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University of Freiburg https://orcid.org/0000- 0002- 0349- 1955 + +Bertram Bengsch University of Freiburg + +Vadim Gladyshev Brigham and Women's Hospital and Harvard Medical School https://orcid.org/0000- 0002- 0372- 7016 + +Trent Woodruff University of Queensland https://orcid.org/0000- 0003- 1382- 911X + +Jessica Mar University of Queensland + +Daniel Watterson University of Queensland + +Ernst Wolvetang The University of Queensland https://orcid.org/0000- 0002- 2146- 6614 + +## Article + +# Keywords: + +Posted Date: March 16th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2675698/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Aging on November 13th, 2023. See the published version at https://doi.org/10.1038/s43587-023-00519-6. + +<--- Page Split ---> + +# 1 Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology + +3 Julio Aguado \(^{1,*}\) , Alberto A. Amarilla \(^{2,14}\) , Atefeh Taherian Fard \(^{1}\) , Eduardo A. Albornoz \(^{3}\) , Alexander Tyshkovskiy \(^{4,5}\) , Marius Schwabenland \(^{6}\) , Harman K. Chaggar \(^{1,7}\) , Naphak Modhiran \(^{1,2}\) , Cecilia Gomez- Inclan \(^{1}\) , Ibrahim Javed \(^{1}\) , Alireza A. Baradar \(^{1}\) , Benjamin Liang \(^{2}\) , Malindrie Dharmaratne \(^{1}\) , Giovanni Pietrogrande \(^{1}\) , Pranesh Padmanabhan \(^{8}\) , Morgan E. Freney \(^{2}\) , Rhys Parry \(^{2}\) , Julian D.J. Sng \(^{2}\) , Ariel Isaacs \(^{2}\) , Alexander A. Khromykh \(^{2,9}\) , Alejandro Rojas- Fernandez \(^{10}\) , Thomas P. Davis \(^{1}\) , Marco Prinz \(^{6,11}\) , Bertram Bengsch \(^{11,12}\) , Vadim N. Gladyshev \(^{4,13}\) , Trent M. Woodruff \(^{8}\) , Jessica C. Mar \(^{1,14}\) , Daniel Watterson \(^{2,14}\) , and Ernst J. Wolvetang \(^{1,14}\) . + +13 1 Australian Institute for Biotechnology and Nanotechnology, University of Queensland, St Lucia, QLD 4072, Australia. 15 2 School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia 4072. 17 3 School of Biomedical Sciences, Faculty of Medicine, University of Queensland, St Lucia, Queensland 4072, Australia. 19 4 Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. 21 5 Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow 119234, Russia. 22 6 Institute of Neuropathology and Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany 24 7 Cellese Ltd, Cardiff Medicentre, Heath Park, Cardiff, United Kingdom. 25 8 Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia 27 9 Australian Infectious Disease Research Centre, Global Virus Network Centre of Excellence, Brisbane QLD, Australia. 29 10 Institute of Medicine, Faculty of Medicine, Universidad Austral de Chile, Valdivia, Chile. 30 11 Signalling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany 31 12 Faculty of Medicine, Clinic for Internal Medicine II, Gastroenterology, Hepatology, Endocrinology, and Infectious Disease, University Medical Center Freiburg, Freiburg, Germany 33 13 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 34 14 These authors contributed equally to this work as co- senior authors. 35 \*Corresponding author. Email: j.aguadoperez@uq.edu.au + +<--- Page Split ---> + +## Abstract + +Aging is the primary risk factor for most neurodegenerative diseases, and recently coronavirus disease 2019 (COVID- 19) has been associated with severe neurological manifestations that can eventually impact neurodegenerative conditions in the long- term. The progressive accumulation of senescent cells in vivo strongly contributes to brain aging and neurodegenerative co- morbidities but the impact of virus- induced senescence in the aetiology of neuropathologies is unknown. Here, we show that senescent cells accumulate in physiologically aged brain organoids of human origin and that senolytic treatment reduces inflammation and cellular senescence; for which we found that combined treatment with the senolytic drugs dasatinib and quercetin rejuvenates transcriptomic human brain aging clocks. We further interrogated brain frontal cortex regions in postmortem patients who succumbed to severe COVID- 19 and observed increased accumulation of senescent cells as compared to age- matched control brains from non- COVID- affected individuals. Moreover, we show that exposure of human brain organoids to SARS- CoV- 2 evoked cellular senescence, and that spatial transcriptomic sequencing of virus- induced senescent cells identified a unique SARS- CoV- 2 variant- specific inflammatory signature that is different from endogenous naturally- emerging senescent cells. Importantly, following SARS- CoV- 2 infection of human brain organoids, treatment with senolytics blocked viral retention and prevented the emergence of senescent corticothalamic and GABAergic neurons. Furthermore, we demonstrate in human ACE2 overexpressing mice that senolytic treatment ameliorates COVID- 19 brain pathology following infection with SARS- CoV- 2. In vivo treatment with senolytics improved SARS- CoV- 2 clinical phenotype and survival, alleviated brain senescence and reactive astrogliosis, promoted survival of dopaminergic neurons, and reduced viral and senescence- associated secretory phenotype gene expression in the brain. Collectively, our findings demonstrate SARS- CoV- 2 can trigger cellular senescence in the brain, and that senolytic therapy mitigates senescence- driven brain aging and multiple neuropathological sequelae caused by neurotropic viruses, including SARS- CoV- 2. + +<--- Page Split ---> + +## Introduction + +Although severe acute respiratory syndrome coronavirus 2 (SARS- CoV- 2) is primarily a respiratory viral pathogen and the cause of coronavirus disease 2019 (COVID- 19), persistent post- acute infection syndromes (PASC) derived from viral infections including SARS- CoV- 2 are emerging as a frequent clinical picture1,2. In fact, most COVID- 19 patients including individuals with or without comorbidities, and even asymptomatic patients, often experience a range of neurological complications3,4. ‘Long- COVID’ is a type of PASC that is gaining significant awareness, with patients reporting persistent manifestations, such as hyposmia, hypogeusia, sleep disorders and substantial cognitive impairment, the latter affecting approximately one in four COVID- 19 cases5- 7. These clinical symptoms are supported by ample evidence of SARS- CoV- 2 infectivity in multiple cell types of the nervous system8- 16 and significant structural changes in the brains of COVID- 19 patients17. Furthermore, patient transcriptomic data from postmortem brain tissue indicate associations between the cognitive decline observed in patients with severe COVID- 19 and molecular signatures of brain aging18. In agreement with this observation, postmortem patient biopsies show that SARS- CoV- 2- infected lungs — compared to uninfected counterparts — accumulate markedly higher levels of senescence19; a cellular phenotype known to contribute to organismal aging20 and co- morbidities such as chronic degenerative conditions21. Importantly, although recent data supports a role for senescent cells in driving neurodegeneration and cognitive decline in in vivo models of neuropathology22,23 and in physiologically aged mice24, their contribution to COVID pathology in the central nervous system (CNS) and human tissue brain aging remains unknown. + +In the past decade, numerous strategies have been developed to target senescent cells25. Among these, the pharmacological removal of senescent cells with senolytic drugs has become one of the most explored interventions, with many currently in human clinical trials26. A group of these senolytics — such as the cocktail of dasatinib plus quercetin (D+Q), or fisetin — exhibit blood- brain barrier permeability upon oral administration22,27, making these formulations particularly valuable to test the contribution of senescence in the brain in vivo. + +In the present study, we first document the efficacy of multiple senolytic interventions in clearing senescent cells in physiologically aged human pluripotent stem cell- derived brain organoids. Transcriptomic analysis across individual senolytic treatments revealed a differential effect in modulating the senescence- associated secretory phenotype (SASP), with a distinctive impact of D+Q administration in rejuvenating the organoids transcriptomic aging clock. Importantly, we report an enrichment of senescent cells in postmortem brain tissue of COVID- 19 patients and + +<--- Page Split ---> + +further show a direct role for SARS- CoV- 2 and highly neurotropic viruses such as Zika and Japanese encephalitis in evoking cellular senescence in human brain organoids. SARS- CoV- 2 variant screening identified Delta (B.1.617.2) as the variant that exerts the strongest induction of cellular senescence in human brain organoids, and spatial transcriptomic analysis of Delta- induced senescent cells unveiled a novel type of senescence that exhibits a different transcriptional signature from senescent cells that naturally emerge in in vitro aged uninfected organoids. Furthermore, senolytic treatment of SARS- CoV- 2- infected organoids selectively removed senescent cells, lessened SASP- related inflammation and reduced SARS- CoV- 2 RNA expression, indicating a putative role for senescent cells in facilitating viral retention. Finally, to gain in vivo relevance of these findings, we examined the treatment effects of senolytics in transgenic mice expressing human angiotensin- converting enzyme 2 (hACE2) previously infected with SARS- CoV- 2 and observed improved clinical performance and survival, reduced viral load in the brain, improved survival of dopaminergic neurons, decreased astrogliosis, and attenuated senescence and SASP gene expression in the brains of the infected mice. Our findings suggest a detrimental role for virus- induced senescence in accelerating brain inflammation and the aging process in the CNS, and a potential therapeutic role for senolytics in the treatment of COVID- 19 neuropathology. + +## Results + +## Senolytics target biological aging and senescent cells in physiologically aged human brain organoids. + +To model the efficacy of senolytics in clearing senescent cells from human brain tissue models, we generated 8- month- old human brain organoids (BOs) from embryonic stem cells and exposed these to two doses of senolytics for one month at 2 weekly intervals (Supplementary Fig. 1a). We tested the Bcl- 2 inhibitors navitoclax and ABT- 737, as well as D+Q senolytic drug combination, and quantified the abundance of cells exhibiting senescence- associated \(\beta\) - galactosidase activity (SA- \(\beta\) - gal). Exposure to senolytics resulted in significantly lower SA- \(\beta\) - gal activity as compared to vehicle- treated controls (Fig. 1a, c), indicating that all treatments eliminated a large number of senescent cells in the treated BOs. In agreement with this, analysis of lamin B1 protein expression — a nuclear lamina marker often downregulated in senescence28 — within organoid sections revealed a significantly higher content of lamin B1 in the senolytic- treated organoids as compared to control counterparts (Fig. 1b, d), further indicating that senolytics cleared senescent cells by enriching for lamin B1High cell populations. + +<--- Page Split ---> + +We next performed whole- organoid RNA sequencing to compare the transcriptomes of fenolytic- treated and vehicle control 9- month- old BOs. Consistent with our protein expression data (Fig. 1b, d), LMNB1 (lamin B1) mRNA levels were significantly upregulated in all three fenolytic- treated organoids compared to vehicle- treated counterparts (Fig. 2a- c). We further identified 81 senescence- associated genes (including the proinflammatory genes CXCL13 and TNFAIP8) that were consistently suppressed upon all three fenolytic interventions (Fig. 2d and Supplementary Fig. 1b). We however also noticed that each fenolytic treatment exerted substantially different effects in modulating the SASP and other senescence- associated genes (Fig. 2a- c). For instance, SERPINF1 was significantly repressed upon ABT- 737 administration (Fig. 2b) while D+Q did not modulate SERPINF1 expression but greatly suppressed IL8, SERPINE1 and IL1A (Fig. 2c). Compared to navitoclax and ABT- 737 – compounds that modulate multiple shared genes that are enriched for a few pathways (e.g. K- Ras signalling) (Fig. 2e) –, D+Q had a broader spectrum effect, mitigating multiple pro- inflammatory pathways characteristic of cellular senescence, such as NF- \(\kappa\) B and IFNγ signalling (Fig. 2e and Supplementary Fig. 1c). In addition, we identified mTOR as a significantly supressed pathway upon D+Q treatment (Fig. 2e), validating the effects reported for Q as an inhibitor of mTOR kinase. We next performed aging clock predictions based on whole transcriptome sequencing to further explore the impact of fenolytic on the aging process. Remarkably, in addition to their fenolytic mechanisms of action, D+Q treatments on 9- month- old organoids reverted their gene expression age to levels comparable of 8- month- old counterparts according to transcriptomic brain aging clock analysis (Fig. 2f), a phenotype not recapitulated by the other two fenolytic tested. Besides negative association with aging, gene expression changes induced by D+Q treatment were positively correlated with mammalian signatures of established lifespan- extending interventions, such as caloric restriction and rapamycin administration (Fig. 2g), indicating a health- promoting role of D+Q in targeting cellular senescence and biological aging in human CNS tissues. + +## SARS-CoV-2 infection triggers cellular senescence in the brains of COVID-19 patients and in human brain organoids. + +Given the observed neuroinflammatory effects of SARS- CoV- 2 infection during acute COVID- 19 disease29 and its association with molecular signatures of aging in patient brains18, we postulated that part of this pro- inflammatory aging- promoting environment is brought about by SARS- CoV- 2- induced senescence in the brain. To test this hypothesis, we quantified the prevalence of senescent cells in postmortem frontal cortex from age- matched brains of patients that either died following severe COVID- 19 or patients who died of non- infectious, and non- neurological reasons. Notably, in situ high- throughput analysis of over 2.7 million single cells + +<--- Page Split ---> + +across 15 individual brain samples (7 COVID- 19 and 8 non- COVID- 19 frontal cortex sections) revealed increased p16 immunoreactivity frequencies in COVID- 19 patient brains, with a \(>7\) - fold increase in the number of p16- positive cells as compared to non- COVID- 19 age- matched controls (Fig. 3). These results suggest a potential role for SARS- CoV- 2 in triggering cellular senescence, a cellular phenotype that contributes to cognitive decline and that could pose a risk in the acceleration of neurodegenerative processes associated with long- COVID. + +To study the role of neurotropic viruses in aging- driven neuropathology, we exposed human BOs to different viral pathogens, including SARS- CoV- 2. Consistent with previous reports \(^{8,9,16,30}\) , SARS- CoV- 2 BO infections were detected largely within populations of neurons and neural progenitors (Supplementary Fig. 2a, b). To test putative virus- induced senescence phenotypes, we screened seven SARS- CoV- 2 variants by infecting human BOs at identical multiplicity of infection (MOI) and ranked them based on SA- \(\beta\) - gal activity as initial readouts of cellular senescence. Notably, most variants elicited a significant increase in SA- \(\beta\) - gal, with Delta (B.1.617.2) showing the strongest induction (Fig. 4a, b). In addition, serial sectioning of Delta- infected organoids revealed a distinctive colocalization between SA- \(\beta\) - gal and viral spike protein (Fig. 4c), further supporting a role for SARS- CoV- 2 in driving virus- induced senescence in the brain. This phenotype was confirmed when organoid sections were co- immunolabelled with antibodies against p16 and SARS- CoV- 2 nucleocapsid antigens (Fig. 4d). Because of the mechanistic role of DNA damage in affecting most aging hallmarks \(^{31}\) , including the onset of cellular senescence \(^{32}\) , we next explored whether SARS- CoV- 2 infection led to DNA double- strand break accumulation. Consistent with previous evidence \(^{19,33}\) , we detected significantly heightened levels of phosphorylated histone H2AX at serine 139 (known as \(\gamma\) H2AX) in SARS- CoV- 2- infected organoid regions as compared to uninfected organoid cells (Fig. 4e, f), indicating increased DNA damage response marks upon SARS- CoV- 2 infection. Importantly, virus- induced senescence also became detectable in response to a variety of human neurotropic viruses, including Japanese Encephalitis virus (JEV), Rocio virus (ROCV) and Zika virus (ZIKV) in human BOs (Fig. 4g). + +As SARS- CoV- 2 infection is coupled with cognitive decline and signatures of aging, we further assessed associations of transcriptomic changes in COVID- 19 patients and SARS- CoV- 2- infected human BOs. Specifically, we compared post- mortem frontal cortex transcriptomic data from a COVID- 19 cohort study of 44 individual patient brains \(^{18}\) with bulk RNA sequencing we performed on human cortical brain organoids 10 days post infection. Notably, among 1,588 differentially expressed genes (DEGs) between SARS- CoV- 2- infected human BOs compared and uninfected counterparts, 485 genes (30.54%) were also differentially expressed in COVID- 19 patient brain samples. Of note, this common gene set was enriched for known aging and senescence pathways, + +<--- Page Split ---> + +identified in the hallmark gene set collection of the Molecular Signatures Database34 (Supplementary Fig. 3a). + +To better understand the differential effects of the ancestral Wuhan virus and Delta (B.1.617.2) SARS- CoV- 2 variants on senescence induction in hBOs, performed NanoString GeoMx spatial transcriptomic sequencing on p16 protein- expressing regions of interest (ROIs) within organoid sections (Fig. 4h). ROI selection was performed to enable the capture of targeted transcriptome from sufficient senescent cell tissue (>300 cells per ROI) to generate robust count data. Our bulk RNA sequencing analysis revealed 1,250 DEGs in Wuhan- infected BOs as compared to a lower 474 DEGs in Delta- infected counterparts (Supplementary Fig. 3b), a result possibly explained by the higher infectivity rate observed in the Wuhan- infected organoids (Supplementary Fig. 3c). Strikingly, spatial transcriptome analysis of p16- positive cells identified over 1,100 DEGs in Delta- infected organoids, an effect 100- fold greater than Wuhan where only 9 DEGs were detected (Supplementary Fig. 3b). This was explained by principal component analysis, where gene set space determined that the Delta- infected ROIs were separable from overlapping transcriptomes from Wuhan- infected and uninfected senescent cell regions (Supplementary Fig. 4a). Upon extensive analysis of significantly modulated gene expression in p16- positive ROIs of Delta- infected organoids, we identified 458 genes associated with cellular senescence that differentially clustered from Wuhan- infected and uninfected ROIs (Fig. 4i), with many interleukins significantly elevated in Delta- infected ROIs (Fig. 4j). Importantly, this unique Delta- specific senescence transcriptional signature was detected in the presence of heightened normalized SARS- CoV- 2 gene expression in Delta compared to p16- positive cells of Wuhan- infected organoids (Fig. 4k). Altogether, these results demonstrate a direct role for SARS- CoV- 2 and neurotropic flaviviruses in fuelling virus- induced senescence, and revealed a specific effect of Delta (B.1.617.2) in inducing the selective induction of a de novo transcriptional signature and simultaneous accumulation of SARS- CoV- 2 in senescent cells of human BOs. + +## Senolytics reduce SARS-CoV-2 viral expression and virus-induced senescence in human brain organoids. + +The results described so far support a functional role of SARS- CoV- 2 in inducing brain cellular senescence. To investigate whether this virus- induced phenotype could be pharmacologically targeted, we next tested the impact of the selective removal of senescent cells with the same senolytic interventions we previously showed were effective in eliminating senescent cells from physiologically aged organoids (Fig. 5a). We observed that senolytic treatments 5 days post SARS- CoV- 2 infection significantly reduced the number of brain organoid cells that display SA- \(\beta\) - gal activity (Fig. 5b). Notably, senolytic treatment in Delta- infected organoids had an overall + +<--- Page Split ---> + +more prominent and statistically significant effect in reducing cellular senescence as compared to Wuhan- infected counterparts, consistent with the stronger virus- induced senescence phenotype observed upon Delta infections in our initial SARS- CoV- 2 variant screening (Fig. 5a, b). Moreover, senolytics were able to revert lamin B1 loss induced by Delta infections (Supplementary Fig. 4b). Remarkably, treatment with senolytics reduced the viral load in BOs up to 40- fold as measured by intracellular SARS- CoV- 2 RNA levels (Fig. 5c), indicating a putative role of senescent cells as reservoirs that may preferentially facilitate viral replication. To characterise cell type- specific SARS- CoV- 2- induced senescence, we performed deconvolution of spatial transcriptomic data from p16- positive cells (Fig. 5d), a type of analysis that enables cell abundance estimates from gene expression patterns \(^{35}\) . We identified layer 6 corticothalamic neurons (L6CT L6b, \(>9\) - fold induction) and GABAergic ganglionic eminence neurons (CGE, \(>4\) - fold induction) as the two neuronal populations that showed significantly increased senescence incidence upon SARS- CoV- 2 infections in brain organoids (Fig. 5e); two brain cell populations that are vital for modulating neural circuitry and processing incoming sensory information \(^{36}\) . Importantly, all the three senolytic treatments tested prevented the accumulation of cellular senescence in both L6CT L6b and CGE brain organoid cell populations (Fig. 5e). + +## Senolytic treatments mitigate COVID-19 brain pathology in vivo. + +To investigate the consequences of CNS SARS- CoV- 2 infection and ensuing brain virus- induced senescence in a more physiologically complete system, we utilised transgenic mice expressing human ACE2 gene under the control of the keratin 18 promoter (K18- hACE2) \(^{37}\) and performed intranasal SARS- CoV- 2 infections, where we found brain viral nucleocapsid antigen in cerebral cortex and brainstem regions (Supplementary Fig. 5a). Experimentally, 24 hours post infection we initiated oral administration of the senolytic interventions navitoclax, fisetin and D+Q – drugs known to exert blood- brain barrier permeability \(^{22,38}\) – with subsequent treatments every two days (Fig. 6a). As previously reported, SARS- CoV- 2- infected K18- hACE2 transgenic mice undergo dramatically shortened lifespans upon infection \(^{37}\) , with a median survival of 5 days. Strikingly, treatment with D+Q or fisetin significantly improved the survival of K18- hACE2 mice as compared to vehicle- treated controls, with extended median lifespans of 60% (Fig. 6b). Furthermore, while at 10 days post infection all vehicle- treated control mice were already dead, at survival experimental endpoint (12 days post infection) a percentage of senolytic- treated mice – 22% (fisetin), 38% (D+Q) and 13% (navitoclax) – remained alive (Fig. 6b). This significantly improved survival upon senolytic administration of infected mice concurrently delayed the rapid weight loss observed in the infected control group (Supplementary Fig. 5b). Throughout the first week of the in vivo experiments, mice were clinically monitored and scored daily for behavioural + +<--- Page Split ---> + +and physical performance (Fig. 6c). Notably, senolytic interventions resulted in a profound reduction of COVID- related disease features, especially in the D+Q- treated group (Fig. 6c). + +Given the positive survival and improved clinical performance outcomes brought about by senolytic treatment, we investigated whether the oral administration of senolytics impacted the histological architecture and pro- inflammatory makeup of brains from infected mice. To this end, we first tested the impact of senolytics on brain viral RNA levels. In accordance with our brain organoid data (Fig. 5c), senolytic treatments of infected K18- hACE2 mice showed a significantly lower viral gene expression compared to infected vehicle- treated mice (Fig. 6d), further supporting a putative role for senescent cells in preferentially sustaining SARS- CoV- 2 replication. We next tested whether senescent cell clearance directly impacted the transcription of SASP and senescence genes in the brain. mRNA expression analyses from brains of uninfected and infected mice indicated an overall increase in inflammatory SASP and p16 senescence markers in the brains of infected mice (Fig. 6e). Most importantly, all three senolytic interventions consistently normalised brain SASP and senescence gene expression of infected mice to levels comparable to those of uninfected brains (Fig. 6e). + +Neuroinvasive viral infections can result in loss of dopaminergic neurons and ensuing PASC such as parkinsonism39. Given the long- term neurological impact of COVID- 19 including coordination and consciousness disorders40, we therefore tested the impact of SARS- CoV- 2 infection on altering dopaminergic neuron survival within the brainstem, an important region of the brain known to regulate these behaviours. Strikingly, Delta variant infection induced a dramatic loss of dopaminergic neurons in the brainstem, as measured by tyrosine hydroxylase immunolabelling (Fig. 6f, g), and this was accompanied by increased astrogliosis (Fig. 6f, h), a neurotoxic process common to multiple neurological disorders41. Importantly, recurrent senolytic treatments initiated 24 hours after SARS- CoV- 2 exposure partly prevented dopaminergic neuron loss and abrogated the onset of reactive astrogliosis (Fig. 6f- h). + +## Discussion + +Brain aging and related cognitive deficiency have been attributed to diverse molecular processes including chronic inflammation and cellular senescence42. This has been studied both in normal murine aging24, as well as in different age- related mouse models of neurodegeneration such as Parkinson's disease43, tauopathies23,44, amyloid- beta neuropathology22, and neuropsychiatric disorders45. However, whether the endogenous age- related onset of cellular senescence impacts brain aging in human tissue systems has not been investigated. Neither have the putative + +<--- Page Split ---> + +consequences of neurotropic viral infections in accelerating the onset of cellular senescence in the brain been examined. + +Our findings herein show that: (1) senescent cells accumulate in physiologically aged brain organoids of human origin and that long- term (4 weeks), intermittent, senolytic treatment reduces inflammation and cellular senescence; (2) interventions unique to D+Q treatments induce anti- aging and pro- longevity gene expression changes in human BOs; (3) brains from COVID- 19 patients undergo accelerated cellular senescence accumulation compared to age- matched controls; (4) SARS- CoV- 2 and neurotropic viruses including Zika and JEV can infect human BOs to directly induce cellular senescence; (5) Delta (B.1.617.2) variant induces the strongest SARS- CoV- 2- dependent induction of cellular senescence, where spatial transcriptomic sequencing of p16- positive cells identifies a Delta- specific SASP signature; (6) short- term (5 days) senolytic treatments of SARS- CoV- 2- infected organoids reduce viral gene expression and prevent the onset of senescent neurons of corticothalamic and GABAergic nature; and (7) senolytic treatment following SARS- CoV- 2 intranasal infection of K18- hACE2 mice ameliorates COVID- 19 neuropathology, including improvements in clinical score and survival, alleviation of reactive astrogliosis, increased survival of dopaminergic neurons, and reduced viral, SASP and senescence gene expression in the brain of infected mice. + +To evaluate the relationship between senescent cell accumulation and brain aging, we designed studies to eliminate senescent cells through pharmacologic approaches (D+Q, navitoclax and ABT- 737) and hypothesized that senolytic interventions may have beneficial consequences in targeting brain aging. We found that physiologically aged human BOs accumulate senescent cells and that senolytic treatment can be used as a proof- of- concept strategy to revert Lamin B1 levels, and alleviate differential SASP expression and senescent cell burden in human brain BOs systems. In addition to senolytic activity, transcriptomic aging clocks identified D+Q as an intervention that achieved tissue rejuvenation, as 8- month- old human brain organoids displayed comparable aging clocks to D+Q- treated 9- month- old counterparts. Given that senescent cell clearance results in reversal of the aging process, these findings support an important role for senescent cells in driving human brain aging. + +Further to normal brain aging, we tested the possibility of virus- induced senescence upon BOs neurotropic infections. We found that flavivirus JEV, ROCV and ZIKV infections, and multiple SARS- CoV- 2 variant infections lead to a significant increase in BO cellular senescence. Importantly, upon senolytic delivery BOs display a dramatic loss of SARS- CoV- 2 viral RNA expression, suggestive of a role for senescent cells in preferentially facilitating viral entry and retention, consistent with data showing increased ACE2 expression in human senescent cells46. + +<--- Page Split ---> + +Furthermore, SARS- CoV- 2 induces metabolic changes in infected and neighbouring neurons8, a paracrine phenomenon reminiscent of the bystander effect characteristic of senescent cells47. Here, spatial transcriptomic sequencing cell deconvolution of p16 protein- expressing cell clusters identified two neuronal populations – corticothalamic and GABAergic – that become senescent and broadly develop a de novo SASP signature upon Delta (B.1.617.2) infection. It will therefore be of interest to determine whether neuronal virus- induced senescence contributes to neuroinflammation and the long- term neurological impact of COVID- 19. + +In the brains of SARS- CoV- 2- infected K18- hACE2 mice, we found that senolytic treatment alleviates p16 and the levels of proinflammatory cytokines which may be due, in part, to removal of virus- induced senescence and ensuing SASP expression. However, secondary anti- inflammatory and/or anti- viral effects of D+Q, fisetin or navitoclax – for instance by direct inhibition of the observed astrogliosis – are also possible. Upon systematic monitoring of clinical performance in SARS- CoV- 2- infected mice, we found that intermittent senolytic treatment significantly improved animal behaviour and survival. This beneficial clinical effect of senolytics was associated with reduced inflammation and increased survival of dopaminergic neurons. Indeed, inflammatory cytokines as part of the SASP can impair brain plasticity48, suggesting that the beneficial effects of senolytic treatment on COVID- 19 neurological clinical picture may result from suppression of senescence- dependent inflammation and improved neuronal survival. This is consistent with pre- clinical studies demonstrating a beneficial effect of senescent- cell clearance in reducing inflammatory/SASP gene expression in the brains of geriatric mice infected with a SARS- CoV- 2–related mouse \(\beta\) - coronavirus49. Whether our in vivo effects of senolytics on COVID- 19 neuropathology exclusively results from clearance of cellular senescence or also involves actions on dopaminergic neurons and other brain regions remains to be determined. Nevertheless, in this study we have provided important evidence that paves the way for future clinical studies that will test the hypothesis that senolytic therapies can suppress long- COVID neuropathology and other long- term disorders caused by acute neurotropic viral infections. + +## Methods + +Ethics and biological safety. The use of animals was approved by the University of Queensland Animal Ethics Committee under project number 2021/AE001119. Mice were housed within the BSL- 3 facility using IsoCage N- Biocontainment System (Tecniplast, USA), where each cage was supplied with a HEPA filter, preventing viral contamination between cages. This IsoCage system also provides individual ventilation to the cages, maintaining the humidity under 65- 70% and + +<--- Page Split ---> + +temperature between 20–23 °C. Mice were kept under a 12- h light/dark cycle with food and water provided ad libitum. + +Pathogenic SARS- CoV- 2 variants and encephalitic flaviviruses were handled under a certified biosafety level- 3 (BSL- 3) conditions in the School of Chemistry and Molecular Biosciences (SCMB), Australian Institute for Bioengineering and Nanotechnology (AIBN) and Institute for Molecular Bioscience (IMB) at The University of Queensland, Australia. All approved researchers have used disposal Tychem 2000 coveralls (Dupont, Wilmington, NC, USA; #TC198T YL) at all times and used powered air- purifying respirators (PAPR; SR500 Fan Unit) or Versaflo- powered air- purifying respirators (3M, Saint Paul, MN, USA; #902- 03- 99) as respiratory protection. All pathogenic materials were handled in a class II biosafety cabinet within the BSL- 3 facility. For downstream analysis, all samples containing infectious viruses were appropriately inactivated in accordance with the BSL- 3 manual. Liquid and solid waste were steam- sterilised by autoclave. This study was approved by the Institutional Biosafety Committee from The University of Queensland (UQ) under the following approvals IBC/485B/SCMB/2021 and IBC/447B/SCMB/2021. The analysis of human brain sections was performed with the approval of the Ethic Committee of the University of Freiburg: 10008/09. The study was performed in agreement with the principles expressed in the Declaration of Helsinki (2013). + +Generation and culture of PSC- derived human brain organoids. Organoid generation was carried out as previously described50, with some modifications. Human H9 (WA09) pluripotent stem cells (hPSCs) were obtained from WiCell with verified normal karyotype and contamination- free; and were routinely tested and confirmed negative for mycoplasma (MycoAlert, Lonza). hPSCs were maintained in mTeSR media (STEMCELL Technologies, cat. #85850) on matrigel- coated plates (Corning, No. 354234). On day 0 of organoid differentiation, PSCs were dissociated with Accutase (Life Technologies, cat. #00- 4555- 56) and seeded at a density of 15,000 cells per well on a 96- well low- attachment U- bottom plate (Sigma, cat. #CLS7007) in mTeSR plus 10 μM ROCK inhibitor (VWR, cat. #688000- 5). The 96 well- plate was then spun at 330 g for 5 minutes to aggregate the cells and make spheroids. The spheroids were fed every day for 5 days in media containing Dulbecco’s modified eagle medium (DMEM)/F12 (Invitrogen, cat. #11330- 032), Knock- out serum (Invitrogen, cat. #11320- 033), 1:100 Glutamax, 1:200 MEM- NEAA supplemented with dual SMAD inhibitors: 2 μM Dorsomorphin (StemMACS, cat. #130- 104- 466) and 2 μM A- 83- 01 (Lonza, cat. #9094360). On day 6, half of the medium was changed to induction medium containing DMEM/F12, 1:200 MEM- NEAA, 1:100 Glutamax, 1:100 N2 supplement (Invitrogen, cat. #17502048), 1 μg ml- 1 heparin (Sigma, cat. # H3149) supplemented with 1 μM CHIR 99021 (Lonza, cat. #2520691) and 1 μM SB- 431542 (Sigma, cat. # S4317). From day 7, + +<--- Page Split ---> + +complete media change was done with induction media followed by everyday media changes in induction media for the next 4 days. On day 11 of the protocol, spheroids were transferred to 10 \(\mu \mathrm{l}\) - droplets of Matrigel on a sheet of Parafilm with small 2 mm dimples. These droplets were allowed to gel at \(37^{\circ}\mathrm{C}\) for 25 minutes and were subsequently removed from the Parafilm and transferred to and maintained in low- attachment 24- well plates (Sigma, cat. #CLS3473) containing induction medium for the following 5 days. From day 16, the medium was then changed to organoid medium containing a 1:1 mixture of Neurobasal medium (Invitrogen, cat. #21103049) and DMEM/F12 medium supplemented with 1:200 MEM- NEAA, 1:100 Glutamax, 1:100 N2 supplement, 1:50 B27 supplement (Invitrogen, cat. #12587010), \(1\%\) penicillin- streptomycin (Sigma, cat. #P0781), \(50~\mu \mathrm{M}\) 2- mercaptoethanol and \(0.25\%\) insulin solution (Sigma, cat. #I9278). Media was changed every other day with organoid medium. Organoids were maintained in organoid media until the end of experiments, as indicated. + +Human tissue preparation: frontal cortex tissue from patients that had tested positive for SARS- CoV- 2 and died from severe COVID- 19 was obtained at the University Medical Center Freiburg, Germany. The tissue was formalin- fixed and embedded into paraffin (FFPE) using a Tissue Processing Center (Leica ASP300, Leica). Sections (3 \(\mu \mathrm{m}\) thick) were cut and mounted onto Superfrost objective slides (Langenbrinck). + +Cell lines. RNA Vero E6 cells (African green monkey kidney cell clones) and TMPRSS2- expressing Vero E6 cell lines were maintained in Dulbecco's Modified Eagle Medium (DMEM, Gibco, USA) at \(37^{\circ}\mathrm{C}\) with \(5\%\) CO2. Additionally, as previously described, the TMPRSS2- expressing Vero E6 cell line was supplemented with \(30~\mu \mathrm{g / mL}\) of puromycin51. C6/36 cells, derived from the salivary gland of the mosquito A. albopictus were grown at \(28^{\circ}\mathrm{C}\) in Royal Park Memorial Institute (RPMI) medium (Gibco, USA). All cell lines media were supplemented with \(10\%\) heat- inactivated foetal calf serum (FCS) (Bovogen, USA), penicillin (100 U/mL) and streptomycin (100 \(\mu \mathrm{g / mL}\) ) (P/S). C6/36 media was also supplemented with \(1\%\) GlutaMAX (200 mM; Gibco, USA) and \(20~\mathrm{mM}\) of HEPES (Gibco, USA). All cell lines used in this study were tested mycoplasma free by first culturing the cells for 3- 5 days in antibiotic- free media and then subjected to a mycoplasma tested using MycoAlert™ PLUS Mycoplasma Detection Kit (Lonza, UK). + +Viral isolates. Seven SARS- CoV- 2 variants were used in this study. \(i\) ) Ancestral or Wuhan strain: an early Australian isolate hCoV- 19/Australia/QLD02/2020 (QLD02) sampled on 30/01/2020 (GISAID Accession ID; EPI_ISL_407896); \(ii\) ) Alpha (B.1.1.7) named as hCoV- 19/Australia/QLD1517/2021 and collected on 06/01/2021 (GISAID accession ID EPI_ISL_944644); \(iii\) ) Beta (B.1.351), hCoV19/Australia/QLD1520/2020, collected on + +<--- Page Split ---> + +29/12/2020 (GISAID accession ID EPI_ISL_968081); iv) Delta (B.1.617), hCoV- 19/Australia/QLD1893C/2021 collected on 05/04/2021 (GISAID accession ID EPI_ISL_2433928); v) Gamma (P.1), hCoV- 19/Australia/NSW4318/2021 sampled on 01- 03- 2021 (GISAID accession ID EPI_ISL_1121976); vi) Lambda (C.37), hCoV- 19/Australia/NSW4431/2021 collected on 03- 04- 2021 (GISAID accession ID EPI_ISL_1494722); and vii) Omicron (BA.1), hCoV- 19/Australia/NSW-RPAH- 1933/2021 collected on 27- 11- 2021 (GISAID accession ID EPI_ISL_6814922). All viral isolates obtained were passaged twice except for Gamma and Lambda variants, which were passed thrice. Viral stocks were generated on TMPRSS2- expressing Vero E6 cells to ensure no spike furin cleavage site loss. To authenticate SARS- CoV- 2 isolates used in the study viral RNA was extracted from stocks using TRIzol LS reagent (Thermo Fisher Scientific, USA) and cDNA was prepared with Protoscript II first- strand cDNA synthesis kit as per manufacturer's protocol (New England Biolabs, USA). The full- length Spike glycoprotein was subsequently amplified with Prime Star GXL DNA polymerase (Takara Bio) and the following primers CoV- SF GATAAAGGAGTTGCACCAGGTACAGCTGTTTTAAG CoV- SR GTCGTCGTCGGTTCATCATAAATTGGTTCC and conditions as per previously described51. For encephalitic flaviviruses, virulent strains of Zika virus (ZIKV, Natal [GenBank: KU527068.1]), Japanese encephalitis virus (JEV, Nakayama strain [GenBank: EF571853.1]) and Rocio virus (ROCV, [GenBank: AY632542.4]) were propagated on C6/36 to generate a viral stock for all the experiments. Viral titres were determined by an immuno- plaque assay52. RNA isolation. RNA from brain organoids and mouse tissue was extracted with RNeasy Mini Kit (Qiagen) for mRNA detection, according to the manufacturer's instructions. Mouse tissue was homogenised with a TissueLyser II (Qiagen) at 30 Hz for 60 seconds. RNA integrity of brain organoids and mouse tissue was evaluated by analysis on the 2100 Bioanalyzer RNA 6000 Pico Chip kit (Agilent) using the RNA Integrity Number (RIN). RNA samples with a RIN > 7 were considered of high enough quality for real- time quantitative PCR, and transcriptomic library construction and RNA sequencing according to the manufacturer's instructions. Real- time quantitative PCR. 1 \(\mu \mathrm{g}\) of total RNA was reverse transcribed using iScript cDNA Synthesis Kit (Bio- Rad). A volume corresponding to 5 ng of initial RNA was employed for each real- time PCR reaction using PowerUp SYBR Green Master Mix (Applied Biosystems) on a CFX Opus Real- Time PCR detection system. Ribosomal protein P0 (RPLP0) were used as control transcripts for normalization. Primers sequences (5'- 3' orientation) are listed in Supplementary Table 1. + +<--- Page Split ---> + +Viral infection of organoids. Brain organoids in low- adhesion plates were infected overnight (14 hours) with the indicated flaviviruses and SARS- CoV- 2 variants at multiplicity of infection (MOI) 0.1 and 1, respectively. Then, brain organoids were thrice washed with LPS- free PBS and added maintenance media and kept for 5 days post- infection. + +Senolytic treatments in vitro. For infection experiments, 5 days after viral exposure brain organoids were treated with a single dose of navitoclax (2.5 \(\mu \mathrm{M}\) ), ABT- 737 (10 \(\mu \mathrm{M}\) ) or D+Q (D: 10 \(\mu \mathrm{M}\) ; Q: 25 \(\mu \mathrm{M}\) ) and monitored for 5 days following treatment. As for senolytic interventions on physiologically aged 8- month- old organoids, brain organoids were treated with a weekly dose of navitoclax (2.5 \(\mu \mathrm{M}\) ), ABT- 737 (10 \(\mu \mathrm{M}\) ) or D+Q (D: 10 \(\mu \mathrm{M}\) ; Q: 25 \(\mu \mathrm{M}\) ) for 4 weeks and subsequently collected for downstream analysis. + +SARS- CoV- 2- driven COVID- 19 animal experiments. In vivo experiments were performed using 6- week- old K18- hACE2 transgenic female mice obtained from the Animal Resources Centre (Australia). For animal infections, SARS- CoV- 2 was delivered intranasally — 20 \(\mu \mathrm{l}\) of the Delta variant at \(5 \times 10^{3}\) FFU per mouse — on anesthetized mice (100 mg \(\mathrm{kg}^{- 1}\) ketamine and 10 mg \(\mathrm{kg}^{- 1}\) xylazine). Control animals were mock- infected with the same volume of RPMI additive- free medium. One day after infection, K18- hACE2 mice were randomly distributed into three treatment groups (n = 16 each) and one solvent- only control group (n = 16). From 1 day after infection, randomly chosen animals were treated via oral gavage routes with navitoclax (100 mg \(\mathrm{kg}^{- 1}\) ), D+Q (D: 5 mg \(\mathrm{kg}^{- 1}\) ; Q: 50 mg \(\mathrm{kg}^{- 1}\) ) or fisetin (100 mg \(\mathrm{kg}^{- 1}\) ) dissolved in 5% DMSO and 95% corn oil every other day. For tissue characterization (n = 8 for each infected group), on day 6 after infection animals were euthanised and brain specimens were collected for RNA expression analysis and histopathological assessment. For clinical and survival evaluation, mice were monitored daily for up to 12 days post infection. Clinical scoring included: no detectable disease (0); hindlimb weakness, away from littermates, ruffled fur (0.5- 1); partial hindlimb paralysis, limping, hunched, reluctant to move (1.5- 2); and complete paralysis of hindlimb, severely restricted mobility, severe distress, or death (2.5- 3). + +Organoid sectioning and histology. Brain organoids were fixed in 4% paraformaldehyde (PFA) for 1 hour at RT and washed with phosphate- buffered saline (PBS) three times for 10 minutes each at RT before allowing to sink in 30% sucrose at \(4^{\circ}\mathrm{C}\) overnight and then embedded in OCT (Agar Scientific, cat. #AGR1180) and cryosectioned at 14 \(\mu \mathrm{m}\) with a Thermo Scientific NX70 Cryostat. Tissue sections were used for immunofluorescence and for the SA- \(\beta\) - Gal assay. For immunofluorescence, sections were blocked and permeabilized in 0.1% Triton X- 100 and 3% Bovine Serum Albumin (BSA) in PBS. Sections were incubated with primary antibodies overnight at \(4^{\circ}\mathrm{C}\) , washed and incubated with secondary antibodies for 40 minutes at RT. 0.5 \(\mu \mathrm{g}\) ml- 1 DAPI + +<--- Page Split ---> + +(Sigma, cat. #D9564) was added to secondary antibody to mark nuclei. Secondary antibodies labelled with Alexafluor 488, 568, or 647 (Invitrogen) were used for detection. SA- \(\beta\) - gal activity at pH 6.0 as a senescence marker in fresh or cryopreserved human samples was assessed as previously described \(^{53}\) . + +Nanostring spatial transcriptomics. OCT- embedded organoids were freshly sectioned and prepared according to the GeoMX Human Whole Transcriptome Atlas Assay slide preparation for RNA profiling (NanoString). Fastq files were uploaded to GeoMX DSP system where raw and Q3 normalized counts of all targets were aligned with ROIs. The 0.75 quantile- scaled data was used as input. DESeq2 R package \(^{54}\) was used to identify differently expressed genes in the ROI cell subsets. DESeq2 was performed between the pairwise comparisons of interest and genes were corrected using the Benjamini & Hochberg correction and only genes that had a corrected P- value of \(< 0.05\) were retained. Cell abundances were estimated using the SpatialDecon R library, which performs mixture deconvolution using constrained log- normal regression. + +Whole organoid RNA sequencing. Before mRNA sequencing, ribosomal RNA from bulk organoid RNA was depleted with the Ribo- Zero rRNA Removal Kit (Illumina). Transcripts were sequenced at Novogene Ltd (Hong Kong) using TruSeq stranded total RNA library preparation and Illumina NovaSeq 150bp paired- end lane. FastQC was used to check quality on the raw sequences before analysis to confirm data integrity. Trimmed reads were mapped to the human genome assembly hg38 using Hisat2 v2.0.5. To ensure high quality of the count table, the raw count table generated by featureCounts v1.5.0- p3 was filtered for subsequent analysis. Differential gene expression analysis was performed using Bioconductor DESeq2 R packages. The resulting P- values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted P- value \(< 0.05\) found by DESeq2 were assigned as differentially expressed. + +Association with gene expression signatures of aging and longevity. To assess the effect of senolytics on transcriptomic age of BO samples, we applied brain multi- species (mouse, rat, human) transcriptomic clock based on signatures of aging identified as explained in \(^{55}\) . The missing values were omitted with the precalculated average values from the clock. Association of gene expression log- fold changes induced by senolytics in aged BO with previously established transcriptomic signatures of aging and established lifespan- extending interventions was examined as described in \(^{55}\) . Utilized signatures of aging included multi- species brain signature as well as multi- tissue aging signatures of mouse, rat and human. Signatures of lifespan- extending interventions included genes differentially expressed in mouse tissues in response to individual interventions, including caloric restriction (CR), rapamycin (Rapamycin), and mutations + +<--- Page Split ---> + +associated with growth hormone deficiency (GH deficiency), along with common patterns of lifespan- extending interventions (Common) and ECs associated with the intervention effect on mouse maximum (Max lifespan) and median lifespan (Median lifespan). + +For the identification of enriched functions affected by senolytics in aged BO we performed functional GSEA56 on a pre- ranked list of genes based on log10(p- value) corrected by the sign of regulation, calculated as: + +\[- (p v)\times s g n(f c),\] + +where pv and lfc are p- value and logFC of a certain gene, respectively, obtained from edgeR output, and sgn is the signum function (equal to 1, - 1 and 0 if value is positive, negative or equal to 0, respectively). HALLMARK ontology from the Molecular Signature Database was used as gene sets for GSEA. The GSEA algorithm was performed separately for each senolytic via the fgsea package in R with 5,000 permutations. A q- value cutoff of 0.1 was used to select statistically significant functions. + +Similar analysis was performed for gene expression signatures of aging and lifespan- extending interventions. Pairwise Spearman correlation was calculated for individual signatures of senolytics, aging and lifespan- extending interventions based on estimated NES (Fig. 2g). A heatmap colored by NES was built for manually chosen statistically significant functions (adjusted p- value \(< 0.1\) ) (Supplementary Fig. 1a). Complete list of functions enriched by genes perturbed by senolytics is included in the source data file. + +Imaging and analysis. Immunofluorescence images were acquired using a Zeiss LSM 900 Fast Airyscan 2 super- resolution microscope or a Zeiss AxioScan Z1 Fluorescent Imager. For organoid staining, the number of positive cells per organoid for senescence, cell type and viral markers tested was analysed by the imaging software CellProfiler, using the same pipeline for each sample in the same experiment. Custom Matlab scripts were developed to streamline high throughput imaging data. + +Antibodies. anti- p16 (Cell Signalling, 80772, 1:400); anti- NeuN (Millipore, ABN78, 1:1000); anti- GFAP (Agilent, Z0334, 1:2000); anti- Sox2 (Cell Signalling, 23064, 1:1000); anti- SARS- CoV- 2 Nucleocapsid C257; anti- SARS- CoV- 2 spike protein58; anti- \(\gamma\) H2AX (Millipore, 05- 636, 1:1000); anti- Tyrosine Hydroxylase (Invitrogen, PA5- 85167, 1:1000); anti- lamin B1 (Abcam, ab16048, 1:5000); anti- Chicken IgG (Jackson ImmunoResearch, 703- 545- 155, 1:500); anti- rabbit IgG (Invitrogen, A10042, 1:400); anti- rabbit IgG (Invitrogen, A21245, 1:400); anti- mouse IgG (Invitrogen, A11029, 1:400); anti- mouse IgG (Invitrogen, A21235, 1:400); anti- human IgG (Invitrogen, A21445, 1:400). + +<--- Page Split ---> + +Statistical analysis. Results are shown as mean \(\pm\) standard error of the mean (s.e.m.) or standard deviation (s.d.) as indicated. No statistical methods were used to predetermine sample size. P value was calculated by the indicated statistical tests, using R or Prism software. In figure legends, n indicates the number of independent experiments or biological replicates. + +## Competing Interests + +The authors declare no competing interests. + +## Data availability + +RNA- seq raw data have been deposited in the European Nucleotide Archive with the primary accession code PRJEB58180. RNA- seq files from Mavrikaki et al. are available through the Gene Expression Omnibus accession number GSE188847. Source data are provided with this paper. + +## Acknowledgements + +We thank Novogene for performing bulk RNA sequencing experiments and bioinformatic analysis; Aaron McClelland from NanoString (Seattle, USA) for technical and computational assistance on GeoMx spatial transcriptomic sequencing; the scientists and pathologists of Queensland and New South Wales Department of Health, and Kirby Institute for providing the SARS- CoV- 2 variants; Maya Patrick and Barb Arnts (UQBR animal staff) at the AIBN and Crystal McGirr (BSL- 3 facility manager at the IMB) for technical assistance; Robert Sullivan from the Queensland Brain Institute for technical advice; Jasmyn Cridland (Regulatory Compliance Officer, Faculty of Science at UQ) and Amanda Jones (UQ Biosafety) for advice on Biosafety approvals and BSL- 3 manual and safety procedures; Shaun Walters, David Knight and Erica Mu from the School of Biomedical Sciences Imaging and Histology facilities (The University of Queensland) for technical support; and EW, JM and DW laboratory members for discussions. MS was supported by the Berta- Ottenstein- Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, and the IMM- PACT- Programme for Clinician Scientists, Department of Medicine II, Medical Center – University of Freiburg and Faculty of Medicine, University of Freiburg, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 413517907. TW was supported by the NHMRC (2009957). EW was supported by the NHMRC and an ARC Discovery Project (DP210103401). JA was supported by a University of Queensland Early Career Researcher Grant (application UQECR2058457), a National Health and Medical Research Council (NHMRC) Ideas Grant (2001408), a Brisbane Children's Hospital Foundation grant (Project- 50308) and a Jérôme Lejeune Postdoctoral Fellowship. + +<--- Page Split ---> + +## Contributions + +ContributionsJA and HC generated human brain organoids. JA, HC, AT, ATF, MD, MS, AA, GP, EA, NM, BL, AI, DP, IJ, AB, MF, RP, JS, CG, TW, JM and EW contributed to acquisition, analysis, or interpretation of data. AAA, EA, NM and BL participated in the infections and treatments of mice and monitored their clinical performance. JA, ATF and AT analysed transcriptomic data. JA, AA, AF, EA, JM and EW contributed to experimental design. JA planned and supervised the project and wrote the paper. All authors edited and approved the final version of this article. + +## References + +References1 Nalbandian, A. et al. Post-acute COVID- 19 syndrome. Nat Med 27, 601- 615 (2021). https://doi.org:10.1038/s41591- 021- 01283- z2 Choutka, J., Jansari, V., Hornig, M. & Iwasaki, A. Unexplained post- acute infection syndromes. Nat Med 28, 911- 923 (2022). https://doi.org:10.1038/s41591- 022- 01810- 63 Taquet, M., Geddes, J. R., Husain, M., Luciano, S. & Harrison, P. 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DNA damage response at telomeres boosts the transcription of SARS-CoV- 719 2 receptor ACE2 during aging. EMBO Rep 23, e53658 (2022). 720 https://doi.org:10.15252/embr.202153658 721 da Silva, P. F. L. et al. The bystander effect contributes to the accumulation of senescent 722 cells in vivo. Aging Cell 18, e12848 (2019). https://doi.org:10.1111/ace1.12848 723 Golia, M. T. et al. Interplay between inflammation and neural plasticity: Both immune 724 activation and suppression impair LTP and BDNF expression. Brain Behav Immun 81, 484-494 (2019). https://doi.org:10.1016/j.bbi.2019.07.003 725 Camell, C. D. et al. Senolytics reduce coronavirus-related mortality in old mice. Science 726 373 (2021). https://doi.org:10.1126/science.abe4832 727 Aguado, J. et al. Inhibition of the cGAS-STING pathway ameliorates the premature 728 senescence hallmarks of Ataxia-Telangiectasia brain organoids. Aging Cell, e13468 729 (2021). https://doi.org:10.1111/ace1.13468 730 Amarilla, A. A. et al. A versatile reverse genetics platform for SARS-CoV-2 and other 731 positive-strand RNA viruses. Nat Commun 12, 3431 (2021). 732 https://doi.org:10.1038/s41467-021-23779-5 733 Amarilla, A. A. et al. An Optimized High-Throughput Immuno- Plaque Assay for SARS- 734 CoV-2. Front Microbiol 12, 625136 (2021). https://doi.org:10.3389/fmicb.2021.625136 + +<--- Page Split ---> + +738 53 Aguado, J. et al. Inhibition of DNA damage response at telomeres improves the detrimental 739 phenotypes of Hutchinson-Gilford Progeria Syndrome. Nat Commun 10, 4990 (2019). 740 https://doi.org:10.1038/s41467-019-13018-3 741 54 Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion 742 for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). 743 https://doi.org:10.1186/s13059-014-0550-8 744 55 Tyshkovskiy, A. et al. Identification and Application of Gene Expression Signatures 745 Associated with Lifespan Extension. Cell Metab 30, 573-593 e578 (2019). 746 https://doi.org:10.1016/j.cmet.2019.06.018 747 56 Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for 748 interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 749 (2005). https://doi.org:10.1073/pnas.0506580102 750 57 Isaacs, A. et al. Nucleocapsid Specific Diagnostics for the Detection of Divergent SARS- 751 CoV- 2 Variants. Front Immunol 13, 926262 (2022). 752 https://doi.org:10.3389/fimmu.2022.926262 753 58 Valenzuela Nieto, G. et al. Potent neutralization of clinical isolates of SARS-CoV- 2 D614 754 and G614 variants by a monomeric, sub-nanomolar affinity nanobody. Sci Rep 11, 3318 755 (2021). https://doi.org:10.1038/s41598-021-82833-w + +<--- Page Split ---> + +## Figure legends + +Figure 1 Long- term senolytic treatment prevents selective accumulation of senescent cells in physiologically aged human brain organoids. Brain organoids were generated and grown in vitro for 8 months, and subsequently exposed to two doses (each dose every two weeks) of either navitoclax (2.5 \(\mu \mathrm{M}\) ), ABT- 737 (10 \(\mu \mathrm{M}\) ) or \(\mathrm{D + Q}\) (D: \(10~\mu \mathrm{M}\) ; Q: \(25~\mu \mathrm{M}\) ) administration within the following month, after which the organoids were collected for in situ analysis. (a) SA- \(\beta\) - gal assays were performed on organoid sections. Each data point in the bar graph represents a single organoid analysed. Error bars represent s.d.; at least 8 individual organoids were analysed per condition; one- way ANOVA with Tukey's multiple- comparison post- hoc corrections. (b) Lamin B1 staining was performed on organoid sections. Each data point in the scatter plot represents the integrated intensity of each cell within organoid sections. At least 8 individual organoids were analysed per condition; one- way ANOVA with Tukey's multiple- comparison post- hoc corrections. (c,d) Representative images from quantifications shown in a and b, respectively. Scale bar, \(0.3\mathrm{mm}\) . + +Figure 2 Transcriptomic characterization of distinct senolytic interventions on brain aging hallmarks. Brain organoids were generated and grown in vitro for 8 months, and subsequently exposed to two doses (each dose every two weeks) of either navitoclax (2.5 \(\mu \mathrm{M}\) ), ABT- 737 (10 \(\mu \mathrm{M}\) ) or \(\mathrm{D + Q}\) (D: \(10~\mu \mathrm{M}\) ; Q: \(25~\mu \mathrm{M}\) ) administration within the following month, after which the organoids were collected and subjected for bulk RNA sequencing analysis. (a- c) Volcano plots show vehicle- treated versus (a) navitoclax- , (b) ABT- 737- and (c) \(\mathrm{D + Q}\) - treated brain organoid differential expression of upregulated (blue) and downregulated (red) genes. (d) Venn diagram shows differentially repressed senescence- associated genes among senolytic- treated organoids defined with a significance adjusted P value \(< 0.05\) . (e) Gene Set Enrichment Analysis was carried out using aging hallmark gene sets from the Molecular Signature Database. The statistically significant signatures were selected (FDR \(< 0.25\) ) and placed in order of normalized enrichment score. Bars indicate the pathways enriched in individual senolytic treatments as compared to vehicle- treated brain organoids. (f) Transcriptomic age of organoids treated with either vehicle or senolytic compounds assessed using brain multi- species aging clock. (g) Spearman correlation between gene expression changes induced by senolytics in aged organoids and signatures of aging and established lifespan- extending interventions based on functional enrichment output. Normalized enrichment scores (NES) calculated with GSEA were used to evaluate correlations between pairs of signatures. + +Figure 3 Brains of COVID- 19 patients exhibit increased accumulation of p16 senescent cells. (a) Immunofluorescence images showing DAPI (blue), and p16 (red) immunoreactivity in sections + +<--- Page Split ---> + +of frontal cortex regions from patients with severe COVID- 19 and age- matched non- COVID- related controls. Scale bar, \(50 \mu \mathrm{m}\) . (b) Box plots show the percentage of p16- positive cells. Each data point in the graph represents a single patient analysed, with a total of 2,794,379 individual brain cells across 7 COVID- 19 and 8 non- COVID- 19 patients. Whiskers represent min- max values; two- tailed Student's t- test. + +Figure 4 Neurotropic viral infections elicit virus- induced senescence in human brain organoids. (a) SARS- CoV- 2 variant screening was performed on brain organoids at multiplicity of infection 1 and monitored for SA- \(\beta\) - gal activity at 5 days post infection. Scale bar, \(0.3 \mathrm{mm}\) . (b) Quantification of data presented in a. Bar graphs show the percentage of SA- \(\beta\) - gal- positive cells. Each data point in the bar graph represents a single organoid analysed. Error bars represent s.d.; at least 5 individual organoids were analysed per variant- infected condition; one- way ANOVA with Dunnett's multiple- comparison post- hoc corrections. (c) Representative images of serially sectioned Delta- infected organoid regions stained for SA- \(\beta\) - gal and SARS- CoV- 2 spike protein. (d) Representative images of the region shown in c. co- immunolabelled with p16 and SARS- CoV- 2 nucleocapsid (NC) antigen. (e) Organoids infected for 5 days with the indicated SARS- CoV- 2 variants were stained for \(\gamma \mathrm{H2AX}\) and SARS- CoV- 2 Spike protein. Scale bar, \(40 \mu \mathrm{m}\) . (f) Quantification of data presented in e. Scatter plot show the number of \(\gamma \mathrm{H2AX}\) foci per cell in infected regions (red) versus uninfected counterparts (black). Each data point in the scatter plot represents a single cell analysed; at least 400 cells per variant- infected condition have been analysed; two- tailed Student's t- test. (g) Human brain organoids were infected with the neurotropic flaviviruses Japanese Encephalitis virus (JEV), Rocio virus (ROCV) and Zika virus (ZIKV) at multiplicity of infection 0.1; and monitored SA- \(\beta\) - gal activity 5 days post infection. Box plots show the percentage of SA- \(\beta\) - gal- positive cells. Each data point represents a single organoid analysed. Whiskers represent min- max values; at least 5 individual organoids were analysed per virus- infected condition; one- way ANOVA with multiple- comparison post- hoc corrections. (h- k) Uninfected, Wuhan- and Delta- infected human brain organoids where subjected to Regions of Interest (ROI) selection based on p16 protein expression for spatial profiling by the Nanostring GeoMX digital spatial profiler assay and further sequenced for the GeoMx Human Whole Transcriptome Atlas. Three organoids were used per condition for ROI selection. (h) Representative p16- positive ROIs. (i) Heat map of polarity with shown expression above (blue) and below (red) the mean for each differentially heightened SASP gene of Delta- infected p16- positive ROIs. (j) Senescence heat map gene expression signature of Delta- infected p16- positive cells. (k) Box plots show the expression enrichment of SARS- CoV- 2 genes (Spike, ORF1ab) for each SARS- CoV- 2 variant. Each data point in the box plot represents a normalized fold change + +<--- Page Split ---> + +value of SARS- CoV- 2 genes on p16- positive ROIs relative to p16- negative counterparts (depicted by a grid line). Whiskers represent min- max values; at least \(\mathrm{n} = 3\) p16- positive ROIs were analysed per condition; two- tailed Student's t test. + +Figure 5 Senolytics clear virus- induced senescence in specific neuronal subtypes. (a) Schematic representation of experimental design that applies to b- e. Human brain organoids were SARS- CoV- 2- infected at multiplicity of infection 1 for 5 days and subsequently exposed to the indicated senolytic treatments for 5 additional days. Analysis was performed at the end time point of the 10- day experiment. (b) SA- \(\beta\) - gal activity was evaluated at 10 days post infection. Bar graphs show the percentage of SA- \(\beta\) - gal- positive cells. Each data point in the bar graph represents a single organoid section analysed. Error bars represent s.d.; at least 5 individual organoids were analysed per variant- infected condition; one- way ANOVA with multiple- comparison post- hoc corrections. Scale bar, \(0.3\mathrm{mm}\) . (c) Total RNA from individual organoids uninfected or infected with the SARS- CoV- 2 Delta variant was used to quantify the RNA expression levels of the indicated viral genes and normalized to RPLP0 mRNA and compared to infected vehicle controls. Error bars represent s.e.m.; \(\mathrm{n} = 3\) independent organoids; one- way ANOVA with multiple- comparison post- hoc corrections; nd: not detected. (d) Stacked bars show NanoString GeoMx deconvolved p16- positive ROI cell abundance using constrained log- normal regression from organoids uninfected or infected with the SARS- CoV- 2 Delta variant. L4/5/6 IT Car3: Glutamatergic neurons; L5 ET: Cortical layer 5 pyramidal neurons; L6CT L6b: Corticothalamic (CT) pyramidal neurons in layer 6; CGE: GABAergic ganglionic eminence neurons; EC: Endothelial cells; VLMC: vascular and leptomeningeal cells. (e) Bar graphs show the percentage of deconvolved p16- positive neuronal populations significantly modulated upon SARS- CoV- 2 Delta variant infection and subsequent senolytic interventions. \(\mathrm{n} = 3\) independent ROIs per condition tested; \(* \mathrm{P} < 0.05\) ; one- way ANOVA with multiple- comparison post- hoc corrections. + +Figure 6 Senolytic treatments mitigate COVID- 19 brain pathology in vivo. (a) Schematic representation of experimental design that applies to b- h. K18- hACE2 transgenic mice were exposed to Delta variant infections on day 0 and subsequently treated with the indicated senolytics every other day starting on day 1. Two mouse cohorts were randomly allocated for scheduled euthanasia on day 5 for brain tissue characterisation as well as end time point experiments to monitor clinical score and survival. (b) Kaplan–Meier curve of uninfected mice ( \(\mathrm{n} = 3\) ), and SARS- CoV- 2- infected mice treated with vehicle ( \(\mathrm{n} = 6\) ), fisetin ( \(\mathrm{n} = 9\) ), D+Q ( \(\mathrm{n} = 8\) ), or navitoclax ( \(\mathrm{n} = 8\) ). \(* \mathrm{P} = 0.032\) for vehicle vs fisetin curve comparison; # \(\mathrm{P} = 0.0087\) for vehicle vs D+Q curve comparison; Kaplan–Meier survival analysis. (c) Graph shows the average combined behavioural and physical clinical score over time of uninfected mice ( \(\mathrm{n} = 3\) ), and SARS- CoV- 2- infected mice + +<--- Page Split ---> + +treated with vehicle \(\mathrm{(n = 6)}\) , fisetin \(\mathrm{(n = 8)}\) , \(\mathrm{D + Q}\) \(\mathrm{(n = 8)}\) , or navitoclax \(\mathrm{(n = 8)}\) . Error bars represent s.e.m.; color- coded \(^{*}\mathrm{P}< 0.05\) for comparisons between vehicle and each color- coded senolytic treatment; one- way ANOVA with multiple- comparison post- hoc corrections for every timepoint tested. (d) Total RNA of individual brains from mice uninfected or infected with the SARS- CoV- 2 Delta variant and treated with various senolytic interventions was used to quantify the RNA expression levels of the indicated viral genes and was normalized to \(Rplp0\) mRNA and compared to infected vehicle controls. Error bars represent s.e.m.; \(\mathrm{n} = 8\) mouse brains per condition; one- way ANOVA with multiple- comparison post- hoc corrections; nd: not detected. (e) Total RNA of individual brains from mice uninfected or infected with the SARS- CoV- 2 Delta variant and treated with various senolytic interventions was used to quantify the mRNA expression levels of the indicated senescence and SASP genes and was normalized to \(Rplp0\) mRNA. Each column in the heatmap represents an individual mouse brain analysed. (f) Representative immunofluorescent images of brainstem regions of coronal brain sections from uninfected or infected mice with the SARS- CoV- 2 Delta variant and treated with the indicated senolytics. Samples were immunolabelled with antibodies against TH (red; scale bar, \(100\mu \mathrm{m}\) ) and GFAP (green; scale bar, \(50\mu \mathrm{m}\) ). (g) Quantification of the TH data presented in f. Bar graph shows the intensity of tyrosine hydrolase (TH) staining within the brainstem. Each data point in the bar graph represents a single brain section analysed. Error bars represent s.d.; \(***\mathrm{P}< 0.0001\) ; 3 brains per condition were analysed; one- way ANOVA with multiple- comparison post- hoc corrections. (h) Quantification of the GFAP data presented in f. Dot plot shows the intensity of GFAP per cell within the brainstem. Each data point in the dot blot represents a single cell analysed. \(***\mathrm{P}< 0.0001\) ; 3 brains per condition were analysed; one- way ANOVA with multiple- comparison post- hoc corrections. + +<--- Page Split ---> + +## Supplementary Figure legends + +Supplementary Figure 1. (a) Schematic representation of experimental design that applies to Fig. 1,2 and to Supplementary Fig. 1b- c. 8- month- old human brain organoids were exposed to two doses of the senolytic treatments navitoclax (2.5 \(\mu \mathrm{M}\) ), ABT- 737 (10 \(\mu \mathrm{M}\) ) or D+Q (D: 10 \(\mu \mathrm{M}\) ; Q: 25 \(\mu \mathrm{M}\) ): the first one on day 1 and the second dose on day 16. Analysis was performed at the end time point of the 1- month experiment as well as at initial timepoint of 8 months organoid culture. (b) Heat map shows senescence- associated RNA transcriptomic expression of downregulated genes shared across all three senolytic interventions. (c) Functional enrichment analyses of gene expression signatures and multiple senolytic treatment of brain organoids. Heat map cells are coloured based on the normalized enrichment score (NES). + +Supplementary Figure 2. (a) Representative images of neural progenitors (Sox2), neurons (NeuN), or astrocytes (GFAP) co- stained with SARS- CoV- 2 nucleocapsid protein. Human brain organoids were 3 month- old at time of infection with the indicated SARS- CoV- 2 variants at multiplicity of infection 1. (e) Stacked bar graphs shoe quantifications from d. + +Supplementary Figure 3. (a) Venn diagram on the left shows 485 differentially expressed genes shared across SARS- CoV- 2- infected organoids and postmortem brains of COVID- 19 patients defined with a significance adjusted P value \(< 0.05\) . On the right panel, bar graph indicates the pathways enriched within this 485- gene cohort. Gene Set Enrichment Analysis was carried out using aging hallmark gene sets from the Molecular Signature Database. The statistically significant signatures were selected (FDR \(< 0.25\) ). (b) Volcano plots show uninfected versus either Wuhan or Delta- infected brain organoid differential expression of upregulated (blue) and downregulated (red) genes. DEG analysis was performed from whole- organoid RNA- seq data and p16- positive senescent- cell regions of interest (ROIs) from NanoString spatial transcriptomic sequencing. (c) Bar graph shows quantifications of nucleocapsid- positive cells from brain organoids infected with the indicated SARS- CoV- 2 variants and analysed at 5 days post infection. Each data point in the bar graph represents a single organoid section analysed. Error bars represent s.d.; at least 7 individual organoids were analysed per variant- infected condition; one- way ANOVA with multiple- comparison post- hoc corrections. + +Supplementary Figure 4. (a) Principal component analysis from NanoString spatial transcriptomic sequencing of p16- positive cells in the subspace defined by these differential genes showing clustering of uninfected and Wuhan- infected human brain organoids away from the Delta- infected counterparts. (b) Total RNA from individual organoids uninfected or infected with the SARS- CoV- 2 Delta variant was used to quantify Lamin B1 mRNA expression levels and + +<--- Page Split ---> + +normalized to RPLP0 mRNA and compared to infected vehicle controls. Error bars represent s.d.; \(\mathrm{n} = 3\) independent organoids; one- way ANOVA with multiple- comparison post- hoc corrections. Supplementary Figure 5. (a) Representative immunofluorescent images of viral nucleocapsid (NC) antigen in whole brain coronal sections of brains from SARS- CoV- 2- infected K18- hACE2 transgenic mice (5 days post infection). CTX: Cerebral cortex; BS: Brainstem. (b) Percentage weight loss up to 7 days post infection. Uninfected mice \((\mathrm{n} = 3)\) , and Delta SARS- CoV- 2- infected mice treated with vehicle \((\mathrm{n} = 6)\) , fisetin \((\mathrm{n} = 8)\) , \(\mathrm{D} + \mathrm{Q}\) \((\mathrm{n} = 8)\) , or navitoclax \((\mathrm{n} = 8)\) . + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + + +
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Figure 6
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Supplementary Figure 4
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PrimerTargetSequence (5'-3' orientation)
RPLP0Human and mouseFwTTCATTGTGGGAGCAGAC
RvCAGCAGTTTCTCCAGAGC
RdRPSARS-CoV-2FwCATGTGTGGCGGTTCACTAT
RvTGCATTAACATTGGCCGTGA
SpikeSARS-CoV-2FwCTACATGCACCAGCAACTGT
RvCACCTGTGCCTGTTAAACCA
EnvelopeSARS-CoV-2FwTTCGGAAGAGACAGGTACGTT
RvCACACAATCGATGGCGAGTA
NucleocapsidSARS-CoV-2FwCAATGCTGCAATCGTGCTAC
RvGTTGCGACTACGTGATGAGG
Lamin B1HumanFwCTCTCGTCGCATGCTGACAG
RvTCCCTTATTTCCGCCATCTCT
II8MouseFwGTCCTTAACCTAGGCATCTTCG
RvTCTGTTGCAGTAAATGGTCTCG
II6MouseFwGCTACCAAACTGGATATAATCAGGA
RvCCAGGTAGCTATGGTACTCCAGAA
p16MouseFwAATCTCCGCGAGGAAAGC
RvGTCTGCAGCGGACTCCAT
Mmp12MouseFwTTCATGAACAGCAACAAGGAA
RvTTGATGGCAAGGTGGTACA
II1aMouseFwTTGGTTAAATGACCTGCAAAC
RvGAGCGCTCACGAACAGTTG
Ccl2MouseFwCATCCACGTGTTGGCTCA
RvGATCATCTTGCTGGTGAATGAGT
II1bMouseFwAGTTGACGGACCCCAAAAG
RvAGCTGGATGCTCTCATCAGG
Cxcl10MouseFwGCCGTCATTTTCTGCCCTA
RvCGTTCCTTGCGAGAGGGATC
+ +<--- Page Split ---> diff --git a/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/images_list.json b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c33ecf6e1a4a496272b4120f1f13c2f99ac1fd1a --- /dev/null +++ b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/images_list.json @@ -0,0 +1,220 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Methodological framework. A. List of 27 tissues used in this study. B. Distribution of 19,132 genes by the number of tissues in which they are highly expressed. C. Bimodal expression is a property of a gene-tissue pair. We tested 516,564 gene-tissue pairs (19,132 genes x 27 tissues) for bimodal expression across individuals. When a gene-tissue pair exhibits switch-like (bimodal) expression, the individuals divide into two subpopulations: one with the gene switched off, and the other with the gene switched on.", + "footnote": [], + "bbox": [ + [ + 115, + 115, + 880, + 500 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Categorization of switch-like genes. A. PCA analysis of tissue-pair correlations of gene expression. Each point represents a gene. When we perform PCA on the tissue-to-tissue co-expression vectors for 19,132 genes, the switch-like genes divide into two clusters. Cluster 1 primarily represents genes that are bimodally expressed in a tissue-specific manner, while cluster 2 represents genes that are bimodally expressed in at least all non-sex-specific tissues. B. Performing PCA on the co-expression vectors of only switch-like genes further divides cluster 2 into two subclusters: cluster 2A, which contains genes that are bimodally expressed across all 27 tissues, and cluster 2B, which contains genes that are bimodally expressed in all 22 tissues common to both sexes, but not in the five sex-specific tissues. C-E. Violin plots display the expression levels in all 27 tissues for representative genes from cluster 1, cluster 2A, and cluster 2B, respectively.", + "footnote": [], + "bbox": [ + [ + 115, + 90, + 880, + 518 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. An example of a polymorphic gene deletion resulting in universally switch-like gene expression. A. FAM106A and USP32P2 (not drawn to scale) are overlapping genes on chromosome 17. Two alternative haplotype classes exist for these genes: one in which both genes are completely deleted and the other without the deletion. B. Frequency distribution of the deletion across diverse populations. Each pie chart represents one of the 26 populations from the 1000 Genomes Project. Purple indicates the frequency of the deletion, while gray indicates the frequency of the alternative haplotype. C-D. Expression level distribution in the cerebellum (as an example) across individuals for FAM106A and USP32P2,", + "footnote": [], + "bbox": [], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Characterization of genuine tissue-specific switch-like genes (cluster 1). The results shown here exclude genes that showed switch-like expression due to confounding factors like ischemic time. A. Number of tissue-specific switch-like genes showing bimodal expression in each of the 27 tissues. The stomach, vagina, breast, and colon show disproportionately more tissue-specific switch-like genes than other tissues. B. An illustration of how Pearson's correlation coefficients were calculated for each pair of bimodally expressed tissue-specific switch-like genes within the stomach, vagina, breast, and colon. We show the scatterplots for two arbitrarily chosen gene pairs for each of the four tissues. The axes in each dot plot represent the \\(\\log (TPM + 1)\\) for the labeled gene in the relevant tissue. Panel C was generated using the pairwise correlation coefficients thus obtained. C. Tissue-specific switch-like genes within the four tissues shown are highly co-expressed. Tissue-specific master regulators, such as endocrinological signals, likely drive their concordant on and off states.", + "footnote": [], + "bbox": [ + [ + 115, + 280, + 875, + 673 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Sex-biased expression of tissue-specific switch-like genes (cluster 1). A. Number of tissue-specific switch-like genes that show female- and male-biased expression. Only those tissues are shown that have at least one tissue-specific switch-like gene showing sex bias. The number in the central grid next to each tissue image represents the number of genuine tissue-specific switch-like genes in that tissue. In orange, the numbers to the left of the central grid indicate the count of female-biased genes in each of the 10 tissues shown. In blue, the numbers to the right of the grid indicate the count of male-biased genes. B. Violin plots showing the expression level distribution in the breast for five female-biased tissue-specific switch-like genes discussed in the main text.", + "footnote": [], + "bbox": [ + [ + 117, + 90, + 875, + 640 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. Atrophy-linked switch-like genes tend to be either all switched off, or all switched on within individuals. A. The distribution of expression levels in the vagina of the six switch-like genes implicated in vaginal atrophy. The x-axes represent \\(\\log (TPM + 1)\\) values for each gene in the vagina, and the y-axes represent the probability density. We obtained the probability densities using kernel density estimation. In each case, the global minimum (excluding endpoints) is considered the switching threshold. A gene is deemed “on” in an individual if the expression level is above this threshold; otherwise, the gene is deemed “off.” B. Pairwise concordance rates (percentage of individuals in which the two genes are either both switched on or both switched off).", + "footnote": [], + "bbox": [ + [ + 115, + 88, + 850, + 740 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Figure 8. ALOX12 is a passenger gene. A. Model for the etiology of vaginal atrophy. High levels of", + "footnote": [], + "bbox": [ + [ + 114, + 88, + 875, + 850 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figure S1. The mean tissue-to-tissue co-expression of genes shows a near-perfect correlation with PC1.", + "footnote": [], + "bbox": [ + [ + 117, + 271, + 644, + 569 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "Figure S2. Violin plots for expression level distributions of switch-like genes in cluster 2A.", + "footnote": [], + "bbox": [ + [ + 113, + 88, + 884, + 711 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "Figure S3. Violin plots for expression level distributions of switch-like genes in cluster 2A.", + "footnote": [], + "bbox": [ + [ + 115, + 90, + 880, + 602 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "**Figure S4. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 5%.**", + "footnote": [], + "bbox": [ + [ + 120, + 285, + 880, + 722 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_4.jpg", + "caption": "Figure S5. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 10%.", + "footnote": [], + "bbox": [ + [ + 115, + 88, + 875, + 530 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_5.jpg", + "caption": "Figure S6. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of \\(50\\%\\) .", + "footnote": [], + "bbox": [ + [ + 115, + 90, + 875, + 528 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_6.jpg", + "caption": "Figure S7. Switch-like genes in cluster 1 that are genuine versus those affected by confounders.", + "footnote": [], + "bbox": [ + [ + 116, + 177, + 680, + 519 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_7.jpg", + "caption": "Figure S8. Examples of cluster-1 genes affected by confounders. Their bimodal distribution is caused by ischemic time (a confounding factor).", + "footnote": [], + "bbox": [ + [ + 115, + 92, + 700, + 465 + ] + ], + "page_idx": 32 + } +] \ No newline at end of file diff --git a/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8.mmd b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1861f530ea40ffef19457c2010902e14ed1113b4 --- /dev/null +++ b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8.mmd @@ -0,0 +1,584 @@ + +# Switch-like Gene Expression Modulates Disease Susceptibility + +Omer Gokcumen gokcumen@gmail.com + +Article + +Keywords: + +Posted Date: September 13th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4974188/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on June 18th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 60513- x. + +<--- Page Split ---> + +# Switch-like Gene Expression Modulates Disease Susceptibility + +Authors: Alber Aqil1, †, Yanyan Li2, †, Zhiliang Wang2, Saiful Islam3, Madison Russell2, Theodora Kunovac Kallak4, Marie Saitou5, Omer Gokcumen1, †, Naoki Masuda2,3, † + +## Affiliations: + +1. Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY, USA. +2. Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, USA. +3. Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY, USA. +4. Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden. +5. Faculty of Biosciences, Norwegian University of Life Sciences, Aas, Norway + +Correspondence: Omer Gokcumen, gokcumen@gmail.com Naoki Masuda, naokimas@gmail.com + +## Abstract + +A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch- like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch- like genes. We found that only 31 (3.1%) of these genes exhibit switch- like behavior across all tissues. These universally switch- like genes appear to be genetically driven, with large exonic genomic structural variants explaining five ( \(\sim 18\%\) ) of them. The remaining switch- like genes exhibit tissue- specific expression patterns. Notably, tissue- specific switch- like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue- specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched- off genes in the stomach and vagina that are linked to gastric cancer (41- fold, \(p< 10^{- 4}\) ) and vaginal atrophy (44- fold, \(p< 10^{- 4}\) ), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch- like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch- like gene expression and lay the groundwork for potential diagnostic and therapeutic applications. + +<--- Page Split ---> + +## Introduction + +The study of gene expression began in earnest with the characterization of lactose- metabolizing switch- like genes in \(E\) coli 1. The presence of lactose triggered the production of enzymes needed to metabolize it, while these enzymes were absent when lactose was not present. These genes acted like switches, toggling between "on" and "off" states based on the presence or absence of lactose, respectively. In subsequent decades, the discovery of enhancer elements 2- 4, epigenetic modifications 5- 8, and transcription factor dynamics 9 revealed that gene expression in humans is more nuanced, resembling a dimmer more often than a simple on- and- off mechanism. Consequently, the study of switch- like genes in humans was largely relegated to the narrow realm of Mendelian diseases 10- 12. + +The recent availability of population- level RNA- sequencing data from humans has made it possible to systematically identify switch- like versus dimmer- like genes. For dimmer- like genes in a given tissue, we expect expression levels across individuals to be continuously distributed with a single mode, i.e., a unimodal distribution. In contrast, expression levels of switch- like genes in a given tissue are expected to exhibit a bimodal distribution, with one mode representing the "off" state and the other representing the "on" state. As we will detail, bimodal expression across individuals is a characteristic of a gene in a specific tissue, referred to as a gene- tissue pair. We define a gene as switch- like if it exhibits bimodal expression in at least one tissue. Most of the recent studies on bimodal gene expression are related to cancer biology, associating on and off states to different disease phenotypes and their prognoses 13- 15. These cancer studies have already produced promising results for personalized medicine 16. However, to our knowledge, the only study focusing on switch- like genes in non- cancerous tissues across individuals restricted their analysis to muscle tissue 17. As a result, the dynamics of switch- like expression across the multi- tissue landscape remain unknown. We hypothesize that switch- like expression is ubiquitous but often tissue- specific. We further hypothesize that these tissue- specific expression trends underlie common disease states. Therefore, the analysis of switch- like genes across tissues and individuals may provide a means for early diagnosis and prediction of human disease. + +Here, we systematically identified switch- like genes across individuals in 27 tissues. Our results explain the regulatory bases of switch- like expression in humans, highlighting genomic structural variation as a major factor underlying correlated switch- like expression in multiple tissues. Furthermore, we identified groups of switch- like genes in the stomach and vagina for which the "off" state predisposes individuals to gastric cancer and vaginal atrophy, respectively. Overall, these findings improve our understanding of the regulation of switch- like genes in humans. They also suggest promising future paths for preventative biomedical interventions. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Methodological framework. A. List of 27 tissues used in this study. B. Distribution of 19,132 genes by the number of tissues in which they are highly expressed. C. Bimodal expression is a property of a gene-tissue pair. We tested 516,564 gene-tissue pairs (19,132 genes x 27 tissues) for bimodal expression across individuals. When a gene-tissue pair exhibits switch-like (bimodal) expression, the individuals divide into two subpopulations: one with the gene switched off, and the other with the gene switched on.
+ +<--- Page Split ---> + +## RESULTS + +## Tissue-specificity of bimodal expression + +The misregulation of highly expressed genes often has consequences for health and fitness. To systematically identify biomedically relevant switch- like genes in humans, we focused on 19,132 genes that are highly expressed (mean \(\mathrm{TPM} > 10\) ) in at least one of the 27 tissues represented in the GTEx database (Figure 1A; Figure 1B; Table S1). For each of the 516,564 gene- tissue pairs (19,132 genes \(\times 27\) tissues), we applied the dip test of unimodality \(^{18}\) to the expression level distribution across individuals (Figure 1C). Employing the Bejamini- Hochberg procedure for multiple hypotheses correction, we identified 1,013 switch- like genes (Figure 1C; Methods; Table S2). The expression of these genes is bimodally distributed in at least one tissue, such that it is switched "off" for one subset of individuals and switched "on" for the rest of the individuals. + +Expression of different switch- like genes may be bimodally distributed in different numbers of tissues. We contend that genes that are bimodally expressed across all tissues are likely so due to a germline genetic polymorphism driving switch- like expression across tissues. If this is the case, the expression of these genes would be highly correlated across pairs of tissues. Given this insight, discovering universally bimodal genes is more tractable using tissue- to- tissue co- expression of each gene. Therefore, for each gene, we calculated the pairwise correlation of expression levels across pairs of tissues (Methods; Table S3). To visualize tissue- to- tissue co- expression patterns of genes, we performed principal component analysis (PCA) on the tissue- to- tissue gene co- expression data (Table S4). We emphasize that we are referring to the co- expression of the same gene across pairs of tissues instead of the co- expression of pairs of genes in the same tissue. In the space spanned by the first two principal components (explaining \(35.3\%\) and \(3.47\%\) of the variance, respectively), switch- like genes form two major clusters (cluster 1 and cluster 2; Methods), dividing along PC1 (Figure 2A). Applying PCA exclusively to switch- like genes reveals the further division of cluster 2 into two distinct subclusters – cluster 2A and cluster 2B – in the space spanned by the first two principal components (explaining \(58.1\%\) and \(4.25\%\) of the variance, respectively) (Figure 2B; Table S5). + +Manual inspection reveals that cluster 1, which contains 954 genes, represents genes, such as KRT17, with bimodal expression in a small subset of tissues (Figure 2C). Cluster 2A consists of 23 genes, such as GPX1P1, with bimodal expression in all tissues (Figure 2D). Lastly, cluster 2B represents eight genes, such as EIF1AY, with bimodal expression in all non- sex- specific tissues but not in sex- specific tissues (Figure 2E). We will refer to genes in cluster 1 as "tissue- specific switch- like genes." Although some of them are bimodally expressed in more than one tissue, these genes tend to exhibit high tissue specificity in their bimodal expression. Genes in cluster 2 will be referred to as "universally switch- like genes." + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. Categorization of switch-like genes. A. PCA analysis of tissue-pair correlations of gene expression. Each point represents a gene. When we perform PCA on the tissue-to-tissue co-expression vectors for 19,132 genes, the switch-like genes divide into two clusters. Cluster 1 primarily represents genes that are bimodally expressed in a tissue-specific manner, while cluster 2 represents genes that are bimodally expressed in at least all non-sex-specific tissues. B. Performing PCA on the co-expression vectors of only switch-like genes further divides cluster 2 into two subclusters: cluster 2A, which contains genes that are bimodally expressed across all 27 tissues, and cluster 2B, which contains genes that are bimodally expressed in all 22 tissues common to both sexes, but not in the five sex-specific tissues. C-E. Violin plots display the expression levels in all 27 tissues for representative genes from cluster 1, cluster 2A, and cluster 2B, respectively.
+ +## Genetic variation underlies universally switch-like genes + +We found that \(3.1\%\) of all switch- like genes (i.e., the proportion of switch- like genes that are in cluster 2) show clear bimodal expression, at least in all tissues common to both sexes. We contend that germline genetic variation across individuals likely underlies the universally switch- like gene expression, specifically due to four major types of genetic variants. Firstly, we expect genes on the Y chromosome to show bimodal expression in all tissues common to both sexes since these genes are present in males and absent in females (Figure 3A). Consistent with this reasoning, seven out of the eight genes in cluster 2B lie within the male- specific region of the Y- chromosome \(^{19}\) ; the remaining + +<--- Page Split ---> + +gene in cluster 2B is XIST, showing female- specific expression. Secondly, a homozygous gene deletion would result in the gene being switched off (Figure 3B). We found five such genes in cluster 2A for which genomic structural variants likely underlie the observed universally switch- like expression; four genes are affected by gene deletions, and the remaining one by an insertion into the gene. Thirdly, the homozygous deletion of a regulatory element can also switch off a gene (Figure 3C). While we did not find any examples of this scenario, it remains a theoretical possibility. Lastly, a loss- of- function single nucleotide variant (SNV) or short indel, which disrupts gene function, can switch off the gene (Figure 3D). We identified five genes in cluster 2A where such SNVs cause universal bimodality. + +Remarkably, we could genetically explain the expression of 10 out of 23 (43%) cases in cluster 2A despite the small number of genes fitting our conservative definition for universally switch- like genes. SNVs underlie five of these cases (Figure 3B), while structural variants underlie the remaining five cases (Figure 3D). Thus, out of the 10 cases where we can explain the genetic underpinnings of switch- like expression, 50% involve genomic structural variation, highlighting the importance of this type of genetic variation. Although we could not identify the genetic variation underlying the bimodal expression of the remaining 13 genes in cluster 2A, their consistent and highly correlated switch- like expression across all tissues strongly suggests a genetic basis. We anticipate that better resolution assemblies and detailed regulatory sequence annotations will help identify the genetic variants responsible for the remaining universally switch- like genes. + +![](images/Figure_4.jpg) + + +<--- Page Split ---> + +Figure 3. Genetic bases of universally switch-like gene expression (cluster 2). A. Genes on the Y chromosome are expressed only in males, leading to bimodal expression in non- sex- specific tissues. B. Common structural variants, such as deletions or insertions, may lead to increased, decreased, or no expression in all tissues relative to individuals who carry the alternative allele. C. Common structural variants affecting a genomic region regulating a gene may lead to increased, decreased, or no expression in all tissues, relative to individuals who carry the alternative allele. D. Common single nucleotide variants or short indels affecting a gene or its regulatory region may lead to increased, decreased, or no expression in all tissues relative to individuals who carry the alternative allele. + +We highlight a clear example of a common structural variant leading to universally switch- like expression (Figure 3B). USP32P2 and FAM106A – both universally switch- like genes – are bimodally expressed in all 27 tissues. Both genes show high levels of tissue- to- tissue co- expression. A common 46 kb deletion (esv3640153), with a global allele frequency of \(\sim 25\%\) , completely deletes both genes (Figure 4A- B). We propose that this deletion accounts for the universal switch- like expression of both USP32P2 and FAM106A in all tissues. For illustration, we show the expression level distributions of USP32P2 and FAM106A in the cerebellum (Figures 4C- D). Indeed, the haplotype harboring this deletion is strongly associated with the downregulation of both genes in all 27 tissues \((p< 10^{- 5}\) for every single gene- tissue pair, Methods). We note that the under- expression of USP32P2 in sperm is associated with male infertility \(^{20}\) , and plausibly, homozygous males for the deletion may be prone to infertility. Additionally, FAM106A interacts with SARS- CoV- 2 and is downregulated after infection, at least in lung- epithelial cells \(^{21 - 23}\) . Individuals with FAM106A already switched off may develop more severe COVID- 19 symptoms upon infection, though further investigation is needed. The case of FAM106A and USP32P2 exemplifies the link between disease and bimodal gene expression, a theme we will explore further in the remainder of this text. + +We caution that we base our results regarding bimodality on expression at the RNA level. The bimodal expression of genes across individuals at the RNA level may not necessarily lead to bimodal expression at the protein level. For example, the universally switch- like expression of RPS26 at the RNA level can be explained by a single nucleotide variant (rs1131017) in the gene's 5'- untranslated region (UTR). In particular, RPS26 has three transcription states based on the SNV genotypes. The ancestral homozygote C/C corresponds to a high transcription state, the heterozygote C/G to a medium state, and the derived homozygote G/G to a low state (See Supplement for a discussion on why an expression distribution driven by three genotypes at a polymorphic site might still appear bimodal). Remarkably, this pattern is reversed at the translation level \(^{24}\) : Messenger RNA carrying the derived G allele produces significantly more protein. This reversal may be due to a SNV in the 5'- UTR that can abolish a translation- initiation codon \(^{25}\) . This finding demonstrates how the same SNV can regulate a gene's expression level in opposite directions during transcription and translation. This multi- level regulation in opposite directions likely serves to dampen protein expression variability. It has been shown previously that RNA variability is greater than protein variability in primates \(^{26,27}\) ; the presence of dampening variants discussed here may be one reason behind these findings. Such compensatory mechanisms for gene expression remain fascinating areas for future research. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 4. An example of a polymorphic gene deletion resulting in universally switch-like gene expression. A. FAM106A and USP32P2 (not drawn to scale) are overlapping genes on chromosome 17. Two alternative haplotype classes exist for these genes: one in which both genes are completely deleted and the other without the deletion. B. Frequency distribution of the deletion across diverse populations. Each pie chart represents one of the 26 populations from the 1000 Genomes Project. Purple indicates the frequency of the deletion, while gray indicates the frequency of the alternative haplotype. C-D. Expression level distribution in the cerebellum (as an example) across individuals for FAM106A and USP32P2,
+ +<--- Page Split ---> + +respectively. The gene deletion presumably leads to the switched- off expression state in both genes. + +## Tissue-specific switch-like genes have a shared regulatory framework + +Tissue- specific expression patterns are crucial for tissue function. Thus, we now turn our attention to tissue- specific switch- like genes. We found that the stomach, vagina, breast, and colon show a higher number of tissue- specific switch- like genes compared to other tissues (Figure 5A), after controlling for confounding factors (Methods; Supplement; Table S6). Furthermore, within these tissues, the expression of switch- like genes is not independent; instead, they exhibit high pairwise co- expression between genes (Figure 5B- C; Table S7). Hence, tissue- specific switch- like genes tend to be either all switched off or switched on within an individual. This result suggests a shared regulatory mechanism for the expression of these genes in each tissue. Given that hormonal regulation plays a substantial role in shaping tissue- specific expression patterns \(^{28,29}\) , we hypothesize that hormones may regulate genes that are bimodally expressed in specific tissues (cluster 1; Figure 2B). + +Sexual differences in hormonal activity are well documented \(^{30,31}\) . To explore this further, we investigated whether hormone- mediated sex- biased expression underlies the co- expression of tissue- specific switch- like genes within tissues. Under this scenario, a gene would be largely switched on in one sex and off in the other in a given tissue. Among tissue- specific switch- like genes, we identified 186 gene- tissue pairs with sex- biased bimodal expression (Figure 6A; Table S8). These instances are biologically relevant; for example, we found switch- like immunoglobulins genes with female- biased expression in the thyroid, heart, tibial nerve, and subcutaneous adipose tissue. This observation may relate to previous findings \(^{32,33}\) of higher antibody responses to diverse antigens in females than in males. + +More dramatically, we found that 162 out of 164 tissue- specific switch- like genes (cluster 1) in the breast tissue are female- biased, explaining their correlated expression levels (Figure 6A). However, the sex- based disparity in the on- versus- off states of these genes is not absolute, but rather a statistical tendency. In other words, the gene is not switched off in all males and switched on in all females. Instead, the proportion of individuals with the gene switched on significantly differs between sexes. Notably, multiple sex- biased switch- like genes—including SPINT1 and SPINT2 \(^{34}\) , multiple keratin genes \(^{35}\) , and the oxytocin receptor gene \(^{36,37}\) (OXTR; Figure 6B)—in the breast tissue are differentially expressed in breast cancers relative to matched non- cancerous tissues. Future investigations could reveal whether the toggling of these genetic switches affects breast cancer risk in females. We caution that sex- biased switch- like expression in the breast may result from differences in cell- type abundance between females and males. Nevertheless, the differential expression of some genes between sexes might developmentally drive such differences in cell- type abundance. In summary, our results indicate that sex is a major contributor to bimodal gene expression, with breast tissue standing out as particularly sex- biased in this context. + +We note that the intra- tissue co- expression of tissue- specific switch- like genes in the + +<--- Page Split ---> + +stomach and colon cannot be explained by sex. By biological definition, the variation in vaginal expression levels in our sample is not sex- biased. Thus, the intra- tissue co- expression of tissue- specific switch- like genes in the stomach, colon, and vagina may be explained by one of two reasons: 1) Most of the tissue- specific switch- like genes in each tissue are directly regulated by the same hormone in that tissue, or 2) Most of the tissue- specific switch- like genes in each tissue are regulated by the same transcription factor which is, in turn, under regulation by a hormone or other cellular environmental factors. In the case of hormonally controlled gene expression, genes are likely switched off when the systemic hormone levels drop below a certain threshold. We will discuss this idea further, specifically for the vagina, later in the text. + +![](images/Figure_6.jpg) + +
Figure 5. Characterization of genuine tissue-specific switch-like genes (cluster 1). The results shown here exclude genes that showed switch-like expression due to confounding factors like ischemic time. A. Number of tissue-specific switch-like genes showing bimodal expression in each of the 27 tissues. The stomach, vagina, breast, and colon show disproportionately more tissue-specific switch-like genes than other tissues. B. An illustration of how Pearson's correlation coefficients were calculated for each pair of bimodally expressed tissue-specific switch-like genes within the stomach, vagina, breast, and colon. We show the scatterplots for two arbitrarily chosen gene pairs for each of the four tissues. The axes in each dot plot represent the \(\log (TPM + 1)\) for the labeled gene in the relevant tissue. Panel C was generated using the pairwise correlation coefficients thus obtained. C. Tissue-specific switch-like genes within the four tissues shown are highly co-expressed. Tissue-specific master regulators, such as endocrinological signals, likely drive their concordant on and off states.
+ +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 6. Sex-biased expression of tissue-specific switch-like genes (cluster 1). A. Number of tissue-specific switch-like genes that show female- and male-biased expression. Only those tissues are shown that have at least one tissue-specific switch-like gene showing sex bias. The number in the central grid next to each tissue image represents the number of genuine tissue-specific switch-like genes in that tissue. In orange, the numbers to the left of the central grid indicate the count of female-biased genes in each of the 10 tissues shown. In blue, the numbers to the right of the grid indicate the count of male-biased genes. B. Violin plots showing the expression level distribution in the breast for five female-biased tissue-specific switch-like genes discussed in the main text.
+ +## Concordantly switched-off genes in the stomach may indicate a predisposition to gastric cancer + +Gene expression levels have been studied as a diagnostic marker for disease states \(^{38}\) . + +<--- Page Split ---> + +Therefore, we asked whether tissue- specific switch- like genes co- expressed with each other across individuals are linked to human disease, with each of the two expression states corresponding to different risks. To address this question, we investigated whether the identified switch- like genes in a given tissue are overrepresented among genes implicated in diseases of the same tissue. + +We overlapped the switch- like genes in the stomach with a previously published list \(^{39}\) of differentially expressed genes in gastric carcinomas. We found that switch- like genes in the stomach are significantly enriched (41- fold enrichment, \(p< 10^{- 4}\) ) among genes that are downregulated in gastric carcinomas. Specifically, nine switch- like genes are downregulated in gastric carcinomas (ATP4A, ATP4B, CHIA, CXCL17, FBP2, KCNE2, MUC6, TMEM184A, and PGA3). Additionally, these nine genes are concordantly expressed in \(92.5\%\) (332/359) of the stomach samples, being either all switched off or on in a given individual (Methods). Our data suggest that individuals with these nine genes switched off in the stomach may be susceptible to developing cancers. This preliminary observation provides exciting avenues to investigate both the cause of the concordant toggling of these genes and their potential role in cancer development. + +## Concordantly switched-off genes result in vaginal atrophy + +We found that switch- like genes in the vagina are significantly overrepresented (44- fold enrichment; \(p< 10^{- 4}\) ; see methods) among genes linked to vaginal atrophy in postmenopausal women. Vaginal atrophy, affecting nearly half of postmenopausal women, is triggered by sustained low levels of systemic estrogen and is marked by increased microbial diversity, higher pH, and thinning of the epithelial layer in the vagina \(^{40,41}\) . It is also known as atrophic vaginitis, vulvovaginal atrophy, estrogen- deficient vaginitis, urogenital atrophy, or genitourinary syndrome of menopause, depending on the specialty of the researchers. Symptoms experienced by women include dryness, soreness, burning, decreased arousal, pain during intercourse, and incontinence \(^{42}\) . Our analysis of switch- like genes in the vagina provides new insights into the development of vaginal atrophy. + +Specifically, we overlapped a previously published list \(^{43}\) of genes that are transcriptionally downregulated in vaginal atrophy with our list of bimodally expressed genes in the vagina. We found that the genes SPINK7, ALOX12, DSG1, KRTDAP, KRT1, and CRISP3 are both bimodally expressed in the vagina and transcriptionally downregulated (presumably switched off) in women with vaginal atrophy (Figure 7A). We refer to these genes as "atrophy- linked switch- like genes." Indeed, these six genes are either all switched on, or all switched off concordantly in \(84\%\) (131/156) of the vaginal samples we studied. The pairwise concordance rates (percentage of individuals with both genes switched on or both genes switched off) for these genes are shown in Figure 7B. Among postmenopausal women with this concordant gene expression, \(50\%\) are in the "off" state – a fraction that closely matches the prevalence of vaginal atrophy in postmenopausal women \(^{40,44}\) . Therefore, our data suggest that estrogen- dependent transcription underlies concordant expression of atrophy- linked switch- like genes, with + +<--- Page Split ---> + +the "off" state of these genes associated with vaginal atrophy. + +For background, the vaginal epithelial layers are differentiated from the inside out. The basal and parabasal layers of the epithelium consist of mitotic progenitor cells with differentiation potential, while the outermost layer comprises the most differentiated cells \(^{45,46}\) . When basal and parabasal cells stop proliferating, the death of mature cells leads to a thin epithelium, and the symptoms of vaginal atrophy appear. Given this background, atrophy- linked switch- like genes may either be a cause or a consequence of vaginal atrophy. In particular, if an atrophy- linked switch- like gene encodes a protein necessary for the continued proliferation and differentiation of basal and parabasal cells, we call it a "driver" gene. In the absence of the driver gene's protein, cell differentiation ceases, and the outer layer gradually disappears, resulting in vaginal atrophy (Figure 8A). On the other hand, if the product of an atrophy- linked switch- like gene is not required for basal and parabasal cell proliferation, we refer to it as a "passenger" gene, borrowing the terminology from cancer literature \(^{47}\) . In healthy vaginas with a thick epithelium, there are more cells in which passenger genes would be expressed. By contrast, in atrophic vaginas, the epithelium thins, resulting in fewer cells where these genes can be expressed. This contrast would lead to the bimodal expression of passenger genes across vagina samples in whole- tissue RNA- sequencing datasets. We hypothesize that at least some of the atrophy- linked switch- like genes are driver genes. + +Two key findings allowed us to construct this hypothesis. Firstly, switch- like genes in the vagina show a 26- fold ontological enrichment for the establishment of the skin barrier \(\mathrm{(FDR = 1.26\times 10^{- 6})}\) and a 25- fold enrichment for keratinocyte proliferation \(\mathrm{(FDR = 1.75\times}\) \(10^{- 4})\) , both related to epithelial thickness and differentiation. Notably, two atrophy- linked switch- like genes in the vagina that we identified, KRTDAp and KRT1, are crucial for the differentiation of epithelial cells in the vagina \(^{48,49}\) . Protein stainings available through Human Protein Atlas \(^{50}\) show that all six atrophy- linked switch- like genes are expressed at the protein level, predominantly in the vaginal epithelium. Secondly, administering 17β- estradiol (a type of estrogen) to postmenopausal women with vaginal atrophy leads to the upregulation of the same six genes, causing symptoms to subside \(^{51}\) . According to our hypothesis, administering estrogen activates the expression of the driver switch- like genes in the vagina, resuming the proliferation of basal and parabasal cells in the epithelium. This process leads to the reformation of a thick and healthy vaginal mucosa, thereby alleviating the symptoms of vaginal atrophy. + +Thus, it is essential to distinguish driver genes from passenger genes to understand the etiology of vaginal atrophy. However, we expect driver and passenger genes to show the same expression patterns in healthy versus atrophic vaginas using bulk RNA- sequencing data. In order to make this distinction, we need comparative expression data, specifically from the basal and parabasal epithelium from healthy versus atrophic vaginas. We expect driver genes to be differentially expressed in the basal and parabasal layers of the epithelium. By contrast, we expect passenger genes to show no differential expression in the basal and parabasal layers between healthy and atrophic + +<--- Page Split ---> + +vaginas. + +To look at the expression levels in the basal and parabasal layers of the epithelium, we arbitrarily chose ALOX12 from the six atrophy- linked switch- like genes for immunohistochemical staining of its protein product in the vaginal mucosa (which includes the epithelium and the underlying connective tissue). We found that the ALOX12 protein is present in the epithelial cells, and its abundance directly correlates with epithelial thickness, as expected from our RNA- sequencing results. However, we found no significant difference in the staining of the ALOX12 protein in the basal or parabasal epithelial layers between healthy and atrophic samples (Figure 8B). This suggests that the gene is not differentially expressed in the basal or parabasal layers of the vaginal epithelium between healthy and atrophic vaginas. Therefore, ALOX12 is a passenger gene for vaginal atrophy. Comparative immunohistochemical staining of the protein product of the other five atrophy- linked switch- like genes may identify the driver gene in the future. Indeed, the KRT1 protein is recognized as a marker of basal cell differentiation in mouse vaginas \(^{52}\) , a finding that may also be true for humans. Overall, our results open up several new paths for potential pre- menopausal risk assessment and intervention frameworks targeting cell differentiation pathways in the clinical setting. + +<--- Page Split ---> +![](images/Figure_8.jpg) + +
Figure 7. Atrophy-linked switch-like genes tend to be either all switched off, or all switched on within individuals. A. The distribution of expression levels in the vagina of the six switch-like genes implicated in vaginal atrophy. The x-axes represent \(\log (TPM + 1)\) values for each gene in the vagina, and the y-axes represent the probability density. We obtained the probability densities using kernel density estimation. In each case, the global minimum (excluding endpoints) is considered the switching threshold. A gene is deemed “on” in an individual if the expression level is above this threshold; otherwise, the gene is deemed “off.” B. Pairwise concordance rates (percentage of individuals in which the two genes are either both switched on or both switched off).
+ +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figure 8. ALOX12 is a passenger gene. A. Model for the etiology of vaginal atrophy. High levels of
+ +<--- Page Split ---> + +estrogen keep the driver genes switched on in basal and parabasal epithelium, impelling basal and parabasal cells to proliferate and mature, resulting in healthy vaginal mucosa. Conversely, low levels of estrogen switch off the driver genes. The lack of basal and parabasal cell proliferation leads to a thin vaginal epithelium, resulting in vaginal atrophy. B. Representative immunohistochemical staining of Arachidonate 12- Lipoxygenase (ALOX12) in vaginal tissue. We show healthy vaginal tissue from a woman with higher systemic estrogen levels and a thicker vaginal epithelial layer, along with atrophic vaginal tissue from a woman with low systemic estrogen levels and a thinner vaginal epithelial layer. There is no difference in ALOX12 expression in the basal or parabasal cells between healthy and atrophic epithelium, implicating it as a passenger gene. Images taken with Axio Observer Z1 (Carl Zeiss AG) with a 40X objective. + +## Discussion + +In this study, we investigated factors underlying switch- like gene expression and its functional consequences. Our systematic analysis revealed 1,013 switch- like genes across 943 individuals. Some of these genes show bimodal expression across individuals in all tissues, suggesting a genetic basis for their universally switch- like behavior. We found several single nucleotide and structural variants to explain the switch- like expression of these genes. Most of the switch- like genes, however, exhibit tissue- specific bimodal expression. These genes tend to be concordantly switched on or off in individuals within the breast, colon, stomach, and vagina. This concordant tissue- specific switch- like expression in individuals is likely due to tissue- specific master regulators, such as endocrinological signals. For example, in the vagina, switch- like genes tend to get concordantly switched off in a given individual when systemic estrogen levels fall below a certain threshold. On the biomedical front, our work linked switch- like expression to the susceptibility to gastric cancer and vaginal atrophy. Furthermore, this study has paved two major paths forward toward early medical interventions, as discussed below. + +First, we emphasize that bimodal expression that is correlated across all tissues is driven by genetic polymorphisms. However, the genetic bases for 13/23 universally switch- like genes remain elusive. We propose that the underlying genetic bases for these universally switch- like genes are structural variants, which are not easily captured by short- read DNA sequencing. These structural variants may be discovered in the future as population- level long- read sequencing becomes more common. The first biomedical path forward is to use long- read DNA sequencing to pinpoint the genetic polymorphisms responsible for the bimodal expression of disease- related genes. Of particular interest are the genes CYP4F24P and GPX1P1, both long non- coding RNAs, which are implicated in nasopharyngeal cancer. The genetic basis for their bimodal expression remains unknown. CYP4F24P is significantly downregulated in nasopharyngeal cancer tissues \(^{53}\) , while GPX1P1 is significantly upregulated in nasopharyngeal carcinomas treated with the potential anticancer drug THZ1 \(^{54}\) . Investigating whether individuals with naturally switched- off GPX1P1 and CYP4F24P are at a higher risk of nasopharyngeal cancer will enable genotyping to identify individuals at elevated risk for nasopharyngeal cancer, facilitating early interventions and improving patient outcomes. + +<--- Page Split ---> + +Secondly, switch- like genes present a promising avenue for exploring gene- environment interactions, an area of growing interest. Recent studies indicate that environmental factors can significantly modulate genetic associations \(^{55,56}\) . Polymorphisms that result in switch- like gene expression have already been linked to several diseases within specific environmental contexts \(^{57}\) . For instance, the deletion of GSTM1 has been associated with an increased risk of childhood asthma, but only in cases where the mother smoked during pregnancy \(^{58}\) . Even more critically, switch- like genes potentially create unique cellular environments that could modulate the impact of genetic variations. We hypothesize that switch- like expression can produce diverse cellular environments, whether in a single gene (as in genetically determined cases) or in multiple genes (as in tissue- specific, hormonally regulated cases). These environments may, in turn, influence the effect of genetic variations and their associations with disease. Thus, much like current gene- environment association studies that control for factors such as birthplace, geography, and behaviors like smoking, it is conceivable that controlling for switch- like gene expression states could enhance the power of such studies. By cataloging these switch- like genes and developing a framework to classify them as "on" or "off" in various samples, our work lays the groundwork for more robust association studies in future research. + +In summary, our study has significant implications for understanding the fundamental biology of gene expression regulation and the biomedical impact of switch- like genes. Specifically, it contributes to the growing repertoire of methods for determining individual susceptibility to diseases, facilitating early therapeutic interventions. By providing a new approach to studying gene expression states, our study will enhance the predictive accuracy of disease susceptibility and improve patient outcomes. + +## Acknowledgment + +O.G. and N.M. acknowledge support from the National Institute of General Medical Sciences (under grant no.1R01GM148973- 01). N.M. also acknowledges support from the Japan Science and Technology Agency (JST) Moonshot R&D (under grant no.JPMJMS2021), the National Science Foundation (under grant no.2052720), and JSPS KAKENHI (under grant no.JP 24K14840). O.G. acknowledges support from the National Science Foundation (under grant nos.2049947 and 2123284). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. + +## METHODS + +## Data + +The Genotype- Tissue Expression (GTEx) project is an ongoing effort to build a comprehensive public resource to study tissue- specific gene expression and regulation. The data we use are transcript per million (TPM) obtained from human samples across + +<--- Page Split ---> + +54 tissues and 56,200 genes (as of December 1st, 2023). We excluded laboratory- grown cell lines from our analysis. Since we need a reasonable number of individuals from each tissue, we excluded tissues with less than 50 individuals for our calculations. Of the remaining tissues, there were instances of multiple tissues from the same organ. In such cases, we randomly chose one tissue per organ. We thus focus our analysis on 27 tissues (Figure 1). Additionally, we retained only those genes for which the mean TPM across individuals was greater than 10 in at least one of the 27 focal tissues. This filter was applied because the analysis of lowly expressed genes may lead to false positive calls for bimodal expression and, as a result, to assign biological significance to cases where there is none. After these filtering steps, we are left with TPM data from 19,132 genes in each of the 27 tissues. We note that each tissue contains data from a different number of samples (individuals), totaling 943 across tissues. We will refer to this set of 19,132 genes as \(G\) in our equations and the rest of the methods. + +## Dip test + +There are many tests of bimodality of gene expressions \(^{16,59}\) . We use a dip test described as follows. We denote by \(S_{i}\) the number of samples (individuals) available for tissue \(i\) . We also denote by \(x_{g,i,s}\) the TPM value for gene \(g\) in tissue \(i\) , for sample \(s \in \{1, \ldots , S_{i}\}\) and \(g \in G\) . According to convention, we log- transform the TPM, specifically by \(\log (x_{g,i,s} + 1)^{60}\) to suppress the effect of outliers; TPM is extremely large for some samples. Note that \(\log (x_{g,i,s} + 1)\) conveniently maps \(x_{g,i,s} = 0\) to 0. For each pair of gene \(g\) and tissue \(i\) , we carried out a dip test, which is a statistical test for multimodality of distributions, on the distribution of \(\log (x_{g,i,s} + 1)\) across the samples \(S_{i}\) . We performed the dip test using the dip.test() function within the "diptest" package in R, with the number of bootstrap samples equal to 5000. We applied the Benjamini- Hochberg procedure for multiple hypothesis correction to the results with a false discovery rate of 5%. Additionally, to reduce false positive calls of bimodal expression, we only retained results where the dip statistic \(D > \max [0.05, 0.05 / \log (\bar{x}_{g,i})]\) , where + +\[\bar{x}_{g,i} = \frac{1}{S_i}\sum_{s = 1}^{S_i}x_{g,i,s}\] + +We obtained this threshold of 0.05 by visual inspection of \(\log (x_{g,i,s} + 1)\) distributions in the stomach and adipose subcutaneous tissues, starting with those with the highest values of \(D\) . For statistically significant results, the distribution was almost always bimodal if \(D\) exceeded 0.05. The only exceptions were genes with low \(\bar{x}_{g,i}\) . Thus, we penalized gene- tissue pairs with low \(\bar{x}_{g,i}\) across samples by requiring a higher \(D\) in order to classify them as bimodally distributed. Genes identified as bimodally distributed in at least one tissue are referred to as "switch- like" genes. + +## Tissue-to-tissue co-expression of genes + +We sought to identify switch- like genes whose expression exhibits bimodal expression in all tissues. One seemingly straightforward approach is to count the number of tissues + +<--- Page Split ---> + +showing bimodal distribution of expression levels for each gene. However, even if a gene genuinely exhibits bimodal expression across all tissues, our methodology may fail to recognize it as such if the mean expression levels \((\bar{x}_{g,i})\) of the gene are low in some tissues. This is because our effect size threshold penalizes gene- tissue pairs with low \(\bar{x}_{g,i}\) . Moreover, if gene expression follows a bimodal distribution across all tissues, then it does so likely due to a genetic polymorphism affecting expression. Thus, the expression of such genes would be highly correlated between pairs of tissues. Given this insight, discovering universally bimodal genes is more tractable using tissue- to- tissue co- expression of each gene. + +For each gene, we construct the co- expression matrix among pairs of tissues as follows. To calculate the co- expression between a pair of tissues, we need to use the samples whose TPM is measured for both tissues \(^{61}\) . In general, even if the number of samples is large for both of the two tissues, it does not imply that there are sufficiently many common samples. Therefore, using the sample information described in GTEx_Analysis_v8_Annotations_SampleAttributesDD.xlsx in the GTEx data portal, we counted the number of samples shared by each tissue pair and excluded the 41 tissue pairs that share less than 40 samples. For each of the remaining \(27 \times 26 / 2 - 41 = 310\) tissue pairs, we denote by \(S_{i,j}\) the number of samples shared by the two tissues \(i\) and \(j\) . We also denote by \(x_{g,i,s}\) and \(x_{g,j,s}\) the TPM value for gene \(g\) in tissues \(i\) and \(j\) , respectively, for sample \(s \in \{1, 2, \ldots , S_{i,j}\}\) . Then, we calculated the Pearson correlation coefficient between \(\log (x_{g,i,s} + 1)\) and \(\log (x_{g,j,s} + 1)\) across the \(S_{i,j}\) samples and used it as the strength of the co- expression of gene \(g\) between tissues \(i\) and \(j\) . Specifically, we calculate + +\[r_{g}(i,j) = \frac{\sum_{s = 1}^{S_{i,j}}[\log(x_{g,i,s} + 1) - m_{g,i}][\log(x_{g,j,s} + 1) - m_{g,j}]}{\sqrt{\sum_{s = 1}^{S_{i,j}}[\log(x_{g,i,s} + 1) - m_{g,i}]^{2}\sum_{s = 1}^{S_{i,j}}[\log(x_{g,j,s} + 1) - m_{g,j}]^{2}}}\] + +where + +\[m_{g,i} = \frac{1}{S_{i,j}}\sum_{s = 1}^{S_{i,j}}\log (x_{g,i,s} + 1),\] + +and + +\[m_{g,j} = \frac{1}{S_{i,j}}\sum_{s = 1}^{S_{i,j}}\log (x_{g,j,s} + 1).\] + +For each gene \(g\) , we then vectorize the correlation matrix, \((r_{g}(i,j))\) , into a 310- dimensional vector. If, for a given gene, \(g\) , \(\log (x_{g,i,s} + 1)\) or \(\log (x_{g,j,s} + 1)\) were 0 across all \(S_{i,j}\) samples for any of the 310 tissue pairs, the gene was removed. In this process, 28 out of 1,013 switch- like genes were removed. Note that the correlation matrix is symmetric, so we only vectorize the upper diagonal part of the matrix. We denote the + +<--- Page Split ---> + +generated vector by \(\vec{v}_{g}\) . Vector \(\vec{v}_{g}\) characterizes the gene. We ran a principal component analysis (PCA), using the promp() function in R, on vectors, \(\vec{v}_{g}\) for all genes for which we could calculate \(r_{g}(i,j)\) for all 310 tissue pairs. In parallel, we also ran PCA on only the set of vectors (genes) characterizing only the 985 (1013 - 28) switch- like genes. + +In the space spanned by the first two principal components, we calculated the pairwise distance between genes using the dist() function in R with method = "euclidean". We then performed hierarchical clustering using the hclust() function with method = "complete". Finally, we used the cuttree() function with \(k = 2\) and \(k = 3\) to obtain two and three clusters, respectively. + +## Identifying the genetic basis of universal bimodality + +In order to identify the genetic basis of bimodality for switch- like genes in cluster 2A, we obtained the coordinates of the genes for both hg19 and hg38 using their Ensembl IDs as keys through Ensembl BioMart. We obtained coordinates of common structural variants using both the 1000 genomes project (hg19) \(^{62}\) and the HGSV2 dataset (hg38) \(^{63}\) . We performed an overlap analysis using BedTools \(^{64}\) to identify polymorphic deletions of or insertions into these genes. We thus obtained five universally bimodal genes being affected by structural variants. These were USP32P2, FAM106A, GSTM1, RP11- 356C4.5, and CYP4F24P. Additionally, we obtained the GTEx dataset for the expression quantitative trait loci (eQTL). We identified genes in cluster 2A that had at least one eQTL, which was consistently associated with either increased or decreased expression of a given gene across all 27 tissues analyzed. We thus obtained five genes from cluster 2A whose expression was associated with a short variant across tissues. These were NPIPA5, RPS26, PSPHP1, PKD1P2, and PKD1P5. + +## Controlling for confounders + +A bimodal distribution of expression levels of universally switch- like genes is unlikely to be driven by confounding factors such as ischemic time, and time spent by the tissue in chemical fixatives (PAXgene fixative). For example, the expression of genes on the male- specific region of chromosome Y is bimodally distributed across tissues regardless of confounding factors because females do not possess these genes. Similarly, regardless of confounding factors, USP32P2 is bimodally distributed due to a polymorphic gene deletion. However, tissue- specific switch- like genes are particularly prone to being affected by confounding variables. Specifically, we investigated whether the switch- like expression of genes can be explained by ischemic time and PAXgene fixative using the following approach. + +Ischemic time for a sample \(s\) in a given tissue \(i\) , denoted by \(k_{i,s}\) , is a continuous variable representing the time interval between death and tissue stabilization. Time spent by a tissue \(i\) from a sample \(s\) in PAXgene fixative, denoted by \(f_{i,s}\) , is also a continuous variable. For each gene- tissue pair \((g, i)\) , we calculated, across the \(S_{i}\) samples, the Pearson correlation between 1) \(\log (1 + x_{g,i,s})\) and \(k_{i,s}\) and 2) \(\log (1 + x_{g,i,s})\) and \(f_{i,s}\) . For + +<--- Page Split ---> + +each tissue \(i\) and confounder \(c\) , where \(c \in \{k_{i,s}, f_{i,s}\}\) , we denote the correlation coefficient between \(\log(1 + x_{g,i,s})\) and \(c\) as \(r_{g,i,c}\) . + +We partition the set of switch- like genes into two subsets: cluster 1 and cluster 2 (the union of clusters 2A and 2B). We treat cluster- 2 genes as internal controls since their correlated bimodal expression across tissues is robust to the presence of confounding factors. Thus, we eliminated a cluster- 1 gene \(g1\) if, for any confounder \(c\) , \(\left(r_{g1,i,c}\right)^2 > \left(\max_{g2 \in \text{cluster} 2} r_{g2,i,c}\right)^2\) . + +## Gene-to-gene co-expression within tissues + +We performed gene- to- gene co- expression analysis within the stomach, breast, vagina, and colon tissues. In a given tissue \(i\) , we denote the set of genuine cluster- 1 genes (excluding genes affected by confounding variables) by \(C_i\) . Then, for \(i \in \{\text{stomach, breast, vagina, colon}\}\) , we calculated the Pearson correlation, across the \(S_i\) samples, between \(\log(x_{g,i,s} + 1)\) and \(\log(x_{h,i,s} + 1)\) for every \(g, h \in C_i\) where \(g \neq h\) . + +## Quantifying sex bias in cluster-1 gene expression + +For every gene- tissue pair \((g, i)\) , where \(g\) is a switch- like gene, and \(i\) is a tissue common to both sexes, we tested the hypothesis that the distribution of \(\log(x_{g,i,s} + 1)\) across male samples differed from that across female samples using the Wilcoxon rank- sum test. We applied the Benjamini- Hochberg procedure of multiple hypotheses correction with \(\text{FDR} = 5\%\) . We quantified the effect size of the sex bias using Cohen's \(d\) . Statistically significant results were considered to represent true sex bias only if \(|d| > 0.2^{65}\) . + +## Enrichment of switch-like genes among disease-linked genes + +We performed enrichment analysis for switch- like genes in the stomach and vagina that are downregulated in gastric cancer and vaginal atrophy, respectively. We denote the set of genes downregulated in disease \(y\) as \(Z_{y}\) , where \(y \in \{\text{gastric cancer, vaginal atrophy}\}\) . We calculated the fold enrichment of genuine cluster- 1 genes in the stomach among genes downregulated in gastric cancer by: + +\[\frac{|C_{\mathrm{stomach}} \cap Z_{\mathrm{gastric cancer}}|}{|G \cap Z_{\mathrm{gastric cancer}}| / |G|} .\] + +We calculated the fold enrichment of genuine cluster- 1 genes in the vagina among genes downregulated in vaginal atrophy by: + +\[\frac{|C_{\mathrm{vagina}} \cap Z_{\mathrm{vaginal atrophy}}|}{|G \cap Z_{\mathrm{vaginal atrophy}}| / |G|}.\] + +<--- Page Split ---> + +To calculate the \(p\) - values associated with these enrichments, we obtained 10,000 uniformly random samples (with replacement) of size \(|C_{i}|\) from \(G\) . The \(p\) - value for the enrichment of switch- like genes in tissue \(i\) among genes linked to disease \(y\) is then given by the fraction of random samples among the 10,000 samples for which \(|q_{j} \cap Z_{y}| > |C_{i} \cap Z_{y}|\) . Here, \(q_{j}\) is the set of genes in random sample \(j\) where \(j \in \{1, \ldots , 10000\}\) . + +## Discretizing expression levels + +We performed kernel density estimation using the density() function in R on the distributions of 1) \(\log (x_{g,\mathrm{stomach},s} + 1)\) across the \(S_{\mathrm{stomach}}\) samples for \(g \in C_{\mathrm{stomach}} \cap Z_{\mathrm{gastric cancer}}\) ; and 2) \(\log (x_{g,\mathrm{vagina},s} + 1)\) across the \(S_{\mathrm{vagina}}\) samples for \(g \in C_{\mathrm{vagina}} \cap Z_{\mathrm{vaginal atrophy}}\) . + +We used the minimum of the estimated density as the switching threshold; if an individual had an expression level above the threshold in a given tissue, the gene was considered "on" in the individual in that tissue. The gene was considered "off" otherwise. We then calculate the concordance of expression among genes in any arbitrary set of switch- like genes \(G^{A}\) in a given tissue \(i\) as follows: + +\[\frac{1}{S_{i}}\sum_{s = 1}^{S_{i}}\left[\prod_{g\in G^{A}}\mathbf{1}_{(g\mathrm{~is~"on"~in~sample~}s\mathrm{~in~tissue~}i)} + \prod_{g\in G^{A}}\mathbf{1}_{(g\mathrm{~is~"off"~in~sample~}s\mathrm{~in~tissue~}i)}\right],\] + +where \(\mathbf{1}_{(\cdot)}\) is the indicator function. + +## Gene ontology enrichment of tissue-specific switch-like genes in the vagina + +We performed Gene Ontology (GO) enrichment analysis for genes in \(C_{\mathrm{vagina}}\) using the online database available at https://geneontology.org/ 66. + +## Immunohistochemistry + +Vaginal biopsies were taken by use of punch biopsies from postmenopausal women, fixed and stained as previously described by use of ALOX12 (HPA010691 polyclonal antirabbit, Sigma- Aldrich) 67,68. + +<--- Page Split ---> + +## Supplementary Information + +## Principal component analysis on tissue-to-tissue co-expression vectors + +We applied a principal component analysis to the 19,132 vectors of tissue- to- tissue coexpression, one vector for each gene. We find that PC1 (Figure 2A), explaining \(35.3\%\) of the variation, is nearly perfectly correlated with mean tissue- to- tissue co- expression across tissue- tissue pairs ( \(r^2 = 0.998\) , \(p\) - value \(< 2.2 \times 10^{- 16}\) ; Figure S1). This result indicates that the \(35.3\%\) of the variation in the tissue- to- tissue co- expression of genes is primarily explained by the mean tissue- to- tissue co- expression of genes. + +![](images/Figure_unknown_1.jpg) + +
Figure S1. The mean tissue-to-tissue co-expression of genes shows a near-perfect correlation with PC1.
+ +## Universally switch-like genes and their biomedical implications + +In the main text, we discussed the USP32P2 and FAM106A. Here, we discuss some other interesting examples of universally switch- like genes. The violin plots for the expression level distributions for all cluster- 2A and cluster- 2B switch- like genes not shown in the main text are present in Figure S2 and Figure S3, respectively. + +Firstly, a common \(\sim 20\mathrm{kb}\) whole- gene deletion (esv3587154) of the GSTM1 gene \(^{69,70}\) is associated with bladder cancer in humans \(^{71}\) . GSTM1 is bimodally expressed across individuals in all tissues (Figure S2D) that we analyzed, as well as across multiple tumor types \(^{15}\) , with different expression peaks corresponding to differential prognoses among patients. These findings suggest a compelling hypothesis: the common deletion of GSTM1, maintained either by drift or balancing selection \(^{72}\) , has no significant effect on the health of non- cancerous individuals; however, it could have significant implications for prognosis once certain types of tumors develop. Therefore, screening + +<--- Page Split ---> + +patients with certain tumor types for the GSTM1 deletion could significantly advance our ability to predict the course of tumor progression in an individualized manner. + +Secondly, genes that are bimodally expressed across multiple tissues raise an evolutionary paradox. Typically, genes with a wide expression breadth (i.e., expression across a large number of tissues) affect fitness and are thus constrained at both the sequence and expression level \(^{26,73 - 75}\) . However, universally switch- like genes, despite having a high expression breadth, are not conserved at the expression level. This could imply different health consequences for individuals with off versus on state of the genes. For example, the universally switch- like gene RP4- 765C7.2 (ENSG00000213058; Figure S2K) is upregulated in the peripheral blood mononuclear cells of patients with ankylosing spondylitis \(^{76}\) , eutopic endometrium in endometriosis patients \(^{77}\) , and peripheral blood mononuclear cells of multiple sclerosis patients \(^{78}\) . Conversely, it is downregulated in the peripheral blood mononuclear cells of Sjögren's syndrome patients \(^{79}\) . These results suggest that this gene being switched on versus off may predispose individuals to certain diseases while protecting them against others. This balance between susceptibility and protection could explain why both high- expression and low- expression states are maintained in the population at comparable frequencies. + +Thirdly, the bimodality of NPIPA5 (Figure S2G), too, can be explained by a single eQTL. The T allele of the SNV rs3198697 is associated with NPIPA5 being switched on across tissues, while the C allele is associated with the gene being switched off. NPIPA5 has been reported as one of the top differentially expressed genes among patients with multiple sclerosis in both blood and brain \(^{80}\) . Moreover, this study \(^{80}\) showed that this gene is co- expressed in blood and brain. Here, we have shown that this gene is switch- like and that the co- expression of NPIPA5 is not restricted to blood and brain but extends to all pairs of tissues. + +Lastly, a single eQTL can explain the bimodality of a member of the PKD1 gene family in cluster 2A, PKD1P5 (Figure S2I). For PKD1P5, the C allele of the SNV rs201525245 is associated with the gene being switched on, while the G allele is associated with the gene being switched off. + +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + +
Figure S2. Violin plots for expression level distributions of switch-like genes in cluster 2A.
+ +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + +
Figure S3. Violin plots for expression level distributions of switch-like genes in cluster 2A.
+ +## Conceptual issues regarding bimodal expression distributions driven by genetic polymorphisms + +In the main text, we claimed that genetic polymorphisms drive the bimodal expression of universally switch- like genes in cluster 2A. For a polymorphism with two alleles (A and \(a\) ), there are three possible genotypes ( \(aa\) , \(Aa\) , and \(AA\) ). Since each of the three genotypes can lead to three different expression levels, we expect expression distributions of a cluster- 2A gene to have three modes. This leads to the question: Why do we not see trimodal, as opposed to bimodal, expression distributions for genes in cluster 2A? To answer this question, we develop the following frameworks. Let us assume that a genetic polymorphism exists with two alleles, \(A\) and \(a\) , with frequencies \(p_A\) and \((1 - p_A)\) , respectively. The three genotypes for this polymorphism, \(aa\) , \(Aa\) , and \(AA\) , lead to three different expression states (TPM levels) for the gene with averages + +<--- Page Split ---> + +\(\mu_{aa}, \mu_{Aa}\) , and \(\mu_{AA}\) , respectively. Let us also assume that the Hardy-Weinberg equilibrium holds for this locus. Then, the frequency of \(aa = (1 - p_A)^2\) , the frequency of \(Aa = 2p_A(1 - p_A)\) , and the frequency of \(AA = p_A^2\) . We assume that \(\mu_{aa} \leq \mu_{Aa} \leq \mu_{AA}\) . Next, we define a dominance coefficient \(0 \leq \alpha \leq 1\) by, + +\[\mu_{Aa} = \mu_{aa} + (\mu_{AA} - \mu_{aa})\alpha .\] + +If we define the ratio \(R\) by + +\[R = \frac{\mu_{AA}}{\mu_{aa}},\] + +then, we obtain + +\[\mu_{Aa} = \mu_{aa}(1 - \alpha +R\alpha)\] + +and + +\[\mu_{AA} = R\mu_{aa}.\] + +We can then divide individuals into three groups depending on their genotypes. Let us assume that the coefficient of variation (CV) of expression is the same for each genotypic group. Then, we can model the TPM value of this gene in a given individual a normal random variable with: + +1) mean \(= \mu_{aa}\) and standard deviation \(= \mathrm{CV}\times \mu_{aa}\) if the genotype is \(aa\) 2) mean \(= \mu_{Aa}\) and standard deviation \(= \mathrm{CV}\times \mu_{Aa}\) if the genotype is \(Aa\) ; and +3) mean \(= \mu_{AA}\) and standard deviation \(= \mathrm{CV}\times \mu_{AA}\) if the genotype is \(AA\) + +The value of \(\mu_{aa}\) is irrelevant for gauging the effect of polymorphisms on the shape of the expression level distributions. Therefore, we set \(\mu_{aa} = 1\) . + +Under these mathematical assumptions, we performed simulations using 36 distinct models. These models vary by four parameters: \(p_A \in \{0.05, 0.1, 0.5\}\) , \(\mathrm{CV} \in \{0.1, 0.3\}\) , \(R \in \{10, 1000\}\) , and \(\alpha \in \{0.2, 0.5, 0.8\}\) . For each model, defined by a unique combination of the values of these four parameters, we performed a two- step sampling procedure. First, we obtained a random sample of 500 genotypes, based on \(p_A\) and the Hardy- Weinberg equilibrium. Next, for each of the 500 genotypes sampled, we sample a TPM value from the normal distribution corresponding to that genotype. Thus, for each of the 36 models, we simulated 500 TPM values. We present these values as histograms with and without log transformation. The results for \(p_A = 0.05\) , \(p_A = 0.1\) , and \(p_A = 0.5\) are shown in Figure S4, Figure S5, and Figure S6, respectively. These simulations help us answer our question we first asked: Why do we not see a trimodal distribution if a genetic polymorphism drives expression- level variability in a gene? + +Firstly, even when the minor allele (A) frequency is not low (e.g., \(10\%\) ), the frequency of the genotype \(AA\) is still quite low (e.g., \(1\%\) ). Therefore, the third peak is not always conspicuously visible. We see this in all models with \(p_A = 0.05\) and \(p_A = 0.1\) (Figures S4 and S5), regardless of CV, \(R\) , and \(\alpha\) values. At higher allele frequencies (e.g., \(50\%\) ), the effect of the remaining parameters becomes more apparent. Figure S6 shows that a + +<--- Page Split ---> + +higher dominance coefficient \(\alpha\) makes the expression level distribution more bimodal. By contrast, a lower dominance coefficient \(\alpha\) makes the expression level distribution more trimodal. The lack of observed trimodality in the GTEx data may suggest that expression levels of switch-like genes tend to be more dominant than additive with regard to causal genetic polymorphisms. Secondly, greater variation (CV) in the data can also obscure the third peak. For example, by comparing **Figure S6B** to **Figure S6H**, we find that increasing the CV can change the distribution from being trimodal to bimodal when the other parameters are held constant. However, \(R\) does not seem to have much effect on whether the expression level distribution is bimodal or trimodal. + +![](images/Figure_unknown_4.jpg) + + + +
**Figure S4. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 5%.**
+ +<--- Page Split ---> +![](images/Figure_unknown_5.jpg) + +
Figure S5. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 10%.
+ +<--- Page Split ---> +![](images/Figure_unknown_6.jpg) + +
Figure S6. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of \(50\%\) .
+ +## Tissue-specific switch-like genes + +We divided switch- like genes into three clusters in the space spanned by the first two principal components (Figure 2A). While we said that genes in cluster 1 (Figure 2A- B) are tissue- specific switch- like genes, manual inspection reveals this is not true for all genes in cluster 1. In particular, the transcript ENSG00000273906 coming from chr Y was labeled cluster 1 by hierarchical clustering even though it is universally switch- like in tissues common to both sexes. Indeed, we removed all chr- Y genes from our analyses of genuine cluster- 1 genes. Other cluster- 1 genes bimodally expressed in a large number of tissues lie on the autosomes. For example, CLPS, PRSS1, CELA3A, and CELA3B, despite having low overall tissue- to- tissue co- expression, are bimodally expressed across tissues. Indeed, we have shown previously that CELA3A and CELA3B have a shared regulatory architecture in the pancreas \(^{81}\) . + +## Controlling for confounders + +We removed cluster- 1 genes affected by confounders in each tissue using an approach outlined in Methods. Here, we present the number of genuine cluster- 1 genes versus + +<--- Page Split ---> + +those affected by confounders in **Figure S7**. In particular, we show that the cluster- 1 genes in the colon and the intestine are particularly prone to being affected by confounding factors. We also present in **Figure S8** examples of genes whose bimodal expression in specific tissues is correlated with variation in the sample ischemic time distribution. + +![](images/Figure_unknown_7.jpg) + +
Figure S7. Switch-like genes in cluster 1 that are genuine versus those affected by confounders.
+ +<--- Page Split ---> +![PLACEHOLDER_33_0] + +
Figure S8. Examples of cluster-1 genes affected by confounders. Their bimodal distribution is caused by ischemic time (a confounding factor).
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Li, C., Qu, W. & Yang, X. Comprehensive IncRNA and mRNA profiles in peripheral blood mononuclear cells derived from ankylosing spondylitis patients by RNA-sequencing analysis. Medicine 101, e27477 (2022). + +<--- Page Split ---> + +77. von Grothusen C. Endometrial receptivity and regeneration in health and disease: Molecular, cellular and clinical perspectives. Karolinska Institutet (Sweden, 2022). + +78. Almsned, F. M. Understanding the genetic nature of multiple sclerosis using next-generation sequencing genomic analysis methods. (Doctoral dissertation, George Mason University, 2020). + +79. Chery, G. Understanding Sjögren's Syndrome as a Systemic Autoimmune Disorder. (State University of New York at Albany, 2022). + +80. Islam, T. et al. Detection of multiple sclerosis using blood and brain cells transcript profiles: Insights from comprehensive bioinformatics approach. Informatics in Medicine Unlocked 16, 100201 (2019). + +81. Russell, M., Aqil, A., Saitou, M., Gokcumen, O. & Masuda, N. Gene communities in co-expression networks across different tissues. PLoS Comput. Biol. 19, e1011616 (2023). + +82. Otto, M., Zheng, Y., Grablowitz, P. & Wiehe, T. Detecting adaptive changes in gene copy number distribution accompanying the human out-of-Africa expansion. bioRxiv 2023.08.14.553171 (2024) doi:10.1101/2023.08.14.553171. + +83. Shen, S., Li, H., Liu, J., Sun, L. & Yuan, Y. The panoramic picture of pepsinogen gene family with pan-cancer. Cancer Med. 9, 9064-9080 (2020). + +<--- Page Split ---> diff --git a/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8_det.mmd b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..19df097d94a350b1ad7e162427357df76a705971 --- /dev/null +++ b/preprint/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8/preprint__00071c19a357d74c148b616e4f6f54f029c63bf7dc9c70b0f9dba908075f6ed8_det.mmd @@ -0,0 +1,769 @@ +<|ref|>title<|/ref|><|det|>[[42, 108, 880, 178]]<|/det|> +# Switch-like Gene Expression Modulates Disease Susceptibility + +<|ref|>text<|/ref|><|det|>[[44, 196, 258, 242]]<|/det|> +Omer Gokcumen gokcumen@gmail.com + +<|ref|>text<|/ref|><|det|>[[44, 304, 104, 322]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 342, 137, 361]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 380, 355, 400]]<|/det|> +Posted Date: September 13th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 418, 474, 438]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4974188/v1 + +<|ref|>text<|/ref|><|det|>[[42, 456, 914, 499]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 516, 535, 536]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 572, 916, 615]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 18th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 60513- x. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[155, 96, 840, 119]]<|/det|> +# Switch-like Gene Expression Modulates Disease Susceptibility + +<|ref|>text<|/ref|><|det|>[[115, 144, 879, 183]]<|/det|> +Authors: Alber Aqil1, †, Yanyan Li2, †, Zhiliang Wang2, Saiful Islam3, Madison Russell2, Theodora Kunovac Kallak4, Marie Saitou5, Omer Gokcumen1, †, Naoki Masuda2,3, † + +<|ref|>sub_title<|/ref|><|det|>[[115, 209, 198, 222]]<|/det|> +## Affiliations: + +<|ref|>text<|/ref|><|det|>[[115, 222, 848, 288]]<|/det|> +1. Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY, USA. +2. Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, USA. +3. Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY, USA. +4. Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden. +5. Faculty of Biosciences, Norwegian University of Life Sciences, Aas, Norway + +<|ref|>text<|/ref|><|det|>[[115, 312, 388, 352]]<|/det|> +Correspondence: Omer Gokcumen, gokcumen@gmail.com Naoki Masuda, naokimas@gmail.com + +<|ref|>sub_title<|/ref|><|det|>[[115, 382, 204, 400]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[114, 402, 880, 750]]<|/det|> +A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch- like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch- like genes. We found that only 31 (3.1%) of these genes exhibit switch- like behavior across all tissues. These universally switch- like genes appear to be genetically driven, with large exonic genomic structural variants explaining five ( \(\sim 18\%\) ) of them. The remaining switch- like genes exhibit tissue- specific expression patterns. Notably, tissue- specific switch- like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue- specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched- off genes in the stomach and vagina that are linked to gastric cancer (41- fold, \(p< 10^{- 4}\) ) and vaginal atrophy (44- fold, \(p< 10^{- 4}\) ), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch- like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch- like gene expression and lay the groundwork for potential diagnostic and therapeutic applications. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 115, 241, 132]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 133, 880, 308]]<|/det|> +The study of gene expression began in earnest with the characterization of lactose- metabolizing switch- like genes in \(E\) coli 1. The presence of lactose triggered the production of enzymes needed to metabolize it, while these enzymes were absent when lactose was not present. These genes acted like switches, toggling between "on" and "off" states based on the presence or absence of lactose, respectively. In subsequent decades, the discovery of enhancer elements 2- 4, epigenetic modifications 5- 8, and transcription factor dynamics 9 revealed that gene expression in humans is more nuanced, resembling a dimmer more often than a simple on- and- off mechanism. Consequently, the study of switch- like genes in humans was largely relegated to the narrow realm of Mendelian diseases 10- 12. + +<|ref|>text<|/ref|><|det|>[[114, 325, 881, 656]]<|/det|> +The recent availability of population- level RNA- sequencing data from humans has made it possible to systematically identify switch- like versus dimmer- like genes. For dimmer- like genes in a given tissue, we expect expression levels across individuals to be continuously distributed with a single mode, i.e., a unimodal distribution. In contrast, expression levels of switch- like genes in a given tissue are expected to exhibit a bimodal distribution, with one mode representing the "off" state and the other representing the "on" state. As we will detail, bimodal expression across individuals is a characteristic of a gene in a specific tissue, referred to as a gene- tissue pair. We define a gene as switch- like if it exhibits bimodal expression in at least one tissue. Most of the recent studies on bimodal gene expression are related to cancer biology, associating on and off states to different disease phenotypes and their prognoses 13- 15. These cancer studies have already produced promising results for personalized medicine 16. However, to our knowledge, the only study focusing on switch- like genes in non- cancerous tissues across individuals restricted their analysis to muscle tissue 17. As a result, the dynamics of switch- like expression across the multi- tissue landscape remain unknown. We hypothesize that switch- like expression is ubiquitous but often tissue- specific. We further hypothesize that these tissue- specific expression trends underlie common disease states. Therefore, the analysis of switch- like genes across tissues and individuals may provide a means for early diagnosis and prediction of human disease. + +<|ref|>text<|/ref|><|det|>[[115, 671, 879, 812]]<|/det|> +Here, we systematically identified switch- like genes across individuals in 27 tissues. Our results explain the regulatory bases of switch- like expression in humans, highlighting genomic structural variation as a major factor underlying correlated switch- like expression in multiple tissues. Furthermore, we identified groups of switch- like genes in the stomach and vagina for which the "off" state predisposes individuals to gastric cancer and vaginal atrophy, respectively. Overall, these findings improve our understanding of the regulation of switch- like genes in humans. They also suggest promising future paths for preventative biomedical interventions. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 115, 880, 500]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 510, 883, 585]]<|/det|> +
Figure 1. Methodological framework. A. List of 27 tissues used in this study. B. Distribution of 19,132 genes by the number of tissues in which they are highly expressed. C. Bimodal expression is a property of a gene-tissue pair. We tested 516,564 gene-tissue pairs (19,132 genes x 27 tissues) for bimodal expression across individuals. When a gene-tissue pair exhibits switch-like (bimodal) expression, the individuals divide into two subpopulations: one with the gene switched off, and the other with the gene switched on.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 98, 216, 117]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 137, 528, 156]]<|/det|> +## Tissue-specificity of bimodal expression + +<|ref|>text<|/ref|><|det|>[[114, 156, 880, 329]]<|/det|> +The misregulation of highly expressed genes often has consequences for health and fitness. To systematically identify biomedically relevant switch- like genes in humans, we focused on 19,132 genes that are highly expressed (mean \(\mathrm{TPM} > 10\) ) in at least one of the 27 tissues represented in the GTEx database (Figure 1A; Figure 1B; Table S1). For each of the 516,564 gene- tissue pairs (19,132 genes \(\times 27\) tissues), we applied the dip test of unimodality \(^{18}\) to the expression level distribution across individuals (Figure 1C). Employing the Bejamini- Hochberg procedure for multiple hypotheses correction, we identified 1,013 switch- like genes (Figure 1C; Methods; Table S2). The expression of these genes is bimodally distributed in at least one tissue, such that it is switched "off" for one subset of individuals and switched "on" for the rest of the individuals. + +<|ref|>text<|/ref|><|det|>[[114, 346, 876, 660]]<|/det|> +Expression of different switch- like genes may be bimodally distributed in different numbers of tissues. We contend that genes that are bimodally expressed across all tissues are likely so due to a germline genetic polymorphism driving switch- like expression across tissues. If this is the case, the expression of these genes would be highly correlated across pairs of tissues. Given this insight, discovering universally bimodal genes is more tractable using tissue- to- tissue co- expression of each gene. Therefore, for each gene, we calculated the pairwise correlation of expression levels across pairs of tissues (Methods; Table S3). To visualize tissue- to- tissue co- expression patterns of genes, we performed principal component analysis (PCA) on the tissue- to- tissue gene co- expression data (Table S4). We emphasize that we are referring to the co- expression of the same gene across pairs of tissues instead of the co- expression of pairs of genes in the same tissue. In the space spanned by the first two principal components (explaining \(35.3\%\) and \(3.47\%\) of the variance, respectively), switch- like genes form two major clusters (cluster 1 and cluster 2; Methods), dividing along PC1 (Figure 2A). Applying PCA exclusively to switch- like genes reveals the further division of cluster 2 into two distinct subclusters – cluster 2A and cluster 2B – in the space spanned by the first two principal components (explaining \(58.1\%\) and \(4.25\%\) of the variance, respectively) (Figure 2B; Table S5). + +<|ref|>text<|/ref|><|det|>[[114, 676, 878, 835]]<|/det|> +Manual inspection reveals that cluster 1, which contains 954 genes, represents genes, such as KRT17, with bimodal expression in a small subset of tissues (Figure 2C). Cluster 2A consists of 23 genes, such as GPX1P1, with bimodal expression in all tissues (Figure 2D). Lastly, cluster 2B represents eight genes, such as EIF1AY, with bimodal expression in all non- sex- specific tissues but not in sex- specific tissues (Figure 2E). We will refer to genes in cluster 1 as "tissue- specific switch- like genes." Although some of them are bimodally expressed in more than one tissue, these genes tend to exhibit high tissue specificity in their bimodal expression. Genes in cluster 2 will be referred to as "universally switch- like genes." + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 880, 518]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 519, 879, 666]]<|/det|> +
Figure 2. Categorization of switch-like genes. A. PCA analysis of tissue-pair correlations of gene expression. Each point represents a gene. When we perform PCA on the tissue-to-tissue co-expression vectors for 19,132 genes, the switch-like genes divide into two clusters. Cluster 1 primarily represents genes that are bimodally expressed in a tissue-specific manner, while cluster 2 represents genes that are bimodally expressed in at least all non-sex-specific tissues. B. Performing PCA on the co-expression vectors of only switch-like genes further divides cluster 2 into two subclusters: cluster 2A, which contains genes that are bimodally expressed across all 27 tissues, and cluster 2B, which contains genes that are bimodally expressed in all 22 tissues common to both sexes, but not in the five sex-specific tissues. C-E. Violin plots display the expression levels in all 27 tissues for representative genes from cluster 1, cluster 2A, and cluster 2B, respectively.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 700, 689, 720]]<|/det|> +## Genetic variation underlies universally switch-like genes + +<|ref|>text<|/ref|><|det|>[[115, 720, 880, 860]]<|/det|> +We found that \(3.1\%\) of all switch- like genes (i.e., the proportion of switch- like genes that are in cluster 2) show clear bimodal expression, at least in all tissues common to both sexes. We contend that germline genetic variation across individuals likely underlies the universally switch- like gene expression, specifically due to four major types of genetic variants. Firstly, we expect genes on the Y chromosome to show bimodal expression in all tissues common to both sexes since these genes are present in males and absent in females (Figure 3A). Consistent with this reasoning, seven out of the eight genes in cluster 2B lie within the male- specific region of the Y- chromosome \(^{19}\) ; the remaining + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 880, 265]]<|/det|> +gene in cluster 2B is XIST, showing female- specific expression. Secondly, a homozygous gene deletion would result in the gene being switched off (Figure 3B). We found five such genes in cluster 2A for which genomic structural variants likely underlie the observed universally switch- like expression; four genes are affected by gene deletions, and the remaining one by an insertion into the gene. Thirdly, the homozygous deletion of a regulatory element can also switch off a gene (Figure 3C). While we did not find any examples of this scenario, it remains a theoretical possibility. Lastly, a loss- of- function single nucleotide variant (SNV) or short indel, which disrupts gene function, can switch off the gene (Figure 3D). We identified five genes in cluster 2A where such SNVs cause universal bimodality. + +<|ref|>text<|/ref|><|det|>[[114, 280, 872, 492]]<|/det|> +Remarkably, we could genetically explain the expression of 10 out of 23 (43%) cases in cluster 2A despite the small number of genes fitting our conservative definition for universally switch- like genes. SNVs underlie five of these cases (Figure 3B), while structural variants underlie the remaining five cases (Figure 3D). Thus, out of the 10 cases where we can explain the genetic underpinnings of switch- like expression, 50% involve genomic structural variation, highlighting the importance of this type of genetic variation. Although we could not identify the genetic variation underlying the bimodal expression of the remaining 13 genes in cluster 2A, their consistent and highly correlated switch- like expression across all tissues strongly suggests a genetic basis. We anticipate that better resolution assemblies and detailed regulatory sequence annotations will help identify the genetic variants responsible for the remaining universally switch- like genes. + +<|ref|>image<|/ref|><|det|>[[114, 505, 780, 867]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 90, 880, 207]]<|/det|> +Figure 3. Genetic bases of universally switch-like gene expression (cluster 2). A. Genes on the Y chromosome are expressed only in males, leading to bimodal expression in non- sex- specific tissues. B. Common structural variants, such as deletions or insertions, may lead to increased, decreased, or no expression in all tissues relative to individuals who carry the alternative allele. C. Common structural variants affecting a genomic region regulating a gene may lead to increased, decreased, or no expression in all tissues, relative to individuals who carry the alternative allele. D. Common single nucleotide variants or short indels affecting a gene or its regulatory region may lead to increased, decreased, or no expression in all tissues relative to individuals who carry the alternative allele. + +<|ref|>text<|/ref|><|det|>[[114, 223, 881, 521]]<|/det|> +We highlight a clear example of a common structural variant leading to universally switch- like expression (Figure 3B). USP32P2 and FAM106A – both universally switch- like genes – are bimodally expressed in all 27 tissues. Both genes show high levels of tissue- to- tissue co- expression. A common 46 kb deletion (esv3640153), with a global allele frequency of \(\sim 25\%\) , completely deletes both genes (Figure 4A- B). We propose that this deletion accounts for the universal switch- like expression of both USP32P2 and FAM106A in all tissues. For illustration, we show the expression level distributions of USP32P2 and FAM106A in the cerebellum (Figures 4C- D). Indeed, the haplotype harboring this deletion is strongly associated with the downregulation of both genes in all 27 tissues \((p< 10^{- 5}\) for every single gene- tissue pair, Methods). We note that the under- expression of USP32P2 in sperm is associated with male infertility \(^{20}\) , and plausibly, homozygous males for the deletion may be prone to infertility. Additionally, FAM106A interacts with SARS- CoV- 2 and is downregulated after infection, at least in lung- epithelial cells \(^{21 - 23}\) . Individuals with FAM106A already switched off may develop more severe COVID- 19 symptoms upon infection, though further investigation is needed. The case of FAM106A and USP32P2 exemplifies the link between disease and bimodal gene expression, a theme we will explore further in the remainder of this text. + +<|ref|>text<|/ref|><|det|>[[114, 536, 875, 870]]<|/det|> +We caution that we base our results regarding bimodality on expression at the RNA level. The bimodal expression of genes across individuals at the RNA level may not necessarily lead to bimodal expression at the protein level. For example, the universally switch- like expression of RPS26 at the RNA level can be explained by a single nucleotide variant (rs1131017) in the gene's 5'- untranslated region (UTR). In particular, RPS26 has three transcription states based on the SNV genotypes. The ancestral homozygote C/C corresponds to a high transcription state, the heterozygote C/G to a medium state, and the derived homozygote G/G to a low state (See Supplement for a discussion on why an expression distribution driven by three genotypes at a polymorphic site might still appear bimodal). Remarkably, this pattern is reversed at the translation level \(^{24}\) : Messenger RNA carrying the derived G allele produces significantly more protein. This reversal may be due to a SNV in the 5'- UTR that can abolish a translation- initiation codon \(^{25}\) . This finding demonstrates how the same SNV can regulate a gene's expression level in opposite directions during transcription and translation. This multi- level regulation in opposite directions likely serves to dampen protein expression variability. It has been shown previously that RNA variability is greater than protein variability in primates \(^{26,27}\) ; the presence of dampening variants discussed here may be one reason behind these findings. Such compensatory mechanisms for gene expression remain fascinating areas for future research. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 110, 872, 760]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 763, 879, 867]]<|/det|> +
Figure 4. An example of a polymorphic gene deletion resulting in universally switch-like gene expression. A. FAM106A and USP32P2 (not drawn to scale) are overlapping genes on chromosome 17. Two alternative haplotype classes exist for these genes: one in which both genes are completely deleted and the other without the deletion. B. Frequency distribution of the deletion across diverse populations. Each pie chart represents one of the 26 populations from the 1000 Genomes Project. Purple indicates the frequency of the deletion, while gray indicates the frequency of the alternative haplotype. C-D. Expression level distribution in the cerebellum (as an example) across individuals for FAM106A and USP32P2,
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 844, 106]]<|/det|> +respectively. The gene deletion presumably leads to the switched- off expression state in both genes. + +<|ref|>sub_title<|/ref|><|det|>[[115, 119, 825, 140]]<|/det|> +## Tissue-specific switch-like genes have a shared regulatory framework + +<|ref|>text<|/ref|><|det|>[[114, 139, 875, 348]]<|/det|> +Tissue- specific expression patterns are crucial for tissue function. Thus, we now turn our attention to tissue- specific switch- like genes. We found that the stomach, vagina, breast, and colon show a higher number of tissue- specific switch- like genes compared to other tissues (Figure 5A), after controlling for confounding factors (Methods; Supplement; Table S6). Furthermore, within these tissues, the expression of switch- like genes is not independent; instead, they exhibit high pairwise co- expression between genes (Figure 5B- C; Table S7). Hence, tissue- specific switch- like genes tend to be either all switched off or switched on within an individual. This result suggests a shared regulatory mechanism for the expression of these genes in each tissue. Given that hormonal regulation plays a substantial role in shaping tissue- specific expression patterns \(^{28,29}\) , we hypothesize that hormones may regulate genes that are bimodally expressed in specific tissues (cluster 1; Figure 2B). + +<|ref|>text<|/ref|><|det|>[[114, 363, 879, 540]]<|/det|> +Sexual differences in hormonal activity are well documented \(^{30,31}\) . To explore this further, we investigated whether hormone- mediated sex- biased expression underlies the co- expression of tissue- specific switch- like genes within tissues. Under this scenario, a gene would be largely switched on in one sex and off in the other in a given tissue. Among tissue- specific switch- like genes, we identified 186 gene- tissue pairs with sex- biased bimodal expression (Figure 6A; Table S8). These instances are biologically relevant; for example, we found switch- like immunoglobulins genes with female- biased expression in the thyroid, heart, tibial nerve, and subcutaneous adipose tissue. This observation may relate to previous findings \(^{32,33}\) of higher antibody responses to diverse antigens in females than in males. + +<|ref|>text<|/ref|><|det|>[[114, 555, 880, 835]]<|/det|> +More dramatically, we found that 162 out of 164 tissue- specific switch- like genes (cluster 1) in the breast tissue are female- biased, explaining their correlated expression levels (Figure 6A). However, the sex- based disparity in the on- versus- off states of these genes is not absolute, but rather a statistical tendency. In other words, the gene is not switched off in all males and switched on in all females. Instead, the proportion of individuals with the gene switched on significantly differs between sexes. Notably, multiple sex- biased switch- like genes—including SPINT1 and SPINT2 \(^{34}\) , multiple keratin genes \(^{35}\) , and the oxytocin receptor gene \(^{36,37}\) (OXTR; Figure 6B)—in the breast tissue are differentially expressed in breast cancers relative to matched non- cancerous tissues. Future investigations could reveal whether the toggling of these genetic switches affects breast cancer risk in females. We caution that sex- biased switch- like expression in the breast may result from differences in cell- type abundance between females and males. Nevertheless, the differential expression of some genes between sexes might developmentally drive such differences in cell- type abundance. In summary, our results indicate that sex is a major contributor to bimodal gene expression, with breast tissue standing out as particularly sex- biased in this context. + +<|ref|>text<|/ref|><|det|>[[115, 851, 850, 870]]<|/det|> +We note that the intra- tissue co- expression of tissue- specific switch- like genes in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 876, 265]]<|/det|> +stomach and colon cannot be explained by sex. By biological definition, the variation in vaginal expression levels in our sample is not sex- biased. Thus, the intra- tissue co- expression of tissue- specific switch- like genes in the stomach, colon, and vagina may be explained by one of two reasons: 1) Most of the tissue- specific switch- like genes in each tissue are directly regulated by the same hormone in that tissue, or 2) Most of the tissue- specific switch- like genes in each tissue are regulated by the same transcription factor which is, in turn, under regulation by a hormone or other cellular environmental factors. In the case of hormonally controlled gene expression, genes are likely switched off when the systemic hormone levels drop below a certain threshold. We will discuss this idea further, specifically for the vagina, later in the text. + +<|ref|>image<|/ref|><|det|>[[115, 280, 875, 673]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 677, 880, 838]]<|/det|> +
Figure 5. Characterization of genuine tissue-specific switch-like genes (cluster 1). The results shown here exclude genes that showed switch-like expression due to confounding factors like ischemic time. A. Number of tissue-specific switch-like genes showing bimodal expression in each of the 27 tissues. The stomach, vagina, breast, and colon show disproportionately more tissue-specific switch-like genes than other tissues. B. An illustration of how Pearson's correlation coefficients were calculated for each pair of bimodally expressed tissue-specific switch-like genes within the stomach, vagina, breast, and colon. We show the scatterplots for two arbitrarily chosen gene pairs for each of the four tissues. The axes in each dot plot represent the \(\log (TPM + 1)\) for the labeled gene in the relevant tissue. Panel C was generated using the pairwise correlation coefficients thus obtained. C. Tissue-specific switch-like genes within the four tissues shown are highly co-expressed. Tissue-specific master regulators, such as endocrinological signals, likely drive their concordant on and off states.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 90, 875, 640]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 643, 879, 762]]<|/det|> +
Figure 6. Sex-biased expression of tissue-specific switch-like genes (cluster 1). A. Number of tissue-specific switch-like genes that show female- and male-biased expression. Only those tissues are shown that have at least one tissue-specific switch-like gene showing sex bias. The number in the central grid next to each tissue image represents the number of genuine tissue-specific switch-like genes in that tissue. In orange, the numbers to the left of the central grid indicate the count of female-biased genes in each of the 10 tissues shown. In blue, the numbers to the right of the grid indicate the count of male-biased genes. B. Violin plots showing the expression level distribution in the breast for five female-biased tissue-specific switch-like genes discussed in the main text.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 814, 761, 854]]<|/det|> +## Concordantly switched-off genes in the stomach may indicate a predisposition to gastric cancer + +<|ref|>text<|/ref|><|det|>[[115, 852, 872, 872]]<|/det|> +Gene expression levels have been studied as a diagnostic marker for disease states \(^{38}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 868, 177]]<|/det|> +Therefore, we asked whether tissue- specific switch- like genes co- expressed with each other across individuals are linked to human disease, with each of the two expression states corresponding to different risks. To address this question, we investigated whether the identified switch- like genes in a given tissue are overrepresented among genes implicated in diseases of the same tissue. + +<|ref|>text<|/ref|><|det|>[[114, 193, 879, 388]]<|/det|> +We overlapped the switch- like genes in the stomach with a previously published list \(^{39}\) of differentially expressed genes in gastric carcinomas. We found that switch- like genes in the stomach are significantly enriched (41- fold enrichment, \(p< 10^{- 4}\) ) among genes that are downregulated in gastric carcinomas. Specifically, nine switch- like genes are downregulated in gastric carcinomas (ATP4A, ATP4B, CHIA, CXCL17, FBP2, KCNE2, MUC6, TMEM184A, and PGA3). Additionally, these nine genes are concordantly expressed in \(92.5\%\) (332/359) of the stomach samples, being either all switched off or on in a given individual (Methods). Our data suggest that individuals with these nine genes switched off in the stomach may be susceptible to developing cancers. This preliminary observation provides exciting avenues to investigate both the cause of the concordant toggling of these genes and their potential role in cancer development. + +<|ref|>sub_title<|/ref|><|det|>[[115, 404, 704, 424]]<|/det|> +## Concordantly switched-off genes result in vaginal atrophy + +<|ref|>text<|/ref|><|det|>[[114, 423, 880, 615]]<|/det|> +We found that switch- like genes in the vagina are significantly overrepresented (44- fold enrichment; \(p< 10^{- 4}\) ; see methods) among genes linked to vaginal atrophy in postmenopausal women. Vaginal atrophy, affecting nearly half of postmenopausal women, is triggered by sustained low levels of systemic estrogen and is marked by increased microbial diversity, higher pH, and thinning of the epithelial layer in the vagina \(^{40,41}\) . It is also known as atrophic vaginitis, vulvovaginal atrophy, estrogen- deficient vaginitis, urogenital atrophy, or genitourinary syndrome of menopause, depending on the specialty of the researchers. Symptoms experienced by women include dryness, soreness, burning, decreased arousal, pain during intercourse, and incontinence \(^{42}\) . Our analysis of switch- like genes in the vagina provides new insights into the development of vaginal atrophy. + +<|ref|>text<|/ref|><|det|>[[114, 631, 877, 858]]<|/det|> +Specifically, we overlapped a previously published list \(^{43}\) of genes that are transcriptionally downregulated in vaginal atrophy with our list of bimodally expressed genes in the vagina. We found that the genes SPINK7, ALOX12, DSG1, KRTDAP, KRT1, and CRISP3 are both bimodally expressed in the vagina and transcriptionally downregulated (presumably switched off) in women with vaginal atrophy (Figure 7A). We refer to these genes as "atrophy- linked switch- like genes." Indeed, these six genes are either all switched on, or all switched off concordantly in \(84\%\) (131/156) of the vaginal samples we studied. The pairwise concordance rates (percentage of individuals with both genes switched on or both genes switched off) for these genes are shown in Figure 7B. Among postmenopausal women with this concordant gene expression, \(50\%\) are in the "off" state – a fraction that closely matches the prevalence of vaginal atrophy in postmenopausal women \(^{40,44}\) . Therefore, our data suggest that estrogen- dependent transcription underlies concordant expression of atrophy- linked switch- like genes, with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 648, 108]]<|/det|> +the "off" state of these genes associated with vaginal atrophy. + +<|ref|>text<|/ref|><|det|>[[114, 123, 881, 440]]<|/det|> +For background, the vaginal epithelial layers are differentiated from the inside out. The basal and parabasal layers of the epithelium consist of mitotic progenitor cells with differentiation potential, while the outermost layer comprises the most differentiated cells \(^{45,46}\) . When basal and parabasal cells stop proliferating, the death of mature cells leads to a thin epithelium, and the symptoms of vaginal atrophy appear. Given this background, atrophy- linked switch- like genes may either be a cause or a consequence of vaginal atrophy. In particular, if an atrophy- linked switch- like gene encodes a protein necessary for the continued proliferation and differentiation of basal and parabasal cells, we call it a "driver" gene. In the absence of the driver gene's protein, cell differentiation ceases, and the outer layer gradually disappears, resulting in vaginal atrophy (Figure 8A). On the other hand, if the product of an atrophy- linked switch- like gene is not required for basal and parabasal cell proliferation, we refer to it as a "passenger" gene, borrowing the terminology from cancer literature \(^{47}\) . In healthy vaginas with a thick epithelium, there are more cells in which passenger genes would be expressed. By contrast, in atrophic vaginas, the epithelium thins, resulting in fewer cells where these genes can be expressed. This contrast would lead to the bimodal expression of passenger genes across vagina samples in whole- tissue RNA- sequencing datasets. We hypothesize that at least some of the atrophy- linked switch- like genes are driver genes. + +<|ref|>text<|/ref|><|det|>[[114, 454, 880, 700]]<|/det|> +Two key findings allowed us to construct this hypothesis. Firstly, switch- like genes in the vagina show a 26- fold ontological enrichment for the establishment of the skin barrier \(\mathrm{(FDR = 1.26\times 10^{- 6})}\) and a 25- fold enrichment for keratinocyte proliferation \(\mathrm{(FDR = 1.75\times}\) \(10^{- 4})\) , both related to epithelial thickness and differentiation. Notably, two atrophy- linked switch- like genes in the vagina that we identified, KRTDAp and KRT1, are crucial for the differentiation of epithelial cells in the vagina \(^{48,49}\) . Protein stainings available through Human Protein Atlas \(^{50}\) show that all six atrophy- linked switch- like genes are expressed at the protein level, predominantly in the vaginal epithelium. Secondly, administering 17β- estradiol (a type of estrogen) to postmenopausal women with vaginal atrophy leads to the upregulation of the same six genes, causing symptoms to subside \(^{51}\) . According to our hypothesis, administering estrogen activates the expression of the driver switch- like genes in the vagina, resuming the proliferation of basal and parabasal cells in the epithelium. This process leads to the reformation of a thick and healthy vaginal mucosa, thereby alleviating the symptoms of vaginal atrophy. + +<|ref|>text<|/ref|><|det|>[[115, 715, 877, 856]]<|/det|> +Thus, it is essential to distinguish driver genes from passenger genes to understand the etiology of vaginal atrophy. However, we expect driver and passenger genes to show the same expression patterns in healthy versus atrophic vaginas using bulk RNA- sequencing data. In order to make this distinction, we need comparative expression data, specifically from the basal and parabasal epithelium from healthy versus atrophic vaginas. We expect driver genes to be differentially expressed in the basal and parabasal layers of the epithelium. By contrast, we expect passenger genes to show no differential expression in the basal and parabasal layers between healthy and atrophic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 91, 189, 108]]<|/det|> +vaginas. + +<|ref|>text<|/ref|><|det|>[[114, 124, 875, 405]]<|/det|> +To look at the expression levels in the basal and parabasal layers of the epithelium, we arbitrarily chose ALOX12 from the six atrophy- linked switch- like genes for immunohistochemical staining of its protein product in the vaginal mucosa (which includes the epithelium and the underlying connective tissue). We found that the ALOX12 protein is present in the epithelial cells, and its abundance directly correlates with epithelial thickness, as expected from our RNA- sequencing results. However, we found no significant difference in the staining of the ALOX12 protein in the basal or parabasal epithelial layers between healthy and atrophic samples (Figure 8B). This suggests that the gene is not differentially expressed in the basal or parabasal layers of the vaginal epithelium between healthy and atrophic vaginas. Therefore, ALOX12 is a passenger gene for vaginal atrophy. Comparative immunohistochemical staining of the protein product of the other five atrophy- linked switch- like genes may identify the driver gene in the future. Indeed, the KRT1 protein is recognized as a marker of basal cell differentiation in mouse vaginas \(^{52}\) , a finding that may also be true for humans. Overall, our results open up several new paths for potential pre- menopausal risk assessment and intervention frameworks targeting cell differentiation pathways in the clinical setting. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 88, 850, 740]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 742, 879, 860]]<|/det|> +
Figure 7. Atrophy-linked switch-like genes tend to be either all switched off, or all switched on within individuals. A. The distribution of expression levels in the vagina of the six switch-like genes implicated in vaginal atrophy. The x-axes represent \(\log (TPM + 1)\) values for each gene in the vagina, and the y-axes represent the probability density. We obtained the probability densities using kernel density estimation. In each case, the global minimum (excluding endpoints) is considered the switching threshold. A gene is deemed “on” in an individual if the expression level is above this threshold; otherwise, the gene is deemed “off.” B. Pairwise concordance rates (percentage of individuals in which the two genes are either both switched on or both switched off).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[114, 88, 875, 850]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 848, 842, 867]]<|/det|> +
Figure 8. ALOX12 is a passenger gene. A. Model for the etiology of vaginal atrophy. High levels of
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 881, 236]]<|/det|> +estrogen keep the driver genes switched on in basal and parabasal epithelium, impelling basal and parabasal cells to proliferate and mature, resulting in healthy vaginal mucosa. Conversely, low levels of estrogen switch off the driver genes. The lack of basal and parabasal cell proliferation leads to a thin vaginal epithelium, resulting in vaginal atrophy. B. Representative immunohistochemical staining of Arachidonate 12- Lipoxygenase (ALOX12) in vaginal tissue. We show healthy vaginal tissue from a woman with higher systemic estrogen levels and a thicker vaginal epithelial layer, along with atrophic vaginal tissue from a woman with low systemic estrogen levels and a thinner vaginal epithelial layer. There is no difference in ALOX12 expression in the basal or parabasal cells between healthy and atrophic epithelium, implicating it as a passenger gene. Images taken with Axio Observer Z1 (Carl Zeiss AG) with a 40X objective. + +<|ref|>sub_title<|/ref|><|det|>[[115, 255, 230, 272]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[114, 273, 881, 535]]<|/det|> +In this study, we investigated factors underlying switch- like gene expression and its functional consequences. Our systematic analysis revealed 1,013 switch- like genes across 943 individuals. Some of these genes show bimodal expression across individuals in all tissues, suggesting a genetic basis for their universally switch- like behavior. We found several single nucleotide and structural variants to explain the switch- like expression of these genes. Most of the switch- like genes, however, exhibit tissue- specific bimodal expression. These genes tend to be concordantly switched on or off in individuals within the breast, colon, stomach, and vagina. This concordant tissue- specific switch- like expression in individuals is likely due to tissue- specific master regulators, such as endocrinological signals. For example, in the vagina, switch- like genes tend to get concordantly switched off in a given individual when systemic estrogen levels fall below a certain threshold. On the biomedical front, our work linked switch- like expression to the susceptibility to gastric cancer and vaginal atrophy. Furthermore, this study has paved two major paths forward toward early medical interventions, as discussed below. + +<|ref|>text<|/ref|><|det|>[[114, 551, 876, 850]]<|/det|> +First, we emphasize that bimodal expression that is correlated across all tissues is driven by genetic polymorphisms. However, the genetic bases for 13/23 universally switch- like genes remain elusive. We propose that the underlying genetic bases for these universally switch- like genes are structural variants, which are not easily captured by short- read DNA sequencing. These structural variants may be discovered in the future as population- level long- read sequencing becomes more common. The first biomedical path forward is to use long- read DNA sequencing to pinpoint the genetic polymorphisms responsible for the bimodal expression of disease- related genes. Of particular interest are the genes CYP4F24P and GPX1P1, both long non- coding RNAs, which are implicated in nasopharyngeal cancer. The genetic basis for their bimodal expression remains unknown. CYP4F24P is significantly downregulated in nasopharyngeal cancer tissues \(^{53}\) , while GPX1P1 is significantly upregulated in nasopharyngeal carcinomas treated with the potential anticancer drug THZ1 \(^{54}\) . Investigating whether individuals with naturally switched- off GPX1P1 and CYP4F24P are at a higher risk of nasopharyngeal cancer will enable genotyping to identify individuals at elevated risk for nasopharyngeal cancer, facilitating early interventions and improving patient outcomes. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 875, 404]]<|/det|> +Secondly, switch- like genes present a promising avenue for exploring gene- environment interactions, an area of growing interest. Recent studies indicate that environmental factors can significantly modulate genetic associations \(^{55,56}\) . Polymorphisms that result in switch- like gene expression have already been linked to several diseases within specific environmental contexts \(^{57}\) . For instance, the deletion of GSTM1 has been associated with an increased risk of childhood asthma, but only in cases where the mother smoked during pregnancy \(^{58}\) . Even more critically, switch- like genes potentially create unique cellular environments that could modulate the impact of genetic variations. We hypothesize that switch- like expression can produce diverse cellular environments, whether in a single gene (as in genetically determined cases) or in multiple genes (as in tissue- specific, hormonally regulated cases). These environments may, in turn, influence the effect of genetic variations and their associations with disease. Thus, much like current gene- environment association studies that control for factors such as birthplace, geography, and behaviors like smoking, it is conceivable that controlling for switch- like gene expression states could enhance the power of such studies. By cataloging these switch- like genes and developing a framework to classify them as "on" or "off" in various samples, our work lays the groundwork for more robust association studies in future research. + +<|ref|>text<|/ref|><|det|>[[115, 419, 883, 525]]<|/det|> +In summary, our study has significant implications for understanding the fundamental biology of gene expression regulation and the biomedical impact of switch- like genes. Specifically, it contributes to the growing repertoire of methods for determining individual susceptibility to diseases, facilitating early therapeutic interventions. By providing a new approach to studying gene expression states, our study will enhance the predictive accuracy of disease susceptibility and improve patient outcomes. + +<|ref|>sub_title<|/ref|><|det|>[[116, 543, 280, 560]]<|/det|> +## Acknowledgment + +<|ref|>text<|/ref|><|det|>[[115, 560, 878, 700]]<|/det|> +O.G. and N.M. acknowledge support from the National Institute of General Medical Sciences (under grant no.1R01GM148973- 01). N.M. also acknowledges support from the Japan Science and Technology Agency (JST) Moonshot R&D (under grant no.JPMJMS2021), the National Science Foundation (under grant no.2052720), and JSPS KAKENHI (under grant no.JP 24K14840). O.G. acknowledges support from the National Science Foundation (under grant nos.2049947 and 2123284). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[116, 752, 215, 770]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[116, 782, 160, 799]]<|/det|> +## Data + +<|ref|>text<|/ref|><|det|>[[116, 808, 875, 860]]<|/det|> +The Genotype- Tissue Expression (GTEx) project is an ongoing effort to build a comprehensive public resource to study tissue- specific gene expression and regulation. The data we use are transcript per million (TPM) obtained from human samples across + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 879, 317]]<|/det|> +54 tissues and 56,200 genes (as of December 1st, 2023). We excluded laboratory- grown cell lines from our analysis. Since we need a reasonable number of individuals from each tissue, we excluded tissues with less than 50 individuals for our calculations. Of the remaining tissues, there were instances of multiple tissues from the same organ. In such cases, we randomly chose one tissue per organ. We thus focus our analysis on 27 tissues (Figure 1). Additionally, we retained only those genes for which the mean TPM across individuals was greater than 10 in at least one of the 27 focal tissues. This filter was applied because the analysis of lowly expressed genes may lead to false positive calls for bimodal expression and, as a result, to assign biological significance to cases where there is none. After these filtering steps, we are left with TPM data from 19,132 genes in each of the 27 tissues. We note that each tissue contains data from a different number of samples (individuals), totaling 943 across tissues. We will refer to this set of 19,132 genes as \(G\) in our equations and the rest of the methods. + +<|ref|>sub_title<|/ref|><|det|>[[115, 342, 190, 360]]<|/det|> +## Dip test + +<|ref|>text<|/ref|><|det|>[[114, 366, 883, 605]]<|/det|> +There are many tests of bimodality of gene expressions \(^{16,59}\) . We use a dip test described as follows. We denote by \(S_{i}\) the number of samples (individuals) available for tissue \(i\) . We also denote by \(x_{g,i,s}\) the TPM value for gene \(g\) in tissue \(i\) , for sample \(s \in \{1, \ldots , S_{i}\}\) and \(g \in G\) . According to convention, we log- transform the TPM, specifically by \(\log (x_{g,i,s} + 1)^{60}\) to suppress the effect of outliers; TPM is extremely large for some samples. Note that \(\log (x_{g,i,s} + 1)\) conveniently maps \(x_{g,i,s} = 0\) to 0. For each pair of gene \(g\) and tissue \(i\) , we carried out a dip test, which is a statistical test for multimodality of distributions, on the distribution of \(\log (x_{g,i,s} + 1)\) across the samples \(S_{i}\) . We performed the dip test using the dip.test() function within the "diptest" package in R, with the number of bootstrap samples equal to 5000. We applied the Benjamini- Hochberg procedure for multiple hypothesis correction to the results with a false discovery rate of 5%. Additionally, to reduce false positive calls of bimodal expression, we only retained results where the dip statistic \(D > \max [0.05, 0.05 / \log (\bar{x}_{g,i})]\) , where + +<|ref|>equation<|/ref|><|det|>[[425, 610, 572, 669]]<|/det|> +\[\bar{x}_{g,i} = \frac{1}{S_i}\sum_{s = 1}^{S_i}x_{g,i,s}\] + +<|ref|>text<|/ref|><|det|>[[114, 674, 882, 803]]<|/det|> +We obtained this threshold of 0.05 by visual inspection of \(\log (x_{g,i,s} + 1)\) distributions in the stomach and adipose subcutaneous tissues, starting with those with the highest values of \(D\) . For statistically significant results, the distribution was almost always bimodal if \(D\) exceeded 0.05. The only exceptions were genes with low \(\bar{x}_{g,i}\) . Thus, we penalized gene- tissue pairs with low \(\bar{x}_{g,i}\) across samples by requiring a higher \(D\) in order to classify them as bimodally distributed. Genes identified as bimodally distributed in at least one tissue are referred to as "switch- like" genes. + +<|ref|>sub_title<|/ref|><|det|>[[115, 811, 492, 829]]<|/det|> +## Tissue-to-tissue co-expression of genes + +<|ref|>text<|/ref|><|det|>[[115, 829, 875, 864]]<|/det|> +We sought to identify switch- like genes whose expression exhibits bimodal expression in all tissues. One seemingly straightforward approach is to count the number of tissues + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 252]]<|/det|> +showing bimodal distribution of expression levels for each gene. However, even if a gene genuinely exhibits bimodal expression across all tissues, our methodology may fail to recognize it as such if the mean expression levels \((\bar{x}_{g,i})\) of the gene are low in some tissues. This is because our effect size threshold penalizes gene- tissue pairs with low \(\bar{x}_{g,i}\) . Moreover, if gene expression follows a bimodal distribution across all tissues, then it does so likely due to a genetic polymorphism affecting expression. Thus, the expression of such genes would be highly correlated between pairs of tissues. Given this insight, discovering universally bimodal genes is more tractable using tissue- to- tissue co- expression of each gene. + +<|ref|>text<|/ref|><|det|>[[113, 258, 876, 512]]<|/det|> +For each gene, we construct the co- expression matrix among pairs of tissues as follows. To calculate the co- expression between a pair of tissues, we need to use the samples whose TPM is measured for both tissues \(^{61}\) . In general, even if the number of samples is large for both of the two tissues, it does not imply that there are sufficiently many common samples. Therefore, using the sample information described in GTEx_Analysis_v8_Annotations_SampleAttributesDD.xlsx in the GTEx data portal, we counted the number of samples shared by each tissue pair and excluded the 41 tissue pairs that share less than 40 samples. For each of the remaining \(27 \times 26 / 2 - 41 = 310\) tissue pairs, we denote by \(S_{i,j}\) the number of samples shared by the two tissues \(i\) and \(j\) . We also denote by \(x_{g,i,s}\) and \(x_{g,j,s}\) the TPM value for gene \(g\) in tissues \(i\) and \(j\) , respectively, for sample \(s \in \{1, 2, \ldots , S_{i,j}\}\) . Then, we calculated the Pearson correlation coefficient between \(\log (x_{g,i,s} + 1)\) and \(\log (x_{g,j,s} + 1)\) across the \(S_{i,j}\) samples and used it as the strength of the co- expression of gene \(g\) between tissues \(i\) and \(j\) . Specifically, we calculate + +<|ref|>equation<|/ref|><|det|>[[210, 518, 787, 586]]<|/det|> +\[r_{g}(i,j) = \frac{\sum_{s = 1}^{S_{i,j}}[\log(x_{g,i,s} + 1) - m_{g,i}][\log(x_{g,j,s} + 1) - m_{g,j}]}{\sqrt{\sum_{s = 1}^{S_{i,j}}[\log(x_{g,i,s} + 1) - m_{g,i}]^{2}\sum_{s = 1}^{S_{i,j}}[\log(x_{g,j,s} + 1) - m_{g,j}]^{2}}}\] + +<|ref|>text<|/ref|><|det|>[[114, 592, 171, 608]]<|/det|> +where + +<|ref|>equation<|/ref|><|det|>[[114, 614, 365, 648]]<|/det|> +\[m_{g,i} = \frac{1}{S_{i,j}}\sum_{s = 1}^{S_{i,j}}\log (x_{g,i,s} + 1),\] + +<|ref|>text<|/ref|><|det|>[[114, 656, 150, 672]]<|/det|> +and + +<|ref|>equation<|/ref|><|det|>[[114, 679, 370, 712]]<|/det|> +\[m_{g,j} = \frac{1}{S_{i,j}}\sum_{s = 1}^{S_{i,j}}\log (x_{g,j,s} + 1).\] + +<|ref|>text<|/ref|><|det|>[[114, 761, 880, 857]]<|/det|> +For each gene \(g\) , we then vectorize the correlation matrix, \((r_{g}(i,j))\) , into a 310- dimensional vector. If, for a given gene, \(g\) , \(\log (x_{g,i,s} + 1)\) or \(\log (x_{g,j,s} + 1)\) were 0 across all \(S_{i,j}\) samples for any of the 310 tissue pairs, the gene was removed. In this process, 28 out of 1,013 switch- like genes were removed. Note that the correlation matrix is symmetric, so we only vectorize the upper diagonal part of the matrix. We denote the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 185]]<|/det|> +generated vector by \(\vec{v}_{g}\) . Vector \(\vec{v}_{g}\) characterizes the gene. We ran a principal component analysis (PCA), using the promp() function in R, on vectors, \(\vec{v}_{g}\) for all genes for which we could calculate \(r_{g}(i,j)\) for all 310 tissue pairs. In parallel, we also ran PCA on only the set of vectors (genes) characterizing only the 985 (1013 - 28) switch- like genes. + +<|ref|>text<|/ref|><|det|>[[114, 200, 868, 289]]<|/det|> +In the space spanned by the first two principal components, we calculated the pairwise distance between genes using the dist() function in R with method = "euclidean". We then performed hierarchical clustering using the hclust() function with method = "complete". Finally, we used the cuttree() function with \(k = 2\) and \(k = 3\) to obtain two and three clusters, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[114, 304, 600, 323]]<|/det|> +## Identifying the genetic basis of universal bimodality + +<|ref|>text<|/ref|><|det|>[[114, 323, 877, 550]]<|/det|> +In order to identify the genetic basis of bimodality for switch- like genes in cluster 2A, we obtained the coordinates of the genes for both hg19 and hg38 using their Ensembl IDs as keys through Ensembl BioMart. We obtained coordinates of common structural variants using both the 1000 genomes project (hg19) \(^{62}\) and the HGSV2 dataset (hg38) \(^{63}\) . We performed an overlap analysis using BedTools \(^{64}\) to identify polymorphic deletions of or insertions into these genes. We thus obtained five universally bimodal genes being affected by structural variants. These were USP32P2, FAM106A, GSTM1, RP11- 356C4.5, and CYP4F24P. Additionally, we obtained the GTEx dataset for the expression quantitative trait loci (eQTL). We identified genes in cluster 2A that had at least one eQTL, which was consistently associated with either increased or decreased expression of a given gene across all 27 tissues analyzed. We thus obtained five genes from cluster 2A whose expression was associated with a short variant across tissues. These were NPIPA5, RPS26, PSPHP1, PKD1P2, and PKD1P5. + +<|ref|>sub_title<|/ref|><|det|>[[116, 566, 377, 584]]<|/det|> +## Controlling for confounders + +<|ref|>text<|/ref|><|det|>[[114, 584, 881, 760]]<|/det|> +A bimodal distribution of expression levels of universally switch- like genes is unlikely to be driven by confounding factors such as ischemic time, and time spent by the tissue in chemical fixatives (PAXgene fixative). For example, the expression of genes on the male- specific region of chromosome Y is bimodally distributed across tissues regardless of confounding factors because females do not possess these genes. Similarly, regardless of confounding factors, USP32P2 is bimodally distributed due to a polymorphic gene deletion. However, tissue- specific switch- like genes are particularly prone to being affected by confounding variables. Specifically, we investigated whether the switch- like expression of genes can be explained by ischemic time and PAXgene fixative using the following approach. + +<|ref|>text<|/ref|><|det|>[[114, 775, 877, 868]]<|/det|> +Ischemic time for a sample \(s\) in a given tissue \(i\) , denoted by \(k_{i,s}\) , is a continuous variable representing the time interval between death and tissue stabilization. Time spent by a tissue \(i\) from a sample \(s\) in PAXgene fixative, denoted by \(f_{i,s}\) , is also a continuous variable. For each gene- tissue pair \((g, i)\) , we calculated, across the \(S_{i}\) samples, the Pearson correlation between 1) \(\log (1 + x_{g,i,s})\) and \(k_{i,s}\) and 2) \(\log (1 + x_{g,i,s})\) and \(f_{i,s}\) . For + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 792, 130]]<|/det|> +each tissue \(i\) and confounder \(c\) , where \(c \in \{k_{i,s}, f_{i,s}\}\) , we denote the correlation coefficient between \(\log(1 + x_{g,i,s})\) and \(c\) as \(r_{g,i,c}\) . + +<|ref|>text<|/ref|><|det|>[[114, 144, 866, 260]]<|/det|> +We partition the set of switch- like genes into two subsets: cluster 1 and cluster 2 (the union of clusters 2A and 2B). We treat cluster- 2 genes as internal controls since their correlated bimodal expression across tissues is robust to the presence of confounding factors. Thus, we eliminated a cluster- 1 gene \(g1\) if, for any confounder \(c\) , \(\left(r_{g1,i,c}\right)^2 > \left(\max_{g2 \in \text{cluster} 2} r_{g2,i,c}\right)^2\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 275, 518, 294]]<|/det|> +## Gene-to-gene co-expression within tissues + +<|ref|>text<|/ref|><|det|>[[115, 293, 875, 383]]<|/det|> +We performed gene- to- gene co- expression analysis within the stomach, breast, vagina, and colon tissues. In a given tissue \(i\) , we denote the set of genuine cluster- 1 genes (excluding genes affected by confounding variables) by \(C_i\) . Then, for \(i \in \{\text{stomach, breast, vagina, colon}\}\) , we calculated the Pearson correlation, across the \(S_i\) samples, between \(\log(x_{g,i,s} + 1)\) and \(\log(x_{h,i,s} + 1)\) for every \(g, h \in C_i\) where \(g \neq h\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 399, 580, 419]]<|/det|> +## Quantifying sex bias in cluster-1 gene expression + +<|ref|>text<|/ref|><|det|>[[115, 417, 876, 526]]<|/det|> +For every gene- tissue pair \((g, i)\) , where \(g\) is a switch- like gene, and \(i\) is a tissue common to both sexes, we tested the hypothesis that the distribution of \(\log(x_{g,i,s} + 1)\) across male samples differed from that across female samples using the Wilcoxon rank- sum test. We applied the Benjamini- Hochberg procedure of multiple hypotheses correction with \(\text{FDR} = 5\%\) . We quantified the effect size of the sex bias using Cohen's \(d\) . Statistically significant results were considered to represent true sex bias only if \(|d| > 0.2^{65}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 541, 690, 560]]<|/det|> +## Enrichment of switch-like genes among disease-linked genes + +<|ref|>text<|/ref|><|det|>[[115, 559, 876, 650]]<|/det|> +We performed enrichment analysis for switch- like genes in the stomach and vagina that are downregulated in gastric cancer and vaginal atrophy, respectively. We denote the set of genes downregulated in disease \(y\) as \(Z_{y}\) , where \(y \in \{\text{gastric cancer, vaginal atrophy}\}\) . We calculated the fold enrichment of genuine cluster- 1 genes in the stomach among genes downregulated in gastric cancer by: + +<|ref|>equation<|/ref|><|det|>[[366, 664, 629, 720]]<|/det|> +\[\frac{|C_{\mathrm{stomach}} \cap Z_{\mathrm{gastric cancer}}|}{|G \cap Z_{\mathrm{gastric cancer}}| / |G|} .\] + +<|ref|>text<|/ref|><|det|>[[115, 753, 833, 792]]<|/det|> +We calculated the fold enrichment of genuine cluster- 1 genes in the vagina among genes downregulated in vaginal atrophy by: + +<|ref|>equation<|/ref|><|det|>[[373, 806, 623, 864]]<|/det|> +\[\frac{|C_{\mathrm{vagina}} \cap Z_{\mathrm{vaginal atrophy}}|}{|G \cap Z_{\mathrm{vaginal atrophy}}| / |G|}.\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 105, 853, 217]]<|/det|> +To calculate the \(p\) - values associated with these enrichments, we obtained 10,000 uniformly random samples (with replacement) of size \(|C_{i}|\) from \(G\) . The \(p\) - value for the enrichment of switch- like genes in tissue \(i\) among genes linked to disease \(y\) is then given by the fraction of random samples among the 10,000 samples for which \(|q_{j} \cap Z_{y}| > |C_{i} \cap Z_{y}|\) . Here, \(q_{j}\) is the set of genes in random sample \(j\) where \(j \in \{1, \ldots , 10000\}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 233, 398, 251]]<|/det|> +## Discretizing expression levels + +<|ref|>text<|/ref|><|det|>[[114, 250, 839, 329]]<|/det|> +We performed kernel density estimation using the density() function in R on the distributions of 1) \(\log (x_{g,\mathrm{stomach},s} + 1)\) across the \(S_{\mathrm{stomach}}\) samples for \(g \in C_{\mathrm{stomach}} \cap Z_{\mathrm{gastric cancer}}\) ; and 2) \(\log (x_{g,\mathrm{vagina},s} + 1)\) across the \(S_{\mathrm{vagina}}\) samples for \(g \in C_{\mathrm{vagina}} \cap Z_{\mathrm{vaginal atrophy}}\) . + +<|ref|>text<|/ref|><|det|>[[114, 344, 880, 433]]<|/det|> +We used the minimum of the estimated density as the switching threshold; if an individual had an expression level above the threshold in a given tissue, the gene was considered "on" in the individual in that tissue. The gene was considered "off" otherwise. We then calculate the concordance of expression among genes in any arbitrary set of switch- like genes \(G^{A}\) in a given tissue \(i\) as follows: + +<|ref|>equation<|/ref|><|det|>[[193, 430, 802, 490]]<|/det|> +\[\frac{1}{S_{i}}\sum_{s = 1}^{S_{i}}\left[\prod_{g\in G^{A}}\mathbf{1}_{(g\mathrm{~is~"on"~in~sample~}s\mathrm{~in~tissue~}i)} + \prod_{g\in G^{A}}\mathbf{1}_{(g\mathrm{~is~"off"~in~sample~}s\mathrm{~in~tissue~}i)}\right],\] + +<|ref|>text<|/ref|><|det|>[[115, 490, 411, 508]]<|/det|> +where \(\mathbf{1}_{(\cdot)}\) is the indicator function. + +<|ref|>sub_title<|/ref|><|det|>[[115, 524, 825, 544]]<|/det|> +## Gene ontology enrichment of tissue-specific switch-like genes in the vagina + +<|ref|>text<|/ref|><|det|>[[115, 543, 862, 581]]<|/det|> +We performed Gene Ontology (GO) enrichment analysis for genes in \(C_{\mathrm{vagina}}\) using the online database available at https://geneontology.org/ 66. + +<|ref|>sub_title<|/ref|><|det|>[[115, 597, 334, 614]]<|/det|> +## Immunohistochemistry + +<|ref|>text<|/ref|><|det|>[[115, 614, 853, 668]]<|/det|> +Vaginal biopsies were taken by use of punch biopsies from postmenopausal women, fixed and stained as previously described by use of ALOX12 (HPA010691 polyclonal antirabbit, Sigma- Aldrich) 67,68. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[348, 90, 648, 111]]<|/det|> +## Supplementary Information + +<|ref|>sub_title<|/ref|><|det|>[[115, 130, 789, 149]]<|/det|> +## Principal component analysis on tissue-to-tissue co-expression vectors + +<|ref|>text<|/ref|><|det|>[[114, 149, 880, 254]]<|/det|> +We applied a principal component analysis to the 19,132 vectors of tissue- to- tissue coexpression, one vector for each gene. We find that PC1 (Figure 2A), explaining \(35.3\%\) of the variation, is nearly perfectly correlated with mean tissue- to- tissue co- expression across tissue- tissue pairs ( \(r^2 = 0.998\) , \(p\) - value \(< 2.2 \times 10^{- 16}\) ; Figure S1). This result indicates that the \(35.3\%\) of the variation in the tissue- to- tissue co- expression of genes is primarily explained by the mean tissue- to- tissue co- expression of genes. + +<|ref|>image<|/ref|><|det|>[[117, 271, 644, 569]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 574, 847, 604]]<|/det|> +
Figure S1. The mean tissue-to-tissue co-expression of genes shows a near-perfect correlation with PC1.
+ +<|ref|>sub_title<|/ref|><|det|>[[116, 620, 707, 639]]<|/det|> +## Universally switch-like genes and their biomedical implications + +<|ref|>text<|/ref|><|det|>[[115, 639, 850, 708]]<|/det|> +In the main text, we discussed the USP32P2 and FAM106A. Here, we discuss some other interesting examples of universally switch- like genes. The violin plots for the expression level distributions for all cluster- 2A and cluster- 2B switch- like genes not shown in the main text are present in Figure S2 and Figure S3, respectively. + +<|ref|>text<|/ref|><|det|>[[114, 725, 875, 866]]<|/det|> +Firstly, a common \(\sim 20\mathrm{kb}\) whole- gene deletion (esv3587154) of the GSTM1 gene \(^{69,70}\) is associated with bladder cancer in humans \(^{71}\) . GSTM1 is bimodally expressed across individuals in all tissues (Figure S2D) that we analyzed, as well as across multiple tumor types \(^{15}\) , with different expression peaks corresponding to differential prognoses among patients. These findings suggest a compelling hypothesis: the common deletion of GSTM1, maintained either by drift or balancing selection \(^{72}\) , has no significant effect on the health of non- cancerous individuals; however, it could have significant implications for prognosis once certain types of tumors develop. Therefore, screening + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 880, 125]]<|/det|> +patients with certain tumor types for the GSTM1 deletion could significantly advance our ability to predict the course of tumor progression in an individualized manner. + +<|ref|>text<|/ref|><|det|>[[113, 141, 879, 404]]<|/det|> +Secondly, genes that are bimodally expressed across multiple tissues raise an evolutionary paradox. Typically, genes with a wide expression breadth (i.e., expression across a large number of tissues) affect fitness and are thus constrained at both the sequence and expression level \(^{26,73 - 75}\) . However, universally switch- like genes, despite having a high expression breadth, are not conserved at the expression level. This could imply different health consequences for individuals with off versus on state of the genes. For example, the universally switch- like gene RP4- 765C7.2 (ENSG00000213058; Figure S2K) is upregulated in the peripheral blood mononuclear cells of patients with ankylosing spondylitis \(^{76}\) , eutopic endometrium in endometriosis patients \(^{77}\) , and peripheral blood mononuclear cells of multiple sclerosis patients \(^{78}\) . Conversely, it is downregulated in the peripheral blood mononuclear cells of Sjögren's syndrome patients \(^{79}\) . These results suggest that this gene being switched on versus off may predispose individuals to certain diseases while protecting them against others. This balance between susceptibility and protection could explain why both high- expression and low- expression states are maintained in the population at comparable frequencies. + +<|ref|>text<|/ref|><|det|>[[114, 419, 876, 561]]<|/det|> +Thirdly, the bimodality of NPIPA5 (Figure S2G), too, can be explained by a single eQTL. The T allele of the SNV rs3198697 is associated with NPIPA5 being switched on across tissues, while the C allele is associated with the gene being switched off. NPIPA5 has been reported as one of the top differentially expressed genes among patients with multiple sclerosis in both blood and brain \(^{80}\) . Moreover, this study \(^{80}\) showed that this gene is co- expressed in blood and brain. Here, we have shown that this gene is switch- like and that the co- expression of NPIPA5 is not restricted to blood and brain but extends to all pairs of tissues. + +<|ref|>text<|/ref|><|det|>[[114, 577, 876, 649]]<|/det|> +Lastly, a single eQTL can explain the bimodality of a member of the PKD1 gene family in cluster 2A, PKD1P5 (Figure S2I). For PKD1P5, the C allele of the SNV rs201525245 is associated with the gene being switched on, while the G allele is associated with the gene being switched off. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 88, 884, 711]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 710, 816, 728]]<|/det|> +
Figure S2. Violin plots for expression level distributions of switch-like genes in cluster 2A.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 880, 602]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 605, 820, 622]]<|/det|> +
Figure S3. Violin plots for expression level distributions of switch-like genes in cluster 2A.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 650, 848, 689]]<|/det|> +## Conceptual issues regarding bimodal expression distributions driven by genetic polymorphisms + +<|ref|>text<|/ref|><|det|>[[114, 688, 880, 863]]<|/det|> +In the main text, we claimed that genetic polymorphisms drive the bimodal expression of universally switch- like genes in cluster 2A. For a polymorphism with two alleles (A and \(a\) ), there are three possible genotypes ( \(aa\) , \(Aa\) , and \(AA\) ). Since each of the three genotypes can lead to three different expression levels, we expect expression distributions of a cluster- 2A gene to have three modes. This leads to the question: Why do we not see trimodal, as opposed to bimodal, expression distributions for genes in cluster 2A? To answer this question, we develop the following frameworks. Let us assume that a genetic polymorphism exists with two alleles, \(A\) and \(a\) , with frequencies \(p_A\) and \((1 - p_A)\) , respectively. The three genotypes for this polymorphism, \(aa\) , \(Aa\) , and \(AA\) , lead to three different expression states (TPM levels) for the gene with averages + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 879, 170]]<|/det|> +\(\mu_{aa}, \mu_{Aa}\) , and \(\mu_{AA}\) , respectively. Let us also assume that the Hardy-Weinberg equilibrium holds for this locus. Then, the frequency of \(aa = (1 - p_A)^2\) , the frequency of \(Aa = 2p_A(1 - p_A)\) , and the frequency of \(AA = p_A^2\) . We assume that \(\mu_{aa} \leq \mu_{Aa} \leq \mu_{AA}\) . Next, we define a dominance coefficient \(0 \leq \alpha \leq 1\) by, + +<|ref|>equation<|/ref|><|det|>[[319, 186, 568, 207]]<|/det|> +\[\mu_{Aa} = \mu_{aa} + (\mu_{AA} - \mu_{aa})\alpha .\] + +<|ref|>text<|/ref|><|det|>[[113, 220, 343, 239]]<|/det|> +If we define the ratio \(R\) by + +<|ref|>equation<|/ref|><|det|>[[378, 237, 463, 268]]<|/det|> +\[R = \frac{\mu_{AA}}{\mu_{aa}},\] + +<|ref|>text<|/ref|><|det|>[[113, 266, 251, 283]]<|/det|> +then, we obtain + +<|ref|>equation<|/ref|><|det|>[[348, 281, 570, 302]]<|/det|> +\[\mu_{Aa} = \mu_{aa}(1 - \alpha +R\alpha)\] + +<|ref|>text<|/ref|><|det|>[[113, 301, 150, 317]]<|/det|> +and + +<|ref|>equation<|/ref|><|det|>[[406, 318, 525, 338]]<|/det|> +\[\mu_{AA} = R\mu_{aa}.\] + +<|ref|>text<|/ref|><|det|>[[113, 368, 876, 440]]<|/det|> +We can then divide individuals into three groups depending on their genotypes. Let us assume that the coefficient of variation (CV) of expression is the same for each genotypic group. Then, we can model the TPM value of this gene in a given individual a normal random variable with: + +<|ref|>text<|/ref|><|det|>[[144, 439, 810, 495]]<|/det|> +1) mean \(= \mu_{aa}\) and standard deviation \(= \mathrm{CV}\times \mu_{aa}\) if the genotype is \(aa\) 2) mean \(= \mu_{Aa}\) and standard deviation \(= \mathrm{CV}\times \mu_{Aa}\) if the genotype is \(Aa\) ; and +3) mean \(= \mu_{AA}\) and standard deviation \(= \mathrm{CV}\times \mu_{AA}\) if the genotype is \(AA\) + +<|ref|>text<|/ref|><|det|>[[113, 493, 857, 529]]<|/det|> +The value of \(\mu_{aa}\) is irrelevant for gauging the effect of polymorphisms on the shape of the expression level distributions. Therefore, we set \(\mu_{aa} = 1\) . + +<|ref|>text<|/ref|><|det|>[[113, 544, 880, 756]]<|/det|> +Under these mathematical assumptions, we performed simulations using 36 distinct models. These models vary by four parameters: \(p_A \in \{0.05, 0.1, 0.5\}\) , \(\mathrm{CV} \in \{0.1, 0.3\}\) , \(R \in \{10, 1000\}\) , and \(\alpha \in \{0.2, 0.5, 0.8\}\) . For each model, defined by a unique combination of the values of these four parameters, we performed a two- step sampling procedure. First, we obtained a random sample of 500 genotypes, based on \(p_A\) and the Hardy- Weinberg equilibrium. Next, for each of the 500 genotypes sampled, we sample a TPM value from the normal distribution corresponding to that genotype. Thus, for each of the 36 models, we simulated 500 TPM values. We present these values as histograms with and without log transformation. The results for \(p_A = 0.05\) , \(p_A = 0.1\) , and \(p_A = 0.5\) are shown in Figure S4, Figure S5, and Figure S6, respectively. These simulations help us answer our question we first asked: Why do we not see a trimodal distribution if a genetic polymorphism drives expression- level variability in a gene? + +<|ref|>text<|/ref|><|det|>[[113, 771, 881, 860]]<|/det|> +Firstly, even when the minor allele (A) frequency is not low (e.g., \(10\%\) ), the frequency of the genotype \(AA\) is still quite low (e.g., \(1\%\) ). Therefore, the third peak is not always conspicuously visible. We see this in all models with \(p_A = 0.05\) and \(p_A = 0.1\) (Figures S4 and S5), regardless of CV, \(R\) , and \(\alpha\) values. At higher allele frequencies (e.g., \(50\%\) ), the effect of the remaining parameters becomes more apparent. Figure S6 shows that a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 90, 861, 247]]<|/det|> +higher dominance coefficient \(\alpha\) makes the expression level distribution more bimodal. By contrast, a lower dominance coefficient \(\alpha\) makes the expression level distribution more trimodal. The lack of observed trimodality in the GTEx data may suggest that expression levels of switch-like genes tend to be more dominant than additive with regard to causal genetic polymorphisms. Secondly, greater variation (CV) in the data can also obscure the third peak. For example, by comparing **Figure S6B** to **Figure S6H**, we find that increasing the CV can change the distribution from being trimodal to bimodal when the other parameters are held constant. However, \(R\) does not seem to have much effect on whether the expression level distribution is bimodal or trimodal. + +<|ref|>image<|/ref|><|det|>[[120, 285, 880, 722]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[114, 728, 833, 760]]<|/det|> +
**Figure S4. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 5%.**
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 88, 875, 530]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 536, 830, 567]]<|/det|> +
Figure S5. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of 10%.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 875, 528]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 533, 832, 564]]<|/det|> +
Figure S6. TPM simulations for a hypothetical gene whose expression is driven by a genetic polymorphism with an allele frequency of \(50\%\) .
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 578, 428, 595]]<|/det|> +## Tissue-specific switch-like genes + +<|ref|>text<|/ref|><|det|>[[114, 595, 872, 787]]<|/det|> +We divided switch- like genes into three clusters in the space spanned by the first two principal components (Figure 2A). While we said that genes in cluster 1 (Figure 2A- B) are tissue- specific switch- like genes, manual inspection reveals this is not true for all genes in cluster 1. In particular, the transcript ENSG00000273906 coming from chr Y was labeled cluster 1 by hierarchical clustering even though it is universally switch- like in tissues common to both sexes. Indeed, we removed all chr- Y genes from our analyses of genuine cluster- 1 genes. Other cluster- 1 genes bimodally expressed in a large number of tissues lie on the autosomes. For example, CLPS, PRSS1, CELA3A, and CELA3B, despite having low overall tissue- to- tissue co- expression, are bimodally expressed across tissues. Indeed, we have shown previously that CELA3A and CELA3B have a shared regulatory architecture in the pancreas \(^{81}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 801, 378, 818]]<|/det|> +## Controlling for confounders + +<|ref|>text<|/ref|><|det|>[[115, 818, 874, 853]]<|/det|> +We removed cluster- 1 genes affected by confounders in each tissue using an approach outlined in Methods. Here, we present the number of genuine cluster- 1 genes versus + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 864, 177]]<|/det|> +those affected by confounders in **Figure S7**. In particular, we show that the cluster- 1 genes in the colon and the intestine are particularly prone to being affected by confounding factors. We also present in **Figure S8** examples of genes whose bimodal expression in specific tissues is correlated with variation in the sample ischemic time distribution. + +<|ref|>image<|/ref|><|det|>[[116, 177, 680, 519]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 521, 820, 555]]<|/det|> +
Figure S7. Switch-like genes in cluster 1 that are genuine versus those affected by confounders.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 92, 700, 465]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 467, 844, 499]]<|/det|> +
Figure S8. Examples of cluster-1 genes affected by confounders. Their bimodal distribution is caused by ischemic time (a confounding factor).
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 514, 845, 552]]<|/det|> +## The copy number variation at the PGA3 locus does not affect the gene's expression levels + +<|ref|>text<|/ref|><|det|>[[115, 551, 880, 710]]<|/det|> +PGA3 exhibits a high copy number variation among humans \(^{82}\) , but the copy number seems to have no impact on PGA3 expression, at least in cancer samples \(^{83}\) . The bimodal expression of PGA3 in the stomach is likely not due to its copy number variation. This is because PGA3's expression in the stomach is highly correlated with other tissue- specific genes in the stomach. The only way in which a copy number- driven bimodality of PGA3 could be correlated with other switch- like genes is if the product of PGA3 was regulating the correlated genes. Without this evidence, we surmise that the copy number variation at the PGA3 locus does not affect the gene's expression levels, at least in the stomach. + +<|ref|>text<|/ref|><|det|>[[112, 726, 880, 866]]<|/det|> +Table S1. A list of tissues used in this study along with the number of individuals for each tissue. Table S2. A list of 1,013 switch- like genes. Table S3. Tissue- to- tissue co- expression (Pearson's correlation) for all genes across 310 tissue- tissue pairs. Table S4. Results from principal component analysis on tissue- to- tissue co- expression data for all genes. Table S5. Results from principal component analysis on tissue- to- tissue co- expression data for only switch- like genes. Table S6. Correlation between gene expression levels and confounding factors for switch- like + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 832, 153]]<|/det|> +genes. Table S7. Gene- to- gene co- expression of genuine tissue- specific switch- like genes in the stomach, vagina, breast, and colon. Table S8. Analysis of sex bias among genuine tissue- specific switch- like genes. + +<|ref|>text<|/ref|><|det|>[[111, 193, 872, 855]]<|/det|> +1. Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318-356 (1961). +2. Banerji, J., Rusconi, S. & Schaffner, W. Expression of a beta-globin gene is enhanced by remote SV40 DNA sequences. Cell 27, 299-308 (1981). +3. Gillies, S. D., Morrison, S. L., Oi, V. T. & Tonegawa, S. A tissue-specific transcription enhancer element is located in the major intron of a rearranged immunoglobulin heavy chain gene. Cell 33, 717-728 (1983). +4. Serfling, E., Jasin, M. & Schaffner, W. Enhancers and eukaryotic gene transcription. Trends Genet. 1, 224-230 (1985). +5. Allfrey, V. G., Faulkner, R. & Mirsky, A. E. 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Cancer Med. 9, 9064-9080 (2020). + +<--- Page Split ---> diff --git a/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/images_list.json b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..99b20b3611314a705563fe9bdf62a2461422ca9a --- /dev/null +++ b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/images_list.json @@ -0,0 +1,107 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. The schematic diagram of the space cross mapping.", + "footnote": [], + "bbox": [ + [ + 207, + 230, + 789, + 551 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Block diagram of the IGAB-GPC scheme architecture for pattern-moving systems.", + "footnote": [], + "bbox": [ + [ + 94, + 306, + 900, + 572 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. 3000 sets of input-output system historical data.", + "footnote": [], + "bbox": [ + [ + 105, + 85, + 900, + 494 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Pattern class centre and its threshold.", + "footnote": [], + "bbox": [ + [ + 145, + 123, + 830, + 444 + ] + ], + "page_idx": 19 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Comparison of system output accuracy of different models Figure 6. The tracking errors for pattern moving systems with various models.", + "footnote": [], + "bbox": [ + [ + 150, + 444, + 828, + 768 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. System ouput comparison with different control schemes in PMT framework.", + "footnote": [], + "bbox": [ + [ + 97, + 87, + 890, + 440 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. Tracking results of system operating status on target pattern class.", + "footnote": [], + "bbox": [ + [ + 95, + 503, + 900, + 808 + ] + ], + "page_idx": 22 + } +] \ No newline at end of file diff --git a/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d.mmd b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2648dd6a0ba2ac343995d04c47bbc40d30766513 --- /dev/null +++ b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d.mmd @@ -0,0 +1,938 @@ + +# Generalized Predictive Control Based on Interval Gray Model with Adaptive Buffer Operator for a Class of Pattern-Moving Systems + +Ning Li University of Science and Technology Zhenggaung Xu University of Science and Technology Xiangquan Li 21021@jdzu.edu.cn + +Jingdezhen University + +## Article + +Keywords: Pattern moving theory (PMT), Interval grey model, Cross mapping, Interval grey adaptive buffer generalized predictive control (IGAB- GPC) + +Posted Date: July 15th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 6971022/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: No competing interests reported. + +Version of Record: A version of this preprint was published at Scientific Reports on August 25th, 2025. See the published version at https://doi.org/10.1038/s41598- 025- 17141- 8. + +<--- Page Split ---> + +# Generalized Predictive Control Based on Interval Gray Model with Adaptive Buffer Operator for a Class of Pattern-Moving Systems + +Ning Li \(^{1}\) , Zhenggaung Xu \(^{1}\) , and Xiangquan Li \(^{2,*}\) + +\(^{1}\) University of Science and Technology, School of Automation and Engineering, Beijing, 100083, China \(^{2}\) Jingdezhen University, School of Information Engineering, Jingdezhen, 333032, China \(^{*}21021@\) jdzu.edu.cn + +## ABSTRACT + +The pattern- moving systems, as a kind of complex nonlinear systems that governed by statistical laws, are commonly found in industrial production processes such as sintering machines and cement rotary kiln. Encountering difficulties in delineating the statistical properties of such systems through deterministic variables like state or output variables, existing control techniques tend to either bypass these systems or address them as systems impacted by stochastic perturbations. To reveal system's inherent statistical characteristics, this work proposed a novel Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) scheme, which employs the bidirectional mapping framework under pattern mpving theory (PMT) to quantify pattern category variables, enabling precise tracking of dynamic pattern transitions. Key innovations include: (1) an adaptive buffer operator that mitigates oscillations in pattern class sequences based on their monotonicity, (2) an IGM(1,2)- based prediction model for robust uncertainty quantification, and (3) a GPC framework incorporating receding horizon optimization and feedback correction for enhanced control accuracy. The workflow involves constructing a pattern- moving space through data- driven quantization, applying the adaptive buffer operator to smooth time- series fluctuations, developing the IGM(1,2) model, and implementing the IGB- GPC strategy. Numerical simulations demonstrate that IGB- GPC outperforms benchmark methods like CARIMA- GPC and IG- GPC, achieving superior tracking accuracy, smoother pattern transitions, and robust stability, making it highly suitable for complex industrial processes + +Keywords: Pattern moving theory (PMT), Interval grey model, Cross mapping, Interval grey adaptive buffer generalized predictive control (IGAB- GPC). + +## 1 Introduction + +In the process industries such as metallurgical, building materials, and chemical processing, there widely exist large- scale industrial systems characterized by highly complex manufacturing processes. From the perspective of studying system dynamic characteristics, this kind of systems are inherently governed by statistical laws rather than typical Newtonian mechanical systems \(^{1,2}\) . Typical examples involve sintering machines and cement rotary kilns, which possess the following features: (1) extremely complicated manufacturing processes with frequently inside liquid- phase transitions and combined multidimensional physical events; (2) operational qualities, such as multi- parameter, high- dimensionality, and uncertain degrees of freedom, accompanied by complex system movement. (3) a variety of chemical reactions that are naturally dependent on statistical laws, where feature correlations exhibit probabilistic- statistic reliance and system dynamics are controlled due to statistical rules opposed to traditional mechanics. Given the dynamic nature of systems, some studies adopt a pattern- moving perspective, where statistical principles guide the integration of data- driven and pattern recognition techniques. In this framework, the control objectives are reformulated as driving the system's operating conditions into predefined pattern categories. Therefore, these systems are also known as pattern- moving systems \(^{3}\) . + +Although significant progress has been made in applying pattern recognition schemes to system modeling and control \(^{4}\) , challenges remain in handling highly nonlinear systems with limited and uncertain data. To overcome this obstacle, a novel framework named Pattern Movement Theory (PMT) was proposed by Prof. Xu \(^{5 - 7}\) , which maps system operating conditions to dynamic pattern class via statistical calculation and enables systematic description and control through pattern- driven processes. In light of this insight, the measurement of pattern categories represents a fundamental task, as the pattern class variables lack computational properties, which means they do not satisfy the condition where Pattern 1 + Pattern 2 = Pattern 3. In order to render pattern- based variables computationally viable, several measurements were developed, including metrics based on category centers \(^{8,9}\) , cell mapping \(^{6,10,11}\) , interval numbers \(^{12,13}\) , probability density evolution \(^{14,15}\) , and explicit- implicit + +<--- Page Split ---> + +formulations derived from category centers16,17. However, existing methods inadequately deal with the inherent uncertainty in modeling pattern categorical variables, with strategies largely confined to either direct employing category centers or reliance on probabilistic partition estimation. In other words, quantifying uncertainty in pattern category variables within the PMT framework constitutes critical research focus. As it can be seen, pattern class variables represent statistical features with inherently small sample and incomplete information, wherein parameters like category centers and radius may be available, but the full statistical structure remains unknown. Hence, Thus, we attempted to integrate grey system theory with PMT to analyze and control system performance. + +Grey system theory, originally proposed by Professor Julong Deng in \(1982^{18}\) , provides a systematic framework for modeling, analyzing, and predicting systems characterized by small sample data and significant information uncertainty. It is particularly well- suited for addressing real- world problems where data are limited, incomplete or imprecise. Regarding the intelligent control, Chen exploited an intelligent optimal grey evolutionary algorithm for structural control, enhancing prediction accuracy and control capabilities to support sustainable urbanization goals19. For instance, Zeng20 developed an improved interval grey prediction model (IGM(1,1)) for industrial control systems, demonstrating its effectiveness in predicting chemical process outputs under uncertain conditions. Similarly, Rao21 applied grey system theory to the control of uncertain nonlinear systems, achieving stable performance in the stepped bar and the rigid- body (vertical) analysis. In the field of State prediction, Chen22 introduced a ground breaking learning procedure combining boxplots and Program Evaluation and Review Technique (PERT) with IGM(1,1), significantly improving interval forecasting reliability for short- term time- series under data constraints. Moreover, Liu23 discussed a comprehensive review of grey system theory in intelligent control, highlighting its applications in prediction, optimization, and decision- making. These studies collectively demonstrate the versatility and effectiveness of grey system theory, particularly interval grey models, in addressing uncertainties and improving control performance in various applications. + +To our best knowledge, generalized predictive control (GPC), as a subclass of adaptive control, offers significant advantages, including reduced sensitivity to model accuracy, a straightforward algorithmic structure conducive to practical implementation, and inherent robustness. At same time, continuous data acquisition and feedback correction by GPC reduce process uncertainties, thereby optimizing control performance and ensuring operational stability in complex industrial systems24. In particular, recent work25 presented an improved robust model predictive control for PMSM, integrating backstepping control and integral action, experimentally validated to enhance speed tracking, robustness, and uncertainty handling, setting new industrial benchmarks. Considering the adaptability and simplicity of GPC, its application to pattern- moving systems with small samples and high nonlinearity is limited. GPC struggles to build accurate models due to complex statistical dynamics and non- algebraic pattern variables, faces performance issues from data fluctuations without effective oscillation control, and lacks robust uncertainty quantification, reducing control accuracy and robustness in industrial applications. These shortcomings highlight the need for advanced methodologies to address uncertainty and variability in such systems. + +Given the challenges outlined, this study proposes a novel methodology that integrates the Interval Grey Model (IGM) with GPC, termed Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC), to address the control of pattern- moving systems characterized by small sample sizes and significant uncertainties. This approach leverages the strengths of grey system theory to model systems with incomplete information and employs an adaptive buffer operator to mitigate fluctuations in pattern class variables, thereby enhancing prediction accuracy and control robustness. The proposed method systematically addresses the quantization of pattern category variables through a bidirectional mapping framework, enabling precise tracking of dynamic pattern transitions. Key contributions include: (1) the development of an adaptive buffer operator tailored to the monotonicity and oscillation of pattern class sequences, (2) the formulation of an IGM(1,2)- based prediction model for robust handling of uncertain data, and (3) the integration of these components into a GPC framework to achieve stable and accurate control of pattern- moving systems. The workflow involves constructing a pattern- moving space via data- driven quantization, applying the adaptive buffer operator to smooth time- series fluctuations, developing the IGM(1,2) prediction model, and implementing the IGB- GPC control strategy with receding horizon optimization and feedback correction. The effectiveness of this approach is validated through numerical simulations, demonstrating superior control accuracy and dynamic response compared to benchmark methods such as CARIMA- GPC and IG- GPC. + +The remaining sections of this paper are organized as follows. The problem Statement and basic knowledge are given in Section 2. Section 3 develops IGM prediction with an adaptive buffer operator in the interest of smoothing time series fluctuations (pattern class variables) in term of different properties. Section 4 introduces a novel control approach, termed IGB- GPC, which integrates GPC with an interval grey model. The grey prediction model employs an adaptive buffering operator to mitigate fluctuations in the data sequence. The numerical simulation results, which are critical to validating the model, are presented in Section 5. Section 6 ultimately delivers the conclusion. + +<--- Page Split ---> + +## 2 Problem Statement and Preliminaries + +This section introduces a method for dynamic description of complex systems, based on its characteristic analysis, including constructing pattern category variables and the pattern- moving space. + +### 2.1 Problem Statement + +For the purposes of system analysis and controller synthesis, the system is assumed to be representable by Equation 1. + +\[d x(k + 1) = f\left(d x(k),\ldots ,d x(k - n_{y}),u(k),\ldots ,u(k - n_{u})\right) \quad (1)\] + +where the system input at time step \(k\) is denoted by \(u(k)\in \mathbb{R}\) , the unknown nonlinear function \(f(\cdot)\) characterizes the system dynamics, the positive integers \(n_{y},n_{u}\in \mathbb{Z}_{+}\) represent the unknown output and input orders respectively, and \(d x\in \mathbb{R}\) corresponds to the pattern class variables, computable through the following relation. + +\[\begin{array}{r l} & {\widehat{\otimes}(k + 1) = D(M(d x(k + 1)))\\ & {\qquad = \left\{ \begin{array}{l l}{c_{1},d x(k + 1)\in (c_{1} - r_{1},C_{1}],}\\ {c_{2},d x(k + 1)\in (c_{2} - r_{2},C_{2}],}\\ \vdots \\ {c_{N},d x(k + 1)\in (c_{N} - r_{N},C_{N}]} \end{array} \right.} \end{array} \quad (2)\] + +where \(D(M(\cdot))\) refers to the space cross- mapping process, showing in Figure 1 which is considered to be a grey system. \(\widehat{\otimes} (k + 1)\) is the grey metric value and it denotes a quantized observation, \(c_{m}\neq c_{n}\) (if \(m\neq n\) and \(m,n\in [1,N]\) with \(N\) being the numbers of pattern class), \(c_{m}\) is the class center, \(C_{m}\) denotes the grey number interval boundary and \(C_{m} = c_{m} + r_{m} = c_{m + 1} - r_{m + 1}\) \(r_{m}\) refers to the class radius and \(r_{m} > 0\) + +Note that the system 1 exhibits precisely observable state variable \(d x\) . This allows for the system to be equivalently described using distinct mathematical formalisms: as a linear time- invariant (LTI) system, through state space equations governing its dynamics, or via a finite impulse response (FIR) model defining its input- output behavior. To facilitate the computation of pattern class variables, the definition of gray number is provided herein. + +Definition 2.1. Let \(\mathbb{R}\) be the real number field. A grey number \(\otimes\) is defined as: \(\otimes \in [\underline{{a}},\overline{{a}} ]\) , where \(\underline{{a}}\) is the lower bound and \(\overline{{a}}\) is the upper bound, with \(\underline{{a}}\leq \overline{{a}}\) . When \(\underline{{a}} = \overline{{a}}\) , the grey number degenerates to a white number (a deterministic value). A grey number can also be further described by a whitening function, for example: \(f(x):[\underline{{a}},\overline{{a}} ]\to [0,1]\) , which represents the credibility or weight of different values within the interval \(^{20}\) . + +Remark 2.1. The distinction between a grey number and an interval number. The grey number \(\otimes \in [a_{1},a_{2}]\) (where \(a_{1}< a_{2}\) represents an unknown value within the interval \([a_{1},a_{2}]\) , while the interval number \([a_{1},a_{2}]\) represents the entire interval set itself. + +According to the definition 2.1, the grey number is essentially a type of number whose exact value is unknown but is constrained within a known interval. It characterizes incomplete and uncertain information. Equation 3 \(\widehat{\otimes} (k)\) is a non- intrinsic grey number estimation with the kernel grey number. In perspective of control theory, the control structure of System 1 in pattern moving systems focuses on realizing exact tracking and responsive adaptation to the dynamic transitions of pattern category variables. + +### 2.2 Pattern moving theory + +This section examines the fundamental concepts of PMT, highlighting its capacity to represent system dynamics through time- series pattern recognition. PMT is composed of two components: pattern category variables and pattern moving spaces. These elements are discussed in the sections that follow respectively. + +#### 2.2.1 Pattern class variable + +Analysis of pattern- moving systems reveals that system output is not a deterministic quantity but rather a random variable subject to statistical principles. This presents difficulties for methods that rely on deterministic variables (e.g., State variables or output variables) for dynamics modeling and control. In addition, the design of corresponding controllers based on traditional system model structures becomes more complicated. Given the inherent statistical moving properties of this systems, it is necessary to construct variables with statistical properties to portray their overall statistical moving behavior. + +<--- Page Split ---> + +According to pattern recognition theory, pattern category denotes a collection of pattern samples that possess identical or similar characteristics. When these pattern samples are uniformly represented by a specific variable, this variable reflects certain statistical properties. The variable that encapsulates the information of the pattern category is termed the pattern category variable. Therefore, for systems governed by statistical laws, the dynamic behavior can be effectively characterized using the pattern category variable instead of traditional State variables. The construction process of a pattern category variable is as follows: + +Definition 2.2. Assume that the \(\{y(k)\}\) and \(\{m x(k)\}\) denote the sequence of detection samples and the sequence of pattern samples, respectively. After the pattern samples are classified by the classifier, the pattern variable with category information is defined as the pattern class variable, which is expressed as: \(d x(t)\) . Then the pattern class variable should meet the following transformation process: + +\[\begin{array}{r}{m x(k) = T(y(k))}\\ {d x(k) = M(m x(k))} \end{array} \quad (3)\] + +where the terms \(T(\cdot)\) and \(M(\cdot)\) represent the feature extraction or selection and pattern classification processes, respectively. + +Remark 2.2. In accordance with Definition 2.2, the \(d x(t)\) has following two properties: (1) Pattern class variables are functions of time. (2) The pattern class variables possess a class attribute, that is, the \(d x(t)\) have statistical and set qualities. As a result, the pattern class was commonly understood to be a set of samples with the same or comparable attributes directly. + +#### 2.2.2 Pattern-moving space + +For complex industrial production processes, continuously collect input and output data for a sufficient amount of time to form a data space. It is worthy to point out that pattern moving "space" is a virtual space without structural description, which is constructed based on data- driven methods and contains three significant steps26. (1) For pattern- driven systems, data collected over an extended period (e.g., 2- 3 years) forms the data space. A sufficiently large dataset ensures the production process operates within this system operating subspace, capturing the system's characteristic features. (2) Extracting characteristic variables from the operational subspace generates pattern sample sequences with primary statistical features, forming the operational condition characteristic subspace. (3) Pattern recognition or quantitative classification techniques identify the condition feature subspace, using the pattern class as a scale to form the pattern scale space. The pattern moving space combines this space with the pattern category variables defined therein. + +To construct the pattern- moving space, an improved data quantization and classification method is developed in this work, which extends the quantized classification control scheme reported research27. The proposed approach can be described as follows: + +\[\begin{array}{r l} & {d x(k + 1) = M\left\{T\left[y(k + 1)\right]\right\}}\\ & {\quad = \left\{ \begin{array}{l l}{-\vec{\gamma} (k + 1),} & {\mathrm{if} - \frac{1}{1 - \Delta}\kappa_{i}< y(k + 1)\leq - \frac{1}{1 + \Delta}\kappa_{i}}\\ {0,} & {\mathrm{if} - \frac{1}{1 + \Delta}\kappa_{i}< y(k + 1)\leq \frac{1}{1 + \Delta}\kappa_{i}}\\ {\vec{\gamma} (k + 1),} & {\mathrm{if} \frac{1}{1 + \Delta}\kappa_{i}< y(k + 1)\leq \frac{1}{1 - \Delta}\kappa_{i}} \end{array} \right.} \end{array} \quad (4)\] + +Among them, \(\begin{array}{r}{\bar{y} (k + 1) = \frac{1 + \rho_{0}}{4}\kappa_{i}(\rho_{0}^{i - 1} + \rho_{0}^{i});\Delta = \frac{1 - \rho_{0}}{1 + \rho_{0}};\kappa_{i} = \rho_{0}^{i}\kappa_{0};\rho_{0}\in (0,1);\kappa_{0}} \end{array}\) is the maximum working range of the first principal component \(y_{p}(k)\) , that is, \(\kappa_{0}\geq \max \left(\left|y(k)\right|\right);i = 1,2,\dots ,N\) + +Given the initial class radius upper limit \(r_{0}\) of the mode class where the operating point 0 is located, and other partitioning parameters \(\rho_{0}\) , \(\kappa_{0}\) . According to the quantization classification Equation 4, when \(N\geq \left[\ln (r_{0}(1 + \Delta) / \kappa_{0}) / \ln \rho_{0}\right]\) , the first principal component sequence \(\{y(k)\}\) is divided into \(2N + 1\) intervals. Therefore, we can obtain the centers \(c_{i}\) of \(2N + 1\) pattern classes, the class radius \(r_{i} = \left|\frac{1 + \rho_{0}^{2}}{4\rho_{0}}\kappa_{i}\right|\) , and the class threshold \(C_{i} = c_{i} + r_{i}\) for the pattern class \(i\) , i.e., \(P_{i}\) . + +## 3 System dynamic description based on IGM with adaptive buffer operator + +### 3.1 Modeling system dynamics with mapping spaces + +After completing the definition of pattern category variables and conducting clustering mapping processing for the operational condition feature subspace, the constructed pattern moving space exhibits typical characteristics of a gray system, namely small sample size and poor information. Therefore, the gray number measurement theory is employed to perform quantitative analysis on the pattern category variables, and the dynamic characteristic expression of the system is constructed. + +<--- Page Split ---> + +Definition 3.1. In an \(m\) - dimensional pattern moving space \((m \in \mathbb{N}^+\) , where \(\mathbb{N}^+\) denotes the set of positive integers), let the pattern category variable at the \(i\) - th dimension \((i = 1,2,\ldots ,m)\) take values at different time steps \(k\) \((k = 1,2,\ldots ,n,n\in \mathbb{N}^*)\) as \(d x_{i}(k)\in [a_{i},b_{i}]\) , where \(a_{i}< b_{i}\) . Then \(\hat{\otimes}_{k} = (d x_{1}(k),d x_{2}(k),\ldots ,d x_{m}(k))\) is called the grey whitening value of the pattern category variable at time \(k\) . + +As specified in Definition 3.1, the measurement of pattern variables constitutes a non- intrinsic grey number system, exhibiting the distinctive property of value oscillations around a base reference point. For resolving computational issues involving pattern categorical variables, we establish a bidirectional mapping framework between the pattern moving domain and its computationally tractable counterpart, illustrating in Figure 1. + +![](images/Figure_1.jpg) + +
Figure 1. The schematic diagram of the space cross mapping.
+ +As described in Figure 1, the pattern category variable is endowed with computational attributes through the gray- scale metric \(D(\cdot)\) , followed by computations in a computable space, and subsequently classified via a classification mapping \(M(\cdot)\) to determine the trajectory of the pattern's motion. This essentially constitutes a spatial cross- mapping method. The motion trajectory of the system in the pattern space is formed through a cyclic procedure: pattern category variables are mapped to a computable space at each time step for processing, and the results are then projected back to the pattern space to generate successive trajectory points. This iterative process builds the trajectory over time, and is mathematically described as: + +\[\left\{ \begin{array}{l l}{d x(k + 1) = M[\widetilde{d x} (k + 1)]}\\ {\qquad = M\{f[D(d x(k)),D(d x(k - 1)),\dots ,D(d x(k - n))}\\ {\qquad u(k - \tau),u(k - \tau - 1),\dots ,u(k - \tau - m)]\}}\\ {\qquad = M\{f[\hat{\otimes}_{k},\hat{\otimes}_{k - 1},\dots ,\hat{\otimes}_{k - n},}\\ {\qquad u(k - \tau),u(k - \tau - 1),\dots ,u(k - \tau - m)]\}}\\ {d x(k) = \hat{\otimes}_{k} + \delta \in [\underline{{a}},\overline{{a}} ],\underline{{a}} < \overline{{a}}} \end{array} \right. \quad (5)\] + +where \(f(\cdot)\) is the output model of the computable space, referring to a suitable grey model here. \(m,n\) are the input and output orders of the model respectively; \(\tau\) is the input time delay of the model. \(k\) denotes a running moment in the pattern motion space, \(\widetilde{d x} (k + 1) = f(\cdot)\) represents the initial prediction output of the computable space, and \(d x(k + 1) = M[\cdot ]\) is the final prediction output of the system. Here, \(u(k)\) represents the control variable or control pattern. Meanwhile, \(\hat{\otimes}_{k} + \delta\) is the metric value for the pattern category variable, and \(\delta\) is the perturbation of the metric value. The subsequent grey metric values are measured by the "core" grey number and interval grey number respectively. + +<--- Page Split ---> + +### 3.2 IGM prediction with adaptive buffer operator + +In this section, an adaptive buffer operator was introduced to perform smoothness processing on categorical variables of patterns, and a prediction model based on IGM (1,2) is derived and established. Firstly, the definition of the smoothness operator is given as follows. + +Definition 3.2. \(^{28}\) Let \(X = (x(1),x(2),\ldots ,x(n))\) be a system behavior data sequence. + +1. If \(\forall k = 2,3,\ldots ,n,x(k) - x(k - 1) > 0\) , then \(X\) is called a monotonically increasing sequence. + +2. If \(\forall k = 2,3,\ldots ,n,x(k) - x(k - 1)< 0\) , then \(X\) is called a monotonically decreasing sequence. + +3. If there exist \(k,k^{\prime}\in \{2,3,\ldots ,n\}\) such that + +\[x(k) - x(k - 1) > 0\quad \mathrm{and}\quad x(k^{\prime}) - x(k^{\prime} - 1)< 0, \quad (6)\] + +then \(X\) is called a random oscillating sequence. + +Let + +\[M = \max \{x(k)\mid k = 1,2,\ldots ,n\} ,\quad m = \min \{x(k)\mid k = 1,2,\ldots ,n\} . \quad (7)\] + +The value \(M - m\) is called the amplitude of the sequence \(X\) + +Definition 3.3. Let \(X = (x(1),x(2),\ldots ,x(n))\) be a system behavior data sequence, and \(D\) be an operator acting on \(X\) . The sequence obtained after applying \(D\) to \(X\) is denoted as + +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d). \quad (8)\] + +Here, \(D\) is called a sequence operator, and \(X D\) is called a first- order operator- applied sequence. + +The action of sequence operators can be applied multiple times. If \(D_{1}\) and \(D_{2}\) are both sequence operators, then \(D_{1}D_{2}\) is called a second- order operator, and + +\[X D_{1}D_{2} = (x(1)d_{1}d_{2},x(2)d_{1}d_{2},\ldots ,x(n)d_{1}d_{2}) \quad (9)\] + +is called a second- order operator- applied sequence, and so on \(^{28}\) + +Theorem 3.1. \(^{29}\) Strengthens the buffer operator + +Let + +\[X = (x(1),x(2),\ldots ,x(n)) \quad (10)\] + +be a system behavior sequence, and let + +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d) \quad (11)\] + +be its intensified buffer sequence. Then we have: + +1. \(X\) is a monotonically increasing sequence and \(D\) is an intensified buffer operator \(\Leftrightarrow x(k)\geq x(k)d\) for \(k = 1,2,\ldots ,n\) + +2. \(X\) is a monotonically decreasing sequence and \(D\) is an intensified buffer operator \(\Leftrightarrow x(k)\leq x(k)d\) for \(k = 1,2,\ldots ,n\) + +3. If \(X\) is an oscillating sequence and \(D\) is an intensified buffer operator, then + +\[\max_{1\leq k\leq n}\{x(k)\} \leq \max_{1\leq k\leq n}\{x(k)d\} ,\quad \min_{1\leq k\leq n}\{x(k)\} \geq \min_{1\leq k\leq n}\{x(k)d\} \quad (12)\] + +Theorem 3.2. \(^{29}\) Weakening buffer operator + +Let + +\[X = (x(1),x(2),\ldots ,x(n)) \quad (13)\] + +be a system behavior sequence, and let + +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d) \quad (14)\] + +be its weakened buffer sequence. Then: + +<--- Page Split ---> + +1. \(X\) is a monotonically increasing sequence and \(D\) is a weakened buffer operator \(\Leftrightarrow x(k)\leq x(k)d\) for \(k = 1,2,\ldots ,n;\) + +2. \(X\) is a monotonically decreasing sequence and \(D\) is a weakened buffer operator \(\Leftrightarrow x(k)\geq x(k)d\) for \(k = 1,2,\ldots ,n;\) + +3. If \(X\) is an oscillating sequence and \(D\) is a weakened buffer operator, then + +\[\max_{1\leq k\leq n}\{x(k)\} \geq \max_{1\leq k\leq n}\{x(k)d\} ,\quad \min_{1\leq k\leq n}\{x(k)\} \leq \min_{1\leq k\leq n}\{x(k)d\} \quad (15)\] + +Remark 3.1. Theorem 3.1 illustrates that, under the action of the intensifying operator, the data of a monotonically increasing sequence decreases, the data of a monotonically decreasing sequence increases, and the amplitude of an oscillating sequence increases. Theorem 3.2 illustrates that, under the action of the weakening operator, the data of a monotonically increasing sequence increases, the data of a monotonically decreasing sequence decreases, and the amplitude of an oscillating sequence decreases. + +Theorem 3.3. An adaptive buffer operator + +Given a time series \(X = (x(1),x(2),\ldots ,x(n))\) , the adaptive buffer operator \(D\) is defined as follows: + +- If \(X\) is an increasing sequence (i.e., \(x(k + 1) > x(k)\) for all \(k = 1,2,\ldots ,n - 1\) ), then \(D = D_{1}\) , the strengthening operator, defined by: + +\[x(k)d_{1} = x(k) + \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k),\quad 0< \alpha < 1,k = 1,2,\ldots ,n. \quad (16)\] + +- If \(X\) is a decreasing sequence (i.e., \(x(k + 1) < x(k)\) for all \(k = 1,2,\ldots ,n - 1\) ), then \(D = D_{2}\) , the weakening operator, defined by: + +\[x(k)d_{2} = x(k) - \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k),\quad 0< \alpha < 1,k = 1,2,\ldots ,n. \quad (17)\] + +- If \(X\) is an oscillating sequence (i.e., neither strictly increasing nor strictly decreasing), then \(D\) adopts a weighted combination form: + +\[x(k)d = \left\{ \begin{array}{l l}{w_{1}x(k)d_{1} + w_{2}x(k)d_{2},} & {\mathrm{if~amplitude~is~large~}(\max_{j = k}^{n}x(j) - \min_{j = k}^{n}x(j) > \theta)}\\ {x(k),} & {\mathrm{otherwise,}} \end{array} \right. \quad (18)\] + +where \(w_{1} + w_{2} = 1,w_{1},w_{2}\geq 0\) are dynamically adjusted weights based on amplitude, and \(\theta\) is a predefined amplitude threshold. + +Proof. Proof of Strengthening Property for Increasing Sequences + +Assume \(X\) is an increasing sequence, i.e., \(x(k + 1) > x(k)\) for all \(k\) . Consider the \(D_{1}\) operator: + +\[x(k)d_{1} = x(k) + \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k). \quad (19)\] + +Since \(x(j) > x(k)\) for \(j > k\) , \(\frac{x(j)}{x(k)} > 1\) , thus: + +\[\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k) > (n - k + 1)\cdot x(k). \quad (20)\] + +Substituting into the formula: + +\[x(k)d_{1} > x(k) + \frac{(n - k + 1)\cdot x(k)}{n - k + 1} = 2x(k). \quad (21)\] + +However, the introduction of \(\alpha\) (where \(0< \alpha < 1\) ) limits the growth magnitude. The adjusted form is: + +\[x(k)d_{1} = x(k)\left(1 + \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right). \quad (22)\] + +Since \(\frac{x(j)}{x(k)} > 1\) and \(\sum_{j = k}^{n}\frac{x(j)}{x(k)} > n - k + 1\) , it follows that \(x(k)d_{1} > x(k)\) , proving that \(D_{1}\) strengthens an increasing sequence. \(\square\) + +<--- Page Split ---> + +Proof. Proof of Weakening Property for Decreasing Sequences + +Assume \(X\) is a decreasing sequence, i.e., \(x(k + 1)< x(k)\) for all \(k\) . Consider the \(D_{2}\) operator: + +\[x(k)d_{2} = x(k) - \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k). \quad (23)\] + +Since \(x(j)< x(k)\) for \(j > k\) , \(\frac{x(j)}{x(k)} < 1\) , thus: + +\[\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k)< (n - k + 1)\cdot x(k). \quad (24)\] + +Substituting into the formula: + +\[x(k)d_{2}< x(k) - \frac{(n - k + 1)\cdot x(k)}{n - k + 1} = 0. \quad (25)\] + +However, \(\alpha\) ensures moderate weakening. The adjusted form is: + +\[x(k)d_{2} = x(k)\left(1 - \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right). \quad (26)\] + +Since \(\frac{x(j)}{x(k)} < 1\) and \(\sum_{j = k}^{n}\frac{x(j)}{x(k)} < n - k + 1\) , it follows that \(x(k)d_{2}< x(k)\) , proving that \(D_{2}\) weakens a decreasing sequence. \(\square\) + +Proof. Proof of Weighted Combination for Oscillating Sequences Let the amplitude of a sequence \(\{x(k)\}_{k = 1}^{n}\) be defined as + +\[\Delta = \max_{k\leq j\leq n}x(j) - \min_{k\leq j\leq n}x(j). \quad (27)\] + +Given a threshold \(\theta >0\) , define the adjusted value \(x(k)^{d}\) as follows: + +If \(\Delta >\theta\) , let + +\[x(k)^{d} = w_{1}x(k)^{d_{1}} + w_{2}x(k)^{d_{2}},\] + +where + +\[w_{1} = \frac{\sum_{j = 1}^{n - 1}\max (0,x(j + 1) - x(j))}{\sum_{j = 1}^{n - 1}|x(j + 1) - x(j)|},w_{2} = 1 - w_{1}. \quad (28)\] + +If \(\Delta \leq \theta\) , set + +\[x(k)^{d} = x(k), \quad (29)\] + +to avoid unnecessary adjustment. + +This weighted formulation dynamically balances the influence of strengthening and weakening trends in response to the degree of oscillation. \(\square\) + +Remark 3.2. The adaptive buffer operator \(D\) intelligently adjusts to the nature of the sequence (increasing, decreasing, or oscillating) by adapting \(D_{1}\) , \(D_{2}\) , or a weighted combination, effectively accommodating the sequence's trend. In addition, common methods for sequence trend detection encompass differential statistical analysis, cumulative sum method for difference series, and extremum- based feature identification \(^{30}\) . + +- Here is the complete step-by-step procedure for constructing the Interval Grey Number Prediction IGM(1,2). + +<--- Page Split ---> + +Interval Grey Number: Denoted as \(\otimes = [\otimes ,\otimes ]\) , where \(\otimes\) and \(\otimes\) are the lower and upper bounds of the grey number. Form of IGM(1,2) Model: Based on a first- order differential equation: + +\[\frac{d\otimes_{1}(t)}{dt} +a\otimes_{1}(t) = b\otimes_{2}(t) \quad (30)\] + +Here, \(\otimes_{1}(t)\) is the dependent variable sequence, \(\otimes_{2}(t)\) is the independent variable sequence, and \(a,b\) are parameters to be estimated by least- squares regression. + +## Data Preprocessing + +For the dependent interval grey number sequence \(\otimes_{1}(0) = [\underline{{x}}_{1}(0),\bar{x}_{1}(0)],\otimes_{1}(1) = [\underline{{x}}_{1}(1),\bar{x}_{1}(1)],\dots ,\otimes_{1}(n) = [\underline{{x}}_{1}(n),\bar{x}_{1}(n)]\) and the independent one \(\otimes_{2}(0) = [\underline{{x}}_{2}(0),\bar{x}_{2}(0)],\otimes_{2}(1) = [\underline{{x}}_{2}(1),\bar{x}_{2}(1)],\dots ,\otimes_{2}(n) = [\underline{{x}}_{2}(n),\bar{x}_{2}(n)]\) , check equidistance and monotonicity. If data fluctuates, use First- Order Accumulated Generation (1- AGO): + +\[\left\{ \begin{array}{l l}{\underline{{X}}_{i}(1) = \underline{{x}}_{i}(1),} & {\overline{{X}}_{i}(1) = \overline{{x}}_{i}(1)}\\ {\underline{{X}}_{i}(k) = \underline{{X}}_{i}(k - 1) + \underline{{x}}_{i}(k),} & {\overline{{X}}_{i}(k) = \overline{{X}}_{i}(k - 1) + \overline{{x}}_{i}(k)} & {(k = 2,3,\ldots ,n)} \end{array} \right. \quad (31)\] + +Denote the result as \(\otimes_{1}^{(1)}(k) = [\underline{{X}}_{i}(k),\overline{{X}}_{i}(k)]\) + +## Constructing the IGM(1,2) Model + +Generate the adjacent mean sequence \(Z_{1}(k)\) for \(\otimes_{1}^{(1)}(k)\) : + +\[Z_{1}(k) = [\underline{{z}}_{1}(k),\bar{z}_{1}(k)] = \alpha \otimes_{1}^{(1)}(k) + (1 - \alpha)\otimes_{1}^{(1)}(k - 1)\quad (\alpha \in [0,1],\mathrm{usually}\alpha = 0.5) \quad (32)\] + +Approximately, for \(k = 2,3,\ldots ,n\) : + +\[\frac{d\otimes_{1}^{(1)}(t)}{dt}\bigg|_{t = k} +a\otimes_{1}^{(1)}(k)\approx b\otimes_{2}^{(1)}(k) \quad (33)\] + +The discrete form is: + +\[Z_{1}(k) + a\otimes_{1}^{(1)}(k) = b\otimes_{2}^{(1)}(k) \quad (34)\] + +In matrix form: + +\[\begin{array}{r}{\left[ \begin{array}{c c}{-Z_{1}(2)} & {\otimes_{1}^{(1)}(2)}\\ {-Z_{1}(3)} & {\otimes_{2}^{(1)}(3)}\\ \vdots & \vdots \\ {-Z_{1}(n)} & {\otimes_{2}^{(1)}(n)} \end{array} \right]\left[ \begin{array}{c}{\otimes_{1}^{(1)}(2) - \otimes_{1}^{(1)}(1)}\\ {\otimes_{1}^{(1)}(3) - \otimes_{1}^{(1)}(2)}\\ \vdots \\ {\otimes_{1}^{(1)}(n) - \otimes_{1}^{(1)}(n - 1)} \end{array} \right]} \end{array} \quad (35)\] + +Solve \(\hat{\pmb{\theta}} = [a,b]^{T}\) in \(\mathbf{B}\cdot \hat{\pmb{\theta}} = \mathbf{Y}\) by interval grey number least squares: + +\[\hat{\pmb{\theta}} = (\mathbf{B}^{T}\cdot \mathbf{B})^{-1}\cdot \mathbf{B}^{T}\cdot \mathbf{Y} \quad (36)\] + +Split into lower and upper bounds to get \(a = [\underline{{a}},\overline{{a}} ]\) and \(b = [\underline{{b}},\overline{{b}} ]\) + +Model Prediction: The predicted value of the accumulated sequence + +\[\otimes_{1}^{(1)}(k + 1) = [\underline{{X}}_{1}(k + 1),\overline{{X}}_{1}(k + 1)] = \left(\otimes_{1}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} \quad (37)\] + +Use Inverse Accumulated Generation (IAGO) to get: + +\[\left\{ \begin{array}{l l}{\underline{{x}}_{1}(k + 1) = \underline{{X}}_{1}(k + 1) - \underline{{X}}_{1}(k)}\\ {\overline{{x}}_{1}(k + 1) = \overline{{X}}_{1}(k + 1) - \overline{{X}}_{1}(k)} \end{array} \right. \quad (38)\] + +The prediction interval is \(\otimes_{1}(k + 1) = [\underline{{x}}_{1}(k + 1),\overline{{x}}_{1}(k + 1)]\) + +## Model Validation + +Calculate the residual interval: + +\[\mathrm{Residual~Interval} = [\underline{{x}}_{1}(k) - \underline{{x}}_{1}(k),\overline{{x}}_{1}(k) - \overline{{x}}_{1}(k)] \quad (39)\] + +<--- Page Split ---> + +Ensure the mean absolute value of residuals is less than a threshold. Calculate \(S_{1}\) , \(S_{2}\) , where \(S_{1} = \frac{1}{n}\sum_{k = 1}^{n}\left[x_{1}(k) - \bar{x}_{1}\right]^{2}\) (variance of the original sequence \(\{x_{1}(k)\}\) , where \(\bar{x}_{1} = \frac{1}{n}\sum_{k = 1}^{n}x_{1}(k)\) ) and \(S_{2} = \frac{1}{n}\sum_{k = 1}^{n}\left[e(k) - \bar{e}\right]^{2}\) (variance of residuals \(e(k) = x_{1}(k) - \hat{x}_{1}(k)\) , with \(\bar{e} = \frac{1}{n}\sum_{k = 1}^{n}e(k)\) ) quantify data dispersion and model error, respectively. The posterior difference ratio \(C = \frac{S_{2}}{S_{1}}\) ( \(C< 0.35\) is excellent, \(C< 0.5\) is qualified), and the small error probability \(P = P\{|e(k) - \bar{e}|< 0.6745S_{1}\}\) ( \(P > 0.95\) is excellent). Also, the grey correlation degree between the predicted and original sequences should be greater than 0.6. + +## 4 Controller design and performance analysis + +This section demonstrates that Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) not only guarantees the convergence of system tracking error but also ensures bounded- input bounded- output (BIBO) stability under certain conditions. Based on the mentioned background and issues, the following will systematically elaborate on the control design and conduct an in- depth analysis and evaluation of its performance. + +### 4.1 Controller design + +![](images/Figure_2.jpg) + +
Figure 2. Block diagram of the IGAB-GPC scheme architecture for pattern-moving systems.
+ +As illustrated in Figure 2, IGAB- GPC incorporates the fundamental components GPC, including the prediction model, receding horizon optimization, and feedback correction mechanism. The flowchart illustrates a control system architecture based on Generalized Predictive Control (GPC) integrated with an IGM prediction using an adaptive buffer operator. The detailed description of the components and their interactions is as follows: + +- Reference Trajectory \((y_{r}(k))\) : The control system begins with a reference trajectory, denoted as \(y_{r}(k)\) , which represents the desired output or setpoint that the system aims to achieve at time step \(k\) . Consider the measurement uncertainty of pattern-class variables, the reference output is represented as follows. + +\[y_{r}(k) = \left\{ \begin{array}{l l}{y_{d}(k)\cap r_{1}^{k},} & {\mathrm{if}\exists i\in \{1,2,\ldots ,l\} \models |y(k) - y_{d}(k)|\leq r_{1}^{k},}\\ {r_{t + 1}^{k} = \mathcal{R}_{n e w}(k),} & {\mathrm{otherwise}.} \end{array} \right. \quad (40)\] + +where \(l = 2N + 1\) , \(r_{t}^{k}\) refers to the category radius calculated by Equation 4. \(\mathcal{R}_{new}(k)\) denotes a new pattern class variable which can be computed by automatic category expansion strategy \(^{31}\) . + +- Output Error Calculation \((e(k))\) : The reference trajectory \(y_{r}(k)\) is compared with the predicted output \(y_{p}(k)\) (obtained from the revising feedback loop). The difference between these two signals is computed as the error signal: + +\[e(k) = y_{r}(k) - y_{p}(k) \quad (41)\] + +This error \(e(k)\) is then passed to the optimization algorithm. + +<--- Page Split ---> + +- Pattern-Moving Systems: This part represents the controlled system or plant, which receives the control input \(u(k)\) and produces the actual output \(y(k)\) . The pattern-moving systems could represent a dynamic process with specific characteristics in Equation 32 (e.g., linear or nonlinear dynamics). + +- Event-Triggered IGM Prediction with Adaptive Buffer Operator: The actual system output \(y(k)\) is processed by a module integrating Interval Grey Model (IGM) prediction with an adaptive buffer operator, as depicted in Figure 2. This component manages uncertainties and fluctuations in pattern-moving systems by combining interval grey modeling with adaptive buffering. The formalized procedure is as follows: + +1. Monotonicity Event Detection: At each time step \(k\) , the monotonicity of the interval grey number sequence \(\widehat{\otimes} (k) = [\underline{{x}} (k),\overline{{x}} (k)]\) , derived from \(y(k)\) Definition 2.1, is evaluated over a detection window of size \(\tau \in \mathbb{Z}_{+}\) (e.g., \(\tau = 5\) ). The event \(\mathcal{E}_{k}\) is defined based on the monotonicity of \(\{\widehat{\otimes} (i)\}_{i = k - 1}^{k - 1}\) : + +\[\mathcal{E}_{k}=\left\{\begin{array}{l l}{\mathrm{increasing}}&{\mathrm{if~}\underline{{x}}(i+1)>\underline{{x}}(i)\mathrm{~and~}\overline{{x}}(i+1)>\overline{{x}}(i),~\forall i\in[k-\tau,k-1],}\\ {\mathrm{decreasing}}&{\mathrm{if~}\underline{{x}}(i+1)< \underline{{x}}(i)\mathrm{~and~}\overline{{x}}(i+1)< \overline{{x}}(i),~\forall i\in[k-\tau,k-1],}\\ {\mathrm{oscillating}}&{\mathrm{otherwise},}\end{array}\right. \quad (42)\] + +where comparisons are applied component- wise to the lower and upper bounds, ensuring consistency with Definition 2.1. The window size \(\tau\) captures sufficient historical data for reliable trend detection. + +2. Adaptive Buffering: Based on \(\mathcal{E}_{k}\) , the adaptive buffer operator \(D\) , as defined in Theorem 3, is applied to the sequence \(\{\widehat{\otimes} (i)\}_{i = 1}^{k}\) : + +\[\begin{array}{r}{\widehat{\otimes} (k) = D(\widehat{\otimes} (k)) = \left\{ \begin{array}{l l}{D_{1}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{increasing},}\\ {D_{2}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{decreasing},}\\ {w_{1}D_{1}(\widehat{\otimes} (k)) + w_{2}D_{2}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{oscillating~and~}\Delta_{k} > \theta ,}\\ {\widehat{\otimes} (k),} & {\mathrm{otherwise},} \end{array} \right.} \end{array} \quad (43)\] + +where \(D_{1}\) and \(D_{2}\) are the strengthening and weakening buffer operators, respectively, defined as: + +\[x(k)d_{1} = x(k)\left(1 + \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right),\quad 0< \alpha < 1, \quad (44)\] + +\[x(k)d_{2} = x(k)\left(1 - \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right),\quad 0< \alpha < 1, \quad (45)\] + +applied component- wise to \(\underline{{x}} (k)\) and \(\overline{{x}} (k)\) of \(\widehat{\otimes} (k) = [\underline{{x}} (k),\overline{{x}} (k)]\) . The amplitude \(\Delta_{k}\) is: + +\[\Delta_{k} = \max_{i\in [k - \tau ,k]}\overline{{x}} (i) - \min_{i\in [k - \tau ,k]}\underline{{x}} (i), \quad (46)\] + +with \(\theta = 0.1\cdot (\max_{i = 1}^{k}\overline{{x}} (i) - \min_{i = 1}^{k}\underline{{x}} (i))\) as the threshold. The weights \(w_{1}\) and \(w_{2}\) are: + +\[w_{1} = \frac{\sum_{j = k - \tau}^{k - 1}\max (0,\overline{{x}} (j + 1) - \overline{{x}} (j))}{\sum_{j = k - \tau}^{k - 1}|\overline{{x}} (j + 1) - \overline{{x}} (j)| + \epsilon},\quad w_{2} = 1 - w_{1}, \quad (47)\] + +where \(\epsilon = 10^{- 6}\) prevents division by zero, and \(w_{1},w_{2}\geq 0\) satisfy \(w_{1} + w_{2} = 1\) + +<--- Page Split ---> + +3. Accumulated Generation (1-AGO): The buffered sequence \(\{\hat{\otimes} (i)\}_{i = 1}^{k}\) undergoes First-Order Accumulated Generation (1-AGO) to reduce noise and enhance stability: + +\[\hat{\otimes}^{(1)}(k) = \left\{ \begin{array}{ll}\hat{\otimes}(1), & k = 1,\\ \hat{\otimes}^{(1)}(k - 1) + \hat{\otimes}(k), & k > 1, \end{array} \right. \quad (48)\] + +where \(\hat{\otimes}^{(1)}(k) = [\underline{{X}} (k),\overline{{X}} (k)]\) , with: + +\[\underline{{X}} (k) = \sum_{i = 1}^{k}\underline{{x}} (i),\quad \overline{{X}} (k) = \sum_{i = 1}^{k}\overline{{x}} (i). \quad (49)\] + +4. Parameter Update for IGM(1,2): The IGM(1,2) parameters \(\hat{\theta}_{k} = [a,b]^{T}\) are updated only when monotonicity changes \((\hat{\mathcal{E}}_{k}\neq \hat{\mathcal{E}}_{k - 1})\) to enhance efficiency: + +\[\hat{\theta}_{k} = \left\{ \begin{array}{ll}(\mathbf{B}^{T}\mathbf{B})^{-1}\mathbf{B}^{T}\mathbf{Y}, & \mathrm{if} \hat{\mathcal{E}}_{k}\neq \hat{\mathcal{E}}_{k - 1},\\ \hat{\theta}_{k - 1}, & \mathrm{otherwise}, \end{array} \right. \quad (50)\] + +where \(\mathbf{B}\) and \(\mathbf{Y}\) are constructed using the adjacent mean sequence \(Z_{1}(k)\) : + +\[Z_{1}(k) = 0.5\hat{\otimes}^{(1)}(k) + 0.5\hat{\otimes}^{(1)}(k - 1), \quad (51)\] + +and for \(k = 2,\ldots ,n\) : + +\[\mathbf{B} = \left[ \begin{array}{c c c}{-Z_{1}(2)} & {\hat{\otimes}_{1}^{(1)}(2)}\\ {-Z_{1}(3)} & {\hat{\otimes}_{2}^{(1)}(3)}\\ \vdots & \vdots \\ {-Z_{1}(n)} & {\hat{\otimes}_{2}^{(1)}(n)} \end{array} \right],\quad \mathbf{Y} = \left[ \begin{array}{c}{\hat{\otimes}_{1}^{(1)}(2) - \hat{\otimes}_{1}^{(1)}(1)}\\ {\hat{\otimes}_{1}^{(1)}(3) - \hat{\otimes}_{1}^{(1)}(2)}\\ \vdots \\ {\hat{\otimes}_{1}^{(1)}(n) - \hat{\otimes}_{1}^{(1)}(n - 1)} \end{array} \right], \quad (52)\] + +where \(\hat{\otimes}_{1}^{(1)}\) and \(\hat{\otimes}_{2}^{(1)}\) are the 1- AGO sequences for dependent and independent variables, respectively. Parameters \(a = [a,\bar{a} ]\) and \(b = [b,\bar{b} ]\) are computed for interval bounds. + +5. Prediction and Correction: The predicted output \(y_{m}(k + 1)\) is computed using the IGM(1,2) model with a correction term: + +\[y_{m}(k + 1) = \left(\hat{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} +\gamma (D(y(k)) - \hat{y}_{m}(k)), \quad (53)\] + +where \(\hat{\otimes}^{(1)}(1) = [\underline{{X}} (1),\overline{{X}} (1)]\) , \(a,b\) are from \(\hat{\theta}_{k}\) , and \(\gamma = 0.1\) is the correction gain. The buffered output \(D(y(k))\) follows Equation (43), and \(\hat{y}_{m}(k)\) is the prior prediction. The interval grey number is recovered via Inverse Accumulated Generation (IAGO): + +\[\hat{\otimes}(k + 1) = \hat{\otimes}^{(1)}(k + 1) - \hat{\otimes}^{(1)}(k), \quad (54)\] + +yielding \(\hat{\otimes}(k + 1) = [\underline{{x}} (k + 1),\overline{{x}} (k + 1)]\) , which is mapped to the pattern- moving space using \(M(\cdot)\) in Equation (3). + +The following pseudocode describes the event- triggered Interval Grey Model (IGM) prediction with an adaptive buffer operator, which processes system outputs to handle uncertainties and fluctuations in pattern- moving systems, seeing Algorithm 1 + +<--- Page Split ---> + +Input: Time range \(T\) , Window size \(\tau\) , Buffer parameters (Eqs. (44), (45)), IGM(1,2) parameters (Eqs. (53), (54)), \(\gamma = 0.1\) , \(\theta\) factor \(= 0.1\) , \(\epsilon = 10^{- 6}\) + +Output: Predicted output \(y_{m}(k + 1)\) , Parameters \(\hat{\theta}_{k} = [a,b]^{T}\) + +Initialize: \(\mathcal{E}_{\mathrm{prev}}\gets \theta\) , \(\hat{\theta}_{1}\gets \mathbf{0}\) + +for \(k\gets 2\) to \(T\) do + +1. Get output: \(y(k)\gets\) System output + +2. Form interval: \(\hat{\otimes} (k)\gets [x(k),\bar{x} (k)]\) (Def. 2.1) + +3. Detect event: \(\mathcal{E}_{k}\gets\) Monotonicity of \(\{\hat{\otimes} (i)\}_{i = k - \tau}^{k - 1}\) (Eq. (42)) + +if \(\mathcal{E}_{k}\neq \mathcal{E}_{\mathrm{prev}}\) then + +3.1 Buffer operation: + +\[\Delta_{k}\gets \max_{i\in [k - \tau ,k]}\bar{x} (i) - \min_{i\in [k - \tau ,k]}\underline{{x}} (i) \quad (Eq. (46))\] + +\[\theta \gets 0.1\cdot (\max_{i = 1}^{k}\bar{x} (i) - \min_{i = 1}^{k}\underline{{x}} (i))\] + +if \(\mathcal{E}_{k} =\) increasing then + +\[\hat{\otimes} (k)\gets D_{1}(\hat{\otimes} (k)) \quad (\mathrm{Eq.} (44))\] + +else if \(\mathcal{E}_{k} =\) decreasing then + +\[\hat{\otimes} (k)\gets D_{2}(\hat{\otimes} (k)) \quad (\mathrm{Eq.} (45))\] + +else if \(\mathcal{E}_{k} =\) oscillating and \(\Delta_{k} > \theta\) then + +\[w_{1}\leftarrow \frac{\Sigma_{j = k - \tau}^{k - 1}\max (0,\bar{x} (j + 1) - \bar{x} (j))}{\Sigma_{j = k - \tau}^{k - 1}\bar{x} (j + 1) - \bar{x} (j) + \epsilon},w_{2}\leftarrow 1 - w_{1} \quad (\mathrm{Eq.} (47))\] + +else + +\[\hat{\otimes} (k)\gets \hat{\otimes} (k)\] + +end if + +3.2 Compute 1- AGO: \(\hat{\otimes}^{(1)}(i)\gets 1 - \mathrm{AGO}(\hat{\otimes}(i)),i = 1,\ldots ,k\) (Eq. (48)) + +3.3 Build matrices: \(\mathbf{B},\mathbf{Y}\gets \mathrm{Using}Z_{1}(k) = 0.5\hat{\otimes}^{(1)}(k) + 0.5\hat{\otimes}^{(1)}(k - 1)\) (Eqs. (51), (52)) + +3.4 Update parameters: \(\hat{\theta}_{k}\gets (\mathbf{B}^{T}\mathbf{B})^{- 1}\mathbf{B}^{T}\mathbf{Y}\) (Eq. (50)) + +3.5 Set \(\mathcal{E}_{\mathrm{prev}}\gets \mathcal{E}_{k}\) + +else + +\[\hat{\theta}_{k}\gets \hat{\theta}_{k - 1}\] + +end if + +4. Predict: \(y_{m}(k + 1)\gets \left(\hat{\otimes}^{(1)}(1) - \frac{a}{b}\right)e^{-a k} + \frac{b}{a}\) , IAGO (Eqs. (53), (54)), map via \(M(\cdot)\) (Eq. (3)) + +5. Correct: \(y_{m}(k + 1)\gets y_{m}(k + 1) + \gamma (D(y(k)) - \hat{y}_{m}(k))\) (Eq. (53)) + +end for + +- Optimization Algorithm: The optimization algorithm block takes the error signal \(e(k)\) as input and computes the optimal control input \(u(k)\) . The optimization process typically minimizes a cost function that balances the tracking error and control effort, a hallmark of GPC. The control input \(u(k)\) is then applied to the pattern-moving systems. The cost function is designed as follows. + +\[J(N_{1},N_{y},N_{u}) = \sum_{j = N_{1}}^{N_{y}}[\hat{y} (k + j|k) - y_{r}(k + j)|_{Q_{j}}^{2} + \sum j = 1^{N_{u}}|\Delta u(k + j - 1)|_{R_{j}}^{2} \quad (55)\] + +where \(N_{1}\) is the minimum costing horizon (typically \(N_{1} = 1\) ), \(N_{y}\) is the prediction horizon, \(N_{u}\) is the control horizon \((N_{u}\leq N_{y})\) , \(\hat{y} (k + j|k)\) is the predicted output at step \(k + j\) based on information at step \(k\) , \(y_{r}(k + j)\) is the reference trajectory at step \(k + j\) , \(\Delta u(k + j - 1) = u(k + j - 1) - u(k + j - 2)\) is the control increment, \(\mathbf{Q}_{j}\succeq 0\) is the output weighting matrix, and \(\mathbf{R}_{j}\succ 0\) is the control weighting matrix. The reference trajectory is generated through setpoint smoothing: \(y_{r}(k + j) = \alpha y_{r}(k + j - 1) + (1 - \alpha)w(k + j)\) with \(\alpha \in [0,1)\) , where \(w(k + j)\) is the desired setpoint. + +The optimal control sequence is derived by expressing predictions in vector form: + +\[\hat{\mathbf{Y}} = \mathbf{G}\Delta \mathbf{U} + \mathbf{F} \quad (56)\] + +where \(\hat{\mathbf{Y}} = [\hat{y} (k + N_{1}|k),\ldots ,\hat{y} (k + N_{y}|k)]^{T}\) , \(\Delta \mathbf{U} = [\Delta u(k),\ldots ,\Delta u(k + N_{u} - 1)]^{T}\) , \(\mathbf{F}\) is the free response vector (prediction + +<--- Page Split ---> + +with \(\Delta u = 0\) ), and \(\mathbf{G}\) is the dynamic matrix containing step response coefficients. The cost function then becomes: + +\[J = (\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r})^{T}\mathbf{Q}(\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}) + \Delta \mathbf{U}^{T}\mathbf{R}\Delta \mathbf{U} \quad (57)\] + +with \(\mathbf{Q} = \mathrm{diag}(\mathbf{Q}_{N_{1}},\dots ,\mathbf{Q}_{N_{s}})\) and \(\mathbf{R} = \mathrm{diag}(\mathbf{R}_{1},\dots ,\mathbf{R}_{N_{u}})\) . The optimal solution is obtained by solving: + +\[\frac{\partial J}{\partial\Delta\mathbf{U}} = 2\mathbf{G}^{T}\mathbf{Q}(\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}) + 2\mathbf{R}\Delta \mathbf{U} = 0 \quad (58)\] + +yielding: + +\[\Delta \mathbf{U}^{*} = (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) \quad (59)\] + +Only the first element is implemented: \(u(k) = u(k - 1) + [1,0,\dots ,0]\Delta \mathbf{U}^{*}\) . + +Key implementation aspects include: (1) Dynamic matrix \(\mathbf{G}\) construction using step response coefficients from IGM(1,2): \(g_{i} = \partial \hat{y} (k + i|k) / \partial \Delta u(k)\approx [\hat{y} (k + i|k,\Delta u(k) = \delta) - \hat{y} (k + i|k,\Delta u(k) = 0)] / \delta\) ; (2) Free response \(\mathbf{F}\) calculation by propagating IGM(1,2) with \(\Delta u = 0\) : \(\hat{y}_{0}(k + j|k) = [\hat{\otimes}^{(1)}(1) - b / a]e^{-a(k + j - 1)} + b / a\) ; (3) Physical constraints handling \((u_{\mathrm{min}}\leq u(k + j)\leq u_{\mathrm{max}}\) , \(\Delta u_{\mathrm{min}}\leq \Delta u(k + j)\leq \Delta u_{\mathrm{max}}\) , \(\mathrm{y}_{\mathrm{min}}\leq \hat{y} (k + j|k)\leq \mathrm{y}_{\mathrm{max}})\) transforming the problem into constrained QP; and (4) Recalculation of optimization each time step with updated parameters. + +# Algorithm 2 IGB-GPC Optimization Procedure + +Input: Current state \(\mathbf{x}(k)\) , Reference \(\mathbf{Y}_{r}\) , Model \(\{a,b\}\) , Weights \(\mathbf{Q}\) , \(\mathbf{R}\) + +Output: Optimal control \(u(k)\) + +procedure OPTIMIZE + +1. Compute \(\mathbf{Y}_{r}\gets [y_{r}(k + N_{1}),\dots ,y_{r}(k + N_{y})]^{T}\) + +2. Calculate free response \(\mathbf{F}\) via IGM(1,2) with \(\Delta u = 0\) + +3. Construct \(\mathbf{G}\) using step response coefficients + +4. Solve \(\Delta \mathbf{U}^{*}\leftarrow (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F})\) + +5. Extract \(\Delta u^{*}(k)\gets [1,0,\dots ,0]\Delta \mathbf{U}^{*}\) + +6. Apply \(u(k)\gets u(k - 1) + \Delta u^{*}(k)\) + +return \(u(k)\) + +end procedure + +- Summation Block for Predicted Output Adjustment: The predicted output \(y_{m}(k)\) is compared with the actual output \(y(k)\) in another summation block. The difference between these two signals is calculated as: + +\[y_{p}(k) = y_{m}(k) + (y(k) - y_{m}(k)) \quad (60)\] + +However, in practice, this step may involve additional correction mechanisms to refine the predicted output \(y_{p}(k)\) . \(p\) represents the optimized time domain parameter, satisfying the condition that \(m < p\) + +- Revising Feedback \((y_{p}(k))\) : The adjusted predicted output \(y_{p}(k)\) is fed back into the system through a revising feedback loop. This feedback is used to compute the error \(e(k)\) in the first summation block, closing the control loop. + +Figure 2 represents the GPC control system using IGM prediction with adaptive buffer operators to manage dynamic uncertainties; real- time feedback enables continuous output correction for pattern- moving systems with uncertain behavior. Algorithm 3 details the signal flow process within this control system. + +In summary, this diagram depicts a GPC- based control system featuring IGM prediction named as IGB- GPC for handling uncertainty and limited data, an adaptive buffer operator for robustness against disturbances, and a feedback loop for continuous adjustment. It is tailored for complex, dynamic pattern- moving systems. + +### 4.2 Performance analysis + +This section rigorously analyzes the stability and convergence properties of the proposed IGB- GPC framework. We establish formal guarantees for bounded- input bounded- output (BIBO) stability and tracking error convergence under specified conditions. The analysis leverages Lyapunov stability theory and incorporates the effects of the adaptive buffer operator on prediction accuracy. + +<--- Page Split ---> + +Input: Reference trajectory \(y_{r}(k)\) , System parameters + +Output: Control input \(u(k)\) , Actual output \(y(k)\) + +Initialize: \(y_{p}(0) \leftarrow\) initial value + +for each time step \(k\) do + +1. Compute error between reference and predicted output: \(e(k) \leftarrow y_{r}(k) - y_{p}(k)\) + +2. Generate control input through optimization \(u(k) \leftarrow\) Optimization Algorithm \((e(k))\) + +3. Apply control to the system \(y(k) \leftarrow\) Pattern Moving System \((u(k))\) + +4. Predict output using IGM and adaptive buffer \(y_{m}(k) \leftarrow\) IGMP prediction \((y(k))\) using Adaptive Buffer Operator + +5. Revise prediction with actual output \(y_{p}(k + 1) \leftarrow\) Revise Prediction \((y_{m}(k), y(k))\) + +end for + +Theorem 4.1. (Bounded prediction error) For the interval grey prediction model with adaptive buffer operator, the prediction error \(e_{p}(k) = y(k) - y_{m}(k)\) satisfies: + +\[|e_{p}(k)|\leq \epsilon_{1} + \epsilon_{2}\exp (-\lambda k) \quad (61)\] + +where \(\epsilon_{1} = \sup_{k}|\delta (k)|\) is the supremum of metric perturbation, \(\epsilon_{2}\) depends on initial conditions, and \(\lambda >0\) is the convergence rate of the grey model. + +Proof. From Definition 3.1, the pattern class variable satisfies \(d x(k) = \tilde{\otimes}_{k} + \delta\) where \(|\delta |\leq \tilde{\delta}\) . The buffered sequence \(\tilde{\otimes} (k) = D(\tilde{\otimes} (k))\) reduces amplitude \(\Delta_{k}\) according to Theorems 3.1 and 3.2. For the IGM(1,2) solution: + +\[\tilde{\otimes}^{(1)}(k + 1) = \left(\tilde{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} \quad (62)\] + +The prediction error dynamics follow: + +\[e_{p}(k + 1) = y(k + 1) - y_{m}(k + 1)\] \[\qquad = \left[f(\cdot) - \left(\tilde{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} - \frac{b}{a}\right] - \gamma (D(y(k)) - \hat{y}_{m}(k))\] + +Applying the adaptive buffer operator bounds the high- frequency components, yielding exponentially stable error dynamics. The correction term \(\gamma (\cdot)\) further attenuates residual errors. \(\square\) + +Theorem 4.2. (BIBO stability) The closed- loop system under IGAB- GPC is BIBO stable if: 1. The prediction horizon \(N_{y}\) exceeds the system's degree of freedom 2. Control weighting matrix \(\mathbf{R} > 0\) 3. The class radius satisfies \(r_{i}< \min_{j\neq i}|c_{i} - c_{j}| / 2\) + +Proof. Consider the Lyapunov function candidate: + +\[V(k) = \Delta \mathbf{U}^{T}(k)\mathbf{\Delta}\mathbf{U}(k) + \sum_{i = k - N_{u} + 1}^{k}e_{p}^{2}(i) \quad (63)\] + +where \(\mathbf{\delta} = \mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R} > 0\) . The difference \(\Delta V(k) = V(k + 1) - V(k)\) satisfies: + +\[\Delta V(k)\leq -\Delta \mathbf{U}^{T}(k)\mathbf{R}\Delta \mathbf{U}(k) + 2L_{g}\| \Delta \mathbf{U}(k)\| |e_{p}(k)|\] \[\qquad +L_{f}e_{p}^{2}(k) - e_{p}^{2}(k - N_{u})\] + +where \(L_{g}\) and \(L_{f}\) are Lipschitz constants for \(\mathbf{G}\) and system dynamics \(f(\cdot)\) . From Theorem 4.1, \(\| e_{p}(k)\| \leq \bar{e}_{p}\) . Selecting \(\mathbf{R}\) such that \(\lambda_{\min}(\mathbf{R}) > L_{g}^{2} / L_{f}\) ensures: + +\[\Delta V(k)\leq -\eta \| \Delta \mathbf{U}(k)\|^{2} - \mu e_{p}^{2}(k)\quad (\eta ,\mu >0) \quad (64)\] + +Thus \(V(k)\) decreases monotonically, proving bounded states and outputs. + +<--- Page Split ---> + +Theorem 4.3. (Tracking error convergence) The tracking error \(e(k) = y_{r}(k) - y(k)\) converges exponentially to a bounded set: + +\[\lim_{k\to \infty}\sup |e(k)|\leq \frac{\epsilon_{1} + \bar{w}}{1 - \alpha} \quad (65)\] + +where \(\alpha\) is the reference trajectory smoothing factor and \(\bar{w}\) is the disturbance bound. + +Proof. The optimized control increment \(\Delta \mathbf{U}^{*}\) from Eq. (59) minimizes: + +\[J(k) = \| \mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}\|_{\mathbf{Q}}^{2} + \| \Delta \mathbf{U}\|_{\mathbf{R}}^{2}\] \[\qquad = \| \mathbf{G}(\Delta \mathbf{U} - \Delta \mathbf{U}^{*})\|_{\mathbf{Q}}^{2} + \| \Delta \mathbf{U} - \Delta \mathbf{U}^{*}\|_{\mathbf{P}}^{2} + J^{*}(k)\] + +where \(\mathbf{P} = \mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R}\) . The error dynamics satisfy: + +\[e(k + 1) = \alpha e(k) + (1 - \alpha)[w(k) - y(k)] + \Delta f(\cdot) \quad (66)\] + +with \(\| \Delta f(\cdot)\| \leq L_{\delta}\) due to metric perturbation. From Lemma 4.1 and Theorem 4.2, we have: + +\[|y(k) - w(k)|\leq |y(k) - y_{m}(k)| + |y_{m}(k) - w(k)|\] \[\qquad \leq \epsilon_{1} + \kappa \| \Delta \mathbf{U}^{*}(k)\|\] + +where \(\kappa = \| \mathbf{G}(1, \cdot)\|\) . Since \(\| \Delta \mathbf{U}^{*}(k)\|\) decays exponentially, the error converges to the stated bound. + +Remark 4.1. The adaptive buffer operator enhances performance by: 1. Reducing prediction error amplitude by \(30 - 50\%\) compared to unbuffered models 2. Decreasing the Lipschitz constant \(L_{f}\) by smoothing system dynamics 3. Accelerating the convergence rate \(\lambda\) in Lemma 4.1 + +Corollary 1. (Pattern convergence) The system operating point converges to the target pattern class \(P_{d}\) within finite steps \(K\) satisfying: + +\[K\leq \frac{1}{\mu}\log \left(\frac{\|d x(0) - c_{d}\|}{\min_{i\neq d}r_{i}}\right) \quad (67)\] + +where \(\mu\) is the convergence rate from Theorem 4.3, and \(c_{d}\) is the target class center. + +Proof. From quantization properties in Eq. (4), convergence to \(P_{d}\) occurs when \(\| d x(k) - c_{d}\| < r_{d}\) . Theorem 4.3 ensures \(\| d x(k) - c_{d}\|\) decreases exponentially, yielding the step bound. + +The analysis demonstrates that IGAB- GPC guarantees closed- loop stability and pattern convergence while accommodating inherent uncertainties in pattern- moving systems through grey modeling and adaptive buffering. + +## 5 Simulation results + +An numerical simulation case are given in this section to illustrate the effectiveness of the proposed IGAB- GPC scheme. In order to properly assess its efficacy and applicability of the proposed method, the classical CARIMA- GPC \(^{24}\) (controlled autoregressive integral moving average generalized predictive control) and IG- GPC \(^{32}\) (interval grey generalized predictive control) are selected as the benchmark comparison methods. By constructing simulation experiments, the control accuracy, dynamic response characteristics are compared and analyzed. Furthermore, the advantages and potential disadvantages of this method in practical applications are comprehensively revealed. + +Consider the the nonlinear discrete- time system with one input and three outputs as follows. + +\[\left\{ \begin{array}{l l}{y_{1}(k) = 0.3y_{1}(k - 1) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 2) + d(k)}\\ {y_{2}(k) = 0.2y_{2}(k - 1) + 0.4y_{2}(k - 2) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 3) + d(k)}\\ {y_{3}(k) = 0.3y_{3}(k - 1) + 0.1y_{3}(k - 2) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 2) + d(k)} \end{array} \right.. \quad (68)\] + +Among them, the system input \(u(k) \in [- 4, 4]\) ; the system noise satisfies \(d(k) \sim N(0, 0.1^{2})\) and is assumed to be known. + +Through the following 3 steps, first, construct the description mode of system dynamics; then, complete the system tracking control by using the designed control algorithm; finally, compare the accuracy with CARIMA- GPC and IG- GPC, respectively. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. 3000 sets of input-output system historical data.
+ +Step 1: Constructing the pattern moving space and system output prediction with IGAB. Based on the construction process described in Section 2.2.2, the input signals \(u(k)\) are introduced to the system, generating 3000 sets of historical input- output data (Figure 3) that form the moving subspace. + +After normalizing the output data, dimensionality reduction was performed via Principal Component Analysis (PCA) to a one- dimensional feature space, with the contribution rate reaching \(87.23\%\) . Subsequently, for quantitative evaluation using the class - specific metric defined in Equation 4, we set the initial parameters of the improved quantization classification algorithm as \(\kappa_{0} = 5\) , \(\rho_{0} = 0.6\) , and \(r_{0} = 0.4\) . By taking \(N = 5\) , the number of classes can be obtained as \(2N + 1 = 11\) . Meanwhile, the center values, class interval values and class radius of each class are acquired. The detailed results are found in Table 1. + +Table 1 shows the construction of the pattern moving space over time, with the variations of the pattern center and threshold illustrated in Figure 4. As indicated in the Definition 2.2, the variation scope of the pattern class variable aligns with the class threshold, involving epistemic uncertainty, exhibiting inherent uncertainty, whereas the grey measure can utilize the variables center as measurement basis. After the quantization algorithm produces category divisions, the first 11 input variables and \(dx(k)\) measures are used to construct IGM(1,2), and the process is as follows. + +1) The adaptive buffer operator is applied to the interval sequence \(\otimes_{1}(k)\) , resulting in a smoothed sequence \(\hat{\otimes}_{1}(k)\) . This process reduces oscillations and enhances the stability of the subsequent IGM(1,2) modeling steps. + +\[\frac{d\otimes_{1}^{(1)}(t)}{dt} +a\otimes_{1}^{(1)}(t) = b\otimes_{2}^{(1)}(t) \quad (69)\] + +where \(\otimes_{1}^{(1)}(t)\) is the first- order accumulated generation (1- AGO) sequence of the buffered output interval \(\hat{\otimes}_{1}(k),\hat{\otimes}_{2}^{(1)}(t)\) is the 1- AGO sequence of the input interval \(\otimes_{2}(k)\) , \(a\) and \(b\) are parameters to be estimated. + +2) With estimated parameters \(a = [0.12, 0.15]\) and \(b = [0.08, 0.10]\) , the specific differential equations for the lower and upper bounds are formulated as follows: + +<--- Page Split ---> + + +Table 1. Results of pattern class and pattern moving space by Equation 4. + +
Class NoClass CenterClass RadiusClass ThresholdClass Interval
14.5331.5006.033[3.033, 6.033]
22.7300.3033.033[2.427, 3.033]
31.9320.4952.427[1.437, 2.427]
40.9790.4581.437[0.521, 1.437]
50.2880.2330.521[0.055, 0.521]
600.5210.521[-0.521, 0.521]
7-0.2880.233-0.055[-0.521, -0.055]
8-0.9790.458-0.521[-1.437, -0.521]
9-1.9320.495-1.437[-2.427, -1.437]
10-2.7300.303-2.427[-3.033, -2.427]
11-4.5331.500-3.033[-6.033, -3.033]
+ +\[\left\{ \begin{array}{l}\frac{dX_1(t)}{dt} +0.12X_1(t) = 0.08X_2(t)\quad (Lower\ Bounded)\\ \displaystyle \frac{d\overline{X}_1(t)}{dt} +0.15\overline{X}_1(t) = 0.10\overline{X}_2(t)\quad (Upper\ Bounded) \end{array} \right. \quad (70)\] + +3) The prediction formulas for the accumulated sequences are derived as: + +\[\left\{ \begin{array}{l}\underline{X}_1(k + 1) = -1.443e^{-0.12k} + 0.667\quad (Lower\ Bounded)\\ \overline{X}_1(k + 1) = -1.369e^{-0.15k} + 0.125\quad (Upper\ Bounded) \end{array} \right. \quad (71)\] + +Finally, the predicted interval for the pattern class variable is then obtained via inverse accumulated generation operation (IAGO): + +\[\hat{\otimes}_{1}(k + 1) = \left[\underline{X}_{1}(k + 1) - \underline{X}_{1}(k),\overline{X}_{1}(k + 1) - \overline{X}_{1}(k)\right]\] + +The IGM(1,2) model, constructed with parameters \(a = [0.12, 0.15]\) and \(b = [0.08, 0.10]\) , effectively predicts \(y_{1}(k)\) using \(u(k)\) in pattern- moving systems. + +Step 2: Design of IGB- GPC controller and comparison of control effects for pattern moving system. Differing from the control targets of traditional purpose control systems, the objective of pattern moving regulation is to assign the system output to the specified product quality. The expected pattern class are respectively established as category 2 (2.739) and category 5 (0.288), i.e., + +\[y_{d} = \left\{ \begin{array}{ll}2, & 0\leq k\leq 100\\ 5, & 100< k\leq 200 \end{array} \right. \quad (72)\] + +we proceed to elaborate the design of the control input \(u(k)\) , the optimization process for determining the optimal control sequence, and a comparative simulation framework to evaluate the performance of the proposed IGB- GPC against benchmark methods, namely CARIMA- GPC and IG- GPC. + +The control objective in this pattern- moving system is to drive the system output \(y(k)\) to match the specified pattern class centers corresponding to \(y_{d}\) , which represent the desired product quality indices. Specifically, \(y_{d} = 2\) corresponds to pattern class 2 with center \(c_{2} = 2.739\) for \(0 \leq k \leq 100\) , and \(y_{d} = 5\) corresponds to pattern class 5 with center \(c_{5} = 0.288\) for \(100 < k \leq 200\) (see Table 1). To simplify the control design, we assume \(y_{d}\) directly approximates these center values, i.e., \(y_{d}(k) = 2.739\) and \(y_{d}(k) = 0.288\) . + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4. Pattern class centre and its threshold.
+ +Using the IGM(1,2) parameters estimated in Step 1 \((a = [0.12,0.15],b = [0.08,0.10])\) , the predicted output at step \(k + j\) is expressed as: + +\[\hat{y} (k + j|k) = \left(\hat{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a\cdot j} + \frac{b}{a} \quad (73)\] + +With initial conditions \(\underline{{X}}_{1}(1) = \underline{{x}}_{1}(1) = 2.739\) (for \(y_{d} = 2\) ) and \(\overline{{X}}_{1}(1) = \overline{{x}}_{1}(1) = 2.739 + r_{2} = 3.033\) , the predicted interval at \(j = 1\) is: + +\[\hat{y} (k + 1|k) = [2.739\cdot e^{-0.12} + 0.667,3.033\cdot e^{-0.15} + 0.667]\approx [2.435,2.712] \quad (74)\] + +The dynamic matrix \(\mathbf{G}\) is constructed using step response coefficients. For a prediction horizon \(N_{\mathrm{y}} = 5\) and control horizon \(N_{u} = 3\) , each element \(g_{i,j}\) represents the effect of a unit control increment at step \(k + j - 1\) on the output at step \(k + i\) : + +\[g_{i,j} = \frac{\partial\hat{y} (k + i|k)}{\partial\Delta u(k + j - 1)}\approx \frac{\hat{y} (k + i|k,\Delta u = 1) - \hat{y} (k + i|k,\Delta u = 0)}{1} \quad (75)\] + +For \(i = 1,j = 1\) : + +\[g_{1,1} = \left[\left(\underline{{X}}_{1}(1) - \frac{0.08}{0.12} +\frac{0.08}{0.12}\right)e^{-0.12\cdot 1} + \frac{0.08}{0.12}\right] - \hat{y} (k + 1|k) = e^{-0.12}\approx 0.886 \quad (76)\] + +Subsequent elements yield: + +\[\mathbf{G} = \left[ \begin{array}{lll}0.886 & 0 & 0\\ 0.789 & 0.886 & 0\\ 0.703 & 0.789 & 0.886\\ 0.627 & 0.703 & 0.789\\ 0.560 & 0.627 & 0.703 \end{array} \right] \quad (77)\] + +<--- Page Split ---> + +The free response vector \(\mathbf{F}\) under zero control increments: + +\[\mathbf{F} = [\hat{y} (k + 1|k,\Delta u = 0),\hat{y} (k + 2|k,\Delta u = 0),\dots ,\hat{y} (k + N_{y}|k,\Delta u = 0)]^{T} \quad (78)\] + +For \(k = 0\) and \(y_{d} = 2.739\) : + +\[\begin{array}{r l} & {\hat{y} (1|0,\Delta u = 0) = 2.739\cdot e^{-0.12} + 0.667\approx 2.435}\\ & {\hat{y} (2|0,\Delta u = 0) = 2.739\cdot e^{-0.24} + 0.667\approx 2.160} \end{array} \quad (80)\] + +Thus: + +\[\mathbf{F} = [2.435,2.160,1.911,1.686,1.483]^{T} \quad (81)\] + +With weighting matrices \(\mathbf{Q} = \mathrm{diag}(10,8,6,4,2)\) and \(\mathbf{R} = \mathrm{diag}(1,1,1)\) , the reference trajectory is: + +\[\mathbf{Y}_{r} = [2.739,2.739,2.739,2.739,2.739]^{T} \quad (82)\] + +The optimal control increment is solved via: + +\[\Delta \mathbf{U}^{*} = (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) \quad (83)\] + +Matrix computations: + +\[\begin{array}{r l} & {\mathbf{G}^{T}\mathbf{Q}\mathbf{G} = \left[ \begin{array}{l l l}{23.25} & {18.64} & {14.08}\\ {18.64} & {15.76} & {12.28}\\ {14.08} & {12.28} & {9.78} \end{array} \right]}\\ & {\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) = \left[ \begin{array}{l}{10.24}\\ {8.32}\\ {6.41} \end{array} \right]} \end{array} \quad (84)\] + +Solution: + +\[\Delta \mathbf{U}^{*} = \left[ \begin{array}{l}{0.32}\\ {0.25}\\ {0.18} \end{array} \right],\quad u(0) = u(- 1) + \Delta u^{*}(0) = 0 + 0.32 = 0.32 \quad (86)\] + +The control input update rule: + +\[u(k) = u(k - 1) + \Delta u^{*}(k),\quad \Delta u^{*}(k) = [1,0,0]\Delta \mathbf{U}^{*}(k) \quad (87)\] + +At \(k = 100\) , switching to \(y_{d} = 0.288\) with \(\underline{{X}}_{1}(1) = 0.288\) , \(\overline{{X}}_{1}(1) = 0.521\) , the calculation yields \(u(101)\approx - 0.25\) + +In summary, the procedure of proposed method was demonstrated in Algorithm 4. Moreover, the tracking results and its errors were shown in Figures 5 and 6, respectively. + +<--- Page Split ---> + +Input: \(a, b, \tilde{\otimes}^{(1)}(1), \mathbf{Y}_r, \mathbf{Q}, \mathbf{R}, N_y, N_u\) + +Output: Control input \(u(k)\) + +Initialize \(u(- 1) = 0, \Delta \mathbf{U} = \mathbf{0}\) + +for \(k = 0\) to 199 do + +if \(k \leq 100\) then + +\(y_d \leftarrow 2.739\) + +else + +\(y_d \leftarrow 0.288\) + +end if + +Construct \(\mathbf{Y}_r = [y_d]^{N_y \times 1}\) + +Compute \(\mathbf{F}\) via IGM(1,2) with \(\Delta u = 0\) + +Build \(\mathbf{G}\) using step responses + +Solve \(\Delta \mathbf{U}^* = (\mathbf{G}^T \mathbf{Q} \mathbf{G} + \mathbf{R})^{- 1} \mathbf{G}^T \mathbf{Q}(\mathbf{Y}_r - \mathbf{F})\) + +\(\Delta u(k) \leftarrow \Delta \mathbf{U}^* (1)\) + +\(u(k) \leftarrow u(k - 1) + \Delta u(k)\) + +\(u(k) \leftarrow \text{saturate}(u(k), [- 4, 4])\) + +end for + +![](images/Figure_6.jpg) + +
Comparison of system output accuracy of different models Figure 6. The tracking errors for pattern moving systems with various models.
+ +Figure 5 indicates that IGB- GPC maintains the output closest to the reference trajectory over the time change. Notably, around the transition point at 100, where the reference shifts from 2.739 to 0.288, IGB- GPC exhibits a smoother and faster response, minimizing overshoot and stabilizing more quickly compared to CARIMA- GPC and IG- GPC. Meanwhile, Figure 6 indicates IGB- GPC has the lowest median error and smallest interquartile range, while CARIMA- GPC and IG- GPC show higher errors and greater variability, highlighting IGB- GPC's superior accuracy and consistency. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5. System ouput comparison with different control schemes in PMT framework.
+ +![](images/Figure_7.jpg) + +
Figure 7. Tracking results of system operating status on target pattern class.
+ +For this comparison, CARIMA- GPC shows a significant drop and oscillatory behavior post- transition, while IG- GPC also struggles with stability, particularly after 100, with larger fluctuations. This suggests that IGAB- GPC's adaptive buffer operator and interval grey modeling enhance tracking accuracy and robustness against dynamic changes. The results can be attributed to two main reasons. The superior performance of IGAB- GPC can be attributed to two key factors. First, the adaptive buffer + +<--- Page Split ---> + +operator reduces fluctuations in pattern class variables by \(30 - 50\%\) (Remark following Theorem 4.3), enhancing prediction accuracy for small- sample, uncertain data compared to CARIMA- GPC and IG- GPC, as shown in Figure 6. Second, integrating IGM(1,2) (Equation (70)) with GPC's receding horizon optimization (Equations (55), (59)) ensures robust tracking of pattern transitions (e.g., class 2 to class 5 at \(k = 100\) ), outperforming CARIMA- GPC's deterministic approach and IG- GPC's less adaptive buffer operator, as evidenced by smoother responses and lower errors in Figures 5. + +Additionally, Figure 7 presents the actual pattern class using IGB- GPC by space cross mapping \((M(\cdot))\) , which indicates the system's ability to accurately track the target pattern classes (class 2 with center \(c_{2} = 2.739\) for \(0 \leq k \leq 100\) and class 5 with center \(c_{5} = 0.288\) for \(100 < k \leq 200\) ). The figure demonstrates that IGB- GPC successfully drives the system operating condition to the desired pattern classes with minimal deviation, maintaining the output within the corresponding class intervals as defined in Table 1. Specifically, the system remains within the class threshold of \([2.427, 3.033)\) for class 2 and \([0.055, 0.521)\) for class 5, with rapid convergence to the target class centers post- transition at \(k = 100\) . Compared to CARIMA- GPC and IG- GPC, IGB- GPC exhibits fewer misclassifications and smoother transitions between pattern classes, underscoring the effectiveness of the adaptive buffer operator and interval grey modeling in handling the inherent uncertainties and dynamic shifts in pattern- moving systems. + +## 6 Conclusions + +This study introduces a novel Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) framework specifically designed for pattern- moving systems characterized by limited sample sizes, pronounced nonlinearity, and significant uncertainties. By synergistically integrating the Interval Grey Model (IGM(1,2)) with an adaptive buffer operator and Generalized Predictive Control (GPC), the proposed methodology effectively addresses the challenges associated with modeling and controlling complex industrial systems governed by statistical dynamics. The primary contributions of this work encompass: (1) the development of an adaptive buffer operator to mitigate oscillations in pattern class variables, (2) the formulation of an IGM(1,2)- based predictive model for robust handling of epistemic uncertainties, and (3) the seamless integration of these components within a GPC framework to achieve precise, stable, and robust control performance. + +Theoretical analysis, grounded in Lyapunov stability theory, rigorously establishes that IGB- GPC guarantees bounded- input bounded- output (BIBO) stability and exponential convergence of tracking errors under well- defined conditions. The adaptive buffer operator reduces the amplitude of prediction errors by \(30 - 50\%\) , thereby enhancing robustness against dynamic fluctuations, while the IGM(1,2) model provides a reliable framework for quantifying uncertainties inherent in pattern category variables. Numerical simulations substantiate the superiority of IGB- GPC over established benchmark methods, namely Controlled AutoRegressive Integrated Moving Average Generalized Predictive Control (CARIMA- GPC) and Interval Grey Generalized Predictive Control (IG- GPC). The results demonstrate that IGB- GPC achieves smoother transitions, significantly lower tracking errors, and reduced misclassifications during pattern class shifts, as evidenced by its performance on a nonlinear discrete- time system. + +The proposed IGB- GPC framework holds considerable promise for applications in process industries, such as metallurgy and chemical engineering, where pattern- moving systems are prevalent. Future research directions include extending the framework to accommodate multi- input multi- output systems, incorporating real- time adaptive parameter estimation to further enhance robustness, and conducting experimental validation on industrial platforms to bridge the gap between simulation and practical deployment. Additionally, exploring hybrid methodologies that combine IGB- GPC with advanced machine learning techniques could further elevate prediction accuracy and control efficacy in highly dynamic and uncertain environments. + +## Data availability + +The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. + +## References + +1. Xu, Z. Pattern recognition method of intelligent automation and its implementation in engineering. Univ. Sci. Technol. Beijing, Beijing, China (2001). +2. Shoude, J. Pattern recognition approach to intelligent automation for complex industrial processes. Chin. J. 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System asymptotic stability analysis of a kind of complex production processes based on multi-dimensional moving pattern. SN Appl. Sci. 5, 32 (2023). + +9. Wang, M., Pan, W. & Lu, Y. A pattern-based controller for a class of production processes with input delay. Asian J. Control. 25, 1074-1085 (2023). + +10. Guo, L. L. & Wang, Y. The application of cell mapping to dynamic modeling and control. Appl. Mech. Mater. 687, 661-664 (2014). + +11. Li, N., Xu, Z. & Li, X. Pattern-moving-modelling and analysis based on clustered generalized cell mapping for a class of complex systems. Processes 12, 492 (2024). + +12. Changping, S. & Zhengguang, X. Extended ts fuzzy model based on interval arithmetic and its application to interval nonlinear regression analysis. In 2009 IEEE International Conference on Fuzzy Systems, 1773-1778 (IEEE, 2009). + +13. Zhengguang, X. & Changping, S. Moving pattern-based approach to modeling of a class of complex production processes. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2282-2287 (IEEE, 2011). + +14. Han, C. & Xu, Z. Prediction and output estimation of pattern moving in non-newtonian mechanical systems based on probability density evolution. CMES-Computer Model. Eng. & Sci. 139 (2024). + +15. Han, C. & Xu, Z. Pattern-moving based data-driven control for multi-input continuous-time non-newtonian mechanical systems. Int. J. Syst. Sci. 1-25 (2025). + +16. Li, X., Xu, Z., Lu, Y., Cui, J. & Zhang, L. Modified model free adaptive control for a class of nonlinear systems with multi-threshold quantized observations. Int. J. Control. Autom. Syst. 19, 3285-3296 (2021). + +17. Li, X., Xu, Z., Han, C. & Li, N. Pattern-moving-based parameter identification of output error models with multi-threshold quantized observations. CMES-Computer Model. Eng. & Sci. 130 (2022). + +18. Ju-Long, D. Control problems of grey systems. Syst. & control letters 1, 288-294 (1982). + +19. Chen, Z., Meng, Y., Wang, R.-Y. & Chen, T. Intelligent optimal grey evolutionary algorithm for structural control and analysis. Smart Struct. Syst. 33, 365-374 (2024). + +20. Zeng, B., Li, C., Zhou, X.-Y. & Long, X.-J. Prediction model of interval grey numbers with a real parameter and its application. In Abstract and Applied Analysis, vol. 2014, 939404 (Wiley Online Library, 2014). + +21. Rao, S. S. & Liu, X. Universal grey system theory for analysis of uncertain structural systems. AIAA journal 55, 3966-3979 (2017). + +22. Chen, C.-C. & Tsai, C. M. Interval forecasting with grey models: a novel learning procedure for improved decision-making. Grey Syst. Theory Appl. (2025). + +23. Liu, S., Yang, Y., Xie, N. & Forrest, J. New progress of grey system theory in the new millennium. Grey Syst. Theory Appl. 6, 2-31 (2016). + +24. Schwenzer, M., Ay, M., Bergs, T. & Abel, D. Review on model predictive control: An engineering perspective. The Int. J. Adv. Manuf. Technol. 117, 1327-1349 (2021). + +25. Djouadi, H. et al. Improved robust model predictive control for pmsm using backstepping control and incorporating integral action with experimental validation. Results Eng. 23, 102416 (2024). + +26. Li, N., Xu, Z. G., Zhao, C. T. & Li, X. Q. Pattern-moving-based dynamic description and optimal control for non-newtonian mechanical systems with generalized cell mapping. The Can. J. Chem. Eng. 103, 3208-3229 (2025). + +27. Liu, G., Pan, Y., Lam, H.-K. & Liang, H. Event-triggered fuzzy adaptive quantized control for nonlinear multi-agent systems in nonaffine pure-feedback form. Fuzzy Sets Syst. 416, 27-46 (2021). + +28. Liu, S., Yang, Y. & Forrest, J. Y.-L. Grey systems analysis: Methods, models and applications (Springer, 2022). + +29. Liu, S.-H. A new grey buffer operator and its application. J. Intell. & Fuzzy Syst. 1-9 (2024). + +30. Yu, X. & Cheng, Y. A comprehensive review and comparison of cusum and change-point-analysis methods to detect test speededness. Multivar. Behav. Res. 57, 112-133 (2022). + +<--- Page Split ---> + +31. Xu, Z., Wu, J. & Qu, S. Prediction model based on moving pattern. J. Comput. 7, 2695–2701 (2012). + +32. Xie, N. & Liu, S. Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model. J. Syst. Eng. Electron. 26, 96–102 (2015). + +## Author contributions Statement + +Ning Li: Writing original draft, Methodology, Conceptualization, Formal analysis. Zhenggaung Xu: Methodology, Conceptualization, Data Collection, Validation. Xiangquan Li: Writing - review & editing, Visualization, Supervision, Project administration. + +## Funding + +Open access funding provided by Natural Science Foundation Project of Guizhou Province, Grant Number ZK[2023] Genera004; Science and Technology Project of Jiangxi Provincial Department of Education, Grant Number GJJ2202404; Natural Science Foundation Project of JiangXi Province, Grant Number 20242BAB25091. + +<--- Page Split ---> diff --git a/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d_det.mmd b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b2fbe9b9d41d9fd7823c610e3620c6ce1dd3d3ab --- /dev/null +++ b/preprint/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d/preprint__0009fc9e7d8fea70f828fc27ba1001b8e0dc12dc0cba8580ba8fe8f9865c469d_det.mmd @@ -0,0 +1,1342 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 880, 208]]<|/det|> +# Generalized Predictive Control Based on Interval Gray Model with Adaptive Buffer Operator for a Class of Pattern-Moving Systems + +<|ref|>text<|/ref|><|det|>[[42, 230, 383, 370]]<|/det|> +Ning Li University of Science and Technology Zhenggaung Xu University of Science and Technology Xiangquan Li 21021@jdzu.edu.cn + +<|ref|>text<|/ref|><|det|>[[55, 396, 250, 415]]<|/det|> +Jingdezhen University + +<|ref|>sub_title<|/ref|><|det|>[[44, 456, 103, 474]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 494, 904, 536]]<|/det|> +Keywords: Pattern moving theory (PMT), Interval grey model, Cross mapping, Interval grey adaptive buffer generalized predictive control (IGAB- GPC) + +<|ref|>text<|/ref|><|det|>[[44, 554, 295, 573]]<|/det|> +Posted Date: July 15th, 2025 + +<|ref|>text<|/ref|><|det|>[[42, 592, 475, 611]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 6971022/v1 + +<|ref|>text<|/ref|><|det|>[[42, 630, 914, 672]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 690, 545, 710]]<|/det|> +Additional Declarations: No competing interests reported. + +<|ref|>text<|/ref|><|det|>[[42, 745, 930, 789]]<|/det|> +Version of Record: A version of this preprint was published at Scientific Reports on August 25th, 2025. See the published version at https://doi.org/10.1038/s41598- 025- 17141- 8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[90, 73, 907, 168]]<|/det|> +# Generalized Predictive Control Based on Interval Gray Model with Adaptive Buffer Operator for a Class of Pattern-Moving Systems + +<|ref|>text<|/ref|><|det|>[[90, 177, 523, 199]]<|/det|> +Ning Li \(^{1}\) , Zhenggaung Xu \(^{1}\) , and Xiangquan Li \(^{2,*}\) + +<|ref|>text<|/ref|><|det|>[[90, 214, 826, 262]]<|/det|> +\(^{1}\) University of Science and Technology, School of Automation and Engineering, Beijing, 100083, China \(^{2}\) Jingdezhen University, School of Information Engineering, Jingdezhen, 333032, China \(^{*}21021@\) jdzu.edu.cn + +<|ref|>sub_title<|/ref|><|det|>[[90, 283, 198, 301]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[92, 317, 904, 512]]<|/det|> +The pattern- moving systems, as a kind of complex nonlinear systems that governed by statistical laws, are commonly found in industrial production processes such as sintering machines and cement rotary kiln. Encountering difficulties in delineating the statistical properties of such systems through deterministic variables like state or output variables, existing control techniques tend to either bypass these systems or address them as systems impacted by stochastic perturbations. To reveal system's inherent statistical characteristics, this work proposed a novel Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) scheme, which employs the bidirectional mapping framework under pattern mpving theory (PMT) to quantify pattern category variables, enabling precise tracking of dynamic pattern transitions. Key innovations include: (1) an adaptive buffer operator that mitigates oscillations in pattern class sequences based on their monotonicity, (2) an IGM(1,2)- based prediction model for robust uncertainty quantification, and (3) a GPC framework incorporating receding horizon optimization and feedback correction for enhanced control accuracy. The workflow involves constructing a pattern- moving space through data- driven quantization, applying the adaptive buffer operator to smooth time- series fluctuations, developing the IGM(1,2) model, and implementing the IGB- GPC strategy. Numerical simulations demonstrate that IGB- GPC outperforms benchmark methods like CARIMA- GPC and IG- GPC, achieving superior tracking accuracy, smoother pattern transitions, and robust stability, making it highly suitable for complex industrial processes + +<|ref|>text<|/ref|><|det|>[[90, 544, 904, 575]]<|/det|> +Keywords: Pattern moving theory (PMT), Interval grey model, Cross mapping, Interval grey adaptive buffer generalized predictive control (IGAB- GPC). + +<|ref|>sub_title<|/ref|><|det|>[[90, 594, 226, 611]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[89, 618, 909, 800]]<|/det|> +In the process industries such as metallurgical, building materials, and chemical processing, there widely exist large- scale industrial systems characterized by highly complex manufacturing processes. From the perspective of studying system dynamic characteristics, this kind of systems are inherently governed by statistical laws rather than typical Newtonian mechanical systems \(^{1,2}\) . Typical examples involve sintering machines and cement rotary kilns, which possess the following features: (1) extremely complicated manufacturing processes with frequently inside liquid- phase transitions and combined multidimensional physical events; (2) operational qualities, such as multi- parameter, high- dimensionality, and uncertain degrees of freedom, accompanied by complex system movement. (3) a variety of chemical reactions that are naturally dependent on statistical laws, where feature correlations exhibit probabilistic- statistic reliance and system dynamics are controlled due to statistical rules opposed to traditional mechanics. Given the dynamic nature of systems, some studies adopt a pattern- moving perspective, where statistical principles guide the integration of data- driven and pattern recognition techniques. In this framework, the control objectives are reformulated as driving the system's operating conditions into predefined pattern categories. Therefore, these systems are also known as pattern- moving systems \(^{3}\) . + +<|ref|>text<|/ref|><|det|>[[89, 800, 909, 920]]<|/det|> +Although significant progress has been made in applying pattern recognition schemes to system modeling and control \(^{4}\) , challenges remain in handling highly nonlinear systems with limited and uncertain data. To overcome this obstacle, a novel framework named Pattern Movement Theory (PMT) was proposed by Prof. Xu \(^{5 - 7}\) , which maps system operating conditions to dynamic pattern class via statistical calculation and enables systematic description and control through pattern- driven processes. In light of this insight, the measurement of pattern categories represents a fundamental task, as the pattern class variables lack computational properties, which means they do not satisfy the condition where Pattern 1 + Pattern 2 = Pattern 3. In order to render pattern- based variables computationally viable, several measurements were developed, including metrics based on category centers \(^{8,9}\) , cell mapping \(^{6,10,11}\) , interval numbers \(^{12,13}\) , probability density evolution \(^{14,15}\) , and explicit- implicit + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 78, 908, 187]]<|/det|> +formulations derived from category centers16,17. However, existing methods inadequately deal with the inherent uncertainty in modeling pattern categorical variables, with strategies largely confined to either direct employing category centers or reliance on probabilistic partition estimation. In other words, quantifying uncertainty in pattern category variables within the PMT framework constitutes critical research focus. As it can be seen, pattern class variables represent statistical features with inherently small sample and incomplete information, wherein parameters like category centers and radius may be available, but the full statistical structure remains unknown. Hence, Thus, we attempted to integrate grey system theory with PMT to analyze and control system performance. + +<|ref|>text<|/ref|><|det|>[[89, 190, 908, 401]]<|/det|> +Grey system theory, originally proposed by Professor Julong Deng in \(1982^{18}\) , provides a systematic framework for modeling, analyzing, and predicting systems characterized by small sample data and significant information uncertainty. It is particularly well- suited for addressing real- world problems where data are limited, incomplete or imprecise. Regarding the intelligent control, Chen exploited an intelligent optimal grey evolutionary algorithm for structural control, enhancing prediction accuracy and control capabilities to support sustainable urbanization goals19. For instance, Zeng20 developed an improved interval grey prediction model (IGM(1,1)) for industrial control systems, demonstrating its effectiveness in predicting chemical process outputs under uncertain conditions. Similarly, Rao21 applied grey system theory to the control of uncertain nonlinear systems, achieving stable performance in the stepped bar and the rigid- body (vertical) analysis. In the field of State prediction, Chen22 introduced a ground breaking learning procedure combining boxplots and Program Evaluation and Review Technique (PERT) with IGM(1,1), significantly improving interval forecasting reliability for short- term time- series under data constraints. Moreover, Liu23 discussed a comprehensive review of grey system theory in intelligent control, highlighting its applications in prediction, optimization, and decision- making. These studies collectively demonstrate the versatility and effectiveness of grey system theory, particularly interval grey models, in addressing uncertainties and improving control performance in various applications. + +<|ref|>text<|/ref|><|det|>[[89, 405, 908, 571]]<|/det|> +To our best knowledge, generalized predictive control (GPC), as a subclass of adaptive control, offers significant advantages, including reduced sensitivity to model accuracy, a straightforward algorithmic structure conducive to practical implementation, and inherent robustness. At same time, continuous data acquisition and feedback correction by GPC reduce process uncertainties, thereby optimizing control performance and ensuring operational stability in complex industrial systems24. In particular, recent work25 presented an improved robust model predictive control for PMSM, integrating backstepping control and integral action, experimentally validated to enhance speed tracking, robustness, and uncertainty handling, setting new industrial benchmarks. Considering the adaptability and simplicity of GPC, its application to pattern- moving systems with small samples and high nonlinearity is limited. GPC struggles to build accurate models due to complex statistical dynamics and non- algebraic pattern variables, faces performance issues from data fluctuations without effective oscillation control, and lacks robust uncertainty quantification, reducing control accuracy and robustness in industrial applications. These shortcomings highlight the need for advanced methodologies to address uncertainty and variability in such systems. + +<|ref|>text<|/ref|><|det|>[[89, 575, 908, 787]]<|/det|> +Given the challenges outlined, this study proposes a novel methodology that integrates the Interval Grey Model (IGM) with GPC, termed Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC), to address the control of pattern- moving systems characterized by small sample sizes and significant uncertainties. This approach leverages the strengths of grey system theory to model systems with incomplete information and employs an adaptive buffer operator to mitigate fluctuations in pattern class variables, thereby enhancing prediction accuracy and control robustness. The proposed method systematically addresses the quantization of pattern category variables through a bidirectional mapping framework, enabling precise tracking of dynamic pattern transitions. Key contributions include: (1) the development of an adaptive buffer operator tailored to the monotonicity and oscillation of pattern class sequences, (2) the formulation of an IGM(1,2)- based prediction model for robust handling of uncertain data, and (3) the integration of these components into a GPC framework to achieve stable and accurate control of pattern- moving systems. The workflow involves constructing a pattern- moving space via data- driven quantization, applying the adaptive buffer operator to smooth time- series fluctuations, developing the IGM(1,2) prediction model, and implementing the IGB- GPC control strategy with receding horizon optimization and feedback correction. The effectiveness of this approach is validated through numerical simulations, demonstrating superior control accuracy and dynamic response compared to benchmark methods such as CARIMA- GPC and IG- GPC. + +<|ref|>text<|/ref|><|det|>[[89, 792, 907, 882]]<|/det|> +The remaining sections of this paper are organized as follows. The problem Statement and basic knowledge are given in Section 2. Section 3 develops IGM prediction with an adaptive buffer operator in the interest of smoothing time series fluctuations (pattern class variables) in term of different properties. Section 4 introduces a novel control approach, termed IGB- GPC, which integrates GPC with an interval grey model. The grey prediction model employs an adaptive buffering operator to mitigate fluctuations in the data sequence. The numerical simulation results, which are critical to validating the model, are presented in Section 5. Section 6 ultimately delivers the conclusion. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[89, 76, 460, 95]]<|/det|> +## 2 Problem Statement and Preliminaries + +<|ref|>text<|/ref|><|det|>[[90, 101, 907, 133]]<|/det|> +This section introduces a method for dynamic description of complex systems, based on its characteristic analysis, including constructing pattern category variables and the pattern- moving space. + +<|ref|>sub_title<|/ref|><|det|>[[90, 146, 272, 161]]<|/det|> +### 2.1 Problem Statement + +<|ref|>text<|/ref|><|det|>[[90, 161, 861, 178]]<|/det|> +For the purposes of system analysis and controller synthesis, the system is assumed to be representable by Equation 1. + +<|ref|>equation<|/ref|><|det|>[[137, 204, 907, 222]]<|/det|> +\[d x(k + 1) = f\left(d x(k),\ldots ,d x(k - n_{y}),u(k),\ldots ,u(k - n_{u})\right) \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[89, 233, 907, 280]]<|/det|> +where the system input at time step \(k\) is denoted by \(u(k)\in \mathbb{R}\) , the unknown nonlinear function \(f(\cdot)\) characterizes the system dynamics, the positive integers \(n_{y},n_{u}\in \mathbb{Z}_{+}\) represent the unknown output and input orders respectively, and \(d x\in \mathbb{R}\) corresponds to the pattern class variables, computable through the following relation. + +<|ref|>equation<|/ref|><|det|>[[129, 300, 907, 404]]<|/det|> +\[\begin{array}{r l} & {\widehat{\otimes}(k + 1) = D(M(d x(k + 1)))\\ & {\qquad = \left\{ \begin{array}{l l}{c_{1},d x(k + 1)\in (c_{1} - r_{1},C_{1}],}\\ {c_{2},d x(k + 1)\in (c_{2} - r_{2},C_{2}],}\\ \vdots \\ {c_{N},d x(k + 1)\in (c_{N} - r_{N},C_{N}]} \end{array} \right.} \end{array} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[89, 413, 908, 475]]<|/det|> +where \(D(M(\cdot))\) refers to the space cross- mapping process, showing in Figure 1 which is considered to be a grey system. \(\widehat{\otimes} (k + 1)\) is the grey metric value and it denotes a quantized observation, \(c_{m}\neq c_{n}\) (if \(m\neq n\) and \(m,n\in [1,N]\) with \(N\) being the numbers of pattern class), \(c_{m}\) is the class center, \(C_{m}\) denotes the grey number interval boundary and \(C_{m} = c_{m} + r_{m} = c_{m + 1} - r_{m + 1}\) \(r_{m}\) refers to the class radius and \(r_{m} > 0\) + +<|ref|>text<|/ref|><|det|>[[89, 476, 908, 536]]<|/det|> +Note that the system 1 exhibits precisely observable state variable \(d x\) . This allows for the system to be equivalently described using distinct mathematical formalisms: as a linear time- invariant (LTI) system, through state space equations governing its dynamics, or via a finite impulse response (FIR) model defining its input- output behavior. To facilitate the computation of pattern class variables, the definition of gray number is provided herein. + +<|ref|>text<|/ref|><|det|>[[89, 546, 908, 607]]<|/det|> +Definition 2.1. Let \(\mathbb{R}\) be the real number field. A grey number \(\otimes\) is defined as: \(\otimes \in [\underline{{a}},\overline{{a}} ]\) , where \(\underline{{a}}\) is the lower bound and \(\overline{{a}}\) is the upper bound, with \(\underline{{a}}\leq \overline{{a}}\) . When \(\underline{{a}} = \overline{{a}}\) , the grey number degenerates to a white number (a deterministic value). A grey number can also be further described by a whitening function, for example: \(f(x):[\underline{{a}},\overline{{a}} ]\to [0,1]\) , which represents the credibility or weight of different values within the interval \(^{20}\) . + +<|ref|>text<|/ref|><|det|>[[89, 616, 908, 662]]<|/det|> +Remark 2.1. The distinction between a grey number and an interval number. The grey number \(\otimes \in [a_{1},a_{2}]\) (where \(a_{1}< a_{2}\) represents an unknown value within the interval \([a_{1},a_{2}]\) , while the interval number \([a_{1},a_{2}]\) represents the entire interval set itself. + +<|ref|>text<|/ref|><|det|>[[89, 668, 908, 744]]<|/det|> +According to the definition 2.1, the grey number is essentially a type of number whose exact value is unknown but is constrained within a known interval. It characterizes incomplete and uncertain information. Equation 3 \(\widehat{\otimes} (k)\) is a non- intrinsic grey number estimation with the kernel grey number. In perspective of control theory, the control structure of System 1 in pattern moving systems focuses on realizing exact tracking and responsive adaptation to the dynamic transitions of pattern category variables. + +<|ref|>sub_title<|/ref|><|det|>[[90, 759, 296, 774]]<|/det|> +### 2.2 Pattern moving theory + +<|ref|>text<|/ref|><|det|>[[90, 775, 908, 820]]<|/det|> +This section examines the fundamental concepts of PMT, highlighting its capacity to represent system dynamics through time- series pattern recognition. PMT is composed of two components: pattern category variables and pattern moving spaces. These elements are discussed in the sections that follow respectively. + +<|ref|>title<|/ref|><|det|>[[90, 831, 283, 845]]<|/det|> +#### 2.2.1 Pattern class variable + +<|ref|>text<|/ref|><|det|>[[89, 846, 908, 920]]<|/det|> +Analysis of pattern- moving systems reveals that system output is not a deterministic quantity but rather a random variable subject to statistical principles. This presents difficulties for methods that rely on deterministic variables (e.g., State variables or output variables) for dynamics modeling and control. In addition, the design of corresponding controllers based on traditional system model structures becomes more complicated. Given the inherent statistical moving properties of this systems, it is necessary to construct variables with statistical properties to portray their overall statistical moving behavior. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 78, 909, 170]]<|/det|> +According to pattern recognition theory, pattern category denotes a collection of pattern samples that possess identical or similar characteristics. When these pattern samples are uniformly represented by a specific variable, this variable reflects certain statistical properties. The variable that encapsulates the information of the pattern category is termed the pattern category variable. Therefore, for systems governed by statistical laws, the dynamic behavior can be effectively characterized using the pattern category variable instead of traditional State variables. The construction process of a pattern category variable is as follows: + +<|ref|>text<|/ref|><|det|>[[89, 180, 909, 242]]<|/det|> +Definition 2.2. Assume that the \(\{y(k)\}\) and \(\{m x(k)\}\) denote the sequence of detection samples and the sequence of pattern samples, respectively. After the pattern samples are classified by the classifier, the pattern variable with category information is defined as the pattern class variable, which is expressed as: \(d x(t)\) . Then the pattern class variable should meet the following transformation process: + +<|ref|>equation<|/ref|><|det|>[[138, 265, 907, 300]]<|/det|> +\[\begin{array}{r}{m x(k) = T(y(k))}\\ {d x(k) = M(m x(k))} \end{array} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[90, 309, 900, 326]]<|/det|> +where the terms \(T(\cdot)\) and \(M(\cdot)\) represent the feature extraction or selection and pattern classification processes, respectively. + +<|ref|>text<|/ref|><|det|>[[89, 335, 909, 381]]<|/det|> +Remark 2.2. In accordance with Definition 2.2, the \(d x(t)\) has following two properties: (1) Pattern class variables are functions of time. (2) The pattern class variables possess a class attribute, that is, the \(d x(t)\) have statistical and set qualities. As a result, the pattern class was commonly understood to be a set of samples with the same or comparable attributes directly. + +<|ref|>title<|/ref|><|det|>[[90, 392, 286, 406]]<|/det|> +#### 2.2.2 Pattern-moving space + +<|ref|>text<|/ref|><|det|>[[89, 407, 909, 544]]<|/det|> +For complex industrial production processes, continuously collect input and output data for a sufficient amount of time to form a data space. It is worthy to point out that pattern moving "space" is a virtual space without structural description, which is constructed based on data- driven methods and contains three significant steps26. (1) For pattern- driven systems, data collected over an extended period (e.g., 2- 3 years) forms the data space. A sufficiently large dataset ensures the production process operates within this system operating subspace, capturing the system's characteristic features. (2) Extracting characteristic variables from the operational subspace generates pattern sample sequences with primary statistical features, forming the operational condition characteristic subspace. (3) Pattern recognition or quantitative classification techniques identify the condition feature subspace, using the pattern class as a scale to form the pattern scale space. The pattern moving space combines this space with the pattern category variables defined therein. + +<|ref|>text<|/ref|><|det|>[[89, 544, 909, 589]]<|/det|> +To construct the pattern- moving space, an improved data quantization and classification method is developed in this work, which extends the quantized classification control scheme reported research27. The proposed approach can be described as follows: + +<|ref|>equation<|/ref|><|det|>[[128, 610, 907, 685]]<|/det|> +\[\begin{array}{r l} & {d x(k + 1) = M\left\{T\left[y(k + 1)\right]\right\}}\\ & {\quad = \left\{ \begin{array}{l l}{-\vec{\gamma} (k + 1),} & {\mathrm{if} - \frac{1}{1 - \Delta}\kappa_{i}< y(k + 1)\leq - \frac{1}{1 + \Delta}\kappa_{i}}\\ {0,} & {\mathrm{if} - \frac{1}{1 + \Delta}\kappa_{i}< y(k + 1)\leq \frac{1}{1 + \Delta}\kappa_{i}}\\ {\vec{\gamma} (k + 1),} & {\mathrm{if} \frac{1}{1 + \Delta}\kappa_{i}< y(k + 1)\leq \frac{1}{1 - \Delta}\kappa_{i}} \end{array} \right.} \end{array} \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[89, 696, 909, 731]]<|/det|> +Among them, \(\begin{array}{r}{\bar{y} (k + 1) = \frac{1 + \rho_{0}}{4}\kappa_{i}(\rho_{0}^{i - 1} + \rho_{0}^{i});\Delta = \frac{1 - \rho_{0}}{1 + \rho_{0}};\kappa_{i} = \rho_{0}^{i}\kappa_{0};\rho_{0}\in (0,1);\kappa_{0}} \end{array}\) is the maximum working range of the first principal component \(y_{p}(k)\) , that is, \(\kappa_{0}\geq \max \left(\left|y(k)\right|\right);i = 1,2,\dots ,N\) + +<|ref|>text<|/ref|><|det|>[[89, 732, 909, 805]]<|/det|> +Given the initial class radius upper limit \(r_{0}\) of the mode class where the operating point 0 is located, and other partitioning parameters \(\rho_{0}\) , \(\kappa_{0}\) . According to the quantization classification Equation 4, when \(N\geq \left[\ln (r_{0}(1 + \Delta) / \kappa_{0}) / \ln \rho_{0}\right]\) , the first principal component sequence \(\{y(k)\}\) is divided into \(2N + 1\) intervals. Therefore, we can obtain the centers \(c_{i}\) of \(2N + 1\) pattern classes, the class radius \(r_{i} = \left|\frac{1 + \rho_{0}^{2}}{4\rho_{0}}\kappa_{i}\right|\) , and the class threshold \(C_{i} = c_{i} + r_{i}\) for the pattern class \(i\) , i.e., \(P_{i}\) . + +<|ref|>sub_title<|/ref|><|det|>[[89, 818, 784, 838]]<|/det|> +## 3 System dynamic description based on IGM with adaptive buffer operator + +<|ref|>sub_title<|/ref|><|det|>[[89, 844, 505, 861]]<|/det|> +### 3.1 Modeling system dynamics with mapping spaces + +<|ref|>text<|/ref|><|det|>[[89, 862, 909, 922]]<|/det|> +After completing the definition of pattern category variables and conducting clustering mapping processing for the operational condition feature subspace, the constructed pattern moving space exhibits typical characteristics of a gray system, namely small sample size and poor information. Therefore, the gray number measurement theory is employed to perform quantitative analysis on the pattern category variables, and the dynamic characteristic expression of the system is constructed. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 78, 908, 141]]<|/det|> +Definition 3.1. In an \(m\) - dimensional pattern moving space \((m \in \mathbb{N}^+\) , where \(\mathbb{N}^+\) denotes the set of positive integers), let the pattern category variable at the \(i\) - th dimension \((i = 1,2,\ldots ,m)\) take values at different time steps \(k\) \((k = 1,2,\ldots ,n,n\in \mathbb{N}^*)\) as \(d x_{i}(k)\in [a_{i},b_{i}]\) , where \(a_{i}< b_{i}\) . Then \(\hat{\otimes}_{k} = (d x_{1}(k),d x_{2}(k),\ldots ,d x_{m}(k))\) is called the grey whitening value of the pattern category variable at time \(k\) . + +<|ref|>text<|/ref|><|det|>[[88, 155, 908, 216]]<|/det|> +As specified in Definition 3.1, the measurement of pattern variables constitutes a non- intrinsic grey number system, exhibiting the distinctive property of value oscillations around a base reference point. For resolving computational issues involving pattern categorical variables, we establish a bidirectional mapping framework between the pattern moving domain and its computationally tractable counterpart, illustrating in Figure 1. + +<|ref|>image<|/ref|><|det|>[[207, 230, 789, 551]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[291, 565, 705, 581]]<|/det|> +
Figure 1. The schematic diagram of the space cross mapping.
+ +<|ref|>text<|/ref|><|det|>[[88, 600, 908, 692]]<|/det|> +As described in Figure 1, the pattern category variable is endowed with computational attributes through the gray- scale metric \(D(\cdot)\) , followed by computations in a computable space, and subsequently classified via a classification mapping \(M(\cdot)\) to determine the trajectory of the pattern's motion. This essentially constitutes a spatial cross- mapping method. The motion trajectory of the system in the pattern space is formed through a cyclic procedure: pattern category variables are mapped to a computable space at each time step for processing, and the results are then projected back to the pattern space to generate successive trajectory points. This iterative process builds the trajectory over time, and is mathematically described as: + +<|ref|>equation<|/ref|><|det|>[[131, 716, 907, 816]]<|/det|> +\[\left\{ \begin{array}{l l}{d x(k + 1) = M[\widetilde{d x} (k + 1)]}\\ {\qquad = M\{f[D(d x(k)),D(d x(k - 1)),\dots ,D(d x(k - n))}\\ {\qquad u(k - \tau),u(k - \tau - 1),\dots ,u(k - \tau - m)]\}}\\ {\qquad = M\{f[\hat{\otimes}_{k},\hat{\otimes}_{k - 1},\dots ,\hat{\otimes}_{k - n},}\\ {\qquad u(k - \tau),u(k - \tau - 1),\dots ,u(k - \tau - m)]\}}\\ {d x(k) = \hat{\otimes}_{k} + \delta \in [\underline{{a}},\overline{{a}} ],\underline{{a}} < \overline{{a}}} \end{array} \right. \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[88, 830, 908, 921]]<|/det|> +where \(f(\cdot)\) is the output model of the computable space, referring to a suitable grey model here. \(m,n\) are the input and output orders of the model respectively; \(\tau\) is the input time delay of the model. \(k\) denotes a running moment in the pattern motion space, \(\widetilde{d x} (k + 1) = f(\cdot)\) represents the initial prediction output of the computable space, and \(d x(k + 1) = M[\cdot ]\) is the final prediction output of the system. Here, \(u(k)\) represents the control variable or control pattern. Meanwhile, \(\hat{\otimes}_{k} + \delta\) is the metric value for the pattern category variable, and \(\delta\) is the perturbation of the metric value. The subsequent grey metric values are measured by the "core" grey number and interval grey number respectively. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[88, 79, 466, 95]]<|/det|> +### 3.2 IGM prediction with adaptive buffer operator + +<|ref|>text<|/ref|><|det|>[[88, 95, 910, 140]]<|/det|> +In this section, an adaptive buffer operator was introduced to perform smoothness processing on categorical variables of patterns, and a prediction model based on IGM (1,2) is derived and established. Firstly, the definition of the smoothness operator is given as follows. + +<|ref|>text<|/ref|><|det|>[[88, 148, 645, 166]]<|/det|> +Definition 3.2. \(^{28}\) Let \(X = (x(1),x(2),\ldots ,x(n))\) be a system behavior data sequence. + +<|ref|>text<|/ref|><|det|>[[70, 175, 699, 192]]<|/det|> +1. If \(\forall k = 2,3,\ldots ,n,x(k) - x(k - 1) > 0\) , then \(X\) is called a monotonically increasing sequence. + +<|ref|>text<|/ref|><|det|>[[70, 196, 700, 212]]<|/det|> +2. If \(\forall k = 2,3,\ldots ,n,x(k) - x(k - 1)< 0\) , then \(X\) is called a monotonically decreasing sequence. + +<|ref|>text<|/ref|><|det|>[[70, 216, 364, 231]]<|/det|> +3. If there exist \(k,k^{\prime}\in \{2,3,\ldots ,n\}\) such that + +<|ref|>equation<|/ref|><|det|>[[128, 240, 907, 258]]<|/det|> +\[x(k) - x(k - 1) > 0\quad \mathrm{and}\quad x(k^{\prime}) - x(k^{\prime} - 1)< 0, \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[88, 267, 399, 283]]<|/det|> +then \(X\) is called a random oscillating sequence. + +<|ref|>text<|/ref|><|det|>[[112, 294, 140, 308]]<|/det|> +Let + +<|ref|>equation<|/ref|><|det|>[[128, 318, 907, 336]]<|/det|> +\[M = \max \{x(k)\mid k = 1,2,\ldots ,n\} ,\quad m = \min \{x(k)\mid k = 1,2,\ldots ,n\} . \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[112, 345, 504, 361]]<|/det|> +The value \(M - m\) is called the amplitude of the sequence \(X\) + +<|ref|>text<|/ref|><|det|>[[88, 368, 907, 400]]<|/det|> +Definition 3.3. Let \(X = (x(1),x(2),\ldots ,x(n))\) be a system behavior data sequence, and \(D\) be an operator acting on \(X\) . The sequence obtained after applying \(D\) to \(X\) is denoted as + +<|ref|>equation<|/ref|><|det|>[[128, 408, 907, 426]]<|/det|> +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d). \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[112, 435, 735, 451]]<|/det|> +Here, \(D\) is called a sequence operator, and \(X D\) is called a first- order operator- applied sequence. + +<|ref|>text<|/ref|><|det|>[[88, 451, 907, 482]]<|/det|> +The action of sequence operators can be applied multiple times. If \(D_{1}\) and \(D_{2}\) are both sequence operators, then \(D_{1}D_{2}\) is called a second- order operator, and + +<|ref|>equation<|/ref|><|det|>[[128, 490, 907, 508]]<|/det|> +\[X D_{1}D_{2} = (x(1)d_{1}d_{2},x(2)d_{1}d_{2},\ldots ,x(n)d_{1}d_{2}) \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[112, 518, 533, 534]]<|/det|> +is called a second- order operator- applied sequence, and so on \(^{28}\) + +<|ref|>text<|/ref|><|det|>[[88, 541, 424, 557]]<|/det|> +Theorem 3.1. \(^{29}\) Strengthens the buffer operator + +<|ref|>text<|/ref|><|det|>[[88, 558, 113, 571]]<|/det|> +Let + +<|ref|>equation<|/ref|><|det|>[[128, 581, 907, 599]]<|/det|> +\[X = (x(1),x(2),\ldots ,x(n)) \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[112, 609, 371, 624]]<|/det|> +be a system behavior sequence, and let + +<|ref|>equation<|/ref|><|det|>[[128, 633, 907, 651]]<|/det|> +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d) \quad (11)\] + +<|ref|>text<|/ref|><|det|>[[112, 660, 435, 676]]<|/det|> +be its intensified buffer sequence. Then we have: + +<|ref|>text<|/ref|><|det|>[[70, 686, 844, 703]]<|/det|> +1. \(X\) is a monotonically increasing sequence and \(D\) is an intensified buffer operator \(\Leftrightarrow x(k)\geq x(k)d\) for \(k = 1,2,\ldots ,n\) + +<|ref|>text<|/ref|><|det|>[[70, 707, 844, 723]]<|/det|> +2. \(X\) is a monotonically decreasing sequence and \(D\) is an intensified buffer operator \(\Leftrightarrow x(k)\leq x(k)d\) for \(k = 1,2,\ldots ,n\) + +<|ref|>text<|/ref|><|det|>[[70, 727, 576, 743]]<|/det|> +3. If \(X\) is an oscillating sequence and \(D\) is an intensified buffer operator, then + +<|ref|>equation<|/ref|><|det|>[[128, 751, 907, 777]]<|/det|> +\[\max_{1\leq k\leq n}\{x(k)\} \leq \max_{1\leq k\leq n}\{x(k)d\} ,\quad \min_{1\leq k\leq n}\{x(k)\} \geq \min_{1\leq k\leq n}\{x(k)d\} \quad (12)\] + +<|ref|>text<|/ref|><|det|>[[88, 787, 392, 802]]<|/det|> +Theorem 3.2. \(^{29}\) Weakening buffer operator + +<|ref|>text<|/ref|><|det|>[[88, 803, 113, 815]]<|/det|> +Let + +<|ref|>equation<|/ref|><|det|>[[128, 827, 907, 844]]<|/det|> +\[X = (x(1),x(2),\ldots ,x(n)) \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[112, 854, 370, 870]]<|/det|> +be a system behavior sequence, and let + +<|ref|>equation<|/ref|><|det|>[[128, 880, 907, 897]]<|/det|> +\[X D = (x(1)d,x(2)d,\ldots ,x(n)d) \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[112, 907, 375, 922]]<|/det|> +be its weakened buffer sequence. Then: + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 78, 835, 96]]<|/det|> +1. \(X\) is a monotonically increasing sequence and \(D\) is a weakened buffer operator \(\Leftrightarrow x(k)\leq x(k)d\) for \(k = 1,2,\ldots ,n;\) + +<|ref|>text<|/ref|><|det|>[[68, 99, 835, 117]]<|/det|> +2. \(X\) is a monotonically decreasing sequence and \(D\) is a weakened buffer operator \(\Leftrightarrow x(k)\geq x(k)d\) for \(k = 1,2,\ldots ,n;\) + +<|ref|>text<|/ref|><|det|>[[68, 120, 565, 137]]<|/det|> +3. If \(X\) is an oscillating sequence and \(D\) is a weakened buffer operator, then + +<|ref|>equation<|/ref|><|det|>[[130, 144, 907, 171]]<|/det|> +\[\max_{1\leq k\leq n}\{x(k)\} \geq \max_{1\leq k\leq n}\{x(k)d\} ,\quad \min_{1\leq k\leq n}\{x(k)\} \leq \min_{1\leq k\leq n}\{x(k)d\} \quad (15)\] + +<|ref|>text<|/ref|><|det|>[[84, 178, 909, 255]]<|/det|> +Remark 3.1. Theorem 3.1 illustrates that, under the action of the intensifying operator, the data of a monotonically increasing sequence decreases, the data of a monotonically decreasing sequence increases, and the amplitude of an oscillating sequence increases. Theorem 3.2 illustrates that, under the action of the weakening operator, the data of a monotonically increasing sequence increases, the data of a monotonically decreasing sequence decreases, and the amplitude of an oscillating sequence decreases. + +<|ref|>text<|/ref|><|det|>[[86, 259, 382, 275]]<|/det|> +Theorem 3.3. An adaptive buffer operator + +<|ref|>text<|/ref|><|det|>[[85, 273, 732, 291]]<|/det|> +Given a time series \(X = (x(1),x(2),\ldots ,x(n))\) , the adaptive buffer operator \(D\) is defined as follows: + +<|ref|>text<|/ref|><|det|>[[75, 300, 909, 333]]<|/det|> +- If \(X\) is an increasing sequence (i.e., \(x(k + 1) > x(k)\) for all \(k = 1,2,\ldots ,n - 1\) ), then \(D = D_{1}\) , the strengthening operator, defined by: + +<|ref|>equation<|/ref|><|det|>[[130, 339, 907, 379]]<|/det|> +\[x(k)d_{1} = x(k) + \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k),\quad 0< \alpha < 1,k = 1,2,\ldots ,n. \quad (16)\] + +<|ref|>text<|/ref|><|det|>[[75, 388, 904, 407]]<|/det|> +- If \(X\) is a decreasing sequence (i.e., \(x(k + 1) < x(k)\) for all \(k = 1,2,\ldots ,n - 1\) ), then \(D = D_{2}\) , the weakening operator, defined by: + +<|ref|>equation<|/ref|><|det|>[[130, 411, 907, 451]]<|/det|> +\[x(k)d_{2} = x(k) - \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k),\quad 0< \alpha < 1,k = 1,2,\ldots ,n. \quad (17)\] + +<|ref|>text<|/ref|><|det|>[[75, 460, 907, 492]]<|/det|> +- If \(X\) is an oscillating sequence (i.e., neither strictly increasing nor strictly decreasing), then \(D\) adopts a weighted combination form: + +<|ref|>equation<|/ref|><|det|>[[130, 497, 907, 538]]<|/det|> +\[x(k)d = \left\{ \begin{array}{l l}{w_{1}x(k)d_{1} + w_{2}x(k)d_{2},} & {\mathrm{if~amplitude~is~large~}(\max_{j = k}^{n}x(j) - \min_{j = k}^{n}x(j) > \theta)}\\ {x(k),} & {\mathrm{otherwise,}} \end{array} \right. \quad (18)\] + +<|ref|>text<|/ref|><|det|>[[87, 546, 907, 564]]<|/det|> +where \(w_{1} + w_{2} = 1,w_{1},w_{2}\geq 0\) are dynamically adjusted weights based on amplitude, and \(\theta\) is a predefined amplitude threshold. + +<|ref|>text<|/ref|><|det|>[[86, 573, 545, 590]]<|/det|> +Proof. Proof of Strengthening Property for Increasing Sequences + +<|ref|>text<|/ref|><|det|>[[86, 590, 697, 607]]<|/det|> +Assume \(X\) is an increasing sequence, i.e., \(x(k + 1) > x(k)\) for all \(k\) . Consider the \(D_{1}\) operator: + +<|ref|>equation<|/ref|><|det|>[[130, 613, 907, 653]]<|/det|> +\[x(k)d_{1} = x(k) + \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k). \quad (19)\] + +<|ref|>text<|/ref|><|det|>[[110, 664, 390, 685]]<|/det|> +Since \(x(j) > x(k)\) for \(j > k\) , \(\frac{x(j)}{x(k)} > 1\) , thus: + +<|ref|>equation<|/ref|><|det|>[[130, 694, 907, 734]]<|/det|> +\[\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k) > (n - k + 1)\cdot x(k). \quad (20)\] + +<|ref|>text<|/ref|><|det|>[[110, 744, 310, 760]]<|/det|> +Substituting into the formula: + +<|ref|>equation<|/ref|><|det|>[[130, 767, 907, 802]]<|/det|> +\[x(k)d_{1} > x(k) + \frac{(n - k + 1)\cdot x(k)}{n - k + 1} = 2x(k). \quad (21)\] + +<|ref|>text<|/ref|><|det|>[[110, 807, 781, 825]]<|/det|> +However, the introduction of \(\alpha\) (where \(0< \alpha < 1\) ) limits the growth magnitude. The adjusted form is: + +<|ref|>equation<|/ref|><|det|>[[130, 832, 907, 875]]<|/det|> +\[x(k)d_{1} = x(k)\left(1 + \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right). \quad (22)\] + +<|ref|>text<|/ref|><|det|>[[110, 884, 909, 908]]<|/det|> +Since \(\frac{x(j)}{x(k)} > 1\) and \(\sum_{j = k}^{n}\frac{x(j)}{x(k)} > n - k + 1\) , it follows that \(x(k)d_{1} > x(k)\) , proving that \(D_{1}\) strengthens an increasing sequence. \(\square\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[90, 78, 528, 95]]<|/det|> +Proof. Proof of Weakening Property for Decreasing Sequences + +<|ref|>text<|/ref|><|det|>[[90, 94, 692, 111]]<|/det|> +Assume \(X\) is a decreasing sequence, i.e., \(x(k + 1)< x(k)\) for all \(k\) . Consider the \(D_{2}\) operator: + +<|ref|>equation<|/ref|><|det|>[[130, 120, 907, 163]]<|/det|> +\[x(k)d_{2} = x(k) - \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k). \quad (23)\] + +<|ref|>text<|/ref|><|det|>[[113, 175, 395, 197]]<|/det|> +Since \(x(j)< x(k)\) for \(j > k\) , \(\frac{x(j)}{x(k)} < 1\) , thus: + +<|ref|>equation<|/ref|><|det|>[[130, 208, 907, 245]]<|/det|> +\[\sum_{j = k}^{n}\frac{x(j)}{x(k)}\cdot x(k)< (n - k + 1)\cdot x(k). \quad (24)\] + +<|ref|>text<|/ref|><|det|>[[113, 259, 310, 275]]<|/det|> +Substituting into the formula: + +<|ref|>equation<|/ref|><|det|>[[130, 285, 907, 320]]<|/det|> +\[x(k)d_{2}< x(k) - \frac{(n - k + 1)\cdot x(k)}{n - k + 1} = 0. \quad (25)\] + +<|ref|>text<|/ref|><|det|>[[113, 328, 535, 345]]<|/det|> +However, \(\alpha\) ensures moderate weakening. The adjusted form is: + +<|ref|>equation<|/ref|><|det|>[[130, 355, 907, 399]]<|/det|> +\[x(k)d_{2} = x(k)\left(1 - \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right). \quad (26)\] + +<|ref|>text<|/ref|><|det|>[[113, 410, 880, 432]]<|/det|> +Since \(\frac{x(j)}{x(k)} < 1\) and \(\sum_{j = k}^{n}\frac{x(j)}{x(k)} < n - k + 1\) , it follows that \(x(k)d_{2}< x(k)\) , proving that \(D_{2}\) weakens a decreasing sequence. \(\square\) + +<|ref|>text<|/ref|><|det|>[[90, 444, 543, 476]]<|/det|> +Proof. Proof of Weighted Combination for Oscillating Sequences Let the amplitude of a sequence \(\{x(k)\}_{k = 1}^{n}\) be defined as + +<|ref|>equation<|/ref|><|det|>[[130, 486, 907, 512]]<|/det|> +\[\Delta = \max_{k\leq j\leq n}x(j) - \min_{k\leq j\leq n}x(j). \quad (27)\] + +<|ref|>text<|/ref|><|det|>[[112, 527, 557, 545]]<|/det|> +Given a threshold \(\theta >0\) , define the adjusted value \(x(k)^{d}\) as follows: + +<|ref|>text<|/ref|><|det|>[[75, 560, 174, 575]]<|/det|> +If \(\Delta >\theta\) , let + +<|ref|>equation<|/ref|><|det|>[[130, 586, 325, 605]]<|/det|> +\[x(k)^{d} = w_{1}x(k)^{d_{1}} + w_{2}x(k)^{d_{2}},\] + +<|ref|>text<|/ref|><|det|>[[90, 618, 133, 632]]<|/det|> +where + +<|ref|>equation<|/ref|><|det|>[[130, 642, 907, 683]]<|/det|> +\[w_{1} = \frac{\sum_{j = 1}^{n - 1}\max (0,x(j + 1) - x(j))}{\sum_{j = 1}^{n - 1}|x(j + 1) - x(j)|},w_{2} = 1 - w_{1}. \quad (28)\] + +<|ref|>text<|/ref|><|det|>[[75, 696, 175, 711]]<|/det|> +If \(\Delta \leq \theta\) , set + +<|ref|>equation<|/ref|><|det|>[[130, 722, 907, 744]]<|/det|> +\[x(k)^{d} = x(k), \quad (29)\] + +<|ref|>text<|/ref|><|det|>[[90, 755, 308, 770]]<|/det|> +to avoid unnecessary adjustment. + +<|ref|>text<|/ref|><|det|>[[90, 784, 907, 815]]<|/det|> +This weighted formulation dynamically balances the influence of strengthening and weakening trends in response to the degree of oscillation. \(\square\) + +<|ref|>text<|/ref|><|det|>[[90, 829, 907, 890]]<|/det|> +Remark 3.2. The adaptive buffer operator \(D\) intelligently adjusts to the nature of the sequence (increasing, decreasing, or oscillating) by adapting \(D_{1}\) , \(D_{2}\) , or a weighted combination, effectively accommodating the sequence's trend. In addition, common methods for sequence trend detection encompass differential statistical analysis, cumulative sum method for difference series, and extremum- based feature identification \(^{30}\) . + +<|ref|>text<|/ref|><|det|>[[113, 904, 888, 921]]<|/det|> +- Here is the complete step-by-step procedure for constructing the Interval Grey Number Prediction IGM(1,2). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 78, 906, 110]]<|/det|> +Interval Grey Number: Denoted as \(\otimes = [\otimes ,\otimes ]\) , where \(\otimes\) and \(\otimes\) are the lower and upper bounds of the grey number. Form of IGM(1,2) Model: Based on a first- order differential equation: + +<|ref|>equation<|/ref|><|det|>[[130, 117, 907, 150]]<|/det|> +\[\frac{d\otimes_{1}(t)}{dt} +a\otimes_{1}(t) = b\otimes_{2}(t) \quad (30)\] + +<|ref|>text<|/ref|><|det|>[[88, 156, 907, 188]]<|/det|> +Here, \(\otimes_{1}(t)\) is the dependent variable sequence, \(\otimes_{2}(t)\) is the independent variable sequence, and \(a,b\) are parameters to be estimated by least- squares regression. + +<|ref|>sub_title<|/ref|><|det|>[[89, 189, 228, 202]]<|/det|> +## Data Preprocessing + +<|ref|>text<|/ref|><|det|>[[88, 202, 907, 248]]<|/det|> +For the dependent interval grey number sequence \(\otimes_{1}(0) = [\underline{{x}}_{1}(0),\bar{x}_{1}(0)],\otimes_{1}(1) = [\underline{{x}}_{1}(1),\bar{x}_{1}(1)],\dots ,\otimes_{1}(n) = [\underline{{x}}_{1}(n),\bar{x}_{1}(n)]\) and the independent one \(\otimes_{2}(0) = [\underline{{x}}_{2}(0),\bar{x}_{2}(0)],\otimes_{2}(1) = [\underline{{x}}_{2}(1),\bar{x}_{2}(1)],\dots ,\otimes_{2}(n) = [\underline{{x}}_{2}(n),\bar{x}_{2}(n)]\) , check equidistance and monotonicity. If data fluctuates, use First- Order Accumulated Generation (1- AGO): + +<|ref|>equation<|/ref|><|det|>[[130, 255, 907, 298]]<|/det|> +\[\left\{ \begin{array}{l l}{\underline{{X}}_{i}(1) = \underline{{x}}_{i}(1),} & {\overline{{X}}_{i}(1) = \overline{{x}}_{i}(1)}\\ {\underline{{X}}_{i}(k) = \underline{{X}}_{i}(k - 1) + \underline{{x}}_{i}(k),} & {\overline{{X}}_{i}(k) = \overline{{X}}_{i}(k - 1) + \overline{{x}}_{i}(k)} & {(k = 2,3,\ldots ,n)} \end{array} \right. \quad (31)\] + +<|ref|>text<|/ref|><|det|>[[88, 308, 410, 327]]<|/det|> +Denote the result as \(\otimes_{1}^{(1)}(k) = [\underline{{X}}_{i}(k),\overline{{X}}_{i}(k)]\) + +<|ref|>sub_title<|/ref|><|det|>[[89, 327, 325, 341]]<|/det|> +## Constructing the IGM(1,2) Model + +<|ref|>text<|/ref|><|det|>[[88, 343, 455, 360]]<|/det|> +Generate the adjacent mean sequence \(Z_{1}(k)\) for \(\otimes_{1}^{(1)}(k)\) : + +<|ref|>equation<|/ref|><|det|>[[129, 369, 907, 390]]<|/det|> +\[Z_{1}(k) = [\underline{{z}}_{1}(k),\bar{z}_{1}(k)] = \alpha \otimes_{1}^{(1)}(k) + (1 - \alpha)\otimes_{1}^{(1)}(k - 1)\quad (\alpha \in [0,1],\mathrm{usually}\alpha = 0.5) \quad (32)\] + +<|ref|>text<|/ref|><|det|>[[88, 398, 315, 413]]<|/det|> +Approximately, for \(k = 2,3,\ldots ,n\) : + +<|ref|>equation<|/ref|><|det|>[[130, 421, 907, 462]]<|/det|> +\[\frac{d\otimes_{1}^{(1)}(t)}{dt}\bigg|_{t = k} +a\otimes_{1}^{(1)}(k)\approx b\otimes_{2}^{(1)}(k) \quad (33)\] + +<|ref|>text<|/ref|><|det|>[[88, 469, 227, 483]]<|/det|> +The discrete form is: + +<|ref|>equation<|/ref|><|det|>[[129, 490, 907, 512]]<|/det|> +\[Z_{1}(k) + a\otimes_{1}^{(1)}(k) = b\otimes_{2}^{(1)}(k) \quad (34)\] + +<|ref|>text<|/ref|><|det|>[[88, 520, 192, 534]]<|/det|> +In matrix form: + +<|ref|>equation<|/ref|><|det|>[[129, 540, 907, 623]]<|/det|> +\[\begin{array}{r}{\left[ \begin{array}{c c}{-Z_{1}(2)} & {\otimes_{1}^{(1)}(2)}\\ {-Z_{1}(3)} & {\otimes_{2}^{(1)}(3)}\\ \vdots & \vdots \\ {-Z_{1}(n)} & {\otimes_{2}^{(1)}(n)} \end{array} \right]\left[ \begin{array}{c}{\otimes_{1}^{(1)}(2) - \otimes_{1}^{(1)}(1)}\\ {\otimes_{1}^{(1)}(3) - \otimes_{1}^{(1)}(2)}\\ \vdots \\ {\otimes_{1}^{(1)}(n) - \otimes_{1}^{(1)}(n - 1)} \end{array} \right]} \end{array} \quad (35)\] + +<|ref|>text<|/ref|><|det|>[[112, 630, 562, 648]]<|/det|> +Solve \(\hat{\pmb{\theta}} = [a,b]^{T}\) in \(\mathbf{B}\cdot \hat{\pmb{\theta}} = \mathbf{Y}\) by interval grey number least squares: + +<|ref|>equation<|/ref|><|det|>[[130, 655, 907, 675]]<|/det|> +\[\hat{\pmb{\theta}} = (\mathbf{B}^{T}\cdot \mathbf{B})^{-1}\cdot \mathbf{B}^{T}\cdot \mathbf{Y} \quad (36)\] + +<|ref|>text<|/ref|><|det|>[[88, 684, 512, 700]]<|/det|> +Split into lower and upper bounds to get \(a = [\underline{{a}},\overline{{a}} ]\) and \(b = [\underline{{b}},\overline{{b}} ]\) + +<|ref|>text<|/ref|><|det|>[[88, 700, 543, 715]]<|/det|> +Model Prediction: The predicted value of the accumulated sequence + +<|ref|>equation<|/ref|><|det|>[[130, 735, 907, 770]]<|/det|> +\[\otimes_{1}^{(1)}(k + 1) = [\underline{{X}}_{1}(k + 1),\overline{{X}}_{1}(k + 1)] = \left(\otimes_{1}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} \quad (37)\] + +<|ref|>text<|/ref|><|det|>[[88, 777, 437, 792]]<|/det|> +Use Inverse Accumulated Generation (IAGO) to get: + +<|ref|>equation<|/ref|><|det|>[[130, 800, 907, 840]]<|/det|> +\[\left\{ \begin{array}{l l}{\underline{{x}}_{1}(k + 1) = \underline{{X}}_{1}(k + 1) - \underline{{X}}_{1}(k)}\\ {\overline{{x}}_{1}(k + 1) = \overline{{X}}_{1}(k + 1) - \overline{{X}}_{1}(k)} \end{array} \right. \quad (38)\] + +<|ref|>text<|/ref|><|det|>[[88, 850, 508, 866]]<|/det|> +The prediction interval is \(\otimes_{1}(k + 1) = [\underline{{x}}_{1}(k + 1),\overline{{x}}_{1}(k + 1)]\) + +<|ref|>sub_title<|/ref|><|det|>[[88, 866, 213, 879]]<|/det|> +## Model Validation + +<|ref|>text<|/ref|><|det|>[[88, 881, 291, 895]]<|/det|> +Calculate the residual interval: + +<|ref|>equation<|/ref|><|det|>[[130, 904, 907, 922]]<|/det|> +\[\mathrm{Residual~Interval} = [\underline{{x}}_{1}(k) - \underline{{x}}_{1}(k),\overline{{x}}_{1}(k) - \overline{{x}}_{1}(k)] \quad (39)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 77, 908, 164]]<|/det|> +Ensure the mean absolute value of residuals is less than a threshold. Calculate \(S_{1}\) , \(S_{2}\) , where \(S_{1} = \frac{1}{n}\sum_{k = 1}^{n}\left[x_{1}(k) - \bar{x}_{1}\right]^{2}\) (variance of the original sequence \(\{x_{1}(k)\}\) , where \(\bar{x}_{1} = \frac{1}{n}\sum_{k = 1}^{n}x_{1}(k)\) ) and \(S_{2} = \frac{1}{n}\sum_{k = 1}^{n}\left[e(k) - \bar{e}\right]^{2}\) (variance of residuals \(e(k) = x_{1}(k) - \hat{x}_{1}(k)\) , with \(\bar{e} = \frac{1}{n}\sum_{k = 1}^{n}e(k)\) ) quantify data dispersion and model error, respectively. The posterior difference ratio \(C = \frac{S_{2}}{S_{1}}\) ( \(C< 0.35\) is excellent, \(C< 0.5\) is qualified), and the small error probability \(P = P\{|e(k) - \bar{e}|< 0.6745S_{1}\}\) ( \(P > 0.95\) is excellent). Also, the grey correlation degree between the predicted and original sequences should be greater than 0.6. + +<|ref|>sub_title<|/ref|><|det|>[[89, 177, 520, 197]]<|/det|> +## 4 Controller design and performance analysis + +<|ref|>text<|/ref|><|det|>[[89, 201, 908, 263]]<|/det|> +This section demonstrates that Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) not only guarantees the convergence of system tracking error but also ensures bounded- input bounded- output (BIBO) stability under certain conditions. Based on the mentioned background and issues, the following will systematically elaborate on the control design and conduct an in- depth analysis and evaluation of its performance. + +<|ref|>sub_title<|/ref|><|det|>[[89, 274, 257, 290]]<|/det|> +### 4.1 Controller design + +<|ref|>image<|/ref|><|det|>[[94, 306, 900, 572]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[188, 580, 805, 597]]<|/det|> +
Figure 2. Block diagram of the IGAB-GPC scheme architecture for pattern-moving systems.
+ +<|ref|>text<|/ref|><|det|>[[88, 608, 908, 670]]<|/det|> +As illustrated in Figure 2, IGAB- GPC incorporates the fundamental components GPC, including the prediction model, receding horizon optimization, and feedback correction mechanism. The flowchart illustrates a control system architecture based on Generalized Predictive Control (GPC) integrated with an IGM prediction using an adaptive buffer operator. The detailed description of the components and their interactions is as follows: + +<|ref|>text<|/ref|><|det|>[[115, 678, 908, 725]]<|/det|> +- Reference Trajectory \((y_{r}(k))\) : The control system begins with a reference trajectory, denoted as \(y_{r}(k)\) , which represents the desired output or setpoint that the system aims to achieve at time step \(k\) . Consider the measurement uncertainty of pattern-class variables, the reference output is represented as follows. + +<|ref|>equation<|/ref|><|det|>[[168, 746, 907, 789]]<|/det|> +\[y_{r}(k) = \left\{ \begin{array}{l l}{y_{d}(k)\cap r_{1}^{k},} & {\mathrm{if}\exists i\in \{1,2,\ldots ,l\} \models |y(k) - y_{d}(k)|\leq r_{1}^{k},}\\ {r_{t + 1}^{k} = \mathcal{R}_{n e w}(k),} & {\mathrm{otherwise}.} \end{array} \right. \quad (40)\] + +<|ref|>text<|/ref|><|det|>[[128, 797, 907, 830]]<|/det|> +where \(l = 2N + 1\) , \(r_{t}^{k}\) refers to the category radius calculated by Equation 4. \(\mathcal{R}_{new}(k)\) denotes a new pattern class variable which can be computed by automatic category expansion strategy \(^{31}\) . + +<|ref|>text<|/ref|><|det|>[[115, 837, 907, 869]]<|/det|> +- Output Error Calculation \((e(k))\) : The reference trajectory \(y_{r}(k)\) is compared with the predicted output \(y_{p}(k)\) (obtained from the revising feedback loop). The difference between these two signals is computed as the error signal: + +<|ref|>equation<|/ref|><|det|>[[170, 877, 907, 895]]<|/det|> +\[e(k) = y_{r}(k) - y_{p}(k) \quad (41)\] + +<|ref|>text<|/ref|><|det|>[[129, 906, 521, 922]]<|/det|> +This error \(e(k)\) is then passed to the optimization algorithm. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 78, 907, 126]]<|/det|> +- Pattern-Moving Systems: This part represents the controlled system or plant, which receives the control input \(u(k)\) and produces the actual output \(y(k)\) . The pattern-moving systems could represent a dynamic process with specific characteristics in Equation 32 (e.g., linear or nonlinear dynamics). + +<|ref|>text<|/ref|><|det|>[[115, 136, 907, 198]]<|/det|> +- Event-Triggered IGM Prediction with Adaptive Buffer Operator: The actual system output \(y(k)\) is processed by a module integrating Interval Grey Model (IGM) prediction with an adaptive buffer operator, as depicted in Figure 2. This component manages uncertainties and fluctuations in pattern-moving systems by combining interval grey modeling with adaptive buffering. The formalized procedure is as follows: + +<|ref|>text<|/ref|><|det|>[[151, 215, 907, 264]]<|/det|> +1. Monotonicity Event Detection: At each time step \(k\) , the monotonicity of the interval grey number sequence \(\widehat{\otimes} (k) = [\underline{{x}} (k),\overline{{x}} (k)]\) , derived from \(y(k)\) Definition 2.1, is evaluated over a detection window of size \(\tau \in \mathbb{Z}_{+}\) (e.g., \(\tau = 5\) ). The event \(\mathcal{E}_{k}\) is defined based on the monotonicity of \(\{\widehat{\otimes} (i)\}_{i = k - 1}^{k - 1}\) : + +<|ref|>equation<|/ref|><|det|>[[210, 290, 907, 350]]<|/det|> +\[\mathcal{E}_{k}=\left\{\begin{array}{l l}{\mathrm{increasing}}&{\mathrm{if~}\underline{{x}}(i+1)>\underline{{x}}(i)\mathrm{~and~}\overline{{x}}(i+1)>\overline{{x}}(i),~\forall i\in[k-\tau,k-1],}\\ {\mathrm{decreasing}}&{\mathrm{if~}\underline{{x}}(i+1)< \underline{{x}}(i)\mathrm{~and~}\overline{{x}}(i+1)< \overline{{x}}(i),~\forall i\in[k-\tau,k-1],}\\ {\mathrm{oscillating}}&{\mathrm{otherwise},}\end{array}\right. \quad (42)\] + +<|ref|>text<|/ref|><|det|>[[170, 359, 907, 391]]<|/det|> +where comparisons are applied component- wise to the lower and upper bounds, ensuring consistency with Definition 2.1. The window size \(\tau\) captures sufficient historical data for reliable trend detection. + +<|ref|>text<|/ref|><|det|>[[150, 400, 907, 433]]<|/det|> +2. Adaptive Buffering: Based on \(\mathcal{E}_{k}\) , the adaptive buffer operator \(D\) , as defined in Theorem 3, is applied to the sequence \(\{\widehat{\otimes} (i)\}_{i = 1}^{k}\) : + +<|ref|>equation<|/ref|><|det|>[[211, 460, 907, 536]]<|/det|> +\[\begin{array}{r}{\widehat{\otimes} (k) = D(\widehat{\otimes} (k)) = \left\{ \begin{array}{l l}{D_{1}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{increasing},}\\ {D_{2}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{decreasing},}\\ {w_{1}D_{1}(\widehat{\otimes} (k)) + w_{2}D_{2}(\widehat{\otimes} (k)),} & {\mathrm{if~}\mathcal{E}_{k} = \mathrm{oscillating~and~}\Delta_{k} > \theta ,}\\ {\widehat{\otimes} (k),} & {\mathrm{otherwise},} \end{array} \right.} \end{array} \quad (43)\] + +<|ref|>text<|/ref|><|det|>[[170, 546, 800, 562]]<|/det|> +where \(D_{1}\) and \(D_{2}\) are the strengthening and weakening buffer operators, respectively, defined as: + +<|ref|>equation<|/ref|><|det|>[[210, 590, 907, 635]]<|/det|> +\[x(k)d_{1} = x(k)\left(1 + \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right),\quad 0< \alpha < 1, \quad (44)\] + +<|ref|>equation<|/ref|><|det|>[[210, 662, 907, 707]]<|/det|> +\[x(k)d_{2} = x(k)\left(1 - \alpha \cdot \frac{1}{n - k + 1}\sum_{j = k}^{n}\frac{x(j)}{x(k)}\right),\quad 0< \alpha < 1, \quad (45)\] + +<|ref|>text<|/ref|><|det|>[[170, 716, 729, 735]]<|/det|> +applied component- wise to \(\underline{{x}} (k)\) and \(\overline{{x}} (k)\) of \(\widehat{\otimes} (k) = [\underline{{x}} (k),\overline{{x}} (k)]\) . The amplitude \(\Delta_{k}\) is: + +<|ref|>equation<|/ref|><|det|>[[210, 764, 907, 789]]<|/det|> +\[\Delta_{k} = \max_{i\in [k - \tau ,k]}\overline{{x}} (i) - \min_{i\in [k - \tau ,k]}\underline{{x}} (i), \quad (46)\] + +<|ref|>text<|/ref|><|det|>[[170, 804, 736, 824]]<|/det|> +with \(\theta = 0.1\cdot (\max_{i = 1}^{k}\overline{{x}} (i) - \min_{i = 1}^{k}\underline{{x}} (i))\) as the threshold. The weights \(w_{1}\) and \(w_{2}\) are: + +<|ref|>equation<|/ref|><|det|>[[210, 850, 907, 890]]<|/det|> +\[w_{1} = \frac{\sum_{j = k - \tau}^{k - 1}\max (0,\overline{{x}} (j + 1) - \overline{{x}} (j))}{\sum_{j = k - \tau}^{k - 1}|\overline{{x}} (j + 1) - \overline{{x}} (j)| + \epsilon},\quad w_{2} = 1 - w_{1}, \quad (47)\] + +<|ref|>text<|/ref|><|det|>[[170, 904, 685, 920]]<|/det|> +where \(\epsilon = 10^{- 6}\) prevents division by zero, and \(w_{1},w_{2}\geq 0\) satisfy \(w_{1} + w_{2} = 1\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 77, 907, 111]]<|/det|> +3. Accumulated Generation (1-AGO): The buffered sequence \(\{\hat{\otimes} (i)\}_{i = 1}^{k}\) undergoes First-Order Accumulated Generation (1-AGO) to reduce noise and enhance stability: + +<|ref|>equation<|/ref|><|det|>[[210, 134, 907, 178]]<|/det|> +\[\hat{\otimes}^{(1)}(k) = \left\{ \begin{array}{ll}\hat{\otimes}(1), & k = 1,\\ \hat{\otimes}^{(1)}(k - 1) + \hat{\otimes}(k), & k > 1, \end{array} \right. \quad (48)\] + +<|ref|>text<|/ref|><|det|>[[170, 187, 407, 207]]<|/det|> +where \(\hat{\otimes}^{(1)}(k) = [\underline{{X}} (k),\overline{{X}} (k)]\) , with: + +<|ref|>equation<|/ref|><|det|>[[210, 228, 907, 271]]<|/det|> +\[\underline{{X}} (k) = \sum_{i = 1}^{k}\underline{{x}} (i),\quad \overline{{X}} (k) = \sum_{i = 1}^{k}\overline{{x}} (i). \quad (49)\] + +<|ref|>text<|/ref|><|det|>[[150, 283, 907, 318]]<|/det|> +4. Parameter Update for IGM(1,2): The IGM(1,2) parameters \(\hat{\theta}_{k} = [a,b]^{T}\) are updated only when monotonicity changes \((\hat{\mathcal{E}}_{k}\neq \hat{\mathcal{E}}_{k - 1})\) to enhance efficiency: + +<|ref|>equation<|/ref|><|det|>[[210, 341, 907, 384]]<|/det|> +\[\hat{\theta}_{k} = \left\{ \begin{array}{ll}(\mathbf{B}^{T}\mathbf{B})^{-1}\mathbf{B}^{T}\mathbf{Y}, & \mathrm{if} \hat{\mathcal{E}}_{k}\neq \hat{\mathcal{E}}_{k - 1},\\ \hat{\theta}_{k - 1}, & \mathrm{otherwise}, \end{array} \right. \quad (50)\] + +<|ref|>text<|/ref|><|det|>[[170, 391, 644, 408]]<|/det|> +where \(\mathbf{B}\) and \(\mathbf{Y}\) are constructed using the adjacent mean sequence \(Z_{1}(k)\) : + +<|ref|>equation<|/ref|><|det|>[[210, 433, 907, 455]]<|/det|> +\[Z_{1}(k) = 0.5\hat{\otimes}^{(1)}(k) + 0.5\hat{\otimes}^{(1)}(k - 1), \quad (51)\] + +<|ref|>text<|/ref|><|det|>[[170, 464, 304, 480]]<|/det|> +and for \(k = 2,\ldots ,n\) : + +<|ref|>equation<|/ref|><|det|>[[210, 503, 907, 590]]<|/det|> +\[\mathbf{B} = \left[ \begin{array}{c c c}{-Z_{1}(2)} & {\hat{\otimes}_{1}^{(1)}(2)}\\ {-Z_{1}(3)} & {\hat{\otimes}_{2}^{(1)}(3)}\\ \vdots & \vdots \\ {-Z_{1}(n)} & {\hat{\otimes}_{2}^{(1)}(n)} \end{array} \right],\quad \mathbf{Y} = \left[ \begin{array}{c}{\hat{\otimes}_{1}^{(1)}(2) - \hat{\otimes}_{1}^{(1)}(1)}\\ {\hat{\otimes}_{1}^{(1)}(3) - \hat{\otimes}_{1}^{(1)}(2)}\\ \vdots \\ {\hat{\otimes}_{1}^{(1)}(n) - \hat{\otimes}_{1}^{(1)}(n - 1)} \end{array} \right], \quad (52)\] + +<|ref|>text<|/ref|><|det|>[[170, 597, 907, 631]]<|/det|> +where \(\hat{\otimes}_{1}^{(1)}\) and \(\hat{\otimes}_{2}^{(1)}\) are the 1- AGO sequences for dependent and independent variables, respectively. Parameters \(a = [a,\bar{a} ]\) and \(b = [b,\bar{b} ]\) are computed for interval bounds. + +<|ref|>text<|/ref|><|det|>[[150, 639, 907, 671]]<|/det|> +5. Prediction and Correction: The predicted output \(y_{m}(k + 1)\) is computed using the IGM(1,2) model with a correction term: + +<|ref|>equation<|/ref|><|det|>[[210, 695, 907, 730]]<|/det|> +\[y_{m}(k + 1) = \left(\hat{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} +\gamma (D(y(k)) - \hat{y}_{m}(k)), \quad (53)\] + +<|ref|>text<|/ref|><|det|>[[170, 740, 907, 789]]<|/det|> +where \(\hat{\otimes}^{(1)}(1) = [\underline{{X}} (1),\overline{{X}} (1)]\) , \(a,b\) are from \(\hat{\theta}_{k}\) , and \(\gamma = 0.1\) is the correction gain. The buffered output \(D(y(k))\) follows Equation (43), and \(\hat{y}_{m}(k)\) is the prior prediction. The interval grey number is recovered via Inverse Accumulated Generation (IAGO): + +<|ref|>equation<|/ref|><|det|>[[210, 814, 907, 835]]<|/det|> +\[\hat{\otimes}(k + 1) = \hat{\otimes}^{(1)}(k + 1) - \hat{\otimes}^{(1)}(k), \quad (54)\] + +<|ref|>text<|/ref|><|det|>[[170, 844, 907, 862]]<|/det|> +yielding \(\hat{\otimes}(k + 1) = [\underline{{x}} (k + 1),\overline{{x}} (k + 1)]\) , which is mapped to the pattern- moving space using \(M(\cdot)\) in Equation (3). + +<|ref|>text<|/ref|><|det|>[[130, 875, 907, 922]]<|/det|> +The following pseudocode describes the event- triggered Interval Grey Model (IGM) prediction with an adaptive buffer operator, which processes system outputs to handle uncertainties and fluctuations in pattern- moving systems, seeing Algorithm 1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[92, 97, 908, 129]]<|/det|> +Input: Time range \(T\) , Window size \(\tau\) , Buffer parameters (Eqs. (44), (45)), IGM(1,2) parameters (Eqs. (53), (54)), \(\gamma = 0.1\) , \(\theta\) factor \(= 0.1\) , \(\epsilon = 10^{- 6}\) + +<|ref|>text<|/ref|><|det|>[[105, 129, 501, 145]]<|/det|> +Output: Predicted output \(y_{m}(k + 1)\) , Parameters \(\hat{\theta}_{k} = [a,b]^{T}\) + +<|ref|>text<|/ref|><|det|>[[105, 145, 300, 160]]<|/det|> +Initialize: \(\mathcal{E}_{\mathrm{prev}}\gets \theta\) , \(\hat{\theta}_{1}\gets \mathbf{0}\) + +<|ref|>text<|/ref|><|det|>[[105, 161, 225, 174]]<|/det|> +for \(k\gets 2\) to \(T\) do + +<|ref|>text<|/ref|><|det|>[[131, 175, 375, 190]]<|/det|> +1. Get output: \(y(k)\gets\) System output + +<|ref|>text<|/ref|><|det|>[[131, 190, 440, 205]]<|/det|> +2. Form interval: \(\hat{\otimes} (k)\gets [x(k),\bar{x} (k)]\) (Def. 2.1) + +<|ref|>text<|/ref|><|det|>[[131, 205, 540, 220]]<|/det|> +3. Detect event: \(\mathcal{E}_{k}\gets\) Monotonicity of \(\{\hat{\otimes} (i)\}_{i = k - \tau}^{k - 1}\) (Eq. (42)) + +<|ref|>text<|/ref|><|det|>[[131, 220, 245, 234]]<|/det|> +if \(\mathcal{E}_{k}\neq \mathcal{E}_{\mathrm{prev}}\) then + +<|ref|>text<|/ref|><|det|>[[168, 235, 310, 248]]<|/det|> +3.1 Buffer operation: + +<|ref|>equation<|/ref|><|det|>[[186, 248, 520, 266]]<|/det|> +\[\Delta_{k}\gets \max_{i\in [k - \tau ,k]}\bar{x} (i) - \min_{i\in [k - \tau ,k]}\underline{{x}} (i) \quad (Eq. (46))\] + +<|ref|>equation<|/ref|><|det|>[[186, 266, 421, 283]]<|/det|> +\[\theta \gets 0.1\cdot (\max_{i = 1}^{k}\bar{x} (i) - \min_{i = 1}^{k}\underline{{x}} (i))\] + +<|ref|>text<|/ref|><|det|>[[152, 283, 306, 296]]<|/det|> +if \(\mathcal{E}_{k} =\) increasing then + +<|ref|>equation<|/ref|><|det|>[[210, 297, 404, 313]]<|/det|> +\[\hat{\otimes} (k)\gets D_{1}(\hat{\otimes} (k)) \quad (\mathrm{Eq.} (44))\] + +<|ref|>text<|/ref|><|det|>[[152, 313, 338, 327]]<|/det|> +else if \(\mathcal{E}_{k} =\) decreasing then + +<|ref|>equation<|/ref|><|det|>[[210, 328, 404, 343]]<|/det|> +\[\hat{\otimes} (k)\gets D_{2}(\hat{\otimes} (k)) \quad (\mathrm{Eq.} (45))\] + +<|ref|>text<|/ref|><|det|>[[152, 343, 417, 357]]<|/det|> +else if \(\mathcal{E}_{k} =\) oscillating and \(\Delta_{k} > \theta\) then + +<|ref|>equation<|/ref|><|det|>[[208, 357, 567, 400]]<|/det|> +\[w_{1}\leftarrow \frac{\Sigma_{j = k - \tau}^{k - 1}\max (0,\bar{x} (j + 1) - \bar{x} (j))}{\Sigma_{j = k - \tau}^{k - 1}\bar{x} (j + 1) - \bar{x} (j) + \epsilon},w_{2}\leftarrow 1 - w_{1} \quad (\mathrm{Eq.} (47))\] + +<|ref|>text<|/ref|><|det|>[[152, 401, 186, 414]]<|/det|> +else + +<|ref|>equation<|/ref|><|det|>[[210, 415, 303, 432]]<|/det|> +\[\hat{\otimes} (k)\gets \hat{\otimes} (k)\] + +<|ref|>text<|/ref|><|det|>[[152, 432, 198, 446]]<|/det|> +end if + +<|ref|>text<|/ref|><|det|>[[168, 446, 630, 463]]<|/det|> +3.2 Compute 1- AGO: \(\hat{\otimes}^{(1)}(i)\gets 1 - \mathrm{AGO}(\hat{\otimes}(i)),i = 1,\ldots ,k\) (Eq. (48)) + +<|ref|>text<|/ref|><|det|>[[168, 463, 748, 480]]<|/det|> +3.3 Build matrices: \(\mathbf{B},\mathbf{Y}\gets \mathrm{Using}Z_{1}(k) = 0.5\hat{\otimes}^{(1)}(k) + 0.5\hat{\otimes}^{(1)}(k - 1)\) (Eqs. (51), (52)) + +<|ref|>text<|/ref|><|det|>[[170, 480, 530, 495]]<|/det|> +3.4 Update parameters: \(\hat{\theta}_{k}\gets (\mathbf{B}^{T}\mathbf{B})^{- 1}\mathbf{B}^{T}\mathbf{Y}\) (Eq. (50)) + +<|ref|>text<|/ref|><|det|>[[170, 495, 290, 510]]<|/det|> +3.5 Set \(\mathcal{E}_{\mathrm{prev}}\gets \mathcal{E}_{k}\) + +<|ref|>text<|/ref|><|det|>[[128, 510, 160, 523]]<|/det|> +else + +<|ref|>equation<|/ref|><|det|>[[170, 523, 242, 540]]<|/det|> +\[\hat{\theta}_{k}\gets \hat{\theta}_{k - 1}\] + +<|ref|>text<|/ref|><|det|>[[128, 540, 171, 553]]<|/det|> +end if + +<|ref|>text<|/ref|><|det|>[[128, 552, 761, 573]]<|/det|> +4. Predict: \(y_{m}(k + 1)\gets \left(\hat{\otimes}^{(1)}(1) - \frac{a}{b}\right)e^{-a k} + \frac{b}{a}\) , IAGO (Eqs. (53), (54)), map via \(M(\cdot)\) (Eq. (3)) + +<|ref|>text<|/ref|><|det|>[[128, 573, 570, 590]]<|/det|> +5. Correct: \(y_{m}(k + 1)\gets y_{m}(k + 1) + \gamma (D(y(k)) - \hat{y}_{m}(k))\) (Eq. (53)) + +<|ref|>text<|/ref|><|det|>[[102, 590, 155, 603]]<|/det|> +end for + +<|ref|>text<|/ref|><|det|>[[115, 630, 908, 692]]<|/det|> +- Optimization Algorithm: The optimization algorithm block takes the error signal \(e(k)\) as input and computes the optimal control input \(u(k)\) . The optimization process typically minimizes a cost function that balances the tracking error and control effort, a hallmark of GPC. The control input \(u(k)\) is then applied to the pattern-moving systems. The cost function is designed as follows. + +<|ref|>equation<|/ref|><|det|>[[169, 716, 907, 757]]<|/det|> +\[J(N_{1},N_{y},N_{u}) = \sum_{j = N_{1}}^{N_{y}}[\hat{y} (k + j|k) - y_{r}(k + j)|_{Q_{j}}^{2} + \sum j = 1^{N_{u}}|\Delta u(k + j - 1)|_{R_{j}}^{2} \quad (55)\] + +<|ref|>text<|/ref|><|det|>[[130, 771, 907, 850]]<|/det|> +where \(N_{1}\) is the minimum costing horizon (typically \(N_{1} = 1\) ), \(N_{y}\) is the prediction horizon, \(N_{u}\) is the control horizon \((N_{u}\leq N_{y})\) , \(\hat{y} (k + j|k)\) is the predicted output at step \(k + j\) based on information at step \(k\) , \(y_{r}(k + j)\) is the reference trajectory at step \(k + j\) , \(\Delta u(k + j - 1) = u(k + j - 1) - u(k + j - 2)\) is the control increment, \(\mathbf{Q}_{j}\succeq 0\) is the output weighting matrix, and \(\mathbf{R}_{j}\succ 0\) is the control weighting matrix. The reference trajectory is generated through setpoint smoothing: \(y_{r}(k + j) = \alpha y_{r}(k + j - 1) + (1 - \alpha)w(k + j)\) with \(\alpha \in [0,1)\) , where \(w(k + j)\) is the desired setpoint. + +<|ref|>text<|/ref|><|det|>[[130, 852, 666, 869]]<|/det|> +The optimal control sequence is derived by expressing predictions in vector form: + +<|ref|>equation<|/ref|><|det|>[[170, 876, 907, 895]]<|/det|> +\[\hat{\mathbf{Y}} = \mathbf{G}\Delta \mathbf{U} + \mathbf{F} \quad (56)\] + +<|ref|>text<|/ref|><|det|>[[130, 903, 907, 922]]<|/det|> +where \(\hat{\mathbf{Y}} = [\hat{y} (k + N_{1}|k),\ldots ,\hat{y} (k + N_{y}|k)]^{T}\) , \(\Delta \mathbf{U} = [\Delta u(k),\ldots ,\Delta u(k + N_{u} - 1)]^{T}\) , \(\mathbf{F}\) is the free response vector (prediction + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[130, 78, 875, 95]]<|/det|> +with \(\Delta u = 0\) ), and \(\mathbf{G}\) is the dynamic matrix containing step response coefficients. The cost function then becomes: + +<|ref|>equation<|/ref|><|det|>[[168, 103, 907, 123]]<|/det|> +\[J = (\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r})^{T}\mathbf{Q}(\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}) + \Delta \mathbf{U}^{T}\mathbf{R}\Delta \mathbf{U} \quad (57)\] + +<|ref|>text<|/ref|><|det|>[[130, 133, 800, 152]]<|/det|> +with \(\mathbf{Q} = \mathrm{diag}(\mathbf{Q}_{N_{1}},\dots ,\mathbf{Q}_{N_{s}})\) and \(\mathbf{R} = \mathrm{diag}(\mathbf{R}_{1},\dots ,\mathbf{R}_{N_{u}})\) . The optimal solution is obtained by solving: + +<|ref|>equation<|/ref|><|det|>[[170, 161, 907, 196]]<|/det|> +\[\frac{\partial J}{\partial\Delta\mathbf{U}} = 2\mathbf{G}^{T}\mathbf{Q}(\mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}) + 2\mathbf{R}\Delta \mathbf{U} = 0 \quad (58)\] + +<|ref|>text<|/ref|><|det|>[[130, 204, 191, 219]]<|/det|> +yielding: + +<|ref|>equation<|/ref|><|det|>[[170, 227, 907, 247]]<|/det|> +\[\Delta \mathbf{U}^{*} = (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) \quad (59)\] + +<|ref|>text<|/ref|><|det|>[[130, 257, 610, 275]]<|/det|> +Only the first element is implemented: \(u(k) = u(k - 1) + [1,0,\dots ,0]\Delta \mathbf{U}^{*}\) . + +<|ref|>text<|/ref|><|det|>[[130, 277, 910, 358]]<|/det|> +Key implementation aspects include: (1) Dynamic matrix \(\mathbf{G}\) construction using step response coefficients from IGM(1,2): \(g_{i} = \partial \hat{y} (k + i|k) / \partial \Delta u(k)\approx [\hat{y} (k + i|k,\Delta u(k) = \delta) - \hat{y} (k + i|k,\Delta u(k) = 0)] / \delta\) ; (2) Free response \(\mathbf{F}\) calculation by propagating IGM(1,2) with \(\Delta u = 0\) : \(\hat{y}_{0}(k + j|k) = [\hat{\otimes}^{(1)}(1) - b / a]e^{-a(k + j - 1)} + b / a\) ; (3) Physical constraints handling \((u_{\mathrm{min}}\leq u(k + j)\leq u_{\mathrm{max}}\) , \(\Delta u_{\mathrm{min}}\leq \Delta u(k + j)\leq \Delta u_{\mathrm{max}}\) , \(\mathrm{y}_{\mathrm{min}}\leq \hat{y} (k + j|k)\leq \mathrm{y}_{\mathrm{max}})\) transforming the problem into constrained QP; and (4) Recalculation of optimization each time step with updated parameters. + +<|ref|>title<|/ref|><|det|>[[90, 371, 417, 386]]<|/det|> +# Algorithm 2 IGB-GPC Optimization Procedure + +<|ref|>text<|/ref|><|det|>[[108, 391, 562, 407]]<|/det|> +Input: Current state \(\mathbf{x}(k)\) , Reference \(\mathbf{Y}_{r}\) , Model \(\{a,b\}\) , Weights \(\mathbf{Q}\) , \(\mathbf{R}\) + +<|ref|>text<|/ref|><|det|>[[108, 408, 305, 421]]<|/det|> +Output: Optimal control \(u(k)\) + +<|ref|>text<|/ref|><|det|>[[108, 422, 253, 435]]<|/det|> +procedure OPTIMIZE + +<|ref|>text<|/ref|><|det|>[[135, 436, 432, 452]]<|/det|> +1. Compute \(\mathbf{Y}_{r}\gets [y_{r}(k + N_{1}),\dots ,y_{r}(k + N_{y})]^{T}\) + +<|ref|>text<|/ref|><|det|>[[135, 452, 490, 466]]<|/det|> +2. Calculate free response \(\mathbf{F}\) via IGM(1,2) with \(\Delta u = 0\) + +<|ref|>text<|/ref|><|det|>[[135, 468, 437, 481]]<|/det|> +3. Construct \(\mathbf{G}\) using step response coefficients + +<|ref|>text<|/ref|><|det|>[[135, 482, 437, 497]]<|/det|> +4. Solve \(\Delta \mathbf{U}^{*}\leftarrow (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F})\) + +<|ref|>text<|/ref|><|det|>[[135, 498, 370, 512]]<|/det|> +5. Extract \(\Delta u^{*}(k)\gets [1,0,\dots ,0]\Delta \mathbf{U}^{*}\) + +<|ref|>text<|/ref|><|det|>[[135, 513, 360, 528]]<|/det|> +6. Apply \(u(k)\gets u(k - 1) + \Delta u^{*}(k)\) + +<|ref|>text<|/ref|><|det|>[[135, 529, 210, 542]]<|/det|> +return \(u(k)\) + +<|ref|>text<|/ref|><|det|>[[108, 543, 210, 556]]<|/det|> +end procedure + +<|ref|>text<|/ref|><|det|>[[112, 582, 907, 613]]<|/det|> +- Summation Block for Predicted Output Adjustment: The predicted output \(y_{m}(k)\) is compared with the actual output \(y(k)\) in another summation block. The difference between these two signals is calculated as: + +<|ref|>equation<|/ref|><|det|>[[170, 624, 907, 642]]<|/det|> +\[y_{p}(k) = y_{m}(k) + (y(k) - y_{m}(k)) \quad (60)\] + +<|ref|>text<|/ref|><|det|>[[115, 656, 907, 688]]<|/det|> +However, in practice, this step may involve additional correction mechanisms to refine the predicted output \(y_{p}(k)\) . \(p\) represents the optimized time domain parameter, satisfying the condition that \(m < p\) + +<|ref|>text<|/ref|><|det|>[[115, 696, 907, 728]]<|/det|> +- Revising Feedback \((y_{p}(k))\) : The adjusted predicted output \(y_{p}(k)\) is fed back into the system through a revising feedback loop. This feedback is used to compute the error \(e(k)\) in the first summation block, closing the control loop. + +<|ref|>text<|/ref|><|det|>[[90, 739, 909, 785]]<|/det|> +Figure 2 represents the GPC control system using IGM prediction with adaptive buffer operators to manage dynamic uncertainties; real- time feedback enables continuous output correction for pattern- moving systems with uncertain behavior. Algorithm 3 details the signal flow process within this control system. + +<|ref|>text<|/ref|><|det|>[[90, 786, 907, 831]]<|/det|> +In summary, this diagram depicts a GPC- based control system featuring IGM prediction named as IGB- GPC for handling uncertainty and limited data, an adaptive buffer operator for robustness against disturbances, and a feedback loop for continuous adjustment. It is tailored for complex, dynamic pattern- moving systems. + +<|ref|>sub_title<|/ref|><|det|>[[90, 845, 290, 860]]<|/det|> +### 4.2 Performance analysis + +<|ref|>text<|/ref|><|det|>[[90, 862, 909, 922]]<|/det|> +This section rigorously analyzes the stability and convergence properties of the proposed IGB- GPC framework. We establish formal guarantees for bounded- input bounded- output (BIBO) stability and tracking error convergence under specified conditions. The analysis leverages Lyapunov stability theory and incorporates the effects of the adaptive buffer operator on prediction accuracy. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[105, 100, 460, 115]]<|/det|> +Input: Reference trajectory \(y_{r}(k)\) , System parameters + +<|ref|>text<|/ref|><|det|>[[106, 116, 416, 130]]<|/det|> +Output: Control input \(u(k)\) , Actual output \(y(k)\) + +<|ref|>text<|/ref|><|det|>[[106, 131, 316, 145]]<|/det|> +Initialize: \(y_{p}(0) \leftarrow\) initial value + +<|ref|>text<|/ref|><|det|>[[106, 146, 260, 160]]<|/det|> +for each time step \(k\) do + +<|ref|>text<|/ref|><|det|>[[130, 161, 651, 176]]<|/det|> +1. Compute error between reference and predicted output: \(e(k) \leftarrow y_{r}(k) - y_{p}(k)\) + +<|ref|>text<|/ref|><|det|>[[130, 176, 690, 191]]<|/det|> +2. Generate control input through optimization \(u(k) \leftarrow\) Optimization Algorithm \((e(k))\) + +<|ref|>text<|/ref|><|det|>[[130, 191, 580, 206]]<|/det|> +3. Apply control to the system \(y(k) \leftarrow\) Pattern Moving System \((u(k))\) + +<|ref|>text<|/ref|><|det|>[[130, 206, 860, 221]]<|/det|> +4. Predict output using IGM and adaptive buffer \(y_{m}(k) \leftarrow\) IGMP prediction \((y(k))\) using Adaptive Buffer Operator + +<|ref|>text<|/ref|><|det|>[[130, 221, 672, 236]]<|/det|> +5. Revise prediction with actual output \(y_{p}(k + 1) \leftarrow\) Revise Prediction \((y_{m}(k), y(k))\) + +<|ref|>text<|/ref|><|det|>[[105, 237, 157, 250]]<|/det|> +end for + +<|ref|>text<|/ref|><|det|>[[88, 279, 905, 312]]<|/det|> +Theorem 4.1. (Bounded prediction error) For the interval grey prediction model with adaptive buffer operator, the prediction error \(e_{p}(k) = y(k) - y_{m}(k)\) satisfies: + +<|ref|>equation<|/ref|><|det|>[[130, 323, 905, 341]]<|/det|> +\[|e_{p}(k)|\leq \epsilon_{1} + \epsilon_{2}\exp (-\lambda k) \quad (61)\] + +<|ref|>text<|/ref|><|det|>[[88, 352, 905, 384]]<|/det|> +where \(\epsilon_{1} = \sup_{k}|\delta (k)|\) is the supremum of metric perturbation, \(\epsilon_{2}\) depends on initial conditions, and \(\lambda >0\) is the convergence rate of the grey model. + +<|ref|>text<|/ref|><|det|>[[88, 397, 905, 430]]<|/det|> +Proof. From Definition 3.1, the pattern class variable satisfies \(d x(k) = \tilde{\otimes}_{k} + \delta\) where \(|\delta |\leq \tilde{\delta}\) . The buffered sequence \(\tilde{\otimes} (k) = D(\tilde{\otimes} (k))\) reduces amplitude \(\Delta_{k}\) according to Theorems 3.1 and 3.2. For the IGM(1,2) solution: + +<|ref|>equation<|/ref|><|det|>[[130, 440, 905, 476]]<|/det|> +\[\tilde{\otimes}^{(1)}(k + 1) = \left(\tilde{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} + \frac{b}{a} \quad (62)\] + +<|ref|>text<|/ref|><|det|>[[88, 486, 343, 502]]<|/det|> +The prediction error dynamics follow: + +<|ref|>equation<|/ref|><|det|>[[130, 512, 580, 572]]<|/det|> +\[e_{p}(k + 1) = y(k + 1) - y_{m}(k + 1)\] \[\qquad = \left[f(\cdot) - \left(\tilde{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a k} - \frac{b}{a}\right] - \gamma (D(y(k)) - \hat{y}_{m}(k))\] + +<|ref|>text<|/ref|><|det|>[[88, 579, 905, 609]]<|/det|> +Applying the adaptive buffer operator bounds the high- frequency components, yielding exponentially stable error dynamics. The correction term \(\gamma (\cdot)\) further attenuates residual errors. \(\square\) + +<|ref|>text<|/ref|><|det|>[[88, 623, 905, 656]]<|/det|> +Theorem 4.2. (BIBO stability) The closed- loop system under IGAB- GPC is BIBO stable if: 1. The prediction horizon \(N_{y}\) exceeds the system's degree of freedom 2. Control weighting matrix \(\mathbf{R} > 0\) 3. The class radius satisfies \(r_{i}< \min_{j\neq i}|c_{i} - c_{j}| / 2\) + +<|ref|>text<|/ref|><|det|>[[88, 669, 422, 685]]<|/det|> +Proof. Consider the Lyapunov function candidate: + +<|ref|>equation<|/ref|><|det|>[[130, 696, 905, 739]]<|/det|> +\[V(k) = \Delta \mathbf{U}^{T}(k)\mathbf{\Delta}\mathbf{U}(k) + \sum_{i = k - N_{u} + 1}^{k}e_{p}^{2}(i) \quad (63)\] + +<|ref|>text<|/ref|><|det|>[[88, 750, 602, 767]]<|/det|> +where \(\mathbf{\delta} = \mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R} > 0\) . The difference \(\Delta V(k) = V(k + 1) - V(k)\) satisfies: + +<|ref|>equation<|/ref|><|det|>[[130, 777, 461, 820]]<|/det|> +\[\Delta V(k)\leq -\Delta \mathbf{U}^{T}(k)\mathbf{R}\Delta \mathbf{U}(k) + 2L_{g}\| \Delta \mathbf{U}(k)\| |e_{p}(k)|\] \[\qquad +L_{f}e_{p}^{2}(k) - e_{p}^{2}(k - N_{u})\] + +<|ref|>text<|/ref|><|det|>[[88, 830, 905, 862]]<|/det|> +where \(L_{g}\) and \(L_{f}\) are Lipschitz constants for \(\mathbf{G}\) and system dynamics \(f(\cdot)\) . From Theorem 4.1, \(\| e_{p}(k)\| \leq \bar{e}_{p}\) . Selecting \(\mathbf{R}\) such that \(\lambda_{\min}(\mathbf{R}) > L_{g}^{2} / L_{f}\) ensures: + +<|ref|>equation<|/ref|><|det|>[[130, 873, 905, 895]]<|/det|> +\[\Delta V(k)\leq -\eta \| \Delta \mathbf{U}(k)\|^{2} - \mu e_{p}^{2}(k)\quad (\eta ,\mu >0) \quad (64)\] + +<|ref|>text<|/ref|><|det|>[[88, 905, 565, 921]]<|/det|> +Thus \(V(k)\) decreases monotonically, proving bounded states and outputs. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 78, 901, 95]]<|/det|> +Theorem 4.3. (Tracking error convergence) The tracking error \(e(k) = y_{r}(k) - y(k)\) converges exponentially to a bounded set: + +<|ref|>equation<|/ref|><|det|>[[129, 104, 907, 139]]<|/det|> +\[\lim_{k\to \infty}\sup |e(k)|\leq \frac{\epsilon_{1} + \bar{w}}{1 - \alpha} \quad (65)\] + +<|ref|>text<|/ref|><|det|>[[88, 147, 640, 164]]<|/det|> +where \(\alpha\) is the reference trajectory smoothing factor and \(\bar{w}\) is the disturbance bound. + +<|ref|>text<|/ref|><|det|>[[88, 172, 558, 189]]<|/det|> +Proof. The optimized control increment \(\Delta \mathbf{U}^{*}\) from Eq. (59) minimizes: + +<|ref|>equation<|/ref|><|det|>[[128, 196, 463, 242]]<|/det|> +\[J(k) = \| \mathbf{G}\Delta \mathbf{U} + \mathbf{F} - \mathbf{Y}_{r}\|_{\mathbf{Q}}^{2} + \| \Delta \mathbf{U}\|_{\mathbf{R}}^{2}\] \[\qquad = \| \mathbf{G}(\Delta \mathbf{U} - \Delta \mathbf{U}^{*})\|_{\mathbf{Q}}^{2} + \| \Delta \mathbf{U} - \Delta \mathbf{U}^{*}\|_{\mathbf{P}}^{2} + J^{*}(k)\] + +<|ref|>text<|/ref|><|det|>[[88, 250, 430, 267]]<|/det|> +where \(\mathbf{P} = \mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R}\) . The error dynamics satisfy: + +<|ref|>equation<|/ref|><|det|>[[129, 277, 907, 296]]<|/det|> +\[e(k + 1) = \alpha e(k) + (1 - \alpha)[w(k) - y(k)] + \Delta f(\cdot) \quad (66)\] + +<|ref|>text<|/ref|><|det|>[[88, 305, 691, 322]]<|/det|> +with \(\| \Delta f(\cdot)\| \leq L_{\delta}\) due to metric perturbation. From Lemma 4.1 and Theorem 4.2, we have: + +<|ref|>equation<|/ref|><|det|>[[129, 330, 444, 370]]<|/det|> +\[|y(k) - w(k)|\leq |y(k) - y_{m}(k)| + |y_{m}(k) - w(k)|\] \[\qquad \leq \epsilon_{1} + \kappa \| \Delta \mathbf{U}^{*}(k)\|\] + +<|ref|>text<|/ref|><|det|>[[88, 378, 744, 396]]<|/det|> +where \(\kappa = \| \mathbf{G}(1, \cdot)\|\) . Since \(\| \Delta \mathbf{U}^{*}(k)\|\) decays exponentially, the error converges to the stated bound. + +<|ref|>text<|/ref|><|det|>[[88, 403, 908, 450]]<|/det|> +Remark 4.1. The adaptive buffer operator enhances performance by: 1. Reducing prediction error amplitude by \(30 - 50\%\) compared to unbuffered models 2. Decreasing the Lipschitz constant \(L_{f}\) by smoothing system dynamics 3. Accelerating the convergence rate \(\lambda\) in Lemma 4.1 + +<|ref|>text<|/ref|><|det|>[[88, 459, 907, 491]]<|/det|> +Corollary 1. (Pattern convergence) The system operating point converges to the target pattern class \(P_{d}\) within finite steps \(K\) satisfying: + +<|ref|>equation<|/ref|><|det|>[[129, 499, 907, 536]]<|/det|> +\[K\leq \frac{1}{\mu}\log \left(\frac{\|d x(0) - c_{d}\|}{\min_{i\neq d}r_{i}}\right) \quad (67)\] + +<|ref|>text<|/ref|><|det|>[[88, 544, 636, 560]]<|/det|> +where \(\mu\) is the convergence rate from Theorem 4.3, and \(c_{d}\) is the target class center. + +<|ref|>text<|/ref|><|det|>[[88, 568, 907, 601]]<|/det|> +Proof. From quantization properties in Eq. (4), convergence to \(P_{d}\) occurs when \(\| d x(k) - c_{d}\| < r_{d}\) . Theorem 4.3 ensures \(\| d x(k) - c_{d}\|\) decreases exponentially, yielding the step bound. + +<|ref|>text<|/ref|><|det|>[[88, 608, 907, 640]]<|/det|> +The analysis demonstrates that IGAB- GPC guarantees closed- loop stability and pattern convergence while accommodating inherent uncertainties in pattern- moving systems through grey modeling and adaptive buffering. + +<|ref|>sub_title<|/ref|><|det|>[[88, 656, 281, 674]]<|/det|> +## 5 Simulation results + +<|ref|>text<|/ref|><|det|>[[88, 680, 908, 771]]<|/det|> +An numerical simulation case are given in this section to illustrate the effectiveness of the proposed IGAB- GPC scheme. In order to properly assess its efficacy and applicability of the proposed method, the classical CARIMA- GPC \(^{24}\) (controlled autoregressive integral moving average generalized predictive control) and IG- GPC \(^{32}\) (interval grey generalized predictive control) are selected as the benchmark comparison methods. By constructing simulation experiments, the control accuracy, dynamic response characteristics are compared and analyzed. Furthermore, the advantages and potential disadvantages of this method in practical applications are comprehensively revealed. + +<|ref|>text<|/ref|><|det|>[[110, 771, 714, 786]]<|/det|> +Consider the the nonlinear discrete- time system with one input and three outputs as follows. + +<|ref|>equation<|/ref|><|det|>[[129, 795, 907, 865]]<|/det|> +\[\left\{ \begin{array}{l l}{y_{1}(k) = 0.3y_{1}(k - 1) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 2) + d(k)}\\ {y_{2}(k) = 0.2y_{2}(k - 1) + 0.4y_{2}(k - 2) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 3) + d(k)}\\ {y_{3}(k) = 0.3y_{3}(k - 1) + 0.1y_{3}(k - 2) + \frac{u(k - 1)}{1 + u^{2}(k - 1)} +u(k - 2) + d(k)} \end{array} \right.. \quad (68)\] + +<|ref|>text<|/ref|><|det|>[[110, 875, 900, 891]]<|/det|> +Among them, the system input \(u(k) \in [- 4, 4]\) ; the system noise satisfies \(d(k) \sim N(0, 0.1^{2})\) and is assumed to be known. + +<|ref|>text<|/ref|><|det|>[[88, 891, 907, 921]]<|/det|> +Through the following 3 steps, first, construct the description mode of system dynamics; then, complete the system tracking control by using the designed control algorithm; finally, compare the accuracy with CARIMA- GPC and IG- GPC, respectively. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[105, 85, 900, 494]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[301, 509, 693, 525]]<|/det|> +
Figure 3. 3000 sets of input-output system historical data.
+ +<|ref|>text<|/ref|><|det|>[[88, 553, 907, 599]]<|/det|> +Step 1: Constructing the pattern moving space and system output prediction with IGAB. Based on the construction process described in Section 2.2.2, the input signals \(u(k)\) are introduced to the system, generating 3000 sets of historical input- output data (Figure 3) that form the moving subspace. + +<|ref|>text<|/ref|><|det|>[[88, 600, 908, 675]]<|/det|> +After normalizing the output data, dimensionality reduction was performed via Principal Component Analysis (PCA) to a one- dimensional feature space, with the contribution rate reaching \(87.23\%\) . Subsequently, for quantitative evaluation using the class - specific metric defined in Equation 4, we set the initial parameters of the improved quantization classification algorithm as \(\kappa_{0} = 5\) , \(\rho_{0} = 0.6\) , and \(r_{0} = 0.4\) . By taking \(N = 5\) , the number of classes can be obtained as \(2N + 1 = 11\) . Meanwhile, the center values, class interval values and class radius of each class are acquired. The detailed results are found in Table 1. + +<|ref|>text<|/ref|><|det|>[[88, 676, 908, 752]]<|/det|> +Table 1 shows the construction of the pattern moving space over time, with the variations of the pattern center and threshold illustrated in Figure 4. As indicated in the Definition 2.2, the variation scope of the pattern class variable aligns with the class threshold, involving epistemic uncertainty, exhibiting inherent uncertainty, whereas the grey measure can utilize the variables center as measurement basis. After the quantization algorithm produces category divisions, the first 11 input variables and \(dx(k)\) measures are used to construct IGM(1,2), and the process is as follows. + +<|ref|>text<|/ref|><|det|>[[88, 752, 905, 783]]<|/det|> +1) The adaptive buffer operator is applied to the interval sequence \(\otimes_{1}(k)\) , resulting in a smoothed sequence \(\hat{\otimes}_{1}(k)\) . This process reduces oscillations and enhances the stability of the subsequent IGM(1,2) modeling steps. + +<|ref|>equation<|/ref|><|det|>[[130, 808, 907, 843]]<|/det|> +\[\frac{d\otimes_{1}^{(1)}(t)}{dt} +a\otimes_{1}^{(1)}(t) = b\otimes_{2}^{(1)}(t) \quad (69)\] + +<|ref|>text<|/ref|><|det|>[[88, 857, 907, 890]]<|/det|> +where \(\otimes_{1}^{(1)}(t)\) is the first- order accumulated generation (1- AGO) sequence of the buffered output interval \(\hat{\otimes}_{1}(k),\hat{\otimes}_{2}^{(1)}(t)\) is the 1- AGO sequence of the input interval \(\otimes_{2}(k)\) , \(a\) and \(b\) are parameters to be estimated. + +<|ref|>text<|/ref|><|det|>[[88, 890, 905, 921]]<|/det|> +2) With estimated parameters \(a = [0.12, 0.15]\) and \(b = [0.08, 0.10]\) , the specific differential equations for the lower and upper bounds are formulated as follows: + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[155, 103, 838, 365]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[252, 78, 741, 94]]<|/det|> +Table 1. Results of pattern class and pattern moving space by Equation 4. + +
Class NoClass CenterClass RadiusClass ThresholdClass Interval
14.5331.5006.033[3.033, 6.033]
22.7300.3033.033[2.427, 3.033]
31.9320.4952.427[1.437, 2.427]
40.9790.4581.437[0.521, 1.437]
50.2880.2330.521[0.055, 0.521]
600.5210.521[-0.521, 0.521]
7-0.2880.233-0.055[-0.521, -0.055]
8-0.9790.458-0.521[-1.437, -0.521]
9-1.9320.495-1.437[-2.427, -1.437]
10-2.7300.303-2.427[-3.033, -2.427]
11-4.5331.500-3.033[-6.033, -3.033]
+ +<|ref|>equation<|/ref|><|det|>[[130, 404, 907, 472]]<|/det|> +\[\left\{ \begin{array}{l}\frac{dX_1(t)}{dt} +0.12X_1(t) = 0.08X_2(t)\quad (Lower\ Bounded)\\ \displaystyle \frac{d\overline{X}_1(t)}{dt} +0.15\overline{X}_1(t) = 0.10\overline{X}_2(t)\quad (Upper\ Bounded) \end{array} \right. \quad (70)\] + +<|ref|>text<|/ref|><|det|>[[113, 478, 593, 494]]<|/det|> +3) The prediction formulas for the accumulated sequences are derived as: + +<|ref|>equation<|/ref|><|det|>[[130, 512, 907, 558]]<|/det|> +\[\left\{ \begin{array}{l}\underline{X}_1(k + 1) = -1.443e^{-0.12k} + 0.667\quad (Lower\ Bounded)\\ \overline{X}_1(k + 1) = -1.369e^{-0.15k} + 0.125\quad (Upper\ Bounded) \end{array} \right. \quad (71)\] + +<|ref|>text<|/ref|><|det|>[[88, 563, 907, 594]]<|/det|> +Finally, the predicted interval for the pattern class variable is then obtained via inverse accumulated generation operation (IAGO): + +<|ref|>equation<|/ref|><|det|>[[130, 616, 470, 637]]<|/det|> +\[\hat{\otimes}_{1}(k + 1) = \left[\underline{X}_{1}(k + 1) - \underline{X}_{1}(k),\overline{X}_{1}(k + 1) - \overline{X}_{1}(k)\right]\] + +<|ref|>text<|/ref|><|det|>[[88, 644, 905, 677]]<|/det|> +The IGM(1,2) model, constructed with parameters \(a = [0.12, 0.15]\) and \(b = [0.08, 0.10]\) , effectively predicts \(y_{1}(k)\) using \(u(k)\) in pattern- moving systems. + +<|ref|>text<|/ref|><|det|>[[88, 676, 907, 737]]<|/det|> +Step 2: Design of IGB- GPC controller and comparison of control effects for pattern moving system. Differing from the control targets of traditional purpose control systems, the objective of pattern moving regulation is to assign the system output to the specified product quality. The expected pattern class are respectively established as category 2 (2.739) and category 5 (0.288), i.e., + +<|ref|>equation<|/ref|><|det|>[[128, 756, 907, 792]]<|/det|> +\[y_{d} = \left\{ \begin{array}{ll}2, & 0\leq k\leq 100\\ 5, & 100< k\leq 200 \end{array} \right. \quad (72)\] + +<|ref|>text<|/ref|><|det|>[[88, 800, 907, 846]]<|/det|> +we proceed to elaborate the design of the control input \(u(k)\) , the optimization process for determining the optimal control sequence, and a comparative simulation framework to evaluate the performance of the proposed IGB- GPC against benchmark methods, namely CARIMA- GPC and IG- GPC. + +<|ref|>text<|/ref|><|det|>[[88, 846, 907, 922]]<|/det|> +The control objective in this pattern- moving system is to drive the system output \(y(k)\) to match the specified pattern class centers corresponding to \(y_{d}\) , which represent the desired product quality indices. Specifically, \(y_{d} = 2\) corresponds to pattern class 2 with center \(c_{2} = 2.739\) for \(0 \leq k \leq 100\) , and \(y_{d} = 5\) corresponds to pattern class 5 with center \(c_{5} = 0.288\) for \(100 < k \leq 200\) (see Table 1). To simplify the control design, we assume \(y_{d}\) directly approximates these center values, i.e., \(y_{d}(k) = 2.739\) and \(y_{d}(k) = 0.288\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 123, 830, 444]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[338, 468, 657, 484]]<|/det|> +
Figure 4. Pattern class centre and its threshold.
+ +<|ref|>text<|/ref|><|det|>[[88, 508, 905, 540]]<|/det|> +Using the IGM(1,2) parameters estimated in Step 1 \((a = [0.12,0.15],b = [0.08,0.10])\) , the predicted output at step \(k + j\) is expressed as: + +<|ref|>equation<|/ref|><|det|>[[128, 550, 907, 584]]<|/det|> +\[\hat{y} (k + j|k) = \left(\hat{\otimes}^{(1)}(1) - \frac{b}{a}\right)e^{-a\cdot j} + \frac{b}{a} \quad (73)\] + +<|ref|>text<|/ref|><|det|>[[88, 594, 907, 626]]<|/det|> +With initial conditions \(\underline{{X}}_{1}(1) = \underline{{x}}_{1}(1) = 2.739\) (for \(y_{d} = 2\) ) and \(\overline{{X}}_{1}(1) = \overline{{x}}_{1}(1) = 2.739 + r_{2} = 3.033\) , the predicted interval at \(j = 1\) is: + +<|ref|>equation<|/ref|><|det|>[[128, 635, 907, 656]]<|/det|> +\[\hat{y} (k + 1|k) = [2.739\cdot e^{-0.12} + 0.667,3.033\cdot e^{-0.15} + 0.667]\approx [2.435,2.712] \quad (74)\] + +<|ref|>text<|/ref|><|det|>[[88, 666, 907, 699]]<|/det|> +The dynamic matrix \(\mathbf{G}\) is constructed using step response coefficients. For a prediction horizon \(N_{\mathrm{y}} = 5\) and control horizon \(N_{u} = 3\) , each element \(g_{i,j}\) represents the effect of a unit control increment at step \(k + j - 1\) on the output at step \(k + i\) : + +<|ref|>equation<|/ref|><|det|>[[128, 707, 907, 741]]<|/det|> +\[g_{i,j} = \frac{\partial\hat{y} (k + i|k)}{\partial\Delta u(k + j - 1)}\approx \frac{\hat{y} (k + i|k,\Delta u = 1) - \hat{y} (k + i|k,\Delta u = 0)}{1} \quad (75)\] + +<|ref|>text<|/ref|><|det|>[[88, 752, 198, 767]]<|/det|> +For \(i = 1,j = 1\) : + +<|ref|>equation<|/ref|><|det|>[[128, 775, 907, 812]]<|/det|> +\[g_{1,1} = \left[\left(\underline{{X}}_{1}(1) - \frac{0.08}{0.12} +\frac{0.08}{0.12}\right)e^{-0.12\cdot 1} + \frac{0.08}{0.12}\right] - \hat{y} (k + 1|k) = e^{-0.12}\approx 0.886 \quad (76)\] + +<|ref|>text<|/ref|><|det|>[[88, 821, 270, 836]]<|/det|> +Subsequent elements yield: + +<|ref|>equation<|/ref|><|det|>[[128, 845, 907, 925]]<|/det|> +\[\mathbf{G} = \left[ \begin{array}{lll}0.886 & 0 & 0\\ 0.789 & 0.886 & 0\\ 0.703 & 0.789 & 0.886\\ 0.627 & 0.703 & 0.789\\ 0.560 & 0.627 & 0.703 \end{array} \right] \quad (77)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 80, 495, 95]]<|/det|> +The free response vector \(\mathbf{F}\) under zero control increments: + +<|ref|>equation<|/ref|><|det|>[[129, 118, 907, 138]]<|/det|> +\[\mathbf{F} = [\hat{y} (k + 1|k,\Delta u = 0),\hat{y} (k + 2|k,\Delta u = 0),\dots ,\hat{y} (k + N_{y}|k,\Delta u = 0)]^{T} \quad (78)\] + +<|ref|>text<|/ref|><|det|>[[88, 160, 262, 177]]<|/det|> +For \(k = 0\) and \(y_{d} = 2.739\) : + +<|ref|>equation<|/ref|><|det|>[[129, 198, 907, 240]]<|/det|> +\[\begin{array}{r l} & {\hat{y} (1|0,\Delta u = 0) = 2.739\cdot e^{-0.12} + 0.667\approx 2.435}\\ & {\hat{y} (2|0,\Delta u = 0) = 2.739\cdot e^{-0.24} + 0.667\approx 2.160} \end{array} \quad (80)\] + +<|ref|>text<|/ref|><|det|>[[88, 263, 129, 278]]<|/det|> +Thus: + +<|ref|>equation<|/ref|><|det|>[[129, 301, 907, 321]]<|/det|> +\[\mathbf{F} = [2.435,2.160,1.911,1.686,1.483]^{T} \quad (81)\] + +<|ref|>text<|/ref|><|det|>[[112, 349, 748, 367]]<|/det|> +With weighting matrices \(\mathbf{Q} = \mathrm{diag}(10,8,6,4,2)\) and \(\mathbf{R} = \mathrm{diag}(1,1,1)\) , the reference trajectory is: + +<|ref|>equation<|/ref|><|det|>[[129, 388, 907, 409]]<|/det|> +\[\mathbf{Y}_{r} = [2.739,2.739,2.739,2.739,2.739]^{T} \quad (82)\] + +<|ref|>text<|/ref|><|det|>[[88, 432, 380, 448]]<|/det|> +The optimal control increment is solved via: + +<|ref|>equation<|/ref|><|det|>[[129, 470, 907, 490]]<|/det|> +\[\Delta \mathbf{U}^{*} = (\mathbf{G}^{T}\mathbf{Q}\mathbf{G} + \mathbf{R})^{-1}\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) \quad (83)\] + +<|ref|>text<|/ref|><|det|>[[88, 514, 232, 529]]<|/det|> +Matrix computations: + +<|ref|>equation<|/ref|><|det|>[[128, 550, 907, 653]]<|/det|> +\[\begin{array}{r l} & {\mathbf{G}^{T}\mathbf{Q}\mathbf{G} = \left[ \begin{array}{l l l}{23.25} & {18.64} & {14.08}\\ {18.64} & {15.76} & {12.28}\\ {14.08} & {12.28} & {9.78} \end{array} \right]}\\ & {\mathbf{G}^{T}\mathbf{Q}(\mathbf{Y}_{r} - \mathbf{F}) = \left[ \begin{array}{l}{10.24}\\ {8.32}\\ {6.41} \end{array} \right]} \end{array} \quad (84)\] + +<|ref|>text<|/ref|><|det|>[[88, 674, 152, 689]]<|/det|> +Solution: + +<|ref|>equation<|/ref|><|det|>[[129, 710, 907, 760]]<|/det|> +\[\Delta \mathbf{U}^{*} = \left[ \begin{array}{l}{0.32}\\ {0.25}\\ {0.18} \end{array} \right],\quad u(0) = u(- 1) + \Delta u^{*}(0) = 0 + 0.32 = 0.32 \quad (86)\] + +<|ref|>text<|/ref|><|det|>[[113, 789, 311, 804]]<|/det|> +The control input update rule: + +<|ref|>equation<|/ref|><|det|>[[129, 826, 907, 846]]<|/det|> +\[u(k) = u(k - 1) + \Delta u^{*}(k),\quad \Delta u^{*}(k) = [1,0,0]\Delta \mathbf{U}^{*}(k) \quad (87)\] + +<|ref|>text<|/ref|><|det|>[[88, 867, 825, 886]]<|/det|> +At \(k = 100\) , switching to \(y_{d} = 0.288\) with \(\underline{{X}}_{1}(1) = 0.288\) , \(\overline{{X}}_{1}(1) = 0.521\) , the calculation yields \(u(101)\approx - 0.25\) + +<|ref|>text<|/ref|><|det|>[[88, 890, 905, 922]]<|/det|> +In summary, the procedure of proposed method was demonstrated in Algorithm 4. Moreover, the tracking results and its errors were shown in Figures 5 and 6, respectively. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[105, 100, 345, 118]]<|/det|> +Input: \(a, b, \tilde{\otimes}^{(1)}(1), \mathbf{Y}_r, \mathbf{Q}, \mathbf{R}, N_y, N_u\) + +<|ref|>text<|/ref|><|det|>[[108, 118, 288, 132]]<|/det|> +Output: Control input \(u(k)\) + +<|ref|>text<|/ref|><|det|>[[108, 133, 299, 147]]<|/det|> +Initialize \(u(- 1) = 0, \Delta \mathbf{U} = \mathbf{0}\) + +<|ref|>text<|/ref|><|det|>[[108, 149, 235, 162]]<|/det|> +for \(k = 0\) to 199 do + +<|ref|>text<|/ref|><|det|>[[131, 164, 234, 178]]<|/det|> +if \(k \leq 100\) then + +<|ref|>text<|/ref|><|det|>[[131, 180, 234, 194]]<|/det|> +\(y_d \leftarrow 2.739\) + +<|ref|>text<|/ref|><|det|>[[131, 196, 160, 208]]<|/det|> +else + +<|ref|>text<|/ref|><|det|>[[131, 210, 234, 223]]<|/det|> +\(y_d \leftarrow 0.288\) + +<|ref|>text<|/ref|><|det|>[[131, 226, 171, 238]]<|/det|> +end if + +<|ref|>text<|/ref|><|det|>[[131, 239, 290, 254]]<|/det|> +Construct \(\mathbf{Y}_r = [y_d]^{N_y \times 1}\) + +<|ref|>text<|/ref|><|det|>[[131, 255, 383, 269]]<|/det|> +Compute \(\mathbf{F}\) via IGM(1,2) with \(\Delta u = 0\) + +<|ref|>text<|/ref|><|det|>[[131, 270, 325, 284]]<|/det|> +Build \(\mathbf{G}\) using step responses + +<|ref|>text<|/ref|><|det|>[[131, 284, 418, 299]]<|/det|> +Solve \(\Delta \mathbf{U}^* = (\mathbf{G}^T \mathbf{Q} \mathbf{G} + \mathbf{R})^{- 1} \mathbf{G}^T \mathbf{Q}(\mathbf{Y}_r - \mathbf{F})\) + +<|ref|>text<|/ref|><|det|>[[131, 300, 250, 314]]<|/det|> +\(\Delta u(k) \leftarrow \Delta \mathbf{U}^* (1)\) + +<|ref|>text<|/ref|><|det|>[[131, 315, 295, 329]]<|/det|> +\(u(k) \leftarrow u(k - 1) + \Delta u(k)\) + +<|ref|>text<|/ref|><|det|>[[131, 330, 325, 345]]<|/det|> +\(u(k) \leftarrow \text{saturate}(u(k), [- 4, 4])\) + +<|ref|>text<|/ref|><|det|>[[108, 346, 160, 359]]<|/det|> +end for + +<|ref|>image<|/ref|><|det|>[[150, 444, 828, 768]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[240, 428, 711, 443]]<|/det|> +
Comparison of system output accuracy of different models Figure 6. The tracking errors for pattern moving systems with various models.
+ +<|ref|>text<|/ref|><|det|>[[88, 846, 910, 922]]<|/det|> +Figure 5 indicates that IGB- GPC maintains the output closest to the reference trajectory over the time change. Notably, around the transition point at 100, where the reference shifts from 2.739 to 0.288, IGB- GPC exhibits a smoother and faster response, minimizing overshoot and stabilizing more quickly compared to CARIMA- GPC and IG- GPC. Meanwhile, Figure 6 indicates IGB- GPC has the lowest median error and smallest interquartile range, while CARIMA- GPC and IG- GPC show higher errors and greater variability, highlighting IGB- GPC's superior accuracy and consistency. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[97, 87, 890, 440]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[205, 458, 787, 474]]<|/det|> +
Figure 5. System ouput comparison with different control schemes in PMT framework.
+ +<|ref|>image<|/ref|><|det|>[[95, 503, 900, 808]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[244, 825, 750, 841]]<|/det|> +
Figure 7. Tracking results of system operating status on target pattern class.
+ +<|ref|>text<|/ref|><|det|>[[89, 860, 908, 920]]<|/det|> +For this comparison, CARIMA- GPC shows a significant drop and oscillatory behavior post- transition, while IG- GPC also struggles with stability, particularly after 100, with larger fluctuations. This suggests that IGAB- GPC's adaptive buffer operator and interval grey modeling enhance tracking accuracy and robustness against dynamic changes. The results can be attributed to two main reasons. The superior performance of IGAB- GPC can be attributed to two key factors. First, the adaptive buffer + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 78, 908, 155]]<|/det|> +operator reduces fluctuations in pattern class variables by \(30 - 50\%\) (Remark following Theorem 4.3), enhancing prediction accuracy for small- sample, uncertain data compared to CARIMA- GPC and IG- GPC, as shown in Figure 6. Second, integrating IGM(1,2) (Equation (70)) with GPC's receding horizon optimization (Equations (55), (59)) ensures robust tracking of pattern transitions (e.g., class 2 to class 5 at \(k = 100\) ), outperforming CARIMA- GPC's deterministic approach and IG- GPC's less adaptive buffer operator, as evidenced by smoother responses and lower errors in Figures 5. + +<|ref|>text<|/ref|><|det|>[[89, 155, 908, 291]]<|/det|> +Additionally, Figure 7 presents the actual pattern class using IGB- GPC by space cross mapping \((M(\cdot))\) , which indicates the system's ability to accurately track the target pattern classes (class 2 with center \(c_{2} = 2.739\) for \(0 \leq k \leq 100\) and class 5 with center \(c_{5} = 0.288\) for \(100 < k \leq 200\) ). The figure demonstrates that IGB- GPC successfully drives the system operating condition to the desired pattern classes with minimal deviation, maintaining the output within the corresponding class intervals as defined in Table 1. Specifically, the system remains within the class threshold of \([2.427, 3.033)\) for class 2 and \([0.055, 0.521)\) for class 5, with rapid convergence to the target class centers post- transition at \(k = 100\) . Compared to CARIMA- GPC and IG- GPC, IGB- GPC exhibits fewer misclassifications and smoother transitions between pattern classes, underscoring the effectiveness of the adaptive buffer operator and interval grey modeling in handling the inherent uncertainties and dynamic shifts in pattern- moving systems. + +<|ref|>sub_title<|/ref|><|det|>[[89, 305, 230, 323]]<|/det|> +## 6 Conclusions + +<|ref|>text<|/ref|><|det|>[[89, 328, 908, 451]]<|/det|> +This study introduces a novel Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB- GPC) framework specifically designed for pattern- moving systems characterized by limited sample sizes, pronounced nonlinearity, and significant uncertainties. By synergistically integrating the Interval Grey Model (IGM(1,2)) with an adaptive buffer operator and Generalized Predictive Control (GPC), the proposed methodology effectively addresses the challenges associated with modeling and controlling complex industrial systems governed by statistical dynamics. The primary contributions of this work encompass: (1) the development of an adaptive buffer operator to mitigate oscillations in pattern class variables, (2) the formulation of an IGM(1,2)- based predictive model for robust handling of epistemic uncertainties, and (3) the seamless integration of these components within a GPC framework to achieve precise, stable, and robust control performance. + +<|ref|>text<|/ref|><|det|>[[89, 450, 908, 586]]<|/det|> +Theoretical analysis, grounded in Lyapunov stability theory, rigorously establishes that IGB- GPC guarantees bounded- input bounded- output (BIBO) stability and exponential convergence of tracking errors under well- defined conditions. The adaptive buffer operator reduces the amplitude of prediction errors by \(30 - 50\%\) , thereby enhancing robustness against dynamic fluctuations, while the IGM(1,2) model provides a reliable framework for quantifying uncertainties inherent in pattern category variables. Numerical simulations substantiate the superiority of IGB- GPC over established benchmark methods, namely Controlled AutoRegressive Integrated Moving Average Generalized Predictive Control (CARIMA- GPC) and Interval Grey Generalized Predictive Control (IG- GPC). The results demonstrate that IGB- GPC achieves smoother transitions, significantly lower tracking errors, and reduced misclassifications during pattern class shifts, as evidenced by its performance on a nonlinear discrete- time system. + +<|ref|>text<|/ref|><|det|>[[89, 586, 908, 676]]<|/det|> +The proposed IGB- GPC framework holds considerable promise for applications in process industries, such as metallurgy and chemical engineering, where pattern- moving systems are prevalent. Future research directions include extending the framework to accommodate multi- input multi- output systems, incorporating real- time adaptive parameter estimation to further enhance robustness, and conducting experimental validation on industrial platforms to bridge the gap between simulation and practical deployment. Additionally, exploring hybrid methodologies that combine IGB- GPC with advanced machine learning techniques could further elevate prediction accuracy and control efficacy in highly dynamic and uncertain environments. + +<|ref|>sub_title<|/ref|><|det|>[[90, 690, 240, 708]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[90, 713, 886, 730]]<|/det|> +The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[90, 744, 197, 761]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[95, 768, 910, 920]]<|/det|> +1. Xu, Z. Pattern recognition method of intelligent automation and its implementation in engineering. Univ. Sci. Technol. Beijing, Beijing, China (2001). +2. Shoude, J. Pattern recognition approach to intelligent automation for complex industrial processes. Chin. J. Eng. 20, 385-389 (1998). +3. Han, C., Xu, Z. & Deng, N. Minimum entropy control for non-newtonian mechanical systems based on pattern moving probability density evolution. J. Frankl. Inst. 362, 107597 (2025). +4. Nandy, D. & Padariya, R. An overview of pattern recognition. Int. J. for Innov. Res. Sci. & Technol. 2 (2016). +5. Han, C. & Xu, Z. Pattern-moving-based dynamic description for a class of nonlinear systems using the generalized probability density evolution. Probabilistic Eng. Mech. 74, 103543 (2023). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[90, 78, 910, 111]]<|/det|> +6. Guo, L., Xu, Z. & Wang, Y. Dynamic modeling and optimal control for complex systems with statistical trajectory. Discret. Dyn. Nat. Soc. 2015, 245685 (2015). + +<|ref|>text<|/ref|><|det|>[[92, 115, 910, 147]]<|/det|> +7. Sun, C. & Xu, Z. Multi-dimensional moving pattern prediction based on multi-dimensional interval ts fuzzy model. Control. Decis. 31, 1569-1576 (2016). + +<|ref|>text<|/ref|><|det|>[[92, 151, 910, 183]]<|/det|> +8. Wu, J. & Li, J. System asymptotic stability analysis of a kind of complex production processes based on multi-dimensional moving pattern. SN Appl. Sci. 5, 32 (2023). + +<|ref|>text<|/ref|><|det|>[[92, 187, 910, 218]]<|/det|> +9. Wang, M., Pan, W. & Lu, Y. A pattern-based controller for a class of production processes with input delay. Asian J. Control. 25, 1074-1085 (2023). + +<|ref|>text<|/ref|><|det|>[[92, 222, 910, 253]]<|/det|> +10. Guo, L. L. & Wang, Y. The application of cell mapping to dynamic modeling and control. Appl. Mech. Mater. 687, 661-664 (2014). + +<|ref|>text<|/ref|><|det|>[[92, 257, 910, 290]]<|/det|> +11. Li, N., Xu, Z. & Li, X. Pattern-moving-modelling and analysis based on clustered generalized cell mapping for a class of complex systems. Processes 12, 492 (2024). + +<|ref|>text<|/ref|><|det|>[[92, 293, 910, 325]]<|/det|> +12. Changping, S. & Zhengguang, X. Extended ts fuzzy model based on interval arithmetic and its application to interval nonlinear regression analysis. In 2009 IEEE International Conference on Fuzzy Systems, 1773-1778 (IEEE, 2009). + +<|ref|>text<|/ref|><|det|>[[92, 329, 910, 360]]<|/det|> +13. Zhengguang, X. & Changping, S. Moving pattern-based approach to modeling of a class of complex production processes. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2282-2287 (IEEE, 2011). + +<|ref|>text<|/ref|><|det|>[[92, 364, 910, 396]]<|/det|> +14. Han, C. & Xu, Z. Prediction and output estimation of pattern moving in non-newtonian mechanical systems based on probability density evolution. CMES-Computer Model. Eng. & Sci. 139 (2024). + +<|ref|>text<|/ref|><|det|>[[92, 400, 910, 431]]<|/det|> +15. Han, C. & Xu, Z. Pattern-moving based data-driven control for multi-input continuous-time non-newtonian mechanical systems. Int. J. Syst. Sci. 1-25 (2025). + +<|ref|>text<|/ref|><|det|>[[92, 436, 910, 467]]<|/det|> +16. Li, X., Xu, Z., Lu, Y., Cui, J. & Zhang, L. Modified model free adaptive control for a class of nonlinear systems with multi-threshold quantized observations. Int. J. Control. Autom. Syst. 19, 3285-3296 (2021). + +<|ref|>text<|/ref|><|det|>[[92, 471, 910, 503]]<|/det|> +17. Li, X., Xu, Z., Han, C. & Li, N. Pattern-moving-based parameter identification of output error models with multi-threshold quantized observations. CMES-Computer Model. Eng. & Sci. 130 (2022). + +<|ref|>text<|/ref|><|det|>[[92, 507, 707, 523]]<|/det|> +18. Ju-Long, D. Control problems of grey systems. Syst. & control letters 1, 288-294 (1982). + +<|ref|>text<|/ref|><|det|>[[92, 527, 910, 559]]<|/det|> +19. Chen, Z., Meng, Y., Wang, R.-Y. & Chen, T. Intelligent optimal grey evolutionary algorithm for structural control and analysis. Smart Struct. Syst. 33, 365-374 (2024). + +<|ref|>text<|/ref|><|det|>[[92, 563, 910, 595]]<|/det|> +20. Zeng, B., Li, C., Zhou, X.-Y. & Long, X.-J. Prediction model of interval grey numbers with a real parameter and its application. In Abstract and Applied Analysis, vol. 2014, 939404 (Wiley Online Library, 2014). + +<|ref|>text<|/ref|><|det|>[[92, 599, 910, 630]]<|/det|> +21. Rao, S. S. & Liu, X. Universal grey system theory for analysis of uncertain structural systems. AIAA journal 55, 3966-3979 (2017). + +<|ref|>text<|/ref|><|det|>[[92, 634, 910, 666]]<|/det|> +22. Chen, C.-C. & Tsai, C. M. Interval forecasting with grey models: a novel learning procedure for improved decision-making. Grey Syst. Theory Appl. (2025). + +<|ref|>text<|/ref|><|det|>[[92, 670, 910, 701]]<|/det|> +23. Liu, S., Yang, Y., Xie, N. & Forrest, J. New progress of grey system theory in the new millennium. Grey Syst. Theory Appl. 6, 2-31 (2016). + +<|ref|>text<|/ref|><|det|>[[92, 706, 910, 737]]<|/det|> +24. Schwenzer, M., Ay, M., Bergs, T. & Abel, D. Review on model predictive control: An engineering perspective. The Int. J. Adv. Manuf. Technol. 117, 1327-1349 (2021). + +<|ref|>text<|/ref|><|det|>[[92, 741, 910, 773]]<|/det|> +25. Djouadi, H. et al. Improved robust model predictive control for pmsm using backstepping control and incorporating integral action with experimental validation. Results Eng. 23, 102416 (2024). + +<|ref|>text<|/ref|><|det|>[[92, 777, 910, 809]]<|/det|> +26. Li, N., Xu, Z. G., Zhao, C. T. & Li, X. Q. Pattern-moving-based dynamic description and optimal control for non-newtonian mechanical systems with generalized cell mapping. The Can. J. Chem. Eng. 103, 3208-3229 (2025). + +<|ref|>text<|/ref|><|det|>[[92, 813, 910, 844]]<|/det|> +27. Liu, G., Pan, Y., Lam, H.-K. & Liang, H. Event-triggered fuzzy adaptive quantized control for nonlinear multi-agent systems in nonaffine pure-feedback form. Fuzzy Sets Syst. 416, 27-46 (2021). + +<|ref|>text<|/ref|><|det|>[[92, 848, 848, 865]]<|/det|> +28. Liu, S., Yang, Y. & Forrest, J. Y.-L. Grey systems analysis: Methods, models and applications (Springer, 2022). + +<|ref|>text<|/ref|><|det|>[[92, 869, 730, 886]]<|/det|> +29. Liu, S.-H. A new grey buffer operator and its application. J. Intell. & Fuzzy Syst. 1-9 (2024). + +<|ref|>text<|/ref|><|det|>[[92, 890, 910, 921]]<|/det|> +30. Yu, X. & Cheng, Y. A comprehensive review and comparison of cusum and change-point-analysis methods to detect test speededness. Multivar. Behav. Res. 57, 112-133 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[90, 78, 781, 95]]<|/det|> +31. Xu, Z., Wu, J. & Qu, S. Prediction model based on moving pattern. J. Comput. 7, 2695–2701 (2012). + +<|ref|>text<|/ref|><|det|>[[90, 99, 905, 131]]<|/det|> +32. Xie, N. & Liu, S. Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model. J. Syst. Eng. Electron. 26, 96–102 (2015). + +<|ref|>sub_title<|/ref|><|det|>[[90, 147, 386, 165]]<|/det|> +## Author contributions Statement + +<|ref|>text<|/ref|><|det|>[[90, 171, 909, 217]]<|/det|> +Ning Li: Writing original draft, Methodology, Conceptualization, Formal analysis. Zhenggaung Xu: Methodology, Conceptualization, Data Collection, Validation. Xiangquan Li: Writing - review & editing, Visualization, Supervision, Project administration. + +<|ref|>sub_title<|/ref|><|det|>[[90, 234, 169, 252]]<|/det|> +## Funding + +<|ref|>text<|/ref|><|det|>[[90, 257, 909, 303]]<|/det|> +Open access funding provided by Natural Science Foundation Project of Guizhou Province, Grant Number ZK[2023] Genera004; Science and Technology Project of Jiangxi Provincial Department of Education, Grant Number GJJ2202404; Natural Science Foundation Project of JiangXi Province, Grant Number 20242BAB25091. + +<--- Page Split ---> diff --git a/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/images_list.json b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d84d0b13d107eec9f5c149440048ae2c346b4f9b --- /dev/null +++ b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: CD-XRMS experiments. (a) Experimental configuration with the incident beams of the IR pump and the x-ray probe. (b) Magnetic diffraction pattern, (CL+CR) (c) Dichroic pattern (CL-CR), displaying the typical signature of clockwise Néel", + "footnote": [], + "bbox": [ + [ + 120, + 702, + 860, + 860 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Evolution of the XRMS signal over the first 5 ps: (a) intensity of integrated diffraction ring \\((CL + CR)\\) and dichroism \\((CL - CR)\\) normalized at their values at negative time delays; (b) experimental asymmetry ratio \\((CL - CR) / (CL + CR)\\) normalized by its value at \\(t< 0\\) in grey circles and black dots. The simulations for different models discussed in the main text appear as colored lines (see Supplementary Materials S3 for details). (c) Full width at half maximum (FWHM) (red dots) and the position (blue circles) in reciprocal space of the magnetic dichroic peak as a function of time.", + "footnote": [], + "bbox": [ + [ + 120, + 90, + 486, + 556 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Magnetization texture modification by hot electrons. (a) Schematic representation of the torque (black arrows) imposed by the 'hot spins' flowing from the domains to the DWs resulting in transient mixed Bloch/Neel/Bloch contributions. (b) Transient DW shape. (c) Precession angles (red) and DW magnetization normalized by Domain one (blue) used in the simulations of the asymmetry ratio shown in Fig. 2(b).", + "footnote": [], + "bbox": [ + [ + 118, + 421, + 876, + 608 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 45, + 97, + 944, + 303 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 50, + 52, + 551, + 775 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 45, + 92, + 940, + 333 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d.mmd b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6621452ed7e8897c7488fcb2e2d4f27a58f360a9 --- /dev/null +++ b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d.mmd @@ -0,0 +1,204 @@ + +# Ultrafast time-evolution of chiral Néel magnetic domain walls probed by circular dichroism in x-ray resonant magnetic scattering. + +Cyril Lévéillé Synchrotron SOLEIL + +Erick Burgos- Parra Synchrotron SOLEIL + +Yanis Sassi Unité Mixte de Physique, CNRS, Thales, Université Paris- Saclay https://orcid.org/0000- 0003- 0703- 6068 + +Fernando Ajejas Unité Mixte de Physique CNRS/Thales https://orcid.org/0000- 0001- 8980- 4475 + +Valentin Chardonnet Sorbonne Université, CNRS, Laboratoire Chimie Physique – Matière et Rayonnement, LCPMR + +Emanuele Pedersoli Elettra- Sincrotrone Trieste https://orcid.org/0000- 0003- 0572- 6735 + +Flavio Capotondi Elettra Sincrotrone Trieste https://orcid.org/0000- 0003- 1980- 6162 + +Giovanni De Ninno University of Nova Gorica and Elettra- Sincrotrone Trieste https://orcid.org/0000- 0002- 4648- 4413 + +Francesco Maccherozzi Diamond Light Source, Chilton, Didcot, Oxfordshire, OX11 0DE, UK. + +Samjeet Dhesi Diamond Light Source https://orcid.org/0000- 0003- 4966- 0002 + +David Bum Diamond Light Source (United Kingdom) https://orcid.org/0000- 0001- 7540- 1616 + +Gerrit van der Laan Diamond Light Source https://orcid.org/0000- 0001- 6852- 2495 + +Oliver Latcham University of Exeter + +Andrei Shytov University of Exeter + +Volodymyr Kruglyak University of Exeter https://orcid.org/0000- 0001- 6607- 0886 + +Emmanuelle Jal + +<--- Page Split ---> + +Sorbonne Université https://orcid.org/0000- 0001- 5297- 9124 + +## Vincent Cros + +Unité Mixte de Physique CNRS,Thales, Université Paris- Saclay https://orcid.org/0000- 0003- 0272- 3651 + +## Jean-Yves Chauleau + +Service de Physique de l'Etat Condensé + +## Nicolas Reyren + +Unité Mixte de Physique CNRS/Thales https://orcid.org/0000- 0002- 7745- 7282 + +## Michel Viret + +SPEC, CEA,CNRS, Université Paris- Saclay, 91191 Gif- sur- Yvette https://orcid.org/0000- 0001- 6320- 6100 + +Nicolas Jaouen ( Nicolas.jaouen@synchrotron- soleil.fr) + +Synchrotron SOLEIL https://orcid.org/0000- 0003- 1781- 7669 + +## Article + +Keywords: Dzyaloshinskii- Moriya interaction, chiral Néel magnetic domain walls + +Posted Date: March 3rd, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 271463/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on March 17th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28899- 0. + +<--- Page Split ---> + +1 Ultrafast time-evolution of chiral Néel magnetic domain walls 2 probed by circular dichroism in x-ray resonant magnetic 3 scattering. + +6 Cyril Léveillé \(^{1}\) , Erick Burgos-Parra \(^{1,2}\) , Yanis Sassi \(^{2}\) , Fernando Ajejas \(^{2}\) , Valentin Chardonnet \(^{3}\) , Emanuele Pedersoli \(^{4}\) , Flavio Capotondi \(^{4}\) , Giovanni De Ninno \(^{4,5}\) , Francesco Maccherozzi \(^{6}\) , Sarnjeet Dhesi \(^{6}\) , David M. Burn \(^{6}\) , Gerrit van der Laan \(^{6}\) , Oliver S. Latcham \(^{7}\) , Andrey V. Shytov \(^{7}\) , Volodymyr V. Kruglyak \(^{7}\) , Emmanuelle Jal \(^{3}\) , Vincent Cros \(^{2}\) , Jean-Yves Chauleau \(^{8}\) , Nicolas Reyren \(^{2}\) , Michel Viret \(^{8}\) and Nicolas Jaouen \(^{1}\) + +\(^{1}\) Synchrotron SOLEIL, Saint-Aubin, Boite Postale 48, 91192 Gif-sur-Yvette Cedex, France + +\(^{2}\) Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767 Palaiseau, France + +\(^{3}\) Sorbonne Université, CNRS, Laboratoire Chimie Physique – Matière et Rayonnement, LCPMR, 75005 Paris, France + +\(^{4}\) Elettra-Sincrotrone Trieste, 34149 Basovizza, Trieste, Italy + +\(^{5}\) University of Nova Gorica, 5000 Nova Gorica, Slovenia + +\(^{6}\) Diamond Light Source, Didcot OX11 0DE, United Kingdom. + +\(^{7}\) University of Exeter, Stocker road, Exeter, EX4 4QL, United Kingdom. + +\(^{8}\) SPEC, CEA, CNRS, Université Paris-Saclay, 91191 Gif-sur-Yvette, France + +Non- collinear spin textures in ferromagnetic ultrathin films are attracting a renewed interest fueled by possible fine engineering of several magnetic interactions, notably the interfacial Dzyaloshinskii- Moriya interaction. This allows the stabilization of complex chiral spin textures such as chiral magnetic domain walls (DWs), spin spirals, and magnetic skyrmions. We report here on the ultrafast behavior of chiral DWs after optical pumping in perpendicularly magnetized asymmetric multilayers, probed using time- resolved circular dichroism in x- ray resonant magnetic scattering (CD- XRMS). We observe a picosecond transient reduction of the CD- XRMS, which is attributed to the spin current- induced coherent and incoherent torques within the continuously dependent spin texture of the DWs. We argue that a specific demagnetization of the inner structure of the DW induces a flow of spins from the interior of the neighboring magnetic domains. We identify this time- varying change of the DW texture shortly after the laser pulse as + +<--- Page Split ---> + +a distortion of the homochiral Néel shape toward a transient mixed Bloch- Néel- Bloch texture along a direction transverse to the DW. + +Ultrafast demagnetization of a ferromagnet by an optical pulse was first demonstrated in 1996 in the seminal study by Beaurepaire et al [Beaurepaire96], which is widely considered as the birth of the research field of femtomagnetism, i.e., the magnetism modulated ("pumped") by femtosecond long laser pulses. While several underlying mechanisms are considered to explain these ultrafast processes, the central role of spin dependent transport of hot electrons has been clearly evidenced [Melnikov11, Siegrist19]. Such phenomena were first experimentally demonstrated in spin valves, in which the demagnetization process is faster for antiparallel alignment of the magnetization in the magnetic layers [Malinowski08]. Models based on polarized electron transport in the superdiffusive regime have been subsequently developed [Battiat0o]. The optically excited hot electrons, initially ballistic, with spin- dependent lifetimes and velocities, generate non- equilibrium spin currents either within a ferromagnetic layer or in adjacent non- magnetic layer. The induced loss of angular momentum greatly participates in ultrafast dynamical behavior of the magnetization [vodungbo16]. The existence of this phenomenon has also been tested in single magnetic layers with a heterogeneous magnetization configuration, i.e., containing a large density of magnetic domains and DWs, albeit with different conclusions [Moisan14, vodungbo16, Pfau2012]. X- ray diffraction experiments are in this latter case more powerful for probing the behavior of DWs [zusin2020, Kerber20, Hennes20b]. For example, Pfau et al. [Pfau2012] inferred that the DW size changes in the first few ps by investigating the variations of the first- order Bragg peak of the magnetic configuration. More recently, the studies of Zuzin et al. [Zuzin2020] and Hennes et al. [hennes2020b] have shown that a more precise way to extract insights about DWs is to study the position and width of higher order diffraction peaks. + +In this Letter, we use circular dichroism in x- ray resonant magnetic scattering (CD- XRMS) to gain access to the internal spin texture of the domain walls. This technique permits indeed a direct determination of the type (Néel or Bloch) as well as of the effective chirality of the DWs [Dürr99, chauleau2018]. Magnetic multilayers with homochiral Néel DWs stabilized by a large interfacial Dzyaloshinskii- Moriya (DM) interaction [Fert80, Fert90] are ideal systems to study DW dynamics at the fs timescales. In recent studies, this approach was used [Zhang17, chauleau2018, Legrand2018, Zhang20] to investigate the intrinsic nature of DWs and skyrmionic systems, which is currently a topic of the utmost relevance from both fundamental and technological viewpoints [Thiaville12, Ruy13, Nagoasa13, Fert17, Yang15]. The degree of circular dichroism in these experiments is not only related to the homochiral nature of the magnetic textures but also to the intrinsic DW configuration and allows us to probe the size and magnetization ratio of domain/domain- wall with unprecedented sensitivity. We hence unveil the ultrafast dynamics of these domain walls, unambiguously showing a specific behavior compared to that of the domains. + +<--- Page Split ---> + +The system under study is an asymmetric magnetic multilayer \(\mathrm{[Pt(3nm)|Co(1.5nm)|Al(1.4nm)|x_5}\) grown by sputtering on a thermally oxidized Si wafer buffered by \(\mathrm{Ta(5)|Pt(5)}\) (see Supplementary Sec. S1 for details) presenting perpendicular magnetic anisotropy and large interfacial DM interaction. At remanence, domains adopt a typical disordered labyrinthine structure, but with a narrow distribution of domain widths. The magnetization and anisotropy are measured by SQUID magnetometry, while the DM amplitude is determined by comparing the experimentally measured (by magnetic force microscopy) domain periodicity to those simulated using micromagnetic calculations with MuMax3 [Vansteenkiste14] (see Supplemental Material S1 for details about the magnetic preparation and the simulations). From these calculations, we can also estimate the DW width to be \(\sim 20 \mathrm{nm}\) . The micromagnetic simulations are also used as inputs in the empirical XRMS model with accurate values for the width of the DW. + +The time- resolved XRMS experiments have been performed on the DiProI beam line [Capotondi13] at the FERMI free electron laser [Allaria12] (Trieste, Italy). Time resolution is achieved using a standard pump- probe approach [Fig. 1(a)] in which the probe is a 60 fs XUV pulse at the Co \(M\) edge energy (photon energy \(\sim 60 \mathrm{eV}\) ) and the pump is a 100 fs infrared laser pulse (780 nm). The overall time resolution is therefore \(\sim 120 \mathrm{fs}\) . The scattering experiments have been conducted under reflectivity condition at \(45^{\circ}\) incidence for circularly left (CL) and right (CR) x- ray polarization allowing to acquire ultrafast snapshots of diffraction diagrams (Fig. 1b) and their corresponding circular dichroism (Fig. 1c) at each delay time of the infrared (IR) excitation (see S2 for details). Noteworthy, the degree of x- ray circular polarization is between \(92 - 95\%\) [Allaria14]. Regarding the probe and pump energy densities, the IR fluence was set to \(4.8 \mathrm{mJ / cm^2}\) (at a repetition rate of \(50 \mathrm{Hz}\) ) and the FEL fluence was set to \(0.5 \mathrm{mJ / cm^2}\) . At the Co \(M\) edge, with \(45^{\circ}\) photon incidence angle, the penetration depth is \(\sim 10 \mathrm{nm}\) , therefore most of the scattered signal comes from the uppermost Co layers. Such a small penetration depth also ensures that the expected tilting of the Ewald sphere is negligible in our experiment. Finally, we decided to perform the experiment at the peak of the absorption resonance to avoid any spurious effect caused by the energy shift of the XAS edge at ultrafast timescales [yao20, hennes20]. + +![](images/Figure_1.jpg) + +
Figure 1: CD-XRMS experiments. (a) Experimental configuration with the incident beams of the IR pump and the x-ray probe. (b) Magnetic diffraction pattern, (CL+CR) (c) Dichroic pattern (CL-CR), displaying the typical signature of clockwise Néel
+ +<--- Page Split ---> + +domain walls. The images in panels b and c have been geometrically corrected to account for the projection related to the photon incidence angle \(\theta = 45^{\circ}\) , and the scale corresponds to the sum of the counts (500 XFEL pulse of each polarization) for \((CL + CR)\) (b) and \((CL - CR)\) (c). + +A typical diffraction pattern of the magnetic system at negative time delays, i.e. before the laser pulse excitation, is displayed in Fig. 1(b) in which the diffracted intensity is the sum of the two circular polarizations (CL+CR). It results from the x- ray diffraction on the labyrinth structure with a period of \((330 \pm 20) \mathrm{nm}\) (estimated from the ring radius). The total magnetic scattering intensity mainly comes from the alternating out- of- plane magnetic domains. The diffraction intensity also displays circular dichroism (CL- CR) [Fig. 1(c)], which reverses its sign on each side (along \(Q_{y}\) ) of the specular reflection, and reaches about \(10\%\) . Such dichroic signal is known [Durr99] to be a signature of an uncompensated sense of rotation in non- collinear magnetic textures. In our experiment, the sign of the dichroism indeed reveals the stabilization of clockwise (CW) Néel DW as we recently demonstrated [Chauleau2018]. The observed features have been corroborated by static scattering measurements at the Co \(L\) edge performed at the SEXTANTS beamline at SOLEIL [Sacchi13], for which the interpretation is now well established (see Supplementary Materials S1). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Evolution of the XRMS signal over the first 5 ps: (a) intensity of integrated diffraction ring \((CL + CR)\) and dichroism \((CL - CR)\) normalized at their values at negative time delays; (b) experimental asymmetry ratio \((CL - CR) / (CL + CR)\) normalized by its value at \(t< 0\) in grey circles and black dots. The simulations for different models discussed in the main text appear as colored lines (see Supplementary Materials S3 for details). (c) Full width at half maximum (FWHM) (red dots) and the position (blue circles) in reciprocal space of the magnetic dichroic peak as a function of time.
+ +The time dependence of both the magnetic intensity \((CL + CR)\) of the overall diffraction ring [Fig 2(a)] and the dichroism \((CL - CR)\) shows a typical signature of ultrafast demagnetization in metallic magnetic ultrathin layers: first, a quench of the magnetization reaching a minimum value after a few hundreds of fs, followed by a log- like recovery over a few ps. The experimental results are further analyzed by plotting the asymmetry ratio, i.e., \((CL - CR) / (CL + CR)\) as a function of time [see Fig. 2(b)], which represents the DW behavior normalized by the total magnetic moment. If the DW magnetization follows the same dynamics as that of the domains, this ratio should not vary. It is plotted in Fig. 2b (normalized by its value before the pump pulse) where one clearly observes a \(15\%\) dip at \(\sim 0.7\) ps. This has been reproducibly observed when repeating the experiment, as demonstrated by the overlapping series of black filled and open circles in Fig. 2(b) showing identical behavior within error bars (inferred + +<--- Page Split ---> + +from the statistical fit of the background and peak intensity, see Supplemental Material Section S2). The normalized ratio remains below 1.0 up to 2 ps. The time evolution of the peak position defined by the maximum of its Gaussian fit, and of the full width at half maximum (FWHM) in reciprocal space of the magnetic dichroic peak are displayed in Fig. 2(c). Those two quantities generally correspond respectively to the variation of the domain size and their distribution. However, this apparent domain extension corresponds in fact to an expansion of the DW in the first ps after optical excitation, as reported by Pfau et al. [Pfau 2012]. When considering this expansion according to the value reported in Fig. 2(c), an increase of the asymmetry ratio is predicted as shown by the blue curve in Fig. 2(b). + +To explain this ultrafast deviation of the dichroism asymmetry ratio, we first exclude an origin due to a change in the scattering factors induced by hot electrons filling the \(d\) band. Indeed, the IR laser fluence of our experiment is much lower ( \(\sim 10\%\) ) than the one used to probe the change of electron occupation induced by the IR pulse using x- ray absorption spectroscopy (XAS) [Mathieu18]. Thus, we explain our observation by the fact that during the demagnetization (resp. remagnetization), the magnetic moments do not decrease (resp. increase) by the same amount simultaneously inside the DWs and inside the domains. If the magnetization decreased uniformly, the expected asymmetry ratio would be constant, as shown by simulation using a model that is detailed in Supplemental Material S3 [magenta line in Fig. 2(b)]. As explained above, the sole expansion of the DW widths cannot explain our data [blue curve in Fig. 2(c)]. To explain an asymmetry ratio dropping below its initial value, we resort to a reduction of the degree of magnetic chirality. In other words, it corresponds to a change of the ratio between the out- of- plane and the in- plane magnetization. In our interpretation, the ultra- fast decrease of the asymmetry ratio below 1.0 is linked to a different demagnetization rate between the DWs and the domains. Note that a scenario that would correspond to a faster remagnetization of the DWs than the domains shall result into an asymmetry ratio larger than 1 (similarly to the expansion of the DW), and therefore can also be safely ruled out. In the following, in order to reproduce our experimental observations, the simulations include both coherent evolution of the hot electron spins that induce a spin torque on the DW and spin temperature (incoherent) variations within the DWs. + +The understanding of the ultrafast DW width expansion requires considering the intense flow of spin currents in the ps regime. These can efficiently transfer angular momentum to and from the ferromagnetic material as shown, e.g., when Pt layers absorb it and generate ps electrical pulses [Kampfrath13]. Angular momentum transfer and dissipation often results in both enhanced demagnetization as well as a faster magnetization recovery. We argue that this is exactly what is happening with the non- collinear magnetic regions inside the DWs. The enhanced spin scattering within DWs is a rather old topic born with studies of the extra contribution to the static magnetoresistance [Viret96] or the induced spin transfer torques resulting in their current- induced displacement. To this aim, ballistic models have been developed and can be appropriately adapted for the ultrafast demagnetization scenario in which superdiffusive spin currents play a central role [Battiato10]. The + +<--- Page Split ---> + +behavior of ballistic spin carriers can be described such as a classical spinned particle perceiving a time varying exchange field while crossing the wall [Viret96, Vanhaverbeke07]. Let us recaller salient features. First, these are band particles that are coupled by exchange to the localized spins (through the so- called \(s - d\) Hamiltonian). Their velocity perpendicular to the wall is related to their momentum in \(k\) space. With the appropriate parameter renormalization, the problem is equivalent to the "fast adiabatic passage" known, e.g., in NMR theory. The spin evolution is given by the Landau- Lifshitz equation: + +\[\frac{d\vec{\mu}}{dt} = \frac{J_{ex}S}{\hbar}\vec{m}\times \vec{\mu}\] + +where \(\vec{\mu}\) is the electron spin, \(J_{\mathrm{ex}}S\) the exchange energy with the localized moment \((S)\) and \(\vec{m}\) the direction of the time varying exchange field seen by the ballistic electrons. The localized moments are rotating in a Neel fashion within the DW and the problem is generally treated in this rotating frame [Vanhaverbeke07]. Basically, the electronic spins will precess around the localized moment effective field and thus acquire a component out of the plane of rotation, inducing a torque parallel to the chiral vector: \(S_{i}\times S_{j}\) . The electron spin precession angle \(\omega\) is proportional to the velocity \(v\) divided by exchange times and the DW width \(2\pi \Delta\) [Viret96]: \(< \omega > = \frac{\pi h\nu}{J_{\mathrm{ex}}S2\pi\Delta}\) . Typically, for electrons at the Fermi level, this precession angle is found to be around 7 degrees for a DW width \(2\pi \Delta\) of \(15\mathrm{nm}\) [Vanhaverbeke07]. However, it is to be noticed that this angle can be quite different for the hot electrons produced in the demagnetization process as the relevant parameter values are hard to quantify. Although their velocities should not be too far from those at the Fermi level (in the \(10^{6}\mathrm{m / s}\) range [Kampfrath13]), the exchange energies effective in bands over \(1\mathrm{eV}\) above the Fermi level can be dramatically reduced \((\sim 0.1\mathrm{eV})\) . Therefore, the expected mistracking angle could be significantly greater for a large part of the hot electrons' distribution. All these processes shall in turn generate a torque applied on the localized moments [Waintal04]. However, because the hot spin currents flow in all directions, mistracking angles can be both positive and negative, resulting in cancellation of the net torque acting on the DWs. The overall effect of the incoherent precession results in an average loss of angular momentum. This should speed up the spin relaxation processes within the DW so that after some \(100\mathrm{fs}\) , a net spin current is established from the domains into the interior of the DWs. + +The new components of the spin- transfer torque resulting from this latter spin current originating from the coherent evolution of the hot electron spins are not cancelled out. Importantly such torques are of opposite sign on the two sides of the DW and should induce a sizeable tilting of the DW magnetization out of the Neel plane as illustrated in Fig. 3(a). This phenomenon is at the origin of a new transient DW shape, made of a Neel type center surrounded by opposite Bloch types as depicted in Fig. 3(b). Such a mixed Bloch/Neel/Bloch contribution will in turn lead to a transient reduction of the measured chirality as it adds two (opposite) Bloch components on both sides of the DW compared to the originally purely Neel character. In order to estimate the amplitude of this DW distortion, it is useful to realize that unlike small current- induced electron flows at the Fermi level, spin fluxes during demagnetization are + +<--- Page Split ---> + +enormous as for each pulse, typically 0.5 electrons per Co atom are excited to higher bands for the used laser fluence [Kampfrath13]. The timescale for the onset of the induced torques is given by the exchange energy and falls in the 10- fs range, ensuring that the wall distortion does not lag from the population of hot electrons. For a spin temperature sufficiently different between domains and DWs, a quantitative estimate using the abovementioned parameters gives a precession angle of the magnetization inside the DW that is larger than 10 degrees. Moreover, the onset of this Bloch component in the DW must leaks out into the domains, thus slightly increasing the effective DW width as also observed experimentally. The measured expansion of the DW can be directly derived from the variation of the dichroic peak position and width shown in Fig. 2(c). We find that the DW width (slightly) increases rapidly and its magnetization reaches a minimum around 1 ps (blue curve), as reported previously for Bloch type DWs [Pfau2012]. Note that this DW expansion takes place when the quenched magnetization starts to recover (1 ps). After reaching it maximum expansion, the DW width then recovers its original (unpumped represented as dotted lines in Fig. 2(c) size at a timescale of \(\sim 5\) ps. + +![](images/Figure_3.jpg) + +
Figure 3: Magnetization texture modification by hot electrons. (a) Schematic representation of the torque (black arrows) imposed by the 'hot spins' flowing from the domains to the DWs resulting in transient mixed Bloch/Neel/Bloch contributions. (b) Transient DW shape. (c) Precession angles (red) and DW magnetization normalized by Domain one (blue) used in the simulations of the asymmetry ratio shown in Fig. 2(b).
+ +Using a 1D magnetization profile (described in Supplementary Material S3) and considering the experimental change of magnetization (extracted directly from the square root of the (CL+CR) intensity), the time evolution of the asymmetry ratio can be simulated. We consider a magnetization in the domains extracted from the (CL+CR) data, along with a further \(12\%\) reduction of the magnetization inside the DWs to account for incoherent effects, as well as a transient Bloch- Néel- Bloch wall as shown in Fig. 3(a) for coherent ones. With these simulations, we find that the precession angle can reach at the maximum about 8 degrees after a time delay of \(\sim 0.6\) ps [red curve in Fig. 3(c)] simultaneously with the reduction of the DW magnetization [relative to domain magnetization blue curve in Fig. 3(c)], The + +<--- Page Split ---> + +resulting simulated asymmetry ratio using the described model is plotted as the green curve in Fig. 2(b), and is in excellent agreement with the experimental measurements. Even accounting for DW expansion [red curve in Fig. 2(b)], the agreement can be obtained for a \(\sim 10\) degrees tilt angle. Although the exchange driven DW distortion is established on a very short timescale, it should last for the nanosecond timescale of the micromagnetic evolution. On the other hand, the incoherent part of the spin current shall relax at the ps timescale of the remagnetization processes, similarly to what we have measured. Interestingly, enhanced spin relaxation existing inside the DWs should speed up remagnetization, explaining that the asymmetry ratio can exceed 1, again in agreement with the experimental results. + +In conclusion, we report here about the experimental investigation of the ultra- short timescale evolution of complex chiral Néel spin textures after laser induced demagnetization. Circular dichroism in x- ray resonant magnetic scattering is used to obtain information in the time domain about both the magnetic domain configuration and the magnetic chirality. Beyond the evolution of the period of the magnetic domains in magnetic multilayers with large perpendicular anisotropy, we acquire new insights into the way that the chirality of the non- collinear spin textures, and their long- range ordering, is evolving in the few ps after demagnetization by a strong optical pulse. We observe that the magnetic difference CL- CR (reflecting the DW properties) reduces faster than the diffracted sum signal (associated to domain magnetization) in the first 2 ps after the laser pulse. To explain this unexpected change of XRMS chirality signal at this short timescale, we propose that angular momentum flowing from the interior of the domains inside the DWs associated to hot electrons induces an ultrafast distortion of the DW magnetization. This transient in- plane deformation of the DWs leads to a transient mixed Bloch- Néel- Bloch DW accompanied by an increase of the DWs width and a reduction of the magnetization inside the DW. These original experimental results are reproduced by calculations, considering a magnetization reduction of \(12\%\) with a 7.5 degrees distortion of the DW. On a longer timescale, i.e., after a few ps, the DWs return to their pure chiral Néel configuration preserving the original sense of rotation (i.e., chirality) together with a recovery of their magnetization. We emphasize that our approach using dichroism in x- ray resonant scattering is applicable to any other magnetic chiral texture and should provide a better understanding of the evolution of the chirality of spin textures on the ultrafast timescale. + +Acknowledgment: We acknowledge Ivaylo Nikolov, Michele Manfredda, Luca Giannessi, and Giuseppe Penco for their inestimable help to set up the FEL and the laser for our experiment. NJ would like to thanks S. Flewett for discussion on XRMS simulations. Financial supports from FLAG- ERA SographMEM (ANR- 15- GRFL- 0005), funding from the Agence Nationale de la Recherche, France, under grant agreement no. ANR- 17- CE24- 0025 (TOPSKY) and 18- CE24- 0018- 01 (SANTA), the Horizon2020 Framework Program of the European Commission under FET- Proactive Grant agreement + +<--- Page Split ---> + +268 no. 824123 (SKYTOP). E. J. is grateful for financial support received from the CNRS- MOMENTUM. 269 O. S. L., A. V. S., and V. V. K. acknowledge funding from the Engineering and Physical Sciences 270 Research Council (EPSRC) in the United Kingdom (Grant No. EP/T016574/1). 271 272 273 References: 274 [beaurepaire96] E. Beaurepaire et al., Ultrafast Spin Dynamics in Ferromagnetic Nickel 275 Phys. Rev. Lett. 76, 4250 (1996). 276 [Melnikov11] A. Melnikov et al. Ultrafast Transport of Laser- Excited Spin- Polarized Carriers 277 in Au/Fe/MgO(001). Phys. Rev. Lett. 107, 076601 (2011). 278 [Siegrist19] F. Siegrist, Light- wave dynamic control of magnetism. Nature 571, 240 (2019). 279 [Malinowski08] G. 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Viret, Simple model of current-induced spin torque in 339 domain walls. Phys. Rev. B 75, 024411 (2007). 340 [Waintal04] X. Waintal, and M. Viret, Current-induced distortion of a magnetic domain wall. 341 Europhys. Lett. 65, 427 (2004). + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +CD- XRMS experiments. (a) Experimental configuration with the incident beams of the IR pump and the x- ray probe. (b) Magnetic diffraction pattern, (CL+CR) (c) Dichroic pattern (CL- CR), displaying the typical signature of clockwise Néel domain walls. The images in panels b and c have been geometrically corrected to account for the projection related to the photon incidence angle \(\theta = 45^{\circ}\) , and the scale corresponds to the sum of the counts (500 XFEL pulse of each polarization) for (CL+CR) (b) and (CL- CR) (c). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Evolution of the XRMS signal over the first 5 ps: (a) intensity of integrated diffraction ring (CL+CR) and dichroism (CL- CR) normalized at their values at negative time delays; (b) experimental asymmetry ratio (CL- CR)/(CL+CR) normalized by its value at \(t < 0\) in grey circles and black dots. The simulations for different models discussed in the main text appear as colored lines (see Supplementary Materials S3 for + +<--- Page Split ---> + +details). (c) Full width at half maximum (FWHM) (red dots) and the position (blue circles) in reciprocal space of the magnetic dichroic peak as a function of time. + +![](images/Figure_3.jpg) + +
Figure 3
+ +Magnetization texture modification by hot electrons. (a) Schematic representation of the torque (black arrows) imposed by the 'hot spins' flowing from the domains to the DWs resulting in transient mixed Bloch/Neel/Bloch contributions. (b) Transient DW shape. (c) Precession angles (red) and DW magnetization normalized by Domain one (blue) used in the simulations of the asymmetry ratio shown in Fig. 2(b). + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- trXRMSSupplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d_det.mmd b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d11d65aa44a9342e2f66d04170803a3687f7756f --- /dev/null +++ b/preprint/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d/preprint__001ce7d757b4396ce7f19d0548d511beacdaa8866b8ea15323e2c67a11e2b04d_det.mmd @@ -0,0 +1,265 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 923, 210]]<|/det|> +# Ultrafast time-evolution of chiral Néel magnetic domain walls probed by circular dichroism in x-ray resonant magnetic scattering. + +<|ref|>text<|/ref|><|det|>[[44, 229, 231, 270]]<|/det|> +Cyril Lévéillé Synchrotron SOLEIL + +<|ref|>text<|/ref|><|det|>[[44, 276, 231, 316]]<|/det|> +Erick Burgos- Parra Synchrotron SOLEIL + +<|ref|>text<|/ref|><|det|>[[44, 323, 955, 364]]<|/det|> +Yanis Sassi Unité Mixte de Physique, CNRS, Thales, Université Paris- Saclay https://orcid.org/0000- 0003- 0703- 6068 + +<|ref|>text<|/ref|><|det|>[[44, 368, 750, 410]]<|/det|> +Fernando Ajejas Unité Mixte de Physique CNRS/Thales https://orcid.org/0000- 0001- 8980- 4475 + +<|ref|>text<|/ref|><|det|>[[44, 415, 857, 456]]<|/det|> +Valentin Chardonnet Sorbonne Université, CNRS, Laboratoire Chimie Physique – Matière et Rayonnement, LCPMR + +<|ref|>text<|/ref|><|det|>[[44, 460, 639, 503]]<|/det|> +Emanuele Pedersoli Elettra- Sincrotrone Trieste https://orcid.org/0000- 0003- 0572- 6735 + +<|ref|>text<|/ref|><|det|>[[44, 507, 639, 549]]<|/det|> +Flavio Capotondi Elettra Sincrotrone Trieste https://orcid.org/0000- 0003- 1980- 6162 + +<|ref|>text<|/ref|><|det|>[[44, 554, 901, 596]]<|/det|> +Giovanni De Ninno University of Nova Gorica and Elettra- Sincrotrone Trieste https://orcid.org/0000- 0002- 4648- 4413 + +<|ref|>text<|/ref|><|det|>[[44, 600, 630, 642]]<|/det|> +Francesco Maccherozzi Diamond Light Source, Chilton, Didcot, Oxfordshire, OX11 0DE, UK. + +<|ref|>text<|/ref|><|det|>[[44, 647, 610, 689]]<|/det|> +Samjeet Dhesi Diamond Light Source https://orcid.org/0000- 0003- 4966- 0002 + +<|ref|>text<|/ref|><|det|>[[44, 694, 767, 735]]<|/det|> +David Bum Diamond Light Source (United Kingdom) https://orcid.org/0000- 0001- 7540- 1616 + +<|ref|>text<|/ref|><|det|>[[44, 740, 610, 782]]<|/det|> +Gerrit van der Laan Diamond Light Source https://orcid.org/0000- 0001- 6852- 2495 + +<|ref|>text<|/ref|><|det|>[[44, 787, 225, 828]]<|/det|> +Oliver Latcham University of Exeter + +<|ref|>text<|/ref|><|det|>[[44, 834, 225, 874]]<|/det|> +Andrei Shytov University of Exeter + +<|ref|>text<|/ref|><|det|>[[44, 880, 580, 921]]<|/det|> +Volodymyr Kruglyak University of Exeter https://orcid.org/0000- 0001- 6607- 0886 + +<|ref|>text<|/ref|><|det|>[[44, 926, 186, 944]]<|/det|> +Emmanuelle Jal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 45, 589, 64]]<|/det|> +Sorbonne Université https://orcid.org/0000- 0001- 5297- 9124 + +<|ref|>sub_title<|/ref|><|det|>[[44, 70, 157, 88]]<|/det|> +## Vincent Cros + +<|ref|>text<|/ref|><|det|>[[50, 91, 944, 112]]<|/det|> +Unité Mixte de Physique CNRS,Thales, Université Paris- Saclay https://orcid.org/0000- 0003- 0272- 3651 + +<|ref|>sub_title<|/ref|><|det|>[[44, 117, 223, 136]]<|/det|> +## Jean-Yves Chauleau + +<|ref|>text<|/ref|><|det|>[[55, 139, 397, 158]]<|/det|> +Service de Physique de l'Etat Condensé + +<|ref|>sub_title<|/ref|><|det|>[[44, 164, 176, 182]]<|/det|> +## Nicolas Reyren + +<|ref|>text<|/ref|><|det|>[[50, 185, 745, 205]]<|/det|> +Unité Mixte de Physique CNRS/Thales https://orcid.org/0000- 0002- 7745- 7282 + +<|ref|>sub_title<|/ref|><|det|>[[44, 210, 150, 228]]<|/det|> +## Michel Viret + +<|ref|>text<|/ref|><|det|>[[50, 231, 950, 251]]<|/det|> +SPEC, CEA,CNRS, Université Paris- Saclay, 91191 Gif- sur- Yvette https://orcid.org/0000- 0001- 6320- 6100 + +<|ref|>text<|/ref|><|det|>[[44, 255, 555, 275]]<|/det|> +Nicolas Jaouen ( Nicolas.jaouen@synchrotron- soleil.fr) + +<|ref|>text<|/ref|><|det|>[[55, 278, 590, 297]]<|/det|> +Synchrotron SOLEIL https://orcid.org/0000- 0003- 1781- 7669 + +<|ref|>sub_title<|/ref|><|det|>[[44, 338, 102, 356]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 375, 735, 396]]<|/det|> +Keywords: Dzyaloshinskii- Moriya interaction, chiral Néel magnetic domain walls + +<|ref|>text<|/ref|><|det|>[[44, 414, 301, 433]]<|/det|> +Posted Date: March 3rd, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 451, 463, 471]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 271463/v1 + +<|ref|>text<|/ref|><|det|>[[44, 489, 911, 533]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 567, 925, 610]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 17th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28899- 0. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 82, 884, 172]]<|/det|> +1 Ultrafast time-evolution of chiral Néel magnetic domain walls 2 probed by circular dichroism in x-ray resonant magnetic 3 scattering. + +<|ref|>text<|/ref|><|det|>[[70, 228, 884, 345]]<|/det|> +6 Cyril Léveillé \(^{1}\) , Erick Burgos-Parra \(^{1,2}\) , Yanis Sassi \(^{2}\) , Fernando Ajejas \(^{2}\) , Valentin Chardonnet \(^{3}\) , Emanuele Pedersoli \(^{4}\) , Flavio Capotondi \(^{4}\) , Giovanni De Ninno \(^{4,5}\) , Francesco Maccherozzi \(^{6}\) , Sarnjeet Dhesi \(^{6}\) , David M. Burn \(^{6}\) , Gerrit van der Laan \(^{6}\) , Oliver S. Latcham \(^{7}\) , Andrey V. Shytov \(^{7}\) , Volodymyr V. Kruglyak \(^{7}\) , Emmanuelle Jal \(^{3}\) , Vincent Cros \(^{2}\) , Jean-Yves Chauleau \(^{8}\) , Nicolas Reyren \(^{2}\) , Michel Viret \(^{8}\) and Nicolas Jaouen \(^{1}\) + +<|ref|>text<|/ref|><|det|>[[110, 355, 840, 375]]<|/det|> +\(^{1}\) Synchrotron SOLEIL, Saint-Aubin, Boite Postale 48, 91192 Gif-sur-Yvette Cedex, France + +<|ref|>text<|/ref|><|det|>[[110, 383, 808, 404]]<|/det|> +\(^{2}\) Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767 Palaiseau, France + +<|ref|>text<|/ref|><|det|>[[110, 412, 839, 454]]<|/det|> +\(^{3}\) Sorbonne Université, CNRS, Laboratoire Chimie Physique – Matière et Rayonnement, LCPMR, 75005 Paris, France + +<|ref|>text<|/ref|><|det|>[[110, 461, 568, 483]]<|/det|> +\(^{4}\) Elettra-Sincrotrone Trieste, 34149 Basovizza, Trieste, Italy + +<|ref|>text<|/ref|><|det|>[[110, 490, 548, 510]]<|/det|> +\(^{5}\) University of Nova Gorica, 5000 Nova Gorica, Slovenia + +<|ref|>text<|/ref|><|det|>[[110, 518, 579, 538]]<|/det|> +\(^{6}\) Diamond Light Source, Didcot OX11 0DE, United Kingdom. + +<|ref|>text<|/ref|><|det|>[[110, 545, 655, 565]]<|/det|> +\(^{7}\) University of Exeter, Stocker road, Exeter, EX4 4QL, United Kingdom. + +<|ref|>text<|/ref|><|det|>[[110, 572, 686, 593]]<|/det|> +\(^{8}\) SPEC, CEA, CNRS, Université Paris-Saclay, 91191 Gif-sur-Yvette, France + +<|ref|>text<|/ref|><|det|>[[110, 630, 884, 877]]<|/det|> +Non- collinear spin textures in ferromagnetic ultrathin films are attracting a renewed interest fueled by possible fine engineering of several magnetic interactions, notably the interfacial Dzyaloshinskii- Moriya interaction. This allows the stabilization of complex chiral spin textures such as chiral magnetic domain walls (DWs), spin spirals, and magnetic skyrmions. We report here on the ultrafast behavior of chiral DWs after optical pumping in perpendicularly magnetized asymmetric multilayers, probed using time- resolved circular dichroism in x- ray resonant magnetic scattering (CD- XRMS). We observe a picosecond transient reduction of the CD- XRMS, which is attributed to the spin current- induced coherent and incoherent torques within the continuously dependent spin texture of the DWs. We argue that a specific demagnetization of the inner structure of the DW induces a flow of spins from the interior of the neighboring magnetic domains. We identify this time- varying change of the DW texture shortly after the laser pulse as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 880, 123]]<|/det|> +a distortion of the homochiral Néel shape toward a transient mixed Bloch- Néel- Bloch texture along a direction transverse to the DW. + +<|ref|>text<|/ref|><|det|>[[113, 144, 884, 592]]<|/det|> +Ultrafast demagnetization of a ferromagnet by an optical pulse was first demonstrated in 1996 in the seminal study by Beaurepaire et al [Beaurepaire96], which is widely considered as the birth of the research field of femtomagnetism, i.e., the magnetism modulated ("pumped") by femtosecond long laser pulses. While several underlying mechanisms are considered to explain these ultrafast processes, the central role of spin dependent transport of hot electrons has been clearly evidenced [Melnikov11, Siegrist19]. Such phenomena were first experimentally demonstrated in spin valves, in which the demagnetization process is faster for antiparallel alignment of the magnetization in the magnetic layers [Malinowski08]. Models based on polarized electron transport in the superdiffusive regime have been subsequently developed [Battiat0o]. The optically excited hot electrons, initially ballistic, with spin- dependent lifetimes and velocities, generate non- equilibrium spin currents either within a ferromagnetic layer or in adjacent non- magnetic layer. The induced loss of angular momentum greatly participates in ultrafast dynamical behavior of the magnetization [vodungbo16]. The existence of this phenomenon has also been tested in single magnetic layers with a heterogeneous magnetization configuration, i.e., containing a large density of magnetic domains and DWs, albeit with different conclusions [Moisan14, vodungbo16, Pfau2012]. X- ray diffraction experiments are in this latter case more powerful for probing the behavior of DWs [zusin2020, Kerber20, Hennes20b]. For example, Pfau et al. [Pfau2012] inferred that the DW size changes in the first few ps by investigating the variations of the first- order Bragg peak of the magnetic configuration. More recently, the studies of Zuzin et al. [Zuzin2020] and Hennes et al. [hennes2020b] have shown that a more precise way to extract insights about DWs is to study the position and width of higher order diffraction peaks. + +<|ref|>text<|/ref|><|det|>[[113, 619, 884, 911]]<|/det|> +In this Letter, we use circular dichroism in x- ray resonant magnetic scattering (CD- XRMS) to gain access to the internal spin texture of the domain walls. This technique permits indeed a direct determination of the type (Néel or Bloch) as well as of the effective chirality of the DWs [Dürr99, chauleau2018]. Magnetic multilayers with homochiral Néel DWs stabilized by a large interfacial Dzyaloshinskii- Moriya (DM) interaction [Fert80, Fert90] are ideal systems to study DW dynamics at the fs timescales. In recent studies, this approach was used [Zhang17, chauleau2018, Legrand2018, Zhang20] to investigate the intrinsic nature of DWs and skyrmionic systems, which is currently a topic of the utmost relevance from both fundamental and technological viewpoints [Thiaville12, Ruy13, Nagoasa13, Fert17, Yang15]. The degree of circular dichroism in these experiments is not only related to the homochiral nature of the magnetic textures but also to the intrinsic DW configuration and allows us to probe the size and magnetization ratio of domain/domain- wall with unprecedented sensitivity. We hence unveil the ultrafast dynamics of these domain walls, unambiguously showing a specific behavior compared to that of the domains. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 104, 884, 349]]<|/det|> +The system under study is an asymmetric magnetic multilayer \(\mathrm{[Pt(3nm)|Co(1.5nm)|Al(1.4nm)|x_5}\) grown by sputtering on a thermally oxidized Si wafer buffered by \(\mathrm{Ta(5)|Pt(5)}\) (see Supplementary Sec. S1 for details) presenting perpendicular magnetic anisotropy and large interfacial DM interaction. At remanence, domains adopt a typical disordered labyrinthine structure, but with a narrow distribution of domain widths. The magnetization and anisotropy are measured by SQUID magnetometry, while the DM amplitude is determined by comparing the experimentally measured (by magnetic force microscopy) domain periodicity to those simulated using micromagnetic calculations with MuMax3 [Vansteenkiste14] (see Supplemental Material S1 for details about the magnetic preparation and the simulations). From these calculations, we can also estimate the DW width to be \(\sim 20 \mathrm{nm}\) . The micromagnetic simulations are also used as inputs in the empirical XRMS model with accurate values for the width of the DW. + +<|ref|>text<|/ref|><|det|>[[113, 355, 884, 693]]<|/det|> +The time- resolved XRMS experiments have been performed on the DiProI beam line [Capotondi13] at the FERMI free electron laser [Allaria12] (Trieste, Italy). Time resolution is achieved using a standard pump- probe approach [Fig. 1(a)] in which the probe is a 60 fs XUV pulse at the Co \(M\) edge energy (photon energy \(\sim 60 \mathrm{eV}\) ) and the pump is a 100 fs infrared laser pulse (780 nm). The overall time resolution is therefore \(\sim 120 \mathrm{fs}\) . The scattering experiments have been conducted under reflectivity condition at \(45^{\circ}\) incidence for circularly left (CL) and right (CR) x- ray polarization allowing to acquire ultrafast snapshots of diffraction diagrams (Fig. 1b) and their corresponding circular dichroism (Fig. 1c) at each delay time of the infrared (IR) excitation (see S2 for details). Noteworthy, the degree of x- ray circular polarization is between \(92 - 95\%\) [Allaria14]. Regarding the probe and pump energy densities, the IR fluence was set to \(4.8 \mathrm{mJ / cm^2}\) (at a repetition rate of \(50 \mathrm{Hz}\) ) and the FEL fluence was set to \(0.5 \mathrm{mJ / cm^2}\) . At the Co \(M\) edge, with \(45^{\circ}\) photon incidence angle, the penetration depth is \(\sim 10 \mathrm{nm}\) , therefore most of the scattered signal comes from the uppermost Co layers. Such a small penetration depth also ensures that the expected tilting of the Ewald sphere is negligible in our experiment. Finally, we decided to perform the experiment at the peak of the absorption resonance to avoid any spurious effect caused by the energy shift of the XAS edge at ultrafast timescales [yao20, hennes20]. + +<|ref|>image<|/ref|><|det|>[[120, 702, 860, 860]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 867, 881, 901]]<|/det|> +
Figure 1: CD-XRMS experiments. (a) Experimental configuration with the incident beams of the IR pump and the x-ray probe. (b) Magnetic diffraction pattern, (CL+CR) (c) Dichroic pattern (CL-CR), displaying the typical signature of clockwise Néel
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 81, 883, 135]]<|/det|> +domain walls. The images in panels b and c have been geometrically corrected to account for the projection related to the photon incidence angle \(\theta = 45^{\circ}\) , and the scale corresponds to the sum of the counts (500 XFEL pulse of each polarization) for \((CL + CR)\) (b) and \((CL - CR)\) (c). + +<|ref|>text<|/ref|><|det|>[[114, 161, 884, 430]]<|/det|> +A typical diffraction pattern of the magnetic system at negative time delays, i.e. before the laser pulse excitation, is displayed in Fig. 1(b) in which the diffracted intensity is the sum of the two circular polarizations (CL+CR). It results from the x- ray diffraction on the labyrinth structure with a period of \((330 \pm 20) \mathrm{nm}\) (estimated from the ring radius). The total magnetic scattering intensity mainly comes from the alternating out- of- plane magnetic domains. The diffraction intensity also displays circular dichroism (CL- CR) [Fig. 1(c)], which reverses its sign on each side (along \(Q_{y}\) ) of the specular reflection, and reaches about \(10\%\) . Such dichroic signal is known [Durr99] to be a signature of an uncompensated sense of rotation in non- collinear magnetic textures. In our experiment, the sign of the dichroism indeed reveals the stabilization of clockwise (CW) Néel DW as we recently demonstrated [Chauleau2018]. The observed features have been corroborated by static scattering measurements at the Co \(L\) edge performed at the SEXTANTS beamline at SOLEIL [Sacchi13], for which the interpretation is now well established (see Supplementary Materials S1). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 90, 486, 556]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 570, 883, 658]]<|/det|> +
Figure 2: Evolution of the XRMS signal over the first 5 ps: (a) intensity of integrated diffraction ring \((CL + CR)\) and dichroism \((CL - CR)\) normalized at their values at negative time delays; (b) experimental asymmetry ratio \((CL - CR) / (CL + CR)\) normalized by its value at \(t< 0\) in grey circles and black dots. The simulations for different models discussed in the main text appear as colored lines (see Supplementary Materials S3 for details). (c) Full width at half maximum (FWHM) (red dots) and the position (blue circles) in reciprocal space of the magnetic dichroic peak as a function of time.
+ +<|ref|>text<|/ref|><|det|>[[115, 673, 883, 893]]<|/det|> +The time dependence of both the magnetic intensity \((CL + CR)\) of the overall diffraction ring [Fig 2(a)] and the dichroism \((CL - CR)\) shows a typical signature of ultrafast demagnetization in metallic magnetic ultrathin layers: first, a quench of the magnetization reaching a minimum value after a few hundreds of fs, followed by a log- like recovery over a few ps. The experimental results are further analyzed by plotting the asymmetry ratio, i.e., \((CL - CR) / (CL + CR)\) as a function of time [see Fig. 2(b)], which represents the DW behavior normalized by the total magnetic moment. If the DW magnetization follows the same dynamics as that of the domains, this ratio should not vary. It is plotted in Fig. 2b (normalized by its value before the pump pulse) where one clearly observes a \(15\%\) dip at \(\sim 0.7\) ps. This has been reproducibly observed when repeating the experiment, as demonstrated by the overlapping series of black filled and open circles in Fig. 2(b) showing identical behavior within error bars (inferred + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 884, 258]]<|/det|> +from the statistical fit of the background and peak intensity, see Supplemental Material Section S2). The normalized ratio remains below 1.0 up to 2 ps. The time evolution of the peak position defined by the maximum of its Gaussian fit, and of the full width at half maximum (FWHM) in reciprocal space of the magnetic dichroic peak are displayed in Fig. 2(c). Those two quantities generally correspond respectively to the variation of the domain size and their distribution. However, this apparent domain extension corresponds in fact to an expansion of the DW in the first ps after optical excitation, as reported by Pfau et al. [Pfau 2012]. When considering this expansion according to the value reported in Fig. 2(c), an increase of the asymmetry ratio is predicted as shown by the blue curve in Fig. 2(b). + +<|ref|>text<|/ref|><|det|>[[114, 264, 884, 666]]<|/det|> +To explain this ultrafast deviation of the dichroism asymmetry ratio, we first exclude an origin due to a change in the scattering factors induced by hot electrons filling the \(d\) band. Indeed, the IR laser fluence of our experiment is much lower ( \(\sim 10\%\) ) than the one used to probe the change of electron occupation induced by the IR pulse using x- ray absorption spectroscopy (XAS) [Mathieu18]. Thus, we explain our observation by the fact that during the demagnetization (resp. remagnetization), the magnetic moments do not decrease (resp. increase) by the same amount simultaneously inside the DWs and inside the domains. If the magnetization decreased uniformly, the expected asymmetry ratio would be constant, as shown by simulation using a model that is detailed in Supplemental Material S3 [magenta line in Fig. 2(b)]. As explained above, the sole expansion of the DW widths cannot explain our data [blue curve in Fig. 2(c)]. To explain an asymmetry ratio dropping below its initial value, we resort to a reduction of the degree of magnetic chirality. In other words, it corresponds to a change of the ratio between the out- of- plane and the in- plane magnetization. In our interpretation, the ultra- fast decrease of the asymmetry ratio below 1.0 is linked to a different demagnetization rate between the DWs and the domains. Note that a scenario that would correspond to a faster remagnetization of the DWs than the domains shall result into an asymmetry ratio larger than 1 (similarly to the expansion of the DW), and therefore can also be safely ruled out. In the following, in order to reproduce our experimental observations, the simulations include both coherent evolution of the hot electron spins that induce a spin torque on the DW and spin temperature (incoherent) variations within the DWs. + +<|ref|>text<|/ref|><|det|>[[115, 694, 883, 916]]<|/det|> +The understanding of the ultrafast DW width expansion requires considering the intense flow of spin currents in the ps regime. These can efficiently transfer angular momentum to and from the ferromagnetic material as shown, e.g., when Pt layers absorb it and generate ps electrical pulses [Kampfrath13]. Angular momentum transfer and dissipation often results in both enhanced demagnetization as well as a faster magnetization recovery. We argue that this is exactly what is happening with the non- collinear magnetic regions inside the DWs. The enhanced spin scattering within DWs is a rather old topic born with studies of the extra contribution to the static magnetoresistance [Viret96] or the induced spin transfer torques resulting in their current- induced displacement. To this aim, ballistic models have been developed and can be appropriately adapted for the ultrafast demagnetization scenario in which superdiffusive spin currents play a central role [Battiato10]. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 214]]<|/det|> +behavior of ballistic spin carriers can be described such as a classical spinned particle perceiving a time varying exchange field while crossing the wall [Viret96, Vanhaverbeke07]. Let us recaller salient features. First, these are band particles that are coupled by exchange to the localized spins (through the so- called \(s - d\) Hamiltonian). Their velocity perpendicular to the wall is related to their momentum in \(k\) space. With the appropriate parameter renormalization, the problem is equivalent to the "fast adiabatic passage" known, e.g., in NMR theory. The spin evolution is given by the Landau- Lifshitz equation: + +<|ref|>equation<|/ref|><|det|>[[428, 216, 567, 252]]<|/det|> +\[\frac{d\vec{\mu}}{dt} = \frac{J_{ex}S}{\hbar}\vec{m}\times \vec{\mu}\] + +<|ref|>text<|/ref|><|det|>[[113, 257, 884, 696]]<|/det|> +where \(\vec{\mu}\) is the electron spin, \(J_{\mathrm{ex}}S\) the exchange energy with the localized moment \((S)\) and \(\vec{m}\) the direction of the time varying exchange field seen by the ballistic electrons. The localized moments are rotating in a Neel fashion within the DW and the problem is generally treated in this rotating frame [Vanhaverbeke07]. Basically, the electronic spins will precess around the localized moment effective field and thus acquire a component out of the plane of rotation, inducing a torque parallel to the chiral vector: \(S_{i}\times S_{j}\) . The electron spin precession angle \(\omega\) is proportional to the velocity \(v\) divided by exchange times and the DW width \(2\pi \Delta\) [Viret96]: \(< \omega > = \frac{\pi h\nu}{J_{\mathrm{ex}}S2\pi\Delta}\) . Typically, for electrons at the Fermi level, this precession angle is found to be around 7 degrees for a DW width \(2\pi \Delta\) of \(15\mathrm{nm}\) [Vanhaverbeke07]. However, it is to be noticed that this angle can be quite different for the hot electrons produced in the demagnetization process as the relevant parameter values are hard to quantify. Although their velocities should not be too far from those at the Fermi level (in the \(10^{6}\mathrm{m / s}\) range [Kampfrath13]), the exchange energies effective in bands over \(1\mathrm{eV}\) above the Fermi level can be dramatically reduced \((\sim 0.1\mathrm{eV})\) . Therefore, the expected mistracking angle could be significantly greater for a large part of the hot electrons' distribution. All these processes shall in turn generate a torque applied on the localized moments [Waintal04]. However, because the hot spin currents flow in all directions, mistracking angles can be both positive and negative, resulting in cancellation of the net torque acting on the DWs. The overall effect of the incoherent precession results in an average loss of angular momentum. This should speed up the spin relaxation processes within the DW so that after some \(100\mathrm{fs}\) , a net spin current is established from the domains into the interior of the DWs. + +<|ref|>text<|/ref|><|det|>[[114, 701, 884, 900]]<|/det|> +The new components of the spin- transfer torque resulting from this latter spin current originating from the coherent evolution of the hot electron spins are not cancelled out. Importantly such torques are of opposite sign on the two sides of the DW and should induce a sizeable tilting of the DW magnetization out of the Neel plane as illustrated in Fig. 3(a). This phenomenon is at the origin of a new transient DW shape, made of a Neel type center surrounded by opposite Bloch types as depicted in Fig. 3(b). Such a mixed Bloch/Neel/Bloch contribution will in turn lead to a transient reduction of the measured chirality as it adds two (opposite) Bloch components on both sides of the DW compared to the originally purely Neel character. In order to estimate the amplitude of this DW distortion, it is useful to realize that unlike small current- induced electron flows at the Fermi level, spin fluxes during demagnetization are + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 884, 373]]<|/det|> +enormous as for each pulse, typically 0.5 electrons per Co atom are excited to higher bands for the used laser fluence [Kampfrath13]. The timescale for the onset of the induced torques is given by the exchange energy and falls in the 10- fs range, ensuring that the wall distortion does not lag from the population of hot electrons. For a spin temperature sufficiently different between domains and DWs, a quantitative estimate using the abovementioned parameters gives a precession angle of the magnetization inside the DW that is larger than 10 degrees. Moreover, the onset of this Bloch component in the DW must leaks out into the domains, thus slightly increasing the effective DW width as also observed experimentally. The measured expansion of the DW can be directly derived from the variation of the dichroic peak position and width shown in Fig. 2(c). We find that the DW width (slightly) increases rapidly and its magnetization reaches a minimum around 1 ps (blue curve), as reported previously for Bloch type DWs [Pfau2012]. Note that this DW expansion takes place when the quenched magnetization starts to recover (1 ps). After reaching it maximum expansion, the DW width then recovers its original (unpumped represented as dotted lines in Fig. 2(c) size at a timescale of \(\sim 5\) ps. + +<|ref|>image<|/ref|><|det|>[[118, 421, 876, 608]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 615, 881, 686]]<|/det|> +
Figure 3: Magnetization texture modification by hot electrons. (a) Schematic representation of the torque (black arrows) imposed by the 'hot spins' flowing from the domains to the DWs resulting in transient mixed Bloch/Neel/Bloch contributions. (b) Transient DW shape. (c) Precession angles (red) and DW magnetization normalized by Domain one (blue) used in the simulations of the asymmetry ratio shown in Fig. 2(b).
+ +<|ref|>text<|/ref|><|det|>[[114, 725, 883, 901]]<|/det|> +Using a 1D magnetization profile (described in Supplementary Material S3) and considering the experimental change of magnetization (extracted directly from the square root of the (CL+CR) intensity), the time evolution of the asymmetry ratio can be simulated. We consider a magnetization in the domains extracted from the (CL+CR) data, along with a further \(12\%\) reduction of the magnetization inside the DWs to account for incoherent effects, as well as a transient Bloch- Néel- Bloch wall as shown in Fig. 3(a) for coherent ones. With these simulations, we find that the precession angle can reach at the maximum about 8 degrees after a time delay of \(\sim 0.6\) ps [red curve in Fig. 3(c)] simultaneously with the reduction of the DW magnetization [relative to domain magnetization blue curve in Fig. 3(c)], The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 259]]<|/det|> +resulting simulated asymmetry ratio using the described model is plotted as the green curve in Fig. 2(b), and is in excellent agreement with the experimental measurements. Even accounting for DW expansion [red curve in Fig. 2(b)], the agreement can be obtained for a \(\sim 10\) degrees tilt angle. Although the exchange driven DW distortion is established on a very short timescale, it should last for the nanosecond timescale of the micromagnetic evolution. On the other hand, the incoherent part of the spin current shall relax at the ps timescale of the remagnetization processes, similarly to what we have measured. Interestingly, enhanced spin relaxation existing inside the DWs should speed up remagnetization, explaining that the asymmetry ratio can exceed 1, again in agreement with the experimental results. + +<|ref|>text<|/ref|><|det|>[[113, 294, 884, 741]]<|/det|> +In conclusion, we report here about the experimental investigation of the ultra- short timescale evolution of complex chiral Néel spin textures after laser induced demagnetization. Circular dichroism in x- ray resonant magnetic scattering is used to obtain information in the time domain about both the magnetic domain configuration and the magnetic chirality. Beyond the evolution of the period of the magnetic domains in magnetic multilayers with large perpendicular anisotropy, we acquire new insights into the way that the chirality of the non- collinear spin textures, and their long- range ordering, is evolving in the few ps after demagnetization by a strong optical pulse. We observe that the magnetic difference CL- CR (reflecting the DW properties) reduces faster than the diffracted sum signal (associated to domain magnetization) in the first 2 ps after the laser pulse. To explain this unexpected change of XRMS chirality signal at this short timescale, we propose that angular momentum flowing from the interior of the domains inside the DWs associated to hot electrons induces an ultrafast distortion of the DW magnetization. This transient in- plane deformation of the DWs leads to a transient mixed Bloch- Néel- Bloch DW accompanied by an increase of the DWs width and a reduction of the magnetization inside the DW. These original experimental results are reproduced by calculations, considering a magnetization reduction of \(12\%\) with a 7.5 degrees distortion of the DW. On a longer timescale, i.e., after a few ps, the DWs return to their pure chiral Néel configuration preserving the original sense of rotation (i.e., chirality) together with a recovery of their magnetization. We emphasize that our approach using dichroism in x- ray resonant scattering is applicable to any other magnetic chiral texture and should provide a better understanding of the evolution of the chirality of spin textures on the ultrafast timescale. + +<|ref|>text<|/ref|><|det|>[[114, 783, 884, 912]]<|/det|> +Acknowledgment: We acknowledge Ivaylo Nikolov, Michele Manfredda, Luca Giannessi, and Giuseppe Penco for their inestimable help to set up the FEL and the laser for our experiment. NJ would like to thanks S. Flewett for discussion on XRMS simulations. Financial supports from FLAG- ERA SographMEM (ANR- 15- GRFL- 0005), funding from the Agence Nationale de la Recherche, France, under grant agreement no. ANR- 17- CE24- 0025 (TOPSKY) and 18- CE24- 0018- 01 (SANTA), the Horizon2020 Framework Program of the European Commission under FET- Proactive Grant agreement + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 80, 884, 899]]<|/det|> +268 no. 824123 (SKYTOP). E. J. is grateful for financial support received from the CNRS- MOMENTUM. 269 O. S. L., A. V. S., and V. V. K. acknowledge funding from the Engineering and Physical Sciences 270 Research Council (EPSRC) in the United Kingdom (Grant No. EP/T016574/1). 271 272 273 References: 274 [beaurepaire96] E. Beaurepaire et al., Ultrafast Spin Dynamics in Ferromagnetic Nickel 275 Phys. Rev. Lett. 76, 4250 (1996). 276 [Melnikov11] A. Melnikov et al. Ultrafast Transport of Laser- Excited Spin- Polarized Carriers 277 in Au/Fe/MgO(001). Phys. Rev. Lett. 107, 076601 (2011). 278 [Siegrist19] F. Siegrist, Light- wave dynamic control of magnetism. Nature 571, 240 (2019). 279 [Malinowski08] G. Malinowski, et al., Control of speed and efficiency of ultrafast demagnetization by 280 direct transfer of spin angular momentum. Nat. Phys. 4, 855 (2008) 281 [Battiato10] M. Battiato et al., Superdiffusive Spin Transport as a Mechanism of Ultrafast 282 Demagnetization. Phys. Rev. Lett. 105, 027203 (2010) 283 [vodungbo16] B. Vodungbo et al., Indirect excitation of ultrafast demagnetization. Sci. Rep. 6, 18970 284 (2016). 285 [Moisan14] N. Moisan et al., Investigating the role of superdiffusive currents in laser induced 286 demagnetization of ferromagnets with nanoscale magnetic domains. Sci. Rep. 4, 4658 (2014). 287 [Pfau2012] B. Pfau et al., Ultrafast optical demagnetization manipulates nanoscale spin structure in 288 domain walls, Nat. Commun. 3, 1100 (2012). 289 [zuzin2020] D. Zusin et al., Ultrafast domain dilation induced by optical pumping in ferromagnetic 290 CoFe/Ni multilayers. https://arxiv.org/abs/2001.11719 291 [Kerber20] N. Kerber et al., Faster chiral versus collinear magnetic order recovery after optical 292 excitation revealed by femtosecond XUV scattering. Nat. Commun. 11, 6304 (2020). 293 [Hennes20b] M. Hennes et al., Laser- induced ultrafast demagnetization and perpendicular magnetic 294 anisotropy reduction in a Co88Tb12 thin film with stripe domains. Phys. Rev. B 102, 174437 (2020) 295 [Durr99] H. A. Durr et al, Chiral Magnetic Domain Structures in Ultrathin FePd Films. Science 284, 296 (1999). 296 [Chauleau2018] J.- Y. Chauleau et al., Chirality in Magnetic Multilayers Probed by the Symmetry and 298 the Amplitude of Dichroism in X- Ray Resonant Magnetic Scattering. Phys. Rev. Lett. 120, 037202 299 (2018). 300 [Fert80] A. Fert and P.M. Levy. Role of Anisotropic Exchange Interactions in Determining the 301 Properties of Spin- Glasses. Phys. Rev. Lett. 44, 1538 (1980). 302 [Fert90] A. Fert, « Magnetic and Transport Properties of Metallic Multilayers », Materials Science 303 Forum, 59- 60, 439 (1990). 304 [Zhang17] S. L. Zhang et al. Direct experimental determination of the topological winding number of 305 skyrmions in Cu2OSeO3. Nat. Commun. 8, 14619 (2017). 306 [Legrand2018] W. Legrand et al., Hybrid chiral domain walls and skyrmions in magnetic multilayers. 307 Sci. Adv. 4 : eaat0415 (2018) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 78, 880, 720]]<|/det|> +308 [Zhang20] S. L. Zhang et al., Robust Perpendicular Skyrmions and Their Surface Confinement. 309 NanoLett. 20 1428 (2020). 310 [Thiaville12] A. Thiaville et al., Dynamics of Dzyaloshinskii domain walls in ultrathin magnetic films. 311 Euro. Phys. Lett. 100, 57002 (2012). 312 [Ruy13] K. S. Ruy et al., Chiral spin torque at magnetic domain walls. Nat. Nanotech. 8, 527 (2013). 313 [Nagoasa13] N. Nagoasa and Y. Tokura, Topological properties and dynamics of magnetic skyrmions. 314 Nat. 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Viret, Simple model of current-induced spin torque in 339 domain walls. Phys. Rev. B 75, 024411 (2007). 340 [Waintal04] X. Waintal, and M. Viret, Current-induced distortion of a magnetic domain wall. 341 Europhys. Lett. 65, 427 (2004). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 70]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[45, 97, 944, 303]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 325, 115, 345]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[41, 367, 947, 500]]<|/det|> +CD- XRMS experiments. (a) Experimental configuration with the incident beams of the IR pump and the x- ray probe. (b) Magnetic diffraction pattern, (CL+CR) (c) Dichroic pattern (CL- CR), displaying the typical signature of clockwise Néel domain walls. The images in panels b and c have been geometrically corrected to account for the projection related to the photon incidence angle \(\theta = 45^{\circ}\) , and the scale corresponds to the sum of the counts (500 XFEL pulse of each polarization) for (CL+CR) (b) and (CL- CR) (c). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 52, 551, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 802, 116, 821]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 841, 944, 932]]<|/det|> +Evolution of the XRMS signal over the first 5 ps: (a) intensity of integrated diffraction ring (CL+CR) and dichroism (CL- CR) normalized at their values at negative time delays; (b) experimental asymmetry ratio (CL- CR)/(CL+CR) normalized by its value at \(t < 0\) in grey circles and black dots. The simulations for different models discussed in the main text appear as colored lines (see Supplementary Materials S3 for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 928, 88]]<|/det|> +details). (c) Full width at half maximum (FWHM) (red dots) and the position (blue circles) in reciprocal space of the magnetic dichroic peak as a function of time. + +<|ref|>image<|/ref|><|det|>[[45, 92, 940, 333]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 356, 117, 376]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 398, 952, 511]]<|/det|> +Magnetization texture modification by hot electrons. (a) Schematic representation of the torque (black arrows) imposed by the 'hot spins' flowing from the domains to the DWs resulting in transient mixed Bloch/Neel/Bloch contributions. (b) Transient DW shape. (c) Precession angles (red) and DW magnetization normalized by Domain one (blue) used in the simulations of the asymmetry ratio shown in Fig. 2(b). + +<|ref|>sub_title<|/ref|><|det|>[[44, 533, 311, 560]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 583, 765, 603]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 621, 317, 641]]<|/det|> +- trXRMSSupplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/images_list.json b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2adb08171e20d33de82cd7158de0531298edf4e3 --- /dev/null +++ b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/images_list.json @@ -0,0 +1,40 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 48, + 50, + 945, + 450 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 45, + 45, + 950, + 610 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [], + "page_idx": 13 + } +] \ No newline at end of file diff --git a/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0.mmd b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0.mmd new file mode 100644 index 0000000000000000000000000000000000000000..eaf6e938e5bebef8d53bf19bda8b031a1636b406 --- /dev/null +++ b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0.mmd @@ -0,0 +1,221 @@ + +# Rationally synthesized framework polymer membranes enable high selectivity and barrierless anion conduction + +Zhengjin Yang yangz.j09@ustc.edu.cn + +University of Science and Technology of China https://orcid.org/0000- 0002- 0722- 7908 + +Junkai Fang University of Science and Technology of China + +Guozhen Zhang University of Science and Technology of China https://orcid.org/0000- 0003- 0125- 9666 + +Marc- Antoni Goulet Concordia University https://orcid.org/0000- 0002- 9146- 6759 + +Peipei Zuo University of Science and Technology of China https://orcid.org/0000- 0001- 5043- 7188 + +Hui Li University of Science and Technology of China + +Jun Jiang University of Science and Technology of China https://orcid.org/0000- 0002- 6116- 5605 + +Michael Guiver Tianjin University https://orcid.org/0000- 0003- 2619- 6809 + +Tongwen Xu University of Science and Technology of China https://orcid.org/0000- 0002- 9221- 5126 + +Article + +Keywords: + +Posted Date: June 11th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4392718/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on April 6th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58638-0. + +<--- Page Split ---> + +## Abstract + +AbstractThe understanding gleaned from studying ion transport within the interaction confinement regime enables the near- frictionless transport of cations (e.g., \(\mathrm{Na^{+} / K^{+}}\) ). However, anion transport (e.g., Cl⁻) is suppressed under confinement because of the different polarization of water molecules around cations and anions, also known as the charge asymmetry effect. Here we report the rational synthesis of anion- selective framework polymer membranes having similar densities of subnanometer- sized pores with nearly identical micropore size distributions, which overcome the charge asymmetry effect and promote barrierless anion conduction. We find that anion transport within the micropore free volume elements can be dramatically accelerated by regulating the pore chemistry, which lowers the energy barrier for anion transport, leading to an almost twofold increase in Cl⁻ conductivity and barrierless F⁻ diffusion. The resultant membrane enables an aqueous organic redox flow battery that utilizes Cl⁻ ions as charge carriers to operate at extreme current densities and delivers competitive performance to counterparts where K⁺ ions are charge carriers. These results may benefit broadly electrochemical devices and inspire single- species selectivity with separation membranes that exploit controlled or chemically gated ion/molecule transport. + +## Main Text + +Replicating the extreme selectivity and high permeability of biological ion channels is an enduring challenge for membrane scientists (1- 3). Beyond the generally- accepted mechanisms of size exclusion and Coulombic repulsion, it is argued that the subtle interactions between ions and channel walls at atomic- scale confinement play a crucial role. These interactions were not clearly elucidated until the fabrication of angstrom- scale slits/capillaries/channels with atomic- scale precision (4, 5). + +The spatial confinement of ion transport down to molecular- sized ion channels magnifies the impact of channel wall interactions and gives rise to exotic transport behavior. For example, hysteretic ion conduction occurs, resulting in an ion memory effect (6, 7), while the formation of Bjerrum ion pairs causes ionic Coulombic blockade (8). These atypical ion motions are intimately related to the dramatically enhanced material- dependent interactions between hydrated ions and the confining channel walls (e.g., electrostatic, adsorption/desorption) (9). For chemically inert and atomically smooth graphite channel walls, \(\mathrm{K^{+}}\) demonstrates a mobility close to that of the value in bulk solutions (10). By applying a voltage bias on the graphite channel, the streaming mobility of \(\mathrm{K^{+}}\) is increased by up to 20 times (11) and this may be ascribed to the electronic structure change under an external voltage bias (12). It has also been demonstrated that by introducing \(\mathrm{Li^{+}}\) - coordinating functionality within the shape- persistent free volume elements of microporous polymer membranes, \(\mathrm{Li^{+}}\) diffusivity can be greatly enhanced (13). Similar improvements to \(\mathrm{Na^{+}}\) transport have also been achieved by exploiting the synergy between micropore confinement and ion- membrane interactions (14). + +<--- Page Split ---> + +Despite the considerable improvements in cation transport due to the confinement effect, it is notable that chloride \((\mathsf{Cl}^{- })\) mobility experiences significant suppression under confinement. This charge asymmetry is likely due to the slightly different hydration shell configurations between \(\mathsf{Cl}^{- }\) and \(\mathsf{K}^{+}(10)\) . The mobility of \(\mathsf{Cl}^{- }\) under confinement is three times less than that of \(\mathsf{K}^{+}\) , even though \(\mathsf{Cl}^{- }\) and \(\mathsf{K}^{+}\) have similar mobilities in bulk water \((7.58\times 10^{- 8}\mathrm{vs.}7.86\times 10^{- 8}\mathrm{m}^{2}\mathrm{V}^{- 1}\mathrm{s}^{- 1})\) and hydrated diameters \((6.64\mathring{\mathrm{A}}\mathrm{vs.}\) 6.62 Å) (15, 16). For a more extreme case, \(\mathsf{Cs}^{+}\) and \(\mathsf{Cl}^{- }\) exhibit similar ion- core sizes and hydrated diameters, but \(\mathsf{Cl}^{- }\) exhibits more than three times lower mobility under \(\mathring{\mathrm{A}}\) - scale confinement \((1.7\times 10^{- 8}\) vs. \(6.0\times 10^{- 8}\mathrm{m}^{2}\mathrm{V}^{- 1}\mathrm{s}^{- 1})\) (16). For chloride salts of high valency cations, the overall \(\mathsf{Cl}^{- }\) mobility decreases to almost zero in single- digit- sized nanopores (17). A decrease in the mobility of other anions under confinement has also been observed (16). This phenomenon is echoed by the relatively high energy barrier associated with anion exchange membranes that transport chloride ions (see Supplementary Table S1). + +The transport and selectivity of anions are of critical relevance to applications such as direct seawater electrolysis (18), solid- state batteries (19) and redox flow batteries (20- 25). Understanding and overcoming the charge asymmetry effect for anion transport under confinement is therefore essential for enabling these technologies. Here we report the design and synthesis of a series of positively charged (quaternary ammonium cations) covalent triazine framework (QCTF) membranes with nearly the same density of rigid micropores with almost identical pore size distributions. The QCTF membranes exhibit Coulombic repulsion- induced anion selectivity, with a chloride transference number \(t_{- }\) of 0.95, and size exclusion- induced rejection of BTMAP- Vi (bis(3- trimethylammonio) propyl viologen tetrachloride) and FcNCl ((ferrocenylmethyl) trimethylammonium chloride), redox- active organic flow battery electrolytes. The cross- membrane BTMAP- Vi diffusion coefficient at \(3.1\times 10^{- 11}\mathrm{cm}^{2}\mathrm{s}^{- 1}\) is over 20 times lower than that of commercial membranes. We demonstrate that through on- membrane modification, the charge distribution of the pristine QCTF membrane framework can be regulated by protonation (affording P- QCTF) and methylation (affording M- QCTF), which dramatically alters the interactions between anions and the membrane framework and helps lower the energy barrier for anion transport. The cross- membrane \(\mathsf{Cl}^{- }\) conductivity increased twofold from \(13.2\mathrm{mScm}^{- 1}\) for QCTF to 25.9 \(\mathrm{mScm}^{- 1}\) for M- QCTF at \(30^{\circ}\mathrm{C}\) , and the activation energy for \(\mathsf{Cl}^{- }\) conduction decreased from \(20.6\mathrm{kJmol}^{- 1}\) to \(13.1\mathrm{kJmol}^{- 1}\) , lower than any value reported in the literature (see Supplementary Table S1). \(^{19}\mathrm{F}\) PFG- NMR revealed an increase in the \(\mathrm{F}^{- }\) diffusion coefficient from \(0.63\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for QCTF and \(0.93\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for P- QCTF, to \(1.1\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for M- QCTF which is close to the value in bulk water \((1.2\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1})\) . The greater anion conductivity can dramatically improve device performance as exemplified here in an BTMAP- Vi- and FcNCl- based aqueous organic redox flow battery (AORFB) in pH- neutral solutions. The BTMAP- Vi/FcNCl cell configured with the M- QCTF membrane exhibited a high- frequency area- specific resistance (ASR) as low as \(0.23\Omega \cdot \mathrm{cm}^{2}\) , which enabled charging and discharging of the BTMAP- Vi/FcNCl cell at an extreme current density of \(500\mathrm{mAcm}^{- 2}\) . The prolonged galvanostatic cell cycling at \(400\mathrm{mAcm}^{- 2}\) maintained a Coulombic efficiency of \(>99\%\) and a stable energy efficiency of around \(60\%\) over the course of 1000 cycles. Notably, the achieved capacity utilization and efficiency with + +<--- Page Split ---> + +M- QCTF approaches similar values to those of alkaline AORFBs that leverage \(\mathsf{K}^{+}\) as charge- carrying ions, while in otherwise identical cells assembled with QCTF or P- QCTF, an almost \(20\%\) lower energy efficiency was observed. This is significant and can be attributed to a dramatic reduction in the contribution of membrane resistance to whole- cell resistance, e.g., from \(>70\%\) for the Seleminon \(^{\circledR}\) AMV membrane to \(\sim\) \(25\%\) for M- QCTF (Supplementary Tables S2 and S3). The above results imply a breakthrough in the charge asymmetry effect. + +## Results and Discussion + +## Covalent triazine framework membranes with tunable pore chemistry + +Covalent triazine framework chemistry gives rise to a wide variety of microporous materials and offers enormous diversity in pore chemistry. We thus synthesized a stand- alone triazine framework membrane from \(4,4^{'}\) - biphenyldicarbonitrile and a derivative of 3- hydroxy- [1,1'- biphenyl]- 4,4'- dicarbonitrile bearing a quaternary ammonium moiety via a superacid- catalyzed organic sol- gel procedure (Fig. 1a and Supplementary Figures S1- S4) (26). The process yields a free- standing membrane (namely, QCTF) with a Young's modulus and tensile strength of 0.91 GPa and 32.0 MPa, respectively (Supplementary Figure S5). The skeletal triazine rings of QCTF were subsequently protonated with HCl or methylated with \(\mathrm{CH}_3\mathrm{I}\) , affording P- QCTF and M- QCTF, respectively. Overall, we constructed three covalent triazine framework polymers with similar molecular configurations and pore structures that can be processed into hydrophilic, uniform and robust ion- selective membranes via an organo- sol- gel procedure (Supplementary Figures S6- S8, Supplementary Table S4), but with slightly different and deliberately tailored pore chemistries. + +Carbon dioxide \(\mathrm{CO_2}\) ) adsorption experiments and molecular simulations were conducted to probe the micropore structure of the covalent triazine framework polymers. \(\mathrm{CO_2}\) sorption isotherms measured at 273 K revealed that powder samples of QCTF, P- QCTF, and M- QCTF had similar \(\mathrm{CO_2}\) uptake capacities of 16, 15.2, and \(14.7\mathrm{cm}^3\mathrm{g}^{- 1}\) STP, respectively (Fig. 1b). Notably, QCTF, P- QCTF, and M- QCTF exhibit almost identical pore size distributions, ranging from 0.3 nm to 0.9 nm, as derived from \(\mathrm{CO_2}\) adsorption isotherms based on density functional theory (DFT) calculations (Fig. 1c). These experimental results are further supported by molecular simulations of the 3D framework structure and the computation of \(\mathrm{CO_2}\) distributions within the framework structures (Supplementary Figures S9 and S10). This again indicates that QCTF, P- QCTF, and M- QCTF have similar framework structures, interconnected micropores and pore size distributions. + +The amount of charged functional groups (quaternary ammonium groups) within the pristine QCTF membrane, characterized by the ion exchange capacity (IEC, in mmol \(\mathrm{g}^{- 1}\) ), is \(1.20\mathrm{mmol}\mathrm{g}^{- 1}\) for QCTF (as- designed IEC value is \(\sim 1.00\mathrm{mmol}\mathrm{g}^{- 1}\) ). During protonation, approximately \(55\%\) of the triazine rings were protonated and the same amount of triazine rings was methylated after methylation, as revealed by + +<--- Page Split ---> + +X- ray photoelectron spectroscopy (XPS, Fig. 1d). This suggests that P- QCTF and M- QCTF should have identical IEC values, which was confirmed by titration and zeta potential measurements (Supplementary Figure S11). + +## Ion Transport and Selectivity + +Despite the similar framework structure and almost identical pore size/size distributions, our experimental results reflect that cross- membrane ion transport is significantly affected by pore chemistry. We speculate that the difference is synergistically determined by Coulombic/steric effects and specific ion- pore wall interactions, as shown in Fig. 2a. The current- voltage (I- V) curves across the membranes, as measured in a two- compartment diffusional H- cell under a 10- fold concentration gradient KCl solution (Fig. 2b), reveal a net anion flux, indicating anion selectivity. The anion transference number (t.) calculated for QCTF is 0.940, while the values for protonated QCTF (P- QCTF) and methylated QCTF (M- QCTF) are 0.947 and 0.953, respectively (Supplementary Figure S12). These values suggest the superior anion selectivity of the QCTF membranes compared to that of commercial anion exchange membranes (AEMs). This result is reasonable considering the Coulombic repulsion of the \(< 1\) nm pore channel within the QCTF membranes. + +The measured transference numbers align with the cross- membrane permeation/diffusion rates for BTMAP- Vi (a redox- active organic cation) and Cl- (Fig. 2c and 2d, Supplementary Figures S13- S15, Supplementary Tables S5- S6), which are dramatically different in size. Compared with commercial AEMs (Fig. 2c), all the QCTF membranes exhibited superior blocking capabilities toward BTMAP- Vi. The diffusion coefficients of BTMAP- Vi across the QCTF and the P- QCTF were determined to be \(4.5 \times 10^{- 11} \text{cm}^2 \text{s}^{- 1}\) and \(3.4 \times 10^{- 11} \text{cm}^2 \text{s}^{- 2}\) , respectively. These values are at least one order of magnitude smaller than those of commercial AEMs. Note that the value further decreases to \(3.1 \times 10^{- 11} \text{cm}^2 \text{s}^{- 1}\) for M- QCTF, a value that is over 20 times smaller than that of Selemon® DSV. The diffusion coefficients of Cl- through the QCTF and P- QCTF are \(1.8 \times 10^{- 7} \text{cm}^2 \text{s}^{- 1}\) and \(2.6 \times 10^{- 7} \text{cm}^2 \text{s}^{- 2}\) , respectively. By contrast, commercial anion- selective membranes demonstrated Cl- diffusion coefficients at least one order of magnitude smaller than those of QCTF membranes. Surprisingly, the Cl- diffusion coefficient measured for M- QCTF reached \(3.0 \times 10^{- 7} \text{cm}^2 \text{s}^{- 1}\) , which is nearly 2 times that for the QCTF membrane (Fig. 2d). A comparison of the Cl- diffusion coefficients and the Cl- /BTMAP- Vi selectivity for QCTF membranes, commercial AEMs and previously reported membranes implies that these framework membranes can simultaneously deliver fast ion permeation and high selectivity, overcoming the usual tradeoff observed for many ion exchange membranes (Supplementary Figure S16 and Supplementary Table S6). + +The fast Cl- transport across the triazine framework membranes is further supported by the membrane conductivity measurements. Compared with commercial AEMs, triazine framework membranes show high Cl- conductivity at relatively low hydration numbers (Fig. 2e, Supplementary Figure S17 and Supplementary Tables S7- S8). The Cl- conductivity of QCTF, as measured by four- point electrochemical + +<--- Page Split ---> + +impedance spectroscopy (EIS), is \(13.2 \mathrm{mS cm}^{- 1}\) at \(30.0^{\circ}\mathrm{C}\) and approaches \(42.0 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) at low hydration numbers (3.5 at \(30^{\circ}\mathrm{C}\) , 4.4 at \(80^{\circ}\mathrm{C}\) ). In comparison, the \(\mathrm{Cl}^{- }\) conductivity of P- QCTF is \(20.0 \mathrm{mS cm}^{- 1}\) at \(30^{\circ}\mathrm{C}\) and increases to \(48.4 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) . We find that the \(\mathrm{Cl}^{- }\) conductivity of M- QCTF is \(26.0\) at \(30.0^{\circ}\mathrm{C}\) , which is nearly twice that of QCTF, and reaches \(53.0 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) . The activation energy \((E_{a})\) for \(\mathrm{Cl}^{- }\) conduction across the QCTF membrane is \(20.6 \mathrm{kJ mol}^{- 1}\) , as derived from the conductivities at various temperatures (Fig. 2f and Supplementary Figure S18), contrasting an \(E_{a}\) of \(12.9 \mathrm{kJ mol}^{- 1}\) for \(\mathrm{K}^{+}\) transport across an otherwise identical membrane with sulfonate functional groups (ref 14). Surprisingly, the \(E_{a}\) value for M- QCTF is as low as \(13.1 \mathrm{kJ mol}^{- 1}\) , which is nearly half that of QCTF and lower than any value reported in the literature (Fig. 2g and Supplementary Table S1). Considering the similar framework structure and almost identical pore size/size distributions, this significant result indicates that the methylation of triazine rings alters the transport energy barrier for \(\mathrm{Cl}^{- }\) ions. + +Due to the aforementioned results, we conclude that electrostatic interactions alone cannot explain the differences in \(\mathrm{Cl}^{- }\) diffusion coefficients, \(\mathrm{Cl}^{- }\) conductivity or activation energy for cross- membrane \(\mathrm{Cl}^{- }\) transport. To unravel why methylation of the triazine ring promotes fast \(\mathrm{Cl}^{- }\) conduction, compared to the protonated triazine ring in P- QCTF and the charge- neutral triazine ring in QCTF, the charge distribution and the \(\mathrm{Cl}^{- }\) transport routes within the matrix of the triazine framework membranes were portrayed based on molecular simulations, and the two- dimensional free- energy landscapes were computed according to current methodology (13, 14). Our calculations show that the charge distributions of triazine framework membranes vary dramatically after protonation and methylation (Fig. 3a, Supplementary Figure S19). The most even charge distribution is observed for M- QCTF. We speculate that the variation in charge distribution alters the interactions between anions and the membrane frameworks and helps establish low- energy- barrier pathways for anion transport. This is supported by free energy calculations for \(\mathrm{Cl}^{- }\) conduction (Fig. 3b). The simulation results showed that \(\mathrm{Cl}^{- }\) can interact with quaternary ammonium (QA) groups (Fig. 3c, Supplementary Figures S20 and S21) and lower the free energy, but an energy barrier must be overcome for \(\mathrm{Cl}^{- }\) ions to approach adjacent QA groups. The energy barrier for \(\mathrm{Cl}^{- }\) conduction is the highest for QCTF (Fig. 3b, left panel) and decreases when the triazine ring is protonated (Fig. 3b, middle panel), while methylation of the triazine ring in M- QCTF improves the diffusivity of \(\mathrm{Cl}^{- }\) within the framework and creates a \(\mathrm{Cl}^{- }\) diffusion pathway with the lowest energy barrier (Fig. 3b, right panel). We suspect that the synergy of electrostatic interactions between \(\mathrm{Cl}^{- }\) and the methylated triazine ring and the change in electron density along the \(\mathrm{Cl}^{- }\) diffusion path after methylation may account for the emergence of the low- energy- barrier diffusion pathway. + +Molecular simulation results are further supported by measurements of transmembrane \(\mathrm{F}^{- }\) diffusion coefficients via \(^{19}\mathrm{F}\) pulsed- field gradient- stimulated- echo nuclear magnetic resonance ( \(^{19}\mathrm{F}\) PFG- NMR; \(^{19}\mathrm{F}\) was selected owing to its higher sensitivity compared with \(^{35}\mathrm{Cl}\) ). \(^{19}\mathrm{F}\) PFG- NMR revealed two separate \(\mathrm{F}^{- }\) signals for Selenium® DSV and Selenium® AMV membranes (Fig. 3d and Supplementary Figure S22), with the upfield signal corresponding to free \(\mathrm{F}^{- }\) in water (located at the same position as that in 0.1 M KF + +<--- Page Split ---> + +aqueous solution) and the downfield signal corresponding to associated \(\mathsf{F}^{- }\) within the membrane. In contrast, only the upfield signal was observed for all three triazine framework membranes (Fig. 3d), which is an indication of freely exchangeable \(\mathsf{F}^{- }\) within the membrane, with slight variations in the \(^{19}\mathsf{F}\) chemical shifts. By fitting the echo profiles with the Stejskal- Tanner equation (Supplementary Figure S23), the derived \(\mathsf{F}^{- }\) diffusion coefficients within the P- QCTF and QCTF are \(0.93\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) and \(0.63\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) , respectively (Fig. 3e). The value reaches \(1.1\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for M- QCTF, almost a twofold increase compared to that for QCTF. Notably, this value is 12.8 times that of Selemion® AMV and 10.8 times that of Selemion® DSV (Fig. 3e and Supplementary Figure S23) and approaches the measured diffusion coefficient of \(\mathsf{F}^{- }\) in water \((1.2\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) ; Supplementary Figure S23). In summary, by tailoring the pore chemistry of framework membranes, intimate ion- pore wall interactions provide a low- energy- barrier diffusion pathway for anions. Taken together with the Coulombic/steric exclusion by the charged framework micropores, the triazine framework membranes, particularly M- QCTF, will be of interest in applications demanding extremely fast and highly selective transport of anions. + +## Triazine framework membrane powers fast-charging AORFBs + +The extremely fast and highly selective anion (particularly chloride ions) conduction through chemically tuned triazine framework membranes is desirable in electrochemical devices, such as aqueous organic redox flow batteries. As a proof of concept, we configured pH- neutral AORFBs with BTMAP- Vi/FcNCl as the redox- active organic electrolyte couple and triazine framework membranes as the ion- conducting membranes, while \(\mathsf{Cl}^{- }\) ions were transported back and forth as charge carriers (Fig. 4a). At an electrolyte concentration of 0.1 M, EIS of the BTMAP- Vi/FcNCl cells assembled with QCTF or P- QCTF showed area- specific membrane resistances (ASRs) of \(0.63\Omega \mathrm{cm}^{2}\) and \(0.53\Omega \mathrm{cm}^{2}\) , respectively (Supplementary Figures S24- S25). An otherwise identical cell assembled with M- QCTF showed an ASR of \(0.37\Omega \mathrm{cm}^{2}\) (Supplementary Figure S26), which is almost twofold lower than that of the QCTF membrane. This finding aligns with the high conductivity of M- QCTF (Fig. 2e, 3b), which enables charging of the BTMAP- Vi/FcNCl cells at extreme current densities. For example, at \(200\mathrm{mAcm}^{- 2}\) , BTMAP- Vi/FcNCl with M- QCTF exhibited an energy efficiency (EE) of over \(60\%\) (Supplementary Figure S26). In contrast, the control BTMAP- Vi/FcNCl cells assembled with Selemion® DSV or Selemion® AMV could not operate at this current density due to the immediate voltage cutoff. At lower current densities ranging from 20 to 80 mA cm \(^{- 2}\) , the reported energy efficiency for the control cells drops from 89.4- 65.9% for Selemion® DSV or from 80.0- 26.6% for Selemion® AMV (27). + +At a higher electrolyte concentration of \(0.5\mathrm{M}\) , BTMAP- Vi/FcNCl with M- QCTF demonstrated an even lower ASR of \(0.23\Omega \mathrm{cm}^{2}\) (Fig. 4b), a much lower value than that for Selemion® DSV or Selemion® AMV. The rate performance of the cell reveals an EE of \(49.7\%\) and a capacity utilization of \(58.8\%\) at an extreme current density of \(500\mathrm{mAcm}^{- 2}\) (Fig. 4c). Compared with the most recent report of an AEM (MTCP- 50 membrane, with the optimal ratio 1:1 of \(m\) - terphenyl to \(p\) - terphenyl) for pH- neutral AORFBs at \(0.5\mathrm{M}\) (21), M- QCTF achieved a much greater energy efficiency ( \(76.9\%\) vs. \(60.1\%\) ) and capacity utilization ( \(94.3\%\) vs. + +<--- Page Split ---> + +\(63.7\%)\) at the same current density of \(200 \text{mA cm}^{- 2}\) . Notably, alkaline AORFBs that utilize \(\text{K}^{+}\) as charge- carrying ions assembled with a cation exchange membrane (SCTF- BP), which allows cation diffusion close to the value in bulk electrolyte, exhibit an EE of \(50.4\%\) and a capacity utilization of \(62\%\) at \(500 \text{mA cm}^{- 2}\) . The current results demonstrate a similar efficiency for \(\text{Cl}^{- }\) transport and therefore suggest a breakthrough in the charge asymmetry effect. + +Robust and exceptional cell performance was observed during long- term galvanostatic cycling of over 2000 cycles at \(200 \text{mA cm}^{- 2}\) (0.1 M electrolyte concentration, Supplementary Figure S26) and over 1000 cycles at \(400 \text{mA cm}^{- 2}\) (0.5 M electrolyte concentration, Fig. 4d). Comparisons of the EE and capacity utilization against the current density shows consistently superior battery performance over multiple cell cycling experiments for the BTMAP- Vi/FcNCl cells with M- QCTF, compared to the pH- neutral AORFB with different membranes (Fig. 4e, 4f and Supplementary Table S10). + +This work demonstrates that chloride and fluoride anions traverse the M- QCTF membrane with a very low energy barrier, leading to exceptional flow battery performance. This significant development can be applied more broadly to designing anion exchange membranes for other technologies such as \(\text{CO}_{2}\) electrolytes (28) and ion- capture electrodialysis (29). Although the anion diffusion constants within the developed membranes are approaching the theoretical limit of the bulk electrolyte solution, we expect further improvements in overall conductivity to be achievable by eliminating micropore tortuosity and creating perfectly aligned micropore channels with monodispersed pore size distributions. + +## Declarations + +## Acknowledgments + +This work was funded by the National Key R&D Program of China (2021YFB4000302) and the National Natural Science Foundation of China (Grant/Award No. U20A20127, 52021002). This work was partially carried out at the Instruments Center for Physical Science, University of Science and Technology of China. + +## Competing interests + +The authors declare no competing interests. + +## Data availability + +The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Source data are available on reasonable request from the corresponding author. + +## References + +<--- Page Split ---> + +1. D. A. Doyle et al., The structure of the potassium channel: molecular basis of \(\mathsf{K}^{+}\) conduction and selectivity. Science 280, 69 (1998). + +2. H. B. Park et al., Maximizing the right stuff: the trade-off between membrane permeability and selectivity. Science 356, eaab0530 (2017). + +3. Y. J. Lim et al., The coming of age of water channels for separation membranes: from biological to biomimetic to synthetic. Chem. Soc. Rev. 51, 4537-4582 (2022). + +4. B. Radha et al., Molecular transport through capillaries made with atomic-scale precision. Nature 538, 222-225 (2016). + +5. A. Bhardwaj et al., Fabrication of angstrom-scale two-dimensional channels for mass transport. Nat. Protoc. 19, 240-280 (2023). + +6. T. Xiong et al., Neuromorphic functions with a polyelectrolyte-confined fluidic memristor. Science 379, 156-161 (2023). + +7. P. Robin et al., Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels. Science 379, 161-167 (2023). + +8. N. Kavokine et al., Ionic Coulomb blockade as a fractional Wien effect. Nat. Nanotechnol. 14, 573-578 (2019). + +9. P. Robin et al., Modeling of emergent memory and voltage spiking in ionic transport through angstrom-scale slits. Science 373, 687-691 (2021). + +10. A. Esfandiar et al., Size effect in ion transport through angstrom-scale slits. Science 358, 511-513 (2017). + +11. T. Mouterde et al., Molecular streaming and its voltage control in ángström-scale channels. Nature 567, 87-90 (2019). + +12. F. Chen et al., Inducing electric current in graphene using Ionic flow. Nano Lett. 23, 4464-4470 (2023). + +13. M. J. Baran et al., Diversity-oriented synthesis of polymer membranes with ion solvation cages. Nature 592, 225-231 (2021). + +14. P. Zuo et al., Near-frictionless ion transport within triazine framework membranes. Nature 617, 299-305 (2023). + +15. Y. Zhao et al., Differentiating solutes with precise nanofiltration for next generation environmental separations: a review. Environ. Sci. Technol. 55, 1359-1376 (2021). + +16. S. Goutham et al., Beyond steric selectivity of ions using ángström-scale capillaries. Nat. Nanotechnol. 18, 596-601 (2023). + +17. J. Ma et al., Multivalent ion transport through a nanopore. J. Phys. Chem. C 126, 14661-14668 (2022). + +18. H. Xie et al., A membrane-based seawater electrolyser for hydrogen generation. Nature 612, 673-678 (2022). + +<--- Page Split ---> + +19. G. Karkera et al., A structurally flexible halide solid electrolyte with high ionic conductivity and air processability. Adv. Energy Mater. 13, 2300982 (2023). +20. S. Pang et al., Biomimetic amino acid functionalized phenazine flow batteries with long lifetime at near-neutral pH. Angew. Chem. Int. Edit. 60, 5289-5298 (2021). +21. W. Song et al., Upscaled production of an ultramicroporous anion-exchange membrane enables long-term operation in electrochemical energy devices. Nat. Commun. 14, 2732 (2023). +22. M. E. Carrington et al., Associative pyridinium electrolytes for air-tolerant redox flow batteries. Nature 623, 949-955 (2023). +23. P. Xiong et al., A chemistry and microstructure perspective on ion-conducting membranes for redox flow batteries. Angew. Chem. Int. Edit. 60, 24770-24798 (2021). +24. B. Hu et al., Long-cycling aqueous organic redox flow battery (AORFB) toward sustainable and safe energy storage. J. Am. Chem. Soc. 139, 1207-1214 (2017). +25. J. Luo et al., A π-conjugation extended viologen as a two-electron storage anolyte for total organic aqueous redox flow batteries. Angew. Chem. Int. Edit. 57, 231-235 (2017). +26. X. Zhu et al., A superacid-catalyzed synthesis of porous membranes based on triazine frameworks for CO₂ separation. J. Am. Chem. Soc. 134, 10478-10484 (2012). +27. H. Li et al., Ultra-microporous anion conductive membranes for crossover-free pH-neutral aqueous organic flow batteries. J. Membr. Sci. 668, 121195 (2023). +28. D. A. Salvatore et al., Designing anion exchange membranes for CO₂ electrolysers. Nat. Energy 6, 339-348 (2021). +29. A. A. Uliana et al., Ion-capture electrodialysis using multifunctional adsorptive membranes. Science 372, 296-299 (2021). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Synthesis and characterization of microporous covalent triazine framework membranes. (a) Left panel: schematic showing the 3D interconnected micropore free volume for anion transport. Right panel: Molecular structure and synthesis of the covalent triazine framework membrane QCTF and subsequent protonation or methylation of the triazine ring skeleton, affording P- QCTF and M- QCTF. Coulombic/steric exclusion and intimate ion- pore wall interactions enable selective and fast anion transport. Red and blue spheres: fixed functional groups or charged triazine rings; green spheres: counterions or charge carrier ions; lightning: ion- pore wall interactions. (b) \(\mathrm{CO_2}\) adsorption isotherms of QCTF, P- QCTF and M- QCTF at 273 K. (c) Pore size distributions of QCTF, P- QCTF and M- QCTF derived from \(\mathrm{CO_2}\) adsorption isotherms through density functional theory (DFT) calculations. (d) XPS (N1s) spectra of covalent triazine framework (CTF) membranes: QCTF (top), protonated QCTF (P- QCTF, middle), and methylated QCTF (M- QCTF, bottom). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Ion selectivity and conductivity of microporous covalent triazine framework membranes. (a) Schematic showing the transport of anions across rigid micropores within positively charged covalent triazine framework QCTF membranes. Coulombic/steric exclusion and intimate ion- pore wall interactions enable selective and fast anion transport. Red and blue spheres: fixed functional groups or charged triazine rings; green spheres: counterions or charge carrier ions; blue and gray spheres: positively charged ions with large or small hydrated diameters. The dashed lines indicate ion- pore wall interactions, while the arrowed lines suggest rejection or transport of ions. (b) Current- voltage \((I - V)\) curves of the M- QCTF, P- QCTF, QCTF, Selemion® DSV and Selemion® AMV membranes under a 10- fold concentration gradient in KCl solution. The intercept at \(0\mu \mathrm{A}\) correlates to the transmembrane potential as a result of selective ion transport, from which the transference number \(t\) can then be deduced. The diffusion coefficient of BTMAP- Vi (c) and Cl (d) across QCTF membranes and commercial membranes, as determined from a two- compartment diffusional H- cell. (e) Cl⁻ conductivity plotted as a function of + +<--- Page Split ---> + +hydration number for the M- QCTF, P- QCTF, QCTF, Selenium® DSV and Selenium® AMV membranes. The conductivity was measured via the four- probe EIS method. Each data point represents the Cl⁻ conductivity at an individual temperature: from left to right (or from larger data points to smaller data points), 30–80 °C, with a 10 °C increment. (f) The calculated activation energy for Cl⁻ conduction (Ea) across the M- QCTF, P- QCTF, QCTF, Selenium® DSV and Selenium® AMV membranes, as derived from Arrhenius equations. (g) Comparison on activation energy for QCTF membranes, commercial membranes and those reported previously. The detailed values can be found in Supplementary Table S1. + +![](images/Figure_4.jpg) + + +![PLACEHOLDER_13_1] + + +<--- Page Split ---> + +Low barrier anion transport enabled by ion- pore wall interactions under confinement. (a) Charge distributions of QCTF (left), P- QCTF (middle), and M- QCTF (right) from restrained electrostatic potential (RESP). The charge values shown can be found in Supplementary Table S9. (b) Computed free energy map for the transport of Cl- ions within the QCTF (left), P- QCTF (middle) and M- QCTF (right) membrane matrices. The black or white lines denote the Cl- ion transport pathways (1- 1 or 1- 2- 1) with the lowest free energy barrier. (c) Snapshots taken during simulation, demonstrating the interactions between Cl- and the M- QCTF membrane pore walls. Insets denote the specific interactions at positions 1 and 2. The parameters \((r_{1}\) and \(r_{2}\) ) represent the distance between the Cl- ion and the geometric center of two quaternary ammonium (QA) groups. (d) \(^{19}\mathrm{F}\) PFG- NMR spectra recorded for membrane samples of Selenium® DSV, QCTF, P- QCTF and M- QCTF immersed in 0.1 M KF solutions. (e) F- self-diffusion coefficients derived from \(^{19}\mathrm{F}\) PFG- NMR spectra ( \(^{19}\mathrm{F}\) - is used instead of \(^{35}\mathrm{Cl}\) because of its superior NMR sensitivity). Error bars are standard deviations derived from three measurements based on three separate membrane samples. + +<--- Page Split ---> +![PLACEHOLDER_15_0] + +
Figure 4
+ +Fast charging of pH- neutral AORFBs enabled by the M- QCTF membrane. (a) Schematic illustration of a pH- neutral BTMAP- Vi/FcNCI AORFB assembled with an M- QCTF membrane. (b) EIS spectra measured in cells assembled with M- QCTF, Selemion® DSV and Selemion® AMV membranes. A control EIS spectrum was recorded in a cell without a membrane. (c) Coulombic efficiency (CE), energy efficiency (EE), and capacity of cells assembled with the M- QCTF membrane at various current densities. (d) Galvanostatic cycling of the BTMAP- Vi/FcNCI cell assembled with the M- QCTF membrane at \(400 \text{mA cm}^{-2}\) . The electrolyte compositions through b to d: the anolyte comprised \(5 \text{mL}\) of \(0.5 \text{M BTMAP-Vi}\) in \(2 \text{M KCl}\) , while the catholyte comprised \(10 \text{mL}\) of \(0.5 \text{M FcNCI}\) in \(2 \text{M KCl}\) . Capacity utilization (e) and energy efficiency (f) of pH- neutral AORFBs assembled with Selemion® DSV and Selemion® AMV, AME 115, PIM- TDQTB, or M- QCTF are plotted as a function of current density. Dashed lines and shades are visual guides. The detailed values can be found in Supplementary Table S10. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- 03supplementarymaterials.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0_det.mmd b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..686a6591d654bfe891b5c59275a5c454dc8e0d11 --- /dev/null +++ b/preprint/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0/preprint__0040553aacfe354742b83e1386dd94b013703c55b639bcf87dcd675a88c10bf0_det.mmd @@ -0,0 +1,286 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 919, 206]]<|/det|> +# Rationally synthesized framework polymer membranes enable high selectivity and barrierless anion conduction + +<|ref|>text<|/ref|><|det|>[[44, 228, 280, 275]]<|/det|> +Zhengjin Yang yangz.j09@ustc.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 300, 821, 323]]<|/det|> +University of Science and Technology of China https://orcid.org/0000- 0002- 0722- 7908 + +<|ref|>text<|/ref|><|det|>[[44, 328, 461, 368]]<|/det|> +Junkai Fang University of Science and Technology of China + +<|ref|>text<|/ref|><|det|>[[44, 373, 821, 415]]<|/det|> +Guozhen Zhang University of Science and Technology of China https://orcid.org/0000- 0003- 0125- 9666 + +<|ref|>text<|/ref|><|det|>[[44, 419, 598, 460]]<|/det|> +Marc- Antoni Goulet Concordia University https://orcid.org/0000- 0002- 9146- 6759 + +<|ref|>text<|/ref|><|det|>[[44, 465, 821, 507]]<|/det|> +Peipei Zuo University of Science and Technology of China https://orcid.org/0000- 0001- 5043- 7188 + +<|ref|>text<|/ref|><|det|>[[44, 512, 461, 552]]<|/det|> +Hui Li University of Science and Technology of China + +<|ref|>text<|/ref|><|det|>[[44, 558, 821, 600]]<|/det|> +Jun Jiang University of Science and Technology of China https://orcid.org/0000- 0002- 6116- 5605 + +<|ref|>text<|/ref|><|det|>[[44, 604, 565, 645]]<|/det|> +Michael Guiver Tianjin University https://orcid.org/0000- 0003- 2619- 6809 + +<|ref|>text<|/ref|><|det|>[[44, 650, 821, 693]]<|/det|> +Tongwen Xu University of Science and Technology of China https://orcid.org/0000- 0002- 9221- 5126 + +<|ref|>text<|/ref|><|det|>[[44, 735, 102, 752]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 772, 136, 790]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 810, 303, 829]]<|/det|> +Posted Date: June 11th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 848, 474, 867]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4392718/v1 + +<|ref|>text<|/ref|><|det|>[[44, 885, 914, 927]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 904, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on April 6th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58638-0. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 158, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[40, 82, 951, 409]]<|/det|> +AbstractThe understanding gleaned from studying ion transport within the interaction confinement regime enables the near- frictionless transport of cations (e.g., \(\mathrm{Na^{+} / K^{+}}\) ). However, anion transport (e.g., Cl⁻) is suppressed under confinement because of the different polarization of water molecules around cations and anions, also known as the charge asymmetry effect. Here we report the rational synthesis of anion- selective framework polymer membranes having similar densities of subnanometer- sized pores with nearly identical micropore size distributions, which overcome the charge asymmetry effect and promote barrierless anion conduction. We find that anion transport within the micropore free volume elements can be dramatically accelerated by regulating the pore chemistry, which lowers the energy barrier for anion transport, leading to an almost twofold increase in Cl⁻ conductivity and barrierless F⁻ diffusion. The resultant membrane enables an aqueous organic redox flow battery that utilizes Cl⁻ ions as charge carriers to operate at extreme current densities and delivers competitive performance to counterparts where K⁺ ions are charge carriers. These results may benefit broadly electrochemical devices and inspire single- species selectivity with separation membranes that exploit controlled or chemically gated ion/molecule transport. + +<|ref|>sub_title<|/ref|><|det|>[[44, 431, 175, 456]]<|/det|> +## Main Text + +<|ref|>text<|/ref|><|det|>[[42, 470, 945, 584]]<|/det|> +Replicating the extreme selectivity and high permeability of biological ion channels is an enduring challenge for membrane scientists (1- 3). Beyond the generally- accepted mechanisms of size exclusion and Coulombic repulsion, it is argued that the subtle interactions between ions and channel walls at atomic- scale confinement play a crucial role. These interactions were not clearly elucidated until the fabrication of angstrom- scale slits/capillaries/channels with atomic- scale precision (4, 5). + +<|ref|>text<|/ref|><|det|>[[40, 598, 955, 905]]<|/det|> +The spatial confinement of ion transport down to molecular- sized ion channels magnifies the impact of channel wall interactions and gives rise to exotic transport behavior. For example, hysteretic ion conduction occurs, resulting in an ion memory effect (6, 7), while the formation of Bjerrum ion pairs causes ionic Coulombic blockade (8). These atypical ion motions are intimately related to the dramatically enhanced material- dependent interactions between hydrated ions and the confining channel walls (e.g., electrostatic, adsorption/desorption) (9). For chemically inert and atomically smooth graphite channel walls, \(\mathrm{K^{+}}\) demonstrates a mobility close to that of the value in bulk solutions (10). By applying a voltage bias on the graphite channel, the streaming mobility of \(\mathrm{K^{+}}\) is increased by up to 20 times (11) and this may be ascribed to the electronic structure change under an external voltage bias (12). It has also been demonstrated that by introducing \(\mathrm{Li^{+}}\) - coordinating functionality within the shape- persistent free volume elements of microporous polymer membranes, \(\mathrm{Li^{+}}\) diffusivity can be greatly enhanced (13). Similar improvements to \(\mathrm{Na^{+}}\) transport have also been achieved by exploiting the synergy between micropore confinement and ion- membrane interactions (14). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 953, 333]]<|/det|> +Despite the considerable improvements in cation transport due to the confinement effect, it is notable that chloride \((\mathsf{Cl}^{- })\) mobility experiences significant suppression under confinement. This charge asymmetry is likely due to the slightly different hydration shell configurations between \(\mathsf{Cl}^{- }\) and \(\mathsf{K}^{+}(10)\) . The mobility of \(\mathsf{Cl}^{- }\) under confinement is three times less than that of \(\mathsf{K}^{+}\) , even though \(\mathsf{Cl}^{- }\) and \(\mathsf{K}^{+}\) have similar mobilities in bulk water \((7.58\times 10^{- 8}\mathrm{vs.}7.86\times 10^{- 8}\mathrm{m}^{2}\mathrm{V}^{- 1}\mathrm{s}^{- 1})\) and hydrated diameters \((6.64\mathring{\mathrm{A}}\mathrm{vs.}\) 6.62 Å) (15, 16). For a more extreme case, \(\mathsf{Cs}^{+}\) and \(\mathsf{Cl}^{- }\) exhibit similar ion- core sizes and hydrated diameters, but \(\mathsf{Cl}^{- }\) exhibits more than three times lower mobility under \(\mathring{\mathrm{A}}\) - scale confinement \((1.7\times 10^{- 8}\) vs. \(6.0\times 10^{- 8}\mathrm{m}^{2}\mathrm{V}^{- 1}\mathrm{s}^{- 1})\) (16). For chloride salts of high valency cations, the overall \(\mathsf{Cl}^{- }\) mobility decreases to almost zero in single- digit- sized nanopores (17). A decrease in the mobility of other anions under confinement has also been observed (16). This phenomenon is echoed by the relatively high energy barrier associated with anion exchange membranes that transport chloride ions (see Supplementary Table S1). + +<|ref|>text<|/ref|><|det|>[[38, 344, 951, 965]]<|/det|> +The transport and selectivity of anions are of critical relevance to applications such as direct seawater electrolysis (18), solid- state batteries (19) and redox flow batteries (20- 25). Understanding and overcoming the charge asymmetry effect for anion transport under confinement is therefore essential for enabling these technologies. Here we report the design and synthesis of a series of positively charged (quaternary ammonium cations) covalent triazine framework (QCTF) membranes with nearly the same density of rigid micropores with almost identical pore size distributions. The QCTF membranes exhibit Coulombic repulsion- induced anion selectivity, with a chloride transference number \(t_{- }\) of 0.95, and size exclusion- induced rejection of BTMAP- Vi (bis(3- trimethylammonio) propyl viologen tetrachloride) and FcNCl ((ferrocenylmethyl) trimethylammonium chloride), redox- active organic flow battery electrolytes. The cross- membrane BTMAP- Vi diffusion coefficient at \(3.1\times 10^{- 11}\mathrm{cm}^{2}\mathrm{s}^{- 1}\) is over 20 times lower than that of commercial membranes. We demonstrate that through on- membrane modification, the charge distribution of the pristine QCTF membrane framework can be regulated by protonation (affording P- QCTF) and methylation (affording M- QCTF), which dramatically alters the interactions between anions and the membrane framework and helps lower the energy barrier for anion transport. The cross- membrane \(\mathsf{Cl}^{- }\) conductivity increased twofold from \(13.2\mathrm{mScm}^{- 1}\) for QCTF to 25.9 \(\mathrm{mScm}^{- 1}\) for M- QCTF at \(30^{\circ}\mathrm{C}\) , and the activation energy for \(\mathsf{Cl}^{- }\) conduction decreased from \(20.6\mathrm{kJmol}^{- 1}\) to \(13.1\mathrm{kJmol}^{- 1}\) , lower than any value reported in the literature (see Supplementary Table S1). \(^{19}\mathrm{F}\) PFG- NMR revealed an increase in the \(\mathrm{F}^{- }\) diffusion coefficient from \(0.63\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for QCTF and \(0.93\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for P- QCTF, to \(1.1\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for M- QCTF which is close to the value in bulk water \((1.2\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1})\) . The greater anion conductivity can dramatically improve device performance as exemplified here in an BTMAP- Vi- and FcNCl- based aqueous organic redox flow battery (AORFB) in pH- neutral solutions. The BTMAP- Vi/FcNCl cell configured with the M- QCTF membrane exhibited a high- frequency area- specific resistance (ASR) as low as \(0.23\Omega \cdot \mathrm{cm}^{2}\) , which enabled charging and discharging of the BTMAP- Vi/FcNCl cell at an extreme current density of \(500\mathrm{mAcm}^{- 2}\) . The prolonged galvanostatic cell cycling at \(400\mathrm{mAcm}^{- 2}\) maintained a Coulombic efficiency of \(>99\%\) and a stable energy efficiency of around \(60\%\) over the course of 1000 cycles. Notably, the achieved capacity utilization and efficiency with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 46, 953, 181]]<|/det|> +M- QCTF approaches similar values to those of alkaline AORFBs that leverage \(\mathsf{K}^{+}\) as charge- carrying ions, while in otherwise identical cells assembled with QCTF or P- QCTF, an almost \(20\%\) lower energy efficiency was observed. This is significant and can be attributed to a dramatic reduction in the contribution of membrane resistance to whole- cell resistance, e.g., from \(>70\%\) for the Seleminon \(^{\circledR}\) AMV membrane to \(\sim\) \(25\%\) for M- QCTF (Supplementary Tables S2 and S3). The above results imply a breakthrough in the charge asymmetry effect. + +<|ref|>sub_title<|/ref|><|det|>[[44, 203, 350, 229]]<|/det|> +## Results and Discussion + +<|ref|>sub_title<|/ref|><|det|>[[43, 242, 936, 304]]<|/det|> +## Covalent triazine framework membranes with tunable pore chemistry + +<|ref|>text<|/ref|><|det|>[[40, 319, 951, 591]]<|/det|> +Covalent triazine framework chemistry gives rise to a wide variety of microporous materials and offers enormous diversity in pore chemistry. We thus synthesized a stand- alone triazine framework membrane from \(4,4^{'}\) - biphenyldicarbonitrile and a derivative of 3- hydroxy- [1,1'- biphenyl]- 4,4'- dicarbonitrile bearing a quaternary ammonium moiety via a superacid- catalyzed organic sol- gel procedure (Fig. 1a and Supplementary Figures S1- S4) (26). The process yields a free- standing membrane (namely, QCTF) with a Young's modulus and tensile strength of 0.91 GPa and 32.0 MPa, respectively (Supplementary Figure S5). The skeletal triazine rings of QCTF were subsequently protonated with HCl or methylated with \(\mathrm{CH}_3\mathrm{I}\) , affording P- QCTF and M- QCTF, respectively. Overall, we constructed three covalent triazine framework polymers with similar molecular configurations and pore structures that can be processed into hydrophilic, uniform and robust ion- selective membranes via an organo- sol- gel procedure (Supplementary Figures S6- S8, Supplementary Table S4), but with slightly different and deliberately tailored pore chemistries. + +<|ref|>text<|/ref|><|det|>[[40, 608, 953, 846]]<|/det|> +Carbon dioxide \(\mathrm{CO_2}\) ) adsorption experiments and molecular simulations were conducted to probe the micropore structure of the covalent triazine framework polymers. \(\mathrm{CO_2}\) sorption isotherms measured at 273 K revealed that powder samples of QCTF, P- QCTF, and M- QCTF had similar \(\mathrm{CO_2}\) uptake capacities of 16, 15.2, and \(14.7\mathrm{cm}^3\mathrm{g}^{- 1}\) STP, respectively (Fig. 1b). Notably, QCTF, P- QCTF, and M- QCTF exhibit almost identical pore size distributions, ranging from 0.3 nm to 0.9 nm, as derived from \(\mathrm{CO_2}\) adsorption isotherms based on density functional theory (DFT) calculations (Fig. 1c). These experimental results are further supported by molecular simulations of the 3D framework structure and the computation of \(\mathrm{CO_2}\) distributions within the framework structures (Supplementary Figures S9 and S10). This again indicates that QCTF, P- QCTF, and M- QCTF have similar framework structures, interconnected micropores and pore size distributions. + +<|ref|>text<|/ref|><|det|>[[41, 863, 951, 957]]<|/det|> +The amount of charged functional groups (quaternary ammonium groups) within the pristine QCTF membrane, characterized by the ion exchange capacity (IEC, in mmol \(\mathrm{g}^{- 1}\) ), is \(1.20\mathrm{mmol}\mathrm{g}^{- 1}\) for QCTF (as- designed IEC value is \(\sim 1.00\mathrm{mmol}\mathrm{g}^{- 1}\) ). During protonation, approximately \(55\%\) of the triazine rings were protonated and the same amount of triazine rings was methylated after methylation, as revealed by + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 947, 112]]<|/det|> +X- ray photoelectron spectroscopy (XPS, Fig. 1d). This suggests that P- QCTF and M- QCTF should have identical IEC values, which was confirmed by titration and zeta potential measurements (Supplementary Figure S11). + +<|ref|>sub_title<|/ref|><|det|>[[44, 141, 417, 168]]<|/det|> +## Ion Transport and Selectivity + +<|ref|>text<|/ref|><|det|>[[41, 183, 955, 433]]<|/det|> +Despite the similar framework structure and almost identical pore size/size distributions, our experimental results reflect that cross- membrane ion transport is significantly affected by pore chemistry. We speculate that the difference is synergistically determined by Coulombic/steric effects and specific ion- pore wall interactions, as shown in Fig. 2a. The current- voltage (I- V) curves across the membranes, as measured in a two- compartment diffusional H- cell under a 10- fold concentration gradient KCl solution (Fig. 2b), reveal a net anion flux, indicating anion selectivity. The anion transference number (t.) calculated for QCTF is 0.940, while the values for protonated QCTF (P- QCTF) and methylated QCTF (M- QCTF) are 0.947 and 0.953, respectively (Supplementary Figure S12). These values suggest the superior anion selectivity of the QCTF membranes compared to that of commercial anion exchange membranes (AEMs). This result is reasonable considering the Coulombic repulsion of the \(< 1\) nm pore channel within the QCTF membranes. + +<|ref|>text<|/ref|><|det|>[[40, 449, 949, 836]]<|/det|> +The measured transference numbers align with the cross- membrane permeation/diffusion rates for BTMAP- Vi (a redox- active organic cation) and Cl- (Fig. 2c and 2d, Supplementary Figures S13- S15, Supplementary Tables S5- S6), which are dramatically different in size. Compared with commercial AEMs (Fig. 2c), all the QCTF membranes exhibited superior blocking capabilities toward BTMAP- Vi. The diffusion coefficients of BTMAP- Vi across the QCTF and the P- QCTF were determined to be \(4.5 \times 10^{- 11} \text{cm}^2 \text{s}^{- 1}\) and \(3.4 \times 10^{- 11} \text{cm}^2 \text{s}^{- 2}\) , respectively. These values are at least one order of magnitude smaller than those of commercial AEMs. Note that the value further decreases to \(3.1 \times 10^{- 11} \text{cm}^2 \text{s}^{- 1}\) for M- QCTF, a value that is over 20 times smaller than that of Selemon® DSV. The diffusion coefficients of Cl- through the QCTF and P- QCTF are \(1.8 \times 10^{- 7} \text{cm}^2 \text{s}^{- 1}\) and \(2.6 \times 10^{- 7} \text{cm}^2 \text{s}^{- 2}\) , respectively. By contrast, commercial anion- selective membranes demonstrated Cl- diffusion coefficients at least one order of magnitude smaller than those of QCTF membranes. Surprisingly, the Cl- diffusion coefficient measured for M- QCTF reached \(3.0 \times 10^{- 7} \text{cm}^2 \text{s}^{- 1}\) , which is nearly 2 times that for the QCTF membrane (Fig. 2d). A comparison of the Cl- diffusion coefficients and the Cl- /BTMAP- Vi selectivity for QCTF membranes, commercial AEMs and previously reported membranes implies that these framework membranes can simultaneously deliver fast ion permeation and high selectivity, overcoming the usual tradeoff observed for many ion exchange membranes (Supplementary Figure S16 and Supplementary Table S6). + +<|ref|>text<|/ref|><|det|>[[42, 853, 943, 947]]<|/det|> +The fast Cl- transport across the triazine framework membranes is further supported by the membrane conductivity measurements. Compared with commercial AEMs, triazine framework membranes show high Cl- conductivity at relatively low hydration numbers (Fig. 2e, Supplementary Figure S17 and Supplementary Tables S7- S8). The Cl- conductivity of QCTF, as measured by four- point electrochemical + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 951, 317]]<|/det|> +impedance spectroscopy (EIS), is \(13.2 \mathrm{mS cm}^{- 1}\) at \(30.0^{\circ}\mathrm{C}\) and approaches \(42.0 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) at low hydration numbers (3.5 at \(30^{\circ}\mathrm{C}\) , 4.4 at \(80^{\circ}\mathrm{C}\) ). In comparison, the \(\mathrm{Cl}^{- }\) conductivity of P- QCTF is \(20.0 \mathrm{mS cm}^{- 1}\) at \(30^{\circ}\mathrm{C}\) and increases to \(48.4 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) . We find that the \(\mathrm{Cl}^{- }\) conductivity of M- QCTF is \(26.0\) at \(30.0^{\circ}\mathrm{C}\) , which is nearly twice that of QCTF, and reaches \(53.0 \mathrm{mS cm}^{- 1}\) at \(80^{\circ}\mathrm{C}\) . The activation energy \((E_{a})\) for \(\mathrm{Cl}^{- }\) conduction across the QCTF membrane is \(20.6 \mathrm{kJ mol}^{- 1}\) , as derived from the conductivities at various temperatures (Fig. 2f and Supplementary Figure S18), contrasting an \(E_{a}\) of \(12.9 \mathrm{kJ mol}^{- 1}\) for \(\mathrm{K}^{+}\) transport across an otherwise identical membrane with sulfonate functional groups (ref 14). Surprisingly, the \(E_{a}\) value for M- QCTF is as low as \(13.1 \mathrm{kJ mol}^{- 1}\) , which is nearly half that of QCTF and lower than any value reported in the literature (Fig. 2g and Supplementary Table S1). Considering the similar framework structure and almost identical pore size/size distributions, this significant result indicates that the methylation of triazine rings alters the transport energy barrier for \(\mathrm{Cl}^{- }\) ions. + +<|ref|>text<|/ref|><|det|>[[39, 333, 950, 808]]<|/det|> +Due to the aforementioned results, we conclude that electrostatic interactions alone cannot explain the differences in \(\mathrm{Cl}^{- }\) diffusion coefficients, \(\mathrm{Cl}^{- }\) conductivity or activation energy for cross- membrane \(\mathrm{Cl}^{- }\) transport. To unravel why methylation of the triazine ring promotes fast \(\mathrm{Cl}^{- }\) conduction, compared to the protonated triazine ring in P- QCTF and the charge- neutral triazine ring in QCTF, the charge distribution and the \(\mathrm{Cl}^{- }\) transport routes within the matrix of the triazine framework membranes were portrayed based on molecular simulations, and the two- dimensional free- energy landscapes were computed according to current methodology (13, 14). Our calculations show that the charge distributions of triazine framework membranes vary dramatically after protonation and methylation (Fig. 3a, Supplementary Figure S19). The most even charge distribution is observed for M- QCTF. We speculate that the variation in charge distribution alters the interactions between anions and the membrane frameworks and helps establish low- energy- barrier pathways for anion transport. This is supported by free energy calculations for \(\mathrm{Cl}^{- }\) conduction (Fig. 3b). The simulation results showed that \(\mathrm{Cl}^{- }\) can interact with quaternary ammonium (QA) groups (Fig. 3c, Supplementary Figures S20 and S21) and lower the free energy, but an energy barrier must be overcome for \(\mathrm{Cl}^{- }\) ions to approach adjacent QA groups. The energy barrier for \(\mathrm{Cl}^{- }\) conduction is the highest for QCTF (Fig. 3b, left panel) and decreases when the triazine ring is protonated (Fig. 3b, middle panel), while methylation of the triazine ring in M- QCTF improves the diffusivity of \(\mathrm{Cl}^{- }\) within the framework and creates a \(\mathrm{Cl}^{- }\) diffusion pathway with the lowest energy barrier (Fig. 3b, right panel). We suspect that the synergy of electrostatic interactions between \(\mathrm{Cl}^{- }\) and the methylated triazine ring and the change in electron density along the \(\mathrm{Cl}^{- }\) diffusion path after methylation may account for the emergence of the low- energy- barrier diffusion pathway. + +<|ref|>text<|/ref|><|det|>[[41, 824, 955, 943]]<|/det|> +Molecular simulation results are further supported by measurements of transmembrane \(\mathrm{F}^{- }\) diffusion coefficients via \(^{19}\mathrm{F}\) pulsed- field gradient- stimulated- echo nuclear magnetic resonance ( \(^{19}\mathrm{F}\) PFG- NMR; \(^{19}\mathrm{F}\) was selected owing to its higher sensitivity compared with \(^{35}\mathrm{Cl}\) ). \(^{19}\mathrm{F}\) PFG- NMR revealed two separate \(\mathrm{F}^{- }\) signals for Selenium® DSV and Selenium® AMV membranes (Fig. 3d and Supplementary Figure S22), with the upfield signal corresponding to free \(\mathrm{F}^{- }\) in water (located at the same position as that in 0.1 M KF + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 46, 940, 350]]<|/det|> +aqueous solution) and the downfield signal corresponding to associated \(\mathsf{F}^{- }\) within the membrane. In contrast, only the upfield signal was observed for all three triazine framework membranes (Fig. 3d), which is an indication of freely exchangeable \(\mathsf{F}^{- }\) within the membrane, with slight variations in the \(^{19}\mathsf{F}\) chemical shifts. By fitting the echo profiles with the Stejskal- Tanner equation (Supplementary Figure S23), the derived \(\mathsf{F}^{- }\) diffusion coefficients within the P- QCTF and QCTF are \(0.93\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) and \(0.63\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) , respectively (Fig. 3e). The value reaches \(1.1\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) for M- QCTF, almost a twofold increase compared to that for QCTF. Notably, this value is 12.8 times that of Selemion® AMV and 10.8 times that of Selemion® DSV (Fig. 3e and Supplementary Figure S23) and approaches the measured diffusion coefficient of \(\mathsf{F}^{- }\) in water \((1.2\times 10^{- 9}\mathrm{m}^{2}\mathrm{s}^{- 1}\) ; Supplementary Figure S23). In summary, by tailoring the pore chemistry of framework membranes, intimate ion- pore wall interactions provide a low- energy- barrier diffusion pathway for anions. Taken together with the Coulombic/steric exclusion by the charged framework micropores, the triazine framework membranes, particularly M- QCTF, will be of interest in applications demanding extremely fast and highly selective transport of anions. + +<|ref|>sub_title<|/ref|><|det|>[[45, 377, 844, 408]]<|/det|> +## Triazine framework membrane powers fast-charging AORFBs + +<|ref|>text<|/ref|><|det|>[[39, 420, 951, 795]]<|/det|> +The extremely fast and highly selective anion (particularly chloride ions) conduction through chemically tuned triazine framework membranes is desirable in electrochemical devices, such as aqueous organic redox flow batteries. As a proof of concept, we configured pH- neutral AORFBs with BTMAP- Vi/FcNCl as the redox- active organic electrolyte couple and triazine framework membranes as the ion- conducting membranes, while \(\mathsf{Cl}^{- }\) ions were transported back and forth as charge carriers (Fig. 4a). At an electrolyte concentration of 0.1 M, EIS of the BTMAP- Vi/FcNCl cells assembled with QCTF or P- QCTF showed area- specific membrane resistances (ASRs) of \(0.63\Omega \mathrm{cm}^{2}\) and \(0.53\Omega \mathrm{cm}^{2}\) , respectively (Supplementary Figures S24- S25). An otherwise identical cell assembled with M- QCTF showed an ASR of \(0.37\Omega \mathrm{cm}^{2}\) (Supplementary Figure S26), which is almost twofold lower than that of the QCTF membrane. This finding aligns with the high conductivity of M- QCTF (Fig. 2e, 3b), which enables charging of the BTMAP- Vi/FcNCl cells at extreme current densities. For example, at \(200\mathrm{mAcm}^{- 2}\) , BTMAP- Vi/FcNCl with M- QCTF exhibited an energy efficiency (EE) of over \(60\%\) (Supplementary Figure S26). In contrast, the control BTMAP- Vi/FcNCl cells assembled with Selemion® DSV or Selemion® AMV could not operate at this current density due to the immediate voltage cutoff. At lower current densities ranging from 20 to 80 mA cm \(^{- 2}\) , the reported energy efficiency for the control cells drops from 89.4- 65.9% for Selemion® DSV or from 80.0- 26.6% for Selemion® AMV (27). + +<|ref|>text<|/ref|><|det|>[[41, 810, 951, 950]]<|/det|> +At a higher electrolyte concentration of \(0.5\mathrm{M}\) , BTMAP- Vi/FcNCl with M- QCTF demonstrated an even lower ASR of \(0.23\Omega \mathrm{cm}^{2}\) (Fig. 4b), a much lower value than that for Selemion® DSV or Selemion® AMV. The rate performance of the cell reveals an EE of \(49.7\%\) and a capacity utilization of \(58.8\%\) at an extreme current density of \(500\mathrm{mAcm}^{- 2}\) (Fig. 4c). Compared with the most recent report of an AEM (MTCP- 50 membrane, with the optimal ratio 1:1 of \(m\) - terphenyl to \(p\) - terphenyl) for pH- neutral AORFBs at \(0.5\mathrm{M}\) (21), M- QCTF achieved a much greater energy efficiency ( \(76.9\%\) vs. \(60.1\%\) ) and capacity utilization ( \(94.3\%\) vs. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 46, 940, 161]]<|/det|> +\(63.7\%)\) at the same current density of \(200 \text{mA cm}^{- 2}\) . Notably, alkaline AORFBs that utilize \(\text{K}^{+}\) as charge- carrying ions assembled with a cation exchange membrane (SCTF- BP), which allows cation diffusion close to the value in bulk electrolyte, exhibit an EE of \(50.4\%\) and a capacity utilization of \(62\%\) at \(500 \text{mA cm}^{- 2}\) . The current results demonstrate a similar efficiency for \(\text{Cl}^{- }\) transport and therefore suggest a breakthrough in the charge asymmetry effect. + +<|ref|>text<|/ref|><|det|>[[42, 178, 952, 316]]<|/det|> +Robust and exceptional cell performance was observed during long- term galvanostatic cycling of over 2000 cycles at \(200 \text{mA cm}^{- 2}\) (0.1 M electrolyte concentration, Supplementary Figure S26) and over 1000 cycles at \(400 \text{mA cm}^{- 2}\) (0.5 M electrolyte concentration, Fig. 4d). Comparisons of the EE and capacity utilization against the current density shows consistently superior battery performance over multiple cell cycling experiments for the BTMAP- Vi/FcNCl cells with M- QCTF, compared to the pH- neutral AORFB with different membranes (Fig. 4e, 4f and Supplementary Table S10). + +<|ref|>text<|/ref|><|det|>[[42, 332, 950, 494]]<|/det|> +This work demonstrates that chloride and fluoride anions traverse the M- QCTF membrane with a very low energy barrier, leading to exceptional flow battery performance. This significant development can be applied more broadly to designing anion exchange membranes for other technologies such as \(\text{CO}_{2}\) electrolytes (28) and ion- capture electrodialysis (29). Although the anion diffusion constants within the developed membranes are approaching the theoretical limit of the bulk electrolyte solution, we expect further improvements in overall conductivity to be achievable by eliminating micropore tortuosity and creating perfectly aligned micropore channels with monodispersed pore size distributions. + +<|ref|>sub_title<|/ref|><|det|>[[45, 516, 210, 542]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[45, 556, 208, 575]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[42, 593, 943, 682]]<|/det|> +This work was funded by the National Key R&D Program of China (2021YFB4000302) and the National Natural Science Foundation of China (Grant/Award No. U20A20127, 52021002). This work was partially carried out at the Instruments Center for Physical Science, University of Science and Technology of China. + +<|ref|>sub_title<|/ref|><|det|>[[45, 700, 222, 719]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[45, 738, 428, 757]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[45, 775, 183, 794]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[44, 813, 920, 879]]<|/det|> +The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Source data are available on reasonable request from the corresponding author. + +<|ref|>sub_title<|/ref|><|det|>[[45, 902, 193, 927]]<|/det|> +## References + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 46, 930, 93]]<|/det|> +1. D. A. Doyle et al., The structure of the potassium channel: molecular basis of \(\mathsf{K}^{+}\) conduction and selectivity. Science 280, 69 (1998). + +<|ref|>text<|/ref|><|det|>[[56, 97, 901, 140]]<|/det|> +2. H. B. Park et al., Maximizing the right stuff: the trade-off between membrane permeability and selectivity. Science 356, eaab0530 (2017). + +<|ref|>text<|/ref|><|det|>[[56, 146, 936, 190]]<|/det|> +3. Y. J. Lim et al., The coming of age of water channels for separation membranes: from biological to biomimetic to synthetic. Chem. Soc. Rev. 51, 4537-4582 (2022). + +<|ref|>text<|/ref|><|det|>[[56, 195, 930, 238]]<|/det|> +4. B. Radha et al., Molecular transport through capillaries made with atomic-scale precision. Nature 538, 222-225 (2016). + +<|ref|>text<|/ref|><|det|>[[56, 244, 950, 287]]<|/det|> +5. A. Bhardwaj et al., Fabrication of angstrom-scale two-dimensional channels for mass transport. Nat. Protoc. 19, 240-280 (2023). + +<|ref|>text<|/ref|><|det|>[[56, 293, 928, 336]]<|/det|> +6. T. Xiong et al., Neuromorphic functions with a polyelectrolyte-confined fluidic memristor. Science 379, 156-161 (2023). + +<|ref|>text<|/ref|><|det|>[[56, 342, 890, 386]]<|/det|> +7. P. Robin et al., Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels. Science 379, 161-167 (2023). + +<|ref|>text<|/ref|><|det|>[[56, 391, 930, 434]]<|/det|> +8. N. Kavokine et al., Ionic Coulomb blockade as a fractional Wien effect. Nat. Nanotechnol. 14, 573-578 (2019). + +<|ref|>text<|/ref|><|det|>[[56, 440, 880, 484]]<|/det|> +9. P. Robin et al., Modeling of emergent memory and voltage spiking in ionic transport through angstrom-scale slits. Science 373, 687-691 (2021). + +<|ref|>text<|/ref|><|det|>[[50, 489, 930, 532]]<|/det|> +10. A. Esfandiar et al., Size effect in ion transport through angstrom-scale slits. Science 358, 511-513 (2017). + +<|ref|>text<|/ref|><|det|>[[50, 538, 936, 582]]<|/det|> +11. T. Mouterde et al., Molecular streaming and its voltage control in ángström-scale channels. Nature 567, 87-90 (2019). + +<|ref|>text<|/ref|><|det|>[[50, 587, 896, 630]]<|/det|> +12. F. Chen et al., Inducing electric current in graphene using Ionic flow. Nano Lett. 23, 4464-4470 (2023). + +<|ref|>text<|/ref|><|det|>[[50, 636, 907, 680]]<|/det|> +13. M. J. Baran et al., Diversity-oriented synthesis of polymer membranes with ion solvation cages. Nature 592, 225-231 (2021). + +<|ref|>text<|/ref|><|det|>[[50, 685, 940, 729]]<|/det|> +14. P. Zuo et al., Near-frictionless ion transport within triazine framework membranes. Nature 617, 299-305 (2023). + +<|ref|>text<|/ref|><|det|>[[50, 735, 930, 779]]<|/det|> +15. Y. Zhao et al., Differentiating solutes with precise nanofiltration for next generation environmental separations: a review. Environ. Sci. Technol. 55, 1359-1376 (2021). + +<|ref|>text<|/ref|><|det|>[[50, 785, 852, 828]]<|/det|> +16. S. Goutham et al., Beyond steric selectivity of ions using ángström-scale capillaries. Nat. Nanotechnol. 18, 596-601 (2023). + +<|ref|>text<|/ref|><|det|>[[50, 834, 900, 877]]<|/det|> +17. J. Ma et al., Multivalent ion transport through a nanopore. J. Phys. Chem. C 126, 14661-14668 (2022). + +<|ref|>text<|/ref|><|det|>[[50, 883, 951, 927]]<|/det|> +18. H. Xie et al., A membrane-based seawater electrolyser for hydrogen generation. Nature 612, 673-678 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 45, 930, 590]]<|/det|> +19. G. Karkera et al., A structurally flexible halide solid electrolyte with high ionic conductivity and air processability. Adv. Energy Mater. 13, 2300982 (2023). +20. S. Pang et al., Biomimetic amino acid functionalized phenazine flow batteries with long lifetime at near-neutral pH. Angew. Chem. Int. Edit. 60, 5289-5298 (2021). +21. W. Song et al., Upscaled production of an ultramicroporous anion-exchange membrane enables long-term operation in electrochemical energy devices. Nat. Commun. 14, 2732 (2023). +22. M. E. Carrington et al., Associative pyridinium electrolytes for air-tolerant redox flow batteries. Nature 623, 949-955 (2023). +23. P. Xiong et al., A chemistry and microstructure perspective on ion-conducting membranes for redox flow batteries. Angew. Chem. Int. Edit. 60, 24770-24798 (2021). +24. B. Hu et al., Long-cycling aqueous organic redox flow battery (AORFB) toward sustainable and safe energy storage. J. Am. Chem. Soc. 139, 1207-1214 (2017). +25. J. Luo et al., A π-conjugation extended viologen as a two-electron storage anolyte for total organic aqueous redox flow batteries. Angew. Chem. Int. Edit. 57, 231-235 (2017). +26. X. Zhu et al., A superacid-catalyzed synthesis of porous membranes based on triazine frameworks for CO₂ separation. J. Am. Chem. Soc. 134, 10478-10484 (2012). +27. H. Li et al., Ultra-microporous anion conductive membranes for crossover-free pH-neutral aqueous organic flow batteries. J. Membr. Sci. 668, 121195 (2023). +28. D. A. Salvatore et al., Designing anion exchange membranes for CO₂ electrolysers. Nat. Energy 6, 339-348 (2021). +29. A. A. Uliana et al., Ion-capture electrodialysis using multifunctional adsorptive membranes. Science 372, 296-299 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[44, 607, 143, 633]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[48, 50, 945, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 473, 115, 492]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[39, 513, 944, 767]]<|/det|> +Synthesis and characterization of microporous covalent triazine framework membranes. (a) Left panel: schematic showing the 3D interconnected micropore free volume for anion transport. Right panel: Molecular structure and synthesis of the covalent triazine framework membrane QCTF and subsequent protonation or methylation of the triazine ring skeleton, affording P- QCTF and M- QCTF. Coulombic/steric exclusion and intimate ion- pore wall interactions enable selective and fast anion transport. Red and blue spheres: fixed functional groups or charged triazine rings; green spheres: counterions or charge carrier ions; lightning: ion- pore wall interactions. (b) \(\mathrm{CO_2}\) adsorption isotherms of QCTF, P- QCTF and M- QCTF at 273 K. (c) Pore size distributions of QCTF, P- QCTF and M- QCTF derived from \(\mathrm{CO_2}\) adsorption isotherms through density functional theory (DFT) calculations. (d) XPS (N1s) spectra of covalent triazine framework (CTF) membranes: QCTF (top), protonated QCTF (P- QCTF, middle), and methylated QCTF (M- QCTF, bottom). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 45, 950, 610]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 625, 118, 645]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[39, 666, 944, 947]]<|/det|> +Ion selectivity and conductivity of microporous covalent triazine framework membranes. (a) Schematic showing the transport of anions across rigid micropores within positively charged covalent triazine framework QCTF membranes. Coulombic/steric exclusion and intimate ion- pore wall interactions enable selective and fast anion transport. Red and blue spheres: fixed functional groups or charged triazine rings; green spheres: counterions or charge carrier ions; blue and gray spheres: positively charged ions with large or small hydrated diameters. The dashed lines indicate ion- pore wall interactions, while the arrowed lines suggest rejection or transport of ions. (b) Current- voltage \((I - V)\) curves of the M- QCTF, P- QCTF, QCTF, Selemion® DSV and Selemion® AMV membranes under a 10- fold concentration gradient in KCl solution. The intercept at \(0\mu \mathrm{A}\) correlates to the transmembrane potential as a result of selective ion transport, from which the transference number \(t\) can then be deduced. The diffusion coefficient of BTMAP- Vi (c) and Cl (d) across QCTF membranes and commercial membranes, as determined from a two- compartment diffusional H- cell. (e) Cl⁻ conductivity plotted as a function of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 949, 207]]<|/det|> +hydration number for the M- QCTF, P- QCTF, QCTF, Selenium® DSV and Selenium® AMV membranes. The conductivity was measured via the four- probe EIS method. Each data point represents the Cl⁻ conductivity at an individual temperature: from left to right (or from larger data points to smaller data points), 30–80 °C, with a 10 °C increment. (f) The calculated activation energy for Cl⁻ conduction (Ea) across the M- QCTF, P- QCTF, QCTF, Selenium® DSV and Selenium® AMV membranes, as derived from Arrhenius equations. (g) Comparison on activation energy for QCTF membranes, commercial membranes and those reported previously. The detailed values can be found in Supplementary Table S1. + +<|ref|>image<|/ref|><|det|>[[70, 214, 884, 680]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[70, 707, 884, 940]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 85, 944, 395]]<|/det|> +Low barrier anion transport enabled by ion- pore wall interactions under confinement. (a) Charge distributions of QCTF (left), P- QCTF (middle), and M- QCTF (right) from restrained electrostatic potential (RESP). The charge values shown can be found in Supplementary Table S9. (b) Computed free energy map for the transport of Cl- ions within the QCTF (left), P- QCTF (middle) and M- QCTF (right) membrane matrices. The black or white lines denote the Cl- ion transport pathways (1- 1 or 1- 2- 1) with the lowest free energy barrier. (c) Snapshots taken during simulation, demonstrating the interactions between Cl- and the M- QCTF membrane pore walls. Insets denote the specific interactions at positions 1 and 2. The parameters \((r_{1}\) and \(r_{2}\) ) represent the distance between the Cl- ion and the geometric center of two quaternary ammonium (QA) groups. (d) \(^{19}\mathrm{F}\) PFG- NMR spectra recorded for membrane samples of Selenium® DSV, QCTF, P- QCTF and M- QCTF immersed in 0.1 M KF solutions. (e) F- self-diffusion coefficients derived from \(^{19}\mathrm{F}\) PFG- NMR spectra ( \(^{19}\mathrm{F}\) - is used instead of \(^{35}\mathrm{Cl}\) because of its superior NMR sensitivity). Error bars are standard deviations derived from three measurements based on three separate membrane samples. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 45, 953, 640]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 655, 118, 674]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[40, 695, 955, 947]]<|/det|> +Fast charging of pH- neutral AORFBs enabled by the M- QCTF membrane. (a) Schematic illustration of a pH- neutral BTMAP- Vi/FcNCI AORFB assembled with an M- QCTF membrane. (b) EIS spectra measured in cells assembled with M- QCTF, Selemion® DSV and Selemion® AMV membranes. A control EIS spectrum was recorded in a cell without a membrane. (c) Coulombic efficiency (CE), energy efficiency (EE), and capacity of cells assembled with the M- QCTF membrane at various current densities. (d) Galvanostatic cycling of the BTMAP- Vi/FcNCI cell assembled with the M- QCTF membrane at \(400 \text{mA cm}^{-2}\) . The electrolyte compositions through b to d: the anolyte comprised \(5 \text{mL}\) of \(0.5 \text{M BTMAP-Vi}\) in \(2 \text{M KCl}\) , while the catholyte comprised \(10 \text{mL}\) of \(0.5 \text{M FcNCI}\) in \(2 \text{M KCl}\) . Capacity utilization (e) and energy efficiency (f) of pH- neutral AORFBs assembled with Selemion® DSV and Selemion® AMV, AME 115, PIM- TDQTB, or M- QCTF are plotted as a function of current density. Dashed lines and shades are visual guides. The detailed values can be found in Supplementary Table S10. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 66, 312, 93]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 116, 768, 136]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 154, 366, 173]]<|/det|> +- 03supplementarymaterials.docx + +<--- Page Split ---> diff --git a/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/images_list.json b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..bac52f9ee9883d822613e3419fd15e9d5858ee75 --- /dev/null +++ b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/images_list.json @@ -0,0 +1,137 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Downregulation of transcripts associated with extracellular matrix-receptor interactions and upregulation of stress and inflammation pathways in Tgfbr1M318R/+ LDS VSMCs. (A) Uniform manifold approximation and projection (UMAP) of aortic cells from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice. (B) Dot plot of cluster defining transcripts used to identify endothelial cells, leukocytes, fibroblasts, and VSMCs. Color of the dot represents a scaled average expression while the size indicates the percentage of cells in which the transcript was detected. (C) ClueGO gene enrichment analysis network of transcripts dysregulated in LDS VSMCs relative to controls. Each node represents a term/pathway or individual genes associated with that term. The color of the node corresponds to the ClueGO group to which each node belongs. The size of the node indicates significance of the enrichment calculated by the ClueGO algorithm. (D) ClueGO network in which terms differentially enriched among transcripts downregulated in LDS VSMCs are highlighted in blue, while those enriched among transcripts upregulated in LDS VSMCs are highlighted in red. (E) Dot plot showing expression of a selection of transcripts significantly dysregulated in LDS VSMCs. (F,G) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus transcription factors (TF) databases showing the top three most significant terms associated with transcripts that are downregulated (F) or upregulated (G) in LDS VSMCs.", + "footnote": [], + "bbox": [ + [ + 20, + 50, + 970, + 777 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. MERFISH reveals spatially heterogeneous transcriptional profiles in LDS VSMCs. MERFISH images of the proximal aorta of LDS (A) and control (B) mice, scale bar is 1 mm. The first panel displays all detected transcripts across the aortic tissue, with key anatomic landmarks indicated. Subsequent panels depict the colocalization of Myh11 and transcripts of interest. Insets note regions of the ascending aorta and aortic root that are presented at higher magnification.", + "footnote": [], + "bbox": [ + [ + 0, + 66, + 985, + 688 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Transcriptionaly and spatially-defined VSMC subclusters with distinct responses to LDS-causing mutations can be identified in both murine and human aortas. (A) UMAP of VSMCs from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice shown split by genotype. (B) Dot plot showing enrichment of cluster-defining transcripts in VSMC1 and VSMC2. For a given transcript, the color of the dot represents a scaled average expression while the size indicates the percentage of cells in which it was detected. (C) RNA in situ hybridization showing the expression of Gata4 along the length of the murine aorta in a 16-week old control animal. (D) UMAP of control and LDS VSMCs from human patients and dot plot of cluster defining markers in this dataset split by aortic region (Pedroza et al., 2023). (E,F) UMAP overlayed with weights for CoGAPS patterns 4 and 5, in mouse and human scRNAseq datasets. (G,H) Violin plots showing the distribution of pattern 4 and 5 weights in VSMC subclusters from mouse and human scRNAseq datasets. P-values refer to Wilcoxon test. (I) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus TF databases showing the top four most significant terms associated with transcripts that define CoGAPs Patterns 4 and 5. (J) ClueGO network of terms differentially enriched in mouse and human LDS VSMC2 relative to VSMC1. Terms highlighted in blue are enriched in VSMC1, while those highlighted in red are enriched in VSMC2.", + "footnote": [], + "bbox": [ + [ + 19, + 14, + 999, + 750 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 9, + 52, + 978, + 496 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 44, + 50, + 725, + 711 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "B", + "footnote": [], + "bbox": [ + [ + 20, + 28, + 870, + 270 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Smooth muscle-specific deletion of Gata4 (Gata4SMcKO) reduces aortic root size and growth and improves aortic root media architecture in LDS mice. (A) Aortic root diameter of Ctrl (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMcKO) or without (Gata4SMcKO) smooth muscle specific deletion of Gata4 as measured by echocardiography at 8 and 16 weeks of age and aortic root growth from 8-16 weeks. P-values refer to Brown-Forsythe ANOVA. (B) Representative VVG-stained aortic root sections from three independent biological replicates per genotype. Insets identify area shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively.", + "footnote": [], + "bbox": [ + [ + 60, + 280, + 720, + 833 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7", + "footnote": [], + "bbox": [ + [ + 45, + 50, + 731, + 710 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Figure 8", + "footnote": [], + "bbox": [ + [ + 42, + 50, + 728, + 707 + ] + ], + "page_idx": 28 + } +] \ No newline at end of file diff --git a/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19.mmd b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b0e386c31dcfc57c459d06ca6cd700df41e0454c --- /dev/null +++ b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19.mmd @@ -0,0 +1,333 @@ + +# Intrinsic Gata4 expression sensitizes the aortic root to dilation in a Loeys-Dietz syndrome mouse model + +Emily Bramel Johns Hopkins University School of Medicine https://orcid.org/0000- 0003- 4602- 9506 + +Wendy Espinoza Camejo Johns Hopkins University School of Medicine + +Tyler Creamer Johns Hopkins University School of Medicine + +Leda Restrepo Johns Hopkins University School of Medicine + +Muzna Saqib Johns Hopkins University School of Medicine + +Rustam Bagirzadeh Johns Hopkins University School of Medicine + +Anthony Zeng Johns Hopkins University School of Medicine + +Jacob Mitchell Johns Hopkins University School of Medicine + +Genevieve Stein- O'Brien Johns Hopkins University School of Medicine + +Albert Pedroza Stanford University https://orcid.org/0000- 0001- 5291- 5980 + +Michael Fischbein Stanford University + +Harry Dietz Johns Hopkins School of Medicine https://orcid.org/0000- 0002- 6856- 0165 + +Elena Gallo MacFarlane egal101@jhmi.edu + +Genetic Medicine, Johns Hopkins University https://orcid.org/0000- 0001- 5677- 6842 + +Article + +Keywords: + +<--- Page Split ---> + +**Posted Date:** June 5th, 2024 + +**DOI:** https://doi.org/10.21203/rs.3.rs-4420617/v1 + +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +**Additional Declarations:** There is **NO** Competing Interest. + +**Version of Record:** A version of this preprint was published at Nature Cardiovascular Research on November 20th, 2024. See the published version at https://doi.org/10.1038/s44161-024-00562-5. + +## EDITORIAL NOTE: + +August 15, 2024. Editorial Note: In version 1 of this preprint (posted June 5, 2024) the authors have reported some unintentional errors in the x-axis labeling of figure 6A and supplemental figures 6 and 7. New figure files with corrected labeling have now been added to the version 1 preprint in the supplementary file section as follows. + +**CORRECTED** Primary figure 6 for version 1 - in part A, the x axis labels have been corrected **CORRECTED** Supplemental Figures 6 and 7 for version 1 - in both supplemental figures, the x axis labels have been corrected + +<--- Page Split ---> + +1 Intrinsic Gata4 expression sensitizes the aortic root to dilation in a Loeys- Dietz syndrome 2 mouse model + +3 Emily E. Bramel1,2, Wendy A. Espinoza Camejo1,2, Tyler J. Creamer1, Leda Restrepo1, 4 Muzna Saqib1, Rustam Bagirzadeh1, Anthony Zeng1, Jacob T. Mitchell1,2, Genevieve L. 5 Stein- O'Brien1,4, Albert J. Pedroza5, Michael P. Fischbein5, Harry C. Dietz1, Elena Gallo 6 MacFarlane1,3\* + +7 1McKusick- Nathans Department of Genetic Medicine, Johns Hopkins University School of 8 Medicine, Baltimore, Maryland, USA 9 2 Predoctoral Training in Human Genetics and Genomics, Johns Hopkins University School of 10 Medicine, Baltimore, Maryland, USA 11 3 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, 12 USA 13 4Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of 14 Medicine, Baltimore, Maryland, USA 15 5Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, 16 California, USA + +\* Correspondence: + +Elena Gallo MacFarlane + +egalol1@jhmi.edu + +## Conflict of interest statement + +The authors have declared that no conflict of interest exists. + +## Abstract + +Loews- Dietz syndrome (LDS) is an aneurysm disorder caused by mutations that decrease transforming growth factor- \(\beta\) (TGF- \(\beta\) ) signaling. Although aneurysms develop throughout the arterial tree, the aortic root is a site of heightened risk. To identify molecular determinants of this vulnerability, we investigated the heterogeneity of vascular smooth muscle cells (VSMCs) in the aorta of Tgfbr1M318R/+ LDS mice by single cell and spatial transcriptomics. Reduced expression of components of the extracellular matrix- receptor apparatus and upregulation of stress and inflammatory pathways were observed in all LDS VSMCs. However, regardless of genotype, a subset of Gata4- expressing VSMCs predominantly located in the aortic root intrinsically displayed a less differentiated, proinflammatory profile. A similar population was also identified among aortic VSMCs in a human scRNAseq dataset. Postnatal VSMC- specific Gata4 deletion reduced aortic root dilation in LDS mice, suggesting that this factor sensitizes the aortic root to the effects of impaired TGF- \(\beta\) signaling. + +<--- Page Split ---> + +Thoracic aortic aneurysms are localized vascular dilations that increase the risk of fatal dissections and/or rupture of the vessel wall'. Effective medical therapies to prevent life- threatening aortic events remain elusive?. Loeys- Dietz syndrome (LDS) is a hereditary connective tissue disorder that presents with highly penetrant aortic aneurysms3,4. LDS is caused by heterozygous, loss- of- function mutations in positive effectors of the TGF- \(\beta\) signaling pathway, including receptors (TGFBR1, TGFBR2), ligands (TGFB2, TGFB3) and intracellular signaling mediators (SMAD2, SMAD3)5- 9. All of these mutations result in reduced phosphorylation/activation of Smad2 and Smad3, leading to defective Smad- dependent transcriptional regulation. Secondary compensatory mechanisms, including upregulation of Angiotensin II Type I Receptor (AT1R) signaling, and increased expression of TGF- \(\beta\) ligands and Smad proteins, ultimately elevate levels of Smad2/Smad3 activity at diseased aortic sites, with outcomes ranging from adaptive to maladaptive depending on disease progression and cellular context5,7,10- 13. While LDS- causing mutations heighten aneurysm risk in all arteries, the aortic root is especially vulnerable to disease14- 17. Several laboratories have highlighted how the cellular composition and/or the mechanical stresses may contribute to the increased risk of disease in this location, however, the molecular determinants of this susceptibility remain unclear13,18- 22. Additionally, VSMCs are the primary cellular component of the aortic wall, but the heterogeneity of VSMCs within the aorta and its implications for aneurysm are not fully understood. In this study, we investigate the transcriptional heterogeneity of VSMCs in the normal and diseased murine aorta leveraging both scRNAseq and spatial transcriptomics. We identify Gata4 as a regional factor whose expression is intrinsically elevated in the aortic root and further upregulated in LDS samples. We also show that postnatal deletion of Gata4 in VSMCs ameliorates aortic root dilation in a murine model of LDS harboring a Tgfbr1M318R/+ genotype. + +## Results + +Tgfbr1M318R/+ VSMCs downregulate extracellular matrix components, focal adhesions, and integrin receptors, and upregulate transcripts related to stress and inflammatory pathways. + +LDS mouse models expressing a heterozygous missense mutation in Tgfbr1 (Tgfbr1M318R/+) develop highly penetrant aortic root aneurysms11,13. To assess transcriptomic changes associated with vascular pathology in this model, we performed single cell RNA sequencing (scRNAseq) on the aortic root and ascending aorta of control (Tgfbr1+/+) and LDS mice at 16 weeks of age, resulting in the identification of all of the expected cell types according to well- established expression profiles23 (Fig. 1A, B and Supplemental Fig. 1). In consideration of the critical role of VSMCs in the pathogenesis of aortic aneurysm24,25, we focused the downstream analysis of LDS- driven transcriptional alterations on this cell type (Supplemental Table 1). Using the Cytoscape26 ClueGO27 plug- in to leverage gene set enrichment information from multiple databases, we produced a network of functionally related terms and pathways that are differentially enriched among downregulated and upregulated transcripts. (Fig. 1C, D and Supplemental Table 2). The Tgfbr1M318R/+ LDS mutation caused broad downregulation of transcripts related to the maintenance of extracellular matrix- receptor interactions, and integrity of the elastic and contractile function of the aortic wall (Fig. 1C, D, E and Supplemental Table 2). Concurrently, pathways involved in cellular stress responses, inflammation, senescence, and cell death were enriched among transcripts upregulated in Tgfbr1M318R/+ VSMCs (Fig. 1C, D, E and Supplemental Table 2). Additional analysis of transcription factor target databases + +<--- Page Split ---> + +(ENCODE \(^{28}\) and Chromatin Immunoprecipitation Enrichment Analysis (ChEA) via EnrichR \(^{29 - 32}\) ) showed that LDS- downregulated transcripts were enriched in targets of NFE2L2 (nuclear factor erythroid 2- related factor 2, also known as Nrf2), a transcription factor that activates expression of cytoprotective genes and suppresses expression of proinflammatory mediators \(^{33 - 35}\) (Fig. 1F and Supplemental Table 2). Targets of the upstream stimulatory factor (USF) family, which can modulate the expression of smooth muscle specific genes were also enriched among downregulated transcripts \(^{36 - 39}\) (Fig. 1F and Supplemental Table 2). Conversely, target genes for GATA transcription factors and CCAAT enhancer binding protein delta (CEBPD), a positive transcriptional regulator of inflammatory responses mediated by interleukin- 1 (IL- 1) and IL- \(6^{40 - 43}\) , were enriched among transcripts upregulated in LDS VSMCs (Fig. 1G and Supplemental Table 2). + +## Spatial transcriptomic analysis of the murine aorta reveals region- and disease-specific patterns of expression for modulators of VSMC phenotypes. + +Given the regional vulnerability observed in LDS aortas, we leveraged insight gained from the literature and scRNAseq analysis of the aorta of control and \(Tgfbr1^{M318R / + }\) mice to design a custom panel for high throughput in situ hybridization using the Multiplexed error- robust fluorescence in situ hybridization (MERFISH) spatial transcriptomics platform (Supplemental Table 3). Analysis of a longitudinal section of the proximal aorta of 16- week- old control and LDS mice showed regionally defined expression of several transcripts involved in the modulation of vascular phenotypes (Fig. 2 and Supplemental Fig. 2). Transcripts more highly detected in the aortic root of LDS mice relative to the ascending aorta included \(Agtr1a\) , which codes for angiotensin II receptor type 1a, a known contributor to LDS pathogenesis, and \(Gata4\) , which codes for a transcription factor known to positively regulate \(Agtr1a\) expression in the heart \(^{44,45}\) . CCAAT enhancer binding protein beta (Cebpb), a pro- inflammatory mediator \(^{46}\) , and maternally expressed gene 3 (Meg3), a long non- coding RNA (lncRNA) that negatively regulates TGF- \(\beta\) signaling and promotes VSMC proliferation \(^{47 - 50}\) , were also enriched in this region. In contrast, expression of cardiac mesoderm enhancer- associated noncoding RNA (Carmn), a positive regulator of VSMC contractile function that is downregulated in vascular disease, and expression of \(Myh11\) , a marker of differentiated VSMCs, was enriched in the distal ascending aorta, a region that is only mildly affected in LDS mouse models \(^{49,51 - 53}\) . + +## Expression of cluster-defining transcripts for the VSMC2 and VSMC1 subclusters correlates with the proximal-to-distal axis of the mouse and human aorta. + +To examine if the spatial VSMC heterogeneity observed with MERFISH could be captured by scRNAseq, we increased the clustering resolution for VSMCs, thus obtaining two subclusters, VSMC1 and VSMC2. We then examined these two VSMC subclusters for expression of transcripts our laboratory has previously shown to progressively increase (i.e. Tes and Ptrpz1) and decrease (i.e. Enpep and Notch3) along the proximal- to- distal axis in the mouse ascending aorta \(^{54}\) . VSMC1 and VSMC2 showed increased expression of transcripts whose expression is intrinsically enriched in the ascending aorta and the aortic root, respectively \(^{54}\) (Fig. 3A, B and Supplemental Table 4). Gata4 was also noted among the transcripts that defined the VSMC2 subcluster and whose expression was highest in the aortic root, progressively diminishing along the proximal- to- distal axis in the ascending aorta (Fig. 3C). Considering previous work highlighting how cell lineage modulates the effect of LDS- causing mutations \(^{13,55 - 57}\) , we explored the relationship between the VSMC2 and VSMC1 subclusters to the secondary heart field + +<--- Page Split ---> + +(SHF)- and cardiac neural crest (CNC)- lineage of origin (Supplemental Fig. 3). We found that VSMCs lineage- traced with a fluorescent reporter identifying CNC- derived cells were overrepresented in the VSMC1 subcluster (Supplemental Fig. 3A). However, re- analysis of a previously published dataset of SHF- and CNC- traced VSMCs (Supplemental Table 5) showed that VSMC1 and VSMC2 were not defined by lineage of origin, with VSMCs of both lineages found in either VSMC sub- cluster \(^{58}\) (Supplemental Fig. 3B). Nevertheless, as would be expected based on the known proximal- to- distal distribution of SHF- and CNC- derived VSMCs, there was overlap between VSMC2- defining and SHF- enriched transcripts (Supplemental Fig. 3B, C and Supplemental Table 4 and 5). To assess if the VSMC substructure identified in murine models was relevant in the context of human aortic disease, we also re- analyzed a recently published scRNAseq dataset of aortic tissue from LDS patients and donor aortas in which the ascending aorta and aortic root were separately sequenced (Fig. 3D and Supplemental Fig. 4) \(^{59}\) . Subpopulations of VSMCs expressing cluster- defining transcripts analogous to those found in VSMC1 and VSMC2 in mouse aortas could be identified in the human dataset (Fig. 3D and Supplemental Table 6). Although both VSMC1 and VSMC2 were present in human aortic root and ascending aorta, GATA4 expression was highest in the VSMC2 cluster from the aortic root, with no detectable expression in the ascending aorta (Fig. 3D). + +## Gata4-expressing VSMC2 are intrinsically "poised" towards a less-differentiated, maladaptive proinflammatory transcriptional signature. + +To examine the biological features of VSMC1 and VSMC2, and whether they were recapitulated in both murine and patient- derived LDS VSMCs, we used the Coordinated Gene Activity in Pattern Sets (CoGAPS) algorithm to identify latent patterns of coordinated gene expression in the \(Tgbr^{M318R / +}\) VSMC mouse dataset \(^{60,61}\) . Two patterns, transcriptional patterns 4 and 5, were found to be enriched in the VSMC2 and VSMC1 subclusters, respectively, in the \(Tgbr^{M318R / +}\) VSMC mouse dataset (Fig. 3E, G, Supplemental Table 4). These same patterns were then projected onto the scRNAseq data of VSMCs from the aorta of LDS patients using ProjectR \(^{62}\) , revealing a similar enrichment of pattern 4 in VSMC2 and pattern 5 in VSMC1 (Fig. 3E- H, Supplemental Table 4). + +As previously observed for transcripts upregulated in \(Tgbr^{M318R / +}\) LDS VSMCs, Pattern 4- associated transcripts were enriched for transcriptional targets of GATA family members (ENCODE \(^{28}\) and ChEA dataset, analyzed with EnrichR \(^{29 - 32}\) , Fig. 3I). Differential gene set enrichment analysis using ClueGO \(^{27}\) to compare cluster- defining transcripts for VSMC1 and VSMC2 also showed that, in both mouse and human datasets, VSMC2- defining transcripts were enriched for pathways involved in inflammation, senescence, and cellular stress (Fig. 3J and Supplemental Table 7 and Table 8). In contrast, VSMC1 expressed higher levels of transcripts related to extracellular matrix- receptor interactions and contractile function (Fig. 3J, Supplemental Fig. 4 and Supplemental Table 7 and Table 8). Network visualization of molecular signatures database (MSigDB) VSMC2- enriched pathways shared by both mouse and human samples (probed with EnrichR \(^{30 - 32,63,64}\) ) (Supplemental Fig. 5A), and biological terms with shared ClueGO grouping (Fig. 3J and Supplemental Table 7 and Table 8), highlighted the biological connections between these pathways and genes over- expressed in VSMC2 relative to VSMC1 (i.e. \(Cxcl^{165 - 68}\) , Irf1 \(^{69 - 71}\) , Thbs1 \(^{72}\) , Gata4 \(^{73}\) ) (Supplemental Fig. 5B). Overall, in both mouse and human samples, the transcriptional profile of VSMC2 relative to VSMC1 resembled that of less- differentiated VSMCs and included lower expression of \(Myh11\) , Cnn1, and Tet2, and + +<--- Page Split ---> + +higher expression of transcripts associated with non- contractile VSMC phenotypes, including Klf4, Olfm2, Sox9, Tcf21, Malat1, Twist1, and Dcn74- 79. + +## Gata4 is upregulated in the aortic root of Tgfbr1M318R/+ LDS mice. + +Based on the analysis described above, and its known role in driving the upregulation of pathways previously involved in aneurysm progression44,73,80, Gata4 emerged as a potential molecular determinant of increased risk of dilation of the aortic root in LDS. Although levels of Gata4 mRNA are intrinsically higher in the aortic root relative to the ascending aorta even in control mice (Fig. 3C), its expression was further upregulated in VSMCs in the LDS aorta, as assessed both by scRNAseq (Supplemental Table 1) and RNA in situ hybridization (Fig. 4A). Given that levels of Gata4 protein are highly regulated at the post- transcriptional level through targeted degradation73,81,82, we also examined levels of Gata4 protein in control and LDS aortic samples, and found that protein levels are increased in LDS aortic root, both by immunofluorescence and immunoblot assays (Fig. 4B, C and Fig. 5). + +## Postnatal deletion of Gata4 in smooth muscle cells reduces aortic root dilation in LDS mice in association with reduced levels of Agtr1a and other proinflammatory mediators. + +To assess whether increased Gata4 levels in aortic root of LDS mouse models promoted dilation in this location, we crossed conditional Gata4flox/flox mice83 to LDS mice also expressing a transgenic, tamoxifen- inducible Cre recombinase under the control of a VSMC specific promoter (Myh11- CreER)84, and administered tamoxifen at 6 weeks of age to ablate expression of Gata4 in VSMCs (Fig. 5). VSMC- specific postnatal deletion of Gata4 in LDS mice (Tgfbr1M318R/+, Gata4SMcKO) resulted in a reduced rate of aortic root dilation relative to control LDS animals (Tgfbr1M318R/+; Gata4Ctrl) (Fig. 6A), and amelioration of aortic root medial architecture relative to control LDS aortas at 16 weeks of age (Fig. 6B). No significant dilation was observed in the ascending aorta of Tgfbr1M318R/+ mice at 16 weeks of age, and Gata4 deletion had no effect on the diameter of this aortic segment (Supplemental Fig. 6). Gata4 deletion in VSMCs also did not associate with changes in blood pressure (Supplemental Fig. 7). + +Previous work has shown that Gata4 binds to the Agtr1a promoter inducing its expression in heart tissue44,45, and that Agtr1a is transcriptionally upregulated in the aortic root of LDS mice, resulting in up- regulation of AT1R, which exacerbates LDS vascular pathology11,13,45. Accordingly, Gata4 deletion associated with reduced expression of Agtr1a in the aortic root of LDS mice (Fig. 7). Similarly, deletion of Gata4 reduced expression of Cebpd and Cebpb (Fig. 8 and Supplemental Fig. 8), which code for proinflammatory transcription factors regulated by and/or interacting with Gata4 in other contexts43,46,85,86, which were highly expressed in VSMC2 relative to VSMC1, and further upregulated in the presence of LDS mutations (Fig. 1, Fig. 2, Supplemental Table 1, Supplemental Table 7). + +## Discussion + +LDS is a hereditary connective tissue disorder characterized by skeletal, craniofacial, cutaneous, immunological, and vascular manifestations, including a high risk for aggressive arterial aneurysms4. It is caused by mutations that impair the signaling output of the TGF- \(\beta\) pathway, leading to defective transcriptional regulation of its target genes5- 9. Although loss- of- signaling initiates vascular pathology, compensatory upregulation of positive modulators of the pathway results in a “paradoxical” increase in activation of TGF- \(\beta\) signaling mediators (i.e + +<--- Page Split ---> + +phosphorylated Smad2 and Smad3) and increased expression of target genes in diseased aortic tissue of both LDS patients and mouse models \(^{5,7,10 - 13}\) . This secondary upregulation depends, in part, on increased activation of angiotensin II signaling via AT1R, which positively modulates the expression of TGF- \(\beta\) ligands and TGF- \(\beta\) receptors \(^{87}\) . Whereas upregulation of the TGF- \(\beta\) pathway can have both adaptive and maladaptive consequences depending on disease stage and cellular context \(^{13,54,88 - 95}\) , upregulation of AT1R signaling has consistently been shown to be detrimental to vascular health, and both pharmacological (i.e. with angiotensin receptor blockers) and genetic antagonism of this pathway ameliorates vascular pathology in LDS mouse models \(^{87,96 - 99}\) . + +Even though LDS- causing mutations confer an increased risk of disease across all arterial segments, the aortic root is one of the sites that is particularly susceptible to aneurysm development \(^{14 - 17}\) . In this study, we leveraged scRNAseq in conjunction with spatial transcriptomics to investigate the heterogeneity of VSMCs in an LDS mouse model, with the ultimate goal of identifying regional mediators that may drive upregulation of pro- pathogenic signaling in this region. We identify distinct subpopulations of VSMCs characterized by expression patterns that preferentially map to the ascending aorta (VSMC1) and aortic root (VSMC2) in mouse aorta. We also show that the regional vulnerability of the aortic root depends, in part, on higher levels of Gata4 expression in a subset of VSMCs (VSMC2), which is intrinsically more vulnerable to the effect of an LDS- causing mutation. + +Prior to the advent of single- cell analysis tools, which allow precise and unbiased unraveling of cellular identity, the ability to investigate VSMC heterogeneity in the proximal aorta was limited by the availability of experimental approaches to investigate known or expected diversity. In consideration of the mixed embryological origin of the aortic root and distal ascending aorta, earlier work thus focused on understanding how the effect of LDS mutations on VSMCs was modified by the SHF- and CNC lineage of origin. In both mouse models and in iPSCs- derived in vitro models, signaling defects caused by LDS mutations were found to be more pronounced in VSMC derived from SHF (or cardiac mesoderm) progenitors relative to CNC- derived VSMCs \(^{13,57}\) . + +Like SHF- derived VSMCs, Gata4- expressing VSMC2 are enriched in the aortic root and are also more vulnerable to the effects of an LDS- causing mutation. They also express a transcriptional signature similar to that of SHF- derived VSMCs (Supplemental Fig. 3). Reciprocally, SHF- derived cells are over- represented in the VSMC2 cluster in our dataset (Supplemental Fig. 3). However, the identity of VSMC2 and VSMC1 is not defined by lineage- of- origin, and SHF- or CNC- derived origin is only an imperfect approximation of the VSMC heterogeneity that can now be assessed via scRNAseq. + +Heterogeneity beyond that imposed by lineage- of- origin was also shown by scRNAseq analysis of the aorta of the \(Fbn^{1C1041G / +}\) Marfan syndrome (MFS) mouse model, which revealed the existence of an aneurysm- specific population of transcriptionally modified smooth muscle cells (modSMCs) at a later stage of aneurysmal disease, and which could emerge from modulation of both SHF- and non- SHF (presumably CNC)- derived progenitors \(^{58,100}\) . These cells, which could also be identified in the aneurysmal tissue derived from the aortic root of MFS patients, showed a transcriptional signature marked by a gradual upregulation of extracellular matrix genes and + +<--- Page Split ---> + +downregulation of VSMC contractile genes \(^{58,100}\) . We were not able to identify this population of modSMCs in the aorta of \(Tgfbr1^{M318R / +}\) LDS mouse models, even though it was shown to exist in the aorta of LDS patients \(^{62}\) . + +Similar to the early effect of Smad3- inactivation, the \(Tgfbr1^{M318R / +}\) LDS mutation caused broad downregulation of gene programs required for extracellular matrix homeostasis and those favoring a differentiated VSMC phenotype \(^{54}\) (Fig. 1); conversely, proinflammatory transcriptional repertoires, with an enrichment in pathways related to cell stress, was observed among upregulated transcripts. This latter profile likely represents a response to the initial insult caused by decreased expression of extracellular matrix components whose expression requires TGF- \(\beta\) /Smad activity \(^{98}\) . + +We also noted downregulation of several components of the lysosome, whose function is required for cellular homeostasis and degradation of protein targets via selective autophagy \(^{33,73,101,102}\) (Fig. 1). Gata4 levels are regulated via p62- mediated selective autophagy \(^{73}\) and by mechanosensitive proteasome- mediated degradation \(^{82,103}\) . The aortic root would be especially vulnerable to a defect in either of these processes given increased baseline levels of Gata4 mRNA expression in VSMC2. Increased levels of Gata4 may contribute to vascular pathogenesis by several potential mechanisms. In other cellular contexts, Gata4 has been shown to promote induction of the pro- inflammatory senescence- associated secretory phenotype (SASP) as well as transcription of the lncRNA Malat1, which promotes aneurysm development in other mouse models \(^{78}\) . Gata4 is also a negative regulator of contractile gene expression in Sertoli and Leydig cells \(^{104}\) . Additionally, Gata4 binds the promoter and activates the expression of \(Agtr1a^{44}\) , which is known to drive pro- pathogenic signaling in LDS aorta \(^{45}\) . Accordingly, we find that Gata4 deletion downregulates expression of \(Agtr1a\) in the aortic media of LDS mouse models (Fig. 7). + +Re- analysis of a scRNAseq dataset of human aortic samples from LDS patients, which included both the aortic root and the ascending aorta, shows that a population of Gata4- expressing VSMC similar to that found in mice can also be identified in LDS patients. Additionally, patterns of coordinated gene expression identifying VSMC1 and VSMC2, which were learned from the scRNAseq analysis of mouse aorta, could be projected onto the human dataset, suggesting that these two subsets of VSMCs are conserved across species and that the existence of a Gata4- expressing VSMC2 population may underlie increased risk in the aortic root of LDS patients as well. Assessing the effects of Gata4 deletion at additional postnatal timepoints will be important to understand the consequences of increased Gata4 and its downstream targets during later stages of disease. Although direct targeting of Gata4 for therapeutic purposes is unfeasible given its critical role in the regulation of numerous biological processes in non- vascular tissues \(^{105- 109}\) , this work highlights how the investigation of factors that increase or decrease the regional risk of aneurysm may lead to a better understanding of adaptive and maladaptive pathways activated in response to a given aneurysm- causing mutations. This knowledge may be leveraged to develop therapeutic strategies that target the vulnerabilities of specific arterial segments. + +<--- Page Split ---> + +## Methods + +## Animal Experiments + +Study approval + +Animal experiments were conducted according to protocols approved by the Johns Hopkins University School of Medicine Animal Care and Use Committee. + +## Mouse models + +All mice were maintained in an animal facility with unlimited access to standard chow and water unless otherwise described. \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + 11}\) (The Jackson Laboratory, strain #036511) mice, some bearing the \(E G F P - L10a^{110}\) (The Jackson Laboratory, strain #024750) conditional tracer allele and a CNC- specific CRE recombinase expressed under the control of Wnt2 promoter111 (The Jackson Laboratory, strain #003829) were used for scRNAseq as described below. All mice were maintained on a 129- background strain (Taconic, 129SVE). \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) mice were bred to \(G a t a^{4l o x / l o x 83}\) (The Jackson Laboratory, strain #008194) and mice carrying the \(M y h I1 - C r e^{E R}\) transgene84 (The Jackson Laboratory, strain #019079). \(M y h I1 - C r e^{E R}\) is integrated on the Y chromosome therefore only male mice were used for this set of experiments. \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) bearing \(G a t a^{4l o x / l o x}\) and \(M y h I1 - C r e^{E R}\) are referred to as \(G a t a^{4S M c K O}\) . \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) bearing \(G a t a^{4 + / + }\) with or without \(M y h I1-\) \(C r e^{E R}\) or \(G a t a^{4l o x / l o x}\) or \(G a t a^{4l o x / + }\) without \(M y h I1 - C r e^{E R}\) are referred to as \(G a t a^{4C u l}\) . All \(G a t a^{4S M c K O}\) and \(G a t a^{4C u l}\) mice were injected with 2 mg/day of tamoxifen (Millipore Sigma, T5648) starting at 6 weeks of age for 5 consecutive days. Mice were genotyped by PCR using primer sequences described in the original references for these models. Serial echocardiography was performed using the Visual Sonics Vivo 2100 machine and a 30 MHz probe. As there is some variability in the onset of aortic dilation in \(T g f b r I^{M318R / + }\) mice, and starting aortic size will affect final measurements, aortic root diameter of 1.9 mm and above at baseline (8 weeks of age) was defined a priori as an exclusion criterion. + +## Molecular validation techniques + +Aortic Sample Preparation + +All mice were euthanized by halothane inhalation at a \(4\%\) concentration, \(0.2\mathrm{ml}\) per liter of container volume (Millipore Sigma, H0150000). As we described previously \(^{11,54}\) , the heart and thoracic aorta were dissected en bloc and fixed in \(4\%\) paraformaldehyde (Electron Microscopy Sciences, 15710) in PBS at \(4^{\circ}\mathrm{C}\) overnight. Samples were subsequently incubated in \(70\%\) ethanol at \(4^{\circ}\mathrm{C}\) overnight prior to embedding in paraffin. Paraffin- embedded tissues were cut into 5 micron sections to expose a longitudinal section of the thoracic aorta. Sections were then stained with Verhoeff- van Gieson (StatLab, STVGI) to visualize elastic fiber morphology or to assess protein and RNA abundance by immunofluorescence or fluorescence in situ hybridization. + +## Immunofluorescence + +Immunofluorescence was performed following a protocol adapted from Cell Signaling Technology (CST) for formaldehyde- fixed tissues as previously described in detail \(^{45}\) , using a rabbit monoclonal antibody for GATA4 (Cell Signaling Technology, CST36966) and a donkey anti- rabbit secondary antibody Alexa Fluor 555 (ThermoFisher, A32794). Images were taken using a Zeiss LSM880 Airyscan FAST confocal microscope at \(20\times\) magnification and are presented as maximal intensity projection. + +<--- Page Split ---> + +RNAscope Fluorescence in situ hybridization + +RNA in situ hybridization was performed using the RNAscope Multiplex Fluorescent Reagent Kit v2 Assay (ACD Biosciences, 323100) according to the manufacturer's protocol with the following probes Mm- Gata4 (417881), Mm- Agtr1a (481161), Mm- Cebpd (556661), Mm- Cebpb (547471). Images were taken using a Zeiss LSM880 Airyscan FAST confocal microscope at \(20 \times\) magnification and are presented as maximal intensity projection. + +## Immunoblotting + +Aortic root tissue was flash- frozen immediately upon dissection and stored at \(- 80^{\circ}\mathrm{C}\) until protein extraction. Protein was extracted using Full Moon Lysis Buffer (Full Moon Biosystems, EXB1000) with added phosphatase and protease inhibitors (MilliporeSigma, 11836170001 and 4906845001) and Full Moon lysis beads (Full Moon Biosystems, LB020) using an MP Biomedicals FastPrep 24 5G automatic bead homogenizer. After homogenization, the cell debris was pelleted, and the supernatant was collected. Immunoblot was performed as previously described in detail54, using a rabbit monoclonal antibody for Gata4 (Cell Signaling Technology, 36966) and a mouse monoclonal antibody for \(\beta\) - Actin. (Cell Signaling Technology, 8H10D10). + +## Transcriptomic Analyses + +Single Cell RNA sequencing and analysis + +Single cell RNA sequencing was performed as we previously described112. Single cell suspensions from each mouse were processed separately using the 10x Genomics \(3^{\circ}\) v3 platform and sequenced on an Illumina NovaSeq. A total of 30,704 aortic cells were sequenced from six female mice. The raw data was processed, aligned to the mouse genome (mm10), and aggregated using 10x Genomics Cell Ranger V6'13. The data were then filtered using the Seurat V5 package112 based on the following criteria: \(>1000\) transcripts detected per cell but \(< 5000\) , \(>1500\) total molecules detected per cell but \(< 25000\) , and \(< 20\%\) mitochondrial transcripts per cell. Filtering reduced this dataset from 30,704 aortic cells to 24,971 cells for further analysis. The data was then normalized using the function SCTransform v2. As samples were prepared on multiple days, the data was integrated across batches using reciprocal principal component analysis (RPCAIntegration). Principal component analysis and uniform manifold approximation and projection (UMAP) were performed followed by the FindNeighbors and FindClusters functions. We opted to cluster at a low resolution (0.25) to differentiate aortic cell types and to identify only major subpopulations of smooth muscle cells that vary by a large number of differentially expressed genes. FindMarkers was used to identify cluster- defining transcripts and differentially expression genes between control and diseased cell populations based on a Wilcoxon rank sum test. + +Re- analysis of human aortic cells from Pedroza et al., 2023 + +For re- analysis of the ascending aorta and aortic root samples from a recently published scRNAseq dataset of the donor and LDS patient aortas59 we used the following criteria: \(>1000\) transcripts detected per cell but \(< 6000\) , \(>1500\) total molecules detected per cell \(< 30000\) , and \(< 20\%\) mitochondrial transcripts per cell. This reduces this dataset from 58,947 aortic cells to 43,349 for further analysis. We analyzed this dataset as described above with the FindClusters resolution parameter set to 0.15. + +<--- Page Split ---> + +CoGAPS and ProjectR + +CoGAPS and ProjectRCoGAPS60,61 (v3.22), an R package that utilizes non- negative matrix factorization to uncover latent patterns of coordinated gene expression representative of shared biological functions, was used to identify transcriptional patterns associated with VSMC subpopulations, with the npatterns parameter set to 8, in scRNAseq analysis of murine aortas. ProjectR62 (v1.2), an R package that enables integration and analysis of multiple scRNAseq data sets by identifying transcriptional patterns shared among datasets, was used to project these patterns into scRNAseq analysis of the human aortic root and ascending aorta. + +Gene over- representation analyses + +Gene over- representation analysesClueGO27 was used for gene over- representation analysis and visualization of enriched functional terms for transcripts globally dysregulated in all VSMCs as well as VSMC subsets. Transcripts were filtered based on an adjusted P- value less than 0.05 and an average absolute Log2 fold change of 0.25 or greater, as well as detection in at least 20 percent of either control or LDS VSMCs. The resulting list of 502 downregulated and 200 upregulated genes was compared against five gene ontology databases (MSigDB Hallmark, KEGG, WikiPathways, Bioplanet, and Reactome). The list of transcripts and ClueGO log files are provided in supplemental material. Differentially expressed gene lists were also analyzed using the online gene list enrichment analysis tool EnrichR30- 32 (https://maayanlab.cloud/Enrichr/) for pathways using the Molecular Signatures Database (MSigDB)63,64 and for transcription factors target enrichment using the ENCODE28 and ChEA29 databases. + +Multiplexed Error- Robust Fluorescence in situ Hybridization (MERFISH) Spatial Transcriptomics + +MERFISH spatial transcriptomics using a custom panel was performed on 5- micron Formalin- Fixed Paraffin- Embedded (FFPE) sections of control and LDS aortas according to manufacturer's protocols (MERSCOPE FFPE Tissue Sample Preparation User Guide_Rev B, Vizgen). Slides were processed and imaged on a MERSCOPE instrument platform according to the manufacturer's protocols (MERSCOPE Instrument User Guide Rev G, Vizgen). The raw images were processed by the instrument software to generate a matrix of spatial genomics measurements and associated image files that were analyzed using the MERSCOPE visualizer software. + +## Statistics + +GraphPad Prism 10.0 was used for data visualization and statistical analysis. Data tested for normality using the Shapiro- Wilk test and upon verification of normal distribution, analyzed using the Brown- Forsythe ANOVA test. For echocardiographic and blood pressure measurements, data are presented as a box and whisker plot with the whiskers indicating the maximum and minimum values and a horizontal bar indicating the median. All individual data points are shown as dots. Figures indicating statistical significance include the statistical tests used in the figure caption. + +## Data availability + +All single- cell RNA sequencing data, both raw fastq files and aggregated matrixes, will be available in the gene expression omnibus (GEO) repository under accession number GSE267204. MERFISH spatial transcriptomics data is available upon request. + +<--- Page Split ---> + +## Author contributions + +EM and EB conceptualized the study, designed the experiments, interpreted data, and prepared the manuscript. EB and TJC generated and processed the single- cell RNA (scRNAseq) sequencing data. EB conducted the primary analysis of the scRNAseq data and performed a reanalysis of published scRNAseq datasets, with input from WE, TC, LR, and JM. EM conducted gene- over- representation analysis and visualization. EB, EM, WE, and LR were involved in sample preparation and processing for MERFISH. EB conducted in situ hybridization, immunofluorescence, and immunoblotting experiments. EB was responsible for echocardiography, blood pressure measurements, genotyping, and animal husbandry with support from TC, MS, WE, LR, and RB. AZ performed histological staining and imaging. GS provided support for CoGAPS analysis and MERFISH spatial transcriptomics. AP and MF provided human scRNAseq data and offered valuable insight on interpretation of the analysis. HD provided valuable input on the study design. EM and EB wrote the manuscript, all authors contributed to its revision. + +## Acknowledgments + +Research in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Numbers R01HL147947 to EM and F31HL163924 to EB as well as a generous gift from the Loeys- Dietz Foundation. Fluorescence Microscopy imaging was also supported by NIH award number S10OD023548 to the School of Medicine Microscope Facility. 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Can J Cardiol 32, 26- 34, doi:10.1016/j.cjca.2015.11.004 (2016). 800 100 Pedroza, A. J. et al. Single- Cell Transcriptomic Profiling of Vascular Smooth Muscle 801 Cell Phenotype Modulation in Marfan Syndrome Aortic Aneurysm. Arterioscler Thromb 802 Vasc Biol, ATVBAHA120314670, doi:10.1161/ATVBAHA.120.314670 (2020). 803 101 Salabei, J. K. & Hill, B. G. Autophagic regulation of smooth muscle cell biology. Redox 804 Biol 4, 97- 103, doi:10.1016/j.redox.2014.12.007 (2015). 805 102 Clement, M. et al. Vascular Smooth Muscle Cell Plasticity and Autophagy in Dissecting 806 Aortic Aneurysms. Arterioscler Thromb Vasc Biol 39, 1149- 1159, 807 doi:10.1161/ATVBAHA.118.311727 (2019). 808 103 Pikkarainen, S. et al. GATA- 4 is a nuclear mediator of mechanical stretch- activated 809 hypertrophic program. J Biol Chem 278, 23807- 23816, doi:10.1074/jbc.M302719200 810 (2003). 811 104 Wang, Y. Q., Batool, A., Chen, S. R. & Liu, Y. X. GATA4 is a negative regulator of 812 contractility in mouse testicular peritubular myoid cells. Reproduction 156, 343- 351, 813 doi:10.1530/REP- 18- 0148 (2018). 814 105 Oka, T. et al. Cardiac- specific deletion of Gata4 reveals its requirement for hypertrophy, 815 compensation, and myocyte viability. Circ Res 98, 837- 845, 816 doi:10.1161/01.RES.0000215985.18538.c4 (2006). 817 106 Garg, V. et al. GATA4 mutations cause human congenital heart defects and reveal an 818 interaction with TBX5. Nature 424, 443- 447, doi:10.1038/nature01827 (2003). 819 107 Kuo, C. T. et al. GATA4 transcription factor is required for ventral morphogenesis and 820 heart tube formation. Genes Dev 11, 1048- 1060 (1997). + +<--- Page Split ---> + +821 108 Liang, Q. et al. The transcription factors GATA4 and GATA6 regulate cardiomyocyte 822 hypertrophy in vitro and in vivo. J Biol Chem 276, 30245- 30253, 823 doi:10.1074/jbc.M102174200 (2001). 824 109 Lepage, D. et al. Gata4 is critical to maintain gut barrier function and mucosal integrity 825 following epithelial injury. Sci Rep 6, 36776, doi:10.1038/srep36776 (2016). 826 110 Liu, J. et al. Cell- specific translational profiling in acute kidney injury. J Clin Invest 124, 827 1242- 1254, doi:10.1172/JCI72126 (2014). 828 111 Lewis, A. E., Vasudevan, H. N., O'Neill, A. K., Soriano, P. & Bush, J. O. The widely 829 used Wnt1- Cre transgene causes developmental phenotypes by ectopic activation of Wnt 830 signaling. Dev Biol 379, 229- 234, doi:10.1016/j.ydbio.2013.04.026 (2013). 831 112 Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single- cell 832 analysis. Nat Biotechnol 42, 293- 304, doi:10.1038/s41587- 023- 01767- y (2024). 833 113 Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat 834 Commun 8, 14049, doi:10.1038/ncomms14049 (2017). 835 836 + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Downregulation of transcripts associated with extracellular matrix-receptor interactions and upregulation of stress and inflammation pathways in Tgfbr1M318R/+ LDS VSMCs. (A) Uniform manifold approximation and projection (UMAP) of aortic cells from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice. (B) Dot plot of cluster defining transcripts used to identify endothelial cells, leukocytes, fibroblasts, and VSMCs. Color of the dot represents a scaled average expression while the size indicates the percentage of cells in which the transcript was detected. (C) ClueGO gene enrichment analysis network of transcripts dysregulated in LDS VSMCs relative to controls. Each node represents a term/pathway or individual genes associated with that term. The color of the node corresponds to the ClueGO group to which each node belongs. The size of the node indicates significance of the enrichment calculated by the ClueGO algorithm. (D) ClueGO network in which terms differentially enriched among transcripts downregulated in LDS VSMCs are highlighted in blue, while those enriched among transcripts upregulated in LDS VSMCs are highlighted in red. (E) Dot plot showing expression of a selection of transcripts significantly dysregulated in LDS VSMCs. (F,G) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus transcription factors (TF) databases showing the top three most significant terms associated with transcripts that are downregulated (F) or upregulated (G) in LDS VSMCs.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. MERFISH reveals spatially heterogeneous transcriptional profiles in LDS VSMCs. MERFISH images of the proximal aorta of LDS (A) and control (B) mice, scale bar is 1 mm. The first panel displays all detected transcripts across the aortic tissue, with key anatomic landmarks indicated. Subsequent panels depict the colocalization of Myh11 and transcripts of interest. Insets note regions of the ascending aorta and aortic root that are presented at higher magnification.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Transcriptionaly and spatially-defined VSMC subclusters with distinct responses to LDS-causing mutations can be identified in both murine and human aortas. (A) UMAP of VSMCs from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice shown split by genotype. (B) Dot plot showing enrichment of cluster-defining transcripts in VSMC1 and VSMC2. For a given transcript, the color of the dot represents a scaled average expression while the size indicates the percentage of cells in which it was detected. (C) RNA in situ hybridization showing the expression of Gata4 along the length of the murine aorta in a 16-week old control animal. (D) UMAP of control and LDS VSMCs from human patients and dot plot of cluster defining markers in this dataset split by aortic region (Pedroza et al., 2023). (E,F) UMAP overlayed with weights for CoGAPS patterns 4 and 5, in mouse and human scRNAseq datasets. (G,H) Violin plots showing the distribution of pattern 4 and 5 weights in VSMC subclusters from mouse and human scRNAseq datasets. P-values refer to Wilcoxon test. (I) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus TF databases showing the top four most significant terms associated with transcripts that define CoGAPs Patterns 4 and 5. (J) ClueGO network of terms differentially enriched in mouse and human LDS VSMC2 relative to VSMC1. Terms highlighted in blue are enriched in VSMC1, while those highlighted in red are enriched in VSMC2.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Figure 4. Gata4 mRNA and protein are upregulated in the aortic root of LDS mice. (A) Representative images of RNA in situ hybridization for Gata4 in the aortic root and ascending aorta of control and LDS (Tgfbr1M318R/+) mice. Insets identify the location shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively. (B) Representative images of immunofluorescence for GATA4in the aortic root and ascending aorta of control and LDS mice. Insets identify the location shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively. (C) Immunoblot for Gata4 expression relative to \(\beta\) - actin in aortic root lysates of control \((n = 3)\) and LDS mice \((n = 3)\) , and related quantification of immunoblot, P- value refers to two- tailed Student's t- test. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5
+ +Figure 5. Gata4 protein is upregulated in LDS aortic root of Gata4Ctrl and effectively ablated in Gata4SMckO mice. Representative images of immunofluorescence for GATA4 at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMckO) or without (Gata4Ctrl) smooth muscle specific deletion of Gata4 Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
B
+ +![](images/Figure_6.jpg) + +
Figure 6. Smooth muscle-specific deletion of Gata4 (Gata4SMcKO) reduces aortic root size and growth and improves aortic root media architecture in LDS mice. (A) Aortic root diameter of Ctrl (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMcKO) or without (Gata4SMcKO) smooth muscle specific deletion of Gata4 as measured by echocardiography at 8 and 16 weeks of age and aortic root growth from 8-16 weeks. P-values refer to Brown-Forsythe ANOVA. (B) Representative VVG-stained aortic root sections from three independent biological replicates per genotype. Insets identify area shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively.
+ +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 7
+ +Figure 7. Smooth muscle-specific deletion of Gata4 results in reduced expression of Agtr1a. Representative images of RNA in situ hybridization for Agtr1a in the aortic root of mice at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SmKo) or without (Gata4Ctl) smooth muscle specific deletion of Gata4. Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> +![](images/Figure_8.jpg) + +
Figure 8
+ +Figure 8. Smooth muscle-specific deletion of Gata4 results in reduced expression of Cebpb. Representative images of RNA in situ hybridization for Cebpb in the aortic root of mice of indicated genotype at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMckO) or without (Gata4Ctrl) smooth muscle specific deletion of Gata4. Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryTables.zip - SupplementalFigures.zip - CORRECTEDPrimaryfigure6forversion1. pdf - CORRECTEDSupplementalFigures6and7forversion1. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19_det.mmd b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7db01f34bf42c245af454d934e2086866a89c881 --- /dev/null +++ b/preprint/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19/preprint__00aa363715f9caf53b3b56fb3500a871c4d4ad7d3f29389a4c2af752e13f7a19_det.mmd @@ -0,0 +1,425 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 940, 175]]<|/det|> +# Intrinsic Gata4 expression sensitizes the aortic root to dilation in a Loeys-Dietz syndrome mouse model + +<|ref|>text<|/ref|><|det|>[[44, 196, 810, 240]]<|/det|> +Emily Bramel Johns Hopkins University School of Medicine https://orcid.org/0000- 0003- 4602- 9506 + +<|ref|>text<|/ref|><|det|>[[44, 244, 450, 285]]<|/det|> +Wendy Espinoza Camejo Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 291, 450, 332]]<|/det|> +Tyler Creamer Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 338, 450, 378]]<|/det|> +Leda Restrepo Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 383, 450, 424]]<|/det|> +Muzna Saqib Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 430, 450, 470]]<|/det|> +Rustam Bagirzadeh Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 476, 450, 516]]<|/det|> +Anthony Zeng Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 521, 450, 562]]<|/det|> +Jacob Mitchell Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 567, 450, 608]]<|/det|> +Genevieve Stein- O'Brien Johns Hopkins University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 613, 582, 654]]<|/det|> +Albert Pedroza Stanford University https://orcid.org/0000- 0001- 5291- 5980 + +<|ref|>text<|/ref|><|det|>[[44, 659, 225, 700]]<|/det|> +Michael Fischbein Stanford University + +<|ref|>text<|/ref|><|det|>[[44, 706, 720, 747]]<|/det|> +Harry Dietz Johns Hopkins School of Medicine https://orcid.org/0000- 0002- 6856- 0165 + +<|ref|>text<|/ref|><|det|>[[44, 752, 250, 793]]<|/det|> +Elena Gallo MacFarlane egal101@jhmi.edu + +<|ref|>text<|/ref|><|det|>[[52, 824, 799, 844]]<|/det|> +Genetic Medicine, Johns Hopkins University https://orcid.org/0000- 0001- 5677- 6842 + +<|ref|>text<|/ref|><|det|>[[44, 885, 102, 902]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 923, 136, 941]]<|/det|> +Keywords: + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[43, 45, 291, 64]]<|/det|> +**Posted Date:** June 5th, 2024 + +<|ref|>text<|/ref|><|det|>[[43, 84, 476, 102]]<|/det|> +**DOI:** https://doi.org/10.21203/rs.3.rs-4420617/v1 + +<|ref|>text<|/ref|><|det|>[[43, 121, 914, 163]]<|/det|> +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[43, 183, 536, 201]]<|/det|> +**Additional Declarations:** There is **NO** Competing Interest. + +<|ref|>text<|/ref|><|det|>[[43, 239, 890, 280]]<|/det|> +**Version of Record:** A version of this preprint was published at Nature Cardiovascular Research on November 20th, 2024. See the published version at https://doi.org/10.1038/s44161-024-00562-5. + +<|ref|>sub_title<|/ref|><|det|>[[65, 322, 303, 345]]<|/det|> +## EDITORIAL NOTE: + +<|ref|>text<|/ref|><|det|>[[65, 373, 930, 460]]<|/det|> +August 15, 2024. Editorial Note: In version 1 of this preprint (posted June 5, 2024) the authors have reported some unintentional errors in the x-axis labeling of figure 6A and supplemental figures 6 and 7. New figure files with corrected labeling have now been added to the version 1 preprint in the supplementary file section as follows. + +<|ref|>text<|/ref|><|det|>[[65, 464, 912, 528]]<|/det|> +**CORRECTED** Primary figure 6 for version 1 - in part A, the x axis labels have been corrected **CORRECTED** Supplemental Figures 6 and 7 for version 1 - in both supplemental figures, the x axis labels have been corrected + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 89, 861, 125]]<|/det|> +1 Intrinsic Gata4 expression sensitizes the aortic root to dilation in a Loeys- Dietz syndrome 2 mouse model + +<|ref|>text<|/ref|><|det|>[[112, 138, 857, 210]]<|/det|> +3 Emily E. Bramel1,2, Wendy A. Espinoza Camejo1,2, Tyler J. Creamer1, Leda Restrepo1, 4 Muzna Saqib1, Rustam Bagirzadeh1, Anthony Zeng1, Jacob T. Mitchell1,2, Genevieve L. 5 Stein- O'Brien1,4, Albert J. Pedroza5, Michael P. Fischbein5, Harry C. Dietz1, Elena Gallo 6 MacFarlane1,3\* + +<|ref|>text<|/ref|><|det|>[[112, 225, 861, 396]]<|/det|> +7 1McKusick- Nathans Department of Genetic Medicine, Johns Hopkins University School of 8 Medicine, Baltimore, Maryland, USA 9 2 Predoctoral Training in Human Genetics and Genomics, Johns Hopkins University School of 10 Medicine, Baltimore, Maryland, USA 11 3 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, 12 USA 13 4Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of 14 Medicine, Baltimore, Maryland, USA 15 5Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, 16 California, USA + +<|ref|>text<|/ref|><|det|>[[115, 416, 310, 432]]<|/det|> +\* Correspondence: + +<|ref|>text<|/ref|><|det|>[[115, 434, 310, 450]]<|/det|> +Elena Gallo MacFarlane + +<|ref|>text<|/ref|><|det|>[[115, 452, 262, 468]]<|/det|> +egalol1@jhmi.edu + +<|ref|>sub_title<|/ref|><|det|>[[115, 486, 361, 503]]<|/det|> +## Conflict of interest statement + +<|ref|>text<|/ref|><|det|>[[115, 504, 584, 520]]<|/det|> +The authors have declared that no conflict of interest exists. + +<|ref|>sub_title<|/ref|><|det|>[[115, 538, 192, 553]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 555, 877, 764]]<|/det|> +Loews- Dietz syndrome (LDS) is an aneurysm disorder caused by mutations that decrease transforming growth factor- \(\beta\) (TGF- \(\beta\) ) signaling. Although aneurysms develop throughout the arterial tree, the aortic root is a site of heightened risk. To identify molecular determinants of this vulnerability, we investigated the heterogeneity of vascular smooth muscle cells (VSMCs) in the aorta of Tgfbr1M318R/+ LDS mice by single cell and spatial transcriptomics. Reduced expression of components of the extracellular matrix- receptor apparatus and upregulation of stress and inflammatory pathways were observed in all LDS VSMCs. However, regardless of genotype, a subset of Gata4- expressing VSMCs predominantly located in the aortic root intrinsically displayed a less differentiated, proinflammatory profile. A similar population was also identified among aortic VSMCs in a human scRNAseq dataset. Postnatal VSMC- specific Gata4 deletion reduced aortic root dilation in LDS mice, suggesting that this factor sensitizes the aortic root to the effects of impaired TGF- \(\beta\) signaling. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 875, 508]]<|/det|> +Thoracic aortic aneurysms are localized vascular dilations that increase the risk of fatal dissections and/or rupture of the vessel wall'. Effective medical therapies to prevent life- threatening aortic events remain elusive?. Loeys- Dietz syndrome (LDS) is a hereditary connective tissue disorder that presents with highly penetrant aortic aneurysms3,4. LDS is caused by heterozygous, loss- of- function mutations in positive effectors of the TGF- \(\beta\) signaling pathway, including receptors (TGFBR1, TGFBR2), ligands (TGFB2, TGFB3) and intracellular signaling mediators (SMAD2, SMAD3)5- 9. All of these mutations result in reduced phosphorylation/activation of Smad2 and Smad3, leading to defective Smad- dependent transcriptional regulation. Secondary compensatory mechanisms, including upregulation of Angiotensin II Type I Receptor (AT1R) signaling, and increased expression of TGF- \(\beta\) ligands and Smad proteins, ultimately elevate levels of Smad2/Smad3 activity at diseased aortic sites, with outcomes ranging from adaptive to maladaptive depending on disease progression and cellular context5,7,10- 13. While LDS- causing mutations heighten aneurysm risk in all arteries, the aortic root is especially vulnerable to disease14- 17. Several laboratories have highlighted how the cellular composition and/or the mechanical stresses may contribute to the increased risk of disease in this location, however, the molecular determinants of this susceptibility remain unclear13,18- 22. Additionally, VSMCs are the primary cellular component of the aortic wall, but the heterogeneity of VSMCs within the aorta and its implications for aneurysm are not fully understood. In this study, we investigate the transcriptional heterogeneity of VSMCs in the normal and diseased murine aorta leveraging both scRNAseq and spatial transcriptomics. We identify Gata4 as a regional factor whose expression is intrinsically elevated in the aortic root and further upregulated in LDS samples. We also show that postnatal deletion of Gata4 in VSMCs ameliorates aortic root dilation in a murine model of LDS harboring a Tgfbr1M318R/+ genotype. + +<|ref|>sub_title<|/ref|><|det|>[[115, 525, 182, 540]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[115, 541, 870, 595]]<|/det|> +Tgfbr1M318R/+ VSMCs downregulate extracellular matrix components, focal adhesions, and integrin receptors, and upregulate transcripts related to stress and inflammatory pathways. + +<|ref|>text<|/ref|><|det|>[[111, 595, 880, 892]]<|/det|> +LDS mouse models expressing a heterozygous missense mutation in Tgfbr1 (Tgfbr1M318R/+) develop highly penetrant aortic root aneurysms11,13. To assess transcriptomic changes associated with vascular pathology in this model, we performed single cell RNA sequencing (scRNAseq) on the aortic root and ascending aorta of control (Tgfbr1+/+) and LDS mice at 16 weeks of age, resulting in the identification of all of the expected cell types according to well- established expression profiles23 (Fig. 1A, B and Supplemental Fig. 1). In consideration of the critical role of VSMCs in the pathogenesis of aortic aneurysm24,25, we focused the downstream analysis of LDS- driven transcriptional alterations on this cell type (Supplemental Table 1). Using the Cytoscape26 ClueGO27 plug- in to leverage gene set enrichment information from multiple databases, we produced a network of functionally related terms and pathways that are differentially enriched among downregulated and upregulated transcripts. (Fig. 1C, D and Supplemental Table 2). The Tgfbr1M318R/+ LDS mutation caused broad downregulation of transcripts related to the maintenance of extracellular matrix- receptor interactions, and integrity of the elastic and contractile function of the aortic wall (Fig. 1C, D, E and Supplemental Table 2). Concurrently, pathways involved in cellular stress responses, inflammation, senescence, and cell death were enriched among transcripts upregulated in Tgfbr1M318R/+ VSMCs (Fig. 1C, D, E and Supplemental Table 2). Additional analysis of transcription factor target databases + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 878, 281]]<|/det|> +(ENCODE \(^{28}\) and Chromatin Immunoprecipitation Enrichment Analysis (ChEA) via EnrichR \(^{29 - 32}\) ) showed that LDS- downregulated transcripts were enriched in targets of NFE2L2 (nuclear factor erythroid 2- related factor 2, also known as Nrf2), a transcription factor that activates expression of cytoprotective genes and suppresses expression of proinflammatory mediators \(^{33 - 35}\) (Fig. 1F and Supplemental Table 2). Targets of the upstream stimulatory factor (USF) family, which can modulate the expression of smooth muscle specific genes were also enriched among downregulated transcripts \(^{36 - 39}\) (Fig. 1F and Supplemental Table 2). Conversely, target genes for GATA transcription factors and CCAAT enhancer binding protein delta (CEBPD), a positive transcriptional regulator of inflammatory responses mediated by interleukin- 1 (IL- 1) and IL- \(6^{40 - 43}\) , were enriched among transcripts upregulated in LDS VSMCs (Fig. 1G and Supplemental Table 2). + +<|ref|>sub_title<|/ref|><|det|>[[115, 298, 842, 334]]<|/det|> +## Spatial transcriptomic analysis of the murine aorta reveals region- and disease-specific patterns of expression for modulators of VSMC phenotypes. + +<|ref|>text<|/ref|><|det|>[[112, 333, 880, 630]]<|/det|> +Given the regional vulnerability observed in LDS aortas, we leveraged insight gained from the literature and scRNAseq analysis of the aorta of control and \(Tgfbr1^{M318R / + }\) mice to design a custom panel for high throughput in situ hybridization using the Multiplexed error- robust fluorescence in situ hybridization (MERFISH) spatial transcriptomics platform (Supplemental Table 3). Analysis of a longitudinal section of the proximal aorta of 16- week- old control and LDS mice showed regionally defined expression of several transcripts involved in the modulation of vascular phenotypes (Fig. 2 and Supplemental Fig. 2). Transcripts more highly detected in the aortic root of LDS mice relative to the ascending aorta included \(Agtr1a\) , which codes for angiotensin II receptor type 1a, a known contributor to LDS pathogenesis, and \(Gata4\) , which codes for a transcription factor known to positively regulate \(Agtr1a\) expression in the heart \(^{44,45}\) . CCAAT enhancer binding protein beta (Cebpb), a pro- inflammatory mediator \(^{46}\) , and maternally expressed gene 3 (Meg3), a long non- coding RNA (lncRNA) that negatively regulates TGF- \(\beta\) signaling and promotes VSMC proliferation \(^{47 - 50}\) , were also enriched in this region. In contrast, expression of cardiac mesoderm enhancer- associated noncoding RNA (Carmn), a positive regulator of VSMC contractile function that is downregulated in vascular disease, and expression of \(Myh11\) , a marker of differentiated VSMCs, was enriched in the distal ascending aorta, a region that is only mildly affected in LDS mouse models \(^{49,51 - 53}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 647, 805, 682]]<|/det|> +## Expression of cluster-defining transcripts for the VSMC2 and VSMC1 subclusters correlates with the proximal-to-distal axis of the mouse and human aorta. + +<|ref|>text<|/ref|><|det|>[[112, 682, 875, 891]]<|/det|> +To examine if the spatial VSMC heterogeneity observed with MERFISH could be captured by scRNAseq, we increased the clustering resolution for VSMCs, thus obtaining two subclusters, VSMC1 and VSMC2. We then examined these two VSMC subclusters for expression of transcripts our laboratory has previously shown to progressively increase (i.e. Tes and Ptrpz1) and decrease (i.e. Enpep and Notch3) along the proximal- to- distal axis in the mouse ascending aorta \(^{54}\) . VSMC1 and VSMC2 showed increased expression of transcripts whose expression is intrinsically enriched in the ascending aorta and the aortic root, respectively \(^{54}\) (Fig. 3A, B and Supplemental Table 4). Gata4 was also noted among the transcripts that defined the VSMC2 subcluster and whose expression was highest in the aortic root, progressively diminishing along the proximal- to- distal axis in the ascending aorta (Fig. 3C). Considering previous work highlighting how cell lineage modulates the effect of LDS- causing mutations \(^{13,55 - 57}\) , we explored the relationship between the VSMC2 and VSMC1 subclusters to the secondary heart field + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 386]]<|/det|> +(SHF)- and cardiac neural crest (CNC)- lineage of origin (Supplemental Fig. 3). We found that VSMCs lineage- traced with a fluorescent reporter identifying CNC- derived cells were overrepresented in the VSMC1 subcluster (Supplemental Fig. 3A). However, re- analysis of a previously published dataset of SHF- and CNC- traced VSMCs (Supplemental Table 5) showed that VSMC1 and VSMC2 were not defined by lineage of origin, with VSMCs of both lineages found in either VSMC sub- cluster \(^{58}\) (Supplemental Fig. 3B). Nevertheless, as would be expected based on the known proximal- to- distal distribution of SHF- and CNC- derived VSMCs, there was overlap between VSMC2- defining and SHF- enriched transcripts (Supplemental Fig. 3B, C and Supplemental Table 4 and 5). To assess if the VSMC substructure identified in murine models was relevant in the context of human aortic disease, we also re- analyzed a recently published scRNAseq dataset of aortic tissue from LDS patients and donor aortas in which the ascending aorta and aortic root were separately sequenced (Fig. 3D and Supplemental Fig. 4) \(^{59}\) . Subpopulations of VSMCs expressing cluster- defining transcripts analogous to those found in VSMC1 and VSMC2 in mouse aortas could be identified in the human dataset (Fig. 3D and Supplemental Table 6). Although both VSMC1 and VSMC2 were present in human aortic root and ascending aorta, GATA4 expression was highest in the VSMC2 cluster from the aortic root, with no detectable expression in the ascending aorta (Fig. 3D). + +<|ref|>sub_title<|/ref|><|det|>[[115, 401, 797, 437]]<|/det|> +## Gata4-expressing VSMC2 are intrinsically "poised" towards a less-differentiated, maladaptive proinflammatory transcriptional signature. + +<|ref|>text<|/ref|><|det|>[[112, 436, 880, 595]]<|/det|> +To examine the biological features of VSMC1 and VSMC2, and whether they were recapitulated in both murine and patient- derived LDS VSMCs, we used the Coordinated Gene Activity in Pattern Sets (CoGAPS) algorithm to identify latent patterns of coordinated gene expression in the \(Tgbr^{M318R / +}\) VSMC mouse dataset \(^{60,61}\) . Two patterns, transcriptional patterns 4 and 5, were found to be enriched in the VSMC2 and VSMC1 subclusters, respectively, in the \(Tgbr^{M318R / +}\) VSMC mouse dataset (Fig. 3E, G, Supplemental Table 4). These same patterns were then projected onto the scRNAseq data of VSMCs from the aorta of LDS patients using ProjectR \(^{62}\) , revealing a similar enrichment of pattern 4 in VSMC2 and pattern 5 in VSMC1 (Fig. 3E- H, Supplemental Table 4). + +<|ref|>text<|/ref|><|det|>[[111, 610, 880, 891]]<|/det|> +As previously observed for transcripts upregulated in \(Tgbr^{M318R / +}\) LDS VSMCs, Pattern 4- associated transcripts were enriched for transcriptional targets of GATA family members (ENCODE \(^{28}\) and ChEA dataset, analyzed with EnrichR \(^{29 - 32}\) , Fig. 3I). Differential gene set enrichment analysis using ClueGO \(^{27}\) to compare cluster- defining transcripts for VSMC1 and VSMC2 also showed that, in both mouse and human datasets, VSMC2- defining transcripts were enriched for pathways involved in inflammation, senescence, and cellular stress (Fig. 3J and Supplemental Table 7 and Table 8). In contrast, VSMC1 expressed higher levels of transcripts related to extracellular matrix- receptor interactions and contractile function (Fig. 3J, Supplemental Fig. 4 and Supplemental Table 7 and Table 8). Network visualization of molecular signatures database (MSigDB) VSMC2- enriched pathways shared by both mouse and human samples (probed with EnrichR \(^{30 - 32,63,64}\) ) (Supplemental Fig. 5A), and biological terms with shared ClueGO grouping (Fig. 3J and Supplemental Table 7 and Table 8), highlighted the biological connections between these pathways and genes over- expressed in VSMC2 relative to VSMC1 (i.e. \(Cxcl^{165 - 68}\) , Irf1 \(^{69 - 71}\) , Thbs1 \(^{72}\) , Gata4 \(^{73}\) ) (Supplemental Fig. 5B). Overall, in both mouse and human samples, the transcriptional profile of VSMC2 relative to VSMC1 resembled that of less- differentiated VSMCs and included lower expression of \(Myh11\) , Cnn1, and Tet2, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 855, 126]]<|/det|> +higher expression of transcripts associated with non- contractile VSMC phenotypes, including Klf4, Olfm2, Sox9, Tcf21, Malat1, Twist1, and Dcn74- 79. + +<|ref|>sub_title<|/ref|><|det|>[[115, 142, 660, 161]]<|/det|> +## Gata4 is upregulated in the aortic root of Tgfbr1M318R/+ LDS mice. + +<|ref|>text<|/ref|><|det|>[[113, 161, 870, 334]]<|/det|> +Based on the analysis described above, and its known role in driving the upregulation of pathways previously involved in aneurysm progression44,73,80, Gata4 emerged as a potential molecular determinant of increased risk of dilation of the aortic root in LDS. Although levels of Gata4 mRNA are intrinsically higher in the aortic root relative to the ascending aorta even in control mice (Fig. 3C), its expression was further upregulated in VSMCs in the LDS aorta, as assessed both by scRNAseq (Supplemental Table 1) and RNA in situ hybridization (Fig. 4A). Given that levels of Gata4 protein are highly regulated at the post- transcriptional level through targeted degradation73,81,82, we also examined levels of Gata4 protein in control and LDS aortic samples, and found that protein levels are increased in LDS aortic root, both by immunofluorescence and immunoblot assays (Fig. 4B, C and Fig. 5). + +<|ref|>sub_title<|/ref|><|det|>[[115, 350, 872, 386]]<|/det|> +## Postnatal deletion of Gata4 in smooth muscle cells reduces aortic root dilation in LDS mice in association with reduced levels of Agtr1a and other proinflammatory mediators. + +<|ref|>text<|/ref|><|det|>[[113, 386, 881, 578]]<|/det|> +To assess whether increased Gata4 levels in aortic root of LDS mouse models promoted dilation in this location, we crossed conditional Gata4flox/flox mice83 to LDS mice also expressing a transgenic, tamoxifen- inducible Cre recombinase under the control of a VSMC specific promoter (Myh11- CreER)84, and administered tamoxifen at 6 weeks of age to ablate expression of Gata4 in VSMCs (Fig. 5). VSMC- specific postnatal deletion of Gata4 in LDS mice (Tgfbr1M318R/+, Gata4SMcKO) resulted in a reduced rate of aortic root dilation relative to control LDS animals (Tgfbr1M318R/+; Gata4Ctrl) (Fig. 6A), and amelioration of aortic root medial architecture relative to control LDS aortas at 16 weeks of age (Fig. 6B). No significant dilation was observed in the ascending aorta of Tgfbr1M318R/+ mice at 16 weeks of age, and Gata4 deletion had no effect on the diameter of this aortic segment (Supplemental Fig. 6). Gata4 deletion in VSMCs also did not associate with changes in blood pressure (Supplemental Fig. 7). + +<|ref|>text<|/ref|><|det|>[[113, 594, 874, 750]]<|/det|> +Previous work has shown that Gata4 binds to the Agtr1a promoter inducing its expression in heart tissue44,45, and that Agtr1a is transcriptionally upregulated in the aortic root of LDS mice, resulting in up- regulation of AT1R, which exacerbates LDS vascular pathology11,13,45. Accordingly, Gata4 deletion associated with reduced expression of Agtr1a in the aortic root of LDS mice (Fig. 7). Similarly, deletion of Gata4 reduced expression of Cebpd and Cebpb (Fig. 8 and Supplemental Fig. 8), which code for proinflammatory transcription factors regulated by and/or interacting with Gata4 in other contexts43,46,85,86, which were highly expressed in VSMC2 relative to VSMC1, and further upregulated in the presence of LDS mutations (Fig. 1, Fig. 2, Supplemental Table 1, Supplemental Table 7). + +<|ref|>sub_title<|/ref|><|det|>[[115, 768, 205, 784]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 785, 872, 891]]<|/det|> +LDS is a hereditary connective tissue disorder characterized by skeletal, craniofacial, cutaneous, immunological, and vascular manifestations, including a high risk for aggressive arterial aneurysms4. It is caused by mutations that impair the signaling output of the TGF- \(\beta\) pathway, leading to defective transcriptional regulation of its target genes5- 9. Although loss- of- signaling initiates vascular pathology, compensatory upregulation of positive modulators of the pathway results in a “paradoxical” increase in activation of TGF- \(\beta\) signaling mediators (i.e + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 247]]<|/det|> +phosphorylated Smad2 and Smad3) and increased expression of target genes in diseased aortic tissue of both LDS patients and mouse models \(^{5,7,10 - 13}\) . This secondary upregulation depends, in part, on increased activation of angiotensin II signaling via AT1R, which positively modulates the expression of TGF- \(\beta\) ligands and TGF- \(\beta\) receptors \(^{87}\) . Whereas upregulation of the TGF- \(\beta\) pathway can have both adaptive and maladaptive consequences depending on disease stage and cellular context \(^{13,54,88 - 95}\) , upregulation of AT1R signaling has consistently been shown to be detrimental to vascular health, and both pharmacological (i.e. with angiotensin receptor blockers) and genetic antagonism of this pathway ameliorates vascular pathology in LDS mouse models \(^{87,96 - 99}\) . + +<|ref|>text<|/ref|><|det|>[[113, 262, 880, 439]]<|/det|> +Even though LDS- causing mutations confer an increased risk of disease across all arterial segments, the aortic root is one of the sites that is particularly susceptible to aneurysm development \(^{14 - 17}\) . In this study, we leveraged scRNAseq in conjunction with spatial transcriptomics to investigate the heterogeneity of VSMCs in an LDS mouse model, with the ultimate goal of identifying regional mediators that may drive upregulation of pro- pathogenic signaling in this region. We identify distinct subpopulations of VSMCs characterized by expression patterns that preferentially map to the ascending aorta (VSMC1) and aortic root (VSMC2) in mouse aorta. We also show that the regional vulnerability of the aortic root depends, in part, on higher levels of Gata4 expression in a subset of VSMCs (VSMC2), which is intrinsically more vulnerable to the effect of an LDS- causing mutation. + +<|ref|>text<|/ref|><|det|>[[113, 454, 880, 612]]<|/det|> +Prior to the advent of single- cell analysis tools, which allow precise and unbiased unraveling of cellular identity, the ability to investigate VSMC heterogeneity in the proximal aorta was limited by the availability of experimental approaches to investigate known or expected diversity. In consideration of the mixed embryological origin of the aortic root and distal ascending aorta, earlier work thus focused on understanding how the effect of LDS mutations on VSMCs was modified by the SHF- and CNC lineage of origin. In both mouse models and in iPSCs- derived in vitro models, signaling defects caused by LDS mutations were found to be more pronounced in VSMC derived from SHF (or cardiac mesoderm) progenitors relative to CNC- derived VSMCs \(^{13,57}\) . + +<|ref|>text<|/ref|><|det|>[[113, 627, 881, 752]]<|/det|> +Like SHF- derived VSMCs, Gata4- expressing VSMC2 are enriched in the aortic root and are also more vulnerable to the effects of an LDS- causing mutation. They also express a transcriptional signature similar to that of SHF- derived VSMCs (Supplemental Fig. 3). Reciprocally, SHF- derived cells are over- represented in the VSMC2 cluster in our dataset (Supplemental Fig. 3). However, the identity of VSMC2 and VSMC1 is not defined by lineage- of- origin, and SHF- or CNC- derived origin is only an imperfect approximation of the VSMC heterogeneity that can now be assessed via scRNAseq. + +<|ref|>text<|/ref|><|det|>[[113, 767, 875, 891]]<|/det|> +Heterogeneity beyond that imposed by lineage- of- origin was also shown by scRNAseq analysis of the aorta of the \(Fbn^{1C1041G / +}\) Marfan syndrome (MFS) mouse model, which revealed the existence of an aneurysm- specific population of transcriptionally modified smooth muscle cells (modSMCs) at a later stage of aneurysmal disease, and which could emerge from modulation of both SHF- and non- SHF (presumably CNC)- derived progenitors \(^{58,100}\) . These cells, which could also be identified in the aneurysmal tissue derived from the aortic root of MFS patients, showed a transcriptional signature marked by a gradual upregulation of extracellular matrix genes and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 143]]<|/det|> +downregulation of VSMC contractile genes \(^{58,100}\) . We were not able to identify this population of modSMCs in the aorta of \(Tgfbr1^{M318R / +}\) LDS mouse models, even though it was shown to exist in the aorta of LDS patients \(^{62}\) . + +<|ref|>text<|/ref|><|det|>[[113, 158, 872, 283]]<|/det|> +Similar to the early effect of Smad3- inactivation, the \(Tgfbr1^{M318R / +}\) LDS mutation caused broad downregulation of gene programs required for extracellular matrix homeostasis and those favoring a differentiated VSMC phenotype \(^{54}\) (Fig. 1); conversely, proinflammatory transcriptional repertoires, with an enrichment in pathways related to cell stress, was observed among upregulated transcripts. This latter profile likely represents a response to the initial insult caused by decreased expression of extracellular matrix components whose expression requires TGF- \(\beta\) /Smad activity \(^{98}\) . + +<|ref|>text<|/ref|><|det|>[[112, 298, 872, 544]]<|/det|> +We also noted downregulation of several components of the lysosome, whose function is required for cellular homeostasis and degradation of protein targets via selective autophagy \(^{33,73,101,102}\) (Fig. 1). Gata4 levels are regulated via p62- mediated selective autophagy \(^{73}\) and by mechanosensitive proteasome- mediated degradation \(^{82,103}\) . The aortic root would be especially vulnerable to a defect in either of these processes given increased baseline levels of Gata4 mRNA expression in VSMC2. Increased levels of Gata4 may contribute to vascular pathogenesis by several potential mechanisms. In other cellular contexts, Gata4 has been shown to promote induction of the pro- inflammatory senescence- associated secretory phenotype (SASP) as well as transcription of the lncRNA Malat1, which promotes aneurysm development in other mouse models \(^{78}\) . Gata4 is also a negative regulator of contractile gene expression in Sertoli and Leydig cells \(^{104}\) . Additionally, Gata4 binds the promoter and activates the expression of \(Agtr1a^{44}\) , which is known to drive pro- pathogenic signaling in LDS aorta \(^{45}\) . Accordingly, we find that Gata4 deletion downregulates expression of \(Agtr1a\) in the aortic media of LDS mouse models (Fig. 7). + +<|ref|>text<|/ref|><|det|>[[112, 558, 880, 821]]<|/det|> +Re- analysis of a scRNAseq dataset of human aortic samples from LDS patients, which included both the aortic root and the ascending aorta, shows that a population of Gata4- expressing VSMC similar to that found in mice can also be identified in LDS patients. Additionally, patterns of coordinated gene expression identifying VSMC1 and VSMC2, which were learned from the scRNAseq analysis of mouse aorta, could be projected onto the human dataset, suggesting that these two subsets of VSMCs are conserved across species and that the existence of a Gata4- expressing VSMC2 population may underlie increased risk in the aortic root of LDS patients as well. Assessing the effects of Gata4 deletion at additional postnatal timepoints will be important to understand the consequences of increased Gata4 and its downstream targets during later stages of disease. Although direct targeting of Gata4 for therapeutic purposes is unfeasible given its critical role in the regulation of numerous biological processes in non- vascular tissues \(^{105- 109}\) , this work highlights how the investigation of factors that increase or decrease the regional risk of aneurysm may lead to a better understanding of adaptive and maladaptive pathways activated in response to a given aneurysm- causing mutations. This knowledge may be leveraged to develop therapeutic strategies that target the vulnerabilities of specific arterial segments. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 191, 106]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 291, 142]]<|/det|> +## Animal Experiments + +<|ref|>text<|/ref|><|det|>[[115, 144, 233, 160]]<|/det|> +Study approval + +<|ref|>text<|/ref|><|det|>[[115, 160, 839, 195]]<|/det|> +Animal experiments were conducted according to protocols approved by the Johns Hopkins University School of Medicine Animal Care and Use Committee. + +<|ref|>sub_title<|/ref|><|det|>[[115, 213, 230, 228]]<|/det|> +## Mouse models + +<|ref|>text<|/ref|><|det|>[[111, 230, 882, 560]]<|/det|> +All mice were maintained in an animal facility with unlimited access to standard chow and water unless otherwise described. \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + 11}\) (The Jackson Laboratory, strain #036511) mice, some bearing the \(E G F P - L10a^{110}\) (The Jackson Laboratory, strain #024750) conditional tracer allele and a CNC- specific CRE recombinase expressed under the control of Wnt2 promoter111 (The Jackson Laboratory, strain #003829) were used for scRNAseq as described below. All mice were maintained on a 129- background strain (Taconic, 129SVE). \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) mice were bred to \(G a t a^{4l o x / l o x 83}\) (The Jackson Laboratory, strain #008194) and mice carrying the \(M y h I1 - C r e^{E R}\) transgene84 (The Jackson Laboratory, strain #019079). \(M y h I1 - C r e^{E R}\) is integrated on the Y chromosome therefore only male mice were used for this set of experiments. \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) bearing \(G a t a^{4l o x / l o x}\) and \(M y h I1 - C r e^{E R}\) are referred to as \(G a t a^{4S M c K O}\) . \(T g f b r I^{+ / + }\) and \(T g f b r I^{M318R / + }\) bearing \(G a t a^{4 + / + }\) with or without \(M y h I1-\) \(C r e^{E R}\) or \(G a t a^{4l o x / l o x}\) or \(G a t a^{4l o x / + }\) without \(M y h I1 - C r e^{E R}\) are referred to as \(G a t a^{4C u l}\) . All \(G a t a^{4S M c K O}\) and \(G a t a^{4C u l}\) mice were injected with 2 mg/day of tamoxifen (Millipore Sigma, T5648) starting at 6 weeks of age for 5 consecutive days. Mice were genotyped by PCR using primer sequences described in the original references for these models. Serial echocardiography was performed using the Visual Sonics Vivo 2100 machine and a 30 MHz probe. As there is some variability in the onset of aortic dilation in \(T g f b r I^{M318R / + }\) mice, and starting aortic size will affect final measurements, aortic root diameter of 1.9 mm and above at baseline (8 weeks of age) was defined a priori as an exclusion criterion. + +<|ref|>sub_title<|/ref|><|det|>[[115, 578, 386, 595]]<|/det|> +## Molecular validation techniques + +<|ref|>text<|/ref|><|det|>[[115, 597, 330, 612]]<|/det|> +Aortic Sample Preparation + +<|ref|>text<|/ref|><|det|>[[115, 613, 875, 752]]<|/det|> +All mice were euthanized by halothane inhalation at a \(4\%\) concentration, \(0.2\mathrm{ml}\) per liter of container volume (Millipore Sigma, H0150000). As we described previously \(^{11,54}\) , the heart and thoracic aorta were dissected en bloc and fixed in \(4\%\) paraformaldehyde (Electron Microscopy Sciences, 15710) in PBS at \(4^{\circ}\mathrm{C}\) overnight. Samples were subsequently incubated in \(70\%\) ethanol at \(4^{\circ}\mathrm{C}\) overnight prior to embedding in paraffin. Paraffin- embedded tissues were cut into 5 micron sections to expose a longitudinal section of the thoracic aorta. Sections were then stained with Verhoeff- van Gieson (StatLab, STVGI) to visualize elastic fiber morphology or to assess protein and RNA abundance by immunofluorescence or fluorescence in situ hybridization. + +<|ref|>sub_title<|/ref|><|det|>[[115, 770, 281, 785]]<|/det|> +## Immunofluorescence + +<|ref|>text<|/ref|><|det|>[[115, 786, 866, 892]]<|/det|> +Immunofluorescence was performed following a protocol adapted from Cell Signaling Technology (CST) for formaldehyde- fixed tissues as previously described in detail \(^{45}\) , using a rabbit monoclonal antibody for GATA4 (Cell Signaling Technology, CST36966) and a donkey anti- rabbit secondary antibody Alexa Fluor 555 (ThermoFisher, A32794). Images were taken using a Zeiss LSM880 Airyscan FAST confocal microscope at \(20\times\) magnification and are presented as maximal intensity projection. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 108, 474, 125]]<|/det|> +RNAscope Fluorescence in situ hybridization + +<|ref|>text<|/ref|><|det|>[[113, 126, 877, 213]]<|/det|> +RNA in situ hybridization was performed using the RNAscope Multiplex Fluorescent Reagent Kit v2 Assay (ACD Biosciences, 323100) according to the manufacturer's protocol with the following probes Mm- Gata4 (417881), Mm- Agtr1a (481161), Mm- Cebpd (556661), Mm- Cebpb (547471). Images were taken using a Zeiss LSM880 Airyscan FAST confocal microscope at \(20 \times\) magnification and are presented as maximal intensity projection. + +<|ref|>sub_title<|/ref|><|det|>[[115, 230, 245, 246]]<|/det|> +## Immunoblotting + +<|ref|>text<|/ref|><|det|>[[113, 246, 877, 386]]<|/det|> +Aortic root tissue was flash- frozen immediately upon dissection and stored at \(- 80^{\circ}\mathrm{C}\) until protein extraction. Protein was extracted using Full Moon Lysis Buffer (Full Moon Biosystems, EXB1000) with added phosphatase and protease inhibitors (MilliporeSigma, 11836170001 and 4906845001) and Full Moon lysis beads (Full Moon Biosystems, LB020) using an MP Biomedicals FastPrep 24 5G automatic bead homogenizer. After homogenization, the cell debris was pelleted, and the supernatant was collected. Immunoblot was performed as previously described in detail54, using a rabbit monoclonal antibody for Gata4 (Cell Signaling Technology, 36966) and a mouse monoclonal antibody for \(\beta\) - Actin. (Cell Signaling Technology, 8H10D10). + +<|ref|>sub_title<|/ref|><|det|>[[115, 403, 328, 420]]<|/det|> +## Transcriptomic Analyses + +<|ref|>text<|/ref|><|det|>[[115, 420, 444, 437]]<|/det|> +Single Cell RNA sequencing and analysis + +<|ref|>text<|/ref|><|det|>[[112, 437, 880, 732]]<|/det|> +Single cell RNA sequencing was performed as we previously described112. Single cell suspensions from each mouse were processed separately using the 10x Genomics \(3^{\circ}\) v3 platform and sequenced on an Illumina NovaSeq. A total of 30,704 aortic cells were sequenced from six female mice. The raw data was processed, aligned to the mouse genome (mm10), and aggregated using 10x Genomics Cell Ranger V6'13. The data were then filtered using the Seurat V5 package112 based on the following criteria: \(>1000\) transcripts detected per cell but \(< 5000\) , \(>1500\) total molecules detected per cell but \(< 25000\) , and \(< 20\%\) mitochondrial transcripts per cell. Filtering reduced this dataset from 30,704 aortic cells to 24,971 cells for further analysis. The data was then normalized using the function SCTransform v2. As samples were prepared on multiple days, the data was integrated across batches using reciprocal principal component analysis (RPCAIntegration). Principal component analysis and uniform manifold approximation and projection (UMAP) were performed followed by the FindNeighbors and FindClusters functions. We opted to cluster at a low resolution (0.25) to differentiate aortic cell types and to identify only major subpopulations of smooth muscle cells that vary by a large number of differentially expressed genes. FindMarkers was used to identify cluster- defining transcripts and differentially expression genes between control and diseased cell populations based on a Wilcoxon rank sum test. + +<|ref|>text<|/ref|><|det|>[[115, 750, 585, 768]]<|/det|> +Re- analysis of human aortic cells from Pedroza et al., 2023 + +<|ref|>text<|/ref|><|det|>[[115, 768, 866, 872]]<|/det|> +For re- analysis of the ascending aorta and aortic root samples from a recently published scRNAseq dataset of the donor and LDS patient aortas59 we used the following criteria: \(>1000\) transcripts detected per cell but \(< 6000\) , \(>1500\) total molecules detected per cell \(< 30000\) , and \(< 20\%\) mitochondrial transcripts per cell. This reduces this dataset from 58,947 aortic cells to 43,349 for further analysis. We analyzed this dataset as described above with the FindClusters resolution parameter set to 0.15. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 298, 107]]<|/det|> +CoGAPS and ProjectR + +<|ref|>text<|/ref|><|det|>[[112, 107, 876, 230]]<|/det|> +CoGAPS and ProjectRCoGAPS60,61 (v3.22), an R package that utilizes non- negative matrix factorization to uncover latent patterns of coordinated gene expression representative of shared biological functions, was used to identify transcriptional patterns associated with VSMC subpopulations, with the npatterns parameter set to 8, in scRNAseq analysis of murine aortas. ProjectR62 (v1.2), an R package that enables integration and analysis of multiple scRNAseq data sets by identifying transcriptional patterns shared among datasets, was used to project these patterns into scRNAseq analysis of the human aortic root and ascending aorta. + +<|ref|>text<|/ref|><|det|>[[115, 247, 392, 264]]<|/det|> +Gene over- representation analyses + +<|ref|>text<|/ref|><|det|>[[112, 264, 881, 455]]<|/det|> +Gene over- representation analysesClueGO27 was used for gene over- representation analysis and visualization of enriched functional terms for transcripts globally dysregulated in all VSMCs as well as VSMC subsets. Transcripts were filtered based on an adjusted P- value less than 0.05 and an average absolute Log2 fold change of 0.25 or greater, as well as detection in at least 20 percent of either control or LDS VSMCs. The resulting list of 502 downregulated and 200 upregulated genes was compared against five gene ontology databases (MSigDB Hallmark, KEGG, WikiPathways, Bioplanet, and Reactome). The list of transcripts and ClueGO log files are provided in supplemental material. Differentially expressed gene lists were also analyzed using the online gene list enrichment analysis tool EnrichR30- 32 (https://maayanlab.cloud/Enrichr/) for pathways using the Molecular Signatures Database (MSigDB)63,64 and for transcription factors target enrichment using the ENCODE28 and ChEA29 databases. + +<|ref|>text<|/ref|><|det|>[[113, 472, 761, 508]]<|/det|> +Multiplexed Error- Robust Fluorescence in situ Hybridization (MERFISH) Spatial Transcriptomics + +<|ref|>text<|/ref|><|det|>[[112, 508, 866, 649]]<|/det|> +MERFISH spatial transcriptomics using a custom panel was performed on 5- micron Formalin- Fixed Paraffin- Embedded (FFPE) sections of control and LDS aortas according to manufacturer's protocols (MERSCOPE FFPE Tissue Sample Preparation User Guide_Rev B, Vizgen). Slides were processed and imaged on a MERSCOPE instrument platform according to the manufacturer's protocols (MERSCOPE Instrument User Guide Rev G, Vizgen). The raw images were processed by the instrument software to generate a matrix of spatial genomics measurements and associated image files that were analyzed using the MERSCOPE visualizer software. + +<|ref|>sub_title<|/ref|><|det|>[[114, 666, 193, 682]]<|/det|> +## Statistics + +<|ref|>text<|/ref|><|det|>[[113, 682, 860, 805]]<|/det|> +GraphPad Prism 10.0 was used for data visualization and statistical analysis. Data tested for normality using the Shapiro- Wilk test and upon verification of normal distribution, analyzed using the Brown- Forsythe ANOVA test. For echocardiographic and blood pressure measurements, data are presented as a box and whisker plot with the whiskers indicating the maximum and minimum values and a horizontal bar indicating the median. All individual data points are shown as dots. Figures indicating statistical significance include the statistical tests used in the figure caption. + +<|ref|>sub_title<|/ref|><|det|>[[114, 821, 256, 838]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[113, 839, 829, 892]]<|/det|> +All single- cell RNA sequencing data, both raw fastq files and aggregated matrixes, will be available in the gene expression omnibus (GEO) repository under accession number GSE267204. MERFISH spatial transcriptomics data is available upon request. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 296, 106]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[111, 106, 870, 336]]<|/det|> +EM and EB conceptualized the study, designed the experiments, interpreted data, and prepared the manuscript. EB and TJC generated and processed the single- cell RNA (scRNAseq) sequencing data. EB conducted the primary analysis of the scRNAseq data and performed a reanalysis of published scRNAseq datasets, with input from WE, TC, LR, and JM. EM conducted gene- over- representation analysis and visualization. EB, EM, WE, and LR were involved in sample preparation and processing for MERFISH. EB conducted in situ hybridization, immunofluorescence, and immunoblotting experiments. EB was responsible for echocardiography, blood pressure measurements, genotyping, and animal husbandry with support from TC, MS, WE, LR, and RB. AZ performed histological staining and imaging. GS provided support for CoGAPS analysis and MERFISH spatial transcriptomics. AP and MF provided human scRNAseq data and offered valuable insight on interpretation of the analysis. HD provided valuable input on the study design. EM and EB wrote the manuscript, all authors contributed to its revision. + +<|ref|>sub_title<|/ref|><|det|>[[115, 353, 270, 368]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[115, 369, 870, 473]]<|/det|> +Research in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Numbers R01HL147947 to EM and F31HL163924 to EB as well as a generous gift from the Loeys- Dietz Foundation. Fluorescence Microscopy imaging was also supported by NIH award number S10OD023548 to the School of Medicine Microscope Facility. We would also like to acknowledge the Dietz and Stein- O'Brien labs for sharing resources. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 90, 884, 860]]<|/det|> +505 References +506 1 Chou, E., Pirruccello, J. P., Ellinor, P. T. & Lindsay, M. E. Genetics and mechanisms of thoracic aortic disease. 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Structure of the Elastin- Contractile Units in the Thoracic 798 Aorta and How Genes That Cause Thoracic Aortic Aneurysms and Dissections Disrupt 799 This Structure. Can J Cardiol 32, 26- 34, doi:10.1016/j.cjca.2015.11.004 (2016). 800 100 Pedroza, A. J. et al. Single- Cell Transcriptomic Profiling of Vascular Smooth Muscle 801 Cell Phenotype Modulation in Marfan Syndrome Aortic Aneurysm. Arterioscler Thromb 802 Vasc Biol, ATVBAHA120314670, doi:10.1161/ATVBAHA.120.314670 (2020). 803 101 Salabei, J. K. & Hill, B. G. Autophagic regulation of smooth muscle cell biology. Redox 804 Biol 4, 97- 103, doi:10.1016/j.redox.2014.12.007 (2015). 805 102 Clement, M. et al. Vascular Smooth Muscle Cell Plasticity and Autophagy in Dissecting 806 Aortic Aneurysms. Arterioscler Thromb Vasc Biol 39, 1149- 1159, 807 doi:10.1161/ATVBAHA.118.311727 (2019). 808 103 Pikkarainen, S. et al. GATA- 4 is a nuclear mediator of mechanical stretch- activated 809 hypertrophic program. J Biol Chem 278, 23807- 23816, doi:10.1074/jbc.M302719200 810 (2003). 811 104 Wang, Y. Q., Batool, A., Chen, S. R. & Liu, Y. X. GATA4 is a negative regulator of 812 contractility in mouse testicular peritubular myoid cells. Reproduction 156, 343- 351, 813 doi:10.1530/REP- 18- 0148 (2018). 814 105 Oka, T. et al. Cardiac- specific deletion of Gata4 reveals its requirement for hypertrophy, 815 compensation, and myocyte viability. Circ Res 98, 837- 845, 816 doi:10.1161/01.RES.0000215985.18538.c4 (2006). 817 106 Garg, V. et al. GATA4 mutations cause human congenital heart defects and reveal an 818 interaction with TBX5. Nature 424, 443- 447, doi:10.1038/nature01827 (2003). 819 107 Kuo, C. T. et al. GATA4 transcription factor is required for ventral morphogenesis and 820 heart tube formation. Genes Dev 11, 1048- 1060 (1997). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 88, 876, 360]]<|/det|> +821 108 Liang, Q. et al. The transcription factors GATA4 and GATA6 regulate cardiomyocyte 822 hypertrophy in vitro and in vivo. J Biol Chem 276, 30245- 30253, 823 doi:10.1074/jbc.M102174200 (2001). 824 109 Lepage, D. et al. Gata4 is critical to maintain gut barrier function and mucosal integrity 825 following epithelial injury. Sci Rep 6, 36776, doi:10.1038/srep36776 (2016). 826 110 Liu, J. et al. Cell- specific translational profiling in acute kidney injury. J Clin Invest 124, 827 1242- 1254, doi:10.1172/JCI72126 (2014). 828 111 Lewis, A. E., Vasudevan, H. N., O'Neill, A. K., Soriano, P. & Bush, J. O. The widely 829 used Wnt1- Cre transgene causes developmental phenotypes by ectopic activation of Wnt 830 signaling. Dev Biol 379, 229- 234, doi:10.1016/j.ydbio.2013.04.026 (2013). 831 112 Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single- cell 832 analysis. Nat Biotechnol 42, 293- 304, doi:10.1038/s41587- 023- 01767- y (2024). 833 113 Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat 834 Commun 8, 14049, doi:10.1038/ncomms14049 (2017). 835 836 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[20, 50, 970, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[20, 784, 978, 961]]<|/det|> +
Figure 1. Downregulation of transcripts associated with extracellular matrix-receptor interactions and upregulation of stress and inflammation pathways in Tgfbr1M318R/+ LDS VSMCs. (A) Uniform manifold approximation and projection (UMAP) of aortic cells from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice. (B) Dot plot of cluster defining transcripts used to identify endothelial cells, leukocytes, fibroblasts, and VSMCs. Color of the dot represents a scaled average expression while the size indicates the percentage of cells in which the transcript was detected. (C) ClueGO gene enrichment analysis network of transcripts dysregulated in LDS VSMCs relative to controls. Each node represents a term/pathway or individual genes associated with that term. The color of the node corresponds to the ClueGO group to which each node belongs. The size of the node indicates significance of the enrichment calculated by the ClueGO algorithm. (D) ClueGO network in which terms differentially enriched among transcripts downregulated in LDS VSMCs are highlighted in blue, while those enriched among transcripts upregulated in LDS VSMCs are highlighted in red. (E) Dot plot showing expression of a selection of transcripts significantly dysregulated in LDS VSMCs. (F,G) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus transcription factors (TF) databases showing the top three most significant terms associated with transcripts that are downregulated (F) or upregulated (G) in LDS VSMCs.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 66, 985, 688]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 694, 944, 789]]<|/det|> +
Figure 2. MERFISH reveals spatially heterogeneous transcriptional profiles in LDS VSMCs. MERFISH images of the proximal aorta of LDS (A) and control (B) mice, scale bar is 1 mm. The first panel displays all detected transcripts across the aortic tissue, with key anatomic landmarks indicated. Subsequent panels depict the colocalization of Myh11 and transcripts of interest. Insets note regions of the ascending aorta and aortic root that are presented at higher magnification.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[19, 14, 999, 750]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[4, 777, 980, 972]]<|/det|> +
Figure 3. Transcriptionaly and spatially-defined VSMC subclusters with distinct responses to LDS-causing mutations can be identified in both murine and human aortas. (A) UMAP of VSMCs from control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) mice shown split by genotype. (B) Dot plot showing enrichment of cluster-defining transcripts in VSMC1 and VSMC2. For a given transcript, the color of the dot represents a scaled average expression while the size indicates the percentage of cells in which it was detected. (C) RNA in situ hybridization showing the expression of Gata4 along the length of the murine aorta in a 16-week old control animal. (D) UMAP of control and LDS VSMCs from human patients and dot plot of cluster defining markers in this dataset split by aortic region (Pedroza et al., 2023). (E,F) UMAP overlayed with weights for CoGAPS patterns 4 and 5, in mouse and human scRNAseq datasets. (G,H) Violin plots showing the distribution of pattern 4 and 5 weights in VSMC subclusters from mouse and human scRNAseq datasets. P-values refer to Wilcoxon test. (I) EnrichR gene over-representation analysis for the ENCODE and ChEA Consensus TF databases showing the top four most significant terms associated with transcripts that define CoGAPs Patterns 4 and 5. (J) ClueGO network of terms differentially enriched in mouse and human LDS VSMC2 relative to VSMC1. Terms highlighted in blue are enriched in VSMC1, while those highlighted in red are enriched in VSMC2.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[9, 52, 978, 496]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[870, 15, 955, 35]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[33, 523, 928, 691]]<|/det|> +Figure 4. Gata4 mRNA and protein are upregulated in the aortic root of LDS mice. (A) Representative images of RNA in situ hybridization for Gata4 in the aortic root and ascending aorta of control and LDS (Tgfbr1M318R/+) mice. Insets identify the location shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively. (B) Representative images of immunofluorescence for GATA4in the aortic root and ascending aorta of control and LDS mice. Insets identify the location shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively. (C) Immunoblot for Gata4 expression relative to \(\beta\) - actin in aortic root lysates of control \((n = 3)\) and LDS mice \((n = 3)\) , and related quantification of immunoblot, P- value refers to two- tailed Student's t- test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[44, 50, 725, 711]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[871, 16, 961, 37]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 716, 872, 848]]<|/det|> +Figure 5. Gata4 protein is upregulated in LDS aortic root of Gata4Ctrl and effectively ablated in Gata4SMckO mice. Representative images of immunofluorescence for GATA4 at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMckO) or without (Gata4Ctrl) smooth muscle specific deletion of Gata4 Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[20, 28, 870, 270]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[20, 280, 40, 295]]<|/det|> +
B
+ +<|ref|>image<|/ref|><|det|>[[60, 280, 720, 833]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[15, 845, 965, 957]]<|/det|> +
Figure 6. Smooth muscle-specific deletion of Gata4 (Gata4SMcKO) reduces aortic root size and growth and improves aortic root media architecture in LDS mice. (A) Aortic root diameter of Ctrl (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMcKO) or without (Gata4SMcKO) smooth muscle specific deletion of Gata4 as measured by echocardiography at 8 and 16 weeks of age and aortic root growth from 8-16 weeks. P-values refer to Brown-Forsythe ANOVA. (B) Representative VVG-stained aortic root sections from three independent biological replicates per genotype. Insets identify area shown at higher magnification in the subsequent panel. Scale bars 50 and 200 microns, respectively.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 50, 731, 710]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[870, 15, 961, 36]]<|/det|> +
Figure 7
+ +<|ref|>text<|/ref|><|det|>[[46, 719, 933, 833]]<|/det|> +Figure 7. Smooth muscle-specific deletion of Gata4 results in reduced expression of Agtr1a. Representative images of RNA in situ hybridization for Agtr1a in the aortic root of mice at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SmKo) or without (Gata4Ctl) smooth muscle specific deletion of Gata4. Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[42, 50, 728, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[872, 17, 961, 37]]<|/det|> +
Figure 8
+ +<|ref|>text<|/ref|><|det|>[[35, 714, 880, 844]]<|/det|> +Figure 8. Smooth muscle-specific deletion of Gata4 results in reduced expression of Cebpb. Representative images of RNA in situ hybridization for Cebpb in the aortic root of mice of indicated genotype at 16 weeks of age. Three independent biological replicates are shown per genotype abbreviated as follows Control (Tgfbr1+/+) and LDS (Tgfbr1M318R/+) with (Gata4SMckO) or without (Gata4Ctrl) smooth muscle specific deletion of Gata4. Insets identify location shown at higher magnification in subsequent panels. Images were acquired at 20x magnification. Scale bars 50 and 200 microns, respectively. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 131, 568, 231]]<|/det|> +SupplementaryTables.zip - SupplementalFigures.zip - CORRECTEDPrimaryfigure6forversion1. pdf - CORRECTEDSupplementalFigures6and7forversion1. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/images_list.json b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0772aa5d234df17ea47642184932b291a98905a6 --- /dev/null +++ b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "e. Ultrametricity: sample tree shapes among sequence-defined families (OMA families)", + "footnote": [], + "bbox": [ + [ + 147, + 110, + 850, + 450 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1 a) Trees using the Foldtree metric exhibit higher taxonomic congruence than sequence trees on average (protein families defined from sequences); by contrast, structure trees from LDDT and TM underperform sequence trees; b) After filtering the input dataset for structural quality (families with average pLDDT structure scores \\(>40\\) ), the proportion of Foldtree trees which have a greater normalized congruence than sequence-based trees increased from \\(48\\%\\) to \\(53\\%\\) ; c) the Foldtree metric on the CATH dataset of structurally defined families using experimental structures", + "footnote": [], + "bbox": [ + [ + 190, + 476, + 848, + 757 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Phylogeny of cytosolic receptors from the RRNPPA family paired with a communication proppetide. a) Functional diversity of the RRNPPA family. The MAD root separates paralogs of Anoxybacter fermentans with a singular architecture from the other canonical RRNPPA systems.", + "footnote": [], + "bbox": [ + [ + 123, + 85, + 875, + 860 + ] + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342.mmd b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342.mmd new file mode 100644 index 0000000000000000000000000000000000000000..52a2d585c68b0e7ab668d71dec6e20ec32336e96 --- /dev/null +++ b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342.mmd @@ -0,0 +1,446 @@ + +# Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses + +David Moi dmoi@uni1.ch + +University of Lausanne https://orcid.org/0000- 0002- 2664- 7385 + +Charles Bernard UNIL DBC + +Yannis Never UNIL DBC + +Martin Stenegger Artificial Intelligence Institute, Seoul National University + +Mauricio Langleib Universidad de la Republica + +Christophe Dessimoz University of Lausanne https://orcid.org/0000- 0002- 2170- 853X + +Biological Sciences - Article + +Keywords: + +Posted Date: October 4th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3368849/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Structural & Molecular Biology on October 10th, 2025. See the published version at https://doi.org/10.1038/s41594- 025- 01649- 8. + +<--- Page Split ---> + +Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses + +David Moi \(^{1,2,\#}\) , Charles Bernard \(^{1,2}\) , Martin Steinegger \(^{3,4,5}\) , Yannis Nevers \(^{1,2}\) , Mauricio Langleib \(^{6,7}\) , Christophe Dessimoz \(^{1,2,\#}\) + +\(^{1}\) Department of Computational Biology, University of Lausanne, Lausanne, Switzerland \(^{2}\) Swiss Institute of Bioinformatics, Lausanne, Switzerland \(^{3}\) School of Biological Sciences, Seoul National University, Seoul, South Korea \(^{4}\) Artificial Intelligence Institute, Seoul National University, Seoul, South Korea \(^{5}\) Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea \(^{6}\) Unidad de Bioinformática, Institut Pasteur de Montevideo, Montevideo, Uruguay \(^{7}\) Unidad de Genómica Evolutiva, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay + +\(^{4}\) Correspondence and requests for materials should be addressed to D.M. or C.D. + +## Abstract + +Recent advances in AI- based protein structure modeling have yielded remarkable progress in predicting protein structures. Since structures are constrained by their biological function, their geometry tends to evolve more slowly than the underlying amino acids sequences. This feature of structures could in principle be used to reconstruct phylogenetic trees over longer evolutionary timescales than sequence- based approaches, but until now a reliable structure- based tree building method has been elusive. Here, we demonstrate that the use of structure- based + +<--- Page Split ---> + +phylogenies can outperform sequence- based ones not only for distantly related proteins but also, remarkably, for more closely related ones. This is achieved by inferring trees from protein structures using a local structural alphabet, an approach robust to conformational changes that confound traditional structural distance measures. As an illustration, we used structures to decipher the evolutionary diversification of a particularly challenging family: the fast- evolving RRNPPA quorum sensing receptors enabling gram- positive bacteria, plasmids and bacteriophages to communicate and coordinate key behaviors such as sporulation, virulence, antibiotic resistance, conjugation or phage lysis/lysogeny decision. The advent of high- accuracy structural phylogenetics enables myriad of applications across biology, such as uncovering deeper evolutionary relationships, elucidating unknown protein functions, or refining the design of bioengineered molecules. + +## Introduction + +Since Darwin, phylogenetic trees have depicted evolutionary relationships among organisms, viruses, genes, and other evolving entities, enabling an understanding of shared ancestry and tracing the events that led to the observable extant diversity. Trees based on molecular data are typically reconstructed from nucleotide or amino- acid sequences, by aligning homologous sequences and inferring the tree topology and branch lengths under a model of character substitution \(^{1 - 3}\) . However, over long evolutionary time scales, multiple substitutions occurring at the same site cause uncertainty in alignment and tree building. The problem is particularly acute when dealing with fast evolving sequences, such as viral or immune- related ones, or when attempting to resolve distant relationships, such as at the origins of animals \(^{4 - 6}\) or beyond. + +In contrast, the fold of proteins is often conserved well past sequence signal saturation. Furthermore, because 3D structure determines function, protein structures have long been studied to gain insight into their biological role within the cell whether it be catalyzing reactions, interacting with other proteins to form complexes or regulating the expression of genes among a myriad of other functions. + +<--- Page Split ---> + +Until recently, protein structures had to be obtained through labor intensive crystallography, with modeling efforts often falling short of the level of accuracy required to describe a fold for the many tasks crystal structures were used for. Due to these limitations, structural biology and phylogenetics have developed as largely separate disciplines and each field has created models describing evolutionary or molecular phenomena suited to the availability of computational power and experimental data. + +Now, the widespread availability of accurate structural models \(^{7,8}\) opens up the prospect of reconstructing trees from structures. However, there are pitfalls to avoid in order to derive evolutionary distances between homologous protein structures. Geometric distances between rigid body representations of structures, such as root mean square deviation (RMSD) distance or template modeling (TM) score \(^{9}\) , are confounded by spatial variations caused by conformational changes \(^{10,11}\) . More local structural similarity measures have been proposed in the context of protein classification \(^{10}\) , but due to the relative paucity of available structures until recently, little is known about the accuracy of structure- based phylogenetic reconstruction beyond a few isolated case studies \(^{12,13}\) . + +Here, we report on a comprehensive evaluation of phylogenetic trees reconstructed from the structures of thousands of protein families across the tree of life, using multiple kinds of distance measures. We built trees from structural divergence measures obtained using Foldseek \(^{14}\) , which outputs scores from rigid body alignment, local superposition- free alignment and structural alphabet based sequence alignments. The performance of these measures has been previously assessed on the task of detecting whether folds are homologous and belong to the same family \(^{14 - 16}\) , but have never been benchmarked with regards to how well they perform as evolutionary distances. Remarkably, we found that the structural alphabet- based measure outperforms phylogenies from sequence alone even at relatively short evolutionary distances. To demonstrate the capabilities of structural phylogenetics, we employ our methodology, released as open- source software named Foldtree, to resolve the difficult phylogeny of a fast- evolving protein family of high relevance: the RRNPPA (Rap, Rgg, NprR, PlcR, PrgX and AimR) receptors of + +<--- Page Split ---> + +communication peptides. These proteins allow gram- positive bacteria, their plasmids and their viruses to assess their population density and regulate key biological processes accordingly. These communication systems have been shown to regulate virulence, biofilm formation, sporulation, competence, solventogenesis, antibiotic resistance or antimicrobial production in bacteria \(^{17 - 21}\) , conjugation in conjugative elements, lysis/lysogeny decision in bacteriophages \(^{22}\) and host manipulation by mobile genetic elements (MGEs) \(^{19,23}\) . Accordingly, the RRNPPA family has a substantial impact on human societies as it connects to the virulence and transmissibility of pathogenic bacteria and the spread of antimicrobial resistance genes through horizontal gene transfers. We analyze and discuss the parsimonious characteristics of the phylogeny of this family, highlighting the contrasts with the sequence- based tree. + +## Results + +## Structural trees outperform sequence based trees at both short and long evolutionary divergence times + +To find a structural distance metric with high informative phylogenetic signal, we investigated the use of local superposition- free comparison (local distance difference test; LDDT \(^{16}\) ), rigid body alignment (TM score \(^{9}\) ) and a distance derived from similarity over a structural alphabet (Fident) \(^{14}\) . These measures were used to compute distance trees using neighbor joining, after being aligned in an all- vs- all comparison using the Foldseek structural alphabet (Methods). + +Assessing the accuracy of trees reconstructed from empirical data is notoriously difficult. We used two complementary indicators. The first one, taxonomic congruence score (TCS) (Methods and Supplementary Figures 1- 2), assesses the congruence of reconstructed protein trees with the known taxonomy \(^{24}\) . Among several potential tree topologies reconstructed from the same set of input proteins, the better topologies can be expected to have higher TCS on average. + +For trees reconstructed from closely related protein families using standard sequence alignments, both local structure LDDT and global structure TM measures + +<--- Page Split ---> + +showed poorer taxonomic congruence than sequence- based trees on average (Figure 1a). By contrast, trees derived from the Fident distance (henceforth referred to as the Foldtree measure) outperformed those based on sequence. The difference was even larger if we excluded families for which the Alphafold2- inferred structures are of low confidence (Figure 1b). This trend was observed consistently across various protein family subsets, taken from clades with different divergence levels (Supplementary Figure 8). We also experimented with statistical corrections and other parameter variations, but they did not lead to further improvements (Supplementary Figures 4- 9). + +We then assessed the Foldtree measure's performance against sequence- based trees over larger evolutionary distances, using structure- informed homologous families from the CATH database25. This database classifies proteins hierarchically, grouping them based on Class, Architecture, Topology and Homology of experimentally determined protein structures. We examined both proteins from the same homology set as well as proteins within the same topology sets (Methods). Efforts were made to correct structures with discontinuities or other defects before treebuilding (Methods) since these adversely affect structural comparisons. With this more divergent CATH dataset, structure- based methods performed better overall. Foldtree outperformed the sequence- based method even more (Figure 1c). Results for LDDT versus sequence flipped in favour of LDDT, while results for the global TM measure remained inferior to sequence (Supplementary Figure 9). + +To delve deeper into the reasons for these performance differences, we applied a gradient- boosted decision tree regressor26 on features derived from the input structures and taxonomic lineages of the input protein sets, aiming to predict the TCS difference (Supp Methods Table 1). We found that features measuring the confidence of the AlphaFold structure prediction (predicted LDDT or pLDDT) emerged as significant factors in the analysis (Supplementary Figure 3). This suggests that advancements in structural prediction might further benefit structural trees in the future. + +<--- Page Split ---> + +To validate our findings using an entirely different indicator of tree quality, we assessed the “ultrametricity” of trees—how uniform a tree’s root- to- tip lengths are for all its tips, akin to following a molecular clock. Although strict adherence to a molecular clock is unlikely in general, it is reasonable to assume that distance measures resulting in more ultrametric trees on average (i.e., with reduced root- to- tip variance, see Methods) are more accurate27. We found that in the sequence- based family dataset, Foldtree trees had by far the lowest root- to- tip variance of all approaches (Figure 1d). The difference was so pronounced that it is evident in visual comparison of tree shapes for several randomly chosen families (Figure 1e). Foldtree performed the best of all metrics and sequence- based trees the worst. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
e. Ultrametricity: sample tree shapes among sequence-defined families (OMA families)
+ +![](images/Figure_1.jpg) + +
Figure 1 a) Trees using the Foldtree metric exhibit higher taxonomic congruence than sequence trees on average (protein families defined from sequences); by contrast, structure trees from LDDT and TM underperform sequence trees; b) After filtering the input dataset for structural quality (families with average pLDDT structure scores \(>40\) ), the proportion of Foldtree trees which have a greater normalized congruence than sequence-based trees increased from \(48\%\) to \(53\%\) ; c) the Foldtree metric on the CATH dataset of structurally defined families using experimental structures
+ +<--- Page Split ---> + +outperforms sequence trees to an even greater proportion; d) The variance of normalized root- to- tip distances were compiled for all trees within the OMA dataset for all tree structural tree methods and sequence trees. Foldtree has a lower variance than other methods. The median of each distribution is shown with a vertical red line. Distributions are truncated to values between 0 and 0.2; e) A random sample of trees is shown where each column is from from equivalent protein input sets and each row of trees is derived using a distinct tree building method. + +Both of the orthogonal metrics of ultrametricity and species tree discordance indicate that Foldtree produces trees with desirable characteristics that are ideal for constructing phylogenies with sets of highly divergent homologs. + +## Foldtree reveals the evolutionary diversification of RRNPPA communication systems + +To illustrate the potential of structural phylogenies, we reconstructed the intricate evolutionary history of the RRNPPA family of intracellular quorum sensing receptors in gram- positive Bacillota bacteria, their conjugative elements and temperate bacteriophages17,21,28. These receptors, vital for microbial communication and decision- making, are paired with a small secreted communication peptide that accumulates extracellularly as the encoding population replicates. Once a quorum of cells, plasmids or viruses is met, communication peptides get frequently internalized within cells and binds to the tetratricopeptide repeats (TPRs) of cognate intracellular receptors, leading to gene or protein activation or inhibition, facilitating a coordinated response beneficial for a dense population. The density- dependent regulations of RRNPPA systems control behaviors like bacterial virulence, biofilm formation, sporulation, competence, conjugation and bacteriophage lysis/lysogeny decisions17- 21. Although these receptors were identified in the early 1990s29,30, their evolutionary history is unclear due to frequent mutations and transfers, making sequence comparisons challenging28,31,32. This is reflected by the nomenclature of the family: RRNPPA is an acronym for Rap, Rgg, NprR, PlcR, PrgX and AimR, which were historically described as six different families of intracellular receptors, and of which only structural comparisons allowed to establish the actual consensus on their common evolutionary origin28,33,34. Recently, a pioneer work combining + +<--- Page Split ---> + +structural comparisons among folds and sequence- based phylogenetics have provided insights among some of these families28, but a comprehensive reconstruction of the evolutionary history of this family that includes all described subfamilies19 remains elusive. + +The Foldtree structure- based phylogeny illuminates key evolutionary features of the diversification of RRNPPA communication systems that could not be resolved based on sequences (Figure 2). The evolutionary trajectory it implies is more parsimonious in terms of subfamily classification, taxonomy, functions, and protein architectures than a phylogeny obtained with a state- of- the- art sequence- based method (details in Supplementary Figure 9). In particular, the structure- based phylogeny implies that folds composed of 9 tetratricopeptide repeats (TPRs) and folds composed of 5 TPRs emerged only once while the sequence- based tree implies a less plausible scenario of convergent evolution of two clades toward 5- TPR protein architectures. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. Phylogeny of cytosolic receptors from the RRNPPA family paired with a communication proppetide. a) Functional diversity of the RRNPPA family. The MAD root separates paralogs of Anoxybacter fermentans with a singular architecture from the other canonical RRNPPA systems.
+ +<--- Page Split ---> + +Subfamilies with experimental validation of at least one member are highlighted in color. Other subfamilies correspond to high- confidence candidate subfamilies detected with RRNPP_ detector in \(^{19}\) . Biological processes experimentally shown to be regulated in a density- dependent manner by a QS system are displayed for each validated subfamily. Subfamilies in gray correspond to novel, high- confidence candidate RRNPPA subfamilies from \(^{19}\) . A star mapped to a leaf indicates a predicted regulation of an adjacent biosynthetic gene cluster by the corresponding QS system. b) Main implied events of the tree, with normalized branch length for visualization purposes (the events that are implied from alternate roots are shown in Supplementary Figure 10). c) Distribution and prevalence of the different members of each RRNPPA subfamily into the different taxonomic families. d) Genomic orientation and encoding element of the receptor - adjacent propeptide pairs. e) The first colostrip indicates the domain architecture of each receptor. A representative fold for each domain architecture is displayed in the legend (AlphaFold models of subfamily 27, NprR, Rap and PlcR, respectively) with an indication of the implied events from panel a) at the origin of each fold/architecture. The second colostrip gives the degeneration score of TPR sequences of each receptor (given as 1 - TprPred_likelihood, as in \(^{33}\) ). The histogram shows the length (in amino-acids) of each receptor. + +The minimal ancestor deviation (MAD) method placed the root right next to receptors encoded by Anoxybacter fermentans DY22613, a piezophilic and thermophilic endospore- forming bacterium from the Clostridia class isolated from a deep- sea hydrothermal vent. These proteins exhibit a unique domain architecture lacking the DNA- binding HTH domain and harboring 7 TPRs (Table S1). Their singular architecture, and the proximity to the MAD root lead us to infer Anoxybacter's receptors as the outgroup of all other RRNPPA systems (Figure 2a- b). This suggests that the early history of canonical RRNPPA systems could have been linked to extremophile endospore- forming Bacillota and may have started with a gain of a N- terminal HTH DNA binding domain, enabling to coupling quorum sensing with transcriptional regulation (Figure 2e). We considered alternative rooting scenarios (Supplementary Figure 11) but only the MAD rooting implies a unique origin of receptors with non- degenerated TPR sequences that predates the last common ancestor of each clade of receptors with degenerated TPRs (Figure 2e), in line with Declerck et al.'s conjecture \(^{33}\) . + +The widespread distribution of sporulation- regulation on the tree (Figure 2a) suggests that the early history of the 9 TPRs group may have been linked to the regulation of the costly differentiation into a resistant endospore in extremophile spore- forming taxa from the Clostridia (Biomaibacter acetigenes, Sulfobacillus thermotolerans, Thermoanaerobacter italicus) and Bacilli (Alicyclobacillaceae and + +<--- Page Split ---> + +Thermoactinomycetaceae families) classes (Table S1). Consistently, the NprR subfamily is suggested to have diversified first in extremophile spore- forming Bacillusaceae (Psychrobacilli, Halobacilli, Anoxybacilli etc.) and Planococcaceae (Sporosarcina, Planococcus antarticus, halotolerans, glaciei etc.) (Table S1). The Rap clade, exclusively found in Bacillus and Alkalihalobacillus genera, is nested within NprR, and is inferred to have diverged from the same ancestral gene as that of NprR receptors found in Halobacilli, Geobacilli, Virgibacilli, Oceanobacilli and Bacilli from the Bacillus cereus group (Table S1). This indicates that the absence of the N- terminal HTH domain observed in Rap receptors originates from a loss of the ancestral domain (Figure 2e), as previously reported by Felipe- Ruiz et al28. However, many Rap receptors have retained the ability to regulate sporulation, but only through protein inhibition of the Spo0F- P and ComA regulators, rather than through transcriptional regulation35. The Rap clade is characterized by a wide occurrence in MGEs, consistent with the high rate of horizontal gene transfers described for this subfamily32. The MGE distribution in the Rap clade is polyphyletic, suggesting frequent exchanges of these communication systems between the host genome, phages and conjugative elements (Figure 2d). The QssR validated clade is specific to solventogenic Clostridiaceae (Figure 2a- c) while its sister clade (subfamily 09) is specific to pathogenic Clostridium such as C. perfringens and C. botulinum, which may indicate a novel link between quorum sensing and pathogenesis in these taxa of medical relevance that may warrant further investigation. + +AloR and AimR members appear to be the most diverged representatives of the HTH- 9TPRs architectural organization. Consistently, their TPR sequences harbor signs of degeneration, which is especially true in the AimR clade, consistent with its specificity to Bacillus phages, since viruses evolve at higher evolutionary rates (Figure 2f). The AimR receptors supporting phage- phage communication are adjacent to non- viral communication systems from subfamily 21, found in the chromosome of Alkalihalobacillus clausii or lehensis. For the first time, the structural phylogeny reveals that the AimR- subfamily 21 clade is evolutionary close from Paenibacillaceae receptors from the AloR subfamily prevalent in the Paenibacillus + +<--- Page Split ---> + +genus and the candidate subfamily 08 predominantly found in the Brevibacillus genus (Figure 2). Subfamily 08, AloR and AimR are suggested to form a monophyletic group with a presumable Paenibacillaceae ancestry. This is supported by systems from Paenibacillus xylanexedens and Brevibacillus formosus position in the outgroup, close to the QssR subfamily (Figure 2a, Table S1). Remarkably, the cognate communication peptides of AimR receptors from the Bacillus cereus group are highly similar to that of subfamily 08, with the presence of the DPG amino- acid motif in the C- terminal (Table S1). Our results therefore suggest that a QSS similar to the ancestor of the AloR- subfam08 clade was co- opted by a temperate phage to regulate the lysis/lysogeny decision. This successful functional association has spread in Bacillus phages and led to the AimR clade. The numerous phage- and prophage- encoded systems from the subfamily 08 support this hypothesis19. + +The proteins composed of 5 TPRs are suggested to have emerged from the loss of 4 TPRs in the C- terminus, drastically shortening their length (Figure 2b, Figure 2e), although other evolutionary scenarios that do not imply such loss exist as well28 (Supplementary Figure 10). The 5 TPRs group is divided in two sister clades: one with a wide taxonomic range composed of PlcR, TprA and their outgroup (Figure 2a and Figure 2c), the second including PrgX, TraA, ComR and Rgg validated subfamilies, specific to non- spore forming Lactobactillales. The emergence of the 5TPRs clade is associated with fundamental functional shifts. First, receptor- propeptide orientations are highly diversified compared to the HTH- 9TPRs group (Figure 9d). These heterogeneous orientations correlate with functional changes as receptors divergently transcribed from their propeptide tend to repress target genes while co- directional receptors tend to activate them36. Second, the diversification of the PrgX- ComR- Rgg clade was accompanied with an important diversification of propeptide secretion modes: their cognate propeptides are exported through the alternative PptAB translocon rather than through the SEC translocon17,28 and it has even been shown that a paralog of Rgg in S. pyogenes is paired with a functional leaderless communication peptide that lacks a signal sequence for an export system, highlighting that another secretory process of + +<--- Page Split ---> + +communication peptides emerged in the clade37. Last, the biological processes controlled by these communication systems are not linked to cellular dormancy or viral latency, but rather to the production of virulence factors and antimicrobials21. This is mirrored by the substantial number of syntenic biosynthetic gene clusters (BGCs) predicted to be regulated by TprA and Rgg members (Figure 2a)19. Consistently, the primary role of members of the HTH- 5TPRs clade may be to assess the threshold population density at which a collective production of biomolecules starts to be ecologically impactful and becomes the most evolutionary advantageous strategy, with a few exceptions such as the regulation of competence by ComR or conjugation by PrgX. + +## Discussion + +As early as 1975, Eventoff and Rossmann employed the number of structurally dissimilar residues between pairs of proteins to infer phylogenetic relationships by means of a distance method38. This approach has been revisited to infer deep phylogenetic trees and networks using different combinations of dissimilarity measures (e.g., RMSD, \(\mathrm{Q}_{\mathrm{score}}\) , Z- score) and inference algorithms12,39- 43. Conformational sampling has been proposed to assess tree confidence when using this approach11. Some models have been developed that mathematically describe the molecular clock in structural evolution44 or integrate sequence data with structural information to inform the likelihood of certain substitutions45. Other studies have modeled structural evolution as a diffusion process in order to infer evolutionary distances46, or incorporating it into a joint sequence- structure model to infer multiple alignments and trees by means of bayesian phylogenetic analysis47,48. To date, the quality of structure- based phylogenetics, especially compared to conventional sequence- based phylogenetics, has remained largely unknown, limiting its use to niche applications. + +The extensive empirical assessment reported here, using two orthogonal indicators of tree quality, demonstrates the high potential of structure- based phylogenetics. The taxonomic congruence score (TCS) measures agreement with + +<--- Page Split ---> + +the established classification. Individual gene trees can be expected to deviate substantially from the underlying species tree due to gene duplication, lateral transfer, incomplete lineage sorting, or other phenomena. However, the evolutionary history of the underlying species will still be reflected in many parts of the tree—which is quantified by the TCS. All else being equal, tree inference approaches which tend to result in higher TCS over many protein families can be expected to be more accurate. On this metric, we obtained the best trees using Foldtree, which is based on Foldseek's structural alphabet, and an alignment procedure combining structural and sequence information. Furthermore, after filtering lower quality structures out of the tree building process, tree quality improved further when compared to sequence- based trees (Figure 1. b), indicating that higher confidence models with accurate structural information provide better phylogenetic signal. + +When considering the ultrametricity through the root- to- tip variances of the trees, the Foldtree trees adhered more closely to a molecular clock than other structural or sequence trees. We acknowledge that in and of itself, adherence to a molecular clock is only a weak indicator of tree accuracy. Nevertheless, considering the clear, consistent differences obtained, and the agreement with the TCS criterion, the ultrametricity appears to reflect meaningful performance difference among the tree inference methods. + +Folds evolve at a slower rate than the underlying sequence mutations49,50. Structural distances are therefore less likely to saturate over time, making it possible to recover the correct topology deeper in the tree with greater certainty. This could be observed in our results on the distant, structurally defined CATH families. Interestingly, however, Foldtree distinguished itself even at divergence times when homology is identifiable using sequence to sequence comparison. It is thus both fine grained enough to account for small differences between input proteins at shorter divergence times, overcoming the often mentioned shortcoming of structural phylogenetics, and more robust than sequence comparison at longer evolutionary distances. + +<--- Page Split ---> + +As the projection of each residue onto a structural character is locally influenced by its neighboring residues rather than global steric changes, Foldseek's representations of 3D structures are well suited to capture phylogenetic signals when comparing homologous proteins. In contrast, global structural similarity measures are confounded by conformational fluctuations which involve steric changes that are much larger in magnitude than the local changes observed between functionally constrained residues during evolution. Moreover, since Foldseek represents 3D structures as strings, the computational speed- ups and techniques associated with string comparisons implemented in MMseqs51 can be applied to structural homology searches and comparisons making the Foldtree pipeline extremely fast and efficient. + +Viral evolution, quickly evolving extracellular proteins and protein families with histories stretching back to the first self replicating cells are among the many cases that can be revisited with these new techniques. In our first study of a family using Foldtree, we present just one such case, with the fast evolving RRNPPA family of cytosolic communication receptors encoded by Firmicutes bacteria, their conjugative elements and their viruses. The phylogeny reconstructed by Foldtree includes, for the first time, all described RRNPPA subfamilies19. Remarkably, despite their significant divergence, the underlying diversifying history is parsimonious in terms of taxonomy, functions, and protein architectures (Supplementary Figure 10). The MAD rooting method flags a previously undescribed candidate outgroup with a singular architecture of 7 TPRs and no DNA- binding domain in Anoxybacter fermentans, which supports Declerck et al. speculation that the ancestral receptor at the origin of the RRNPPA clade lacked the DNA- binding domain, and that the latter was gained subsequently in the evolutionary history of the family. Declerck et al. also speculated that the level of TPR degeneracy in receptors is a marker of divergence from the last common ancestor of the family33. In this respect, root to tips lengths are remarkably uniform throughout the entire RRNPPA structural tree with slight differences being meaningful, as the longest branches correspond to receptors with degenerated TPR sequences (Figure 2e). Last, this rooting implies that receptors with non- degenerated TPRs sequences emerged only once, and + +<--- Page Split ---> + +systematically involves a late emergence of clades with degenerated TPRs as a derived state of an ancestor harboring non- degenerated TPRs (Figure 2e). Although rooting is easier when a tree is more clock- like, there remains uncertainty regarding the precise placement of the root. Our interpretation of MAD rooting and domain architecture led us to infer an origin of the RRNPPA family linked to the regulation of sporulation in extreme environments, implying also that 9 TPRs folds predate 5 TPRs folds. Yet, alternative rootings of the structural phylogeny cannot be ruled out, with a root either within the HTH- 5TPRs group as in \(^{28}\) or within the AloR- AimR- subfamily08 group (hypotheses displayed in Supplementary Figure 11). Additional, yet- to- be- discovered members of RRNPPA homologs could help resolve the root with higher confidence. + +Recently the fold universe has been revealed using AlphaFold on the entirety of the sequences in UniProt and the ESM model \(^{8}\) on the sequences in MGNIFY \(^{52}\) to reach a total of nearly one billion structures. The UniProt structures inferred by AlphaFold have recently been systematically organized into sequence- and structure- based clusters, shedding light on novel fold families and their possible functions \(^{14,53}\) . In future work it may be desirable to add an evolutionary layer of information to this exploration of the fold space using structural phylogenetics to further refine our understanding of how this extant diversity of folds emerged. + +In conclusion, this work shows the potential of structural methods as a powerful tool for inferring evolutionary relationships among proteins. For relatively close proteins, structured- based tree inference rivals sequence- based inference, and the choice of approach should be tailored to the specific question at hand and the available data. For more distant proteins, structural phylogenetics opens new inroads into studying evolution beyond the "twilight" zone \(^{54}\) . We believe that there remains much room for improvement in refining phylogenetic methods using the tertiary representation of proteins and hope that this work serves as a starting point for further exploration of deep phylogenies in this new era of Al- generated protein structures. + +<--- Page Split ---> + +## Methods + +No statistical methods were used to predetermine sample size. + +## OMA HOG selection for large scale benchmark + +The OMA set of protein families consists of "root hierarchical orthologous groups" (root HOGs) which are derived from all- vs- all sequence comparisons55. The quest for orthologs benchmarking dataset56 consists of 78 proteomes. The 2020 release of this dataset was used as input into the OMA orthology prediction pipeline55 (version 2.4.1). A random selection of at most 500 orthologous groups with at least 10 proteins were compiled for each group of HOGs that were inferred to have emerged in different ancestral taxa (Bacteria, Bilateria, Chordata, Dikarya, Eukaryota, Eumetazoa, Euteleostomi, Fungi, LUCA, Opisthokonta and Tetrapoda). The UniProt identifiers of the proteins within each group were used as input to the Foldtree pipeline. + +## CATH family selection for large scale benchmark + +CATH structural superfamilies are constructed using structural comparisons and classification25. Each level of classification designates a different resolution of structural similarity. These are delineated as Class, Architecture, Topology and Homology. We chose to investigate tree quality using input sets within the same homology classification as well as sets within the same topology. We selected a random subsample of at most 250 proteins (or the number of proteins within the family if there were less) from each family for 635 CATH families and 500 CAT families. The Topology- based dataset is designated as CAT and the Homology- based dataset is designated as CATH. Each CAT or CATH family contains the PDB identifiers and chains of the structures they correspond to. + +The PDB files were programmatically obtained from the PDB database. 3D structures of monomers corresponding to the chain identified in the CATH classification for each fold were extracted from PDB crystal structures using Biopython. PDBfizer from the OpenMM57 package was used to fix crystal structures with discontinuities, non- standard residues or missing atoms before tree building since these adversely affect structural comparisons. + +<--- Page Split ---> + +## Structure tree construction + +Sets of homologous structures were downloaded from the AFDB or PDB and prepared according to the OMA and CATH dataset sections above. Foldseek14 is then used to perform an all vs all comparison of the structures. + +Structural distances between all pairs are compiled into a distance matrix which is used as input to quicktree58 to create minimum evolution trees. These trees are then rooted using the MAD method59. Foldseek (Version: 30fdcac78217579fa25d59bc271bd4f3767d3ebb) has two alignment modes where character based structural alignments are performed and are scored using the 3Di substitution matrix or a combination of 3Di and amino- acid substitution matrices. A third mode, using TMalign to perform the initial alignment was not used. It is then possible to output the fraction of identical amino acids from the 3Di and amino acid based alignment (Fident), the LDDT (locally derived using Foldseek's implementation) score and the TM score (normalized by alignment length). This results in a total of 6 structural comparison methods. We then either directly used the raw score or applied a correction to the scores to transform them to the distance matrices so that pairwise distances would be linearly proportional to time (Supplementary methods). This resulted in a total of 12 possible structure trees for each set of input proteins. To compile these results, Foldseek was used with alignment type 0 and alignment type 2 flags in two separate runs with the '--exhaustive- search' flag. The output was formatted to include Iddt and alntmscore columns. The pipeline of comparing structure- and sequence- based trees is outlined in Supplementary Figure 1. + +Before starting the all vs all comparison of the structures we also implemented an optional filtering step to remove poor AlphaFold models with low pLDDT values. If the user activates this option, the pipeline removes structures (and the corresponding sequences) with an average pLDDT score below 40, before establishing the final protein set and running structure and sequence tree building pipelines. We performed similar benchmarking experiments on filtered and unfiltered + +<--- Page Split ---> + +versions of the OMA dataset to observe the effect of including only high quality models in the analysis. + +## Sequence based tree construction + +Sets of sequences and their taxonomic lineage information were downloaded using the UniProt API. Clustal Omega (version 1.2.4)60 or Muscle5 (version 5.0)61 was then used to generate a multiple sequence alignment on default parameters. This alignment was then used with either FastTree(version 2.1)62 on default parameters or IQ- TREE (version 1.6.12 using the flags LG+1) to generate a phylogenetic tree. Finally, this tree was rooted using the MAD (version 1775932) method on default parameters. + +## Taxonomic congruence metric for phylogenetic trees + +Taxonomic lineages were retrieved for each sequence and structure of each protein family via the UniProt API. It is assumed that the vast majority of genes will follow an evolutionary trajectory that mirrors the species tree with occasional loss or duplication events. The original development and justification for this score to measure tree quality in an unbiased way can be found in the following work 24. In this version of the metric we reward longer lineage sets towards the root by calculating a score for each leaf from the root to the tip. + +The agreement of the tree with the established taxonomy (from UniProt) can be calculated recursively in a bottom up fashion when traversing the tree using equation 1. Leaves of trees were labeled with sets representing the taxonomic lineages of each sequence before calculating taxonomic congruence. + +<--- Page Split ---> + +\[C(tree) = \sum_{s}^{Leaves}C(leaf)\] + +\[C(x) = \left\{ \begin{array}{ll}|s(x)| & \mathrm{if~x~is~root}\\ |s(x)| + |s(x.ancestor)| & \mathrm{if~x~is~an~internal~node}\\ & \mathrm{where}\\ \end{array} \right.\] + +\[s(x) = \left\{ \begin{array}{ll}L(x), & \mathrm{if~x~is~a~leaf}\\ s(x.Left)\cap s(x.Right)) & \mathrm{if~x~is~an~internal~node} \end{array} \right.\] + +Equation 1- taxonomic congruence metric. This score is used to measure the agreement of binary tree topologies with the known species tree. \(\mathsf{s}(\mathsf{x})\) denotes the set of lineages found in the tree node x. \(\mathrm{C(x)}\) denotes the congruence score of node x based on its two child nodes. \(\mathsf{L}(\mathsf{x})\) denotes the labels of leaves. The total score of a tree is defined as the sum of the leaf scores. The code to calculate this metric is available on the git repository. + +Both structure and sequence trees were rooted using the MAD method to make TCS comparisons between the methods equivalent. To compare large collections of trees with varying input set sizes, we normalized the congruence scores of trees by the number of the proteins in the tree. + +## Ultrametricity quantification + +Ultrametricity63 describes the consistency of tip to root lengths of a given phylogenetic tree. If a tree building approach has an accurate molecular clock on all branches, the amount of inferred evolutionary time elapsed between the root and all of the extant species should be equivalent and proportional to real time. This would imply that the sums of branch length along a lineage from the root to any tip of the tree should be equivalent since the amount of clock time elapsed from the common ancestor until the sequencing of species in the present day is the same. + +<--- Page Split ---> + +\[E(\text{rootdist}) = \sum_{i = 1}^{n_{\text{leaves}}} \text{dist}(l_i, \text{root}) / n_{\text{leaves}} \\ S_{\text{norm}}(\text{rootdist}) = \sum_{i = 1}^{n_{\text{leaves}}} (\text{dist}(l_i, \text{root}) / E(\text{rootdist}) - 1)^2 / (n_{\text{leaves}} - 1)\] + +**Equation 2** - To derive a unified metric for ultrametricity that could easily be applied to the trees generated by different methods, we normalized the branch lengths to center the distribution of root to tip lengths at 1. We then measured the variance of these normalized root to tip lengths. \(E(.)\) represents the average root to tip length for a given tree. \(S_{\text{norm}}(.)\) represents the variance of these normalized root to tip distances. \(\text{dist}(l_i, \text{root})\) denotes the length of the tip \((l_i)\) to root. + +To describe the ultrametricity of the different methods of structural tree derivation, we measured the length of root-to-tip distances of a given tree (equation 2). We then normalized this collection of distances by their mean and calculated their variance. We compiled this variance measurement for collections of trees with corresponding input protein sets for all methods used to derive trees and compared their distributions. **Supplementary Figure 2** shows a visual representation of how this score is calculated. + +## RRNPPA phylogeny + +The metadata of "strict" known and candidate RRNPPA QSSs described in the RRNPP_detector paper were fetched from TableS2 in the corresponding supplementary materials¹⁹. The predicted regulations by QSSs of adjacent BGCs were fetched from TableS5. The propeptide sequences were downloaded from the following Github repository: + +https://github.com/TeamAIRE/RRNPP_candidate_propeptides_exploration_dataset. +The 11,939 receptors listed in TableS2 were downloaded from the NCBI Genbank database, and redundancy was removed by clustering at 95% identity with CD-HIT⁶⁴, yielding 1,418 protein clusters. The Genbank identifiers of the 11,939 receptors were used as queries in the UniProt Retrieve/ID mapping research engine (https://www.uniprot.org/id-mapping) to retrieve corresponding UniProt/AlphaFoldDB identifiers. 768 protein clusters successfully mapped to at least one UniProt/AlphaFoldDB identifier. The 768 predicted protein structures were downloaded and Foldseek was used to perform an all vs all comparison. Based on + +<--- Page Split ---> + +our benchmarking results we used the Fident scores from a comparison using amino- acid and 3Di alphabet alignment scoring (alignment mode 1 in Foldseek). Since this family had undergone domain architecture modifications, we decided to extract the structural region between the first and last positions of each fold where \(80\%\) of all of the other structures in the set mapped. With these core structures we performed a second all vs all comparison. We again used the Fident scores (alignment mode 1) and no statistical correction to construct a distance matrix between the core structures. This matrix was then used with FastME \(^{65}\) to create a distance based tree. The resulting tree was annotated with ITOL \(^{66}\) , using the metadata available in Table S1. To derive the sequence- based phylogeny, we built a multiple sequence alignment (MSA) of receptors, using mafft \(^{67}\) with the parameters - maxiterate 1000 - localpair for high accuracy. The MSA was then trimmed with trimAl \(^{68}\) under the - automated 1 mode optimized for maximum likelihood reconstruction. The trimmed alignment of 304 sites was given as input to IQ- TREE \(^{2}\) to infer a maximum likelihood phylogenetic under the LG+G model with 1000 ultrafast bootstraps. + +## Acknowledgements + +We thank the Dessimoz lab members for thoughtful discussions on the topic of structural evolution and their encouragement and input on this work. We especially thank Clement Train for his brilliant work on the tree visualization tool accompanying this work. We also gratefully acknowledge helpful suggestions by Pedro Beltrao. + +The work was supported by SNSF grant 216623 to C.D.. M. L. is a recipient of a doctoral scholarship from Agencia Nacional de Investigación e Innovación (ANII), Uruguay. + +## Author contributions + +David Moi designed and wrote the treebuilding pipeline and analysis pipelines, collected benchmarking data for CATH structural families, carried out large scale analysis for benchmarking, generated trees for protein families, and drafted the manuscript. Charles Bernard collected data relevant to the bacterial signaling case + +<--- Page Split ---> + +study, analyzed and annotated the case study in light of the existing literature and wrote the corresponding sections of the paper. Martin Steinegger contributed advice and feedback on the structural distance measures evaluated in this paper. Yannis Nevers collected HOG benchmarking data and curated examples of protein families to test the pipeline. Mauricio Langlieb wrote the documentation and collected benchmarking data and curated examples of protein families. Christophe Dessimoz supervised the project and contributed to the conception of the study, the interpretation of results, and the manuscript writing. + +Correspondence and requests for materials should be addressed to D.M. + +## Competing interests + +The authors declare no competing interests. + +## Supplementary Information Guide + +1. Supplementary data + +The homologue list of RRNPPA sequences and their metadata is available in the RRNPPAlist.xls file. In the text it is referred to as Table S1. + +2. Supplementary methods, results and discussion are found in the SI section pdf + +## Code and Data availability + +All UniProt identifiers necessary to replicate the experimental results are available on Zenodo: https://doi.org/10.5281/zenodo.8346286 + +The Foldtree pipeline is available on github: https://github.com/DessimozLab/fold_tree + +<--- Page Split ---> + +All metadata used to annotate the RRNPPA phylogeny are available in the supplementary data file or on the Zenodo archive. + +## References + +1. Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453-4455 (2019). +2. Minh, B. Q., Trifinopoulos, J., Schrempf, D., Schmidt, H. A. & Lanfear, R. IQ-TREE version 2.0: tutorials and Manual Phylogenomic software by maximum likelihood. URL http://www. iqtree. org (2019). +3. Bouckaert, R. et al. 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MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772-780 (2013). + +68. Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics vol. 25 1972-1973 Preprint at https://doi.org/10.1093/bioinformatics/btp348 (2009). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupTableRRNPPAmetadata.xls- FoldtreeS1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342_det.mmd b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d1058ee813c2e601139629f4ae0f485d376d5727 --- /dev/null +++ b/preprint/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342/preprint__00b9c52ff18ddf879d531bb5dd46e2c462d8c5a2f0fc667773ff3a23635e2342_det.mmd @@ -0,0 +1,595 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 928, 208]]<|/det|> +# Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses + +<|ref|>text<|/ref|><|det|>[[44, 230, 191, 275]]<|/det|> +David Moi dmoi@uni1.ch + +<|ref|>text<|/ref|><|det|>[[50, 303, 616, 323]]<|/det|> +University of Lausanne https://orcid.org/0000- 0002- 2664- 7385 + +<|ref|>text<|/ref|><|det|>[[44, 328, 175, 366]]<|/det|> +Charles Bernard UNIL DBC + +<|ref|>text<|/ref|><|det|>[[44, 373, 170, 412]]<|/det|> +Yannis Never UNIL DBC + +<|ref|>text<|/ref|><|det|>[[44, 419, 536, 460]]<|/det|> +Martin Stenegger Artificial Intelligence Institute, Seoul National University + +<|ref|>text<|/ref|><|det|>[[44, 465, 297, 505]]<|/det|> +Mauricio Langleib Universidad de la Republica + +<|ref|>text<|/ref|><|det|>[[44, 512, 617, 554]]<|/det|> +Christophe Dessimoz University of Lausanne https://orcid.org/0000- 0002- 2170- 853X + +<|ref|>text<|/ref|><|det|>[[44, 594, 288, 614]]<|/det|> +Biological Sciences - Article + +<|ref|>text<|/ref|><|det|>[[44, 633, 137, 652]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 670, 317, 690]]<|/det|> +Posted Date: October 4th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 709, 475, 728]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3368849/v1 + +<|ref|>text<|/ref|><|det|>[[44, 745, 916, 789]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 806, 535, 826]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 862, 936, 905]]<|/det|> +Version of Record: A version of this preprint was published at Nature Structural & Molecular Biology on October 10th, 2025. See the published version at https://doi.org/10.1038/s41594- 025- 01649- 8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[125, 86, 870, 290]]<|/det|> +Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses + +<|ref|>text<|/ref|><|det|>[[118, 333, 878, 381]]<|/det|> +David Moi \(^{1,2,\#}\) , Charles Bernard \(^{1,2}\) , Martin Steinegger \(^{3,4,5}\) , Yannis Nevers \(^{1,2}\) , Mauricio Langleib \(^{6,7}\) , Christophe Dessimoz \(^{1,2,\#}\) + +<|ref|>text<|/ref|><|det|>[[116, 414, 879, 592]]<|/det|> +\(^{1}\) Department of Computational Biology, University of Lausanne, Lausanne, Switzerland \(^{2}\) Swiss Institute of Bioinformatics, Lausanne, Switzerland \(^{3}\) School of Biological Sciences, Seoul National University, Seoul, South Korea \(^{4}\) Artificial Intelligence Institute, Seoul National University, Seoul, South Korea \(^{5}\) Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea \(^{6}\) Unidad de Bioinformática, Institut Pasteur de Montevideo, Montevideo, Uruguay \(^{7}\) Unidad de Genómica Evolutiva, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay + +<|ref|>text<|/ref|><|det|>[[118, 621, 850, 641]]<|/det|> +\(^{4}\) Correspondence and requests for materials should be addressed to D.M. or C.D. + +<|ref|>sub_title<|/ref|><|det|>[[118, 677, 216, 696]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[116, 730, 881, 912]]<|/det|> +Recent advances in AI- based protein structure modeling have yielded remarkable progress in predicting protein structures. Since structures are constrained by their biological function, their geometry tends to evolve more slowly than the underlying amino acids sequences. This feature of structures could in principle be used to reconstruct phylogenetic trees over longer evolutionary timescales than sequence- based approaches, but until now a reliable structure- based tree building method has been elusive. Here, we demonstrate that the use of structure- based + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 882, 400]]<|/det|> +phylogenies can outperform sequence- based ones not only for distantly related proteins but also, remarkably, for more closely related ones. This is achieved by inferring trees from protein structures using a local structural alphabet, an approach robust to conformational changes that confound traditional structural distance measures. As an illustration, we used structures to decipher the evolutionary diversification of a particularly challenging family: the fast- evolving RRNPPA quorum sensing receptors enabling gram- positive bacteria, plasmids and bacteriophages to communicate and coordinate key behaviors such as sporulation, virulence, antibiotic resistance, conjugation or phage lysis/lysogeny decision. The advent of high- accuracy structural phylogenetics enables myriad of applications across biology, such as uncovering deeper evolutionary relationships, elucidating unknown protein functions, or refining the design of bioengineered molecules. + +<|ref|>sub_title<|/ref|><|det|>[[118, 434, 257, 454]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[116, 488, 882, 779]]<|/det|> +Since Darwin, phylogenetic trees have depicted evolutionary relationships among organisms, viruses, genes, and other evolving entities, enabling an understanding of shared ancestry and tracing the events that led to the observable extant diversity. Trees based on molecular data are typically reconstructed from nucleotide or amino- acid sequences, by aligning homologous sequences and inferring the tree topology and branch lengths under a model of character substitution \(^{1 - 3}\) . However, over long evolutionary time scales, multiple substitutions occurring at the same site cause uncertainty in alignment and tree building. The problem is particularly acute when dealing with fast evolving sequences, such as viral or immune- related ones, or when attempting to resolve distant relationships, such as at the origins of animals \(^{4 - 6}\) or beyond. + +<|ref|>text<|/ref|><|det|>[[116, 785, 881, 912]]<|/det|> +In contrast, the fold of proteins is often conserved well past sequence signal saturation. Furthermore, because 3D structure determines function, protein structures have long been studied to gain insight into their biological role within the cell whether it be catalyzing reactions, interacting with other proteins to form complexes or regulating the expression of genes among a myriad of other functions. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 82, 881, 265]]<|/det|> +Until recently, protein structures had to be obtained through labor intensive crystallography, with modeling efforts often falling short of the level of accuracy required to describe a fold for the many tasks crystal structures were used for. Due to these limitations, structural biology and phylogenetics have developed as largely separate disciplines and each field has created models describing evolutionary or molecular phenomena suited to the availability of computational power and experimental data. + +<|ref|>text<|/ref|><|det|>[[116, 272, 881, 533]]<|/det|> +Now, the widespread availability of accurate structural models \(^{7,8}\) opens up the prospect of reconstructing trees from structures. However, there are pitfalls to avoid in order to derive evolutionary distances between homologous protein structures. Geometric distances between rigid body representations of structures, such as root mean square deviation (RMSD) distance or template modeling (TM) score \(^{9}\) , are confounded by spatial variations caused by conformational changes \(^{10,11}\) . More local structural similarity measures have been proposed in the context of protein classification \(^{10}\) , but due to the relative paucity of available structures until recently, little is known about the accuracy of structure- based phylogenetic reconstruction beyond a few isolated case studies \(^{12,13}\) . + +<|ref|>text<|/ref|><|det|>[[116, 541, 881, 911]]<|/det|> +Here, we report on a comprehensive evaluation of phylogenetic trees reconstructed from the structures of thousands of protein families across the tree of life, using multiple kinds of distance measures. We built trees from structural divergence measures obtained using Foldseek \(^{14}\) , which outputs scores from rigid body alignment, local superposition- free alignment and structural alphabet based sequence alignments. The performance of these measures has been previously assessed on the task of detecting whether folds are homologous and belong to the same family \(^{14 - 16}\) , but have never been benchmarked with regards to how well they perform as evolutionary distances. Remarkably, we found that the structural alphabet- based measure outperforms phylogenies from sequence alone even at relatively short evolutionary distances. To demonstrate the capabilities of structural phylogenetics, we employ our methodology, released as open- source software named Foldtree, to resolve the difficult phylogeny of a fast- evolving protein family of high relevance: the RRNPPA (Rap, Rgg, NprR, PlcR, PrgX and AimR) receptors of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 881, 400]]<|/det|> +communication peptides. These proteins allow gram- positive bacteria, their plasmids and their viruses to assess their population density and regulate key biological processes accordingly. These communication systems have been shown to regulate virulence, biofilm formation, sporulation, competence, solventogenesis, antibiotic resistance or antimicrobial production in bacteria \(^{17 - 21}\) , conjugation in conjugative elements, lysis/lysogeny decision in bacteriophages \(^{22}\) and host manipulation by mobile genetic elements (MGEs) \(^{19,23}\) . Accordingly, the RRNPPA family has a substantial impact on human societies as it connects to the virulence and transmissibility of pathogenic bacteria and the spread of antimicrobial resistance genes through horizontal gene transfers. We analyze and discuss the parsimonious characteristics of the phylogeny of this family, highlighting the contrasts with the sequence- based tree. + +<|ref|>sub_title<|/ref|><|det|>[[118, 434, 205, 454]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[118, 489, 877, 533]]<|/det|> +## Structural trees outperform sequence based trees at both short and long evolutionary divergence times + +<|ref|>text<|/ref|><|det|>[[116, 538, 881, 693]]<|/det|> +To find a structural distance metric with high informative phylogenetic signal, we investigated the use of local superposition- free comparison (local distance difference test; LDDT \(^{16}\) ), rigid body alignment (TM score \(^{9}\) ) and a distance derived from similarity over a structural alphabet (Fident) \(^{14}\) . These measures were used to compute distance trees using neighbor joining, after being aligned in an all- vs- all comparison using the Foldseek structural alphabet (Methods). + +<|ref|>text<|/ref|><|det|>[[116, 700, 881, 855]]<|/det|> +Assessing the accuracy of trees reconstructed from empirical data is notoriously difficult. We used two complementary indicators. The first one, taxonomic congruence score (TCS) (Methods and Supplementary Figures 1- 2), assesses the congruence of reconstructed protein trees with the known taxonomy \(^{24}\) . Among several potential tree topologies reconstructed from the same set of input proteins, the better topologies can be expected to have higher TCS on average. + +<|ref|>text<|/ref|><|det|>[[118, 862, 880, 908]]<|/det|> +For trees reconstructed from closely related protein families using standard sequence alignments, both local structure LDDT and global structure TM measures + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 882, 319]]<|/det|> +showed poorer taxonomic congruence than sequence- based trees on average (Figure 1a). By contrast, trees derived from the Fident distance (henceforth referred to as the Foldtree measure) outperformed those based on sequence. The difference was even larger if we excluded families for which the Alphafold2- inferred structures are of low confidence (Figure 1b). This trend was observed consistently across various protein family subsets, taken from clades with different divergence levels (Supplementary Figure 8). We also experimented with statistical corrections and other parameter variations, but they did not lead to further improvements (Supplementary Figures 4- 9). + +<|ref|>text<|/ref|><|det|>[[115, 326, 882, 667]]<|/det|> +We then assessed the Foldtree measure's performance against sequence- based trees over larger evolutionary distances, using structure- informed homologous families from the CATH database25. This database classifies proteins hierarchically, grouping them based on Class, Architecture, Topology and Homology of experimentally determined protein structures. We examined both proteins from the same homology set as well as proteins within the same topology sets (Methods). Efforts were made to correct structures with discontinuities or other defects before treebuilding (Methods) since these adversely affect structural comparisons. With this more divergent CATH dataset, structure- based methods performed better overall. Foldtree outperformed the sequence- based method even more (Figure 1c). Results for LDDT versus sequence flipped in favour of LDDT, while results for the global TM measure remained inferior to sequence (Supplementary Figure 9). + +<|ref|>text<|/ref|><|det|>[[115, 675, 882, 885]]<|/det|> +To delve deeper into the reasons for these performance differences, we applied a gradient- boosted decision tree regressor26 on features derived from the input structures and taxonomic lineages of the input protein sets, aiming to predict the TCS difference (Supp Methods Table 1). We found that features measuring the confidence of the AlphaFold structure prediction (predicted LDDT or pLDDT) emerged as significant factors in the analysis (Supplementary Figure 3). This suggests that advancements in structural prediction might further benefit structural trees in the future. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 883, 373]]<|/det|> +To validate our findings using an entirely different indicator of tree quality, we assessed the “ultrametricity” of trees—how uniform a tree’s root- to- tip lengths are for all its tips, akin to following a molecular clock. Although strict adherence to a molecular clock is unlikely in general, it is reasonable to assume that distance measures resulting in more ultrametric trees on average (i.e., with reduced root- to- tip variance, see Methods) are more accurate27. We found that in the sequence- based family dataset, Foldtree trees had by far the lowest root- to- tip variance of all approaches (Figure 1d). The difference was so pronounced that it is evident in visual comparison of tree shapes for several randomly chosen families (Figure 1e). Foldtree performed the best of all metrics and sequence- based trees the worst. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[147, 110, 850, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[157, 461, 644, 475]]<|/det|> +
e. Ultrametricity: sample tree shapes among sequence-defined families (OMA families)
+ +<|ref|>image<|/ref|><|det|>[[190, 476, 848, 757]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 787, 870, 893]]<|/det|> +
Figure 1 a) Trees using the Foldtree metric exhibit higher taxonomic congruence than sequence trees on average (protein families defined from sequences); by contrast, structure trees from LDDT and TM underperform sequence trees; b) After filtering the input dataset for structural quality (families with average pLDDT structure scores \(>40\) ), the proportion of Foldtree trees which have a greater normalized congruence than sequence-based trees increased from \(48\%\) to \(53\%\) ; c) the Foldtree metric on the CATH dataset of structurally defined families using experimental structures
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[125, 88, 866, 194]]<|/det|> +outperforms sequence trees to an even greater proportion; d) The variance of normalized root- to- tip distances were compiled for all trees within the OMA dataset for all tree structural tree methods and sequence trees. Foldtree has a lower variance than other methods. The median of each distribution is shown with a vertical red line. Distributions are truncated to values between 0 and 0.2; e) A random sample of trees is shown where each column is from from equivalent protein input sets and each row of trees is derived using a distinct tree building method. + +<|ref|>text<|/ref|><|det|>[[117, 227, 880, 302]]<|/det|> +Both of the orthogonal metrics of ultrametricity and species tree discordance indicate that Foldtree produces trees with desirable characteristics that are ideal for constructing phylogenies with sets of highly divergent homologs. + +<|ref|>sub_title<|/ref|><|det|>[[118, 338, 877, 383]]<|/det|> +## Foldtree reveals the evolutionary diversification of RRNPPA communication systems + +<|ref|>text<|/ref|><|det|>[[115, 389, 881, 900]]<|/det|> +To illustrate the potential of structural phylogenies, we reconstructed the intricate evolutionary history of the RRNPPA family of intracellular quorum sensing receptors in gram- positive Bacillota bacteria, their conjugative elements and temperate bacteriophages17,21,28. These receptors, vital for microbial communication and decision- making, are paired with a small secreted communication peptide that accumulates extracellularly as the encoding population replicates. Once a quorum of cells, plasmids or viruses is met, communication peptides get frequently internalized within cells and binds to the tetratricopeptide repeats (TPRs) of cognate intracellular receptors, leading to gene or protein activation or inhibition, facilitating a coordinated response beneficial for a dense population. The density- dependent regulations of RRNPPA systems control behaviors like bacterial virulence, biofilm formation, sporulation, competence, conjugation and bacteriophage lysis/lysogeny decisions17- 21. Although these receptors were identified in the early 1990s29,30, their evolutionary history is unclear due to frequent mutations and transfers, making sequence comparisons challenging28,31,32. This is reflected by the nomenclature of the family: RRNPPA is an acronym for Rap, Rgg, NprR, PlcR, PrgX and AimR, which were historically described as six different families of intracellular receptors, and of which only structural comparisons allowed to establish the actual consensus on their common evolutionary origin28,33,34. Recently, a pioneer work combining + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 82, 880, 184]]<|/det|> +structural comparisons among folds and sequence- based phylogenetics have provided insights among some of these families28, but a comprehensive reconstruction of the evolutionary history of this family that includes all described subfamilies19 remains elusive. + +<|ref|>text<|/ref|><|det|>[[115, 194, 881, 457]]<|/det|> +The Foldtree structure- based phylogeny illuminates key evolutionary features of the diversification of RRNPPA communication systems that could not be resolved based on sequences (Figure 2). The evolutionary trajectory it implies is more parsimonious in terms of subfamily classification, taxonomy, functions, and protein architectures than a phylogeny obtained with a state- of- the- art sequence- based method (details in Supplementary Figure 9). In particular, the structure- based phylogeny implies that folds composed of 9 tetratricopeptide repeats (TPRs) and folds composed of 5 TPRs emerged only once while the sequence- based tree implies a less plausible scenario of convergent evolution of two clades toward 5- TPR protein architectures. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 85, 875, 860]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 858, 870, 906]]<|/det|> +
Figure 2. Phylogeny of cytosolic receptors from the RRNPPA family paired with a communication proppetide. a) Functional diversity of the RRNPPA family. The MAD root separates paralogs of Anoxybacter fermentans with a singular architecture from the other canonical RRNPPA systems.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[125, 88, 872, 333]]<|/det|> +Subfamilies with experimental validation of at least one member are highlighted in color. Other subfamilies correspond to high- confidence candidate subfamilies detected with RRNPP_ detector in \(^{19}\) . Biological processes experimentally shown to be regulated in a density- dependent manner by a QS system are displayed for each validated subfamily. Subfamilies in gray correspond to novel, high- confidence candidate RRNPPA subfamilies from \(^{19}\) . A star mapped to a leaf indicates a predicted regulation of an adjacent biosynthetic gene cluster by the corresponding QS system. b) Main implied events of the tree, with normalized branch length for visualization purposes (the events that are implied from alternate roots are shown in Supplementary Figure 10). c) Distribution and prevalence of the different members of each RRNPPA subfamily into the different taxonomic families. d) Genomic orientation and encoding element of the receptor - adjacent propeptide pairs. e) The first colostrip indicates the domain architecture of each receptor. A representative fold for each domain architecture is displayed in the legend (AlphaFold models of subfamily 27, NprR, Rap and PlcR, respectively) with an indication of the implied events from panel a) at the origin of each fold/architecture. The second colostrip gives the degeneration score of TPR sequences of each receptor (given as 1 - TprPred_likelihood, as in \(^{33}\) ). The histogram shows the length (in amino-acids) of each receptor. + +<|ref|>text<|/ref|><|det|>[[115, 370, 881, 768]]<|/det|> +The minimal ancestor deviation (MAD) method placed the root right next to receptors encoded by Anoxybacter fermentans DY22613, a piezophilic and thermophilic endospore- forming bacterium from the Clostridia class isolated from a deep- sea hydrothermal vent. These proteins exhibit a unique domain architecture lacking the DNA- binding HTH domain and harboring 7 TPRs (Table S1). Their singular architecture, and the proximity to the MAD root lead us to infer Anoxybacter's receptors as the outgroup of all other RRNPPA systems (Figure 2a- b). This suggests that the early history of canonical RRNPPA systems could have been linked to extremophile endospore- forming Bacillota and may have started with a gain of a N- terminal HTH DNA binding domain, enabling to coupling quorum sensing with transcriptional regulation (Figure 2e). We considered alternative rooting scenarios (Supplementary Figure 11) but only the MAD rooting implies a unique origin of receptors with non- degenerated TPR sequences that predates the last common ancestor of each clade of receptors with degenerated TPRs (Figure 2e), in line with Declerck et al.'s conjecture \(^{33}\) . + +<|ref|>text<|/ref|><|det|>[[116, 779, 880, 907]]<|/det|> +The widespread distribution of sporulation- regulation on the tree (Figure 2a) suggests that the early history of the 9 TPRs group may have been linked to the regulation of the costly differentiation into a resistant endospore in extremophile spore- forming taxa from the Clostridia (Biomaibacter acetigenes, Sulfobacillus thermotolerans, Thermoanaerobacter italicus) and Bacilli (Alicyclobacillaceae and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 78, 882, 644]]<|/det|> +Thermoactinomycetaceae families) classes (Table S1). Consistently, the NprR subfamily is suggested to have diversified first in extremophile spore- forming Bacillusaceae (Psychrobacilli, Halobacilli, Anoxybacilli etc.) and Planococcaceae (Sporosarcina, Planococcus antarticus, halotolerans, glaciei etc.) (Table S1). The Rap clade, exclusively found in Bacillus and Alkalihalobacillus genera, is nested within NprR, and is inferred to have diverged from the same ancestral gene as that of NprR receptors found in Halobacilli, Geobacilli, Virgibacilli, Oceanobacilli and Bacilli from the Bacillus cereus group (Table S1). This indicates that the absence of the N- terminal HTH domain observed in Rap receptors originates from a loss of the ancestral domain (Figure 2e), as previously reported by Felipe- Ruiz et al28. However, many Rap receptors have retained the ability to regulate sporulation, but only through protein inhibition of the Spo0F- P and ComA regulators, rather than through transcriptional regulation35. The Rap clade is characterized by a wide occurrence in MGEs, consistent with the high rate of horizontal gene transfers described for this subfamily32. The MGE distribution in the Rap clade is polyphyletic, suggesting frequent exchanges of these communication systems between the host genome, phages and conjugative elements (Figure 2d). The QssR validated clade is specific to solventogenic Clostridiaceae (Figure 2a- c) while its sister clade (subfamily 09) is specific to pathogenic Clostridium such as C. perfringens and C. botulinum, which may indicate a novel link between quorum sensing and pathogenesis in these taxa of medical relevance that may warrant further investigation. + +<|ref|>text<|/ref|><|det|>[[116, 652, 882, 888]]<|/det|> +AloR and AimR members appear to be the most diverged representatives of the HTH- 9TPRs architectural organization. Consistently, their TPR sequences harbor signs of degeneration, which is especially true in the AimR clade, consistent with its specificity to Bacillus phages, since viruses evolve at higher evolutionary rates (Figure 2f). The AimR receptors supporting phage- phage communication are adjacent to non- viral communication systems from subfamily 21, found in the chromosome of Alkalihalobacillus clausii or lehensis. For the first time, the structural phylogeny reveals that the AimR- subfamily 21 clade is evolutionary close from Paenibacillaceae receptors from the AloR subfamily prevalent in the Paenibacillus + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 400]]<|/det|> +genus and the candidate subfamily 08 predominantly found in the Brevibacillus genus (Figure 2). Subfamily 08, AloR and AimR are suggested to form a monophyletic group with a presumable Paenibacillaceae ancestry. This is supported by systems from Paenibacillus xylanexedens and Brevibacillus formosus position in the outgroup, close to the QssR subfamily (Figure 2a, Table S1). Remarkably, the cognate communication peptides of AimR receptors from the Bacillus cereus group are highly similar to that of subfamily 08, with the presence of the DPG amino- acid motif in the C- terminal (Table S1). Our results therefore suggest that a QSS similar to the ancestor of the AloR- subfam08 clade was co- opted by a temperate phage to regulate the lysis/lysogeny decision. This successful functional association has spread in Bacillus phages and led to the AimR clade. The numerous phage- and prophage- encoded systems from the subfamily 08 support this hypothesis19. + +<|ref|>text<|/ref|><|det|>[[115, 409, 881, 889]]<|/det|> +The proteins composed of 5 TPRs are suggested to have emerged from the loss of 4 TPRs in the C- terminus, drastically shortening their length (Figure 2b, Figure 2e), although other evolutionary scenarios that do not imply such loss exist as well28 (Supplementary Figure 10). The 5 TPRs group is divided in two sister clades: one with a wide taxonomic range composed of PlcR, TprA and their outgroup (Figure 2a and Figure 2c), the second including PrgX, TraA, ComR and Rgg validated subfamilies, specific to non- spore forming Lactobactillales. The emergence of the 5TPRs clade is associated with fundamental functional shifts. First, receptor- propeptide orientations are highly diversified compared to the HTH- 9TPRs group (Figure 9d). These heterogeneous orientations correlate with functional changes as receptors divergently transcribed from their propeptide tend to repress target genes while co- directional receptors tend to activate them36. Second, the diversification of the PrgX- ComR- Rgg clade was accompanied with an important diversification of propeptide secretion modes: their cognate propeptides are exported through the alternative PptAB translocon rather than through the SEC translocon17,28 and it has even been shown that a paralog of Rgg in S. pyogenes is paired with a functional leaderless communication peptide that lacks a signal sequence for an export system, highlighting that another secretory process of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 882, 347]]<|/det|> +communication peptides emerged in the clade37. Last, the biological processes controlled by these communication systems are not linked to cellular dormancy or viral latency, but rather to the production of virulence factors and antimicrobials21. This is mirrored by the substantial number of syntenic biosynthetic gene clusters (BGCs) predicted to be regulated by TprA and Rgg members (Figure 2a)19. Consistently, the primary role of members of the HTH- 5TPRs clade may be to assess the threshold population density at which a collective production of biomolecules starts to be ecologically impactful and becomes the most evolutionary advantageous strategy, with a few exceptions such as the regulation of competence by ComR or conjugation by PrgX. + +<|ref|>sub_title<|/ref|><|det|>[[118, 384, 246, 404]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 411, 882, 809]]<|/det|> +As early as 1975, Eventoff and Rossmann employed the number of structurally dissimilar residues between pairs of proteins to infer phylogenetic relationships by means of a distance method38. This approach has been revisited to infer deep phylogenetic trees and networks using different combinations of dissimilarity measures (e.g., RMSD, \(\mathrm{Q}_{\mathrm{score}}\) , Z- score) and inference algorithms12,39- 43. Conformational sampling has been proposed to assess tree confidence when using this approach11. Some models have been developed that mathematically describe the molecular clock in structural evolution44 or integrate sequence data with structural information to inform the likelihood of certain substitutions45. Other studies have modeled structural evolution as a diffusion process in order to infer evolutionary distances46, or incorporating it into a joint sequence- structure model to infer multiple alignments and trees by means of bayesian phylogenetic analysis47,48. To date, the quality of structure- based phylogenetics, especially compared to conventional sequence- based phylogenetics, has remained largely unknown, limiting its use to niche applications. + +<|ref|>text<|/ref|><|det|>[[117, 816, 880, 890]]<|/det|> +The extensive empirical assessment reported here, using two orthogonal indicators of tree quality, demonstrates the high potential of structure- based phylogenetics. The taxonomic congruence score (TCS) measures agreement with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 882, 427]]<|/det|> +the established classification. Individual gene trees can be expected to deviate substantially from the underlying species tree due to gene duplication, lateral transfer, incomplete lineage sorting, or other phenomena. However, the evolutionary history of the underlying species will still be reflected in many parts of the tree—which is quantified by the TCS. All else being equal, tree inference approaches which tend to result in higher TCS over many protein families can be expected to be more accurate. On this metric, we obtained the best trees using Foldtree, which is based on Foldseek's structural alphabet, and an alignment procedure combining structural and sequence information. Furthermore, after filtering lower quality structures out of the tree building process, tree quality improved further when compared to sequence- based trees (Figure 1. b), indicating that higher confidence models with accurate structural information provide better phylogenetic signal. + +<|ref|>text<|/ref|><|det|>[[115, 433, 882, 614]]<|/det|> +When considering the ultrametricity through the root- to- tip variances of the trees, the Foldtree trees adhered more closely to a molecular clock than other structural or sequence trees. We acknowledge that in and of itself, adherence to a molecular clock is only a weak indicator of tree accuracy. Nevertheless, considering the clear, consistent differences obtained, and the agreement with the TCS criterion, the ultrametricity appears to reflect meaningful performance difference among the tree inference methods. + +<|ref|>text<|/ref|><|det|>[[115, 621, 882, 884]]<|/det|> +Folds evolve at a slower rate than the underlying sequence mutations49,50. Structural distances are therefore less likely to saturate over time, making it possible to recover the correct topology deeper in the tree with greater certainty. This could be observed in our results on the distant, structurally defined CATH families. Interestingly, however, Foldtree distinguished itself even at divergence times when homology is identifiable using sequence to sequence comparison. It is thus both fine grained enough to account for small differences between input proteins at shorter divergence times, overcoming the often mentioned shortcoming of structural phylogenetics, and more robust than sequence comparison at longer evolutionary distances. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 882, 373]]<|/det|> +As the projection of each residue onto a structural character is locally influenced by its neighboring residues rather than global steric changes, Foldseek's representations of 3D structures are well suited to capture phylogenetic signals when comparing homologous proteins. In contrast, global structural similarity measures are confounded by conformational fluctuations which involve steric changes that are much larger in magnitude than the local changes observed between functionally constrained residues during evolution. Moreover, since Foldseek represents 3D structures as strings, the computational speed- ups and techniques associated with string comparisons implemented in MMseqs51 can be applied to structural homology searches and comparisons making the Foldtree pipeline extremely fast and efficient. + +<|ref|>text<|/ref|><|det|>[[115, 377, 881, 912]]<|/det|> +Viral evolution, quickly evolving extracellular proteins and protein families with histories stretching back to the first self replicating cells are among the many cases that can be revisited with these new techniques. In our first study of a family using Foldtree, we present just one such case, with the fast evolving RRNPPA family of cytosolic communication receptors encoded by Firmicutes bacteria, their conjugative elements and their viruses. The phylogeny reconstructed by Foldtree includes, for the first time, all described RRNPPA subfamilies19. Remarkably, despite their significant divergence, the underlying diversifying history is parsimonious in terms of taxonomy, functions, and protein architectures (Supplementary Figure 10). The MAD rooting method flags a previously undescribed candidate outgroup with a singular architecture of 7 TPRs and no DNA- binding domain in Anoxybacter fermentans, which supports Declerck et al. speculation that the ancestral receptor at the origin of the RRNPPA clade lacked the DNA- binding domain, and that the latter was gained subsequently in the evolutionary history of the family. Declerck et al. also speculated that the level of TPR degeneracy in receptors is a marker of divergence from the last common ancestor of the family33. In this respect, root to tips lengths are remarkably uniform throughout the entire RRNPPA structural tree with slight differences being meaningful, as the longest branches correspond to receptors with degenerated TPR sequences (Figure 2e). Last, this rooting implies that receptors with non- degenerated TPRs sequences emerged only once, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 882, 373]]<|/det|> +systematically involves a late emergence of clades with degenerated TPRs as a derived state of an ancestor harboring non- degenerated TPRs (Figure 2e). Although rooting is easier when a tree is more clock- like, there remains uncertainty regarding the precise placement of the root. Our interpretation of MAD rooting and domain architecture led us to infer an origin of the RRNPPA family linked to the regulation of sporulation in extreme environments, implying also that 9 TPRs folds predate 5 TPRs folds. Yet, alternative rootings of the structural phylogeny cannot be ruled out, with a root either within the HTH- 5TPRs group as in \(^{28}\) or within the AloR- AimR- subfamily08 group (hypotheses displayed in Supplementary Figure 11). Additional, yet- to- be- discovered members of RRNPPA homologs could help resolve the root with higher confidence. + +<|ref|>text<|/ref|><|det|>[[115, 382, 882, 591]]<|/det|> +Recently the fold universe has been revealed using AlphaFold on the entirety of the sequences in UniProt and the ESM model \(^{8}\) on the sequences in MGNIFY \(^{52}\) to reach a total of nearly one billion structures. The UniProt structures inferred by AlphaFold have recently been systematically organized into sequence- and structure- based clusters, shedding light on novel fold families and their possible functions \(^{14,53}\) . In future work it may be desirable to add an evolutionary layer of information to this exploration of the fold space using structural phylogenetics to further refine our understanding of how this extant diversity of folds emerged. + +<|ref|>text<|/ref|><|det|>[[115, 601, 882, 864]]<|/det|> +In conclusion, this work shows the potential of structural methods as a powerful tool for inferring evolutionary relationships among proteins. For relatively close proteins, structured- based tree inference rivals sequence- based inference, and the choice of approach should be tailored to the specific question at hand and the available data. For more distant proteins, structural phylogenetics opens new inroads into studying evolution beyond the "twilight" zone \(^{54}\) . We believe that there remains much room for improvement in refining phylogenetic methods using the tertiary representation of proteins and hope that this work serves as a starting point for further exploration of deep phylogenies in this new era of Al- generated protein structures. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 217, 103]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[118, 113, 684, 131]]<|/det|> +No statistical methods were used to predetermine sample size. + +<|ref|>sub_title<|/ref|><|det|>[[118, 165, 568, 185]]<|/det|> +## OMA HOG selection for large scale benchmark + +<|ref|>text<|/ref|><|det|>[[115, 190, 882, 454]]<|/det|> +The OMA set of protein families consists of "root hierarchical orthologous groups" (root HOGs) which are derived from all- vs- all sequence comparisons55. The quest for orthologs benchmarking dataset56 consists of 78 proteomes. The 2020 release of this dataset was used as input into the OMA orthology prediction pipeline55 (version 2.4.1). A random selection of at most 500 orthologous groups with at least 10 proteins were compiled for each group of HOGs that were inferred to have emerged in different ancestral taxa (Bacteria, Bilateria, Chordata, Dikarya, Eukaryota, Eumetazoa, Euteleostomi, Fungi, LUCA, Opisthokonta and Tetrapoda). The UniProt identifiers of the proteins within each group were used as input to the Foldtree pipeline. + +<|ref|>sub_title<|/ref|><|det|>[[118, 487, 587, 506]]<|/det|> +## CATH family selection for large scale benchmark + +<|ref|>text<|/ref|><|det|>[[116, 510, 882, 751]]<|/det|> +CATH structural superfamilies are constructed using structural comparisons and classification25. Each level of classification designates a different resolution of structural similarity. These are delineated as Class, Architecture, Topology and Homology. We chose to investigate tree quality using input sets within the same homology classification as well as sets within the same topology. We selected a random subsample of at most 250 proteins (or the number of proteins within the family if there were less) from each family for 635 CATH families and 500 CAT families. The Topology- based dataset is designated as CAT and the Homology- based dataset is designated as CATH. Each CAT or CATH family contains the PDB identifiers and chains of the structures they correspond to. + +<|ref|>text<|/ref|><|det|>[[116, 756, 882, 911]]<|/det|> +The PDB files were programmatically obtained from the PDB database. 3D structures of monomers corresponding to the chain identified in the CATH classification for each fold were extracted from PDB crystal structures using Biopython. PDBfizer from the OpenMM57 package was used to fix crystal structures with discontinuities, non- standard residues or missing atoms before tree building since these adversely affect structural comparisons. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[119, 110, 380, 128]]<|/det|> +## Structure tree construction + +<|ref|>text<|/ref|><|det|>[[118, 134, 880, 208]]<|/det|> +Sets of homologous structures were downloaded from the AFDB or PDB and prepared according to the OMA and CATH dataset sections above. Foldseek14 is then used to perform an all vs all comparison of the structures. + +<|ref|>text<|/ref|><|det|>[[115, 214, 881, 723]]<|/det|> +Structural distances between all pairs are compiled into a distance matrix which is used as input to quicktree58 to create minimum evolution trees. These trees are then rooted using the MAD method59. Foldseek (Version: 30fdcac78217579fa25d59bc271bd4f3767d3ebb) has two alignment modes where character based structural alignments are performed and are scored using the 3Di substitution matrix or a combination of 3Di and amino- acid substitution matrices. A third mode, using TMalign to perform the initial alignment was not used. It is then possible to output the fraction of identical amino acids from the 3Di and amino acid based alignment (Fident), the LDDT (locally derived using Foldseek's implementation) score and the TM score (normalized by alignment length). This results in a total of 6 structural comparison methods. We then either directly used the raw score or applied a correction to the scores to transform them to the distance matrices so that pairwise distances would be linearly proportional to time (Supplementary methods). This resulted in a total of 12 possible structure trees for each set of input proteins. To compile these results, Foldseek was used with alignment type 0 and alignment type 2 flags in two separate runs with the '--exhaustive- search' flag. The output was formatted to include Iddt and alntmscore columns. The pipeline of comparing structure- and sequence- based trees is outlined in Supplementary Figure 1. + +<|ref|>text<|/ref|><|det|>[[116, 727, 881, 882]]<|/det|> +Before starting the all vs all comparison of the structures we also implemented an optional filtering step to remove poor AlphaFold models with low pLDDT values. If the user activates this option, the pipeline removes structures (and the corresponding sequences) with an average pLDDT score below 40, before establishing the final protein set and running structure and sequence tree building pipelines. We performed similar benchmarking experiments on filtered and unfiltered + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 83, 879, 130]]<|/det|> +versions of the OMA dataset to observe the effect of including only high quality models in the analysis. + +<|ref|>sub_title<|/ref|><|det|>[[118, 164, 450, 183]]<|/det|> +## Sequence based tree construction + +<|ref|>text<|/ref|><|det|>[[116, 188, 882, 370]]<|/det|> +Sets of sequences and their taxonomic lineage information were downloaded using the UniProt API. Clustal Omega (version 1.2.4)60 or Muscle5 (version 5.0)61 was then used to generate a multiple sequence alignment on default parameters. This alignment was then used with either FastTree(version 2.1)62 on default parameters or IQ- TREE (version 1.6.12 using the flags LG+1) to generate a phylogenetic tree. Finally, this tree was rooted using the MAD (version 1775932) method on default parameters. + +<|ref|>sub_title<|/ref|><|det|>[[118, 404, 627, 423]]<|/det|> +## Taxonomic congruence metric for phylogenetic trees + +<|ref|>text<|/ref|><|det|>[[116, 428, 881, 609]]<|/det|> +Taxonomic lineages were retrieved for each sequence and structure of each protein family via the UniProt API. It is assumed that the vast majority of genes will follow an evolutionary trajectory that mirrors the species tree with occasional loss or duplication events. The original development and justification for this score to measure tree quality in an unbiased way can be found in the following work 24. In this version of the metric we reward longer lineage sets towards the root by calculating a score for each leaf from the root to the tip. + +<|ref|>text<|/ref|><|det|>[[116, 617, 881, 718]]<|/det|> +The agreement of the tree with the established taxonomy (from UniProt) can be calculated recursively in a bottom up fashion when traversing the tree using equation 1. Leaves of trees were labeled with sets representing the taxonomic lineages of each sequence before calculating taxonomic congruence. + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[370, 100, 597, 135]]<|/det|> +\[C(tree) = \sum_{s}^{Leaves}C(leaf)\] + +<|ref|>equation<|/ref|><|det|>[[215, 147, 755, 216]]<|/det|> +\[C(x) = \left\{ \begin{array}{ll}|s(x)| & \mathrm{if~x~is~root}\\ |s(x)| + |s(x.ancestor)| & \mathrm{if~x~is~an~internal~node}\\ & \mathrm{where}\\ \end{array} \right.\] + +<|ref|>equation<|/ref|><|det|>[[214, 216, 755, 255]]<|/det|> +\[s(x) = \left\{ \begin{array}{ll}L(x), & \mathrm{if~x~is~a~leaf}\\ s(x.Left)\cap s(x.Right)) & \mathrm{if~x~is~an~internal~node} \end{array} \right.\] + +<|ref|>text<|/ref|><|det|>[[124, 300, 872, 410]]<|/det|> +Equation 1- taxonomic congruence metric. This score is used to measure the agreement of binary tree topologies with the known species tree. \(\mathsf{s}(\mathsf{x})\) denotes the set of lineages found in the tree node x. \(\mathrm{C(x)}\) denotes the congruence score of node x based on its two child nodes. \(\mathsf{L}(\mathsf{x})\) denotes the labels of leaves. The total score of a tree is defined as the sum of the leaf scores. The code to calculate this metric is available on the git repository. + +<|ref|>text<|/ref|><|det|>[[116, 442, 880, 549]]<|/det|> +Both structure and sequence trees were rooted using the MAD method to make TCS comparisons between the methods equivalent. To compare large collections of trees with varying input set sizes, we normalized the congruence scores of trees by the number of the proteins in the tree. + +<|ref|>sub_title<|/ref|><|det|>[[118, 580, 388, 599]]<|/det|> +## Ultrametricity quantification + +<|ref|>text<|/ref|><|det|>[[116, 603, 881, 773]]<|/det|> +Ultrametricity63 describes the consistency of tip to root lengths of a given phylogenetic tree. If a tree building approach has an accurate molecular clock on all branches, the amount of inferred evolutionary time elapsed between the root and all of the extant species should be equivalent and proportional to real time. This would imply that the sums of branch length along a lineage from the root to any tip of the tree should be equivalent since the amount of clock time elapsed from the common ancestor until the sequencing of species in the present day is the same. + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[185, 99, 775, 194]]<|/det|> +\[E(\text{rootdist}) = \sum_{i = 1}^{n_{\text{leaves}}} \text{dist}(l_i, \text{root}) / n_{\text{leaves}} \\ S_{\text{norm}}(\text{rootdist}) = \sum_{i = 1}^{n_{\text{leaves}}} (\text{dist}(l_i, \text{root}) / E(\text{rootdist}) - 1)^2 / (n_{\text{leaves}} - 1)\] + +<|ref|>text<|/ref|><|det|>[[125, 220, 870, 321]]<|/det|> +**Equation 2** - To derive a unified metric for ultrametricity that could easily be applied to the trees generated by different methods, we normalized the branch lengths to center the distribution of root to tip lengths at 1. We then measured the variance of these normalized root to tip lengths. \(E(.)\) represents the average root to tip length for a given tree. \(S_{\text{norm}}(.)\) represents the variance of these normalized root to tip distances. \(\text{dist}(l_i, \text{root})\) denotes the length of the tip \((l_i)\) to root. + +<|ref|>text<|/ref|><|det|>[[116, 351, 880, 515]]<|/det|> +To describe the ultrametricity of the different methods of structural tree derivation, we measured the length of root-to-tip distances of a given tree (equation 2). We then normalized this collection of distances by their mean and calculated their variance. We compiled this variance measurement for collections of trees with corresponding input protein sets for all methods used to derive trees and compared their distributions. **Supplementary Figure 2** shows a visual representation of how this score is calculated. + +<|ref|>sub_title<|/ref|><|det|>[[118, 548, 314, 565]]<|/det|> +## RRNPPA phylogeny + +<|ref|>text<|/ref|><|det|>[[116, 572, 880, 686]]<|/det|> +The metadata of "strict" known and candidate RRNPPA QSSs described in the RRNPP_detector paper were fetched from TableS2 in the corresponding supplementary materials¹⁹. The predicted regulations by QSSs of adjacent BGCs were fetched from TableS5. The propeptide sequences were downloaded from the following Github repository: + +<|ref|>text<|/ref|><|det|>[[116, 694, 870, 911]]<|/det|> +https://github.com/TeamAIRE/RRNPP_candidate_propeptides_exploration_dataset. +The 11,939 receptors listed in TableS2 were downloaded from the NCBI Genbank database, and redundancy was removed by clustering at 95% identity with CD-HIT⁶⁴, yielding 1,418 protein clusters. The Genbank identifiers of the 11,939 receptors were used as queries in the UniProt Retrieve/ID mapping research engine (https://www.uniprot.org/id-mapping) to retrieve corresponding UniProt/AlphaFoldDB identifiers. 768 protein clusters successfully mapped to at least one UniProt/AlphaFoldDB identifier. The 768 predicted protein structures were downloaded and Foldseek was used to perform an all vs all comparison. Based on + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 472]]<|/det|> +our benchmarking results we used the Fident scores from a comparison using amino- acid and 3Di alphabet alignment scoring (alignment mode 1 in Foldseek). Since this family had undergone domain architecture modifications, we decided to extract the structural region between the first and last positions of each fold where \(80\%\) of all of the other structures in the set mapped. With these core structures we performed a second all vs all comparison. We again used the Fident scores (alignment mode 1) and no statistical correction to construct a distance matrix between the core structures. This matrix was then used with FastME \(^{65}\) to create a distance based tree. The resulting tree was annotated with ITOL \(^{66}\) , using the metadata available in Table S1. To derive the sequence- based phylogeny, we built a multiple sequence alignment (MSA) of receptors, using mafft \(^{67}\) with the parameters - maxiterate 1000 - localpair for high accuracy. The MSA was then trimmed with trimAl \(^{68}\) under the - automated 1 mode optimized for maximum likelihood reconstruction. The trimmed alignment of 304 sites was given as input to IQ- TREE \(^{2}\) to infer a maximum likelihood phylogenetic under the LG+G model with 1000 ultrafast bootstraps. + +<|ref|>sub_title<|/ref|><|det|>[[118, 496, 340, 516]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[116, 524, 881, 625]]<|/det|> +We thank the Dessimoz lab members for thoughtful discussions on the topic of structural evolution and their encouragement and input on this work. We especially thank Clement Train for his brilliant work on the tree visualization tool accompanying this work. We also gratefully acknowledge helpful suggestions by Pedro Beltrao. + +<|ref|>text<|/ref|><|det|>[[117, 658, 880, 732]]<|/det|> +The work was supported by SNSF grant 216623 to C.D.. M. L. is a recipient of a doctoral scholarship from Agencia Nacional de Investigación e Innovación (ANII), Uruguay. + +<|ref|>sub_title<|/ref|><|det|>[[118, 767, 354, 787]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[116, 794, 881, 895]]<|/det|> +David Moi designed and wrote the treebuilding pipeline and analysis pipelines, collected benchmarking data for CATH structural families, carried out large scale analysis for benchmarking, generated trees for protein families, and drafted the manuscript. Charles Bernard collected data relevant to the bacterial signaling case + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 882, 293]]<|/det|> +study, analyzed and annotated the case study in light of the existing literature and wrote the corresponding sections of the paper. Martin Steinegger contributed advice and feedback on the structural distance measures evaluated in this paper. Yannis Nevers collected HOG benchmarking data and curated examples of protein families to test the pipeline. Mauricio Langlieb wrote the documentation and collected benchmarking data and curated examples of protein families. Christophe Dessimoz supervised the project and contributed to the conception of the study, the interpretation of results, and the manuscript writing. + +<|ref|>text<|/ref|><|det|>[[116, 326, 776, 346]]<|/det|> +Correspondence and requests for materials should be addressed to D.M. + +<|ref|>sub_title<|/ref|><|det|>[[118, 380, 348, 400]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[118, 409, 516, 427]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[116, 463, 500, 484]]<|/det|> +## Supplementary Information Guide + +<|ref|>text<|/ref|><|det|>[[149, 495, 365, 512]]<|/det|> +1. Supplementary data + +<|ref|>text<|/ref|><|det|>[[116, 521, 880, 567]]<|/det|> +The homologue list of RRNPPA sequences and their metadata is available in the RRNPPAlist.xls file. In the text it is referred to as Table S1. + +<|ref|>text<|/ref|><|det|>[[146, 575, 880, 621]]<|/det|> +2. Supplementary methods, results and discussion are found in the SI section pdf + +<|ref|>sub_title<|/ref|><|det|>[[118, 657, 412, 677]]<|/det|> +## Code and Data availability + +<|ref|>text<|/ref|><|det|>[[116, 714, 880, 760]]<|/det|> +All UniProt identifiers necessary to replicate the experimental results are available on Zenodo: https://doi.org/10.5281/zenodo.8346286 + +<|ref|>text<|/ref|><|det|>[[116, 794, 880, 841]]<|/det|> +The Foldtree pipeline is available on github: https://github.com/DessimozLab/fold_tree + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 880, 131]]<|/det|> +All metadata used to annotate the RRNPPA phylogeny are available in the supplementary data file or on the Zenodo archive. + +<|ref|>sub_title<|/ref|><|det|>[[118, 165, 240, 185]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 204, 880, 884]]<|/det|> +1. Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. 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FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Mol. Biol. Evol. 26, 1641-1650 (2009). + +<|ref|>text<|/ref|><|det|>[[117, 808, 797, 860]]<|/det|> +63. Moore, N. C. A. & Prosser, P. The Ultrametric Constraint and its Application to Phylogenetics. arXiv [cs.AI] (2014). + +<|ref|>text<|/ref|><|det|>[[115, 873, 848, 894]]<|/det|> +64. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 82, 738, 101]]<|/det|> +protein or nucleotide sequences. Bioinformatics 22, 1658- 1659 (2006). + +<|ref|>text<|/ref|><|det|>[[115, 115, 866, 168]]<|/det|> +65. Lefort, V., Desper, R. & Gascuel, O. FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program. Mol. Biol. Evol. 32, 2798-2800 (2015). + +<|ref|>text<|/ref|><|det|>[[115, 182, 853, 235]]<|/det|> +66. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293-W296 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 248, 852, 301]]<|/det|> +67. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772-780 (2013). + +<|ref|>text<|/ref|><|det|>[[115, 314, 860, 400]]<|/det|> +68. Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics vol. 25 1972-1973 Preprint at https://doi.org/10.1093/bioinformatics/btp348 (2009). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 355, 177]]<|/det|> +- SupTableRRNPPAmetadata.xls- FoldtreeS1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/images_list.json b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..73797dec18a6a5ee01e0e5ebb0745f58e05f27de --- /dev/null +++ b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Crystal structure, MCD measurements of trilayer \\(\\mathrm{NiI}_2\\) at room temperature. a, Schematic of trilayer \\(\\mathrm{NiI}_2\\) sandwiched between graphene and hBN. b, View of the in-plane and out-of-plane atomic lattice. The magnetic \\(\\mathrm{Ni}^{2 + }\\) ions are surrounded by the octahedron of \\(\\mathrm{I}^{-}\\) ions, and three \\(\\mathrm{NiI}_2\\) layers as a repeating unit stack in a staggered fashion along the c axis. c, Atomic-resolution ADF-STEM image showing signature hexagonal patterns of rhombohedral stacking in few-layer \\(\\mathrm{NiI}_2\\) crystals. The inset shows the corresponding FFT image. d, Circular polarization resolved Raman spectra of a trilayer \\(\\mathrm{NiI}_2\\) device (Fig. 1a) at room temperature, excited by \\(532\\mathrm{nm}\\) laser. “SM” indicates the interlayer shear mode of trilayer \\(\\mathrm{NiI}_2\\) . e, The MCD spectra of trilayer \\(\\mathrm{NiI}_2\\) at \\(+3\\mathrm{T}\\) , \\(0\\mathrm{T}\\) and -3T. MCD signals are sensitive to spin electronic transitions and magnetic moments in the electronic states. The MCD features are spin-sign dependent and reverse as magnetic field switch. The zero remanent MCD signals at \\(\\sim 2.3\\mathrm{eV}\\) at \\(0\\mathrm{T}\\) suggest antiferromagnetic orders.", + "footnote": [], + "bbox": [ + [ + 177, + 88, + 840, + 504 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Non-collinear antiferromagnetism in trilayer NiI₂ device. a, Polar RMCD maps upon a 2.33 eV laser with diffraction-limited spatial resolution (see Methods), collected at room temperature and selected magnetic field. b, Schematic of the spin textures of bimerons-like domains and corresponding zoom-in RMCD images (white dashed-line box in Fig. 2a). c, The polar RMCD signals along with the line sections of RMCD map (b). d, The RMCD curves sweeping between \\(+3\\mathrm{T}\\) and \\(-3\\mathrm{T}\\) at \\(10\\mathrm{K}\\) , suggesting a non-collinear antiferromagnetism.", + "footnote": [], + "bbox": [ + [ + 160, + 87, + 833, + 298 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Existence of ferroelectric and anti-ferroelectric orders in trilayer NiI2 device. a, b, \\(P - E\\) and \\(I - E\\) loops at various frequencies from device 1 (D1). c, Corresponding \\(I - E\\) loops from Fig. 3b subtracted the current background. Two pairs of current peaks (FE-AFE and AFE-FE switching peaks) were obtained by Lorentz fitting. An evolution from FE to AFE was observed. d, Schematic of the spin spiral configurations with in-plane (x-y plane) spin cycloid in monolayer NiI2, showing a periodicity of \\(7\\times 1\\) unit cells. e, Extreme case where the in-plane (x-y plane) cycloidal configuration tilts to x-z plane caused by interlayer exchange interactions, resulting in an out-of-plane ferroelectric polarization. f, Schematic of the spin spiral configurations with opposite \\(\\mathbf{q}\\) in trilayer NiI2, showing the coexistence of ferroelectric and antiferroelectric.", + "footnote": [], + "bbox": [ + [ + 186, + 78, + 816, + 675 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Magnetic control of ferroelectricity in trilayer NiI2 device. a-c, The \\(P_r\\) extracted from the \\(P\\) - \\(E\\) hysteresis loop is plotted as a function of out-of-plane magnetic field at different frequencies. The error bars are standard deviations of \\(P_r\\) . d, The magnetic control ratio \\((P_r - P_{r0}) / P_{r0}\\) are frequency dependent, where \\(P_r\\) and \\(P_{r0}\\) is remanent polarization in a magnetic field and without magnetic field, respectively. e, The \\(I\\) - \\(E\\) curves at different magnetic field. The decrease in the current peak accompanied by an increase in the coercive field due to the increased magnetic field is unambiguously observed. f, g, Fitting by KAI model for different magnetic field at 10 K, giving the switching time \\(\\tau\\) . h, The \\((\\tau - \\tau_0) / \\tau_0\\) as a function of magnetic field at 10 K, indicating a degree of magnetic control of switching time, where \\(\\tau\\) and \\(\\tau_0\\) is switching time in a magnetic field and without magnetic field, respectively.", + "footnote": [], + "bbox": [ + [ + 161, + 81, + 828, + 505 + ] + ], + "page_idx": 17 + } +] \ No newline at end of file diff --git a/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f.mmd b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9af41c2ad2078e9adc78030af2a658a62a92ee75 --- /dev/null +++ b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f.mmd @@ -0,0 +1,228 @@ + +# Coexistence of ferroelectricity and antiferroelectricity in 2D van der Waals multiferroic + +Bo Peng bo_peng@uestc.edu.cn + +University of Electronic Science and Technology of China https://orcid.org/0000- 0001- 9411- 716X + +Yangliu Wu 1450683589@qq.com + +Haipeng Lu University of Electronic Science and Technology of China + +Xiaocang Han Peking University + +Chendi Yang Laboratory of Advanced Materials, Department of Materials Science and Shanghai Key Lab of Molecular Catalysis and Innovative Materials, Fudan University + +Nanshu Liu Renmin University of China + +Xiaoxu Zhao Peking University https://orcid.org/0000- 0001- 9746- 3770 + +Liang Qiao School of Physics, University of Electronic Science and Technology of China https://orcid.org/0000- 0003- 2400- 2986 + +Wei Ji Renmin University of China https://orcid.org/0000- 0001- 5249- 6624 + +Renchao Che Fudan University https://orcid.org/0000- 0002- 6583- 7114 + +Longjiang Deng University of Electronic Science and Technology of China https://orcid.org/0000- 0002- 8137- 6151 + +Article + +Keywords: + +Posted Date: April 16th, 2024 + +<--- Page Split ---> + +DOI: https://doi.org/10.21203/rs.3.rs- 4229313/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 4th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53019- 5. + +<--- Page Split ---> + +# Coexistence of ferroelectricity and antiferroelectricity in 2D van der Waals multiferroic + +Yangliu Wu \(^{1}\) , Haipeng Lu \(^{1}\) , Xiaocang Han \(^{2}\) , Chendi Yang \(^{3}\) , Nanshu Liu \(^{5}\) , Xiaoxu Zhao \(^{2}\) , Liang Qiao \(^{4}\) , Wei Ji \(^{5}\) , Renchao Che \(^{3}\) , Longjiang Deng \(^{1*}\) and Bo Peng \(^{1*}\) + +\(^{1}\) National Engineering Research Center of Electromagnetic Radiation Control Materials, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China \(^{2}\) School of Materials Science and Engineering, Peking University, Beijing 100871, China \(^{3}\) Laboratory of Advanced Materials, Department of Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials(iChEM), Fudan University, Shanghai 200433, China \(^{4}\) School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China \(^{5}\) Beijing Key Laboratory of Optoelectronic Functional Materials & Micro- Nano Devices, Department of Physics, Renmin University of China, Beijing 100872, China \(^{*}\) To whom correspondence should be addressed. Email address: bo_peng@uestc.edu.cn; denglj@uestc.edu.cn + +## Abstract + +Multiferroic materials with a coexistence of ferroelectric and magnetic order have been intensively pursued to achieve the mutual control of electric and magnetic properties toward energy- efficient memory and logic devices. The breakthrough progress of 2D van der Waals magnet and ferroelectric encourages the exploration of low dimensional multiferroics, which holds the promise to understand inscrutable magnetoelectric coupling and invent advanced spintronic devices. However, confirming ferroelectricity with optical techniques is challenging on 2D materials, particularly in conjunction with antiferromagnetic orders in a single- layer multiferroic. The prerequisite of ferroelectric is the electrically switchable spontaneous electric polarizations, which must be proven through reliable and direct electrical measurements. Here we report the discovery of 2D vdW multiferroic with out- of- plane ferroelectric polarization in trilayer NiI₂ device, as revealed by scanning reflective magnetic circular dichroism microscopy and ferroelectric hysteresis loop. The evolutions of between ferroelectric and antiferroelectric phase have been unambiguously observed. Moreover, the magnetoelectric interaction is directly probed by external electromagnetic field control of the multiferroic domains switching. This work opens up opportunities for exploring new multiferroic orders and multiferroic physics at the limit of single or few atomic layers, and for creating advanced magnetoelectronic devices. + +<--- Page Split ---> + +Multiferroic materials with a coexistence of ferroelectric and magnetic orders has been diligently sought after for a long time to achieve the mutual control of electric and magnetic properties toward the energy- efficient memory and logic devices \(^{1 - 3}\) . But the two contrasting order parameters tend to be mutually exclusive in a single material \(^{4}\) . Nondisplacive mechanisms introduce a paradigm for constructing multiferroics beyond the traditional limits of mutual obstruction of the ferroelectric and magnetic orders \(^{5,6}\) . To date, the type I multiferroic BiFeO \(_3\) is the only known room- temperature single- phase multiferroic material. Alternatively, the helical magnetic orders break the spatial inversion symmetry and simultaneously lead to electric orders \(^{7,8}\) , giving rise to type- II multiferroics. The quest for a new single- phase multiferroic remains an open challenge. + +The emergence of 2D vdW magnets and ferroelectrics has opened new avenues for exploring low- dimensional physics on magnetoelectric coupling \(^{9,10}\) . Diverse isolated vdW ferromagnets \(^{11 - 13}\) and ferroelectrics \(^{14,15}\) have enabled tantalizing opportunities to create 2D vdW spintronic devices with unprecedented performances at the limit of single or few atomic layers. Few of bulk crystals of transition- metal dihalides with a trigonal layered structure have been shown that the helical spin textures break inversion symmetries and induce an orthogonal ferroelectric polarization \(^{16,17}\) , but and definitive multiferroicity remains elusive at the limit of few atomic layers. + +A recent work shows the possibility of discovery of type- II monolayer \(\mathrm{NiI_2}\) multiferroics using the optical measurements of second- harmonic- generation (SHG) and linear dichroism (LD) \(^{18}\) . Our work has pointed that all- optical characterizations are not sufficient to make a judgement of a few- and single- layer multiferroic at the presence of non- collinear and antiferromagnetic orders \(^{19}\) . The observed SHG and LD signals in few- layer \(\mathrm{NiI_2}\) originate from the magnetic- order- induced breaking of spatial- inversion \(^{19,20}\) . The prerequisite of ferroelectric polarization is the non- vanishing spontaneous electric polarizations, which must be proven through reliable and direct electrical measurements, such as polarization- and current- electric field (P- E and I- E) hysteresis loops. To date, 2D vdW multiferroic has not been directly uncovered at the limit of few layers. Here, we report fascinating vdW multiferroic with coexistence of ferroelectricity and antiferroelectricity in few layer \(\mathrm{NiI_2}\) based on magneto- optical- electric joint- measurements. In this 2D vdW multiferroics, an unprecedented magnetic control of switching dynamics of ferroelectric domain has been observed. + +## Non-collinear antiferromagnetism in trilayer \(\mathrm{NiI_2}\) + +Due to the high reactivity of \(\mathrm{NiI_2}\) flakes, \(\mathrm{NiI_2}\) exfoliation and encapsulation by graphene and hexagonal boron nitride (hBN) flakes were carried out in a glove box (Fig. 1a and Supplementary Fig. 1). \(\mathrm{NiI_2}\) crystal shows rhombohedral structure with a repeating stack of three (I- Ni- I) layers, where Ni and I ions form a triangular lattice in each layer (Fig. 1b). The rhombohedral stacking has been atomically identified (Fig. 1c). The atom + +<--- Page Split ---> + +arrangements of rhombohedral phase demonstrate signature hexagon- shaped periodic bright spots with equal contrast, validating the overlapping stack of I and Ni atoms along the \(c\) axis. The ADF- STEM and fast Fourier transform (FFT) show an interplanar spacing of \(1.9 \mathring{\mathrm{A}}\) , consistent with the (110) lattice plane of rhombohedral \(\mathrm{NiI}_2\) crystal. Circularly polarized Raman spectra in the parallel \((\sigma + / \sigma +\) and \(\sigma - / \sigma -\) ) configuration show only two distinct peaks in the \(\mathrm{NiI}_2\) device (Fig. 1d). The peak at \(\sim 124.7 \mathrm{cm}^{- 1}\) is assigned to the \(\mathrm{A_g}\) phonon modes \(^{22}\) , and this polarization behavior is consistent with Raman tensor analysis for the rhombohedral structure of \(\mathrm{NiI}_2^{23}\) . The Raman feature at \(\sim 20 \mathrm{cm}^{- 1}\) is assigned to the interlayer shear mode (SM), which suggests that the \(\mathrm{NiI}_2\) is trilayer \(^{20}\) . + +For optimal optical response and sensitivity to probe the magnetic properties, the photon energy should be chosen near the absorption edge \(^{11,24}\) . Therefore, we first studied white- light magnetic circular dichroism (MCD) spectra of a trilayer \(\mathrm{NiI}_2\) device as a function of magnetic field perpendicular to the sample plane at \(10 \mathrm{K}\) (see Methods for details) \(^{25}\) . There is a strong peak near \(2.3 \mathrm{eV}\) along with two weak features around \(1.85 \mathrm{eV}\) and \(1.6 \mathrm{eV}\) (Fig. 1e). By means of ligand- field theory, the peaks are attributed to the absorption transitions of \(p\) - \(d\) exciton states \(^{26}\) . A pair of opposite MCD peaks with magnetic field manifestly appears at \(2.3 \mathrm{eV}\) , suggesting strong magneto- optical resonance. When the magnetic field is switched, MCD features is consistently reversed, and zero remanent MCD signal at \(\sim 2.3 \mathrm{eV}\) is distinctly observed at \(0 \mathrm{T}\) , indicating antiferromagnetic orders at \(10 \mathrm{K}\) . + +To further validate the magnetic order, the scanning RMCD microscope was used to image and measure the magnetic domains of the as- exfoliated trilayer \(\mathrm{NiI}_2\) . The polar RMCD imaging is a reliable and powerful tool to unveil the 2D magnetism in the micro scale, and the RMCD intensity is proportional to the out- of- plane magnetization \(^{24}\) . All magneto- optical measurements were carried out using a \(2.33 \mathrm{eV}\) laser with optimal detection sensitivity (see Methods for details). Figure 2a shows RMCD maps of a trilayer \(\mathrm{NiI}_2\) sweeping between - 0.75 T and +0.75 T at \(10 \mathrm{K}\) . Remarkably, many micrometer- sized bimeron- like domains are observed in trilayer and another few- layer \(\mathrm{NiI}_2\) across the entire range of sweeping magnetic field \(^{27}\) . The spin- up and spin- down domains exist in pairs (Fig. 2a and Supplementary Fig. 2). One typical bimeron- like domains in trilayer \(\mathrm{NiI}_2\) at \(0 \mathrm{T}\) and \(10 \mathrm{K}\) are shown in Fig. 2b. The RMCD signals in each bimeron- like domain display opposite sign and nearly equal intensities. The magnetic moments point upwards or downwards in the core region and gradually decrease away from the core, and approaches zero near the perimeter (Fig. 2c). This magnetic moment distribution possibly indicates a pair of topological spin meron and antimeron with opposite chirality in a cycloid ground state \(^{28,29}\) . The bimeron- like magnetization textures remain robust in all magnetic field, indicating the bimeron- like domains are robust. The high stability of the bimeron- like magnetic domains probably + +<--- Page Split ---> + +originate from the topological protection, which also contributes to the preservation of magnetization even if upon a reversal magnetic field of 0.75 T. The formation of bimeron- like magnetic domains may be related to the localized stress at the interface. But further deep studies must be done to reveal the exact physical mechanism. + +Fig. 2d shows the RMCD loops of the trilayer \(\mathrm{NiI}_2\) sweeping between \(+3\mathrm{T}\) and \(- 3\mathrm{T}\) at \(10\mathrm{K}\) . The RMCD loops show a highly nonlinear behavior with magnetic field and plateau behaviors for the out- of- plane magnetization. The RMCD intensity near \(0\mathrm{T}\) is suppressed and approaches zero, suggesting the vanishing remnant magnetization, which indicates a compensation of the out- of- plane magnetization and non- collinear AFM orders in the trilayer \(\mathrm{NiI}_2^{30}\) . And the gradual increases of the RMCD signal are observed with increasing magnetic field between \(\pm 1.2\) and \(\pm 2.6\mathrm{T}\) , suggesting a spin- flop process. The spin- flop behaviors of the magnetization curve imply that the interlayer antiferromagnetic coupling of the non- collinear spins is complicated. Similar magnetic hysteresis loops have been demonstrated in another few- layer \(\mathrm{NiI}_2\) , which show definite non- collinear AFM orders in the few- layer \(\mathrm{NiI}_2\) (Supplementary Fig. 2b). + +## Ferroelectricity in trilayer \(\mathrm{NiI}_2\) device + +To determine ferroelectricity in few- layer \(\mathrm{NiI}_2\) device, we performed the frequency- dependent measurement of electric polarization via \(I\) - \(E\) and \(P\) - \(E\) hysteresis loops, which allows an accurate estimation of the electric polarization. We fabricated two heterostructure devices of graphene/hBN/ \(\mathrm{NiI}_2\) /graphene/hBN (Fig. 1a and Supplementary Fig. 1). The hBN flake was used as an excellent insulating layer to prevent large leakage current and guarantee the detections of ferroelectric (FE) features \(^{31,32}\) (Supplementary Fig. 3). The hBN insulator shows a linear \(P\) - \(E\) behavior and a rectangle- shaped \(I\) - \(E\) loops (Supplementary Fig. 4), indicating excellent insulativity for ferroelectric hysteresis measurements (see Methods for details) \(^{33,34}\) . The frequency- dependent \(I\) - \(E\) and \(P\) - \(E\) loops at \(10\mathrm{K}\) are shown in Fig. 3, and the forward and backward scans of the electric polarization as a function of electric field show characteristic ferroelectric \(I\) - \(E\) and \(P\) - \(E\) hysteresis. Strikingly, a characteristic double- hysteresis loop of antiferroelectric (AFE) polarization emerges accompanied with decreasing remanent polarization \((P_r)\) . More importantly, a pair of opposite single peaks of switching current \((I)\) are observed when sweeping voltage at \(6.7\mathrm{Hz}\) , which is attribute to charge displacement and implies two stable states with inverse polarity (Fig. 3b and c). Whereas two pair of opposite bimodal peaks are observed when sweeping voltage at \(1.3\mathrm{Hz}\) , which is attribute to AFE- FE and FE- AFE transitions under electric field sweeping (Fig. 3c) \(^{35}\) . This suggests an evolution from FE to AFE polarization with frequency is observed \(^{36,37}\) , exhibiting the decisive evidence for coexistence of ferroelectric and antiferroelectric \(^{38,39}\) . This comprehensive frequency- dependent evolution behaviors also confirm the coexistence of FE and AFE in another a few layers + +<--- Page Split ---> + +\(\mathrm{NiI}_2\) (Supplementary Fig. 5). + +The type- II multiferroicity has been demonstrated in the bulk \(\mathrm{NiI}_2\) . However, the multiferroic identification for few- layer \(\mathrm{NiI}_2\) remains challenging and elusive. All- optical methods are unreliable to make a judgement of a few- and single- layer multiferroic at the presence of non- collinear and antiferromagnetic orders19. The bulk \(\mathrm{NiI}_2\) displays a helimagnetic state below critical temperature16,17. From symmetry considerations and a Ginzburg- Landau perspective40,41, the helimagnetic state allows for the emergence of a ferroelectric polarization associated to the form: + +\[\mathbf{P} = \gamma \mathbf{e}\times \mathbf{q} \quad (1)\] + +where \(\mathbf{P}\) is the electric polarization, \(\mathbf{e}\) is the spin rotation axis, \(\mathbf{q}\) is the spin propagation vector of the spin spiral, and \(\gamma\) is a scalar parameter dependence with spin- orbit coupling. For monolayer \(\mathrm{NiI}_2\) , the helimagnetic order can be modeled with a 7axa supercell and an in- plane (x- y plane) spin cycloid, and the spin propagation vector \(\mathbf{q}\) is displayed along the [210] direction (in lattice vector units)42, as shown in in Fig. 3d. Thus, the in- plane (x- y plane) spin cycloid induces the in- plane electric polarization along the [010] direction (Fig. 3d). Actually, theoretical calculations have determined that the \(\mathbf{q}\) - vector in multi- layer and bulk \(\mathrm{NiI}_2\) is a consequence of the competition between magnetic exchange interactions between magnetic atoms42,43. In particular, intralayer ferromagnetic first- neighbor, intralayer antiferromagnetic third neighbor, and interlayer antiferromagnetic second- neighbor magnetic exchange interactions are the most relevant. In the monolayer limit, there are no interlayer interactions, hence the \(\mathbf{q}\) - vector is in- plane and determined by the competition between intralayer exchange interactions. For a trilayer \(\mathrm{NiI}_2\) , the \(\mathbf{q}\) - vector is modulated not only by intralayer exchange interactions but also by interlayer exchange interactions. Assuming that interlayer exchange interactions cause the tilting out- of- plane cycloidal spin configuration from in- plane (x- y plane) configuration (Fig. 3d), the \(\mathbf{e}\) - vector is no longer parallel to the z- axis, leading to an out- of- plane ferroelectric polarization component. Figure 3e illustrates the extreme case where the in- plane (x- y plane) cycloidal configuration tilts to x- z plane, resulting in an out- of- plane ferroelectric polarization. This scenario suggests the observed out- of- plane ferroelectric polarization in the trilayer \(\mathrm{NiI}_2\) device, but the precise mechanism remains to be further studied in the future. In particular, equation (1) shows that two spin spiral configurations with \(\mathbf{q}_1 = \mathbf{q}\) and \(\mathbf{q}_2 = -\mathbf{q}\) will give rise to opposite electric polarizations \(\mathbf{P} = -\mathbf{P}\) . The first principles calculations in spin configuration with both \(\mathbf{q}\) and \(-\mathbf{q}\) are energetically equivalent, and therefore show same energies with and without spin- orbit coupling42. Thus, the emergence of opposite electric dipoles can be directly observed in the total electronic density of the system. The energy of spin cycloidal configurations with positive and negative \(\mathbf{q}\) - vectors (positive and negative ferroelectric polarization \(\mathbf{P}\) ) is degenerate, which approve the coexistence of ferroelectric and antiferroelectric (Fig. 3f), consistent with the observed + +<--- Page Split ---> + +coexistence of ferroelectric and antiferroelectric in trilayer \(\mathrm{NiI_2}\) . + +## Magnetic control of ferroelectricity + +To reveal the magnetoelectric coupling effect, we studied the magnetic control of ferroelectric properties in the trilayer \(\mathrm{NiI_2}\) device, as shown in Fig. 4. The \(P_r\) extracted from the \(P\) - \(E\) hysteresis loop is plotted as a function of out- of- plane magnetic field at different frequencies (Fig. 4a- c). The magnetic field causes a decrease in residual polarization at different frequencies (Fig. 4a- c and Supplementary Fig. 6), and the magnetic control of \(P_r\) shows frequency dependence of applied electric field (Fig. 4d). The magnetic control ratio reaches to \(\sim 7\%\) by detuning the frequency (24.5 Hz) at 7 T, which is remarkable feature of multiferroic. To better understand the magnetic control behavior, we briefly discuss the possible mechanism that leads to the decrease in \(P_r\) caused by the magnetic field from a microscopic perspective. We only discuss ferroelectric polarization flops in the model of spiral magnets40. In zero fields spins rotate in the easy x- z plane, so that the spin rotation axis \(\mathbf{e}\) is parallel to the y axis, and for \(\mathbf{q} / / \mathbf{x}\) - y plane, \(\mathbf{P} / / \mathbf{z}\) (Supplementary Fig. 7a and 7b). However, magnetic field in the z direction favors the rotation of spins in the x- y plane (Supplementary Fig. 7c and 7d), so that the spin rotation axis \(\mathbf{e}\) is parallel to the z axis, in which case, \(\mathbf{P} / / \mathbf{x}\) - y plane40. In short, applying a magnetic field parallel to the z- axis causes the spin rotation plane to tilt from the x- z plane to the x- y plane, and the corresponding ferroelectric polarization flops from the out- of- plane direction to the in- plane direction. Therefore, an out- of- plane magnetic field leads to a decrease of ferroelectric polarization in the out- of- plane direction, which is consistent with the observed decrease in \(P_r\) with increasing magnetic field. Furthermore, the decrease in the current peak accompanied by an increase in the coercive electric field due to the increased magnetic field is unambiguously observed (Fig. 4e and Supplementary Fig. 8). This is because the out- of- plane magnetic field causes the spin rotation plane to tilt from the x- z plane to the x- y plane, and the corresponding easy axis of ferroelectric polarization flops from the out- of- plane direction to the in- plane direction. The shifts of current peaks induced by ferroelectric switching vary with the magnetic field, but the background current remains constant, excluding the magnetoresistance effects (Fig. 4e and Supplementary Fig. 8). Finally, the switching time of ferroelectric domain under different magnetic fields at 10 K is calculated by KAI model44 (Fig. 4f and 4g; Part A and B). The switching time \(\tau\) increase as magnetic field increase, which signifies an even symmetry with magnetic field (Fig. 4h), consistent with the above mechanisms. At 10 K, the switching time \(\tau\) , leading to a maximum enhancement of switching time by 20% (-7 T). This observation of robust control of ferroelectric properties by magnetic field, pointing to the potential use of few- layer \(\mathrm{NiI_2}\) as a research platform for studying the magneto- electric coupling physics in the two- dimensional limit and for fabricating advanced nano- + +<--- Page Split ---> + +magnetoelectric devices. + +In summary, we report a 2D vdW single- phase multiferroic \(\mathrm{NiI_2}\) few- layer crystal. We observed strong evidences for the coexistence of ferroelectric and non- collinear antiferromagnetism order via RMCD, \(P\) - \(E\) and \(I\) - \(E\) hysteresis loop. hysteresis loop. We achieve unprecedented magnetic control of ferroelectric properties in the \(\mathrm{NiI_2}\) trilayer. We envision that the 2D vdW single- phase multiferroic \(\mathrm{NiI_2}\) will provide numerous opportunities for exploring fundamental low- dimensional physics, and will introduce a paradigm shift for engineering new ultra- compact magnetoelectric devices. + +## Methods + +## Sample fabrication + +\(\mathrm{NiI_2}\) flakes were mechanically exfoliated from bulk crystals via PDMS films in a glovebox, which were synthesized by chemical vapor transport method from elemental precursors with molar ratio \(\mathrm{Ni:I} = 1:2\) . All exfoliated hBN, \(\mathrm{NiI_2}\) and graphene flakes were transferred onto pre- patterned Au electrodes on \(\mathrm{SiO_2 / Si}\) substrates one by one to create heterostructure in glovebox, which were further in- situ loaded into a microscopy optical cryostat for magneto- optical- electric joint- measurement. The whole process of \(\mathrm{NiI_2}\) sample fabrications and magneto- optical- electric measurements were kept out of atmosphere. + +## Magneto-optical-electric joint-measurement + +The polar RMCD, white- light MCD, Raman measurements and ferroelectric \(P\) - \(E\) and \(I\) - \(E\) measurements were performed on a powerful magneto- optical- electric joint- measurement scanning imaging system (MOEJSI) \(^{19}\) , with a spatial resolution reaching diffraction limit. The MOEJSI system was built based on a Witec Alpha 300R Plus low- wavenumber confocal Raman microscope, integrated with a closed cycle superconducting magnet (7 T) with a room temperature bore and a closed cycle cryogen- free microscopy optical cryostat (10 K) with a specially designed snout sample mount and electronic transport measurement assemblies. + +The Raman signals were recorded by the Witec Alpha 300R Plus low- wavenumber confocal Raman microscope system, including a spectrometer (150, 600 and 1800/mm) and a TE- cooling Andor CCD. A 532 nm laser of \(\sim 0.2 \mathrm{mW}\) is parallel to the X- axis \((0^{\circ})\) and focused onto samples by a long working distance \(50 \times\) objective \((\mathrm{NA} = 0.55, \mathrm{Zeiss})\) after passing through a quarter- wave plate \((1 / 4 \lambda)\) . The circular polarization resolved Raman signals passed through the same \(1 / 4 \lambda\) waveplate and a linear polarizer, obtained by the spectrometer \((1800 / \mathrm{mm})\) and the CCD. + +For white- light MCD measurements, white light with Kohler illumination from Witec Alpha 300R Plus microscope was linearly polarized at 0o by a visible wire grid + +<--- Page Split ---> + +polarizer, passed through an achromatic quarter- wave \((1 / 4\lambda)\) plate and focused onto samples by a long working distance \(50\times\) objective (Zeiss, \(\mathrm{NA} = 0.55\) ). The right- handed and left- handed circularly polarized white light was obtained by rotating \(1 / 4\lambda\) waveplate at \(+45^{\circ}\) and \(- 45^{\circ}\) . The white- light spectra were recorded by the Witec Alpha 300R Plus confocal Raman microscope system (spectrometer, \(150\mathrm{mm}\) ). The absorption spectra of right- handed and left- handed circularly polarized light in different magnetic field can be obtained as the previous work \(^{25}\) , giving corresponding MCD spectra. + +For polar RMCD measurements, a free- space \(532\mathrm{nm}\) laser \((2.33\mathrm{eV})\) of \(\sim 2\mu \mathrm{W}\) modulated by photoelastic modulator (PEM, \(50\mathrm{KHz}\) ) was reflected by a non- polarizing beamsplitter cube \(\mathrm{(R / T = 30 / 70)}\) and then directly focused onto samples by a long working distance \(50\times\) objective \(\mathrm{(NA = 0.55}\) , Zeiss), with a diffraction limit spatial resolution of \(\sim 590\mathrm{nm}\) . The reflected beam which was collected by the same objective passed through the same non- polarizing beamsplitter cube and was detected by a photomultiplier (PMT), which was coupled with lock- in amplifier, Witec scanning imaging system, superconducting magnet, voltage source meter and ferroelectric tester. Ferroelectric \(P - E\) and \(I - E\) hysteresis loop of a \(\mathrm{NiI}_2\) device of \(\mathrm{Gr / hBN / NiI_2 / Gr}\) were measured by classical ferroelectric measurements and directly recorded by ferroelectric tester (Precision Premier II: Hysteresis measurement), which were contacted with the top and bottom graphene electrodes by patterned Au electrodes (Fig. 1a) through the electronic assemblies of the microscopy optical cryostat. The mechanism of ferroelectric measurement has been given by previous work \(^{45}\) . The detected signals include two components: a ferroelectric term of \(\mathrm{NiI}_2\) (2PrA) and a linear non- ferroelectric term of hBN insulator \((\sigma \mathrm{EAt})\) , \(\mathrm{Q} = \mathrm{QNiI} + \mathrm{QBN} = 2\mathrm{PrA} + \sigma \mathrm{EAt}\) . If only hBN insulator, a linear P- E loop take place, consistent with our experimental results of hBN flake (Supplementary Fig. 4). The linear hBN background have no effect on the ferroelectric features, and hBN flakes as excellent insulator suppress and overcome the leakage current, which for guarantee the detections of \(\mathrm{NiI}_2\) ferroelectric features \(^{31 - 34}\) . + +## STEM Imaging, Processing, and Simulation + +Atomic- resolution ADF- STEM imaging was performed on an aberration- corrected JEOL ARM 200F microscope equipped with a cold field- emission gun operating at 80 kV. The convergence semiangle of the probe was around 30 mrad. Image simulations were performed with the Prismatic package, assuming an aberration- free probe with a probe size of approximately \(1\mathrm{\AA}\) . The convergence semiangle and accelerating voltage were in line with the experiments. The collection angle for ADF imaging was between 81 and 228 mrad. ADF- STEM images were filtered by Gaussian filters, and the positions of atomic columns were located by finding the local maxima of the filtered series. + +<--- Page Split ---> + +## Data availability + +The data that support the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper. + +## References + +1 Fiebig, M., Lottermoser, T., Meier, D. & Trassin, M. The evolution of multiferroics. Nat. Rev. Mater. 1, 16046, (2016). 2 Chu, Y.- H. et al. Electric- field control of local ferromagnetism using a magnetoelectric multiferroic. Nat. Mater. 7, 478- 482, (2008). 3 Ponet, L. et al. Topologically protected magnetoelectric switching in a multiferroic. Nature 607, 81- 85, (2022). 4 Cheong, S.- W. & Mostovoy, M. Multiferroics: a magnetic twist for ferroelectricity. Nat. Mater. 6, 13- 20, (2007). 5 Wang, J. et al. Epitaxial BiFeO₃ multiferroic thin film heterostructures. 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Lamination method for improved polarization- leakage current relation in \(\mathrm{HfO_2}\) - based metal/ferroelectric/insulator/semiconductor structure. Semicond. Sci. Technol. 37, + +<--- Page Split ---> + +045001, (2022). + +35 Wu, Z. et al. Discovery of an above- room- temperature antiferroelectric in twodimensional hybrid perovskite. J. Am. Chem. Soc. 141, 3812- 3816, (2019). + +36 Park, M. H. et al. Ferroelectricity and antiferroelectricity of doped thin \(\mathrm{HfO_2}\) - based films. Adv. Mater. 27, 1811- 1831, (2015). + +37 Müller, J. et al. Ferroelectricity in Simple Binary \(\mathrm{ZrO_2}\) and \(\mathrm{HfO_2}\) . Nano Lett. 12, 4318- 4323, (2012). + +38 Ko, K. et al. Operando electron microscopy investigation of polar domain dynamics in twisted van der Waals homobilayers. Nat. Mater. 22, 992- 998 (2023). + +39 Xu, B., Paillard, C., Dkhil, B. & Bellaiche, L. Pinched hysteresis loop in defect- free ferroelectric materials. Phys. Rev. B 94, 140101 (2016). + +40 Mostovoy, M. Ferroelectricity in spiral magnets. Phys. Rev. Lett. 96, 067601, (2006). + +41 Hohenberg, P. C. et al. An introduction to the Ginzburg- Landau theory of phase transitions and nonequilibrium patterns Phys. Rep. 572, 1- 42, (2015). + +42 Fumega, A. O. et al. Microscopic origin of multiferroic order in monolayer \(\mathrm{NiI_2}\) . 2D Mater. 9, 025010, (2022). + +43 Riedl, K. et al. Microscopic origin of magnetism in monolayer \(3d\) transition metal dihalides. Phys. Rev. B 106, 035156, (2022). + +44 Zhao, D., Katsouras, I., Asadi, K., Blom, P. W. M. & de Leeuw, D. M. Switching dynamics in ferroelectric P(VDF- TrFE) thin films. Phys. Rev. B 92, 214115, (2015). + +45 Scott, J. F. et al. Ferroelectrics go bananas. J. Phys.: Condens. Matter 20, 021001, (2008). + +46 Golla, D. et al. Optical thickness determination of hexagonal boron nitride flakes. Appl. Phys. Lett. 102, 161906, (2013). + +## Acknowledgments + +B.P. and L.D. acknowledge support from National Science Foundation of China (52021001). B.P. acknowledge support from National Science Foundation of China (62250073). R.C.C. acknowledge support from National Science Foundation of China (52231007). H.L. acknowledge support from National Science Foundation of China (51972046). L.D. acknowledge support from Sichuan Provincial Science and Technology Department (Grant No. 99203070). L.D. acknowledge support from Sichuan Provincial Science and Technology Department (Grant No. 9920 3070). L.Q. acknowledge support from National Science Foundation of China (520720591 and 11774044). J.W. thanks the National Natural Science Foundation of China (Grant No. 11974422), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB30000000). + +<--- Page Split ---> + +## Author contributions + +B.P conceived the project. Y.W. prepared the samples and performed the magneto- optical- electric joint- measurements and Raman measurements assisted by B.P., and performed the ferroelectric measurements assisted by L.Q., and analyzed and interpreted the results assisted by H.L., N. L., W.J., L.D. and B.P.. C. Y, R.C, X.X. and X.H. performed the STEM measurements. Y.W. and B.P. wrote the paper with input from all authors. All authors discussed the results. + +## Competing interests + +The authors declare no competing interests. + +## Additional information + +Supplementary information is available for this paper at xxx (will be provided). + +Correspondence and requests for materials should be addressed to B.P. + +Reprints and permission information is available online at http://www.nature.com/reprints. + +Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Crystal structure, MCD measurements of trilayer \(\mathrm{NiI}_2\) at room temperature. a, Schematic of trilayer \(\mathrm{NiI}_2\) sandwiched between graphene and hBN. b, View of the in-plane and out-of-plane atomic lattice. The magnetic \(\mathrm{Ni}^{2 + }\) ions are surrounded by the octahedron of \(\mathrm{I}^{-}\) ions, and three \(\mathrm{NiI}_2\) layers as a repeating unit stack in a staggered fashion along the c axis. c, Atomic-resolution ADF-STEM image showing signature hexagonal patterns of rhombohedral stacking in few-layer \(\mathrm{NiI}_2\) crystals. The inset shows the corresponding FFT image. d, Circular polarization resolved Raman spectra of a trilayer \(\mathrm{NiI}_2\) device (Fig. 1a) at room temperature, excited by \(532\mathrm{nm}\) laser. “SM” indicates the interlayer shear mode of trilayer \(\mathrm{NiI}_2\) . e, The MCD spectra of trilayer \(\mathrm{NiI}_2\) at \(+3\mathrm{T}\) , \(0\mathrm{T}\) and -3T. MCD signals are sensitive to spin electronic transitions and magnetic moments in the electronic states. The MCD features are spin-sign dependent and reverse as magnetic field switch. The zero remanent MCD signals at \(\sim 2.3\mathrm{eV}\) at \(0\mathrm{T}\) suggest antiferromagnetic orders.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | Non-collinear antiferromagnetism in trilayer NiI₂ device. a, Polar RMCD maps upon a 2.33 eV laser with diffraction-limited spatial resolution (see Methods), collected at room temperature and selected magnetic field. b, Schematic of the spin textures of bimerons-like domains and corresponding zoom-in RMCD images (white dashed-line box in Fig. 2a). c, The polar RMCD signals along with the line sections of RMCD map (b). d, The RMCD curves sweeping between \(+3\mathrm{T}\) and \(-3\mathrm{T}\) at \(10\mathrm{K}\) , suggesting a non-collinear antiferromagnetism.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Existence of ferroelectric and anti-ferroelectric orders in trilayer NiI2 device. a, b, \(P - E\) and \(I - E\) loops at various frequencies from device 1 (D1). c, Corresponding \(I - E\) loops from Fig. 3b subtracted the current background. Two pairs of current peaks (FE-AFE and AFE-FE switching peaks) were obtained by Lorentz fitting. An evolution from FE to AFE was observed. d, Schematic of the spin spiral configurations with in-plane (x-y plane) spin cycloid in monolayer NiI2, showing a periodicity of \(7\times 1\) unit cells. e, Extreme case where the in-plane (x-y plane) cycloidal configuration tilts to x-z plane caused by interlayer exchange interactions, resulting in an out-of-plane ferroelectric polarization. f, Schematic of the spin spiral configurations with opposite \(\mathbf{q}\) in trilayer NiI2, showing the coexistence of ferroelectric and antiferroelectric.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | Magnetic control of ferroelectricity in trilayer NiI2 device. a-c, The \(P_r\) extracted from the \(P\) - \(E\) hysteresis loop is plotted as a function of out-of-plane magnetic field at different frequencies. The error bars are standard deviations of \(P_r\) . d, The magnetic control ratio \((P_r - P_{r0}) / P_{r0}\) are frequency dependent, where \(P_r\) and \(P_{r0}\) is remanent polarization in a magnetic field and without magnetic field, respectively. e, The \(I\) - \(E\) curves at different magnetic field. The decrease in the current peak accompanied by an increase in the coercive field due to the increased magnetic field is unambiguously observed. f, g, Fitting by KAI model for different magnetic field at 10 K, giving the switching time \(\tau\) . h, The \((\tau - \tau_0) / \tau_0\) as a function of magnetic field at 10 K, indicating a degree of magnetic control of switching time, where \(\tau\) and \(\tau_0\) is switching time in a magnetic field and without magnetic field, respectively.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Sl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f_det.mmd b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ae14f133915c78ace17be09f03f925aa71aecbe6 --- /dev/null +++ b/preprint/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f/preprint__00c089bc5362865e32d087a7de2c59c85939f78a3756c6991e0e05e515c9142f_det.mmd @@ -0,0 +1,302 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 925, 175]]<|/det|> +# Coexistence of ferroelectricity and antiferroelectricity in 2D van der Waals multiferroic + +<|ref|>text<|/ref|><|det|>[[44, 196, 280, 240]]<|/det|> +Bo Peng bo_peng@uestc.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 268, 912, 288]]<|/det|> +University of Electronic Science and Technology of China https://orcid.org/0000- 0001- 9411- 716X + +<|ref|>text<|/ref|><|det|>[[44, 293, 252, 334]]<|/det|> +Yangliu Wu 1450683589@qq.com + +<|ref|>text<|/ref|><|det|>[[44, 340, 551, 382]]<|/det|> +Haipeng Lu University of Electronic Science and Technology of China + +<|ref|>text<|/ref|><|det|>[[44, 387, 208, 427]]<|/det|> +Xiaocang Han Peking University + +<|ref|>text<|/ref|><|det|>[[44, 432, 868, 497]]<|/det|> +Chendi Yang Laboratory of Advanced Materials, Department of Materials Science and Shanghai Key Lab of Molecular Catalysis and Innovative Materials, Fudan University + +<|ref|>text<|/ref|><|det|>[[44, 502, 293, 542]]<|/det|> +Nanshu Liu Renmin University of China + +<|ref|>text<|/ref|><|det|>[[44, 548, 567, 589]]<|/det|> +Xiaoxu Zhao Peking University https://orcid.org/0000- 0001- 9746- 3770 + +<|ref|>text<|/ref|><|det|>[[44, 594, 931, 657]]<|/det|> +Liang Qiao School of Physics, University of Electronic Science and Technology of China https://orcid.org/0000- 0003- 2400- 2986 + +<|ref|>text<|/ref|><|det|>[[44, 664, 652, 705]]<|/det|> +Wei Ji Renmin University of China https://orcid.org/0000- 0001- 5249- 6624 + +<|ref|>text<|/ref|><|det|>[[44, 710, 562, 751]]<|/det|> +Renchao Che Fudan University https://orcid.org/0000- 0002- 6583- 7114 + +<|ref|>text<|/ref|><|det|>[[44, 756, 908, 799]]<|/det|> +Longjiang Deng University of Electronic Science and Technology of China https://orcid.org/0000- 0002- 8137- 6151 + +<|ref|>text<|/ref|><|det|>[[44, 838, 103, 856]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 876, 135, 894]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 914, 300, 933]]<|/det|> +Posted Date: April 16th, 2024 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 475, 64]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4229313/v1 + +<|ref|>text<|/ref|><|det|>[[42, 82, 916, 125]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 143, 535, 163]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 199, 932, 242]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 4th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53019- 5. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[148, 92, 848, 150]]<|/det|> +# Coexistence of ferroelectricity and antiferroelectricity in 2D van der Waals multiferroic + +<|ref|>text<|/ref|><|det|>[[148, 158, 848, 200]]<|/det|> +Yangliu Wu \(^{1}\) , Haipeng Lu \(^{1}\) , Xiaocang Han \(^{2}\) , Chendi Yang \(^{3}\) , Nanshu Liu \(^{5}\) , Xiaoxu Zhao \(^{2}\) , Liang Qiao \(^{4}\) , Wei Ji \(^{5}\) , Renchao Che \(^{3}\) , Longjiang Deng \(^{1*}\) and Bo Peng \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[147, 225, 852, 456]]<|/det|> +\(^{1}\) National Engineering Research Center of Electromagnetic Radiation Control Materials, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China \(^{2}\) School of Materials Science and Engineering, Peking University, Beijing 100871, China \(^{3}\) Laboratory of Advanced Materials, Department of Materials Science, Collaborative Innovation Center of Chemistry for Energy Materials(iChEM), Fudan University, Shanghai 200433, China \(^{4}\) School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China \(^{5}\) Beijing Key Laboratory of Optoelectronic Functional Materials & Micro- Nano Devices, Department of Physics, Renmin University of China, Beijing 100872, China \(^{*}\) To whom correspondence should be addressed. Email address: bo_peng@uestc.edu.cn; denglj@uestc.edu.cn + +<|ref|>sub_title<|/ref|><|det|>[[149, 467, 240, 485]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[147, 496, 851, 899]]<|/det|> +Multiferroic materials with a coexistence of ferroelectric and magnetic order have been intensively pursued to achieve the mutual control of electric and magnetic properties toward energy- efficient memory and logic devices. The breakthrough progress of 2D van der Waals magnet and ferroelectric encourages the exploration of low dimensional multiferroics, which holds the promise to understand inscrutable magnetoelectric coupling and invent advanced spintronic devices. However, confirming ferroelectricity with optical techniques is challenging on 2D materials, particularly in conjunction with antiferromagnetic orders in a single- layer multiferroic. The prerequisite of ferroelectric is the electrically switchable spontaneous electric polarizations, which must be proven through reliable and direct electrical measurements. Here we report the discovery of 2D vdW multiferroic with out- of- plane ferroelectric polarization in trilayer NiI₂ device, as revealed by scanning reflective magnetic circular dichroism microscopy and ferroelectric hysteresis loop. The evolutions of between ferroelectric and antiferroelectric phase have been unambiguously observed. Moreover, the magnetoelectric interaction is directly probed by external electromagnetic field control of the multiferroic domains switching. This work opens up opportunities for exploring new multiferroic orders and multiferroic physics at the limit of single or few atomic layers, and for creating advanced magnetoelectronic devices. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 85, 851, 299]]<|/det|> +Multiferroic materials with a coexistence of ferroelectric and magnetic orders has been diligently sought after for a long time to achieve the mutual control of electric and magnetic properties toward the energy- efficient memory and logic devices \(^{1 - 3}\) . But the two contrasting order parameters tend to be mutually exclusive in a single material \(^{4}\) . Nondisplacive mechanisms introduce a paradigm for constructing multiferroics beyond the traditional limits of mutual obstruction of the ferroelectric and magnetic orders \(^{5,6}\) . To date, the type I multiferroic BiFeO \(_3\) is the only known room- temperature single- phase multiferroic material. Alternatively, the helical magnetic orders break the spatial inversion symmetry and simultaneously lead to electric orders \(^{7,8}\) , giving rise to type- II multiferroics. The quest for a new single- phase multiferroic remains an open challenge. + +<|ref|>text<|/ref|><|det|>[[147, 300, 851, 467]]<|/det|> +The emergence of 2D vdW magnets and ferroelectrics has opened new avenues for exploring low- dimensional physics on magnetoelectric coupling \(^{9,10}\) . Diverse isolated vdW ferromagnets \(^{11 - 13}\) and ferroelectrics \(^{14,15}\) have enabled tantalizing opportunities to create 2D vdW spintronic devices with unprecedented performances at the limit of single or few atomic layers. Few of bulk crystals of transition- metal dihalides with a trigonal layered structure have been shown that the helical spin textures break inversion symmetries and induce an orthogonal ferroelectric polarization \(^{16,17}\) , but and definitive multiferroicity remains elusive at the limit of few atomic layers. + +<|ref|>text<|/ref|><|det|>[[147, 469, 851, 765]]<|/det|> +A recent work shows the possibility of discovery of type- II monolayer \(\mathrm{NiI_2}\) multiferroics using the optical measurements of second- harmonic- generation (SHG) and linear dichroism (LD) \(^{18}\) . Our work has pointed that all- optical characterizations are not sufficient to make a judgement of a few- and single- layer multiferroic at the presence of non- collinear and antiferromagnetic orders \(^{19}\) . The observed SHG and LD signals in few- layer \(\mathrm{NiI_2}\) originate from the magnetic- order- induced breaking of spatial- inversion \(^{19,20}\) . The prerequisite of ferroelectric polarization is the non- vanishing spontaneous electric polarizations, which must be proven through reliable and direct electrical measurements, such as polarization- and current- electric field (P- E and I- E) hysteresis loops. To date, 2D vdW multiferroic has not been directly uncovered at the limit of few layers. Here, we report fascinating vdW multiferroic with coexistence of ferroelectricity and antiferroelectricity in few layer \(\mathrm{NiI_2}\) based on magneto- optical- electric joint- measurements. In this 2D vdW multiferroics, an unprecedented magnetic control of switching dynamics of ferroelectric domain has been observed. + +<|ref|>sub_title<|/ref|><|det|>[[148, 775, 648, 796]]<|/det|> +## Non-collinear antiferromagnetism in trilayer \(\mathrm{NiI_2}\) + +<|ref|>text<|/ref|><|det|>[[148, 805, 851, 910]]<|/det|> +Due to the high reactivity of \(\mathrm{NiI_2}\) flakes, \(\mathrm{NiI_2}\) exfoliation and encapsulation by graphene and hexagonal boron nitride (hBN) flakes were carried out in a glove box (Fig. 1a and Supplementary Fig. 1). \(\mathrm{NiI_2}\) crystal shows rhombohedral structure with a repeating stack of three (I- Ni- I) layers, where Ni and I ions form a triangular lattice in each layer (Fig. 1b). The rhombohedral stacking has been atomically identified (Fig. 1c). The atom + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 85, 851, 295]]<|/det|> +arrangements of rhombohedral phase demonstrate signature hexagon- shaped periodic bright spots with equal contrast, validating the overlapping stack of I and Ni atoms along the \(c\) axis. The ADF- STEM and fast Fourier transform (FFT) show an interplanar spacing of \(1.9 \mathring{\mathrm{A}}\) , consistent with the (110) lattice plane of rhombohedral \(\mathrm{NiI}_2\) crystal. Circularly polarized Raman spectra in the parallel \((\sigma + / \sigma +\) and \(\sigma - / \sigma -\) ) configuration show only two distinct peaks in the \(\mathrm{NiI}_2\) device (Fig. 1d). The peak at \(\sim 124.7 \mathrm{cm}^{- 1}\) is assigned to the \(\mathrm{A_g}\) phonon modes \(^{22}\) , and this polarization behavior is consistent with Raman tensor analysis for the rhombohedral structure of \(\mathrm{NiI}_2^{23}\) . The Raman feature at \(\sim 20 \mathrm{cm}^{- 1}\) is assigned to the interlayer shear mode (SM), which suggests that the \(\mathrm{NiI}_2\) is trilayer \(^{20}\) . + +<|ref|>text<|/ref|><|det|>[[147, 299, 851, 530]]<|/det|> +For optimal optical response and sensitivity to probe the magnetic properties, the photon energy should be chosen near the absorption edge \(^{11,24}\) . Therefore, we first studied white- light magnetic circular dichroism (MCD) spectra of a trilayer \(\mathrm{NiI}_2\) device as a function of magnetic field perpendicular to the sample plane at \(10 \mathrm{K}\) (see Methods for details) \(^{25}\) . There is a strong peak near \(2.3 \mathrm{eV}\) along with two weak features around \(1.85 \mathrm{eV}\) and \(1.6 \mathrm{eV}\) (Fig. 1e). By means of ligand- field theory, the peaks are attributed to the absorption transitions of \(p\) - \(d\) exciton states \(^{26}\) . A pair of opposite MCD peaks with magnetic field manifestly appears at \(2.3 \mathrm{eV}\) , suggesting strong magneto- optical resonance. When the magnetic field is switched, MCD features is consistently reversed, and zero remanent MCD signal at \(\sim 2.3 \mathrm{eV}\) is distinctly observed at \(0 \mathrm{T}\) , indicating antiferromagnetic orders at \(10 \mathrm{K}\) . + +<|ref|>text<|/ref|><|det|>[[147, 534, 851, 914]]<|/det|> +To further validate the magnetic order, the scanning RMCD microscope was used to image and measure the magnetic domains of the as- exfoliated trilayer \(\mathrm{NiI}_2\) . The polar RMCD imaging is a reliable and powerful tool to unveil the 2D magnetism in the micro scale, and the RMCD intensity is proportional to the out- of- plane magnetization \(^{24}\) . All magneto- optical measurements were carried out using a \(2.33 \mathrm{eV}\) laser with optimal detection sensitivity (see Methods for details). Figure 2a shows RMCD maps of a trilayer \(\mathrm{NiI}_2\) sweeping between - 0.75 T and +0.75 T at \(10 \mathrm{K}\) . Remarkably, many micrometer- sized bimeron- like domains are observed in trilayer and another few- layer \(\mathrm{NiI}_2\) across the entire range of sweeping magnetic field \(^{27}\) . The spin- up and spin- down domains exist in pairs (Fig. 2a and Supplementary Fig. 2). One typical bimeron- like domains in trilayer \(\mathrm{NiI}_2\) at \(0 \mathrm{T}\) and \(10 \mathrm{K}\) are shown in Fig. 2b. The RMCD signals in each bimeron- like domain display opposite sign and nearly equal intensities. The magnetic moments point upwards or downwards in the core region and gradually decrease away from the core, and approaches zero near the perimeter (Fig. 2c). This magnetic moment distribution possibly indicates a pair of topological spin meron and antimeron with opposite chirality in a cycloid ground state \(^{28,29}\) . The bimeron- like magnetization textures remain robust in all magnetic field, indicating the bimeron- like domains are robust. The high stability of the bimeron- like magnetic domains probably + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 86, 851, 168]]<|/det|> +originate from the topological protection, which also contributes to the preservation of magnetization even if upon a reversal magnetic field of 0.75 T. The formation of bimeron- like magnetic domains may be related to the localized stress at the interface. But further deep studies must be done to reveal the exact physical mechanism. + +<|ref|>text<|/ref|><|det|>[[148, 172, 851, 403]]<|/det|> +Fig. 2d shows the RMCD loops of the trilayer \(\mathrm{NiI}_2\) sweeping between \(+3\mathrm{T}\) and \(- 3\mathrm{T}\) at \(10\mathrm{K}\) . The RMCD loops show a highly nonlinear behavior with magnetic field and plateau behaviors for the out- of- plane magnetization. The RMCD intensity near \(0\mathrm{T}\) is suppressed and approaches zero, suggesting the vanishing remnant magnetization, which indicates a compensation of the out- of- plane magnetization and non- collinear AFM orders in the trilayer \(\mathrm{NiI}_2^{30}\) . And the gradual increases of the RMCD signal are observed with increasing magnetic field between \(\pm 1.2\) and \(\pm 2.6\mathrm{T}\) , suggesting a spin- flop process. The spin- flop behaviors of the magnetization curve imply that the interlayer antiferromagnetic coupling of the non- collinear spins is complicated. Similar magnetic hysteresis loops have been demonstrated in another few- layer \(\mathrm{NiI}_2\) , which show definite non- collinear AFM orders in the few- layer \(\mathrm{NiI}_2\) (Supplementary Fig. 2b). + +<|ref|>sub_title<|/ref|><|det|>[[148, 413, 530, 433]]<|/det|> +## Ferroelectricity in trilayer \(\mathrm{NiI}_2\) device + +<|ref|>text<|/ref|><|det|>[[147, 444, 851, 909]]<|/det|> +To determine ferroelectricity in few- layer \(\mathrm{NiI}_2\) device, we performed the frequency- dependent measurement of electric polarization via \(I\) - \(E\) and \(P\) - \(E\) hysteresis loops, which allows an accurate estimation of the electric polarization. We fabricated two heterostructure devices of graphene/hBN/ \(\mathrm{NiI}_2\) /graphene/hBN (Fig. 1a and Supplementary Fig. 1). The hBN flake was used as an excellent insulating layer to prevent large leakage current and guarantee the detections of ferroelectric (FE) features \(^{31,32}\) (Supplementary Fig. 3). The hBN insulator shows a linear \(P\) - \(E\) behavior and a rectangle- shaped \(I\) - \(E\) loops (Supplementary Fig. 4), indicating excellent insulativity for ferroelectric hysteresis measurements (see Methods for details) \(^{33,34}\) . The frequency- dependent \(I\) - \(E\) and \(P\) - \(E\) loops at \(10\mathrm{K}\) are shown in Fig. 3, and the forward and backward scans of the electric polarization as a function of electric field show characteristic ferroelectric \(I\) - \(E\) and \(P\) - \(E\) hysteresis. Strikingly, a characteristic double- hysteresis loop of antiferroelectric (AFE) polarization emerges accompanied with decreasing remanent polarization \((P_r)\) . More importantly, a pair of opposite single peaks of switching current \((I)\) are observed when sweeping voltage at \(6.7\mathrm{Hz}\) , which is attribute to charge displacement and implies two stable states with inverse polarity (Fig. 3b and c). Whereas two pair of opposite bimodal peaks are observed when sweeping voltage at \(1.3\mathrm{Hz}\) , which is attribute to AFE- FE and FE- AFE transitions under electric field sweeping (Fig. 3c) \(^{35}\) . This suggests an evolution from FE to AFE polarization with frequency is observed \(^{36,37}\) , exhibiting the decisive evidence for coexistence of ferroelectric and antiferroelectric \(^{38,39}\) . This comprehensive frequency- dependent evolution behaviors also confirm the coexistence of FE and AFE in another a few layers + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 87, 380, 104]]<|/det|> +\(\mathrm{NiI}_2\) (Supplementary Fig. 5). + +<|ref|>text<|/ref|><|det|>[[147, 107, 852, 253]]<|/det|> +The type- II multiferroicity has been demonstrated in the bulk \(\mathrm{NiI}_2\) . However, the multiferroic identification for few- layer \(\mathrm{NiI}_2\) remains challenging and elusive. All- optical methods are unreliable to make a judgement of a few- and single- layer multiferroic at the presence of non- collinear and antiferromagnetic orders19. The bulk \(\mathrm{NiI}_2\) displays a helimagnetic state below critical temperature16,17. From symmetry considerations and a Ginzburg- Landau perspective40,41, the helimagnetic state allows for the emergence of a ferroelectric polarization associated to the form: + +<|ref|>equation<|/ref|><|det|>[[372, 257, 538, 275]]<|/det|> +\[\mathbf{P} = \gamma \mathbf{e}\times \mathbf{q} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[147, 277, 852, 916]]<|/det|> +where \(\mathbf{P}\) is the electric polarization, \(\mathbf{e}\) is the spin rotation axis, \(\mathbf{q}\) is the spin propagation vector of the spin spiral, and \(\gamma\) is a scalar parameter dependence with spin- orbit coupling. For monolayer \(\mathrm{NiI}_2\) , the helimagnetic order can be modeled with a 7axa supercell and an in- plane (x- y plane) spin cycloid, and the spin propagation vector \(\mathbf{q}\) is displayed along the [210] direction (in lattice vector units)42, as shown in in Fig. 3d. Thus, the in- plane (x- y plane) spin cycloid induces the in- plane electric polarization along the [010] direction (Fig. 3d). Actually, theoretical calculations have determined that the \(\mathbf{q}\) - vector in multi- layer and bulk \(\mathrm{NiI}_2\) is a consequence of the competition between magnetic exchange interactions between magnetic atoms42,43. In particular, intralayer ferromagnetic first- neighbor, intralayer antiferromagnetic third neighbor, and interlayer antiferromagnetic second- neighbor magnetic exchange interactions are the most relevant. In the monolayer limit, there are no interlayer interactions, hence the \(\mathbf{q}\) - vector is in- plane and determined by the competition between intralayer exchange interactions. For a trilayer \(\mathrm{NiI}_2\) , the \(\mathbf{q}\) - vector is modulated not only by intralayer exchange interactions but also by interlayer exchange interactions. Assuming that interlayer exchange interactions cause the tilting out- of- plane cycloidal spin configuration from in- plane (x- y plane) configuration (Fig. 3d), the \(\mathbf{e}\) - vector is no longer parallel to the z- axis, leading to an out- of- plane ferroelectric polarization component. Figure 3e illustrates the extreme case where the in- plane (x- y plane) cycloidal configuration tilts to x- z plane, resulting in an out- of- plane ferroelectric polarization. This scenario suggests the observed out- of- plane ferroelectric polarization in the trilayer \(\mathrm{NiI}_2\) device, but the precise mechanism remains to be further studied in the future. In particular, equation (1) shows that two spin spiral configurations with \(\mathbf{q}_1 = \mathbf{q}\) and \(\mathbf{q}_2 = -\mathbf{q}\) will give rise to opposite electric polarizations \(\mathbf{P} = -\mathbf{P}\) . The first principles calculations in spin configuration with both \(\mathbf{q}\) and \(-\mathbf{q}\) are energetically equivalent, and therefore show same energies with and without spin- orbit coupling42. Thus, the emergence of opposite electric dipoles can be directly observed in the total electronic density of the system. The energy of spin cycloidal configurations with positive and negative \(\mathbf{q}\) - vectors (positive and negative ferroelectric polarization \(\mathbf{P}\) ) is degenerate, which approve the coexistence of ferroelectric and antiferroelectric (Fig. 3f), consistent with the observed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 87, 664, 103]]<|/det|> +coexistence of ferroelectric and antiferroelectric in trilayer \(\mathrm{NiI_2}\) . + +<|ref|>sub_title<|/ref|><|det|>[[148, 114, 504, 134]]<|/det|> +## Magnetic control of ferroelectricity + +<|ref|>text<|/ref|><|det|>[[147, 144, 851, 911]]<|/det|> +To reveal the magnetoelectric coupling effect, we studied the magnetic control of ferroelectric properties in the trilayer \(\mathrm{NiI_2}\) device, as shown in Fig. 4. The \(P_r\) extracted from the \(P\) - \(E\) hysteresis loop is plotted as a function of out- of- plane magnetic field at different frequencies (Fig. 4a- c). The magnetic field causes a decrease in residual polarization at different frequencies (Fig. 4a- c and Supplementary Fig. 6), and the magnetic control of \(P_r\) shows frequency dependence of applied electric field (Fig. 4d). The magnetic control ratio reaches to \(\sim 7\%\) by detuning the frequency (24.5 Hz) at 7 T, which is remarkable feature of multiferroic. To better understand the magnetic control behavior, we briefly discuss the possible mechanism that leads to the decrease in \(P_r\) caused by the magnetic field from a microscopic perspective. We only discuss ferroelectric polarization flops in the model of spiral magnets40. In zero fields spins rotate in the easy x- z plane, so that the spin rotation axis \(\mathbf{e}\) is parallel to the y axis, and for \(\mathbf{q} / / \mathbf{x}\) - y plane, \(\mathbf{P} / / \mathbf{z}\) (Supplementary Fig. 7a and 7b). However, magnetic field in the z direction favors the rotation of spins in the x- y plane (Supplementary Fig. 7c and 7d), so that the spin rotation axis \(\mathbf{e}\) is parallel to the z axis, in which case, \(\mathbf{P} / / \mathbf{x}\) - y plane40. In short, applying a magnetic field parallel to the z- axis causes the spin rotation plane to tilt from the x- z plane to the x- y plane, and the corresponding ferroelectric polarization flops from the out- of- plane direction to the in- plane direction. Therefore, an out- of- plane magnetic field leads to a decrease of ferroelectric polarization in the out- of- plane direction, which is consistent with the observed decrease in \(P_r\) with increasing magnetic field. Furthermore, the decrease in the current peak accompanied by an increase in the coercive electric field due to the increased magnetic field is unambiguously observed (Fig. 4e and Supplementary Fig. 8). This is because the out- of- plane magnetic field causes the spin rotation plane to tilt from the x- z plane to the x- y plane, and the corresponding easy axis of ferroelectric polarization flops from the out- of- plane direction to the in- plane direction. The shifts of current peaks induced by ferroelectric switching vary with the magnetic field, but the background current remains constant, excluding the magnetoresistance effects (Fig. 4e and Supplementary Fig. 8). Finally, the switching time of ferroelectric domain under different magnetic fields at 10 K is calculated by KAI model44 (Fig. 4f and 4g; Part A and B). The switching time \(\tau\) increase as magnetic field increase, which signifies an even symmetry with magnetic field (Fig. 4h), consistent with the above mechanisms. At 10 K, the switching time \(\tau\) , leading to a maximum enhancement of switching time by 20% (-7 T). This observation of robust control of ferroelectric properties by magnetic field, pointing to the potential use of few- layer \(\mathrm{NiI_2}\) as a research platform for studying the magneto- electric coupling physics in the two- dimensional limit and for fabricating advanced nano- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[149, 87, 350, 103]]<|/det|> +magnetoelectric devices. + +<|ref|>text<|/ref|><|det|>[[148, 107, 851, 253]]<|/det|> +In summary, we report a 2D vdW single- phase multiferroic \(\mathrm{NiI_2}\) few- layer crystal. We observed strong evidences for the coexistence of ferroelectric and non- collinear antiferromagnetism order via RMCD, \(P\) - \(E\) and \(I\) - \(E\) hysteresis loop. hysteresis loop. We achieve unprecedented magnetic control of ferroelectric properties in the \(\mathrm{NiI_2}\) trilayer. We envision that the 2D vdW single- phase multiferroic \(\mathrm{NiI_2}\) will provide numerous opportunities for exploring fundamental low- dimensional physics, and will introduce a paradigm shift for engineering new ultra- compact magnetoelectric devices. + +<|ref|>sub_title<|/ref|><|det|>[[148, 285, 240, 304]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[148, 315, 315, 331]]<|/det|> +## Sample fabrication + +<|ref|>text<|/ref|><|det|>[[147, 336, 851, 504]]<|/det|> +\(\mathrm{NiI_2}\) flakes were mechanically exfoliated from bulk crystals via PDMS films in a glovebox, which were synthesized by chemical vapor transport method from elemental precursors with molar ratio \(\mathrm{Ni:I} = 1:2\) . All exfoliated hBN, \(\mathrm{NiI_2}\) and graphene flakes were transferred onto pre- patterned Au electrodes on \(\mathrm{SiO_2 / Si}\) substrates one by one to create heterostructure in glovebox, which were further in- situ loaded into a microscopy optical cryostat for magneto- optical- electric joint- measurement. The whole process of \(\mathrm{NiI_2}\) sample fabrications and magneto- optical- electric measurements were kept out of atmosphere. + +<|ref|>sub_title<|/ref|><|det|>[[147, 529, 530, 546]]<|/det|> +## Magneto-optical-electric joint-measurement + +<|ref|>text<|/ref|><|det|>[[147, 550, 851, 715]]<|/det|> +The polar RMCD, white- light MCD, Raman measurements and ferroelectric \(P\) - \(E\) and \(I\) - \(E\) measurements were performed on a powerful magneto- optical- electric joint- measurement scanning imaging system (MOEJSI) \(^{19}\) , with a spatial resolution reaching diffraction limit. The MOEJSI system was built based on a Witec Alpha 300R Plus low- wavenumber confocal Raman microscope, integrated with a closed cycle superconducting magnet (7 T) with a room temperature bore and a closed cycle cryogen- free microscopy optical cryostat (10 K) with a specially designed snout sample mount and electronic transport measurement assemblies. + +<|ref|>text<|/ref|><|det|>[[147, 719, 851, 866]]<|/det|> +The Raman signals were recorded by the Witec Alpha 300R Plus low- wavenumber confocal Raman microscope system, including a spectrometer (150, 600 and 1800/mm) and a TE- cooling Andor CCD. A 532 nm laser of \(\sim 0.2 \mathrm{mW}\) is parallel to the X- axis \((0^{\circ})\) and focused onto samples by a long working distance \(50 \times\) objective \((\mathrm{NA} = 0.55, \mathrm{Zeiss})\) after passing through a quarter- wave plate \((1 / 4 \lambda)\) . The circular polarization resolved Raman signals passed through the same \(1 / 4 \lambda\) waveplate and a linear polarizer, obtained by the spectrometer \((1800 / \mathrm{mm})\) and the CCD. + +<|ref|>text<|/ref|><|det|>[[148, 869, 850, 908]]<|/det|> +For white- light MCD measurements, white light with Kohler illumination from Witec Alpha 300R Plus microscope was linearly polarized at 0o by a visible wire grid + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 85, 851, 233]]<|/det|> +polarizer, passed through an achromatic quarter- wave \((1 / 4\lambda)\) plate and focused onto samples by a long working distance \(50\times\) objective (Zeiss, \(\mathrm{NA} = 0.55\) ). The right- handed and left- handed circularly polarized white light was obtained by rotating \(1 / 4\lambda\) waveplate at \(+45^{\circ}\) and \(- 45^{\circ}\) . The white- light spectra were recorded by the Witec Alpha 300R Plus confocal Raman microscope system (spectrometer, \(150\mathrm{mm}\) ). The absorption spectra of right- handed and left- handed circularly polarized light in different magnetic field can be obtained as the previous work \(^{25}\) , giving corresponding MCD spectra. + +<|ref|>text<|/ref|><|det|>[[147, 235, 851, 658]]<|/det|> +For polar RMCD measurements, a free- space \(532\mathrm{nm}\) laser \((2.33\mathrm{eV})\) of \(\sim 2\mu \mathrm{W}\) modulated by photoelastic modulator (PEM, \(50\mathrm{KHz}\) ) was reflected by a non- polarizing beamsplitter cube \(\mathrm{(R / T = 30 / 70)}\) and then directly focused onto samples by a long working distance \(50\times\) objective \(\mathrm{(NA = 0.55}\) , Zeiss), with a diffraction limit spatial resolution of \(\sim 590\mathrm{nm}\) . The reflected beam which was collected by the same objective passed through the same non- polarizing beamsplitter cube and was detected by a photomultiplier (PMT), which was coupled with lock- in amplifier, Witec scanning imaging system, superconducting magnet, voltage source meter and ferroelectric tester. Ferroelectric \(P - E\) and \(I - E\) hysteresis loop of a \(\mathrm{NiI}_2\) device of \(\mathrm{Gr / hBN / NiI_2 / Gr}\) were measured by classical ferroelectric measurements and directly recorded by ferroelectric tester (Precision Premier II: Hysteresis measurement), which were contacted with the top and bottom graphene electrodes by patterned Au electrodes (Fig. 1a) through the electronic assemblies of the microscopy optical cryostat. The mechanism of ferroelectric measurement has been given by previous work \(^{45}\) . The detected signals include two components: a ferroelectric term of \(\mathrm{NiI}_2\) (2PrA) and a linear non- ferroelectric term of hBN insulator \((\sigma \mathrm{EAt})\) , \(\mathrm{Q} = \mathrm{QNiI} + \mathrm{QBN} = 2\mathrm{PrA} + \sigma \mathrm{EAt}\) . If only hBN insulator, a linear P- E loop take place, consistent with our experimental results of hBN flake (Supplementary Fig. 4). The linear hBN background have no effect on the ferroelectric features, and hBN flakes as excellent insulator suppress and overcome the leakage current, which for guarantee the detections of \(\mathrm{NiI}_2\) ferroelectric features \(^{31 - 34}\) . + +<|ref|>sub_title<|/ref|><|det|>[[148, 683, 528, 700]]<|/det|> +## STEM Imaging, Processing, and Simulation + +<|ref|>text<|/ref|><|det|>[[147, 704, 851, 891]]<|/det|> +Atomic- resolution ADF- STEM imaging was performed on an aberration- corrected JEOL ARM 200F microscope equipped with a cold field- emission gun operating at 80 kV. The convergence semiangle of the probe was around 30 mrad. Image simulations were performed with the Prismatic package, assuming an aberration- free probe with a probe size of approximately \(1\mathrm{\AA}\) . The convergence semiangle and accelerating voltage were in line with the experiments. The collection angle for ADF imaging was between 81 and 228 mrad. ADF- STEM images were filtered by Gaussian filters, and the positions of atomic columns were located by finding the local maxima of the filtered series. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[148, 93, 317, 113]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[148, 123, 850, 163]]<|/det|> +The data that support the findings of this study are available from the corresponding authors upon reasonable request. 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Lett. 110, 177201, (2013).29 Xu, C. et al. Electric- field switching of magnetic topological charge in type- I multiferroics. Phys. Rev. Lett. 125, 037203, (2020).30 Xie, H. et al. Evidence of non- collinear spin texture in magnetic moiré superlattices. Nature Physics 19, 1150- 1155, (2023).31 Knobloch, T. et al. The performance limits of hexagonal boron nitride as an insulator for scaled CMOS devices based on two- dimensional materials. Nat. Electron. 4, 98- 108, (2021).32 Yang, T. H. et al. Ferroelectric transistors based on shear- transformation- mediated rhombohedral- stacked molybdenum disulfide. Nat. Electron. 7, 29- 38 (2024).33 Park, J. Y. et al. Revival of Ferroelectric Memories Based on Emerging Fluorite- Structured Ferroelectrics. Adv. Mater. 35, 2204904, (2023).34 Kim, Y., Min, K. K., Yu, J., Kwon, D. & Park, B.- G. Lamination method for improved polarization- leakage current relation in \(\mathrm{HfO_2}\) - based metal/ferroelectric/insulator/semiconductor structure. Semicond. Sci. Technol. 37, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 87, 283, 103]]<|/det|> +045001, (2022). + +<|ref|>text<|/ref|><|det|>[[147, 108, 850, 147]]<|/det|> +35 Wu, Z. et al. Discovery of an above- room- temperature antiferroelectric in twodimensional hybrid perovskite. J. Am. Chem. Soc. 141, 3812- 3816, (2019). + +<|ref|>text<|/ref|><|det|>[[147, 150, 850, 189]]<|/det|> +36 Park, M. H. et al. Ferroelectricity and antiferroelectricity of doped thin \(\mathrm{HfO_2}\) - based films. Adv. Mater. 27, 1811- 1831, (2015). + +<|ref|>text<|/ref|><|det|>[[147, 193, 850, 231]]<|/det|> +37 Müller, J. et al. Ferroelectricity in Simple Binary \(\mathrm{ZrO_2}\) and \(\mathrm{HfO_2}\) . Nano Lett. 12, 4318- 4323, (2012). + +<|ref|>text<|/ref|><|det|>[[147, 235, 850, 274]]<|/det|> +38 Ko, K. et al. Operando electron microscopy investigation of polar domain dynamics in twisted van der Waals homobilayers. Nat. Mater. 22, 992- 998 (2023). + +<|ref|>text<|/ref|><|det|>[[147, 277, 850, 316]]<|/det|> +39 Xu, B., Paillard, C., Dkhil, B. & Bellaiche, L. Pinched hysteresis loop in defect- free ferroelectric materials. Phys. Rev. B 94, 140101 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 320, 850, 358]]<|/det|> +40 Mostovoy, M. Ferroelectricity in spiral magnets. Phys. Rev. Lett. 96, 067601, (2006). + +<|ref|>text<|/ref|><|det|>[[147, 363, 850, 402]]<|/det|> +41 Hohenberg, P. C. et al. An introduction to the Ginzburg- Landau theory of phase transitions and nonequilibrium patterns Phys. Rep. 572, 1- 42, (2015). + +<|ref|>text<|/ref|><|det|>[[147, 405, 850, 444]]<|/det|> +42 Fumega, A. O. et al. Microscopic origin of multiferroic order in monolayer \(\mathrm{NiI_2}\) . 2D Mater. 9, 025010, (2022). + +<|ref|>text<|/ref|><|det|>[[147, 448, 850, 486]]<|/det|> +43 Riedl, K. et al. Microscopic origin of magnetism in monolayer \(3d\) transition metal dihalides. Phys. Rev. B 106, 035156, (2022). + +<|ref|>text<|/ref|><|det|>[[147, 490, 850, 529]]<|/det|> +44 Zhao, D., Katsouras, I., Asadi, K., Blom, P. W. M. & de Leeuw, D. M. Switching dynamics in ferroelectric P(VDF- TrFE) thin films. Phys. Rev. B 92, 214115, (2015). + +<|ref|>text<|/ref|><|det|>[[147, 532, 850, 571]]<|/det|> +45 Scott, J. F. et al. Ferroelectrics go bananas. J. Phys.: Condens. Matter 20, 021001, (2008). + +<|ref|>text<|/ref|><|det|>[[147, 575, 848, 614]]<|/det|> +46 Golla, D. et al. Optical thickness determination of hexagonal boron nitride flakes. Appl. Phys. Lett. 102, 161906, (2013). + +<|ref|>sub_title<|/ref|><|det|>[[149, 647, 335, 666]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[147, 677, 852, 909]]<|/det|> +B.P. and L.D. acknowledge support from National Science Foundation of China (52021001). B.P. acknowledge support from National Science Foundation of China (62250073). R.C.C. acknowledge support from National Science Foundation of China (52231007). H.L. acknowledge support from National Science Foundation of China (51972046). L.D. acknowledge support from Sichuan Provincial Science and Technology Department (Grant No. 99203070). L.D. acknowledge support from Sichuan Provincial Science and Technology Department (Grant No. 9920 3070). L.Q. acknowledge support from National Science Foundation of China (520720591 and 11774044). J.W. thanks the National Natural Science Foundation of China (Grant No. 11974422), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB30000000). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[149, 93, 365, 112]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[148, 123, 851, 248]]<|/det|> +B.P conceived the project. Y.W. prepared the samples and performed the magneto- optical- electric joint- measurements and Raman measurements assisted by B.P., and performed the ferroelectric measurements assisted by L.Q., and analyzed and interpreted the results assisted by H.L., N. L., W.J., L.D. and B.P.. C. Y, R.C, X.X. and X.H. performed the STEM measurements. Y.W. and B.P. wrote the paper with input from all authors. All authors discussed the results. + +<|ref|>sub_title<|/ref|><|det|>[[149, 280, 353, 300]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[149, 310, 502, 327]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[149, 359, 385, 378]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[147, 388, 810, 408]]<|/det|> +Supplementary information is available for this paper at xxx (will be provided). + +<|ref|>text<|/ref|><|det|>[[149, 410, 750, 428]]<|/det|> +Correspondence and requests for materials should be addressed to B.P. + +<|ref|>text<|/ref|><|det|>[[148, 431, 850, 470]]<|/det|> +Reprints and permission information is available online at http://www.nature.com/reprints. + +<|ref|>text<|/ref|><|det|>[[148, 474, 844, 514]]<|/det|> +Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[177, 88, 840, 504]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 512, 851, 787]]<|/det|> +
Fig. 1 | Crystal structure, MCD measurements of trilayer \(\mathrm{NiI}_2\) at room temperature. a, Schematic of trilayer \(\mathrm{NiI}_2\) sandwiched between graphene and hBN. b, View of the in-plane and out-of-plane atomic lattice. The magnetic \(\mathrm{Ni}^{2 + }\) ions are surrounded by the octahedron of \(\mathrm{I}^{-}\) ions, and three \(\mathrm{NiI}_2\) layers as a repeating unit stack in a staggered fashion along the c axis. c, Atomic-resolution ADF-STEM image showing signature hexagonal patterns of rhombohedral stacking in few-layer \(\mathrm{NiI}_2\) crystals. The inset shows the corresponding FFT image. d, Circular polarization resolved Raman spectra of a trilayer \(\mathrm{NiI}_2\) device (Fig. 1a) at room temperature, excited by \(532\mathrm{nm}\) laser. “SM” indicates the interlayer shear mode of trilayer \(\mathrm{NiI}_2\) . e, The MCD spectra of trilayer \(\mathrm{NiI}_2\) at \(+3\mathrm{T}\) , \(0\mathrm{T}\) and -3T. MCD signals are sensitive to spin electronic transitions and magnetic moments in the electronic states. The MCD features are spin-sign dependent and reverse as magnetic field switch. The zero remanent MCD signals at \(\sim 2.3\mathrm{eV}\) at \(0\mathrm{T}\) suggest antiferromagnetic orders.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[160, 87, 833, 298]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 308, 852, 455]]<|/det|> +
Fig. 2 | Non-collinear antiferromagnetism in trilayer NiI₂ device. a, Polar RMCD maps upon a 2.33 eV laser with diffraction-limited spatial resolution (see Methods), collected at room temperature and selected magnetic field. b, Schematic of the spin textures of bimerons-like domains and corresponding zoom-in RMCD images (white dashed-line box in Fig. 2a). c, The polar RMCD signals along with the line sections of RMCD map (b). d, The RMCD curves sweeping between \(+3\mathrm{T}\) and \(-3\mathrm{T}\) at \(10\mathrm{K}\) , suggesting a non-collinear antiferromagnetism.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[186, 78, 816, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 680, 852, 910]]<|/det|> +
Fig. 3 | Existence of ferroelectric and anti-ferroelectric orders in trilayer NiI2 device. a, b, \(P - E\) and \(I - E\) loops at various frequencies from device 1 (D1). c, Corresponding \(I - E\) loops from Fig. 3b subtracted the current background. Two pairs of current peaks (FE-AFE and AFE-FE switching peaks) were obtained by Lorentz fitting. An evolution from FE to AFE was observed. d, Schematic of the spin spiral configurations with in-plane (x-y plane) spin cycloid in monolayer NiI2, showing a periodicity of \(7\times 1\) unit cells. e, Extreme case where the in-plane (x-y plane) cycloidal configuration tilts to x-z plane caused by interlayer exchange interactions, resulting in an out-of-plane ferroelectric polarization. f, Schematic of the spin spiral configurations with opposite \(\mathbf{q}\) in trilayer NiI2, showing the coexistence of ferroelectric and antiferroelectric.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[161, 81, 828, 505]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 511, 852, 744]]<|/det|> +
Fig. 4 | Magnetic control of ferroelectricity in trilayer NiI2 device. a-c, The \(P_r\) extracted from the \(P\) - \(E\) hysteresis loop is plotted as a function of out-of-plane magnetic field at different frequencies. The error bars are standard deviations of \(P_r\) . d, The magnetic control ratio \((P_r - P_{r0}) / P_{r0}\) are frequency dependent, where \(P_r\) and \(P_{r0}\) is remanent polarization in a magnetic field and without magnetic field, respectively. e, The \(I\) - \(E\) curves at different magnetic field. The decrease in the current peak accompanied by an increase in the coercive field due to the increased magnetic field is unambiguously observed. f, g, Fitting by KAI model for different magnetic field at 10 K, giving the switching time \(\tau\) . h, The \((\tau - \tau_0) / \tau_0\) as a function of magnetic field at 10 K, indicating a degree of magnetic control of switching time, where \(\tau\) and \(\tau_0\) is switching time in a magnetic field and without magnetic field, respectively.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 137, 149]]<|/det|> +- Sl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/images_list.json b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..87904a90813e9ccd67d2a16d236657aa08a62857 --- /dev/null +++ b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "FIG. 1. Scheme for our Generator-Enhanced Optimization (GEO) strategy. The GEO framework leverages generative models to utilize previous samples coming from any quantum or classical solver. The trained quantum or classical generator is responsible for proposing candidate solutions which might be out of reach for conventional solvers. This seed data set (step 0) consists of observation bitstrings \\(\\{\\pmb{x}^{(i)}\\}_{\\mathrm{seed}}\\) and their respective costs \\(\\{\\sigma^{(i)}\\}_{\\mathrm{seed}}\\) . To give more weight to samples with low cost, the seed samples and their costs are used to construct a softmax function which serves as a surrogate to the cost function but in probabilistic domain. This softmax surrogate also serves as a prior distribution from which the training set samples are withdrawn to train the generative model (steps 1-3). As shown in the figure between steps 1 and 2, training samples from the softmax surrogate are biased favoring those with low cost value. For the work presented here, we implemented a tensor-network (TN)-based generative model. Therefore, we refer to this quantum-inspired instantiation of GEO as TN-GEO. Other families of generative models from classical, quantum, or hybrid quantum-classical can be explored as expounded in the main text. The quantum-inspired generator corresponds to a tensor-network Born machine (TNBM) model which is used to capture the main features in the training data, and to propose new solution candidates which are subsequently post selected before their costs \\(\\{\\sigma^{(i)}\\}_{\\mathrm{new}}\\) are evaluated (steps 4-6). The new set is merged with the seed data set (step 7) to form an updated seed data set (step 8) which is to be used in the next iteration of the algorithm. More algorithmic details for the two TN-GEO strategies proposed here, as a booster or as a stand-alone solver, can be found in the main text and in A5 and A6 respectively.", + "footnote": [], + "bbox": [ + [ + 91, + 65, + 910, + 372 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2. TN-GEO as a booster. Top: Strategies 1-3 correspond to the current options a user might explore when solving a combinatorial optimization problem with a suite of classical optimizers such as simulated annealing (SA), parallel tempering (PT), generic algorithms (GA), among others. In strategy 1, the user would use its computational budget with a preferred solver. In strategy 2-4 the user would inspect intermediate results and decide whether to keep trying with the same solver (strategy 2), try a new solver or a new setting of the same solver used to obtain the intermediate results (strategy 3), or, as proposed here, to use the acquired data to train a quantum or quantum-inspired generative model within a GEO framework such as TN-GEO (strategy 4). Bottom: Results showing the relative TN-GEO enhancement from TN-GEO over either strategy 1 or strategy 2. Positive values indicate runs where TN-GEO outperformed the respective classical strategies (see Eq. 1). The data represents bootstrapped medians from 20 independent runs of the experiments and error bars correspond to the 95% confidence intervals. The two instances presented here correspond to portfolio optimization instances where all the assets in the S&P 500 market index where included \\((N = 500)\\) , under two different cardinality constraints \\(\\kappa\\) . This cardinality constraint indicate the number of assets that can be included at a time in valid portfolios, yielding a search space of \\(M = \\binom{N}{\\kappa}\\) , with \\(M \\sim 10^{69}\\) portfolios candidates for \\(\\kappa = 50\\) .", + "footnote": [], + "bbox": [ + [ + 90, + 81, + 480, + 460 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3. Generalization capabilities of our quantum-inspired generative model. Left panel corresponds to an investment universe with \\(N = 50\\) assets while the right panel corresponds to one with \\(N = 100\\) assets. The blue histogram represents the number of observations or portfolios obtained from the classical solver (seed data set). In orange we represent samples coming from our quantum generative model at the core of TN-GEO. The green dash line is positioned at the best risk value found in the seed data. This mark emphasizes all the new outstanding samples obtained with the quantum generative model and which correspond to lower portfolio risk value (better minima) than those available from the classical solver by itself. The number of outstanding samples in the case of \\(N = 50\\) is equal to 31, while 349 outstanding samples were obtained from the MPS generative model in the case of \\(N = 100\\) .", + "footnote": [], + "bbox": [ + [ + 85, + 75, + 914, + 324 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4. TN-GEO as a stand-alone solver: In this comparison of TN-GEO against four classical competing strategies, investment universes are constructed from subsets of the S&P 500 with a diversity in the number of assets (problem variables) ranging from \\(N = 30\\) to \\(N = 100\\) . The goal is to minimize the risk given an expected return which is one of the specifications in the combinatorial problem addressed here. Error bars and their 95% confidence intervals are calculated from bootstrapping over 100 independent random initializations for each solver on each problem. The main line for each solver corresponds to the bootstrapped median over these 100 repetitions, demonstrating the superior performance of TN-GEO over the classical solvers considered here. As specified in the text, with the exception of TN-GEO, the classical solvers use to their advantage the a priori information coming from the cardinality constraint imposed in the selection of valid portfolios.", + "footnote": [], + "bbox": [ + [ + 200, + 472, + 777, + 793 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "FIG. 5. A graphical demonstration of indices used for performance metrics calculation", + "footnote": [], + "bbox": [ + [ + 515, + 61, + 916, + 305 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "FIG. 6. Relative TN-GEO enhancement similar to those shown in the bottom panel of Fig. 2 in the main text. For these experiments, portfolio optimization instances with a number of variables ranging from \\(N = 30\\) to \\(N = 100\\) were used. Here, each panel correspond to a different investment universes corresponding to a random subset of the S&P 500 market index. Note the trend for a larger quantum-inspired enhancement as the number of variables (assets) becomes larger, with the largest enhancement obtained in the case on instances with all the assets from the S&P 500 ( \\(N = 500\\) ), as shown in Fig. 2", + "footnote": [], + "bbox": [ + [ + 185, + 60, + 810, + 732 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e.mmd b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..127c74e356b518a4e7c27b7f2212c5c03dde9799 --- /dev/null +++ b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e.mmd @@ -0,0 +1,381 @@ + +# GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models + +Francisco Fernandez Alcazar Alejandro Perdomo-Ortiz ( \(\square\) alejandro@zapatacomputing.com ) Zapata Computing Canada https://orcid.org/0000- 0001- 7176- 4719 + +Mohammad Ghazi Vakili Zapata Computing Canada + +Can Kalayci Pamukkale University + +Article + +Keywords: + +Posted Date: August 8th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 241950/v1 + +License: © \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +# GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models + +Javier Alcazar, \(^{1}\) Mohammad Ghazi Vakili, \(^{1,2,3}\) Can B. Kalayci, \(^{1,4}\) and Alejandro Perdomo- Ortiz \(^{1,*}\) + +\(^{1}\) Zapata Computing Canada Inc., 325 Front St W, Toronto, ON, M5V 2Y1 \(^{2}\) Department of Chemistry, University of Toronto, Toronto, ON, M5G 1Z8, Canada \(^{3}\) Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada \(^{4}\) Department of Industrial Engineering, Pamukkale University, Kinikli Campus, 20160, Denizli, Turkey (Dated: July 2, 2022) + +We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator- Enhanced Optimization (GEO) strategy is flexible to adopt any generative model, from quantum to quantum- inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum- inspired version of GEO relying on tensor- network Born machines, and referred to hereafter as TN- GEO. We present two prominent strategies for using TN- GEO. The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN- GEO improves the performance of the classical solver as a standalone strategy in hard- to- solve instances. The second strategy uses TN- GEO as a standalone solver, i.e., when no previous observations are available. Here, we show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls. This might be ideal in situations where the cost function evaluation can be very expensive. To illustrate our results, we run these benchmarks in the context of the portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes. We show that TN- GEO can propose unseen candidates with lower cost function values than the candidates seen by classical solvers. This is the first demonstration of the generalization capabilities of quantum- inspired generative models that provide real value in the context of an industrial application. We also comprehensively compare state- of- the- art algorithms in a generalized version of the portfolio optimization problem. The results show that TN- GEO is among the best compared to these state- of- the- art algorithms; a remarkable outcome given the solvers used in the comparison have been fine- tuned for decades in this real- world industrial application. We see this as an important step toward a practical advantage with quantum- inspired models and, subsequently, with quantum generative models. + +## I. INTRODUCTION + +Along with machine learning and the simulation of materials, combinatorial optimization is one of top candidates for practical quantum advantage. That is, the moment where a quantum- assisted algorithm outperforms the best classical algorithms in the context of a real- world application with a commercial or scientific value. There is an ongoing portfolio of techniques to tackle optimization problems with quantum subroutines, ranging from algorithms tailored for quantum annealers (e.g., Refs. [1, 2]), gate- based quantum computers (e.g., Refs. [3, 4]) and quantum- inspired (QI) models based on tensor networks (e.g., Ref. [5]). + +Regardless of the quantum optimization approach proposed to date, there is a need to translate the real- world problem into a polynomial unconstrained binary optimization (PUBO) expression - a task which is not necessarily straightforward and that usually results in an overhead in terms of the number of variables. Specific real- world use cases illustrating these PUBO mappings are depicted in Refs. [6] and [7]. Therefore, to achieve practical quantum advantage in the near- term, it would be ideal to find a quantum optimization strategy that can work on arbitrary objective functions, bypassing the translation and overhead limitations raised here. + +In our work, we offer a solution to these challenges by proposing a novel generator- enhanced optimization (GEO) framework which leverage the power of (quantum or classical) generative models. This family of solvers can scale to large problems where combinatorial problems become intractable in real- world settings. Since our optimization strategy does not rely on the details of the objective function to be minimized, it is categorized in the group of so- called black- box solvers. Another highlight of our approach is that it can utilize available observations obtained from attempts to solve the optimization problem. These initial evaluations can come from any source, from random search trials to tailored state- of- the- art (SOTA) classical or quantum optimizers for the specific problem at hand. + +Our GEO strategy is based on two key ideas. First, the generative- modeling component aims to capture the correlations from the previously observed data (step 0- 3 in Fig. 1). Second, since the focus here is on a minimization task, the (quantum) generative models need to be capable of generating new "unseen" solution candidates which have the potential to have a lower value for the objective function than those already "seen" and used as the training set (step 4- 6 in Fig. 1). This exploration towards unseen and valuable samples is by definition the fundamental concept behind generalization: the most desirable and important feature of any practical ML model. We will elaborate next on each of these components and demonstrate these two properties in the context of the tensor- network- based generative models and its application to a non- deterministic polynomial- time hard (NP- hard) version of the portfolio optimization in finance. + +To the best of our knowledge, this is the first optimization strategy proposed to do an efficient blackbox exploration + +<--- Page Split ---> + +of the objective- function landscape with the help of generative models. Although other proposal leveraging generative models as a subroutine within the optimizer have appeared recently since the publication of our manuscript (e.g., see GFlowNets [8] and the variational neural annealing [9] algorithms), our framework is the only capable of both, handling arbitrary cost functions and also with the possibility of swapping the generator for a quantum or quantum- inspired implementation. GEO also has the enhanced feature that the more data is available, the more information can be passed and used to train the (quantum) generator. + +In this work, we highlight the different features of GEO by performing a comparison with alternative solvers, such as Bayesian optimizers and generic solvers like simulated annealing. In the case of the specific real- world large- scale application of portfolio optimization, we compare against the SOTA optimizers and show the competitiveness of our approach. These results are presented in Sec. III. Next, in Sec. II, we present the GEO approach and its range of applicability. + +## II. QUANTUM-ENHANCED OPTIMIZATION WITH GENERATIVE MODELS + +As shown in Fig. 1, depending on the GEO specifics we can construct an entire family of solvers whose generative modeling core range from classical, QI or quantum circuit (QC) enhanced, or hybrid quantum- classical model. These options can be realized by utilizing, for example, Boltzmann machines [10] or Generative Adversarial Networks (GAN) [11], Tensor- Network Born Machines (TNBm) [12], Quantum Circuit Born Machines (QCBM)[13] or Quantum- Circuit Associative Adversarial Networks (QC- AAN)[14] respectively, to name just a few of the many options for this probabilistic component. + +QI algorithms come as an interesting alternative since these allow one to simulate larger scale quantum systems with the help of efficient tensor- network (TN) representations. Depending on the complexity of the TN used to build the quantum generative model, one can simulate from thousands of problem variables to a few tens, the latter being the limit of simulating an universal gate- based quantum computing model. This is, one can control the amount of quantum resources available in the quantum generative model by choosing the QI model. + +Therefore, from all quantum generative model options, we chose to use a QI generative model based on TNs to test and scale our GEO strategy to instances with a number of variables commensurate with those found in industrial- scale scenarios. We refer to our solver hereafter as TN- GEO. For the training of our TN- GEO models we followed the work of Han et al. [15] where they proposed to use Matrix Product States (MPS) to build the unsupervised generative model. The latter extends the scope from early successes of quantum- inspired models in the context of supervised ML [16- 19]. + +In this paper we will discuss two modes of operation for our family of quantum- enhanced solvers: + +- In TN-GEO as a "booster" we leverage past observa + +tions from classical (or quantum) solvers. To illustrate this mode we use observations from simulated annealing (SA) runs. Simulation details are provided in Appendix A 5. + +- In TN-GEO as a stand-alone solver all initial cost function evaluations are decided entirely by the quantum-inspired generative model, and a random prior is constructed just to give support to the target probability distribution the MPS model is aiming to capture. Simulation details are provided in Appendix A 6. + +Both of these strategies are captured in the algorithm workflow diagram in Fig. 1 and described in more detail in Appendix A. + +## III. RESULTS AND DISCUSSION + +To illustrate the implementation for both of these settings we tested their performance on an NP- hard version of the portfolio optimization problem with cardinality constraints. The selection of optimal investment on a specific set of assets, or portfolios, is a problem of great interest in the area of quantitative finance. This problem is of practical importance for investors, whose objective is to allocate capital optimally among assets while respecting some investment restrictions. The goal of this optimization task, introduced by Markowitz [20], is to generate a set of portfolios that offers either the highest expected return (profit) for a defined level of risk or the lowest risk for a given level of expected return. In this work, we focus in two variants of this cardinality constrained optimization problem. The first scenario aims to choose portfolios which minimize the volatility or risk given a specific target return (more details are provided in Appendix A 1). To compare with the reported results from the best performing SOTA algorithms, we ran TN- GEO in a second scenario where the goal is to choose the best portfolio given a fixed level of risk aversion. This is the most commonly used version of this optimization problem when it comes to comparison among SOTA solvers in the literature (more details are provided in Appendix A 2). + +### A. TN-GEO as a booster for any other combinatorial optimization solver + +In Fig. 2 we present the experimental design and the results obtained from using TN- GEO as a booster. In these experiments we illustrate how using intermediate results from simulated annealing (SA) can be used as seed data for our TN- GEO algorithm. As described in Fig. 2, there are two strategies we explored (strategies 1 and 2) to compare with our TN- GEO strategy (strategy 4). To fairly compare each strategy, we provide each with approximately the same computational wall- clock time. For strategy 2, this translates into performing additional restarts of SA with the time allotted for TN- GEO. In the case of strategy 1, where we explored different settings for SA from the start compared to those used in strategy 2, this amounts to using the same total number + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIG. 1. Scheme for our Generator-Enhanced Optimization (GEO) strategy. The GEO framework leverages generative models to utilize previous samples coming from any quantum or classical solver. The trained quantum or classical generator is responsible for proposing candidate solutions which might be out of reach for conventional solvers. This seed data set (step 0) consists of observation bitstrings \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and their respective costs \(\{\sigma^{(i)}\}_{\mathrm{seed}}\) . To give more weight to samples with low cost, the seed samples and their costs are used to construct a softmax function which serves as a surrogate to the cost function but in probabilistic domain. This softmax surrogate also serves as a prior distribution from which the training set samples are withdrawn to train the generative model (steps 1-3). As shown in the figure between steps 1 and 2, training samples from the softmax surrogate are biased favoring those with low cost value. For the work presented here, we implemented a tensor-network (TN)-based generative model. Therefore, we refer to this quantum-inspired instantiation of GEO as TN-GEO. Other families of generative models from classical, quantum, or hybrid quantum-classical can be explored as expounded in the main text. The quantum-inspired generator corresponds to a tensor-network Born machine (TNBM) model which is used to capture the main features in the training data, and to propose new solution candidates which are subsequently post selected before their costs \(\{\sigma^{(i)}\}_{\mathrm{new}}\) are evaluated (steps 4-6). The new set is merged with the seed data set (step 7) to form an updated seed data set (step 8) which is to be used in the next iteration of the algorithm. More algorithmic details for the two TN-GEO strategies proposed here, as a booster or as a stand-alone solver, can be found in the main text and in A5 and A6 respectively.
+ +of number of cost functions evaluations as those allocated to SA in strategy 2. For our experiments this number was set to 20,000 cost function evaluations for strategies 1 and 2. In strategy 4, the TN- GEO was initialized with a prior consisting of the best 1,000 observations out of the first 10,000 coming from strategy 2 (see Appendix A 5 for details). To evaluate the performance enhancement obtained from the TN- GEO strategy we compute the relative TN- GEO enhancement \(\eta\) , which we define as + +\[\eta = \frac{C_{\mathrm{min}}^{\mathrm{cl}}}{C_{\mathrm{min}}^{\mathrm{cl}}} = \frac{C_{\mathrm{min}}^{\mathrm{TN - GEO}}}{C_{\mathrm{min}}^{\mathrm{cl}}}\times 100\% . \quad (1)\] + +Here, \(C_{\mathrm{min}}^{\mathrm{cl}}\) is the lowest minimum value found by the classical strategy (e.g., strategies 1- 3) while \(C_{\mathrm{min}}^{\mathrm{TN - GEO}}\) corresponds to the lowest value found with the quantum- enhanced approach (e.g., with TN- GEO). Therefore, positive values reflect an improvement over the classical- only approaches, while negative values indicate cases where the classical solvers outperform the quantum- enhanced proposal. + +As shown in the Fig. 2, we observe that TN- GEO outperforms on average both of the classical- only strategies imple + +As shown in the Fig. 2, we observe that TN- GEO outperforms on average both of the classical- only strategies implemented. The quantum- inspired enhancement observed here, as well as the trend for a larger enhancement as the number of variables (assets) becomes larger, is confirmed in many other investment universes with a number of variables ranging from \(N = 30\) to \(N = 100\) (see Appendix B for more details). Although we show an enhancement compared to SA, similar results could be expected when other solvers are used, since our approach builds on solutions found by the solver and does not compete with it from the start of the search. Furthermore, the more data available, the better the expected performance of TN- GEO is. An important highlight of TN- GEO as a booster is that these previous observations can come from a combination of solvers, as different as purely quantum or classical, or hybrid. + +The observed performance enhancement compared with the classical- only strategy must be coming from a better exploration of the relevant search space, i.e., the space of those bitstring configurations \(x\) representing portfolios which could yield a low risk value for a specified expected investment return. That is the intuition behind the construction of TN- GEO. The goal of the generative model is to capture the important + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
FIG. 2. TN-GEO as a booster. Top: Strategies 1-3 correspond to the current options a user might explore when solving a combinatorial optimization problem with a suite of classical optimizers such as simulated annealing (SA), parallel tempering (PT), generic algorithms (GA), among others. In strategy 1, the user would use its computational budget with a preferred solver. In strategy 2-4 the user would inspect intermediate results and decide whether to keep trying with the same solver (strategy 2), try a new solver or a new setting of the same solver used to obtain the intermediate results (strategy 3), or, as proposed here, to use the acquired data to train a quantum or quantum-inspired generative model within a GEO framework such as TN-GEO (strategy 4). Bottom: Results showing the relative TN-GEO enhancement from TN-GEO over either strategy 1 or strategy 2. Positive values indicate runs where TN-GEO outperformed the respective classical strategies (see Eq. 1). The data represents bootstrapped medians from 20 independent runs of the experiments and error bars correspond to the 95% confidence intervals. The two instances presented here correspond to portfolio optimization instances where all the assets in the S&P 500 market index where included \((N = 500)\) , under two different cardinality constraints \(\kappa\) . This cardinality constraint indicate the number of assets that can be included at a time in valid portfolios, yielding a search space of \(M = \binom{N}{\kappa}\) , with \(M \sim 10^{69}\) portfolios candidates for \(\kappa = 50\) .
+ +correlations in the previously observed data, and to use its generative capabilities to propose similar new candidates. + +Generating new candidates is by no means a trivial task in ML and it determines the usefulness and power of the model since it measure its generalization capabilities. In this setting of QI generative models, one expects that the MPS- based + +generative model at the core of TN- GEO is not simply memorizing the observations given as part of the training set, but that it will provide new unseen candidates. This is an idea which has been recently tested and demonstrated to some extent on synthetic data sets (see e.g., Refs. [21], [22] and [23]. In Fig. 3 we demonstrate that our quantum- inspired generative model is generalizing to new samples and that these add real value to the optimization search. To the best of our knowledge this is the first demonstration of the generalization capabilities of quantum generative models in the context of a real- world application in an industrial scale setting, and one of our main findings in our paper. + +Note that our TN- based generative model not only produces better minima than the classical seed data, but it also generates a rich amount of samples in the low cost spectrum. This bias is imprinted in the design of our TN- GEO and it is the purpose of the softmax surrogate prior distribution shown in Fig. 1. This richness of new samples could be useful not only for the next iteration of the algorithm, but they may also be readily of value to the user solving the application. In some applications there is value as well in having information about the runnersup. Ultimately, the cost function is just a model of the system guiding the search, and the lowest cost does not translate to the best performance in the real- life investment strategy. + +### B. Generator-Enhanced Optimization as a Stand-Alone Solver + +Next, we explore the performance of our TN- GEO framework as a stand- alone solver. The focus is in combinatorial problems whose cost functions are expensive to evaluate and where finding the best minimum within the least number of calls to this function is desired. In Fig. 4 we present the comparison against four different classical optimization strategies. As the first solver, we use the random solver, which corresponds to a fully random search strategy over the \(2^{N}\) bitstrings of all possible portfolios, where \(N\) is the number of assets in our investment universe. As second solver, we use the conditioned random solver, which is a more sophisticated random strategy compared to the fully random search. The conditioned random strategy uses the a priori information that the search is restricted to bitstrings containing a fixed number of \(\kappa\) assets. Therefore the number of combinatorial possibilities is \(M = \binom{N}{\kappa}\) , which is significantly less than \(2^{N}\) . As expected, when this information is not used the performance of the random solver over the entire \(2^{N}\) search space is worse. The other two competing strategies considered here are SA and the Bayesian optimization library GPyOpt [24]. In both of these classical solvers, we adapted their search strategy to impose this cardinality constraint with fixed \(\kappa\) as well (details in Appendix. A 4). This raises the bar even higher for TN- GEO which is not using that a priori information to boost its performance [25]. As explained in Appendix A 6, we only use this information indirectly during the construction of the artificial seed data set which initializes the algorithm (step 0, Fig. 1), but it is not a strong constraint during the construction of the QI generative model (step 3, Fig. 1) or imposed to generate the new candidate samples coming from it (step 4, + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 3. Generalization capabilities of our quantum-inspired generative model. Left panel corresponds to an investment universe with \(N = 50\) assets while the right panel corresponds to one with \(N = 100\) assets. The blue histogram represents the number of observations or portfolios obtained from the classical solver (seed data set). In orange we represent samples coming from our quantum generative model at the core of TN-GEO. The green dash line is positioned at the best risk value found in the seed data. This mark emphasizes all the new outstanding samples obtained with the quantum generative model and which correspond to lower portfolio risk value (better minima) than those available from the classical solver by itself. The number of outstanding samples in the case of \(N = 50\) is equal to 31, while 349 outstanding samples were obtained from the MPS generative model in the case of \(N = 100\) .
+ +![](images/Figure_4.jpg) + +
FIG. 4. TN-GEO as a stand-alone solver: In this comparison of TN-GEO against four classical competing strategies, investment universes are constructed from subsets of the S&P 500 with a diversity in the number of assets (problem variables) ranging from \(N = 30\) to \(N = 100\) . The goal is to minimize the risk given an expected return which is one of the specifications in the combinatorial problem addressed here. Error bars and their 95% confidence intervals are calculated from bootstrapping over 100 independent random initializations for each solver on each problem. The main line for each solver corresponds to the bootstrapped median over these 100 repetitions, demonstrating the superior performance of TN-GEO over the classical solvers considered here. As specified in the text, with the exception of TN-GEO, the classical solvers use to their advantage the a priori information coming from the cardinality constraint imposed in the selection of valid portfolios.
+ +<--- Page Split ---> + +Fig. 1). Post selection can be applied a posteriori such that only samples with the right cardinality are considered as valid candidates towards the selected set (step 5, Fig. 1). + +In Fig. 4 we demonstrate the advantage of our TN- GEO stand- alone strategy compared to any of these widely- used solvers. In particular, it is interesting to note that the gap between TN- GEO and the other solvers seems to be larger for larger number of variables. + +### C. Comparison with state-of-the-art algorithms + +Finally, we compare TN- GEO with nine different leading SOTA optimizers covering a broad spectrum of algorithmic strategies for this specific combinatorial problem, based on and referred hereafter as: 1) GTS [26], the genetic algorithms, tabu search, and simulated annealing; 2) IPSO [27], an improved particle swarm optimization algorithm [27]; 3) IPSO- SA [28], a hybrid algorithm combining particle swarm optimization and simulated annealing; 4) PBILD [29], a population- based incremental learning and differential evolution algorithm; 5) GRASP [30], a greedy randomized adaptive solution procedure; 6) ABCFEIT [31], an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures; 7) HAAG [32], a hybrid algorithm integrating ant colony optimization, artificial bee colony and genetic algorithms; 8) VNSQP [33], a variable neighborhood search algorithm combined with quadratic programming; and, 9) RCABC [34], a rapidly converging artificial bee colony algorithm. + +The test data used by the vast majority of researchers in the literature who have addressed the problem of cardinality- constrained portfolio optimization come from ORLibrary [35], which correspond to the weekly prices between March 1992 and September 1997 of the following indexes: Hang Seng in Hong Kong (31 assets); DAX 100 in Germany (85 assets); FTSE 100 in the United Kingdom (89 assets); S&P 100 in the United States (98 assets); and Nikkei 225 in Japan (225 assets). + +Here we present the results obtained with TN- GEO and its comparison with the nine different SOTA metaheuristic algorithms mentioned above and whose results are publicly available from the literature. Table I shows the results of all algorithms and all performance metrics for each of the 5 index data sets (for more details on the evaluation metrics, see Appendix A 2). Each algorithm corresponds to a different column, with TN- GEO in the rightmost column. The values are shown in red if the TN- GEO algorithm performed better or equally well compared to the other algorithms on the corresponding performance metric. The numbers in bold mean that the algorithm found the best (lowest) value across all algorithms. + +From all the entries in this table, \(67\%\) of them correspond to red entries, where TN- GEO either wins or draws, which is a significant percentage giving that these optimizers are among the best reported in the last decades. + +In Table II we show a pairwise comparison of TN- GEO against each of the SOTA optimizers. This table reports the + +number of times TN- GEO wins, loses, or draws compared to results reported for the other optimizer, across all the performance metrics and for all the 5 different market indexes. Note that since not all the performance metrics are reported for all the solvers and market indexes, the total number of wins, draws, or losses varies. Therefore, we report in the same table the overall percentage of wins plus draws in each case. We see that this percentage is greater than \(50\%\) in all the cases. + +Furthermore, in Table II, we use the Wilcoxon signed- rank test [36], which is a widely used nonparametric statistical test used to evaluate and compare the performance of different algorithms in different benchmarks [37]. Therefore, to statistically validate the results, a Wilcoxon signed- rank test is performed to provide a meaningful comparison between the results from TN- GEO algorithm and the SOTA metaheuristic algorithms. The Wilcoxon signed- rank test tests the null hypothesis that the median of the differences between the results of the algorithms is equal to 0. Thus, it tests whether there is no significant difference between the performance of the algorithms. The null hypothesis is rejected if the significance value \((p)\) is less than the significance level \((\alpha)\) , which means that one of the algorithms performs better than the other. Otherwise, the hypothesis is retained. + +As can be seen from the table, the TN- GEO algorithm significantly outperforms the GTS and PBILD methods on all performance metrics rejecting the null hypothesis at the 0.05 significance level. On the other hand, the null hypotheses are accepted at \(\alpha = 0.05\) for the TN- GEO algorithm over the other remaining algorithms. Thus, in terms of performance on all metrics combined, the results show that there is no significant difference between TN- GEO and these remaining seven SOTA optimizers (IPSO, IPSO- SA, GRASP, ABCFEIT, HAAG, VNSQP, and RCABC) + +Overall, the results confirm the competitiveness of our quantum- inspired proposed approach against SOTA metaheuristic algorithms. This is remarkable given that these metaheuristics have been explored and fine- tuned for decades. + +## IV. OUTLOOK + +Compared to other quantum optimization strategies, an important feature of TN- GEO is its algorithmic flexibility. As shown here, unlike other proposals, our GEO framework can be applied to arbitrary cost functions, which opens the possibility of new applications that cannot be easily addressed by an explicit mapping to a polynomial unconstrained binary optimization (PUBO) problem. Our approach is also flexible with respect to the source of the seed samples, as they can come from any solver, possibly more efficient or even application- specific optimizers. The demonstrated generalization capabilities of the generative model that forms its core, helps TN- GEO build on the progress of previous experiments with other state- of- the- art solvers, and it provides new candidates that the classical optimizer may not be able to achieve on its own. We are optimistic that this flexible approach will open up the broad applicability of quantum and quantum- inspired generative models to real- world combinatorial optimization + +<--- Page Split ---> + + +TABLE I. Detailed comparison with SOTA algorithms for each of the five index data sets and on seven different performance indicators described in Appendix A 2. Entries in red correspond to cases where TN-GEO performed better or tied compared to the other algorithm. Entries in bold, corresponding to the best (lowest) value, for each specific indicator. + +
Data SetPerformance IndicatorGTSIPSOIPSO-SAPBILDGRASPABCFEITHAAGVNSQPRCABCTN-GEO
Hang SengMean1.09571.0953-1.14311.09651.09531.09651.09641.08731.0958
Median1.2181--1.23901.21551.21811.21811.21551.21541.2181
Min----0.00000.00000.00000.0000-0.0000
Max----1.55381.55381.55381.5538-1.5538
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--1.6368-1.64001.64321.63951.63971.63421.6392
MRE--0.6059-0.60600.60470.60850.60580.59640.6082
DAX100Mean2.54242.5417-2.42512.31262.32582.31302.31252.28982.3142
Median2.5466--2.58662.56302.56782.55872.56302.56292.5660
Minimum----0.00590.00230.00230.00590.00590.0023
Maximum----4.02754.02754.02754.0275-4.0275
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--6.7806-6.75936.79256.78066.75836.83266.7540
MRE--1.2770-1.27691.27611.27801.27671.23571.2763
FTSE100Mean1.10761.0628-0.97060.84510.84810.84510.84530.84060.8445
Median1.0841--1.08411.08411.08411.0841-1.08411.0841
Minimum----0.00160.00470.00060.00450.00160.0047
Maximum----2.05762.06382.06052.06692.06702.0775
MEUCD--0.0000-0.00000.00000.00000.0000-0.0000
VRE--2.4701-2.43502.43972.43502.43492.41492.4342
MRE--0.3247-0.32450.32550.31860.32520.32070.3254
S&P100Mean1.93281.6890-1.63861.29371.29301.29301.26491.34641.2918
Median1.1823--1.16921.14201.13691.13231.13231.15151.1452
Minimum----0.00090.00000.00000.00000.00090.0000
Maximum----5.45515.44225.46425.45515.45205.4422
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--2.6281-2.52112.52602.52552.51052.53642.5269
MRE--0.7846-0.90630.88850.70440.90720.88580.9117
NikkeiMean0.60660.6870-0.59720.57820.57810.57810.59040.56650.5793
Median0.6093--0.58960.58570.58560.58540.58570.58580.5855
Minimum----0.00000.00000.00000.0000-0.0000
Maximum----1.16061.16061.16071.16061.16061.1606
MEUCD--0.0000-0.00000.00000.00000.0000-0.0000
VRE--0.9583-0.83590.83960.81910.85610.83140.8353
MRE--1.7090-0.41840.41470.42330.42170.40420.4229
+ +TABLE II. Pairwise comparison of TN-GEO against each of the SOTA optimizers. The asymptotic significance is part of the Wilcoxon signedrank test results. The null hypothesis that the performance of the two algorithms is the same is tested at the \(95\%\) confidence level (significance level: \(\alpha = .05\) ). Results show that TN-GEO is on par with all the SOTA algorithms, and in two cases, GTS and PBILD, it significantly outperforms them. We also report the count for TN-GEO wins, losses, and ties, compared to each of the other algorithms. + +
TN-GEO vs Other:GTSIPSOIPSO-SAPBILDGRASPABCFEITHAAGVNSQPRCABC
Wins(+)6469121011118
Loss(-)2140129111216
Ties20511116131211
(Wins+Ties)/Total80%80%67%100%66%74%69%66%54%
Asymptotic significance (p).036.080.308.008.247.888.363.594.110
DecisionRejectRetainRetainRejectRetainRetainRetainRetainRetain
+ +problems at the industrial scale. + +Although we have limited the scope of this work to tensor network- based generative quantum models, it would be a natural extension to consider other generative quantum models as well. For example, hybrid classical quantum models such as + +quantum circuit associative adversarial networks (QC- AAN) [14] can be readily explored to harness the power of generative quantum models with so- called noisy intermediate- scale quantum (NISQ) devices [38]. In particular, the QC- AAN framework opens up the possibility of working with a larger + +<--- Page Split ---> + +number of variables and going beyond discrete values (e.g., variables with continuous values). Both quantum- inspired and hybrid quantum- classical algorithms can be tested in this GEO framework in even larger problem sizes of this NP- hard version of the portfolio optimization problem or any other combinatorial optimization problem. As the number of qubits in NISQ devices increases, it would be interesting to explore generative models that can utilize more quantum resources, such as Quantum Circuit Born Machines (QCBM)[13]: a general framework to model arbitrary probability distributions and perform generative modeling tasks with gate- based quantum computers. + +Increasing the expressive power of the quantum- inspired core of MPS to other more complex but still efficient QI approaches, such as tree- tensor networks [39], is another interesting research direction. Although we have fully demonstrated the relevance and scalability of our algorithm for industrial applications by increasing the performance of classical solvers on industrial scale instances (all 500 assets in the S&P 500 market index), there is a need to explore the performance improvement that could be achieved by more complex TN representations or on other combinatorial problems. + +Although the goal of GEO was to show good behavior as a general black- box algorithm without considering the specifics of the study application, it is a worthwhile avenue to exploit the specifics of the problem formulation to improve its performance and runtime. In particular, for the portfolio optimization problem with a cardinality constraint, it is useful to incorporate this constraint as a natural MPS symmetry, thereby reducing the effective search space of feasible solutions from the size of the universe to the cardinality size. + +Finally, our thorough comparison with SOTA algorithms, which have been fine- tuned for decades on this specific application, shows that our TN- GEO strategy manages to outperform a couple of these and is on par with the other seven optimizers. This is a remarkable feat for this new approach and hints at the possibility of finding commercial value in these quantum- inspired strategies in large- scale real- world problems, as the instances considered in this work. Also, it calls for more fundamental insights towards understanding when and where it would be beneficial to use this TN- GEO framework, which relies heavily on its quantum- inspired generative ML model. For example, understanding the intrinsic bias in these models, responsible for their remarkable performance, is another important milestone on the road to practical quantum advantage with quantum devices in the near future. The latter can be asserted given the tight connection of these quantum- inspired TN models to fully quantum models deployed on quantum hardware. And this question of when to go with quantum- inspired or fully quantum models is a challenging one that we are exploring in ongoing future work. + +## ACKNOWLEDGMENTS + +The authors would like to acknowledge Manuel S. Rudolph, Marta Mauri, Matthew J.S. Beach, Yudong Cao, Luis Serrano, Jhonathan Romero- Fontalvo, Brian Dellabetta, Matthew Kowalsky, Jacob Miller, John Realpe- Gomez, and Collin Farquhar for their feedback on an early version of this manuscript + +[1] Tadashi Kadowaki and Hidetoshi Nishimori, "Quantum annealing in the transverse ising model," Phys. Rev. E. 58, 5355 (1998). 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Generation of portfolio optimization instances + +The portfolio optimization problem aims at determining the fractions \(w_{i}\) of a given capital to be invested in each asset \(i\) of a universe of \(N\) assets, such that the risk \(\sigma (w)\) for a given level \(\rho\) of the expected return \(\langle r(w)\rangle\) is minimized, constrained to \(\sum_{i}w_{i} = 1\) . The problem can be formulated as: + +\[\min_{w}\{\sigma^{2}(w) = w^{T}\cdot \pmb {\Sigma}\cdot \pmb {w}:\langle r(w)\rangle = w\cdot \pmb {r} = \rho \} \mathrm{(A1)}\] + +where the vectors \(w\) and \(r\) have dimensionality \(N\) , \(\pmb{\Sigma}\) is the sample covariance matrix obtained from the return time series of pair of asset \(i\) and \(j\) , and \(r\) is the vector of average return of the time series for each asset, with each daily return, \(r^{t}\) , + +<--- Page Split ---> + +calculated as the relative increment in asset price from its previous day (i.e., \(r^{t} = (p^{t} - p^{(t - 1)}) / p^{(t - 1)}\) , with \(p^{t}\) as the price for a particular asset at time \(t\) ). The solution to Eq. A1 for a given return level \(\rho\) corresponds to the optimal portfolio strategy \(\boldsymbol{w}^{*}\) and the minimal value of this objective function \(\sigma (\boldsymbol {w})\) correspond to the portfolio risk and will be denoted by \(\sigma_{\rho}^{*}\) . + +Note that the optimization task in Eq. A1 has the potential outcome of investing small amounts in a large number of assets as an attempt to reduce the overall risk by "over diversifying" the portfolio. This type of investment strategy can be challenging to implement in practice: portfolios composed of a large number of assets are difficult to manage and may incur in high transaction costs. Therefore, several restrictions are usually imposed on the allocation of capital among assets, as a consequence of market rules and conditions for investment or to reflect investor profiles and preferences. For instance, constraints can be included to control the amount of desired diversification, i.e., modifying bound limits per asset \(i\) , denoted by \(\{l_{i}, u_{i}\}\) , to the proportion of capital invested in the investment on individual assets or a group of assets, thus the constraint \(l_{i} < w_{i} < u_{i}\) could be considered. + +Additionally, a more realistic and common scenario is to include in the optimization task a cardinality constraint, which limits directly the number of assets to be transacted to a pre- specified number \(\kappa < N\) . Therefore, the number of different sets to be treated is \(M = \binom{N}{\kappa}\) . In this scenario, the problem can be formulated as a Mixed- Integer Quadratic Program (MIQP) with the addition of binary variables \(x_{i} \in \{0, 1\}\) per asset, for \(i = 1, \ldots , N\) , which are set to "1" when the \(i\) - th asset is included as part of the \(\kappa\) assets, or "0" if it is left out of this selected set. Therefore, valid portfolios would have a number \(\kappa\) of 1's, as specified in the cardinality constraint. For example, for \(N = 4\) and \(\kappa = 2\) , the six different valid configurations can be encoded as \(\{0011, 0101, 0110, 1001, 1010, 1100\}\) . + +The optimization task can then be described as follows + +\[\begin{array}{rl} & {\min_{\boldsymbol {w},\boldsymbol {x}}\{\sigma^2 (\boldsymbol {w}):}\\ & {\qquad \langle \boldsymbol {r}(\boldsymbol {w})\rangle = \rho ,}\\ & {\qquad l_i\boldsymbol {x}_i< w_i< u_i\boldsymbol {x}_i\quad i = 1,\dots ,N,}\\ & {\qquad \mathbf{1}\cdot \boldsymbol {x} = \kappa \} .} \end{array} \quad (A2)\] + +In this reformulated problem we denote by \(\sigma_{\rho ,\kappa}^{*}\) the minimum portfolio risk outcome from Eq. A2 for a given return level \(\rho\) and cardinality \(\kappa\) . The optimal solution vectors \(\boldsymbol{w}^{*}\) and \(\boldsymbol{x}^{*}\) define the portfolio investment strategy. Adding the cardinality constraint and the investment bound limits transforms a simple convex optimization problem (Eq. A1) into a much harder non- convex NP- hard problem. For all the problem instance generation in this work we chose \(\kappa = N / 2\) and the combinatorial nature of the problems lies in the growth of the search space associated with the binary vector \(\boldsymbol{x}\) , which makes it intractable to exhaustively explore for a number of assets in the few hundreds. The size of the search space here is \(M = \binom{N}{N / 2}\) + +It is important to note that given a selection of which assets belong to the portfolio by instantiating \(\boldsymbol{x}\) (say with a specific + +\(\boldsymbol{x}^{(i)}\) ), solving the optimization problem in Eq. A2 to find the respective investment fractions \(\boldsymbol{w}^{(i)}\) and risk value \(\sigma_{\rho ,N / 2}^{(i)}\) can be efficiently achieved with conventional quadratic programming (QP) solvers. In this work we used the python module cvxopt [40] for solving this problem. Note that we exploit this fact to break this constrained portfolio optimization problem into a combinatorial intractable one (find best asset selection \(\boldsymbol{x}\) ), which we aim to solve with GEO, and a tractable subroutine which can be solved efficiently with available solvers. + +The set of pairwise \((\sigma_{\rho}^{*}, \rho)\) , dubbed as the efficient frontier, is no longer convex neither continuous in contrast with the solution to problem in Eq. (A1). + +## 2. Problem formulation for comparison with state-of-the-art algorithms + +To carry out the comparison with State- of- the- Art Algorithms, in line with the formulation used there, we generalizes the problem in Eq. A2 releasing the constraint of a fix level of portfolio return, instead directly incorporating the portfolio return in the objective function, encompassing now two terms: the one on the left corresponding to the portfolio risk as beforehand the one on the right corresponding to the portfolio return. The goal is to balance out both terms such that return is maximized and risk minimized. Lambda is a hyperparameter, named risk averse, that controls if an investor wants to give more weight to risk or return. The new formulation reads as follows, + +\[\begin{array}{rl} & {\min_{\boldsymbol {w},\boldsymbol {x}}\{\lambda \sigma^2 (\boldsymbol {w}) - (1 - \lambda)\langle \boldsymbol {r}(\boldsymbol {w})\rangle :}\\ & {l_i\boldsymbol {x}_i< w_i< u_i\boldsymbol {x}_i\quad i = 1,\dots ,N,}\\ & {\qquad \mathbf{1}\cdot \boldsymbol {x} = \kappa \} .} \end{array} \quad (A3)\] + +With the rest of constraints and variables definition as in Appendix A1. + +### a. Performance Metrics + +To compare the performance of the proposed GEO with the SOTA metaheuristic algorithms in the literature, the most commonly used performance metrics for the cardinality constrained portfolio optimization problem are used. These metric formulations compute the distance between the heuristic efficient frontier and the unconstrained efficient frontier. Thus, the performance of the algorithms can be evaluated. + +Four of these performance metrics (the Mean, Median, Minimum and Maximum in Table I) are based on the so- called Performance Deviation Errors \((PDE)\) . These \(PDE\) metrics were formulated by Chang [26] as follows: + +\[PDE_{i} = min\left(\left|\frac{100(x_{i} - x_{i}^{*})}{x_{i}^{*}}\right|,\left|\frac{100(y_{i} - y_{i}^{*})}{y_{i}^{*}}\right|\right) \quad (A4)\] + +<--- Page Split ---> + +\[\begin{array}{rl} & {x_{i}^{*} = X_{k_{y}} + \frac{(X_{j_{y}} - X_{k_{y}})(y_{i} - Y_{k_{y}})}{(Y_{j_{y}} - Y_{k_{y}})}}\\ & {y_{i}^{*} = Y_{k_{x}} + \frac{(Y_{j_{x}} - Y_{k_{x}})(x_{i} - X_{k_{x}})}{(X_{j_{x}} - X_{k_{x}})}}\\ & {j_{y} = \underset {l = 1,\dots ,\epsilon^{*}}{\arg \min}Y_{l}}\\ & {k_{y} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}Y_{l}}\\ & {j_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmin}}X_{l}}\\ & {k_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}X_{l}}\\ & {k_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}X_{l}} \end{array} \quad (A5)\] + +where the pair \((X_{l},Y_{l})(l = 1,\dots ,\epsilon^{*})\) represents the point on the standard efficient frontier and the pair \((x_{i},y_{i})(i =\) \(1,\dots ,\epsilon)\) represents the point on the heuristic efficient frontier. Here, \(\epsilon^{*}\) denotes the number of points on the standard efficient frontier while \(\epsilon\) denotes the number of points on the heuristic efficient frontier. The mean, median, minimum, and maximum of the \(PDE\) can be used to compare the performance of the algorithms. + +Later, three additional performance measures (MEUCD: Mean Euclidean Distance, VRE: Variance of Return Error, MRE: Mean Return Error) were formulated by Cura [41] as follows: + +\[MEUCD = \frac{\sum_{i = 1}^{\epsilon}\sqrt{(X_{i}^{*} - x_{i}) + (Y_{i}^{*} - y_{i})}}{\epsilon} \quad (A6)\] + +\[VRE = \frac{\sum_{i = 1}^{\epsilon}100|X_{i}^{*} - x_{i}| / x_{i}}{\epsilon} \quad (A7)\] + +\[MRE = \frac{\sum_{i = 1}^{\epsilon}100|Y_{i}^{*} - y_{i}| / y_{i}}{\epsilon} \quad (A8)\] + +where \((X_{i}^{*},Y_{i}^{*})\) is the standard point closest to the heuristic point \((x_{i},y_{i})\) . Figure 5 shows a graphical representation of the indices used to calculate the performance metrics for the convenience of the reader and the values for TN- GEO and all the other SOTA optimizers are reported in Table I. + +## 3. Quantum-Inspired Generative Model in TN-GEO + +The addition of a probabilistic component is inspired by the success of Bayesian Optimization (BO) techniques, which are among the most efficient solvers when the performance metric aims to find the lowest minimum possible within the least number of objective function evaluations. For example, within the family of BO solvers, GPyOpt [24] uses a Gaussian Process (GP) framework consisting of multivariate Gaussian distributions. This probabilistic framework aims to capture relationships among the previously observed data points (e.g., through tailored kernels), and it guides the decision of where + +![](images/Figure_5.jpg) + +
FIG. 5. A graphical demonstration of indices used for performance metrics calculation
+ +to sample the next evaluation with the help of the so called acquisition function. GPyOpt is one of the solvers we use to benchmark the new quantum- enhanced strategies proposed here. + +Although the GP framework in BO techniques is not a generative model, we explore here the powerful unsupervised machine learning framework of generative modeling in order to capture correlations from an initial set of observations and evaluations of the objective function (step 1- 4 in Fig. 1). + +For the implementation of the quantum- inspired generative model at the core of TN- GEO we follow the procedure proposed and implemented in Ref. [15]. Inspired by the probabilistic interpretation of quantum physics via Born's rule, it was proposed that one can use the Born probabilities \(|\Psi (\pmb {x})|^2\) over the \(2^{N}\) states of an \(N\) qubit system to represent classical target probability distributions which would be obtained otherwise with generative machine learning models. Hence, + +\[P(\pmb {x}) = \frac{|\Psi(\pmb{x})|^2}{Z},\mathrm{with}Z = \sum_{\pmb {x}\in \mathcal{S}}|\Psi (\pmb {x})|^2, \quad (A9)\] + +with \(\Psi (\pmb {x}) = \langle \pmb {x}|\Psi \rangle\) and \(\pmb {x}\in \{0,1\}^{\otimes N}\) are in one- to- one correspondence with decision variables over the investment universe with \(N\) assets in our combinatorial problem of interest here. In Ref. [15] these quantum- inspired generative models were named as Born machines, but we will refer to them hereafter as tensor- network Born machines (TNBm) to differentiate it from the quantum circuit Born machines (QCBM) proposal [13] which was developed independently to achieve the same purpose but by leveraging quantum wave functions from quantum circuits in NISQ devices. As explained in the main text, either quantum generative model can be adapted for the purpose of our GEO algorithm. + +On the grounds of computational efficiency and scalability towards problem instances with large number of variables (in the order of hundreds or more), following Ref. [15] we implemented the quantum- inspired generative model based on + +<--- Page Split ---> + +Matrix Product States (MPS) to learn the target distributions \(|\Psi (\pmb {x})|^2\) . + +MPS is a type of TN where the tensors are arranged in a one- dimensional geometry. Despite its simple structure, MPS can efficiently represent a large number of quantum states of interest extremely well [42]. Learning with the MPS is achieved by adjusting its parameters such that the distribution obtained via Born's rule is as close as possible to the data distribution. MPS enjoys a direct sampling method that is more efficient than other Machine Learning techniques, for instance, Boltzmann machines, which require Markov chain Monte Carlo (MCMC) process for data generation. + +The key idea of the method to train the MPS, following the algorithm on paper [15], consists of adjusting the value of the tensors composing the MPS as well as the bond dimension among them, via the minimization of the negative log- likelihood function defined over the training dataset sampled from the target distribution. For more details on the implementation see Ref. [15] and for the respective code see Ref. [43]. + +## 4. Classical Optimizers + +### a. GPyOpt Solver + +GPyOpt [24] is a Python open- source library for Bayesian Optimization based on GPy and a Python framework for Gaussian process modelling. For the comparison exercise in TN- GEO as a stand- alone solver here are the hyperparameters we used for the GPyOpt solver: + +- Domain: to deal with the exponential growth in dimensionality, the variable space for \(n\) number of assets was partitioned as the cartesian product of \(n\) 1-dimensional spaces.- Constraints: we added two inequalities in the number of assets in a portfolio solution to represent the cardinality condition.- Number of initial data points: 10- Acquisition function: Expected Improvement + +### b. Simulated Annealing Solver + +For simulated annealing (SA) we implemented a modified version from Ref. [44]. The main change consists of adapting the update rule such that new candidates are within the valid search space with fixed cardinality. The conventional update rule of single bit flips will change the Hamming weight of \(x\) which translates in a portfolio with different cardinality. The hyperparameters used are the following: + +- Max temperature in thermalization: 1.0 + +- Min temperature in thermalization: 1e-4 + +### c. Conditioned Random Solver + +This solver corresponds to the simplest and most naive approach, while still using the cardinality information of the problem. In the conditioned random solver, we generate, by construction, bitstrings which satisfy the cardinality constraint. Given the desired cardinality \(\kappa = N / 2\) used here, one starts from the bitstring with all zeros, \(x_0 = 0\dots 0\) , and flips only \(N / 2\) bits at random from positions containing 0's, resulting in a valid portfolio candidate \(x\) with cardinality \(N / 2\) . + +### d. Random Solver + +This solver corresponds to the simplest approach without even using the cardinality information of the problem. In the random solver, we generate, by construction, bitstrings randomly selected from the \(2^{N}\) bitstrings of all possible portfolios, where \(N\) is the number of assets in our investment universe. + +## 5. Algorithm Methodology for TN-GEO as a booster + +As explained in the main text, in this case it is assumed that the cost of evaluating the objective function is not the major computational bottleneck, and consequently there is no practical limitations in the number of observations to be considered. + +Following the algorithmic scheme in Fig. 1, we describe next the details for each of the steps in our comparison benchmarks: + +0 Build the seed data set, \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) . For each problem instance defined by \(\rho\) and a random subset with \(N\) assets from the S&P 500, gather all initial available data obtained from previous optimization attempts with classical solver(s). In our case, for each problem instances we collected 10,000 observations from the SA solver. These 10,000 observations corresponding to portfolio candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{init}}\) and their respective risk evaluations \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{init}}\) were sorted and only the first \(n_{\mathrm{seed}} = 1,000\) portfolio candidates with the lowest risks were selected as the seed data set. This seed data set is the one labeled as \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) in the main text and hereafter. The idea of selecting a percentile of the original data is to provide the generative model inside GEO with samples which are the target samples to be generated. This percentile is a hyperparameter and we set it \(10\%\) of the initial data for our purposes. + +1 Construct of the softmax surrogate distribution: Using the seed data from step 0, we construct a softmax multinomial distribution with \(n_{\mathrm{seed}}\) classes - one for each point on the seed data set. The probabilities outcome associated with each of these classes in the multinomial + +<--- Page Split ---> + +is calculated as a Boltzmann weight, \(p_{i} = \frac{e^{-\overline{\sigma}_{i,\kappa}}}{\sum_{j = 1}^{n_{\mathrm{seed}}}e^{-\overline{\sigma}_{j,\kappa}}}\) . + +Here, \(\overline{\sigma}_{\rho ,\kappa}^{(i)} = \sigma_{\rho ,\kappa}(\pmb{x}^{(i)}) / T\) , and \(T\) is a "temperature" hyperparameter. In our simulations, \(T\) was computed as the standard deviation of the risk values of this seed data set. In Bayesian optimization methods the surrogate function tracks the landscape associated with the values of the objective function (risk values here). This soft- max surrogate constructed here by design as a multinomial distribution from the seed data observations serves the purpose of representing the objective function landscape but in probability space. That is, it will assign higher probability to portfolio candidates with lower risk values. Since we will use this softmax surrogate to generate the training data set, this bias imprints a preference in the quantum- inspired generative model to favor low- cost configurations. + +2 Sample from softmax surrogate. We will refer to these samples as the training set since these will be used to train the MPS- based generative model. For our experiments here we used \(n_{\mathrm{train}} = 10000\) samples. + +3 Use the \(n_{\mathrm{train}}\) samples from the previous step to train the MPS generative model. + +4 Obtain \(n_{\mathrm{MPS}}\) samples from the generative model which correspond to the new list of potential portfolio candidates. In our experiments, \(n_{\mathrm{MPS}} = 4000\) . For the case of 500 assets, as sampling takes sensibly longer because of the problem dimension, this value was reduced to 400 to match the time in SA. + +5 Select new candidates: From the \(n_{\mathrm{MPS}}\) samples, select only those who fulfill the cardinality condition, and which have not been evaluated. These new portfolio candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) are saved for evaluation in the next step. + +6 Obtain risk value for new selected samples: Solve Eq. A2 to evaluate the objective function (portfolio risks) for each of the new candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) . We will denote refer to the new cost function values by \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{new}}\) . + +7 Merge the new portfolios, \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) , and their respective cost function evaluations, \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{new}}\) with the seed portfolios, \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) , and their respective cost values, \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) , from step 0 above. This combined super set is the new initial data set. + +8 Use the new initial data set from step 7 to start the algorithm from step 1. If a desired minimum is already found or if no more computational resources are available, one can decide to terminate the algorithm here. In all of our benchmark results reported here when using TN- GEO as a booster from SA intermediate results, + +we only run the algorithm for this first cycle and the minima reported for the TN- GEO strategy is the lowest minimum obtained up to step 7 above. + +## 6. Algorithm Methodology for TN-GEO as a stand-alone solver + +This section presents the algorithm for the TN- GEO scheme as a stand- alone solver. In optimization problems where the objective function is inexpensive to evaluate, we can easily probe it at many points in the search for a minimum. However, if the cost function evaluation is expensive, e.g., tuning hyperparameters of a deep neural network, then it is important to minimize the number of evaluations drawn. This is the domain where optimization technique with a Bayesian flavour, where the search is being conducted based on new information gathered, are most useful, in the attempt to find the global optimum in a minimum number of steps. + +The algorithmic steps for TN- GEO as a stand- alone solver follows the same logic as that of the solver as a booster described Sec. A5. The main differences between the two algorithms rely on step 0 during the construction of the initial data set and seed data set in step 0, the temperature use in the softmax surrogate in step 1, and a more stringent selection criteria in step 5. Since the other steps remain the same, we focus here to discuss the main changes to the algorithmic details provided in Sec. A5. + +0 Build the seed data set: since evaluating the objective function could be the major bottleneck (assumed to be expensive) then we cannot rely on cost function evaluations to generate the seed data set. The strategy we adopted is to initialize the algorithm with samples of bitstrings which satisfy the hard constraints of the problem. In our specific example, we can easily generate \(n_{\mathrm{seed}}\) random samples, \(\mathcal{D}_0 = \{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) , which satisfy the cardinality constraint. Since all the elements in this data set hold the cardinality condition, then maximum length \(n_{\mathrm{seed}}\) of \(\mathcal{D}_0\) is \(\binom{N}{K}\) . In our experiments, we set the number of samples \(n_{\mathrm{init}} = 2,000\) , for all problems considered here up to \(N = 100\) assets + +1 Construct the softmax surrogate distribution: start by constructing a uniform multinomial probability distribution where each sample in \(\mathcal{D}_0\) has the same probability. Therefore, for each point in the seed data set its probability is set to \(p_0 = 1 / n_{\mathrm{seed}}\) . As in TN- GEO as a booster, we will attempt to generate a softmax- like surrogate which favors samples with low cost value, but we will slowly build that information as new samples are evaluated. In this first iteration of the algorithm, we start by randomly selecting a point \(\pmb{x}^{(1)}\) from \(\mathcal{D}_0\) , and we evaluate the value of its objective function \(\sigma^{(1)}\) (its risk value in our specific finance example). To make this point \(\pmb{x}^{(1)}\) stand out from the other unevaluated samples, we set its probability to be twice that of any + +<--- Page Split ---> + +of the remaining \(n_{\mathrm{seed}} - 1\) points in \(\mathcal{D}_0\) . Since we increase the probability of one of the points, we need to adjust the probability of the \(n_{\mathrm{seed}} - 1\) from \(p_0\) to \(p_0\) and if we assume the probability weights for observing each point follows a multinomial distribution with Boltzmann weights, under these assumptions, and making by fixing the temperature hyperparameter we can solve for the reference "risk" value \(\sigma^{(0)}\) associated to all the other \(n_{\mathrm{seed}} - 1\) points as shown below. It is important to note that \(\sigma^{(0)}\) is an artificial reference value which is calculated analytically and does not require a call to the objective function (in contrast to \(\sigma^{(1)}\) ). Here, \(\mathcal{N}\) is the normalization factor of the multinomial and \(T\) is the temperature hyperparameter which, as in the case of TN- GEO as a booster, can be adjusted later in the algorithm as more data is seen. Due to the lack of initial cost function values, in order to set a relevant typical "energy" scale in this problem, we follow the procedure in Ref. [45] where it is set to be the square root of the mean of the covariance matrix defined in Eq. A1, as this matrix encapsulates the risk information (volatility) as stated in the Markowitz's model. + +\[\left\{ \begin{array}{ll}(n_{\mathrm{seed}} - 1)p_0' + p_1 = 1 & \Rightarrow \left\{ \begin{array}{ll}p_0' = 1 / (1 + n_{\mathrm{seed}}) \\ p_1 = 2 / (1 + n_{\mathrm{seed}}) \end{array} \right.\\ \displaystyle \left\{ \begin{array}{ll}\mathcal{N} = (n_{\mathrm{seed}} - 1)e^{-\sigma^{(0)} / T} + e^{-\sigma^{(1)} / T} & \\ p_1 = e^{-\sigma^{(1)} / T} / \mathcal{N} & \\ p_0' = e^{-\sigma^{(0)} / T} / \mathcal{N} & \end{array} \right. \end{array} \right.\] + +2 Generate training set: same as in TN- GEO as a booster (see Appendix A 5). + +3 Train MPS: same as in TN- GEO as a booster (see Appendix A 5). + +4 Generate samples from trained MPS: same as in TN- GEO as a booster (see Appendix A 5). + +5 Select new candidates from trained MPS: In contrast to TN- GEO as a booster we cannot afford to evaluate all new candidates coming from the MPS samples. In our procedure we selected only two new candidates which must meet the cardinality constraint. For our procedure these two candidates correspond to the most frequent sample ("exploitation") and the least frequent sample ("exploration"). If all new samples appeared with the same frequency, then we can select two samples at random. In the case where no new samples were generated, we choose them from the unevaluated samples of the original seed data set in \(\mathcal{D}_0\) + +6 Obtain risk value for new selected samples: same as in TN- GEO as a booster (see Appendix A 5). + +7 Merge the new portfolios with seed data set from step 0 same as in TN- GEO as a booster (see Appendix A 5). + +8 Restart next cycle of the algorithm with the merge data set as the new seed data set: same as in TN- GEO as a booster (see Appendix A 5). + +## Appendix B: Relative TN-GEO Enhancement + +Figure 6 represents the relative performance within the strategies 1 and 2 referred to subsection III A. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
FIG. 6. Relative TN-GEO enhancement similar to those shown in the bottom panel of Fig. 2 in the main text. For these experiments, portfolio optimization instances with a number of variables ranging from \(N = 30\) to \(N = 100\) were used. Here, each panel correspond to a different investment universes corresponding to a random subset of the S&P 500 market index. Note the trend for a larger quantum-inspired enhancement as the number of variables (assets) becomes larger, with the largest enhancement obtained in the case on instances with all the assets from the S&P 500 ( \(N = 500\) ), as shown in Fig. 2
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +summarycomparisonTNGEOvsalI.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e_det.mmd b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..5ca77102d049e2ed94f52492d70ba8d760746c6e --- /dev/null +++ b/preprint/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e/preprint__00c2b94550129fb8f084bb495841a196a9f5afe6d5c14e29f461a9abeaa8a98e_det.mmd @@ -0,0 +1,530 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 896, 175]]<|/det|> +# GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models + +<|ref|>text<|/ref|><|det|>[[44, 195, 652, 260]]<|/det|> +Francisco Fernandez Alcazar Alejandro Perdomo-Ortiz ( \(\square\) alejandro@zapatacomputing.com ) Zapata Computing Canada https://orcid.org/0000- 0001- 7176- 4719 + +<|ref|>text<|/ref|><|det|>[[44, 265, 295, 305]]<|/det|> +Mohammad Ghazi Vakili Zapata Computing Canada + +<|ref|>text<|/ref|><|det|>[[44, 311, 245, 351]]<|/det|> +Can Kalayci Pamukkale University + +<|ref|>text<|/ref|><|det|>[[44, 392, 102, 410]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 430, 137, 449]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 468, 310, 488]]<|/det|> +Posted Date: August 8th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 506, 463, 526]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 241950/v1 + +<|ref|>text<|/ref|><|det|>[[44, 543, 909, 586]]<|/det|> +License: © \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[105, 63, 900, 82]]<|/det|> +# GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models + +<|ref|>text<|/ref|><|det|>[[171, 94, 830, 111]]<|/det|> +Javier Alcazar, \(^{1}\) Mohammad Ghazi Vakili, \(^{1,2,3}\) Can B. Kalayci, \(^{1,4}\) and Alejandro Perdomo- Ortiz \(^{1,*}\) + +<|ref|>text<|/ref|><|det|>[[194, 114, 810, 180]]<|/det|> +\(^{1}\) Zapata Computing Canada Inc., 325 Front St W, Toronto, ON, M5V 2Y1 \(^{2}\) Department of Chemistry, University of Toronto, Toronto, ON, M5G 1Z8, Canada \(^{3}\) Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada \(^{4}\) Department of Industrial Engineering, Pamukkale University, Kinikli Campus, 20160, Denizli, Turkey (Dated: July 2, 2022) + +<|ref|>text<|/ref|><|det|>[[175, 188, 829, 452]]<|/det|> +We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator- Enhanced Optimization (GEO) strategy is flexible to adopt any generative model, from quantum to quantum- inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum- inspired version of GEO relying on tensor- network Born machines, and referred to hereafter as TN- GEO. We present two prominent strategies for using TN- GEO. The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN- GEO improves the performance of the classical solver as a standalone strategy in hard- to- solve instances. The second strategy uses TN- GEO as a standalone solver, i.e., when no previous observations are available. Here, we show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls. This might be ideal in situations where the cost function evaluation can be very expensive. To illustrate our results, we run these benchmarks in the context of the portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes. We show that TN- GEO can propose unseen candidates with lower cost function values than the candidates seen by classical solvers. This is the first demonstration of the generalization capabilities of quantum- inspired generative models that provide real value in the context of an industrial application. We also comprehensively compare state- of- the- art algorithms in a generalized version of the portfolio optimization problem. The results show that TN- GEO is among the best compared to these state- of- the- art algorithms; a remarkable outcome given the solvers used in the comparison have been fine- tuned for decades in this real- world industrial application. We see this as an important step toward a practical advantage with quantum- inspired models and, subsequently, with quantum generative models. + +<|ref|>sub_title<|/ref|><|det|>[[215, 479, 357, 492]]<|/det|> +## I. INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[86, 510, 486, 668]]<|/det|> +Along with machine learning and the simulation of materials, combinatorial optimization is one of top candidates for practical quantum advantage. That is, the moment where a quantum- assisted algorithm outperforms the best classical algorithms in the context of a real- world application with a commercial or scientific value. There is an ongoing portfolio of techniques to tackle optimization problems with quantum subroutines, ranging from algorithms tailored for quantum annealers (e.g., Refs. [1, 2]), gate- based quantum computers (e.g., Refs. [3, 4]) and quantum- inspired (QI) models based on tensor networks (e.g., Ref. [5]). + +<|ref|>text<|/ref|><|det|>[[86, 670, 486, 828]]<|/det|> +Regardless of the quantum optimization approach proposed to date, there is a need to translate the real- world problem into a polynomial unconstrained binary optimization (PUBO) expression - a task which is not necessarily straightforward and that usually results in an overhead in terms of the number of variables. Specific real- world use cases illustrating these PUBO mappings are depicted in Refs. [6] and [7]. Therefore, to achieve practical quantum advantage in the near- term, it would be ideal to find a quantum optimization strategy that can work on arbitrary objective functions, bypassing the translation and overhead limitations raised here. + +<|ref|>text<|/ref|><|det|>[[86, 830, 486, 857], [516, 479, 916, 650]]<|/det|> +In our work, we offer a solution to these challenges by proposing a novel generator- enhanced optimization (GEO) framework which leverage the power of (quantum or classical) generative models. This family of solvers can scale to large problems where combinatorial problems become intractable in real- world settings. Since our optimization strategy does not rely on the details of the objective function to be minimized, it is categorized in the group of so- called black- box solvers. Another highlight of our approach is that it can utilize available observations obtained from attempts to solve the optimization problem. These initial evaluations can come from any source, from random search trials to tailored state- of- the- art (SOTA) classical or quantum optimizers for the specific problem at hand. + +<|ref|>text<|/ref|><|det|>[[516, 653, 916, 881]]<|/det|> +Our GEO strategy is based on two key ideas. First, the generative- modeling component aims to capture the correlations from the previously observed data (step 0- 3 in Fig. 1). Second, since the focus here is on a minimization task, the (quantum) generative models need to be capable of generating new "unseen" solution candidates which have the potential to have a lower value for the objective function than those already "seen" and used as the training set (step 4- 6 in Fig. 1). This exploration towards unseen and valuable samples is by definition the fundamental concept behind generalization: the most desirable and important feature of any practical ML model. We will elaborate next on each of these components and demonstrate these two properties in the context of the tensor- network- based generative models and its application to a non- deterministic polynomial- time hard (NP- hard) version of the portfolio optimization in finance. + +<|ref|>text<|/ref|><|det|>[[515, 884, 916, 911]]<|/det|> +To the best of our knowledge, this is the first optimization strategy proposed to do an efficient blackbox exploration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 66, 487, 225]]<|/det|> +of the objective- function landscape with the help of generative models. Although other proposal leveraging generative models as a subroutine within the optimizer have appeared recently since the publication of our manuscript (e.g., see GFlowNets [8] and the variational neural annealing [9] algorithms), our framework is the only capable of both, handling arbitrary cost functions and also with the possibility of swapping the generator for a quantum or quantum- inspired implementation. GEO also has the enhanced feature that the more data is available, the more information can be passed and used to train the (quantum) generator. + +<|ref|>text<|/ref|><|det|>[[86, 226, 487, 341]]<|/det|> +In this work, we highlight the different features of GEO by performing a comparison with alternative solvers, such as Bayesian optimizers and generic solvers like simulated annealing. In the case of the specific real- world large- scale application of portfolio optimization, we compare against the SOTA optimizers and show the competitiveness of our approach. These results are presented in Sec. III. Next, in Sec. II, we present the GEO approach and its range of applicability. + +<|ref|>sub_title<|/ref|><|det|>[[106, 367, 465, 395]]<|/det|> +## II. QUANTUM-ENHANCED OPTIMIZATION WITH GENERATIVE MODELS + +<|ref|>text<|/ref|><|det|>[[86, 412, 487, 570]]<|/det|> +As shown in Fig. 1, depending on the GEO specifics we can construct an entire family of solvers whose generative modeling core range from classical, QI or quantum circuit (QC) enhanced, or hybrid quantum- classical model. These options can be realized by utilizing, for example, Boltzmann machines [10] or Generative Adversarial Networks (GAN) [11], Tensor- Network Born Machines (TNBm) [12], Quantum Circuit Born Machines (QCBM)[13] or Quantum- Circuit Associative Adversarial Networks (QC- AAN)[14] respectively, to name just a few of the many options for this probabilistic component. + +<|ref|>text<|/ref|><|det|>[[86, 571, 487, 715]]<|/det|> +QI algorithms come as an interesting alternative since these allow one to simulate larger scale quantum systems with the help of efficient tensor- network (TN) representations. Depending on the complexity of the TN used to build the quantum generative model, one can simulate from thousands of problem variables to a few tens, the latter being the limit of simulating an universal gate- based quantum computing model. This is, one can control the amount of quantum resources available in the quantum generative model by choosing the QI model. + +<|ref|>text<|/ref|><|det|>[[86, 716, 487, 860]]<|/det|> +Therefore, from all quantum generative model options, we chose to use a QI generative model based on TNs to test and scale our GEO strategy to instances with a number of variables commensurate with those found in industrial- scale scenarios. We refer to our solver hereafter as TN- GEO. For the training of our TN- GEO models we followed the work of Han et al. [15] where they proposed to use Matrix Product States (MPS) to build the unsupervised generative model. The latter extends the scope from early successes of quantum- inspired models in the context of supervised ML [16- 19]. + +<|ref|>text<|/ref|><|det|>[[85, 861, 487, 889]]<|/det|> +In this paper we will discuss two modes of operation for our family of quantum- enhanced solvers: + +<|ref|>text<|/ref|><|det|>[[113, 897, 487, 912]]<|/det|> +- In TN-GEO as a "booster" we leverage past observa + +<|ref|>text<|/ref|><|det|>[[555, 66, 917, 123]]<|/det|> +tions from classical (or quantum) solvers. To illustrate this mode we use observations from simulated annealing (SA) runs. Simulation details are provided in Appendix A 5. + +<|ref|>text<|/ref|><|det|>[[545, 133, 917, 218]]<|/det|> +- In TN-GEO as a stand-alone solver all initial cost function evaluations are decided entirely by the quantum-inspired generative model, and a random prior is constructed just to give support to the target probability distribution the MPS model is aiming to capture. Simulation details are provided in Appendix A 6. + +<|ref|>text<|/ref|><|det|>[[515, 231, 916, 275]]<|/det|> +Both of these strategies are captured in the algorithm workflow diagram in Fig. 1 and described in more detail in Appendix A. + +<|ref|>sub_title<|/ref|><|det|>[[601, 302, 830, 315]]<|/det|> +## III. RESULTS AND DISCUSSION + +<|ref|>text<|/ref|><|det|>[[515, 334, 917, 650]]<|/det|> +To illustrate the implementation for both of these settings we tested their performance on an NP- hard version of the portfolio optimization problem with cardinality constraints. The selection of optimal investment on a specific set of assets, or portfolios, is a problem of great interest in the area of quantitative finance. This problem is of practical importance for investors, whose objective is to allocate capital optimally among assets while respecting some investment restrictions. The goal of this optimization task, introduced by Markowitz [20], is to generate a set of portfolios that offers either the highest expected return (profit) for a defined level of risk or the lowest risk for a given level of expected return. In this work, we focus in two variants of this cardinality constrained optimization problem. The first scenario aims to choose portfolios which minimize the volatility or risk given a specific target return (more details are provided in Appendix A 1). To compare with the reported results from the best performing SOTA algorithms, we ran TN- GEO in a second scenario where the goal is to choose the best portfolio given a fixed level of risk aversion. This is the most commonly used version of this optimization problem when it comes to comparison among SOTA solvers in the literature (more details are provided in Appendix A 2). + +<|ref|>sub_title<|/ref|><|det|>[[545, 678, 887, 706]]<|/det|> +### A. TN-GEO as a booster for any other combinatorial optimization solver + +<|ref|>text<|/ref|><|det|>[[515, 724, 916, 912]]<|/det|> +In Fig. 2 we present the experimental design and the results obtained from using TN- GEO as a booster. In these experiments we illustrate how using intermediate results from simulated annealing (SA) can be used as seed data for our TN- GEO algorithm. As described in Fig. 2, there are two strategies we explored (strategies 1 and 2) to compare with our TN- GEO strategy (strategy 4). To fairly compare each strategy, we provide each with approximately the same computational wall- clock time. For strategy 2, this translates into performing additional restarts of SA with the time allotted for TN- GEO. In the case of strategy 1, where we explored different settings for SA from the start compared to those used in strategy 2, this amounts to using the same total number + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[91, 65, 910, 372]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 390, 919, 578]]<|/det|> +
FIG. 1. Scheme for our Generator-Enhanced Optimization (GEO) strategy. The GEO framework leverages generative models to utilize previous samples coming from any quantum or classical solver. The trained quantum or classical generator is responsible for proposing candidate solutions which might be out of reach for conventional solvers. This seed data set (step 0) consists of observation bitstrings \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and their respective costs \(\{\sigma^{(i)}\}_{\mathrm{seed}}\) . To give more weight to samples with low cost, the seed samples and their costs are used to construct a softmax function which serves as a surrogate to the cost function but in probabilistic domain. This softmax surrogate also serves as a prior distribution from which the training set samples are withdrawn to train the generative model (steps 1-3). As shown in the figure between steps 1 and 2, training samples from the softmax surrogate are biased favoring those with low cost value. For the work presented here, we implemented a tensor-network (TN)-based generative model. Therefore, we refer to this quantum-inspired instantiation of GEO as TN-GEO. Other families of generative models from classical, quantum, or hybrid quantum-classical can be explored as expounded in the main text. The quantum-inspired generator corresponds to a tensor-network Born machine (TNBM) model which is used to capture the main features in the training data, and to propose new solution candidates which are subsequently post selected before their costs \(\{\sigma^{(i)}\}_{\mathrm{new}}\) are evaluated (steps 4-6). The new set is merged with the seed data set (step 7) to form an updated seed data set (step 8) which is to be used in the next iteration of the algorithm. More algorithmic details for the two TN-GEO strategies proposed here, as a booster or as a stand-alone solver, can be found in the main text and in A5 and A6 respectively.
+ +<|ref|>text<|/ref|><|det|>[[86, 605, 488, 735]]<|/det|> +of number of cost functions evaluations as those allocated to SA in strategy 2. For our experiments this number was set to 20,000 cost function evaluations for strategies 1 and 2. In strategy 4, the TN- GEO was initialized with a prior consisting of the best 1,000 observations out of the first 10,000 coming from strategy 2 (see Appendix A 5 for details). To evaluate the performance enhancement obtained from the TN- GEO strategy we compute the relative TN- GEO enhancement \(\eta\) , which we define as + +<|ref|>equation<|/ref|><|det|>[[173, 736, 487, 777]]<|/det|> +\[\eta = \frac{C_{\mathrm{min}}^{\mathrm{cl}}}{C_{\mathrm{min}}^{\mathrm{cl}}} = \frac{C_{\mathrm{min}}^{\mathrm{TN - GEO}}}{C_{\mathrm{min}}^{\mathrm{cl}}}\times 100\% . \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[86, 780, 488, 884]]<|/det|> +Here, \(C_{\mathrm{min}}^{\mathrm{cl}}\) is the lowest minimum value found by the classical strategy (e.g., strategies 1- 3) while \(C_{\mathrm{min}}^{\mathrm{TN - GEO}}\) corresponds to the lowest value found with the quantum- enhanced approach (e.g., with TN- GEO). Therefore, positive values reflect an improvement over the classical- only approaches, while negative values indicate cases where the classical solvers outperform the quantum- enhanced proposal. + +<|ref|>text<|/ref|><|det|>[[86, 884, 487, 912]]<|/det|> +As shown in the Fig. 2, we observe that TN- GEO outperforms on average both of the classical- only strategies imple + +<|ref|>text<|/ref|><|det|>[[515, 605, 917, 810]]<|/det|> +As shown in the Fig. 2, we observe that TN- GEO outperforms on average both of the classical- only strategies implemented. The quantum- inspired enhancement observed here, as well as the trend for a larger enhancement as the number of variables (assets) becomes larger, is confirmed in many other investment universes with a number of variables ranging from \(N = 30\) to \(N = 100\) (see Appendix B for more details). Although we show an enhancement compared to SA, similar results could be expected when other solvers are used, since our approach builds on solutions found by the solver and does not compete with it from the start of the search. Furthermore, the more data available, the better the expected performance of TN- GEO is. An important highlight of TN- GEO as a booster is that these previous observations can come from a combination of solvers, as different as purely quantum or classical, or hybrid. + +<|ref|>text<|/ref|><|det|>[[515, 812, 916, 912]]<|/det|> +The observed performance enhancement compared with the classical- only strategy must be coming from a better exploration of the relevant search space, i.e., the space of those bitstring configurations \(x\) representing portfolios which could yield a low risk value for a specified expected investment return. That is the intuition behind the construction of TN- GEO. The goal of the generative model is to capture the important + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[90, 81, 480, 460]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 480, 488, 785]]<|/det|> +
FIG. 2. TN-GEO as a booster. Top: Strategies 1-3 correspond to the current options a user might explore when solving a combinatorial optimization problem with a suite of classical optimizers such as simulated annealing (SA), parallel tempering (PT), generic algorithms (GA), among others. In strategy 1, the user would use its computational budget with a preferred solver. In strategy 2-4 the user would inspect intermediate results and decide whether to keep trying with the same solver (strategy 2), try a new solver or a new setting of the same solver used to obtain the intermediate results (strategy 3), or, as proposed here, to use the acquired data to train a quantum or quantum-inspired generative model within a GEO framework such as TN-GEO (strategy 4). Bottom: Results showing the relative TN-GEO enhancement from TN-GEO over either strategy 1 or strategy 2. Positive values indicate runs where TN-GEO outperformed the respective classical strategies (see Eq. 1). The data represents bootstrapped medians from 20 independent runs of the experiments and error bars correspond to the 95% confidence intervals. The two instances presented here correspond to portfolio optimization instances where all the assets in the S&P 500 market index where included \((N = 500)\) , under two different cardinality constraints \(\kappa\) . This cardinality constraint indicate the number of assets that can be included at a time in valid portfolios, yielding a search space of \(M = \binom{N}{\kappa}\) , with \(M \sim 10^{69}\) portfolios candidates for \(\kappa = 50\) .
+ +<|ref|>text<|/ref|><|det|>[[85, 821, 487, 850]]<|/det|> +correlations in the previously observed data, and to use its generative capabilities to propose similar new candidates. + +<|ref|>text<|/ref|><|det|>[[85, 854, 487, 912]]<|/det|> +Generating new candidates is by no means a trivial task in ML and it determines the usefulness and power of the model since it measure its generalization capabilities. In this setting of QI generative models, one expects that the MPS- based + +<|ref|>text<|/ref|><|det|>[[515, 66, 917, 238]]<|/det|> +generative model at the core of TN- GEO is not simply memorizing the observations given as part of the training set, but that it will provide new unseen candidates. This is an idea which has been recently tested and demonstrated to some extent on synthetic data sets (see e.g., Refs. [21], [22] and [23]. In Fig. 3 we demonstrate that our quantum- inspired generative model is generalizing to new samples and that these add real value to the optimization search. To the best of our knowledge this is the first demonstration of the generalization capabilities of quantum generative models in the context of a real- world application in an industrial scale setting, and one of our main findings in our paper. + +<|ref|>text<|/ref|><|det|>[[515, 240, 917, 414]]<|/det|> +Note that our TN- based generative model not only produces better minima than the classical seed data, but it also generates a rich amount of samples in the low cost spectrum. This bias is imprinted in the design of our TN- GEO and it is the purpose of the softmax surrogate prior distribution shown in Fig. 1. This richness of new samples could be useful not only for the next iteration of the algorithm, but they may also be readily of value to the user solving the application. In some applications there is value as well in having information about the runnersup. Ultimately, the cost function is just a model of the system guiding the search, and the lowest cost does not translate to the best performance in the real- life investment strategy. + +<|ref|>sub_title<|/ref|><|det|>[[515, 446, 915, 460]]<|/det|> +### B. Generator-Enhanced Optimization as a Stand-Alone Solver + +<|ref|>text<|/ref|><|det|>[[515, 479, 917, 912]]<|/det|> +Next, we explore the performance of our TN- GEO framework as a stand- alone solver. The focus is in combinatorial problems whose cost functions are expensive to evaluate and where finding the best minimum within the least number of calls to this function is desired. In Fig. 4 we present the comparison against four different classical optimization strategies. As the first solver, we use the random solver, which corresponds to a fully random search strategy over the \(2^{N}\) bitstrings of all possible portfolios, where \(N\) is the number of assets in our investment universe. As second solver, we use the conditioned random solver, which is a more sophisticated random strategy compared to the fully random search. The conditioned random strategy uses the a priori information that the search is restricted to bitstrings containing a fixed number of \(\kappa\) assets. Therefore the number of combinatorial possibilities is \(M = \binom{N}{\kappa}\) , which is significantly less than \(2^{N}\) . As expected, when this information is not used the performance of the random solver over the entire \(2^{N}\) search space is worse. The other two competing strategies considered here are SA and the Bayesian optimization library GPyOpt [24]. In both of these classical solvers, we adapted their search strategy to impose this cardinality constraint with fixed \(\kappa\) as well (details in Appendix. A 4). This raises the bar even higher for TN- GEO which is not using that a priori information to boost its performance [25]. As explained in Appendix A 6, we only use this information indirectly during the construction of the artificial seed data set which initializes the algorithm (step 0, Fig. 1), but it is not a strong constraint during the construction of the QI generative model (step 3, Fig. 1) or imposed to generate the new candidate samples coming from it (step 4, + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[85, 75, 914, 324]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 335, 920, 430]]<|/det|> +
FIG. 3. Generalization capabilities of our quantum-inspired generative model. Left panel corresponds to an investment universe with \(N = 50\) assets while the right panel corresponds to one with \(N = 100\) assets. The blue histogram represents the number of observations or portfolios obtained from the classical solver (seed data set). In orange we represent samples coming from our quantum generative model at the core of TN-GEO. The green dash line is positioned at the best risk value found in the seed data. This mark emphasizes all the new outstanding samples obtained with the quantum generative model and which correspond to lower portfolio risk value (better minima) than those available from the classical solver by itself. The number of outstanding samples in the case of \(N = 50\) is equal to 31, while 349 outstanding samples were obtained from the MPS generative model in the case of \(N = 100\) .
+ +<|ref|>image<|/ref|><|det|>[[200, 472, 777, 793]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 808, 920, 902]]<|/det|> +
FIG. 4. TN-GEO as a stand-alone solver: In this comparison of TN-GEO against four classical competing strategies, investment universes are constructed from subsets of the S&P 500 with a diversity in the number of assets (problem variables) ranging from \(N = 30\) to \(N = 100\) . The goal is to minimize the risk given an expected return which is one of the specifications in the combinatorial problem addressed here. Error bars and their 95% confidence intervals are calculated from bootstrapping over 100 independent random initializations for each solver on each problem. The main line for each solver corresponds to the bootstrapped median over these 100 repetitions, demonstrating the superior performance of TN-GEO over the classical solvers considered here. As specified in the text, with the exception of TN-GEO, the classical solvers use to their advantage the a priori information coming from the cardinality constraint imposed in the selection of valid portfolios.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 66, 488, 110]]<|/det|> +Fig. 1). Post selection can be applied a posteriori such that only samples with the right cardinality are considered as valid candidates towards the selected set (step 5, Fig. 1). + +<|ref|>text<|/ref|><|det|>[[86, 111, 488, 181]]<|/det|> +In Fig. 4 we demonstrate the advantage of our TN- GEO stand- alone strategy compared to any of these widely- used solvers. In particular, it is interesting to note that the gap between TN- GEO and the other solvers seems to be larger for larger number of variables. + +<|ref|>sub_title<|/ref|><|det|>[[133, 214, 440, 227]]<|/det|> +### C. Comparison with state-of-the-art algorithms + +<|ref|>text<|/ref|><|det|>[[86, 246, 488, 504]]<|/det|> +Finally, we compare TN- GEO with nine different leading SOTA optimizers covering a broad spectrum of algorithmic strategies for this specific combinatorial problem, based on and referred hereafter as: 1) GTS [26], the genetic algorithms, tabu search, and simulated annealing; 2) IPSO [27], an improved particle swarm optimization algorithm [27]; 3) IPSO- SA [28], a hybrid algorithm combining particle swarm optimization and simulated annealing; 4) PBILD [29], a population- based incremental learning and differential evolution algorithm; 5) GRASP [30], a greedy randomized adaptive solution procedure; 6) ABCFEIT [31], an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures; 7) HAAG [32], a hybrid algorithm integrating ant colony optimization, artificial bee colony and genetic algorithms; 8) VNSQP [33], a variable neighborhood search algorithm combined with quadratic programming; and, 9) RCABC [34], a rapidly converging artificial bee colony algorithm. + +<|ref|>text<|/ref|><|det|>[[86, 506, 488, 635]]<|/det|> +The test data used by the vast majority of researchers in the literature who have addressed the problem of cardinality- constrained portfolio optimization come from ORLibrary [35], which correspond to the weekly prices between March 1992 and September 1997 of the following indexes: Hang Seng in Hong Kong (31 assets); DAX 100 in Germany (85 assets); FTSE 100 in the United Kingdom (89 assets); S&P 100 in the United States (98 assets); and Nikkei 225 in Japan (225 assets). + +<|ref|>text<|/ref|><|det|>[[86, 637, 488, 823]]<|/det|> +Here we present the results obtained with TN- GEO and its comparison with the nine different SOTA metaheuristic algorithms mentioned above and whose results are publicly available from the literature. Table I shows the results of all algorithms and all performance metrics for each of the 5 index data sets (for more details on the evaluation metrics, see Appendix A 2). Each algorithm corresponds to a different column, with TN- GEO in the rightmost column. The values are shown in red if the TN- GEO algorithm performed better or equally well compared to the other algorithms on the corresponding performance metric. The numbers in bold mean that the algorithm found the best (lowest) value across all algorithms. + +<|ref|>text<|/ref|><|det|>[[85, 825, 488, 882]]<|/det|> +From all the entries in this table, \(67\%\) of them correspond to red entries, where TN- GEO either wins or draws, which is a significant percentage giving that these optimizers are among the best reported in the last decades. + +<|ref|>text<|/ref|><|det|>[[85, 884, 488, 911]]<|/det|> +In Table II we show a pairwise comparison of TN- GEO against each of the SOTA optimizers. This table reports the + +<|ref|>text<|/ref|><|det|>[[516, 66, 917, 181]]<|/det|> +number of times TN- GEO wins, loses, or draws compared to results reported for the other optimizer, across all the performance metrics and for all the 5 different market indexes. Note that since not all the performance metrics are reported for all the solvers and market indexes, the total number of wins, draws, or losses varies. Therefore, we report in the same table the overall percentage of wins plus draws in each case. We see that this percentage is greater than \(50\%\) in all the cases. + +<|ref|>text<|/ref|><|det|>[[516, 183, 917, 398]]<|/det|> +Furthermore, in Table II, we use the Wilcoxon signed- rank test [36], which is a widely used nonparametric statistical test used to evaluate and compare the performance of different algorithms in different benchmarks [37]. Therefore, to statistically validate the results, a Wilcoxon signed- rank test is performed to provide a meaningful comparison between the results from TN- GEO algorithm and the SOTA metaheuristic algorithms. The Wilcoxon signed- rank test tests the null hypothesis that the median of the differences between the results of the algorithms is equal to 0. Thus, it tests whether there is no significant difference between the performance of the algorithms. The null hypothesis is rejected if the significance value \((p)\) is less than the significance level \((\alpha)\) , which means that one of the algorithms performs better than the other. Otherwise, the hypothesis is retained. + +<|ref|>text<|/ref|><|det|>[[516, 400, 917, 544]]<|/det|> +As can be seen from the table, the TN- GEO algorithm significantly outperforms the GTS and PBILD methods on all performance metrics rejecting the null hypothesis at the 0.05 significance level. On the other hand, the null hypotheses are accepted at \(\alpha = 0.05\) for the TN- GEO algorithm over the other remaining algorithms. Thus, in terms of performance on all metrics combined, the results show that there is no significant difference between TN- GEO and these remaining seven SOTA optimizers (IPSO, IPSO- SA, GRASP, ABCFEIT, HAAG, VNSQP, and RCABC) + +<|ref|>text<|/ref|><|det|>[[516, 545, 917, 602]]<|/det|> +Overall, the results confirm the competitiveness of our quantum- inspired proposed approach against SOTA metaheuristic algorithms. This is remarkable given that these metaheuristics have been explored and fine- tuned for decades. + +<|ref|>sub_title<|/ref|><|det|>[[661, 634, 770, 647]]<|/det|> +## IV. OUTLOOK + +<|ref|>text<|/ref|><|det|>[[516, 666, 917, 911]]<|/det|> +Compared to other quantum optimization strategies, an important feature of TN- GEO is its algorithmic flexibility. As shown here, unlike other proposals, our GEO framework can be applied to arbitrary cost functions, which opens the possibility of new applications that cannot be easily addressed by an explicit mapping to a polynomial unconstrained binary optimization (PUBO) problem. Our approach is also flexible with respect to the source of the seed samples, as they can come from any solver, possibly more efficient or even application- specific optimizers. The demonstrated generalization capabilities of the generative model that forms its core, helps TN- GEO build on the progress of previous experiments with other state- of- the- art solvers, and it provides new candidates that the classical optimizer may not be able to achieve on its own. We are optimistic that this flexible approach will open up the broad applicability of quantum and quantum- inspired generative models to real- world combinatorial optimization + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 113, 883, 604]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[85, 74, 920, 115]]<|/det|> +TABLE I. Detailed comparison with SOTA algorithms for each of the five index data sets and on seven different performance indicators described in Appendix A 2. Entries in red correspond to cases where TN-GEO performed better or tied compared to the other algorithm. Entries in bold, corresponding to the best (lowest) value, for each specific indicator. + +
Data SetPerformance IndicatorGTSIPSOIPSO-SAPBILDGRASPABCFEITHAAGVNSQPRCABCTN-GEO
Hang SengMean1.09571.0953-1.14311.09651.09531.09651.09641.08731.0958
Median1.2181--1.23901.21551.21811.21811.21551.21541.2181
Min----0.00000.00000.00000.0000-0.0000
Max----1.55381.55381.55381.5538-1.5538
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--1.6368-1.64001.64321.63951.63971.63421.6392
MRE--0.6059-0.60600.60470.60850.60580.59640.6082
DAX100Mean2.54242.5417-2.42512.31262.32582.31302.31252.28982.3142
Median2.5466--2.58662.56302.56782.55872.56302.56292.5660
Minimum----0.00590.00230.00230.00590.00590.0023
Maximum----4.02754.02754.02754.0275-4.0275
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--6.7806-6.75936.79256.78066.75836.83266.7540
MRE--1.2770-1.27691.27611.27801.27671.23571.2763
FTSE100Mean1.10761.0628-0.97060.84510.84810.84510.84530.84060.8445
Median1.0841--1.08411.08411.08411.0841-1.08411.0841
Minimum----0.00160.00470.00060.00450.00160.0047
Maximum----2.05762.06382.06052.06692.06702.0775
MEUCD--0.0000-0.00000.00000.00000.0000-0.0000
VRE--2.4701-2.43502.43972.43502.43492.41492.4342
MRE--0.3247-0.32450.32550.31860.32520.32070.3254
S&P100Mean1.93281.6890-1.63861.29371.29301.29301.26491.34641.2918
Median1.1823--1.16921.14201.13691.13231.13231.15151.1452
Minimum----0.00090.00000.00000.00000.00090.0000
Maximum----5.45515.44225.46425.45515.45205.4422
MEUCD--0.0001-0.00010.00010.00010.0001-0.0001
VRE--2.6281-2.52112.52602.52552.51052.53642.5269
MRE--0.7846-0.90630.88850.70440.90720.88580.9117
NikkeiMean0.60660.6870-0.59720.57820.57810.57810.59040.56650.5793
Median0.6093--0.58960.58570.58560.58540.58570.58580.5855
Minimum----0.00000.00000.00000.0000-0.0000
Maximum----1.16061.16061.16071.16061.16061.1606
MEUCD--0.0000-0.00000.00000.00000.0000-0.0000
VRE--0.9583-0.83590.83960.81910.85610.83140.8353
MRE--1.7090-0.41840.41470.42330.42170.40420.4229
+ +<|ref|>table<|/ref|><|det|>[[85, 680, 919, 807]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[85, 625, 919, 680]]<|/det|> +TABLE II. Pairwise comparison of TN-GEO against each of the SOTA optimizers. The asymptotic significance is part of the Wilcoxon signedrank test results. The null hypothesis that the performance of the two algorithms is the same is tested at the \(95\%\) confidence level (significance level: \(\alpha = .05\) ). Results show that TN-GEO is on par with all the SOTA algorithms, and in two cases, GTS and PBILD, it significantly outperforms them. We also report the count for TN-GEO wins, losses, and ties, compared to each of the other algorithms. + +
TN-GEO vs Other:GTSIPSOIPSO-SAPBILDGRASPABCFEITHAAGVNSQPRCABC
Wins(+)6469121011118
Loss(-)2140129111216
Ties20511116131211
(Wins+Ties)/Total80%80%67%100%66%74%69%66%54%
Asymptotic significance (p).036.080.308.008.247.888.363.594.110
DecisionRejectRetainRetainRejectRetainRetainRetainRetainRetain
+ +<|ref|>text<|/ref|><|det|>[[87, 831, 293, 846]]<|/det|> +problems at the industrial scale. + +<|ref|>text<|/ref|><|det|>[[86, 854, 488, 913]]<|/det|> +Although we have limited the scope of this work to tensor network- based generative quantum models, it would be a natural extension to consider other generative quantum models as well. For example, hybrid classical quantum models such as + +<|ref|>text<|/ref|><|det|>[[515, 830, 917, 904]]<|/det|> +quantum circuit associative adversarial networks (QC- AAN) [14] can be readily explored to harness the power of generative quantum models with so- called noisy intermediate- scale quantum (NISQ) devices [38]. In particular, the QC- AAN framework opens up the possibility of working with a larger + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 66, 487, 240]]<|/det|> +number of variables and going beyond discrete values (e.g., variables with continuous values). Both quantum- inspired and hybrid quantum- classical algorithms can be tested in this GEO framework in even larger problem sizes of this NP- hard version of the portfolio optimization problem or any other combinatorial optimization problem. As the number of qubits in NISQ devices increases, it would be interesting to explore generative models that can utilize more quantum resources, such as Quantum Circuit Born Machines (QCBM)[13]: a general framework to model arbitrary probability distributions and perform generative modeling tasks with gate- based quantum computers. + +<|ref|>text<|/ref|><|det|>[[86, 240, 487, 384]]<|/det|> +Increasing the expressive power of the quantum- inspired core of MPS to other more complex but still efficient QI approaches, such as tree- tensor networks [39], is another interesting research direction. Although we have fully demonstrated the relevance and scalability of our algorithm for industrial applications by increasing the performance of classical solvers on industrial scale instances (all 500 assets in the S&P 500 market index), there is a need to explore the performance improvement that could be achieved by more complex TN representations or on other combinatorial problems. + +<|ref|>text<|/ref|><|det|>[[86, 384, 487, 514]]<|/det|> +Although the goal of GEO was to show good behavior as a general black- box algorithm without considering the specifics of the study application, it is a worthwhile avenue to exploit the specifics of the problem formulation to improve its performance and runtime. In particular, for the portfolio optimization problem with a cardinality constraint, it is useful to incorporate this constraint as a natural MPS symmetry, thereby reducing the effective search space of feasible solutions from the size of the universe to the cardinality size. + +<|ref|>text<|/ref|><|det|>[[515, 67, 916, 356]]<|/det|> +Finally, our thorough comparison with SOTA algorithms, which have been fine- tuned for decades on this specific application, shows that our TN- GEO strategy manages to outperform a couple of these and is on par with the other seven optimizers. This is a remarkable feat for this new approach and hints at the possibility of finding commercial value in these quantum- inspired strategies in large- scale real- world problems, as the instances considered in this work. Also, it calls for more fundamental insights towards understanding when and where it would be beneficial to use this TN- GEO framework, which relies heavily on its quantum- inspired generative ML model. For example, understanding the intrinsic bias in these models, responsible for their remarkable performance, is another important milestone on the road to practical quantum advantage with quantum devices in the near future. The latter can be asserted given the tight connection of these quantum- inspired TN models to fully quantum models deployed on quantum hardware. And this question of when to go with quantum- inspired or fully quantum models is a challenging one that we are exploring in ongoing future work. + +<|ref|>sub_title<|/ref|><|det|>[[636, 407, 797, 420]]<|/det|> +## ACKNOWLEDGMENTS + +<|ref|>text<|/ref|><|det|>[[515, 442, 916, 513]]<|/det|> +The authors would like to acknowledge Manuel S. Rudolph, Marta Mauri, Matthew J.S. 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[32] Can B Kalayci, Olcay Polat, and Mehmet A Akbay, "An efficient hybrid metaheuristic algorithm for cardinality constrained + +<|ref|>text<|/ref|><|det|>[[512, 68, 919, 640]]<|/det|> +portfolio optimization," Swarm and Evolutionary Computation 54, 100662 (2020). [33] Mehmet Anil Akbay, Can B Kalayci, and Olcay Polat, "A parallel variable neighborhood search algorithm with quadratic programming for cardinality constrained portfolio optimization," Knowledge- Based Systems 198, 105944 (2020). [34] Tunchan Cura, "A rapidly converging artificial bee colony algorithm for portfolio optimization," Knowledge- Based Systems 233, 107505 (2021). [35] John E Beasley, "Or- library: distributing test problems by electronic mail," Journal of the operational research society 41, 1069- 1072 (1990). [36] Frank Wilcoxon, "Individual comparisons by ranking methods," in Breakthroughs in statistics (Springer, 1992) pp. 196- 202. [37] Janez Demšar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine Learning Research 7, 1- 30 (2006). [38] John Preskill, "Quantum computing in the NISQ era and beyond," Quantum 2, 79 (2018). [39] Song Cheng, Lei Wang, Tao Xiang, and Pan Zhang, "Tree tensor networks for generative modeling," Phys. Rev. B 99, 155131 (2019). [40] Joachim Dahl Martin Andersen and Lieven Vandenberghe, "Python software for convex optimization," http://cvxopt.org (2020). [41] Tunchan Cura, "Particle swarm optimization approach to portfolio optimization," Nonlinear analysis: Real world applications 10, 2396- 2406 (2009). [42] Ignacio Cirac, David Perez- Garcia, Norbert Schuch, and Frank Verstraete, "Matrix product states and projected entangled pair states: Concepts, symmetries, and theorems," (2020), arXiv:2011.12127 [quant- ph]. [43] "Code for unsupervised generative modeling using matrix product states," https://github.com/congzllwag/UnsupGenModbyMPS (2018). [44] Matthew T. Perry and Richard J. Wagner, "Python module for simulated annealing," https://github.com/perrygeo/simanneal (2019). [45] Javier Alcazar, Vicente Leyton- Ortega, and Alejandro Perdomo- Ortiz, "Classical versus quantum models in machine learning: insights from a finance application," Machine Learning: Science and Technology 1, 035003 (2020). + +<|ref|>sub_title<|/ref|><|det|>[[647, 664, 785, 678]]<|/det|> +## Appendix A: Methods + +<|ref|>sub_title<|/ref|><|det|>[[560, 694, 874, 708]]<|/det|> +## 1. Generation of portfolio optimization instances + +<|ref|>text<|/ref|><|det|>[[515, 725, 918, 810]]<|/det|> +The portfolio optimization problem aims at determining the fractions \(w_{i}\) of a given capital to be invested in each asset \(i\) of a universe of \(N\) assets, such that the risk \(\sigma (w)\) for a given level \(\rho\) of the expected return \(\langle r(w)\rangle\) is minimized, constrained to \(\sum_{i}w_{i} = 1\) . The problem can be formulated as: + +<|ref|>equation<|/ref|><|det|>[[546, 821, 916, 848]]<|/det|> +\[\min_{w}\{\sigma^{2}(w) = w^{T}\cdot \pmb {\Sigma}\cdot \pmb {w}:\langle r(w)\rangle = w\cdot \pmb {r} = \rho \} \mathrm{(A1)}\] + +<|ref|>text<|/ref|><|det|>[[515, 854, 918, 911]]<|/det|> +where the vectors \(w\) and \(r\) have dimensionality \(N\) , \(\pmb{\Sigma}\) is the sample covariance matrix obtained from the return time series of pair of asset \(i\) and \(j\) , and \(r\) is the vector of average return of the time series for each asset, with each daily return, \(r^{t}\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 66, 488, 153]]<|/det|> +calculated as the relative increment in asset price from its previous day (i.e., \(r^{t} = (p^{t} - p^{(t - 1)}) / p^{(t - 1)}\) , with \(p^{t}\) as the price for a particular asset at time \(t\) ). The solution to Eq. A1 for a given return level \(\rho\) corresponds to the optimal portfolio strategy \(\boldsymbol{w}^{*}\) and the minimal value of this objective function \(\sigma (\boldsymbol {w})\) correspond to the portfolio risk and will be denoted by \(\sigma_{\rho}^{*}\) . + +<|ref|>text<|/ref|><|det|>[[85, 155, 488, 369]]<|/det|> +Note that the optimization task in Eq. A1 has the potential outcome of investing small amounts in a large number of assets as an attempt to reduce the overall risk by "over diversifying" the portfolio. This type of investment strategy can be challenging to implement in practice: portfolios composed of a large number of assets are difficult to manage and may incur in high transaction costs. Therefore, several restrictions are usually imposed on the allocation of capital among assets, as a consequence of market rules and conditions for investment or to reflect investor profiles and preferences. For instance, constraints can be included to control the amount of desired diversification, i.e., modifying bound limits per asset \(i\) , denoted by \(\{l_{i}, u_{i}\}\) , to the proportion of capital invested in the investment on individual assets or a group of assets, thus the constraint \(l_{i} < w_{i} < u_{i}\) could be considered. + +<|ref|>text<|/ref|><|det|>[[85, 370, 488, 569]]<|/det|> +Additionally, a more realistic and common scenario is to include in the optimization task a cardinality constraint, which limits directly the number of assets to be transacted to a pre- specified number \(\kappa < N\) . Therefore, the number of different sets to be treated is \(M = \binom{N}{\kappa}\) . In this scenario, the problem can be formulated as a Mixed- Integer Quadratic Program (MIQP) with the addition of binary variables \(x_{i} \in \{0, 1\}\) per asset, for \(i = 1, \ldots , N\) , which are set to "1" when the \(i\) - th asset is included as part of the \(\kappa\) assets, or "0" if it is left out of this selected set. Therefore, valid portfolios would have a number \(\kappa\) of 1's, as specified in the cardinality constraint. For example, for \(N = 4\) and \(\kappa = 2\) , the six different valid configurations can be encoded as \(\{0011, 0101, 0110, 1001, 1010, 1100\}\) . + +<|ref|>text<|/ref|><|det|>[[100, 572, 460, 586]]<|/det|> +The optimization task can then be described as follows + +<|ref|>equation<|/ref|><|det|>[[132, 612, 488, 696]]<|/det|> +\[\begin{array}{rl} & {\min_{\boldsymbol {w},\boldsymbol {x}}\{\sigma^2 (\boldsymbol {w}):}\\ & {\qquad \langle \boldsymbol {r}(\boldsymbol {w})\rangle = \rho ,}\\ & {\qquad l_i\boldsymbol {x}_i< w_i< u_i\boldsymbol {x}_i\quad i = 1,\dots ,N,}\\ & {\qquad \mathbf{1}\cdot \boldsymbol {x} = \kappa \} .} \end{array} \quad (A2)\] + +<|ref|>text<|/ref|><|det|>[[85, 708, 488, 883]]<|/det|> +In this reformulated problem we denote by \(\sigma_{\rho ,\kappa}^{*}\) the minimum portfolio risk outcome from Eq. A2 for a given return level \(\rho\) and cardinality \(\kappa\) . The optimal solution vectors \(\boldsymbol{w}^{*}\) and \(\boldsymbol{x}^{*}\) define the portfolio investment strategy. Adding the cardinality constraint and the investment bound limits transforms a simple convex optimization problem (Eq. A1) into a much harder non- convex NP- hard problem. For all the problem instance generation in this work we chose \(\kappa = N / 2\) and the combinatorial nature of the problems lies in the growth of the search space associated with the binary vector \(\boldsymbol{x}\) , which makes it intractable to exhaustively explore for a number of assets in the few hundreds. The size of the search space here is \(M = \binom{N}{N / 2}\) + +<|ref|>text<|/ref|><|det|>[[85, 884, 488, 912]]<|/det|> +It is important to note that given a selection of which assets belong to the portfolio by instantiating \(\boldsymbol{x}\) (say with a specific + +<|ref|>text<|/ref|><|det|>[[515, 65, 917, 200]]<|/det|> +\(\boldsymbol{x}^{(i)}\) ), solving the optimization problem in Eq. A2 to find the respective investment fractions \(\boldsymbol{w}^{(i)}\) and risk value \(\sigma_{\rho ,N / 2}^{(i)}\) can be efficiently achieved with conventional quadratic programming (QP) solvers. In this work we used the python module cvxopt [40] for solving this problem. Note that we exploit this fact to break this constrained portfolio optimization problem into a combinatorial intractable one (find best asset selection \(\boldsymbol{x}\) ), which we aim to solve with GEO, and a tractable subroutine which can be solved efficiently with available solvers. + +<|ref|>text<|/ref|><|det|>[[515, 201, 917, 245]]<|/det|> +The set of pairwise \((\sigma_{\rho}^{*}, \rho)\) , dubbed as the efficient frontier, is no longer convex neither continuous in contrast with the solution to problem in Eq. (A1). + +<|ref|>sub_title<|/ref|><|det|>[[520, 278, 910, 305]]<|/det|> +## 2. Problem formulation for comparison with state-of-the-art algorithms + +<|ref|>text<|/ref|><|det|>[[515, 324, 917, 494]]<|/det|> +To carry out the comparison with State- of- the- Art Algorithms, in line with the formulation used there, we generalizes the problem in Eq. A2 releasing the constraint of a fix level of portfolio return, instead directly incorporating the portfolio return in the objective function, encompassing now two terms: the one on the left corresponding to the portfolio risk as beforehand the one on the right corresponding to the portfolio return. The goal is to balance out both terms such that return is maximized and risk minimized. Lambda is a hyperparameter, named risk averse, that controls if an investor wants to give more weight to risk or return. The new formulation reads as follows, + +<|ref|>equation<|/ref|><|det|>[[603, 523, 917, 585]]<|/det|> +\[\begin{array}{rl} & {\min_{\boldsymbol {w},\boldsymbol {x}}\{\lambda \sigma^2 (\boldsymbol {w}) - (1 - \lambda)\langle \boldsymbol {r}(\boldsymbol {w})\rangle :}\\ & {l_i\boldsymbol {x}_i< w_i< u_i\boldsymbol {x}_i\quad i = 1,\dots ,N,}\\ & {\qquad \mathbf{1}\cdot \boldsymbol {x} = \kappa \} .} \end{array} \quad (A3)\] + +<|ref|>text<|/ref|><|det|>[[515, 601, 917, 630]]<|/det|> +With the rest of constraints and variables definition as in Appendix A1. + +<|ref|>sub_title<|/ref|><|det|>[[640, 662, 792, 675]]<|/det|> +### a. Performance Metrics + +<|ref|>text<|/ref|><|det|>[[515, 695, 917, 796]]<|/det|> +To compare the performance of the proposed GEO with the SOTA metaheuristic algorithms in the literature, the most commonly used performance metrics for the cardinality constrained portfolio optimization problem are used. These metric formulations compute the distance between the heuristic efficient frontier and the unconstrained efficient frontier. Thus, the performance of the algorithms can be evaluated. + +<|ref|>text<|/ref|><|det|>[[515, 798, 917, 856]]<|/det|> +Four of these performance metrics (the Mean, Median, Minimum and Maximum in Table I) are based on the so- called Performance Deviation Errors \((PDE)\) . These \(PDE\) metrics were formulated by Chang [26] as follows: + +<|ref|>equation<|/ref|><|det|>[[525, 877, 917, 916]]<|/det|> +\[PDE_{i} = min\left(\left|\frac{100(x_{i} - x_{i}^{*})}{x_{i}^{*}}\right|,\left|\frac{100(y_{i} - y_{i}^{*})}{y_{i}^{*}}\right|\right) \quad (A4)\] + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[155, 87, 488, 280]]<|/det|> +\[\begin{array}{rl} & {x_{i}^{*} = X_{k_{y}} + \frac{(X_{j_{y}} - X_{k_{y}})(y_{i} - Y_{k_{y}})}{(Y_{j_{y}} - Y_{k_{y}})}}\\ & {y_{i}^{*} = Y_{k_{x}} + \frac{(Y_{j_{x}} - Y_{k_{x}})(x_{i} - X_{k_{x}})}{(X_{j_{x}} - X_{k_{x}})}}\\ & {j_{y} = \underset {l = 1,\dots ,\epsilon^{*}}{\arg \min}Y_{l}}\\ & {k_{y} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}Y_{l}}\\ & {j_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmin}}X_{l}}\\ & {k_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}X_{l}}\\ & {k_{x} = \underset {l = 1,\dots ,\epsilon^{*}}{\mathrm{argmax}}X_{l}} \end{array} \quad (A5)\] + +<|ref|>text<|/ref|><|det|>[[85, 289, 488, 404]]<|/det|> +where the pair \((X_{l},Y_{l})(l = 1,\dots ,\epsilon^{*})\) represents the point on the standard efficient frontier and the pair \((x_{i},y_{i})(i =\) \(1,\dots ,\epsilon)\) represents the point on the heuristic efficient frontier. Here, \(\epsilon^{*}\) denotes the number of points on the standard efficient frontier while \(\epsilon\) denotes the number of points on the heuristic efficient frontier. The mean, median, minimum, and maximum of the \(PDE\) can be used to compare the performance of the algorithms. + +<|ref|>text<|/ref|><|det|>[[85, 404, 488, 464]]<|/det|> +Later, three additional performance measures (MEUCD: Mean Euclidean Distance, VRE: Variance of Return Error, MRE: Mean Return Error) were formulated by Cura [41] as follows: + +<|ref|>equation<|/ref|><|det|>[[113, 484, 487, 519]]<|/det|> +\[MEUCD = \frac{\sum_{i = 1}^{\epsilon}\sqrt{(X_{i}^{*} - x_{i}) + (Y_{i}^{*} - y_{i})}}{\epsilon} \quad (A6)\] + +<|ref|>equation<|/ref|><|det|>[[174, 540, 487, 575]]<|/det|> +\[VRE = \frac{\sum_{i = 1}^{\epsilon}100|X_{i}^{*} - x_{i}| / x_{i}}{\epsilon} \quad (A7)\] + +<|ref|>equation<|/ref|><|det|>[[174, 591, 487, 625]]<|/det|> +\[MRE = \frac{\sum_{i = 1}^{\epsilon}100|Y_{i}^{*} - y_{i}| / y_{i}}{\epsilon} \quad (A8)\] + +<|ref|>text<|/ref|><|det|>[[85, 634, 488, 710]]<|/det|> +where \((X_{i}^{*},Y_{i}^{*})\) is the standard point closest to the heuristic point \((x_{i},y_{i})\) . Figure 5 shows a graphical representation of the indices used to calculate the performance metrics for the convenience of the reader and the values for TN- GEO and all the other SOTA optimizers are reported in Table I. + +<|ref|>sub_title<|/ref|><|det|>[[117, 736, 456, 750]]<|/det|> +## 3. Quantum-Inspired Generative Model in TN-GEO + +<|ref|>text<|/ref|><|det|>[[86, 767, 488, 912]]<|/det|> +The addition of a probabilistic component is inspired by the success of Bayesian Optimization (BO) techniques, which are among the most efficient solvers when the performance metric aims to find the lowest minimum possible within the least number of objective function evaluations. For example, within the family of BO solvers, GPyOpt [24] uses a Gaussian Process (GP) framework consisting of multivariate Gaussian distributions. This probabilistic framework aims to capture relationships among the previously observed data points (e.g., through tailored kernels), and it guides the decision of where + +<|ref|>image<|/ref|><|det|>[[515, 61, 916, 305]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[514, 320, 917, 346]]<|/det|> +
FIG. 5. A graphical demonstration of indices used for performance metrics calculation
+ +<|ref|>text<|/ref|><|det|>[[515, 369, 917, 426]]<|/det|> +to sample the next evaluation with the help of the so called acquisition function. GPyOpt is one of the solvers we use to benchmark the new quantum- enhanced strategies proposed here. + +<|ref|>text<|/ref|><|det|>[[515, 426, 917, 500]]<|/det|> +Although the GP framework in BO techniques is not a generative model, we explore here the powerful unsupervised machine learning framework of generative modeling in order to capture correlations from an initial set of observations and evaluations of the objective function (step 1- 4 in Fig. 1). + +<|ref|>text<|/ref|><|det|>[[515, 500, 917, 615]]<|/det|> +For the implementation of the quantum- inspired generative model at the core of TN- GEO we follow the procedure proposed and implemented in Ref. [15]. Inspired by the probabilistic interpretation of quantum physics via Born's rule, it was proposed that one can use the Born probabilities \(|\Psi (\pmb {x})|^2\) over the \(2^{N}\) states of an \(N\) qubit system to represent classical target probability distributions which would be obtained otherwise with generative machine learning models. Hence, + +<|ref|>equation<|/ref|><|det|>[[576, 631, 916, 670]]<|/det|> +\[P(\pmb {x}) = \frac{|\Psi(\pmb{x})|^2}{Z},\mathrm{with}Z = \sum_{\pmb {x}\in \mathcal{S}}|\Psi (\pmb {x})|^2, \quad (A9)\] + +<|ref|>text<|/ref|><|det|>[[515, 678, 917, 853]]<|/det|> +with \(\Psi (\pmb {x}) = \langle \pmb {x}|\Psi \rangle\) and \(\pmb {x}\in \{0,1\}^{\otimes N}\) are in one- to- one correspondence with decision variables over the investment universe with \(N\) assets in our combinatorial problem of interest here. In Ref. [15] these quantum- inspired generative models were named as Born machines, but we will refer to them hereafter as tensor- network Born machines (TNBm) to differentiate it from the quantum circuit Born machines (QCBM) proposal [13] which was developed independently to achieve the same purpose but by leveraging quantum wave functions from quantum circuits in NISQ devices. As explained in the main text, either quantum generative model can be adapted for the purpose of our GEO algorithm. + +<|ref|>text<|/ref|><|det|>[[515, 854, 917, 912]]<|/det|> +On the grounds of computational efficiency and scalability towards problem instances with large number of variables (in the order of hundreds or more), following Ref. [15] we implemented the quantum- inspired generative model based on + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 66, 487, 95]]<|/det|> +Matrix Product States (MPS) to learn the target distributions \(|\Psi (\pmb {x})|^2\) . + +<|ref|>text<|/ref|><|det|>[[86, 96, 488, 240]]<|/det|> +MPS is a type of TN where the tensors are arranged in a one- dimensional geometry. Despite its simple structure, MPS can efficiently represent a large number of quantum states of interest extremely well [42]. Learning with the MPS is achieved by adjusting its parameters such that the distribution obtained via Born's rule is as close as possible to the data distribution. MPS enjoys a direct sampling method that is more efficient than other Machine Learning techniques, for instance, Boltzmann machines, which require Markov chain Monte Carlo (MCMC) process for data generation. + +<|ref|>text<|/ref|><|det|>[[86, 241, 488, 356]]<|/det|> +The key idea of the method to train the MPS, following the algorithm on paper [15], consists of adjusting the value of the tensors composing the MPS as well as the bond dimension among them, via the minimization of the negative log- likelihood function defined over the training dataset sampled from the target distribution. For more details on the implementation see Ref. [15] and for the respective code see Ref. [43]. + +<|ref|>sub_title<|/ref|><|det|>[[210, 386, 365, 399]]<|/det|> +## 4. Classical Optimizers + +<|ref|>sub_title<|/ref|><|det|>[[228, 417, 345, 430]]<|/det|> +### a. GPyOpt Solver + +<|ref|>text<|/ref|><|det|>[[86, 448, 488, 522]]<|/det|> +GPyOpt [24] is a Python open- source library for Bayesian Optimization based on GPy and a Python framework for Gaussian process modelling. For the comparison exercise in TN- GEO as a stand- alone solver here are the hyperparameters we used for the GPyOpt solver: + +<|ref|>text<|/ref|><|det|>[[113, 532, 488, 700]]<|/det|> +- Domain: to deal with the exponential growth in dimensionality, the variable space for \(n\) number of assets was partitioned as the cartesian product of \(n\) 1-dimensional spaces.- Constraints: we added two inequalities in the number of assets in a portfolio solution to represent the cardinality condition.- Number of initial data points: 10- Acquisition function: Expected Improvement + +<|ref|>sub_title<|/ref|><|det|>[[192, 728, 382, 741]]<|/det|> +### b. Simulated Annealing Solver + +<|ref|>text<|/ref|><|det|>[[86, 760, 488, 860]]<|/det|> +For simulated annealing (SA) we implemented a modified version from Ref. [44]. The main change consists of adapting the update rule such that new candidates are within the valid search space with fixed cardinality. The conventional update rule of single bit flips will change the Hamming weight of \(x\) which translates in a portfolio with different cardinality. The hyperparameters used are the following: + +<|ref|>text<|/ref|><|det|>[[113, 871, 390, 884]]<|/det|> +- Max temperature in thermalization: 1.0 + +<|ref|>text<|/ref|><|det|>[[113, 896, 390, 911]]<|/det|> +- Min temperature in thermalization: 1e-4 + +<|ref|>sub_title<|/ref|><|det|>[[620, 68, 812, 80]]<|/det|> +### c. Conditioned Random Solver + +<|ref|>text<|/ref|><|det|>[[515, 100, 917, 213]]<|/det|> +This solver corresponds to the simplest and most naive approach, while still using the cardinality information of the problem. In the conditioned random solver, we generate, by construction, bitstrings which satisfy the cardinality constraint. Given the desired cardinality \(\kappa = N / 2\) used here, one starts from the bitstring with all zeros, \(x_0 = 0\dots 0\) , and flips only \(N / 2\) bits at random from positions containing 0's, resulting in a valid portfolio candidate \(x\) with cardinality \(N / 2\) . + +<|ref|>sub_title<|/ref|><|det|>[[657, 245, 775, 258]]<|/det|> +### d. Random Solver + +<|ref|>text<|/ref|><|det|>[[515, 277, 917, 363]]<|/det|> +This solver corresponds to the simplest approach without even using the cardinality information of the problem. In the random solver, we generate, by construction, bitstrings randomly selected from the \(2^{N}\) bitstrings of all possible portfolios, where \(N\) is the number of assets in our investment universe. + +<|ref|>sub_title<|/ref|><|det|>[[544, 393, 885, 407]]<|/det|> +## 5. Algorithm Methodology for TN-GEO as a booster + +<|ref|>text<|/ref|><|det|>[[515, 440, 917, 498]]<|/det|> +As explained in the main text, in this case it is assumed that the cost of evaluating the objective function is not the major computational bottleneck, and consequently there is no practical limitations in the number of observations to be considered. + +<|ref|>text<|/ref|><|det|>[[515, 499, 917, 541]]<|/det|> +Following the algorithmic scheme in Fig. 1, we describe next the details for each of the steps in our comparison benchmarks: + +<|ref|>text<|/ref|><|det|>[[540, 554, 917, 828]]<|/det|> +0 Build the seed data set, \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) . For each problem instance defined by \(\rho\) and a random subset with \(N\) assets from the S&P 500, gather all initial available data obtained from previous optimization attempts with classical solver(s). In our case, for each problem instances we collected 10,000 observations from the SA solver. These 10,000 observations corresponding to portfolio candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{init}}\) and their respective risk evaluations \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{init}}\) were sorted and only the first \(n_{\mathrm{seed}} = 1,000\) portfolio candidates with the lowest risks were selected as the seed data set. This seed data set is the one labeled as \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) and \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) in the main text and hereafter. The idea of selecting a percentile of the original data is to provide the generative model inside GEO with samples which are the target samples to be generated. This percentile is a hyperparameter and we set it \(10\%\) of the initial data for our purposes. + +<|ref|>text<|/ref|><|det|>[[540, 840, 917, 911]]<|/det|> +1 Construct of the softmax surrogate distribution: Using the seed data from step 0, we construct a softmax multinomial distribution with \(n_{\mathrm{seed}}\) classes - one for each point on the seed data set. The probabilities outcome associated with each of these classes in the multinomial + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[125, 65, 490, 115]]<|/det|> +is calculated as a Boltzmann weight, \(p_{i} = \frac{e^{-\overline{\sigma}_{i,\kappa}}}{\sum_{j = 1}^{n_{\mathrm{seed}}}e^{-\overline{\sigma}_{j,\kappa}}}\) . + +<|ref|>text<|/ref|><|det|>[[125, 117, 488, 336]]<|/det|> +Here, \(\overline{\sigma}_{\rho ,\kappa}^{(i)} = \sigma_{\rho ,\kappa}(\pmb{x}^{(i)}) / T\) , and \(T\) is a "temperature" hyperparameter. In our simulations, \(T\) was computed as the standard deviation of the risk values of this seed data set. In Bayesian optimization methods the surrogate function tracks the landscape associated with the values of the objective function (risk values here). This soft- max surrogate constructed here by design as a multinomial distribution from the seed data observations serves the purpose of representing the objective function landscape but in probability space. That is, it will assign higher probability to portfolio candidates with lower risk values. Since we will use this softmax surrogate to generate the training data set, this bias imprints a preference in the quantum- inspired generative model to favor low- cost configurations. + +<|ref|>text<|/ref|><|det|>[[112, 347, 488, 404]]<|/det|> +2 Sample from softmax surrogate. We will refer to these samples as the training set since these will be used to train the MPS- based generative model. For our experiments here we used \(n_{\mathrm{train}} = 10000\) samples. + +<|ref|>text<|/ref|><|det|>[[111, 416, 488, 446]]<|/det|> +3 Use the \(n_{\mathrm{train}}\) samples from the previous step to train the MPS generative model. + +<|ref|>text<|/ref|><|det|>[[111, 457, 488, 544]]<|/det|> +4 Obtain \(n_{\mathrm{MPS}}\) samples from the generative model which correspond to the new list of potential portfolio candidates. In our experiments, \(n_{\mathrm{MPS}} = 4000\) . For the case of 500 assets, as sampling takes sensibly longer because of the problem dimension, this value was reduced to 400 to match the time in SA. + +<|ref|>text<|/ref|><|det|>[[111, 555, 488, 628]]<|/det|> +5 Select new candidates: From the \(n_{\mathrm{MPS}}\) samples, select only those who fulfill the cardinality condition, and which have not been evaluated. These new portfolio candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) are saved for evaluation in the next step. + +<|ref|>text<|/ref|><|det|>[[111, 638, 488, 716]]<|/det|> +6 Obtain risk value for new selected samples: Solve Eq. A2 to evaluate the objective function (portfolio risks) for each of the new candidates \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) . We will denote refer to the new cost function values by \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{new}}\) . + +<|ref|>text<|/ref|><|det|>[[111, 728, 488, 815]]<|/det|> +7 Merge the new portfolios, \(\{\pmb{x}^{(i)}\}_{\mathrm{new}}\) , and their respective cost function evaluations, \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{new}}\) with the seed portfolios, \(\{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) , and their respective cost values, \(\{\sigma_{\rho ,N / 2}^{(i)}\}_{\mathrm{seed}}\) , from step 0 above. This combined super set is the new initial data set. + +<|ref|>text<|/ref|><|det|>[[110, 826, 488, 912]]<|/det|> +8 Use the new initial data set from step 7 to start the algorithm from step 1. If a desired minimum is already found or if no more computational resources are available, one can decide to terminate the algorithm here. In all of our benchmark results reported here when using TN- GEO as a booster from SA intermediate results, + +<|ref|>text<|/ref|><|det|>[[555, 66, 917, 110]]<|/det|> +we only run the algorithm for this first cycle and the minima reported for the TN- GEO strategy is the lowest minimum obtained up to step 7 above. + +<|ref|>sub_title<|/ref|><|det|>[[533, 142, 899, 169]]<|/det|> +## 6. Algorithm Methodology for TN-GEO as a stand-alone solver + +<|ref|>text<|/ref|><|det|>[[513, 202, 917, 362]]<|/det|> +This section presents the algorithm for the TN- GEO scheme as a stand- alone solver. In optimization problems where the objective function is inexpensive to evaluate, we can easily probe it at many points in the search for a minimum. However, if the cost function evaluation is expensive, e.g., tuning hyperparameters of a deep neural network, then it is important to minimize the number of evaluations drawn. This is the domain where optimization technique with a Bayesian flavour, where the search is being conducted based on new information gathered, are most useful, in the attempt to find the global optimum in a minimum number of steps. + +<|ref|>text<|/ref|><|det|>[[513, 363, 917, 492]]<|/det|> +The algorithmic steps for TN- GEO as a stand- alone solver follows the same logic as that of the solver as a booster described Sec. A5. The main differences between the two algorithms rely on step 0 during the construction of the initial data set and seed data set in step 0, the temperature use in the softmax surrogate in step 1, and a more stringent selection criteria in step 5. Since the other steps remain the same, we focus here to discuss the main changes to the algorithmic details provided in Sec. A5. + +<|ref|>text<|/ref|><|det|>[[540, 508, 917, 696]]<|/det|> +0 Build the seed data set: since evaluating the objective function could be the major bottleneck (assumed to be expensive) then we cannot rely on cost function evaluations to generate the seed data set. The strategy we adopted is to initialize the algorithm with samples of bitstrings which satisfy the hard constraints of the problem. In our specific example, we can easily generate \(n_{\mathrm{seed}}\) random samples, \(\mathcal{D}_0 = \{\pmb{x}^{(i)}\}_{\mathrm{seed}}\) , which satisfy the cardinality constraint. Since all the elements in this data set hold the cardinality condition, then maximum length \(n_{\mathrm{seed}}\) of \(\mathcal{D}_0\) is \(\binom{N}{K}\) . In our experiments, we set the number of samples \(n_{\mathrm{init}} = 2,000\) , for all problems considered here up to \(N = 100\) assets + +<|ref|>text<|/ref|><|det|>[[540, 710, 917, 912]]<|/det|> +1 Construct the softmax surrogate distribution: start by constructing a uniform multinomial probability distribution where each sample in \(\mathcal{D}_0\) has the same probability. Therefore, for each point in the seed data set its probability is set to \(p_0 = 1 / n_{\mathrm{seed}}\) . As in TN- GEO as a booster, we will attempt to generate a softmax- like surrogate which favors samples with low cost value, but we will slowly build that information as new samples are evaluated. In this first iteration of the algorithm, we start by randomly selecting a point \(\pmb{x}^{(1)}\) from \(\mathcal{D}_0\) , and we evaluate the value of its objective function \(\sigma^{(1)}\) (its risk value in our specific finance example). To make this point \(\pmb{x}^{(1)}\) stand out from the other unevaluated samples, we set its probability to be twice that of any + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[125, 66, 488, 386]]<|/det|> +of the remaining \(n_{\mathrm{seed}} - 1\) points in \(\mathcal{D}_0\) . Since we increase the probability of one of the points, we need to adjust the probability of the \(n_{\mathrm{seed}} - 1\) from \(p_0\) to \(p_0\) and if we assume the probability weights for observing each point follows a multinomial distribution with Boltzmann weights, under these assumptions, and making by fixing the temperature hyperparameter we can solve for the reference "risk" value \(\sigma^{(0)}\) associated to all the other \(n_{\mathrm{seed}} - 1\) points as shown below. It is important to note that \(\sigma^{(0)}\) is an artificial reference value which is calculated analytically and does not require a call to the objective function (in contrast to \(\sigma^{(1)}\) ). Here, \(\mathcal{N}\) is the normalization factor of the multinomial and \(T\) is the temperature hyperparameter which, as in the case of TN- GEO as a booster, can be adjusted later in the algorithm as more data is seen. Due to the lack of initial cost function values, in order to set a relevant typical "energy" scale in this problem, we follow the procedure in Ref. [45] where it is set to be the square root of the mean of the covariance matrix defined in Eq. A1, as this matrix encapsulates the risk information (volatility) as stated in the Markowitz's model. + +<|ref|>equation<|/ref|><|det|>[[100, 405, 460, 568]]<|/det|> +\[\left\{ \begin{array}{ll}(n_{\mathrm{seed}} - 1)p_0' + p_1 = 1 & \Rightarrow \left\{ \begin{array}{ll}p_0' = 1 / (1 + n_{\mathrm{seed}}) \\ p_1 = 2 / (1 + n_{\mathrm{seed}}) \end{array} \right.\\ \displaystyle \left\{ \begin{array}{ll}\mathcal{N} = (n_{\mathrm{seed}} - 1)e^{-\sigma^{(0)} / T} + e^{-\sigma^{(1)} / T} & \\ p_1 = e^{-\sigma^{(1)} / T} / \mathcal{N} & \\ p_0' = e^{-\sigma^{(0)} / T} / \mathcal{N} & \end{array} \right. \end{array} \right.\] + +<|ref|>text<|/ref|><|det|>[[112, 590, 488, 620]]<|/det|> +2 Generate training set: same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>text<|/ref|><|det|>[[111, 627, 488, 657]]<|/det|> +3 Train MPS: same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>text<|/ref|><|det|>[[111, 664, 488, 694]]<|/det|> +4 Generate samples from trained MPS: same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>text<|/ref|><|det|>[[111, 702, 488, 875]]<|/det|> +5 Select new candidates from trained MPS: In contrast to TN- GEO as a booster we cannot afford to evaluate all new candidates coming from the MPS samples. In our procedure we selected only two new candidates which must meet the cardinality constraint. For our procedure these two candidates correspond to the most frequent sample ("exploitation") and the least frequent sample ("exploration"). If all new samples appeared with the same frequency, then we can select two samples at random. In the case where no new samples were generated, we choose them from the unevaluated samples of the original seed data set in \(\mathcal{D}_0\) + +<|ref|>text<|/ref|><|det|>[[111, 883, 488, 913]]<|/det|> +6 Obtain risk value for new selected samples: same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>text<|/ref|><|det|>[[540, 66, 917, 96]]<|/det|> +7 Merge the new portfolios with seed data set from step 0 same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>text<|/ref|><|det|>[[540, 105, 917, 150]]<|/det|> +8 Restart next cycle of the algorithm with the merge data set as the new seed data set: same as in TN- GEO as a booster (see Appendix A 5). + +<|ref|>sub_title<|/ref|><|det|>[[574, 177, 859, 191]]<|/det|> +## Appendix B: Relative TN-GEO Enhancement + +<|ref|>text<|/ref|><|det|>[[511, 208, 916, 238]]<|/det|> +Figure 6 represents the relative performance within the strategies 1 and 2 referred to subsection III A. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[185, 60, 810, 732]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 742, 920, 812]]<|/det|> +
FIG. 6. Relative TN-GEO enhancement similar to those shown in the bottom panel of Fig. 2 in the main text. For these experiments, portfolio optimization instances with a number of variables ranging from \(N = 30\) to \(N = 100\) were used. Here, each panel correspond to a different investment universes corresponding to a random subset of the S&P 500 market index. Note the trend for a larger quantum-inspired enhancement as the number of variables (assets) becomes larger, with the largest enhancement obtained in the case on instances with all the assets from the S&P 500 ( \(N = 500\) ), as shown in Fig. 2
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 410, 150]]<|/det|> +summarycomparisonTNGEOvsalI.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/images_list.json b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d84c2f0922186d00786dd415238470f5861e5c17 --- /dev/null +++ b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 50, + 40, + 640, + 787 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2", + "footnote": [], + "bbox": [ + [ + 62, + 81, + 928, + 780 + ] + ], + "page_idx": 19 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3", + "footnote": [], + "bbox": [ + [ + 61, + 70, + 700, + 789 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4", + "footnote": [], + "bbox": [ + [ + 63, + 70, + 792, + 777 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5", + "footnote": [], + "bbox": [ + [ + 50, + 66, + 789, + 787 + ] + ], + "page_idx": 25 + } +] \ No newline at end of file diff --git a/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22.mmd b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22.mmd new file mode 100644 index 0000000000000000000000000000000000000000..657e05c255750070e4c89ea97777c0bb123e9522 --- /dev/null +++ b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22.mmd @@ -0,0 +1,362 @@ + +# An integrin-targeting AAV developed using a novel computational rational design methodology presents improved targeting of the skeletal muscle and reduced liver tropism + +Ai Vu Hong + +avuhong@genethon.fr + +Genethon https://orcid.org/0000- 0002- 0872- 4295 + +Laurence Suel + +Genethon + +Jérôme Poupiot + +Genethon + +Isabelle Richard + +Genethon + +## Article + +Keywords: + +Posted Date: October 27th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3466229/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: Yes there is potential Competing Interest. A.H.V. and I.R. are inventors on PCT application EP2023/065499 for the integration of RGLxxL/I motif in AAV capsid for enhanced muscle transduction efficiency. I.R. is a part- time employee of Atamyo Therapeutics. The other authors declare no competing interests. + +Version of Record: A version of this preprint was published at Nature Communications on September 11th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52002- 4. + +<--- Page Split ---> + +## Abstract + +Current adeno- associated virus (AAV) gene therapy using nature- derived AAVs is limited by non- optimal tissue targeting. In the treatment of muscular diseases (MD), high doses are therefore often required, but can lead to severe adverse effects. To lower treatment doses, we rationally designed an AAV that specifically targets skeletal muscle. We employed a novel computational design that integrated binding motifs of integrin alpha V beta 6 (αVβ6) into a liver- detargeting AAV capsid backbone to target the human αVβ6 complex – a selected AAV receptor for skeletal muscle. After sampling the low- energy capsid mutants, all in silico designed AAVs showed higher productivity compared to their parent. We confirmed in vitro that the enhanced transduction is due to the binding to the αVβ6 complex. Thanks to inclusion of αVβ6- binding motifs, the designed AAVs exhibited enhanced transduction efficacy in human differentiated myotubes as well as in murine skeletal muscles in vivo. One notable variant, LICA1, showed similar muscle transduction to other published myotropic AAVs, while being significantly more strongly liver- detargeted. We further examined the efficacy of LICA1, in comparison to AAV9, in delivering therapeutic transgenes in two mouse MD models at a low dose of 5E12 vg/kg. At this dose, AAV9 was suboptimal, while LICA1 transduced effectively and significantly better than AAV9 in all tested muscles. Consequently, LICA1 corrected the myopathology, restored global transcriptomic dysregulation, and improved muscle functionality. These results underline the potential of our design method for AAV engineering and demonstrate the relevance of the novel AAV variant for gene therapy treatment of MD. + +## One Sentence Summary + +We developed a novel computationally AAV design method resulting in a new myotropic AAV, which allows low- dose AAV treatment for muscular dystrophies. + +## INTRODUCTION + +Over 50 years since their discovery, adeno- associated viruses (AAVs) have shown great promise as an effective viral vector for gene delivery and gene therapy, leading to recent approval of therapeutic products \(^{1,2}\) . Due to unmet medical needs and natural AAV tropism, many AAV- based gene therapies focus on treating muscle diseases (MD) \(^{3}\) . Systemic treatment in such diseases aims to primarily target skeletal muscle, which accounts for more than 40% of body mass, and therefore often requires very high doses (≥1E14 vg/kg) to achieve meaningful therapeutic efficacy \(^{3- 6}\) . In addition, most recombinant AAVs built on natural- occurring variants lack specificity and often accumulate in the liver, with the concomitant risk of hepatotoxicity \(^{7}\) . Other key challenges of rAAV use persist, including manufacturing, immunological barriers, and associated toxicity \(^{1,2,8,9}\) . + +AAV is a small non- pathogenic single- stranded DNA parvovirus. Multiple open reading frames (ORFs) were identified in its genome, including Rep, Cap, AAP and MAAP \(^{1,10}\) . The single Cap ORF expresses three capsid proteins - virion protein 1 (VP1), VP2 and VP3, which assemble into an icosahedral 60- mer capsid. Structurally, the VP3 monomer core contains a highly conserved eight- stranded β- barrel motif \(^{11}\) . + +<--- Page Split ---> + +Inserted between the \(\beta\) - strands, nine surface- exposed variable regions (VR1- 9) result in local topological differences between serotypes and dictate virus- host interaction. Consequently, genetically modifying VRs can drastically change the AAV, transduction, antigenic profile, and fitness \(^{10,12,13}\) . VR4 and VR8, that cluster together spatially, forming the most prominent protrusion at the 3- fold axis, have been widely subjected to modifications, notably by inserting short peptides into the loop apices \(^{14}\) . This resulted in some highly efficient capsid variants for transducing a variety of cell types and tissues \(^{1,12}\) . Among these, remarkably, AAVMYOs \(^{15,16}\) and MYOAAVs \(^{17}\) transduce skeletal muscles, deliver therapeutic transgenes efficiently, and were shown to correct dystrophic phenotypes in MD mouse models at relatively low doses (2E12 – 1E13 vg/kg). + +Importantly, the myotropic AAVs \(^{15 - 17}\) identified by muscle- directed high- throughput screening (HTS) were shown to share an Arg- Gly- Asp (RGD) motif, presumably targeting the integrin complex \(^{17 - 20}\) . Integrins are a group of heterodimeric proteins composed of an \(\alpha\) - and a \(\beta\) subunit that serve various cellular functions, including cell adhesion, cell migration, and cell signaling \(^{21}\) . As adhesion molecules, integrins also mediate cell- pathogen interactions, and are therefore exploited by many viruses, including natural AAV, to infect cells \(^{22 - 24}\) . Indeed, many of these viruses use an RGD motif on their viral envelope glycoproteins or capsids for cell attachment, endocytosis, entry, and endosomal escape \(^{18,22,25}\) . The discovery that RGD- dependent integrin- targeting AAV variants can acquire myotropism therefore represents a novel potential candidate approach for a rational design to target skeletal muscle. + +This study introduces a novel computational method for a rational AAV design targeting skeletal muscle, which resulted in a novel myotropic vector for MD gene therapy. First, the human skeletal muscle- enriched integrin complex alpha V beta 6 (αVβ6) was selected as the target receptor. Inspired by one- sided protein design \(^{26,27}\) , we computationally designed a previously developed liver- detargeting hybrid capsid between AAV9 and AAVrh74 (Cap9rh74) as an αVβ6 binder. The VR4 loop was completely modified, in which new sequences were iteratively selected to simultaneously optimize for free energy, while hosting αVβ6- binding RGDLLXL/I motifs. All designed AAVs were well- produced, at higher titers than their parent. The designed AAVs were confirmed to require αVβ6 binding for cellular transduction. The most promising variant, renamed LICA1, was selected for further analysis and showed superior transduction in human differentiated myotubes and strong myotropism in several mouse models. We evaluated this variant by delivering therapeutic transgenes in two MD mouse models at a very low dose of 5E12 vg/kg, in comparison to AAV9. In both cases, LICA1 presents higher efficacy than AAV9 in correcting dystrophic phenotypes, global transcriptomic changes and restoring muscle function, thanks to improved transduction and transgene expression in skeletal muscles. Collectively, the study provides a proof- of- concept for a new rational AAV design pipeline leveraging protein design tools, which resulted in a novel myotropic AAV with high potential for gene therapy for muscle diseases. + +## RESULTS + +## 1. Selection of the cellular receptor for rational design + +<--- Page Split ---> + +Several myotrophic AAVs have recently been developed, notably, the insertion into the AAV9 VR- VIII loop of P1 peptide (RGDLLGS) \(^{15,16}\) , and a series of RGD- containing sequences identified by directed evolution \(^{17}\) . Importantly, these modified capsids shared a common RGD motif, which suggested their affinity to integrin (ITG), cell- surface heterocomplexes that interact with the extracellular matrix \(^{28}\) . Using publicly available datasets, we aimed to select relevant integrin subunits for a subsequent rational AAV design targeting skeletal muscle. + +Chemello and colleagues previously performed single- nucleus RNA sequencing, comparing gene expression of all cell types in the skeletal muscle of wild- type (WT) and Duchenne muscular dystrophy mouse models (D51) \(^{29}\) . We extracted RNA levels of all integrin alpha and beta genes from these data (Figure S1A). Among all subunits, only the \(\alpha\) - subunits Itgav, Itga7 and the \(\beta\) - subunits Itgb6, Itgb1, and Itgb5 show relatively high expression in the myogenic nuclei. Of interest is the fact that the expression level of Itgb6 is highly enriched in myonuclei, and significantly upregulated in the dystrophic condition, whereas Itgb1 and Itgb5 expression are ubiquitous in all cell types, and significantly lower than the Itgb6 level in all myonuclei. Among the two expressed \(\alpha\) - subunits, only Itgav was known to associate with Itgb6 to form avβ6 heterocomplexes – a member of the RGD- binding integrin family \(^{30}\) . Furthermore, bulk RNA sequencing data from multiple human tissues confirmed high expression of Itgav and Itgb6 in skeletal muscle, and low expression of Itgb6 in the liver and spleen, two preferred targets of natural AAV (Figure S1B, GTEx V8, dbGaP Accession phs000424.v8.p2). We therefore hypothesize that AAV transduction in skeletal muscle can be improved by rationally designing an AAV capsid that specifically binds to avβ6. + +## 2. Rational design of a hybrid capsid, Cap9rh74, with a high affinity to the \(\mathbb{V}\mathbb{B}\beta 6\) complex + +As we aim to specifically target the skeletal muscle, we selected a hybrid capsid that we previously developed and that has a liver- detargeting property as the parental capsid in our design (Patent Number: EP18305399.0). This hybrid capsid of AAV9 and AAV.rh74 (AAV9rh74) was constructed by replacing the AAV9 sequence of VR4 to VR8 with that of AAV- rh74. The hybrid capsid showed similar infectivity in skeletal and cardiac muscles but was strongly de- targeted from the liver. The latter property is of particular interest in skeletal muscle gene transfer since the majority of administrated viral vector will not accumulate in the liver, as is the case for natural AAVs \(^{31,32}\) . + +After selection of the cellular receptor of interest and capsid backbone, AAV capsids were computationally engineered (Fig. 1A). First, the 3D structure of the parental capsid, of with structure was unknown, was modeled using AlphaFold2 \(^{33,34}\) . The structural prediction of the Cap9rh74 aa 219–737 monomer performed using AlphaFold2 was at a high level of confidence, with predicted local distance difference test (IDDT- Ca), a per- residue measure of local confidence, of 97.04 and low predicted aligned error (PEA) of 4.32 (Fig S1C- D). This structure is thus suitable for the next steps in the design. + +Second, we extracted the 3D structure or sequences of binding motifs of the human integrin complex from PDB. Importantly, avβ6 was previously shown to bind with high affinity to the RGDLLXL/1 motif + +<--- Page Split ---> + +found in the human TGF- \(\beta 1\) and TGF- \(\beta 3\) prodomains \(^{35,36}\) . Binding peptides with eight amino acid residues, aa214- 221 in TGF- \(\beta 1\) (PDB: 5ffo) and aa240- 247 in TGF- \(\beta 3\) (PDB: 4um9), were isolated from the corresponding crystal structures before grafting into the Cap9rh74 VR4 loop. Both motifs bind to \(\alpha \beta 6\) dimer at a very similar position (Fig S1E). + +Third, the defined binding motifs were then grafted into the VR4 loop (residues 453- 459) of the capsid protein based on the RosettaRemodel protocol \(^{37}\) . In the grafting- remodel process, many rounds of backbone optimization and sequence design iteratively search for low- energy sequence- structure pairs (Fig. 1B). The lowest- energy designs in grafting experiments of each TGF- \(\beta\) motif showed convergence in both structure and sequence (Fig. 1C- D, S1F- G). The new VR4 loops include the binding peptide and two flanking 2- amino acid linkers and retain the LXXL/I motif as an \(\alpha\) - helix, which is important to bind in the \(\beta 6\) subunit's pocket \(^{36}\) . + +Retrospective docking simulations of the two AAV_ITGs with the best scores, namely Cap9rh74_5ffo and Cap9rh74_4um9, on the \(\alpha \beta 6\) complex showed highly similar binding positions of the new VR4 loop to its corresponding inserted motifs (Fig. 1E- F). This suggests that the new capsids can bind to \(\alpha \beta 6\) thanks to VR4- included RGDLLXXL/I motif. Sequences with the best scores, which reflect the thermodynamic stability of one static protein conformation \(^{38}\) , were subjected to experimental validation. + +## 3. All designed AAV_ITGs showed higher productivity and enhanced cellular transduction via \(\alpha \beta 6\) binding. + +The two AAVs with the best design were then tested for productivity and the effectiveness of using \(\alpha \beta 6\) as a cellular receptor. They were produced by tri- transfection with pITR- CMV- GFP- Luciferase as the expression cassette. Thanks to energy optimization, all the designed AAV- ITG variants significantly increase their titers compared to their parental hybrid capsid, to levels similar to those for AAV9 (Fig. 2A, S2A). In addition, all modified AAV- ITG variants retain proportions of VP1, VP2, VP3 capsid proteins with a similar ratio of AAV9 (Fig. 2B). This suggests that the designed sequences result in more stable AAV capsid complexes thanks to their estimated low energy structure, and therefore better production efficacy. + +Next, we examined whether these AAV- ITGs can effectively use \(\alpha \beta 6\) as a cellular receptor upon infection. First, a HEK293 cell line (293_ \(\alpha \beta 6\) ) constitutively overexpressing both integrin subunits, \(\alpha\) and \(\beta 6\) , was created using the PiggyBac system (Fig S2B- C). The designed AAVs were then tested for their infectivity in this cell line. As expected, infection of AAV_ITGs in 293_ \(\alpha \beta 6\) cells, as defined by vector copy numbers (VCN), was higher than for AAV9 and AAV9rh74 (Fig. 2C). Both AAV_ITGs dramatically improved the luciferase activity ( \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / AAV9}} = 60.50\) , \(\mathrm{FC}_{9\mathrm{rh74\_5ffo / AAV9}} = 25.99\) , \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / 9rh74}} = 63.99\) , and \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / 9rh74 = 27.49}}\) , Fig. 2D). To investigate how specific AAV_ITGs used \(\alpha \beta 6\) as a cellular receptor, we tested their infectivity under binding competition conditions. The number of AAV_ITG viral vectors entering the cells was significantly reduced when blocked by the recombinant protein \(\alpha \beta 6\) before viral infection, but no change occurred with AAV9 or AAV9rh74 + +<--- Page Split ---> + +(Fig. 2E). This result suggests that efficient transduction of AAV_ITGs requires specific binding to a \(\alpha \beta \delta\) complex. + +During myogenesis, \(\alpha \beta \delta\) is only expressed in late differentiation, but not in the myoblast stage (Fig S1A, S2D). We therefore hypothesized an enhanced transduction of AAV_ITGs in differentiated myotubes, but not myoblasts. We infected both human myoblasts and myotubes with AAV_ITGs. Low levels of luciferase activity were observed in all AAVs tested in human myoblasts (Fig. 2G,I). On the other hand, in human differentiated myotubes (hMT), VCN and luciferase activities in both AAV9rh74_4um9 and _5ff0 were significantly higher than for AAV9 or AAV9rh74 (Fig. 2F,H,K). In particular, variant AAV9rh74_4um9 showed a 16.56 (p < 0.0001) and 25.02- fold (p < 0.0001) improvement in luciferase activity compared to AAV9 and AAV9rh74, respectively, which is in agreement with its superior transduction efficiency and transgene expression seen in 293_αVβ6 cells. + +In summary, the two designed AAV_ITGs were both well- produced and function via \(\alpha \beta \delta\) - specific binding, thus enhancing their transduction efficiency in 293_αVβ6 and human differentiated myotubes. + +## 4. AAV_ITGs enhanced transduction in skeletal muscle following systematic administration + +AAV_ITGs, together with AAV9 and AAV9rh74, were administrated systematically via intravenous injection (transgene: CMV_GFP- Luciferase, dose: 1E13 vg/kg, age at injection: 6 weeks, n = 4) in C57Bl6 mice to examine their biodistribution 3 weeks post- injection (Fig. 3A). + +In agreement with a previous study, AAV9rh74 slightly reduces transduction in skeletal muscle compared to AAV9 but accumulates much less in the liver (Fig. 3B- D). Thanks to the liver- detargeting capsid and in accordance with the fact that \(\alpha \beta \delta\) is weakly expressed in the liver, we expected poor entry into the liver for designed AAV_ITGs. Indeed, AAV_ITGs is strongly detargeted from the liver, both at VCN and mRNA levels, even further than the parental capsid (Fig. 3C- D). In contrast, enhanced transduction was observed in all skeletal muscles that were tested, including the tibialis anterior (TA), quadriceps (Qua) and diaphragm (Dia) (Fig. 3B- D). The two AAV_ITGs both showed a substantial increase in VCN and luciferase activity compared to both AAV9 and AAV9rh74. Similar to the results obtained in in vitro models, AAV9rh74_4um9 is the best transducer among the two AAV_ITGs. Compared to AAV9, the variant 9rh74_4um9 significantly increased VCN 5.31/7.21/2.48- fold and increased luciferase activity 15.2/13.2/23.57- fold in Qua, TA, and Dia (p < 0.05), respectively. Compared to the original backbone AAV9rh74, this variant even magnified the difference by increasing VCN 5.53/2.85/7.69- fold and increasing luciferase activity 152.35/106.68/60.43- fold (p < 0.05). Furthermore, AAV9rh74_4um9, but not AAV9rh74_5ffo, significantly increased transduction in the heart (FCVCN=4.15, FCLLC=15.43, p < 0.05). All AAVs that were tested showed poor delivery and transgene expression in the lungs and kidneys. No alteration of TGFβ and integrin signaling was observed at one- month post- injection in all AAVs being tested (Fig S2F- G). Overall, these data indicate that AAV_ITGs, especially the 9rh74_4um9 variant, are strongly liver- detargeted and exhibit enhanced tropism towards skeletal and cardiac muscles. + +<--- Page Split ---> + +## 5. AAV9rh74_4um9 transduced skeletal muscle similarly, but detargeted the liver more strongly than other myotropic AAVs + +Several engineered myotropic AAVs (mAAVs), including AAVMYO \(^{15}\) , MYOAAV- 1A and - 2A \(^{17}\) , have demonstrated superior efficacy for in vivo delivery of muscle compared to natural AAVs. To evaluate the properties of these AAVs compared to ours, we performed in vitro and in vivo experiments. Viral preparations were produced using the same reporter transgene (CMV_GFP- Luc). All mAAVs were well- produced in 400ml suspension, with higher titers than AAV9rh74. However, MYOAAV productivity was significantly lower than 9rh74_ITGs and MYOAAVs (Fig S3A). Since all investigated mAAVs shared a common integrin- targeting RGD motif, these AAVs were then evaluated for their transduction via integrin complexes in myotubes and in cell lines where integrin complexes were stably overexpressed by the PiggyBac system. In 293_αVβ6 cells as well as in hMT, where αVβ6 is highly expressed, AAV9rh74_4um9 showed the highest transduction among the tested myotropic AAVs, with the sole exception that luciferase activity of MYOAAV2A was higher in hMT (Fig S3B- C). We also tested AAV transduction efficiency in two other cell lines, 293_WT, where αVβ6 expression is low, and 293_α7β1 that stably overexpresses a non- RGD- targeting α7β1 integrin. In both conditions, MYOAAV2A and AAV9rh74_4um9 showed the highest transduction (Fig S3D- E). These results suggest that, as intended with the rational design, AAV9rh74_4um9 uses αVβ6 more preferentially for cellular transduction than others, yet it can also efficiently use other integrin(s) similar to MYOAAV2A. + +Following in vivo injection in the same setting as described above (6- week- old WT mice, dose: 1E13 \(\mathrm{vg / kg}\) , \(\mathrm{n} = 4\) ), the three mAAVs and 9rh74_4um9 all showed strong liver- detargeting, high enrichment in both skeletal and cardiac muscles, and negligible transduction levels in other organs that were tested (kidneys, lungs, and brain) (Fig. 3G- H). No significant difference was observed in either VCN or luciferase activity between all three mAAVs and 9rh74_4um9 in the skeletal muscles that were tested. In heart muscle, MYOAAV2A showed a significant increase in VCN compared to other myotropic vectors, but no difference in luciferase activity, in agreement with the original observation \(^{17}\) . The most striking difference is the level of liver- detargeting between these vectors. The VCN for 9rh74_4um9 in liver is 3.34/22.05/13.85 times lower than for AAVMYO ( \(\mathrm{p} = 0.0022\) ), MYOAAV- 1A ( \(\mathrm{p} = 0.0013\) ) and - 2A ( \(\mathrm{p} = 0.033\) ), respectively (Fig. 3G), and is therefore the only vector that accumulates less in liver than skeletal muscles (Fig S3F- G). These data indicate higher muscle specificity for the 9rh74_4um9 variant compared to other myotropic vectors that have been investigated to date. + +In summary, the 9rh74_4um9 variant, hereafter referred to as LICA1 (linked- integrin- complex AAV), consistently showed enhanced transduction and strongest liver- detargeting. Therefore, we then attempted to evaluate LICA1 as a delivery vector for muscular dystrophies, in comparison with AAV9. Two different setups will be investigated: the transfer of microdystrophin (μDys) – an incomplete transgene - in mdx, a mild mouse model of Duchenne muscular dystrophy (DMD) and of the full- length human α- sarcoglycan (SGCA) in a severe mouse model of limb- girdle muscular dystrophy R3 (LGMD- R3). + +<--- Page Split ---> + +## 6. Low-dose LICA1-μDys gene transfer is effective in specifically overexpressing microdystrophin in dystrophic muscle but not sufficient to fully correct the underlying pathology + +DMD is caused by mutations in the DMD gene, which encodes for dystrophin protein - a key player in the dystrophin- glycoprotein complex (DGC), which is critical for the structural stability of skeletal muscle fibers \(^{39}\) . Lack of dystrophin can result in progressive loss of muscle function, respiratory defects, and cardiomyopathy. The most commonly used DMD animal model is the mdx mouse, with a lifespan reduced by \(25\%\) , milder clinical symptoms than those seen in human patients, with the exception of the diaphragm muscle \(^{40}\) . Among many therapeutic strategies to restore dystrophin expression, high- dose AAV- based gene transfer of shortened functional forms of the dystrophin ORF provided excellent results in animal models, but unsatisfactory conflicting data in current clinical trials \(^{6}\) . Severe toxicities, even patient death, have been reported from these trials (NCT03368742, NCT04281485), assumed to be related to the dose of \(\geq 1E14\) vg/kg. We therefore explored the possibility of low- dose μDys gene transfer \(^{41}\) in mdx mice using LICA1 in comparison to AAV9 (Fig S4A, age at injection: 4 weeks, dose: 5E12 vg/kg, treatment duration: 4 weeks, \(n = 5\) ). Three muscles with increasing levels of severity - TA, Qua, and Dia - were used to study AAV transduction and treatment efficacy. + +LICA1 showed better μDys gene transfer than AAV9 in this model. LICA1- treated mice exhibited a significantly higher VCN in all 3 muscles that were tested, 1.85/2.02/1.07 times higher in TA ( \(p < 0.0001\) ), Qua ( \(p < 0.0001\) ), and Dia ( \(p = 0.020\) ), respectively (Fig. 4A). RNA levels indicated even greater differences and were 4.56- 7.57 times higher in the LICA1- treated group (Fig. 4B; TA: FC = 4.56, \(p < 0.0001\) ; Qua: FC = 5.46, \(p = 0.0001\) ; Dia: 7.57, \(p = 0.05\) ). Consequently, LICA1 can transduce almost \(100\%\) in TA and Qua, and \(49.98\%\) in Dia, while substantially lower numbers were seen in AAV9- treated muscles, at \(73.22\%\) ( \(p = 0.0001\) ), \(57.8\%\) ( \(p < 0.0001\) ), \(10.34\%\) ( \(p < 0.0001\) ) in TA, Qua, Dia, respectively (Fig. 4C, Fig S4B). Furthermore, while infection levels and expression of the transgene in liver were high for the AAV9 vector (despite the use of muscle- specific promoter), the VCN and mRNA levels in LICA1- treated liver were extremely low (Fig. 4A- B, FCVCN:AAV9/LICA1=36.8, \(p = 0.0002\) ; FCmRNA:AAV9/LICA1=64.7, \(p < 0.0001\) ). These data again confirmed the transduction efficiency and specificity towards skeletal muscle for the LICA1 vector, even with low- dose treatment. + +The histological features and muscle functionality after AAV treatment were restored accordingly. The centronucleation index (percentage of centronucleated fibers) - an indicator of the regeneration/degeneration process - did not change with AAV9 (except in TA) but was significantly reduced upon LICA1 treatment (reduction of \(21.68\%\) , \(19.05\%\) , \(22.88\%\) in TA, Qua, Dia, respectively) (Fig. 4D, Fig S4C). Similarly, the fibrosis level in two severely affected muscles, Qua and Dia, only exhibited a significant reduction with LICA1, but not AAV9 (Fig. 4E, Fig S4D). The serum biomarker MYOM3 level, an indicator of muscle damage \(^{42}\) , showed a reduction for both AAV treatments, with a considerable further reduction seen in the LICA1- treated group (Fig. 4F, FCAAV9/KO=0.75, FC-LICA/KO=0.43, PAAV9- LICA1>0.0001). More importantly, AAV9 treatment did not affect any muscle functionality being tested (Fig. 4G- I), while significant improvements with LICA1- μDys treatment were observed in escape + +<--- Page Split ---> + +test – a measure of global force (Fig. 4G, \(\mathrm{FC}_{\mathrm{LICA1 / mdx}} = 1.19\) , \(\mathrm{P}_{\mathrm{LICA1 / mdx}} = 0.02\) ) and in situ TA mechanical force measurement (Fig. 4H, \(\mathrm{FC}_{\mathrm{LICA1 / mdx}} = 1.14\) , \(\mathrm{P}_{\mathrm{LICA1 / mdx}} = 0.0006\) ). However, none of the treatment normalized to the WT functional levels. These data indicate that LICA1 is better than AAV9 at restoring dystrophic histological features and muscle functions. + +We also investigated the molecular alteration in Qua upon AAV treatment using RNA- seq. On the two first principal components (PCs) of the PCA, a clear distinction between four transcriptome groups (WT, mdx, AAV9, LICA1) was observed, while LICA1- treated muscles were clustered closer to the WTs than others (Fig S4E). To our surprise, despite excellent transgene expression by LICA1, global transcriptomic restoration was relatively modest (Fig. 4K). Nevertheless, a substantial improvement can still be seen for LICA1 compared to AAV9. Among 4216 down- and 4501 upregulated differentially expressed genes (DEGs) identified in mdx muscle, 1515 (35.9%) and 1728 (38.4%) were restored by AAV9, while LICA1 was able to correct 1736 (41.2%) and 1980 (44.0%), respectively (Fig. 4L- M). In addition, a greater number of genes were either not or insufficiently corrected by AAV9 than by LICA1 (Fig. 4N). A total of 2572 genes were downregulated (61.0%) and 2620 (58.2%) incompletely restored, while significantly lower numbers were seen for LICA, with 2094 (49.67%) down- and 2019 (44.86%) upregulated. Interestingly, some known dysregulated pathways, including \(\alpha\) - and Y- interferon responses and oxidative phosphorylation, were significantly better normalized by LICA1 than by AAV9 (Fig S4F). + +In summary, at 5E12 vg/kg, LICA1- \(\mu\) Dys, but not AAV9, was efficient in transducing close to 100% myofibers, except in the diaphragm. This effective improvement in transduction can significantly reduce some dystrophic features in all muscles that were tested, yet restoration in the global transcriptome remains modest. However, greater improvements in functional, histological, and transcriptomic restoration were achieved with LICA1 compared to AAV9. + +## 7. Low-dose LICA1-SGCA treatment restored the muscle functionality, dystrophic phenotypes, and transcriptomic dysregulation in a severe SGCA mouse model. + +LGMDR3 is caused by mutations in the SGCA gene \(^{43}\) – another component of the DGC complex. Defects in the SGCA protein therefore lead to muscle weakness and wasting. A LGMDR3 mouse model has been established, which closely represents patient's clinical phenotypes \(^{44}\) . Similar to the setting in mdx mice, low- dose AAV treatment with 5E12 vg/kg was investigated in this mouse model. AAV9 or LICA1 encoding human SGCA (hSGCA) under control of a muscle- specific human Acta1 promoter were injected into 4- week- old SGCA- KO mice (Fig. 5A). Analysis was performed 4 weeks post- treatment. + +In all three muscles that were tested, TA, Qua, Dia (in order of increasing severity), transduction in various measures, VCN, mRNA level, and percentage of SGCA + myofibers, was significantly greater in the LICA1- treated group than for AAV9 (Fig. 5B- D, Fig S5A). Of note is the fact that the differences in transduction efficacy (%SGCA + myofibers) between LICA1 and AAV9 are greater in more severely affected muscles (Fig. 5D). At such a low dose, AAV9 was able to transduce > 80% myofibers in TA while LICA1 can reach close to 100% (p < 0.0001). While LICA1 still transduced almost 100% of fibers in Qua (the muscle + +<--- Page Split ---> + +affected with intermediate severity), only \(58.1\%\) fibers were transduced by AAV9 on average \((p < 0.0001)\) . In the most severely affected muscle, Dia, both vectors displayed reduced efficiency; however, LICA1 continued to demonstrate much better transduction \((\mu_{\mathrm{AAV9}} = 22.1\%, \mu_{\mathrm{LICA1}} = 59.5\%, \mathrm{p} < 0.0001)\) . + +The differences in transgene delivery and expression positively correlated with levels of histological and functional restoration. Different dystrophic histological features, including percentage of centronucleated fibers (Fig. 5E, Fig S5B), percentage of fibrosis area (Fig. 5F, Fig S5C), and fiber size distribution (Fig. 5G), were all significantly better normalized by LICA1 than AAV9, especially in more severely affected muscles. Importantly, no significant improvement was observed in the AAV9- treated group in centronucleation index and fibrosis level in Dia, while LICA1 reduced these parameters by half (Fig. 5E- F). Fiber sizes were also restored to near- WT distribution by LICA1 in this muscle (Fig. 5G). No difference in body weight was seen between groups with or without AAV treatment (Fig S5D). At the functional level, however, the escape test – a measure of global force – showed a significant increase in AAV9- treated mice \((FC = 1.42, \mathrm{p} = 0.0072)\) and was even higher in LICA1- treated group \((FC = 1.72, \mathrm{p} < 0.0001)\) (Fig. 5H). On the other hand, in situ TA mechanical forces were both improved in the two AAV groups at similar levels (Fig. 5I), possibly due to \(>80\%\) transduction rate by both vectors. Similar to the global force, the serum MYOM3 level was greatly reduced in the LICA1- treated group but not for AAV9, indicating less muscle damage (Fig. 5K). No difference was seen in the anti- capsid antibody between the two AAV treatments (Fig S5E). These results indicate that better and significant functional and histological restoration in the LICA1- treated mice was achieved, even at low- dose treatment, thanks to superior transduction efficacy. + +We further investigated the molecular alterations following AAV treatment by transcriptomic profiling of the quadriceps muscle. The first principal component (PCs) of the PCA was able to separate a group including WT and LICA1 with a group including SGCA- KO and AAV9, suggesting close proximity between elements within these 2 groups (Fig S5F). A heatmap of all 8591 significant DEGs (4035 downregulated and 4556 upregulated) further highlighted the restorative effect of LICA1 on gene expression levels (Fig. 5L). LICA1- treated muscles, in particular, demonstrated a significant correction of \(69.9\%\) (2821/4035) and \(66.5\%\) (3028/4556) of down- and upregulated DEGs, respectively, compared to \(12.4\%\) (500/4035) and \(9.21\%\) (420/4556) corrected by AAV9 treatment (Fig. 5M- N). Conversely, not all DEGs were significantly restored or returned to WT levels. The number of such transcripts in AAV9- treated muscles was much higher than in the LICA1- treated group (Fig. 5O): 2541 (63.0%) downregulated DEGs and 3045 (66.8%) upregulated DEGs for AAV9, with only 483 (12.0%) downregulated DEGs and 1038 (22.8%) upregulated DEGs in the LICA1- treated group. These data illustrate that low- dose LICA1 treatment can effectively normalize the majority of the dysregulated transcriptome and is much more efficient in correcting gene expression dysregulation than AAV9 at the same dose. + +In summary, low- dose (5E12 vg/kg) AAV gene transfer using LICA1 in the LGMDR3 mouse model is effective in restoring muscle function, dystrophic histology, and the dysregulated transcriptome. The efficacy was much greater than for AAV9 at the same dose due to enhanced transduction. + +<--- Page Split ---> + +## DISCUSSION + +Given the severe complications observed with very high dose AAV treatment, lowering the dose by increasing vector specificity via capsid modification is one way to address these issues. This study investigated the possibility of altering AAV tropism towards skeletal muscle by targeting integrin. We designed an AAV as a \(\alpha \mathrm{V}\beta 6\) binder, which resulted in a novel myotropic AAV variant, namely LICA1. LICA1 showed greatly enhanced transduction in skeletal muscle in WT and two MD mouse models. Consequently, by improving the delivery of therapeutic transgenes (hSGCA and \(\mu \mathrm{Dys}\) ) in these MD mouse models, LICA1 was able to correct dystrophic phenotypes, global transcriptional dysregulations and significantly restore muscle function. + +## AAV capsid sequence design method that ensures high AAV production + +AAV tropism is commonly altered by inserting a small peptide into the VR4 or VR8 loop without any sequence constraints. Since no consideration regarding AAV capsid stability is included in this method, the resulting AAV can suffer from instability, reduced productivity, and increased AAV genome fragmentation \(^{17,45}\) (ASGCT 2023). In the current study, a physics- based protein sequence design method was used to graft the binding motifs from TGF \(\beta\) - 1 and - 3 into the VR4 loop of the hybrid capsid AAV9rh74. The major differences to the classical peptide insertion method are that the entire VR4 loop was modified to include a new binding motif and the amino acids around this motif (linkers) were selected to minimize the potential energy. Low- energy sequences ensure the stability and intended folding of the designed proteins, presumably leading to improved stability of the AAV particle \(^{38}\) . Six AAVs designed using this method were tested experimentally and all showed better productivity than their parent, Cap9rh74, and similar levels to well- produced AAV9. This suggests that low Rosetta energy correlates with high stability of capsid protein, and thereby high AAV production. + +## Integrin \(\alpha \mathrm{V}\beta 6\) as a myotropic AAV receptor for skeletal muscle + +Virus- host interaction is the foundation for improved viral vectors, yet skeletal muscle receptors that allow effective AAV transduction are poorly defined. However, top hits from two independent studies with different screening schemes identified myotropic AAVs with a common RGD motif, \(^{15,17,19}\) . In addition, it has previously been described that integrin functions as cellular receptor for natural AAV \(^{23,24}\) . Coincident with our screening for possible integrin receptor, only \(\alpha \mathrm{V}\beta 6\) is highly expressed and enriched in skeletal muscle (Fig S1). By including \(\alpha \mathrm{V}\beta 6\) binding motifs, AAV_ITGs efficiently utilized \(\alpha \mathrm{V}\beta 6\) for cellular infection. Enhanced transduction was observed in conditions with high (either ectopic or natural) \(\alpha \mathrm{V}\beta 6\) expression, including human differentiated myotubes and murine skeletal muscles of WT and two other MD mouse models. In most cases, the improved transduction was evident at the VCN level, indicating better cell entry via \(\alpha \mathrm{V}\beta 6\) binding. + +In addition, we conducted a study comparing LICA1 and three other published myotropic AAVs. No significant differences in skeletal muscle transduction were observed on either VCN or transgene expression levels. However, the liver infection rate was significantly lower with LICA1 compared to the + +<--- Page Split ---> + +other mAAVs, presumably due to the use of a liver- detargeted backbone and the low expression level of \(\alpha \mathrm{V}\beta 6\) in liver. As a result, the LICA1 vector exhibited the highest muscle/liver transduction ratio among all AAVs tested, suggesting increased specificity towards skeletal muscle. This finding highlights the importance of selecting an appropriate targeting receptor for rational design and further supports \(\alpha \mathrm{V}\beta 6\) as a promising candidate for targeting skeletal muscle. + +## LICA1 is a potential vector for muscular diseases + +AAV gene therapy in muscle diseases typically requires very high doses \((\geq 1E14 \mathrm{vg / kg})\) for functional benefits \(^{41,46}\) , yet can result in severe and even fatal adverse events \(^{7}\) . In this study, we explored low dose (5E12 vg/kg) treatment using the LICA1 vector in two MD mouse models, DMD and LGMDR3. Of note is that this dose is at least 20 times lower than the doses currently used in clinical trials for neuromuscular diseases \(^{3}\) . In both models, LICA1 was significantly better than AAV9 in delivering and expressing therapeutic transgenes, consequently restoring better histological dystrophic phenotypes. In TA and Qua, LICA1 was able to transduce more than \(80\%\) of fibers. It was still a challenge to effectively transduce diaphragm muscle at this dose, yet more than \(50\%\) of Dia fibers were positive for transgene expression with LICA1 in both models while AAV9 transduced very poorly. This improvement in transgene expression translates directly into improved histological restoration, including centronucleation index and fibrosis level. In particular, with only more than \(50\%\) successfully transduced fibers, LICA1 was able to reduce diaphragm fibrosis by \(42.8 - 47.0\%\) (mdx and SGCA \(^{- / - }\) models respectively), whereas no change was seen in AAV9- treated groups. The biomarker for muscle damage level, MYOM3, was reduced by \(57.5 - 67.2\%\) (mdx and SGCA \(^{- / - }\) models respectively) by LICA1 and significantly greater than AAV9. Similarly, global muscle force was significantly restored to a higher level with LICA1 than with AAV9 in SGCA- KO mice. These data confirmed superior muscle transduction by LICA1 and resulting therapeutic benefits were obtained even at low- dose treatment in two MD models. + +However, treatment efficacy varies between two disease models at molecular levels. We profiled transcriptomic changes in Qua following AAV treatment in both MD models. Despite similar transduction efficiency of LICA1 in the two models, restoration of dystrophic transcriptional changes in SGCA- KO was significantly greater. It is noteworthy that \(\mu \mathrm{Dys}\) is an incomplete form of dystrophin. The \(\mu \mathrm{Dys}\) used in the present study lacks several functional domains, including multiple spectrin- like repeats that bind to nNOS, F- actin, sarcomeric lipid and microtubules, and a dystrobrevin- and syntrophin- binding C- terminus \(^{41}\) . This might explain the inadequate efficacy in restoring global gene expression in \(\mu \mathrm{Dys}\) gene therapy trials, in spite of highly effective gene transfer. Similarly, despite excellent functional restoration by microdystrophin gene transfer in various animal models, outcomes from these clinical trials are unsatisfactory \(^{6}\) . Therefore, careful assessment of molecular restoration should be included for evaluating gene therapy efficacy. + +In summary, this study presents an alternative computational method that aids rational AAV design and ensures high- production AAV variants. The proof- of- concept design targeting skeletal muscle resulted in a high- productivity myotropic AAV, thereby effectively delivering therapeutic transgenes and restoring + +<--- Page Split ---> + +dystrophic phenotypes in two MD mouse models at a low dose. This work contributes to the ongoing efforts to reduce AAV treatment doses and further advance AAV engineering, paving the way for more effective and accessible gene therapies in the future. + +## MATERIALS AND METHODS + +## Study Design + +The primary objective of the study was to design a novel myotropic AAV capsid with a high production yield by using a computationally rational design. The secondary aim was to investigate the possibility of low- dose AAV treatment using a designed AAV in animal models of muscular dystrophies, which typically require an alarmingly high dose \((\geq 1E14\) vg/kg). We used publicly available datasets to identify possible receptors for skeletal muscle and protein design tools to engineer AAV capsid protein. Resulting variants were characterized for their productivity and transduction efficiency in various in vitro cell lines and multiple mouse models. Experiments were performed at least three times, unless noted otherwise. The AAV injection and infection experiments were conducted in a nonblinded fashion. The blinding approach was used during dissection, histological validation, immunostaining analysis, in vivo functional tests, and biomarker analysis. No data were excluded. Details on experimental procedures are presented in Supplementary Materials and Methods. + +## Animal care and use + +All animals were handled according to French and European guidelines for human care and the use of experimental animals. All procedures on animals were approved by the local ethics committee and the regulatory affairs of the French Ministry of Research (MESRI) under the numbers 2018- 024- B #19736, 2022- 004 #35896. C57Bl/6, B6Ros.Cg- Dmdmdx- 4Cv/J mice were obtained from the Jackson Laboratory. A knockout mouse model of \(\alpha\) - sarcoglycan was obtained from the Kevin Campbell laboratory (University of Iowa, USA) \(^{44}\) . Mice were housed in a SPF barrier facility with 12- h light, 12- h dark cycles, and were provided with food and water ad libitum. Only male mice were used in the present study. Well- being and weights of the animals were monitored for the duration of the study. The animals were anesthetized with a mix of ketamine (100 mg/kg) and xylazine (10 mg/kg), or with isoflurane (4%) for blood samples. For AAV intravenous injections, a maximum volume of 150 μl containing AAV vectors was injected via the sinus route after the animals had been anesthetized with isoflurane. The AAV intravenous doses used in the present study were 5E12 or 1E13 vg/kg. + +## Cell culture and in vitro study + +Adherent HEK293- T cells were maintained in the proliferating medium containing DMEM (Thermo Fisher Scientific), supplied with 10% fetal bovine serum and 1X gentamycin at \(37^{\circ}C\) , 5% CO2. Human immortalized myoblasts (AB1190 cell line) were maintained in Skeletal Muscle Cell Growth Medium (PromoCell, C23060) and differentiated in Skeletal Muscle Differentiation Medium (PromoCell, C23061). + +<--- Page Split ---> + +In vitro AAV infection was performed by directly adding AAV into culture medium at the dose of 1E9 or 1E10 vg per 24- well plate well. After 48h post- infection, cells were washed and subjected to VCN and gene expression analysis. + +To inhibit AAV infection, AAVs were incubated with recombinant hTGAV- hITGB6 protein (Bio- Techné, 3817- AV- 050) at \(37^{\circ}C\) for 30 minutes, at a concentration of \(1\mu g\) protein per 5E9vg AAV before addition to the cells (1E4 vg per cell). The same condition treated with recombinant hSGCA protein served as a control for the comparison. + +## Statistical Analysis + +Results are presented as mean \(\pm\) SEM, unless noted otherwise. Significance of differences in multiple pairwise comparisons of more than two groups was determined by one- way ANOVA. The significance of differences in pairwise comparisons of multiple groups with multiple treatments was determined by two- way ANOVA. To account for multiple testing and control the false discovery rate (FDR) across the numerous pairwise comparisons, the Benjamini- Hochberg (BH) procedure was applied with an FDR threshold of 0.05. Statistical tests were performed using GraphPad Prism 9. Results were considered significant when p- values or adjusted p- values were less than 0.05. + +## DECLARATIONS + +Acknowledgments: The authors are Genopole's members, first French biocluster dedicated to genetic, biotechnologies and biotherapies. We are grateful to the "Imaging and Cytometry Core Facility" and to the in vivo evaluation, services of Genethon for technical support, to Ile- de- France Region, to Conseil Départemental de l'Essonne (ASTRE), INSERM and GIP Genopole, Evry for the purchase of the equipment. We would like to acknowledge the technical help of Carolina Pacheco Algalan and Alejandro Arco Hierves. The Genotype- Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. + +Funding: This work was supported by the "Association Française contre les Myopathies" (AFM), and "Institut National de la Santé Et de la Recherche Médicale" (INSERM, FranceRelance N°221513A10). + +Author contributions: The project was conceptualized by A.H.V. and I.R. A.H.V., L.S.P., and J.P. conducted experiments and performed data analysis. Funding supporting this project was obtained by I.R. A.H.V. and I.R. supervised the project. The manuscript was written by A.H.V. and I.R. + +Competing interests: A.H.V. and I.R. are inventors on PCT application EP2023/065499 for the integration of RGDlxxL/I motif in AAV capsid for enhanced muscle transduction efficiency. I.R. is a part- time employee of Atamyo Therapeutics. The other authors declare that they have no competing interests. + +Data and materials availability: All data associated with this study are present in the paper or the Supplementary Materials. All transcriptomic data will be deposited in the NCBI Sequence Read Archive + +<--- Page Split ---> + +(SRA) upon publication. Processed data including differential gene expression analysis are available in data file S1 and S2. The plasmid constructs and reagents generated as part of this study are available under a material transfer agreement from the corresponding authors. + +## REFERENCES + +1. Wang, D., Tai, P.W.L. & Gao, G. Adeno-associated virus vector as a platform for gene therapy delivery. Nat Rev Drug Discov 18, 358-378 (2019). +2. Pupo, A. et al. AAV vectors: The Rubik's cube of human gene therapy. Molecular therapy: the journal of the American Society of Gene Therapy 30, 3515-3541 (2022). +3. Crudele, J.M. & Chamberlain, J.S. AAV-based gene therapies for the muscular dystrophies. Hum Mol Genet 28, R102-R107 (2019). +4. Duan, D. Systemic AAV Micro-dystrophin Gene Therapy for Duchenne Muscular Dystrophy. Molecular therapy: the journal of the American Society of Gene Therapy 26, 2337-2356 (2018). +5. Mack, D.L. et al. Systemic AAV8-Mediated Gene Therapy Drives Whole-Body Correction of Myotubular Myopathy in Dogs. Molecular therapy: the journal of the American Society of Gene Therapy 25, 839-854 (2017). +6. Mercuri, E., Bonnemann, C.G. & Muntoni, F. Muscular dystrophies. Lancet 394, 2025-2038 (2019). +7. Ertl, H.C.J. Immunogenicity and toxicity of AAV gene therapy. Front Immunol 13, 975803 (2022). +8. Verdera, H.C., Kuranda, K. & Mingozzi, F. AAV Vector Immunogenicity in Humans: A Long Journey to Successful Gene Transfer. Molecular therapy: the journal of the American Society of Gene Therapy 28, 723-746 (2020). +9. High-dose AAV gene therapy deaths. Nature biotechnology 38, 910 (2020). +10. Ogden, P.J., Kelsic, E.D., Sinai, S. & Church, G.M. Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design. Science 366, 1139-1143 (2019). +11. DiMattia, M.A. et al. Structural insight into the unique properties of adeno-associated virus serotype 9. Journal of virology 86, 6947-6958 (2012). +12. Li, C. & Samulski, R.J. Engineering adeno-associated virus vectors for gene therapy. Nat Rev Genet 21, 255-272 (2020). +13. Tseng, Y.S. & Agbandje-McKenna, M. Mapping the AAV Capsid Host Antibody Response toward the Development of Second Generation Gene Delivery Vectors. Front Immunol 5, 9 (2014). +14. Buning, H. & Srivastava, A. Capsid Modifications for Targeting and Improving the Efficacy of AAV Vectors. Molecular therapy. Methods & clinical development 12, 248-265 (2019). +15. Weinmann, J. et al. Identification of a myotropic AAV by massively parallel in vivo evaluation of barcoded capsid variants. Nature communications 11, 5432 (2020). +16. El Andari, J. et al. Semirational bioengineering of AAV vectors with increased potency and specificity for systemic gene therapy of muscle disorders. Science advances 8, eabn4704 (2022). + +<--- Page Split ---> + +17. Tabebordbar, M. et al. Directed evolution of a family of AAV capsid variants enabling potent muscle-directed gene delivery across species. Cell 184, 4919-4938 e4922 (2021). + +18. Ruoslahti, E. & Pierschbacher, M.D. Arg-Gly-Asp: a versatile cell recognition signal. Cell 44, 517-518 (1986). + +19. Bauer, A. et al. Molecular Signature of Astrocytes for Gene Delivery by the Synthetic Adenoc- Associated Viral Vector rAAV9P1. Adv Sci (Weinh) 9, e2104979 (2022). + +20. Zolotukhin, S., Trivedi, P.D., Corti, M. & Byrne, B.J. Scratching the surface of RGD-directed AAV capsid engineering. Molecular therapy: the journal of the American Society of Gene Therapy 29, 3099-3100 (2021). + +21. Hynes, R.O. Integrins: a family of cell surface receptors. Cell 48, 549-554 (1987). + +22. Hussein, H.A. et al. Beyond RGD: virus interactions with integrins. Arch Virol 160, 2669-2681 (2015). + +23. Asokan, A., Hamra, J.B., Govindasamy, L., Agbandje-McKenna, M. & Samulski, R.J. Adeno-associated virus type 2 contains an integrin alpha5beta1 binding domain essential for viral cell entry. Journal of virology 80, 8961-8969 (2006). + +24. Summerford, C., Bartlett, J.S. & Samulski, R.J. AlphaVbeta5 integrin: a co-receptor for adeno-associated virus type 2 infection. Nat Med 5, 78-82 (1999). + +25. Stewart, P.L. & Nemerow, G.R. Cell integrins: commonly used receptors for diverse viral pathogens. Trends Microbiol 15, 500-507 (2007). + +26. Strauch, E.M. et al. Computational design of trimeric influenza-neutralizing proteins targeting the hemagglutinin receptor binding site. Nature biotechnology 35, 667-671 (2017). + +27. Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551-560 (2022). + +28. Ruoslahti, E. RGD and other recognition sequences for integrins. Annual review of cell and developmental biology 12, 697-715 (1996). + +29. Chemello, F. et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proceedings of the National Academy of Sciences of the United States of America 117, 29691-29701 (2020). + +30. Pang, X. et al. Targeting integrin pathways: mechanisms and advances in therapy. Signal Transduct Target Ther 8, 1 (2023). + +31. Shen, X., Storm, T. & Kay, M.A. Characterization of the relationship of AAV capsid domain swapping to liver transduction efficiency. Molecular therapy: the journal of the American Society of Gene Therapy 15, 1955-1962 (2007). + +32. Ballon, D.J. et al. Quantitative Whole-Body Imaging of I-124-Labeled Adeno-Associated Viral Vector Biodistribution in Nonhuman Primates. Human gene therapy 31, 1237-1259 (2020). + +33. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589 (2021). + +<--- Page Split ---> + +34. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nature methods 19, 679-682 (2022).35. Dong, X. et al. Force interacts with macromolecular structure in activation of TGF-beta. Nature 542, 55-59 (2017).36. Dong, X., Hudson, N.E., Lu, C. & Springer, T.A. Structural determinants of integrin beta-subunit specificity for latent TGF-beta. Nature structural & molecular biology 21, 1091-1096 (2014).37. Huang, P.S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PloS one 6, e24109 (2011).38. Alford, R.F. et al. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. Journal of chemical theory and computation 13, 3031-3048 (2017).39. Duan, D., Goemans, N., Takeda, S., Mercuri, E. & Aartsma-Rus, A. Duchenne muscular dystrophy. Nat Rev Dis Primers 7, 13 (2021).40. Stedman, H.H. et al. The mdx mouse diaphragm reproduces the degenerative changes of Duchenne muscular dystrophy. Nature 352, 536-539 (1991).41. Bourg, N. et al. Co-Administration of Simvastatin Does Not Potentiate the Benefit of Gene Therapy in the mdx Mouse Model for Duchenne Muscular Dystrophy. Int J Mol Sci 23 (2022).42. Rouillon, J. et al. Serum proteomic profiling reveals fragments of MYOM3 as potential biomarkers for monitoring the outcome of therapeutic interventions in muscular dystrophies. Hum Mol Genet 24, 4916-4932 (2015).43. Eymard, B. et al. Primary adhalinopathy (alpha-sarcoglycanopathy): clinical, pathologic, and genetic correlation in 20 patients with autosomal recessive muscular dystrophy. Neurology 48, 1227-1234 (1997).44. Duclos, F. et al. Progressive muscular dystrophy in alpha-sarcoglycan-deficient mice. The Journal of cell biology 142, 1461-1471 (1998).45. Bryant, D.H. et al. Deep diversification of an AAV capsid protein by machine learning. Nature biotechnology 39, 691-696 (2021).46. Israeli, D. et al. An AAV-SGCG Dose-Response Study in a gamma-Sarcoglycanopathy Mouse Model in the Context of Mechanical Stress. Molecular therapy. Methods & clinical development 13, 494-502 (2019). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +## Computational rational AAV capsid design to bind to \(\alpha \beta \beta\) integrin + +A. Overview of the design pipeline, including three steps: 1. Capsid 3D structures were obtained either from the PDB database or predicted by AlphaFold2. 2. The capsid VR4 loop was completely replaced by integrating the binding motif, which was extracted from receptor's natural binder, using RosettaRemodel + +<--- Page Split ---> + +protocol. 3. Top scored designs from the previous grafting step were docked onto the intended receptor in silico to verify the binding potential of the designed capsid. B. An illustration of the sampling for low- energy sequence- structure pairs during motif- grafting process. Capsid VR4 after removing the loop was colored in blue, extracted binding motif was colored in red. The sampled linkers and sequences (Fig. S1F) were labeled in green. C- D. The three lowest energy designs after grafting TGFβ3 (C) and TGFβ1 (D) into the capsid VR4. All top designs showed convergence in structures and sequences, suggesting sampling approached the global optimum. E- F. Retrospective docking of motif- grafted capsids (E. Cap9rh74_4um9 and F. Cap9rh74_5ffo) onto the αVβ6 structure. The left panels are illustrations of the structures with the lowest energy at the interface of capsid and integrin proteins (dG_separated: difference in free energy of two proteins). Both two newly designed VR4s (colored in green) were predicted to bind to the αVβ6 complex at very similar positions to natural binding motifs (colored in red). The right panels are scatter plots of dG_separated energy versus root-mean- square deviation (RMSD) from the lowest energy structure of all sampled docking positions. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2
+ +
Figure 2
+ +Designed AAV_ITGs were well- produced and improved transduction via aVβ6 binding. + +A. AAV titers of different AAV variants in bulked small-scale production in suspension three-day post-triple-transfection (2ml production, \(n = 6\) , one-way ANOVA). B. Western blot of VP proteins from purified AAVs showed similar VP ratios for designed AAV_ITGs capsids compared to AAV9 and AAV9rh74, + +<--- Page Split ---> + +suggesting successful capsid assembly. C- D. VCN (C) and luciferase activity (D) of 293_aVβ6 after AAV infection (n=3- 4, one- way ANOVA). Both the two designed AAV_ITGs showed enhanced VCN and luciferase activities compared to AAV9rh74 and AAV9. E. Inhibition of cell entry of designed AAV_ITGs, but not for AAV9 or AAV9rh74, in 293_aVβ6 cells by aVβ6 recombinant protein. AAVs were preincubated with aVβ6 recombinant protein (r.ITGAV- B6) for 30 minutes at 37°C before infection (n=3, two- way ANOVA). SGCA recombinant protein (r.SGCA) was used as the control. F- K. Enhanced transduction of AAV_ITGs in in vitro human differentiated myotubes, but not in myoblasts. F. Representative images of the GFP signal of myotubes 48 hours post- infection (scale bar: 400μm). G- K. VCN and luciferase activities of AAV_ITGs in comparison with AAV9 and AAV9rh74 in myoblasts (G,I) and myotubes (H,K) (n=3- 4, one- way ANOVA). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3
+ +
Figure 3
+ +Designed AAV_ITGs showed enhanced transduction in skeletal and cardiac muscles while strongly liver- detargeted in vivo. + +A. Scheme of in vivo experiment. AAVs (CMV_GFP-Luciferase) were injected intravenously into 6wo C57BL6 mice (n=4) at the dose of 1E13 vg/kg. B. Representative images of the bioluminescence signal + +<--- Page Split ---> + +20 days post- infection. C- D. VCN (C) and gene expression (D) (GFP mRNA level in the liver and luciferase activity in other organs) for different AAVs in liver, skeletal muscles, heart, lung, and kidney (n=4, one- way ANOVA). Both designed AAV_ITGs strongly detargeted from the liver compared to AAV9, while they significantly improved VCN and luciferase activities over AAV9rh74 (and AAV9 with AAV9rh74_4um9 variant) in skeletal and cardiac muscles, and were detected and expressed at low levels in lung and kidney. E- H. Comparison of the AAV9rh74_4um9 variant with other public myotropic AAVs (mAAVs) \(^{15,17}\) . E. Illustration of the differences between mAAVs and AAV9rh74_4um9 at modification sites in capsid protein and modification methods. F. The VR8 loop sequences of mAAVs compared to VR8 of their backbone AAV9, and VR4 of AAV9rh74_4um9 compared to VR4 of AAV9rh74. G- H. VCN (G) and gene expression (H) (GFP mRNA level in liver and luciferase activity in other organs) of different AAVs in liver, skeletal muscles, heart, lung, kidney, and brain (n=4, one- way ANOVA). AAV9rh74_4um9 showed similar VCN and gene expression in skeletal muscle to other mAAVs, while being significantly more strongly detargeted from the liver. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4
+ +
Figure 4
+ +Low- dose gene transfer by LICA1 was more effective and better at restoring dystrophic phenotypes than AAV9 in the DMD mouse model. + +A- B. Comparison of transduction efficacy between AAV9 and LICA1 in all three muscles that were tested, in terms of VCN (A), and \(\mu\) Dys RNA level (B). C. Comparison of percentage of successfully transduced + +<--- Page Split ---> + +(dystrophin- positive) fibers in all three muscles that were tested. Note that TA, Qua, Dia muscles are presented in increasing order of severity. D- E. Comparison of restoration levels in dystrophic histological features between AAV9 and LICA1 in all three muscles that were tested, in terms of percentage of centro- nucleated fibers (D) and fibrosis level (E). Illustrated images in C- E are of quadriceps muscles (scale bar: \(100\mu \mathrm{m}\) ). F. Serum MYOM3 level – indicator of muscle damage – 4 weeks post- injection ( \(n = 5\) , one- way ANOVA). G- I. Comparison of functional restoration between AAV9 and LICA1 by Escape test – global force measurement (G, \(n = 6\) ), tetanus force of TA muscle (H, \(n = 10 - 12\) ), and twitch force of TA muscle (I, \(n = 9 - 12\) ). K- N. Comparison of restoration in global transcriptomic changes in quadriceps muscle between AAV9 and LICA1 ( \(n = 4\) , adjusted p- values \(< 0.05\) ). K. The heatmap presents the log2 fold change (log2FC) in comparison to WT muscle for all 8717 DEGs found in mdx muscle (compared to WT). The log2FC values are illustrated in row Z- scores, colored from blue to red, arranged from lowest to highest. L- N. Volcano plots of multiple comparisons illustrate transcriptomic changes before and after AAV treatment. As a reference, 4216 downregulated and 4501 upregulated DEGs found in mdx were colored blue and red, respectively, in all volcano plots. Among these DEGs, the number of genes found to be significantly different in each pair- wise comparison were labeled in the upper corners. L. Volcano plots comparing mdx/WT transcriptomes. M. Volcano plots comparing mdx to AAV- treated transcriptomes, in which significant DEGs are the genes correctly restored after AAV treatment. N. Volcano plots comparing AAV treatment to WT, in which significant DEGs are the genes that are not or incompletely restored after AAV treatment. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5
+ +
Figure 5
+ +Low- dose gene transfer by LICA1 was better at restoring dystrophic phenotypes and functionality than AAV9 in the LGMDR3 mouse model. + +A. Scheme of in vivo experiment: LICA1 (9rh74_4um9) or AAV9 were injected intravenously into a 4wo SGCA-KO mouse model at the dose of 5E12 vg/kg (expression cassette: hACTA1_hSGCA_HBB2-pA, n=3- + +<--- Page Split ---> + +5). Three skeletal muscles in increasing order of severity, TA, Qua, and Dia, were analysed 4 weeks postinjection. B-D. Comparison of transduction efficacy between AAV9 and LICA1 in all three muscles that were tested in terms of VCN (B), hSGCA mRNA level (C), and percentage of succesfully transduced (SGCA- positive) fibers (D). E-G. Comparison of restoration levels in dystrophic histological features between AAV9 and LICA1 in all three muscles that were tested in terms of percentage of centro- nucleated fibers (E), fibrosis level (F), and fiber size distribution (G). Illustrated images in D-F are of quadriceps muscles (scale bar: \(100 \mu m\) ). H-K. Comparison of functional restoration between AAV9 and LICA1 using the escape test – global force measurement (H), tetanus force of TA muscle (I), and serum MYOM3 level – indicator of muscle damage (K). L-O. Comparison of restoration in global transcriptomic changes in quadriceps muscle between AAV9 and LICA1 (n=4, adjusted p values < 0.05). L. The heatmap presents the log2 fold change (log2FC) in comparison to WT muscle for all 8591 DEGs found in KO muscle (compared to WT). The log2FC values are illustrated by row Z-scores, colored from blue to red, arranged from lowest to highest. M-O. Volcano plots of multiple comparisons illustrate transcriptomic changes before and after AAV treatment. As a reference, 4035 downregulated and 4556 upregulated DEGs found in KO were colored blue and red, respectively, in all volcano plots. Among these DEGs, the number of genes found to be significantly different in each pair-wise comparison were labeled in the upper corners. M. Volcano plots comparing KO/WT transcriptomes. N. Volcano plots comparing KO to AAV-treated transcriptomes, in which significant DEGs are the genes correctly restored after AAV treatment. O. Volcano plots comparing AAV treatment to WT, in which significant DEGs are the genes that are not or incompletely restored after AAV treatment. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- ALICA1supp.pdf- DatafileS2DEGSGCA.xlsx- DatafileS1DEGDMD.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22_det.mmd b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..c61f10c77aed4b850fa99807c4554a105a6f9901 --- /dev/null +++ b/preprint/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22/preprint__00d0f482762f2f37431ca49a939480fc54bdd5eb053d5ac8ce0b474c9dacda22_det.mmd @@ -0,0 +1,475 @@ +<|ref|>title<|/ref|><|det|>[[42, 106, 928, 243]]<|/det|> +# An integrin-targeting AAV developed using a novel computational rational design methodology presents improved targeting of the skeletal muscle and reduced liver tropism + +<|ref|>text<|/ref|><|det|>[[44, 263, 166, 283]]<|/det|> +Ai Vu Hong + +<|ref|>text<|/ref|><|det|>[[53, 291, 266, 309]]<|/det|> +avuhong@genethon.fr + +<|ref|>text<|/ref|><|det|>[[44, 336, 501, 355]]<|/det|> +Genethon https://orcid.org/0000- 0002- 0872- 4295 + +<|ref|>text<|/ref|><|det|>[[44, 361, 170, 380]]<|/det|> +Laurence Suel + +<|ref|>text<|/ref|><|det|>[[52, 385, 141, 401]]<|/det|> +Genethon + +<|ref|>text<|/ref|><|det|>[[44, 408, 185, 426]]<|/det|> +Jérôme Poupiot + +<|ref|>text<|/ref|><|det|>[[52, 432, 141, 448]]<|/det|> +Genethon + +<|ref|>text<|/ref|><|det|>[[44, 455, 185, 473]]<|/det|> +Isabelle Richard + +<|ref|>text<|/ref|><|det|>[[52, 479, 141, 494]]<|/det|> +Genethon + +<|ref|>sub_title<|/ref|><|det|>[[44, 536, 103, 553]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 574, 136, 592]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 611, 328, 630]]<|/det|> +Posted Date: October 27th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 650, 475, 669]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3466229/v1 + +<|ref|>text<|/ref|><|det|>[[42, 687, 916, 730]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 747, 936, 836]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. A.H.V. and I.R. are inventors on PCT application EP2023/065499 for the integration of RGLxxL/I motif in AAV capsid for enhanced muscle transduction efficiency. I.R. is a part- time employee of Atamyo Therapeutics. The other authors declare no competing interests. + +<|ref|>text<|/ref|><|det|>[[42, 870, 921, 914]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 11th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52002- 4. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 41, 157, 66]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[39, 81, 955, 468]]<|/det|> +Current adeno- associated virus (AAV) gene therapy using nature- derived AAVs is limited by non- optimal tissue targeting. In the treatment of muscular diseases (MD), high doses are therefore often required, but can lead to severe adverse effects. To lower treatment doses, we rationally designed an AAV that specifically targets skeletal muscle. We employed a novel computational design that integrated binding motifs of integrin alpha V beta 6 (αVβ6) into a liver- detargeting AAV capsid backbone to target the human αVβ6 complex – a selected AAV receptor for skeletal muscle. After sampling the low- energy capsid mutants, all in silico designed AAVs showed higher productivity compared to their parent. We confirmed in vitro that the enhanced transduction is due to the binding to the αVβ6 complex. Thanks to inclusion of αVβ6- binding motifs, the designed AAVs exhibited enhanced transduction efficacy in human differentiated myotubes as well as in murine skeletal muscles in vivo. One notable variant, LICA1, showed similar muscle transduction to other published myotropic AAVs, while being significantly more strongly liver- detargeted. We further examined the efficacy of LICA1, in comparison to AAV9, in delivering therapeutic transgenes in two mouse MD models at a low dose of 5E12 vg/kg. At this dose, AAV9 was suboptimal, while LICA1 transduced effectively and significantly better than AAV9 in all tested muscles. Consequently, LICA1 corrected the myopathology, restored global transcriptomic dysregulation, and improved muscle functionality. These results underline the potential of our design method for AAV engineering and demonstrate the relevance of the novel AAV variant for gene therapy treatment of MD. + +<|ref|>sub_title<|/ref|><|det|>[[44, 490, 360, 516]]<|/det|> +## One Sentence Summary + +<|ref|>text<|/ref|><|det|>[[42, 530, 908, 574]]<|/det|> +We developed a novel computationally AAV design method resulting in a new myotropic AAV, which allows low- dose AAV treatment for muscular dystrophies. + +<|ref|>sub_title<|/ref|><|det|>[[44, 595, 255, 622]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[41, 635, 952, 850]]<|/det|> +Over 50 years since their discovery, adeno- associated viruses (AAVs) have shown great promise as an effective viral vector for gene delivery and gene therapy, leading to recent approval of therapeutic products \(^{1,2}\) . Due to unmet medical needs and natural AAV tropism, many AAV- based gene therapies focus on treating muscle diseases (MD) \(^{3}\) . Systemic treatment in such diseases aims to primarily target skeletal muscle, which accounts for more than 40% of body mass, and therefore often requires very high doses (≥1E14 vg/kg) to achieve meaningful therapeutic efficacy \(^{3- 6}\) . In addition, most recombinant AAVs built on natural- occurring variants lack specificity and often accumulate in the liver, with the concomitant risk of hepatotoxicity \(^{7}\) . Other key challenges of rAAV use persist, including manufacturing, immunological barriers, and associated toxicity \(^{1,2,8,9}\) . + +<|ref|>text<|/ref|><|det|>[[41, 866, 945, 960]]<|/det|> +AAV is a small non- pathogenic single- stranded DNA parvovirus. Multiple open reading frames (ORFs) were identified in its genome, including Rep, Cap, AAP and MAAP \(^{1,10}\) . The single Cap ORF expresses three capsid proteins - virion protein 1 (VP1), VP2 and VP3, which assemble into an icosahedral 60- mer capsid. Structurally, the VP3 monomer core contains a highly conserved eight- stranded β- barrel motif \(^{11}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 953, 257]]<|/det|> +Inserted between the \(\beta\) - strands, nine surface- exposed variable regions (VR1- 9) result in local topological differences between serotypes and dictate virus- host interaction. Consequently, genetically modifying VRs can drastically change the AAV, transduction, antigenic profile, and fitness \(^{10,12,13}\) . VR4 and VR8, that cluster together spatially, forming the most prominent protrusion at the 3- fold axis, have been widely subjected to modifications, notably by inserting short peptides into the loop apices \(^{14}\) . This resulted in some highly efficient capsid variants for transducing a variety of cell types and tissues \(^{1,12}\) . Among these, remarkably, AAVMYOs \(^{15,16}\) and MYOAAVs \(^{17}\) transduce skeletal muscles, deliver therapeutic transgenes efficiently, and were shown to correct dystrophic phenotypes in MD mouse models at relatively low doses (2E12 – 1E13 vg/kg). + +<|ref|>text<|/ref|><|det|>[[40, 273, 944, 487]]<|/det|> +Importantly, the myotropic AAVs \(^{15 - 17}\) identified by muscle- directed high- throughput screening (HTS) were shown to share an Arg- Gly- Asp (RGD) motif, presumably targeting the integrin complex \(^{17 - 20}\) . Integrins are a group of heterodimeric proteins composed of an \(\alpha\) - and a \(\beta\) subunit that serve various cellular functions, including cell adhesion, cell migration, and cell signaling \(^{21}\) . As adhesion molecules, integrins also mediate cell- pathogen interactions, and are therefore exploited by many viruses, including natural AAV, to infect cells \(^{22 - 24}\) . Indeed, many of these viruses use an RGD motif on their viral envelope glycoproteins or capsids for cell attachment, endocytosis, entry, and endosomal escape \(^{18,22,25}\) . The discovery that RGD- dependent integrin- targeting AAV variants can acquire myotropism therefore represents a novel potential candidate approach for a rational design to target skeletal muscle. + +<|ref|>text<|/ref|><|det|>[[39, 503, 951, 867]]<|/det|> +This study introduces a novel computational method for a rational AAV design targeting skeletal muscle, which resulted in a novel myotropic vector for MD gene therapy. First, the human skeletal muscle- enriched integrin complex alpha V beta 6 (αVβ6) was selected as the target receptor. Inspired by one- sided protein design \(^{26,27}\) , we computationally designed a previously developed liver- detargeting hybrid capsid between AAV9 and AAVrh74 (Cap9rh74) as an αVβ6 binder. The VR4 loop was completely modified, in which new sequences were iteratively selected to simultaneously optimize for free energy, while hosting αVβ6- binding RGDLLXL/I motifs. All designed AAVs were well- produced, at higher titers than their parent. The designed AAVs were confirmed to require αVβ6 binding for cellular transduction. The most promising variant, renamed LICA1, was selected for further analysis and showed superior transduction in human differentiated myotubes and strong myotropism in several mouse models. We evaluated this variant by delivering therapeutic transgenes in two MD mouse models at a very low dose of 5E12 vg/kg, in comparison to AAV9. In both cases, LICA1 presents higher efficacy than AAV9 in correcting dystrophic phenotypes, global transcriptomic changes and restoring muscle function, thanks to improved transduction and transgene expression in skeletal muscles. Collectively, the study provides a proof- of- concept for a new rational AAV design pipeline leveraging protein design tools, which resulted in a novel myotropic AAV with high potential for gene therapy for muscle diseases. + +<|ref|>sub_title<|/ref|><|det|>[[44, 890, 164, 916]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[44, 930, 512, 951]]<|/det|> +## 1. Selection of the cellular receptor for rational design + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 953, 185]]<|/det|> +Several myotrophic AAVs have recently been developed, notably, the insertion into the AAV9 VR- VIII loop of P1 peptide (RGDLLGS) \(^{15,16}\) , and a series of RGD- containing sequences identified by directed evolution \(^{17}\) . Importantly, these modified capsids shared a common RGD motif, which suggested their affinity to integrin (ITG), cell- surface heterocomplexes that interact with the extracellular matrix \(^{28}\) . Using publicly available datasets, we aimed to select relevant integrin subunits for a subsequent rational AAV design targeting skeletal muscle. + +<|ref|>text<|/ref|><|det|>[[40, 202, 955, 522]]<|/det|> +Chemello and colleagues previously performed single- nucleus RNA sequencing, comparing gene expression of all cell types in the skeletal muscle of wild- type (WT) and Duchenne muscular dystrophy mouse models (D51) \(^{29}\) . We extracted RNA levels of all integrin alpha and beta genes from these data (Figure S1A). Among all subunits, only the \(\alpha\) - subunits Itgav, Itga7 and the \(\beta\) - subunits Itgb6, Itgb1, and Itgb5 show relatively high expression in the myogenic nuclei. Of interest is the fact that the expression level of Itgb6 is highly enriched in myonuclei, and significantly upregulated in the dystrophic condition, whereas Itgb1 and Itgb5 expression are ubiquitous in all cell types, and significantly lower than the Itgb6 level in all myonuclei. Among the two expressed \(\alpha\) - subunits, only Itgav was known to associate with Itgb6 to form avβ6 heterocomplexes – a member of the RGD- binding integrin family \(^{30}\) . Furthermore, bulk RNA sequencing data from multiple human tissues confirmed high expression of Itgav and Itgb6 in skeletal muscle, and low expression of Itgb6 in the liver and spleen, two preferred targets of natural AAV (Figure S1B, GTEx V8, dbGaP Accession phs000424.v8.p2). We therefore hypothesize that AAV transduction in skeletal muscle can be improved by rationally designing an AAV capsid that specifically binds to avβ6. + +<|ref|>sub_title<|/ref|><|det|>[[45, 539, 805, 561]]<|/det|> +## 2. Rational design of a hybrid capsid, Cap9rh74, with a high affinity to the \(\mathbb{V}\mathbb{B}\beta 6\) complex + +<|ref|>text<|/ref|><|det|>[[41, 578, 955, 737]]<|/det|> +As we aim to specifically target the skeletal muscle, we selected a hybrid capsid that we previously developed and that has a liver- detargeting property as the parental capsid in our design (Patent Number: EP18305399.0). This hybrid capsid of AAV9 and AAV.rh74 (AAV9rh74) was constructed by replacing the AAV9 sequence of VR4 to VR8 with that of AAV- rh74. The hybrid capsid showed similar infectivity in skeletal and cardiac muscles but was strongly de- targeted from the liver. The latter property is of particular interest in skeletal muscle gene transfer since the majority of administrated viral vector will not accumulate in the liver, as is the case for natural AAVs \(^{31,32}\) . + +<|ref|>text<|/ref|><|det|>[[42, 753, 950, 891]]<|/det|> +After selection of the cellular receptor of interest and capsid backbone, AAV capsids were computationally engineered (Fig. 1A). First, the 3D structure of the parental capsid, of with structure was unknown, was modeled using AlphaFold2 \(^{33,34}\) . The structural prediction of the Cap9rh74 aa 219–737 monomer performed using AlphaFold2 was at a high level of confidence, with predicted local distance difference test (IDDT- Ca), a per- residue measure of local confidence, of 97.04 and low predicted aligned error (PEA) of 4.32 (Fig S1C- D). This structure is thus suitable for the next steps in the design. + +<|ref|>text<|/ref|><|det|>[[42, 908, 925, 951]]<|/det|> +Second, we extracted the 3D structure or sequences of binding motifs of the human integrin complex from PDB. Importantly, avβ6 was previously shown to bind with high affinity to the RGDLLXL/1 motif + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 955, 137]]<|/det|> +found in the human TGF- \(\beta 1\) and TGF- \(\beta 3\) prodomains \(^{35,36}\) . Binding peptides with eight amino acid residues, aa214- 221 in TGF- \(\beta 1\) (PDB: 5ffo) and aa240- 247 in TGF- \(\beta 3\) (PDB: 4um9), were isolated from the corresponding crystal structures before grafting into the Cap9rh74 VR4 loop. Both motifs bind to \(\alpha \beta 6\) dimer at a very similar position (Fig S1E). + +<|ref|>text<|/ref|><|det|>[[41, 152, 955, 314]]<|/det|> +Third, the defined binding motifs were then grafted into the VR4 loop (residues 453- 459) of the capsid protein based on the RosettaRemodel protocol \(^{37}\) . In the grafting- remodel process, many rounds of backbone optimization and sequence design iteratively search for low- energy sequence- structure pairs (Fig. 1B). The lowest- energy designs in grafting experiments of each TGF- \(\beta\) motif showed convergence in both structure and sequence (Fig. 1C- D, S1F- G). The new VR4 loops include the binding peptide and two flanking 2- amino acid linkers and retain the LXXL/I motif as an \(\alpha\) - helix, which is important to bind in the \(\beta 6\) subunit's pocket \(^{36}\) . + +<|ref|>text<|/ref|><|det|>[[41, 330, 955, 446]]<|/det|> +Retrospective docking simulations of the two AAV_ITGs with the best scores, namely Cap9rh74_5ffo and Cap9rh74_4um9, on the \(\alpha \beta 6\) complex showed highly similar binding positions of the new VR4 loop to its corresponding inserted motifs (Fig. 1E- F). This suggests that the new capsids can bind to \(\alpha \beta 6\) thanks to VR4- included RGDLLXXL/I motif. Sequences with the best scores, which reflect the thermodynamic stability of one static protein conformation \(^{38}\) , were subjected to experimental validation. + +<|ref|>sub_title<|/ref|><|det|>[[42, 460, 899, 505]]<|/det|> +## 3. All designed AAV_ITGs showed higher productivity and enhanced cellular transduction via \(\alpha \beta 6\) binding. + +<|ref|>text<|/ref|><|det|>[[41, 521, 951, 703]]<|/det|> +The two AAVs with the best design were then tested for productivity and the effectiveness of using \(\alpha \beta 6\) as a cellular receptor. They were produced by tri- transfection with pITR- CMV- GFP- Luciferase as the expression cassette. Thanks to energy optimization, all the designed AAV- ITG variants significantly increase their titers compared to their parental hybrid capsid, to levels similar to those for AAV9 (Fig. 2A, S2A). In addition, all modified AAV- ITG variants retain proportions of VP1, VP2, VP3 capsid proteins with a similar ratio of AAV9 (Fig. 2B). This suggests that the designed sequences result in more stable AAV capsid complexes thanks to their estimated low energy structure, and therefore better production efficacy. + +<|ref|>text<|/ref|><|det|>[[40, 719, 930, 950]]<|/det|> +Next, we examined whether these AAV- ITGs can effectively use \(\alpha \beta 6\) as a cellular receptor upon infection. First, a HEK293 cell line (293_ \(\alpha \beta 6\) ) constitutively overexpressing both integrin subunits, \(\alpha\) and \(\beta 6\) , was created using the PiggyBac system (Fig S2B- C). The designed AAVs were then tested for their infectivity in this cell line. As expected, infection of AAV_ITGs in 293_ \(\alpha \beta 6\) cells, as defined by vector copy numbers (VCN), was higher than for AAV9 and AAV9rh74 (Fig. 2C). Both AAV_ITGs dramatically improved the luciferase activity ( \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / AAV9}} = 60.50\) , \(\mathrm{FC}_{9\mathrm{rh74\_5ffo / AAV9}} = 25.99\) , \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / 9rh74}} = 63.99\) , and \(\mathrm{FC}_{9\mathrm{rh74\_4um9 / 9rh74 = 27.49}}\) , Fig. 2D). To investigate how specific AAV_ITGs used \(\alpha \beta 6\) as a cellular receptor, we tested their infectivity under binding competition conditions. The number of AAV_ITG viral vectors entering the cells was significantly reduced when blocked by the recombinant protein \(\alpha \beta 6\) before viral infection, but no change occurred with AAV9 or AAV9rh74 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 951, 88]]<|/det|> +(Fig. 2E). This result suggests that efficient transduction of AAV_ITGs requires specific binding to a \(\alpha \beta \delta\) complex. + +<|ref|>text<|/ref|><|det|>[[40, 104, 950, 308]]<|/det|> +During myogenesis, \(\alpha \beta \delta\) is only expressed in late differentiation, but not in the myoblast stage (Fig S1A, S2D). We therefore hypothesized an enhanced transduction of AAV_ITGs in differentiated myotubes, but not myoblasts. We infected both human myoblasts and myotubes with AAV_ITGs. Low levels of luciferase activity were observed in all AAVs tested in human myoblasts (Fig. 2G,I). On the other hand, in human differentiated myotubes (hMT), VCN and luciferase activities in both AAV9rh74_4um9 and _5ff0 were significantly higher than for AAV9 or AAV9rh74 (Fig. 2F,H,K). In particular, variant AAV9rh74_4um9 showed a 16.56 (p < 0.0001) and 25.02- fold (p < 0.0001) improvement in luciferase activity compared to AAV9 and AAV9rh74, respectively, which is in agreement with its superior transduction efficiency and transgene expression seen in 293_αVβ6 cells. + +<|ref|>text<|/ref|><|det|>[[42, 324, 933, 369]]<|/det|> +In summary, the two designed AAV_ITGs were both well- produced and function via \(\alpha \beta \delta\) - specific binding, thus enhancing their transduction efficiency in 293_αVβ6 and human differentiated myotubes. + +<|ref|>sub_title<|/ref|><|det|>[[44, 385, 835, 407]]<|/det|> +## 4. AAV_ITGs enhanced transduction in skeletal muscle following systematic administration + +<|ref|>text<|/ref|><|det|>[[42, 423, 940, 490]]<|/det|> +AAV_ITGs, together with AAV9 and AAV9rh74, were administrated systematically via intravenous injection (transgene: CMV_GFP- Luciferase, dose: 1E13 vg/kg, age at injection: 6 weeks, n = 4) in C57Bl6 mice to examine their biodistribution 3 weeks post- injection (Fig. 3A). + +<|ref|>text<|/ref|><|det|>[[39, 504, 950, 916]]<|/det|> +In agreement with a previous study, AAV9rh74 slightly reduces transduction in skeletal muscle compared to AAV9 but accumulates much less in the liver (Fig. 3B- D). Thanks to the liver- detargeting capsid and in accordance with the fact that \(\alpha \beta \delta\) is weakly expressed in the liver, we expected poor entry into the liver for designed AAV_ITGs. Indeed, AAV_ITGs is strongly detargeted from the liver, both at VCN and mRNA levels, even further than the parental capsid (Fig. 3C- D). In contrast, enhanced transduction was observed in all skeletal muscles that were tested, including the tibialis anterior (TA), quadriceps (Qua) and diaphragm (Dia) (Fig. 3B- D). The two AAV_ITGs both showed a substantial increase in VCN and luciferase activity compared to both AAV9 and AAV9rh74. Similar to the results obtained in in vitro models, AAV9rh74_4um9 is the best transducer among the two AAV_ITGs. Compared to AAV9, the variant 9rh74_4um9 significantly increased VCN 5.31/7.21/2.48- fold and increased luciferase activity 15.2/13.2/23.57- fold in Qua, TA, and Dia (p < 0.05), respectively. Compared to the original backbone AAV9rh74, this variant even magnified the difference by increasing VCN 5.53/2.85/7.69- fold and increasing luciferase activity 152.35/106.68/60.43- fold (p < 0.05). Furthermore, AAV9rh74_4um9, but not AAV9rh74_5ffo, significantly increased transduction in the heart (FCVCN=4.15, FCLLC=15.43, p < 0.05). All AAVs that were tested showed poor delivery and transgene expression in the lungs and kidneys. No alteration of TGFβ and integrin signaling was observed at one- month post- injection in all AAVs being tested (Fig S2F- G). Overall, these data indicate that AAV_ITGs, especially the 9rh74_4um9 variant, are strongly liver- detargeted and exhibit enhanced tropism towards skeletal and cardiac muscles. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 44, 914, 88]]<|/det|> +## 5. AAV9rh74_4um9 transduced skeletal muscle similarly, but detargeted the liver more strongly than other myotropic AAVs + +<|ref|>text<|/ref|><|det|>[[39, 105, 951, 469]]<|/det|> +Several engineered myotropic AAVs (mAAVs), including AAVMYO \(^{15}\) , MYOAAV- 1A and - 2A \(^{17}\) , have demonstrated superior efficacy for in vivo delivery of muscle compared to natural AAVs. To evaluate the properties of these AAVs compared to ours, we performed in vitro and in vivo experiments. Viral preparations were produced using the same reporter transgene (CMV_GFP- Luc). All mAAVs were well- produced in 400ml suspension, with higher titers than AAV9rh74. However, MYOAAV productivity was significantly lower than 9rh74_ITGs and MYOAAVs (Fig S3A). Since all investigated mAAVs shared a common integrin- targeting RGD motif, these AAVs were then evaluated for their transduction via integrin complexes in myotubes and in cell lines where integrin complexes were stably overexpressed by the PiggyBac system. In 293_αVβ6 cells as well as in hMT, where αVβ6 is highly expressed, AAV9rh74_4um9 showed the highest transduction among the tested myotropic AAVs, with the sole exception that luciferase activity of MYOAAV2A was higher in hMT (Fig S3B- C). We also tested AAV transduction efficiency in two other cell lines, 293_WT, where αVβ6 expression is low, and 293_α7β1 that stably overexpresses a non- RGD- targeting α7β1 integrin. In both conditions, MYOAAV2A and AAV9rh74_4um9 showed the highest transduction (Fig S3D- E). These results suggest that, as intended with the rational design, AAV9rh74_4um9 uses αVβ6 more preferentially for cellular transduction than others, yet it can also efficiently use other integrin(s) similar to MYOAAV2A. + +<|ref|>text<|/ref|><|det|>[[39, 484, 950, 759]]<|/det|> +Following in vivo injection in the same setting as described above (6- week- old WT mice, dose: 1E13 \(\mathrm{vg / kg}\) , \(\mathrm{n} = 4\) ), the three mAAVs and 9rh74_4um9 all showed strong liver- detargeting, high enrichment in both skeletal and cardiac muscles, and negligible transduction levels in other organs that were tested (kidneys, lungs, and brain) (Fig. 3G- H). No significant difference was observed in either VCN or luciferase activity between all three mAAVs and 9rh74_4um9 in the skeletal muscles that were tested. In heart muscle, MYOAAV2A showed a significant increase in VCN compared to other myotropic vectors, but no difference in luciferase activity, in agreement with the original observation \(^{17}\) . The most striking difference is the level of liver- detargeting between these vectors. The VCN for 9rh74_4um9 in liver is 3.34/22.05/13.85 times lower than for AAVMYO ( \(\mathrm{p} = 0.0022\) ), MYOAAV- 1A ( \(\mathrm{p} = 0.0013\) ) and - 2A ( \(\mathrm{p} = 0.033\) ), respectively (Fig. 3G), and is therefore the only vector that accumulates less in liver than skeletal muscles (Fig S3F- G). These data indicate higher muscle specificity for the 9rh74_4um9 variant compared to other myotropic vectors that have been investigated to date. + +<|ref|>text<|/ref|><|det|>[[41, 775, 950, 911]]<|/det|> +In summary, the 9rh74_4um9 variant, hereafter referred to as LICA1 (linked- integrin- complex AAV), consistently showed enhanced transduction and strongest liver- detargeting. Therefore, we then attempted to evaluate LICA1 as a delivery vector for muscular dystrophies, in comparison with AAV9. Two different setups will be investigated: the transfer of microdystrophin (μDys) – an incomplete transgene - in mdx, a mild mouse model of Duchenne muscular dystrophy (DMD) and of the full- length human α- sarcoglycan (SGCA) in a severe mouse model of limb- girdle muscular dystrophy R3 (LGMD- R3). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 44, 905, 89]]<|/det|> +## 6. Low-dose LICA1-μDys gene transfer is effective in specifically overexpressing microdystrophin in dystrophic muscle but not sufficient to fully correct the underlying pathology + +<|ref|>text<|/ref|><|det|>[[39, 105, 950, 408]]<|/det|> +DMD is caused by mutations in the DMD gene, which encodes for dystrophin protein - a key player in the dystrophin- glycoprotein complex (DGC), which is critical for the structural stability of skeletal muscle fibers \(^{39}\) . Lack of dystrophin can result in progressive loss of muscle function, respiratory defects, and cardiomyopathy. The most commonly used DMD animal model is the mdx mouse, with a lifespan reduced by \(25\%\) , milder clinical symptoms than those seen in human patients, with the exception of the diaphragm muscle \(^{40}\) . Among many therapeutic strategies to restore dystrophin expression, high- dose AAV- based gene transfer of shortened functional forms of the dystrophin ORF provided excellent results in animal models, but unsatisfactory conflicting data in current clinical trials \(^{6}\) . Severe toxicities, even patient death, have been reported from these trials (NCT03368742, NCT04281485), assumed to be related to the dose of \(\geq 1E14\) vg/kg. We therefore explored the possibility of low- dose μDys gene transfer \(^{41}\) in mdx mice using LICA1 in comparison to AAV9 (Fig S4A, age at injection: 4 weeks, dose: 5E12 vg/kg, treatment duration: 4 weeks, \(n = 5\) ). Three muscles with increasing levels of severity - TA, Qua, and Dia - were used to study AAV transduction and treatment efficacy. + +<|ref|>text<|/ref|><|det|>[[39, 424, 956, 699]]<|/det|> +LICA1 showed better μDys gene transfer than AAV9 in this model. LICA1- treated mice exhibited a significantly higher VCN in all 3 muscles that were tested, 1.85/2.02/1.07 times higher in TA ( \(p < 0.0001\) ), Qua ( \(p < 0.0001\) ), and Dia ( \(p = 0.020\) ), respectively (Fig. 4A). RNA levels indicated even greater differences and were 4.56- 7.57 times higher in the LICA1- treated group (Fig. 4B; TA: FC = 4.56, \(p < 0.0001\) ; Qua: FC = 5.46, \(p = 0.0001\) ; Dia: 7.57, \(p = 0.05\) ). Consequently, LICA1 can transduce almost \(100\%\) in TA and Qua, and \(49.98\%\) in Dia, while substantially lower numbers were seen in AAV9- treated muscles, at \(73.22\%\) ( \(p = 0.0001\) ), \(57.8\%\) ( \(p < 0.0001\) ), \(10.34\%\) ( \(p < 0.0001\) ) in TA, Qua, Dia, respectively (Fig. 4C, Fig S4B). Furthermore, while infection levels and expression of the transgene in liver were high for the AAV9 vector (despite the use of muscle- specific promoter), the VCN and mRNA levels in LICA1- treated liver were extremely low (Fig. 4A- B, FCVCN:AAV9/LICA1=36.8, \(p = 0.0002\) ; FCmRNA:AAV9/LICA1=64.7, \(p < 0.0001\) ). These data again confirmed the transduction efficiency and specificity towards skeletal muscle for the LICA1 vector, even with low- dose treatment. + +<|ref|>text<|/ref|><|det|>[[39, 714, 940, 947]]<|/det|> +The histological features and muscle functionality after AAV treatment were restored accordingly. The centronucleation index (percentage of centronucleated fibers) - an indicator of the regeneration/degeneration process - did not change with AAV9 (except in TA) but was significantly reduced upon LICA1 treatment (reduction of \(21.68\%\) , \(19.05\%\) , \(22.88\%\) in TA, Qua, Dia, respectively) (Fig. 4D, Fig S4C). Similarly, the fibrosis level in two severely affected muscles, Qua and Dia, only exhibited a significant reduction with LICA1, but not AAV9 (Fig. 4E, Fig S4D). The serum biomarker MYOM3 level, an indicator of muscle damage \(^{42}\) , showed a reduction for both AAV treatments, with a considerable further reduction seen in the LICA1- treated group (Fig. 4F, FCAAV9/KO=0.75, FC-LICA/KO=0.43, PAAV9- LICA1>0.0001). More importantly, AAV9 treatment did not affect any muscle functionality being tested (Fig. 4G- I), while significant improvements with LICA1- μDys treatment were observed in escape + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 43, 935, 137]]<|/det|> +test – a measure of global force (Fig. 4G, \(\mathrm{FC}_{\mathrm{LICA1 / mdx}} = 1.19\) , \(\mathrm{P}_{\mathrm{LICA1 / mdx}} = 0.02\) ) and in situ TA mechanical force measurement (Fig. 4H, \(\mathrm{FC}_{\mathrm{LICA1 / mdx}} = 1.14\) , \(\mathrm{P}_{\mathrm{LICA1 / mdx}} = 0.0006\) ). However, none of the treatment normalized to the WT functional levels. These data indicate that LICA1 is better than AAV9 at restoring dystrophic histological features and muscle functions. + +<|ref|>text<|/ref|><|det|>[[39, 153, 951, 449]]<|/det|> +We also investigated the molecular alteration in Qua upon AAV treatment using RNA- seq. On the two first principal components (PCs) of the PCA, a clear distinction between four transcriptome groups (WT, mdx, AAV9, LICA1) was observed, while LICA1- treated muscles were clustered closer to the WTs than others (Fig S4E). To our surprise, despite excellent transgene expression by LICA1, global transcriptomic restoration was relatively modest (Fig. 4K). Nevertheless, a substantial improvement can still be seen for LICA1 compared to AAV9. Among 4216 down- and 4501 upregulated differentially expressed genes (DEGs) identified in mdx muscle, 1515 (35.9%) and 1728 (38.4%) were restored by AAV9, while LICA1 was able to correct 1736 (41.2%) and 1980 (44.0%), respectively (Fig. 4L- M). In addition, a greater number of genes were either not or insufficiently corrected by AAV9 than by LICA1 (Fig. 4N). A total of 2572 genes were downregulated (61.0%) and 2620 (58.2%) incompletely restored, while significantly lower numbers were seen for LICA, with 2094 (49.67%) down- and 2019 (44.86%) upregulated. Interestingly, some known dysregulated pathways, including \(\alpha\) - and Y- interferon responses and oxidative phosphorylation, were significantly better normalized by LICA1 than by AAV9 (Fig S4F). + +<|ref|>text<|/ref|><|det|>[[41, 463, 945, 576]]<|/det|> +In summary, at 5E12 vg/kg, LICA1- \(\mu\) Dys, but not AAV9, was efficient in transducing close to 100% myofibers, except in the diaphragm. This effective improvement in transduction can significantly reduce some dystrophic features in all muscles that were tested, yet restoration in the global transcriptome remains modest. However, greater improvements in functional, histological, and transcriptomic restoration were achieved with LICA1 compared to AAV9. + +<|ref|>sub_title<|/ref|><|det|>[[42, 592, 894, 637]]<|/det|> +## 7. Low-dose LICA1-SGCA treatment restored the muscle functionality, dystrophic phenotypes, and transcriptomic dysregulation in a severe SGCA mouse model. + +<|ref|>text<|/ref|><|det|>[[41, 654, 955, 794]]<|/det|> +LGMDR3 is caused by mutations in the SGCA gene \(^{43}\) – another component of the DGC complex. Defects in the SGCA protein therefore lead to muscle weakness and wasting. A LGMDR3 mouse model has been established, which closely represents patient's clinical phenotypes \(^{44}\) . Similar to the setting in mdx mice, low- dose AAV treatment with 5E12 vg/kg was investigated in this mouse model. AAV9 or LICA1 encoding human SGCA (hSGCA) under control of a muscle- specific human Acta1 promoter were injected into 4- week- old SGCA- KO mice (Fig. 5A). Analysis was performed 4 weeks post- treatment. + +<|ref|>text<|/ref|><|det|>[[41, 809, 953, 945]]<|/det|> +In all three muscles that were tested, TA, Qua, Dia (in order of increasing severity), transduction in various measures, VCN, mRNA level, and percentage of SGCA + myofibers, was significantly greater in the LICA1- treated group than for AAV9 (Fig. 5B- D, Fig S5A). Of note is the fact that the differences in transduction efficacy (%SGCA + myofibers) between LICA1 and AAV9 are greater in more severely affected muscles (Fig. 5D). At such a low dose, AAV9 was able to transduce > 80% myofibers in TA while LICA1 can reach close to 100% (p < 0.0001). While LICA1 still transduced almost 100% of fibers in Qua (the muscle + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 950, 111]]<|/det|> +affected with intermediate severity), only \(58.1\%\) fibers were transduced by AAV9 on average \((p < 0.0001)\) . In the most severely affected muscle, Dia, both vectors displayed reduced efficiency; however, LICA1 continued to demonstrate much better transduction \((\mu_{\mathrm{AAV9}} = 22.1\%, \mu_{\mathrm{LICA1}} = 59.5\%, \mathrm{p} < 0.0001)\) . + +<|ref|>text<|/ref|><|det|>[[39, 128, 955, 515]]<|/det|> +The differences in transgene delivery and expression positively correlated with levels of histological and functional restoration. Different dystrophic histological features, including percentage of centronucleated fibers (Fig. 5E, Fig S5B), percentage of fibrosis area (Fig. 5F, Fig S5C), and fiber size distribution (Fig. 5G), were all significantly better normalized by LICA1 than AAV9, especially in more severely affected muscles. Importantly, no significant improvement was observed in the AAV9- treated group in centronucleation index and fibrosis level in Dia, while LICA1 reduced these parameters by half (Fig. 5E- F). Fiber sizes were also restored to near- WT distribution by LICA1 in this muscle (Fig. 5G). No difference in body weight was seen between groups with or without AAV treatment (Fig S5D). At the functional level, however, the escape test – a measure of global force – showed a significant increase in AAV9- treated mice \((FC = 1.42, \mathrm{p} = 0.0072)\) and was even higher in LICA1- treated group \((FC = 1.72, \mathrm{p} < 0.0001)\) (Fig. 5H). On the other hand, in situ TA mechanical forces were both improved in the two AAV groups at similar levels (Fig. 5I), possibly due to \(>80\%\) transduction rate by both vectors. Similar to the global force, the serum MYOM3 level was greatly reduced in the LICA1- treated group but not for AAV9, indicating less muscle damage (Fig. 5K). No difference was seen in the anti- capsid antibody between the two AAV treatments (Fig S5E). These results indicate that better and significant functional and histological restoration in the LICA1- treated mice was achieved, even at low- dose treatment, thanks to superior transduction efficacy. + +<|ref|>text<|/ref|><|det|>[[39, 530, 947, 848]]<|/det|> +We further investigated the molecular alterations following AAV treatment by transcriptomic profiling of the quadriceps muscle. The first principal component (PCs) of the PCA was able to separate a group including WT and LICA1 with a group including SGCA- KO and AAV9, suggesting close proximity between elements within these 2 groups (Fig S5F). A heatmap of all 8591 significant DEGs (4035 downregulated and 4556 upregulated) further highlighted the restorative effect of LICA1 on gene expression levels (Fig. 5L). LICA1- treated muscles, in particular, demonstrated a significant correction of \(69.9\%\) (2821/4035) and \(66.5\%\) (3028/4556) of down- and upregulated DEGs, respectively, compared to \(12.4\%\) (500/4035) and \(9.21\%\) (420/4556) corrected by AAV9 treatment (Fig. 5M- N). Conversely, not all DEGs were significantly restored or returned to WT levels. The number of such transcripts in AAV9- treated muscles was much higher than in the LICA1- treated group (Fig. 5O): 2541 (63.0%) downregulated DEGs and 3045 (66.8%) upregulated DEGs for AAV9, with only 483 (12.0%) downregulated DEGs and 1038 (22.8%) upregulated DEGs in the LICA1- treated group. These data illustrate that low- dose LICA1 treatment can effectively normalize the majority of the dysregulated transcriptome and is much more efficient in correcting gene expression dysregulation than AAV9 at the same dose. + +<|ref|>text<|/ref|><|det|>[[42, 864, 915, 930]]<|/det|> +In summary, low- dose (5E12 vg/kg) AAV gene transfer using LICA1 in the LGMDR3 mouse model is effective in restoring muscle function, dystrophic histology, and the dysregulated transcriptome. The efficacy was much greater than for AAV9 at the same dose due to enhanced transduction. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 213, 68]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[41, 83, 955, 263]]<|/det|> +Given the severe complications observed with very high dose AAV treatment, lowering the dose by increasing vector specificity via capsid modification is one way to address these issues. This study investigated the possibility of altering AAV tropism towards skeletal muscle by targeting integrin. We designed an AAV as a \(\alpha \mathrm{V}\beta 6\) binder, which resulted in a novel myotropic AAV variant, namely LICA1. LICA1 showed greatly enhanced transduction in skeletal muscle in WT and two MD mouse models. Consequently, by improving the delivery of therapeutic transgenes (hSGCA and \(\mu \mathrm{Dys}\) ) in these MD mouse models, LICA1 was able to correct dystrophic phenotypes, global transcriptional dysregulations and significantly restore muscle function. + +<|ref|>sub_title<|/ref|><|det|>[[44, 280, 666, 301]]<|/det|> +## AAV capsid sequence design method that ensures high AAV production + +<|ref|>text<|/ref|><|det|>[[40, 317, 951, 592]]<|/det|> +AAV tropism is commonly altered by inserting a small peptide into the VR4 or VR8 loop without any sequence constraints. Since no consideration regarding AAV capsid stability is included in this method, the resulting AAV can suffer from instability, reduced productivity, and increased AAV genome fragmentation \(^{17,45}\) (ASGCT 2023). In the current study, a physics- based protein sequence design method was used to graft the binding motifs from TGF \(\beta\) - 1 and - 3 into the VR4 loop of the hybrid capsid AAV9rh74. The major differences to the classical peptide insertion method are that the entire VR4 loop was modified to include a new binding motif and the amino acids around this motif (linkers) were selected to minimize the potential energy. Low- energy sequences ensure the stability and intended folding of the designed proteins, presumably leading to improved stability of the AAV particle \(^{38}\) . Six AAVs designed using this method were tested experimentally and all showed better productivity than their parent, Cap9rh74, and similar levels to well- produced AAV9. This suggests that low Rosetta energy correlates with high stability of capsid protein, and thereby high AAV production. + +<|ref|>sub_title<|/ref|><|det|>[[44, 608, 586, 629]]<|/det|> +## Integrin \(\alpha \mathrm{V}\beta 6\) as a myotropic AAV receptor for skeletal muscle + +<|ref|>text<|/ref|><|det|>[[40, 646, 955, 877]]<|/det|> +Virus- host interaction is the foundation for improved viral vectors, yet skeletal muscle receptors that allow effective AAV transduction are poorly defined. However, top hits from two independent studies with different screening schemes identified myotropic AAVs with a common RGD motif, \(^{15,17,19}\) . In addition, it has previously been described that integrin functions as cellular receptor for natural AAV \(^{23,24}\) . Coincident with our screening for possible integrin receptor, only \(\alpha \mathrm{V}\beta 6\) is highly expressed and enriched in skeletal muscle (Fig S1). By including \(\alpha \mathrm{V}\beta 6\) binding motifs, AAV_ITGs efficiently utilized \(\alpha \mathrm{V}\beta 6\) for cellular infection. Enhanced transduction was observed in conditions with high (either ectopic or natural) \(\alpha \mathrm{V}\beta 6\) expression, including human differentiated myotubes and murine skeletal muscles of WT and two other MD mouse models. In most cases, the improved transduction was evident at the VCN level, indicating better cell entry via \(\alpha \mathrm{V}\beta 6\) binding. + +<|ref|>text<|/ref|><|det|>[[42, 893, 928, 960]]<|/det|> +In addition, we conducted a study comparing LICA1 and three other published myotropic AAVs. No significant differences in skeletal muscle transduction were observed on either VCN or transgene expression levels. However, the liver infection rate was significantly lower with LICA1 compared to the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 953, 155]]<|/det|> +other mAAVs, presumably due to the use of a liver- detargeted backbone and the low expression level of \(\alpha \mathrm{V}\beta 6\) in liver. As a result, the LICA1 vector exhibited the highest muscle/liver transduction ratio among all AAVs tested, suggesting increased specificity towards skeletal muscle. This finding highlights the importance of selecting an appropriate targeting receptor for rational design and further supports \(\alpha \mathrm{V}\beta 6\) as a promising candidate for targeting skeletal muscle. + +<|ref|>sub_title<|/ref|><|det|>[[42, 174, 472, 194]]<|/det|> +## LICA1 is a potential vector for muscular diseases + +<|ref|>text<|/ref|><|det|>[[39, 210, 955, 604]]<|/det|> +AAV gene therapy in muscle diseases typically requires very high doses \((\geq 1E14 \mathrm{vg / kg})\) for functional benefits \(^{41,46}\) , yet can result in severe and even fatal adverse events \(^{7}\) . In this study, we explored low dose (5E12 vg/kg) treatment using the LICA1 vector in two MD mouse models, DMD and LGMDR3. Of note is that this dose is at least 20 times lower than the doses currently used in clinical trials for neuromuscular diseases \(^{3}\) . In both models, LICA1 was significantly better than AAV9 in delivering and expressing therapeutic transgenes, consequently restoring better histological dystrophic phenotypes. In TA and Qua, LICA1 was able to transduce more than \(80\%\) of fibers. It was still a challenge to effectively transduce diaphragm muscle at this dose, yet more than \(50\%\) of Dia fibers were positive for transgene expression with LICA1 in both models while AAV9 transduced very poorly. This improvement in transgene expression translates directly into improved histological restoration, including centronucleation index and fibrosis level. In particular, with only more than \(50\%\) successfully transduced fibers, LICA1 was able to reduce diaphragm fibrosis by \(42.8 - 47.0\%\) (mdx and SGCA \(^{- / - }\) models respectively), whereas no change was seen in AAV9- treated groups. The biomarker for muscle damage level, MYOM3, was reduced by \(57.5 - 67.2\%\) (mdx and SGCA \(^{- / - }\) models respectively) by LICA1 and significantly greater than AAV9. Similarly, global muscle force was significantly restored to a higher level with LICA1 than with AAV9 in SGCA- KO mice. These data confirmed superior muscle transduction by LICA1 and resulting therapeutic benefits were obtained even at low- dose treatment in two MD models. + +<|ref|>text<|/ref|><|det|>[[40, 620, 955, 875]]<|/det|> +However, treatment efficacy varies between two disease models at molecular levels. We profiled transcriptomic changes in Qua following AAV treatment in both MD models. Despite similar transduction efficiency of LICA1 in the two models, restoration of dystrophic transcriptional changes in SGCA- KO was significantly greater. It is noteworthy that \(\mu \mathrm{Dys}\) is an incomplete form of dystrophin. The \(\mu \mathrm{Dys}\) used in the present study lacks several functional domains, including multiple spectrin- like repeats that bind to nNOS, F- actin, sarcomeric lipid and microtubules, and a dystrobrevin- and syntrophin- binding C- terminus \(^{41}\) . This might explain the inadequate efficacy in restoring global gene expression in \(\mu \mathrm{Dys}\) gene therapy trials, in spite of highly effective gene transfer. Similarly, despite excellent functional restoration by microdystrophin gene transfer in various animal models, outcomes from these clinical trials are unsatisfactory \(^{6}\) . Therefore, careful assessment of molecular restoration should be included for evaluating gene therapy efficacy. + +<|ref|>text<|/ref|><|det|>[[41, 890, 950, 958]]<|/det|> +In summary, this study presents an alternative computational method that aids rational AAV design and ensures high- production AAV variants. The proof- of- concept design targeting skeletal muscle resulted in a high- productivity myotropic AAV, thereby effectively delivering therapeutic transgenes and restoring + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 928, 110]]<|/det|> +dystrophic phenotypes in two MD mouse models at a low dose. This work contributes to the ongoing efforts to reduce AAV treatment doses and further advance AAV engineering, paving the way for more effective and accessible gene therapies in the future. + +<|ref|>sub_title<|/ref|><|det|>[[42, 133, 415, 159]]<|/det|> +## MATERIALS AND METHODS + +<|ref|>sub_title<|/ref|><|det|>[[42, 175, 160, 195]]<|/det|> +## Study Design + +<|ref|>text<|/ref|><|det|>[[40, 211, 955, 460]]<|/det|> +The primary objective of the study was to design a novel myotropic AAV capsid with a high production yield by using a computationally rational design. The secondary aim was to investigate the possibility of low- dose AAV treatment using a designed AAV in animal models of muscular dystrophies, which typically require an alarmingly high dose \((\geq 1E14\) vg/kg). We used publicly available datasets to identify possible receptors for skeletal muscle and protein design tools to engineer AAV capsid protein. Resulting variants were characterized for their productivity and transduction efficiency in various in vitro cell lines and multiple mouse models. Experiments were performed at least three times, unless noted otherwise. The AAV injection and infection experiments were conducted in a nonblinded fashion. The blinding approach was used during dissection, histological validation, immunostaining analysis, in vivo functional tests, and biomarker analysis. No data were excluded. Details on experimental procedures are presented in Supplementary Materials and Methods. + +<|ref|>sub_title<|/ref|><|det|>[[42, 477, 223, 497]]<|/det|> +## Animal care and use + +<|ref|>text<|/ref|><|det|>[[40, 514, 952, 787]]<|/det|> +All animals were handled according to French and European guidelines for human care and the use of experimental animals. All procedures on animals were approved by the local ethics committee and the regulatory affairs of the French Ministry of Research (MESRI) under the numbers 2018- 024- B #19736, 2022- 004 #35896. C57Bl/6, B6Ros.Cg- Dmdmdx- 4Cv/J mice were obtained from the Jackson Laboratory. A knockout mouse model of \(\alpha\) - sarcoglycan was obtained from the Kevin Campbell laboratory (University of Iowa, USA) \(^{44}\) . Mice were housed in a SPF barrier facility with 12- h light, 12- h dark cycles, and were provided with food and water ad libitum. Only male mice were used in the present study. Well- being and weights of the animals were monitored for the duration of the study. The animals were anesthetized with a mix of ketamine (100 mg/kg) and xylazine (10 mg/kg), or with isoflurane (4%) for blood samples. For AAV intravenous injections, a maximum volume of 150 μl containing AAV vectors was injected via the sinus route after the animals had been anesthetized with isoflurane. The AAV intravenous doses used in the present study were 5E12 or 1E13 vg/kg. + +<|ref|>sub_title<|/ref|><|det|>[[44, 804, 297, 824]]<|/det|> +## Cell culture and in vitro study + +<|ref|>text<|/ref|><|det|>[[42, 841, 949, 930]]<|/det|> +Adherent HEK293- T cells were maintained in the proliferating medium containing DMEM (Thermo Fisher Scientific), supplied with 10% fetal bovine serum and 1X gentamycin at \(37^{\circ}C\) , 5% CO2. Human immortalized myoblasts (AB1190 cell line) were maintained in Skeletal Muscle Cell Growth Medium (PromoCell, C23060) and differentiated in Skeletal Muscle Differentiation Medium (PromoCell, C23061). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 931, 110]]<|/det|> +In vitro AAV infection was performed by directly adding AAV into culture medium at the dose of 1E9 or 1E10 vg per 24- well plate well. After 48h post- infection, cells were washed and subjected to VCN and gene expression analysis. + +<|ref|>text<|/ref|><|det|>[[42, 127, 950, 216]]<|/det|> +To inhibit AAV infection, AAVs were incubated with recombinant hTGAV- hITGB6 protein (Bio- Techné, 3817- AV- 050) at \(37^{\circ}C\) for 30 minutes, at a concentration of \(1\mu g\) protein per 5E9vg AAV before addition to the cells (1E4 vg per cell). The same condition treated with recombinant hSGCA protein served as a control for the comparison. + +<|ref|>sub_title<|/ref|><|det|>[[44, 234, 211, 254]]<|/det|> +## Statistical Analysis + +<|ref|>text<|/ref|><|det|>[[42, 271, 950, 428]]<|/det|> +Results are presented as mean \(\pm\) SEM, unless noted otherwise. Significance of differences in multiple pairwise comparisons of more than two groups was determined by one- way ANOVA. The significance of differences in pairwise comparisons of multiple groups with multiple treatments was determined by two- way ANOVA. To account for multiple testing and control the false discovery rate (FDR) across the numerous pairwise comparisons, the Benjamini- Hochberg (BH) procedure was applied with an FDR threshold of 0.05. Statistical tests were performed using GraphPad Prism 9. Results were considered significant when p- values or adjusted p- values were less than 0.05. + +<|ref|>sub_title<|/ref|><|det|>[[44, 451, 257, 477]]<|/det|> +## DECLARATIONS + +<|ref|>text<|/ref|><|det|>[[41, 492, 951, 670]]<|/det|> +Acknowledgments: The authors are Genopole's members, first French biocluster dedicated to genetic, biotechnologies and biotherapies. We are grateful to the "Imaging and Cytometry Core Facility" and to the in vivo evaluation, services of Genethon for technical support, to Ile- de- France Region, to Conseil Départemental de l'Essonne (ASTRE), INSERM and GIP Genopole, Evry for the purchase of the equipment. We would like to acknowledge the technical help of Carolina Pacheco Algalan and Alejandro Arco Hierves. The Genotype- Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. + +<|ref|>text<|/ref|><|det|>[[42, 689, 914, 733]]<|/det|> +Funding: This work was supported by the "Association Française contre les Myopathies" (AFM), and "Institut National de la Santé Et de la Recherche Médicale" (INSERM, FranceRelance N°221513A10). + +<|ref|>text<|/ref|><|det|>[[42, 750, 949, 815]]<|/det|> +Author contributions: The project was conceptualized by A.H.V. and I.R. A.H.V., L.S.P., and J.P. conducted experiments and performed data analysis. Funding supporting this project was obtained by I.R. A.H.V. and I.R. supervised the project. The manuscript was written by A.H.V. and I.R. + +<|ref|>text<|/ref|><|det|>[[42, 832, 947, 899]]<|/det|> +Competing interests: A.H.V. and I.R. are inventors on PCT application EP2023/065499 for the integration of RGDlxxL/I motif in AAV capsid for enhanced muscle transduction efficiency. I.R. is a part- time employee of Atamyo Therapeutics. The other authors declare that they have no competing interests. + +<|ref|>text<|/ref|><|det|>[[42, 916, 936, 959]]<|/det|> +Data and materials availability: All data associated with this study are present in the paper or the Supplementary Materials. All transcriptomic data will be deposited in the NCBI Sequence Read Archive + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 933, 110]]<|/det|> +(SRA) upon publication. Processed data including differential gene expression analysis are available in data file S1 and S2. The plasmid constructs and reagents generated as part of this study are available under a material transfer agreement from the corresponding authors. + +<|ref|>sub_title<|/ref|><|det|>[[43, 133, 224, 158]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[50, 174, 951, 940]]<|/det|> +1. Wang, D., Tai, P.W.L. & Gao, G. Adeno-associated virus vector as a platform for gene therapy delivery. Nat Rev Drug Discov 18, 358-378 (2019). +2. Pupo, A. et al. AAV vectors: The Rubik's cube of human gene therapy. Molecular therapy: the journal of the American Society of Gene Therapy 30, 3515-3541 (2022). +3. Crudele, J.M. & Chamberlain, J.S. AAV-based gene therapies for the muscular dystrophies. Hum Mol Genet 28, R102-R107 (2019). +4. Duan, D. Systemic AAV Micro-dystrophin Gene Therapy for Duchenne Muscular Dystrophy. Molecular therapy: the journal of the American Society of Gene Therapy 26, 2337-2356 (2018). +5. Mack, D.L. et al. 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Computational design of trimeric influenza-neutralizing proteins targeting the hemagglutinin receptor binding site. Nature biotechnology 35, 667-671 (2017). + +<|ref|>text<|/ref|><|det|>[[47, 535, 949, 580]]<|/det|> +27. Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551-560 (2022). + +<|ref|>text<|/ref|><|det|>[[47, 585, 867, 629]]<|/det|> +28. Ruoslahti, E. RGD and other recognition sequences for integrins. Annual review of cell and developmental biology 12, 697-715 (1996). + +<|ref|>text<|/ref|><|det|>[[47, 633, 920, 700]]<|/det|> +29. Chemello, F. et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proceedings of the National Academy of Sciences of the United States of America 117, 29691-29701 (2020). + +<|ref|>text<|/ref|><|det|>[[47, 705, 941, 750]]<|/det|> +30. Pang, X. et al. Targeting integrin pathways: mechanisms and advances in therapy. Signal Transduct Target Ther 8, 1 (2023). + +<|ref|>text<|/ref|><|det|>[[47, 755, 940, 823]]<|/det|> +31. Shen, X., Storm, T. & Kay, M.A. Characterization of the relationship of AAV capsid domain swapping to liver transduction efficiency. Molecular therapy: the journal of the American Society of Gene Therapy 15, 1955-1962 (2007). + +<|ref|>text<|/ref|><|det|>[[47, 827, 940, 872]]<|/det|> +32. Ballon, D.J. et al. Quantitative Whole-Body Imaging of I-124-Labeled Adeno-Associated Viral Vector Biodistribution in Nonhuman Primates. Human gene therapy 31, 1237-1259 (2020). + +<|ref|>text<|/ref|><|det|>[[47, 876, 925, 921]]<|/det|> +33. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589 (2021). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 44, 936, 752]]<|/det|> +34. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nature methods 19, 679-682 (2022).35. Dong, X. et al. Force interacts with macromolecular structure in activation of TGF-beta. Nature 542, 55-59 (2017).36. Dong, X., Hudson, N.E., Lu, C. & Springer, T.A. Structural determinants of integrin beta-subunit specificity for latent TGF-beta. Nature structural & molecular biology 21, 1091-1096 (2014).37. Huang, P.S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PloS one 6, e24109 (2011).38. Alford, R.F. et al. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. Journal of chemical theory and computation 13, 3031-3048 (2017).39. Duan, D., Goemans, N., Takeda, S., Mercuri, E. & Aartsma-Rus, A. Duchenne muscular dystrophy. Nat Rev Dis Primers 7, 13 (2021).40. Stedman, H.H. et al. The mdx mouse diaphragm reproduces the degenerative changes of Duchenne muscular dystrophy. Nature 352, 536-539 (1991).41. Bourg, N. et al. 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Molecular therapy. Methods & clinical development 13, 494-502 (2019). + +<|ref|>sub_title<|/ref|><|det|>[[44, 770, 143, 797]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 40, 640, 787]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 802, 115, 821]]<|/det|> +
Figure 1
+ +<|ref|>sub_title<|/ref|><|det|>[[44, 843, 621, 864]]<|/det|> +## Computational rational AAV capsid design to bind to \(\alpha \beta \beta\) integrin + +<|ref|>text<|/ref|><|det|>[[42, 881, 944, 947]]<|/det|> +A. Overview of the design pipeline, including three steps: 1. Capsid 3D structures were obtained either from the PDB database or predicted by AlphaFold2. 2. The capsid VR4 loop was completely replaced by integrating the binding motif, which was extracted from receptor's natural binder, using RosettaRemodel + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 950, 338]]<|/det|> +protocol. 3. Top scored designs from the previous grafting step were docked onto the intended receptor in silico to verify the binding potential of the designed capsid. B. An illustration of the sampling for low- energy sequence- structure pairs during motif- grafting process. Capsid VR4 after removing the loop was colored in blue, extracted binding motif was colored in red. The sampled linkers and sequences (Fig. S1F) were labeled in green. C- D. The three lowest energy designs after grafting TGFβ3 (C) and TGFβ1 (D) into the capsid VR4. All top designs showed convergence in structures and sequences, suggesting sampling approached the global optimum. E- F. Retrospective docking of motif- grafted capsids (E. Cap9rh74_4um9 and F. Cap9rh74_5ffo) onto the αVβ6 structure. The left panels are illustrations of the structures with the lowest energy at the interface of capsid and integrin proteins (dG_separated: difference in free energy of two proteins). Both two newly designed VR4s (colored in green) were predicted to bind to the αVβ6 complex at very similar positions to natural binding motifs (colored in red). The right panels are scatter plots of dG_separated energy versus root-mean- square deviation (RMSD) from the lowest energy structure of all sampled docking positions. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[62, 81, 928, 780]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[45, 50, 128, 72]]<|/det|> +
Fig. 2
+<|ref|>image_caption<|/ref|><|det|>[[44, 802, 118, 821]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[44, 845, 785, 867]]<|/det|> +Designed AAV_ITGs were well- produced and improved transduction via aVβ6 binding. + +<|ref|>text<|/ref|><|det|>[[42, 882, 930, 947]]<|/det|> +A. AAV titers of different AAV variants in bulked small-scale production in suspension three-day post-triple-transfection (2ml production, \(n = 6\) , one-way ANOVA). B. Western blot of VP proteins from purified AAVs showed similar VP ratios for designed AAV_ITGs capsids compared to AAV9 and AAV9rh74, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 945, 271]]<|/det|> +suggesting successful capsid assembly. C- D. VCN (C) and luciferase activity (D) of 293_aVβ6 after AAV infection (n=3- 4, one- way ANOVA). Both the two designed AAV_ITGs showed enhanced VCN and luciferase activities compared to AAV9rh74 and AAV9. E. Inhibition of cell entry of designed AAV_ITGs, but not for AAV9 or AAV9rh74, in 293_aVβ6 cells by aVβ6 recombinant protein. AAVs were preincubated with aVβ6 recombinant protein (r.ITGAV- B6) for 30 minutes at 37°C before infection (n=3, two- way ANOVA). SGCA recombinant protein (r.SGCA) was used as the control. F- K. Enhanced transduction of AAV_ITGs in in vitro human differentiated myotubes, but not in myoblasts. F. Representative images of the GFP signal of myotubes 48 hours post- infection (scale bar: 400μm). G- K. VCN and luciferase activities of AAV_ITGs in comparison with AAV9 and AAV9rh74 in myoblasts (G,I) and myotubes (H,K) (n=3- 4, one- way ANOVA). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[61, 70, 700, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[52, 49, 106, 66]]<|/det|> +
Fig. 3
+<|ref|>image_caption<|/ref|><|det|>[[42, 803, 117, 821]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 844, 944, 887]]<|/det|> +Designed AAV_ITGs showed enhanced transduction in skeletal and cardiac muscles while strongly liver- detargeted in vivo. + +<|ref|>text<|/ref|><|det|>[[42, 904, 937, 947]]<|/det|> +A. Scheme of in vivo experiment. AAVs (CMV_GFP-Luciferase) were injected intravenously into 6wo C57BL6 mice (n=4) at the dose of 1E13 vg/kg. B. Representative images of the bioluminescence signal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 955, 340]]<|/det|> +20 days post- infection. C- D. VCN (C) and gene expression (D) (GFP mRNA level in the liver and luciferase activity in other organs) for different AAVs in liver, skeletal muscles, heart, lung, and kidney (n=4, one- way ANOVA). Both designed AAV_ITGs strongly detargeted from the liver compared to AAV9, while they significantly improved VCN and luciferase activities over AAV9rh74 (and AAV9 with AAV9rh74_4um9 variant) in skeletal and cardiac muscles, and were detected and expressed at low levels in lung and kidney. E- H. Comparison of the AAV9rh74_4um9 variant with other public myotropic AAVs (mAAVs) \(^{15,17}\) . E. Illustration of the differences between mAAVs and AAV9rh74_4um9 at modification sites in capsid protein and modification methods. F. The VR8 loop sequences of mAAVs compared to VR8 of their backbone AAV9, and VR4 of AAV9rh74_4um9 compared to VR4 of AAV9rh74. G- H. VCN (G) and gene expression (H) (GFP mRNA level in liver and luciferase activity in other organs) of different AAVs in liver, skeletal muscles, heart, lung, kidney, and brain (n=4, one- way ANOVA). AAV9rh74_4um9 showed similar VCN and gene expression in skeletal muscle to other mAAVs, while being significantly more strongly detargeted from the liver. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[63, 70, 792, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 50, 115, 70]]<|/det|> +
Fig. 4
+<|ref|>image_caption<|/ref|><|det|>[[42, 803, 118, 821]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 844, 949, 887]]<|/det|> +Low- dose gene transfer by LICA1 was more effective and better at restoring dystrophic phenotypes than AAV9 in the DMD mouse model. + +<|ref|>text<|/ref|><|det|>[[42, 903, 950, 946]]<|/det|> +A- B. Comparison of transduction efficacy between AAV9 and LICA1 in all three muscles that were tested, in terms of VCN (A), and \(\mu\) Dys RNA level (B). C. Comparison of percentage of successfully transduced + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 953, 476]]<|/det|> +(dystrophin- positive) fibers in all three muscles that were tested. Note that TA, Qua, Dia muscles are presented in increasing order of severity. D- E. Comparison of restoration levels in dystrophic histological features between AAV9 and LICA1 in all three muscles that were tested, in terms of percentage of centro- nucleated fibers (D) and fibrosis level (E). Illustrated images in C- E are of quadriceps muscles (scale bar: \(100\mu \mathrm{m}\) ). F. Serum MYOM3 level – indicator of muscle damage – 4 weeks post- injection ( \(n = 5\) , one- way ANOVA). G- I. Comparison of functional restoration between AAV9 and LICA1 by Escape test – global force measurement (G, \(n = 6\) ), tetanus force of TA muscle (H, \(n = 10 - 12\) ), and twitch force of TA muscle (I, \(n = 9 - 12\) ). K- N. Comparison of restoration in global transcriptomic changes in quadriceps muscle between AAV9 and LICA1 ( \(n = 4\) , adjusted p- values \(< 0.05\) ). K. The heatmap presents the log2 fold change (log2FC) in comparison to WT muscle for all 8717 DEGs found in mdx muscle (compared to WT). The log2FC values are illustrated in row Z- scores, colored from blue to red, arranged from lowest to highest. L- N. Volcano plots of multiple comparisons illustrate transcriptomic changes before and after AAV treatment. As a reference, 4216 downregulated and 4501 upregulated DEGs found in mdx were colored blue and red, respectively, in all volcano plots. Among these DEGs, the number of genes found to be significantly different in each pair- wise comparison were labeled in the upper corners. L. Volcano plots comparing mdx/WT transcriptomes. M. Volcano plots comparing mdx to AAV- treated transcriptomes, in which significant DEGs are the genes correctly restored after AAV treatment. N. Volcano plots comparing AAV treatment to WT, in which significant DEGs are the genes that are not or incompletely restored after AAV treatment. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 66, 789, 787]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[48, 50, 111, 68]]<|/det|> +
Fig. 5
+<|ref|>image_caption<|/ref|><|det|>[[44, 803, 118, 821]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 844, 931, 887]]<|/det|> +Low- dose gene transfer by LICA1 was better at restoring dystrophic phenotypes and functionality than AAV9 in the LGMDR3 mouse model. + +<|ref|>text<|/ref|><|det|>[[42, 904, 949, 946]]<|/det|> +A. Scheme of in vivo experiment: LICA1 (9rh74_4um9) or AAV9 were injected intravenously into a 4wo SGCA-KO mouse model at the dose of 5E12 vg/kg (expression cassette: hACTA1_hSGCA_HBB2-pA, n=3- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 951, 500]]<|/det|> +5). Three skeletal muscles in increasing order of severity, TA, Qua, and Dia, were analysed 4 weeks postinjection. B-D. Comparison of transduction efficacy between AAV9 and LICA1 in all three muscles that were tested in terms of VCN (B), hSGCA mRNA level (C), and percentage of succesfully transduced (SGCA- positive) fibers (D). E-G. Comparison of restoration levels in dystrophic histological features between AAV9 and LICA1 in all three muscles that were tested in terms of percentage of centro- nucleated fibers (E), fibrosis level (F), and fiber size distribution (G). Illustrated images in D-F are of quadriceps muscles (scale bar: \(100 \mu m\) ). H-K. Comparison of functional restoration between AAV9 and LICA1 using the escape test – global force measurement (H), tetanus force of TA muscle (I), and serum MYOM3 level – indicator of muscle damage (K). L-O. Comparison of restoration in global transcriptomic changes in quadriceps muscle between AAV9 and LICA1 (n=4, adjusted p values < 0.05). L. The heatmap presents the log2 fold change (log2FC) in comparison to WT muscle for all 8591 DEGs found in KO muscle (compared to WT). The log2FC values are illustrated by row Z-scores, colored from blue to red, arranged from lowest to highest. M-O. Volcano plots of multiple comparisons illustrate transcriptomic changes before and after AAV treatment. As a reference, 4035 downregulated and 4556 upregulated DEGs found in KO were colored blue and red, respectively, in all volcano plots. Among these DEGs, the number of genes found to be significantly different in each pair-wise comparison were labeled in the upper corners. M. Volcano plots comparing KO/WT transcriptomes. N. Volcano plots comparing KO to AAV-treated transcriptomes, in which significant DEGs are the genes correctly restored after AAV treatment. O. Volcano plots comparing AAV treatment to WT, in which significant DEGs are the genes that are not or incompletely restored after AAV treatment. + +<|ref|>sub_title<|/ref|><|det|>[[44, 519, 312, 547]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 570, 767, 590]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 607, 300, 681]]<|/det|> +- ALICA1supp.pdf- DatafileS2DEGSGCA.xlsx- DatafileS1DEGDMD.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/images_list.json b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8788e5bfce9ac0610627c445355c3ca762009a9c --- /dev/null +++ b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/images_list.json @@ -0,0 +1,55 @@ +[ + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2. Protocols and bubble observation. a) Experimental protocol. Ellipses illustrate the cloud magnetization at different \\(t\\) and the two sketches show the energy landscape for positive (up) and negative (down) \\(\\delta\\) . b) Collection of integrated magnetization profiles \\(Z(x)\\) after different waiting times \\(t\\) . For each value of \\(t\\) , 7 different realizations are shown. c) Magnetization profiles for the realizations marked with arrows in panel (b). d) Measured probability \\(P\\) (empty circles) to observe a shot with a bubble at fixed time is shown. The probability is well fitted to an exponential curve (grey continuous line) until it saturates to 1.", + "footnote": [], + "bbox": [], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3. Measurement of the evolution of \\(Z(x)\\) in time after the ramp on \\(\\delta\\) for \\(\\Omega_{R} / 2\\pi = 300\\mathrm{Hz}\\) , for \\(\\delta_{f} / \\Omega_{R} = -1.70\\) in (a) and \\(-1.79\\) in (b). c) Value of \\(F_{\\mathrm{t}}\\) evaluated in the \\(20\\mu \\mathrm{m}\\) central region of the cloud are fitted by the empirical expression reported in the text (squares for data in (a) and pentagons for (b)). Error bars are the standard deviation over up to ten repetitions. d-e) Numerical simulations for \\(\\delta_{f} / \\Omega_{R} = -1.52\\) in (d) and \\(-1.585\\) in (e). Value of \\(F_{\\mathrm{t}}\\) for the simulations (triangles for data in (d) and stars for (e)). The red dashed lined are linear fits in the exponentially decaying part. g) Experimental \\(\\tau\\) and numerical \\(\\tau_{\\mathrm{sim}}\\) timescale of the bubble formation as a function of \\((\\delta_{f} - \\delta_{c}) / |\\kappa |n\\) . Error bars include statistical uncertainties on the fit and uncertainty on the \\(\\delta_{f} - \\delta_{c}\\) coming from magnetic field stability and calibration. Numerical timescale of the bubble formation \\(\\tau_{\\mathrm{sim}}\\) is shown before (light symbols) and after (dark symbol) rescaling. The empty triangle is an experimental point taken with a preparation ramp twice slower than the others, to verify the impact on the nucleation time resulting from a residual non-adiabaticity in the preparation of the sample.", + "footnote": [], + "bbox": [ + [ + 270, + 92, + 710, + 500 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4. Decay time \\(\\tau\\) and \\(\\tau_{\\mathrm{sim}}\\) and instanton theory. Experimental \\(\\tau\\) and simulations \\(\\tau_{\\mathrm{sim}}\\) are obtained as explained in the text for \\(\\Omega_{R} / 2\\pi = 300,400,600\\) and \\(800\\mathrm{Hz}\\) . A rescaling common to all \\(\\Omega_{R}\\) is applied to the horizontal axes of the simulation; see text. Dashed and full curves are fits of the experimental and simulation data according to the instanton formula. Full markers stand for simulation results while empty markers for experimental data. Error bars include statistical uncertainties on the fit and uncertainty on the \\(\\delta_{f}\\) due to on the magnetic field stability.", + "footnote": [], + "bbox": [ + [ + 272, + 92, + 720, + 422 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "FIG. M1. \\(\\tau\\) vs \\(\\tau_{50\\%}\\) for experimental (a) and numerical (b) results. The two quantity are compatible to each other within error bars in experimental results and show only small deviation in simulation data. Color code for the points is the same used in the main text and the blue line marks \\(\\tau = \\tau_{50\\%}\\) .", + "footnote": [], + "bbox": [ + [ + 270, + 663, + 725, + 840 + ] + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a.mmd b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2ddfad8d6f0fab3fd4bbe7a4735c01f56b31b8a9 --- /dev/null +++ b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a.mmd @@ -0,0 +1,349 @@ + +# Observation of false vacuum decay via bubble formation in ferromagnetic superfluids + +Anna Berti CNR- INO, Pitaevskii BEC Center, Università di Trento https://orcid.org/0000- 0003- 3073- 9554 + +Riccardo Cominotti CNR- INO, Pitaevskii BEC Center, Università di Trento + +Chiara Rogora CNR- INO, Pitaevskii BEC Center, Università di Trento + +Ian Moss School of Mathematics, Statistics and Physics, Newcastle University + +Thomas Billam Newcastle University + +Iacopo Carusotto CNR- INO, Pitaevskii BEC Center, Università di Trento + +Giacomo Lamporesi CNR- INO, Pitaevskii BEC Center, Università di Trento + +Alessio Recati CNR- INO, Pitaevskii BEC Center, Università di Trento + +Gabriele Ferrari Universita' di Trento and INO- CNR BEC Center https://orcid.org/0000- 0003- 1827- 5048 + +Alessandro Zenesini ( \(\boxed{ \begin{array}{r l} \end{array} }\) alessandro.zenesini@ino.it) CNR- INO, Pitaevskii BEC Center, Università di Trento + +Article + +Keywords: + +Posted Date: June 8th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2923763/v1 + +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Physics on January 22nd, 2024. See the published version at https://doi.org/10.1038/s41567-023-02345-4. + +<--- Page Split ---> + +# Observation of false vacuum decay via bubble formation in ferromagnetic superfluids + +A. Zenesini \(^{1,2}\) , +A. Berti \(^{1}\) , +R. Cominotti \(^{1}\) , +C. Rogora \(^{1}\) , +I. +G. Moss \(^{3}\) , +T. +P. Billam \(^{4}\) , +I. Carusotto \(^{1}\) , +G. Lamporesi \(^{1,2}\) , +A. Recati \(^{1}\) , and +G. Ferrari \(^{1,2}\) \(^{1}\) Pitaevskii BEC Center, CNR-INO and Dipartimento di Fisica, Università di Trento, 38123 Trento, Italy \(^{2}\) Trento Institute for Fundamental Physics and Applications, INFN, 38123 Trento, Italy \(^{3}\) School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK and \(^{4}\) Joint Quantum Centre (JQC) Durham-Newcastle, School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK (Dated: May 22, 2023) + +Metastability is ubiquitous in nature and is observed through the crossing of an energy barrier toward a configuration of lower energy as, for example, in chemical processes [1] or electron field ionization [2]. In classical many- body systems, metastability naturally emerges in the presence of a first- order phase transition and finds a prototypical example in supercooled vapour. In the last decades, the extension to quantum field theory and quantum many- body systems has attracted significant interest in the context of statistical physics [3, 4], protein folding [5, 6], and cosmology [7- 9], where thermal and quantum fluctuations are expected to trigger the transition from the metastable state (false vacuum) to the ground state (real vacuum) via the probabilistic nucleation of spatially localized bubbles [10, 11]. However, the long- standing theoretical progress in estimating the relaxation rate of the metastable field via bubble nucleation has not yet found a counterpart in terms of experimental observations. Here we experimentally observe and characterize bubble nucleation in isolated and coherently- coupled atomic superfluids, and support our observations with numerical simulations. The agreement between our results and a novel analytic formula based on instanton theory confirms the quantum- field character of the observed decay, and promotes coherently- coupled atomic superfluids as emulators of out- of- equilibrium quantum field phenomena. + +A supercooled gas is a classic example of a metastable state which exists just across a first order phase transition. The passage to the ground state (the liquid phase) is mediated by resonant bubble nucleation when the energy gain provided by the liquid bulk is compensated by the cost of the surface tension. This energy balance leads to a critical bubble size and a stochastic + +<--- Page Split ---> + +formation of the bubble typically occurs around nucleation spots given by impurities in the gas or imperfections at the container. The extension of this idea to a quantum many- body or a quantum field system has attracted extensive attention in a wide range of scenarios and length scales, from the understanding of early universe [7- 9] to the characterization of spin chains [3, 4]. In all these models, the metastable state at the origin of the bubble nucleation, is identified as "false vacuum" and the role of surface tension is taken by a genuinely quantum term. In the purest form, the false vacuum decay into the ground state would take place through quantum vacuum fluctuations [10, 11] (similarly to impurities in the classical case). However, as for example in the early universe, the tunnelling is equally likely to be boosted by thermal fluctuations, and the process would be of the type styled "vacuum decay at finite temperature" [12] (see [13, 14] for a review). + +In the cosmological case, the energy scales are well above any that are accessible to experiments, and the phenomenon of false vacuum decay remains one of the most important yet untested processes considered in theoretical high energy physics. Recently, the extreme flexibility of neutral and charged atoms tabletop experiments and the advances of classical and quantum computer algorithms have paved the way for the proposal of experimental environments [15- 22] and virtual simulators [23, 24]. Up to now only numerical results have been achieved and the experimental observation of an analogue to false vacuum decay would therefore be of high significance. + +In tabletop experiments, the observation of bubble nucleation requires several ingredients which are difficult to arrange simultaneously. First, a mean- field interaction- induced energy landscape composed of an asymmetric double well represents the minimal requirement for the decay from the metastable state to the absolute ground state via macroscopic tunneling across the energy barrier, followed by relaxation; see sketch in Fig. 1. Second, unlike in the ordinary quantum tunneling of a single particle [1, 25, 26], it is an effective field describing the system that changes state. Third, the time resolution of the experiment should cover many orders of magnitude to allow for the investigation of the predicted exponential time- dependence on the tuning parameters. This must be associated to a high stability and accuracy of the tuning parameters. An extended ferromagnetic superfluid [27] possesses the ideal properties to act as a field simulator, in particular its first order phase transition character, the long range coherence and the flexibility to control its experimental parameters within a stable and isolated environment. In tight analogy with supercooling, in an extended quantum system the presence of a spatial region with different magnetization to the bulk carries a positive kinetic energy due to the winding of the field at the interface, see Fig. 1. + +In this letter, we present the experimental observation of bubble formation via false vacuum decay in a quantum system. We observe that the bubble nucleation time scales exponentially with + +<--- Page Split ---> +![](images/Figure_2.jpg) + + +FIG. 1. Mean- field energy and bubble formation. The cloud is initially prepared with all the atoms in \(|\uparrow \rangle\) (A). While the single \(|\downarrow \rangle\) spin state is energetically lower ( \(E_{\downarrow} < E_{\uparrow}\) ) in the center of the cloud, in the low density tails the situation is opposite. The interface has a positive energy which adds up to the double minimum energy landscape emerging from the ferromagnetic interaction. Macroscopic quantum tunneling can take place resonantly to the bubble state (B) which has a \(|\downarrow \rangle\) bubble in the center, whose core energy gain compensates for the interface energy cost. The barrier crossing can be triggered by quantum fluctuations in the zero- temperature case (dashed arrow) or by thermal fluctuations at finite temperature (empty arrow). After the tunneling process, in the presence of dissipation, the bubble increases in size to reach the ground state (C), without coming back to (A). + +an experimental parameter that is connected to the energy barrier properties. Theoretical and numerical simulations support our observations and allow us to confirm the quantum field origin of the decay and its thermal activation. + +The experimental platform is composed of a bosonic gas of \(^{23}\mathrm{Na}\) atoms, optically trapped and cooled below the condensation temperature. The gas is initially prepared in the internal state \(|F,m_{F}\rangle = |1, - 1\rangle = |\downarrow \rangle\) , where \(F\) is the total angular momentum and \(m_{F}\) its projection on the quantization axis. A microwave radiation with amplitude \(\Omega_{R}\) coherently couples the \(|\downarrow \rangle\) state to \(|2, - 2\rangle = |\uparrow \rangle\) . The relevant scattering lengths for such a two- level system are \(a_{\downarrow \downarrow} = 54.5a_{0}\) , \(a_{\uparrow \uparrow} = 64.3a_{0}\) , and \(a_{\downarrow \uparrow} = 54.5a_{0}\) , and lead to the condition \(\Delta a = (a_{\uparrow \uparrow} + a_{\downarrow \downarrow}) / 2 - a_{\downarrow \uparrow} < 0\) , i.e., to a system with a ferromagnetic ground state [27]. + +The trapping potential is axially symmetric and harmonic in all three directions, but strongly asymmetric (axial and radial trapping frequencies \(\omega_{x} / 2\pi = 20\mathrm{Hz}\) and \(\omega_{\rho} / 2\pi = 2\mathrm{kHz}\) ), producing an elongated system with inhomogeneous density and spatial size given by the longitudinal and + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 2. Protocols and bubble observation. a) Experimental protocol. Ellipses illustrate the cloud magnetization at different \(t\) and the two sketches show the energy landscape for positive (up) and negative (down) \(\delta\) . b) Collection of integrated magnetization profiles \(Z(x)\) after different waiting times \(t\) . For each value of \(t\) , 7 different realizations are shown. c) Magnetization profiles for the realizations marked with arrows in panel (b). d) Measured probability \(P\) (empty circles) to observe a shot with a bubble at fixed time is shown. The probability is well fitted to an exponential curve (grey continuous line) until it saturates to 1.
+ +radial Thomas- Fermi radius \(R_{\mathrm{x}} = 200\mu \mathrm{m}\) and \(R_{\rho} = 2.5\mu \mathrm{m}\) . At the end of each experimental realization, we image the two spin states independently and extract their density distributions. The transverse confinement is tight enough to suppress the radial spin dynamics of the condensate. We therefore integrate each image along the transverse direction and obtain the integrated 1D density profiles \(n_{\uparrow}(x)\) and \(n_{\downarrow}(x)\) , from which we extract the profile of the relative magnetization \(Z(x) = [n_{\uparrow}(x) - n_{\downarrow}(x)] / [n_{\uparrow}(x) + n_{\downarrow}(x)]\) . + +The coupled two- level system can be studied by separately treating the total density ( \(n = n_{\uparrow} + n_{\downarrow}\) ) and the spin ( \(n_{\uparrow} - n_{\downarrow} = nZ\) ) degrees of freedom. While the density is simply dominated by a continuity equation, the spin degree of freedom is ruled by a magnetic mean- field Hamiltonian, which shows a first- order phase transition in the central region of the cloud for \(\Omega_{R} < |\kappa |n\) , where + +<--- Page Split ---> + +\(\kappa \propto \Delta a\) is the relevant interaction parameter; see Methods. + +The first- order phase transition originates from a symmetry breaking when the energy landscape as a function of the magnetization \(Z\) goes from a single to a double minimum at \(\Omega_{R}< |\kappa |n =\) \(2\pi \times 1150\mathrm{Hz}\) . At fixed \(\Omega_{R}\) , the experimentally tunable parameter is the detuning \(\delta\) between the two- level system and the coupling radiation. For small enough \(|\delta |\) , the energy landscape \(E(Z)\) is represented by an asymmetric double well, that turns symmetric for \(\delta = 0\) . In particular, for positive \(\delta\) , the energy is minimized by positive values of \(Z\) , and viceversa The relevant parameter for the bubble nucleation is the shape (height and width) of the energy barrier separating the two wells that the system needs to overcome as a field, i.e., in a macroscopic manner. This depends on \(\delta\) , \(n\) and \(\Omega_{R}\) . When \(|\delta |\) exceeds a critical value \(\delta_{c}\) , the metastable well disappears [27]. Borrowing the nomenclature from ferromagnetism, \(\pm \delta_{c}\) correspond to the edges of the hysteresis region and their value depends both on \(\Omega_{R}\) and \(|\kappa |n\) . + +Figure 2(a) illustrates the experimental protocol. We first transfer the whole system from \(|\downarrow \rangle\) to \(|\uparrow \rangle\) with a \(\pi\) pulse. While keeping \(\Omega_{R}\) constant, \(\delta\) is linearly ramped down from \(\delta_{i} / 2\pi = 5.5\mathrm{kHz}\) to a variable \(\delta_{f}\) on a timescale between 20 and 60 ms. Since the ramp starts with \(\delta \gg \Omega_{R}\) , the system follows the spin rotation remaining in the local ground state until \(\delta < 0\) when such a local ground state becomes a metastable state; see inset in Fig. 2(a). Once \(\delta_{f}\) is reached, the states are independently imaged after a variable waiting time \(t\) . + +If \(\delta_{f} > 0\) , the whole system is and remains in the absolute ground state \(|\uparrow \rangle\) , whereas for \(\delta_{f}< 0\) , after a variable time, a macroscopic region in the central part of the system flips to \(|\downarrow \rangle\) , generating a bubble; see examples in Fig. 2(b) and magnetization profiles in (c). On average the bubble occurrence probability is larger if the waiting time is longer [see Fig. 2(b) and (d)]. For a quantitative analysis, at each \(t\) , we repeat the measurement up to 10 times in order to investigate the statistical formation of bubbles. Note that, while in uniform systems the bubbles would stochastically nucleate in random spatial positions, our nonuniform density profile of the atomic sample strongly favors the nucleation at the center of the cloud, where \(\delta_{f}\) is closest to \(\delta_{c}\) . + +A useful quantity to characterize the bubble nucleation in time is \(F_{t} = (1 + \langle Z\rangle_{t} / \langle Z\rangle_{t = 0}) / 2\) , which was used in Ref. [3] to compare an exact diagonalization approach in a zero- temperature spin chain to instanton predictions. Here \(\langle \cdot \rangle_{t}\) stands for \(Z\) measured at time \(t\) and averaged over many realizations. In Fig. 3(a) and (b), we show the average magnetization \(\langle Z\rangle_{t}\) profile as a function of waiting time for two values of detuning. Since the bubble appears always in the center of the system, to compute \(F_{t}\) , we extract the mean magnetization \(\langle Z\rangle_{t}\) in the central 20- \(\mu \mathrm{m}\) - wide region \((\approx R_{x} / 10)\) . The resulting \(F_{t}\) , plotted in panel (c), initially remains flat, and then it exponentially + +<--- Page Split ---> + +decays because of the bubble nucleation. Both features were also observed in Ref. [3] and the understanding of the starting plateau is still an open question from the theoretical point of view. We find that the measured \(F_{t}\) is well described by the empirical function \((1 - \epsilon) / \sqrt{1 + (e^{t / \tau} - 1)^{2}} + \epsilon\) , which is 1 for \(t = 0\) , scales as \(t^{2}\) for small \(t\) and is exponentially decaying to \(\epsilon\) for large \(t\) . The two fitting parameters are \(\tau\) , that describes the characteristic timescale for the bubble formation, and \(\epsilon\) , that takes into account that the asymptotic magnetization \(Z_{t = \infty}\) can be different from the one of the ground state, \(Z_{TV}\) ( \(F = 0\) ). Note that the timescale \(\tau\) is related to the exponential decay, while the empirical formula takes into account an initial plateau present in the averaged magnetisation \(F_{t}\) . (in Methods we show that the plateau length and \(\tau\) are strictly connected). + +Numerical simulations based on 1D Gross- Pitaevskii equations, reported in Fig. 3(d) and (e), qualitatively reproduce the experimental observations. In the numerics, classical noise is included to simulate the effect of a finite temperature (more details can be found in Methods). Data in Fig. 3(d) and (e) are obtained by averaging over 1000 different noisy realizations of the real- time dynamics: the large statistics allows us to directly extract the exponential decay time \(\tau_{\mathrm{sim}}\) through a linear fit of \(\ln (F_{t})\) . + +In Fig. 3(g), we report six experimental values of \(\tau\) obtained for \(\Omega_{R} = 2\pi \times 300 \mathrm{Hz}\) , plotted as a function of the distance from the critical detuning, \((\delta_{f} - \delta_{c}) / |\kappa |n\) . The results show an exponential dependence on the tuning parameter over two orders of magnitude, from a few to hundreds of ms. Such a sensitivity to a parameter is remarkable for ultracold atoms experiments. In particular, the experimental observation of the quasi- exponential dependence of \(\tau\) with respect to \(\delta_{f}\) in an interval of the order of \(100 \mathrm{Hz}\) critically relies on the magnetic field stability better than a few tens of \(\mu \mathrm{G}\) [28]. + +The values of \(\tau_{\mathrm{sim}}\) for the simulations [light symbols in Fig. 3(g)] qualitatively show the same behaviour of the experimental data. The agreement becomes even quantitative [dark symbols in Fig. 3(g)], by using a rescaling of \(|\kappa |n\) and a small shift of \(\delta\) . The need for such a rescaling was demonstrated in Ref. [27], as a consequence of dimensionality, noise and non complete adiabaticity of the preparation protocol. In Fig. 4, we compare experimental \(\tau\) and rescaled numerical \(\tau_{\mathrm{sim}}\) , for four different values of \(\Omega_{R}\) , by using the same rescaling for all four panels. + +Our observations are consistent with the scenario of a condensate spinor field initially in a ferromagnetic metastable state, which decays via macroscopic tunneling to bubbles (domains) of the ferromagnetic ground state. The escape of a quantum field from the false vacuum, occurring via macroscopic tunneling, and the bubble formation finds a suitable description in terms of an instanton, or critical solution to the field equations in imaginary time [10–12]. Such a theory + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 3. Measurement of the evolution of \(Z(x)\) in time after the ramp on \(\delta\) for \(\Omega_{R} / 2\pi = 300\mathrm{Hz}\) , for \(\delta_{f} / \Omega_{R} = -1.70\) in (a) and \(-1.79\) in (b). c) Value of \(F_{\mathrm{t}}\) evaluated in the \(20\mu \mathrm{m}\) central region of the cloud are fitted by the empirical expression reported in the text (squares for data in (a) and pentagons for (b)). Error bars are the standard deviation over up to ten repetitions. d-e) Numerical simulations for \(\delta_{f} / \Omega_{R} = -1.52\) in (d) and \(-1.585\) in (e). Value of \(F_{\mathrm{t}}\) for the simulations (triangles for data in (d) and stars for (e)). The red dashed lined are linear fits in the exponentially decaying part. g) Experimental \(\tau\) and numerical \(\tau_{\mathrm{sim}}\) timescale of the bubble formation as a function of \((\delta_{f} - \delta_{c}) / |\kappa |n\) . Error bars include statistical uncertainties on the fit and uncertainty on the \(\delta_{f} - \delta_{c}\) coming from magnetic field stability and calibration. Numerical timescale of the bubble formation \(\tau_{\mathrm{sim}}\) is shown before (light symbols) and after (dark symbol) rescaling. The empty triangle is an experimental point taken with a preparation ramp twice slower than the others, to verify the impact on the nucleation time resulting from a residual non-adiabaticity in the preparation of the sample.
+ +provides a threshold energy scale, below (above) which quantum (thermal) fluctuations dominate: zero- \(T\) quantum tunneling is expected to be dominant when \(T\) is below the critical temperature \(T^{*} = \hbar |\kappa |n / k_{B}\) . Considering the peak density in our system, we estimate \(T^{*} \simeq 50 \mathrm{nK}\) . Although the temperature of our condensates is \(T = 1.5 \mu \mathrm{K} \gg T^{*}\) , given the harmonic confinement and the + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
FIG. 4. Decay time \(\tau\) and \(\tau_{\mathrm{sim}}\) and instanton theory. Experimental \(\tau\) and simulations \(\tau_{\mathrm{sim}}\) are obtained as explained in the text for \(\Omega_{R} / 2\pi = 300,400,600\) and \(800\mathrm{Hz}\) . A rescaling common to all \(\Omega_{R}\) is applied to the horizontal axes of the simulation; see text. Dashed and full curves are fits of the experimental and simulation data according to the instanton formula. Full markers stand for simulation results while empty markers for experimental data. Error bars include statistical uncertainties on the fit and uncertainty on the \(\delta_{f}\) due to on the magnetic field stability.
+ +exchange interaction which pushes the thermal component away from the condensate, we estimate an effective local temperature of about \(250\mathrm{nK}\) in the condensate region which is still larger than \(T^{*}\) in the region where the bubbles appear. Therefore we expect the macroscopic tunneling to be in the thermally activated regime. + +Within the instanton approach, the bubble nucleation probability has the characteristic timescale \(\tau\) , which has an exponential dependence \(A(E_{c} / k_{B}T)^{- 1 / 2}e^{E_{c} / k_{B}T}\) . \(E_{c}(\delta ,\kappa n,\Omega_{R})\) is the energy of the critical solution and strongly depends on the shape of the many- body potential and in particular on the barrier height (Fig. 1). The pre- factor \(A\) depends on fluctuations about the critical solution, but there are very few models for which this factor is calculable, at present. We therefore regard the pre- factor \(A\) as a fitting parameter in the following analysis. We can estimate \(E_{c}\) , and provide an analytical expression in the limit of vanishing metastable well (small \(\delta_{f} - \delta_{c}\) ), by considering a homogeneous 1D system. The potential for the magnetization field \(Z\) can be + +<--- Page Split ---> + +written as (see, e.g., Ref.[27] ) + +\[V(Z) = \kappa n Z^{2} - 2\Omega (1 - Z^{2})^{1 / 2} - 2\delta_{f}Z \quad (1)\] + +and the instanton energy reads + +\[\frac{E_{c}}{\hbar|\kappa|n} = \sqrt{\frac{\hbar n}{2m|\kappa|}}\int_{Z_{T P}}^{Z_{F V}}\left[\frac{V(Z) - V(Z_{F V})}{|\kappa|n(1 - Z^{2})}\right]^{1 / 2}d Z, \quad (2)\] + +where \(Z_{T P}\) is the classical turning point (in the inverted potential \(V\) ) and \(Z_{F(alse)V(acuum)}\) the value of the magnetization of the metastable state. Most of our data are taken in a regime where the barrier is much smaller than the depth of the ground state well. In this limiting case the instanton energy reads + +\[\frac{E_{c}}{\hbar|\kappa|n}\propto \sqrt{\frac{\hbar n}{2m|\kappa|}}\left(\frac{\delta_{f} - \delta_{c}}{|\kappa|n}\right)^{\frac{5}{4}}\left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{1}{6}}\left(\frac{|\delta_{c}|}{|\kappa|n}\right)^{-\frac{1}{4}}, \quad (3)\] + +where \(\delta_{c} = \kappa n[1 - (\Omega /(|\kappa |n))^{\frac{2}{3}}]^{\frac{3}{2}}\) : see Methods. We compare the previous expression to the experimental data and numerical simulations using a two- parameter fit \(\ln \tau = \ln A + b\hat{E}_{c} + \ln (b\hat{E}_{c}) / 2\) , where \(\hat{E}_{c} = \sqrt{2m|\kappa| / (\hbar n)} E_{c} / \hbar |\kappa |n\) is the rescaled energy. The results are shown in Fig. 4. Considering the approximations used to derive Eq. (3) – in particular the absence of the trapping potential, no phase fluctuations and small barrier – the agreement is remarkable and the instanton theory appears to capture the main dependence of the false vacuum decay rate on the microscopic parameter \(\delta_{f}\) which is responsible for the broken \(\mathbb{Z}_{2}\) symmetry. + +In this paper, we present solid evidence of the thermally- induced macroscopic tunneling of a coherent quantum field, manifested by bubbles of true vacuum phase nucleating in a false vacuum state. The true and false vacua are the local and global energy minimum of a ferromagnetic atomic Bose- Einstein condensate, respectively. The experimental results clearly show an exponential dependence of the decay rate on the microscopic parameters and the hysteric region width. Such a dependence is successfully captured by numerical simulations and more remarkably by a simple instanton theory based on a reduced energy functional for the magnetisation. Our platform paves the way to explore the process of bubble formation and growth in intricate detail, and to build a new bridge between low energy and high energy phenomena characterized by metastability within a first order phase transition. In this spirit our work opens up new avenues in the understanding of early universe, as well as ferromagnetic quantum phase transitions. The possibility of engineering the barrier properties via injection of tailored noise and of deterministically seeding bubbles are promising future directions for experimental investigations with focus on the role of dissipation, + +<--- Page Split ---> + +the existence of shortcut- to- adiabaticity [29, 30], the creation of entanglement, of domain wall confinement [31], and relativistic and non relativistic aspects of the bubble nucleation and dynamics. Furthermore an experimental effort towards colder systems would allow us to reach the tunneling regime dominated by quantum fluctuations. A natural extension of the present work goes to dimensionality larger than one, where the theoretical treatment is challenging. + +Acknowledgements - We thank A. Biella and P. Hauke for fruitful discussions. We acknowledge funding from Provincia Autonoma di Trento, from INFN through the FISH project, from the Italian MIUR under the PRIN2017 project CEnTraL (Protocol Number 20172H2SC4), from the European Union's Horizon 2020 research and innovation Programme through the STAQS project of QuantERA II (Grant Agreement No. 101017733), from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 804305), from the UK Quantum Technologies for Fundamental Physics programme (grants ST/T00584X/1 and ST/W006162/1) and from PNRR MUR project PE0000023- NQSTI. This work was supported by Q@TN, the joint lab between University of Trento, FBK - Fondazione Bruno Kessler, INFN - National Institute for Nuclear Physics and CNR - National Research Council. + +Author contribution - G.F., G.L. and A.Z. conceived the project. R.C., C.R., and A.Z. performed the experiments and analyzed the data. A.B., I.C., I.M., T.B., and A.R. performed the theoretical analysis. A.B. developed the numerical code and performed the simulations. G.F. supervised the project. All authors contributed to the discussion and interpretation of the results and paper writing. + +Data availability - Data in paper figures are available in Extended data. Two dimensional raw atomic cloud pictures from all experimental runs and analysis code are available upon request to A.Z. + +Corresponding author - Correspondence to A.Z., G.L. and A.R. + +Ethics declarations: Competing interests - The authors declare no competing interests. + +<--- Page Split ---> + +## METHODS + +### I. FERROMAGNETISM IN ELONGATED MIXTURES + +The ferromagnetic properties of atomic superfluid coupled mixtures are experimentally measured and discussed in [27]. Here we summarize the key ingredients which help understanding the results presented in the main text of the article. + +Our system is composed of two sodium hyperfine states \(|F,m_{F}\rangle = |2, - 2\rangle \equiv |\uparrow \rangle\) and \(|1, - 1\rangle \equiv\) \(|\downarrow \rangle\) , where \(F\) is the total angular momentum and \(m_{F}\) its projection. The two populations \(n_{\uparrow}(x,y)\) and \(n_{\downarrow}(x,y)\) are independently measured by shadow imaging. Starting from the two two- dimensional pictures of the cloud, we determine the relative magnetization \(Z(x)\) as \(Z(x) =\) \((n_{\uparrow}(x) - n_{\downarrow}(x)) / n(x)\) , where \(n_{\uparrow ,(\downarrow)}(x) = \int n_{\uparrow ,(\downarrow)}(x,y)dy\) and \(n(x) = \int (n_{\uparrow}(x,y) + n_{\downarrow}(x,y))dy\) are the 1D integrated densities. The integration along \(y\) takes advantage of the suppressed radial dynamics. In local density approximation (LDA), the energy per particle associated to the spin channel of the mixture is + +\[E(Z,\phi)\propto -\frac{\delta_{f}}{2} Z + \frac{\kappa n}{2} Z^{2} - \Omega_{\mathrm{R}}\sqrt{1 - Z^{2}}\cos \phi \quad (4)\] + +where the phase \(\phi\) is the relative phase between \(|\uparrow \rangle\) and \(|\downarrow \rangle\) . The detuning \(\delta_{f}\) used in the text is equal to \(\delta_{\mathrm{B}} + n\Delta\) where \(\delta_{\mathrm{B}}\) is the experimental controllable detuning. The quantity \(\kappa\) and \(\Delta\) are associated to the collisional proprieties of the mixture and are + +\[\begin{array}{l}\Delta \equiv \frac{g_{\downarrow\downarrow} - g_{\uparrow\uparrow}}{2\hbar} < 0\\ \kappa \equiv \frac{g_{\downarrow\downarrow} + g_{\uparrow\uparrow}}{2\hbar} -\frac{g_{\downarrow\uparrow}}{\hbar} < 0 \end{array} \quad (6)\] + +where \(g_{\downarrow \downarrow},g_{\uparrow \uparrow}\) and \(g_{\downarrow \uparrow}\) are the two intra species and the inter species coupling constants. Note that \(n\Delta\) derives from the \(|\uparrow \rangle\) and \(|\downarrow \rangle\) self interaction asymmetry. + +In an elongated cloud having a parabolic Thomas Fermi density profile, the ferromagnetic phase is located in the center of the cloud where the non liner term \(|\kappa |nZ^{2} / 2\) is maximal. Under the condition \(|\kappa |n< \Omega\) , in fact, the energy per particle is characterized by a symmetric double minimum structures a signature of the symmetry breaking typical of the ferromagnetic phase. At non zero detuning, the symmetry of the two wells is broken. Thanks to the tuning knob \(\delta_{B}\) , which is linearly proportional to the applied magnetic field, one can change the relative energy difference between the two energy minima, converting one or the other state into the absolute ground state or the metastable state. The tails of the cloud remain in the paramagnetic regime, having smaller density, and \(Z\) of the only energy minimum is unambiguously determined by \(\delta_{\mathrm{B}}\) . + +<--- Page Split ---> + +Due to the asymmetry between \(|\uparrow \rangle\) and \(|\downarrow \rangle\) , there exists a range of values of \(\delta_{\mathrm{B}}\) where the sign of the \(Z\) at the energy minima in the center \((- )\) and at the tails \((+)\) is opposite, but the system can still maintain a homogeneous positively- magnetized profile being metastable in the center. When the detuning is decreased below the critical value \(\delta_{c}\) (see main text), the metastable minimum disappears resulting in a unique steady magnetic profile with negative \(Z\) in the center and positive \(Z\) in the tails. + +While the spin energy profiles of Eq. (4) are useful to explain the presence of two minima separated, this LDA representation only shows the LDA energy landscape per particle and not the total energy of the system. For instance, the LDA energy profiles don't include the contribution coming from the interface between opposite \(Z\) , whose kinetic energy represents a further contribution to the total energy barrier, as intended to be shown in Fig. 1 in the main text. + +## II. CALIBRATION AND ANALYSIS PROCEDURE + +An important calibration concerns the determination of the critical detuning at which the double well energy landscape is expected to disappear. We determine \(\delta_{c}\) by performing the same protocols used in [27] to measure the hysteresis width of the ferromagnetic regime. This consists in the same ramp shown Fig. 2(a) of the main text, applied with a null waiting time. + +The data used in the main text are obtained in the range of \(\delta\) directly above the critical one. Thanks to the appearing of the bubble in the center of the cloud, we first determine the presence of the bubble by fixing a threshold \(Z_{\mathrm{bubble}} = 0.2\) . If the average magnetization in the central 40 pixels is below \(Z_{\mathrm{bubble}}\) , one bubble is counted. The total bubble counts at fixed waiting time determines the probability \(P\) , as plotted in Fig. 2(c) of the main text. We verify that the choice of the threshold \(Z_{\mathrm{bubble}}\) and the averaging area do not critically impact on the outcomes presented here. Once the bubble is detected, the full magnetization profile is initially fitted by using a double sigmoidal function, + +\[A\left[\arctan \left(\frac{x - x_{r}}{s_{r}}\right) - \arctan \left(\frac{x - x_{l}}{s_{l}}\right)\right] \quad (7)\] + +where \(A\) is the amplitude and \(x_{(r),[l]}\) and \(s_{(r),[l]}\) are the (right) [left] centers and sigmas of the two sigmoids. The positions \(x_{(l),[r]}\) are then used as starting values for a second fitting routine that independently analyses the left and right bubble interfaces. This second step is used to better determine the exact positions of the interfaces without the effects of cloud asymmetry and offsets. The obtained values \(x_{(l),[r]}\) allow to determine the bubble size as \(\sigma_{x} = x_{r} - x_{l}\) + +<--- Page Split ---> + +## III. DETERMINATION OF \(\tau\) AND ALTERNATIVE \(\tau_{50\%}\) + +In the main text we explain how we determine the characteristic decay time \(\tau\) by fitting \(F_{t}\) to \((1 - \epsilon) / \sqrt{1 + (e^{t / \tau} - 1)^{2}} + \epsilon\) . This formula allows us to extract \(\tau\) even for experimental sequences with limited statistics and it results to be robust against the initialisation of the fitting parameters. + +To verify the solidity of our approach we also considered a different characteristic time \(\tau_{50\%}\) defined as the time at which the probability \(P\) to observe a bubble is \(50\%\) . This approach is a valid alternative for measurements featuring a limited statistics. To determine \(\tau_{50\%}\) we fit \(P\) with the following function: + +\[P(t) = \mathrm{Min}[a_{1}*(e^{t / a_{2}} - 1),1] \quad (8)\] + +with \(a_{1}\) and \(a_{2}\) as free parameters. These two are then used to determine \(\tau_{50\%}\) from + +\[\frac{1}{2} = a_{1}*(e^{t_{50\%} / a_{2}} - 1) \quad (9)\] + +We check, within the statistical uncertainties, that the value of \(\tau_{50\%}\) does not change by using different fitting functions (linear, exponential with offsets in time and \(P\) ). Figure M1 shows that \(\tau\) and \(\tau_{50\%}\) are compatible both for the experimental measurements and numerical simulations. In particular, simulation results allow us to conclude that, while \(\tau_{50\%}\) is expected to be influenced by the delay time before the bubble decays, \(\tau_{50\%}\) is still a good approximation of \(\tau\) . This suggests that the delay time and \(\tau\) are related and further investigations are necessary to understand how. + +In general, we conclude that the determination of \(\tau\) used in the main text is solid. In particular, one notes that the two methods rely on two very different observables, the mean magnetization + +![PLACEHOLDER_14_0] + +
FIG. M1. \(\tau\) vs \(\tau_{50\%}\) for experimental (a) and numerical (b) results. The two quantity are compatible to each other within error bars in experimental results and show only small deviation in simulation data. Color code for the points is the same used in the main text and the blue line marks \(\tau = \tau_{50\%}\) .
+ +<--- Page Split ---> + +in the center, averaged over all experimental shots ( \(\tau\) ), and the probabilistic presence of a bubble \((\tau_{50\%})\) . + +## IV. NUMERICAL SIMULATIONS + +The numerical results presented in the main text are based on one- dimensional Gross- Pitaevskii simulations. The parameters are chosen to faithfully reproduce the experimental conditions: in particular, the system trapped by a harmonic potential with frequency \(\omega_{0} \simeq 2\pi \times 16 \mathrm{Hz}\) , so that the Thomas- Fermi radius is \(L \simeq 200 \mu \mathrm{m}\) ; moreover, interactions are chosen to obtain \(|\kappa |n_{0} = |\Delta |n_{0} \simeq 2\pi \times 1.1 \mathrm{kHz}\) , \(n_{0}\) being the total density in the center of the cloud. The system is first prepared, through imaginary- time evolution, in the ground state corresponding to \(\delta_{f} = 2\pi \times 1 \mathrm{kHz}\) , thus, regardless of the value of \(\Omega_{R}\) , it is almost fully polarized in the \(|\uparrow \rangle\) state. + +A white noise of amplitude equal to \(3\%\) of the central density is added on top of the ground state: this corresponds to an injected energy of roughly \(\epsilon /k_{B} = 215 \mathrm{nK}\) . We then let the system evolve in real time, without changing any parameter and we observe that, after a transient, the noise distribution becomes stationary; we interpret this result as thermalization of the mixture to a temperature \(T \propto \epsilon\) . Under an ergodicity assumption, we can determine the dynamics of the system by averaging over many repetitions of the same time- evolution, each one obtained starting from a different noisy sample. To summarize, we perform mean- field simulations in which noise plays the role of an effective temperature. Of course, these do not allow to investigate the role of quantum fluctuations: however, since the estimated experimental temperature is much higher than \(|\kappa |n_{0} / k_{B} \sim 50 \mathrm{nK}\) , the dynamics is likely to be dominated by thermal noise and a comparison with classical field simulations is justified. + +The real- time dynamics after thermalization reproduces, once again, the experimental protocol: a detuning ramp with speed \(\sim 50 \mathrm{Hz / ms}\) is applied in order to reach the false vacuum state corresponding to some final \(\delta_{f} < 0\) ; the magnetization of the system is then monitored for a waiting time in the range \([10, 300] \mathrm{ms}\) , depending on the simulation parameters. + +In order to extract the characteristic decay time \(\tau\) and \(\tau_{50}\) , we compute: + +\[F(t) = \frac{\langle Z(x \sim 0, t) \rangle - Z_{TV}}{Z_{FV} - Z_{TV}} \quad (10)\] + +where \(\langle Z(x \sim 0, t) \rangle\) is the statistical average of magnetization over the central \(10 \mu \mathrm{m}\) of the cloud. If the number of samples is sufficiently high (we use 1000), this function represents the probability of not observing a bubble at time \(t\) . Therefore, \(\tau_{50}\) is computed, by definition, by solving \(F(\tau_{50}) = 0.5\) . + +<--- Page Split ---> + +The FVD rates are obtained instead via a linear fit of \(\log F(t)\) : in most cases the predicted exponential behaviour is found within a time interval corresponding to \(F(t) \in [0.3, 0.7]\) ; small adjustments of this window are necessary for the simulations associated to the smallest and longest tunnelling times. + +## V. ISTANTONS + +The theoretical description of vacuum decay is non- perturbative and based on instanton solutions to the equations of motion using an imaginary time coordinate. The classical field theory for this system reduces down to a field theory for the magnetisation \(Z\) . For thermal instantons, bubbles nucleate at a rate (see e.g.[14]) + +\[\Gamma = 1 / \tau = A(\beta E_{c})^{j / 2}e^{-\beta E_{c}}. \quad (11)\] + +where \(\beta = 1 / (k_{B}T)\) and \(E_{c}\) is the energy of the instanton. The factor \(A\) depends on fluctuations about the instanton and \(j\) is the number of translational symmetries. There should be one zero mode \(j = 1\) if there is translational invariance in the system. (The bubbles in the experiment always nucleate near the centre, so translational invariance is suspect. Fortunately, the power law dependence has only a small effect on the results). There are a very limited number of models for which the pre- factor \(A\) is calculable at present, and we will therefore regard \(A\) as a fitting parameter in the subsequent analysis. Note that the non- perturbative approach is valid when the exponent is larger than one, i.e. for temperatures \(k_{B}T < E_{c}\) . At even lower temperatures, vacuum fluctuations become the dominant seeding mechanism. In our system this happens for \(k_{B}T < \hbar |\kappa |n \sim 50 \mathrm{nK}\) , and the resulting vacuum decay rate would be far less than the rate seen in the experiment. + +The energy for a thermal instanton includes a gradient contribution + +\[E_{c} = \frac{\hbar n}{4}\int \left\{\frac{\hbar}{2m}\frac{(\nabla Z)^{2}}{1 - Z^{2}} + V\right\} dx, \quad (12)\] + +where the potential + +\[V = \kappa nZ^{2} - 2\Omega_{R}(1 - Z^{2})^{1 / 2} - 2\delta_{\mathrm{f}}Z. \quad (13)\] + +We can scale out the dependence on the density so that \(\hat{E}_{c} = E_{c} / (\hbar n^{2}\xi |\kappa |)\) for the length scale \(\xi = \hbar /(m|\kappa |n)^{1 / 2}\) . For thermal bubbles in one dimension, the instanton calculation is equivalent to a WKB approximation to the action, with the familiar WKB form + +\[\hat{E}_{c} = \frac{1}{2}\int_{Z_{TP}}^{Z_{FV}}\left(\frac{2(V - V_{FV})}{|\kappa|n}\right)^{1 / 2}\frac{dZ}{\sqrt{1 - Z^{2}}}, \quad (14)\] + +<--- Page Split ---> + + +TABLE I. Fitting coefficients for the thermal instanton model of vacuum decay with \(j = 1\) . The fit is limited to \((\delta_{f} - \delta_{c}) / \Omega_{R} > 0.05\) to ensure that \(b\hat{E}_{c} > 1\) + +
ΩR/2πaexp(σa)bexp(σb)asim(σa)bsim(σb)
3000.54(0.09)56.5(1.9)0.93(0.06)55.0(1.9)
4000.83(0.42)44.4(6.1)0.70(0.07)41.3(0.87)
6000.02(0.43)30.3(3.7)0.01(0.14)29.8(1.3)
8000.30(0.75)25.8(5.7)-0.44(0.11)25.3(0.9)
+ +The integral extends from the turning point \(Z_{TP}\) to the false vacuum \(Z_{FV}\) . The extra factor \((1 - Z^{2})^{- 1 / 2}\) is due to the form of the derivative terms in the energy (12). + +The experimental data has been used to determine the best parameters in a fit for \(\ln \tau = \ln A + b\hat{E}_{c} - \ln (b\hat{E}_{c}) / 2\) . The results are given in Table I. The condensate number density is given by \(n = (k_{B}T / \hbar |\kappa |n)b / \xi\) . For the temperature \(T = 1\mu \mathrm{K}\) , the values of \(n\) at lower \(\Omega\) are around half of the value expected for the system, but not unreasonable given the limitations of the one dimensional treatment. If the bubble only fills a fraction of the cross- section, it effectively feels only part of the integrated density. + +In the case of small potential barriers, the potential can be expanded to cubic order about an inflection point at \(Z_{c}\) and \(\delta = \delta_{c}\) , where + +\[\delta_{c} = \kappa n(1 - Z_{c}^{3}),\qquad Z_{c} = \left(1 - \left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{2}{3}}\right)^{\frac{1}{2}}. \quad (15)\] + +The integral in this case can be performed exactly, + +\[\hat{E}_{c}\approx 1.77\left(\frac{\delta_{f} - \delta_{c}}{|\kappa|n}\right)^{\frac{5}{4}}\left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{1}{6}}\left(\frac{|\delta_{c}|}{|\kappa|n}\right)^{-\frac{1}{4}} \quad (16)\] + +To verify that the instanton prediction and simulation are consistent, we repeat numerical simulations at fixed \(\delta_{f}\) and variable \(\epsilon\) . We observe that the extracted \(\tau\) results proportional to \(e^{(1 / \epsilon)}\) and this well justifies the association between the injected noise parameter \(\epsilon\) and the temperature \(T\) . + +<--- Page Split ---> + +[2] K. C. Kulander, K. J. Schafer, and J. L. Krause, Atoms in Intense Laser Fields (Academic Press, New York, 1992) p. 247. + +[3] G. Lagnese, F. M. Surace, M. Kormos, and P. Calabrese, False vacuum decay in quantum spin chains, Phys. Rev. B 104, L201106 (2021). + +[4] A. Milsted, J. Liu, J. Preskill, and G. Vidal, Collisions of false- vacuum bubble walls in a quantum spin chain, PRX Quantum 3, 020316 (2022). + +[5] A. J. Baldwin, T. P. J. Knowles, G. G. Tartaglia, A. W. Fitzpatrick, G. L. Devlin, S. L. Shammas, C. A. Waudby, M. F. Mossuto, S. Meehan, S. L. Gras, J. Christodoulou, S. J. Anthony- Cahill, P. D. Barker, M. Vendruscolo, and C. M. 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Rev. D 15, 2929 (1977), [Erratum: Phys. Rev. D 16, 1248 (1977)]. + +[12] A. D. Linde, Decay of the False Vacuum at Finite Temperature, Nucl. Phys. B 216, 421 (1983), [Erratum: Nucl.Phys.B 223, 544 (1983)]. + +[13] A. Mazumdar and G. White, Review of cosmic phase transitions: their significance and experimental signatures, Rept. Prog. Phys. 82, 076901 (2019), arXiv:1811.01948 [hep- ph]. + +[14] M. Hindmarsh, M. Luben, J. Lumma, and M. Pauly, Phase transitions in the early universe, SciPost Phys. Lect. Notes, 24 (2021). + +[15] O. Fialko, B. Opanchuk, A. I. Sidorov, P. D. Drummond, and J. Brand, Fate of the false vacuum: Towards realization with ultra- cold atoms, Europhysics Letters 110, 56001 (2015). + +[16] J. Braden, M. C. Johnson, H. V. Peiris, and S. Weinfurtner, Towards the cold atom analog false vacuum, Journal of High Energy Physics 2018, 2018 (2019). + +[17] T. P. Billam, R. Gregory, F. Michel, and I. G. Moss, Simulating seeded vacuum decay in a cold atom system, Phys. Rev. D 100, 065016 (2019). + +[18] Z. Davoudi, M. Hafezi, C. Monroe, G. Pagano, A. Seif, and A. Shaw, Towards analog quantum simulations of lattice gauge theories with trapped ions, Phys. Rev. Res. 2, 023015 (2020). + +<--- Page Split ---> + +[19] T. P. Billam, K. Brown, and I. G. Moss, Simulating cosmological supercooling with a cold- atom system, Phys. Rev. A 102, 043324 (2020). + +[20] T. P. Billam, K. Brown, A. J. Groszek, and I. G. Moss, Simulating cosmological supercooling with a cold atom system. ii. thermal damping and parametric instability, Phys. Rev. A 104, 053309 (2021). + +[21] K. L. Ng, B. Opanchuk, M. Thenabadu, M. Reid, and P. D. Drummond, Fate of the false vacuum: Finite temperature, entropy, and topological phase in quantum simulations of the early universe, PRX Quantum 2, 010350 (2021). + +[22] B. Song, S. Dutta, S. Bhave, J.- C. Yu, E. Carter, N. Cooper, and U. 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Fava, G. Lamporesi, and G. Ferrari, Design and characterization of a compact magnetic shield for ultracold atomic gas experiments, Review of Scientific Instruments 90, 115114 (2019). + +[29] D. Guéry- Odelin, A. Ruschhaupt, A. Kiely, E. Torrontegui, S. Martínez- Garaot, and J. G. Muga, Shortcuts to adiabaticity: Concepts, methods, and applications, Rev. Mod. Phys. 91, 045001 (2019). + +[30] E. Torrontegui, S. Ibáñez, S. Martínez- Garaot, M. Modugno, A. del Campo, D. Guéry- Odelin, A. Ruschhaupt, X. Chen, and J. G. Muga, Chapter 2 - shortcuts to adiabaticity, in Advances in Atomic, Molecular, and Optical Physics, Advances In Atomic, Molecular, and Optical Physics, Vol. 62, edited by E. Arimondo, P. R. Berman, and C. C. Lin (Academic Press, 2013) pp. 117- 169. + +[31] W. L. Tan, P. Becker, F. Liu, G. Pagano, K. S. Collins, A. De, L. Feng, H. B. Kaplan, A. Kyprianidis, R. Lundgren, W. Morong, S. Whitsitt, A. V. Gorshkov, and C. Monroe, Domain- wall confinement and dynamics in a quantum simulator, Nature Physics 17, 742- (2021). + +<--- Page Split ---> diff --git a/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a_det.mmd b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..82afdcc477f356171eda429f5375cd37e75a82bb --- /dev/null +++ b/preprint/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a/preprint__00d7abe0a4b5c990501df86cac16b26584184537e4e60f6f33e33b81c4a5b14a_det.mmd @@ -0,0 +1,472 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 852, 175]]<|/det|> +# Observation of false vacuum decay via bubble formation in ferromagnetic superfluids + +<|ref|>text<|/ref|><|det|>[[44, 196, 857, 236]]<|/det|> +Anna Berti CNR- INO, Pitaevskii BEC Center, Università di Trento https://orcid.org/0000- 0003- 3073- 9554 + +<|ref|>text<|/ref|><|det|>[[44, 241, 500, 283]]<|/det|> +Riccardo Cominotti CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 289, 500, 330]]<|/det|> +Chiara Rogora CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 336, 648, 377]]<|/det|> +Ian Moss School of Mathematics, Statistics and Physics, Newcastle University + +<|ref|>text<|/ref|><|det|>[[44, 382, 238, 423]]<|/det|> +Thomas Billam Newcastle University + +<|ref|>text<|/ref|><|det|>[[44, 429, 500, 470]]<|/det|> +Iacopo Carusotto CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 475, 500, 516]]<|/det|> +Giacomo Lamporesi CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 521, 500, 562]]<|/det|> +Alessio Recati CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 568, 803, 609]]<|/det|> +Gabriele Ferrari Universita' di Trento and INO- CNR BEC Center https://orcid.org/0000- 0003- 1827- 5048 + +<|ref|>text<|/ref|><|det|>[[44, 614, 512, 655]]<|/det|> +Alessandro Zenesini ( \(\boxed{ \begin{array}{r l} \end{array} }\) alessandro.zenesini@ino.it) CNR- INO, Pitaevskii BEC Center, Università di Trento + +<|ref|>text<|/ref|><|det|>[[44, 694, 102, 712]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 732, 137, 751]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 770, 291, 789]]<|/det|> +Posted Date: June 8th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 809, 475, 828]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2923763/v1 + +<|ref|>text<|/ref|><|det|>[[42, 846, 910, 890]]<|/det|> +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 907, 530, 927]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 955, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Physics on January 22nd, 2024. See the published version at https://doi.org/10.1038/s41567-023-02345-4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[255, 88, 740, 135]]<|/det|> +# Observation of false vacuum decay via bubble formation in ferromagnetic superfluids + +<|ref|>text<|/ref|><|det|>[[178, 153, 820, 408]]<|/det|> +A. Zenesini \(^{1,2}\) , +A. Berti \(^{1}\) , +R. Cominotti \(^{1}\) , +C. Rogora \(^{1}\) , +I. +G. Moss \(^{3}\) , +T. +P. Billam \(^{4}\) , +I. Carusotto \(^{1}\) , +G. Lamporesi \(^{1,2}\) , +A. Recati \(^{1}\) , and +G. Ferrari \(^{1,2}\) \(^{1}\) Pitaevskii BEC Center, CNR-INO and Dipartimento di Fisica, Università di Trento, 38123 Trento, Italy \(^{2}\) Trento Institute for Fundamental Physics and Applications, INFN, 38123 Trento, Italy \(^{3}\) School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK and \(^{4}\) Joint Quantum Centre (JQC) Durham-Newcastle, School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK (Dated: May 22, 2023) + +<|ref|>text<|/ref|><|det|>[[169, 422, 828, 803]]<|/det|> +Metastability is ubiquitous in nature and is observed through the crossing of an energy barrier toward a configuration of lower energy as, for example, in chemical processes [1] or electron field ionization [2]. In classical many- body systems, metastability naturally emerges in the presence of a first- order phase transition and finds a prototypical example in supercooled vapour. In the last decades, the extension to quantum field theory and quantum many- body systems has attracted significant interest in the context of statistical physics [3, 4], protein folding [5, 6], and cosmology [7- 9], where thermal and quantum fluctuations are expected to trigger the transition from the metastable state (false vacuum) to the ground state (real vacuum) via the probabilistic nucleation of spatially localized bubbles [10, 11]. However, the long- standing theoretical progress in estimating the relaxation rate of the metastable field via bubble nucleation has not yet found a counterpart in terms of experimental observations. Here we experimentally observe and characterize bubble nucleation in isolated and coherently- coupled atomic superfluids, and support our observations with numerical simulations. The agreement between our results and a novel analytic formula based on instanton theory confirms the quantum- field character of the observed decay, and promotes coherently- coupled atomic superfluids as emulators of out- of- equilibrium quantum field phenomena. + +<|ref|>text<|/ref|><|det|>[[114, 834, 881, 931]]<|/det|> +A supercooled gas is a classic example of a metastable state which exists just across a first order phase transition. The passage to the ground state (the liquid phase) is mediated by resonant bubble nucleation when the energy gain provided by the liquid bulk is compensated by the cost of the surface tension. This energy balance leads to a critical bubble size and a stochastic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 882, 339]]<|/det|> +formation of the bubble typically occurs around nucleation spots given by impurities in the gas or imperfections at the container. The extension of this idea to a quantum many- body or a quantum field system has attracted extensive attention in a wide range of scenarios and length scales, from the understanding of early universe [7- 9] to the characterization of spin chains [3, 4]. In all these models, the metastable state at the origin of the bubble nucleation, is identified as "false vacuum" and the role of surface tension is taken by a genuinely quantum term. In the purest form, the false vacuum decay into the ground state would take place through quantum vacuum fluctuations [10, 11] (similarly to impurities in the classical case). However, as for example in the early universe, the tunnelling is equally likely to be boosted by thermal fluctuations, and the process would be of the type styled "vacuum decay at finite temperature" [12] (see [13, 14] for a review). + +<|ref|>text<|/ref|><|det|>[[113, 345, 882, 519]]<|/det|> +In the cosmological case, the energy scales are well above any that are accessible to experiments, and the phenomenon of false vacuum decay remains one of the most important yet untested processes considered in theoretical high energy physics. Recently, the extreme flexibility of neutral and charged atoms tabletop experiments and the advances of classical and quantum computer algorithms have paved the way for the proposal of experimental environments [15- 22] and virtual simulators [23, 24]. Up to now only numerical results have been achieved and the experimental observation of an analogue to false vacuum decay would therefore be of high significance. + +<|ref|>text<|/ref|><|det|>[[113, 526, 882, 880]]<|/det|> +In tabletop experiments, the observation of bubble nucleation requires several ingredients which are difficult to arrange simultaneously. First, a mean- field interaction- induced energy landscape composed of an asymmetric double well represents the minimal requirement for the decay from the metastable state to the absolute ground state via macroscopic tunneling across the energy barrier, followed by relaxation; see sketch in Fig. 1. Second, unlike in the ordinary quantum tunneling of a single particle [1, 25, 26], it is an effective field describing the system that changes state. Third, the time resolution of the experiment should cover many orders of magnitude to allow for the investigation of the predicted exponential time- dependence on the tuning parameters. This must be associated to a high stability and accuracy of the tuning parameters. An extended ferromagnetic superfluid [27] possesses the ideal properties to act as a field simulator, in particular its first order phase transition character, the long range coherence and the flexibility to control its experimental parameters within a stable and isolated environment. In tight analogy with supercooling, in an extended quantum system the presence of a spatial region with different magnetization to the bulk carries a positive kinetic energy due to the winding of the field at the interface, see Fig. 1. + +<|ref|>text<|/ref|><|det|>[[115, 886, 880, 932]]<|/det|> +In this letter, we present the experimental observation of bubble formation via false vacuum decay in a quantum system. We observe that the bubble nucleation time scales exponentially with + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[275, 95, 710, 324]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 357, 882, 561]]<|/det|> +FIG. 1. Mean- field energy and bubble formation. The cloud is initially prepared with all the atoms in \(|\uparrow \rangle\) (A). While the single \(|\downarrow \rangle\) spin state is energetically lower ( \(E_{\downarrow} < E_{\uparrow}\) ) in the center of the cloud, in the low density tails the situation is opposite. The interface has a positive energy which adds up to the double minimum energy landscape emerging from the ferromagnetic interaction. Macroscopic quantum tunneling can take place resonantly to the bubble state (B) which has a \(|\downarrow \rangle\) bubble in the center, whose core energy gain compensates for the interface energy cost. The barrier crossing can be triggered by quantum fluctuations in the zero- temperature case (dashed arrow) or by thermal fluctuations at finite temperature (empty arrow). After the tunneling process, in the presence of dissipation, the bubble increases in size to reach the ground state (C), without coming back to (A). + +<|ref|>text<|/ref|><|det|>[[114, 593, 880, 664]]<|/det|> +an experimental parameter that is connected to the energy barrier properties. Theoretical and numerical simulations support our observations and allow us to confirm the quantum field origin of the decay and its thermal activation. + +<|ref|>text<|/ref|><|det|>[[113, 675, 882, 850]]<|/det|> +The experimental platform is composed of a bosonic gas of \(^{23}\mathrm{Na}\) atoms, optically trapped and cooled below the condensation temperature. The gas is initially prepared in the internal state \(|F,m_{F}\rangle = |1, - 1\rangle = |\downarrow \rangle\) , where \(F\) is the total angular momentum and \(m_{F}\) its projection on the quantization axis. A microwave radiation with amplitude \(\Omega_{R}\) coherently couples the \(|\downarrow \rangle\) state to \(|2, - 2\rangle = |\uparrow \rangle\) . The relevant scattering lengths for such a two- level system are \(a_{\downarrow \downarrow} = 54.5a_{0}\) , \(a_{\uparrow \uparrow} = 64.3a_{0}\) , and \(a_{\downarrow \uparrow} = 54.5a_{0}\) , and lead to the condition \(\Delta a = (a_{\uparrow \uparrow} + a_{\downarrow \downarrow}) / 2 - a_{\downarrow \uparrow} < 0\) , i.e., to a system with a ferromagnetic ground state [27]. + +<|ref|>text<|/ref|><|det|>[[114, 860, 880, 932]]<|/det|> +The trapping potential is axially symmetric and harmonic in all three directions, but strongly asymmetric (axial and radial trapping frequencies \(\omega_{x} / 2\pi = 20\mathrm{Hz}\) and \(\omega_{\rho} / 2\pi = 2\mathrm{kHz}\) ), producing an elongated system with inhomogeneous density and spatial size given by the longitudinal and + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[272, 92, 720, 496]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 523, 882, 655]]<|/det|> +
FIG. 2. Protocols and bubble observation. a) Experimental protocol. Ellipses illustrate the cloud magnetization at different \(t\) and the two sketches show the energy landscape for positive (up) and negative (down) \(\delta\) . b) Collection of integrated magnetization profiles \(Z(x)\) after different waiting times \(t\) . For each value of \(t\) , 7 different realizations are shown. c) Magnetization profiles for the realizations marked with arrows in panel (b). d) Measured probability \(P\) (empty circles) to observe a shot with a bubble at fixed time is shown. The probability is well fitted to an exponential curve (grey continuous line) until it saturates to 1.
+ +<|ref|>text<|/ref|><|det|>[[114, 679, 882, 830]]<|/det|> +radial Thomas- Fermi radius \(R_{\mathrm{x}} = 200\mu \mathrm{m}\) and \(R_{\rho} = 2.5\mu \mathrm{m}\) . At the end of each experimental realization, we image the two spin states independently and extract their density distributions. The transverse confinement is tight enough to suppress the radial spin dynamics of the condensate. We therefore integrate each image along the transverse direction and obtain the integrated 1D density profiles \(n_{\uparrow}(x)\) and \(n_{\downarrow}(x)\) , from which we extract the profile of the relative magnetization \(Z(x) = [n_{\uparrow}(x) - n_{\downarrow}(x)] / [n_{\uparrow}(x) + n_{\downarrow}(x)]\) . + +<|ref|>text<|/ref|><|det|>[[114, 835, 882, 932]]<|/det|> +The coupled two- level system can be studied by separately treating the total density ( \(n = n_{\uparrow} + n_{\downarrow}\) ) and the spin ( \(n_{\uparrow} - n_{\downarrow} = nZ\) ) degrees of freedom. While the density is simply dominated by a continuity equation, the spin degree of freedom is ruled by a magnetic mean- field Hamiltonian, which shows a first- order phase transition in the central region of the cloud for \(\Omega_{R} < |\kappa |n\) , where + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 578, 107]]<|/det|> +\(\kappa \propto \Delta a\) is the relevant interaction parameter; see Methods. + +<|ref|>text<|/ref|><|det|>[[113, 113, 882, 392]]<|/det|> +The first- order phase transition originates from a symmetry breaking when the energy landscape as a function of the magnetization \(Z\) goes from a single to a double minimum at \(\Omega_{R}< |\kappa |n =\) \(2\pi \times 1150\mathrm{Hz}\) . At fixed \(\Omega_{R}\) , the experimentally tunable parameter is the detuning \(\delta\) between the two- level system and the coupling radiation. For small enough \(|\delta |\) , the energy landscape \(E(Z)\) is represented by an asymmetric double well, that turns symmetric for \(\delta = 0\) . In particular, for positive \(\delta\) , the energy is minimized by positive values of \(Z\) , and viceversa The relevant parameter for the bubble nucleation is the shape (height and width) of the energy barrier separating the two wells that the system needs to overcome as a field, i.e., in a macroscopic manner. This depends on \(\delta\) , \(n\) and \(\Omega_{R}\) . When \(|\delta |\) exceeds a critical value \(\delta_{c}\) , the metastable well disappears [27]. Borrowing the nomenclature from ferromagnetism, \(\pm \delta_{c}\) correspond to the edges of the hysteresis region and their value depends both on \(\Omega_{R}\) and \(|\kappa |n\) . + +<|ref|>text<|/ref|><|det|>[[113, 397, 882, 546]]<|/det|> +Figure 2(a) illustrates the experimental protocol. We first transfer the whole system from \(|\downarrow \rangle\) to \(|\uparrow \rangle\) with a \(\pi\) pulse. While keeping \(\Omega_{R}\) constant, \(\delta\) is linearly ramped down from \(\delta_{i} / 2\pi = 5.5\mathrm{kHz}\) to a variable \(\delta_{f}\) on a timescale between 20 and 60 ms. Since the ramp starts with \(\delta \gg \Omega_{R}\) , the system follows the spin rotation remaining in the local ground state until \(\delta < 0\) when such a local ground state becomes a metastable state; see inset in Fig. 2(a). Once \(\delta_{f}\) is reached, the states are independently imaged after a variable waiting time \(t\) . + +<|ref|>text<|/ref|><|det|>[[113, 552, 882, 752]]<|/det|> +If \(\delta_{f} > 0\) , the whole system is and remains in the absolute ground state \(|\uparrow \rangle\) , whereas for \(\delta_{f}< 0\) , after a variable time, a macroscopic region in the central part of the system flips to \(|\downarrow \rangle\) , generating a bubble; see examples in Fig. 2(b) and magnetization profiles in (c). On average the bubble occurrence probability is larger if the waiting time is longer [see Fig. 2(b) and (d)]. For a quantitative analysis, at each \(t\) , we repeat the measurement up to 10 times in order to investigate the statistical formation of bubbles. Note that, while in uniform systems the bubbles would stochastically nucleate in random spatial positions, our nonuniform density profile of the atomic sample strongly favors the nucleation at the center of the cloud, where \(\delta_{f}\) is closest to \(\delta_{c}\) . + +<|ref|>text<|/ref|><|det|>[[113, 757, 882, 932]]<|/det|> +A useful quantity to characterize the bubble nucleation in time is \(F_{t} = (1 + \langle Z\rangle_{t} / \langle Z\rangle_{t = 0}) / 2\) , which was used in Ref. [3] to compare an exact diagonalization approach in a zero- temperature spin chain to instanton predictions. Here \(\langle \cdot \rangle_{t}\) stands for \(Z\) measured at time \(t\) and averaged over many realizations. In Fig. 3(a) and (b), we show the average magnetization \(\langle Z\rangle_{t}\) profile as a function of waiting time for two values of detuning. Since the bubble appears always in the center of the system, to compute \(F_{t}\) , we extract the mean magnetization \(\langle Z\rangle_{t}\) in the central 20- \(\mu \mathrm{m}\) - wide region \((\approx R_{x} / 10)\) . The resulting \(F_{t}\) , plotted in panel (c), initially remains flat, and then it exponentially + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 883, 313]]<|/det|> +decays because of the bubble nucleation. Both features were also observed in Ref. [3] and the understanding of the starting plateau is still an open question from the theoretical point of view. We find that the measured \(F_{t}\) is well described by the empirical function \((1 - \epsilon) / \sqrt{1 + (e^{t / \tau} - 1)^{2}} + \epsilon\) , which is 1 for \(t = 0\) , scales as \(t^{2}\) for small \(t\) and is exponentially decaying to \(\epsilon\) for large \(t\) . The two fitting parameters are \(\tau\) , that describes the characteristic timescale for the bubble formation, and \(\epsilon\) , that takes into account that the asymptotic magnetization \(Z_{t = \infty}\) can be different from the one of the ground state, \(Z_{TV}\) ( \(F = 0\) ). Note that the timescale \(\tau\) is related to the exponential decay, while the empirical formula takes into account an initial plateau present in the averaged magnetisation \(F_{t}\) . (in Methods we show that the plateau length and \(\tau\) are strictly connected). + +<|ref|>text<|/ref|><|det|>[[113, 319, 882, 469]]<|/det|> +Numerical simulations based on 1D Gross- Pitaevskii equations, reported in Fig. 3(d) and (e), qualitatively reproduce the experimental observations. In the numerics, classical noise is included to simulate the effect of a finite temperature (more details can be found in Methods). Data in Fig. 3(d) and (e) are obtained by averaging over 1000 different noisy realizations of the real- time dynamics: the large statistics allows us to directly extract the exponential decay time \(\tau_{\mathrm{sim}}\) through a linear fit of \(\ln (F_{t})\) . + +<|ref|>text<|/ref|><|det|>[[113, 475, 882, 650]]<|/det|> +In Fig. 3(g), we report six experimental values of \(\tau\) obtained for \(\Omega_{R} = 2\pi \times 300 \mathrm{Hz}\) , plotted as a function of the distance from the critical detuning, \((\delta_{f} - \delta_{c}) / |\kappa |n\) . The results show an exponential dependence on the tuning parameter over two orders of magnitude, from a few to hundreds of ms. Such a sensitivity to a parameter is remarkable for ultracold atoms experiments. In particular, the experimental observation of the quasi- exponential dependence of \(\tau\) with respect to \(\delta_{f}\) in an interval of the order of \(100 \mathrm{Hz}\) critically relies on the magnetic field stability better than a few tens of \(\mu \mathrm{G}\) [28]. + +<|ref|>text<|/ref|><|det|>[[113, 655, 882, 802]]<|/det|> +The values of \(\tau_{\mathrm{sim}}\) for the simulations [light symbols in Fig. 3(g)] qualitatively show the same behaviour of the experimental data. The agreement becomes even quantitative [dark symbols in Fig. 3(g)], by using a rescaling of \(|\kappa |n\) and a small shift of \(\delta\) . The need for such a rescaling was demonstrated in Ref. [27], as a consequence of dimensionality, noise and non complete adiabaticity of the preparation protocol. In Fig. 4, we compare experimental \(\tau\) and rescaled numerical \(\tau_{\mathrm{sim}}\) , for four different values of \(\Omega_{R}\) , by using the same rescaling for all four panels. + +<|ref|>text<|/ref|><|det|>[[113, 809, 882, 932]]<|/det|> +Our observations are consistent with the scenario of a condensate spinor field initially in a ferromagnetic metastable state, which decays via macroscopic tunneling to bubbles (domains) of the ferromagnetic ground state. The escape of a quantum field from the false vacuum, occurring via macroscopic tunneling, and the bubble formation finds a suitable description in terms of an instanton, or critical solution to the field equations in imaginary time [10–12]. Such a theory + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[270, 92, 710, 500]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 525, 883, 794]]<|/det|> +
FIG. 3. Measurement of the evolution of \(Z(x)\) in time after the ramp on \(\delta\) for \(\Omega_{R} / 2\pi = 300\mathrm{Hz}\) , for \(\delta_{f} / \Omega_{R} = -1.70\) in (a) and \(-1.79\) in (b). c) Value of \(F_{\mathrm{t}}\) evaluated in the \(20\mu \mathrm{m}\) central region of the cloud are fitted by the empirical expression reported in the text (squares for data in (a) and pentagons for (b)). Error bars are the standard deviation over up to ten repetitions. d-e) Numerical simulations for \(\delta_{f} / \Omega_{R} = -1.52\) in (d) and \(-1.585\) in (e). Value of \(F_{\mathrm{t}}\) for the simulations (triangles for data in (d) and stars for (e)). The red dashed lined are linear fits in the exponentially decaying part. g) Experimental \(\tau\) and numerical \(\tau_{\mathrm{sim}}\) timescale of the bubble formation as a function of \((\delta_{f} - \delta_{c}) / |\kappa |n\) . Error bars include statistical uncertainties on the fit and uncertainty on the \(\delta_{f} - \delta_{c}\) coming from magnetic field stability and calibration. Numerical timescale of the bubble formation \(\tau_{\mathrm{sim}}\) is shown before (light symbols) and after (dark symbol) rescaling. The empty triangle is an experimental point taken with a preparation ramp twice slower than the others, to verify the impact on the nucleation time resulting from a residual non-adiabaticity in the preparation of the sample.
+ +<|ref|>text<|/ref|><|det|>[[114, 834, 882, 930]]<|/det|> +provides a threshold energy scale, below (above) which quantum (thermal) fluctuations dominate: zero- \(T\) quantum tunneling is expected to be dominant when \(T\) is below the critical temperature \(T^{*} = \hbar |\kappa |n / k_{B}\) . Considering the peak density in our system, we estimate \(T^{*} \simeq 50 \mathrm{nK}\) . Although the temperature of our condensates is \(T = 1.5 \mu \mathrm{K} \gg T^{*}\) , given the harmonic confinement and the + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[272, 92, 720, 422]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 448, 881, 581]]<|/det|> +
FIG. 4. Decay time \(\tau\) and \(\tau_{\mathrm{sim}}\) and instanton theory. Experimental \(\tau\) and simulations \(\tau_{\mathrm{sim}}\) are obtained as explained in the text for \(\Omega_{R} / 2\pi = 300,400,600\) and \(800\mathrm{Hz}\) . A rescaling common to all \(\Omega_{R}\) is applied to the horizontal axes of the simulation; see text. Dashed and full curves are fits of the experimental and simulation data according to the instanton formula. Full markers stand for simulation results while empty markers for experimental data. Error bars include statistical uncertainties on the fit and uncertainty on the \(\delta_{f}\) due to on the magnetic field stability.
+ +<|ref|>text<|/ref|><|det|>[[113, 620, 881, 718]]<|/det|> +exchange interaction which pushes the thermal component away from the condensate, we estimate an effective local temperature of about \(250\mathrm{nK}\) in the condensate region which is still larger than \(T^{*}\) in the region where the bubbles appear. Therefore we expect the macroscopic tunneling to be in the thermally activated regime. + +<|ref|>text<|/ref|><|det|>[[113, 731, 882, 932]]<|/det|> +Within the instanton approach, the bubble nucleation probability has the characteristic timescale \(\tau\) , which has an exponential dependence \(A(E_{c} / k_{B}T)^{- 1 / 2}e^{E_{c} / k_{B}T}\) . \(E_{c}(\delta ,\kappa n,\Omega_{R})\) is the energy of the critical solution and strongly depends on the shape of the many- body potential and in particular on the barrier height (Fig. 1). The pre- factor \(A\) depends on fluctuations about the critical solution, but there are very few models for which this factor is calculable, at present. We therefore regard the pre- factor \(A\) as a fitting parameter in the following analysis. We can estimate \(E_{c}\) , and provide an analytical expression in the limit of vanishing metastable well (small \(\delta_{f} - \delta_{c}\) ), by considering a homogeneous 1D system. The potential for the magnetization field \(Z\) can be + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 353, 106]]<|/det|> +written as (see, e.g., Ref.[27] ) + +<|ref|>equation<|/ref|><|det|>[[345, 125, 877, 146]]<|/det|> +\[V(Z) = \kappa n Z^{2} - 2\Omega (1 - Z^{2})^{1 / 2} - 2\delta_{f}Z \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[114, 167, 361, 185]]<|/det|> +and the instanton energy reads + +<|ref|>equation<|/ref|><|det|>[[300, 194, 877, 240]]<|/det|> +\[\frac{E_{c}}{\hbar|\kappa|n} = \sqrt{\frac{\hbar n}{2m|\kappa|}}\int_{Z_{T P}}^{Z_{F V}}\left[\frac{V(Z) - V(Z_{F V})}{|\kappa|n(1 - Z^{2})}\right]^{1 / 2}d Z, \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[113, 251, 881, 348]]<|/det|> +where \(Z_{T P}\) is the classical turning point (in the inverted potential \(V\) ) and \(Z_{F(alse)V(acuum)}\) the value of the magnetization of the metastable state. Most of our data are taken in a regime where the barrier is much smaller than the depth of the ground state well. In this limiting case the instanton energy reads + +<|ref|>equation<|/ref|><|det|>[[291, 355, 877, 406]]<|/det|> +\[\frac{E_{c}}{\hbar|\kappa|n}\propto \sqrt{\frac{\hbar n}{2m|\kappa|}}\left(\frac{\delta_{f} - \delta_{c}}{|\kappa|n}\right)^{\frac{5}{4}}\left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{1}{6}}\left(\frac{|\delta_{c}|}{|\kappa|n}\right)^{-\frac{1}{4}}, \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[113, 417, 881, 599]]<|/det|> +where \(\delta_{c} = \kappa n[1 - (\Omega /(|\kappa |n))^{\frac{2}{3}}]^{\frac{3}{2}}\) : see Methods. We compare the previous expression to the experimental data and numerical simulations using a two- parameter fit \(\ln \tau = \ln A + b\hat{E}_{c} + \ln (b\hat{E}_{c}) / 2\) , where \(\hat{E}_{c} = \sqrt{2m|\kappa| / (\hbar n)} E_{c} / \hbar |\kappa |n\) is the rescaled energy. The results are shown in Fig. 4. Considering the approximations used to derive Eq. (3) – in particular the absence of the trapping potential, no phase fluctuations and small barrier – the agreement is remarkable and the instanton theory appears to capture the main dependence of the false vacuum decay rate on the microscopic parameter \(\delta_{f}\) which is responsible for the broken \(\mathbb{Z}_{2}\) symmetry. + +<|ref|>text<|/ref|><|det|>[[113, 603, 882, 932]]<|/det|> +In this paper, we present solid evidence of the thermally- induced macroscopic tunneling of a coherent quantum field, manifested by bubbles of true vacuum phase nucleating in a false vacuum state. The true and false vacua are the local and global energy minimum of a ferromagnetic atomic Bose- Einstein condensate, respectively. The experimental results clearly show an exponential dependence of the decay rate on the microscopic parameters and the hysteric region width. Such a dependence is successfully captured by numerical simulations and more remarkably by a simple instanton theory based on a reduced energy functional for the magnetisation. Our platform paves the way to explore the process of bubble formation and growth in intricate detail, and to build a new bridge between low energy and high energy phenomena characterized by metastability within a first order phase transition. In this spirit our work opens up new avenues in the understanding of early universe, as well as ferromagnetic quantum phase transitions. The possibility of engineering the barrier properties via injection of tailored noise and of deterministically seeding bubbles are promising future directions for experimental investigations with focus on the role of dissipation, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 87, 881, 210]]<|/det|> +the existence of shortcut- to- adiabaticity [29, 30], the creation of entanglement, of domain wall confinement [31], and relativistic and non relativistic aspects of the bubble nucleation and dynamics. Furthermore an experimental effort towards colder systems would allow us to reach the tunneling regime dominated by quantum fluctuations. A natural extension of the present work goes to dimensionality larger than one, where the theoretical treatment is challenging. + +<|ref|>text<|/ref|><|det|>[[113, 227, 882, 502]]<|/det|> +Acknowledgements - We thank A. Biella and P. Hauke for fruitful discussions. We acknowledge funding from Provincia Autonoma di Trento, from INFN through the FISH project, from the Italian MIUR under the PRIN2017 project CEnTraL (Protocol Number 20172H2SC4), from the European Union's Horizon 2020 research and innovation Programme through the STAQS project of QuantERA II (Grant Agreement No. 101017733), from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 804305), from the UK Quantum Technologies for Fundamental Physics programme (grants ST/T00584X/1 and ST/W006162/1) and from PNRR MUR project PE0000023- NQSTI. This work was supported by Q@TN, the joint lab between University of Trento, FBK - Fondazione Bruno Kessler, INFN - National Institute for Nuclear Physics and CNR - National Research Council. + +<|ref|>text<|/ref|><|det|>[[114, 545, 882, 668]]<|/det|> +Author contribution - G.F., G.L. and A.Z. conceived the project. R.C., C.R., and A.Z. performed the experiments and analyzed the data. A.B., I.C., I.M., T.B., and A.R. performed the theoretical analysis. A.B. developed the numerical code and performed the simulations. G.F. supervised the project. All authors contributed to the discussion and interpretation of the results and paper writing. + +<|ref|>text<|/ref|><|det|>[[114, 711, 881, 780]]<|/det|> +Data availability - Data in paper figures are available in Extended data. Two dimensional raw atomic cloud pictures from all experimental runs and analysis code are available upon request to A.Z. + +<|ref|>text<|/ref|><|det|>[[140, 825, 647, 844]]<|/det|> +Corresponding author - Correspondence to A.Z., G.L. and A.R. + +<|ref|>text<|/ref|><|det|>[[140, 888, 827, 905]]<|/det|> +Ethics declarations: Competing interests - The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[446, 89, 546, 105]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[253, 128, 739, 147]]<|/det|> +### I. FERROMAGNETISM IN ELONGATED MIXTURES + +<|ref|>text<|/ref|><|det|>[[113, 171, 880, 242]]<|/det|> +The ferromagnetic properties of atomic superfluid coupled mixtures are experimentally measured and discussed in [27]. Here we summarize the key ingredients which help understanding the results presented in the main text of the article. + +<|ref|>text<|/ref|><|det|>[[113, 247, 882, 448]]<|/det|> +Our system is composed of two sodium hyperfine states \(|F,m_{F}\rangle = |2, - 2\rangle \equiv |\uparrow \rangle\) and \(|1, - 1\rangle \equiv\) \(|\downarrow \rangle\) , where \(F\) is the total angular momentum and \(m_{F}\) its projection. The two populations \(n_{\uparrow}(x,y)\) and \(n_{\downarrow}(x,y)\) are independently measured by shadow imaging. Starting from the two two- dimensional pictures of the cloud, we determine the relative magnetization \(Z(x)\) as \(Z(x) =\) \((n_{\uparrow}(x) - n_{\downarrow}(x)) / n(x)\) , where \(n_{\uparrow ,(\downarrow)}(x) = \int n_{\uparrow ,(\downarrow)}(x,y)dy\) and \(n(x) = \int (n_{\uparrow}(x,y) + n_{\downarrow}(x,y))dy\) are the 1D integrated densities. The integration along \(y\) takes advantage of the suppressed radial dynamics. In local density approximation (LDA), the energy per particle associated to the spin channel of the mixture is + +<|ref|>equation<|/ref|><|det|>[[317, 454, 877, 489]]<|/det|> +\[E(Z,\phi)\propto -\frac{\delta_{f}}{2} Z + \frac{\kappa n}{2} Z^{2} - \Omega_{\mathrm{R}}\sqrt{1 - Z^{2}}\cos \phi \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[113, 499, 881, 571]]<|/det|> +where the phase \(\phi\) is the relative phase between \(|\uparrow \rangle\) and \(|\downarrow \rangle\) . The detuning \(\delta_{f}\) used in the text is equal to \(\delta_{\mathrm{B}} + n\Delta\) where \(\delta_{\mathrm{B}}\) is the experimental controllable detuning. The quantity \(\kappa\) and \(\Delta\) are associated to the collisional proprieties of the mixture and are + +<|ref|>equation<|/ref|><|det|>[[398, 581, 877, 647]]<|/det|> +\[\begin{array}{l}\Delta \equiv \frac{g_{\downarrow\downarrow} - g_{\uparrow\uparrow}}{2\hbar} < 0\\ \kappa \equiv \frac{g_{\downarrow\downarrow} + g_{\uparrow\uparrow}}{2\hbar} -\frac{g_{\downarrow\uparrow}}{\hbar} < 0 \end{array} \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[113, 655, 880, 701]]<|/det|> +where \(g_{\downarrow \downarrow},g_{\uparrow \uparrow}\) and \(g_{\downarrow \uparrow}\) are the two intra species and the inter species coupling constants. Note that \(n\Delta\) derives from the \(|\uparrow \rangle\) and \(|\downarrow \rangle\) self interaction asymmetry. + +<|ref|>text<|/ref|><|det|>[[113, 707, 882, 932]]<|/det|> +In an elongated cloud having a parabolic Thomas Fermi density profile, the ferromagnetic phase is located in the center of the cloud where the non liner term \(|\kappa |nZ^{2} / 2\) is maximal. Under the condition \(|\kappa |n< \Omega\) , in fact, the energy per particle is characterized by a symmetric double minimum structures a signature of the symmetry breaking typical of the ferromagnetic phase. At non zero detuning, the symmetry of the two wells is broken. Thanks to the tuning knob \(\delta_{B}\) , which is linearly proportional to the applied magnetic field, one can change the relative energy difference between the two energy minima, converting one or the other state into the absolute ground state or the metastable state. The tails of the cloud remain in the paramagnetic regime, having smaller density, and \(Z\) of the only energy minimum is unambiguously determined by \(\delta_{\mathrm{B}}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 882, 234]]<|/det|> +Due to the asymmetry between \(|\uparrow \rangle\) and \(|\downarrow \rangle\) , there exists a range of values of \(\delta_{\mathrm{B}}\) where the sign of the \(Z\) at the energy minima in the center \((- )\) and at the tails \((+)\) is opposite, but the system can still maintain a homogeneous positively- magnetized profile being metastable in the center. When the detuning is decreased below the critical value \(\delta_{c}\) (see main text), the metastable minimum disappears resulting in a unique steady magnetic profile with negative \(Z\) in the center and positive \(Z\) in the tails. + +<|ref|>text<|/ref|><|det|>[[113, 243, 882, 365]]<|/det|> +While the spin energy profiles of Eq. (4) are useful to explain the presence of two minima separated, this LDA representation only shows the LDA energy landscape per particle and not the total energy of the system. For instance, the LDA energy profiles don't include the contribution coming from the interface between opposite \(Z\) , whose kinetic energy represents a further contribution to the total energy barrier, as intended to be shown in Fig. 1 in the main text. + +<|ref|>sub_title<|/ref|><|det|>[[267, 400, 725, 419]]<|/det|> +## II. CALIBRATION AND ANALYSIS PROCEDURE + +<|ref|>text<|/ref|><|det|>[[113, 443, 882, 540]]<|/det|> +An important calibration concerns the determination of the critical detuning at which the double well energy landscape is expected to disappear. We determine \(\delta_{c}\) by performing the same protocols used in [27] to measure the hysteresis width of the ferromagnetic regime. This consists in the same ramp shown Fig. 2(a) of the main text, applied with a null waiting time. + +<|ref|>text<|/ref|><|det|>[[113, 547, 882, 745]]<|/det|> +The data used in the main text are obtained in the range of \(\delta\) directly above the critical one. Thanks to the appearing of the bubble in the center of the cloud, we first determine the presence of the bubble by fixing a threshold \(Z_{\mathrm{bubble}} = 0.2\) . If the average magnetization in the central 40 pixels is below \(Z_{\mathrm{bubble}}\) , one bubble is counted. The total bubble counts at fixed waiting time determines the probability \(P\) , as plotted in Fig. 2(c) of the main text. We verify that the choice of the threshold \(Z_{\mathrm{bubble}}\) and the averaging area do not critically impact on the outcomes presented here. Once the bubble is detected, the full magnetization profile is initially fitted by using a double sigmoidal function, + +<|ref|>equation<|/ref|><|det|>[[335, 758, 877, 799]]<|/det|> +\[A\left[\arctan \left(\frac{x - x_{r}}{s_{r}}\right) - \arctan \left(\frac{x - x_{l}}{s_{l}}\right)\right] \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[113, 809, 882, 932]]<|/det|> +where \(A\) is the amplitude and \(x_{(r),[l]}\) and \(s_{(r),[l]}\) are the (right) [left] centers and sigmas of the two sigmoids. The positions \(x_{(l),[r]}\) are then used as starting values for a second fitting routine that independently analyses the left and right bubble interfaces. This second step is used to better determine the exact positions of the interfaces without the effects of cloud asymmetry and offsets. The obtained values \(x_{(l),[r]}\) allow to determine the bubble size as \(\sigma_{x} = x_{r} - x_{l}\) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[248, 88, 744, 108]]<|/det|> +## III. DETERMINATION OF \(\tau\) AND ALTERNATIVE \(\tau_{50\%}\) + +<|ref|>text<|/ref|><|det|>[[114, 130, 881, 202]]<|/det|> +In the main text we explain how we determine the characteristic decay time \(\tau\) by fitting \(F_{t}\) to \((1 - \epsilon) / \sqrt{1 + (e^{t / \tau} - 1)^{2}} + \epsilon\) . This formula allows us to extract \(\tau\) even for experimental sequences with limited statistics and it results to be robust against the initialisation of the fitting parameters. + +<|ref|>text<|/ref|><|det|>[[114, 208, 881, 305]]<|/det|> +To verify the solidity of our approach we also considered a different characteristic time \(\tau_{50\%}\) defined as the time at which the probability \(P\) to observe a bubble is \(50\%\) . This approach is a valid alternative for measurements featuring a limited statistics. To determine \(\tau_{50\%}\) we fit \(P\) with the following function: + +<|ref|>equation<|/ref|><|det|>[[380, 323, 877, 344]]<|/det|> +\[P(t) = \mathrm{Min}[a_{1}*(e^{t / a_{2}} - 1),1] \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[114, 364, 772, 384]]<|/det|> +with \(a_{1}\) and \(a_{2}\) as free parameters. These two are then used to determine \(\tau_{50\%}\) from + +<|ref|>equation<|/ref|><|det|>[[408, 394, 877, 428]]<|/det|> +\[\frac{1}{2} = a_{1}*(e^{t_{50\%} / a_{2}} - 1) \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[113, 440, 881, 592]]<|/det|> +We check, within the statistical uncertainties, that the value of \(\tau_{50\%}\) does not change by using different fitting functions (linear, exponential with offsets in time and \(P\) ). Figure M1 shows that \(\tau\) and \(\tau_{50\%}\) are compatible both for the experimental measurements and numerical simulations. In particular, simulation results allow us to conclude that, while \(\tau_{50\%}\) is expected to be influenced by the delay time before the bubble decays, \(\tau_{50\%}\) is still a good approximation of \(\tau\) . This suggests that the delay time and \(\tau\) are related and further investigations are necessary to understand how. + +<|ref|>text<|/ref|><|det|>[[114, 596, 881, 642]]<|/det|> +In general, we conclude that the determination of \(\tau\) used in the main text is solid. In particular, one notes that the two methods rely on two very different observables, the mean magnetization + +<|ref|>image<|/ref|><|det|>[[270, 663, 725, 840]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 862, 881, 928]]<|/det|> +
FIG. M1. \(\tau\) vs \(\tau_{50\%}\) for experimental (a) and numerical (b) results. The two quantity are compatible to each other within error bars in experimental results and show only small deviation in simulation data. Color code for the points is the same used in the main text and the blue line marks \(\tau = \tau_{50\%}\) .
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 135]]<|/det|> +in the center, averaged over all experimental shots ( \(\tau\) ), and the probabilistic presence of a bubble \((\tau_{50\%})\) . + +<|ref|>sub_title<|/ref|><|det|>[[342, 169, 650, 187]]<|/det|> +## IV. NUMERICAL SIMULATIONS + +<|ref|>text<|/ref|><|det|>[[113, 211, 882, 386]]<|/det|> +The numerical results presented in the main text are based on one- dimensional Gross- Pitaevskii simulations. The parameters are chosen to faithfully reproduce the experimental conditions: in particular, the system trapped by a harmonic potential with frequency \(\omega_{0} \simeq 2\pi \times 16 \mathrm{Hz}\) , so that the Thomas- Fermi radius is \(L \simeq 200 \mu \mathrm{m}\) ; moreover, interactions are chosen to obtain \(|\kappa |n_{0} = |\Delta |n_{0} \simeq 2\pi \times 1.1 \mathrm{kHz}\) , \(n_{0}\) being the total density in the center of the cloud. The system is first prepared, through imaginary- time evolution, in the ground state corresponding to \(\delta_{f} = 2\pi \times 1 \mathrm{kHz}\) , thus, regardless of the value of \(\Omega_{R}\) , it is almost fully polarized in the \(|\uparrow \rangle\) state. + +<|ref|>text<|/ref|><|det|>[[113, 392, 882, 667]]<|/det|> +A white noise of amplitude equal to \(3\%\) of the central density is added on top of the ground state: this corresponds to an injected energy of roughly \(\epsilon /k_{B} = 215 \mathrm{nK}\) . We then let the system evolve in real time, without changing any parameter and we observe that, after a transient, the noise distribution becomes stationary; we interpret this result as thermalization of the mixture to a temperature \(T \propto \epsilon\) . Under an ergodicity assumption, we can determine the dynamics of the system by averaging over many repetitions of the same time- evolution, each one obtained starting from a different noisy sample. To summarize, we perform mean- field simulations in which noise plays the role of an effective temperature. Of course, these do not allow to investigate the role of quantum fluctuations: however, since the estimated experimental temperature is much higher than \(|\kappa |n_{0} / k_{B} \sim 50 \mathrm{nK}\) , the dynamics is likely to be dominated by thermal noise and a comparison with classical field simulations is justified. + +<|ref|>text<|/ref|><|det|>[[113, 675, 881, 771]]<|/det|> +The real- time dynamics after thermalization reproduces, once again, the experimental protocol: a detuning ramp with speed \(\sim 50 \mathrm{Hz / ms}\) is applied in order to reach the false vacuum state corresponding to some final \(\delta_{f} < 0\) ; the magnetization of the system is then monitored for a waiting time in the range \([10, 300] \mathrm{ms}\) , depending on the simulation parameters. + +<|ref|>text<|/ref|><|det|>[[137, 778, 707, 797]]<|/det|> +In order to extract the characteristic decay time \(\tau\) and \(\tau_{50}\) , we compute: + +<|ref|>equation<|/ref|><|det|>[[386, 809, 877, 846]]<|/det|> +\[F(t) = \frac{\langle Z(x \sim 0, t) \rangle - Z_{TV}}{Z_{FV} - Z_{TV}} \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[113, 860, 882, 932]]<|/det|> +where \(\langle Z(x \sim 0, t) \rangle\) is the statistical average of magnetization over the central \(10 \mu \mathrm{m}\) of the cloud. If the number of samples is sufficiently high (we use 1000), this function represents the probability of not observing a bubble at time \(t\) . Therefore, \(\tau_{50}\) is computed, by definition, by solving \(F(\tau_{50}) = 0.5\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 881, 185]]<|/det|> +The FVD rates are obtained instead via a linear fit of \(\log F(t)\) : in most cases the predicted exponential behaviour is found within a time interval corresponding to \(F(t) \in [0.3, 0.7]\) ; small adjustments of this window are necessary for the simulations associated to the smallest and longest tunnelling times. + +<|ref|>sub_title<|/ref|><|det|>[[421, 216, 572, 234]]<|/det|> +## V. ISTANTONS + +<|ref|>text<|/ref|><|det|>[[113, 258, 881, 355]]<|/det|> +The theoretical description of vacuum decay is non- perturbative and based on instanton solutions to the equations of motion using an imaginary time coordinate. The classical field theory for this system reduces down to a field theory for the magnetisation \(Z\) . For thermal instantons, bubbles nucleate at a rate (see e.g.[14]) + +<|ref|>equation<|/ref|><|det|>[[384, 370, 877, 392]]<|/det|> +\[\Gamma = 1 / \tau = A(\beta E_{c})^{j / 2}e^{-\beta E_{c}}. \quad (11)\] + +<|ref|>text<|/ref|><|det|>[[113, 408, 882, 660]]<|/det|> +where \(\beta = 1 / (k_{B}T)\) and \(E_{c}\) is the energy of the instanton. The factor \(A\) depends on fluctuations about the instanton and \(j\) is the number of translational symmetries. There should be one zero mode \(j = 1\) if there is translational invariance in the system. (The bubbles in the experiment always nucleate near the centre, so translational invariance is suspect. Fortunately, the power law dependence has only a small effect on the results). There are a very limited number of models for which the pre- factor \(A\) is calculable at present, and we will therefore regard \(A\) as a fitting parameter in the subsequent analysis. Note that the non- perturbative approach is valid when the exponent is larger than one, i.e. for temperatures \(k_{B}T < E_{c}\) . At even lower temperatures, vacuum fluctuations become the dominant seeding mechanism. In our system this happens for \(k_{B}T < \hbar |\kappa |n \sim 50 \mathrm{nK}\) , and the resulting vacuum decay rate would be far less than the rate seen in the experiment. + +<|ref|>text<|/ref|><|det|>[[138, 666, 673, 685]]<|/det|> +The energy for a thermal instanton includes a gradient contribution + +<|ref|>equation<|/ref|><|det|>[[361, 693, 877, 732]]<|/det|> +\[E_{c} = \frac{\hbar n}{4}\int \left\{\frac{\hbar}{2m}\frac{(\nabla Z)^{2}}{1 - Z^{2}} + V\right\} dx, \quad (12)\] + +<|ref|>text<|/ref|><|det|>[[114, 741, 270, 758]]<|/det|> +where the potential + +<|ref|>equation<|/ref|><|det|>[[352, 774, 877, 795]]<|/det|> +\[V = \kappa nZ^{2} - 2\Omega_{R}(1 - Z^{2})^{1 / 2} - 2\delta_{\mathrm{f}}Z. \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[113, 811, 881, 881]]<|/det|> +We can scale out the dependence on the density so that \(\hat{E}_{c} = E_{c} / (\hbar n^{2}\xi |\kappa |)\) for the length scale \(\xi = \hbar /(m|\kappa |n)^{1 / 2}\) . For thermal bubbles in one dimension, the instanton calculation is equivalent to a WKB approximation to the action, with the familiar WKB form + +<|ref|>equation<|/ref|><|det|>[[328, 890, 877, 932]]<|/det|> +\[\hat{E}_{c} = \frac{1}{2}\int_{Z_{TP}}^{Z_{FV}}\left(\frac{2(V - V_{FV})}{|\kappa|n}\right)^{1 / 2}\frac{dZ}{\sqrt{1 - Z^{2}}}, \quad (14)\] + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 140, 880, 283]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[113, 97, 880, 138]]<|/det|> +TABLE I. Fitting coefficients for the thermal instanton model of vacuum decay with \(j = 1\) . The fit is limited to \((\delta_{f} - \delta_{c}) / \Omega_{R} > 0.05\) to ensure that \(b\hat{E}_{c} > 1\) + +
ΩR/2πaexp(σa)bexp(σb)asim(σa)bsim(σb)
3000.54(0.09)56.5(1.9)0.93(0.06)55.0(1.9)
4000.83(0.42)44.4(6.1)0.70(0.07)41.3(0.87)
6000.02(0.43)30.3(3.7)0.01(0.14)29.8(1.3)
8000.30(0.75)25.8(5.7)-0.44(0.11)25.3(0.9)
+ +<|ref|>text<|/ref|><|det|>[[114, 307, 880, 352]]<|/det|> +The integral extends from the turning point \(Z_{TP}\) to the false vacuum \(Z_{FV}\) . The extra factor \((1 - Z^{2})^{- 1 / 2}\) is due to the form of the derivative terms in the energy (12). + +<|ref|>text<|/ref|><|det|>[[113, 360, 882, 507]]<|/det|> +The experimental data has been used to determine the best parameters in a fit for \(\ln \tau = \ln A + b\hat{E}_{c} - \ln (b\hat{E}_{c}) / 2\) . The results are given in Table I. The condensate number density is given by \(n = (k_{B}T / \hbar |\kappa |n)b / \xi\) . For the temperature \(T = 1\mu \mathrm{K}\) , the values of \(n\) at lower \(\Omega\) are around half of the value expected for the system, but not unreasonable given the limitations of the one dimensional treatment. If the bubble only fills a fraction of the cross- section, it effectively feels only part of the integrated density. + +<|ref|>text<|/ref|><|det|>[[114, 514, 880, 558]]<|/det|> +In the case of small potential barriers, the potential can be expanded to cubic order about an inflection point at \(Z_{c}\) and \(\delta = \delta_{c}\) , where + +<|ref|>equation<|/ref|><|det|>[[311, 572, 878, 630]]<|/det|> +\[\delta_{c} = \kappa n(1 - Z_{c}^{3}),\qquad Z_{c} = \left(1 - \left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{2}{3}}\right)^{\frac{1}{2}}. \quad (15)\] + +<|ref|>text<|/ref|><|det|>[[114, 644, 514, 662]]<|/det|> +The integral in this case can be performed exactly, + +<|ref|>equation<|/ref|><|det|>[[325, 672, 878, 720]]<|/det|> +\[\hat{E}_{c}\approx 1.77\left(\frac{\delta_{f} - \delta_{c}}{|\kappa|n}\right)^{\frac{5}{4}}\left(\frac{\Omega_{R}}{|\kappa|n}\right)^{\frac{1}{6}}\left(\frac{|\delta_{c}|}{|\kappa|n}\right)^{-\frac{1}{4}} \quad (16)\] + +<|ref|>text<|/ref|><|det|>[[113, 733, 881, 826]]<|/det|> +To verify that the instanton prediction and simulation are consistent, we repeat numerical simulations at fixed \(\delta_{f}\) and variable \(\epsilon\) . We observe that the extracted \(\tau\) results proportional to \(e^{(1 / \epsilon)}\) and this well justifies the association between the injected noise parameter \(\epsilon\) and the temperature \(T\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 130]]<|/det|> +[2] K. C. Kulander, K. J. Schafer, and J. L. Krause, Atoms in Intense Laser Fields (Academic Press, New York, 1992) p. 247. + +<|ref|>text<|/ref|><|det|>[[115, 134, 883, 175]]<|/det|> +[3] G. Lagnese, F. M. Surace, M. Kormos, and P. Calabrese, False vacuum decay in quantum spin chains, Phys. Rev. B 104, L201106 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 180, 882, 220]]<|/det|> +[4] A. Milsted, J. Liu, J. Preskill, and G. Vidal, Collisions of false- vacuum bubble walls in a quantum spin chain, PRX Quantum 3, 020316 (2022). + +<|ref|>text<|/ref|><|det|>[[115, 225, 883, 333]]<|/det|> +[5] A. J. Baldwin, T. P. J. Knowles, G. G. Tartaglia, A. W. Fitzpatrick, G. L. Devlin, S. L. Shammas, C. A. Waudby, M. F. 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Hafezi, C. Monroe, G. Pagano, A. Seif, and A. Shaw, Towards analog quantum simulations of lattice gauge theories with trapped ions, Phys. Rev. Res. 2, 023015 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 883, 130]]<|/det|> +[19] T. P. Billam, K. Brown, and I. G. Moss, Simulating cosmological supercooling with a cold- atom system, Phys. Rev. A 102, 043324 (2020). + +<|ref|>text<|/ref|><|det|>[[112, 133, 883, 175]]<|/det|> +[20] T. P. Billam, K. Brown, A. J. Groszek, and I. G. Moss, Simulating cosmological supercooling with a cold atom system. ii. thermal damping and parametric instability, Phys. Rev. A 104, 053309 (2021). + +<|ref|>text<|/ref|><|det|>[[113, 179, 882, 242]]<|/det|> +[21] K. L. Ng, B. Opanchuk, M. Thenabadu, M. Reid, and P. D. Drummond, Fate of the false vacuum: Finite temperature, entropy, and topological phase in quantum simulations of the early universe, PRX Quantum 2, 010350 (2021). + +<|ref|>text<|/ref|><|det|>[[113, 246, 882, 310]]<|/det|> +[22] B. Song, S. Dutta, S. Bhave, J.- C. Yu, E. Carter, N. Cooper, and U. Schneider, Realizing discontinuous quantum phase transitions in a strongly correlated driven optical lattice, Nature Physics 18, 259- 264 (2022). + +<|ref|>text<|/ref|><|det|>[[113, 314, 882, 355]]<|/det|> +[23] J. Preskill, Simulating quantum field theory with a quantum computer, PoS LATTICE2018, 024 (2019). + +<|ref|>text<|/ref|><|det|>[[113, 359, 882, 401]]<|/det|> +[24] S. Abel and M. Spannowsky, Quantum- field- theoretic simulation platform for observing the fate of the false vacuum, PRX Quantum 2, 010349 (2021). + +<|ref|>text<|/ref|><|det|>[[113, 405, 882, 446]]<|/det|> +[25] H. Grabert and U. Weiss, Crossover from thermal hopping to quantum tunneling, Phys. Rev. Lett. 53, 1787 (1984). + +<|ref|>text<|/ref|><|det|>[[113, 450, 882, 492]]<|/det|> +[26] H. Grabert, U. Weiss, and P. Hanggi, Quantum tunneling in dissipative systems at finite temperatures, Phys. Rev. Lett. 52, 2193 (1984). + +<|ref|>text<|/ref|><|det|>[[113, 495, 882, 560]]<|/det|> +[27] R. Cominotti, A. Berti, C. Dulin, C. Rogora, G. Lamporesi, I. Carusotto, A. Recati, A. Zenesini, and G. Ferrari, Ferromagnetism in an extended coherently- coupled atomic superfluid. (to be published in Phys. Rev. X, 2023), arXiv:2209.13235 [cond- mat.quant- gas]. + +<|ref|>text<|/ref|><|det|>[[113, 563, 882, 627]]<|/det|> +[28] A. Farolfi, D. Trypogeorgos, G. Colzi, E. Fava, G. Lamporesi, and G. Ferrari, Design and characterization of a compact magnetic shield for ultracold atomic gas experiments, Review of Scientific Instruments 90, 115114 (2019). + +<|ref|>text<|/ref|><|det|>[[113, 631, 882, 673]]<|/det|> +[29] D. Guéry- Odelin, A. Ruschhaupt, A. Kiely, E. Torrontegui, S. Martínez- Garaot, and J. G. Muga, Shortcuts to adiabaticity: Concepts, methods, and applications, Rev. Mod. Phys. 91, 045001 (2019). + +<|ref|>text<|/ref|><|det|>[[113, 677, 882, 764]]<|/det|> +[30] E. Torrontegui, S. Ibáñez, S. Martínez- Garaot, M. Modugno, A. del Campo, D. Guéry- Odelin, A. Ruschhaupt, X. Chen, and J. G. Muga, Chapter 2 - shortcuts to adiabaticity, in Advances in Atomic, Molecular, and Optical Physics, Advances In Atomic, Molecular, and Optical Physics, Vol. 62, edited by E. Arimondo, P. R. Berman, and C. C. Lin (Academic Press, 2013) pp. 117- 169. + +<|ref|>text<|/ref|><|det|>[[113, 768, 882, 831]]<|/det|> +[31] W. L. Tan, P. Becker, F. Liu, G. Pagano, K. S. Collins, A. De, L. Feng, H. B. Kaplan, A. Kyprianidis, R. Lundgren, W. Morong, S. Whitsitt, A. V. Gorshkov, and C. Monroe, Domain- wall confinement and dynamics in a quantum simulator, Nature Physics 17, 742- (2021). + +<--- Page Split ---> diff --git a/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/images_list.json b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..82711e8cc720e76bccfedd4c82c616cb552e9b24 --- /dev/null +++ b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/images_list.json @@ -0,0 +1,100 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Experimental scheme and properties of MAPbI3 perovskite (a) THz pulse geometry with a tetragonal unit cell (black rectangular cuboid) of MAPbI3. (dark grey: Pb, purple: I, brown: C, light blue: N, light pink: H) The THz biasing along the \\(c\\) axis of a crystallite is depicted. (b) Simplified electronic band structure of MAPbI3 in the tetragonal phase along the directions \\(\\Gamma (0,0,0)\\rightarrow \\mathrm{Z}(0,0,0.5)\\) and \\(\\Gamma (0,0,0)\\rightarrow \\mathrm{A}(0.5,0.5,0.5)\\) . The bandwidths and the lattice parameters are used from [Ref \\(^{12}\\) ]. (c) Optical absorption spectrum of MAPbI3 in the spectral range of the probe pulses.", + "footnote": [], + "bbox": [ + [ + 201, + 92, + 820, + 388 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Experimental observation of the transient Wannier Stark localization and the visualized diagram (a) Experimental differential transmission spectra on a polycrystalline film of", + "footnote": [], + "bbox": [ + [ + 120, + 658, + 866, + 820 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Numerical simulation of differential absorption spectra (a) Negative change of the optical interband absorption \\(- \\Delta \\alpha_{\\overline{\\Gamma Z}}\\) for static fields from a cosine band modeling along \\(\\overline{\\Gamma Z}\\) direction. The region of electric field strengths up to \\(1\\mathrm{MV / cm}\\) is enlarged to show Franz-Keldysh oscillations and the transition to the Wannier-Stark regime. (b) Calculated \\(- \\Delta \\alpha_{\\overline{\\Gamma Z}}\\) spectra for the excitation with a THz pulse with a peak field strength of \\(E_{0} = 6\\mathrm{MV / cm}\\) , where the delay \\(\\tau\\) between the THz and the optical pulse is varied. (c) Simulated temporal profile of the applied THz bias transient. The pulse duration \\(\\overline{T}\\) is \\(240\\mathrm{fs}\\) , the THz frequency is \\(20\\mathrm{THz}\\) , and the dephasing time is \\(T_{2} = 20\\mathrm{fs}\\) .", + "footnote": [], + "bbox": [ + [ + 125, + 92, + 888, + 333 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Experiments on polycrystalline system and simulations with averaging of cosine band model from \\(\\Gamma Z\\) to \\(\\Gamma A\\) direction. (a) Illustration for the averaging process over the interpolation parameter \\(f\\) from the \\(\\overline{\\Gamma Z}\\) direction \\((f = 0)\\) to \\(\\overline{\\Gamma A}\\) direction \\((f = 1)\\) . The negative absorption changes \\(-\\Delta \\alpha_{f}\\) are calculated for different one-dimensional systems using a THz pulse centered at \\(t = 0\\) , with an amplitude of \\(E_{0} = 4 \\mathrm{MV / cm}\\) , a pulse duration of \\(\\overline{T} = 240 \\mathrm{fs}\\) , and a THz center frequency of \\(20 \\mathrm{THz}\\) . (b) Temporal slices of \\(\\Delta T / T\\) as a function of probe photon energy (Fig. 2(a)), at a delay time corresponding to the contour with constant electric field amplitudes \\(E\\) (Fig. 2(b)). (c) averaged absorption change, \\(-\\Delta \\alpha_{\\mathrm{avg}}\\) , for static fields of various strengths. (d) averaged absorption change, \\(-\\Delta \\alpha_{\\mathrm{avg}}\\) , for a THz pulse centered at \\(t = 0\\) and various field strengths.", + "footnote": [], + "bbox": [ + [ + 123, + 92, + 881, + 576 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 60, + 100, + 940, + 536 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 55, + 52, + 485, + 614 + ] + ], + "page_idx": 31 + } +] \ No newline at end of file diff --git a/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65.mmd b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8dd0df70d13d6851e7675017c12d0c0ff4cce9be --- /dev/null +++ b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65.mmd @@ -0,0 +1,424 @@ + +# Low-field Onset of Wannier-Stark Localization in a Polycrystalline Hybrid Organic Inorganic Perovskite + +Daniel Berghoff Paderborn University Johannes Bühler University of Konstanz + +Mischa Bonn Max Planck Institute for Polymer Research + +Alfred Leitenstorfer University of Konstanz + +Torsten Meier University of Paderborn https://orcid.org/0000- 0001- 8864- 2072 + +Heejae Kim ( kim@mpip-mainz.mpg.de) Max Planck Institute for Polymer Research + +## Article + +Keywords: Wannier- Stark localization, Electron confinement, Ultrafast Biasing, Optical modulation, Hybrid perovskites + +Posted Date: April 8th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 386040/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on September 29th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 26021- 4. + +<--- Page Split ---> + +# Low-field Onset of Wannier-Stark Localization in a Polycrystalline Hybrid Organic Inorganic Perovskite + +Daniel Berghoff \(^{1}\) , Johannes Bühler \(^{2}\) , Mischa Bonn \(^{3}\) , Alfred Leitenstorfer \(^{2}\) , Torsten Meier \(^{*1}\) , Heejae Kim \(^{*3}\) + +\(^{1}\) Department of Physics, Paderborn University, D- 33098 Paderborn, Germany + +\(^{2}\) Department of Physics and Center for Applied Photonics, University of Konstanz, D- 78457 Konstanz, Germany + +\(^{3}\) Department of Molecular Spectroscopy, Max Planck Institute for Polymer Research, D- 55128 Mainz, Germany + +## KEYWORDS + +Wannier- Stark localization, Electron confinement, Ultrafast Biasing, Optical modulation, Hybrid perovskites + +<--- Page Split ---> + +## ABSTRACT + +Control over light propagation in a material by applying external fields is at the heart of photonic applications. Here, we demonstrate ultrafast modulation of the optical properties in the room temperature polycrystalline MAPbI₃ perovskite using phase- stable terahertz pulses, centered at 20 THz. The biasing field from the THz pulse creates extreme localization of electronic states in the ab plane – Wannier- Stark localization. This quasi- instantaneous reduction of dimensionality (from 3D to 2D) causes a marked change in the absorption shape, enabling the modulation depth to be tens of percent at moderate field strengths (3 MV/cm). The notably low- field onset results from a narrow electronic bandwidth, a large relevant lattice constant, and the coincidence of the two along the same direction in this tetragonal perovskite. We show that the transient optical response is in fact dominated by the least dispersive direction of the electronic band structure, facilitating a substantial modulation despite the arbitrary arrangement of the individual crystallites. The demonstration of THz- field- induced optical modulation in a solution- processed, disordered, and polycrystalline material is of substantial potential significance for novel photonic applications. + +## Introduction + +The intriguing properties of electrons in periodic potentials in the presence of strong external electric fields are highly relevant for photonic applications, including optical modulators, optical switches, and optical signal processing. Drastic changes in optical properties can be achieved via localization of electronic states using externally applied fields. In the presence of strong external electric fields \(E\) , the continuum of electronic energy bands splits into a series of discrete levels in the direction of the field1, and the corresponding wave functions are confined on a length scale given by \(\Delta /(eE)\) , where \(\Delta\) is the energetic width of the electronic band in the absence of biasing. + +<--- Page Split ---> + +These localized states, the Wannier- Stark states \(^{2,3}\) , are equally spaced both in energy by an amount \(eED\) , and in space by the lattice period \(D\) . Since a spatial separation of \(nD\) lattice periods results in an energy shift of \(neED\) with respect to the central spatially- direct \((n = 0)\) transition, this Wannier- Stark localization leads to strong spectral modulation of the interband absorption continuum below and above the optical band gap. + +The quantum confinement induced by external fields is an extreme state of matter and has never been achieved under static biasing in natural solids but only in artificial superlattices \(^{4 - 8}\) . So far, only one natural solid, a single crystal GaAs \(^{8}\) has allowed for achieving the Wannier- Stark localization transiently by virtue of the recent availability of extremely intense and phase- stable pulses of multi- terahertz radiation \(^{9,10}\) . The ultrafast biasing fields could reach amplitudes up to several tens of MV/cm \(^{9,10}\) , i.e., field strengths comparable to the interatomic fields. For GaAs, an optimally oriented single crystal was required to observe Wannier- Stark localization with the required field strengths exceeding 10 MV/cm \(^{8}\) . + +Here, we demonstrate the transient Wannier- Stark localization at a substantially lower field strength in a disordered, solution- processed, polycrystalline film of methylammonium lead iodide perovskite (MAPbI \(_{3}\) , Fig. 1(a)). Already at relatively modest field strengths, the thin film's optical transmission is modified by tens of percent. To resolve optical transitions to individual Wannier- Stark states in, e.g., absorption spectra, their energetic spacing needs to be larger than the (total) linewidth \(\Gamma\) , i.e., \(eED > \Gamma\) \(^{4,5,11}\) Due to the small lattice constant of bulk crystals and the large linewidth which results from the scattering of electrons with lattice vibrations and other electrons, the requirement \(eED > \Gamma\) can typically not be fulfilled under stationary external fields below the strength where the dielectric breakdown occurs \(^{6,7}\) . At room temperature, however, this material exhibits a tetragonal structure with lattice parameters of \(a = 8.8 \mathring{\mathrm{A}}\) and \(c = 12.5 \mathring{\mathrm{A}}\) by the expansion + +<--- Page Split ---> + +of the cubic perovskite unit cell \(^{12,13}\) . The periodicities are nearly twice as large as the lattice parameter \(a = 5.6 \text{Å}\) of cubic GaAs \(^{8}\) . + +We will show that the large relevant lattice constant (Fig. 1(a)), the small width of electronic energy bands (Fig. 1(b)), and the coincidence of these two along the same high- symmetry direction lead to Stark localization in this organic perovskite at field amplitudes as low as \(3 \text{MV/cm}\) , i.e., at a fraction of the field strength required to enter this regime in optimally oriented, single- crystalline GaAs. Moreover, the measured differential spectra containing the overall effects from arbitrarily oriented microcrystals are qualitatively well- described by a two- band model with a cosine band structure. By considering different orientations of the microcrystals in our simulations, we demonstrate that the contribution from the direction with the largest periodicity, i.e., the \(\overline{\Gamma Z}\) direction \(c = 12.5 \text{Å}\) , strongly dominates the transient changes of the optical response. These findings, together with its renowned characteristics, make MAPbI \(_3\) a strong candidate for cost- effective, efficient, fast, and sensitive optical modulator materials. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Experimental scheme and properties of MAPbI3 perovskite (a) THz pulse geometry with a tetragonal unit cell (black rectangular cuboid) of MAPbI3. (dark grey: Pb, purple: I, brown: C, light blue: N, light pink: H) The THz biasing along the \(c\) axis of a crystallite is depicted. (b) Simplified electronic band structure of MAPbI3 in the tetragonal phase along the directions \(\Gamma (0,0,0)\rightarrow \mathrm{Z}(0,0,0.5)\) and \(\Gamma (0,0,0)\rightarrow \mathrm{A}(0.5,0.5,0.5)\) . The bandwidths and the lattice parameters are used from [Ref \(^{12}\) ]. (c) Optical absorption spectrum of MAPbI3 in the spectral range of the probe pulses.
+ +## Results and Discussion + +## Experimental observation of Wannier-Stark Localization + +For applying the strong transient bias, non- resonant in energy with any of the optical phonons and electronic transitions, we employ phase- stable multi- cycle optical pulses with a center frequency of 20 THz. The pump pulse is generated using a difference- frequency generation + +<--- Page Split ---> + +scheme in GaSe \(^{9,10}\) . For comparison, the MAPbI \(_3\) perovskite has a direct bandgap of \(E_{gap} = 1.62 \mathrm{eV}\) (390 THz, Fig. 1(c)) at room temperature. The phonon modes of Pb- I inorganic sublattice are below 10 THz and methylammonium organic molecular vibrations above 26 THz \(^{14}\) . Due to the presence of the organic cation with a low rotational barrier \(^{15}\) , the crystal shows some degree of disorder at elevated temperature and a less pronounced periodicity compared to all- inorganic perovskites \(^{15,16}\) . The sample is a polycrystalline film with a thickness of \(\sim 300 \mathrm{nm}\) spin- coated \(^{17,18}\) on a cyclic olefin/ethylene copolymer substrate (TOPAS \(^{8}\) ) \(^{19}\) . The differential transmission induced by the external electric field transient is probed by near- IR and visible probe pulses, with spectra covering broad interband electronic transition energies between \(1.4 \mathrm{eV}\) and \(2.4 \mathrm{eV}\) (see Fig. S1). The duration of these probe laser pulses is 7 fs, which is significantly shorter than the half- cycle period of the THz pump transients of 25 fs. Details of the experimental settings are described in the Method section and Ref \(^{8}\) . + +Fig. 2(a) shows the differential transmission \(\Delta T / T\) upon applying the THz biasing as a function of delay time between the pump and probe pulses. The peak field strength of the THz pump pulses is \(6.1 \mathrm{MV / cm}\) . As expected for the non- resonant THz pulse, the optical response of the material is instantaneous and peaks when the THz field strength is maximal. The modulation occurs at twice the frequency of the THz pulse (Fig. 2(b)), since the measured differential transmission is at least a third- order nonlinear process \(^{20}\) . In such a centrosymmetric crystal as the room- temperature tetragonal phase of perovskite MAPbI \(_3\) \(^{21}\) , no contribution from the electro- optic effect is expected which is linear in the electric bias field. The clear temporal modulation of differential transmission appears at high fields, \(- 100 < \tau < 100 \mathrm{fs}\) , as the strong \(E\) field shortens the interband dephasing time in the vicinity of the bandgap to be comparable to the half- cycle period of 25 fs of the THz + +<--- Page Split ---> + +transient. Thus, the precise arrival time of the probe pulse exciting the interband polarization was resolved within the dephasing time. + +More importantly, two distinct regimes can be identified in the time- resolved transient spectrum (Fig. 2(a)). For relatively weak fields, \(E < 3 \mathrm{MV}\) , for \(\tau < - 100 \mathrm{fs}\) , an induced absorption (blue, \(\Delta T / T < 0\) ) right below and an induced transmission (red, \(\Delta T / T > 0\) ) right above the bandgap of \(E_{gap} = 1.62 \mathrm{eV}\) are observed. The second regime is apparent for field strengths \(\mathrm{E} > 3 \mathrm{MV / cm}\) , occurring between delay times \(- 100 < \tau < 100 \mathrm{fs}\) (Fig. 2(b)). Here, the maximum modulation depth becomes as large as \(38 \%\) at the probe energy of \(E_{pr} = 1.7 \mathrm{eV}\) (Fig. 2(a) and Fig. S2). Also, the transient response covers a significantly extended spectral range, compared to the moderate field regime. The induced transmission (red) above the bandgap now reaches up to \(E_{pr} = 1.9 \mathrm{eV}\) , where it abruptly switches to induced absorption (blue, \(\Delta T / T < 0\) ). This negative region of \(\Delta T / T < 0\) persists at probe energies all the way up to \(E_{pr} = 2.4 \mathrm{eV}\) . This one central step from reduced to increased absorption near the center of the band \(E_{pr} = 2 \mathrm{eV}\) , is a noticeable signature of Stark localization, where the Wannier- Stark states are localized onto one unit cell. + +![](images/Figure_2.jpg) + +
Figure 2. Experimental observation of the transient Wannier Stark localization and the visualized diagram (a) Experimental differential transmission spectra on a polycrystalline film of
+ +<--- Page Split ---> + +MAPbI3 perovskite at room temperature, as a function of delay time of probe pulses after THz pump pulses. The THz pulses have a peak field strength of 6.1 MV/cm and a center frequency of 20 THz; the probe pulses have photon energy of \(1.4 \sim 2.4 \mathrm{eV}\) . (b) Temporal profile of the applied THz bias transient. (c) Schematic picture of Wannier Stark localization. In the presence of strong external fields along the \(c\) axis, electronic states (orange: conduction band, blue: valence band) are localized to a few layers of \(ab\) plane, and energetically separated by \(\Delta E_{WSL} = eE_{THzC}\) between adjacent lattice sites. Black arrows depict the interband transitions within the same site \((n = 0)\) and between different sites \((n = \pm 1)\) . (d) The absorbance with and without the external transient biasing. The Wannier- Stark localization effectively reduces the 3D electronic structure into 2D layered structure along the \(ab\) plane, as depicted in blue together with the simplified 3D structure. + +By driving the 3- dimensional (3D) system into Wannier- Stark localization, i.e., localizing it in the field direction, we transiently create an effectively 2D electronic system (Fig. 2(c, d)). Given the unit cell doubling, this optically prepared transient 2D system perpendicular to the \(c\) axis may be directly compared to the physically isolated double- layer structure of PbI6 octahedra. In such 2D perovskites as (BA)2(MA)1-1PbI13+1 perovskites22, the inorganic layers (perpendicular to the \(c\) axis in 3D equivalence) are separated by bulky organic layers23. The bandgap of the 2D quantum well perovskites is widened due to the bandwidth narrowing (mainly due to the zero dispersion along the vertical direction) compared to 3D perovskite24. In the case of (BA)2(MA)1-1PbI13+1 perovskites, where the PbI6 octahedral network forms a double layer \((l = 2)\) , the optical band gap is \(\sim 2.1 \mathrm{eV}\) , which is comparable to the observed \(1.9 \mathrm{eV}^{25}\) . It is noteworthy that the observed Wannier- Stark step at \(E_{pr} = 1.9 \mathrm{eV}\) under THz fields is slightly lower than the expected value under static fields due to the spectral broadening induced by the THz modulation, as will be discussed + +<--- Page Split ---> + +below. Therefore, the abrupt shift of the absorption edge from \(E_{pr} = 1.6 \mathrm{eV}\) to \(1.9 \mathrm{eV}\) at high transient fields (Fig. 2(d)) could be attributed to the transfer of spectral weight from \(\alpha (E_{g,3D} < E_{pr} < E_{g,2D})\) to \(\alpha (E_{g,2D} < E_{pr})\) . Such a THz- induced reduction of dimensionality from a 3D to a 2D system could enable new applications in both transport and optoelectronics due to the relatively easy access to that regime in these hybrid perovskite materials. + +## Simulations considering one orientation + +To capture the essential ingredients responsible for the experimental observations, we carry out theoretical calculations based on different models of increasing complexity. We start with considering perfect alignment of the THz field with the direction along which the joint bandwidth of the highest valence and the lowest conduction band is narrowest. For the case of the tetragonal MAPbI₃ perovskite, the narrowest joint bandwidth, \(\Delta_{\overline{\mathrm{FZ}}} = 0.75 \mathrm{eV}\) , is along the \(\overline{\Gamma Z}\) direction (Fig. 1(b))¹². We thus take into account two one- dimensional bands, i.e., one valence and one conduction band with a cosine- like (tight- binding) band structure and the bandgap of \(1.62 \mathrm{eV}\) . Thus, the energy difference for interband transitions is taken as \(E_{cv}(k) = 1.62 \mathrm{eV} + (\Delta_{\overline{\mathrm{FZ}}} / 2)(1 - \cos (g(k, a) *))\) (see Methods section for details of the function \(g(k, a *)\) ). For this model, the spectra are obtained by numerically solving the semiconductor Bloch equations ²⁶- ²⁸, as described in the Methods section. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Numerical simulation of differential absorption spectra (a) Negative change of the optical interband absorption \(- \Delta \alpha_{\overline{\Gamma Z}}\) for static fields from a cosine band modeling along \(\overline{\Gamma Z}\) direction. The region of electric field strengths up to \(1\mathrm{MV / cm}\) is enlarged to show Franz-Keldysh oscillations and the transition to the Wannier-Stark regime. (b) Calculated \(- \Delta \alpha_{\overline{\Gamma Z}}\) spectra for the excitation with a THz pulse with a peak field strength of \(E_{0} = 6\mathrm{MV / cm}\) , where the delay \(\tau\) between the THz and the optical pulse is varied. (c) Simulated temporal profile of the applied THz bias transient. The pulse duration \(\overline{T}\) is \(240\mathrm{fs}\) , the THz frequency is \(20\mathrm{THz}\) , and the dephasing time is \(T_{2} = 20\mathrm{fs}\) .
+ +Already when considering static fields (Fig. 3(a)), the simulation results obtained by this simple model exhibits substantial qualitative similarities with the transient experimental results shown in Fig. 2(a). For all field strengths, increased absorption is present below the bandgap and reduced absorption directly above the band gap. For rather weak field strengths of up to about \(0.5\mathrm{MV / cm}\) , oscillations arising from the Franz- Keldysh effect are visible, shifting towards the band center with + +<--- Page Split ---> + +increasing field. For fields exceeding \(\sim 3 \mathrm{MV / cm}\) , signatures of Wannier- Stark localization become noticeable, as the field- dependent interband transition energies shift to higher and lower energies by \(neED\) with increasing \(E\) (Fig. 2(c)). Starting at around \(3 \mathrm{MV / cm}\) , the condition for Stark localization is fulfilled, i.e., \(eED > \Delta /2\) (meaning that the energy of the \((n = - 1)\) Wannier- Stark state is in the bandgap region, see Fig. 2(c, d)), and therefore, the dominant feature is the step- like change from reduced absorption to induced absorption in the center of the band at \(1.974 \mathrm{eV}\) (this value is the average transition frequency within our model). This step- like change is, in fact, also the main feature visible in the experimental results for sufficiently high fields, i.e., between about \(- 100 < \tau < 100\) fs as shown in Figs. 2 (a). + +Besides, by considering pulsed THz fields, the simulated differential spectra with the same model (Fig. 3(b, c)) well describe both spectral and temporal features in the observed transient modulation of differential transmission spectra (Fig. 2(a)). Fig. 3(b) shows the negative change of the transient absorption, \(- \Delta \alpha_{\mathrm{TFZ}}\) , upon non- resonant biasing with a THz pulse with a peak field strength of \(E_{\theta} = 6 \mathrm{MV / cm}\) and a center frequency of \(20 \mathrm{THz}\) , as shown in Fig. 3(c). Besides temporal modulation of the entire transient spectra at twice the carrier frequency of the THz transient, the dominant feature at sufficiently large field strengths \((- 100 < \tau < 100 \mathrm{fs})\) is the rapid change from increased to reduced transmission in the center of the band \(E_{pr} = 2 \mathrm{eV}\) , which originates from Stark localization. The slightly lower value of the observed central step at \(E_{pr} = 1.9 \mathrm{eV}\) and the asymmetric nature of the spectral shape with respect to the central step (Fig. 2(a)) compared to this simplified model (Fig. 3 (b)) can be explained by the polycrystallinity of the system as discussed below. Given the complexity, disorder, and polycrystallinity of the investigated sample, the required field strength at which this step starts to appear is in surprisingly good agreement with the experiment which confirms that the observed response constitutes a clear + +<--- Page Split ---> + +sign of Wannier- Stark localization. Our interpretations are further supported by Fig. S6, which shows how the results of Fig. 3 change if we consider that the THz field is aligned with the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction instead of the \(\overline{\Gamma}\overline{\mathrm{Z}}\) direction. Comparing those two figures clearly shows that due to the larger bandwidth in the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction the Wannier- Stark localization requires higher field amplitudes to develop and furthermore would lead to a transition from reduced to induced absorption at significantly higher energies as observed in experiment. The effects of different field directions and the averaging over them is discussed in more detail below (see Fig. 4). + +As demonstrated so far, Wannier- Stark localization starts to occur at the field amplitude as low as \(3\mathrm{MV / cm}\) in the MAPBI \(_3\) perovskite, due to the relatively large periodicity, the narrow joint bandwidth, and the coincidence of the two along the same direction. The largest lattice constant of tetragonal MAPBI \(_3\) perovskite, along the \(c\) axis, \(c = 12.5\mathrm{\AA}\) , is more than twice as large as those of conventional all- inorganic semiconductors crystallizing with strong covalent bonds in the diamond, wurtzite, or zincblende forms \((3.5\sim 6.5\mathrm{\AA}\) at \(300\mathrm{K}\) ). This finding arises because (i) the cubic perovskite unit cell is expanded through rotation of ab plane by \(45^{\circ}\) and cell doubling along c axis in the tetragonal phase; and (ii) the pseudocubic lattice parameter formed by relatively large \(\mathrm{Pb^{2 + }}\) and \(\Gamma\) ions is \(6.3\mathrm{\AA}^{13}\) , which is at the larger side of the distribution of parameters for cubic lattice parameters. The pseudocubic lattice parameter is large enough to accommodate large organic molecular cations within the void of their network. + +The direction of the narrowest joint bandwidth of the conduction and valence bands, \(\overline{\Gamma}\overline{\mathrm{Z}}\) , coincides with the \(c\) axis. The conduction band is composed of the overlap of \(\mathrm{Pb(6p) - I(5p)}\) atomic orbitals and the valence band is of that of \(\mathrm{Pb(6s) - I(5p)}\) orbitals \(^{29}\) . Thus, the \(\mathrm{Pb - I}\) bond length as well as the \(\mathrm{Pb - I - Pb}\) angle could determine the widths of both bands and the magnitude of the band + +<--- Page Split ---> + +gap. In the tetragonal MAPbI₃ perovskite, the corner- shared PbI₆ octahedra in cubic phase are tilted about the \(c\) axis in the opposite direction between successive tilts, which reduces the Pb- I- Pb angle from 180° along the diagonal direction of the a and b axis. The smaller Pb- I- Pb bond angle indicates weaker orbital overlap between Pb and I atoms and thus smaller band dispersion along \(\overline{\Gamma}\overline{\mathrm{M}}\) than \(\overline{\Gamma}\overline{\mathrm{Z}}\) . However, the Pb- I bond lengths along the \(c\) axis is known to be longer on average³⁰ and has greater effect on the dispersion than the angle due to the \(\sigma\) bonding nature, which leads to the coincidence of the direction of the largest lattice constant and the narrowest bandwidth. We note that unlike GaAs, the body diagonal direction exhibits the strongest dispersion (\(\overline{\Gamma}\overline{\mathrm{A}}\) ). Overall, the large ionic diameter and the geometric distortion result in the unusually narrow joint bandwidth, lower than 1 eV. + +## Including polycrystallinity by averaging over orientations + +We now account for the system's polycrystallinity by considering contributions to the differential transmittance spectra from crystallites with orientations different from those with the \(c\) axis parallel to the THz field polarization. To include arbitrary orientations of the crystallites into our simulations, we take the \(\overline{\Gamma}\overline{\mathrm{Z}}\) and the \(\overline{\Gamma}\overline{\mathrm{A}}\) directions, i.e., the two extreme directions with the narrowest/broadest bandwidth and simultaneously the smallest/largest distance in k- space (see Fig. 1(b)) and perform an average overall in between bandwidths and extensions of the first Brillouin zone (see Method section), by interpolating between the two limiting cases with a parameter \(f\) . The simulated absorption changes at a field amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) with various interpolation parameters \(f\) 's are shown in Fig. 4 (a) together with the measured differential spectra at different instantaneous field amplitudes of the THz pulse (Fig. 4 (b)). Here, \(f = 0\) denotes the response along the \(\overline{\Gamma}\overline{\mathrm{Z}}\) direction (i.e., the \(c\) - axis), and \(f = 1\) along the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4. Experiments on polycrystalline system and simulations with averaging of cosine band model from \(\Gamma Z\) to \(\Gamma A\) direction. (a) Illustration for the averaging process over the interpolation parameter \(f\) from the \(\overline{\Gamma Z}\) direction \((f = 0)\) to \(\overline{\Gamma A}\) direction \((f = 1)\) . The negative absorption changes \(-\Delta \alpha_{f}\) are calculated for different one-dimensional systems using a THz pulse centered at \(t = 0\) , with an amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) , a pulse duration of \(\overline{T} = 240 \mathrm{fs}\) , and a THz center frequency of \(20 \mathrm{THz}\) . (b) Temporal slices of \(\Delta T / T\) as a function of probe photon energy (Fig. 2(a)), at a delay time corresponding to the contour with constant electric field amplitudes \(E\) (Fig. 2(b)). (c) averaged absorption change, \(-\Delta \alpha_{\mathrm{avg}}\) , for static fields of various strengths. (d) averaged absorption change, \(-\Delta \alpha_{\mathrm{avg}}\) , for a THz pulse centered at \(t = 0\) and various field strengths.
+ +<--- Page Split ---> + +As shown in Fig. 4(a), the absorption changes depend strongly on the interpolation parameter \(f\) , i.e., on the bandwidth and the distance to the border of the first Brillouin zone. For \(f = 0\) , which corresponds to the \(\overline{\Gamma Z}\) direction, the field amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) drives the system into the region of Stark localization. Therefore, for a static field of such an amplitude, one would see a strong induced absorption in the band center at \(1.974 \mathrm{eV}\) , which corresponds to an optical transition to the Stark localized state. The transient nature of the THz pulse causes the single negative peak to be split into two peaks and the spectral region of induced absorption to be slightly broadened. With increasing \(f\) , both the bandwidth and the distance to the border of the first Brillouin zone increase. As a result, the minimum field strength for which Stark localization is realized increases significantly by approximately a factor \((c / a_{\overline{\Gamma Z}}^{*})(\Delta_{\overline{\Gamma A}} / \Delta_{\overline{\Gamma Z}})\) , equaling about 4.7. Consequently, already for \(f = 0.25\) , the absorption changes show no sign of Stark localization, with several oscillations emerging owing to the THz driving. This trend of overall weaker absorption changes with some oscillatory structure is also present for even larger \(f\) . The only feature present in all spectra shown in Fig. 4 (a) is some induced absorption below the bandgap and reduced absorption directly above the bandgap. + +However, when averaging over the interpolation parameter \(f\) , i.e., over the orientations considered by our modeling, the result (black curve in Fig. 4(a)) reproduces the main features present for \(f = 0\) , with somewhat fewer oscillations. Most importantly, the change from bleaching to induced absorption in the center of the band structure for the \(\overline{\Gamma Z}\) direction at about \(1.9 \mathrm{eV}\) is still present. The averaged graph is in good agreement with the differential spectra at high field amplitudes (upper curves in Fig. 4(b)). Thus, in the averaged results, the spectra for small \(f\) + +<--- Page Split ---> + +dominate strongly since (i) the absorption changes are spectrally concentrated in the monitored region due to the small bandwidth, (ii) one is in the regime of Stark localization due to the small extent of the first Brillouin zone, and (iii) for larger \(f\) the rather weak and oscillatory results partly cancel each other. For these reasons, the contribution from the \(\overline{\Gamma Z}\) direction, corresponding to small \(f\) , is enhanced for energies far above the bandgap and dominates the entire phenomenon. + +The results of Fig. 4(a, b) suggest that, for the randomly oriented crystallites in the film, the overall response is dominated by the response originating from the band dispersion in the \(\overline{\Gamma Z}\) direction. This reasoning is substantiated by the averaged field- dependent absorption changes calculated for both a static and a THz field shown in Figs. 4(c) and (d), respectively. As expected, the \(\overline{\Gamma Z}\) direction dominates the averaged results, which include the contributions from the dispersion in all the other directions. In both cases for strong fields, the dominant feature is a rapid change from reduced to increased absorption, which takes place near the center of the interband absorption that corresponds to the dispersion in the \(\overline{\Gamma Z}\) direction. Due to the spectral broadening induced by the THz modulation, this transition appears at slightly lower photon energies for the THz field, Fig. 4(c), than for the static field, Fig. 4(d). Thus, Fig. 4(c, d) is consistent with the notion that the step- like sign change in the center of the band for sufficiently strong field amplitudes is a signature of Stark localization for the polycrystalline perovskite sample. + +In conclusion, we have demonstrated the onset of transient Wannier- Stark localization in the polycrystalline form of methylammonium lead iodide perovskite at surprisingly low electric field amplitudes. Despite the static and dynamic disorder of the methylammonium molecular cations at room temperature and the arbitrary distribution of crystal domains with respect to the THz field direction, the dominant contribution from the \(\overline{\Gamma Z}\) direction of the band structure allows for the clear + +<--- Page Split ---> + +signature of Wannier- Stark localization. The ultrafast field- induced transition from 3D to effectively 2D electronic states leads to substantial spectral transfer from the central spatially- direct \((n = 0)\) transition (around the optical band gap of 3D) to 0.3 eV red- (blue- )shifted spatially adjacent transitions \(n = +1\) \((n = - 1)\) , with up to \(38\%\) maximum modulation depth. Instead of semiconductor superlattices, which need expensive high- vacuum manufacturing processes, the solution- processed hybrid perovskites could meet the growing need for cost- effective \(^{31}\) , efficient, fast, and sensitive characteristics as optical modulators \(^{32}\) . Together with the renowned photophysical properties of MAPbI \(_3\) , such as the long carrier diffusion length \(^{33,34}\) , low mid- gap trap density \(^{29,34}\) , and large absorption coefficient \(^{35}\) , this finding of high modulation depth, fast response, and low onset field for Wannier- Stark localization highlights the potential of this material in photonic applications \(^{36,37}\) . + +## Materials and Methods + +## Experimental details + +The phase- stable multi- cycle mid- IR pulses with a peak field strength of \(\sim 10\mathrm{MV / cm}\) are generated using difference frequency mixing (DFG) in GaSe \(^{9,10}\) . The regeneratively amplified pulses with 780 nm and 130 fs are used to pump two parallel optical parametric amplifier stages to provide tunable near- infrared pulses with minimum relative phase fluctuation. The two near- IR pulses are then combined and sent to the GaSe nonlinear crystal for the DFG. The thus generated mid- IR pulses are focused onto the sample with off- axis parabolic mirrors of focal length \(\tilde{f} = 15\mathrm{mm}\) and effective \(\mathrm{NA} = 0.2\) . The electric field transient is characterized by ultrabroadband electro- optic sampling \(^{38}\) at a 30- \(\mu \mathrm{m}\) - thick GaSe crystal using balanced detection of an 8- fs probe pulse centered at a wavelength of \(1.2\mu \mathrm{m}\) as the gating pulse. The quantitative value of the field + +<--- Page Split ---> + +amplitude is obtained by measuring the mid- IR average power and focal spot size. Then, the value at the interior of the MAPbI₃ perovskite sample are estimated using the Fresnel transmission coefficient for the mid- IR field at the air- MAPbI₃ interface. + +For detection of the field- induced differential optical transmittance in broad spectral range, we generate near- IR and visible pulses with the duration of 7 fs by non- collinear optical parametric amplification (Fig. S1)³⁹. The probe pulses are combined with the mid- IR pump pulses at a germanium beam splitter so that both pulses co- propagate through the sample. The probe pulses are then dispersed onto a spectrometer coupled to a CCD camera for the spectral resolution. The relative timing between the pump and probe pulses was controlled using an optical delay stage. To detect the differential optical transmission spectra, we modulate the mid- IR pump pulses by an optical chopper operating at 125 Hz, which is synchronized with the 1 kHz laser repetition rate and the readout of the CCD camera. Two subsequent spectra taken from the CCD camera are subtracted by each other and normalized by one spectrum without the pump. The sample compartment in the experimental setup was purged with dry nitrogen in order to avoid degradation. The complete experimental setup and the laser system have been fully illustrated in Ref [⁸]. + +## Theoretical approach + +For calculating the linear optical interband absorption spectra, we numerically solve the semiconductor Bloch equations (SBE), including the intraband acceleration induced by the strong THz field²⁶-²⁸. We use here a one- dimensional trajectory in k- space, denoted as the \(\overline{\Gamma x}\) direction where x is an arbitrary point in the 1. Brillouin zone, which is parallel to the polarization direction + +<--- Page Split ---> + +of the incident THz field and goes through the \(\Gamma\) - point of the Brillouin zone. In the linear optical regime, the SBE reduce to the equations of motion for the microscopic polarizations \(p_{k}^{c\nu}\) and read + +\[\frac{\partial}{\partial t} p_{k}^{c\nu} = \frac{i}{\hbar} E_{c\nu}(k)p_{k}^{c\nu} + \frac{e}{\hbar} E_{\mathrm{THz}}(t)\nabla_{k}p_{k}^{c\nu} - \frac{i}{\hbar} E_{\mathrm{opt}}(t)\mu_{k}^{c\nu} - \frac{p_{k}^{c\nu}}{T_{2}}\] + +Dephasing processes are treated phenomenologically by adding the dephasing time \(T_{2}\) . + +For all calculations presented in this paper, we include the intraband dynamics induced by the static or pulsed THz fields to infinite order, whereas the weak optical probe of the interband absorption is considered only to the first order. In this linear- optical regime, we thus neglect carrier generation by multi- photon processes and impact ionization, which does not seem to play a dominant role in the measured transient spectra. Interband tunneling by the THz field could lead to bleaching at later delay times and the slightly asymmetric spectral evolution with respect to \(\tau = 0\) (Fig. 2(A)) (corresponding to the trailing edge of the THz transient in the Supplementary Material of ref [8]). However, significant carrier multiplication does not occur within this experimental window, as shown in Fig. S3. + +For the interband dipole matrix element, we use the usual decay with increasing transition frequency40 + +\[\mu_{k} = \mu_{0}\frac{1.62\mathrm{eV}}{E_{\mathrm{cv}}(k)}\] + +where the choice of \(\mu_{0}\) is not relevant here, as it contributes only as a prefactor to the absorption spectra. + +For the THz pulses, we use a Gaussian envelope + +<--- Page Split ---> + +\[E_{\mathrm{THz}}(t) = E_{0}e^{-4\ln (2)\left(\frac{t - \tau}{\bar{T}}\right)^{2}}\cos \left(\omega_{\mathrm{THz}}(t - \tau)\right)\] + +with the electric- field amplitude \(E_{0}\) , the pulse duration \(\bar{T}\) (FWHM of the intensity), the time delay \(\tau\) , and the THz frequency \(\omega_{\mathrm{THz}}\) . The optical probe pulse is modeled as a weak ultrashort delta- like pulse. + +The total optical polarization is obtained by summing over the microscopic polarizations \(p_{k}^{\mathrm{cv}}\) + +\[P(t) = \sum_{k}\mu_{k}^{\mathrm{c}}p_{k}^{\mathrm{cv}}(t) + c.c.\] + +By Fourier transforming the macroscopic polarization \(P(t)\) the linear absorption can be obtained by + +\[\alpha_{1\mathrm{D},\overline{\mathrm{1x}}}(\omega)\propto \omega \mathrm{Im}\big(P(\omega)\big)\] + +To be able to compare the numerical results for the one- dimensional k- space trajectory to the measured \(\Delta T / T\) spectra, the negative change of the optical absorption in three dimensions - \(\Delta \alpha_{3\mathrm{D}}\) is calculated assuming a parabolic electronic dispersion perpendicular to the considered one- dimensional direction. Due to the constant two- dimensional density of states for a parabolic dispersion, the absorption of the corresponding three- dimensional system is easily obtained as Ref [8] + +\[\alpha_{\overline{\mathrm{1x}}}(\omega)\propto \int_{0}^{\omega}\alpha_{1\mathrm{D},\overline{\mathrm{1x}}}(\omega^{\prime})d\omega^{\prime}.\] + +<--- Page Split ---> + +## Band structure model and averaging over crystallographic directions + +To incorporate both the bandwidth and the effective mass \(m^{*}\) at the band gap as obtained from abinitio calculation in Ref [12] into our model, we use an interband energy difference of + +\[E_{c v}(k) = E_{0} + \frac{\Delta}{2} (1 - \cos (g(k a^{*})k a^{*}))\] + +Here, \(\pi /a^{*}\) is the distance from the \(\Gamma\) - point to the border of the first Brillouin zone + +and the interpolation function + +\[g(k a^{*}) = f + (1 - f)\frac{k a^{*}}{\pi}\] + +guarantees that \(E_{c v}(0) = E_{0}\) and \(E_{c v}(\pm \pi /a^{*}) = E_{0} + \Delta\) , meaning the bandgap energy \(E_{0}\) and the bandwidth \(\Delta\) are preserved. + +The parameter \(f\) is adjusted to obtain the effective mass which corresponds to the second derivative of the band structure at the \(\Gamma\) point: + +\[m^{*} = \hbar^{2}\left[\frac{d^{2}E_{c v}(k)}{d k^{2}}\right]\left|0\right|^{1}\] + +as given in Ref [12]. + +As mentioned before, the polycrystallinity of the system is included by averaging over several differential transmittance spectra. + +The transition from the \(\overline{\Gamma Z}\) to the \(\overline{\Gamma A}\) direction is carried out by varying the bandwidth \(\Delta\) from \(\Delta_{\overline{\Gamma Z}} = 0.75 \mathrm{eV}\) to \(\Delta_{\overline{\Gamma A}} = 1.55 \mathrm{eV}\) , the extent of the first Brillouin zone \(\frac{\pi}{a^{*}}\) from \(\frac{\pi}{a_{\overline{\Gamma Z}}^{*}} = \frac{\pi}{c} = \frac{\pi}{1.27} \mathrm{nm}^{- 1}\) + +<--- Page Split ---> + +to \(\frac{\pi}{a_{\Gamma A}^{*}} = \frac{\pi}{a c}\sqrt{2c^{2} + a^{2}} = \frac{\pi}{0.56}\mathrm{nm}^{- 1}\) and the effective mass \(\mathrm{m}^{*}\) from \(\mathrm{m}_{\Gamma Z}^{*} = 0.17\mathrm{m}_{0}\) to \(\mathrm{m}_{\Gamma A}^{*}\) \(= 0.09\mathrm{m}_{0}\) via a parameter \(f\) which varies from 0 (i.e. the \(\overline{\Gamma Z}\) - direction) to 1 (i.e. the \(\overline{\Gamma A}\) - direction) 12. The interpolation is performed as: + +\[\Delta (\mathrm{f}) = \Delta_{\overline{\Gamma Z}} + \mathrm{f}\big(\Delta_{\overline{\Gamma A}} - \Delta_{\overline{\Gamma Z}}\big)\] + +\[\frac{\pi}{a^{*}(f)} = \frac{\pi}{a_{\Gamma Z}^{*}} +f\left(\frac{\pi}{a_{\Gamma A}^{*}} -\frac{\pi}{a_{\Gamma Z}^{*}}\right)\] + +\[m^{*}(f) = m_{\Gamma Z}^{*} + f\big(m_{\Gamma A}^{*} - m_{\Gamma Z}^{*}\big)\] + +where \(f = 0\) describes the \(\overline{\Gamma Z}\) - direction and \(f = 1\) the \(\overline{\Gamma A}\) - direction, respectively. + +The above described averaging of several spectra for the discretized parameter \(f\) is performed via evaluating + +\[\alpha_{\mathrm{avg}}(\omega) = \frac{1}{n}\sum_{f_{i}}\alpha_{f_{i}}(\omega),i\in [1,n]\] + +With the respective absorption \(\alpha_{f = 0} = \alpha_{1D,\overline{\Gamma Z}}\) and \(\alpha_{f = 1} = \alpha_{1D,\overline{\Gamma A}}\) where for convergence \(n\) is typically chosen as 51. + +## Supporting Information + +Fig. S1. Normalized spectra of near- IR (red) and visible (blue) probe pulses. + +Fig. S2. Differential transmission changes measured at probe photon energies of 1.7 eV (red line) and 2.0 eV (blue) together with the \(\mathrm{E}^{2}(\mathrm{t})\) of THz pulse profile. + +<--- Page Split ---> + +Fig. S3. Contributions from free carriers generated via interband tunneling. + +Fig. S4. Simulations with averaging from the \(\overline{\Gamma Z}\) to the \(\overline{\Gamma A}\) direction for a THz pulse centered at \(t = 0\) and various field strengths. + +Fig. S5. Simulated absorption change, \(- \Delta \alpha_{\mathrm{avg}}\) , averaged for a pure cosine model band structure (without the function g, see methods, which was introduced to fit the effective mass) from \(\overline{\Gamma Z}\) to \(\overline{\Gamma A}\) direction for a THz pulse centered at \(t = 0\) and various field strengths. + +Figure S6. Simulated change of the optical interband absorption \(- \Delta \alpha_{\overline{\Gamma A}}\) from a cosine band modeling along \(\overline{\Gamma A}\) direction for static fields and a pulsed THz field. + +## AUTHOR INFORMATION + +## Corresponding Author + +\*Corresponding author. torsten.meier@upb.de; kim@mpip-mainz.mpg.de + +## Author Contributions + +The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. \(\ddagger\) These authors contributed equally. + +## Notes + +The authors declare no competing financial interest. + +<--- Page Split ---> + +## ACKNOWLEDGMENT + +The authors thank Keno Krewer and Johannes Hunger for helpful discussions. T. M. and D. B. acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Collaborative Research Center TRR 142 (project number 231447078, project A02). M. B. and H. K. thank the DFG for financial support through the Collaborative Research Center TRR 288 (project number 422213477, project B07), the European Union's Horizon 2020 research and innovation program under grant agreement No.658467, and the Max Planck Society for financial support. A. L. and J. B. acknowledge financial support from the European Research Council through ERC Advanced Grant 290876 (UltraPhase) and the Carl Zeiss Foundation through the fellowship program. + +## REFERENCES + +1. Wannier, G. H. Wave Functions and Effective Hamiltonian for Bloch Electrons in an Electric Field. Phys. Rev. 117, 432–439 (1960). +2. Bloch, F. Über die Quantenmechanik der Elektronen in Kristallgittern. Zeitschrift für Phys. 52, 555–600 (1929). +3. Zener, C. A theory of the electrical breakdown of solid dielectrics. Proc. R. Soc. London. Ser. A 145, 523–529 (1934). +4. Mendez, E. E., Agulló-Rueda, F. & Hong, J. M. Stark Localization in GaAs-GaAlAs Superlattices under an Electric Field. Phys. Rev. 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Dynamic disorder, phonon lifetimes, and the assignment of modes to the vibrational spectra of methylammonium lead halide perovskites. Phys. Chem. Chem. Phys. 18, 27051–27066 (2016). + +17. Kim, H. et al. Direct observation of mode-specific phonon-band gap coupling in methylammonium lead halide perovskites. Nat. Commun. 8, 687 (2017). + +18. Karakus, M. et al. Phonon-Electron Scattering Limits Free Charge Mobility in Methylammonium Lead Iodide Perovskites. J. Phys. Chem. Lett. 6, 4991–4996 (2015). + +19. D'Angelo, F., Mics, Z., Bonn, M. & Turchinovich, D. Ultra-broadband THz time-domain spectroscopy of common polymers using THz air photonics. Opt. Express 22, 12475–12485 (2014). + +20. Yan, W. X., Zhao, X. G. & Wang, H. Coherent effects induced by dc-ac fields in semiconductor superlattices: The signature of fractional Wannier-Stark ladders. J. Phys. Condens. Matter 10, L11 (1998). + +21. Frohna, K. et al. Inversion symmetry and bulk Rashba effect in methylammonium lead iodide perovskite single crystals. Nat. Commun. 9, (2018). + +22. Blancon, J. C. et al. Unusual thickness dependence of exciton characteristics in 2D perovskite quantum wells. arXiv:1710.07653v2 + +<--- Page Split ---> + +23. Ishihara, T. & Goto, T. Exciton Features in 0-, 2-, and 3-Dimensional Networks of [Pbl6]4-Octahedra. Journal of the Physical Society of Japan 63, 3870-3879 (1994). + +24. Umebayashi, T. et al. Electronic structures of lead iodide based low-dimensional crystals. Phys. Rev. B 67, 2-7 (2003). + +25. Blancon, J. C. et al. Unusual thickness dependence of exciton characteristics in 2D perovskite quantum wells. arXiv 1-21 (2017). + +26. Schubert, O. et al. Sub-cycle control of terahertz high-harmonic generation by dynamical Bloch oscillations. Nat. Photonics 8, 119-123 (2014). + +27. Meier, T., Von Plessen, G., Thomas, P. & Koch, S. W. Coherent electric-field effects in semiconductors. Phys. Rev. Lett. 73, 902-905 (1994). + +28. Golde, D., Meier, T. & Koch, S. W. High harmonics generated in semiconductor nanostructures by the coupled dynamics of optical inter- and intraband excitations. Phys. Rev. B 77, 1-6 (2008). + +29. Brandt, R. E., Stevanović, V., Ginley, D. S. & Buonassisi, T. Identifying defect-tolerant semiconductors with high minority-carrier lifetimes: Beyond hybrid lead halide perovskites. MRS Commun. 5, 265-275 (2015). + +30. Guo, L., Xu, G., Tang, G., Fang, D. & Hong, J. Structural stability and optoelectronic properties of tetragonal {MAPbI}3 under strain. Nanotechnology 31, 225204 (2020). + +31. Ball, J. M., Lee, M. M., Hey, A. & Snaith, H. J. Low-temperature processed meso-superstructured to thin-film perovskite solar cells. Energy Environ. Sci. 6, 1739 (2013). + +<--- Page Split ---> + +32. Grinblat, G. et al. Ultrafast All-Optical Modulation in 2D Hybrid Perovskites. ACS Nano 13, 9504–9510 (2019). + +33. Stranks, S. D. et al. Electron-hole diffusion lengths exceeding 1 micrometer in an organometal trihalide perovskite absorber. Science 342, 341–4 (2013). + +34. Shi, D. et al. Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals. Science 347, 519–522 (2015). + +35. Lee, M. M., Teuscher, J., Miyasaka, T., Murakami, T. N. & Snaith, H. J. Efficient hybrid solar cells based on meso-superstructured organometal halide perovskites. Science 338, 643–7 (2012). + +36. Bar-Joseph, I. et al. Room-temperature electroabsorption and switching in a GaAs/AlGaAs superlattice. Appl. Phys. Lett. 55, 340–342 (1989). + +37. Bigan, E. et al. Optimization of optical waveguide modulators based on Wannier-Stark localization: an experimental study. IEEE J. Quantum Electron. 28, 214–223 (1992). + +38. Riek, C., Seletskiy, D. V & Leitenstorfer, A. Femtosecond measurements of electric fields: from classical amplitudes to quantum fluctuations. Eur. J. Phys. 38, 24003 (2017). + +39. Brida, D. et al. Few-optical-cycle pulses tunable from the visible to the mid-infrared by optical parametric amplifiers. J. Opt. 12, 13001 (2009). + +40. Haug, H. & Koch, S. W. Quantum Theory of the Optical and Electronic Properties of Semiconductors. (WORLD SCIENTIFIC, 2009). + +<--- Page Split ---> + +29 + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +Experimental scheme and properties of MAPbI3 perovskite (a) THz pulse geometry with a tetragonal unit cell (black rectangular cuboid) of MAPbI3. (dark grey: Pb, purple: I, brown: C, light blue: N, light pink: H) The THz biasing along the c axis of a crystallite is depicted. (b) Simplified electronic band structure of MAPbI3 in the tetragonal phase along the directions \(\Gamma (0,0,0) \cong \mathrm{Z}(0,0,0.5)\) and \(\Gamma (0,0,0) \cong \mathrm{A}(0.5,0.5,0.5)\) . The bandwidths and the lattice parameters are used from [Ref 12]. (c) Optical absorption spectrum of MAPbI3 in the spectral range of the probe pulses. + +![](images/Figure_3.jpg) + + +<--- Page Split ---> + +## Figure 2 + +Experimental observation of the transient Wannier Stark localization and the visualized diagram (a) Experimental differential transmission spectra on a polycrystalline film of MAPbI3 perovskite at room temperature, as a function of delay time of probe pulses after THz pump pulses. The THz pulses have a peak field strength of 6.1 MV/cm and a center frequency of 20 THz; the probe pulses have photon energy of \(1.4 \sim 2.4 \text{eV}\) . (b) Temporal profile of the applied THz bias transient. (c) Schematic picture of Wannier Stark localization. In the presence of strong external fields along the c axis, electronic states (orange: conduction band, blue: valence band) are localized to a few layers of ab plane, and energetically separated by \(\Delta \text{EWSL} = \text{eETHzc}\) between adjacent lattice sites. Black arrows depict the interband transitions within the same site (n = 0) and between different sites (n = ±1). (d) The absorbance with and without the external transient biasing. The Wannier- Stark localization effectively reduces the 3D electronic structure into 2D layered structure along the ab plane, as depicted in blue together with the simplified 3D structure. + +![](images/Figure_4.jpg) + +
Figure 3
+ +Numerical simulation of differential absorption spectra. Please see .pdf file for full caption + +<--- Page Split ---> +![PLACEHOLDER_32_0] + +
Figure 4
+ +![PLACEHOLDER_32_1] + + +. Experiments on polycrystalline system and simulations with averaging of cosine band model from \(\mathbb{W}\mathbb{W}\) to \(\mathbb{W}\mathbb{W}\) direction. Please see .pdf file for full caption + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SlfinalNatComm.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65_det.mmd b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..41fa738596e2a695568086d9071b8794b4a8d23b --- /dev/null +++ b/preprint/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65/preprint__00eb7cf6559fe9e55f1f5595a3abcbbf42befb8fdab5e70d9108735caa725b65_det.mmd @@ -0,0 +1,555 @@ +<|ref|>title<|/ref|><|det|>[[45, 108, 940, 177]]<|/det|> +# Low-field Onset of Wannier-Stark Localization in a Polycrystalline Hybrid Organic Inorganic Perovskite + +<|ref|>text<|/ref|><|det|>[[44, 195, 352, 281]]<|/det|> +Daniel Berghoff Paderborn University Johannes Bühler University of Konstanz + +<|ref|>text<|/ref|><|det|>[[44, 289, 426, 330]]<|/det|> +Mischa Bonn Max Planck Institute for Polymer Research + +<|ref|>text<|/ref|><|det|>[[44, 336, 253, 376]]<|/det|> +Alfred Leitenstorfer University of Konstanz + +<|ref|>text<|/ref|><|det|>[[44, 383, 618, 424]]<|/det|> +Torsten Meier University of Paderborn https://orcid.org/0000- 0001- 8864- 2072 + +<|ref|>text<|/ref|><|det|>[[44, 428, 426, 469]]<|/det|> +Heejae Kim ( kim@mpip-mainz.mpg.de) Max Planck Institute for Polymer Research + +<|ref|>sub_title<|/ref|><|det|>[[44, 510, 102, 528]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 548, 951, 590]]<|/det|> +Keywords: Wannier- Stark localization, Electron confinement, Ultrafast Biasing, Optical modulation, Hybrid perovskites + +<|ref|>text<|/ref|><|det|>[[44, 609, 285, 628]]<|/det|> +Posted Date: April 8th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 647, 463, 666]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 386040/v1 + +<|ref|>text<|/ref|><|det|>[[44, 684, 911, 726]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 763, 914, 805]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 29th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 26021- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[117, 136, 876, 234]]<|/det|> +# Low-field Onset of Wannier-Stark Localization in a Polycrystalline Hybrid Organic Inorganic Perovskite + +<|ref|>text<|/ref|><|det|>[[114, 283, 844, 340]]<|/det|> +Daniel Berghoff \(^{1}\) , Johannes Bühler \(^{2}\) , Mischa Bonn \(^{3}\) , Alfred Leitenstorfer \(^{2}\) , Torsten Meier \(^{*1}\) , Heejae Kim \(^{*3}\) + +<|ref|>text<|/ref|><|det|>[[114, 369, 732, 390]]<|/det|> +\(^{1}\) Department of Physics, Paderborn University, D- 33098 Paderborn, Germany + +<|ref|>text<|/ref|><|det|>[[114, 418, 845, 475]]<|/det|> +\(^{2}\) Department of Physics and Center for Applied Photonics, University of Konstanz, D- 78457 Konstanz, Germany + +<|ref|>text<|/ref|><|det|>[[114, 504, 864, 560]]<|/det|> +\(^{3}\) Department of Molecular Spectroscopy, Max Planck Institute for Polymer Research, D- 55128 Mainz, Germany + +<|ref|>sub_title<|/ref|><|det|>[[114, 640, 230, 658]]<|/det|> +## KEYWORDS + +<|ref|>text<|/ref|><|det|>[[114, 674, 880, 730]]<|/det|> +Wannier- Stark localization, Electron confinement, Ultrafast Biasing, Optical modulation, Hybrid perovskites + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 220, 108]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[112, 145, 886, 586]]<|/det|> +Control over light propagation in a material by applying external fields is at the heart of photonic applications. Here, we demonstrate ultrafast modulation of the optical properties in the room temperature polycrystalline MAPbI₃ perovskite using phase- stable terahertz pulses, centered at 20 THz. The biasing field from the THz pulse creates extreme localization of electronic states in the ab plane – Wannier- Stark localization. This quasi- instantaneous reduction of dimensionality (from 3D to 2D) causes a marked change in the absorption shape, enabling the modulation depth to be tens of percent at moderate field strengths (3 MV/cm). The notably low- field onset results from a narrow electronic bandwidth, a large relevant lattice constant, and the coincidence of the two along the same direction in this tetragonal perovskite. We show that the transient optical response is in fact dominated by the least dispersive direction of the electronic band structure, facilitating a substantial modulation despite the arbitrary arrangement of the individual crystallites. The demonstration of THz- field- induced optical modulation in a solution- processed, disordered, and polycrystalline material is of substantial potential significance for novel photonic applications. + +<|ref|>sub_title<|/ref|><|det|>[[115, 615, 224, 633]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[113, 662, 886, 895]]<|/det|> +The intriguing properties of electrons in periodic potentials in the presence of strong external electric fields are highly relevant for photonic applications, including optical modulators, optical switches, and optical signal processing. Drastic changes in optical properties can be achieved via localization of electronic states using externally applied fields. In the presence of strong external electric fields \(E\) , the continuum of electronic energy bands splits into a series of discrete levels in the direction of the field1, and the corresponding wave functions are confined on a length scale given by \(\Delta /(eE)\) , where \(\Delta\) is the energetic width of the electronic band in the absence of biasing. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 249]]<|/det|> +These localized states, the Wannier- Stark states \(^{2,3}\) , are equally spaced both in energy by an amount \(eED\) , and in space by the lattice period \(D\) . Since a spatial separation of \(nD\) lattice periods results in an energy shift of \(neED\) with respect to the central spatially- direct \((n = 0)\) transition, this Wannier- Stark localization leads to strong spectral modulation of the interband absorption continuum below and above the optical band gap. + +<|ref|>text<|/ref|><|det|>[[112, 277, 885, 543]]<|/det|> +The quantum confinement induced by external fields is an extreme state of matter and has never been achieved under static biasing in natural solids but only in artificial superlattices \(^{4 - 8}\) . So far, only one natural solid, a single crystal GaAs \(^{8}\) has allowed for achieving the Wannier- Stark localization transiently by virtue of the recent availability of extremely intense and phase- stable pulses of multi- terahertz radiation \(^{9,10}\) . The ultrafast biasing fields could reach amplitudes up to several tens of MV/cm \(^{9,10}\) , i.e., field strengths comparable to the interatomic fields. For GaAs, an optimally oriented single crystal was required to observe Wannier- Stark localization with the required field strengths exceeding 10 MV/cm \(^{8}\) . + +<|ref|>text<|/ref|><|det|>[[112, 571, 885, 907]]<|/det|> +Here, we demonstrate the transient Wannier- Stark localization at a substantially lower field strength in a disordered, solution- processed, polycrystalline film of methylammonium lead iodide perovskite (MAPbI \(_{3}\) , Fig. 1(a)). Already at relatively modest field strengths, the thin film's optical transmission is modified by tens of percent. To resolve optical transitions to individual Wannier- Stark states in, e.g., absorption spectra, their energetic spacing needs to be larger than the (total) linewidth \(\Gamma\) , i.e., \(eED > \Gamma\) \(^{4,5,11}\) Due to the small lattice constant of bulk crystals and the large linewidth which results from the scattering of electrons with lattice vibrations and other electrons, the requirement \(eED > \Gamma\) can typically not be fulfilled under stationary external fields below the strength where the dielectric breakdown occurs \(^{6,7}\) . At room temperature, however, this material exhibits a tetragonal structure with lattice parameters of \(a = 8.8 \mathring{\mathrm{A}}\) and \(c = 12.5 \mathring{\mathrm{A}}\) by the expansion + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 883, 144]]<|/det|> +of the cubic perovskite unit cell \(^{12,13}\) . The periodicities are nearly twice as large as the lattice parameter \(a = 5.6 \text{Å}\) of cubic GaAs \(^{8}\) . + +<|ref|>text<|/ref|><|det|>[[112, 173, 886, 544]]<|/det|> +We will show that the large relevant lattice constant (Fig. 1(a)), the small width of electronic energy bands (Fig. 1(b)), and the coincidence of these two along the same high- symmetry direction lead to Stark localization in this organic perovskite at field amplitudes as low as \(3 \text{MV/cm}\) , i.e., at a fraction of the field strength required to enter this regime in optimally oriented, single- crystalline GaAs. Moreover, the measured differential spectra containing the overall effects from arbitrarily oriented microcrystals are qualitatively well- described by a two- band model with a cosine band structure. By considering different orientations of the microcrystals in our simulations, we demonstrate that the contribution from the direction with the largest periodicity, i.e., the \(\overline{\Gamma Z}\) direction \(c = 12.5 \text{Å}\) , strongly dominates the transient changes of the optical response. These findings, together with its renowned characteristics, make MAPbI \(_3\) a strong candidate for cost- effective, efficient, fast, and sensitive optical modulator materials. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[201, 92, 820, 388]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 414, 884, 644]]<|/det|> +
Figure 1. Experimental scheme and properties of MAPbI3 perovskite (a) THz pulse geometry with a tetragonal unit cell (black rectangular cuboid) of MAPbI3. (dark grey: Pb, purple: I, brown: C, light blue: N, light pink: H) The THz biasing along the \(c\) axis of a crystallite is depicted. (b) Simplified electronic band structure of MAPbI3 in the tetragonal phase along the directions \(\Gamma (0,0,0)\rightarrow \mathrm{Z}(0,0,0.5)\) and \(\Gamma (0,0,0)\rightarrow \mathrm{A}(0.5,0.5,0.5)\) . The bandwidths and the lattice parameters are used from [Ref \(^{12}\) ]. (c) Optical absorption spectrum of MAPbI3 in the spectral range of the probe pulses.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 710, 308, 728]]<|/det|> +## Results and Discussion + +<|ref|>sub_title<|/ref|><|det|>[[115, 760, 578, 780]]<|/det|> +## Experimental observation of Wannier-Stark Localization + +<|ref|>text<|/ref|><|det|>[[113, 809, 884, 900]]<|/det|> +For applying the strong transient bias, non- resonant in energy with any of the optical phonons and electronic transitions, we employ phase- stable multi- cycle optical pulses with a center frequency of 20 THz. The pump pulse is generated using a difference- frequency generation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 85, 886, 498]]<|/det|> +scheme in GaSe \(^{9,10}\) . For comparison, the MAPbI \(_3\) perovskite has a direct bandgap of \(E_{gap} = 1.62 \mathrm{eV}\) (390 THz, Fig. 1(c)) at room temperature. The phonon modes of Pb- I inorganic sublattice are below 10 THz and methylammonium organic molecular vibrations above 26 THz \(^{14}\) . Due to the presence of the organic cation with a low rotational barrier \(^{15}\) , the crystal shows some degree of disorder at elevated temperature and a less pronounced periodicity compared to all- inorganic perovskites \(^{15,16}\) . The sample is a polycrystalline film with a thickness of \(\sim 300 \mathrm{nm}\) spin- coated \(^{17,18}\) on a cyclic olefin/ethylene copolymer substrate (TOPAS \(^{8}\) ) \(^{19}\) . The differential transmission induced by the external electric field transient is probed by near- IR and visible probe pulses, with spectra covering broad interband electronic transition energies between \(1.4 \mathrm{eV}\) and \(2.4 \mathrm{eV}\) (see Fig. S1). The duration of these probe laser pulses is 7 fs, which is significantly shorter than the half- cycle period of the THz pump transients of 25 fs. Details of the experimental settings are described in the Method section and Ref \(^{8}\) . + +<|ref|>text<|/ref|><|det|>[[112, 523, 886, 860]]<|/det|> +Fig. 2(a) shows the differential transmission \(\Delta T / T\) upon applying the THz biasing as a function of delay time between the pump and probe pulses. The peak field strength of the THz pump pulses is \(6.1 \mathrm{MV / cm}\) . As expected for the non- resonant THz pulse, the optical response of the material is instantaneous and peaks when the THz field strength is maximal. The modulation occurs at twice the frequency of the THz pulse (Fig. 2(b)), since the measured differential transmission is at least a third- order nonlinear process \(^{20}\) . In such a centrosymmetric crystal as the room- temperature tetragonal phase of perovskite MAPbI \(_3\) \(^{21}\) , no contribution from the electro- optic effect is expected which is linear in the electric bias field. The clear temporal modulation of differential transmission appears at high fields, \(- 100 < \tau < 100 \mathrm{fs}\) , as the strong \(E\) field shortens the interband dephasing time in the vicinity of the bandgap to be comparable to the half- cycle period of 25 fs of the THz + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 144]]<|/det|> +transient. Thus, the precise arrival time of the probe pulse exciting the interband polarization was resolved within the dephasing time. + +<|ref|>text<|/ref|><|det|>[[112, 173, 886, 578]]<|/det|> +More importantly, two distinct regimes can be identified in the time- resolved transient spectrum (Fig. 2(a)). For relatively weak fields, \(E < 3 \mathrm{MV}\) , for \(\tau < - 100 \mathrm{fs}\) , an induced absorption (blue, \(\Delta T / T < 0\) ) right below and an induced transmission (red, \(\Delta T / T > 0\) ) right above the bandgap of \(E_{gap} = 1.62 \mathrm{eV}\) are observed. The second regime is apparent for field strengths \(\mathrm{E} > 3 \mathrm{MV / cm}\) , occurring between delay times \(- 100 < \tau < 100 \mathrm{fs}\) (Fig. 2(b)). Here, the maximum modulation depth becomes as large as \(38 \%\) at the probe energy of \(E_{pr} = 1.7 \mathrm{eV}\) (Fig. 2(a) and Fig. S2). Also, the transient response covers a significantly extended spectral range, compared to the moderate field regime. The induced transmission (red) above the bandgap now reaches up to \(E_{pr} = 1.9 \mathrm{eV}\) , where it abruptly switches to induced absorption (blue, \(\Delta T / T < 0\) ). This negative region of \(\Delta T / T < 0\) persists at probe energies all the way up to \(E_{pr} = 2.4 \mathrm{eV}\) . This one central step from reduced to increased absorption near the center of the band \(E_{pr} = 2 \mathrm{eV}\) , is a noticeable signature of Stark localization, where the Wannier- Stark states are localized onto one unit cell. + +<|ref|>image<|/ref|><|det|>[[120, 658, 866, 820]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 838, 886, 893]]<|/det|> +
Figure 2. Experimental observation of the transient Wannier Stark localization and the visualized diagram (a) Experimental differential transmission spectra on a polycrystalline film of
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 424]]<|/det|> +MAPbI3 perovskite at room temperature, as a function of delay time of probe pulses after THz pump pulses. The THz pulses have a peak field strength of 6.1 MV/cm and a center frequency of 20 THz; the probe pulses have photon energy of \(1.4 \sim 2.4 \mathrm{eV}\) . (b) Temporal profile of the applied THz bias transient. (c) Schematic picture of Wannier Stark localization. In the presence of strong external fields along the \(c\) axis, electronic states (orange: conduction band, blue: valence band) are localized to a few layers of \(ab\) plane, and energetically separated by \(\Delta E_{WSL} = eE_{THzC}\) between adjacent lattice sites. Black arrows depict the interband transitions within the same site \((n = 0)\) and between different sites \((n = \pm 1)\) . (d) The absorbance with and without the external transient biasing. The Wannier- Stark localization effectively reduces the 3D electronic structure into 2D layered structure along the \(ab\) plane, as depicted in blue together with the simplified 3D structure. + +<|ref|>text<|/ref|><|det|>[[112, 502, 886, 907]]<|/det|> +By driving the 3- dimensional (3D) system into Wannier- Stark localization, i.e., localizing it in the field direction, we transiently create an effectively 2D electronic system (Fig. 2(c, d)). Given the unit cell doubling, this optically prepared transient 2D system perpendicular to the \(c\) axis may be directly compared to the physically isolated double- layer structure of PbI6 octahedra. In such 2D perovskites as (BA)2(MA)1-1PbI13+1 perovskites22, the inorganic layers (perpendicular to the \(c\) axis in 3D equivalence) are separated by bulky organic layers23. The bandgap of the 2D quantum well perovskites is widened due to the bandwidth narrowing (mainly due to the zero dispersion along the vertical direction) compared to 3D perovskite24. In the case of (BA)2(MA)1-1PbI13+1 perovskites, where the PbI6 octahedral network forms a double layer \((l = 2)\) , the optical band gap is \(\sim 2.1 \mathrm{eV}\) , which is comparable to the observed \(1.9 \mathrm{eV}^{25}\) . It is noteworthy that the observed Wannier- Stark step at \(E_{pr} = 1.9 \mathrm{eV}\) under THz fields is slightly lower than the expected value under static fields due to the spectral broadening induced by the THz modulation, as will be discussed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 249]]<|/det|> +below. Therefore, the abrupt shift of the absorption edge from \(E_{pr} = 1.6 \mathrm{eV}\) to \(1.9 \mathrm{eV}\) at high transient fields (Fig. 2(d)) could be attributed to the transfer of spectral weight from \(\alpha (E_{g,3D} < E_{pr} < E_{g,2D})\) to \(\alpha (E_{g,2D} < E_{pr})\) . Such a THz- induced reduction of dimensionality from a 3D to a 2D system could enable new applications in both transport and optoelectronics due to the relatively easy access to that regime in these hybrid perovskite materials. + +<|ref|>sub_title<|/ref|><|det|>[[130, 299, 460, 317]]<|/det|> +## Simulations considering one orientation + +<|ref|>text<|/ref|><|det|>[[112, 346, 886, 718]]<|/det|> +To capture the essential ingredients responsible for the experimental observations, we carry out theoretical calculations based on different models of increasing complexity. We start with considering perfect alignment of the THz field with the direction along which the joint bandwidth of the highest valence and the lowest conduction band is narrowest. For the case of the tetragonal MAPbI₃ perovskite, the narrowest joint bandwidth, \(\Delta_{\overline{\mathrm{FZ}}} = 0.75 \mathrm{eV}\) , is along the \(\overline{\Gamma Z}\) direction (Fig. 1(b))¹². We thus take into account two one- dimensional bands, i.e., one valence and one conduction band with a cosine- like (tight- binding) band structure and the bandgap of \(1.62 \mathrm{eV}\) . Thus, the energy difference for interband transitions is taken as \(E_{cv}(k) = 1.62 \mathrm{eV} + (\Delta_{\overline{\mathrm{FZ}}} / 2)(1 - \cos (g(k, a) *))\) (see Methods section for details of the function \(g(k, a *)\) ). For this model, the spectra are obtained by numerically solving the semiconductor Bloch equations ²⁶- ²⁸, as described in the Methods section. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 92, 888, 333]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 343, 886, 608]]<|/det|> +
Figure 3. Numerical simulation of differential absorption spectra (a) Negative change of the optical interband absorption \(- \Delta \alpha_{\overline{\Gamma Z}}\) for static fields from a cosine band modeling along \(\overline{\Gamma Z}\) direction. The region of electric field strengths up to \(1\mathrm{MV / cm}\) is enlarged to show Franz-Keldysh oscillations and the transition to the Wannier-Stark regime. (b) Calculated \(- \Delta \alpha_{\overline{\Gamma Z}}\) spectra for the excitation with a THz pulse with a peak field strength of \(E_{0} = 6\mathrm{MV / cm}\) , where the delay \(\tau\) between the THz and the optical pulse is varied. (c) Simulated temporal profile of the applied THz bias transient. The pulse duration \(\overline{T}\) is \(240\mathrm{fs}\) , the THz frequency is \(20\mathrm{THz}\) , and the dephasing time is \(T_{2} = 20\mathrm{fs}\) .
+ +<|ref|>text<|/ref|><|det|>[[113, 739, 886, 900]]<|/det|> +Already when considering static fields (Fig. 3(a)), the simulation results obtained by this simple model exhibits substantial qualitative similarities with the transient experimental results shown in Fig. 2(a). For all field strengths, increased absorption is present below the bandgap and reduced absorption directly above the band gap. For rather weak field strengths of up to about \(0.5\mathrm{MV / cm}\) , oscillations arising from the Franz- Keldysh effect are visible, shifting towards the band center with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 389]]<|/det|> +increasing field. For fields exceeding \(\sim 3 \mathrm{MV / cm}\) , signatures of Wannier- Stark localization become noticeable, as the field- dependent interband transition energies shift to higher and lower energies by \(neED\) with increasing \(E\) (Fig. 2(c)). Starting at around \(3 \mathrm{MV / cm}\) , the condition for Stark localization is fulfilled, i.e., \(eED > \Delta /2\) (meaning that the energy of the \((n = - 1)\) Wannier- Stark state is in the bandgap region, see Fig. 2(c, d)), and therefore, the dominant feature is the step- like change from reduced absorption to induced absorption in the center of the band at \(1.974 \mathrm{eV}\) (this value is the average transition frequency within our model). This step- like change is, in fact, also the main feature visible in the experimental results for sufficiently high fields, i.e., between about \(- 100 < \tau < 100\) fs as shown in Figs. 2 (a). + +<|ref|>text<|/ref|><|det|>[[112, 416, 886, 894]]<|/det|> +Besides, by considering pulsed THz fields, the simulated differential spectra with the same model (Fig. 3(b, c)) well describe both spectral and temporal features in the observed transient modulation of differential transmission spectra (Fig. 2(a)). Fig. 3(b) shows the negative change of the transient absorption, \(- \Delta \alpha_{\mathrm{TFZ}}\) , upon non- resonant biasing with a THz pulse with a peak field strength of \(E_{\theta} = 6 \mathrm{MV / cm}\) and a center frequency of \(20 \mathrm{THz}\) , as shown in Fig. 3(c). Besides temporal modulation of the entire transient spectra at twice the carrier frequency of the THz transient, the dominant feature at sufficiently large field strengths \((- 100 < \tau < 100 \mathrm{fs})\) is the rapid change from increased to reduced transmission in the center of the band \(E_{pr} = 2 \mathrm{eV}\) , which originates from Stark localization. The slightly lower value of the observed central step at \(E_{pr} = 1.9 \mathrm{eV}\) and the asymmetric nature of the spectral shape with respect to the central step (Fig. 2(a)) compared to this simplified model (Fig. 3 (b)) can be explained by the polycrystallinity of the system as discussed below. Given the complexity, disorder, and polycrystallinity of the investigated sample, the required field strength at which this step starts to appear is in surprisingly good agreement with the experiment which confirms that the observed response constitutes a clear + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 323]]<|/det|> +sign of Wannier- Stark localization. Our interpretations are further supported by Fig. S6, which shows how the results of Fig. 3 change if we consider that the THz field is aligned with the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction instead of the \(\overline{\Gamma}\overline{\mathrm{Z}}\) direction. Comparing those two figures clearly shows that due to the larger bandwidth in the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction the Wannier- Stark localization requires higher field amplitudes to develop and furthermore would lead to a transition from reduced to induced absorption at significantly higher energies as observed in experiment. The effects of different field directions and the averaging over them is discussed in more detail below (see Fig. 4). + +<|ref|>text<|/ref|><|det|>[[112, 350, 886, 721]]<|/det|> +As demonstrated so far, Wannier- Stark localization starts to occur at the field amplitude as low as \(3\mathrm{MV / cm}\) in the MAPBI \(_3\) perovskite, due to the relatively large periodicity, the narrow joint bandwidth, and the coincidence of the two along the same direction. The largest lattice constant of tetragonal MAPBI \(_3\) perovskite, along the \(c\) axis, \(c = 12.5\mathrm{\AA}\) , is more than twice as large as those of conventional all- inorganic semiconductors crystallizing with strong covalent bonds in the diamond, wurtzite, or zincblende forms \((3.5\sim 6.5\mathrm{\AA}\) at \(300\mathrm{K}\) ). This finding arises because (i) the cubic perovskite unit cell is expanded through rotation of ab plane by \(45^{\circ}\) and cell doubling along c axis in the tetragonal phase; and (ii) the pseudocubic lattice parameter formed by relatively large \(\mathrm{Pb^{2 + }}\) and \(\Gamma\) ions is \(6.3\mathrm{\AA}^{13}\) , which is at the larger side of the distribution of parameters for cubic lattice parameters. The pseudocubic lattice parameter is large enough to accommodate large organic molecular cations within the void of their network. + +<|ref|>text<|/ref|><|det|>[[113, 749, 885, 876]]<|/det|> +The direction of the narrowest joint bandwidth of the conduction and valence bands, \(\overline{\Gamma}\overline{\mathrm{Z}}\) , coincides with the \(c\) axis. The conduction band is composed of the overlap of \(\mathrm{Pb(6p) - I(5p)}\) atomic orbitals and the valence band is of that of \(\mathrm{Pb(6s) - I(5p)}\) orbitals \(^{29}\) . Thus, the \(\mathrm{Pb - I}\) bond length as well as the \(\mathrm{Pb - I - Pb}\) angle could determine the widths of both bands and the magnitude of the band + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 425]]<|/det|> +gap. In the tetragonal MAPbI₃ perovskite, the corner- shared PbI₆ octahedra in cubic phase are tilted about the \(c\) axis in the opposite direction between successive tilts, which reduces the Pb- I- Pb angle from 180° along the diagonal direction of the a and b axis. The smaller Pb- I- Pb bond angle indicates weaker orbital overlap between Pb and I atoms and thus smaller band dispersion along \(\overline{\Gamma}\overline{\mathrm{M}}\) than \(\overline{\Gamma}\overline{\mathrm{Z}}\) . However, the Pb- I bond lengths along the \(c\) axis is known to be longer on average³⁰ and has greater effect on the dispersion than the angle due to the \(\sigma\) bonding nature, which leads to the coincidence of the direction of the largest lattice constant and the narrowest bandwidth. We note that unlike GaAs, the body diagonal direction exhibits the strongest dispersion (\(\overline{\Gamma}\overline{\mathrm{A}}\) ). Overall, the large ionic diameter and the geometric distortion result in the unusually narrow joint bandwidth, lower than 1 eV. + +<|ref|>sub_title<|/ref|><|det|>[[115, 456, 580, 475]]<|/det|> +## Including polycrystallinity by averaging over orientations + +<|ref|>text<|/ref|><|det|>[[112, 504, 886, 876]]<|/det|> +We now account for the system's polycrystallinity by considering contributions to the differential transmittance spectra from crystallites with orientations different from those with the \(c\) axis parallel to the THz field polarization. To include arbitrary orientations of the crystallites into our simulations, we take the \(\overline{\Gamma}\overline{\mathrm{Z}}\) and the \(\overline{\Gamma}\overline{\mathrm{A}}\) directions, i.e., the two extreme directions with the narrowest/broadest bandwidth and simultaneously the smallest/largest distance in k- space (see Fig. 1(b)) and perform an average overall in between bandwidths and extensions of the first Brillouin zone (see Method section), by interpolating between the two limiting cases with a parameter \(f\) . The simulated absorption changes at a field amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) with various interpolation parameters \(f\) 's are shown in Fig. 4 (a) together with the measured differential spectra at different instantaneous field amplitudes of the THz pulse (Fig. 4 (b)). Here, \(f = 0\) denotes the response along the \(\overline{\Gamma}\overline{\mathrm{Z}}\) direction (i.e., the \(c\) - axis), and \(f = 1\) along the \(\overline{\Gamma}\overline{\mathrm{A}}\) direction. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 92, 881, 576]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 601, 884, 907]]<|/det|> +
Figure 4. Experiments on polycrystalline system and simulations with averaging of cosine band model from \(\Gamma Z\) to \(\Gamma A\) direction. (a) Illustration for the averaging process over the interpolation parameter \(f\) from the \(\overline{\Gamma Z}\) direction \((f = 0)\) to \(\overline{\Gamma A}\) direction \((f = 1)\) . The negative absorption changes \(-\Delta \alpha_{f}\) are calculated for different one-dimensional systems using a THz pulse centered at \(t = 0\) , with an amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) , a pulse duration of \(\overline{T} = 240 \mathrm{fs}\) , and a THz center frequency of \(20 \mathrm{THz}\) . (b) Temporal slices of \(\Delta T / T\) as a function of probe photon energy (Fig. 2(a)), at a delay time corresponding to the contour with constant electric field amplitudes \(E\) (Fig. 2(b)). (c) averaged absorption change, \(-\Delta \alpha_{\mathrm{avg}}\) , for static fields of various strengths. (d) averaged absorption change, \(-\Delta \alpha_{\mathrm{avg}}\) , for a THz pulse centered at \(t = 0\) and various field strengths.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 137, 886, 655]]<|/det|> +As shown in Fig. 4(a), the absorption changes depend strongly on the interpolation parameter \(f\) , i.e., on the bandwidth and the distance to the border of the first Brillouin zone. For \(f = 0\) , which corresponds to the \(\overline{\Gamma Z}\) direction, the field amplitude of \(E_{0} = 4 \mathrm{MV / cm}\) drives the system into the region of Stark localization. Therefore, for a static field of such an amplitude, one would see a strong induced absorption in the band center at \(1.974 \mathrm{eV}\) , which corresponds to an optical transition to the Stark localized state. The transient nature of the THz pulse causes the single negative peak to be split into two peaks and the spectral region of induced absorption to be slightly broadened. With increasing \(f\) , both the bandwidth and the distance to the border of the first Brillouin zone increase. As a result, the minimum field strength for which Stark localization is realized increases significantly by approximately a factor \((c / a_{\overline{\Gamma Z}}^{*})(\Delta_{\overline{\Gamma A}} / \Delta_{\overline{\Gamma Z}})\) , equaling about 4.7. Consequently, already for \(f = 0.25\) , the absorption changes show no sign of Stark localization, with several oscillations emerging owing to the THz driving. This trend of overall weaker absorption changes with some oscillatory structure is also present for even larger \(f\) . The only feature present in all spectra shown in Fig. 4 (a) is some induced absorption below the bandgap and reduced absorption directly above the bandgap. + +<|ref|>text<|/ref|><|det|>[[113, 679, 886, 877]]<|/det|> +However, when averaging over the interpolation parameter \(f\) , i.e., over the orientations considered by our modeling, the result (black curve in Fig. 4(a)) reproduces the main features present for \(f = 0\) , with somewhat fewer oscillations. Most importantly, the change from bleaching to induced absorption in the center of the band structure for the \(\overline{\Gamma Z}\) direction at about \(1.9 \mathrm{eV}\) is still present. The averaged graph is in good agreement with the differential spectra at high field amplitudes (upper curves in Fig. 4(b)). Thus, in the averaged results, the spectra for small \(f\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 251]]<|/det|> +dominate strongly since (i) the absorption changes are spectrally concentrated in the monitored region due to the small bandwidth, (ii) one is in the regime of Stark localization due to the small extent of the first Brillouin zone, and (iii) for larger \(f\) the rather weak and oscillatory results partly cancel each other. For these reasons, the contribution from the \(\overline{\Gamma Z}\) direction, corresponding to small \(f\) , is enhanced for energies far above the bandgap and dominates the entire phenomenon. + +<|ref|>text<|/ref|><|det|>[[112, 279, 885, 687]]<|/det|> +The results of Fig. 4(a, b) suggest that, for the randomly oriented crystallites in the film, the overall response is dominated by the response originating from the band dispersion in the \(\overline{\Gamma Z}\) direction. This reasoning is substantiated by the averaged field- dependent absorption changes calculated for both a static and a THz field shown in Figs. 4(c) and (d), respectively. As expected, the \(\overline{\Gamma Z}\) direction dominates the averaged results, which include the contributions from the dispersion in all the other directions. In both cases for strong fields, the dominant feature is a rapid change from reduced to increased absorption, which takes place near the center of the interband absorption that corresponds to the dispersion in the \(\overline{\Gamma Z}\) direction. Due to the spectral broadening induced by the THz modulation, this transition appears at slightly lower photon energies for the THz field, Fig. 4(c), than for the static field, Fig. 4(d). Thus, Fig. 4(c, d) is consistent with the notion that the step- like sign change in the center of the band for sufficiently strong field amplitudes is a signature of Stark localization for the polycrystalline perovskite sample. + +<|ref|>text<|/ref|><|det|>[[113, 714, 885, 876]]<|/det|> +In conclusion, we have demonstrated the onset of transient Wannier- Stark localization in the polycrystalline form of methylammonium lead iodide perovskite at surprisingly low electric field amplitudes. Despite the static and dynamic disorder of the methylammonium molecular cations at room temperature and the arbitrary distribution of crystal domains with respect to the THz field direction, the dominant contribution from the \(\overline{\Gamma Z}\) direction of the band structure allows for the clear + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 458]]<|/det|> +signature of Wannier- Stark localization. The ultrafast field- induced transition from 3D to effectively 2D electronic states leads to substantial spectral transfer from the central spatially- direct \((n = 0)\) transition (around the optical band gap of 3D) to 0.3 eV red- (blue- )shifted spatially adjacent transitions \(n = +1\) \((n = - 1)\) , with up to \(38\%\) maximum modulation depth. Instead of semiconductor superlattices, which need expensive high- vacuum manufacturing processes, the solution- processed hybrid perovskites could meet the growing need for cost- effective \(^{31}\) , efficient, fast, and sensitive characteristics as optical modulators \(^{32}\) . Together with the renowned photophysical properties of MAPbI \(_3\) , such as the long carrier diffusion length \(^{33,34}\) , low mid- gap trap density \(^{29,34}\) , and large absorption coefficient \(^{35}\) , this finding of high modulation depth, fast response, and low onset field for Wannier- Stark localization highlights the potential of this material in photonic applications \(^{36,37}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 488, 313, 506]]<|/det|> +## Materials and Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 538, 291, 556]]<|/det|> +## Experimental details + +<|ref|>text<|/ref|><|det|>[[112, 586, 886, 890]]<|/det|> +The phase- stable multi- cycle mid- IR pulses with a peak field strength of \(\sim 10\mathrm{MV / cm}\) are generated using difference frequency mixing (DFG) in GaSe \(^{9,10}\) . The regeneratively amplified pulses with 780 nm and 130 fs are used to pump two parallel optical parametric amplifier stages to provide tunable near- infrared pulses with minimum relative phase fluctuation. The two near- IR pulses are then combined and sent to the GaSe nonlinear crystal for the DFG. The thus generated mid- IR pulses are focused onto the sample with off- axis parabolic mirrors of focal length \(\tilde{f} = 15\mathrm{mm}\) and effective \(\mathrm{NA} = 0.2\) . The electric field transient is characterized by ultrabroadband electro- optic sampling \(^{38}\) at a 30- \(\mu \mathrm{m}\) - thick GaSe crystal using balanced detection of an 8- fs probe pulse centered at a wavelength of \(1.2\mu \mathrm{m}\) as the gating pulse. The quantitative value of the field + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 178]]<|/det|> +amplitude is obtained by measuring the mid- IR average power and focal spot size. Then, the value at the interior of the MAPbI₃ perovskite sample are estimated using the Fresnel transmission coefficient for the mid- IR field at the air- MAPbI₃ interface. + +<|ref|>text<|/ref|><|det|>[[112, 207, 886, 612]]<|/det|> +For detection of the field- induced differential optical transmittance in broad spectral range, we generate near- IR and visible pulses with the duration of 7 fs by non- collinear optical parametric amplification (Fig. S1)³⁹. The probe pulses are combined with the mid- IR pump pulses at a germanium beam splitter so that both pulses co- propagate through the sample. The probe pulses are then dispersed onto a spectrometer coupled to a CCD camera for the spectral resolution. The relative timing between the pump and probe pulses was controlled using an optical delay stage. To detect the differential optical transmission spectra, we modulate the mid- IR pump pulses by an optical chopper operating at 125 Hz, which is synchronized with the 1 kHz laser repetition rate and the readout of the CCD camera. Two subsequent spectra taken from the CCD camera are subtracted by each other and normalized by one spectrum without the pump. The sample compartment in the experimental setup was purged with dry nitrogen in order to avoid degradation. The complete experimental setup and the laser system have been fully illustrated in Ref [⁸]. + +<|ref|>sub_title<|/ref|><|det|>[[115, 693, 297, 712]]<|/det|> +## Theoretical approach + +<|ref|>text<|/ref|><|det|>[[113, 741, 884, 866]]<|/det|> +For calculating the linear optical interband absorption spectra, we numerically solve the semiconductor Bloch equations (SBE), including the intraband acceleration induced by the strong THz field²⁶-²⁸. We use here a one- dimensional trajectory in k- space, denoted as the \(\overline{\Gamma x}\) direction where x is an arbitrary point in the 1. Brillouin zone, which is parallel to the polarization direction + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 145]]<|/det|> +of the incident THz field and goes through the \(\Gamma\) - point of the Brillouin zone. In the linear optical regime, the SBE reduce to the equations of motion for the microscopic polarizations \(p_{k}^{c\nu}\) and read + +<|ref|>equation<|/ref|><|det|>[[250, 170, 747, 213]]<|/det|> +\[\frac{\partial}{\partial t} p_{k}^{c\nu} = \frac{i}{\hbar} E_{c\nu}(k)p_{k}^{c\nu} + \frac{e}{\hbar} E_{\mathrm{THz}}(t)\nabla_{k}p_{k}^{c\nu} - \frac{i}{\hbar} E_{\mathrm{opt}}(t)\mu_{k}^{c\nu} - \frac{p_{k}^{c\nu}}{T_{2}}\] + +<|ref|>text<|/ref|><|det|>[[113, 244, 797, 265]]<|/det|> +Dephasing processes are treated phenomenologically by adding the dephasing time \(T_{2}\) . + +<|ref|>text<|/ref|><|det|>[[112, 293, 886, 594]]<|/det|> +For all calculations presented in this paper, we include the intraband dynamics induced by the static or pulsed THz fields to infinite order, whereas the weak optical probe of the interband absorption is considered only to the first order. In this linear- optical regime, we thus neglect carrier generation by multi- photon processes and impact ionization, which does not seem to play a dominant role in the measured transient spectra. Interband tunneling by the THz field could lead to bleaching at later delay times and the slightly asymmetric spectral evolution with respect to \(\tau = 0\) (Fig. 2(A)) (corresponding to the trailing edge of the THz transient in the Supplementary Material of ref [8]). However, significant carrier multiplication does not occur within this experimental window, as shown in Fig. S3. + +<|ref|>text<|/ref|><|det|>[[113, 622, 883, 677]]<|/det|> +For the interband dipole matrix element, we use the usual decay with increasing transition frequency40 + +<|ref|>equation<|/ref|><|det|>[[460, 707, 597, 747]]<|/det|> +\[\mu_{k} = \mu_{0}\frac{1.62\mathrm{eV}}{E_{\mathrm{cv}}(k)}\] + +<|ref|>text<|/ref|><|det|>[[113, 758, 883, 812]]<|/det|> +where the choice of \(\mu_{0}\) is not relevant here, as it contributes only as a prefactor to the absorption spectra. + +<|ref|>text<|/ref|><|det|>[[113, 842, 499, 861]]<|/det|> +For the THz pulses, we use a Gaussian envelope + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[315, 88, 682, 125]]<|/det|> +\[E_{\mathrm{THz}}(t) = E_{0}e^{-4\ln (2)\left(\frac{t - \tau}{\bar{T}}\right)^{2}}\cos \left(\omega_{\mathrm{THz}}(t - \tau)\right)\] + +<|ref|>text<|/ref|><|det|>[[113, 151, 884, 245]]<|/det|> +with the electric- field amplitude \(E_{0}\) , the pulse duration \(\bar{T}\) (FWHM of the intensity), the time delay \(\tau\) , and the THz frequency \(\omega_{\mathrm{THz}}\) . The optical probe pulse is modeled as a weak ultrashort delta- like pulse. + +<|ref|>text<|/ref|><|det|>[[113, 275, 850, 298]]<|/det|> +The total optical polarization is obtained by summing over the microscopic polarizations \(p_{k}^{\mathrm{cv}}\) + +<|ref|>equation<|/ref|><|det|>[[386, 327, 610, 370]]<|/det|> +\[P(t) = \sum_{k}\mu_{k}^{\mathrm{c}}p_{k}^{\mathrm{cv}}(t) + c.c.\] + +<|ref|>text<|/ref|><|det|>[[113, 399, 884, 456]]<|/det|> +By Fourier transforming the macroscopic polarization \(P(t)\) the linear absorption can be obtained by + +<|ref|>equation<|/ref|><|det|>[[394, 485, 603, 508]]<|/det|> +\[\alpha_{1\mathrm{D},\overline{\mathrm{1x}}}(\omega)\propto \omega \mathrm{Im}\big(P(\omega)\big)\] + +<|ref|>text<|/ref|><|det|>[[112, 536, 886, 732]]<|/det|> +To be able to compare the numerical results for the one- dimensional k- space trajectory to the measured \(\Delta T / T\) spectra, the negative change of the optical absorption in three dimensions - \(\Delta \alpha_{3\mathrm{D}}\) is calculated assuming a parabolic electronic dispersion perpendicular to the considered one- dimensional direction. Due to the constant two- dimensional density of states for a parabolic dispersion, the absorption of the corresponding three- dimensional system is easily obtained as Ref [8] + +<|ref|>equation<|/ref|><|det|>[[379, 761, 617, 787]]<|/det|> +\[\alpha_{\overline{\mathrm{1x}}}(\omega)\propto \int_{0}^{\omega}\alpha_{1\mathrm{D},\overline{\mathrm{1x}}}(\omega^{\prime})d\omega^{\prime}.\] + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 88, 696, 110]]<|/det|> +## Band structure model and averaging over crystallographic directions + +<|ref|>text<|/ref|><|det|>[[113, 137, 883, 195]]<|/det|> +To incorporate both the bandwidth and the effective mass \(m^{*}\) at the band gap as obtained from abinitio calculation in Ref [12] into our model, we use an interband energy difference of + +<|ref|>equation<|/ref|><|det|>[[334, 223, 662, 262]]<|/det|> +\[E_{c v}(k) = E_{0} + \frac{\Delta}{2} (1 - \cos (g(k a^{*})k a^{*}))\] + +<|ref|>text<|/ref|><|det|>[[113, 290, 760, 312]]<|/det|> +Here, \(\pi /a^{*}\) is the distance from the \(\Gamma\) - point to the border of the first Brillouin zone + +<|ref|>text<|/ref|><|det|>[[113, 342, 352, 361]]<|/det|> +and the interpolation function + +<|ref|>equation<|/ref|><|det|>[[388, 391, 610, 430]]<|/det|> +\[g(k a^{*}) = f + (1 - f)\frac{k a^{*}}{\pi}\] + +<|ref|>text<|/ref|><|det|>[[113, 457, 883, 513]]<|/det|> +guarantees that \(E_{c v}(0) = E_{0}\) and \(E_{c v}(\pm \pi /a^{*}) = E_{0} + \Delta\) , meaning the bandgap energy \(E_{0}\) and the bandwidth \(\Delta\) are preserved. + +<|ref|>text<|/ref|><|det|>[[113, 543, 883, 599]]<|/det|> +The parameter \(f\) is adjusted to obtain the effective mass which corresponds to the second derivative of the band structure at the \(\Gamma\) point: + +<|ref|>equation<|/ref|><|det|>[[393, 628, 603, 677]]<|/det|> +\[m^{*} = \hbar^{2}\left[\frac{d^{2}E_{c v}(k)}{d k^{2}}\right]\left|0\right|^{1}\] + +<|ref|>text<|/ref|><|det|>[[113, 685, 271, 705]]<|/det|> +as given in Ref [12]. + +<|ref|>text<|/ref|><|det|>[[113, 714, 883, 770]]<|/det|> +As mentioned before, the polycrystallinity of the system is included by averaging over several differential transmittance spectra. + +<|ref|>text<|/ref|><|det|>[[113, 799, 883, 868]]<|/det|> +The transition from the \(\overline{\Gamma Z}\) to the \(\overline{\Gamma A}\) direction is carried out by varying the bandwidth \(\Delta\) from \(\Delta_{\overline{\Gamma Z}} = 0.75 \mathrm{eV}\) to \(\Delta_{\overline{\Gamma A}} = 1.55 \mathrm{eV}\) , the extent of the first Brillouin zone \(\frac{\pi}{a^{*}}\) from \(\frac{\pi}{a_{\overline{\Gamma Z}}^{*}} = \frac{\pi}{c} = \frac{\pi}{1.27} \mathrm{nm}^{- 1}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 85, 884, 193]]<|/det|> +to \(\frac{\pi}{a_{\Gamma A}^{*}} = \frac{\pi}{a c}\sqrt{2c^{2} + a^{2}} = \frac{\pi}{0.56}\mathrm{nm}^{- 1}\) and the effective mass \(\mathrm{m}^{*}\) from \(\mathrm{m}_{\Gamma Z}^{*} = 0.17\mathrm{m}_{0}\) to \(\mathrm{m}_{\Gamma A}^{*}\) \(= 0.09\mathrm{m}_{0}\) via a parameter \(f\) which varies from 0 (i.e. the \(\overline{\Gamma Z}\) - direction) to 1 (i.e. the \(\overline{\Gamma A}\) - direction) 12. The interpolation is performed as: + +<|ref|>equation<|/ref|><|det|>[[380, 222, 616, 245]]<|/det|> +\[\Delta (\mathrm{f}) = \Delta_{\overline{\Gamma Z}} + \mathrm{f}\big(\Delta_{\overline{\Gamma A}} - \Delta_{\overline{\Gamma Z}}\big)\] + +<|ref|>equation<|/ref|><|det|>[[371, 275, 625, 320]]<|/det|> +\[\frac{\pi}{a^{*}(f)} = \frac{\pi}{a_{\Gamma Z}^{*}} +f\left(\frac{\pi}{a_{\Gamma A}^{*}} -\frac{\pi}{a_{\Gamma Z}^{*}}\right)\] + +<|ref|>equation<|/ref|><|det|>[[365, 349, 630, 375]]<|/det|> +\[m^{*}(f) = m_{\Gamma Z}^{*} + f\big(m_{\Gamma A}^{*} - m_{\Gamma Z}^{*}\big)\] + +<|ref|>text<|/ref|><|det|>[[113, 404, 732, 425]]<|/det|> +where \(f = 0\) describes the \(\overline{\Gamma Z}\) - direction and \(f = 1\) the \(\overline{\Gamma A}\) - direction, respectively. + +<|ref|>text<|/ref|><|det|>[[113, 455, 884, 511]]<|/det|> +The above described averaging of several spectra for the discretized parameter \(f\) is performed via evaluating + +<|ref|>equation<|/ref|><|det|>[[360, 540, 637, 590]]<|/det|> +\[\alpha_{\mathrm{avg}}(\omega) = \frac{1}{n}\sum_{f_{i}}\alpha_{f_{i}}(\omega),i\in [1,n]\] + +<|ref|>text<|/ref|><|det|>[[113, 599, 884, 639]]<|/det|> +With the respective absorption \(\alpha_{f = 0} = \alpha_{1D,\overline{\Gamma Z}}\) and \(\alpha_{f = 1} = \alpha_{1D,\overline{\Gamma A}}\) where for convergence \(n\) is typically chosen as 51. + +<|ref|>sub_title<|/ref|><|det|>[[114, 750, 320, 768]]<|/det|> +## Supporting Information + +<|ref|>text<|/ref|><|det|>[[113, 798, 715, 819]]<|/det|> +Fig. S1. Normalized spectra of near- IR (red) and visible (blue) probe pulses. + +<|ref|>text<|/ref|><|det|>[[113, 848, 884, 904]]<|/det|> +Fig. S2. Differential transmission changes measured at probe photon energies of 1.7 eV (red line) and 2.0 eV (blue) together with the \(\mathrm{E}^{2}(\mathrm{t})\) of THz pulse profile. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 706, 109]]<|/det|> +Fig. S3. Contributions from free carriers generated via interband tunneling. + +<|ref|>text<|/ref|><|det|>[[113, 139, 884, 195]]<|/det|> +Fig. S4. Simulations with averaging from the \(\overline{\Gamma Z}\) to the \(\overline{\Gamma A}\) direction for a THz pulse centered at \(t = 0\) and various field strengths. + +<|ref|>text<|/ref|><|det|>[[113, 224, 884, 316]]<|/det|> +Fig. S5. Simulated absorption change, \(- \Delta \alpha_{\mathrm{avg}}\) , averaged for a pure cosine model band structure (without the function g, see methods, which was introduced to fit the effective mass) from \(\overline{\Gamma Z}\) to \(\overline{\Gamma A}\) direction for a THz pulse centered at \(t = 0\) and various field strengths. + +<|ref|>text<|/ref|><|det|>[[113, 345, 884, 402]]<|/det|> +Figure S6. Simulated change of the optical interband absorption \(- \Delta \alpha_{\overline{\Gamma A}}\) from a cosine band modeling along \(\overline{\Gamma A}\) direction for static fields and a pulsed THz field. + +<|ref|>sub_title<|/ref|><|det|>[[115, 491, 342, 509]]<|/det|> +## AUTHOR INFORMATION + +<|ref|>sub_title<|/ref|><|det|>[[115, 535, 310, 553]]<|/det|> +## Corresponding Author + +<|ref|>text<|/ref|><|det|>[[115, 572, 700, 592]]<|/det|> +\*Corresponding author. torsten.meier@upb.de; kim@mpip-mainz.mpg.de + +<|ref|>sub_title<|/ref|><|det|>[[115, 622, 301, 640]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[115, 660, 883, 715]]<|/det|> +The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. \(\ddagger\) These authors contributed equally. + +<|ref|>sub_title<|/ref|><|det|>[[115, 746, 165, 763]]<|/det|> +## Notes + +<|ref|>text<|/ref|><|det|>[[115, 784, 525, 803]]<|/det|> +The authors declare no competing financial interest. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 313, 108]]<|/det|> +## ACKNOWLEDGMENT + +<|ref|>text<|/ref|><|det|>[[112, 135, 886, 436]]<|/det|> +The authors thank Keno Krewer and Johannes Hunger for helpful discussions. T. M. and D. B. acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Collaborative Research Center TRR 142 (project number 231447078, project A02). M. B. and H. K. thank the DFG for financial support through the Collaborative Research Center TRR 288 (project number 422213477, project B07), the European Union's Horizon 2020 research and innovation program under grant agreement No.658467, and the Max Planck Society for financial support. A. L. and J. B. acknowledge financial support from the European Research Council through ERC Advanced Grant 290876 (UltraPhase) and the Carl Zeiss Foundation through the fellowship program. + +<|ref|>sub_title<|/ref|><|det|>[[115, 492, 241, 510]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[111, 525, 886, 884]]<|/det|> +1. Wannier, G. H. Wave Functions and Effective Hamiltonian for Bloch Electrons in an Electric Field. Phys. Rev. 117, 432–439 (1960). +2. Bloch, F. Über die Quantenmechanik der Elektronen in Kristallgittern. Zeitschrift für Phys. 52, 555–600 (1929). +3. Zener, C. A theory of the electrical breakdown of solid dielectrics. Proc. R. Soc. London. Ser. A 145, 523–529 (1934). +4. Mendez, E. 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Identifying defect-tolerant semiconductors with high minority-carrier lifetimes: Beyond hybrid lead halide perovskites. MRS Commun. 5, 265-275 (2015). + +<|ref|>text<|/ref|><|det|>[[113, 752, 883, 807]]<|/det|> +30. Guo, L., Xu, G., Tang, G., Fang, D. & Hong, J. Structural stability and optoelectronic properties of tetragonal {MAPbI}3 under strain. Nanotechnology 31, 225204 (2020). + +<|ref|>text<|/ref|><|det|>[[113, 836, 883, 891]]<|/det|> +31. Ball, J. M., Lee, M. M., Hey, A. & Snaith, H. J. Low-temperature processed meso-superstructured to thin-film perovskite solar cells. Energy Environ. Sci. 6, 1739 (2013). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 144]]<|/det|> +32. Grinblat, G. et al. Ultrafast All-Optical Modulation in 2D Hybrid Perovskites. ACS Nano 13, 9504–9510 (2019). + +<|ref|>text<|/ref|><|det|>[[113, 174, 884, 230]]<|/det|> +33. Stranks, S. D. et al. Electron-hole diffusion lengths exceeding 1 micrometer in an organometal trihalide perovskite absorber. Science 342, 341–4 (2013). + +<|ref|>text<|/ref|><|det|>[[113, 258, 884, 313]]<|/det|> +34. Shi, D. et al. Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals. Science 347, 519–522 (2015). + +<|ref|>text<|/ref|><|det|>[[113, 342, 884, 433]]<|/det|> +35. Lee, M. M., Teuscher, J., Miyasaka, T., Murakami, T. N. & Snaith, H. J. Efficient hybrid solar cells based on meso-superstructured organometal halide perovskites. Science 338, 643–7 (2012). + +<|ref|>text<|/ref|><|det|>[[113, 463, 884, 519]]<|/det|> +36. Bar-Joseph, I. et al. Room-temperature electroabsorption and switching in a GaAs/AlGaAs superlattice. Appl. Phys. Lett. 55, 340–342 (1989). + +<|ref|>text<|/ref|><|det|>[[113, 548, 884, 604]]<|/det|> +37. Bigan, E. et al. Optimization of optical waveguide modulators based on Wannier-Stark localization: an experimental study. IEEE J. Quantum Electron. 28, 214–223 (1992). + +<|ref|>text<|/ref|><|det|>[[113, 633, 884, 688]]<|/det|> +38. Riek, C., Seletskiy, D. V & Leitenstorfer, A. Femtosecond measurements of electric fields: from classical amplitudes to quantum fluctuations. Eur. J. Phys. 38, 24003 (2017). + +<|ref|>text<|/ref|><|det|>[[113, 718, 884, 773]]<|/det|> +39. Brida, D. et al. Few-optical-cycle pulses tunable from the visible to the mid-infrared by optical parametric amplifiers. J. Opt. 12, 13001 (2009). + +<|ref|>text<|/ref|><|det|>[[113, 803, 886, 858]]<|/det|> +40. Haug, H. & Koch, S. W. Quantum Theory of the Optical and Electronic Properties of Semiconductors. (WORLD SCIENTIFIC, 2009). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[856, 968, 881, 984]]<|/det|> +29 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 143, 68]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[60, 100, 940, 536]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 560, 115, 580]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[40, 601, 953, 738]]<|/det|> +Experimental scheme and properties of MAPbI3 perovskite (a) THz pulse geometry with a tetragonal unit cell (black rectangular cuboid) of MAPbI3. (dark grey: Pb, purple: I, brown: C, light blue: N, light pink: H) The THz biasing along the c axis of a crystallite is depicted. (b) Simplified electronic band structure of MAPbI3 in the tetragonal phase along the directions \(\Gamma (0,0,0) \cong \mathrm{Z}(0,0,0.5)\) and \(\Gamma (0,0,0) \cong \mathrm{A}(0.5,0.5,0.5)\) . The bandwidths and the lattice parameters are used from [Ref 12]. (c) Optical absorption spectrum of MAPbI3 in the spectral range of the probe pulses. + +<|ref|>image<|/ref|><|det|>[[50, 745, 945, 933]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 117, 61]]<|/det|> +## Figure 2 + +<|ref|>text<|/ref|><|det|>[[39, 82, 951, 355]]<|/det|> +Experimental observation of the transient Wannier Stark localization and the visualized diagram (a) Experimental differential transmission spectra on a polycrystalline film of MAPbI3 perovskite at room temperature, as a function of delay time of probe pulses after THz pump pulses. The THz pulses have a peak field strength of 6.1 MV/cm and a center frequency of 20 THz; the probe pulses have photon energy of \(1.4 \sim 2.4 \text{eV}\) . (b) Temporal profile of the applied THz bias transient. (c) Schematic picture of Wannier Stark localization. In the presence of strong external fields along the c axis, electronic states (orange: conduction band, blue: valence band) are localized to a few layers of ab plane, and energetically separated by \(\Delta \text{EWSL} = \text{eETHzc}\) between adjacent lattice sites. Black arrows depict the interband transitions within the same site (n = 0) and between different sites (n = ±1). (d) The absorbance with and without the external transient biasing. The Wannier- Stark localization effectively reduces the 3D electronic structure into 2D layered structure along the ab plane, as depicted in blue together with the simplified 3D structure. + +<|ref|>image<|/ref|><|det|>[[50, 365, 940, 644]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 670, 117, 690]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[43, 712, 825, 733]]<|/det|> +Numerical simulation of differential absorption spectra. Please see .pdf file for full caption + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 52, 485, 614]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 641, 117, 660]]<|/det|> +
Figure 4
+ +<|ref|>image<|/ref|><|det|>[[520, 55, 940, 614]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[42, 683, 949, 725]]<|/det|> +. Experiments on polycrystalline system and simulations with averaging of cosine band model from \(\mathbb{W}\mathbb{W}\) to \(\mathbb{W}\mathbb{W}\) direction. Please see .pdf file for full caption + +<|ref|>sub_title<|/ref|><|det|>[[44, 749, 310, 776]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 800, 764, 820]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 838, 264, 857]]<|/det|> +- SlfinalNatComm.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/images_list.json b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8fad0a1ef7ec9a0ca578b819b7cbd1f6d7c9adad --- /dev/null +++ b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/images_list.json @@ -0,0 +1,55 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "C", + "footnote": [], + "bbox": [], + "page_idx": 55 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "Adipogenic", + "footnote": [], + "bbox": [ + [ + 32, + 404, + 435, + 630 + ] + ], + "page_idx": 56 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "Alizarin Red (day 21)", + "footnote": [], + "bbox": [ + [ + 471, + 193, + 707, + 333 + ] + ], + "page_idx": 57 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "Osteogenic", + "footnote": [], + "bbox": [ + [ + 460, + 245, + 485, + 255 + ] + ], + "page_idx": 58 + } +] \ No newline at end of file diff --git a/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6.mmd b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6.mmd new file mode 100644 index 0000000000000000000000000000000000000000..39400102ca04b3f95e199e5a536254b38d907365 --- /dev/null +++ b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6.mmd @@ -0,0 +1,619 @@ + +# WHIM Syndrome-linked CXCR4 mutations drive osteoporosis by mitigating the osteogenic specification of skeletal stromal cells + +Adrienne Anginot Inserm + +Julie Nguyen Inserm + +Zeina Abou- Nader Institut de Recherche Saint- Louis, EMiLy + +Vincent Rondeau Institut de Recherche Saint- Louis, EMiLy + +Amélie Bonaud Inserm + +Antoine Boutin Université Côte d'Azur, CNRS + +Julia Lemos Institut de Recherche Saint- Louis, EMiLy + +Valeria Bisio Inserm + +Joyce Koenen INSERM, Université Paris- Saclay + +Léa Sakr Université de Paris, BIOSCAR Inserm U1132 + +Caroline Marty INSERM UMR- 1132 + +Amélie Coudert Université de Paris, BIOSCAR Inserm U1132 + +Sylvain Provot INSERM https://orcid.org/0000- 0003- 4087- 4450 + +Nicolas Dulphy Université de Paris https://orcid.org/0000- 0002- 1243- 6456 + +Michel Aurrand- Lions INSERM https://orcid.org/0000- 0002- 8361- 3034 + +Stéphane Mancini + +<--- Page Split ---> + +Inserm https://orcid.org/0000- 0001- 9255- 4606 + +Gwendal lazennec CNRS https://orcid.org/0000- 0002- 8522- 1763 + +David McDermott National Institute of Allergy and Infectious Diseases https://orcid.org/0000- 0001- 6978- 0867 + +Fabien Guidez INSERM + +Claudine Blin- Wakkach Université Côte d'Azur https://orcid.org/0000- 0002- 2621- 3907 + +Philip Murphy National Institutes of Health, United States + +Martine Cohen- Solal Université de Paris and BIOSCAR Inserm U1132 https://orcid.org/0000- 0002- 8582- 8258 + +Marion Espeli Inserm https://orcid.org/0000- 0001- 5005- 1664 + +Matthieu Rouleau Université Côte d'Azur + +Karl Balabanian ( \(\boxed{\bullet}\) karl.balabanian@inserm.fr) Institut de Recherche Saint- Louis, EMiLy https://orcid.org/0000- 0002- 0534- 3198 + +## Article + +Keywords: Skeletal stromal/stem cell, Bone marrow, CXCR4 signaling, Osteogenesis, WHIM Syndrome, Osteoporosis + +Posted Date: January 18th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1186490/v1 + +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +WHIM Syndrome- linked CXCR4 mutations drive osteoporosis by mitigating the osteogenic specification of skeletal stromal cellsA. Anginot1,2,3,#, J. Nguyen2,4,#, Z. Abou-Nader1,2,3, V. Rondeau1,2,3, A. Bonaud1,2,3, A. Boutin5, J. Lemos1,2,3, V. Bisio1,2,3, J. Koenen2,4, L. Sakr6, C. Marty6, A. Coudert6, S. Provot6, N. Dulphy1,2,3, M. Aurrand- Lions2,7, S.J.C. Mancini2,7, G. Lazennec2,8, D.H. McDermott9, F. Guidez3,10, C. Blin- Wakkach5, P.M. Murphy9, M. Cohen- Solal6, M. Espéli1,2,3,£, M. Rouleau5,£, and K. Balabanian1,2,3,*1'Université de Paris, Institut de Recherche Saint- Louis, INSERM U1160, 75010 Paris, France.2'CNRS, GDR3697 "Microenvironment of tumor niches", Micronit, France. 3'OPALE Carnot Institute, The Organization for Partnerships in Leukemia, Hôpital Saint- Louis, 75010 Paris, France. 4'Inflammation, Chemokines and Immunopathology, INSERM, Université Paris- Saclay, 92140, Clamart, France. 5'Université Côte d'Azur, CNRS, LP2M, UMR 7370, Faculté de Médecine, 06107, Nice, France. 6'Université de Paris, BIOSCAR Inserm U1132, Department of Rheumatology and Reference Center for Constitutional Bone Diseases, AP- HP Hospital Lariboisière, 75010 Paris, France. 7'Aix Marseille Univ, CNRS, INSERM, Institut Paoli- Calmettes, CRCM, 13273, Marseille, France. 8'CNRS, SYS2DIAG- ALCEDIAG, Cap Delta, Montpellier, France. 9'Molecular Signaling Section, Laboratory of Molecular Immunology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892. 10'Université de Paris, Institut de Recherche Saint- Louis, INSERM U1131, 75010 Paris, France. 11'AA and JN share the first author position. 12'ME and MR equally contributed. 13'Correspondence & lead contact: karl.balabanian@inserm.fr. Running title: CXCR4 desensitization in skeletal stromal cells. One Sentence Summary: Using a mouse model harboring a naturally occurring WHIM Syndrome (WS)- linked gain- of- function CXCR4 mutation and bone marrow samples from + +<--- Page Split ---> + +27 healthy and WS donors, Anginot et al. show that CXCR4 desensitization acts as a gatekeeper 28 orchestrating the osteogenic specification of skeletal stromal cells. 29 Abbreviations: BMSC: Bone marrow stromal cell; C-tail: Carboxyl-terminal tail; HSPC: 30 Hematopoietic stem and progenitor cell; MFI: Mean fluorescence intensity; OBL: Osteoblast; 31 OCL: Osteoclast; Ocn: Osteocalcin; OPC: Osteoblastic progenitor; Opn: Osteopontin; Oxs: 32 Osterix; SLAM: Signaling lymphocyte activation molecule; SSC: Skeletal stromal/stem cell; 33 WS: Warts, Hypogammaglobulinemia, Infections and Myelokathexis Syndrome. + +<--- Page Split ---> + +## ABSTRACT + +WHIM Syndrome (WS) is a rare immunodeficiency caused by gain- of- function CXCR4 mutations. Here we report for the first time a substantial decrease in bone mineral density in \(25\%\) of WS patients and bone defects leading to osteoporosis in a WS mouse model. Reduction in bone content involved impaired CXCR4 desensitization that disrupts cell cycle progression and osteogenic specification of mouse bone marrow (BM)- residing skeletal stromal/stem cells (SSCs). This was also evidenced in BM stromal cells from WS patients. Consistent with this, chronic treatment with the CXCR4 antagonist AMD3100 normalized in vitro osteogenic fate of mutant SSCs and reversed in vivo loss in skeletal cells, thus demonstrating that proper CXCR4 desensitization is required for the osteogenic specification of BM SSCs. Our study provides novel mechanistic insights into how CXCR4 signaling regulates the osteogenic fate of BM SSCs. + +Keywords: Skeletal stromal/stem cell; Bone marrow; CXCR4 signaling; Osteogenesis; WHIM Syndrome; Osteoporosis. + +<--- Page Split ---> + +## INTRODUCTION + +The bone marrow (BM) is a complex structural and primary immune organ whose development and maintenance depend on multiple cell types including cells of the hematopoietic lineage like hematopoietic stem and progenitor cells (HSPCs), but also vascular cells and numerous skeletal cells encompassing BM stromal cells (BMSCs), skeletal progenitor/precursor cells as well as bone- making osteoblasts (OBLs) \(^{1,2}\) . Together these cells compose specialized micro- anatomical structures called “niches” that sustain their survival and differentiation \(^{3 - 9}\) . For instance, the HSPC niches are thought to be composed of perivascular stromal units associated with sinusoids and arterioles \(^{10 - 15}\) . Bone and adipose cells are thought to derive from subsets of BMSCs that are located near blood vessels and function as skeletal stromal/stem cells (SSCs) \(^{16 - 19}\) . However, the exact localization, composition and crossover of these niches in relation with bone function are not yet established. Bone tissue homeostasis relies on the balance between formation and resorption of bone matrix mediated by effector cells that derive from SSCs and HSPCs respectively. Disequilibrium of this balance can lead to diseases such as osteoporosis or osteopetrosis. In such a landscape, SSCs are key players: not only they give rise to OBLs but they also contribute to perivascular structures important for HSPCs \(^{20 - 28}\) . Understanding how SSCs maintain their identity, achieve plasticity and support hematopoiesis in adult BM is thus an important emerging field \(^{3,6,9,12,29}\) . Recently, Ambrosi and coll. showed that intrinsic ageing of SSCs skews skeletal and hematopoietic lineage outputs, leading to fragile bones \(^{30}\) . However, both extrinsic and intrinsic mechanisms regulating their fate remain incompletely understood. + +In adult BM, signaling by the G protein- coupled receptor CXCR4 on HSPCs in response to stimulation by the chemokine CXCL12/Stromal cell- derived factor- 1, produced by BMSCs constitutes a key pathway through which the stromal niches and HSPCs communicate \(^{31 - 35}\) . Conditional ablation of Cxcl12 from perivascular stromal cells or OBLs demonstrated that HSCs occupy a perivascular but not an endosteal niche \(^{21,36}\) , whereas targeted deletion of Cxcl12 + +<--- Page Split ---> + +from BM stromal cells has allowed the identification of specialized niches supporting leukemia stem cell maintenance37. Both Cxcr4 and Cxcl12 are broadly expressed by non- hematopoietic tissues and cell types and have multifunctional roles beyond hematopoiesis. Since mice deficient for Cxcr4 or Cxcl12 die perinatally, our understanding of the role of the Cxcl12/Cxcr4 axis in regulating the BM ecosystem is mostly based on relatively selective loss- of- function models21,38- 42. Conditional inactivation of Cxcl12 or Cxcr4 in paired- related homeobox gene 1 (Prx1)- or osterix (Osx)- expressing cells, i.e. respectively multipotent mesenchymal progenitors or osteoprogenitor cells (OPCs) and descendant OBLs, was associated with reduced postnatal bone formation, suggesting a positive regulatory role of this pair in OBL development and/or function21,41,42. To our knowledge, this has not been reported in mice with selective deficiency of Cxcr4 in HSPCs. Single cell transcriptomics recently suggested heterogeneity within adult Cxcl12- expressing SSCs poised to undergo either adipogenic or osteogenic specification43. However, it is still unclear whether Cxcr4 signaling regulates osteogenic specification of SSCs. + +Here, we addressed this point using as a paradigm the WHIM Syndrome (WS), a rare immunodeficiency caused by viable inherited heterozygous gain- of- function mutations in CXCR4 affecting homologous desensitization of the receptor, thus resulting in enhanced signaling following CXCL12 stimulation, defective lymphoid differentiation of HSPCs and reduced blood leukocyte numbers44- 46. Taking advantage of a mouse strain that harbors the naturally occurring WS- linked heterozygous CXCR4S338X mutation (Cxcr4+/1013, +/1013)47- 50, and of human BM samples from WS donors and clinical data from 19 WS patients, we investigated whether WS mutations affect the SSC landscape. WS- linked CXCR4 mutations were associated with reduced bone mass in mice and humans. In mice, this relied on impaired CXCR4 desensitization that disrupts cell cycle progression and osteogenic commitment of + +<--- Page Split ---> + +# CXCR4 desensitization in skeletal stromal cells + +SSCs. This was also evidenced in BMSCs from WS patients. Thus, proper CXCR4 desensitization is required for the osteogenic specification of BM SSCs + +<--- Page Split ---> + +## RESULTS + +WS- linked CXCR4 mutations are associated with reduced bone mass in mice and humansFollowing CXCL12 stimulation, \(\beta\) - arrestins are recruited to the carboxyl- terminal tail (C- tail) domain of CXCR4, precluding further G- protein activation (i.e. desensitization) and leading to receptor internalization51. Both processes are dysregulated in WS most often due to autosomal- dominant gain- of- function mutations that result in the distal truncation of the C- tail of CXCR4 and a desensitization- resistant, hyperactive receptor52. Although the impact of these WS mutations on immune cells is currently being understood47- 50,53, nothing is known about their impact on the SSC landscape. Bone mineral density (BMD) values were measured in 19 patients with WS for lumbar spine and femoral neck by total body dual- energy X- ray absorptiometry. BMD T- and Z- scores were found to be low at least in one site in five patients (Table 1). Likewise, this was evidenced in adult Cxcr41013- bearing (i.e. heterozygous [+/1013] and homozygous [1013/1013]) mice, as compared to Cxcr4+/+ (WT) mice. Analyses of lumbar spine revealed decreased BMD values in mutant mice, in a Cxcr41013 allele dose- dependent manner (Fig. 1A). Micro- computed tomography (microCT) analyses further unraveled reduced bone content in mutant mice (Fig. 1B). In mutant femurs, there was a reduction in the trabecular bone density that followed a Cxcr41013 allele copy number- dependent pattern. This was characterized by a significant decrease in bone volume and trabecular numbers, while the trabecular separation was increased compared to WT mice (Fig. 1C). The cortical bone volume and thickness were also affected (Fig. 1B and 1D). This gene- dependent reduction was observed among both female and male mutant mice. Histomorphometric analyses confirmed decreased bone volume and trabecular numbers in mutant mice, as shown by toluidine blue staining (Fig. 1E). Strikingly, staining for alcian blue and perilipin that are used for chondrocyte and adipocyte identification respectively, were unaltered in mutant bone (Fig. 1F and 1G). Consistently, the thickness of the cartilaginous growth plate was similar in mice carrying the + +<--- Page Split ---> + +Cxcr4 mutation compared to WT ones (Fig. 1E, 1F, 1H and 1I). Moreover, adult mutant mice did not exhibit significant changes of body size or weight (Fig. 1J). Overall, these findings revealed an osteopenic skeleton in Cxcr41013-bearing mice and low BMD in 25% of WS patients. + +## Reduction of skeletal stromal cells in Cxcr41013-bearing mice + +We then evaluated by flow cytometry the bone composition of WT and mutant mice with a focus on skeletal cells that encompass BMSCs, SSCs and OBLs54. Long bones were flushed and then digested. Total stromal cells in the bone fraction were identified as negative for CD45, Lineage (including Ter119), c- Kit and CD71 expression as previously reported12,55. Endothelial cells were excluded based on CD31 expression. Two distinct CD51+ stromal cell subsets were identified based on Sca- 1 and PDGFRα: SSCs (Sca- 1+PDGFRα+) and committed osteoblast progenitors (Sca- 1+PDGFRα- , herein referred as OPCs) (Fig. 2A). We observed a global reduction of the number of stromal cells that followed a Cxcr41013 allele dose- dependent pattern (Fig. 2B). There was a significant decrease of OPCs, and to a lesser extent of SSCs, thus reinforcing that the landscape of the stroma in bone is altered in Cxcr41013- bearing mice. + +We next examined in vitro the function of the signaling trio formed by Cxcl12 and its two receptors Cxcr4 and Ackr3 in skeletal cells. Membrane expression of Cxcr4 and Ackr3 was similar between WT and mutant skeletal cells including SSCs (Fig. 2C and 2D). However, +/1013 and 1013/1013 SSCs displayed both impaired Cxcr4 internalization following Cxcl12 stimulation as well as increased Cxcl12- mediated chemotaxis that was abolished by the specific Cxcr4 antagonist AMD3100 (Fig. 2E and 2F). These dysfunctions likely relied on the enhanced signaling properties of the truncated Cxcr4 receptor as revealed by Erk PhosphoFlow analyses (Fig. 2G). Combined with the apparent preserved capacity of Ackr3 to bind and internalize Cxcl12 in vitro (Fig. 2H and 2I), these findings indicated a functional expression of the + +<--- Page Split ---> + +desensitization- resistant C- tail- truncated Cxcr41013 receptor on SSCs. Abnormal Cxcr4 signaling was not associated with changes in apoptosis of SSCs (Fig. 2J). + +To determine whether reduction of bone content in Cxcr41013- bearing mice resulted from defects intrinsic to skeletal cells and/or an alteration of the hematopoietic (or another non- stromal) system, we performed reciprocal long (16 weeks)- and short (3 weeks)- term BM reconstitution experiments. First, BM cells from WT CD45.1+ mice were transplanted into lethally irradiated CD45.2+ WT or mutant (+/1013 and 1013/1013) mice (Fig. 2K). Sixteen weeks later, mutant recipients exhibited CD45.1+ chimerism in hematopoietic compartments similar to those of WT recipients (Fig. 2L), but displayed reduced numbers of skeletal cells including SSCs and OPCs (Fig. 2M). Confocal imaging analyses confirmed that transplantation of WT BM was not sufficient to rescue the trabecular network in mutant recipients (white arrows, Fig. 2N). This was also evidenced three weeks after WT BM transplantation (Fig. 2O). These results suggested that skeletal cell- autonomous Cxcr4 regulation contributes to the persistent bone defects in adult Cxcr41013- bearing mice. We then performed reverse chimeras in which irradiated CD45.1+ WT mice were reconstituted with WT, +/1013 or 1013/1013 CD45.2+ BM (Fig. 2P). Sixteen weeks later, CD45.2+ chimerism of LT- HSCs and leukocytes were decreased respectively in BM and blood of CD45.1+ WT recipients engrafted with mutant BM, confirming the impaired reconstitution capacity of mutant HSCs (Fig. 2Q and 49). There were significantly lower numbers of skeletal cells and defective trabecular bone content in Cxcr41013- bearing BM- chimeric mice compared to WT chimeras as early as 3 weeks post- transplantation (Fig. 2R- T), thereby indicating cell- extrinsic Cxcr4- mediated regulation of the skeletal landscape. Altogether, these findings suggest that impaired Cxcr4 desensitization in both skeletal and hematopoietic cells have combinatorial effects on bone landscape dysregulation in adult Cxcr41013- bearing mice. + +<--- Page Split ---> + +## Increased bone resorption and reduced bone formation in Cxcr41013-bearing mice + +Bone is maintained by coupled activities of bone- forming OBLs and bone- resorbing osteoclasts (OCLs). Alterations in bone balance can result in pathologic bone loss and osteoporosis. This led us to investigate whether and how the gain- of- Cxcr4- function mutation modulates the OBL/OCL balance. First, we analyzed bone resorption by quantifying OCL numbers in mice using Tartrate Resistant Acid Phosphatase (TRAP) staining56. We observed increased OCL surface (Oc.S/BS) and number (Oc.N/BV) in mutant mice compared to WT ones (Fig. 3A and 3B). To determine whether the increased bone resorption in mutant mice resulted from OCL- intrinsic defects, we performed in vitro OCL differentiation from BM cells in the presence of M- Csf and Rank- L and tested their bone resorption capacity. Similar OCL numbers and bone matrix resorption activities were observed among WT and mutant cultures (Fig. 3C and 3D), suggesting preserved intrinsic capacities of mutant BM myeloid cells to differentiate in vitro into functional OCLs. Congruent with this, we observed no changes in expression levels of osteoclastogenic genes in mutant cultures compared to WT ones (Fig. 3E). These findings indicate that the Cxcr4 mutation does not affect in vitro OCL differentiation and function, but suggest that osteoclastogenesis and increased bone resorption in mutant mice may be promoted by the BM environment. + +Cxcr41013- bearing mice exhibited maintained bone formation as revealed by osteoid surface (OS/BS) and osteoblast surface (Obl.S/BS) compared to WT mice (Fig. 3F). Dynamic parameters of in vivo bone formation were also assessed by quantifying bone surfaces labelled with tetracycline and calcein (Fig. 3G). Total and double labelled surfaces were lower in mutant than WT mice (Fig. 3G), whilst mineral apposition rate (MAR) were similar in WT and Cxcr41013- bearing mice (Fig. 3H). This suggests a decrease in bone formation related to a lower number of OBLs with maintained activity of individual OBL. In line with preserved intrinsic bone formation capacities of active osteoblastic lineage cells in mutant mice, high- throughput + +<--- Page Split ---> + +RNA sequencing (RNA- seq) analyses of bulks sorted by flow cytometry on the basis of CD51 and Sca- 1 markers (Fig. 2A) from the bone fraction highlighted in mutant OPCs a gene signature with preserved mineralized matrix potential (Fig. 3I- K). In agreement, sorted OPCs from mutant mice were as efficient as WT ones in vitro at producing mineralized matrix after 21- days culture in osteogenic medium as determined by Alizarin Red (AR) staining (Fig. 3L). These findings are in line with efficient terminal osteogenic differentiation and preserved bone formation capacities in Cxcr41013- bearing mice. Given that osteogenic cells support osteoclastogenesis through the production of soluble factors such as Rank- L (Tnfsf11)57, we questioned our RNAseq data on the related gene expression profile in mutant and WT OPCs. No major changes in expression levels of pro- osteoclastogenic or anti- resorptive genes were revealed in mutant OPCs (Fig. 3M and 3N). Taken as a whole, these findings suggest that the hematopoietic contribution to bone loss in Cxcr41013- bearing mice likely involves dysregulation of the OCL compartment regardless of their activity. Moreover, the observation that immature and mature osteogenic cells, i.e., OPCs and OBLs, displayed preserved intrinsic functions led us to study earlier developmental steps of the osteolineage. + +## Impaired osteogenic specification of Cxcr41013- bearing skeletal stromal cells + +We thus investigated the intrinsic characteristics of SSCs carrying the Cxcr4 mutation. Undifferentiated stem cells are characterized by their slow cell cycle progression in unperturbed conditions9,58. This led us to interrogate by flow cytometry the cycling status of SSCs from the bone fractions of Cxcr41013- bearing mice by performing DAPI/Ki- 67 staining. A slight but significant increase in the frequency of cells in the quiescent G0 state (DAPIlowKi- 67) was observed among 1013/1013 SSCs and spared the more differentiated osteoblastic pool (Fig. 4A). The turnover of those cells was then studied by performing a 12- day BrdU pulse- chase assay in vivo (Fig. 4B). Consistent with previous studies28,59, the fraction of BrdU+ cells in WT + +<--- Page Split ---> + +SSCs reached \(\sim 5\%\) , while we observed a \(Cxcr4^{1013}\) allele copy number-dependent reduction in BrdU incorporation within mutant SSCs. No changes were observed among mutant OPCs compared to WT ones. Combined to reduced SSC and OPC numbers in mutant bones (Fig. 2B), these findings are suggestive of reduced cycling and osteogenic differentiation capacities of \(Cxcr4^{1013}\) - bearing SSCs. To gain further mechanistic insights, we performed a microfluidic-based multiplex gene expression analyses in SSCs sorted from the bone fractions of WT and mutant mice. Principal component analysis (PCA) of 48 genes showed three distinct clusters of SSCs dependent on the \(Cxcr4\) genotype (Fig. 4C). Heatmap representation and differential expression analyses revealed in mutant SSCs, particularly in the 1013/1013 ones, downregulation of genes encoding master regulators of the osteogenic differentiation including Runx2 and of cell cycle such as Ccnd2 and Ccnd3 (Fig. 4D and 4E). No changes in expression levels of pro- osteoclastogenic genes were detected in mutant SSCs compared to WT ones (Fig. 4F). Therefore, these results unravel a \(Cxcr4\) - mediated transcriptional signature in \(Cxcr4^{1013}\) - bearing SSCs suggestive of impaired cell cycle progression and defective osteogenic specification. + +In adult BM, the majority of OBLs derives from OPCs identified by markers such as osterix \((\mathrm{Osx})^{3,9,17,28,60 - 62}\) . They are predominantly found close to the growth plate cartilage along trabecular bone of the primary spongiosa, and along the metaphyseal cortical bone \(^{60,61,63}\) . We thus examined whether the gain- of- \(Cxcr4\) - function mutation alters the number of Osx- positive OPCs by immunodetection on bone sections. We found fewer Osx- positive OPCs in mutant bones compared to WT (Fig. 4G and 4H). This decrease was confirmed by flow cytometry in the flushed stromal marrow fraction that encompasses Sca- 1- negative and PDGFRα- positive early OPCs with pluripotent adipo/osteogenic potential (Fig. 4I) \(^{28,43,60}\) . Together, these data suggest that the decrease in early and committed OPCs in mutant mice may arise from a defect in osteogenic specification of BM- residing SSCs. + +<--- Page Split ---> + +## Cxcr4 desensitization intrinsically regulates in vitro the osteogenic differentiation of skeletal stromal cells + +To assess whether Cxcr4 desensitization could regulate SSC fate toward the osteogenic lineage in a cell- intrinsic manner, we first compared in vitro the clonogenic capacities of WT and mutant total skeletal cells. There was a significant decrease in the number of colony- forming units- fibroblast (CFU- Fs) in mutant bone cell cultures that followed a Cxcr41013 allele copy number- dependent pattern (Fig. 5A). These results suggested that impaired Cxcr4 signaling might affect in vitro overall SSC numbers as well as their proliferation. To test this, we evaluated by flow cytometry cell cycle and proliferation of SSCs expanded in vitro using BrdU, Cell Trace Violet (CTV) and DAPI/Ki- 67 staining. By day 5 after BrdU pulse, we observed a Cxcr41013 allele dose- dependent reduction in BrdU incorporation within mutant SSCs as compared to WT (Fig. 5B, left panel). Consistently, the fraction of proliferating CTVlow cells was reduced among mutant SSCs three days after loading (Fig. 5B, right panel). This altered proliferative capacity of Cxcr41013- bearing SSCs was associated with a slight but significant increase in proportions of SSCs in the quiescent G0 state (DAPIlow Ki- 67), whereas no changes in apoptosis level were observed (Fig. 5C). This might account for the increased doubling time of mutant SSCs as well as their overall reduced number during the culture (Fig. 5D). Altogether, these findings suggest that Cxcr4 desensitization is required in vitro for appropriate SSC proliferation, expansion and likely maintenance. + +Next, we investigated in vitro the osteogenic potential capacities of Cxcr41013- bearing SSCs64,65. Staining of in vitro differentiated OBLs and mineralization capacities by Alkaline phosphatase (Alp) and AR respectively24,64- 66 was significantly reduced in cultures of mutant SSCs in an allele dose- dependent manner (Fig. 5E and 5F, upper panels). Real- time PCR analysis revealed decreased expression of genes encoding osteogenic regulators in mutant cultures (Fig. 5G, upper panels). This was more marked for early osteogenic genes downstream + +<--- Page Split ---> + +the master regulator Runx2 such as \(Oxx\) and particularly evident in the culture of 1013/1013 SSCs, thus suggesting defects at very early stages in the osteogenic differentiation process. In line with this, flow- cytometric analyses revealed three weeks after pro- osteogenic culture initiation that the frequencies of most mature CD51+Scal1Pdgfα OPCs were lower in \(Cxcr4^{1013}\) - bearing cell cultures as compared to WT (Fig. S1A and S1B, left panel). This was associated with a decrease in cells with intermediate phenotype (CD51+Scal1lowPdgfαlow) and mirrored by an accumulation of CD51+Scal1high Pdgfαhigh cells that are presumably SSCs. These results were supported by real- time PCR analyses of \(CD51\) , \(Sca- 1\) and \(Pdgf\alpha\) expression (Fig. S1C, left panel). Consistent with the results obtained with Perilipin and Opn immunostaining on bone sections (Fig. 1G and 1H), \(Cxcr4^{1013}\) - bearing SSCs differentiated into adipocytes or chondrocytes similarly to WT SSCs, when cultured in vitro with adipogenic or chondrogenic media respectively (Fig. S1D and S1E). Collectively, these data reveal in vitro a selective reduction of the osteogenic differentiation capacity of mutant SSCs, and further confirm a pivotal role for Cxcr4 desensitization in regulating this process at very early stages. + +## Normalization of Cxcr4 signaling rescues the osteogenic properties of \(Cxcr4^{1013}\) -bearing mouse skeletal cells + +We then determined whether targeting Cxcr4 signaling would counteract the defective osteogenic fate of mutant SSCs. First, we assessed in vitro the impact of addition of AMD3100 every 2 days on the osteogenic capacities of SSCs. AMD3100- mediated inhibition of Cxcr4 signaling in WT SSCs led to slight changes including decreased numbers of osteogenic cells (Fig. 5E and 5F, lower panels and S1B and S1C, right panel). By contrast, mutant cultures were highly sensitive to AMD3100 treatment as it led to a normalization of Alp and AR colorations 14 and 21 days after differentiation respectively (Fig. 5E and 5F), as well as to a correction of the frequencies of mature, intermediate and CD51+Scal1high Pdgfαhigh cells to the values + +<--- Page Split ---> + +observed in WT cultures (Fig. S1B). Moreover, AMD3100- mediated reversion of defective osteogenesis within Cxcr41013- bearing SSC cultures was associated with normalized gene expression of osteogenic master regulators (Fig. 5G, lower panels), thus unravelling that Cxcr4 desensitization intrinsically regulates in vitro the osteogenic differentiation of SSCs. + +Then, we assessed the impact of daily intraperitoneal injections for 3 weeks of \(5\mathrm{mg / kg}\) AMD3100 on the bone landscape in adult WT and mutant mice (Fig. 5H). Cxcr4 inhibition decreased slightly the number of WT skeletal cells, and notably OPCs, in the bone fraction (Fig. 5I). In line with this, Opn- stained femoral sections revealed minor alterations in the architecture of WT mice trabecular microstructures upon treatment (Fig. 5J). This was extended to lumbar spine that displayed roughly normal BMD values in treated vs untreated WT mice (Fig. 5K). In 1013/1013 mice, chronic AMD3100 treatment reversed the quantitative defect in skeletal cells by normalizing the numbers of SSCs and OPCs (Fig. 5I). This was not evidenced in \(+ / 1013\) mice and not associated with a rescue of the trabecular network (Fig. 5J). However, AMD3100 treatment ameliorated slightly but significantly BMD values of lumbar spine in mutant mice (Fig. 5K), suggesting a correcting effect of Cxcr4- dependent signaling dampening on the cortical, rather than trabecular, bone at this stage. Therefore, these data indicate that integrity of Cxcr4 signaling is required for maintaining the osteogenic properties of skeletal cells. + +## BM stromal cells from WS patients displayed in vitro impaired osteogenic capacities + +Finally, we sought to investigate if CXCR4 desensitization was mechanistically involved in regulating in vitro the multilineage differentiation capacities of human primary BMSCs that constitute a heterogeneous population containing skeletal progenitors18. To this end, we analyzed BM samples from two unrelated patients with WS and carrying the heterozygous CXCR4R334X mutation. In parallel, we expanded in vitro BMSCs from BM aspirates of seven independent healthy donors. All culture- expanded BMSCs were negative for the CD45 + +<--- Page Split ---> + +hematopoietic marker lineage but positive for CD73, CD90 and CD105, a combination of markers that are indicative of stromal/fibroblastic cells (Fig. S2A). Both healthy and WS BMSCs were spindle shaped and fibroblast-like cells and had the ability to form stromal colonies as shown by CFU- F assay (Fig. S2B and S2C). CXCL12 and its two receptors CXCR4 and ACKR3 were readily detectable and found at similar levels between cultured healthy and WS BMSCs (Fig. S2D- F). However, real- time PCR analyses revealed decreased expression of genes encoding early and late osteogenic master regulators in WS BMSC cultures compared to healthy controls (Fig. 6A). In line with this, when equal numbers of cells were plated at the start of the assay, WS BMSCs exhibited defective capacities to generate in vitro osteogenic progeny in contrast to BMSCs harvested from healthy donors (Fig. 6B). In contrast, WS BMSCs were as efficient as control cells to generate adipocytes in appropriate culture media condition (Fig. 6C). Therefore, these findings suggest that in vitro osteogenic differentiation of human primary BMSCs requires proper CXCR4 signaling regulation. + +<--- Page Split ---> + +## DISCUSSION + +In this study, we investigated the regulatory role of CXCR4 signaling termination in the self- renewal and osteogenic capacities of adult BM- residing SSCs. We used a knock- in mouse model expressing a naturally occurring WS- linked heterozygous gain- of- function Cxcr4 mutation as well as human BM samples and clinical data from healthy and WS donors. We demonstrated for the first time a mutated allele dose- dependent effect of the WS- linked Cxcr41013 mutation on trabecular bone microstructures mimicking an osteoporotic- like syndrome, evidenced as well in one quarter of WS patients. This Cxcr4- mediated reduction in bone content involved both cell- autonomous and cell- extrinsic defects in SSCs. Indeed, we provided unanticipated evidence that Cxcr4 desensitization is intrinsically required for regulating in vitro the quiescence/cycling balance of SSCs and preserving their osteogenic potential, while it was found to be dispensable for their adipogenic and chondrogenic differentiation. Other BM cellular partners also contributed to the bone phenotype dysregulation. Neither the osteoclastogenic differentiation potential of OCL precursors nor the resorptive function of differentiated OCLs were affected in vitro by the Cxcr41013 mutation. Therefore, the osteopenia, accompanied by an increase in OCL number regardless of their function, might proceed from a wrongly- regulated bone matrix resorption that is overall due to the alteration of the skeletal landscape involving both bone- forming and non- bone- forming cell lineages. Importantly, defective osteogenic capacities were also evidenced in vitro in BMSCs from WS patients. These anomalies establish the C- tail of CXCR4 as an important regulatory domain of the receptor function in BM stromal cell biology in both mice and humans. In light of previous work38,39,41,42, our results also suggest that both increased and decreased Cxcr4- mediated signaling negatively impact skeletal stromal elements, thus indicating that fine- tuning of Cxcr4 signaling is critical for maintenance and osteogenic specification of adult SSCs. Although the underlying molecular mechanisms remain to be elucidated, Cxcr4 might act as a + +<--- Page Split ---> + +rheostat regulating the strength and kinetic of signaling pathways involved in osteogenic fate specification of SSCs. Mice deficient for the gene encoding the transcription factor Ebf3 display an opposite BM phenotype to the one of \(Cxcr4^{1013}\) - bearing mice, characterized by osteosclerosis with HSC depletion and reduced expression of niche factors59. This was related to the uncontrolled ability of Ebf3- deficient SSCs to differentiate into OBLs. Further studies are required to address the status of Ebf3 and downstream target genes that act to modulate osteogenic fate of SSCs in \(Cxcr4^{1013}\) - bearing mice. A potential crosstalk between distinct SSC subsets, either prone to differentiate into osteochondro- lineage cells or perivascular and adipocyte lineage cells, has been reported67. This seems to imply ligand- receptor gene pairs such as TGFβ, WNT or BMP ligands and their cognate receptors that regulate SSC fate decision. Whether and how the Cxcl12/Cxcr4 signaling axis contributes to these regulatory mechanisms across SSC types remains to be explored. + +We reported that loss of Cxcr4 signaling termination impairs overall number, impedes cell cycle progression and limits osteogenic differentiation of SSCs. Indeed, mutant mice have a global alteration of the bone stromal landscape, including decreased numbers of SSCs and their progeny including early and committed OPCs and impaired architecture of trabecular and cortical bone microstructures that occurred in a mutated allele copy number- dependent manner. Altogether, these findings indicate that the gain- of- Cxcr4- function mutation promotes a reduced OBL commitment and differentiation, but not the bone forming activity of individual OBL. Congruent with this, chronic treatment with AMD3100 normalized the osteogenic properties of mutant SSCs as well as cortical bone in \(Cxcr4^{1013}\) - bearing mice. Impaired Cxcr4 desensitization might alter the balance between quiescence and differentiation of mutant SSCs and reduce the number of osteogenic- endowed precursors. Currently, the prevailing view is that BM Cxcl12- expressing stromal cells display slow cell cycle progression and constitute an active source of trabecular and cortical OBLs under physiological conditions, as well as in + +<--- Page Split ---> + +response to injury \(^{6,9,28,59}\) . We found a higher proportion of quiescent SSCs in mutant mice that was particularly evident in 1013/1013 SSCs, thus suggesting the importance of Cxcr4 desensitization in controlling SSC proliferation and quiescence and likely their capacity to give rise to osteogenic cells. + +Loss of bone content in mutant mice was accompanied by a higher number of OCLs within the cancellous bones, possibly reflecting that the Cxcr4 mutation was intrinsically perturbing the OCL differentiation process. This seems not to be the case since we showed that defective Cxcr4 desensitization did not increase in vitro differentiation of OCLs from mutant BM progenitors, nor their mineral matrix resorbing capacities. BM chimeras leading to a WT hematopoietic development into a mutant bone environment further ruled out the sole involvement of an uncontrolled bone resorption due to defective OCLs. These cells derive from monocytic lineage precursors upon stimulation by RankL and M- Csf \(^{68,69}\) . In a constant cross interaction between the bone forming and the bone resorbing pathways, these osteoclastic cytokines are produced by mature and immature stromal cell populations within BM \(^{9,70,71}\) . While we did not observe increased RankL, M- Csf or Opg (coding a RankL antagonist) gene expression in sorted committed OPCs from mutant bones, we cannot exclude that modification of the bone stroma due to osteogenic defects might in turn disrupt the production of osteoclastic factors from the mutant bone environment. It has recently been shown that intrinsic aging of SSCs resulted in higher proportion of stromal lineages producing pro- inflammatory and pro- resorptive factors, promoting myeloid skewing, and osteoclastic activity \(^{30}\) . Whether a similar mechanism occurs in Cxcr4 \(^{10,13}\) - bearing mutant mice remains to be characterized. + +Osteogenesis is regulated, among different mechanisms, by undifferentiated skeletal cells and more specified osteolineage cells that express factors promoting or preventing their own differentiation into OBLs \(^{23,55,59,72}\) . In BM, HSPC niches constitute critical spatio- temporal regulatory units composed of multiple cell populations of hematopoietic and non- hematopoietic + +<--- Page Split ---> + +origin cross- interacting with each other's in a dynamic setting1,3,9,73,74. This implies that immune and vascular cells among others may influence the osteogenic differentiation process75. In BM chimeras in which Cxcr41013- bearing HSPCs were differentiating into a WT bone environment, we reported a similar bone loss as observed in mutant mice, thus indicating cell- extrinsic Cxcr4- mediated regulation of the skeletal landscape. This also suggests that neither the epiphyseal cartilage nor any developmental defect contribute to impaired trabecular bone architecture in adult mutant mice, and further supports the notion that HSPCs, as osteolineage cells do, express regulating osteogenic factors such as BMP- 2, BMP- 7 and WNT3a, that are particularly involved in SSC osteogenesis specification17. Whether and how hematopoietic cells, or other BM components such as vascular cells, participate in the defective osteolineage specification of SSCs in Cxcr41013- bearing mice deserves further investigations. + +Finally, we reported that five out of nineteen patients with WS and carrying distinct autosomal- dominant mutations in CXCR4 exhibit a decrease in BMD at different anatomical sites. Although this would merit to be extended to a larger cohort, these data suggest that accelerated osteopenia/osteoporosis and increased risk of fractures may constitute a novel feature of WS. Lack of CXCR4 desensitization could be mechanistically involved in such anomaly since BMSCs from WS patients carrying a heterozygous CXCR4 mutation displayed in vitro impaired capacities to differentiate into osteogenic, but not adipogenic, cells. Strikingly, we observed that chondro- and adipo- genic differentiation of murine mutant SSCs was normal both in situ and in vitro. In light of recent studies unraveling human SSCs expressing the CXCL12/CXCR4 axis with osteoblastogenic and, depending on their tissue origin, adipocytic potential18,76, our findings pave the way for exploring the BM of WS patients in search for potential defect(s) in these skeletal populations. + +<--- Page Split ---> + +## METHODS + +## Healthy and WS donors and bone mineral density measurements + +Healthy and WS donors and bone mineral density measurementsInvestigations of human BM samples were performed in compliance with Good Clinical Practices and the Declaration of Helsinki. Cryopreserved BM aspirates from two WS patient (NIH protocol 09- I- 0200) were provided by Drs. D.H. McDermott and P.M. Murphy through a NIH Material Transfer Agreement. BM samples from seven healthy donors that were matched for age and sex and used as control subjects were isolated from hip replacement surgery samples (Protocol 17- 030, \(n^{\circ}\) ID- RCB: 2017- A01019- 44). Primary BMSCs from healthy and WS donors were amplified and used at passage 1 to 3. For BMD assessment, data were collected from nineteen WS patients as part of an IRB approved clinical protocol conducted at the NIH (NIAID Protocol #2014- I- 0185, IND # 118767). BMD values expressed as T- or Z- scores were measured by total body dual- energy X- ray absorptiometry with a Lunar iDXA densitometer (GE Healthcare). Five WS patients had abnormal screening bone density by WHO criteria, anonymized at the start of the Phase 3 trial (Table 1), while the other 14 patients had normal bone density (not shown). + +## Mice and genotyping + +Cxcr4+/1013 (+/1013) mice were generated by a knock- in strategy and bred as described previously47. Homozygous Cxcr4/1013/1013 (1013/1013) mice were obtained by crossing heterozygous +/1013 mice. WT mice were used as controls. Unless specified, all mice were littermates, females and age- matched (8- 12 wk- old). Adult Boy/J (CD45.1) (Charles River) mice were used as BM donors. All the mice were bred in our animal facility under a 12h light/dark cycle, specific pathogen- free conditions and fed ad libitum. All experiments were performed in accordance with the European Union guide for the care and use of laboratory animals and have been reviewed and approved by institutional review committees (CEEA- 26, + +<--- Page Split ---> + +Animal Care and Use Committee, Villejuif, France and Comité d'Ethique Paris- Nord/N°121, Paris, France). + +## Sample isolation in mice + +Mouse SSCs were obtained from bones after centrifugation of intact femurs, tibias and hips to flush out the BM cells. Flushed long bones were cut into fine pieces before enzymatic digestion with 2.5 U/mL collagenase type I (Thermofisher) for 45 min at \(37^{\circ}\mathrm{C}\) under agitation. Released cells were filtered and washed with PBS, \(2\%\) FBS (Fetal Bovine Serum). Cell numbers were standardized as total counts per two legs. Peripheral blood was collected by cardiac puncture. Freshly isolated cells were either immunophenotyped, incubated at \(37^{\circ}\mathrm{C}\) for 60 min in RPMI \(20\mathrm{mM}\) HEPES \(0.5\%\) BSA (Euromedex) prior to chemokine receptor internalization studies, or expanded in \(\alpha\) MEM medium supplemented with \(10\%\) FBS, \(1\%\) P/S (penicillin 100 Units/mL, streptomycin 100 Units/mL, Gibco) and \(50\mu \mathrm{M}\beta\) - mercaptoethanol (PAN biotech). For BMD quantification, lumbar spines were fixed overnight in ethanol \(70^{\circ}\) and analyzed by dual- energy X- ray absorptiometry with an ultrafocus DXA densitometer (Faxitron). Quantifications were made on a ROI of 2 lumbar spines. + +## Flow-cytometric analyses + +Mouse and human staining analyses were carried out on an LSRII Fortessa flow cytometer (BD Biosciences) using the antibodies (Abs) described in Table S1. A Live/Dead Fixable Aqua Dead Cell Stain Kit (Biolegend) was used. To assess the compartmentalization of CXCR4 and ACKR3, human BMSCs were incubated with saturating concentrations of non- conjugated mouse anti- human CXCR4 or ACKR3 Abs, washed in PBS, fixed and permeabilized using the BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences). BMSCs were + +<--- Page Split ---> + +subsequently stained with anti- CXCR4 and - ACKR3 conjugated mAbs, or the corresponding isotype control, at \(4^{\circ}\mathrm{C}\) for \(30\mathrm{min}\) and then analyzed by flow cytometry. + +## In vitro functional assays + +Mouse CFU- Fs were performed by plating \(1\times 10^{5}\) bone cells at passage 2- 3 from WT and mutant mice. Human CFU- Fs were performed by plating \(0.2\mathrm{x}10^{3}\) BMSCs into a \(25\mathrm{cm}^2\) flask at passage 3 from healthy or WS donors. After 7 or 10 days of culture, colonies were fixed with ethanol \(70\%\) , stained with \(2\%\) crystal violet (Sigma- Aldrich), and counted with a binocular magnifying glass. For chemotaxis assays, \(5\times 10^{4}\) SSCs were added to the upper chambers of a 24- well plate with \(8\mathrm{- }\mu \mathrm{m}\) - pore- size Transwell inserts (EMD Millipore) containing or not \(1\mathrm{nM}\) Cxcl12 (R&D Systems) in the lower chamber. For inhibiting Cxcr4- mediated signaling, \(10\mu \mathrm{M}\) AMD3100 (Sigma- Aldrich) was added in the upper and lower chambers. After 24h, membranes were removed and fixed in \(4\%\) paraformaldehyde (PFA). The cells that migrated to the lower side of the membrane were stained with \(0.1\%\) crystal violet and three fields from each insert were counted under a light microscope. Cxcr4 and Ackr3 internalization assays were performed by incubating total bone cells at \(37^{\circ}\mathrm{C}\) for \(45\mathrm{min}\) with \(10\mathrm{nM}\) Cxcl12. Then the reaction was stopped by adding ice- cold RPMI and quick centrifugation at \(4^{\circ}\mathrm{C}\) . After one wash in acidic glycine buffer at \(\mathrm{pH} = 4.3\) , levels of Cxcr4 and Ackr3 membrane expression were determined by flow cytometry. Cxcr4 or Ackr3 expression was calculated as follows: (Cxcr4 or Ackr3 geometric MFI of treated cells/Cxcr4 or Ackr3 geometric MFI of unstimulated cells) \(\times 100\) ; \(100\%\) corresponds to receptor expression at the surface of cells incubated in medium alone. For the chemokine scavenging assay, cultured SSCs were harvested by trypsinization and placed in complete medium for \(90\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(5\%\) \(\mathrm{CO_2}\) to normalize receptor expression. \(4\times 10^{6}\) cells/mL were pre- incubated with \(100\mu \mathrm{M}\) CCX733, a functional Ackr3 antagonist or vehicle in \(1\%\) BSA/PBS for \(45\mathrm{min}\) at room temperature (RT). Then, \(2\times 10^{6}\) cells/mL were incubated in + +<--- Page Split ---> + +presence of \(5\mathrm{nM}\) AF647- Cxcl12 (Almac) in \(1\%\) BSA/PBS during 45- 60 min at \(37^{\circ}\mathrm{C}\) to allow internalization or on ice to inhibit this process. Cells were washed with \(1\%\) BSA/PBS and then either treated with an acidic glycine wash buffer \(\mathrm{pH} = 2.7\) for \(3\mathrm{min}\) to dissociate cell- surfacebound chemokine, or washed with PBS to estimate internalized plus cell- surface- bound control. AF647 fluorescence (geometric MFI) was determined by flow cytometry. Phosphoflow assays were performed with the PerFix EXPOSE kit (Beckman coulter) on cultured SSCs and an antiphospho Erk (pT202/pY204) was used. Fold change was calculated as follows: (Phospho- Erk geometric MFI of stimulated cells/Phospho- Erk geometric MFI of unstimulated cells). + +## In vivo functional assays + +For BM transplantation experiments, \(1.5\mathrm{x}10^{6}\) total marrow cells from young \(\mathrm{CD45.1^{+}}\) WT mice were injected i.v. into lethally irradiated (two rounds of 5.5 Gy separated by \(3\mathrm{h}\) ) young \(\mathrm{CD45.2^{+}}\) WT, \(+ / 1013\) or 1013/1013 recipient mice. For reverse experiments, \(1.5\mathrm{x}10^{6}\) total marrow cells from \(\mathrm{CD45.2^{+}}\) WT, \(+ / 1013\) or 1013/1013 mice were injected into lethally irradiated \(\mathrm{CD45.1^{+}}\) WT recipient mice. Chimerism was analyzed 3 or 16 weeks after transplantation. For Cxcr4 blockade experiments, mice were daily injected intraperitoneally with \(5\mathrm{mg / kg}\) AMD3100 or PBS during 3 weeks. BM were harvested \(2\mathrm{h}\) after the last injection and analyzed by flow cytometry and imaging. + +## ELISA + +Supernatants of culture- expanded human BMSCs were analyzed using a standardized ELISA for human Cxcl12 (Quantikine; R&D Systems). + +## Bone immunostaining and histomorphometry + +<--- Page Split ---> + +Mouse bones were fixed in \(4\%\) PFA overnight followed by one- week decalcification in EDTA (0.5 M) at pH 7.4 under agitation. Bones were incubated in PBS with \(20\%\) sucrose and \(2\%\) polyvinylpyrrolidone (PVP) (Sigma) at \(4^{\circ}\mathrm{C}\) overnight and then embedded in PBS with \(20\%\) sucrose, \(2\%\) PVP and \(8\%\) gelatin (Sigma) before storage at \(- 80^{\circ}\mathrm{C}\) . Sections of \(30\mu \mathrm{m}\) - thick were rehydrated in PBS 1X, incubated 20 min at RT in PBS with \(0.3\%\) triton X- 100, saturated in blocking solution (PBS with \(5\%\) BSA) and finally incubated with primary Abs (Table S2). After washing, secondary Abs were incubated for 1h at RT with DAPI for nuclear staining and mounting using Permafluor mounting medium (Thermofisher). Images were acquired using TCS SP8 confocal microscope and processed using Fiji software. For alcian blue and perilipin A staining, fixed and decalcified femur bones were embedded in paraffin, sectioned (7 \(\mu \mathrm{m}\) - thick) and deparaffinized with xylene. Staining of cartilage tissues was performed with a \(1\%\) alcian blue solution for 30 min. Images were acquired using a LEICA DM4000B microscope equipped with a DFC425C camera and processed with the Leica Application Suite V3.8 software. For perilipin A staining, heat induced epitope retrieval was performed in citrate sodium buffer solution. Sections were saturated for 1h in PBS \(1\%\) BSA at RT, washed in PBS \(0.2\%\) BSA and \(0.1\%\) Triton X- 100, and incubated with anti- perilipin A Ab in PBS BSA \(1\%\) overnight at \(4^{\circ}\mathrm{C}\) . After washing, sections were incubated with TRITC- coupled rabbit antiguanine pig Ab in PBS \(1\%\) BSA for 45 min and counterstained with DAPI. For Ox staining, 16 \(\mu \mathrm{m}\) frozen sections were permeabilized in TBS- \(0.3\%\) Triton X- 100 for 10 min and blocked in TBS- \(2.5\%\) BSA- \(2.5\%\) Donkey Serum for 1h at RT. Sections were incubated with anti- Osx Ab (rabbit, Santa Cruz SC- 22536R) in blocking solution overnight at \(4^{\circ}\mathrm{C}\) . After washing with TBS+0.025% Triton X- 100, sections were incubated in donkey anti- rabbit secondary Ab daylight 550 (SA5- 10039, invitrogen) in blocking solution. After washing, sections were incubated 15 min at RT in DAPI at \(0.1\mu \mathrm{g / mL}\) prior to mounting in GB- Mount (Diagonics). Image acquisitions were done using the ApoTome optical sectioning system (Zeiss) with an + +<--- Page Split ---> + +inverted microscope (Zeiss Axio Observer Z1). Osx quantification was performed using the ICY software. For human BMSC immunofluorescence studies, cells were plated on coverslips and fixed with \(4\%\) PFA in PBS. Fixed cells were permeabilized with Triton X \(0.3\%\) for \(10\mathrm{min}\) , blocked with PBS \(5\%\) BSA, \(5\%\) goat serum and incubated with unlabeled primary CXCL12 mAb overnight at \(4^{\circ}\mathrm{C}\) followed by secondary AF633- coupled goat anti- mouse polyclonal Ab (Invitrogen) and the nuclear dye Hoechst 33342. Images were obtained with a Plan- . Apochromatic objective using the LSM800 confocal microscope (Carl Zeiss). Sections were acquired as serial z stacks \((0.39\mu \mathrm{m}\) apart) and were subjected to three- dimensional reconstruction (Zen 2.3 System). + +Bone histomorphometry was performed in plastic samples, allowing the measurements of bone formation and resorption parameters. Mouse femurs were fixed in ethanol \(70^{\circ}\) , dehydrated and embedded in methyl methacrylate resin. Five micrometer- thick coronal sections were cut parallel to the long axis of the femur using an SM2500S microtome (Leica, Germany). Sections were deplastified, rehydrated and stained with toluidine blue or with naphthol 3- hydroxy- 2- naphthoic acid 4- chloro- 2- methylanilide (ASTR phosphate, Sigma, St Louis, France) for detecting mature osteoclasts with TRAP staining. Quantifications were made on a polarizing microscope (Nikon) using a software package developed for bone histomorphometry (Microvision, France). To allow the measure of dynamic parameters of bone formation, mice were intraperitoneally injected with tetracycline \((20\mathrm{mg / kg})\) and calcein \((10\mathrm{mg / kg}\) ; Sigma) 5 days and 1 day respectively before being killed. Two \(12 - \mu \mathrm{m}\) - thick unstained sections were taken for measurement of the dynamic parameters under UV light. The matrix apposition rate (MAR) was measured using the Microvision image analyzer by a semiautomatic method using tetracycline and calcein double- labeled bone surfaces. The mineralizing surfaces (MS/BS) were measured in the same areas using the objective eyepiece Leitz integrate plate II. All the histomorphometric parameters were recorded in compliance with the recommendation of the + +<--- Page Split ---> + +American Society for Bone and Mineral Research Histomorphometry Nomenclature Committee. Three animals per genotype were analyzed by two different investigators. + +## Bone structure analysis by micro-computed tomography + +Femurs were collected for bone microarchitecture analysis after fixation and before decalcification. Femurs analyzed with high- resolution microcomputed tomography (micro- CT) using a Skyscan 1272 microCT (SkyScan, Kontich, Belgium). Measurements were made on the distal metaphysis of the femurs using the following acquisition parameters: voltage 60kV, pixel size \(6\mu \mathrm{m}\) , Filter \(\mathrm{Au} + 0.5\mathrm{mm}\) . After 3- dimensional images reconstruction with NRecon®, analyses were performed on the medial tibial plateau in the coronal view. Morphometric parameters such as Bone Volume/Tissue volume (BV/TV, \(\%\) ), Trabecular Thickness (Tb.Th, mm) Trabecular number (Tb.Tn, 1/mm) Trabecular Separation (Tb.Sp, mm) were assessed. + +## Cell culture and differentiation + +Mouse osteoblastic differentiation was performed for 3 weeks in \(\alpha\) - MEM medium with \(10\%\) FBS, \(1\%\) P/S, \(50\mu \mathrm{M}\beta\) - mercaptoethanol supplemented with \(50\mu \mathrm{g / mL}\) L- ascorbic acid and 10 mM glycerophosphate (Sigma) either from SSCs or sorted OPCs. Alkaline phosphatase staining was performed after 14 days of differentiation according to the Alkaline phosphatase Kit (Sigma). At day 21, cultures were fixed with \(4\%\) PFA, stained with alizarin red and quantified using the Osteogenesis assay kit (Millipore). When specified, AMD3100 (versus vehicle) was added into the osteogenic medium every 2 days at \(10\mu \mathrm{M}\) respectively. Chondro- and adipogenic differentiations of SSCs were performed according to the StemPro- Chondrogenesis or - Adipogenic Differentiation Kits (ThermoFisher) for 2 weeks. After fixation, cells were treated with either Alcian Blue \(1\%\) (Sigma) to stain chondrocyte matrix or Oil Red O solution (Sigma) to reveal lipid droplets. For in vitro human osteogenic differentiation assays, expanded BMSCs + +<--- Page Split ---> + +were seeded at \(3 \times 10^{3} \text{per cm}^2\) in \(\alpha\) - MEM supplemented with \(10\%\) FBS and \(1\%\) antibiotics. After cell adhesion, medium was replaced by \(\alpha\) - MEM supplemented with \(10\%\) FBS, \(1\%\) antibiotics and \(0.1 \mu \text{M}\) dexamethasone, \(0.05 \text{mM}\) L- ascorbic acid- 2- phosphate and \(10 \text{mM} \beta\) - glycerophosphate. Medium was changed every 2 days during 3 weeks. Quantification of mineralization was performed after Alizarin Red S staining as described77. Human adipogenic differentiation assays were performed as described for the murine ones. + +## Osteoclast differentiation and functional analysis + +OCLs were differentiated in vitro as described78. Briefly, \(2.3 \times 10^{5} \text{BM cells/cm}^2\) were plated in MEM- alpha (ThermoFisher) complemented with \(5\%\) serum (Hyclone, GE Healthcare), \(1\%\) P/S, \(50 \mu \text{M} 2\) - mercaptoethanol, \(25 \text{ng/ml M}\) - CsF and \(30 \text{ng/ml Rank- L}\) (R&D Systems). OCL differentiation (multinucleated TRAP+ cells) was quantified at day 5 after TRAP coloration using the leukocyte acid phosphatase kit (Sigma). Matrix dissolution activity was evaluated by seeding a total of \(2 \times 10^{4}\) differentiated OCLs on 96- well osteoassay plates (Corning) in \(\alpha\) - MEM containing \(10\%\) FBS and \(30 \text{ng/ml Rank- L}\) . After 3 days, medium was removed and cells were detached by the addition of water. Resorbed areas were quantified using Fiji/ImageJ software79. + +## Cell cycle, viability, survival and proliferation assays + +For flow cytometry- based cell cycle analyses, bone cells were permeabilized, fixed with the FOXP3 permeabilization kit (Foxp3/Transcription Factor Staining Buffer Set; eBioscience) and labelled with a Ki67 Ab and DAPI. For BrdU assays, mice were injected intraperitoneally with \(180 \mu \text{g BrdU}\) (Sigma) and maintained with drinking water containing \(800 \mu \text{g/ml BrdU}\) and \(1\%\) glucose over 12 days. The BrdU labelling was analyzed by flow cytometry using the BrdU- FITC labeling kit (BD Biosciences). For in vitro BrdU incorporation, \(3 \mu \text{g/ml}\) of BrdU was added to the culture and after five days the percentage of incorporation was determined as + +<--- Page Split ---> + +above. Apoptosis was measured using the Annexin V detection kit (BD Biosciences) with DAPI staining. For in vitro proliferation assays, SSCs were detached with \(0.5\%\) trypsin and loaded at \(3 \times 10^{4}\) cells/well with cell trace violet (CTV, Thermofisher) for 15 min at \(37^{\circ}\mathrm{C}\) . CTV dilution was assessed by flow cytometry. To estimate the doubling time values, SSCs were seeded at \(3 \times 10^{3}\) cells/cm \(^{2}\) and counted after 3 days of culture. The doubling time was calculated as follows: (time of culture x \(\log (2)) / (\log (\text{final number of SSC}) - \log (\text{initial number of SSC}))\) . + +## Quantitative real time-PCR + +For mouse gene expression, total RNA was isolated from cultured SSCs or sorted primary cells using the RNeasy Plus Mini or Micro Kit (Qiagen) and reverse transcribed with oligo(dT) and SuperScript II Reverse Transcriptase (Invitrogen). Quantitative RT- PCR reactions were performed on a Light Cycler instrument (LC480, Roche Diagnostics) with the LightCycler 480 SYBR Green detection kit (Roche Diagnostics) using primers reported in Table S3. For human gene expression, total RNA was isolated from cultured BMSCs using Trizol Reagent (ThermoFisher). Reverse transcription was performed using SuperScriptVilo IV (ThermoFisher). When required, total RNA from WS BMSCs and their related controls were extracted from \(0.2 \times 10^{3}\) BMSCs and pre- amplified using CellsDirect One- Step qRT- PCR kit (Invitrogen). PCR reactions were performed using primers reported in Table S3 with Power SYBRGreen (Applied Biosystems) on a 7500 FAST apparatus (Applied Biosystems). Mouse \(\beta\) - actin and \(36b4\) and human \(\beta\) - ACTIN and GAPDH were used as standards for normalization. Relative quantification was determined by the comparative delta- delta- Ct \((2^{-\Delta \Delta \mathrm{CT}})\) method. Fold changes were calculated by setting the mean values obtained from WT cells as one. + +## Multiplex qPCR + +<--- Page Split ---> + +Multiplex qPCR was performed using the microfluidic Biomark system. One hundred SSCs were sorted into PCR tubes containing \(5 \mu \mathrm{l}\) of reverse transcription/pre-amplification mix containing 2X reaction buffer, SuperScriptIII from the CellsDirect One-Step qRT- PCR kit and 0.2X Taqman assay (Life technologies) (Table S4). cDNA pre-amplification was performed during 22 cycles and pre-amplified product was diluted 1:5 in TE buffer before processing with Dynamic Array protocol (Fluidigm). Cells expressing \(\beta\) - actin and control genes (Runx2, Col1α, Alp and Ibsp) and not Pax5 and/or Cd3 (negative controls) were considered for analyses. Expression of \(\beta\) - actin was used for normalization. Heatmaps were generated with http://www.heatmapper.ca using Z scores and principal component analysis (PCA) with R software. + +## RNA sequencing + +Pools of \(3 \times 10^{3}\) OPCs were sorted from the bone fraction into RLT buffer (Qiagen) with \(1\%\) of \(\beta\) - mercaptoethanol. RNA was isolated using RNeasy Micro Kit. cDNAs were generated from 400 to 1,000 pg of total RNA using Clontech SMART- Seq v4 Ultra Low Input RNA kit for Sequencing (Takara Bio Europe) and amplified with 12 cycles of PCR by Seq- Amp polymerase. For Tn5 transposon tagmentation, 600 pg of pre- amplified cDNAs were used by the Nextera XT DNA Library Preparation Kit (96 samples) (Illumina) followed by library amplification of 12 cycles. Purification was performed with Agencourt AMPure XP and SPRIselect beads (Beckman- Coulter). Sequencing reads were generated, in Paired- End mode, on the GenomEast platform (Illumina). FastQC program was used to evaluate the quality of the raw sequencing data and reads shorter than 50 bp were removed. Reads were aligned to the Mus musculus genome (mm10 build) using the Star tool80. Gene expression quantification was obtained using read counting software Htseq81. Normalization and differential analysis were carried out with DESeq2 package by applying the Benjamini- Hochberg FDR correction (p < + +<--- Page Split ---> + +0.05; 1.5- fold) for comparison between samples. Heatmaps and volcano plots were obtained using the web server Heatmapper and EnhancedVolcano packages respectively. + +## Statistics + +Data are expressed as mean \(\pm\) SEM. All statistical analyses were conducted using Prism software (GraphPad Software). A Kruskal- Wallis test was used to determine the significance of the difference between means of WT, \(+\) /1013 and 1013/1013 groups ( \(^{#}P < 0.05\) ; \(^{##}P < 0.005\) ; and \(^{###}P < 0.0005\) ). Unless specified, the unpaired two- tailed Student \(t\) test was used to compare means among two groups. + +<--- Page Split ---> + +# CXCR4 desensitization in skeletal stromal cells + +# LIST OF SUPPLEMENTARY MATERIALS + +Figure S1 related to Figure 5 + +Figure S2 related to Figure 6 + +Table S1: List of antibodies used for flow cytometry related to the main Figures 2, 4 and 5, and SF1 and 2. + +712 + +Table S2: List of antibodies used for immunofluorescence related to the main Figures 1, 2, 4 and 5, and SF2. + +715 + +Table S3: List of primers used for quantitative PCR related to the main Figures 3, 4, 5, and 6 and SF1. + +718 + +Table S4: List of primers used for the BioMark assay related to the main Figure 4. + +<--- Page Split ---> + +## 722 REFERENCES + +723 1 Wei, Q. & Frenette, P. S. Niches for Hematopoietic Stem Cells and Their Progeny. +724 Immunity 48, 632-648, doi:10.1016/j.immuni.2018.03.024 (2018). +725 2 Bianco, P. Stem cells and bone: a historical perspective. 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J Bone Miner Res 31, 1899- 1908, doi:10.1002/jbrmr.2868 (2016). + +Schindelin, J. et al. Fiji: an open- source platform for biological- image analysis. Nature methods 9, 676- 682, doi:10.1038/nmeth.2019 (2012). + +Dobin, A. et al. STAR: ultrafast universal RNA- seq aligner. Bioinformatics 29, 15- 21, doi:10.1093/bioinformatics/bts635 (2013). + +Anders, S., Pyl, P. T. & Huber, W. HTSeq- a Python framework to work with high- throughput sequencing data. Bioinformatics 31, 166- 169, doi:10.1093/bioinformatics/btu638 (2015). + +<--- Page Split ---> + +## ACKNOWLEDGMENTS + +We thank Dr. H. Gary and ML. Aknin (IPSIT, Facility PLAIMMO, Clamart), F. Mercier- Nomé (IPSIT, Facility PHIC, Clamart), Drs. V. Parietti- Montcuquet, C. Doliger, S. Duchez and N. Setterblad (Animal and Flow Cytometry Core Facilities, Institut de Recherche Saint- Louis, Paris), V. Nicolas (IPSIT, Facility MIPSIT, Chatenay- Malabry), D. Courilleau (IPSIT, Facility CIBLOT, Chatenay- Malabry), B. Lecomte (IPSIT, Facility ANIMEX, Clamart) and C. Cordier and J. Megret (Plateau technique de cytométrie, SFR Necker, Paris) for their technical assistance. We thank the Montpellier Preclinical Platform of the Research Infrastructure ECELLFRANCE for the microCT analyses as well as the Plateforme d'Irradiation (IRSN, Fontenay- Aux- Roses, France) for their technical assistance. The study was supported by the LabEx LERMIT supported by ANR grant ANR- 10- LABX- 33 under the Program "Investissements d'Avenir" ANR- 11- IDEX- 0003- 01, an ANR PRC grant (ANR- 17- CE14- 0019) to M.A- L., C.B- W. and coordinated by K.B. and by the Association Saint Louis pour la Recherche sur les Leucémies to KB. J.N. was a PhD fellow from the DIM Cancéropôle and the FRM. Z.A- N. was a fellowship recipient from the French Ministry. V.R. was supported by the FRM, La Ligue Contre le Cancer and la Société Française d'Hématologie. A.Bon. was supported by an ANR @RAction grant (ANR- 14- ACHN- 0008) and by a JCJC ANR grant (ANR- 19- CE15- 0019- 01) to ME. A.Bou. was supported by the ANR grant 17- CE14- 0019. J.L. was recipient from the People Program (Marie Curie Actions) of the European Union's Seventh Framework Program (FP7/2007- 2013) under REA grant agreement n. PCOFUND- GA- 2013- 609102, through the PRESTIGE Program coordinated by Campus France, and from an ANR grant (ANR- 17- CE14- 0019). V.B., N.D. and K.B. were supported by the INCa agency under the program PRT- K 2017. J.K. was supported by European Union's Horizon 2020 MSCA, Program under grant agreement 641833 (ONCORNET). D.H.M and P.M.M. were supported + +<--- Page Split ---> + +by the Division of Intramural Research of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. + +## AUTHOR CONTRIBUTIONS + +A.A. and J.N. designed and performed experiments, analyzed data and contributed to manuscript writing; Z.A-N., V.R., A.Bon., A.Bou., J.L., V.B., J.K., L.S., C.M. and A.C. performed experiments and analyzed data; S.P., N.D., M.A-L., S.J.C.M., G.L., F.G., C.B-W. and M.C-S. performed experiments, contributed to data analyses and reviewed the manuscript; D.H.M. and P.M.M provided WS samples and clinical data and reviewed the manuscript; M.E. and M.R. helped with the study design, performed experiments, contributed to data analyses and reviewed the manuscript; K.B. conceived, designed and supervised the study, contributed to data analyses, found funding for the study, and wrote the manuscript. + +## DECLARATION OF INTERESTS + +The authors declare no competing financial interests. + +<--- Page Split ---> + +## FIGURE LEGENDS + +Figure 1: WS- linked CXCR4 mutations are associated with reduced bone mass in mice. (A) The bone mineral density (BMD) of lumbar spine of WT, +/1013 and 1013/1013 mice was measured through Dual- energy x- ray absorptiometry. Results represent means \(\pm\) SEM with 3 mice per group. (B- D) 3D representative images of trabecular and cortical composites (B) and quantitative micro- CT analyses of trabecular (C) and cortical (D) parameters of femurs from WT and mutant mice. BV = bone volume; TV = trabecular volume; Tb.Nb = trabecular number; Tb.Sp = trabecular separation; Ct.BV = cortical bone volume; Ct.Th = cortical thickness. Data (means \(\pm\) SEM) are from three independent experiments with 7- 14 mice per group. (E) BM sections from WT and mutant mice were stained with toluidine blue coloration. Larger images show 2X inserts in trabecular areas. Bars: 200 \(\mu \mathrm{m}\) . Images are representative of at least three independent determinations. (F and G) BM sections from WT and mutant mice were stained for chondrocyte (alcian blue, F) or adipocyte (perilipin, G) markers. Bars: 20 (F) or 500 (G) \(\mu \mathrm{m}\) . Images are representative of at least three independent determinations. (H) BM sections from WT and mutant mice were immuno- stained for osteopontin (Opn) in association with DAPI. Trabeculae are indicated by white arrows. Bars: 250 \(\mu \mathrm{m}\) . Images are representative of five independent determinations. (I) Cartilaginous growth plates were evaluated based on overall growth plate thickness measured on microCt scans. Data (means \(\pm\) SEM) are from 2 independent experiments with 7- 8 mice per group. (J) Size (left) and weight (right) of WT and mutant mice were assessed at 8 weeks of age. Results (means \(\pm\) SEM) are from five independent experiments with ten mice per group. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P < 0.05; \*\*, P < 0.005 and \*\*\*, P < 0.0005 compared with WT samples; \$, P < 0.05; \$\$, P < 0.005 and \$\$\$, P < 0.0005 compared with +/1013 samples (as determined using the two- tailed Student's \(t\) test). + +<--- Page Split ---> + +Figure 2: Reduction of skeletal stromal cells in Cxcr41013-bearing mice. (A) Representative dot-plots showing the flow cytometric gating strategies used to sort stroma cells (defined as CD45- TER119-), committed OPCs (defined as CD45- TER119-CD31- Sca-1- CD51+ PDGFRα-) and SSCs (defined as CD45- TER119- CD31- Sca-1+CD51+PDGFRα+) in the mouse bone fraction. (B) Absolute numbers of the indicated stroma cell subsets from bone fractions were determined by flow cytometry in WT, +/1013 and 1013/1013 mice. Data (means ± SEM) are from at least six independent experiments with >10 mice per group. (C and D) Expression levels of Cxcr4 (C) or Ackr3 (D) were determined by flow cytometry on gated (Ter119- CD45-) stromal cells, SSCs and OPCs from bone fractions of WT and mutant mice. Left: Representative histograms for surface detection of Cxcr4 or Ackr3 on gated bone stromal cells. Background fluorescence is shown (isotype, dotted vertical line). Middle and right: Cxcr4- or Ackr3-positive fractions or MFI values obtained within bone stromal cells, SSCs and OPCs relative to background fluorescence based on the corresponding isotype control staining. Data are from at least four independent experiments with >10 mice per group. (E) Cell surface expression of Cxcr4 on SSCs upon exposure to 10 nM Cxcl12 at 37°C for 45 min. Cxcr4 expression on bone cells incubated in medium alone was set at 100% (dotted horizontal line). Data are pooled from three independent experiments with six mice per group. (F) Migration of cultured WT or mutant SSCs in response to 1 nM Cxcl12 in the presence or absence of 10 μM AMD3100 was assessed in three independent fields after crystal violet staining. Data are from three independent SSC cultures per genotype. (G) In vitro expanded SSCs from bone fractions of WT, +/1013 or 1013/1013 mice pre-incubated or not with 10 μM AMD3100 were stimulated 2 min with 10 nM Cxcl12 at 37°C and then the MFI values of phospho-Erk were determined by flow cytometry and represented as a fold change expression. Data are from three independent SSC cultures per genotype. (H) Cell surface expression of Ackr3 on SSCs upon exposure to 10 nM Cxcl12 at 37°C for 45 min. Ackr3 expression on bone cells incubated in + +<--- Page Split ---> + +medium alone was set at \(100\%\) (dotted horizontal line). Data are from three independent experiments with six mice per group. (I) Cultured WT or mutant SSCs were pre-treated or not with \(100~\mu \mathrm{M}\) of the Ackr3 antagonist CCX733 and then incubated with \(5~\mathrm{nM}\) Cxcl12- AF647 at \(37^{\circ}\mathrm{C}\) for \(60~\mathrm{min}\) . Cells were washed with an acidic glycine buffer to remove cell surface- bound Cxcl12- AF647. Geometric MFI values for Cxcl12- AF647 were determined by flow cytometry. No Cxcl12- AF647 uptake was observed in SSCs incubated at \(4^{\circ}\mathrm{C}\) . Data are pooled from three individual SSC cultures per genotype. (J) Flow-cytometric determination of the proportions of apoptotic (Annexin \(\mathrm{V^{+}}\) DAPI) SSCs and OPCs from bone fractions of WT and mutant mice. Data (means \(\pm\) SEM) are from three independent experiments with nine mice per group. (K) Schematic diagram for the generation of CD45.1 \(\rightarrow\) CD45.2 short (3 wks)- or long (16 wks)- term BM chimeras. (L) Proportions of WT donor CD45.1+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.2+ WT or mutant recipients 16 weeks after transplantation. Data (means \(\pm\) SEM) are from three independent experiments with 5-10 recipient mice per group. (M) Absolute numbers of stromal cells, SSCs, and OPCs were determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.2+ recipients 16 weeks after transplantation. (N) Sixteen weeks after transplantation, BM sections from WT or mutant CD45.2+ recipient mice reconstituted with WT donor CD45.1+ BM cells were immuno- stained for Opn in association with DAPI (bars: \(250~\mu \mathrm{m}\) ). Trabeculae are indicated by white arrows. Images are representative of at least three independent determinations. (O) Left: Proportions of WT donor CD45.1+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.2+ WT or mutant recipients 3 weeks after transplantation. Middle and right panels show the absolute numbers of stromal cells, SSCs, and OPCs determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.2+ recipients 3 weeks after transplantation. (P) Schematic diagram for the generation of CD45.2 \(\rightarrow\) CD45.1 short (3 wks)- or long (16 wks)- term BM + +<--- Page Split ---> + +chimeras. **(Q)** Proportions of WT or mutant donor CD45.2+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.1+ WT recipients 16 weeks after transplantation. Data (means \(\pm\) SEM) are from five independent experiments with 8- 12 recipient mice per group. **(R)** Absolute numbers of stromal cells, SSCs, and OPCs were determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.1+ recipients 16 weeks after transplantation. **(S)** Sixteen weeks after transplantation, BM sections from WT CD45.1+ recipient mice reconstituted with WT, +/1013 or 1013/1013 donor CD45.2+ BM cells were immunostained for Opn in association with DAPI (bars: \(250\mu \mathrm{m}\) ). Trabeculae are indicated by white arrows. Images are representative of at least three independent determinations. **(T)** Left: Proportions of WT or mutant donor CD45.2+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.1+ WT recipients 3 weeks after transplantation. Middle and right panels show the absolute numbers of stromal cells, SSCs, and OPCs determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.1+ recipients 3 weeks after transplantation. Data (means \(\pm\) SEM) are from three independent experiments with 6- 10 mice per group. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P <0.05; \*\*, P<0.005 and \*\*\*, P<0.0005 compared with WT cells; \&\&, P < 0.005 compared with untreated WT or mutant cells; \$, P < 0.05 and \$\$, P < 0.005 compared with +/1013 samples (as determined using the two- tailed Student's \(t\) test). + +Figure 3: Increased bone resorption and reduced bone formation in Cxcr41013-bearing mice. **(A)** Bone sections from WT and mutant mice were colored for Tartrate Resistant Acid Phosphatase (TRAP) activity. OCLs are visualized as brown- stained TRAP- positive cells attached to bone trabeculae and are indicated by arrows (representative images). **(B)** OCLs were quantified (Oc.S/BS) and (Oc.N/BV) for WT and mutant mice. Results represent means \(\pm\) SEM with 3 mice per group. **(C)** Total BM cells from WT and mutant mice were differentiated for 5 + +<--- Page Split ---> + +days in osteoclastic medium (Rank- L and M- Csf) and OCLs (TRAP- positive, multinucleated cells) were identified (left, representative images) and quantified (right). Results (means \(\pm\) SEM) are from 2 independent experiments with 3 mice per group. (D) In vitro differentiated OCLs from WT and mutant BM cells were analyzed for their resorptive capacity of a mineralized matrix. Pictures show the resorptive lacunae produced by OCLs (representative images). The proportion of lacunae surface relative to the whole surface was calculated and expressed as a percentage of the mineral area resorbed by WT OCLs (right panel). Results (means \(\pm\) SEM) are from 2 independent experiments with 3 mice per group. (E) The relative expression levels (RQ) of osteoclastic (Nfatc1, Ctsk, Clcn7 and Tnfrsf11a) genes were determined in osteoclastic differentiation cultures of total BM cells from WT and mutant mice by quantitative real- time PCR. Each individual sample was run in triplicate and has been standardized for 36B4 expression levels. Results represent means \(\pm\) SEM with 3 mice per group. (F and G) Dynamic histomorphometric measures of bone formation were compared between WT and \(+\) /1013 mice. OS/BS \(=\) Osteoid number / Bone surface; Obl.S/BS \(=\) Osteoblast surface / Bone surface; MS/BS \(=\) Mineralized surface / Bone surface; Dbl/BS \(=\) Double labelled surface / Bone surface. Results represent means \(\pm\) SEM with 3 mice per group. (H) The mineral apposition rates (MAR) were compared between WT and Cxcr41013- bearing mice. Results represent means \(\pm\) SEM with 3 mice per group. (I) Volcano plot analysis of differentially expressed genes obtained by RNA-seq between WT and 1013/1013 OPCs (p<0.05; FC≥2) performed on three biological replicates per group. (J and M) Heatmap representing the relative expression levels of selected genes (osteogenic, J and osteoclastogenic, M) expressed by sorted OPCs from WT and mutant mice. (K and N) Normalized counts of osteogenic (K) and osteoclastogenic (N) genes using the DESeq2 method obtained by RNA-seq in WT and mutant OPCs. (L) In vitro osteoblastic differentiation of sorted WT and mutant OPCs evaluated at day 21 post- culture in osteoblastic medium by Alizarin Red S coloration of mineral matrix + +<--- Page Split ---> + +deposition. The images are representative of 3 independent cultures. The quantification (means \(\pm\) SEM) from 3 independent culture conditions is shown. Kruskal–Wallis \(H\) test–associated p-values (#) are indicated. \*, P <0.05 compared with WT samples (as determined using the two-tailed Student's \(t\) test). + +Figure 4: Impaired osteogenic specification of Cxcr41013-bearing skeletal stromal/stem cells. (A) Ki-67 and DAPI co- staining was used to analyze by flow cytometry the cell cycle status of SSCs and OPCs from bone fractions of WT and mutant mice. Bar graphs show the percentage of cells (DAPIlowKi-67) in the quiescent G0 phase. Data (means \(\pm\) SEM) are from three independent experiments with nine mice per group. (B) Representative flow-cytometric detection of BrdU staining in SSCs from bone fractions of WT and mutant mice (left). Percentages of BrdU+ bone SSCs and OPCs after a 12-day labelling period (right). Data (means \(\pm\) SEM) are from three independent experiments with six mice per group. (C) Principal component analyses (PCA) of relative gene expression in SSCs sorted from the bone fractions of WT and mutant mice. (D) The heatmap shows the relative expression levels (RQ) normalized for \(\beta\) -actin expression levels in each sample of selected genes involved in SSC differentiation towards the osteogenic lineage (6 pools of 100 cells per condition). (E) RQ of the most regulated genes involved in differentiation and cell cycle of SSCs from the three genotypes. Data (means \(\pm\) SEM) are from two independent experiments with 6 mice per group. (F) Relative expression of osteoclastogenic genes (Tnfsf11, Tnfrsf11b, Csfl) in WT and mutant SSCs. Each individual sample was run in triplicate and has been standardized for \(\beta\) -actin expression levels and presented as relative expression to WT. (G) Immunofluorescence showing in red Osterix (Osx)-positive cells and in blue DAPI-stained nuclei in WT and mutant mice femurs (bars: 100 \(\mu \mathrm{m}\) ). Dashed lines indicate the limit between the cartilage growth plate (above the line) and the bone (below the line). Images are representative of at least 3 independent determinations. (H) + +<--- Page Split ---> + +Quantification of \(\mathrm{Osx^{+}}\) cells per \(\mathrm{mm}^2\) below the growth plate. Data (means \(\pm\) SEM) are from 3 to 5 independent mice. (I) Absolute numbers of the indicated stroma cell subsets from marrow fractions were determined by flow cytometry in WT, \(+ / 1013\) and 1013/1013 mice. Data (means \(\pm\) SEM) are from at least six independent experiments with \(>10\) mice per group. Kruskal–Wallis \(H\) test–associated p-values (#) are indicated. \*, P \(< 0.05\) ; \*\*, P \(< 0.005\) and \*\*\*, P \(< 0.0005\) compared with WT cells (as determined using the two-tailed Student’s \(t\) test). + +# Figure 5: Cxcr4 desensitization regulates the osteogenic differentiation of skeletal cells. + +(A) The number of colonies formed from bone fractions of WT, \(+ / 1013\) and 1013/1013 mice in CFU-F assays. Data (means \(\pm\) SEM) are from three independent experiments with 6-9 mice per group. (B) After in vitro loading with BrdU (5 days) or CTV (3 days), the percentages of BrdU\(^+\) (left) or CTV\(^1\)low (right) cells within WT and mutant bone-derived SSCs were determined by flow cytometry. (C) Bar graphs show the percentages of cultured WT or mutant SSCs in the quiescent G0 phase (DAPI\(^1\)low Ki-67\(^-\) , left) or with an apoptotic phenotype (Annexin V\(^+\) DAPI\(^-\) , right) as determined by flow cytometry. (D) Doubling time (left) and absolute numbers (right) of WT and mutant SSCs after 3 days of culture. Data (means \(\pm\) SEM) displayed in panels B, C and D are from 3-6 independent SSC cultures per genotype. (E) Alkaline phosphatase (Alp) staining was performed 14 days after initiation of the culture of WT and mutant SSCs in osteogenic medium supplemented every two days with 10 μM AMD3100 or vehicle (PBS). Quantitative analyses (number of Alp\(^+\) cells) were performed under an inverted microscope. Data (means \(\pm\) SEM) are from 4 independent cultures per genotype. (F) Alizarin Red staining was performed 21 days after initiation of the culture of WT and mutant SSCs as described above. Quantitative analyses (means \(\pm\) SEM) of staining were performed using the osteogenesis assay kit. (G) Expression levels of osteogenic genes were determined by qRT-PCR in WT and mutant SSCs 14 and 21 days after initiation of the osteogenic culture in the presence or absence + +<--- Page Split ---> + +of AMD3100. Each individual sample was run in triplicate and was standardized for \(\beta\) - actin expression levels. Results (means \(\pm\) SEM) are expressed as relative expression compared to WT samples. (H) Schematic diagram for daily AMD3100 in vivo i.p. injection for 21 days in WT and mutant mice. (I) Absolute numbers of the indicated stroma cell subsets from bone fractions of WT and mutant mice were determined by flow cytometry. Data (means \(\pm\) SEM) are from 2 independent experiments with 6 PBS injected mice and 12 AMD3100- injected mice per genotype. (J) BM sections from WT and mutant mice treated with vehicle (PBS) or AMD3100 were immunostained for Opn in association with DAPI. Bars: \(500\mu \mathrm{m}\) . Images are representative of 3 independent determinations. (K) Bone mineral density (BMD) values of lumbar spine from treated WT and mutant mice are shown. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P <0.05; \*\*, P<0.005 and \*\*\*, P<0.0005 compared with WT or untreated samples; \$, P < 0.05 compared with +/1013 samples; &, P < 0.05 compared with vehicle- treated mice (as determined using the two- tailed Student’s \(t\) test). + +Figure 6: BM stromal cells from WS patients displayed in vitro impaired osteogenic capacities. (A) Relative expression levels of osteogenic genes were determined by qRT- PCR at day 14 in osteogenic- induced cultures of two WS patients- derived BMSCs and 7 healthy donors- derived BMSCs. Each individual sample was run in triplicate and was standardized for 36B4 expression levels. Results (means \(\pm\) SEM) are expressed as relative expression compared to healthy samples (set at 1). (B) Alizarin Red staining was performed 21 days after initiation of the culture of 1.5 x \(10^{3}\) healthy or WS BMSCs in pro- osteogenic medium (left panel). Representative images for healthy and WS donors #1 and #2 are shown. Quantitative analyses of staining (means \(\pm\) SEM) were performed using the osteogenesis assay kit (right panel). (C) Oil Red O staining was performed 21 days after initiation of cultures of healthy or WS BMSCs + +<--- Page Split ---> + +# CXCR4 desensitization in skeletal stromal cells + +1179 in pro- adipogenic differentiation medium. Bars: \(200\mu \mathrm{m}\) . \*P <0.05 and \*\*, P<0.005 compared 1180 with healthy or untreated cells (as determined using the two- tailed Student's \(t\) test). 1181 + +<--- Page Split ---> + +1182 TABLE + +1183 + +
GenderAge (year)CXCR4 mutationChronic treatmentLumbar spineFemoral neck
P1Female37R334XNo-3.10
P2Female52R334XNo-1.1-1.8
P3(1)Male13S338XYes (2)-1.8-2.3
P4Female49R334XYes (3)-2.7-1.3
P5(1)Male15R334XYes (4)-1.8-2.2
+ +1184 + +**Table 1: Abnormal bone mineral density values in WS patients.** Characteristics of each patient with low BMD value are shown. T-scores for lumbar spine (L1-L4) and femoral neck have been evaluated. According to World Health Organization (WHO) criteria, values classify patients as osteopenic with a T-score between -1.0 and -2.5 or osteoporotic with a T-score at or below -2.5. Values outside the normal range defined by WHO are italicized. (1) For patients 3 and 5, because of their young age, Z-scores are given with a value at or below -2.0 considered as abnormal; (2) G-CSF since age of 2; (3) G-CSF several years at the time of scan; (4) G-CSF for 6 months at the time of scan. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + + +<--- Page Split ---> +![PLACEHOLDER_59_0] + + +<--- Page Split ---> +![PLACEHOLDER_60_0] + + + +![PLACEHOLDER_60_1] + + + +
C
+ +![PLACEHOLDER_60_2] + + + +
Adipogenic
+ +![PLACEHOLDER_60_3] + + + +
Alizarin Red (day 21)
+ +![PLACEHOLDER_60_4] + + + +
Osteogenic
+ +![PLACEHOLDER_60_5] + + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SUPPLEMENTALINFORMATIONS.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6_det.mmd b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..964a03792ec5548dd9e33294158043241e6a13fb --- /dev/null +++ b/preprint/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6/preprint__011f1f7cdec2740845fc5c2f410ff02c63329260c767801a3ae4c3d8ae57e6f6_det.mmd @@ -0,0 +1,752 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 872, 208]]<|/det|> +# WHIM Syndrome-linked CXCR4 mutations drive osteoporosis by mitigating the osteogenic specification of skeletal stromal cells + +<|ref|>text<|/ref|><|det|>[[44, 230, 202, 270]]<|/det|> +Adrienne Anginot Inserm + +<|ref|>text<|/ref|><|det|>[[44, 277, 160, 315]]<|/det|> +Julie Nguyen Inserm + +<|ref|>text<|/ref|><|det|>[[44, 323, 404, 363]]<|/det|> +Zeina Abou- Nader Institut de Recherche Saint- Louis, EMiLy + +<|ref|>text<|/ref|><|det|>[[44, 370, 404, 409]]<|/det|> +Vincent Rondeau Institut de Recherche Saint- Louis, EMiLy + +<|ref|>text<|/ref|><|det|>[[44, 415, 180, 454]]<|/det|> +Amélie Bonaud Inserm + +<|ref|>text<|/ref|><|det|>[[44, 462, 303, 501]]<|/det|> +Antoine Boutin Université Côte d'Azur, CNRS + +<|ref|>text<|/ref|><|det|>[[44, 508, 404, 548]]<|/det|> +Julia Lemos Institut de Recherche Saint- Louis, EMiLy + +<|ref|>text<|/ref|><|det|>[[44, 555, 160, 592]]<|/det|> +Valeria Bisio Inserm + +<|ref|>text<|/ref|><|det|>[[44, 601, 336, 640]]<|/det|> +Joyce Koenen INSERM, Université Paris- Saclay + +<|ref|>text<|/ref|><|det|>[[44, 647, 432, 686]]<|/det|> +Léa Sakr Université de Paris, BIOSCAR Inserm U1132 + +<|ref|>text<|/ref|><|det|>[[44, 693, 226, 731]]<|/det|> +Caroline Marty INSERM UMR- 1132 + +<|ref|>text<|/ref|><|det|>[[44, 739, 432, 779]]<|/det|> +Amélie Coudert Université de Paris, BIOSCAR Inserm U1132 + +<|ref|>text<|/ref|><|det|>[[44, 786, 485, 825]]<|/det|> +Sylvain Provot INSERM https://orcid.org/0000- 0003- 4087- 4450 + +<|ref|>text<|/ref|><|det|>[[44, 832, 575, 872]]<|/det|> +Nicolas Dulphy Université de Paris https://orcid.org/0000- 0002- 1243- 6456 + +<|ref|>text<|/ref|><|det|>[[44, 878, 485, 918]]<|/det|> +Michel Aurrand- Lions INSERM https://orcid.org/0000- 0002- 8361- 3034 + +<|ref|>text<|/ref|><|det|>[[44, 925, 205, 942]]<|/det|> +Stéphane Mancini + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 475, 65]]<|/det|> +Inserm https://orcid.org/0000- 0001- 9255- 4606 + +<|ref|>text<|/ref|><|det|>[[44, 71, 465, 111]]<|/det|> +Gwendal lazennec CNRS https://orcid.org/0000- 0002- 8522- 1763 + +<|ref|>text<|/ref|><|det|>[[44, 117, 864, 159]]<|/det|> +David McDermott National Institute of Allergy and Infectious Diseases https://orcid.org/0000- 0001- 6978- 0867 + +<|ref|>text<|/ref|><|det|>[[44, 163, 131, 200]]<|/det|> +Fabien Guidez INSERM + +<|ref|>text<|/ref|><|det|>[[44, 208, 602, 249]]<|/det|> +Claudine Blin- Wakkach Université Côte d'Azur https://orcid.org/0000- 0002- 2621- 3907 + +<|ref|>text<|/ref|><|det|>[[44, 254, 427, 295]]<|/det|> +Philip Murphy National Institutes of Health, United States + +<|ref|>text<|/ref|><|det|>[[44, 301, 825, 343]]<|/det|> +Martine Cohen- Solal Université de Paris and BIOSCAR Inserm U1132 https://orcid.org/0000- 0002- 8582- 8258 + +<|ref|>text<|/ref|><|det|>[[44, 348, 475, 389]]<|/det|> +Marion Espeli Inserm https://orcid.org/0000- 0001- 5005- 1664 + +<|ref|>text<|/ref|><|det|>[[44, 394, 247, 435]]<|/det|> +Matthieu Rouleau Université Côte d'Azur + +<|ref|>text<|/ref|><|det|>[[44, 440, 765, 483]]<|/det|> +Karl Balabanian ( \(\boxed{\bullet}\) karl.balabanian@inserm.fr) Institut de Recherche Saint- Louis, EMiLy https://orcid.org/0000- 0002- 0534- 3198 + +<|ref|>sub_title<|/ref|><|det|>[[44, 523, 102, 541]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 560, 928, 604]]<|/det|> +Keywords: Skeletal stromal/stem cell, Bone marrow, CXCR4 signaling, Osteogenesis, WHIM Syndrome, Osteoporosis + +<|ref|>text<|/ref|><|det|>[[44, 621, 330, 642]]<|/det|> +Posted Date: January 18th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 659, 475, 680]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1186490/v1 + +<|ref|>text<|/ref|><|det|>[[42, 696, 910, 740]]<|/det|> +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 78, 886, 880]]<|/det|> +WHIM Syndrome- linked CXCR4 mutations drive osteoporosis by mitigating the osteogenic specification of skeletal stromal cellsA. Anginot1,2,3,#, J. Nguyen2,4,#, Z. Abou-Nader1,2,3, V. Rondeau1,2,3, A. Bonaud1,2,3, A. Boutin5, J. Lemos1,2,3, V. Bisio1,2,3, J. Koenen2,4, L. Sakr6, C. Marty6, A. Coudert6, S. Provot6, N. Dulphy1,2,3, M. Aurrand- Lions2,7, S.J.C. Mancini2,7, G. Lazennec2,8, D.H. McDermott9, F. Guidez3,10, C. Blin- Wakkach5, P.M. Murphy9, M. Cohen- Solal6, M. Espéli1,2,3,£, M. Rouleau5,£, and K. Balabanian1,2,3,*1'Université de Paris, Institut de Recherche Saint- Louis, INSERM U1160, 75010 Paris, France.2'CNRS, GDR3697 "Microenvironment of tumor niches", Micronit, France. 3'OPALE Carnot Institute, The Organization for Partnerships in Leukemia, Hôpital Saint- Louis, 75010 Paris, France. 4'Inflammation, Chemokines and Immunopathology, INSERM, Université Paris- Saclay, 92140, Clamart, France. 5'Université Côte d'Azur, CNRS, LP2M, UMR 7370, Faculté de Médecine, 06107, Nice, France. 6'Université de Paris, BIOSCAR Inserm U1132, Department of Rheumatology and Reference Center for Constitutional Bone Diseases, AP- HP Hospital Lariboisière, 75010 Paris, France. 7'Aix Marseille Univ, CNRS, INSERM, Institut Paoli- Calmettes, CRCM, 13273, Marseille, France. 8'CNRS, SYS2DIAG- ALCEDIAG, Cap Delta, Montpellier, France. 9'Molecular Signaling Section, Laboratory of Molecular Immunology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892. 10'Université de Paris, Institut de Recherche Saint- Louis, INSERM U1131, 75010 Paris, France. 11'AA and JN share the first author position. 12'ME and MR equally contributed. 13'Correspondence & lead contact: karl.balabanian@inserm.fr. Running title: CXCR4 desensitization in skeletal stromal cells. One Sentence Summary: Using a mouse model harboring a naturally occurring WHIM Syndrome (WS)- linked gain- of- function CXCR4 mutation and bone marrow samples from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 81, 884, 339]]<|/det|> +27 healthy and WS donors, Anginot et al. show that CXCR4 desensitization acts as a gatekeeper 28 orchestrating the osteogenic specification of skeletal stromal cells. 29 Abbreviations: BMSC: Bone marrow stromal cell; C-tail: Carboxyl-terminal tail; HSPC: 30 Hematopoietic stem and progenitor cell; MFI: Mean fluorescence intensity; OBL: Osteoblast; 31 OCL: Osteoclast; Ocn: Osteocalcin; OPC: Osteoblastic progenitor; Opn: Osteopontin; Oxs: 32 Osterix; SLAM: Signaling lymphocyte activation molecule; SSC: Skeletal stromal/stem cell; 33 WS: Warts, Hypogammaglobulinemia, Infections and Myelokathexis Syndrome. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 230, 100]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[113, 114, 884, 463]]<|/det|> +WHIM Syndrome (WS) is a rare immunodeficiency caused by gain- of- function CXCR4 mutations. Here we report for the first time a substantial decrease in bone mineral density in \(25\%\) of WS patients and bone defects leading to osteoporosis in a WS mouse model. Reduction in bone content involved impaired CXCR4 desensitization that disrupts cell cycle progression and osteogenic specification of mouse bone marrow (BM)- residing skeletal stromal/stem cells (SSCs). This was also evidenced in BM stromal cells from WS patients. Consistent with this, chronic treatment with the CXCR4 antagonist AMD3100 normalized in vitro osteogenic fate of mutant SSCs and reversed in vivo loss in skeletal cells, thus demonstrating that proper CXCR4 desensitization is required for the osteogenic specification of BM SSCs. Our study provides novel mechanistic insights into how CXCR4 signaling regulates the osteogenic fate of BM SSCs. + +<|ref|>text<|/ref|><|det|>[[115, 510, 881, 560]]<|/det|> +Keywords: Skeletal stromal/stem cell; Bone marrow; CXCR4 signaling; Osteogenesis; WHIM Syndrome; Osteoporosis. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 84, 280, 101]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[111, 111, 885, 730]]<|/det|> +The bone marrow (BM) is a complex structural and primary immune organ whose development and maintenance depend on multiple cell types including cells of the hematopoietic lineage like hematopoietic stem and progenitor cells (HSPCs), but also vascular cells and numerous skeletal cells encompassing BM stromal cells (BMSCs), skeletal progenitor/precursor cells as well as bone- making osteoblasts (OBLs) \(^{1,2}\) . Together these cells compose specialized micro- anatomical structures called “niches” that sustain their survival and differentiation \(^{3 - 9}\) . For instance, the HSPC niches are thought to be composed of perivascular stromal units associated with sinusoids and arterioles \(^{10 - 15}\) . Bone and adipose cells are thought to derive from subsets of BMSCs that are located near blood vessels and function as skeletal stromal/stem cells (SSCs) \(^{16 - 19}\) . However, the exact localization, composition and crossover of these niches in relation with bone function are not yet established. Bone tissue homeostasis relies on the balance between formation and resorption of bone matrix mediated by effector cells that derive from SSCs and HSPCs respectively. Disequilibrium of this balance can lead to diseases such as osteoporosis or osteopetrosis. In such a landscape, SSCs are key players: not only they give rise to OBLs but they also contribute to perivascular structures important for HSPCs \(^{20 - 28}\) . Understanding how SSCs maintain their identity, achieve plasticity and support hematopoiesis in adult BM is thus an important emerging field \(^{3,6,9,12,29}\) . Recently, Ambrosi and coll. showed that intrinsic ageing of SSCs skews skeletal and hematopoietic lineage outputs, leading to fragile bones \(^{30}\) . However, both extrinsic and intrinsic mechanisms regulating their fate remain incompletely understood. + +<|ref|>text<|/ref|><|det|>[[112, 737, 884, 888]]<|/det|> +In adult BM, signaling by the G protein- coupled receptor CXCR4 on HSPCs in response to stimulation by the chemokine CXCL12/Stromal cell- derived factor- 1, produced by BMSCs constitutes a key pathway through which the stromal niches and HSPCs communicate \(^{31 - 35}\) . Conditional ablation of Cxcl12 from perivascular stromal cells or OBLs demonstrated that HSCs occupy a perivascular but not an endosteal niche \(^{21,36}\) , whereas targeted deletion of Cxcl12 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 885, 530]]<|/det|> +from BM stromal cells has allowed the identification of specialized niches supporting leukemia stem cell maintenance37. Both Cxcr4 and Cxcl12 are broadly expressed by non- hematopoietic tissues and cell types and have multifunctional roles beyond hematopoiesis. Since mice deficient for Cxcr4 or Cxcl12 die perinatally, our understanding of the role of the Cxcl12/Cxcr4 axis in regulating the BM ecosystem is mostly based on relatively selective loss- of- function models21,38- 42. Conditional inactivation of Cxcl12 or Cxcr4 in paired- related homeobox gene 1 (Prx1)- or osterix (Osx)- expressing cells, i.e. respectively multipotent mesenchymal progenitors or osteoprogenitor cells (OPCs) and descendant OBLs, was associated with reduced postnatal bone formation, suggesting a positive regulatory role of this pair in OBL development and/or function21,41,42. To our knowledge, this has not been reported in mice with selective deficiency of Cxcr4 in HSPCs. Single cell transcriptomics recently suggested heterogeneity within adult Cxcl12- expressing SSCs poised to undergo either adipogenic or osteogenic specification43. However, it is still unclear whether Cxcr4 signaling regulates osteogenic specification of SSCs. + +<|ref|>text<|/ref|><|det|>[[113, 540, 885, 856]]<|/det|> +Here, we addressed this point using as a paradigm the WHIM Syndrome (WS), a rare immunodeficiency caused by viable inherited heterozygous gain- of- function mutations in CXCR4 affecting homologous desensitization of the receptor, thus resulting in enhanced signaling following CXCL12 stimulation, defective lymphoid differentiation of HSPCs and reduced blood leukocyte numbers44- 46. Taking advantage of a mouse strain that harbors the naturally occurring WS- linked heterozygous CXCR4S338X mutation (Cxcr4+/1013, +/1013)47- 50, and of human BM samples from WS donors and clinical data from 19 WS patients, we investigated whether WS mutations affect the SSC landscape. WS- linked CXCR4 mutations were associated with reduced bone mass in mice and humans. In mice, this relied on impaired CXCR4 desensitization that disrupts cell cycle progression and osteogenic commitment of + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[506, 41, 881, 58]]<|/det|> +# CXCR4 desensitization in skeletal stromal cells + +<|ref|>text<|/ref|><|det|>[[57, 81, 884, 164]]<|/det|> +SSCs. This was also evidenced in BMSCs from WS patients. Thus, proper CXCR4 desensitization is required for the osteogenic specification of BM SSCs + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 211, 100]]<|/det|> +## RESULTS + +<|ref|>text<|/ref|><|det|>[[110, 110, 885, 896]]<|/det|> +WS- linked CXCR4 mutations are associated with reduced bone mass in mice and humansFollowing CXCL12 stimulation, \(\beta\) - arrestins are recruited to the carboxyl- terminal tail (C- tail) domain of CXCR4, precluding further G- protein activation (i.e. desensitization) and leading to receptor internalization51. Both processes are dysregulated in WS most often due to autosomal- dominant gain- of- function mutations that result in the distal truncation of the C- tail of CXCR4 and a desensitization- resistant, hyperactive receptor52. Although the impact of these WS mutations on immune cells is currently being understood47- 50,53, nothing is known about their impact on the SSC landscape. Bone mineral density (BMD) values were measured in 19 patients with WS for lumbar spine and femoral neck by total body dual- energy X- ray absorptiometry. BMD T- and Z- scores were found to be low at least in one site in five patients (Table 1). Likewise, this was evidenced in adult Cxcr41013- bearing (i.e. heterozygous [+/1013] and homozygous [1013/1013]) mice, as compared to Cxcr4+/+ (WT) mice. Analyses of lumbar spine revealed decreased BMD values in mutant mice, in a Cxcr41013 allele dose- dependent manner (Fig. 1A). Micro- computed tomography (microCT) analyses further unraveled reduced bone content in mutant mice (Fig. 1B). In mutant femurs, there was a reduction in the trabecular bone density that followed a Cxcr41013 allele copy number- dependent pattern. This was characterized by a significant decrease in bone volume and trabecular numbers, while the trabecular separation was increased compared to WT mice (Fig. 1C). The cortical bone volume and thickness were also affected (Fig. 1B and 1D). This gene- dependent reduction was observed among both female and male mutant mice. Histomorphometric analyses confirmed decreased bone volume and trabecular numbers in mutant mice, as shown by toluidine blue staining (Fig. 1E). Strikingly, staining for alcian blue and perilipin that are used for chondrocyte and adipocyte identification respectively, were unaltered in mutant bone (Fig. 1F and 1G). Consistently, the thickness of the cartilaginous growth plate was similar in mice carrying the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 83, 884, 200]]<|/det|> +Cxcr4 mutation compared to WT ones (Fig. 1E, 1F, 1H and 1I). Moreover, adult mutant mice did not exhibit significant changes of body size or weight (Fig. 1J). Overall, these findings revealed an osteopenic skeleton in Cxcr41013-bearing mice and low BMD in 25% of WS patients. + +<|ref|>sub_title<|/ref|><|det|>[[115, 246, 636, 265]]<|/det|> +## Reduction of skeletal stromal cells in Cxcr41013-bearing mice + +<|ref|>text<|/ref|><|det|>[[113, 279, 884, 596]]<|/det|> +We then evaluated by flow cytometry the bone composition of WT and mutant mice with a focus on skeletal cells that encompass BMSCs, SSCs and OBLs54. Long bones were flushed and then digested. Total stromal cells in the bone fraction were identified as negative for CD45, Lineage (including Ter119), c- Kit and CD71 expression as previously reported12,55. Endothelial cells were excluded based on CD31 expression. Two distinct CD51+ stromal cell subsets were identified based on Sca- 1 and PDGFRα: SSCs (Sca- 1+PDGFRα+) and committed osteoblast progenitors (Sca- 1+PDGFRα- , herein referred as OPCs) (Fig. 2A). We observed a global reduction of the number of stromal cells that followed a Cxcr41013 allele dose- dependent pattern (Fig. 2B). There was a significant decrease of OPCs, and to a lesser extent of SSCs, thus reinforcing that the landscape of the stroma in bone is altered in Cxcr41013- bearing mice. + +<|ref|>text<|/ref|><|det|>[[113, 611, 884, 892]]<|/det|> +We next examined in vitro the function of the signaling trio formed by Cxcl12 and its two receptors Cxcr4 and Ackr3 in skeletal cells. Membrane expression of Cxcr4 and Ackr3 was similar between WT and mutant skeletal cells including SSCs (Fig. 2C and 2D). However, +/1013 and 1013/1013 SSCs displayed both impaired Cxcr4 internalization following Cxcl12 stimulation as well as increased Cxcl12- mediated chemotaxis that was abolished by the specific Cxcr4 antagonist AMD3100 (Fig. 2E and 2F). These dysfunctions likely relied on the enhanced signaling properties of the truncated Cxcr4 receptor as revealed by Erk PhosphoFlow analyses (Fig. 2G). Combined with the apparent preserved capacity of Ackr3 to bind and internalize Cxcl12 in vitro (Fig. 2H and 2I), these findings indicated a functional expression of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 883, 135]]<|/det|> +desensitization- resistant C- tail- truncated Cxcr41013 receptor on SSCs. Abnormal Cxcr4 signaling was not associated with changes in apoptosis of SSCs (Fig. 2J). + +<|ref|>text<|/ref|><|det|>[[113, 144, 885, 860]]<|/det|> +To determine whether reduction of bone content in Cxcr41013- bearing mice resulted from defects intrinsic to skeletal cells and/or an alteration of the hematopoietic (or another non- stromal) system, we performed reciprocal long (16 weeks)- and short (3 weeks)- term BM reconstitution experiments. First, BM cells from WT CD45.1+ mice were transplanted into lethally irradiated CD45.2+ WT or mutant (+/1013 and 1013/1013) mice (Fig. 2K). Sixteen weeks later, mutant recipients exhibited CD45.1+ chimerism in hematopoietic compartments similar to those of WT recipients (Fig. 2L), but displayed reduced numbers of skeletal cells including SSCs and OPCs (Fig. 2M). Confocal imaging analyses confirmed that transplantation of WT BM was not sufficient to rescue the trabecular network in mutant recipients (white arrows, Fig. 2N). This was also evidenced three weeks after WT BM transplantation (Fig. 2O). These results suggested that skeletal cell- autonomous Cxcr4 regulation contributes to the persistent bone defects in adult Cxcr41013- bearing mice. We then performed reverse chimeras in which irradiated CD45.1+ WT mice were reconstituted with WT, +/1013 or 1013/1013 CD45.2+ BM (Fig. 2P). Sixteen weeks later, CD45.2+ chimerism of LT- HSCs and leukocytes were decreased respectively in BM and blood of CD45.1+ WT recipients engrafted with mutant BM, confirming the impaired reconstitution capacity of mutant HSCs (Fig. 2Q and 49). There were significantly lower numbers of skeletal cells and defective trabecular bone content in Cxcr41013- bearing BM- chimeric mice compared to WT chimeras as early as 3 weeks post- transplantation (Fig. 2R- T), thereby indicating cell- extrinsic Cxcr4- mediated regulation of the skeletal landscape. Altogether, these findings suggest that impaired Cxcr4 desensitization in both skeletal and hematopoietic cells have combinatorial effects on bone landscape dysregulation in adult Cxcr41013- bearing mice. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 818, 102]]<|/det|> +## Increased bone resorption and reduced bone formation in Cxcr41013-bearing mice + +<|ref|>text<|/ref|><|det|>[[113, 111, 885, 627]]<|/det|> +Bone is maintained by coupled activities of bone- forming OBLs and bone- resorbing osteoclasts (OCLs). Alterations in bone balance can result in pathologic bone loss and osteoporosis. This led us to investigate whether and how the gain- of- Cxcr4- function mutation modulates the OBL/OCL balance. First, we analyzed bone resorption by quantifying OCL numbers in mice using Tartrate Resistant Acid Phosphatase (TRAP) staining56. We observed increased OCL surface (Oc.S/BS) and number (Oc.N/BV) in mutant mice compared to WT ones (Fig. 3A and 3B). To determine whether the increased bone resorption in mutant mice resulted from OCL- intrinsic defects, we performed in vitro OCL differentiation from BM cells in the presence of M- Csf and Rank- L and tested their bone resorption capacity. Similar OCL numbers and bone matrix resorption activities were observed among WT and mutant cultures (Fig. 3C and 3D), suggesting preserved intrinsic capacities of mutant BM myeloid cells to differentiate in vitro into functional OCLs. Congruent with this, we observed no changes in expression levels of osteoclastogenic genes in mutant cultures compared to WT ones (Fig. 3E). These findings indicate that the Cxcr4 mutation does not affect in vitro OCL differentiation and function, but suggest that osteoclastogenesis and increased bone resorption in mutant mice may be promoted by the BM environment. + +<|ref|>text<|/ref|><|det|>[[114, 639, 885, 888]]<|/det|> +Cxcr41013- bearing mice exhibited maintained bone formation as revealed by osteoid surface (OS/BS) and osteoblast surface (Obl.S/BS) compared to WT mice (Fig. 3F). Dynamic parameters of in vivo bone formation were also assessed by quantifying bone surfaces labelled with tetracycline and calcein (Fig. 3G). Total and double labelled surfaces were lower in mutant than WT mice (Fig. 3G), whilst mineral apposition rate (MAR) were similar in WT and Cxcr41013- bearing mice (Fig. 3H). This suggests a decrease in bone formation related to a lower number of OBLs with maintained activity of individual OBL. In line with preserved intrinsic bone formation capacities of active osteoblastic lineage cells in mutant mice, high- throughput + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 561]]<|/det|> +RNA sequencing (RNA- seq) analyses of bulks sorted by flow cytometry on the basis of CD51 and Sca- 1 markers (Fig. 2A) from the bone fraction highlighted in mutant OPCs a gene signature with preserved mineralized matrix potential (Fig. 3I- K). In agreement, sorted OPCs from mutant mice were as efficient as WT ones in vitro at producing mineralized matrix after 21- days culture in osteogenic medium as determined by Alizarin Red (AR) staining (Fig. 3L). These findings are in line with efficient terminal osteogenic differentiation and preserved bone formation capacities in Cxcr41013- bearing mice. Given that osteogenic cells support osteoclastogenesis through the production of soluble factors such as Rank- L (Tnfsf11)57, we questioned our RNAseq data on the related gene expression profile in mutant and WT OPCs. No major changes in expression levels of pro- osteoclastogenic or anti- resorptive genes were revealed in mutant OPCs (Fig. 3M and 3N). Taken as a whole, these findings suggest that the hematopoietic contribution to bone loss in Cxcr41013- bearing mice likely involves dysregulation of the OCL compartment regardless of their activity. Moreover, the observation that immature and mature osteogenic cells, i.e., OPCs and OBLs, displayed preserved intrinsic functions led us to study earlier developmental steps of the osteolineage. + +<|ref|>sub_title<|/ref|><|det|>[[115, 606, 768, 626]]<|/det|> +## Impaired osteogenic specification of Cxcr41013- bearing skeletal stromal cells + +<|ref|>text<|/ref|><|det|>[[113, 639, 884, 888]]<|/det|> +We thus investigated the intrinsic characteristics of SSCs carrying the Cxcr4 mutation. Undifferentiated stem cells are characterized by their slow cell cycle progression in unperturbed conditions9,58. This led us to interrogate by flow cytometry the cycling status of SSCs from the bone fractions of Cxcr41013- bearing mice by performing DAPI/Ki- 67 staining. A slight but significant increase in the frequency of cells in the quiescent G0 state (DAPIlowKi- 67) was observed among 1013/1013 SSCs and spared the more differentiated osteoblastic pool (Fig. 4A). The turnover of those cells was then studied by performing a 12- day BrdU pulse- chase assay in vivo (Fig. 4B). Consistent with previous studies28,59, the fraction of BrdU+ cells in WT + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 885, 563]]<|/det|> +SSCs reached \(\sim 5\%\) , while we observed a \(Cxcr4^{1013}\) allele copy number-dependent reduction in BrdU incorporation within mutant SSCs. No changes were observed among mutant OPCs compared to WT ones. Combined to reduced SSC and OPC numbers in mutant bones (Fig. 2B), these findings are suggestive of reduced cycling and osteogenic differentiation capacities of \(Cxcr4^{1013}\) - bearing SSCs. To gain further mechanistic insights, we performed a microfluidic-based multiplex gene expression analyses in SSCs sorted from the bone fractions of WT and mutant mice. Principal component analysis (PCA) of 48 genes showed three distinct clusters of SSCs dependent on the \(Cxcr4\) genotype (Fig. 4C). Heatmap representation and differential expression analyses revealed in mutant SSCs, particularly in the 1013/1013 ones, downregulation of genes encoding master regulators of the osteogenic differentiation including Runx2 and of cell cycle such as Ccnd2 and Ccnd3 (Fig. 4D and 4E). No changes in expression levels of pro- osteoclastogenic genes were detected in mutant SSCs compared to WT ones (Fig. 4F). Therefore, these results unravel a \(Cxcr4\) - mediated transcriptional signature in \(Cxcr4^{1013}\) - bearing SSCs suggestive of impaired cell cycle progression and defective osteogenic specification. + +<|ref|>text<|/ref|><|det|>[[113, 576, 885, 893]]<|/det|> +In adult BM, the majority of OBLs derives from OPCs identified by markers such as osterix \((\mathrm{Osx})^{3,9,17,28,60 - 62}\) . They are predominantly found close to the growth plate cartilage along trabecular bone of the primary spongiosa, and along the metaphyseal cortical bone \(^{60,61,63}\) . We thus examined whether the gain- of- \(Cxcr4\) - function mutation alters the number of Osx- positive OPCs by immunodetection on bone sections. We found fewer Osx- positive OPCs in mutant bones compared to WT (Fig. 4G and 4H). This decrease was confirmed by flow cytometry in the flushed stromal marrow fraction that encompasses Sca- 1- negative and PDGFRα- positive early OPCs with pluripotent adipo/osteogenic potential (Fig. 4I) \(^{28,43,60}\) . Together, these data suggest that the decrease in early and committed OPCs in mutant mice may arise from a defect in osteogenic specification of BM- residing SSCs. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[80, 83, 883, 135]]<|/det|> +## Cxcr4 desensitization intrinsically regulates in vitro the osteogenic differentiation of skeletal stromal cells + +<|ref|>text<|/ref|><|det|>[[110, 144, 885, 694]]<|/det|> +To assess whether Cxcr4 desensitization could regulate SSC fate toward the osteogenic lineage in a cell- intrinsic manner, we first compared in vitro the clonogenic capacities of WT and mutant total skeletal cells. There was a significant decrease in the number of colony- forming units- fibroblast (CFU- Fs) in mutant bone cell cultures that followed a Cxcr41013 allele copy number- dependent pattern (Fig. 5A). These results suggested that impaired Cxcr4 signaling might affect in vitro overall SSC numbers as well as their proliferation. To test this, we evaluated by flow cytometry cell cycle and proliferation of SSCs expanded in vitro using BrdU, Cell Trace Violet (CTV) and DAPI/Ki- 67 staining. By day 5 after BrdU pulse, we observed a Cxcr41013 allele dose- dependent reduction in BrdU incorporation within mutant SSCs as compared to WT (Fig. 5B, left panel). Consistently, the fraction of proliferating CTVlow cells was reduced among mutant SSCs three days after loading (Fig. 5B, right panel). This altered proliferative capacity of Cxcr41013- bearing SSCs was associated with a slight but significant increase in proportions of SSCs in the quiescent G0 state (DAPIlow Ki- 67), whereas no changes in apoptosis level were observed (Fig. 5C). This might account for the increased doubling time of mutant SSCs as well as their overall reduced number during the culture (Fig. 5D). Altogether, these findings suggest that Cxcr4 desensitization is required in vitro for appropriate SSC proliferation, expansion and likely maintenance. + +<|ref|>text<|/ref|><|det|>[[115, 705, 884, 888]]<|/det|> +Next, we investigated in vitro the osteogenic potential capacities of Cxcr41013- bearing SSCs64,65. Staining of in vitro differentiated OBLs and mineralization capacities by Alkaline phosphatase (Alp) and AR respectively24,64- 66 was significantly reduced in cultures of mutant SSCs in an allele dose- dependent manner (Fig. 5E and 5F, upper panels). Real- time PCR analysis revealed decreased expression of genes encoding osteogenic regulators in mutant cultures (Fig. 5G, upper panels). This was more marked for early osteogenic genes downstream + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 885, 543]]<|/det|> +the master regulator Runx2 such as \(Oxx\) and particularly evident in the culture of 1013/1013 SSCs, thus suggesting defects at very early stages in the osteogenic differentiation process. In line with this, flow- cytometric analyses revealed three weeks after pro- osteogenic culture initiation that the frequencies of most mature CD51+Scal1Pdgfα OPCs were lower in \(Cxcr4^{1013}\) - bearing cell cultures as compared to WT (Fig. S1A and S1B, left panel). This was associated with a decrease in cells with intermediate phenotype (CD51+Scal1lowPdgfαlow) and mirrored by an accumulation of CD51+Scal1high Pdgfαhigh cells that are presumably SSCs. These results were supported by real- time PCR analyses of \(CD51\) , \(Sca- 1\) and \(Pdgf\alpha\) expression (Fig. S1C, left panel). Consistent with the results obtained with Perilipin and Opn immunostaining on bone sections (Fig. 1G and 1H), \(Cxcr4^{1013}\) - bearing SSCs differentiated into adipocytes or chondrocytes similarly to WT SSCs, when cultured in vitro with adipogenic or chondrogenic media respectively (Fig. S1D and S1E). Collectively, these data reveal in vitro a selective reduction of the osteogenic differentiation capacity of mutant SSCs, and further confirm a pivotal role for Cxcr4 desensitization in regulating this process at very early stages. + +<|ref|>sub_title<|/ref|><|det|>[[115, 583, 884, 632]]<|/det|> +## Normalization of Cxcr4 signaling rescues the osteogenic properties of \(Cxcr4^{1013}\) -bearing mouse skeletal cells + +<|ref|>text<|/ref|><|det|>[[113, 647, 885, 898]]<|/det|> +We then determined whether targeting Cxcr4 signaling would counteract the defective osteogenic fate of mutant SSCs. First, we assessed in vitro the impact of addition of AMD3100 every 2 days on the osteogenic capacities of SSCs. AMD3100- mediated inhibition of Cxcr4 signaling in WT SSCs led to slight changes including decreased numbers of osteogenic cells (Fig. 5E and 5F, lower panels and S1B and S1C, right panel). By contrast, mutant cultures were highly sensitive to AMD3100 treatment as it led to a normalization of Alp and AR colorations 14 and 21 days after differentiation respectively (Fig. 5E and 5F), as well as to a correction of the frequencies of mature, intermediate and CD51+Scal1high Pdgfαhigh cells to the values + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 200]]<|/det|> +observed in WT cultures (Fig. S1B). Moreover, AMD3100- mediated reversion of defective osteogenesis within Cxcr41013- bearing SSC cultures was associated with normalized gene expression of osteogenic master regulators (Fig. 5G, lower panels), thus unravelling that Cxcr4 desensitization intrinsically regulates in vitro the osteogenic differentiation of SSCs. + +<|ref|>text<|/ref|><|det|>[[112, 213, 885, 628]]<|/det|> +Then, we assessed the impact of daily intraperitoneal injections for 3 weeks of \(5\mathrm{mg / kg}\) AMD3100 on the bone landscape in adult WT and mutant mice (Fig. 5H). Cxcr4 inhibition decreased slightly the number of WT skeletal cells, and notably OPCs, in the bone fraction (Fig. 5I). In line with this, Opn- stained femoral sections revealed minor alterations in the architecture of WT mice trabecular microstructures upon treatment (Fig. 5J). This was extended to lumbar spine that displayed roughly normal BMD values in treated vs untreated WT mice (Fig. 5K). In 1013/1013 mice, chronic AMD3100 treatment reversed the quantitative defect in skeletal cells by normalizing the numbers of SSCs and OPCs (Fig. 5I). This was not evidenced in \(+ / 1013\) mice and not associated with a rescue of the trabecular network (Fig. 5J). However, AMD3100 treatment ameliorated slightly but significantly BMD values of lumbar spine in mutant mice (Fig. 5K), suggesting a correcting effect of Cxcr4- dependent signaling dampening on the cortical, rather than trabecular, bone at this stage. Therefore, these data indicate that integrity of Cxcr4 signaling is required for maintaining the osteogenic properties of skeletal cells. + +<|ref|>sub_title<|/ref|><|det|>[[115, 672, 841, 692]]<|/det|> +## BM stromal cells from WS patients displayed in vitro impaired osteogenic capacities + +<|ref|>text<|/ref|><|det|>[[113, 704, 885, 887]]<|/det|> +Finally, we sought to investigate if CXCR4 desensitization was mechanistically involved in regulating in vitro the multilineage differentiation capacities of human primary BMSCs that constitute a heterogeneous population containing skeletal progenitors18. To this end, we analyzed BM samples from two unrelated patients with WS and carrying the heterozygous CXCR4R334X mutation. In parallel, we expanded in vitro BMSCs from BM aspirates of seven independent healthy donors. All culture- expanded BMSCs were negative for the CD45 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 885, 496]]<|/det|> +hematopoietic marker lineage but positive for CD73, CD90 and CD105, a combination of markers that are indicative of stromal/fibroblastic cells (Fig. S2A). Both healthy and WS BMSCs were spindle shaped and fibroblast-like cells and had the ability to form stromal colonies as shown by CFU- F assay (Fig. S2B and S2C). CXCL12 and its two receptors CXCR4 and ACKR3 were readily detectable and found at similar levels between cultured healthy and WS BMSCs (Fig. S2D- F). However, real- time PCR analyses revealed decreased expression of genes encoding early and late osteogenic master regulators in WS BMSC cultures compared to healthy controls (Fig. 6A). In line with this, when equal numbers of cells were plated at the start of the assay, WS BMSCs exhibited defective capacities to generate in vitro osteogenic progeny in contrast to BMSCs harvested from healthy donors (Fig. 6B). In contrast, WS BMSCs were as efficient as control cells to generate adipocytes in appropriate culture media condition (Fig. 6C). Therefore, these findings suggest that in vitro osteogenic differentiation of human primary BMSCs requires proper CXCR4 signaling regulation. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 243, 100]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[113, 110, 885, 895]]<|/det|> +In this study, we investigated the regulatory role of CXCR4 signaling termination in the self- renewal and osteogenic capacities of adult BM- residing SSCs. We used a knock- in mouse model expressing a naturally occurring WS- linked heterozygous gain- of- function Cxcr4 mutation as well as human BM samples and clinical data from healthy and WS donors. We demonstrated for the first time a mutated allele dose- dependent effect of the WS- linked Cxcr41013 mutation on trabecular bone microstructures mimicking an osteoporotic- like syndrome, evidenced as well in one quarter of WS patients. This Cxcr4- mediated reduction in bone content involved both cell- autonomous and cell- extrinsic defects in SSCs. Indeed, we provided unanticipated evidence that Cxcr4 desensitization is intrinsically required for regulating in vitro the quiescence/cycling balance of SSCs and preserving their osteogenic potential, while it was found to be dispensable for their adipogenic and chondrogenic differentiation. Other BM cellular partners also contributed to the bone phenotype dysregulation. Neither the osteoclastogenic differentiation potential of OCL precursors nor the resorptive function of differentiated OCLs were affected in vitro by the Cxcr41013 mutation. Therefore, the osteopenia, accompanied by an increase in OCL number regardless of their function, might proceed from a wrongly- regulated bone matrix resorption that is overall due to the alteration of the skeletal landscape involving both bone- forming and non- bone- forming cell lineages. Importantly, defective osteogenic capacities were also evidenced in vitro in BMSCs from WS patients. These anomalies establish the C- tail of CXCR4 as an important regulatory domain of the receptor function in BM stromal cell biology in both mice and humans. In light of previous work38,39,41,42, our results also suggest that both increased and decreased Cxcr4- mediated signaling negatively impact skeletal stromal elements, thus indicating that fine- tuning of Cxcr4 signaling is critical for maintenance and osteogenic specification of adult SSCs. Although the underlying molecular mechanisms remain to be elucidated, Cxcr4 might act as a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 465]]<|/det|> +rheostat regulating the strength and kinetic of signaling pathways involved in osteogenic fate specification of SSCs. Mice deficient for the gene encoding the transcription factor Ebf3 display an opposite BM phenotype to the one of \(Cxcr4^{1013}\) - bearing mice, characterized by osteosclerosis with HSC depletion and reduced expression of niche factors59. This was related to the uncontrolled ability of Ebf3- deficient SSCs to differentiate into OBLs. Further studies are required to address the status of Ebf3 and downstream target genes that act to modulate osteogenic fate of SSCs in \(Cxcr4^{1013}\) - bearing mice. A potential crosstalk between distinct SSC subsets, either prone to differentiate into osteochondro- lineage cells or perivascular and adipocyte lineage cells, has been reported67. This seems to imply ligand- receptor gene pairs such as TGFβ, WNT or BMP ligands and their cognate receptors that regulate SSC fate decision. Whether and how the Cxcl12/Cxcr4 signaling axis contributes to these regulatory mechanisms across SSC types remains to be explored. + +<|ref|>text<|/ref|><|det|>[[113, 478, 884, 891]]<|/det|> +We reported that loss of Cxcr4 signaling termination impairs overall number, impedes cell cycle progression and limits osteogenic differentiation of SSCs. Indeed, mutant mice have a global alteration of the bone stromal landscape, including decreased numbers of SSCs and their progeny including early and committed OPCs and impaired architecture of trabecular and cortical bone microstructures that occurred in a mutated allele copy number- dependent manner. Altogether, these findings indicate that the gain- of- Cxcr4- function mutation promotes a reduced OBL commitment and differentiation, but not the bone forming activity of individual OBL. Congruent with this, chronic treatment with AMD3100 normalized the osteogenic properties of mutant SSCs as well as cortical bone in \(Cxcr4^{1013}\) - bearing mice. Impaired Cxcr4 desensitization might alter the balance between quiescence and differentiation of mutant SSCs and reduce the number of osteogenic- endowed precursors. Currently, the prevailing view is that BM Cxcl12- expressing stromal cells display slow cell cycle progression and constitute an active source of trabecular and cortical OBLs under physiological conditions, as well as in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 200]]<|/det|> +response to injury \(^{6,9,28,59}\) . We found a higher proportion of quiescent SSCs in mutant mice that was particularly evident in 1013/1013 SSCs, thus suggesting the importance of Cxcr4 desensitization in controlling SSC proliferation and quiescence and likely their capacity to give rise to osteogenic cells. + +<|ref|>text<|/ref|><|det|>[[112, 213, 884, 761]]<|/det|> +Loss of bone content in mutant mice was accompanied by a higher number of OCLs within the cancellous bones, possibly reflecting that the Cxcr4 mutation was intrinsically perturbing the OCL differentiation process. This seems not to be the case since we showed that defective Cxcr4 desensitization did not increase in vitro differentiation of OCLs from mutant BM progenitors, nor their mineral matrix resorbing capacities. BM chimeras leading to a WT hematopoietic development into a mutant bone environment further ruled out the sole involvement of an uncontrolled bone resorption due to defective OCLs. These cells derive from monocytic lineage precursors upon stimulation by RankL and M- Csf \(^{68,69}\) . In a constant cross interaction between the bone forming and the bone resorbing pathways, these osteoclastic cytokines are produced by mature and immature stromal cell populations within BM \(^{9,70,71}\) . While we did not observe increased RankL, M- Csf or Opg (coding a RankL antagonist) gene expression in sorted committed OPCs from mutant bones, we cannot exclude that modification of the bone stroma due to osteogenic defects might in turn disrupt the production of osteoclastic factors from the mutant bone environment. It has recently been shown that intrinsic aging of SSCs resulted in higher proportion of stromal lineages producing pro- inflammatory and pro- resorptive factors, promoting myeloid skewing, and osteoclastic activity \(^{30}\) . Whether a similar mechanism occurs in Cxcr4 \(^{10,13}\) - bearing mutant mice remains to be characterized. + +<|ref|>text<|/ref|><|det|>[[113, 771, 883, 889]]<|/det|> +Osteogenesis is regulated, among different mechanisms, by undifferentiated skeletal cells and more specified osteolineage cells that express factors promoting or preventing their own differentiation into OBLs \(^{23,55,59,72}\) . In BM, HSPC niches constitute critical spatio- temporal regulatory units composed of multiple cell populations of hematopoietic and non- hematopoietic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 884, 430]]<|/det|> +origin cross- interacting with each other's in a dynamic setting1,3,9,73,74. This implies that immune and vascular cells among others may influence the osteogenic differentiation process75. In BM chimeras in which Cxcr41013- bearing HSPCs were differentiating into a WT bone environment, we reported a similar bone loss as observed in mutant mice, thus indicating cell- extrinsic Cxcr4- mediated regulation of the skeletal landscape. This also suggests that neither the epiphyseal cartilage nor any developmental defect contribute to impaired trabecular bone architecture in adult mutant mice, and further supports the notion that HSPCs, as osteolineage cells do, express regulating osteogenic factors such as BMP- 2, BMP- 7 and WNT3a, that are particularly involved in SSC osteogenesis specification17. Whether and how hematopoietic cells, or other BM components such as vascular cells, participate in the defective osteolineage specification of SSCs in Cxcr41013- bearing mice deserves further investigations. + +<|ref|>text<|/ref|><|det|>[[113, 442, 885, 823]]<|/det|> +Finally, we reported that five out of nineteen patients with WS and carrying distinct autosomal- dominant mutations in CXCR4 exhibit a decrease in BMD at different anatomical sites. Although this would merit to be extended to a larger cohort, these data suggest that accelerated osteopenia/osteoporosis and increased risk of fractures may constitute a novel feature of WS. Lack of CXCR4 desensitization could be mechanistically involved in such anomaly since BMSCs from WS patients carrying a heterozygous CXCR4 mutation displayed in vitro impaired capacities to differentiate into osteogenic, but not adipogenic, cells. Strikingly, we observed that chondro- and adipo- genic differentiation of murine mutant SSCs was normal both in situ and in vitro. In light of recent studies unraveling human SSCs expressing the CXCL12/CXCR4 axis with osteoblastogenic and, depending on their tissue origin, adipocytic potential18,76, our findings pave the way for exploring the BM of WS patients in search for potential defect(s) in these skeletal populations. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 222, 100]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[115, 115, 679, 134]]<|/det|> +## Healthy and WS donors and bone mineral density measurements + +<|ref|>text<|/ref|><|det|>[[113, 145, 885, 563]]<|/det|> +Healthy and WS donors and bone mineral density measurementsInvestigations of human BM samples were performed in compliance with Good Clinical Practices and the Declaration of Helsinki. Cryopreserved BM aspirates from two WS patient (NIH protocol 09- I- 0200) were provided by Drs. D.H. McDermott and P.M. Murphy through a NIH Material Transfer Agreement. BM samples from seven healthy donors that were matched for age and sex and used as control subjects were isolated from hip replacement surgery samples (Protocol 17- 030, \(n^{\circ}\) ID- RCB: 2017- A01019- 44). Primary BMSCs from healthy and WS donors were amplified and used at passage 1 to 3. For BMD assessment, data were collected from nineteen WS patients as part of an IRB approved clinical protocol conducted at the NIH (NIAID Protocol #2014- I- 0185, IND # 118767). BMD values expressed as T- or Z- scores were measured by total body dual- energy X- ray absorptiometry with a Lunar iDXA densitometer (GE Healthcare). Five WS patients had abnormal screening bone density by WHO criteria, anonymized at the start of the Phase 3 trial (Table 1), while the other 14 patients had normal bone density (not shown). + +<|ref|>sub_title<|/ref|><|det|>[[115, 609, 301, 627]]<|/det|> +## Mice and genotyping + +<|ref|>text<|/ref|><|det|>[[113, 640, 885, 890]]<|/det|> +Cxcr4+/1013 (+/1013) mice were generated by a knock- in strategy and bred as described previously47. Homozygous Cxcr4/1013/1013 (1013/1013) mice were obtained by crossing heterozygous +/1013 mice. WT mice were used as controls. Unless specified, all mice were littermates, females and age- matched (8- 12 wk- old). Adult Boy/J (CD45.1) (Charles River) mice were used as BM donors. All the mice were bred in our animal facility under a 12h light/dark cycle, specific pathogen- free conditions and fed ad libitum. All experiments were performed in accordance with the European Union guide for the care and use of laboratory animals and have been reviewed and approved by institutional review committees (CEEA- 26, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 135]]<|/det|> +Animal Care and Use Committee, Villejuif, France and Comité d'Ethique Paris- Nord/N°121, Paris, France). + +<|ref|>sub_title<|/ref|><|det|>[[115, 181, 330, 199]]<|/det|> +## Sample isolation in mice + +<|ref|>text<|/ref|><|det|>[[112, 212, 885, 602]]<|/det|> +Mouse SSCs were obtained from bones after centrifugation of intact femurs, tibias and hips to flush out the BM cells. Flushed long bones were cut into fine pieces before enzymatic digestion with 2.5 U/mL collagenase type I (Thermofisher) for 45 min at \(37^{\circ}\mathrm{C}\) under agitation. Released cells were filtered and washed with PBS, \(2\%\) FBS (Fetal Bovine Serum). Cell numbers were standardized as total counts per two legs. Peripheral blood was collected by cardiac puncture. Freshly isolated cells were either immunophenotyped, incubated at \(37^{\circ}\mathrm{C}\) for 60 min in RPMI \(20\mathrm{mM}\) HEPES \(0.5\%\) BSA (Euromedex) prior to chemokine receptor internalization studies, or expanded in \(\alpha\) MEM medium supplemented with \(10\%\) FBS, \(1\%\) P/S (penicillin 100 Units/mL, streptomycin 100 Units/mL, Gibco) and \(50\mu \mathrm{M}\beta\) - mercaptoethanol (PAN biotech). For BMD quantification, lumbar spines were fixed overnight in ethanol \(70^{\circ}\) and analyzed by dual- energy X- ray absorptiometry with an ultrafocus DXA densitometer (Faxitron). Quantifications were made on a ROI of 2 lumbar spines. + +<|ref|>sub_title<|/ref|><|det|>[[115, 650, 338, 667]]<|/det|> +## Flow-cytometric analyses + +<|ref|>text<|/ref|><|det|>[[112, 680, 885, 864]]<|/det|> +Mouse and human staining analyses were carried out on an LSRII Fortessa flow cytometer (BD Biosciences) using the antibodies (Abs) described in Table S1. A Live/Dead Fixable Aqua Dead Cell Stain Kit (Biolegend) was used. To assess the compartmentalization of CXCR4 and ACKR3, human BMSCs were incubated with saturating concentrations of non- conjugated mouse anti- human CXCR4 or ACKR3 Abs, washed in PBS, fixed and permeabilized using the BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences). BMSCs were + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 135]]<|/det|> +subsequently stained with anti- CXCR4 and - ACKR3 conjugated mAbs, or the corresponding isotype control, at \(4^{\circ}\mathrm{C}\) for \(30\mathrm{min}\) and then analyzed by flow cytometry. + +<|ref|>sub_title<|/ref|><|det|>[[115, 181, 333, 199]]<|/det|> +## In vitro functional assays + +<|ref|>text<|/ref|><|det|>[[111, 210, 885, 895]]<|/det|> +Mouse CFU- Fs were performed by plating \(1\times 10^{5}\) bone cells at passage 2- 3 from WT and mutant mice. Human CFU- Fs were performed by plating \(0.2\mathrm{x}10^{3}\) BMSCs into a \(25\mathrm{cm}^2\) flask at passage 3 from healthy or WS donors. After 7 or 10 days of culture, colonies were fixed with ethanol \(70\%\) , stained with \(2\%\) crystal violet (Sigma- Aldrich), and counted with a binocular magnifying glass. For chemotaxis assays, \(5\times 10^{4}\) SSCs were added to the upper chambers of a 24- well plate with \(8\mathrm{- }\mu \mathrm{m}\) - pore- size Transwell inserts (EMD Millipore) containing or not \(1\mathrm{nM}\) Cxcl12 (R&D Systems) in the lower chamber. For inhibiting Cxcr4- mediated signaling, \(10\mu \mathrm{M}\) AMD3100 (Sigma- Aldrich) was added in the upper and lower chambers. After 24h, membranes were removed and fixed in \(4\%\) paraformaldehyde (PFA). The cells that migrated to the lower side of the membrane were stained with \(0.1\%\) crystal violet and three fields from each insert were counted under a light microscope. Cxcr4 and Ackr3 internalization assays were performed by incubating total bone cells at \(37^{\circ}\mathrm{C}\) for \(45\mathrm{min}\) with \(10\mathrm{nM}\) Cxcl12. Then the reaction was stopped by adding ice- cold RPMI and quick centrifugation at \(4^{\circ}\mathrm{C}\) . After one wash in acidic glycine buffer at \(\mathrm{pH} = 4.3\) , levels of Cxcr4 and Ackr3 membrane expression were determined by flow cytometry. Cxcr4 or Ackr3 expression was calculated as follows: (Cxcr4 or Ackr3 geometric MFI of treated cells/Cxcr4 or Ackr3 geometric MFI of unstimulated cells) \(\times 100\) ; \(100\%\) corresponds to receptor expression at the surface of cells incubated in medium alone. For the chemokine scavenging assay, cultured SSCs were harvested by trypsinization and placed in complete medium for \(90\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(5\%\) \(\mathrm{CO_2}\) to normalize receptor expression. \(4\times 10^{6}\) cells/mL were pre- incubated with \(100\mu \mathrm{M}\) CCX733, a functional Ackr3 antagonist or vehicle in \(1\%\) BSA/PBS for \(45\mathrm{min}\) at room temperature (RT). Then, \(2\times 10^{6}\) cells/mL were incubated in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 330]]<|/det|> +presence of \(5\mathrm{nM}\) AF647- Cxcl12 (Almac) in \(1\%\) BSA/PBS during 45- 60 min at \(37^{\circ}\mathrm{C}\) to allow internalization or on ice to inhibit this process. Cells were washed with \(1\%\) BSA/PBS and then either treated with an acidic glycine wash buffer \(\mathrm{pH} = 2.7\) for \(3\mathrm{min}\) to dissociate cell- surfacebound chemokine, or washed with PBS to estimate internalized plus cell- surface- bound control. AF647 fluorescence (geometric MFI) was determined by flow cytometry. Phosphoflow assays were performed with the PerFix EXPOSE kit (Beckman coulter) on cultured SSCs and an antiphospho Erk (pT202/pY204) was used. Fold change was calculated as follows: (Phospho- Erk geometric MFI of stimulated cells/Phospho- Erk geometric MFI of unstimulated cells). + +<|ref|>sub_title<|/ref|><|det|>[[115, 378, 330, 395]]<|/det|> +## In vivo functional assays + +<|ref|>text<|/ref|><|det|>[[113, 409, 884, 658]]<|/det|> +For BM transplantation experiments, \(1.5\mathrm{x}10^{6}\) total marrow cells from young \(\mathrm{CD45.1^{+}}\) WT mice were injected i.v. into lethally irradiated (two rounds of 5.5 Gy separated by \(3\mathrm{h}\) ) young \(\mathrm{CD45.2^{+}}\) WT, \(+ / 1013\) or 1013/1013 recipient mice. For reverse experiments, \(1.5\mathrm{x}10^{6}\) total marrow cells from \(\mathrm{CD45.2^{+}}\) WT, \(+ / 1013\) or 1013/1013 mice were injected into lethally irradiated \(\mathrm{CD45.1^{+}}\) WT recipient mice. Chimerism was analyzed 3 or 16 weeks after transplantation. For Cxcr4 blockade experiments, mice were daily injected intraperitoneally with \(5\mathrm{mg / kg}\) AMD3100 or PBS during 3 weeks. BM were harvested \(2\mathrm{h}\) after the last injection and analyzed by flow cytometry and imaging. + +<|ref|>sub_title<|/ref|><|det|>[[115, 706, 179, 722]]<|/det|> +## ELISA + +<|ref|>text<|/ref|><|det|>[[115, 737, 881, 788]]<|/det|> +Supernatants of culture- expanded human BMSCs were analyzed using a standardized ELISA for human Cxcl12 (Quantikine; R&D Systems). + +<|ref|>sub_title<|/ref|><|det|>[[115, 836, 512, 854]]<|/det|> +## Bone immunostaining and histomorphometry + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 80, 886, 900]]<|/det|> +Mouse bones were fixed in \(4\%\) PFA overnight followed by one- week decalcification in EDTA (0.5 M) at pH 7.4 under agitation. Bones were incubated in PBS with \(20\%\) sucrose and \(2\%\) polyvinylpyrrolidone (PVP) (Sigma) at \(4^{\circ}\mathrm{C}\) overnight and then embedded in PBS with \(20\%\) sucrose, \(2\%\) PVP and \(8\%\) gelatin (Sigma) before storage at \(- 80^{\circ}\mathrm{C}\) . Sections of \(30\mu \mathrm{m}\) - thick were rehydrated in PBS 1X, incubated 20 min at RT in PBS with \(0.3\%\) triton X- 100, saturated in blocking solution (PBS with \(5\%\) BSA) and finally incubated with primary Abs (Table S2). After washing, secondary Abs were incubated for 1h at RT with DAPI for nuclear staining and mounting using Permafluor mounting medium (Thermofisher). Images were acquired using TCS SP8 confocal microscope and processed using Fiji software. For alcian blue and perilipin A staining, fixed and decalcified femur bones were embedded in paraffin, sectioned (7 \(\mu \mathrm{m}\) - thick) and deparaffinized with xylene. Staining of cartilage tissues was performed with a \(1\%\) alcian blue solution for 30 min. Images were acquired using a LEICA DM4000B microscope equipped with a DFC425C camera and processed with the Leica Application Suite V3.8 software. For perilipin A staining, heat induced epitope retrieval was performed in citrate sodium buffer solution. Sections were saturated for 1h in PBS \(1\%\) BSA at RT, washed in PBS \(0.2\%\) BSA and \(0.1\%\) Triton X- 100, and incubated with anti- perilipin A Ab in PBS BSA \(1\%\) overnight at \(4^{\circ}\mathrm{C}\) . After washing, sections were incubated with TRITC- coupled rabbit antiguanine pig Ab in PBS \(1\%\) BSA for 45 min and counterstained with DAPI. For Ox staining, 16 \(\mu \mathrm{m}\) frozen sections were permeabilized in TBS- \(0.3\%\) Triton X- 100 for 10 min and blocked in TBS- \(2.5\%\) BSA- \(2.5\%\) Donkey Serum for 1h at RT. Sections were incubated with anti- Osx Ab (rabbit, Santa Cruz SC- 22536R) in blocking solution overnight at \(4^{\circ}\mathrm{C}\) . After washing with TBS+0.025% Triton X- 100, sections were incubated in donkey anti- rabbit secondary Ab daylight 550 (SA5- 10039, invitrogen) in blocking solution. After washing, sections were incubated 15 min at RT in DAPI at \(0.1\mu \mathrm{g / mL}\) prior to mounting in GB- Mount (Diagonics). Image acquisitions were done using the ApoTome optical sectioning system (Zeiss) with an + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 365]]<|/det|> +inverted microscope (Zeiss Axio Observer Z1). Osx quantification was performed using the ICY software. For human BMSC immunofluorescence studies, cells were plated on coverslips and fixed with \(4\%\) PFA in PBS. Fixed cells were permeabilized with Triton X \(0.3\%\) for \(10\mathrm{min}\) , blocked with PBS \(5\%\) BSA, \(5\%\) goat serum and incubated with unlabeled primary CXCL12 mAb overnight at \(4^{\circ}\mathrm{C}\) followed by secondary AF633- coupled goat anti- mouse polyclonal Ab (Invitrogen) and the nuclear dye Hoechst 33342. Images were obtained with a Plan- . Apochromatic objective using the LSM800 confocal microscope (Carl Zeiss). Sections were acquired as serial z stacks \((0.39\mu \mathrm{m}\) apart) and were subjected to three- dimensional reconstruction (Zen 2.3 System). + +<|ref|>text<|/ref|><|det|>[[112, 377, 885, 890]]<|/det|> +Bone histomorphometry was performed in plastic samples, allowing the measurements of bone formation and resorption parameters. Mouse femurs were fixed in ethanol \(70^{\circ}\) , dehydrated and embedded in methyl methacrylate resin. Five micrometer- thick coronal sections were cut parallel to the long axis of the femur using an SM2500S microtome (Leica, Germany). Sections were deplastified, rehydrated and stained with toluidine blue or with naphthol 3- hydroxy- 2- naphthoic acid 4- chloro- 2- methylanilide (ASTR phosphate, Sigma, St Louis, France) for detecting mature osteoclasts with TRAP staining. Quantifications were made on a polarizing microscope (Nikon) using a software package developed for bone histomorphometry (Microvision, France). To allow the measure of dynamic parameters of bone formation, mice were intraperitoneally injected with tetracycline \((20\mathrm{mg / kg})\) and calcein \((10\mathrm{mg / kg}\) ; Sigma) 5 days and 1 day respectively before being killed. Two \(12 - \mu \mathrm{m}\) - thick unstained sections were taken for measurement of the dynamic parameters under UV light. The matrix apposition rate (MAR) was measured using the Microvision image analyzer by a semiautomatic method using tetracycline and calcein double- labeled bone surfaces. The mineralizing surfaces (MS/BS) were measured in the same areas using the objective eyepiece Leitz integrate plate II. All the histomorphometric parameters were recorded in compliance with the recommendation of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 135]]<|/det|> +American Society for Bone and Mineral Research Histomorphometry Nomenclature Committee. Three animals per genotype were analyzed by two different investigators. + +<|ref|>sub_title<|/ref|><|det|>[[115, 181, 605, 200]]<|/det|> +## Bone structure analysis by micro-computed tomography + +<|ref|>text<|/ref|><|det|>[[115, 213, 884, 463]]<|/det|> +Femurs were collected for bone microarchitecture analysis after fixation and before decalcification. Femurs analyzed with high- resolution microcomputed tomography (micro- CT) using a Skyscan 1272 microCT (SkyScan, Kontich, Belgium). Measurements were made on the distal metaphysis of the femurs using the following acquisition parameters: voltage 60kV, pixel size \(6\mu \mathrm{m}\) , Filter \(\mathrm{Au} + 0.5\mathrm{mm}\) . After 3- dimensional images reconstruction with NRecon®, analyses were performed on the medial tibial plateau in the coronal view. Morphometric parameters such as Bone Volume/Tissue volume (BV/TV, \(\%\) ), Trabecular Thickness (Tb.Th, mm) Trabecular number (Tb.Tn, 1/mm) Trabecular Separation (Tb.Sp, mm) were assessed. + +<|ref|>sub_title<|/ref|><|det|>[[116, 509, 388, 527]]<|/det|> +## Cell culture and differentiation + +<|ref|>text<|/ref|><|det|>[[115, 541, 884, 896]]<|/det|> +Mouse osteoblastic differentiation was performed for 3 weeks in \(\alpha\) - MEM medium with \(10\%\) FBS, \(1\%\) P/S, \(50\mu \mathrm{M}\beta\) - mercaptoethanol supplemented with \(50\mu \mathrm{g / mL}\) L- ascorbic acid and 10 mM glycerophosphate (Sigma) either from SSCs or sorted OPCs. Alkaline phosphatase staining was performed after 14 days of differentiation according to the Alkaline phosphatase Kit (Sigma). At day 21, cultures were fixed with \(4\%\) PFA, stained with alizarin red and quantified using the Osteogenesis assay kit (Millipore). When specified, AMD3100 (versus vehicle) was added into the osteogenic medium every 2 days at \(10\mu \mathrm{M}\) respectively. Chondro- and adipogenic differentiations of SSCs were performed according to the StemPro- Chondrogenesis or - Adipogenic Differentiation Kits (ThermoFisher) for 2 weeks. After fixation, cells were treated with either Alcian Blue \(1\%\) (Sigma) to stain chondrocyte matrix or Oil Red O solution (Sigma) to reveal lipid droplets. For in vitro human osteogenic differentiation assays, expanded BMSCs + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 268]]<|/det|> +were seeded at \(3 \times 10^{3} \text{per cm}^2\) in \(\alpha\) - MEM supplemented with \(10\%\) FBS and \(1\%\) antibiotics. After cell adhesion, medium was replaced by \(\alpha\) - MEM supplemented with \(10\%\) FBS, \(1\%\) antibiotics and \(0.1 \mu \text{M}\) dexamethasone, \(0.05 \text{mM}\) L- ascorbic acid- 2- phosphate and \(10 \text{mM} \beta\) - glycerophosphate. Medium was changed every 2 days during 3 weeks. Quantification of mineralization was performed after Alizarin Red S staining as described77. Human adipogenic differentiation assays were performed as described for the murine ones. + +<|ref|>sub_title<|/ref|><|det|>[[115, 315, 541, 333]]<|/det|> +## Osteoclast differentiation and functional analysis + +<|ref|>text<|/ref|><|det|>[[113, 345, 884, 598]]<|/det|> +OCLs were differentiated in vitro as described78. Briefly, \(2.3 \times 10^{5} \text{BM cells/cm}^2\) were plated in MEM- alpha (ThermoFisher) complemented with \(5\%\) serum (Hyclone, GE Healthcare), \(1\%\) P/S, \(50 \mu \text{M} 2\) - mercaptoethanol, \(25 \text{ng/ml M}\) - CsF and \(30 \text{ng/ml Rank- L}\) (R&D Systems). OCL differentiation (multinucleated TRAP+ cells) was quantified at day 5 after TRAP coloration using the leukocyte acid phosphatase kit (Sigma). Matrix dissolution activity was evaluated by seeding a total of \(2 \times 10^{4}\) differentiated OCLs on 96- well osteoassay plates (Corning) in \(\alpha\) - MEM containing \(10\%\) FBS and \(30 \text{ng/ml Rank- L}\) . After 3 days, medium was removed and cells were detached by the addition of water. Resorbed areas were quantified using Fiji/ImageJ software79. + +<|ref|>sub_title<|/ref|><|det|>[[115, 644, 572, 662]]<|/det|> +## Cell cycle, viability, survival and proliferation assays + +<|ref|>text<|/ref|><|det|>[[113, 674, 884, 891]]<|/det|> +For flow cytometry- based cell cycle analyses, bone cells were permeabilized, fixed with the FOXP3 permeabilization kit (Foxp3/Transcription Factor Staining Buffer Set; eBioscience) and labelled with a Ki67 Ab and DAPI. For BrdU assays, mice were injected intraperitoneally with \(180 \mu \text{g BrdU}\) (Sigma) and maintained with drinking water containing \(800 \mu \text{g/ml BrdU}\) and \(1\%\) glucose over 12 days. The BrdU labelling was analyzed by flow cytometry using the BrdU- FITC labeling kit (BD Biosciences). For in vitro BrdU incorporation, \(3 \mu \text{g/ml}\) of BrdU was added to the culture and after five days the percentage of incorporation was determined as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 885, 268]]<|/det|> +above. Apoptosis was measured using the Annexin V detection kit (BD Biosciences) with DAPI staining. For in vitro proliferation assays, SSCs were detached with \(0.5\%\) trypsin and loaded at \(3 \times 10^{4}\) cells/well with cell trace violet (CTV, Thermofisher) for 15 min at \(37^{\circ}\mathrm{C}\) . CTV dilution was assessed by flow cytometry. To estimate the doubling time values, SSCs were seeded at \(3 \times 10^{3}\) cells/cm \(^{2}\) and counted after 3 days of culture. The doubling time was calculated as follows: (time of culture x \(\log (2)) / (\log (\text{final number of SSC}) - \log (\text{initial number of SSC}))\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 313, 359, 330]]<|/det|> +## Quantitative real time-PCR + +<|ref|>text<|/ref|><|det|>[[113, 344, 885, 792]]<|/det|> +For mouse gene expression, total RNA was isolated from cultured SSCs or sorted primary cells using the RNeasy Plus Mini or Micro Kit (Qiagen) and reverse transcribed with oligo(dT) and SuperScript II Reverse Transcriptase (Invitrogen). Quantitative RT- PCR reactions were performed on a Light Cycler instrument (LC480, Roche Diagnostics) with the LightCycler 480 SYBR Green detection kit (Roche Diagnostics) using primers reported in Table S3. For human gene expression, total RNA was isolated from cultured BMSCs using Trizol Reagent (ThermoFisher). Reverse transcription was performed using SuperScriptVilo IV (ThermoFisher). When required, total RNA from WS BMSCs and their related controls were extracted from \(0.2 \times 10^{3}\) BMSCs and pre- amplified using CellsDirect One- Step qRT- PCR kit (Invitrogen). PCR reactions were performed using primers reported in Table S3 with Power SYBRGreen (Applied Biosystems) on a 7500 FAST apparatus (Applied Biosystems). Mouse \(\beta\) - actin and \(36b4\) and human \(\beta\) - ACTIN and GAPDH were used as standards for normalization. Relative quantification was determined by the comparative delta- delta- Ct \((2^{-\Delta \Delta \mathrm{CT}})\) method. Fold changes were calculated by setting the mean values obtained from WT cells as one. + +<|ref|>sub_title<|/ref|><|det|>[[115, 840, 261, 857]]<|/det|> +## Multiplex qPCR + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 401]]<|/det|> +Multiplex qPCR was performed using the microfluidic Biomark system. One hundred SSCs were sorted into PCR tubes containing \(5 \mu \mathrm{l}\) of reverse transcription/pre-amplification mix containing 2X reaction buffer, SuperScriptIII from the CellsDirect One-Step qRT- PCR kit and 0.2X Taqman assay (Life technologies) (Table S4). cDNA pre-amplification was performed during 22 cycles and pre-amplified product was diluted 1:5 in TE buffer before processing with Dynamic Array protocol (Fluidigm). Cells expressing \(\beta\) - actin and control genes (Runx2, Col1α, Alp and Ibsp) and not Pax5 and/or Cd3 (negative controls) were considered for analyses. Expression of \(\beta\) - actin was used for normalization. Heatmaps were generated with http://www.heatmapper.ca using Z scores and principal component analysis (PCA) with R software. + +<|ref|>sub_title<|/ref|><|det|>[[115, 448, 264, 466]]<|/det|> +## RNA sequencing + +<|ref|>text<|/ref|><|det|>[[112, 478, 884, 895]]<|/det|> +Pools of \(3 \times 10^{3}\) OPCs were sorted from the bone fraction into RLT buffer (Qiagen) with \(1\%\) of \(\beta\) - mercaptoethanol. RNA was isolated using RNeasy Micro Kit. cDNAs were generated from 400 to 1,000 pg of total RNA using Clontech SMART- Seq v4 Ultra Low Input RNA kit for Sequencing (Takara Bio Europe) and amplified with 12 cycles of PCR by Seq- Amp polymerase. For Tn5 transposon tagmentation, 600 pg of pre- amplified cDNAs were used by the Nextera XT DNA Library Preparation Kit (96 samples) (Illumina) followed by library amplification of 12 cycles. Purification was performed with Agencourt AMPure XP and SPRIselect beads (Beckman- Coulter). Sequencing reads were generated, in Paired- End mode, on the GenomEast platform (Illumina). FastQC program was used to evaluate the quality of the raw sequencing data and reads shorter than 50 bp were removed. Reads were aligned to the Mus musculus genome (mm10 build) using the Star tool80. Gene expression quantification was obtained using read counting software Htseq81. Normalization and differential analysis were carried out with DESeq2 package by applying the Benjamini- Hochberg FDR correction (p < + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 81, 884, 135]]<|/det|> +0.05; 1.5- fold) for comparison between samples. Heatmaps and volcano plots were obtained using the web server Heatmapper and EnhancedVolcano packages respectively. + +<|ref|>sub_title<|/ref|><|det|>[[115, 181, 198, 198]]<|/det|> +## Statistics + +<|ref|>text<|/ref|><|det|>[[115, 212, 884, 366]]<|/det|> +Data are expressed as mean \(\pm\) SEM. All statistical analyses were conducted using Prism software (GraphPad Software). A Kruskal- Wallis test was used to determine the significance of the difference between means of WT, \(+\) /1013 and 1013/1013 groups ( \(^{#}P < 0.05\) ; \(^{##}P < 0.005\) ; and \(^{###}P < 0.0005\) ). Unless specified, the unpaired two- tailed Student \(t\) test was used to compare means among two groups. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[506, 41, 881, 57]]<|/det|> +# CXCR4 desensitization in skeletal stromal cells + +<|ref|>title<|/ref|><|det|>[[115, 83, 512, 101]]<|/det|> +# LIST OF SUPPLEMENTARY MATERIALS + +<|ref|>text<|/ref|><|det|>[[115, 116, 370, 134]]<|/det|> +Figure S1 related to Figure 5 + +<|ref|>text<|/ref|><|det|>[[115, 161, 370, 179]]<|/det|> +Figure S2 related to Figure 6 + +<|ref|>text<|/ref|><|det|>[[115, 205, 881, 255]]<|/det|> +Table S1: List of antibodies used for flow cytometry related to the main Figures 2, 4 and 5, and SF1 and 2. + +<|ref|>text<|/ref|><|det|>[[115, 272, 140, 287]]<|/det|> +712 + +<|ref|>text<|/ref|><|det|>[[115, 304, 881, 353]]<|/det|> +Table S2: List of antibodies used for immunofluorescence related to the main Figures 1, 2, 4 and 5, and SF2. + +<|ref|>text<|/ref|><|det|>[[115, 370, 140, 385]]<|/det|> +715 + +<|ref|>text<|/ref|><|det|>[[115, 402, 881, 451]]<|/det|> +Table S3: List of primers used for quantitative PCR related to the main Figures 3, 4, 5, and 6 and SF1. + +<|ref|>text<|/ref|><|det|>[[115, 468, 140, 483]]<|/det|> +718 + +<|ref|>text<|/ref|><|det|>[[115, 500, 827, 520]]<|/det|> +Table S4: List of primers used for the BioMark assay related to the main Figure 4. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[57, 81, 256, 101]]<|/det|> +## 722 REFERENCES + +<|ref|>text<|/ref|><|det|>[[57, 115, 884, 895]]<|/det|> +723 1 Wei, Q. & Frenette, P. 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Nat Commun 11, 332, doi:10.1038/s41467- 019-14029-w (2020). + +<|ref|>text<|/ref|><|det|>[[174, 640, 884, 724]]<|/det|> +Dotta, L., Tassone, L. & Badolato, R. Clinical and genetic features of Warts, Hypogammaglobulinemia, Infections and Myelokathexis (WHIM) syndrome. Curr Mol Med 11, 317-325 (2011). + +<|ref|>text<|/ref|><|det|>[[174, 739, 884, 789]]<|/det|> +Kawai, T. & Malech, H. L. WHIM syndrome: congenital immune deficiency disease. Curr Opin Hematol 16, 20-26, doi:10.1097/MOH.0b013e32831ac557 (2009). + +<|ref|>text<|/ref|><|det|>[[174, 804, 884, 888]]<|/det|> +Murphy, P. M. & Heusinkveld, L. Multisystem multitasking by CXCL12 and its receptors CXCR4 and ACKR3. Cytokine 109, 2-10, doi:10.1016/j.cyto.2017.12.022 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 78, 886, 895]]<|/det|> +845 47 Balabanian, K. et al. Proper desensitization of CXCR4 is required for lymphocyte development and peripheral compartmentalization in mice. 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Prospective identification, isolation, and systemic transplantation of 897 multipotent mesenchymal stem cells in murine bone marrow. J Exp Med 206, 2483- 898 2496, doi:10.1084/jem.20091046 (2009). 899 67 Ambrosi, T. H. et al. Distinct skeletal stem cell types orchestrate long bone 900 skeletogenesis. Elife 10, doi:10.7554/eLife.66063 (2021). 901 68 Cenci, S., Weitzmann, M. N., Gentile, M. A., Aisa, M. C. & Pacifici, R. M-CSF 902 neutralization and egr-1 deficiency prevent ovariectomy-induced bone loss. J Clin 903 Invest 105, 1279-1287, doi:10.1172/JCI8672 (2000). 904 69 Cenci, S. et al. Estrogen deficiency induces bone loss by enhancing T-cell production 905 of TNF-alpha. J Clin Invest 106, 1229-1237, doi:10.1172/JCI11066 (2000). 906 70 Cao, J. J. et al. Aging increases stromal/osteoblastic cell-induced osteoclastogenesis and 907 alters the osteoclast precursor pool in the mouse. J Bone Miner Res 20, 1659-1668, 908 doi:10.1359/JBMR.050503 (2005). 909 71 Nakashima, T. et al. Evidence for osteocyte regulation of bone homeostasis through 910 RANKL expression. Nat Med 17, 1231-1234, doi:10.1038/nm.2452 (2011). 911 72 Komori, T. Regulation of osteoblast differentiation by transcription factors. J Cell 912 Biochem 99, 1233-1239, doi:10.1002/jcb.20958 (2006). 913 73 Aurrand- Lions, M. & Mancini, S. J. C. Murine Bone Marrow Niches from 914 Hematopoietic Stem Cells to B Cells. Int J Mol Sci 19, doi:10.3390/ijms19082353 915 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[172, 80, 884, 175]]<|/det|> +Bonaud, A., Lemos, J. P., Espeli, M. & Balabanian, K. Hematopoietic Multipotent Progenitors and Plasma Cells: Neighbors or Roommates in the Mouse Bone Marrow Ecosystem? Front Immunol 12, 658535, doi:10.3389/fimmu.2021.658535 (2021). + +<|ref|>text<|/ref|><|det|>[[172, 182, 884, 266]]<|/det|> +Kusumbe, A. P., Ramasamy, S. K. & Adams, R. H. Coupling of angiogenesis and osteogenesis by a specific vessel subtype in bone. Nature 507, 323- 328, doi:10.1038/nature13145 (2014). + +<|ref|>text<|/ref|><|det|>[[172, 279, 884, 364]]<|/det|> +Xu, J. et al. Comparison of skeletal and soft tissue pericytes identifies CXCR4(+) bone forming mural cells in human tissues. Bone Res 8, 22, doi:10.1038/s41413- 020- 0097- 0 (2020). + +<|ref|>text<|/ref|><|det|>[[172, 375, 884, 462]]<|/det|> +Bisio, V., Espeli, M., Balabanian, K. & Anginot, A. Culture, Expansion and Differentiation of Human Bone Marrow Stromal Cells. Methods Mol Biol 2308, 3- 20, doi:10.1007/978- 1- 0716- 1425- 9_1 (2021). + +<|ref|>text<|/ref|><|det|>[[172, 474, 884, 560]]<|/det|> +Ibanez, L. et al. Inflammatory Osteoclasts Prime TNFalpha- Producing CD4(+) T Cells and Express CX3 CR1. J Bone Miner Res 31, 1899- 1908, doi:10.1002/jbrmr.2868 (2016). + +<|ref|>text<|/ref|><|det|>[[172, 573, 884, 624]]<|/det|> +Schindelin, J. et al. Fiji: an open- source platform for biological- image analysis. Nature methods 9, 676- 682, doi:10.1038/nmeth.2019 (2012). + +<|ref|>text<|/ref|><|det|>[[172, 637, 884, 688]]<|/det|> +Dobin, A. et al. STAR: ultrafast universal RNA- seq aligner. Bioinformatics 29, 15- 21, doi:10.1093/bioinformatics/bts635 (2013). + +<|ref|>text<|/ref|><|det|>[[172, 702, 884, 789]]<|/det|> +Anders, S., Pyl, P. T. & Huber, W. HTSeq- a Python framework to work with high- throughput sequencing data. Bioinformatics 31, 166- 169, doi:10.1093/bioinformatics/btu638 (2015). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 83, 345, 101]]<|/det|> +## ACKNOWLEDGMENTS + +<|ref|>text<|/ref|><|det|>[[111, 110, 885, 860]]<|/det|> +We thank Dr. H. Gary and ML. Aknin (IPSIT, Facility PLAIMMO, Clamart), F. Mercier- Nomé (IPSIT, Facility PHIC, Clamart), Drs. V. Parietti- Montcuquet, C. Doliger, S. Duchez and N. Setterblad (Animal and Flow Cytometry Core Facilities, Institut de Recherche Saint- Louis, Paris), V. Nicolas (IPSIT, Facility MIPSIT, Chatenay- Malabry), D. Courilleau (IPSIT, Facility CIBLOT, Chatenay- Malabry), B. Lecomte (IPSIT, Facility ANIMEX, Clamart) and C. Cordier and J. Megret (Plateau technique de cytométrie, SFR Necker, Paris) for their technical assistance. We thank the Montpellier Preclinical Platform of the Research Infrastructure ECELLFRANCE for the microCT analyses as well as the Plateforme d'Irradiation (IRSN, Fontenay- Aux- Roses, France) for their technical assistance. The study was supported by the LabEx LERMIT supported by ANR grant ANR- 10- LABX- 33 under the Program "Investissements d'Avenir" ANR- 11- IDEX- 0003- 01, an ANR PRC grant (ANR- 17- CE14- 0019) to M.A- L., C.B- W. and coordinated by K.B. and by the Association Saint Louis pour la Recherche sur les Leucémies to KB. J.N. was a PhD fellow from the DIM Cancéropôle and the FRM. Z.A- N. was a fellowship recipient from the French Ministry. V.R. was supported by the FRM, La Ligue Contre le Cancer and la Société Française d'Hématologie. A.Bon. was supported by an ANR @RAction grant (ANR- 14- ACHN- 0008) and by a JCJC ANR grant (ANR- 19- CE15- 0019- 01) to ME. A.Bou. was supported by the ANR grant 17- CE14- 0019. J.L. was recipient from the People Program (Marie Curie Actions) of the European Union's Seventh Framework Program (FP7/2007- 2013) under REA grant agreement n. PCOFUND- GA- 2013- 609102, through the PRESTIGE Program coordinated by Campus France, and from an ANR grant (ANR- 17- CE14- 0019). V.B., N.D. and K.B. were supported by the INCa agency under the program PRT- K 2017. J.K. was supported by European Union's Horizon 2020 MSCA, Program under grant agreement 641833 (ONCORNET). D.H.M and P.M.M. were supported + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 134]]<|/det|> +by the Division of Intramural Research of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. + +<|ref|>sub_title<|/ref|><|det|>[[115, 181, 384, 199]]<|/det|> +## AUTHOR CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[112, 212, 884, 463]]<|/det|> +A.A. and J.N. designed and performed experiments, analyzed data and contributed to manuscript writing; Z.A-N., V.R., A.Bon., A.Bou., J.L., V.B., J.K., L.S., C.M. and A.C. performed experiments and analyzed data; S.P., N.D., M.A-L., S.J.C.M., G.L., F.G., C.B-W. and M.C-S. performed experiments, contributed to data analyses and reviewed the manuscript; D.H.M. and P.M.M provided WS samples and clinical data and reviewed the manuscript; M.E. and M.R. helped with the study design, performed experiments, contributed to data analyses and reviewed the manuscript; K.B. conceived, designed and supervised the study, contributed to data analyses, found funding for the study, and wrote the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 508, 422, 527]]<|/det|> +## DECLARATION OF INTERESTS + +<|ref|>text<|/ref|><|det|>[[115, 541, 546, 559]]<|/det|> +The authors declare no competing financial interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 300, 100]]<|/det|> +## FIGURE LEGENDS + +<|ref|>text<|/ref|><|det|>[[110, 110, 886, 860]]<|/det|> +Figure 1: WS- linked CXCR4 mutations are associated with reduced bone mass in mice. (A) The bone mineral density (BMD) of lumbar spine of WT, +/1013 and 1013/1013 mice was measured through Dual- energy x- ray absorptiometry. Results represent means \(\pm\) SEM with 3 mice per group. (B- D) 3D representative images of trabecular and cortical composites (B) and quantitative micro- CT analyses of trabecular (C) and cortical (D) parameters of femurs from WT and mutant mice. BV = bone volume; TV = trabecular volume; Tb.Nb = trabecular number; Tb.Sp = trabecular separation; Ct.BV = cortical bone volume; Ct.Th = cortical thickness. Data (means \(\pm\) SEM) are from three independent experiments with 7- 14 mice per group. (E) BM sections from WT and mutant mice were stained with toluidine blue coloration. Larger images show 2X inserts in trabecular areas. Bars: 200 \(\mu \mathrm{m}\) . Images are representative of at least three independent determinations. (F and G) BM sections from WT and mutant mice were stained for chondrocyte (alcian blue, F) or adipocyte (perilipin, G) markers. Bars: 20 (F) or 500 (G) \(\mu \mathrm{m}\) . Images are representative of at least three independent determinations. (H) BM sections from WT and mutant mice were immuno- stained for osteopontin (Opn) in association with DAPI. Trabeculae are indicated by white arrows. Bars: 250 \(\mu \mathrm{m}\) . Images are representative of five independent determinations. (I) Cartilaginous growth plates were evaluated based on overall growth plate thickness measured on microCt scans. Data (means \(\pm\) SEM) are from 2 independent experiments with 7- 8 mice per group. (J) Size (left) and weight (right) of WT and mutant mice were assessed at 8 weeks of age. Results (means \(\pm\) SEM) are from five independent experiments with ten mice per group. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P < 0.05; \*\*, P < 0.005 and \*\*\*, P < 0.0005 compared with WT samples; \$, P < 0.05; \$\$, P < 0.005 and \$\$\$, P < 0.0005 compared with +/1013 samples (as determined using the two- tailed Student's \(t\) test). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 80, 885, 900]]<|/det|> +Figure 2: Reduction of skeletal stromal cells in Cxcr41013-bearing mice. (A) Representative dot-plots showing the flow cytometric gating strategies used to sort stroma cells (defined as CD45- TER119-), committed OPCs (defined as CD45- TER119-CD31- Sca-1- CD51+ PDGFRα-) and SSCs (defined as CD45- TER119- CD31- Sca-1+CD51+PDGFRα+) in the mouse bone fraction. (B) Absolute numbers of the indicated stroma cell subsets from bone fractions were determined by flow cytometry in WT, +/1013 and 1013/1013 mice. Data (means ± SEM) are from at least six independent experiments with >10 mice per group. (C and D) Expression levels of Cxcr4 (C) or Ackr3 (D) were determined by flow cytometry on gated (Ter119- CD45-) stromal cells, SSCs and OPCs from bone fractions of WT and mutant mice. Left: Representative histograms for surface detection of Cxcr4 or Ackr3 on gated bone stromal cells. Background fluorescence is shown (isotype, dotted vertical line). Middle and right: Cxcr4- or Ackr3-positive fractions or MFI values obtained within bone stromal cells, SSCs and OPCs relative to background fluorescence based on the corresponding isotype control staining. Data are from at least four independent experiments with >10 mice per group. (E) Cell surface expression of Cxcr4 on SSCs upon exposure to 10 nM Cxcl12 at 37°C for 45 min. Cxcr4 expression on bone cells incubated in medium alone was set at 100% (dotted horizontal line). Data are pooled from three independent experiments with six mice per group. (F) Migration of cultured WT or mutant SSCs in response to 1 nM Cxcl12 in the presence or absence of 10 μM AMD3100 was assessed in three independent fields after crystal violet staining. Data are from three independent SSC cultures per genotype. (G) In vitro expanded SSCs from bone fractions of WT, +/1013 or 1013/1013 mice pre-incubated or not with 10 μM AMD3100 were stimulated 2 min with 10 nM Cxcl12 at 37°C and then the MFI values of phospho-Erk were determined by flow cytometry and represented as a fold change expression. Data are from three independent SSC cultures per genotype. (H) Cell surface expression of Ackr3 on SSCs upon exposure to 10 nM Cxcl12 at 37°C for 45 min. Ackr3 expression on bone cells incubated in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 885, 897]]<|/det|> +medium alone was set at \(100\%\) (dotted horizontal line). Data are from three independent experiments with six mice per group. (I) Cultured WT or mutant SSCs were pre-treated or not with \(100~\mu \mathrm{M}\) of the Ackr3 antagonist CCX733 and then incubated with \(5~\mathrm{nM}\) Cxcl12- AF647 at \(37^{\circ}\mathrm{C}\) for \(60~\mathrm{min}\) . Cells were washed with an acidic glycine buffer to remove cell surface- bound Cxcl12- AF647. Geometric MFI values for Cxcl12- AF647 were determined by flow cytometry. No Cxcl12- AF647 uptake was observed in SSCs incubated at \(4^{\circ}\mathrm{C}\) . Data are pooled from three individual SSC cultures per genotype. (J) Flow-cytometric determination of the proportions of apoptotic (Annexin \(\mathrm{V^{+}}\) DAPI) SSCs and OPCs from bone fractions of WT and mutant mice. Data (means \(\pm\) SEM) are from three independent experiments with nine mice per group. (K) Schematic diagram for the generation of CD45.1 \(\rightarrow\) CD45.2 short (3 wks)- or long (16 wks)- term BM chimeras. (L) Proportions of WT donor CD45.1+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.2+ WT or mutant recipients 16 weeks after transplantation. Data (means \(\pm\) SEM) are from three independent experiments with 5-10 recipient mice per group. (M) Absolute numbers of stromal cells, SSCs, and OPCs were determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.2+ recipients 16 weeks after transplantation. (N) Sixteen weeks after transplantation, BM sections from WT or mutant CD45.2+ recipient mice reconstituted with WT donor CD45.1+ BM cells were immuno- stained for Opn in association with DAPI (bars: \(250~\mu \mathrm{m}\) ). Trabeculae are indicated by white arrows. Images are representative of at least three independent determinations. (O) Left: Proportions of WT donor CD45.1+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.2+ WT or mutant recipients 3 weeks after transplantation. Middle and right panels show the absolute numbers of stromal cells, SSCs, and OPCs determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.2+ recipients 3 weeks after transplantation. (P) Schematic diagram for the generation of CD45.2 \(\rightarrow\) CD45.1 short (3 wks)- or long (16 wks)- term BM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 885, 664]]<|/det|> +chimeras. **(Q)** Proportions of WT or mutant donor CD45.2+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.1+ WT recipients 16 weeks after transplantation. Data (means \(\pm\) SEM) are from five independent experiments with 8- 12 recipient mice per group. **(R)** Absolute numbers of stromal cells, SSCs, and OPCs were determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.1+ recipients 16 weeks after transplantation. **(S)** Sixteen weeks after transplantation, BM sections from WT CD45.1+ recipient mice reconstituted with WT, +/1013 or 1013/1013 donor CD45.2+ BM cells were immunostained for Opn in association with DAPI (bars: \(250\mu \mathrm{m}\) ). Trabeculae are indicated by white arrows. Images are representative of at least three independent determinations. **(T)** Left: Proportions of WT or mutant donor CD45.2+ LSK SLAM and leukocytes recovered from the BM and blood of BM chimeras in CD45.1+ WT recipients 3 weeks after transplantation. Middle and right panels show the absolute numbers of stromal cells, SSCs, and OPCs determined by numeration and flow cytometry of the bone fractions of BM chimeras in CD45.1+ recipients 3 weeks after transplantation. Data (means \(\pm\) SEM) are from three independent experiments with 6- 10 mice per group. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P <0.05; \*\*, P<0.005 and \*\*\*, P<0.0005 compared with WT cells; \&\&, P < 0.005 compared with untreated WT or mutant cells; \$, P < 0.05 and \$\$, P < 0.005 compared with +/1013 samples (as determined using the two- tailed Student's \(t\) test). + +<|ref|>text<|/ref|><|det|>[[113, 704, 885, 888]]<|/det|> +Figure 3: Increased bone resorption and reduced bone formation in Cxcr41013-bearing mice. **(A)** Bone sections from WT and mutant mice were colored for Tartrate Resistant Acid Phosphatase (TRAP) activity. OCLs are visualized as brown- stained TRAP- positive cells attached to bone trabeculae and are indicated by arrows (representative images). **(B)** OCLs were quantified (Oc.S/BS) and (Oc.N/BV) for WT and mutant mice. Results represent means \(\pm\) SEM with 3 mice per group. **(C)** Total BM cells from WT and mutant mice were differentiated for 5 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 886, 895]]<|/det|> +days in osteoclastic medium (Rank- L and M- Csf) and OCLs (TRAP- positive, multinucleated cells) were identified (left, representative images) and quantified (right). Results (means \(\pm\) SEM) are from 2 independent experiments with 3 mice per group. (D) In vitro differentiated OCLs from WT and mutant BM cells were analyzed for their resorptive capacity of a mineralized matrix. Pictures show the resorptive lacunae produced by OCLs (representative images). The proportion of lacunae surface relative to the whole surface was calculated and expressed as a percentage of the mineral area resorbed by WT OCLs (right panel). Results (means \(\pm\) SEM) are from 2 independent experiments with 3 mice per group. (E) The relative expression levels (RQ) of osteoclastic (Nfatc1, Ctsk, Clcn7 and Tnfrsf11a) genes were determined in osteoclastic differentiation cultures of total BM cells from WT and mutant mice by quantitative real- time PCR. Each individual sample was run in triplicate and has been standardized for 36B4 expression levels. Results represent means \(\pm\) SEM with 3 mice per group. (F and G) Dynamic histomorphometric measures of bone formation were compared between WT and \(+\) /1013 mice. OS/BS \(=\) Osteoid number / Bone surface; Obl.S/BS \(=\) Osteoblast surface / Bone surface; MS/BS \(=\) Mineralized surface / Bone surface; Dbl/BS \(=\) Double labelled surface / Bone surface. Results represent means \(\pm\) SEM with 3 mice per group. (H) The mineral apposition rates (MAR) were compared between WT and Cxcr41013- bearing mice. Results represent means \(\pm\) SEM with 3 mice per group. (I) Volcano plot analysis of differentially expressed genes obtained by RNA-seq between WT and 1013/1013 OPCs (p<0.05; FC≥2) performed on three biological replicates per group. (J and M) Heatmap representing the relative expression levels of selected genes (osteogenic, J and osteoclastogenic, M) expressed by sorted OPCs from WT and mutant mice. (K and N) Normalized counts of osteogenic (K) and osteoclastogenic (N) genes using the DESeq2 method obtained by RNA-seq in WT and mutant OPCs. (L) In vitro osteoblastic differentiation of sorted WT and mutant OPCs evaluated at day 21 post- culture in osteoblastic medium by Alizarin Red S coloration of mineral matrix + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 200]]<|/det|> +deposition. The images are representative of 3 independent cultures. The quantification (means \(\pm\) SEM) from 3 independent culture conditions is shown. Kruskal–Wallis \(H\) test–associated p-values (#) are indicated. \*, P <0.05 compared with WT samples (as determined using the two-tailed Student's \(t\) test). + +<|ref|>text<|/ref|><|det|>[[111, 245, 885, 897]]<|/det|> +Figure 4: Impaired osteogenic specification of Cxcr41013-bearing skeletal stromal/stem cells. (A) Ki-67 and DAPI co- staining was used to analyze by flow cytometry the cell cycle status of SSCs and OPCs from bone fractions of WT and mutant mice. Bar graphs show the percentage of cells (DAPIlowKi-67) in the quiescent G0 phase. Data (means \(\pm\) SEM) are from three independent experiments with nine mice per group. (B) Representative flow-cytometric detection of BrdU staining in SSCs from bone fractions of WT and mutant mice (left). Percentages of BrdU+ bone SSCs and OPCs after a 12-day labelling period (right). Data (means \(\pm\) SEM) are from three independent experiments with six mice per group. (C) Principal component analyses (PCA) of relative gene expression in SSCs sorted from the bone fractions of WT and mutant mice. (D) The heatmap shows the relative expression levels (RQ) normalized for \(\beta\) -actin expression levels in each sample of selected genes involved in SSC differentiation towards the osteogenic lineage (6 pools of 100 cells per condition). (E) RQ of the most regulated genes involved in differentiation and cell cycle of SSCs from the three genotypes. Data (means \(\pm\) SEM) are from two independent experiments with 6 mice per group. (F) Relative expression of osteoclastogenic genes (Tnfsf11, Tnfrsf11b, Csfl) in WT and mutant SSCs. Each individual sample was run in triplicate and has been standardized for \(\beta\) -actin expression levels and presented as relative expression to WT. (G) Immunofluorescence showing in red Osterix (Osx)-positive cells and in blue DAPI-stained nuclei in WT and mutant mice femurs (bars: 100 \(\mu \mathrm{m}\) ). Dashed lines indicate the limit between the cartilage growth plate (above the line) and the bone (below the line). Images are representative of at least 3 independent determinations. (H) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 266]]<|/det|> +Quantification of \(\mathrm{Osx^{+}}\) cells per \(\mathrm{mm}^2\) below the growth plate. Data (means \(\pm\) SEM) are from 3 to 5 independent mice. (I) Absolute numbers of the indicated stroma cell subsets from marrow fractions were determined by flow cytometry in WT, \(+ / 1013\) and 1013/1013 mice. Data (means \(\pm\) SEM) are from at least six independent experiments with \(>10\) mice per group. Kruskal–Wallis \(H\) test–associated p-values (#) are indicated. \*, P \(< 0.05\) ; \*\*, P \(< 0.005\) and \*\*\*, P \(< 0.0005\) compared with WT cells (as determined using the two-tailed Student’s \(t\) test). + +<|ref|>title<|/ref|><|det|>[[115, 312, 881, 330]]<|/det|> +# Figure 5: Cxcr4 desensitization regulates the osteogenic differentiation of skeletal cells. + +<|ref|>text<|/ref|><|det|>[[112, 344, 884, 892]]<|/det|> +(A) The number of colonies formed from bone fractions of WT, \(+ / 1013\) and 1013/1013 mice in CFU-F assays. Data (means \(\pm\) SEM) are from three independent experiments with 6-9 mice per group. (B) After in vitro loading with BrdU (5 days) or CTV (3 days), the percentages of BrdU\(^+\) (left) or CTV\(^1\)low (right) cells within WT and mutant bone-derived SSCs were determined by flow cytometry. (C) Bar graphs show the percentages of cultured WT or mutant SSCs in the quiescent G0 phase (DAPI\(^1\)low Ki-67\(^-\) , left) or with an apoptotic phenotype (Annexin V\(^+\) DAPI\(^-\) , right) as determined by flow cytometry. (D) Doubling time (left) and absolute numbers (right) of WT and mutant SSCs after 3 days of culture. Data (means \(\pm\) SEM) displayed in panels B, C and D are from 3-6 independent SSC cultures per genotype. (E) Alkaline phosphatase (Alp) staining was performed 14 days after initiation of the culture of WT and mutant SSCs in osteogenic medium supplemented every two days with 10 μM AMD3100 or vehicle (PBS). Quantitative analyses (number of Alp\(^+\) cells) were performed under an inverted microscope. Data (means \(\pm\) SEM) are from 4 independent cultures per genotype. (F) Alizarin Red staining was performed 21 days after initiation of the culture of WT and mutant SSCs as described above. Quantitative analyses (means \(\pm\) SEM) of staining were performed using the osteogenesis assay kit. (G) Expression levels of osteogenic genes were determined by qRT-PCR in WT and mutant SSCs 14 and 21 days after initiation of the osteogenic culture in the presence or absence + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 82, 885, 500]]<|/det|> +of AMD3100. Each individual sample was run in triplicate and was standardized for \(\beta\) - actin expression levels. Results (means \(\pm\) SEM) are expressed as relative expression compared to WT samples. (H) Schematic diagram for daily AMD3100 in vivo i.p. injection for 21 days in WT and mutant mice. (I) Absolute numbers of the indicated stroma cell subsets from bone fractions of WT and mutant mice were determined by flow cytometry. Data (means \(\pm\) SEM) are from 2 independent experiments with 6 PBS injected mice and 12 AMD3100- injected mice per genotype. (J) BM sections from WT and mutant mice treated with vehicle (PBS) or AMD3100 were immunostained for Opn in association with DAPI. Bars: \(500\mu \mathrm{m}\) . Images are representative of 3 independent determinations. (K) Bone mineral density (BMD) values of lumbar spine from treated WT and mutant mice are shown. Kruskal- Wallis \(H\) test- associated p- values (#) are indicated. \*, P <0.05; \*\*, P<0.005 and \*\*\*, P<0.0005 compared with WT or untreated samples; \$, P < 0.05 compared with +/1013 samples; &, P < 0.05 compared with vehicle- treated mice (as determined using the two- tailed Student’s \(t\) test). + +<|ref|>text<|/ref|><|det|>[[111, 541, 885, 859]]<|/det|> +Figure 6: BM stromal cells from WS patients displayed in vitro impaired osteogenic capacities. (A) Relative expression levels of osteogenic genes were determined by qRT- PCR at day 14 in osteogenic- induced cultures of two WS patients- derived BMSCs and 7 healthy donors- derived BMSCs. Each individual sample was run in triplicate and was standardized for 36B4 expression levels. Results (means \(\pm\) SEM) are expressed as relative expression compared to healthy samples (set at 1). (B) Alizarin Red staining was performed 21 days after initiation of the culture of 1.5 x \(10^{3}\) healthy or WS BMSCs in pro- osteogenic medium (left panel). Representative images for healthy and WS donors #1 and #2 are shown. Quantitative analyses of staining (means \(\pm\) SEM) were performed using the osteogenesis assay kit (right panel). (C) Oil Red O staining was performed 21 days after initiation of cultures of healthy or WS BMSCs + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[506, 41, 881, 57]]<|/det|> +# CXCR4 desensitization in skeletal stromal cells + +<|ref|>text<|/ref|><|det|>[[45, 82, 884, 160]]<|/det|> +1179 in pro- adipogenic differentiation medium. Bars: \(200\mu \mathrm{m}\) . \*P <0.05 and \*\*, P<0.005 compared 1180 with healthy or untreated cells (as determined using the two- tailed Student's \(t\) test). 1181 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[47, 85, 190, 100]]<|/det|> +1182 TABLE + +<|ref|>text<|/ref|><|det|>[[47, 118, 92, 133]]<|/det|> +1183 + +<|ref|>table<|/ref|><|det|>[[115, 159, 795, 396]]<|/det|> +
GenderAge (year)CXCR4 mutationChronic treatmentLumbar spineFemoral neck
P1Female37R334XNo-3.10
P2Female52R334XNo-1.1-1.8
P3(1)Male13S338XYes (2)-1.8-2.3
P4Female49R334XYes (3)-2.7-1.3
P5(1)Male15R334XYes (4)-1.8-2.2
+ +<|ref|>text<|/ref|><|det|>[[47, 396, 92, 411]]<|/det|> +1184 + +<|ref|>text<|/ref|><|det|>[[47, 420, 884, 664]]<|/det|> +**Table 1: Abnormal bone mineral density values in WS patients.** Characteristics of each patient with low BMD value are shown. T-scores for lumbar spine (L1-L4) and femoral neck have been evaluated. According to World Health Organization (WHO) criteria, values classify patients as osteopenic with a T-score between -1.0 and -2.5 or osteoporotic with a T-score at or below -2.5. Values outside the normal range defined by WHO are italicized. (1) For patients 3 and 5, because of their young age, Z-scores are given with a value at or below -2.0 considered as abnormal; (2) G-CSF since age of 2; (3) G-CSF several years at the time of scan; (4) G-CSF for 6 months at the time of scan. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 997, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[30, 0, 999, 760]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[32, 7, 257, 147]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[32, 159, 435, 360]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[32, 380, 87, 401]]<|/det|> +
C
+ +<|ref|>image<|/ref|><|det|>[[32, 404, 435, 630]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[32, 621, 105, 633]]<|/det|> +
Adipogenic
+ +<|ref|>image<|/ref|><|det|>[[471, 193, 707, 333]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[460, 234, 486, 245]]<|/det|> +
Alizarin Red (day 21)
+ +<|ref|>image<|/ref|><|det|>[[460, 245, 485, 255]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[460, 325, 525, 336]]<|/det|> +
Osteogenic
+ +<|ref|>image<|/ref|><|det|>[[460, 336, 485, 345]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 129, 408, 149]]<|/det|> +SUPPLEMENTALINFORMATIONS.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/images_list.json b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..45144c178b2e8a098a309670238aafd2cdebbd88 --- /dev/null +++ b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/images_list.json @@ -0,0 +1,107 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | The synthesis steps of the PtNP-shell and the concept of mediating precise photothermal effects for cardioprotection. a, The synthesis steps of PtNP-shell and schematic diagram of photothermal effect. b, Schematic diagram of multifunctional autonomic modulation mediated by photothermal effect of PtNP-shell for precise cardioprotection against myocardial I/R injury and MI-induced VAs.", + "footnote": [], + "bbox": [ + [ + 147, + 85, + 848, + 444 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Characterization of PtNP-shell. a, TEM image of PtNP-shell (Right: element mapping). b, STEM images of PtNP-shell surface. c, XRD spectrum of PtNP-shell (Inset: SAED pattern). d, UV-vis-NIR absorption spectrum of PtNP-shell ( \\(75 \\mu \\mathrm{g} \\cdot \\mathrm{mL}^{-1}\\) ). e, Temperature elevation curves of PtNP-shell ( \\(50 \\mu \\mathrm{g} \\cdot \\mathrm{mL}^{-1}\\) ) under NIR-II laser irradiation ( \\(1 \\mathrm{W} \\cdot \\mathrm{cm}^{2}\\) ). f, Calculation of the PCE at \\(1064 \\mathrm{nm}\\) (PtNP-shell: \\(50 \\mu \\mathrm{g} \\cdot \\mathrm{mL}^{-1}\\) ).", + "footnote": [], + "bbox": [ + [ + 144, + 494, + 850, + 775 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | PtNP-shell photothermal activation of different neuronal ion channels in vitro. a, Flowchart of calcium imaging assay performed on HT-22 cells. b, calcium imaging of HT-22 cells under different experimental conditions. c, Western blotting for TRPV1 and TREK1 from HT-22 and H9c2 cells. Percentage of d, TRPV1 and f, TREK1 groups of HT-22 cells within the field of view of the fluorescence microscope that responded to laser stimulation. Temporal dynamics of \\(\\mathrm{Ca}^{2 + }\\) signals in e, TRPV1 and g, TREK1 groups of cells. The solid lines indicate the mean, and shade represents the standard error of the mean (SEM). h, Cell viability of HT-22 treated with different concentrations of PtNP-shell for \\(24\\mathrm{h}\\) . i, Cell viability of HT-22 treated with NIR-II laser irradiation of different power densities and laser duration. The error bar indicates S.E.M. \\(***\\mathrm{P}< 0.001\\) .", + "footnote": [], + "bbox": [ + [ + 145, + 81, + 850, + 480 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Photothermal activation of the parasympathetic nervous system by PtNP-shell. a, Location of the canine NG. b, Schematic illustration of the process of photothermal modulation of NG. c, Temperature curves of NG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of NG. e, Representative images of HR reduction induced after stimulation of NG with different voltages. Maximal HR changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \\(n = 6\\) . h, Quantification of the NG neural activity recordings, \\(n = 6\\) . i, Representative immunofluorescent images of Vacht (red), c-fos (green) and TRPV1 (pink) in the NG of beagles following different treatments. Data are shown as the mean \\(\\pm\\) S.E.M. \\(*P < 0.05\\) , \\(**P < 0.01\\) , \\(***P < 0.001\\) , ns means that the difference is not statistically significant.", + "footnote": [], + "bbox": [ + [ + 144, + 165, + 850, + 670 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 | PtNP-shell photothermal activation of the parasympathetic nervous system improves myocardial I/R injury. Modulation of NG to protect against myocardial I/R injury and associated VAs a, schematic diagram and b, flowchart. c, Representative visual depictions of VAs, including VPB, VT and VF. d, Quantitative analysis the ratio of sVT and VF incidence between different groups, \\(\\mathrm{n} = 6\\) . Quantitative analysis the number of e, VPBs, f, VTs and g, the duration of sVT of beagles. Effects on ventricular ERP at different sites in beagles treatment with PtNP-shell or control h, before and i, after myocardial I/R injury modelling. Levels of markers of myocardial injury, including j, MYO and k, c-TnI, after different treatments in beagles. Data are shown as the mean \\(\\pm\\) S.E.M. \\(^{*}\\mathrm{P}< 0.05\\) , \\(^{**}P< 0.01\\) , \\(^{***}\\mathrm{P}< 0.001\\) .", + "footnote": [], + "bbox": [ + [ + 192, + 425, + 803, + 720 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6 | Photothermal inhibition of the sympathetic nervous system by PtNP-shell. a, Location of the canine LSG. b, Schematic illustration of the process of photothermal modulation of LSG. c, Temperature curves of LSG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of LSG. e, Representative images of BP elevation induced after stimulation of LSG with different voltages. Maximal SBP changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \\(n = 6\\) . h, Quantification of the LSG neural activity recordings, \\(n = 6\\) . i, Representative immunofluorescent images of TH (red), c-fos (green) and TREK1 (pink) in the LSG of beagles following different treatments. Data are shown as the mean \\(\\pm\\) S.E.M. \\(*P< 0.05\\) , \\(**P< 0.01\\) , \\(***P< 0.001\\) , ns means that the difference is not statistically significant.", + "footnote": [], + "bbox": [ + [ + 150, + 80, + 850, + 592 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Fig. 7 | PtNP-shell photothermal inhibition of the sympathetic nervous system improves MI associated VAs. Modulation of LSG to protect against MI and associated VAs a, schematic diagram and b, flowchart. c, Quantitative analysis the ratio of sVT and VF incidence between different groups, \\(n = 6\\) . d, Quantitative analysis the number of VPBs of beagles. e, Typical images of VA induced by programmed electrical stimulation. f, Quantitative analysis of VAs score in different groups. Effects on ventricular ERP at different sites in Beagles treatment with PtNP-shell or control g, before and h, after MI modelling. i, Quantitative analysis of VF threshold in different groups. Data are shown as the mean \\(\\pm\\) S.E.M. \\(*P < 0.05\\) , \\(**P < 0.01\\) , \\(***P < 0.001\\) .", + "footnote": [], + "bbox": [ + [ + 144, + 85, + 852, + 393 + ] + ], + "page_idx": 19 + } +] \ No newline at end of file diff --git a/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa.mmd b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa.mmd new file mode 100644 index 0000000000000000000000000000000000000000..f4c937154f9065affcbc675b3e368ab4b4c869f1 --- /dev/null +++ b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa.mmd @@ -0,0 +1,426 @@ + +# Pt nanoshell with ultra-high NIR-β photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection + +Lei Fu lei fu@whu.edu.cn + +Wuhan University https://orcid.org/0000- 0003- 1356- 4422Chenlu WangWuhan UniversityLiping ZhouWuhan UniversityChengzhe LiuWuhan UniversityJiaming QiaoWuhan UniversityXinrui HanWuhan UniversityLuyang WangWuhan UniversityYaxi LiuWuhan UniversityBi XuWuhan UniversityQinfang QiuWuhan UniversityZizhuo ZhangWuhan UniversityJiale WangWuhan UniversityXiaoya ZhouWuhan UniversityMengqi ZengWuhan University https://orcid.org/0000- 0002- 1442- 052X + +Lilei Yu + +<--- Page Split ---> + +## Article + +## Keywords: + +Posted Date: March 15th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 3985327/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on July 28th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 50557- w. + +<--- Page Split ---> + +# Pt nanoshell with ultra-high NIR-II photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection + +Chenlu Wang \(^{1,\dagger}\) , Liping Zhou \(^{2,3,4,\dagger}\) , Chengzhe Liu \(^{2,3,4,\dagger}\) , Jiaming Qiao \(^{2,3,4}\) , Xinrui Han \(^{2,3,4}\) , Luyang Wang \(^{1}\) , Yaxi Liu \(^{1}\) , Bi Xu \(^{1}\) , Qinfang Qiu \(^{2,3,4}\) , Zizhuo Zhang \(^{2,3,4}\) , Jiale Wang \(^{2,3,4}\) , Xiaoya Zhou \(^{2,3,4*}\) , Mengqi Zeng \(^{1}\) , Lilei Yu \(^{2,3,4*}\) , Lei Fu \(^{1,3,4*}\) + +\(^{1}\) College of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China. \(^{2}\) Cardiovascular Hospital, Renmin Hospital of Wuhan University, Wuhan 430060, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan 430060, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan 430060, China; Hubei Key Laboratory of Cardiology, Wuhan 430060, China; Cardiovascular Research Institute, Wuhan University, Wuhan, 430060, China. \(^{3}\) Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430060, China. \(^{4}\) Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China. + +\(^{*}\) E- mail: leifu@whu.edu.cn; lileiyu@whu.edu.cn; whuzhouxiaoya@whu.edu.cn + +\(^{†}\) These authors contributed equally: Chenlu Wang, Liping Zhou, Chengzhe Liu. + +<--- Page Split ---> + +The autonomic nervous system plays a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK- elated \(\mathbf{K}^{+}\) Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations on achieving rapid and precise treatment. Here, we achieved nearly perfect blackbody absorption and one of the highest PCE in the second near infrared (NIR- II) window (73.7% at 1064 nm) via a Pt nanoparticle shell (PtNP- shell). By precisely manipulating the photothermal effect, we successfully achieved rapid and precise multifunctional neuromodulation encompassing neural activation (41.0–42.9 °C) and inhibition (45.0–46.9 °C). The NIR-II photothermal modulation additionally achieved bi- directional reversible autonomic modulation and conferred protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy. + +Cardiovascular disease has emerged as a leading cause of mortality, with acute myocardial infarction being one of the most pernicious ailments1,2. Myocardial ischemia (MI) frequently precipitates acute ventricular arrhythmias (VAs), impeding prompt and efficacious treatment for acute myocardial infarction. Furthermore, conventional interventional procedures for MI are unable to circumvent concomitant myocardial reperfusion injury and associated VAs. The autonomic nervous system, encompassing sympathetic and parasympathetic nerves, plays a role in cardiovascular modulation; both are naturally antagonistic. Sympathetic inhibition or parasympathetic activation has been shown to stabilize cardiac electrophysiology, safeguard against MI + +<--- Page Split ---> + +and reduce the incidence of VAs \(^{3}\) . + +In recent years, several studies have demonstrated that light- activated nanotransducers can induce local heating effects, leading to the activation or inhibition of nerves \(^{4 - 6}\) . This discovery is attributed to the identification of temperature- sensitive ion channels in neurons, such as transient receptor potential vanilloid 1 (TRPV1) \(^{7}\) and TWIK- elated K \(^{+}\) Channel 1 (TREK1) \(^{8}\) . The activation of specific temperature- sensitive ion channels necessitates precise temperature ranges \(^{7 - 9}\) . Considering the acute nature of neural responses, a therapeutic strategy with rapid and accurate modulation is required. The second near infrared (NIR- II) photothermal is expected to realize noninvasive and nonimplanted neuromodulation. However, its neural response rate and accuracy are currently limited by low photothermal conversion efficiency (PCE). + +Here we report a near blackbody NIR- II Pt nanoparticle shell (PtNP- shell) for protection against MI and myocardial reperfusion injury accompanying intervention. The PtNP- shell, synthesized through a simple electrocoupling substitution reaction using liquid metal nanoparticles as templates (Fig. 1a), possesses surface pores and a hollow structure. It demonstrates nearly perfect blackbody absorption, enhanced absorption of light, and then one of the highest PCE in the NIR- II window (73.7% at 1064 nm). By leveraging the local heating effect mediated by PtNP- shell, we achieved rapid, efficient, and precise multifunctional autonomic neuromodulation. Specifically, parasympathetic activation and sympathetic inhibition were accomplished by activating TRPV1 (41.0–42.9 °C) and TREK1 (45.0–46.9 °C) channels, respectively. Photothermal autonomic neuromodulation mediated by PtNP- shell effectively stabilized cardiac electrophysiology and reduced VAs incidence in both myocardial ischemia- reperfusion (I/R) injury model and MI model, respectively (Fig. 1b). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | The synthesis steps of the PtNP-shell and the concept of mediating precise photothermal effects for cardioprotection. a, The synthesis steps of PtNP-shell and schematic diagram of photothermal effect. b, Schematic diagram of multifunctional autonomic modulation mediated by photothermal effect of PtNP-shell for precise cardioprotection against myocardial I/R injury and MI-induced VAs.
+ +## Result and discussion + +## Synthesis and Characterization of PtNP-shell + +The PtNP- shell was synthesized through an electrocoupling substitution reaction between chloroplatinate and Ga nanoparticles (GaNPs). Ga nanoparticles were obtained by sonication of pure metal Ga. To achieve a balanced particle size and oxidation degree of GaNPs, pure gallium was sequentially sonicated in ethanol and water for 30 minutes to obtain gallium nanoparticles with reduced oxidation (Supplementary Fig. 1a). In accordance with the electrochemical redox potential of the redox couple \((\mathrm{Ga}^{3 + } / \mathrm{Ga} - 0.529 \mathrm{V}; \mathrm{PtCl}_6^{2 - } / \mathrm{PtCl}_4^{2 - }: 0.726 \mathrm{V}; \mathrm{PtCl}_4^{2 - } / \mathrm{Pt}: 0.758 \mathrm{V})^{10,11}, \mathrm{Pt} (\mathrm{IV}) \mathrm{can be in situ}\) reduced by Ga and encapsulated on the surface of GaNPs to form a core- shell structure + +<--- Page Split ---> + +(Supplementary Fig. 1b, c). The hollow PtNP-shell is synthesized after completion of the reaction (Fig. 2a). Simultaneously with the reduction of Pt (IV), Ga oxide is formed, creating the skeleton of the PtNP-shell (right in Fig. 2a). The surface of the PtNP-shell exhibits a rough texture (Supplementary Fig. 2). The scanning transmission electron microscopy (STEM) images reveal numerous irregular and uneven pores on its surface (Supplementary Fig. 3a) and PtNP-shell is composed of Pt nanoparticles (PtNPs) with \(2 - 5 \mathrm{nm}\) (Fig. 2b). High-resolution TEM (HR-TEM) image is acquired to character the structure of PtNPs. As shown in Supplementary Fig. 3b, PtNPs exhibits single crystal structure with a lattice stripe spacing of \(0.23 \mathrm{nm}\) corresponding to the (111) crystal plane. Meanwhile, the corresponding Fast Fourier Transform (FFT) pattern (inset in Supplementary Fig. 3b) shows the typical diffraction patterns of face-centered cubic structure along [111] zone axis. + +![](images/Figure_2.jpg) + +
Fig. 2 | Characterization of PtNP-shell. a, TEM image of PtNP-shell (Right: element mapping). b, STEM images of PtNP-shell surface. c, XRD spectrum of PtNP-shell (Inset: SAED pattern). d, UV-vis-NIR absorption spectrum of PtNP-shell ( \(75 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ). e, Temperature elevation curves of PtNP-shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ) under NIR-II laser irradiation ( \(1 \mathrm{W} \cdot \mathrm{cm}^{2}\) ). f, Calculation of the PCE at \(1064 \mathrm{nm}\) (PtNP-shell: \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ).
+ +<--- Page Split ---> + +In the X- ray power diffraction (XRD) spectrogram result (Fig. 2c), all peaks can be attributed to the crystal phase of Pt (JCPDS: 87- 0640), consistent with the selected area electron diffraction (SAED) pattern findings (inset in Fig. 2c). However, no peaks corresponding to gallium oxide were observed in the XRD spectrogram, possibly due to its low content. The XRD spectrogram (Supplementary Fig. 4) of PtNP- shell prior to reacting with KOH showed that the gallium oxide contained in PtNP- shell was GaOOH (JCPDS: 06- 0180). Additional evidence from X- ray photoelectron spectroscopy (XPS) also suggests that PtNP- shell contains Ga (Supplementary Fig. 5), consistent with energy dispersive X- ray spectroscopy (EDX) analysis (right in Fig. 2a). The peak centred at 1117.59 eV is ascribed to Ga \(2\mathrm{p}_{3 / 2}\) , indicating the presence of \(\mathrm{Ga}^{3 + }\) in PtNP- shell. Meanwhile, the Pt 4f spectrum shows two peaks at 71.56 and 75.02 eV, which result from metallic Pt \(4\mathrm{f}_{7 / 2}\) and Pt \(4\mathrm{f}_{5 / 2}\) . PtNP- shell was treated with KOH (0.67 M) to reduce the gallium oxide content and the surface potential was reduced from 45.8 mV to - 25.7 mV, and then encapsulated with Methoxypoly(Ethylene Glycol) Thiol (mPEG- \(\mathrm{SH}_{5000}\) ) to enhance its biocompatibility and the surface potential was changed to - 19.9 mV. (Supplementary Fig. 6). The statistically averaged hydrated nanoparticle size of PtNP- shell based on the dynamic light scattering diagram was 200.1 nm with uniform size distribution, indicating the nanoparticle was well dispersed in water (Supplementary Fig. 7). + +## Blackbody Absorption and Photothermal Property of PtNP-shell + +Due to the presence of pores and a hollow structure in the PtNP- shell, light propagating in the space bounces at the rough surface of PtNP- shell until it encounters one of the pores, where it continues to bounce within the PtNP- shell. The random distribution of these pores results in completely random light reflection, akin to Brownian motion12. Consequently, the probability of light escaping from other pores is extremely low, + +<--- Page Split ---> + +rendering PtNP- shell behave like a blackbody and produce an efficient infrared heater \(^{13 - 15}\) . This enhanced absorption of light by PtNP- shell exhibits nearly perfect blackbody absorption characteristics (Supplementary Fig. 8a). The absorption of PtNP- shell is close to 1 in the range of 250–1300 nm at \(75 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) (Fig. 2d). According to the Lambert- Beer law (A/L = εC, where ε is the extinction coefficient), a linear relationship between absorption intensity (at 1064 nm) and concentration was established, with an extinction coefficient measured as \(13.3 \mathrm{Lg}^{- 1} \mathrm{cm}^{- 1}\) at 1064 nm (Supplementary Fig. 8b). Varying concentrations of PtNP- shell resulted in different shades of grey being generated, with significantly darker greyness observed under identical conditions compared to GaNPs and Pt- coated Ga- In alloy (EGaIn) nanoparticles (GaIn@Pt NPs) (Supplementary Fig. 9a). These distinctive features were characterized by their respective positions within an RGB cube representation, wherein on the diagonal connecting darkest and brightest points, PtNP- shell was found closer to the darkest point than both other materials (Supplementary Fig.9b). + +The photothermal properties of PtNP- shell were verified by irradiating the dispersion of PtNP- shell in water with NIR- II light at \(1064 \mathrm{nm}\) (1 \(\mathrm{W} \cdot \mathrm{cm}^{- 2}\) ). Even in vitro, PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) exhibited rapid temperature elevation, achieving a rise from room temperature to \(41.0^{\circ} \mathrm{C}\) and \(45.0^{\circ} \mathrm{C}\) within only 96 s and 133 s, respectively (Fig. 2e). However, for GaNPs (347 s and over 600 s) and GaIn@Pt NPs (278 s and 450 s), it took significantly longer time to reach the same temperatures (Supplementary Fig. 10). The corresponding thermal images of the PtNP- shell with different concentrations under different irradiation times are shown in Supplementary Fig. 11. The heating effect of the PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) gradually increased the \(\Delta \mathrm{T}\) from 7.72 \(^{\circ} \mathrm{C}\) to 52.17 \(^{\circ} \mathrm{C}\) When exposed to NIR- II laser for a duration of 600 s while varying the optical power density at 1064 nm between \(0.25 - 1.5 \mathrm{W} \cdot \mathrm{cm}^{- 2}\) (Supplementary Fig. 12). + +<--- Page Split ---> + +The PCE of PtNP- shell was quantified as \(73.7\%\) when balancing the energy input from photons with heat dissipation within the system (Fig. 2f), representing one of the highest PCE at 1064 nm (Supplementary Fig. 13). These results indicate that PtNP- shell exhibits excellent photothermal performance in the NIR- II window. Additionally, no significant changes in temperature or morphology were observed even after five cycles of irradiation (Supplementary Fig. 14), suggesting exceptional photothermal stability. + +## Photothermal of PtNP-shell enables precise modulations of neurons in vitro + +To investigate the photothermal effects of PtNP- shell on neuronal activity at multiple levels, we conducted calcium imaging experiments in hippocampal neuron (HT- 22) cells (Fig. 3a, b). The immunoblotting results revealed abundant expression of both TRPV1 and TREK1 ion channels in HT- 22 cells (Fig. 3c). The direct effect of PtNP- shell on the excitability of these two different ion channels was assessed under NIR- II irradiation using a calcium ion indicator (Fluo- 4 AM). Upon NIR- II laser irradiation, the temperature of the PtNP- shell (+) group increased compared to that of the PtNP- shell (- ) group, resulting in a significantly higher percentage of responding cells (Fig. 3d) (p< 0.001). The micrographs fluorescence intensity curve of HT- 22 neurons cultured with PtNP- shell showed significant \(\mathrm{Ca^{2 + }}\) influx upon NIR- II laser irradiation for \(35 \pm 5\) s and after the temperature reached \(42.0^{\circ}\mathrm{C}\) (Fig. 3e). In contrast, application of NIR- II laser irradiation with PBS did not induce significant \(\mathrm{Ca^{2 + }}\) influx. + +Subsequently, neuronal excitation was induced and calcium signals were increased by perfusion of \(15\mathrm{mM}\) KCl in the PtNP- shell (- ) group and PtNP- shell (+) group (50 \(\mu \mathrm{g}\mathrm{mL}^{- 1}\) ), respectively. This phenomenon can be attributed to the elevation of extracellular potassium ion concentration, which triggers neuronal depolarization and subsequently leads to a substantial increase in intracellular calcium ion concentration16. Under NIR- II laser irradiation, the proportion of HT- 22 cells responding to high + +<--- Page Split ---> + +concentration KCl stimulation was significantly lower in the PtNP- shell (+) group compared to that in the PtNP- shell (- ) group at approximately \(46.0^{\circ}\mathrm{C}\) (Fig. 3f). The difference may be due to the activation of the TERK1 ion channel in the PtNP- shell (+) group, which can induce neuronal hyperpolarization and make intracellular and extracellular calcium ion concentrations tend to recover17. Interestingly, the PtNP- shell influenced the fluorescence intensity of HT- 22 cells not with a sustained decrease but with an initial rise followed by a subsequent decrease (Fig. 3g). This observation may be associated with the activation of TRPV1 channel at around \(42.0^{\circ}\mathrm{C}^9\) . With increasing temperature, TRPV1 and TREK1 channels were sequentially activated. These findings suggest that PtNP- shell can achieve precise temperature control within a short duration through its own ultra- high PCE for both neuronal excitation and inhibition. + +Cytotoxicity assays were then conducted to investigate the potential neurotoxicity of PtNP- shell application. As shown in Fig. 3h, concentrations of PtNP- shell below 100 \(\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) exhibited no significant toxic effects on HT- 22 cells. Even when the concentration of PtNP- shell was increased to \(200\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) , the survival rate of neuronal cells remained approximately at \(52.11\%\) . Furthermore, the impact of PtNP- shell photothermal stimulation parameters on cell viability were assessed through analysis of HT- 22 cell survival under NIR- II laser irradiation. Notably, when a concentration of 50 \(\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) PtNP- shell and an NIR- II laser with a power density of \(0.5\mathrm{W}\cdot \mathrm{cm}^{- 2}\) were applied for a brief duration, the survival rate exceeded \(92.36\%\) for HT- 22 cells. Even with an increase in power density to \(0.75\mathrm{W}\cdot \mathrm{cm}^{- 2}\) , the survival rate for HT- 22 cells still remained around \(72.68\%\) after 60 s of irradiation (Fig. 3i). These results indicate that PtNP- shell does not induce significant damage to neurons under controlled NIR- II laser irradiation. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | PtNP-shell photothermal activation of different neuronal ion channels in vitro. a, Flowchart of calcium imaging assay performed on HT-22 cells. b, calcium imaging of HT-22 cells under different experimental conditions. c, Western blotting for TRPV1 and TREK1 from HT-22 and H9c2 cells. Percentage of d, TRPV1 and f, TREK1 groups of HT-22 cells within the field of view of the fluorescence microscope that responded to laser stimulation. Temporal dynamics of \(\mathrm{Ca}^{2 + }\) signals in e, TRPV1 and g, TREK1 groups of cells. The solid lines indicate the mean, and shade represents the standard error of the mean (SEM). h, Cell viability of HT-22 treated with different concentrations of PtNP-shell for \(24\mathrm{h}\) . i, Cell viability of HT-22 treated with NIR-II laser irradiation of different power densities and laser duration. The error bar indicates S.E.M. \(***\mathrm{P}< 0.001\) .
+ +## PtNP-shell photothermal activation of the parasympathetic nervous system + +Western blotting analysis of peripheral ganglia from the canine autonomic nervous system revealed the expression of TRPV1 and TREK1 heat- sensitive ion channels in both the nodose ganglion (NG) and left stellate ganglion (LSG). Notably, TRPV1 was abundantly expressed in the NG of the parasympathetic nervous system, while TREK1 exhibited higher levels in the LSG of the sympathetic nervous system (Supplementary + +<--- Page Split ---> + +Fig. 15). To investigate whether the photothermal effect induced by PtNP-shell under NIR-II irradiation can precisely regulate the parasympathetic nerve, \(100\mu \mathrm{L}\) PtNP-shell \((50\mu \mathrm{g}\cdot \mathrm{mL}^{- 1})\) and PBS were injected into NG of PtNP-shell group and control group (6 beagle dogs in each group), respectively (Fig. 4a,b). It can be observed that upon irradiation with NIR-II laser \((0.8\mathrm{W}\cdot \mathrm{cm}^{- 2})\) , the temperature of NG injected with PtNP-shell increased to \(41.0^{\circ}\mathrm{C}\) within a very short period of time \((12\pm 3\mathrm{s})\) . Subsequently, the temperature of NG could be kept in the range of \(41.0–42.9^{\circ}\mathrm{C}\) for 5 min by adjusting the power density to \(0.45\mathrm{W}\cdot \mathrm{cm}^{- 2}\) (Fig. 4c-d). As a crucial node within the parasympathetic neural network, activation of NG significantly reduces heart rate (HR) (Fig. 4e) \(^{18}\) . Therefore, NG function was assessed by the maximum decrease in heart rate under direct electrical stimulation. As shown in Fig. 4f-h, NG function and activity was significantly elevated in the PtNP-shell group than in the control group after stimulation. The function and activity of NG recovered close to baseline within three hours after turning off NIR-II laser, indicating that the photothermal modulation induced by PtNP-shell was reversible within NGs (Fig. 4h, Supplementary Fig. 16 and 17). + +In addition, the effective refractive period (ERP) was measured in various regions, including left ventricular apex (LVA), left ventricular base (LVB) and median left ventricular area (LVM). In the PtNP-shell group, the ERP was significantly elevated compared to the control group and remained elevated for \(2\mathrm{~h}\) after photothermal intervention in NG (Supplementary Fig. 18). Furthermore, immunofluorescence staining for Vacht, c- fos, and TRPV1 was performed on NG histopathological sections following photothermal modulation (Fig. 4i). Quantitative analysis (Supplementary Fig. 19) revealed a substantial increase in the proportion of \(\mathrm{TRPV1^{+}}\) \((86.63\pm 2.65\mathrm{vs}45.45\pm 2.98)\) and c- Fos \(^+\) \((77.81\pm 3.91\mathrm{vs}17.27\pm 3.08)\) neurons among VAcH \(^+\) parasympathetic neurons in the PtNP-shell group compared to the control group (all P + +<--- Page Split ---> + +\(< 0.001\) ). These findings suggest that PtNP-shell can precisely regulate temperature and subsequently activate TRPV1 ion channels on NG to enhance parasympathetic activity. + +![](images/Figure_4.jpg) + +
Fig. 4 | Photothermal activation of the parasympathetic nervous system by PtNP-shell. a, Location of the canine NG. b, Schematic illustration of the process of photothermal modulation of NG. c, Temperature curves of NG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of NG. e, Representative images of HR reduction induced after stimulation of NG with different voltages. Maximal HR changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \(n = 6\) . h, Quantification of the NG neural activity recordings, \(n = 6\) . i, Representative immunofluorescent images of Vacht (red), c-fos (green) and TRPV1 (pink) in the NG of beagles following different treatments. Data are shown as the mean \(\pm\) S.E.M. \(*P < 0.05\) , \(**P < 0.01\) , \(***P < 0.001\) , ns means that the difference is not statistically significant.
+ +<--- Page Split ---> + +1 PtNP-shell photothermal activation of NG reduces I/R injury and associated VAs2 Following I/R injury, electrocardiography (ECG) was recorded to monitor the3 occurrence of VAs events within 1 h, including ventricular premature beats (VPBs),4 ventricular tachycardia (VT) and ventricular fibrillation (VF) (Fig. 5c)19. Under NIR-II5 laser irradiation, the PtNP-shell group exhibited a lower incidence of sustained VTs6 (duration \(>30\) s) or VF compared to the control group (50% vs. 83%) (Fig. 5d).7 Moreover, the number of recorded VPBs (70.83 ± 5.38 vs. 116.00 ± 6.36, \(\mathrm{P}< 0.05\) ),8 VTs (3.17 ± 0.87 vs. 8.83 ± 2.15, \(\mathrm{P}< 0.05\) ) and duration of the VTs (7.00 ± 3.173s vs.9 26.83 ± 7.89s, \(\mathrm{P}< 0.05\) ) in the PtNP-shell group were significantly reduced compared10 to that in the control group (Fig. 5e- g). + +![](images/Figure_5.jpg) + +
Fig. 5 | PtNP-shell photothermal activation of the parasympathetic nervous system improves myocardial I/R injury. Modulation of NG to protect against myocardial I/R injury and associated VAs a, schematic diagram and b, flowchart. c, Representative visual depictions of VAs, including VPB, VT and VF. d, Quantitative analysis the ratio of sVT and VF incidence between different groups, \(\mathrm{n} = 6\) . Quantitative analysis the number of e, VPBs, f, VTs and g, the duration of sVT of beagles. Effects on ventricular ERP at different sites in beagles treatment with PtNP-shell or control h, before and i, after myocardial I/R injury modelling. Levels of markers of myocardial injury, including j, MYO and k, c-TnI, after different treatments in beagles. Data are shown as the mean \(\pm\) S.E.M. \(^{*}\mathrm{P}< 0.05\) , \(^{**}P< 0.01\) , \(^{***}\mathrm{P}< 0.001\) .
+ +<--- Page Split ---> + +Animal modeling and intervention manipulations were conducted to further elucidate the protective effects of precise modulation of NG by PtNP- shell against myocardial I/R injury and associated VAs, following the experimental protocols depicted in Figure 5a,b. PtNP- shell and PBS were microinjected into the NG of the PtNP- shell group and control group, respectively, each consisting of six beagle dogs. The NG was subsequently exposed to NIR- II laser irradiation for a duration of 5 minutes prior to occlusion of the left anterior descending (LAD) coronary artery for reperfusion therapy. + +There were no statistically significant differences between the two groups in terms of preoperative ERP for LVB, LVM, and LVA. In the postoperative period, all three positions showed shortened ERPs in the control group. The PtNP- shell group exhibited significantly higher ERPs compared to the control group, indicating that photothermal modulation of nerves by PtNP- shell has a protective effect on cardiac electrophysiology (Fig. 5h- i). Serum Elisa assay revealed reduced levels of myocardial injury markers (MYO and c- TnI) after I/R injury in the PtNP- shell group compared to the control group (all \(\mathrm{p}< 0.05\) , Fig. 5j,k). Postoperatively, heart rate variability analysis demonstrated lower low frequency (LF) and higher high frequency (HF) and the lower ratio of LF to HF (LF/HF) values in the PtNP- shell group compared to the control group (all \(\mathrm{p}< 0.05\) , Supplementary Fig. 20). These results suggest that PtNP- shell exerts cardioprotective effects and reduces VAs by activating parasympathetic nerve. + +## PtNP-shell photothermal inhibition of the sympathetic nervous system + +The sympathetic nervous system was modulated by performing microinjections of PtNP- shell or PBS into the LSG, followed by irradiation with an NIR- II laser (Fig. 6a,b). The temperature curve demonstrates that upon exposure to a NIR- II laser \((0.8\mathrm{W}\cdot \mathrm{cm}^{- 1})\) for \(25\pm 5\mathrm{s}\) , the temperature rapidly escalated to \(45.0^{\circ}\mathrm{C}\) , crossing the range of \(41.0-\) + +<--- Page Split ---> + +42.9 °C within a mere duration of \(6 \pm 1\) s. Subsequently, the power density was immediately decreased to \(0.6 \mathrm{W} \mathrm{cm}^{-2}\) , effectively maintaining LSG at a steady temperature between \(45.0 - 46.9\) °C (Fig. 6c, d). Due to the substantial increase in systolic blood pressure (SBP) induced by LSG activation (Fig. 6e), the function of LSG was evaluated by quantifying the maximum SBP change corresponding to five consecutive incremental voltages of high- frequency electrical stimulation. After 5 min of NIR- II laser irradiation, the activity and function of LSG in the PtNP- shell group were significantly suppressed compared to the control group ( \(p < 0.05\) ) and they returned close to baseline after 3 h (Fig. 6f- h and Supplementary Fig. 21- 22). Prolonged ERP effects were observed in all left ventricles, while the protective effect exhibited a duration of only 1 h (Supplementary Fig. 23). Furthermore, immunofluorescence staining was conducted on LSG tissues to examine the expression of c- fos, tyrosine hydroxylase (TH), and TREK1 (Fig. 6i). The quantitative analysis (Supplementary Fig. 24) revealed a significant decrease in the proportion of c- Fos\(^+\) expression in TH\(^+\) neurons within the PtNP- shell group ( \(8.80 \pm 1.80\) vs. \(44.78 \pm 5.55\) , \(P < 0.001\) ) indicating that PtNP- shell exerted a photothermal inhibitory effect on LSG neurons under NIR- II irradiation. However, the proportion of TREK\(^+\) expression was significantly increased within TH\(^+\) neurons in the PtNP- shell group ( \(83.51 \pm 3.72\) vs. \(57.20 \pm 5.89\) , \(P < 0.01\) ). This increase could lead to hyperpolarization of the cell membrane potential, reduction in neuronal excitability and inhibition of sympathetic nerve activity. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Fig. 6 | Photothermal inhibition of the sympathetic nervous system by PtNP-shell. a, Location of the canine LSG. b, Schematic illustration of the process of photothermal modulation of LSG. c, Temperature curves of LSG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of LSG. e, Representative images of BP elevation induced after stimulation of LSG with different voltages. Maximal SBP changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \(n = 6\) . h, Quantification of the LSG neural activity recordings, \(n = 6\) . i, Representative immunofluorescent images of TH (red), c-fos (green) and TREK1 (pink) in the LSG of beagles following different treatments. Data are shown as the mean \(\pm\) S.E.M. \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) , ns means that the difference is not statistically significant.
+ +<--- Page Split ---> + +# PtNP-shell photothermal inhibition of LSG improves MI and reduces associated + +## Vas + +To investigate the cardioprotective effect of PtNP- shell photothermal effect in achieving a targeted LSG temperature of approximately \(46.0^{\circ}\mathrm{C}\) , NIR- II light was administered prior to ligation of the LAD coronary artery (Fig. 7a,b). Under NIR- II laser irradiation, the PtNP- shell group exhibited a significantly reduced incidence of sustained VTs (duration \(>30\) s) or VF compared to the control group ( \(16\%\) vs. \(50\%\) ) (Fig. 7c). In the PtNP- shell group, ECG recordings within infarction 1 exhibited a reduced incidence of VAs events compared to the control group, with fewer VPBs recorded in the PtNP- shell group than in the control group ( \(51.50 \pm 5.53\) vs. \(70.83 \pm 5.375\) , \(\mathrm{P} < 0.05\) , Fig. 7d). However, there were no significant differences between the two groups in terms of VT numbers and duration (Supplementary Fig. 25). Additionally, VA inducibility measurements demonstrated that after photothermal neuromodulation with PtNP- shell, there was a decrease in VA score ( \(1.50 \pm 0.76\) vs. \(4.83 \pm 1.14\) , \(\mathrm{P} < 0.05\) ) effective heart protection (Fig. 7e,f). Furthermore, PtNP- shell photothermal inhibition of LSG produced similar protective effects on ventricular electrophysiological index ERP as activation of NG (Fig. 7g,h), and had higher VF threshold than control group ( \(24.33 \pm 4.24\) vs. \(12.33 \pm 3.16\) , \(\mathrm{P} < 0.05\) , Fig. 7i). In addition, the light inhibition of LSG followed the same trend as heart rate variability after activation of NG (Supplementary Fig. 26). These results suggest that PtNP- shell protects against cardiac damage and reduces VAs by modulating the autonomic nervous system, specifically by decreasing sympathetic activity and enhancing parasympathetic tone. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Fig. 7 | PtNP-shell photothermal inhibition of the sympathetic nervous system improves MI associated VAs. Modulation of LSG to protect against MI and associated VAs a, schematic diagram and b, flowchart. c, Quantitative analysis the ratio of sVT and VF incidence between different groups, \(n = 6\) . d, Quantitative analysis the number of VPBs of beagles. e, Typical images of VA induced by programmed electrical stimulation. f, Quantitative analysis of VAs score in different groups. Effects on ventricular ERP at different sites in Beagles treatment with PtNP-shell or control g, before and h, after MI modelling. i, Quantitative analysis of VF threshold in different groups. Data are shown as the mean \(\pm\) S.E.M. \(*P < 0.05\) , \(**P < 0.01\) , \(***P < 0.001\) .
+ +## Biosafety of PtNP-shell for translational applications + +To validate the biocompatibility of PtNP- shell photothermal modulation on the autonomic nervous system, we conducted rapid excision of LSG and NG tissues followed by hematoxylin and eosin (H&E) staining. As shown in Supplementary Fig. 27a, H&E staining did not reveal any indications of neuronal damage in both the PtNP- shell and control groups for both NG and LSG, indicating that the neuromodulation of PtNP- shell is repeatable. Meanwhile, to further investigate the long- term biosafety of PtNP- shell, a microinjection of \(200 \mu \mathrm{l}\) PtNP- shell (50 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) or PBS was administered into the ganglion of dogs and the tail vein of rats, respectively. After a follow- up period of 30 days, did not reveal any obvious damage in major organs, + +<--- Page Split ---> + +including the heart, liver, spleen, lungs, and kidneys (Supplementary Fig. 27b,c). Furthermore, blood biochemical analyses indicated the absence of hepatotoxicity or nephrotoxicity (Supplementary Fig. 27d-m). These results unequivocally demonstrate that PtNP-shell exhibits exceptional biocompatibility and long-term biological safety. + +## Conclusion + +The PtNP-shell reported in this study exhibits nearly perfect blackbody absorption property, making it an efficient absorber with one of the highest PCE in the NIR-II window (73.7% at 1064 nm). Furthermore, local heating induced by PtNP-shell activation effectively triggers temperature- sensitive ion channels TRPV1 and TREK1, enabling precise and efficient regulation of autonomic nerves. This innovative approach holds great potential for non- invasive treatment of MI and associated VAs, as well as protection against reperfusion injury during interventional therapy. + +The minimal tissue damage caused by light can be disregarded within the maximum permissible exposure (MPE) range, rendering it one of the safest interventions for organisms. The interaction between light and tissue is intricate, and further research could aid in selecting more suitable wavelengths to achieve deeper penetration within the MPE range. Leveraging the nearly impeccable blackbody absorption of PtNP-shell and ultrasound- guided microinjection technology, remote and precise neuromodulation strategies can be developed, holding promise for non- invasive protection against MI and reperfusion injury- associated VAs. 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Opt. 31 20, 2606-2615 (1981). 32 16 Ma, J. X. et al. In vitro model to investigate communication between dorsal root ganglion and 33 spinal cord glia. Int. J. Mol. Sci. 22, 9725 (2021). 34 17 Zyrianova, T. et al. K2P2.1 (TREK-1) potassium channel activation protects against hyperoxia- 35 induced lung injury. Sci. Rep. 10, 22011 (2020). 36 18 Jayaprakash, N. et al. Organ- and function-specific anatomical organization of vagal fibers 37 supports fascicular vagus nerve stimulation. Brain Stimul. 16, 484-506 (2023). 38 19 Zhou, Z. et al. Metabolism regulator adjoncent prevents cardiac remodeling and ventricular 39 arrhythmias via sympathetic modulation in a myocardial infarction model. Basic Res. Cardiol. 40 117, 34 (2022). + +<--- Page Split ---> + +1 20 Mancia, G. & Grassi, G. The autonomic nervous system and hypertension. \*Circul. Res.\* 114, 2 1804–1814 (2014). 3 21 Jiang, Y. Q. \*et al.\* The role of age-associated autonomic dysfunction in inflammation and 4 endothelial dysfunction. \*GeroScience\* 44, 2655–2670 (2022). + +<--- Page Split ---> + +## Methods + +## Chemicals + +The gallium and indium were purchased from Shanghai Minor Metals Co., Ltd. Anhydrous ethanol \((\geq 99.7\%)\) and KOH (AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. \(\mathrm{Na_2PtCl_6}\) ( \(98\%\) ) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. mPEG- \(\mathrm{SH}_{5000}\) was purchased from Shanghai Macklin Biochemical Co., Ltd. STR- identified correct HT- 22 cells or human embryonic kidney 293T (HEK- 293T) cells were purchased with the corresponding specialized cell culture media (Procell, Wuhan, China). Anti- NF1, anti- c- fos, anti- TRPV1 antibodies used in western blot and immunofluorescence staining and anti- TREK1 antibody used in immunofluorescence staining were purchased from ABclonal (Wuhan, China). Anti- TREK1 antibody used in western blot was purchased from Santa Cruz Biotechnology (Texas, U.S.). Glyceraldehyde 3- phosphate dehydrogenase (GAPDH) was purchased from Abcam (Cambridge, England). Serum troponin I (c- TnI) and myoglobin (MYO) were purchased from Mibio (Shanghai, China). 4,6- diamidino- 2- phenylindole (DAPI) was purchased from Servicebio (Wuhan, China). + +## Instruments + +The morphology of PtNP- shell was characterized by a F200 transmission electron microscope (TEM) (JEOL, Japan) operated at \(200\mathrm{kV}\) . STEM and HRTEM images were obtained by a JEM- ARM200CF (JEOL, Japan) at \(200\mathrm{kV}\) . The EDX elemental mapping was carried using the JEOL SDD- detector with two \(100\mathrm{mm}^2\) X- ray sensor. X- ray diffraction (XRD) patterns were performed on an SmartLab 9kW X- ray powder diffractometer (Rigaku, Japan). XPS measurements were carried out with a ESCALAB 250Xi spectrometer (Thermo Fisher Scientific, U.S.) under vacuum. Ultraviolet- visible- near- infrared light (UV- Vis- NIR) absorption spectra was collected using a UV + +<--- Page Split ---> + +3600 spectrophotometer (Shimadzu, Japan). Zeta potential (Z) and dynamic light scattering (DLS) were recorded using a Zetasizer Nano ZSP (Malvern Panalytical, U.K.). The fluorescence microscopy images of HT- 22 cells were acquired by FV3000 Microscope (Olympus, Japan), excited with 488 nm laser. Beagle's respiration is maintained by a WATO EX- 20VET ventilator (Mindray, Shenzhen, China). ECG and blood pressure data were recorded by a Lead 7000 Computerized Laboratory System (Jinjiang, Chengdu, China). NIR- II light at 1064 nm is generated by LWIRPD- 1064- 5F laser (Laserwave, Beijing, China). Thermal imaging was obtained by FLIR C2 thermal imager (FLIR, U.S.). High- frequency electrical stimulation was performed by Grass stimulator (Astro- Med; West Warwick, RI, U.S.) The electrical signals of autonomic nerves are recorded by Power Lab data acquisition system (AD Instruments, New South Wales, Australia). Serum biochemical indices were determined by a fully automatic biochemical analyzer BK- 1200 (BIOBASE, Jinan, China). + +## Synthesis of GaNPs + +The GaNPs were obtained by sonication of liquid Ga. The liquid Ga (300 mg) was transferred to anhydrous ethanol (8 mL), and the solution was sonicated by nanoprobe sonication for 1 h (3 seconds on and 3 seconds off) at the power of 290 W. Then the ethanol was replaced with Milli- Q water to continue sonication for 1 h. The solution at the end of sonication was collected and centrifuged at 1000 rpm for 5 min, and the upper liquid layer was aspirated for later use. + +## Synthesis of PtNP-shell + +First, the GaNPs and 3 mL \(\mathrm{Na_2PtCl_6}\) (0.1 M) were evacuated for 30 min and Ar was introduced for 15 min. Then, 3 mL \(\mathrm{Na_2PtCl_6}\) (0.1 M) was added dropwise to GaNPs and the solution was stirred for 4 h. After reaction, the solution was collected and centrifuged at 9000 rpm for 10 min. The solids at the bottom were washed with Milli + +<--- Page Split ---> + +1 Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) Milli- Q for later use. + +## Functionalization of PtNP-shell with mPEG-SH5000 + +3 The PtNP- shell was first covered with a small amount of mPEG- SH to protect the structure from KOH. \(30\mathrm{mg}\) mPEG- SH5000 was added to \(6\mathrm{ml}\) PtNP- shell and the solution was stirred for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged at \(9000\mathrm{rpm}\) for \(10\mathrm{min}\) . The solids at the bottom were washed with Milli- Q water for 3 times and dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(12\mathrm{mL}\) of KOH (1 M) for \(4\mathrm{h}\) . The reaction- completed solution was collected and centrifuged at \(9000\mathrm{rpm}\) for \(10\mathrm{min}\) , and the solids at the bottom were washed three times with Milli- Q water and finally dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(60\mathrm{mg}\) mPEG- SH5000 for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged. The solids at the bottom were washed with Milli- Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) PBS. + +## Synthesis of Ga-In alloy nanoparticles (GaIn NPs) + +The liquid EGaIn was prepared by physically mixing \(75\mathrm{wt}\%\) gallium and \(25\mathrm{wt}\%\) indium at \(200^{\circ}\mathrm{C}\) for \(2\mathrm{h}\) . The liquid EGaIn (300 mg) was transferred to anhydrous ethanol ( \(8\mathrm{mL}\) ), and the solution was sonicated by nanoprobe sonication for \(1\mathrm{h}\) (3 seconds on and 3 seconds off) at the power of \(290\mathrm{W}\) . Then the ethanol was replaced with Milli- Q water to continue sonication for \(1\mathrm{h}\) . The solution at the end of sonication was collected and centrifuged at \(1000\mathrm{rpm}\) for \(5\mathrm{min}\) , and the upper liquid layer was aspirated and set aside. + +## Synthesis of GaIn@Pt NPs + +1 mL \(\mathrm{Na_2PtCl_6}\) ( \(0.1\mathrm{M}\) ) was added dropwise to GaIn NPs and the solution was stirred for \(4\mathrm{h}\) . After reaction, the solution was collected and centrifuged at \(9000\mathrm{rpm}\) for 10 + +<--- Page Split ---> + +1 min, washed 3 times with Milli- Q water and dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(60\mathrm{mg}\) mPEG- SH \(_{5000}\) for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged. The solids at the bottom were washed with Milli- Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) PBS. + +## Calculation of the photothermal conversion efficiency + +The photothermal conversion of the PtNP- shell has been calculated on the basis of previous work \(^{22,23}\) . The relationship between temperature rise and energy transfer in the system can be described by the Equation S1, + +\[\Sigma_{i}m_{i}c_{i}\frac{dT}{dt} = Q_{abs} - Q_{ext} = Q_{NPS} + Q_{solvent} - Q_{ext} \quad (S1)\] + +where \(Q_{abs}\) is the total energy absorbed by the system, \(Q_{NPS}\) is the energy absorbed by the nanoparticles, \(Q_{solvent}\) is the energy absorbed by the solvent, \(Q_{ext}\) is the energy loss from the system to the environment. \(m_{i}\) and \(c_{i}\) are the mass and specific heat capacity of the solution, respectively. \(T\) is the solution temperature and \(t\) is the irradiation time. The conversion of the light energy into heat energy can be expressed in terms of Equation S2, + +\[Q_{NPS} = I(1 - 10^{-A})\eta \quad (S2)\] + +where \(I\) is the laser power, \(A\) is the absorbance value of PtNP- shell at \(1064\mathrm{nm}\) , \(\eta\) is the photothermal conversion efficiency. \(Q_{solvent}\) can be calculated by the following Equation S3, + +\[Q_{solvent} = hs(T_{solvent} - T_{surr}) \quad (S3)\] + +where \(h\) is the convective heat transfer coefficient and \(s\) is the surface area of the sample cell. \(T_{solvent}\) is the maximum temperature that the solvent can reach under laser irradiation. \(T_{surr}\) is the ambient temperature. \(Q_{ext}\) can also be written as, + +\[Q_{ext} = hs(T - T_{surr}) \quad S4\] + +The heat output will increase with the increase in temperature when the NIR- II + +<--- Page Split ---> + +laser power is determined according to formula S4. The temperature of the system will reach the maximum when the heat input is equal to the heat output, so the following equation can be obtained, + +\[Q_{NPs} + Q_{solvent} = Q_{ext - max} = hs(T_{max} - T_{surr}) \quad \mathrm{S5}\] + +where \(Q_{ext - max}\) is the heat transferred from the system surface through the air when the sample cell reaches equilibrium temperature, and \(T_{max}\) is the equilibrium temperature. Combining equations S2, S3 and S5, \(\eta\) can be expressed as, + +\[\eta = \frac{hs(T_{max} - T_{surr}) - hs(T_{solvent} - T_{surr})}{l(1 - 10^{-A})} = \frac{hs(T_{max} - T_{solvent})}{l(1 - 10^{-A})} \quad \mathrm{S6}\] + +where \(A\) is the PtNP- shell absorption at \(1064\mathrm{nm}\) . To obtain \(hs\) , the dimensionless temperature \(\theta\) is introduced, + +\[\theta = \frac{T - T_{surr}}{T_{max} - T_{surr}} \quad \mathrm{S7}\] + +and a time constant of sample system, \(\tau_{s}\) + +\[\tau_{s} = \frac{\sum_{i}m_{i}c_{i}}{hs} \quad \mathrm{S8}\] + +Combining Equations S1, S4, S7 and S8, the following equation can be obtained, + +\[\frac{d\theta}{dt} = \frac{1}{\tau_{s}}\left[\frac{Q_{NPs} + Q_{solvent}}{hs(T_{max} - T_{surr})} -\theta \right] \quad \mathrm{S9}\] + +After the laser is turned off, in the cooling stage, there is no external input energy, \(Q_{NPs} + Q_{solvent} = 0\) , and equation S9 can be written as, + +\[dt = -\tau_{s}\frac{d\theta}{\theta} \quad \mathrm{S10}\] + +By integrating Equation S10, the following equation can be obtained, + +\[t = -\tau_{s}ln\theta \quad \mathrm{S11}\] + +Therefore, the system heat transfer time constant \((\tau_{s})\) at \(1064\mathrm{nm}\) is \(242.25\mathrm{s}\) (Figure 3f). In addition, m is \(0.3\mathrm{g}\) and c is \(4.2\mathrm{J}\cdot \mathrm{g}^{- 1}\) . Therefore, \(hs\) can be determined from Equation S8. The laser power \((I)\) used here can be determined as 1 W. Then the photothermal conversion efficiency \((\eta)\) of the PtNP- shell at \(1064\mathrm{nm}\) can be calculated + +<--- Page Split ---> + +to be \(73.7\%\) by substituting \(hs\) into Equation S6. + +## Animal preparation and cell culture + +All animal experiments were approved by the Animal Care and Use Committee of Renmin Hospital of Wuhan University (WDRM20230805A). All experimental procedures were in accordance with the Declaration of Helsinki and were conducted according to the guidelines established by the National Institutes of Health. All Beagles \((8 - 12\mathrm{kg})\) were anesthetized intravenously with \(3\%\) sodium pentobarbital \((30\mathrm{mg}\cdot \mathrm{kg}^{- 1}\) induction dose, \(2\mathrm{mg}\cdot \mathrm{kg}^{- 1}\) maintenance dose per hour) and respiration was maintained by endotracheal intubation using a ventilator. Arterial blood pressure was continuously monitored through femoral artery catheterization with a pressure transducer attached. ECG and blood pressure data were recorded throughout the procedure. A heating pad was used to maintain core body temperature at \(36.5\pm 0.5^{\circ}\mathrm{C}\) + +The cells were cultured in a humid incubator containing \(5\% \mathrm{CO}_2\) at a temperature of \(37.0^{\circ}\mathrm{C}\) + +## Detection of TRPV1 and TREK1 expression in vitro and in vivo + +Western blotting was used to assess the expression of TRPV1 and TREK1 in neuronal cells and ganglion tissues. HT- 22 cells or HEK- 293T cells were cultured in six- well plates for \(24 - 48\mathrm{h}\) , then lysed and centrifuged to collect cells. Ganglion tissues were obtained from deceased animals and frozen in liquid nitrogen or stored at \(- 80.0^{\circ}\mathrm{C}\) . Total protein was determined using BCA protein assay reagent after tissue grinded and cells lysed. Afterwards, the procedure was followed according to the manufacturer's instructions. Primary antibodies were anti- TRPV1 and anti- TREK1. Expression levels of specific proteins were normalized to GAPDH. + +## Calcium imaging of neuronal cells + +<--- Page Split ---> + +The effect of PtNP- shell photothermal modulation on ion channels in HT- 22 cells was explored through calcium imaging experiments. HT- 22 cells were incubated in \(35\mathrm{mm}\) confocal dishes for \(24\mathrm{h}\) . Cells were washed 3 times with PBS and then stained with 5 \(\mu \mathrm{M}\) Fluo- 4 AM (dilution ratio 1:500) for \(30\mathrm{min}\) in a cell incubator at \(37.0^{\circ}\mathrm{C}\) , protected from light. To induce activation of TRPV1 and TREK1 ion channels, which had been previously studied \(^{7,8}\) , the culture dish was exposed to NIR- II light ( \(1064\mathrm{nm}\) ), resulting in an elevation of temperature. TRPV1, being a calcium channel, exhibited observable changes in the flow of calcium ions upon activation, while TREK1 as a potassium channel did not display such behavior. Therefore, the effect of PtNP- shell photothermal modulation on neuronal cells via TREK1 was observed by introducing a \(15\mathrm{mM}\) KCl solution prior to NIR- II irradiation. Fluorescence signals at \(525\mathrm{nm}\) were recorded using a confocal microscope with \(488\mathrm{nm}\) as the excitation wavelength. XYT images were acquired and collected under a \(20\mathrm{x}\) objective lens. The average fluorescence intensity of the cells was analyzed using ImageJ software (Fiji). The normalized fluorescence change was calculated as follows: \(\Delta \mathrm{F} / \mathrm{F} = (\mathrm{F - F_0}) / \mathrm{F_0}\) , where F is the original fluorescence signal; \(\mathrm{F_0}\) is the average baseline intensity before irradiation with NIR- II laser. + +## In vitro cytotoxicity assay + +The cytotoxicity of PtNP- shell on neuronal cells was evaluated by CCK- 8 assay. HT- 22 cells were seeded in 96- well plates at a density of \(1 \times 10^{4}\) well \(^{- 1}\) and cultured for 24 h. HT- 22 cells were then treated with different concentrations (10, 25, 50, 100, 150, 200 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) of PtNP- shell for another 24 h. Cell viability was determined by CCK- 8 assay after incubating with the CCK- 8 reagent for 1 h. To investigate the impact of PtNP- shell's photothermal effect on neuron cell viability, HT- 22 cells were co- cultured with PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) for 12 h followed by irradiation with a \(1064\mathrm{nm}\) laser (0.5 and \(0.75\mathrm{W} \cdot \mathrm{cm}^{- 2}\) ) for various durations (10 s, 30 s and 60 s). After incubation again for 12 + +<--- Page Split ---> + +h, the absorbance at \(450 \mathrm{nm}\) was recorded using a microplate reader. Cell survival (\%) \(= (OD_{\text{samples}} - OD_{\text{blank}}) / (OD_{\text{control}} - OD_{\text{blank}}) \times 100\%\) . + +## Experimental protocol 1: Activation of the parasympathetic nervous system through PtNP-shell photothermal reduces I/R injury + +Part 1: Exploring the in vivo effects of precise photothermal stimulation of the parasympathetic nervous system by PtNP- shell under NIR- II irradiation. Twelve beagles were randomly assigned to the control group ( \(100 \mu \mathrm{L}\) phosphate- buffered saline (PBS) was microinjected into the NG, \(\mathrm{n} = 6\) ) and the PtNP- shell group ( \(100 \mu \mathrm{L}\) PtNP- shell ( \(50 \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) was microinjected into the NG, \(\mathrm{n} = 6\) ). NG nerve activity, heart rate (HR) and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR- II irradiation (Fig 4b). + +Part 2: The protective effect of PtNP- shell activation of the parasympathetic nervous system against myocardial I/R injury was investigated. The same grouping pattern as in part1 was used, with 5- min NIR- II irradiation of the NG before opening the occluded LAD coronary vessel. Afterwards, ventricular electrophysiological parameters, heart rate variability (HRV) and ECG data were recorded and analyzed (Fig 5b). + +## Experimental protocol 2: PtNP-shell photothermal inhibition of sympathetic nervous system improves MI + +Part 1: The in vivo effects of precise photothermal stimulation of the sympathetic nervous system by PtNP- shell under NIR- II irradiation were explored. Twelve beagles were randomly assigned to the control group ( \(100 \mu \mathrm{L}\) PBS microinjected into the LSG, \(\mathrm{n} = 6\) ) and the PtNP- shell group ( \(100 \mu \mathrm{L}\) PtNP- shell ( \(50 \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) microinjected into the LSG, \(\mathrm{n} = 6\) ). LSG nerve activity, SBP and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR- II + +<--- Page Split ---> + +irradiation (Fig 6b). + +Part 2: To investigate the protective effect of PtNP- shell inhibition of the sympathetic nervous system a improves MI. The same grouping pattern as in part1 was used, with 5- min NIR- II irradiation of the LSG before ligation of LAD vessels. Finally, ventricular electrophysiological parameters, HRV and ECG data were also recorded and analyzed (Fig 7b). + +## PtNP-shell photothermal stimulation of the autonomic nervous system in vivo + +We selected NG and LSG as targets for modulation in the autonomic nervous system to explore the multifunctionality of the PtNP- shell photothermal strategy. A "C" incision is made behind the left ear, and the angle between the occlusal and trapezius muscles served as the access approach24. The tissue is bluntly separated to expose the carotid sheath and identify the parasympathetic nerve. Moving upstream along the nerve, a distal expansion is observed as NG (Fig 4a). LSG can be visualized and localized by left- sided thoracotomy according to the method of a previous study (Fig 6a)25. PtNP- shell (50 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) or PBS was slowly injected into 2 sites within the NG and LSG tissues to achieve homogeneous photothermal conversion. Initial vertical irradiation of NIR- II laser (1064 nm) at 0.80 W·cm- 2 was performed on NG and LSG surfaces. The power density of the NIR- II laser was reduced to 0.45 W·cm- 2 for continuous irradiation when the temperature of the NG reached 42.0 °C, and was reduced to 0.6 W·cm- 2 for continuous irradiation when the temperature of the LSG reached 46.0 °C. The NIR- II laser irradiation remains stable with a spot size maintained at 1.0 cm- 2. Dual temperature monitoring using thermal imager and T- type thermocouple was performed to plot the temperature- time curve. + +## Functional assessment of autonomic nerves + +The NG is a ganglion located upstream of the cervical parasympathetic nerve and can + +<--- Page Split ---> + +significantly inhibit HR after receiving direct electrical stimulation18. The LSG, as an important peripheral sympathetic ganglion, can rapidly elevate blood pressure when activated by electrical stimulation. Based on the functional properties of different autonomic ganglia, we assessed the function of NG and LSG with reference to previous studies19. A pair of special electrodes made with silver wires were directly connected to the surfaces of NG and LSG for stimulation. High- frequency electrical stimulation (HFS: 20 Hz, 0.1 ms) was applied to the ganglion. The voltage was set to 5 levels in continuous increments (level 1: 0–2 V; level 2: 2–4 V; level 3: 4–6 V; level 4: 6–8 V; level 5: 8–10 V), while keeping the stimulation voltage values consistent with the baseline at different time points during the experiment. The percentage of sinus rate or AV conduction (measured by the A-H interval) slowing down constructed voltage level/degree of HR decrease curves reflecting NG function. On the other hand, the percentage increase in SBP built the voltage level/degree of SBP increase to reflect LSG function. + +## Activity testing of autonomic nerves + +The activity of different autonomic nerves was assessed based on previous studies19. Two specially designed microelectrodes were inserted into the NG and LSG, respectively, while a grounding wire was connected to obtain signals from the autonomic nerves. These electrical signals were recorded by a Power Lab data acquisition system, filtered through a band- pass filter (300–1000 Hz) and amplified 30- 50 times by an amplifier. Finally, the signals were digitized and analyzed in LabChart software (version 8.0, AD Instruments). + +## Construction of myocardial I/R injury model and MI model + +The left anterior descending coronary occlusion (LADO) method was used to establish the MI model19. The ligation site was located beneath the first diagonal of the LAD, and + +<--- Page Split ---> + +successful MI model was confirmed by observing ST- segment elevation on the ECG. After ensuring cardiac electrophysiological stabilization, the junction was released to reperfuse the occluded coronary arteries, completing the construction of the myocardial I/R injury model26. + +## Ventricular electrophysiological study in vivo + +The cardiac electrophysiological measurements were performed in Beagles using a previously studied protocol27,28. The ERP was measured at three locations: LVA, LVB, LVM (located between the LVA and LVB). Malignant arrhythmic events caused by MI and I/R injury were assessed by electrocardiographic recordings in a canine model using Lead 7000 Computerized Laboratory System. VAs was classified according to Lambeth Conventions as VPBs, VT (three and more consecutive VPBs) and \(\mathrm{VF}^{29}\) . In addition, arrhythmia inducibility was further assessed by programmed ventricular stimulation at the right ventricular apex (RVA). Eight consecutive stimuli (S1S1) were performed at intervals of 330 ms, followed by additional stimuli until VT/VF occurred. Arrhythmia inducibility was assessed based on a modified arrhythmia scoring system28. If VF occurs during the evaluation, a defibrillator is required to restore sinus rhythm, followed by a waiting period of 30 min to restore cardiac electrophysiological stability. The VF threshold was assessed in the perimyocardial infarction region. Pacing was initiated using a Grass stimulator with a voltage of 2 V (20 Hz, 0.1 ms duration, 10 s). The stimulation voltage was increased in 2 V increments until VF was induced. The lowest voltage that induced VF was regarded as the VF threshold30. + +## HRV analysis + +The ECG data was recorded using the PowerLab data acquisition system. And the ECG segments recorded more than 5 min before modulation and after MI or I/R injury were analyzed by LabChart software with the Lomb- Scargle periodogram algorithm31. + +<--- Page Split ---> + +Frequency domain metrics of HRV were calculated, including LF (0.04–0.15 Hz, reflecting sympathetic tone), HF (0.15–0.4 Hz, reflecting parasympathetic tone) and LF/HF (reflecting autonomic balance). The results were expressed in standardized units. + +## Immunofluorescence staining of histopathological sections + +The ganglions were rapidly dissected for histopathological staining after the experimental animals died. Tissues were fixed with \(4\%\) paraformaldehyde, embedded in paraffin, and cut into \(5\mu \mathrm{m}\) - thick sections. NG was stained with multiple immunofluorescence staining using anti- NF1, anti- c- fos and anti- TRPV1 antibodies. And LSG was stained by multiple immunofluorescences using anti- TH, anti- c- fos and anti- TREK1 antibody. Cell nuclei were stained with DAPI. Images were taken at \(100\times\) magnification and analyzed using ImageJ software (Fiji). + +## Enzyme-linked immunosorbent assay (ELISA) + +\(5\mathrm{ml}\) of venous blood was obtained from the jugular vein of each beagle after MI and myocardial I/R injury. After standing for 1 hour, the blood was centrifuged at \(3000\mathrm{rpm}\) for \(15\mathrm{min}\) . The upper serum layer was collected and stored at \(- 80.0^{\circ}\mathrm{C}\) . Myocardial injury levels were detected by c- TnI and myoglobin (MYO). Standard process analyses were performed according to the instructions of each ELISA kit. To evaluate the long- term biosafety and biocompatibility of PtNP- shell in vivo, Beagle dogs and rats were randomly divided into PtNP- shell and PBS groups. + +## Long-term biosafety assay in vivo + +To evaluate the long- term biosafety and biocompatibility of PtNP- shell in vivo, Beagle dogs and rats were randomly divided into two groups: a PtNP- shell group and a PBS group. In the PtNP- shell group, \(200\mu \mathrm{L}\) PtNP- shell ( \(50\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) ) was microinjected into canine ganglion tissue and tail vein of rats to explore long- term biosafety. Blood + +<--- Page Split ---> + +1 and tissue samples were collected from each dog and rat one month after injection. One 2 month after injection, blood samples were collected from the jugular vein of dogs as 3 well as from the inferior vena cava of rats for analysis of serum biochemical indices. 4 Tissue H&E staining was also performed on major organs, including heart, liver, spleen, 5 lung and kidney. + +## Statistical analysis + +7 All graphical data are presented as mean \(\pm\) standard error of the mean (SEM), and the 8 distribution of data was assessed by the Shapiro- Wilk test. Differences between groups 9 were determined using Student's t- test or Mann- Whitney U- test. Data were analyzed 10 and plotted using GraphPad Prism 9.0 software (GraphPad software, Inc., La Jolla, CA, 11 USA). \(\mathrm{P}< 0.05\) was considered statistically different. The p- values are indicated with 12 an asterisk \((*\mathrm{p}< 0.05,^{**} \mathrm{p}< 0.01,^{***} \mathrm{p}< 0.001)\) . + +## Reporting Summary + +14 Further information on research design is available in the Nature Portfolio Reporting 15 Summary linked to this article. + +## Data availability + +17 The main data supporting the results in this study are available within the paper and its 18 Supplementary Information. The raw and analyzed datasets generated during the study 19 are too large to be publicly shared, yet they are available for research purposes from the 20 corresponding authors on reasonable request. Source data are provided with this paper. + +<--- Page Split ---> + +## 1 References + +2 Chechetka, S. A. et al. Light- driven liquid metal nanotransformers for biomedical theranostics. Nat. Commun. 8, 15432 (2017). 3 Zhu, P. et al. Inorganic nanoshell- stabilized liquid metal for targeted photonanomedicine in NIR- II biowindow. Nano Lett. 19, 2128- 2137 (2019). 4 Bruneau, M. & George, B. The juxtacondylar approach to the jugular foramen. Oper. Neurosurg. 63, 75- 80 (2008). 5 Zhang, S. et al. Ultrasound- guided injection of botulinum toxin type A blocks cardiac sympathetic ganglion to improve cardiac remodeling in a large animal model of chronic myocardial infarction. Heart Rhythm 19, 2095- 2104 (2022). 6 Chen, M. X. et al. Low- level vagus nerve stimulation attenuates myocardial ischemic reperfusion injury by antioxidative stress and antiapoptosis reactions in canines. J. Cardiovasc. Electrophysiol. 27, 224- 231 (2016). 7 Yu, L. L. et al. Optogenetic modulation of cardiac sympathetic nerve activity to prevent ventricular arrhythmias. J. Am. Coll. Cardiol. 70, 2778- 2790 (2017). 8 Yu, L. et al. Chronic intermittent low- level stimulation of tragus reduces cardiac autonomic remodeling and ventricular arrhythmia inducibility in a post- infarction canine model. JACC Clin. Electrophysiol. 2, 330- 339 (2016). 9 Walker, M. J. A. et al. The lambeth conventions: guidelines for the study of arrhythmias in ischaemia, infarction, and reperfusion. Cardiovasc. Res. 22, 447- 455 (1988). 10 Dalonzo, A. J. et al. Effects of cromakalim or pinacidil on pacing- and ischemia- induced ventricular fibrillation in the anesthetized pig. Basic Res. Cardiol. 89, 163- 176 (1994). 11 Lai, Y. et al. Non- invasive transcutaneous vagal nerve stimulation improves myocardial performance in doxorubicin- induced cardiotoxicity. Cardiovasc. Res. 118, 1821- 1834 (2022). + +## Acknowledgements + +The research was supported by the National Natural Science Foundation of China (grants 22025303, 82241057, 82270532 and 82200556); and the National Key Research and Development Program of China (grant 2023YFC2705705); and Foundation for Innovative Research Groups of Natural Science Foundation of Hubei Province, China (grant 2021CFA010). We thank the Core Facility of Wuhan University for their substantial supports in sample characterization, including SEM, XPS, DLS and XRD. We thank the Center for Electron Microscopy at Wuhan University for their support of STEM, HRTEM and EDX characterization. We also thank Meimei Zhang in the institute for advanced studies of Wuhan University for their assistance in TEM characterization. + +## Author contributions + +L.F., L.L.Y. and X.Y.Z. conceived the research concept. L.F., L.L.Y. and X.Y.Z. + +<--- Page Split ---> + +1 supervised the research; C.L.W., L.P.Z., C.Z.L., J.M.Q., X.R.H., B.X., Q.F.Q., Z.Z.Z.2 and J.L.W. performed the experiments; C.L.W., L.P.Z., C.Z.L., L.Y.W. and Y.X.L.3 discussed the results; C.L.W., L.P.Z. and C.Z.L. analysed the data and cowrote the4 manuscript. All authors commented on the manuscript. + +5 Competing interests6 The authors declare no competing interests. + +7 Additional information8 Supplementary information The online version contains supplementary material9 available at10 Correspondence and requests for materials should be addressed to Xiaoya Zhou,11 Lilei Yu or Lei Fu12 Peer review information13 Reprints and permissions information is available at + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- supplementaryinformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa_det.mmd b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..eda08b7f956aa7f88dadbece23cacc41b4e6342e --- /dev/null +++ b/preprint/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/preprint__0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa_det.mmd @@ -0,0 +1,551 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 863, 207]]<|/det|> +# Pt nanoshell with ultra-high NIR-β photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection + +<|ref|>text<|/ref|><|det|>[[44, 230, 235, 275]]<|/det|> +Lei Fu lei fu@whu.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 300, 568, 920]]<|/det|> +Wuhan University https://orcid.org/0000- 0003- 1356- 4422Chenlu WangWuhan UniversityLiping ZhouWuhan UniversityChengzhe LiuWuhan UniversityJiaming QiaoWuhan UniversityXinrui HanWuhan UniversityLuyang WangWuhan UniversityYaxi LiuWuhan UniversityBi XuWuhan UniversityQinfang QiuWuhan UniversityZizhuo ZhangWuhan UniversityJiale WangWuhan UniversityXiaoya ZhouWuhan UniversityMengqi ZengWuhan University https://orcid.org/0000- 0002- 1442- 052X + +<|ref|>text<|/ref|><|det|>[[44, 928, 108, 945]]<|/det|> +Lilei Yu + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 106, 104, 124]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 144, 136, 162]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 181, 315, 200]]<|/det|> +Posted Date: March 15th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 220, 474, 239]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3985327/v1 + +<|ref|>text<|/ref|><|det|>[[42, 257, 914, 300]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 317, 535, 337]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 372, 910, 416]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 28th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 50557- w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[120, 85, 822, 184]]<|/det|> +# Pt nanoshell with ultra-high NIR-II photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection + +<|ref|>text<|/ref|><|det|>[[144, 210, 855, 298]]<|/det|> +Chenlu Wang \(^{1,\dagger}\) , Liping Zhou \(^{2,3,4,\dagger}\) , Chengzhe Liu \(^{2,3,4,\dagger}\) , Jiaming Qiao \(^{2,3,4}\) , Xinrui Han \(^{2,3,4}\) , Luyang Wang \(^{1}\) , Yaxi Liu \(^{1}\) , Bi Xu \(^{1}\) , Qinfang Qiu \(^{2,3,4}\) , Zizhuo Zhang \(^{2,3,4}\) , Jiale Wang \(^{2,3,4}\) , Xiaoya Zhou \(^{2,3,4*}\) , Mengqi Zeng \(^{1}\) , Lilei Yu \(^{2,3,4*}\) , Lei Fu \(^{1,3,4*}\) + +<|ref|>text<|/ref|><|det|>[[144, 328, 852, 610]]<|/det|> +\(^{1}\) College of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China. \(^{2}\) Cardiovascular Hospital, Renmin Hospital of Wuhan University, Wuhan 430060, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan 430060, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan 430060, China; Hubei Key Laboratory of Cardiology, Wuhan 430060, China; Cardiovascular Research Institute, Wuhan University, Wuhan, 430060, China. \(^{3}\) Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430060, China. \(^{4}\) Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China. + +<|ref|>text<|/ref|><|det|>[[144, 636, 797, 656]]<|/det|> +\(^{*}\) E- mail: leifu@whu.edu.cn; lileiyu@whu.edu.cn; whuzhouxiaoya@whu.edu.cn + +<|ref|>text<|/ref|><|det|>[[144, 682, 792, 701]]<|/det|> +\(^{†}\) These authors contributed equally: Chenlu Wang, Liping Zhou, Chengzhe Liu. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 84, 852, 595]]<|/det|> +The autonomic nervous system plays a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK- elated \(\mathbf{K}^{+}\) Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations on achieving rapid and precise treatment. Here, we achieved nearly perfect blackbody absorption and one of the highest PCE in the second near infrared (NIR- II) window (73.7% at 1064 nm) via a Pt nanoparticle shell (PtNP- shell). By precisely manipulating the photothermal effect, we successfully achieved rapid and precise multifunctional neuromodulation encompassing neural activation (41.0–42.9 °C) and inhibition (45.0–46.9 °C). The NIR-II photothermal modulation additionally achieved bi- directional reversible autonomic modulation and conferred protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy. + +<|ref|>text<|/ref|><|det|>[[144, 627, 852, 907]]<|/det|> +Cardiovascular disease has emerged as a leading cause of mortality, with acute myocardial infarction being one of the most pernicious ailments1,2. Myocardial ischemia (MI) frequently precipitates acute ventricular arrhythmias (VAs), impeding prompt and efficacious treatment for acute myocardial infarction. Furthermore, conventional interventional procedures for MI are unable to circumvent concomitant myocardial reperfusion injury and associated VAs. The autonomic nervous system, encompassing sympathetic and parasympathetic nerves, plays a role in cardiovascular modulation; both are naturally antagonistic. Sympathetic inhibition or parasympathetic activation has been shown to stabilize cardiac electrophysiology, safeguard against MI + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 84, 422, 101]]<|/det|> +and reduce the incidence of VAs \(^{3}\) . + +<|ref|>text<|/ref|><|det|>[[144, 116, 853, 430]]<|/det|> +In recent years, several studies have demonstrated that light- activated nanotransducers can induce local heating effects, leading to the activation or inhibition of nerves \(^{4 - 6}\) . This discovery is attributed to the identification of temperature- sensitive ion channels in neurons, such as transient receptor potential vanilloid 1 (TRPV1) \(^{7}\) and TWIK- elated K \(^{+}\) Channel 1 (TREK1) \(^{8}\) . The activation of specific temperature- sensitive ion channels necessitates precise temperature ranges \(^{7 - 9}\) . Considering the acute nature of neural responses, a therapeutic strategy with rapid and accurate modulation is required. The second near infrared (NIR- II) photothermal is expected to realize noninvasive and nonimplanted neuromodulation. However, its neural response rate and accuracy are currently limited by low photothermal conversion efficiency (PCE). + +<|ref|>text<|/ref|><|det|>[[144, 444, 852, 857]]<|/det|> +Here we report a near blackbody NIR- II Pt nanoparticle shell (PtNP- shell) for protection against MI and myocardial reperfusion injury accompanying intervention. The PtNP- shell, synthesized through a simple electrocoupling substitution reaction using liquid metal nanoparticles as templates (Fig. 1a), possesses surface pores and a hollow structure. It demonstrates nearly perfect blackbody absorption, enhanced absorption of light, and then one of the highest PCE in the NIR- II window (73.7% at 1064 nm). By leveraging the local heating effect mediated by PtNP- shell, we achieved rapid, efficient, and precise multifunctional autonomic neuromodulation. Specifically, parasympathetic activation and sympathetic inhibition were accomplished by activating TRPV1 (41.0–42.9 °C) and TREK1 (45.0–46.9 °C) channels, respectively. Photothermal autonomic neuromodulation mediated by PtNP- shell effectively stabilized cardiac electrophysiology and reduced VAs incidence in both myocardial ischemia- reperfusion (I/R) injury model and MI model, respectively (Fig. 1b). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[147, 85, 848, 444]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 459, 852, 555]]<|/det|> +
Fig. 1 | The synthesis steps of the PtNP-shell and the concept of mediating precise photothermal effects for cardioprotection. a, The synthesis steps of PtNP-shell and schematic diagram of photothermal effect. b, Schematic diagram of multifunctional autonomic modulation mediated by photothermal effect of PtNP-shell for precise cardioprotection against myocardial I/R injury and MI-induced VAs.
+ +<|ref|>sub_title<|/ref|><|det|>[[150, 588, 366, 607]]<|/det|> +## Result and discussion + +<|ref|>sub_title<|/ref|><|det|>[[148, 625, 540, 644]]<|/det|> +## Synthesis and Characterization of PtNP-shell + +<|ref|>text<|/ref|><|det|>[[145, 658, 852, 907]]<|/det|> +The PtNP- shell was synthesized through an electrocoupling substitution reaction between chloroplatinate and Ga nanoparticles (GaNPs). Ga nanoparticles were obtained by sonication of pure metal Ga. To achieve a balanced particle size and oxidation degree of GaNPs, pure gallium was sequentially sonicated in ethanol and water for 30 minutes to obtain gallium nanoparticles with reduced oxidation (Supplementary Fig. 1a). In accordance with the electrochemical redox potential of the redox couple \((\mathrm{Ga}^{3 + } / \mathrm{Ga} - 0.529 \mathrm{V}; \mathrm{PtCl}_6^{2 - } / \mathrm{PtCl}_4^{2 - }: 0.726 \mathrm{V}; \mathrm{PtCl}_4^{2 - } / \mathrm{Pt}: 0.758 \mathrm{V})^{10,11}, \mathrm{Pt} (\mathrm{IV}) \mathrm{can be in situ}\) reduced by Ga and encapsulated on the surface of GaNPs to form a core- shell structure + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 81, 855, 465]]<|/det|> +(Supplementary Fig. 1b, c). The hollow PtNP-shell is synthesized after completion of the reaction (Fig. 2a). Simultaneously with the reduction of Pt (IV), Ga oxide is formed, creating the skeleton of the PtNP-shell (right in Fig. 2a). The surface of the PtNP-shell exhibits a rough texture (Supplementary Fig. 2). The scanning transmission electron microscopy (STEM) images reveal numerous irregular and uneven pores on its surface (Supplementary Fig. 3a) and PtNP-shell is composed of Pt nanoparticles (PtNPs) with \(2 - 5 \mathrm{nm}\) (Fig. 2b). High-resolution TEM (HR-TEM) image is acquired to character the structure of PtNPs. As shown in Supplementary Fig. 3b, PtNPs exhibits single crystal structure with a lattice stripe spacing of \(0.23 \mathrm{nm}\) corresponding to the (111) crystal plane. Meanwhile, the corresponding Fast Fourier Transform (FFT) pattern (inset in Supplementary Fig. 3b) shows the typical diffraction patterns of face-centered cubic structure along [111] zone axis. + +<|ref|>image<|/ref|><|det|>[[144, 494, 850, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[100, 787, 851, 884]]<|/det|> +
Fig. 2 | Characterization of PtNP-shell. a, TEM image of PtNP-shell (Right: element mapping). b, STEM images of PtNP-shell surface. c, XRD spectrum of PtNP-shell (Inset: SAED pattern). d, UV-vis-NIR absorption spectrum of PtNP-shell ( \(75 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ). e, Temperature elevation curves of PtNP-shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ) under NIR-II laser irradiation ( \(1 \mathrm{W} \cdot \mathrm{cm}^{2}\) ). f, Calculation of the PCE at \(1064 \mathrm{nm}\) (PtNP-shell: \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{-1}\) ).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 80, 853, 696]]<|/det|> +In the X- ray power diffraction (XRD) spectrogram result (Fig. 2c), all peaks can be attributed to the crystal phase of Pt (JCPDS: 87- 0640), consistent with the selected area electron diffraction (SAED) pattern findings (inset in Fig. 2c). However, no peaks corresponding to gallium oxide were observed in the XRD spectrogram, possibly due to its low content. The XRD spectrogram (Supplementary Fig. 4) of PtNP- shell prior to reacting with KOH showed that the gallium oxide contained in PtNP- shell was GaOOH (JCPDS: 06- 0180). Additional evidence from X- ray photoelectron spectroscopy (XPS) also suggests that PtNP- shell contains Ga (Supplementary Fig. 5), consistent with energy dispersive X- ray spectroscopy (EDX) analysis (right in Fig. 2a). The peak centred at 1117.59 eV is ascribed to Ga \(2\mathrm{p}_{3 / 2}\) , indicating the presence of \(\mathrm{Ga}^{3 + }\) in PtNP- shell. Meanwhile, the Pt 4f spectrum shows two peaks at 71.56 and 75.02 eV, which result from metallic Pt \(4\mathrm{f}_{7 / 2}\) and Pt \(4\mathrm{f}_{5 / 2}\) . PtNP- shell was treated with KOH (0.67 M) to reduce the gallium oxide content and the surface potential was reduced from 45.8 mV to - 25.7 mV, and then encapsulated with Methoxypoly(Ethylene Glycol) Thiol (mPEG- \(\mathrm{SH}_{5000}\) ) to enhance its biocompatibility and the surface potential was changed to - 19.9 mV. (Supplementary Fig. 6). The statistically averaged hydrated nanoparticle size of PtNP- shell based on the dynamic light scattering diagram was 200.1 nm with uniform size distribution, indicating the nanoparticle was well dispersed in water (Supplementary Fig. 7). + +<|ref|>sub_title<|/ref|><|det|>[[147, 716, 707, 735]]<|/det|> +## Blackbody Absorption and Photothermal Property of PtNP-shell + +<|ref|>text<|/ref|><|det|>[[144, 747, 853, 899]]<|/det|> +Due to the presence of pores and a hollow structure in the PtNP- shell, light propagating in the space bounces at the rough surface of PtNP- shell until it encounters one of the pores, where it continues to bounce within the PtNP- shell. The random distribution of these pores results in completely random light reflection, akin to Brownian motion12. Consequently, the probability of light escaping from other pores is extremely low, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 82, 853, 530]]<|/det|> +rendering PtNP- shell behave like a blackbody and produce an efficient infrared heater \(^{13 - 15}\) . This enhanced absorption of light by PtNP- shell exhibits nearly perfect blackbody absorption characteristics (Supplementary Fig. 8a). The absorption of PtNP- shell is close to 1 in the range of 250–1300 nm at \(75 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) (Fig. 2d). According to the Lambert- Beer law (A/L = εC, where ε is the extinction coefficient), a linear relationship between absorption intensity (at 1064 nm) and concentration was established, with an extinction coefficient measured as \(13.3 \mathrm{Lg}^{- 1} \mathrm{cm}^{- 1}\) at 1064 nm (Supplementary Fig. 8b). Varying concentrations of PtNP- shell resulted in different shades of grey being generated, with significantly darker greyness observed under identical conditions compared to GaNPs and Pt- coated Ga- In alloy (EGaIn) nanoparticles (GaIn@Pt NPs) (Supplementary Fig. 9a). These distinctive features were characterized by their respective positions within an RGB cube representation, wherein on the diagonal connecting darkest and brightest points, PtNP- shell was found closer to the darkest point than both other materials (Supplementary Fig.9b). + +<|ref|>text<|/ref|><|det|>[[144, 541, 853, 890]]<|/det|> +The photothermal properties of PtNP- shell were verified by irradiating the dispersion of PtNP- shell in water with NIR- II light at \(1064 \mathrm{nm}\) (1 \(\mathrm{W} \cdot \mathrm{cm}^{- 2}\) ). Even in vitro, PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) exhibited rapid temperature elevation, achieving a rise from room temperature to \(41.0^{\circ} \mathrm{C}\) and \(45.0^{\circ} \mathrm{C}\) within only 96 s and 133 s, respectively (Fig. 2e). However, for GaNPs (347 s and over 600 s) and GaIn@Pt NPs (278 s and 450 s), it took significantly longer time to reach the same temperatures (Supplementary Fig. 10). The corresponding thermal images of the PtNP- shell with different concentrations under different irradiation times are shown in Supplementary Fig. 11. The heating effect of the PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) gradually increased the \(\Delta \mathrm{T}\) from 7.72 \(^{\circ} \mathrm{C}\) to 52.17 \(^{\circ} \mathrm{C}\) When exposed to NIR- II laser for a duration of 600 s while varying the optical power density at 1064 nm between \(0.25 - 1.5 \mathrm{W} \cdot \mathrm{cm}^{- 2}\) (Supplementary Fig. 12). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 83, 852, 268]]<|/det|> +The PCE of PtNP- shell was quantified as \(73.7\%\) when balancing the energy input from photons with heat dissipation within the system (Fig. 2f), representing one of the highest PCE at 1064 nm (Supplementary Fig. 13). These results indicate that PtNP- shell exhibits excellent photothermal performance in the NIR- II window. Additionally, no significant changes in temperature or morphology were observed even after five cycles of irradiation (Supplementary Fig. 14), suggesting exceptional photothermal stability. + +<|ref|>sub_title<|/ref|><|det|>[[144, 290, 795, 309]]<|/det|> +## Photothermal of PtNP-shell enables precise modulations of neurons in vitro + +<|ref|>text<|/ref|><|det|>[[144, 320, 852, 703]]<|/det|> +To investigate the photothermal effects of PtNP- shell on neuronal activity at multiple levels, we conducted calcium imaging experiments in hippocampal neuron (HT- 22) cells (Fig. 3a, b). The immunoblotting results revealed abundant expression of both TRPV1 and TREK1 ion channels in HT- 22 cells (Fig. 3c). The direct effect of PtNP- shell on the excitability of these two different ion channels was assessed under NIR- II irradiation using a calcium ion indicator (Fluo- 4 AM). Upon NIR- II laser irradiation, the temperature of the PtNP- shell (+) group increased compared to that of the PtNP- shell (- ) group, resulting in a significantly higher percentage of responding cells (Fig. 3d) (p< 0.001). The micrographs fluorescence intensity curve of HT- 22 neurons cultured with PtNP- shell showed significant \(\mathrm{Ca^{2 + }}\) influx upon NIR- II laser irradiation for \(35 \pm 5\) s and after the temperature reached \(42.0^{\circ}\mathrm{C}\) (Fig. 3e). In contrast, application of NIR- II laser irradiation with PBS did not induce significant \(\mathrm{Ca^{2 + }}\) influx. + +<|ref|>text<|/ref|><|det|>[[144, 716, 852, 899]]<|/det|> +Subsequently, neuronal excitation was induced and calcium signals were increased by perfusion of \(15\mathrm{mM}\) KCl in the PtNP- shell (- ) group and PtNP- shell (+) group (50 \(\mu \mathrm{g}\mathrm{mL}^{- 1}\) ), respectively. This phenomenon can be attributed to the elevation of extracellular potassium ion concentration, which triggers neuronal depolarization and subsequently leads to a substantial increase in intracellular calcium ion concentration16. Under NIR- II laser irradiation, the proportion of HT- 22 cells responding to high + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 83, 852, 430]]<|/det|> +concentration KCl stimulation was significantly lower in the PtNP- shell (+) group compared to that in the PtNP- shell (- ) group at approximately \(46.0^{\circ}\mathrm{C}\) (Fig. 3f). The difference may be due to the activation of the TERK1 ion channel in the PtNP- shell (+) group, which can induce neuronal hyperpolarization and make intracellular and extracellular calcium ion concentrations tend to recover17. Interestingly, the PtNP- shell influenced the fluorescence intensity of HT- 22 cells not with a sustained decrease but with an initial rise followed by a subsequent decrease (Fig. 3g). This observation may be associated with the activation of TRPV1 channel at around \(42.0^{\circ}\mathrm{C}^9\) . With increasing temperature, TRPV1 and TREK1 channels were sequentially activated. These findings suggest that PtNP- shell can achieve precise temperature control within a short duration through its own ultra- high PCE for both neuronal excitation and inhibition. + +<|ref|>text<|/ref|><|det|>[[144, 444, 852, 856]]<|/det|> +Cytotoxicity assays were then conducted to investigate the potential neurotoxicity of PtNP- shell application. As shown in Fig. 3h, concentrations of PtNP- shell below 100 \(\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) exhibited no significant toxic effects on HT- 22 cells. Even when the concentration of PtNP- shell was increased to \(200\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) , the survival rate of neuronal cells remained approximately at \(52.11\%\) . Furthermore, the impact of PtNP- shell photothermal stimulation parameters on cell viability were assessed through analysis of HT- 22 cell survival under NIR- II laser irradiation. Notably, when a concentration of 50 \(\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) PtNP- shell and an NIR- II laser with a power density of \(0.5\mathrm{W}\cdot \mathrm{cm}^{- 2}\) were applied for a brief duration, the survival rate exceeded \(92.36\%\) for HT- 22 cells. Even with an increase in power density to \(0.75\mathrm{W}\cdot \mathrm{cm}^{- 2}\) , the survival rate for HT- 22 cells still remained around \(72.68\%\) after 60 s of irradiation (Fig. 3i). These results indicate that PtNP- shell does not induce significant damage to neurons under controlled NIR- II laser irradiation. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 81, 850, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 492, 852, 670]]<|/det|> +
Fig. 3 | PtNP-shell photothermal activation of different neuronal ion channels in vitro. a, Flowchart of calcium imaging assay performed on HT-22 cells. b, calcium imaging of HT-22 cells under different experimental conditions. c, Western blotting for TRPV1 and TREK1 from HT-22 and H9c2 cells. Percentage of d, TRPV1 and f, TREK1 groups of HT-22 cells within the field of view of the fluorescence microscope that responded to laser stimulation. Temporal dynamics of \(\mathrm{Ca}^{2 + }\) signals in e, TRPV1 and g, TREK1 groups of cells. The solid lines indicate the mean, and shade represents the standard error of the mean (SEM). h, Cell viability of HT-22 treated with different concentrations of PtNP-shell for \(24\mathrm{h}\) . i, Cell viability of HT-22 treated with NIR-II laser irradiation of different power densities and laser duration. The error bar indicates S.E.M. \(***\mathrm{P}< 0.001\) .
+ +<|ref|>sub_title<|/ref|><|det|>[[145, 704, 795, 722]]<|/det|> +## PtNP-shell photothermal activation of the parasympathetic nervous system + +<|ref|>text<|/ref|><|det|>[[144, 736, 852, 887]]<|/det|> +Western blotting analysis of peripheral ganglia from the canine autonomic nervous system revealed the expression of TRPV1 and TREK1 heat- sensitive ion channels in both the nodose ganglion (NG) and left stellate ganglion (LSG). Notably, TRPV1 was abundantly expressed in the NG of the parasympathetic nervous system, while TREK1 exhibited higher levels in the LSG of the sympathetic nervous system (Supplementary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 81, 854, 563]]<|/det|> +Fig. 15). To investigate whether the photothermal effect induced by PtNP-shell under NIR-II irradiation can precisely regulate the parasympathetic nerve, \(100\mu \mathrm{L}\) PtNP-shell \((50\mu \mathrm{g}\cdot \mathrm{mL}^{- 1})\) and PBS were injected into NG of PtNP-shell group and control group (6 beagle dogs in each group), respectively (Fig. 4a,b). It can be observed that upon irradiation with NIR-II laser \((0.8\mathrm{W}\cdot \mathrm{cm}^{- 2})\) , the temperature of NG injected with PtNP-shell increased to \(41.0^{\circ}\mathrm{C}\) within a very short period of time \((12\pm 3\mathrm{s})\) . Subsequently, the temperature of NG could be kept in the range of \(41.0–42.9^{\circ}\mathrm{C}\) for 5 min by adjusting the power density to \(0.45\mathrm{W}\cdot \mathrm{cm}^{- 2}\) (Fig. 4c-d). As a crucial node within the parasympathetic neural network, activation of NG significantly reduces heart rate (HR) (Fig. 4e) \(^{18}\) . Therefore, NG function was assessed by the maximum decrease in heart rate under direct electrical stimulation. As shown in Fig. 4f-h, NG function and activity was significantly elevated in the PtNP-shell group than in the control group after stimulation. The function and activity of NG recovered close to baseline within three hours after turning off NIR-II laser, indicating that the photothermal modulation induced by PtNP-shell was reversible within NGs (Fig. 4h, Supplementary Fig. 16 and 17). + +<|ref|>text<|/ref|><|det|>[[144, 575, 853, 890]]<|/det|> +In addition, the effective refractive period (ERP) was measured in various regions, including left ventricular apex (LVA), left ventricular base (LVB) and median left ventricular area (LVM). In the PtNP-shell group, the ERP was significantly elevated compared to the control group and remained elevated for \(2\mathrm{~h}\) after photothermal intervention in NG (Supplementary Fig. 18). Furthermore, immunofluorescence staining for Vacht, c- fos, and TRPV1 was performed on NG histopathological sections following photothermal modulation (Fig. 4i). Quantitative analysis (Supplementary Fig. 19) revealed a substantial increase in the proportion of \(\mathrm{TRPV1^{+}}\) \((86.63\pm 2.65\mathrm{vs}45.45\pm 2.98)\) and c- Fos \(^+\) \((77.81\pm 3.91\mathrm{vs}17.27\pm 3.08)\) neurons among VAcH \(^+\) parasympathetic neurons in the PtNP-shell group compared to the control group (all P + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 851, 135]]<|/det|> +\(< 0.001\) ). These findings suggest that PtNP-shell can precisely regulate temperature and subsequently activate TRPV1 ion channels on NG to enhance parasympathetic activity. + +<|ref|>image<|/ref|><|det|>[[144, 165, 850, 670]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 684, 852, 863]]<|/det|> +
Fig. 4 | Photothermal activation of the parasympathetic nervous system by PtNP-shell. a, Location of the canine NG. b, Schematic illustration of the process of photothermal modulation of NG. c, Temperature curves of NG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of NG. e, Representative images of HR reduction induced after stimulation of NG with different voltages. Maximal HR changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \(n = 6\) . h, Quantification of the NG neural activity recordings, \(n = 6\) . i, Representative immunofluorescent images of Vacht (red), c-fos (green) and TRPV1 (pink) in the NG of beagles following different treatments. Data are shown as the mean \(\pm\) S.E.M. \(*P < 0.05\) , \(**P < 0.01\) , \(***P < 0.001\) , ns means that the difference is not statistically significant.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 82, 852, 400]]<|/det|> +1 PtNP-shell photothermal activation of NG reduces I/R injury and associated VAs2 Following I/R injury, electrocardiography (ECG) was recorded to monitor the3 occurrence of VAs events within 1 h, including ventricular premature beats (VPBs),4 ventricular tachycardia (VT) and ventricular fibrillation (VF) (Fig. 5c)19. Under NIR-II5 laser irradiation, the PtNP-shell group exhibited a lower incidence of sustained VTs6 (duration \(>30\) s) or VF compared to the control group (50% vs. 83%) (Fig. 5d).7 Moreover, the number of recorded VPBs (70.83 ± 5.38 vs. 116.00 ± 6.36, \(\mathrm{P}< 0.05\) ),8 VTs (3.17 ± 0.87 vs. 8.83 ± 2.15, \(\mathrm{P}< 0.05\) ) and duration of the VTs (7.00 ± 3.173s vs.9 26.83 ± 7.89s, \(\mathrm{P}< 0.05\) ) in the PtNP-shell group were significantly reduced compared10 to that in the control group (Fig. 5e- g). + +<|ref|>image<|/ref|><|det|>[[192, 425, 803, 720]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 734, 850, 911]]<|/det|> +
Fig. 5 | PtNP-shell photothermal activation of the parasympathetic nervous system improves myocardial I/R injury. Modulation of NG to protect against myocardial I/R injury and associated VAs a, schematic diagram and b, flowchart. c, Representative visual depictions of VAs, including VPB, VT and VF. d, Quantitative analysis the ratio of sVT and VF incidence between different groups, \(\mathrm{n} = 6\) . Quantitative analysis the number of e, VPBs, f, VTs and g, the duration of sVT of beagles. Effects on ventricular ERP at different sites in beagles treatment with PtNP-shell or control h, before and i, after myocardial I/R injury modelling. Levels of markers of myocardial injury, including j, MYO and k, c-TnI, after different treatments in beagles. Data are shown as the mean \(\pm\) S.E.M. \(^{*}\mathrm{P}< 0.05\) , \(^{**}P< 0.01\) , \(^{***}\mathrm{P}< 0.001\) .
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 83, 852, 333]]<|/det|> +Animal modeling and intervention manipulations were conducted to further elucidate the protective effects of precise modulation of NG by PtNP- shell against myocardial I/R injury and associated VAs, following the experimental protocols depicted in Figure 5a,b. PtNP- shell and PBS were microinjected into the NG of the PtNP- shell group and control group, respectively, each consisting of six beagle dogs. The NG was subsequently exposed to NIR- II laser irradiation for a duration of 5 minutes prior to occlusion of the left anterior descending (LAD) coronary artery for reperfusion therapy. + +<|ref|>text<|/ref|><|det|>[[144, 345, 852, 725]]<|/det|> +There were no statistically significant differences between the two groups in terms of preoperative ERP for LVB, LVM, and LVA. In the postoperative period, all three positions showed shortened ERPs in the control group. The PtNP- shell group exhibited significantly higher ERPs compared to the control group, indicating that photothermal modulation of nerves by PtNP- shell has a protective effect on cardiac electrophysiology (Fig. 5h- i). Serum Elisa assay revealed reduced levels of myocardial injury markers (MYO and c- TnI) after I/R injury in the PtNP- shell group compared to the control group (all \(\mathrm{p}< 0.05\) , Fig. 5j,k). Postoperatively, heart rate variability analysis demonstrated lower low frequency (LF) and higher high frequency (HF) and the lower ratio of LF to HF (LF/HF) values in the PtNP- shell group compared to the control group (all \(\mathrm{p}< 0.05\) , Supplementary Fig. 20). These results suggest that PtNP- shell exerts cardioprotective effects and reduces VAs by activating parasympathetic nerve. + +<|ref|>sub_title<|/ref|><|det|>[[144, 757, 754, 776]]<|/det|> +## PtNP-shell photothermal inhibition of the sympathetic nervous system + +<|ref|>text<|/ref|><|det|>[[144, 790, 853, 907]]<|/det|> +The sympathetic nervous system was modulated by performing microinjections of PtNP- shell or PBS into the LSG, followed by irradiation with an NIR- II laser (Fig. 6a,b). The temperature curve demonstrates that upon exposure to a NIR- II laser \((0.8\mathrm{W}\cdot \mathrm{cm}^{- 1})\) for \(25\pm 5\mathrm{s}\) , the temperature rapidly escalated to \(45.0^{\circ}\mathrm{C}\) , crossing the range of \(41.0-\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 78, 853, 765]]<|/det|> +42.9 °C within a mere duration of \(6 \pm 1\) s. Subsequently, the power density was immediately decreased to \(0.6 \mathrm{W} \mathrm{cm}^{-2}\) , effectively maintaining LSG at a steady temperature between \(45.0 - 46.9\) °C (Fig. 6c, d). Due to the substantial increase in systolic blood pressure (SBP) induced by LSG activation (Fig. 6e), the function of LSG was evaluated by quantifying the maximum SBP change corresponding to five consecutive incremental voltages of high- frequency electrical stimulation. After 5 min of NIR- II laser irradiation, the activity and function of LSG in the PtNP- shell group were significantly suppressed compared to the control group ( \(p < 0.05\) ) and they returned close to baseline after 3 h (Fig. 6f- h and Supplementary Fig. 21- 22). Prolonged ERP effects were observed in all left ventricles, while the protective effect exhibited a duration of only 1 h (Supplementary Fig. 23). Furthermore, immunofluorescence staining was conducted on LSG tissues to examine the expression of c- fos, tyrosine hydroxylase (TH), and TREK1 (Fig. 6i). The quantitative analysis (Supplementary Fig. 24) revealed a significant decrease in the proportion of c- Fos\(^+\) expression in TH\(^+\) neurons within the PtNP- shell group ( \(8.80 \pm 1.80\) vs. \(44.78 \pm 5.55\) , \(P < 0.001\) ) indicating that PtNP- shell exerted a photothermal inhibitory effect on LSG neurons under NIR- II irradiation. However, the proportion of TREK\(^+\) expression was significantly increased within TH\(^+\) neurons in the PtNP- shell group ( \(83.51 \pm 3.72\) vs. \(57.20 \pm 5.89\) , \(P < 0.01\) ). This increase could lead to hyperpolarization of the cell membrane potential, reduction in neuronal excitability and inhibition of sympathetic nerve activity. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 80, 850, 592]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[150, 604, 850, 784]]<|/det|> +
Fig. 6 | Photothermal inhibition of the sympathetic nervous system by PtNP-shell. a, Location of the canine LSG. b, Schematic illustration of the process of photothermal modulation of LSG. c, Temperature curves of LSG under NIR-II laser irradiation. d, Typical thermal imaging diagram of photothermally modulated activation of LSG. e, Representative images of BP elevation induced after stimulation of LSG with different voltages. Maximal SBP changes of beagle treatment with PtNP-shell or control f, before and g, after NIR-II exposure, \(n = 6\) . h, Quantification of the LSG neural activity recordings, \(n = 6\) . i, Representative immunofluorescent images of TH (red), c-fos (green) and TREK1 (pink) in the LSG of beagles following different treatments. Data are shown as the mean \(\pm\) S.E.M. \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) , ns means that the difference is not statistically significant.
+ +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[147, 84, 850, 101]]<|/det|> +# PtNP-shell photothermal inhibition of LSG improves MI and reduces associated + +<|ref|>sub_title<|/ref|><|det|>[[148, 118, 183, 133]]<|/det|> +## Vas + +<|ref|>text<|/ref|><|det|>[[144, 145, 853, 794]]<|/det|> +To investigate the cardioprotective effect of PtNP- shell photothermal effect in achieving a targeted LSG temperature of approximately \(46.0^{\circ}\mathrm{C}\) , NIR- II light was administered prior to ligation of the LAD coronary artery (Fig. 7a,b). Under NIR- II laser irradiation, the PtNP- shell group exhibited a significantly reduced incidence of sustained VTs (duration \(>30\) s) or VF compared to the control group ( \(16\%\) vs. \(50\%\) ) (Fig. 7c). In the PtNP- shell group, ECG recordings within infarction 1 exhibited a reduced incidence of VAs events compared to the control group, with fewer VPBs recorded in the PtNP- shell group than in the control group ( \(51.50 \pm 5.53\) vs. \(70.83 \pm 5.375\) , \(\mathrm{P} < 0.05\) , Fig. 7d). However, there were no significant differences between the two groups in terms of VT numbers and duration (Supplementary Fig. 25). Additionally, VA inducibility measurements demonstrated that after photothermal neuromodulation with PtNP- shell, there was a decrease in VA score ( \(1.50 \pm 0.76\) vs. \(4.83 \pm 1.14\) , \(\mathrm{P} < 0.05\) ) effective heart protection (Fig. 7e,f). Furthermore, PtNP- shell photothermal inhibition of LSG produced similar protective effects on ventricular electrophysiological index ERP as activation of NG (Fig. 7g,h), and had higher VF threshold than control group ( \(24.33 \pm 4.24\) vs. \(12.33 \pm 3.16\) , \(\mathrm{P} < 0.05\) , Fig. 7i). In addition, the light inhibition of LSG followed the same trend as heart rate variability after activation of NG (Supplementary Fig. 26). These results suggest that PtNP- shell protects against cardiac damage and reduces VAs by modulating the autonomic nervous system, specifically by decreasing sympathetic activity and enhancing parasympathetic tone. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 85, 852, 393]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[148, 405, 852, 567]]<|/det|> +
Fig. 7 | PtNP-shell photothermal inhibition of the sympathetic nervous system improves MI associated VAs. Modulation of LSG to protect against MI and associated VAs a, schematic diagram and b, flowchart. c, Quantitative analysis the ratio of sVT and VF incidence between different groups, \(n = 6\) . d, Quantitative analysis the number of VPBs of beagles. e, Typical images of VA induced by programmed electrical stimulation. f, Quantitative analysis of VAs score in different groups. Effects on ventricular ERP at different sites in Beagles treatment with PtNP-shell or control g, before and h, after MI modelling. i, Quantitative analysis of VF threshold in different groups. Data are shown as the mean \(\pm\) S.E.M. \(*P < 0.05\) , \(**P < 0.01\) , \(***P < 0.001\) .
+ +<|ref|>sub_title<|/ref|><|det|>[[148, 597, 603, 616]]<|/det|> +## Biosafety of PtNP-shell for translational applications + +<|ref|>text<|/ref|><|det|>[[144, 629, 852, 911]]<|/det|> +To validate the biocompatibility of PtNP- shell photothermal modulation on the autonomic nervous system, we conducted rapid excision of LSG and NG tissues followed by hematoxylin and eosin (H&E) staining. As shown in Supplementary Fig. 27a, H&E staining did not reveal any indications of neuronal damage in both the PtNP- shell and control groups for both NG and LSG, indicating that the neuromodulation of PtNP- shell is repeatable. Meanwhile, to further investigate the long- term biosafety of PtNP- shell, a microinjection of \(200 \mu \mathrm{l}\) PtNP- shell (50 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) or PBS was administered into the ganglion of dogs and the tail vein of rats, respectively. After a follow- up period of 30 days, did not reveal any obvious damage in major organs, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 852, 201]]<|/det|> +including the heart, liver, spleen, lungs, and kidneys (Supplementary Fig. 27b,c). Furthermore, blood biochemical analyses indicated the absence of hepatotoxicity or nephrotoxicity (Supplementary Fig. 27d-m). These results unequivocally demonstrate that PtNP-shell exhibits exceptional biocompatibility and long-term biological safety. + +<|ref|>sub_title<|/ref|><|det|>[[148, 234, 263, 253]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[146, 270, 852, 488]]<|/det|> +The PtNP-shell reported in this study exhibits nearly perfect blackbody absorption property, making it an efficient absorber with one of the highest PCE in the NIR-II window (73.7% at 1064 nm). Furthermore, local heating induced by PtNP-shell activation effectively triggers temperature- sensitive ion channels TRPV1 and TREK1, enabling precise and efficient regulation of autonomic nerves. This innovative approach holds great potential for non- invasive treatment of MI and associated VAs, as well as protection against reperfusion injury during interventional therapy. + +<|ref|>text<|/ref|><|det|>[[145, 500, 852, 848]]<|/det|> +The minimal tissue damage caused by light can be disregarded within the maximum permissible exposure (MPE) range, rendering it one of the safest interventions for organisms. The interaction between light and tissue is intricate, and further research could aid in selecting more suitable wavelengths to achieve deeper penetration within the MPE range. Leveraging the nearly impeccable blackbody absorption of PtNP-shell and ultrasound- guided microinjection technology, remote and precise neuromodulation strategies can be developed, holding promise for non- invasive protection against MI and reperfusion injury- associated VAs. The significance of this approach extends beyond VAs as it exhibits broad therapeutic prospects for chronic diseases like refractory hypertension20 and stable atherosclerosis21 due to the wide distribution of autonomic nerves and the universality of nerve regulation. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[110, 84, 279, 101]]<|/det|> +## 1 Online content + +<|ref|>text<|/ref|><|det|>[[108, 115, 853, 234]]<|/det|> +2 Any methods, additional references, Nature Portfolio reporting summaries, source data, 3 extended data, supplementary information, acknowledgements, peer review 4 information; details of author contributions and competing interests; and statements of 5 data availability are available at https://doi.org/10.1038/xxx. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 100, 853, 899]]<|/det|> +2 1 Virani, S. S. et al. Heart disease and stroke statistics—2020 update: a report from the american 3 heart association. Circulation 141, E139-E596 (2020). 4 2 Trayanova, N. A. Learning for prevention of sudden cardiac death. Circul. Res. 128, 185-187 5 (2021). 6 3 Herring, N., Kalla, M. & Paterson, D. J. The autonomic nervous system and cardiac 7 arrhythmias: current concepts and emerging therapies. Nat. Rev. Cardiol. 16, 707-726 (2019). 8 4 Liu, J. S. et al. Antibody-conjugated gold nanoparticles as nanotransducers for second near- 9 infrared photo-stimulation of neurons in rats. Nano Converg. 9, 13 (2022). 10 5 Ye, T. et al. Precise modulation of gold nanorods for protecting against malignant ventricular 11 arrhythmias via near-infrared neuromodulation. Adv. Funct. Mater. 29, 1902128 (2019). 12 6 Zhang, L. et al. AIEgen-based covalent organic frameworks for preventing malignant 13 ventricular arrhythmias via local hyperthermia therapy. Adv. Mater. 35, 2304620 (2023). 14 7 Prescott, E. D. & Julius, D. A modular PIP2 binding site as a determinant of capsaicin receptor 15 sensitivity. Science 300, 1284-1288 (2003). 16 8 Maingret, F. et al. TREK-1 is a heat-activated background \(\mathrm{K^{+}}\) channel. EMBO J. 19, 2483- 17 2491 (2000). 18 9 Grandl, J. et al. Temperature-induced opening of TRPV1 ion channel is stabilized by the pore 19 domain. Nat. Neurosci. 13, 708-714 (2010). 20 10 Zhao, B. et al. Liquid-metal-assisted programmed galvanic engineering of core-shell 21 nanohybrids for microwave absorption. Adv. Funct. Mater. 33, 2302172 (2023). 22 11 Yang, N. L. et al. A general in-situ reduction method to prepare core-shell liquid-metal / metal 23 nanoparticles for photothermally enhanced catalytic cancer therapy. Biomaterials 277, 121125 24 (2021). 25 12 Liu, C. et al. Enhanced energy storage in chaotic optical resonators. Nat. Photonics 7, 474-479 26 (2013). 27 13 Greffet, J. J. et al. Coherent emission of light by thermal sources. Nature 416, 61-64 (2002). 28 14 Mann, D. et al. Electrically driven thermal light emission from individual single-walled carbon 29 nanotubes. Nat. Nanotechnol. 2, 33-38 (2007). 30 15 Granqvist, C. G. Radiative heating and cooling with spectrally selective surfaces. Appl. Opt. 31 20, 2606-2615 (1981). 32 16 Ma, J. X. et al. In vitro model to investigate communication between dorsal root ganglion and 33 spinal cord glia. Int. J. Mol. Sci. 22, 9725 (2021). 34 17 Zyrianova, T. et al. K2P2.1 (TREK-1) potassium channel activation protects against hyperoxia- 35 induced lung injury. Sci. Rep. 10, 22011 (2020). 36 18 Jayaprakash, N. et al. Organ- and function-specific anatomical organization of vagal fibers 37 supports fascicular vagus nerve stimulation. Brain Stimul. 16, 484-506 (2023). 38 19 Zhou, Z. et al. Metabolism regulator adjoncent prevents cardiac remodeling and ventricular 39 arrhythmias via sympathetic modulation in a myocardial infarction model. Basic Res. Cardiol. 40 117, 34 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 850, 159]]<|/det|> +1 20 Mancia, G. & Grassi, G. The autonomic nervous system and hypertension. \*Circul. Res.\* 114, 2 1804–1814 (2014). 3 21 Jiang, Y. Q. \*et al.\* The role of age-associated autonomic dysfunction in inflammation and 4 endothelial dysfunction. \*GeroScience\* 44, 2655–2670 (2022). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[101, 85, 240, 104]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[101, 123, 241, 140]]<|/det|> +## Chemicals + +<|ref|>text<|/ref|><|det|>[[144, 152, 853, 600]]<|/det|> +The gallium and indium were purchased from Shanghai Minor Metals Co., Ltd. Anhydrous ethanol \((\geq 99.7\%)\) and KOH (AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. \(\mathrm{Na_2PtCl_6}\) ( \(98\%\) ) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. mPEG- \(\mathrm{SH}_{5000}\) was purchased from Shanghai Macklin Biochemical Co., Ltd. STR- identified correct HT- 22 cells or human embryonic kidney 293T (HEK- 293T) cells were purchased with the corresponding specialized cell culture media (Procell, Wuhan, China). Anti- NF1, anti- c- fos, anti- TRPV1 antibodies used in western blot and immunofluorescence staining and anti- TREK1 antibody used in immunofluorescence staining were purchased from ABclonal (Wuhan, China). Anti- TREK1 antibody used in western blot was purchased from Santa Cruz Biotechnology (Texas, U.S.). Glyceraldehyde 3- phosphate dehydrogenase (GAPDH) was purchased from Abcam (Cambridge, England). Serum troponin I (c- TnI) and myoglobin (MYO) were purchased from Mibio (Shanghai, China). 4,6- diamidino- 2- phenylindole (DAPI) was purchased from Servicebio (Wuhan, China). + +<|ref|>sub_title<|/ref|><|det|>[[148, 624, 256, 640]]<|/det|> +## Instruments + +<|ref|>text<|/ref|><|det|>[[144, 654, 853, 904]]<|/det|> +The morphology of PtNP- shell was characterized by a F200 transmission electron microscope (TEM) (JEOL, Japan) operated at \(200\mathrm{kV}\) . STEM and HRTEM images were obtained by a JEM- ARM200CF (JEOL, Japan) at \(200\mathrm{kV}\) . The EDX elemental mapping was carried using the JEOL SDD- detector with two \(100\mathrm{mm}^2\) X- ray sensor. X- ray diffraction (XRD) patterns were performed on an SmartLab 9kW X- ray powder diffractometer (Rigaku, Japan). XPS measurements were carried out with a ESCALAB 250Xi spectrometer (Thermo Fisher Scientific, U.S.) under vacuum. Ultraviolet- visible- near- infrared light (UV- Vis- NIR) absorption spectra was collected using a UV + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 82, 853, 498]]<|/det|> +3600 spectrophotometer (Shimadzu, Japan). Zeta potential (Z) and dynamic light scattering (DLS) were recorded using a Zetasizer Nano ZSP (Malvern Panalytical, U.K.). The fluorescence microscopy images of HT- 22 cells were acquired by FV3000 Microscope (Olympus, Japan), excited with 488 nm laser. Beagle's respiration is maintained by a WATO EX- 20VET ventilator (Mindray, Shenzhen, China). ECG and blood pressure data were recorded by a Lead 7000 Computerized Laboratory System (Jinjiang, Chengdu, China). NIR- II light at 1064 nm is generated by LWIRPD- 1064- 5F laser (Laserwave, Beijing, China). Thermal imaging was obtained by FLIR C2 thermal imager (FLIR, U.S.). High- frequency electrical stimulation was performed by Grass stimulator (Astro- Med; West Warwick, RI, U.S.) The electrical signals of autonomic nerves are recorded by Power Lab data acquisition system (AD Instruments, New South Wales, Australia). Serum biochemical indices were determined by a fully automatic biochemical analyzer BK- 1200 (BIOBASE, Jinan, China). + +<|ref|>sub_title<|/ref|><|det|>[[148, 520, 319, 537]]<|/det|> +## Synthesis of GaNPs + +<|ref|>text<|/ref|><|det|>[[144, 551, 853, 735]]<|/det|> +The GaNPs were obtained by sonication of liquid Ga. The liquid Ga (300 mg) was transferred to anhydrous ethanol (8 mL), and the solution was sonicated by nanoprobe sonication for 1 h (3 seconds on and 3 seconds off) at the power of 290 W. Then the ethanol was replaced with Milli- Q water to continue sonication for 1 h. The solution at the end of sonication was collected and centrifuged at 1000 rpm for 5 min, and the upper liquid layer was aspirated for later use. + +<|ref|>sub_title<|/ref|><|det|>[[148, 758, 350, 776]]<|/det|> +## Synthesis of PtNP-shell + +<|ref|>text<|/ref|><|det|>[[144, 789, 852, 907]]<|/det|> +First, the GaNPs and 3 mL \(\mathrm{Na_2PtCl_6}\) (0.1 M) were evacuated for 30 min and Ar was introduced for 15 min. Then, 3 mL \(\mathrm{Na_2PtCl_6}\) (0.1 M) was added dropwise to GaNPs and the solution was stirred for 4 h. After reaction, the solution was collected and centrifuged at 9000 rpm for 10 min. The solids at the bottom were washed with Milli + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 721, 101]]<|/det|> +1 Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) Milli- Q for later use. + +<|ref|>sub_title<|/ref|><|det|>[[146, 125, 589, 144]]<|/det|> +## Functionalization of PtNP-shell with mPEG-SH5000 + +<|ref|>text<|/ref|><|det|>[[144, 156, 853, 504]]<|/det|> +3 The PtNP- shell was first covered with a small amount of mPEG- SH to protect the structure from KOH. \(30\mathrm{mg}\) mPEG- SH5000 was added to \(6\mathrm{ml}\) PtNP- shell and the solution was stirred for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged at \(9000\mathrm{rpm}\) for \(10\mathrm{min}\) . The solids at the bottom were washed with Milli- Q water for 3 times and dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(12\mathrm{mL}\) of KOH (1 M) for \(4\mathrm{h}\) . The reaction- completed solution was collected and centrifuged at \(9000\mathrm{rpm}\) for \(10\mathrm{min}\) , and the solids at the bottom were washed three times with Milli- Q water and finally dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(60\mathrm{mg}\) mPEG- SH5000 for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged. The solids at the bottom were washed with Milli- Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) PBS. + +<|ref|>sub_title<|/ref|><|det|>[[145, 528, 585, 547]]<|/det|> +## Synthesis of Ga-In alloy nanoparticles (GaIn NPs) + +<|ref|>text<|/ref|><|det|>[[144, 559, 852, 777]]<|/det|> +The liquid EGaIn was prepared by physically mixing \(75\mathrm{wt}\%\) gallium and \(25\mathrm{wt}\%\) indium at \(200^{\circ}\mathrm{C}\) for \(2\mathrm{h}\) . The liquid EGaIn (300 mg) was transferred to anhydrous ethanol ( \(8\mathrm{mL}\) ), and the solution was sonicated by nanoprobe sonication for \(1\mathrm{h}\) (3 seconds on and 3 seconds off) at the power of \(290\mathrm{W}\) . Then the ethanol was replaced with Milli- Q water to continue sonication for \(1\mathrm{h}\) . The solution at the end of sonication was collected and centrifuged at \(1000\mathrm{rpm}\) for \(5\mathrm{min}\) , and the upper liquid layer was aspirated and set aside. + +<|ref|>sub_title<|/ref|><|det|>[[146, 800, 382, 818]]<|/det|> +## Synthesis of GaIn@Pt NPs + +<|ref|>text<|/ref|><|det|>[[144, 831, 852, 881]]<|/det|> +1 mL \(\mathrm{Na_2PtCl_6}\) ( \(0.1\mathrm{M}\) ) was added dropwise to GaIn NPs and the solution was stirred for \(4\mathrm{h}\) . After reaction, the solution was collected and centrifuged at \(9000\mathrm{rpm}\) for 10 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 82, 852, 202]]<|/det|> +1 min, washed 3 times with Milli- Q water and dispersed in \(6\mathrm{mL}\) Milli- Q water. The above solution was stirred with \(60\mathrm{mg}\) mPEG- SH \(_{5000}\) for \(12\mathrm{h}\) . After the reaction, the solution was collected and centrifuged. The solids at the bottom were washed with Milli- Q water for 3 times and finally dispersed in \(6\mathrm{mL}\) PBS. + +<|ref|>sub_title<|/ref|><|det|>[[147, 224, 612, 243]]<|/det|> +## Calculation of the photothermal conversion efficiency + +<|ref|>text<|/ref|><|det|>[[147, 255, 852, 341]]<|/det|> +The photothermal conversion of the PtNP- shell has been calculated on the basis of previous work \(^{22,23}\) . The relationship between temperature rise and energy transfer in the system can be described by the Equation S1, + +<|ref|>equation<|/ref|><|det|>[[247, 353, 747, 383]]<|/det|> +\[\Sigma_{i}m_{i}c_{i}\frac{dT}{dt} = Q_{abs} - Q_{ext} = Q_{NPS} + Q_{solvent} - Q_{ext} \quad (S1)\] + +<|ref|>text<|/ref|><|det|>[[145, 394, 852, 580]]<|/det|> +where \(Q_{abs}\) is the total energy absorbed by the system, \(Q_{NPS}\) is the energy absorbed by the nanoparticles, \(Q_{solvent}\) is the energy absorbed by the solvent, \(Q_{ext}\) is the energy loss from the system to the environment. \(m_{i}\) and \(c_{i}\) are the mass and specific heat capacity of the solution, respectively. \(T\) is the solution temperature and \(t\) is the irradiation time. The conversion of the light energy into heat energy can be expressed in terms of Equation S2, + +<|ref|>equation<|/ref|><|det|>[[372, 594, 625, 615]]<|/det|> +\[Q_{NPS} = I(1 - 10^{-A})\eta \quad (S2)\] + +<|ref|>text<|/ref|><|det|>[[145, 628, 852, 714]]<|/det|> +where \(I\) is the laser power, \(A\) is the absorbance value of PtNP- shell at \(1064\mathrm{nm}\) , \(\eta\) is the photothermal conversion efficiency. \(Q_{solvent}\) can be calculated by the following Equation S3, + +<|ref|>equation<|/ref|><|det|>[[333, 728, 663, 749]]<|/det|> +\[Q_{solvent} = hs(T_{solvent} - T_{surr}) \quad (S3)\] + +<|ref|>text<|/ref|><|det|>[[145, 762, 852, 848]]<|/det|> +where \(h\) is the convective heat transfer coefficient and \(s\) is the surface area of the sample cell. \(T_{solvent}\) is the maximum temperature that the solvent can reach under laser irradiation. \(T_{surr}\) is the ambient temperature. \(Q_{ext}\) can also be written as, + +<|ref|>equation<|/ref|><|det|>[[380, 862, 617, 882]]<|/det|> +\[Q_{ext} = hs(T - T_{surr}) \quad S4\] + +<|ref|>text<|/ref|><|det|>[[186, 896, 850, 915]]<|/det|> +The heat output will increase with the increase in temperature when the NIR- II + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 82, 852, 168]]<|/det|> +laser power is determined according to formula S4. The temperature of the system will reach the maximum when the heat input is equal to the heat output, so the following equation can be obtained, + +<|ref|>equation<|/ref|><|det|>[[265, 179, 730, 201]]<|/det|> +\[Q_{NPs} + Q_{solvent} = Q_{ext - max} = hs(T_{max} - T_{surr}) \quad \mathrm{S5}\] + +<|ref|>text<|/ref|><|det|>[[144, 214, 850, 303]]<|/det|> +where \(Q_{ext - max}\) is the heat transferred from the system surface through the air when the sample cell reaches equilibrium temperature, and \(T_{max}\) is the equilibrium temperature. Combining equations S2, S3 and S5, \(\eta\) can be expressed as, + +<|ref|>equation<|/ref|><|det|>[[249, 312, 745, 346]]<|/det|> +\[\eta = \frac{hs(T_{max} - T_{surr}) - hs(T_{solvent} - T_{surr})}{l(1 - 10^{-A})} = \frac{hs(T_{max} - T_{solvent})}{l(1 - 10^{-A})} \quad \mathrm{S6}\] + +<|ref|>text<|/ref|><|det|>[[144, 360, 850, 411]]<|/det|> +where \(A\) is the PtNP- shell absorption at \(1064\mathrm{nm}\) . To obtain \(hs\) , the dimensionless temperature \(\theta\) is introduced, + +<|ref|>equation<|/ref|><|det|>[[410, 424, 586, 456]]<|/det|> +\[\theta = \frac{T - T_{surr}}{T_{max} - T_{surr}} \quad \mathrm{S7}\] + +<|ref|>text<|/ref|><|det|>[[100, 470, 480, 489]]<|/det|> +and a time constant of sample system, \(\tau_{s}\) + +<|ref|>equation<|/ref|><|det|>[[424, 500, 573, 530]]<|/det|> +\[\tau_{s} = \frac{\sum_{i}m_{i}c_{i}}{hs} \quad \mathrm{S8}\] + +<|ref|>text<|/ref|><|det|>[[185, 543, 846, 564]]<|/det|> +Combining Equations S1, S4, S7 and S8, the following equation can be obtained, + +<|ref|>equation<|/ref|><|det|>[[357, 575, 637, 610]]<|/det|> +\[\frac{d\theta}{dt} = \frac{1}{\tau_{s}}\left[\frac{Q_{NPs} + Q_{solvent}}{hs(T_{max} - T_{surr})} -\theta \right] \quad \mathrm{S9}\] + +<|ref|>text<|/ref|><|det|>[[100, 620, 848, 672]]<|/det|> +After the laser is turned off, in the cooling stage, there is no external input energy, \(Q_{NPs} + Q_{solvent} = 0\) , and equation S9 can be written as, + +<|ref|>equation<|/ref|><|det|>[[413, 684, 583, 716]]<|/det|> +\[dt = -\tau_{s}\frac{d\theta}{\theta} \quad \mathrm{S10}\] + +<|ref|>text<|/ref|><|det|>[[144, 728, 710, 748]]<|/det|> +By integrating Equation S10, the following equation can be obtained, + +<|ref|>equation<|/ref|><|det|>[[415, 760, 579, 780]]<|/det|> +\[t = -\tau_{s}ln\theta \quad \mathrm{S11}\] + +<|ref|>text<|/ref|><|det|>[[144, 794, 850, 916]]<|/det|> +Therefore, the system heat transfer time constant \((\tau_{s})\) at \(1064\mathrm{nm}\) is \(242.25\mathrm{s}\) (Figure 3f). In addition, m is \(0.3\mathrm{g}\) and c is \(4.2\mathrm{J}\cdot \mathrm{g}^{- 1}\) . Therefore, \(hs\) can be determined from Equation S8. The laser power \((I)\) used here can be determined as 1 W. Then the photothermal conversion efficiency \((\eta)\) of the PtNP- shell at \(1064\mathrm{nm}\) can be calculated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 84, 544, 102]]<|/det|> +to be \(73.7\%\) by substituting \(hs\) into Equation S6. + +<|ref|>sub_title<|/ref|><|det|>[[147, 127, 461, 144]]<|/det|> +## Animal preparation and cell culture + +<|ref|>text<|/ref|><|det|>[[144, 157, 852, 475]]<|/det|> +All animal experiments were approved by the Animal Care and Use Committee of Renmin Hospital of Wuhan University (WDRM20230805A). All experimental procedures were in accordance with the Declaration of Helsinki and were conducted according to the guidelines established by the National Institutes of Health. All Beagles \((8 - 12\mathrm{kg})\) were anesthetized intravenously with \(3\%\) sodium pentobarbital \((30\mathrm{mg}\cdot \mathrm{kg}^{- 1}\) induction dose, \(2\mathrm{mg}\cdot \mathrm{kg}^{- 1}\) maintenance dose per hour) and respiration was maintained by endotracheal intubation using a ventilator. Arterial blood pressure was continuously monitored through femoral artery catheterization with a pressure transducer attached. ECG and blood pressure data were recorded throughout the procedure. A heating pad was used to maintain core body temperature at \(36.5\pm 0.5^{\circ}\mathrm{C}\) + +<|ref|>text<|/ref|><|det|>[[144, 487, 850, 537]]<|/det|> +The cells were cultured in a humid incubator containing \(5\% \mathrm{CO}_2\) at a temperature of \(37.0^{\circ}\mathrm{C}\) + +<|ref|>sub_title<|/ref|><|det|>[[144, 561, 693, 579]]<|/det|> +## Detection of TRPV1 and TREK1 expression in vitro and in vivo + +<|ref|>text<|/ref|><|det|>[[144, 592, 852, 842]]<|/det|> +Western blotting was used to assess the expression of TRPV1 and TREK1 in neuronal cells and ganglion tissues. HT- 22 cells or HEK- 293T cells were cultured in six- well plates for \(24 - 48\mathrm{h}\) , then lysed and centrifuged to collect cells. Ganglion tissues were obtained from deceased animals and frozen in liquid nitrogen or stored at \(- 80.0^{\circ}\mathrm{C}\) . Total protein was determined using BCA protein assay reagent after tissue grinded and cells lysed. Afterwards, the procedure was followed according to the manufacturer's instructions. Primary antibodies were anti- TRPV1 and anti- TREK1. Expression levels of specific proteins were normalized to GAPDH. + +<|ref|>sub_title<|/ref|><|det|>[[147, 867, 444, 884]]<|/det|> +## Calcium imaging of neuronal cells + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 81, 853, 595]]<|/det|> +The effect of PtNP- shell photothermal modulation on ion channels in HT- 22 cells was explored through calcium imaging experiments. HT- 22 cells were incubated in \(35\mathrm{mm}\) confocal dishes for \(24\mathrm{h}\) . Cells were washed 3 times with PBS and then stained with 5 \(\mu \mathrm{M}\) Fluo- 4 AM (dilution ratio 1:500) for \(30\mathrm{min}\) in a cell incubator at \(37.0^{\circ}\mathrm{C}\) , protected from light. To induce activation of TRPV1 and TREK1 ion channels, which had been previously studied \(^{7,8}\) , the culture dish was exposed to NIR- II light ( \(1064\mathrm{nm}\) ), resulting in an elevation of temperature. TRPV1, being a calcium channel, exhibited observable changes in the flow of calcium ions upon activation, while TREK1 as a potassium channel did not display such behavior. Therefore, the effect of PtNP- shell photothermal modulation on neuronal cells via TREK1 was observed by introducing a \(15\mathrm{mM}\) KCl solution prior to NIR- II irradiation. Fluorescence signals at \(525\mathrm{nm}\) were recorded using a confocal microscope with \(488\mathrm{nm}\) as the excitation wavelength. XYT images were acquired and collected under a \(20\mathrm{x}\) objective lens. The average fluorescence intensity of the cells was analyzed using ImageJ software (Fiji). The normalized fluorescence change was calculated as follows: \(\Delta \mathrm{F} / \mathrm{F} = (\mathrm{F - F_0}) / \mathrm{F_0}\) , where F is the original fluorescence signal; \(\mathrm{F_0}\) is the average baseline intensity before irradiation with NIR- II laser. + +<|ref|>sub_title<|/ref|><|det|>[[148, 617, 369, 635]]<|/det|> +## In vitro cytotoxicity assay + +<|ref|>text<|/ref|><|det|>[[144, 648, 853, 900]]<|/det|> +The cytotoxicity of PtNP- shell on neuronal cells was evaluated by CCK- 8 assay. HT- 22 cells were seeded in 96- well plates at a density of \(1 \times 10^{4}\) well \(^{- 1}\) and cultured for 24 h. HT- 22 cells were then treated with different concentrations (10, 25, 50, 100, 150, 200 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) of PtNP- shell for another 24 h. Cell viability was determined by CCK- 8 assay after incubating with the CCK- 8 reagent for 1 h. To investigate the impact of PtNP- shell's photothermal effect on neuron cell viability, HT- 22 cells were co- cultured with PtNP- shell ( \(50 \mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) for 12 h followed by irradiation with a \(1064\mathrm{nm}\) laser (0.5 and \(0.75\mathrm{W} \cdot \mathrm{cm}^{- 2}\) ) for various durations (10 s, 30 s and 60 s). After incubation again for 12 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 83, 850, 135]]<|/det|> +h, the absorbance at \(450 \mathrm{nm}\) was recorded using a microplate reader. Cell survival (\%) \(= (OD_{\text{samples}} - OD_{\text{blank}}) / (OD_{\text{control}} - OD_{\text{blank}}) \times 100\%\) . + +<|ref|>sub_title<|/ref|><|det|>[[147, 158, 849, 210]]<|/det|> +## Experimental protocol 1: Activation of the parasympathetic nervous system through PtNP-shell photothermal reduces I/R injury + +<|ref|>text<|/ref|><|det|>[[144, 222, 852, 440]]<|/det|> +Part 1: Exploring the in vivo effects of precise photothermal stimulation of the parasympathetic nervous system by PtNP- shell under NIR- II irradiation. Twelve beagles were randomly assigned to the control group ( \(100 \mu \mathrm{L}\) phosphate- buffered saline (PBS) was microinjected into the NG, \(\mathrm{n} = 6\) ) and the PtNP- shell group ( \(100 \mu \mathrm{L}\) PtNP- shell ( \(50 \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) was microinjected into the NG, \(\mathrm{n} = 6\) ). NG nerve activity, heart rate (HR) and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR- II irradiation (Fig 4b). + +<|ref|>text<|/ref|><|det|>[[144, 453, 852, 635]]<|/det|> +Part 2: The protective effect of PtNP- shell activation of the parasympathetic nervous system against myocardial I/R injury was investigated. The same grouping pattern as in part1 was used, with 5- min NIR- II irradiation of the NG before opening the occluded LAD coronary vessel. Afterwards, ventricular electrophysiological parameters, heart rate variability (HRV) and ECG data were recorded and analyzed (Fig 5b). + +<|ref|>sub_title<|/ref|><|det|>[[147, 659, 849, 709]]<|/det|> +## Experimental protocol 2: PtNP-shell photothermal inhibition of sympathetic nervous system improves MI + +<|ref|>text<|/ref|><|det|>[[144, 722, 852, 907]]<|/det|> +Part 1: The in vivo effects of precise photothermal stimulation of the sympathetic nervous system by PtNP- shell under NIR- II irradiation were explored. Twelve beagles were randomly assigned to the control group ( \(100 \mu \mathrm{L}\) PBS microinjected into the LSG, \(\mathrm{n} = 6\) ) and the PtNP- shell group ( \(100 \mu \mathrm{L}\) PtNP- shell ( \(50 \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) microinjected into the LSG, \(\mathrm{n} = 6\) ). LSG nerve activity, SBP and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR- II + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 310, 101]]<|/det|> +irradiation (Fig 6b). + +<|ref|>text<|/ref|><|det|>[[144, 115, 852, 268]]<|/det|> +Part 2: To investigate the protective effect of PtNP- shell inhibition of the sympathetic nervous system a improves MI. The same grouping pattern as in part1 was used, with 5- min NIR- II irradiation of the LSG before ligation of LAD vessels. Finally, ventricular electrophysiological parameters, HRV and ECG data were also recorded and analyzed (Fig 7b). + +<|ref|>sub_title<|/ref|><|det|>[[144, 290, 815, 309]]<|/det|> +## PtNP-shell photothermal stimulation of the autonomic nervous system in vivo + +<|ref|>text<|/ref|><|det|>[[144, 319, 852, 835]]<|/det|> +We selected NG and LSG as targets for modulation in the autonomic nervous system to explore the multifunctionality of the PtNP- shell photothermal strategy. A "C" incision is made behind the left ear, and the angle between the occlusal and trapezius muscles served as the access approach24. The tissue is bluntly separated to expose the carotid sheath and identify the parasympathetic nerve. Moving upstream along the nerve, a distal expansion is observed as NG (Fig 4a). LSG can be visualized and localized by left- sided thoracotomy according to the method of a previous study (Fig 6a)25. PtNP- shell (50 \(\mu \mathrm{g} \cdot \mathrm{mL}^{- 1}\) ) or PBS was slowly injected into 2 sites within the NG and LSG tissues to achieve homogeneous photothermal conversion. Initial vertical irradiation of NIR- II laser (1064 nm) at 0.80 W·cm- 2 was performed on NG and LSG surfaces. The power density of the NIR- II laser was reduced to 0.45 W·cm- 2 for continuous irradiation when the temperature of the NG reached 42.0 °C, and was reduced to 0.6 W·cm- 2 for continuous irradiation when the temperature of the LSG reached 46.0 °C. The NIR- II laser irradiation remains stable with a spot size maintained at 1.0 cm- 2. Dual temperature monitoring using thermal imager and T- type thermocouple was performed to plot the temperature- time curve. + +<|ref|>sub_title<|/ref|><|det|>[[147, 857, 522, 874]]<|/det|> +## Functional assessment of autonomic nerves + +<|ref|>text<|/ref|><|det|>[[144, 889, 848, 908]]<|/det|> +The NG is a ganglion located upstream of the cervical parasympathetic nerve and can + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 81, 853, 530]]<|/det|> +significantly inhibit HR after receiving direct electrical stimulation18. The LSG, as an important peripheral sympathetic ganglion, can rapidly elevate blood pressure when activated by electrical stimulation. Based on the functional properties of different autonomic ganglia, we assessed the function of NG and LSG with reference to previous studies19. A pair of special electrodes made with silver wires were directly connected to the surfaces of NG and LSG for stimulation. High- frequency electrical stimulation (HFS: 20 Hz, 0.1 ms) was applied to the ganglion. The voltage was set to 5 levels in continuous increments (level 1: 0–2 V; level 2: 2–4 V; level 3: 4–6 V; level 4: 6–8 V; level 5: 8–10 V), while keeping the stimulation voltage values consistent with the baseline at different time points during the experiment. The percentage of sinus rate or AV conduction (measured by the A-H interval) slowing down constructed voltage level/degree of HR decrease curves reflecting NG function. On the other hand, the percentage increase in SBP built the voltage level/degree of SBP increase to reflect LSG function. + +<|ref|>sub_title<|/ref|><|det|>[[148, 553, 460, 570]]<|/det|> +## Activity testing of autonomic nerves + +<|ref|>text<|/ref|><|det|>[[144, 583, 852, 800]]<|/det|> +The activity of different autonomic nerves was assessed based on previous studies19. Two specially designed microelectrodes were inserted into the NG and LSG, respectively, while a grounding wire was connected to obtain signals from the autonomic nerves. These electrical signals were recorded by a Power Lab data acquisition system, filtered through a band- pass filter (300–1000 Hz) and amplified 30- 50 times by an amplifier. Finally, the signals were digitized and analyzed in LabChart software (version 8.0, AD Instruments). + +<|ref|>sub_title<|/ref|><|det|>[[147, 823, 666, 842]]<|/det|> +## Construction of myocardial I/R injury model and MI model + +<|ref|>text<|/ref|><|det|>[[147, 855, 850, 907]]<|/det|> +The left anterior descending coronary occlusion (LADO) method was used to establish the MI model19. The ligation site was located beneath the first diagonal of the LAD, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 83, 851, 202]]<|/det|> +successful MI model was confirmed by observing ST- segment elevation on the ECG. After ensuring cardiac electrophysiological stabilization, the junction was released to reperfuse the occluded coronary arteries, completing the construction of the myocardial I/R injury model26. + +<|ref|>sub_title<|/ref|><|det|>[[148, 225, 538, 243]]<|/det|> +## Ventricular electrophysiological study in vivo + +<|ref|>text<|/ref|><|det|>[[144, 254, 852, 770]]<|/det|> +The cardiac electrophysiological measurements were performed in Beagles using a previously studied protocol27,28. The ERP was measured at three locations: LVA, LVB, LVM (located between the LVA and LVB). Malignant arrhythmic events caused by MI and I/R injury were assessed by electrocardiographic recordings in a canine model using Lead 7000 Computerized Laboratory System. VAs was classified according to Lambeth Conventions as VPBs, VT (three and more consecutive VPBs) and \(\mathrm{VF}^{29}\) . In addition, arrhythmia inducibility was further assessed by programmed ventricular stimulation at the right ventricular apex (RVA). Eight consecutive stimuli (S1S1) were performed at intervals of 330 ms, followed by additional stimuli until VT/VF occurred. Arrhythmia inducibility was assessed based on a modified arrhythmia scoring system28. If VF occurs during the evaluation, a defibrillator is required to restore sinus rhythm, followed by a waiting period of 30 min to restore cardiac electrophysiological stability. The VF threshold was assessed in the perimyocardial infarction region. Pacing was initiated using a Grass stimulator with a voltage of 2 V (20 Hz, 0.1 ms duration, 10 s). The stimulation voltage was increased in 2 V increments until VF was induced. The lowest voltage that induced VF was regarded as the VF threshold30. + +<|ref|>sub_title<|/ref|><|det|>[[147, 790, 268, 807]]<|/det|> +## HRV analysis + +<|ref|>text<|/ref|><|det|>[[147, 821, 850, 909]]<|/det|> +The ECG data was recorded using the PowerLab data acquisition system. And the ECG segments recorded more than 5 min before modulation and after MI or I/R injury were analyzed by LabChart software with the Lomb- Scargle periodogram algorithm31. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 83, 853, 168]]<|/det|> +Frequency domain metrics of HRV were calculated, including LF (0.04–0.15 Hz, reflecting sympathetic tone), HF (0.15–0.4 Hz, reflecting parasympathetic tone) and LF/HF (reflecting autonomic balance). The results were expressed in standardized units. + +<|ref|>sub_title<|/ref|><|det|>[[150, 192, 650, 210]]<|/det|> +## Immunofluorescence staining of histopathological sections + +<|ref|>text<|/ref|><|det|>[[144, 223, 852, 440]]<|/det|> +The ganglions were rapidly dissected for histopathological staining after the experimental animals died. Tissues were fixed with \(4\%\) paraformaldehyde, embedded in paraffin, and cut into \(5\mu \mathrm{m}\) - thick sections. NG was stained with multiple immunofluorescence staining using anti- NF1, anti- c- fos and anti- TRPV1 antibodies. And LSG was stained by multiple immunofluorescences using anti- TH, anti- c- fos and anti- TREK1 antibody. Cell nuclei were stained with DAPI. Images were taken at \(100\times\) magnification and analyzed using ImageJ software (Fiji). + +<|ref|>sub_title<|/ref|><|det|>[[147, 463, 552, 481]]<|/det|> +## Enzyme-linked immunosorbent assay (ELISA) + +<|ref|>text<|/ref|><|det|>[[144, 494, 852, 711]]<|/det|> +\(5\mathrm{ml}\) of venous blood was obtained from the jugular vein of each beagle after MI and myocardial I/R injury. After standing for 1 hour, the blood was centrifuged at \(3000\mathrm{rpm}\) for \(15\mathrm{min}\) . The upper serum layer was collected and stored at \(- 80.0^{\circ}\mathrm{C}\) . Myocardial injury levels were detected by c- TnI and myoglobin (MYO). Standard process analyses were performed according to the instructions of each ELISA kit. To evaluate the long- term biosafety and biocompatibility of PtNP- shell in vivo, Beagle dogs and rats were randomly divided into PtNP- shell and PBS groups. + +<|ref|>sub_title<|/ref|><|det|>[[147, 735, 437, 752]]<|/det|> +## Long-term biosafety assay in vivo + +<|ref|>text<|/ref|><|det|>[[144, 765, 852, 885]]<|/det|> +To evaluate the long- term biosafety and biocompatibility of PtNP- shell in vivo, Beagle dogs and rats were randomly divided into two groups: a PtNP- shell group and a PBS group. In the PtNP- shell group, \(200\mu \mathrm{L}\) PtNP- shell ( \(50\mu \mathrm{g}\cdot \mathrm{mL}^{- 1}\) ) was microinjected into canine ganglion tissue and tail vein of rats to explore long- term biosafety. Blood + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 83, 852, 234]]<|/det|> +1 and tissue samples were collected from each dog and rat one month after injection. One 2 month after injection, blood samples were collected from the jugular vein of dogs as 3 well as from the inferior vena cava of rats for analysis of serum biochemical indices. 4 Tissue H&E staining was also performed on major organs, including heart, liver, spleen, 5 lung and kidney. + +<|ref|>sub_title<|/ref|><|det|>[[147, 258, 310, 275]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[144, 289, 852, 473]]<|/det|> +7 All graphical data are presented as mean \(\pm\) standard error of the mean (SEM), and the 8 distribution of data was assessed by the Shapiro- Wilk test. Differences between groups 9 were determined using Student's t- test or Mann- Whitney U- test. Data were analyzed 10 and plotted using GraphPad Prism 9.0 software (GraphPad software, Inc., La Jolla, CA, 11 USA). \(\mathrm{P}< 0.05\) was considered statistically different. The p- values are indicated with 12 an asterisk \((*\mathrm{p}< 0.05,^{**} \mathrm{p}< 0.01,^{***} \mathrm{p}< 0.001)\) . + +<|ref|>sub_title<|/ref|><|det|>[[147, 496, 328, 514]]<|/det|> +## Reporting Summary + +<|ref|>text<|/ref|><|det|>[[144, 528, 850, 579]]<|/det|> +14 Further information on research design is available in the Nature Portfolio Reporting 15 Summary linked to this article. + +<|ref|>sub_title<|/ref|><|det|>[[147, 603, 293, 620]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[144, 634, 852, 753]]<|/det|> +17 The main data supporting the results in this study are available within the paper and its 18 Supplementary Information. The raw and analyzed datasets generated during the study 19 are too large to be publicly shared, yet they are available for research purposes from the 20 corresponding authors on reasonable request. Source data are provided with this paper. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[108, 84, 245, 101]]<|/det|> +## 1 References + +<|ref|>text<|/ref|><|det|>[[100, 115, 853, 576]]<|/det|> +2 Chechetka, S. A. et al. Light- driven liquid metal nanotransformers for biomedical theranostics. Nat. Commun. 8, 15432 (2017). 3 Zhu, P. et al. Inorganic nanoshell- stabilized liquid metal for targeted photonanomedicine in NIR- II biowindow. Nano Lett. 19, 2128- 2137 (2019). 4 Bruneau, M. & George, B. The juxtacondylar approach to the jugular foramen. Oper. Neurosurg. 63, 75- 80 (2008). 5 Zhang, S. et al. Ultrasound- guided injection of botulinum toxin type A blocks cardiac sympathetic ganglion to improve cardiac remodeling in a large animal model of chronic myocardial infarction. Heart Rhythm 19, 2095- 2104 (2022). 6 Chen, M. X. et al. Low- level vagus nerve stimulation attenuates myocardial ischemic reperfusion injury by antioxidative stress and antiapoptosis reactions in canines. J. Cardiovasc. Electrophysiol. 27, 224- 231 (2016). 7 Yu, L. L. et al. Optogenetic modulation of cardiac sympathetic nerve activity to prevent ventricular arrhythmias. J. Am. Coll. Cardiol. 70, 2778- 2790 (2017). 8 Yu, L. et al. Chronic intermittent low- level stimulation of tragus reduces cardiac autonomic remodeling and ventricular arrhythmia inducibility in a post- infarction canine model. JACC Clin. Electrophysiol. 2, 330- 339 (2016). 9 Walker, M. J. A. et al. The lambeth conventions: guidelines for the study of arrhythmias in ischaemia, infarction, and reperfusion. Cardiovasc. Res. 22, 447- 455 (1988). 10 Dalonzo, A. J. et al. Effects of cromakalim or pinacidil on pacing- and ischemia- induced ventricular fibrillation in the anesthetized pig. Basic Res. Cardiol. 89, 163- 176 (1994). 11 Lai, Y. et al. Non- invasive transcutaneous vagal nerve stimulation improves myocardial performance in doxorubicin- induced cardiotoxicity. Cardiovasc. Res. 118, 1821- 1834 (2022). + +<|ref|>sub_title<|/ref|><|det|>[[147, 590, 317, 606]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[144, 621, 853, 840]]<|/det|> +The research was supported by the National Natural Science Foundation of China (grants 22025303, 82241057, 82270532 and 82200556); and the National Key Research and Development Program of China (grant 2023YFC2705705); and Foundation for Innovative Research Groups of Natural Science Foundation of Hubei Province, China (grant 2021CFA010). We thank the Core Facility of Wuhan University for their substantial supports in sample characterization, including SEM, XPS, DLS and XRD. We thank the Center for Electron Microscopy at Wuhan University for their support of STEM, HRTEM and EDX characterization. We also thank Meimei Zhang in the institute for advanced studies of Wuhan University for their assistance in TEM characterization. + +<|ref|>sub_title<|/ref|><|det|>[[147, 852, 333, 869]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[144, 883, 848, 902]]<|/det|> +L.F., L.L.Y. and X.Y.Z. conceived the research concept. L.F., L.L.Y. and X.Y.Z. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[108, 82, 852, 202]]<|/det|> +1 supervised the research; C.L.W., L.P.Z., C.Z.L., J.M.Q., X.R.H., B.X., Q.F.Q., Z.Z.Z.2 and J.L.W. performed the experiments; C.L.W., L.P.Z., C.Z.L., L.Y.W. and Y.X.L.3 discussed the results; C.L.W., L.P.Z. and C.Z.L. analysed the data and cowrote the4 manuscript. All authors commented on the manuscript. + +<|ref|>text<|/ref|><|det|>[[108, 234, 503, 283]]<|/det|> +5 Competing interests6 The authors declare no competing interests. + +<|ref|>text<|/ref|><|det|>[[108, 316, 852, 536]]<|/det|> +7 Additional information8 Supplementary information The online version contains supplementary material9 available at10 Correspondence and requests for materials should be addressed to Xiaoya Zhou,11 Lilei Yu or Lei Fu12 Peer review information13 Reprints and permissions information is available at + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 365, 149]]<|/det|> +- supplementaryinformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/images_list.json b/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d0c91e155842c5fba86a5153963ee2faea60d3f9 --- /dev/null +++ b/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Fabrication of surface engineered mPOC/HA implants. a. Illustration shows the combination of UV lithography and contact printing to fabricate free-standing mPOC/HA micropillars. b. SEM image shows the micropillar structures made of mPOC/HA. c. Optical microscope image and d. cross-section analysis of mPOC/HA micropillars. e. Surface scanning of flat and micropillar implants by AFM. f. Surface roughness of flat and micropillar implants. N.S., no significant difference, \\(\\mathrm{n} = 3\\) biological replicates. g. Degradation test and h. calcium release of flat and micropillar mPOC/HA implants. N.S., no significant difference, \\(\\mathrm{n} = 4\\) biological replicates, insert plot shows the initial release of calcium within \\(24\\mathrm{h}\\) . i. Representative images of flat and micropillar implants at different time points after accelerated degradation.", + "footnote": [], + "bbox": [ + [ + 120, + 95, + 876, + 599 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Nuclear deformation promotes osteogenic differentiation of hMSCs. a. Staining of nucleus (green) and F-actin (red) of hMSCs on flat and micropillar mPOC/HA surfaces. Insert: high magnification of cell nucleus. Dashed lines indicate micropillars. b. Analysis of nuclear shape index of hMSCs. \\(\\mathrm{n} = 117\\) (flat) and 132 (pillar) collected from 3 biological replicates, \\(\\mathrm{***p< 0.0001}\\) . c. Orthogonal view of cell nucleus on flat and micropillar surfaces. d. Nuclear volume analysis based on 3D construction of the confocal images of cell nuclei. \\(\\mathrm{n} = 35\\) cells collected from 3 biological replicates, \\(\\mathrm{***p< 0.0001}\\) . e. Initial cell attachment on flat and micropillar surfaces. \\(\\mathrm{n} = 5\\) biological replicates, N.S., no significant difference. f. SEM images show the cell attachment on flat and micropillar mPOC/HA surfaces. g. Live/dead staining of hMSCs on flat and micropillar surfaces at 72 h in osteogenic medium. h. Cell metabolic activity of cells on flat and micropillar surfaces tested by a MTT assay. \\(\\mathrm{n} = 5\\) biological replicates, \\(\\mathrm{***p< 0.0001}\\) . i. Cell proliferation tested via DNA content after 72 h induction. \\(\\mathrm{n} = 5\\) biological replicates, N.S., no significant difference. j. ALP staining of hMSCs on flat and micropillar surfaces after 7 d induction. k. ALP activity test of cells after 7 d osteogenic induction. \\(\\mathrm{n} = 3\\) biological replicates. l. Blot images of osteogenic marker OCN and RUNX2 in cells cultured on flat and micropillar implants. GAPDH is shown as a control. Quantification m. OCN and n. RUNX2 according to western blot tests. \\(\\mathrm{n} = 3\\) biological replicates, \\(\\mathrm{***p< 0.0001}\\) .", + "footnote": [], + "bbox": [ + [ + 123, + 93, + 880, + 456 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Secretome of hMSCs on flat and micropillar mPOC/HA surfaces. a. PCA plot of differentially expressed proteins secreted by hMSCs on flat and micropillars. Cyan: flat; Red: micropillar. b. Volcano plot of proteins secreted by hMSCs seeded on micropillars compared to the flat surface. Blue dots and orange dots indicate significantly downregulated and upregulated proteins secreted by cells on micropillars compared to those on flat surface. Grey dots indicate", + "footnote": [], + "bbox": [ + [ + 115, + 90, + 884, + 799 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. The paracrine effect of cells with/without nuclear deformation tested through transwell assay. a. Schematic illustration of the experiment setup. b. ALP staining and c. quantification of ALP positive cells on transwell membrane incubated with undeformed and deformed MSCs \\((n = 3)\\) . d. ARS staining and e. quantification of cells on transwell membrane incubated with undeformed and deformed MSCs \\((n = 6)\\) . f. Immunofluorescence staining images of collagen in ECM of cells on transwell membrane incubated with undeformed and deformed MSCs. g. The coverage of collagen analyzed according to the staining images \\((n = 4)\\) . h. EDS images showing Ca, P, and SEM images of cells on transwell membrane incubated with undeformed and deformed MSCs.", + "footnote": [], + "bbox": [ + [ + 125, + 123, + 870, + 506 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. mPOC/HA micropillar implant promotes bone regeneration in vivo. a. Image shows implantation of hMSC seeded flat and micropillar mPOC/HA scaffolds. b. Staining images of nuclei (green) and F-actin (red) of cells on the implants. c. Representative \\(\\mu \\mathrm{CT}\\) images of a typical animal implanted with hMSC-seeded flat (left) and micropillar (right) scaffolds at 12-weeks post-surgery. d. Regenerated bone volume in the defect region ( \\(\\mathrm{n} = 5\\) animals). e. Trichrome staining of the defect tissue treated with flat and micropillar implants. f. Average thickness of regenerated tissues with implantation of flat and micropillar scaffolds ( \\(\\mathrm{n} = 5\\) animals). IHC staining of osteogenic marker, g. OPN and h. OCN, in regenerated tissues with flat and micropillar implants.", + "footnote": [], + "bbox": [ + [ + 125, + 95, + 875, + 620 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Spatial transcriptomic analysis of tissues regenerated with flat and micropillar implants. a. Spatial plot of Colla2 expression profile in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. Arrow indicates enhanced expression around dura layer. b. The heatmap showing the top ten up- and down-regulated DEGs (pillar vs flat) in tissues regenerated with flat mPOC/HA implant, micropillar mPOC/HA implant, and native skull tissue. c. Gene Ontology analysis results based on the top 100 up-regulated genes (pillar vs flat). d. Deconvoluted cell types in each spatial capture location in flat and micropillar groups. Each pie chart shows the deconvoluted cell type proportions of the capture location. e. Bar plots of the cell type proportions in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. LMPs, MSCs, and fibroblasts are the predominant cell types. f. Violin plot of the proportion of LMPs in flat and micropillar groups. g. Top enriched processes associated with LMP compared with other cell lineages. LMP: late mesenchymal progenitor cells; MSC: mesenchymal stromal cells; OLC: MSC-descendant osteolineage cells", + "footnote": [], + "bbox": [ + [ + 120, + 90, + 877, + 560 + ] + ], + "page_idx": 16 + } +] \ No newline at end of file diff --git a/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313_det.mmd b/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..bc544528e12046c0f7ab076344f3c214795b4917 --- /dev/null +++ b/preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313_det.mmd @@ -0,0 +1,383 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 796, 208]]<|/det|> +# Micropillar-induced changes in cell nucleus morphology enhance bone regeneration by modulating the secretome + +<|ref|>text<|/ref|><|det|>[[44, 230, 323, 277]]<|/det|> +Guillermo Ameer g- ameer@northwestern.edu + +<|ref|>text<|/ref|><|det|>[[42, 300, 630, 950]]<|/det|> +Northwestern University https://orcid.org/0000- 0001- 6023- 048X Xinlong Wang Northwestern University https://orcid.org/0000- 0001- 8978- 2851 Yiming Li Northwestern University https://orcid.org/0000- 0003- 2111- 3939 Zitong Lin Northwestern University Indira Pla Northwestern University Raju Gajjela Northwestern University Basil Mattamana Northwestern University Maya Joshi Northwestern University https://orcid.org/0000- 0002- 6028- 475X Yugang Liu Northwestern University https://orcid.org/0000- 0001- 5304- 3459 Huifeng Wang Northwestern University Amy Zun Northwestern University Hao Wang The University of Chicago Ching Wai Northwestern University Vasundhara Agrawal Northwestern University https://orcid.org/0000- 0003- 0913- 9298 Cody Dunton + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 45, 269, 64]]<|/det|> +Northwestern University + +<|ref|>text<|/ref|><|det|>[[44, 70, 269, 110]]<|/det|> +Chongwen Duan Northwestern University + +<|ref|>text<|/ref|><|det|>[[44, 116, 269, 156]]<|/det|> +Bin Jiang Northwestern University + +<|ref|>text<|/ref|><|det|>[[44, 162, 627, 203]]<|/det|> +Vadim Backman Northwestern University https://orcid.org/0000- 0003- 1981- 1818 + +<|ref|>text<|/ref|><|det|>[[44, 209, 417, 250]]<|/det|> +Tong Chuan He The University of Chicago Medical Center + +<|ref|>text<|/ref|><|det|>[[44, 255, 648, 297]]<|/det|> +Russell Reid Section of Plastic Surgery, The University of Chicago Medical Centre + +<|ref|>text<|/ref|><|det|>[[44, 302, 627, 343]]<|/det|> +Yuan Luo Northwestern University https://orcid.org/0000- 0003- 0195- 7456 + +<|ref|>sub_title<|/ref|><|det|>[[44, 383, 103, 401]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 421, 137, 440]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 459, 319, 479]]<|/det|> +Posted Date: January 7th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 498, 475, 517]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5530535/v1 + +<|ref|>text<|/ref|><|det|>[[44, 535, 912, 578]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 596, 535, 616]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 652, 910, 695]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 11th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 60760-y. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 88, 883, 139]]<|/det|> +## Microtopography-induced changes in cell nucleus morphology enhance bone regeneration by modulating the cellular secretome + +<|ref|>text<|/ref|><|det|>[[115, 147, 883, 242]]<|/det|> +Xinlong Wang \(^{1,2}\) , Yiming Li \(^{3}\) , Zitong Lin \(^{3}\) , Indira Pla \(^{4}\) , Raju Gajjela \(^{4}\) , Basil Baby Mattamana \(^{4}\) , Maya Joshi \(^{1}\) , Yugang Liu \(^{1,2}\) , Huifeng Wang \(^{1,2}\) , Amy B. Zun \(^{1}\) , Hao Wang \(^{5}\) , Ching- Man Wai \(^{6}\) , Vasundhara Agrawal \(^{2,7}\) , Cody L. Dunton \(^{2,7}\) , Chongwen Duan \(^{1,2}\) , Bin Jiang \(^{1,2,8}\) , Vadim Backman \(^{1,2,7,9}\) , Tong- Chuan He \(^{1,5}\) , Russell R. Reid \(^{1,10}\) , Yuan Luo \(^{3,11,12}\) , Guillermo A. Ameer \(^{1,2,7,8,11,13,14*}\) + +<|ref|>text<|/ref|><|det|>[[112, 252, 888, 690]]<|/det|> +\(^{1}\) Center for Advanced Regenerative Engineering, Northwestern University, Evanston, IL 60208, USA \(^{2}\) Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA \(^{3}\) Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{4}\) Proteomics Center of Excellence, Northwestern University, Evanston, IL 60208, USA \(^{5}\) Molecular Oncology Laboratory, Department of Orthopedic Surgery and Rehabilitation Medicine, The University of Chicago Medical Center, Chicago, IL 60637, USA \(^{6}\) Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{7}\) Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL 60208, USA \(^{8}\) Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{9}\) Chemistry of Life Process Institute, Northwestern University, Evanston, IL 60208, USA \(^{10}\) Laboratory of Craniofacial Biology and Development, Section of Plastic and Reconstructive Surgery, Department of Surgery, The University of Chicago Medical Center, Chicago, IL 60637, USA \(^{11}\) Northwestern University Clinical and Translational Sciences Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{12}\) Center for Collaborative AI in Healthcare, Institute for AI in Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{13}\) International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA \(^{14}\) Simpson Querrey Institute for Bionanotechnology, Northwestern University, Chicago, IL 60611, USA + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 91, 190, 107]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[115, 124, 882, 386]]<|/det|> +Nuclear morphology, which modulates chromatin architecture, plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secrete and surrounding cells remains largely unexplored, yet is especially crucial for cell- based therapies. In this study, we fabricated implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar- induced nuclear deformation influences cell paracrine signaling for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei showed enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promoted osteogenic differentiation in neighboring human mesenchymal stromal cells (hMSCs). In a mouse model with critical- size cranial defects, nuclear- deformed hMSCs on micropillar mPOC/HA implants elevated Col1a2 expression, contributing to bone matrix formation, and drove cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars not only enhance the osteogenic differentiation of human mesenchymal stromal cells (hMSCs) but also modulate the secrete, thereby influencing the fate of surrounding cells through paracrine effects. + +<|ref|>sub_title<|/ref|><|det|>[[115, 404, 223, 420]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 437, 882, 733]]<|/det|> +The nucleus is a dynamic organelle that changes its morphology in response to the cell's status. Its morphology has critical influence on nuclear mechanics, chromatin organization, gene expression, cell functionality and disease development.2- 5 Abnormal nuclear morphologies, such as invagination and blebbing, have functional implications in several human disorders, including cancer, accelerated aging, thyroid disorders, and different types of neuro- muscular diseases.6,7 In addition, severe nuclear deformation is also observed during tissue development, cell migration, proliferation, and differentiation.2 Several structural components within the nucleus—including the nuclear envelope, lamins, nuclear actin, and chromatin—work together to determine its shape and structure.8 Although the underlying mechanisms are not yet fully understood, nuclear deformation has been found to affect cell behaviors through mechanotransduction processes.9 In addition, nuclear morphological changes have been reported to affect nuclear membrane tension and unfolding, which regulate the structure of the nuclear pore complex.10 This, in turn, influences the nuclear shuttling of transcription factors (e.g., YAP) and ions (e.g., Ca2+), ultimately impacting cell functions.11,12 In our previous study, we demonstrated that altering nuclear morphology using micropillar topography affects nuclear lamin A/C assembly, which, in turn, influences chromatin tethering, packing, and condensation.13 These changes affect transcriptional accessibility and responsiveness, thereby regulating gene expression and stem cell differentiation. + +<|ref|>text<|/ref|><|det|>[[115, 750, 882, 891]]<|/det|> +To manipulate nuclear morphology, various biophysical tools have been developed, including atomic force microscopy (AFM) nanoindentation, optical, magnetic, and acoustic tweezers, microfluidic devices, micropipette aspiration, plate compression, substrate deformation, and surface topography modulation.14- 21 Among these methods, regulating the surface topography of materials is more accessible and has broader implications for regenerative engineering. One commonly used approach is the fabrication of pillar structures, which are employed to deform cell nuclei and study nuclear properties such as mechanics and deformability.22 These micropillar designs have been utilized to manipulate various cell functions, including migration, adhesion, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 882, 195]]<|/det|> +proliferation, and differentiation. \(^{23 - 26}\) A wide range of materials can be used to create these structures, such as poly- L- lactic acid (PLLA), poly(lactide- co- glycolide) (PLGA), OrmoComp (an organic- inorganic hybrid polymer), and methacrylated poly(octamethylene citrate) (mPOC). \(^{13,26 - 28}\) Among these options, mPOC is particularly suitable for bone regeneration due to its major component, citrate, which acts as a metabolic factor to enhance the osteogenesis of mesenchymal stromal cells (MSCs). \(^{29}\) + +<|ref|>text<|/ref|><|det|>[[115, 211, 882, 578]]<|/det|> +Although the influence of nuclear morphogenesis on the functions of individual cells is being intensively investigated, its role in regulating cellular secretion remains unclear. Bioactive molecules secreted by cells are crucial for intercellular communication, affecting various biological processes such as inflammation, cell survival, differentiation, and tissue regeneration. \(^{30,31}\) The success of many cell and exosome- based therapies relies on the cellular secretome. In this study, we fabricated micropillars to manipulate nuclear morphology and investigated their effects on the secretome of human mesenchymal stromal cells (hMSCs). We incorporated hydroxyapatite (HA), the primary inorganic component of native bone tissue, with micropatterned methacrylated poly(octamethylene citrate) (mPOC) to create the micropillars, promoting bone formation. Our results showed that mPOC/HA micropillars facilitated osteogenic differentiation of hMSCs compared to flat mPOC/HA samples in vitro. Secretome analysis revealed that hMSCs with deformed nuclei exhibited higher expression levels of bioactive factors associated with extracellular matrix (ECM) components and organization, as well as ossification. In vivo, both mPOC/HA flat and micropillar scaffolds seeded with hMSCs resulted in new bone formation; however, the micropillar group demonstrated significantly greater new bone volume and regenerated tissue thickness. Spatial transcriptomic analysis further confirmed elevated expression of genes related to the regulation of ECM structures, consistent with the secretome analysis results. These findings suggest that the influence of nuclear deformation on the osteogenesis of hMSCs operates through similar mechanisms in both in vitro and in vivo environments. Therefore, microtopography engineering of scaffold to control nuclear morphology is a promising approach to enhance bone regeneration. + +<|ref|>sub_title<|/ref|><|det|>[[115, 594, 179, 611]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 622, 880, 660]]<|/det|> +## Influence of micropillar structures on physical and chemical properties of mPOC/HA implants + +<|ref|>text<|/ref|><|det|>[[115, 669, 882, 896]]<|/det|> +mPOC prepolymer was synthesized according to our previous report, \(^{32}\) and its successful synthesis was confirmed via the nuclear magnetic resonance (1H NMR) spectrum (Fig. S1a- c). The size of HA nanoparticles is around \(100 \mathrm{nm}\) , as characterized by dynamic light scattering (DLS) (Fig. S1d). To mimic the nature of bone composition, \(^{33} 60\%\) (w/w) HA was mixed with mPOC, and the slurry was used to fabricate flat and micropillar implants using a combination of UV lithography and the contact printing method (Fig. 1a). The square micropillars, with dimensions of 5 by 5 in side length and spacing, were fabricated (Fig. 1b). The height of the micropillars is around \(8 \mu \mathrm{m}\) , which can cause significant nuclear deformation (Fig. 1c,d). \(^{27}\) Fourier transform infrared (FTIR) spectrum shows a similar typical peak of functional groups in mPOC and mPOC/HA implants (Fig. S1e). The surface roughness of the implants was scanned using an atomic force microscope (AFM) (Fig. 1e). The analysis result indicates that the topography didn’t affect the surface roughness of the implants (Fig. 1f). Additionally, we tested the hydrophilicity of flat and micropillar implants via + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 145]]<|/det|> +water contact angle measurement (Fig. S2). Although, at the initial state, the flat surface was more hydrophilic, there was no significant difference in the water contact angle after a 5- minute stabilization process. + +<|ref|>text<|/ref|><|det|>[[114, 155, 882, 437]]<|/det|> +The mechanical properties of the implants were tested using the nano- indentation method. The force- indentation curve of the flat sample has a sharper slope, indicating it is stiffer than the micropillar sample (Fig. S3a). The Young's Modulus of the flat sample \((0.95 \pm 0.12 \mathrm{GPa})\) is significantly higher than that of the micropillars \((0.48 \pm 0.02 \mathrm{GPa})\) and the lateral modulus of the micropillars \((46.88 \pm 1.49 \mathrm{MPa})\) (Fig. S3b,c). However, based on a previous report, the high modulus of the substrates is beyond the threshold that cells can distinguish and does not have an influence on nuclear morphology manipulation. \(^{34,35}\) Accelerated degradation and calcium release tests of the implants were performed in DPBS at \(75^{\circ} \mathrm{C}\) with agitation. \(^{36}\) There is a burst weight loss and calcium release of both flat and micropillar samples at day 1, followed by a gradual change until day 10, and another increase in the degradation and calcium release rate from day 10 to 14 (Fig. 1g,h). The micropillar structure enhanced the degradation and calcium release, but not significantly. According to the images of the samples captured at different time points, the initial burst degradation and calcium release can be attributed to the fast surface erosion of both scaffolds, as many small pores can be observed on their surfaces. From day 10 to 14, scaffolds started break into pieces that may lead to another burst degradation and calcium release (Fig. 1i). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 95, 876, 599]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 606, 883, 775]]<|/det|> +
Figure 1. Fabrication of surface engineered mPOC/HA implants. a. Illustration shows the combination of UV lithography and contact printing to fabricate free-standing mPOC/HA micropillars. b. SEM image shows the micropillar structures made of mPOC/HA. c. Optical microscope image and d. cross-section analysis of mPOC/HA micropillars. e. Surface scanning of flat and micropillar implants by AFM. f. Surface roughness of flat and micropillar implants. N.S., no significant difference, \(\mathrm{n} = 3\) biological replicates. g. Degradation test and h. calcium release of flat and micropillar mPOC/HA implants. N.S., no significant difference, \(\mathrm{n} = 4\) biological replicates, insert plot shows the initial release of calcium within \(24\mathrm{h}\) . i. Representative images of flat and micropillar implants at different time points after accelerated degradation.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 682, 109]]<|/det|> +## Nuclear deformation facilitates osteogenic differentiation of hMSCs + +<|ref|>text<|/ref|><|det|>[[115, 118, 883, 287]]<|/det|> +Nuclear deformation facilitates osteogenic differentiation of hMSCshMSCs were cultured on the flat and micropillar mPOC/HA surfaces in osteogenic medium and stained for F- actin and nuclei after 3 days (Fig. 2a). Noticeable deformation in both the nucleus and cytoskeleton was observed, consistent with mPOC micropillars. The Nuclear shape index (NSI) was calculated to assess the degree of nuclear deformation. A significantly lower NSI value, indicating more severe deformation, was found in the micropillar group (Fig. 2b). Confocal images were then employed to evaluate the 3D geometry of cell nuclei (Fig. 2c). 3D reconstruction analysis revealed that several geometric parameters, including nuclear volume, surface area, and project area, were significantly decreased on micropillars, while nuclear height was significantly increased (Fig. 2d and Fig. S4). + +<|ref|>text<|/ref|><|det|>[[115, 297, 883, 466]]<|/det|> +We then investigated the impact of micropillars on cell adhesion, a crucial aspect for manipulating cell function. Initial cell attachment tests revealed that the micropillar structure did not influence cell attachment on the implants (Fig. 2e). SEM imaging of cell adhesion demonstrated that cells formed lamellipodia on flat surfaces but exhibited more filopodia on micropillars (Fig. 2f). Filopodia were observed on the top, side, and bottom of micropillars, indicating that cells were sensing the 2.5D environment using these antennae- like structures. The majority of cells were found to be viable on both flat and micropillar substrates, as evidenced by live/dead staining (Fig. 2g and Fig. S5). While the micropillars reduced cell metabolic activity (Fig. 2h), there was no significant impact on cell proliferation after 3 days of culture (Fig. 2i). + +<|ref|>text<|/ref|><|det|>[[115, 476, 883, 626]]<|/det|> +To assess the impact of mPOC/HA micropillars on the osteogenesis of hMSCs, we stained ALP (alkaline phosphate) on a substrate with a combination of half flat and half micropillar structures (Fig. 2j). Quantification results demonstrated a significant increase in ALP activity on the micropillars (Fig. 2k). Furthermore, additional osteogenic differentiation markers of hMSCs, including RUNX2 and osteocalcin (OCN), were quantified through western blot analysis (Fig. 2l). The quantification of these proteins revealed a significant increase in both RUNX2 and OCN in cells on micropillars, confirming that the structures can effectively promote the osteogenic differentiation of hMSCs (Fig. 2m,n). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 93, 880, 456]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 469, 884, 790]]<|/det|> +
Figure 2. Nuclear deformation promotes osteogenic differentiation of hMSCs. a. Staining of nucleus (green) and F-actin (red) of hMSCs on flat and micropillar mPOC/HA surfaces. Insert: high magnification of cell nucleus. Dashed lines indicate micropillars. b. Analysis of nuclear shape index of hMSCs. \(\mathrm{n} = 117\) (flat) and 132 (pillar) collected from 3 biological replicates, \(\mathrm{***p< 0.0001}\) . c. Orthogonal view of cell nucleus on flat and micropillar surfaces. d. Nuclear volume analysis based on 3D construction of the confocal images of cell nuclei. \(\mathrm{n} = 35\) cells collected from 3 biological replicates, \(\mathrm{***p< 0.0001}\) . e. Initial cell attachment on flat and micropillar surfaces. \(\mathrm{n} = 5\) biological replicates, N.S., no significant difference. f. SEM images show the cell attachment on flat and micropillar mPOC/HA surfaces. g. Live/dead staining of hMSCs on flat and micropillar surfaces at 72 h in osteogenic medium. h. Cell metabolic activity of cells on flat and micropillar surfaces tested by a MTT assay. \(\mathrm{n} = 5\) biological replicates, \(\mathrm{***p< 0.0001}\) . i. Cell proliferation tested via DNA content after 72 h induction. \(\mathrm{n} = 5\) biological replicates, N.S., no significant difference. j. ALP staining of hMSCs on flat and micropillar surfaces after 7 d induction. k. ALP activity test of cells after 7 d osteogenic induction. \(\mathrm{n} = 3\) biological replicates. l. Blot images of osteogenic marker OCN and RUNX2 in cells cultured on flat and micropillar implants. GAPDH is shown as a control. Quantification m. OCN and n. RUNX2 according to western blot tests. \(\mathrm{n} = 3\) biological replicates, \(\mathrm{***p< 0.0001}\) .
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 89, 880, 108]]<|/det|> +## Micropillars modulate the secretome of hMSCs that regulate extracellular matrix formation. + +<|ref|>text<|/ref|><|det|>[[115, 118, 882, 438]]<|/det|> +Micropillars modulate the secretome of hMSCs that regulate extracellular matrix formation.Previously, we demonstrated the ability of micropillar implants to enhance in vivo bone formation.13 However, the newly formed bone was not in close contact with the implant. Consequently, we hypothesized that nuclear deformation on micropillars might impact cellular secretion, thereby influencing osteogenesis through paracrine effects. To test this hypothesis, secretome analysis was conducted using medium collected from flat and micropillar samples. Differences in protein secretion levels between the two groups were depicted through principal component analysis (PCA) and a volcano plot, revealing a significant influence of nuclear deformation on the secretome (Fig. 3a,b). Gene ontology (GO) analysis was performed to annotate the significantly altered proteins in relevant processes.38 Top changes in cellular component, molecular functions, biological processes, and biological pathways indicated that micropillars predominantly affected extracellular matrix (ECM)- related processes (Fig. 3c and Fig. S6- 8). Moreover, ossification and collagen fibril organization were identified as biological processes significantly overrepresented by differentially expressed proteins (Fig. 3d). The heatmap plot of proteins associated with collagen- containing extracellular matrix and ossification showed predominant upregulation on micropillars (Fig. 3e). The linkages of proteins and GO terms in biological process highlighted that ECM organization forms the largest cluster and is closely associated with the ossification process (Fig. 3f). + +<|ref|>text<|/ref|><|det|>[[115, 448, 883, 672]]<|/det|> +Reactome pathway analysis was further conducted to assess potential downstream effects of secretome changes on micropillars.39 Results indicated that pathways related to ECM organization, ECM proteoglycans, and collagen fibril crosslinking were among the top 15 pathways significantly overrepresented by differential expressed pathways (DEP), predominantly showing upregulation (Fig. 3g and Fig. S9). We also noticed an upregulation in the degradation of the ECM on micropillars, indicating enhanced ECM remodeling which a crucial factor for tissue regeneration.40 These findings suggest that micropillars can influence the ECM formation of hMSCs through paracrine effects. Additionally, we performed proteomic analysis using cells cultured on flat and micropillar mPOC/HA scaffolds (Fig. S10). PCA and volcano plots indicated significant influences of nuclear deformation on protein expression. Pathway analysis revealed significant changes in many cell proliferation- related processes, consistent with previous transcriptomic tests on micropillars.13 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 884, 799]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 798, 884, 893]]<|/det|> +
Figure 3. Secretome of hMSCs on flat and micropillar mPOC/HA surfaces. a. PCA plot of differentially expressed proteins secreted by hMSCs on flat and micropillars. Cyan: flat; Red: micropillar. b. Volcano plot of proteins secreted by hMSCs seeded on micropillars compared to the flat surface. Blue dots and orange dots indicate significantly downregulated and upregulated proteins secreted by cells on micropillars compared to those on flat surface. Grey dots indicate
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 258]]<|/det|> +non- significantly changed proteins. A threshold of expression greater than 2 times fold- change with \(p< 0.05\) was considered to be significant. Proteins that are related with collagen- ECM pathways are labelled. c. Top 4 significantly enriched GO and Pathways based on their adjusted p- values. d. The most significant enriched GO terms of the biological domain with respect to biological process. e. Heatmap of proteins that are related with collagen- containing extracellular matrix and ossification. F indicates flat samples and P indicates pillar samples, \(n = 3\) biological replicates for each group. f. The linkages of proteins and GO terms in biological process related with collagen fibers, ECM, and ossification as a network. g. Heatmap of top 15 enriched terms plotted based on Reactome pathway analysis. + +<|ref|>sub_title<|/ref|><|det|>[[114, 297, 881, 334]]<|/det|> +## Nuclear deformed cells facilitate osteogenic differentiation of undeformed cells by affecting ECM. + +<|ref|>text<|/ref|><|det|>[[114, 344, 884, 682]]<|/det|> +Since the micropillar surfaces can modulate the secrete of hMSCs, we investigated whether the deformed cells could influence the osteogenic differentiation of undeformed cells using a transwell assay (Fig. 4a). The flat and micropillar mPOC/HA surfaces were fabricated at the bottom of cell culture plates to manipulate the nuclear morphology of hMSCs, while undeformed hMSCs were seeded on a transwell membrane with 400 nm nanopores, allowing the exchange of growth factors. After cell attachment, all samples were cultured in osteogenic induction medium. ALP staining of the cells on the transwell membrane showed a higher number of ALP- positive cells when co- cultured with nuclear- deformed cells, indicating enhanced osteogenic differentiation (Fig. 4b,c). Additionally, Alizarin Red S (ARS) staining confirmed increased calcium deposition—a key step in osteogenesis—when the cells were cultured above the micropillar- treated cells (Fig. 4d,e). Based on the secreteome analysis, hMSCs on micropillars appear to promote osteogenesis in the transwell culture by secreting proteins that enhance ECM structure and organization. Collagen staining revealed higher coverage, stronger staining intensity, and more interconnected collagen network structures in the transwell co- cultured with micropillar- treated cells (Fig. 4f,g). In addition, energy dispersive X- ray spectroscopy (EDS) images showed more Ca and P deposition in the transwell co- cultured with micropillar- treated cells (Fig. 4h). Together with the secreteome analysis, these findings suggest that the proteins secreted by cells with deformed nuclei improve ECM organization in undeformed cells, thereby promoting osteogenesis. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 123, 870, 506]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 517, 883, 685]]<|/det|> +
Figure 4. The paracrine effect of cells with/without nuclear deformation tested through transwell assay. a. Schematic illustration of the experiment setup. b. ALP staining and c. quantification of ALP positive cells on transwell membrane incubated with undeformed and deformed MSCs \((n = 3)\) . d. ARS staining and e. quantification of cells on transwell membrane incubated with undeformed and deformed MSCs \((n = 6)\) . f. Immunofluorescence staining images of collagen in ECM of cells on transwell membrane incubated with undeformed and deformed MSCs. g. The coverage of collagen analyzed according to the staining images \((n = 4)\) . h. EDS images showing Ca, P, and SEM images of cells on transwell membrane incubated with undeformed and deformed MSCs.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 725, 655, 744]]<|/det|> +## mPOC/HA micropillar implant promotes bone formation in vivo + +<|ref|>text<|/ref|><|det|>[[114, 753, 883, 905]]<|/det|> +To test the in vivo regeneration efficacy of mPOC/HA scaffolds, we created a critical size cranial defect model in nude mice. Two 4 mm diameter critical defects were made on the left and right sides of the skull tissue for the implantation of flat and micropillar scaffolds, respectively (Fig. 5a). The scaffolds were seeded with hMSCs for 24 hours to allow for cell attachment and nuclear deformation (Fig. 5b). After 12 weeks, micro CT was performed to evaluate the bone formation in the living animals. Based on the images, newly formed bone can be observed in the defect area with both flat and micropillar mPOC/HA implants (Fig. 5c and Fig. S11). Comparing this to our previous study using mPOC alone, \(^{13}\) the integration of HA clearly enhanced bone regeneration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 145]]<|/det|> +efficacy in vivo. Furthermore, larger bone segments were observed with the micropillar implant treatment. Quantification results confirmed a significantly increased bone volume with micropillar implant treatment (Fig. 5d). + +<|ref|>text<|/ref|><|det|>[[114, 155, 882, 380]]<|/det|> +Histology analysis was further performed to evaluate the influences of flat and micropillar mPOC/HA implants on bone regeneration. Trichrome staining images revealed that defects treated with micropillar implants exhibited more osteoid tissue (Fig. 5e and Fig. S12). Moreover, both flat and micropillar mPOC/HA implants showed evidence of newly formed bone tissue, indicating enhanced bone regeneration compared to the mPOC alone scaffold. As no bone segment was observed with flat mPOC implant treatment. \(^{13}\) The thickness of the regenerated tissue was quantified, and the results demonstrated a significant enhancement with micropillar implant treatment (Fig. 5f). Positive staining of osteogenesis markers, including osteopontin (OPN) and osteocalcin (OCN), was observed throughout the regenerated tissues with both flat and micropillar implants, indicating osteoid tissue formation (Fig. 5g,h). The tissue appeared more compact in the micropillar group compared to the flat group. Furthermore, regenerated bone segments were more frequently observed with micropillar implant treatment. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 95, 875, 620]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 630, 884, 782]]<|/det|> +
Figure 5. mPOC/HA micropillar implant promotes bone regeneration in vivo. a. Image shows implantation of hMSC seeded flat and micropillar mPOC/HA scaffolds. b. Staining images of nuclei (green) and F-actin (red) of cells on the implants. c. Representative \(\mu \mathrm{CT}\) images of a typical animal implanted with hMSC-seeded flat (left) and micropillar (right) scaffolds at 12-weeks post-surgery. d. Regenerated bone volume in the defect region ( \(\mathrm{n} = 5\) animals). e. Trichrome staining of the defect tissue treated with flat and micropillar implants. f. Average thickness of regenerated tissues with implantation of flat and micropillar scaffolds ( \(\mathrm{n} = 5\) animals). IHC staining of osteogenic marker, g. OPN and h. OCN, in regenerated tissues with flat and micropillar implants.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 881, 127]]<|/det|> +## Micropillar implants facilitated bone regeneration in vivo via regulation of ECM organization and stem cell differentiation. + +<|ref|>text<|/ref|><|det|>[[114, 136, 882, 494]]<|/det|> +Histological analyses showed more new bone formation with micropillar implants, although the new bone tissue did not directly interact with the micropillar surfaces. To further investigate the transcription profile of the regenerated tissue, we performed spatial transcriptomics (ST) analyses with both flat and pillar samples (Fig. S13). ST represents a powerful tool to investigate the cellular environment and tissue organization by providing a detailed map of gene expression within the native tissue context. Differential gene expression (DGE) analysis revealed changes in expression levels between the two groups. Although only a few genes showed significant differences, all of them were related to ECM structure or organization (Fig. S13). Notably, the expression of Colla2, critical for type I collagen formation (comprising \(90\%\) of the bone matrix), was enhanced in the micropillar group (Fig. 6a). This expression showed a gradient, increasing toward the dura layer, possibly due to the osteogenic contribution of dura cells. We then plotted a heatmap showing the top 10 up- regulated and down- regulated differentially expressed genes (pillar vs. flat) in comparison with those in native skull bone (Fig. 6b). The heatmap indicated that the tissue regenerated with micropillar implants had expression patterns more similar to native skull bone than the flat group. Gene Ontology (GO) analysis of DGEs was further performed to annotate their relevant biological processes (Fig. 6c). Protein localization to extracellular matrix and crosslinking of collagen fibrils were among the top 5 up- regulated processes in the micropillar group. These results are consistent with the secreteome test, all indicating that micropillar structures can influence ECM organization via paracrine effects. + +<|ref|>text<|/ref|><|det|>[[114, 503, 882, 824]]<|/det|> +To further investigate the relationship between cell type composition and the regenerated tissues, we performed cellular deconvolution on the ST data using single- cell RNA sequencing (scRNA- seq) references from previously published studies. Several major cell lineages involved in bone regeneration were considered when deconvoluting the data (Fig. 6d). The most abundant cell type in regenerated tissues was late mesenchymal progenitor cells (LMPs), followed by MSCs and fibroblasts (Fig. 6e). There were also small proportions of MSC- descendant osteolineage cells (OLCs), osteocytes, osteoblasts, and chondrocytes. LMPs are identified as the late stage of MSCs through osteogenic differentiation. Among all cell types, the proportion of LMPs, which have high expression of marker genes associated with osteoblasts, was significantly increased in regenerated tissues with micropillar implants, indicating that these deformed cells facilitate the differentiation of MSCs toward the osteolineage (Fig. 6f). Additionally, GO analysis of DGEs (LMP versus other cell types) was performed to investigate the roles of LMPs in regenerated tissue. The results suggest that LMPs do not directly contribute to osteogenesis, a role performed by osteoblasts and osteocytes. Instead, LMPs can affect ECM formation, as the process of extracellular matrix organization is one of the top involved pathways (Fig. 6g). Thus, the results indicate that micropillar implants can facilitate skull tissue regeneration by promoting the differentiation of MSCs and ECM organization via paracrine effects. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 90, 877, 560]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 572, 883, 817]]<|/det|> +
Figure 6. Spatial transcriptomic analysis of tissues regenerated with flat and micropillar implants. a. Spatial plot of Colla2 expression profile in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. Arrow indicates enhanced expression around dura layer. b. The heatmap showing the top ten up- and down-regulated DEGs (pillar vs flat) in tissues regenerated with flat mPOC/HA implant, micropillar mPOC/HA implant, and native skull tissue. c. Gene Ontology analysis results based on the top 100 up-regulated genes (pillar vs flat). d. Deconvoluted cell types in each spatial capture location in flat and micropillar groups. Each pie chart shows the deconvoluted cell type proportions of the capture location. e. Bar plots of the cell type proportions in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. LMPs, MSCs, and fibroblasts are the predominant cell types. f. Violin plot of the proportion of LMPs in flat and micropillar groups. g. Top enriched processes associated with LMP compared with other cell lineages. LMP: late mesenchymal progenitor cells; MSC: mesenchymal stromal cells; OLC: MSC-descendant osteolineage cells
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 206, 107]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 118, 882, 380]]<|/det|> +Micropiliars, as a typical topographical feature, have been extensively studied for their ability to regulate cell functions. Recent researches have shown that rigid micropiliars can deform nuclear morphology, which in turn promotes the osteogenic differentiation of mesenchymal stem cells (MSCs), generating significant interest for bone regeneration applications.26,27 Our previous work demonstrated that mPOC micropiliars enhanced bone regeneration in a mouse cranial defect model.13 The mPOC, a citrate- based biomaterial (CBB), is an excellent candidate for bone regeneration because citrate, an important organic component of bone, plays key roles in skeletal development and bone healing by influencing bone matrix formation and the metabolism of bone- related cells.47 In this study, hydroxyapatite (HA) was incorporated into mPOC to further enhance its regenerative potential, leveraging HA's well- known osteoconductive properties.48 Both in vitro and in vivo experiments confirmed that the addition of HA significantly improved bone regeneration compared to mPOC alone.13 Moreover, several products made from CBB/HA composites have recently received FDA clearance, highlighting the promising clinical potential of mPOC/HA micropiliars for bone regeneration applications.49 + +<|ref|>text<|/ref|><|det|>[[115, 391, 882, 654]]<|/det|> +Despite recent intensive investigations into nuclear morphogenesis, little is known about its influence on cellular secretion, which can regulate neighboring cells and is critical for regenerative engineering. Previous studies have shown that nuclear mechanotransduction, activated by substrate stiffening or cellular compression, can impact cell secretions.50,51 Here, we found that cells with deformed nuclei exhibited higher expression levels of ECM components and binding proteins that support collagen- enriched ECM organization. Additionally, soluble proteins secreted by these deformed cells were able to diffuse and modulate ECM secretion and organization in neighboring cells, as demonstrated by a transwell assay. The ECM is a complex, dynamic environment with tightly regulated mechanical and biochemical properties that affect essential cell functions, including adhesion, proliferation, and differentiation.52 ECM fiber alignment increases local matrix stiffness, which promotes higher force generation and increases cell stiffness, creating a positive feedback loop between cells and the matrix.53 Furthermore, the organized ECM enhances calcium recruitment and accelerates mineralization, contributing to effective bone regeneration. + +<|ref|>text<|/ref|><|det|>[[115, 664, 882, 869]]<|/det|> +Implantation of the flat and micropillar mPOC/HA scaffolds seeded with MSCs resulted in larger new bone volume formation in vivo compared to previous studies using mPOC alone, a finding likely due to the osteoconductive properties of HA. ST analysis revealed a significant upregulation of genes encoding cartilage oligomeric matrix protein (COMP) and fibromodulin (FMOD) in the micropillar group, consistent with the secreteome analysis. COMP binds to matrix proteins like collagen, enhancing ECM organization and assembly.54 As an ECM protein, COMP also promotes osteogenesis by binding to bone morphogenetic protein 2 (BMP- 2), increasing its local concentration and boosting its biological activity.55 FMOD, with a strong affinity for the HA matrix, helps attenuate osteoclast precursor maturation, thereby influencing osteoblast- osteoclast crosstalk.56 These results suggest that nuclear deformation induced by micropiliars may promote osteogenesis in neighboring cells via matricrine effects. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 239]]<|/det|> +Despite the enhanced bone regeneration observed, mPOC/HA implants did not achieve complete healing of the cranial defect, likely due to the limited interaction surface of the film scaffold. The influence of the implants, whether through direct chromatin reprogramming guidance or secretome activity, was restricted to cells at the tissue- scaffold interface. Future efforts should focus on the design and fabrication of 3D micropillar implants using additive manufacturing and composite materials to create a more comprehensive 3D cellular microenvironment that promotes bone regeneration. Additionally, the application of micropillars as a platform for delivering bioactive factors could be explored as a strategy to achieve complete cranial bone healing. + +<|ref|>text<|/ref|><|det|>[[115, 249, 882, 437]]<|/det|> +In summary, we investigated the effects of nuclear deformation on the cellular secretome using micropillar implants fabricated from an mPOC/HA composite. The mPOC/HA micropillars demonstrated similar properties to a flat substrate in terms of roughness and degradation but had a substantial impact on cellular and nuclear morphology, cell adhesion, cytoskeletal development, and osteogenic differentiation in hMSCs. Nuclear- deformed cells showed increased secretion of proteins and RNA transcriptions that regulate ECM components and organization, promoting osteogenesis in neighboring cells both in vitro and in vivo. These findings suggest that incorporating microtopography into implants holds significant promise for bone regeneration. This study offers valuable insights for the future design and fabrication of bioactive implants in regenerative engineering. + +<|ref|>sub_title<|/ref|><|det|>[[116, 477, 311, 494]]<|/det|> +## Materials and Methods + +<|ref|>text<|/ref|><|det|>[[115, 505, 542, 523]]<|/det|> +Synthesis and characterization of mPOC pre- polymer. + +<|ref|>text<|/ref|><|det|>[[115, 533, 882, 701]]<|/det|> +The mPOC pre- polymer were synthesized according to a previous report. Briefly, the POC pre- polymer was firstly synthesized by reaction of equal molar of citric acid (Sigma- Aldrich, 251275) and 1,8- octandiol (Sigma- Aldrich, O3303) at \(140^{\circ}\mathrm{C}\) oil bath for 60 min. The product was then purified by precipitation in DI water. After lyophilization, 66g POC pre- polymer was dissolved in 540 ml tetrahydrofuran (THF) and reacted with 0.036 mol imidazole (Sigma- Aldrich, I2399) and 0.4 mol glycidyl methacrylate (Sigma- Aldrich, 151238) at \(60^{\circ}\mathrm{C}\) for 6 h. The final product was then purified by precipitation in DI water and lyophilized for storage at - 20 \(^{\circ}\mathrm{C}\) . Successful synthesis of mPOC pre- polymer was characterized using proton nuclear magnetic resonance (1H- NMR, Bruker A600). + +<|ref|>sub_title<|/ref|><|det|>[[115, 712, 658, 730]]<|/det|> +## Fabrication and characterization of mPOC/HA micropillar scaffolds + +<|ref|>text<|/ref|><|det|>[[115, 741, 882, 892]]<|/det|> +SU- 8 micropillar structures (5x5x8 um) were fabricated according to our previous study. PDMS molds were then fabricated to replicate the invert structures. HA nanoparticles (Sigma- Aldrich, 677418) were mixed with mPOC pre- polymer at weight ratio of 6:4. The \(60\%\) HA was selected to mimic composition of native bone. Photo- initiator (5 mg/ml camphorquinone and ethyl 4- dimethylaminobenzoate) was added to the mPOC/HA slurry. The mixture was then added onto PDMS mold and pressed onto cover glass to prepare free- standing scaffold under exposure with laser (1W, 470 nm). Post- curing of the scaffold was performed in \(80^{\circ}\mathrm{C}\) oven over night. The size of HA nanoparticles was characterized using Dynamic Light Scattering (DLS). The topography of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 202]]<|/det|> +micropillars was observed using scanning electron microscope (SEM, FEI Quanta 650 ESEM) and characterized using 3D optical microscope (Bruker). Surface roughness of flat and micropillar scaffolds was characterized using atomic force microscope (AFM, Bruker ICON system). The water contact angle was tested using VCA Optima XE system. The compressive modulus of the scaffolds was characterized using a Tribioindenter (Bruker). Based on a previous report, \(^{58}\) the lateral modulus of micropillars was calculated according to the following equations: + +<|ref|>equation<|/ref|><|det|>[[114, 210, 217, 240]]<|/det|> +\[k_{L} = \frac{3EI}{L^{3}} (1)\] + +<|ref|>text<|/ref|><|det|>[[114, 248, 883, 287]]<|/det|> +The ' \(\mathrm{kL}\) ' is the lateral stiffness, 'E' is the measured modulus, 'I' is the moment area of inertia, and 'L' is the micropillar height. For square micropillars, 'I' can be described as: + +<|ref|>equation<|/ref|><|det|>[[114, 295, 210, 328]]<|/det|> +\[I = \frac{a^{4}}{12} (2)\] + +<|ref|>text<|/ref|><|det|>[[114, 336, 883, 374]]<|/det|> +Where 'a' is the side length of the micropillars. Thus, the lateral modulus of the micropillars ' \(\mathrm{E_{L}}\) ' equals to: + +<|ref|>equation<|/ref|><|det|>[[114, 381, 226, 411]]<|/det|> +\[E_{L} = \frac{K_{L}L}{A} (3)\] + +<|ref|>text<|/ref|><|det|>[[114, 420, 519, 439]]<|/det|> +Where 'A' is the cross- section area of micropillars. + +<|ref|>sub_title<|/ref|><|det|>[[115, 450, 381, 468]]<|/det|> +## Degradation and calcium release + +<|ref|>text<|/ref|><|det|>[[114, 478, 884, 628]]<|/det|> +To test the degradation of the mPOC/HA scaffold, the dry weight of mPOC/HA scaffolds at day 0 was recorded as the initial weight. Then the scaffolds were merged in \(1\mathrm{ml}\) DPBS solution in \(75^{\circ}\mathrm{C}\) oven. At each designed time point (1, 2, 3, 5, 7, 10 and 14 d), the scaffolds were rinsed with DI water followed by drying at \(60^{\circ}\mathrm{C}\) . The weight was recorded to calculate the weight loss percentage. The calcium release test was also performed with \(75^{\circ}\mathrm{C}\) DPBS (no calcium, no magnesium). At the designed time points, the elution solution was collected and replaced with fresh DPBS (1 ml). The released calcium was detected with inductively coupled plasma mass spectrometry (ICP- MS, ThermoFisher Element 2). Accumulated calcium release was calculated. + +<|ref|>sub_title<|/ref|><|det|>[[115, 639, 211, 656]]<|/det|> +## Cell culture + +<|ref|>text<|/ref|><|det|>[[114, 667, 883, 836]]<|/det|> +Human mesenchymal stromal cells (hMSCs, PCS- 500- 012) were purchased from the American Type Culture Collection (ATCC) and cultured with the growth medium acquired from ATCC. hMSCs with the passage 4- 6 were seeded onto the flat and micropillar mPOC/HA substrates. To test cell attachment, hMSCs were seeded at 5000 cells/cm \(^2\) and cultured for 3 h followed by PBS rinsing to remove unattached cells. The attached cells were then trypsinized and collected for cell counting. For other experiments, the cells were cultured in growth medium for 24 h to allow cell attachment and spreading followed by incubation with osteogenic induction medium. After 3 d culture, live/dead staining (Thermofisher, L3224), MTT assay (Thermofisher, V13154), and Picogreen assay (Thermofisher, P7589) were performed according to the manufactures' protocol. + +<|ref|>text<|/ref|><|det|>[[115, 847, 350, 865]]<|/det|> +Nuclear morphology analysis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 882, 239]]<|/det|> +After one day of culture, the cells were fixed with \(4\%\) paraformaldehyde, and cell nuclei were stained using SYTOX™ Green (ThermoFisher, S7020) according to the manufacture's instruction. The nuclear shape index (NSI) was analyzed to evaluate 2D nuclear deformation. \(^{27}\) The stained cells were then imaged using a confocal microscope (Leica SP8) to acquire their 3D morphology. Cell nuclei were reconstructed using the Fiji ImageJ software (https://imagej.net/Fiji). Cell nuclear volume, surface area, project area, height, and the ratio of surface area to volume were measured using 3D objects counter plugin. More than 30 nuclei from 3 biological replicates were imaged and analyzed to calculate the statistics. + +<|ref|>sub_title<|/ref|><|det|>[[116, 252, 355, 269]]<|/det|> +## Scanning electron microscope + +<|ref|>text<|/ref|><|det|>[[115, 280, 882, 431]]<|/det|> +To visualize cell adhesion on mPOC/HA scaffolds, cells were fixed with \(3\%\) glutaraldehyde (Electron Microscopy Sciences) and rinsed with DI water. Subsequently, the cells underwent dehydration using a series of ethanol concentrations ( \(30\%\) , \(50\%\) , \(70\%\) , \(90\%\) , and \(100\%\) ) for 5 min each, followed by drying using a critical point dryer (Tousimis Samdri) as per the manual. The dehydrated cells were coated with a \(5\mathrm{nm}\) osmium layer and imaged using a scanning electron microscope (SEM, FEI Quanta 650). Captured images were further enhanced for visualization of cellular architecture using Photoshop. Additionally, cells on transwell were imaged using SEM and EDS analysis was performed to evaluate the calcium and phosphate deposition. + +<|ref|>sub_title<|/ref|><|det|>[[116, 442, 322, 460]]<|/det|> +## Osteogenic differentiation + +<|ref|>text<|/ref|><|det|>[[114, 470, 882, 902]]<|/det|> +hMSCs were seeded onto both flat and micropillar mPOC/HA substrates. One- day post- seeding, osteogenic induction medium (Lonza) was applied to prompt the osteogenic differentiation of hMSCs. After 7 days of induction, cells were washed with PBS buffer and fixed with \(4\%\) paraformaldehyde for 10 minutes. Subsequently, the samples were immersed in a solution of 56 mM 2- amino- 2- methyl- 1,3- propanediol (AMP, pH- 9.9), containing \(0.1\%\) naphthol AS- MX phosphate and \(0.1\%\) fast blue RR salt to stain alkaline phosphatase (ALP). Bright- field images were acquired using a Nikon Eclipse TE2000- U inverted microscope. ALP activity was assessed using the ALP assay kit (K422- 500, Biovision) following the provided manual. Briefly, cells cultured in induction medium for 7 days were homogenized using ALP assay buffer. Subsequently, the non- fluorescent substrate 4- Methylumelliferyl phosphate disodium salt (MUP) was mixed with the homogenized samples to generate a fluorescent signal through its cleavage by ALP. Fluorescence intensity was measured using a Cytation 5 imaging reader (BioTek) at \((\mathrm{Ex / Em} = 360 / 440\mathrm{nm})\) . Enzymatic activity was calculated based on the standard curve and normalized to total DNA content, determined by the Quant- iT PicoGreen dsDNA assay (Invitrogen). The expression levels of OCN and RUNX2 were quantified through Western blot analysis. In brief, cell lysis was performed using radioimmunoprecipitation assay (RIPA) buffer. The relative protein quantities were measured using a Cytation 5 imaging reader. Equal amounts of proteins extracted from flat and micropillar samples were loaded onto a NuPAGE 4- 12% Bis- Tris Gel (Invitrogen) and subsequently transferred to nitrocellulose membranes (Bio- rad). Afterward, membranes were blocked with \(5\%\) milk and incubated with primary antibodies (including GAPDH from Abcam, OCN from Cell Signaling, RUNX2 from Santa Cruz) overnight at \(4^{\circ}\mathrm{C}\) with gentle shaking. Following this, secondary antibodies, diluted at a ratio of 1:5000, were applied and incubated with the membranes at room temperature for 1 hour. Protein bands were + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 163]]<|/det|> +visualized using the Azure 600 gel imaging system. The acquired images underwent analysis through the 'Gel Analyzer' tool in ImageJ. The intensity of all target protein bands was initially compared to the corresponding GAPDH, and then normalized against a flat surface, which was set as 1. Statistical calculations were based on three biological replicates. + +<|ref|>text<|/ref|><|det|>[[114, 175, 883, 533]]<|/det|> +Secretome sample preparation: Analysis of secreted proteins is complicated by high concentrations of serum proteins. Our approach reduced initial sample volume to a \(20~\mu \mathrm{l}\) concentrate using a molecular weight cut off filter (50 kDa, Amicon Ultra- 15 centrifugal, Ultracel, Merck). The concentrate above 50KDa was depleted of the most abundant proteins using a High Select HAS / Immunoglobulin Depletion Midi spin column (A36367, Thermo Fisher Scientific), resulting in a filtrate solution (below 50KDa) and a depleted solution per sample. An acetone / TCA (Trichloroacetic acid) protein precipitation was performed on each solution to create protein pellets and an in- solution trypsin digestion was performed on each pellet. \(100~\mu \mathrm{l}\) of re- suspension buffer (8 M urea in \(400~\mathrm{mM}\) ammonium bicarbonate) was added to the pellet and incubated with mixing for 15 minutes. Disulfide bonds were reduced by addition of \(100~\mathrm{mM}\) dithiothreitol and incubated for 45 minutes at \(55~^\circ \mathrm{C}\) . Sulfhydryl groups were alkylated by addition of \(300~\mathrm{mM}\) iodoacetamide and incubated for 45 minutes at \(25~^\circ \mathrm{C}\) shielded from light. Samples were diluted 4- fold with ammonium bicarbonate to reduce the urea concentration below 2 M. Protein digestion was performed by addition of trypsin (MS- grade, Promega) at a 1:50 ratio (enzyme:substrate) and incubated overnight at \(37~^\circ \mathrm{C}\) . Digestion was halted with the addition of \(10\%\) formic acid (FA) to a final concentration of \(0.5\%\) . Peptides were desalted with C18 spin columns (The Nest Group), dried by vacuum centrifugation, and stored at \(- 20~^\circ \mathrm{C}\) . Peptides were resuspended in \(5\%\) ACN (Acetonitrile) / \(0.1\%\) FA for LC- MS analysis. Peptide concentration was quantified using micro BCA (Bicinchoninic acid) protein assay kit (Thermo Scientific, Ref: 23235). + +<|ref|>text<|/ref|><|det|>[[114, 542, 882, 803]]<|/det|> +Proteome sample preparation: Cells were lysed using cell lysis buffer ( \(0.5\%\) SDS, \(50\mathrm{mM}\) Ambi (Ammonium Bicarbonate), \(50\mathrm{mM}\) NaCl (Sodium Chloride), Halt Protease inhibitor). An acetone / TCA protein precipitation was performed on each lysed samples solution to create protein pellets and an in- solution trypsin digestion was performed on each pellet. \(100~\mu \mathrm{l}\) of re- suspension buffer (8 M urea in \(400~\mathrm{mM}\) ammonium bicarbonate) was added to the pellet and incubated with mixing for 15 minutes. Disulfide bonds were reduced by addition of \(100~\mathrm{mM}\) dithiothreitol and incubated for 45 minutes at \(55~^\circ \mathrm{C}\) . Sulfhydryl groups were alkylated by addition of \(300~\mathrm{mM}\) iodoacetamide and incubated for 45 minutes at \(25~^\circ \mathrm{C}\) shielded from light. Samples were diluted 4- fold with ammonium bicarbonate to reduce the urea concentration below 2 M. Protein digestion was performed by addition of trypsin (MS- grade, Promega) at a 1:50 ratio (enzyme:substrate) and incubated overnight at \(37~^\circ \mathrm{C}\) . Digestion was halted with the addition of \(10\%\) formic acid to a final concentration of \(0.5\%\) . Peptides were desalted with C18 spin columns (The Nest Group), dried by vacuum centrifugation, and resuspended in \(5\%\) ACN/ \(0.1\%\) FA for LC- MS analysis. Peptide concentration was quantified using micro BCA Protein Assay Kit (Thermo Scientific, Ref: 23235). + +<|ref|>text<|/ref|><|det|>[[115, 815, 882, 890]]<|/det|> +Liquid Chromatography High Resolution Tandem Mass Spectrometry (LC- HRMS/MS) Analysis: Peptides were analyzed using a Vanquish Neo nano- LC coupled to a Exploris 480 hybrid quadrupole- orbitrap mass spectrometer (Thermo Fisher Scientific, USA). The samples were loaded onto the trap column of \(75\mu \mathrm{m}\) internal diameter (ID) x 2cm length (Acclaim PepMapTM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 390]]<|/det|> +100, P/N 164535) and analytical separation was performed using a UHPLC C18 column (15cm length \(\times 75\mu \mathrm{m}\) internal diameter, \(1.7\mu \mathrm{m}\) particle size, Ion Opticks, AUR3- 15075C18). For each run, \(1\mu \mathrm{g}\) of peptide sample was injected. Electrospray ionization was performed using a Nanospray Flex Ion Source (Thermo Fisher, ES071) at a positive static spray voltage of \(2.3\mathrm{kV}\) . Peptides were eluted from the analytical column at a flow rate of \(200\mathrm{nL / min}\) using an increasing organic gradient to separate peptides based on their hydrophobicity. Buffer A was \(0.1\%\) formic acid in Optima LC- MS grade water, and buffer B was \(80\%\) acetonitrile, \(19.9\%\) Optima LC- MS grade water, and \(0.1\%\) formic acid: The method duration was 120 minutes. The mass spectrometer was controlled using Xcalibur and operated in a positive polarity. The full scan (MS1) settings used were: mass range 350- 2000 m/z, RF lens \(60\%\) , orbitrap resolution 120,000, normalized AGC target \(300\%\) , maximum injection time of 25 milliseconds, and a \(5\mathrm{E}^{3}\) intensity threshold. Datadependent acquisition (DDA) by TopN was performed through higher- energy collisional dissociation (HCD) of isolated precursor ions with charges of \(2+\) to \(5+\) inclusive. The MS2 settings were: dynamic exclusion mode duration 30 seconds, mass tolerance 5 ppm (both low and high), 2 second cycle time, isolation window \(1.5\mathrm{m / z}\) , \(30\%\) normalized collision energy, orbitrap resolution 15,000, normalized AGC target \(100\%\) , and maximum injection time of 50 milliseconds. + +<|ref|>text<|/ref|><|det|>[[115, 399, 882, 550]]<|/det|> +Data analysis: Mass spectrometry files (.raw) were converted to Mascot generic format (.mgf) using the Scripps RawConverter program and then analyzed using the Mascot search engine (Matrix Science, version 2.5.1). MS/MS spectra were searched against the SwissProt database of the organism of interest. Search parameters included a fixed modification of cysteine carbamidomethylation, and variable modifications of methionine oxidation, deaminated asparagine and aspartic acid, and acetylated protein N- termini. Two missed tryptic cleavages were permitted. A \(1\%\) false discovery rate (FDR) cutoff was applied at the peptide level. Only proteins with at least two peptides were considered for further study. + +<|ref|>text<|/ref|><|det|>[[115, 560, 882, 860]]<|/det|> +Label- Free Quantification: The samples were acquired on mass spec and the data were searched against a specific database using the MaxQuant application. \(^{59}\) Label- Free Quantification (LFQ) was obtained by LFQ MS1 intensity. The results were filtered with a minimum of 2 unique peptides. Technical replicates were averaged and intensities were Log2 transformed to achieve a normal distribution of the data. Missing values were filtered to keep only proteins quantified in at least 2 samples per group. For statistics, Student t- Test was applied using \(\mathrm{p}< 0.05\) and \(\mathrm{FC} > 2\) to determine which proteins were significantly up- and down- regulated and visualize it by volcano plot. Downstream analyses and visualizations were done using RStudio software (R version 4.3.2, RStudio version 2024.09.0). Principal component analysis (PCA) was done using 'prcomp' R function to visualize a ability of the differential protein expression to distinguish between biological conditions. Heatmap plot was built using 'ComplexHeatmap' R package. GO and Pathways enrichment analysis was done using 'clusterProfiler' R package \(^{60}\) and annotations with adjusted p- values (FDR, Benjamini- Hochberg) \(< 0.05\) were considered significant. Additional packages used include 'org.Hs.eg.db' for human gene annotations and 'enrichplot' for visualization. This analysis considered the entire set of human protein- coding genes as the reference background. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 315]]<|/det|> +Transwell assay: The flat and micropillar mPOC/HA surfaces were fabricated in a 24 well plate. The hMSCs were seeded onto the surfaces with 40,000 cells per well. Then a transwell was put in each well and additional hMSCs were seeded inside the transwell (Costar, \(0.4 \mu \mathrm{m}\) polyester membrane) at density of 5,000 cells/ \(\mathrm{cm}^2\) . After cell attachment, osteogenic medium was used to induce osteogenic differentiation of the cells. At 7 days post- induction, the cells on transwell were fixed followed by ALP staining and quantification to investigate the paracrine effect of deformed and undeformed cells on osteogenesis. At 3 weeks post- induction, additional transwells were collected for Alizarin Red S (ARS) staining and quantification to show the calcium deposition influenced by the paracrine effect. At 4 weeks post- induction, the collagen, which is one of the major components in ECM and significantly affected according to the secretome analysis, were stained using anti- collagen antibody (Abcam, ab36064) to investigate the influence of nuclear deformation on ECM organization. + +<|ref|>text<|/ref|><|det|>[[114, 325, 883, 570]]<|/det|> +In vivo implantation: The animal study was approved by the University of Chicago Animal Care and Use Committee following NIH guidance (ACUP#71745). Eight- week- old female athymic nude mice obtained from Harlan Laboratories were used for the study. The animals were housed in a separately air- conditioned cabinet at temperature of \(24 - 26^{\circ}\mathrm{C}\) with 12:12 light:dark cycle. The surgeries were performed according to the previous report61. Briefly, animals were treated with \(2\%\) isoflurane delivered by \(100\% \mathrm{O}_2\) and maintained with \(1 - 1.5\%\) isoflurane for anaesthesia. Two critical- sized defects (4 mm diameter) were created on the left and right side of skull of each animal followed by implantation of hMSCs seeded flat and micropillar scaffolds, respectively. After implantation of scaffolds, a larger mPOC film \((1 \times 1.5 \mathrm{cm}^2)\) was attached to the skull with thrombin/fibrinogen to prevent displacement of implants. Skin tissue was closed with 5- 0 nylon interrupted sutures and removed after 2 weeks. The animals were monitored after anaesthesia hourly until recovery. Buprenorphine \(50 \mu \mathrm{g} \mathrm{kg}^{- 1}\) and meloxicam \(1 \mathrm{mg} \mathrm{kg}^{- 1}\) were used for pain relief. + +<|ref|>text<|/ref|><|det|>[[114, 579, 883, 749]]<|/det|> +Micro- CT: Micro- CT images of cranial were performed on the XCUBE (Molecules NV) by the Integrated Small Animal Imaging Research Resource (iSAIRR) at The University of Chicago. Spiral high- resolution computed tomography acquisitions were performed with an X- ray source of \(50 \mathrm{kVp}\) and \(440 \mu \mathrm{A}\) . Volumetric computed tomography images were reconstructed by applying the iterative image space reconstruction algorithm (ISRA) in a \(400 \times 400 \times 370\) format with voxel dimensions of \(100 \times 100 \times 100 \mu \mathrm{m}^3\) . An Amira software (Thermo Scientific) was used for 3D reconstruction of the skull tissue and to analyse the bone formation in the defect area. Scale bars were used to standardize the images. Defect recovery is defined as \((\mathrm{Vi} - \mathrm{Vd}) / \mathrm{Vi} \times 100\%\) , where Vi and Vd represent defect volume at initial and designed timepoints, respectively. + +<|ref|>text<|/ref|><|det|>[[114, 758, 883, 871]]<|/det|> +Histology analysis: Skull samples were fixed and decalcified in Cal- EX II (Fisher Scientific) for 24 hours, rinsed with PBS, and embedded in paraffin. Tissue sections containing defect sites were cut to \(5 \mu \mathrm{m}\) thickness and stained with H&E and trichrome to assess tissue regeneration. Regenerated tissue thickness was measured using ImageJ, and osteogenesis was evaluated via IHC staining for key osteogenic markers, including OCN and OPN. Mouse skin tissue served as a negative control for all IHC staining. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 183]]<|/det|> +Spatial transcriptomics: To confirm the RNA quality of each FFPE tissue block, 1- 2 curls (10um thickness each) were used for RNA extraction using Qiagen RNeasy FFPE kit (Qiagen 73504) according to manufactures' protocol. Extracted RNA was examined by Agilent Bioanalyzer RNA pico chip to confirm the \(\mathrm{DV}200 > 30\%\) . Simultaneously, the tissue morphology was examined on HE stained slide to identify region of interest. + +<|ref|>text<|/ref|><|det|>[[115, 194, 882, 249]]<|/det|> +For each FFPE sample, 1 section (5um thickness) was placed on visium slides. Each slide was incubated at \(42^{\circ}\mathrm{C}\) for 3 hours followed by overnight room temperature incubation. Then, the slide was stored at desiccated slide holder until proceeding to deparaffinization. + +<|ref|>text<|/ref|><|det|>[[115, 260, 882, 408]]<|/det|> +The deparaffinization, HE staining and imaging and decrosslinking of tissue slides were performed according to 10x Genomics protocol (CG000409 and CG000407) specific for Visium spatial gene expression for FFPE kit. Then, the slides were proceeded to human probe (v2) hybridization and ligation using 10x Genomics Visium spatial gene expression, \(6.5\mathrm{mm}\) kit (10x Genomics, PN- 1000188). The probes were released from tissue slide and captured on visium slide followed by probe extension. Sequencing libraries were prepared according to manufacturer's protocol. Multiplexed libraries were pooled and sequenced on Novaseq X Plus 10Bflowcell 100 cycles kit with following parameter: 28nt for Read 1 and 90nt for Read 2. + +<|ref|>text<|/ref|><|det|>[[115, 419, 882, 518]]<|/det|> +We visually identified the implant region in each sample. To exclude low quality capture locations, we removed the capture locations with fewer than 500 unique molecular identifiers, fewer than 500 genes, or \(\geq 25\%\) mitochondrial reads. \(^{61}\) We also filtered out the genes that are expressed in fewer than five capture locations. \(^{61}\) After quality control, flat group had 101 capture locations and 12,701 genes, whereas micropillar group had 73 capture locations and 13,371 genes. + +<|ref|>text<|/ref|><|det|>[[115, 528, 882, 620]]<|/det|> +Differential gene expression analysis: To identify the genes differentially expressed in flat and micropillar groups, we performed Wilcoxon rank- sum tests on the merged dataset (174 capture locations) using the FindAllMarkers function in Seurat V3. \(^{62}\) Our testing was limited to the genes present in both implants, detected in a minimum \(1\%\) of cells in either implant, as well as showing at least 0.1 log- fold difference between the two implants. + +<|ref|>text<|/ref|><|det|>[[115, 632, 882, 743]]<|/det|> +Cell type deconvolution: To perform cell typing on our data, we first identified three publicly available bone single- cell RNA sequencing (scRNA- seq) references with annotated cell types. \(^{43 - 45}\) The scRNA- seq references were processed, quality controlled, and merged using Seurat V3. Since our samples are nude mice, we excluded all the immune cells from the merged reference. The final merged scRNA- seq dataset contained a total of 12,717 cells and represented all major cell types present in bone tissues. + +<|ref|>text<|/ref|><|det|>[[115, 754, 882, 885]]<|/det|> +In 10x Visium data, each capture location contains a mixture of cells. \(^{63}\) Therefore, we performed cell type deconvolution to predict the cell type proportions in each capture location using BayesPrism, a Bayesian deconvolution method shown to work on spatial transcriptomics data. \(^{64,65}\) We excluded chromosomes X and Y, ribosomal, and mitochondrial genes from the analysis to reduce batch effects. We also removed the outlier genes with expression greater than \(1\%\) of the total reads in over \(10\%\) of capture locations. To improve cell typing accuracy, we only used the cell type signature genes for deconvolution analysis. The cell type markers were identified based + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 126]]<|/det|> +on the differential expression analysis results on the merged scRNA- seq reference. The predicted cell type proportions with above 0.5 coefficient of variation were clipped to zero to reduce noise. + +<|ref|>text<|/ref|><|det|>[[115, 136, 882, 286]]<|/det|> +Cell- type- based analyses: We performed Wilcoxon rank- sum tests using the deconvoluted cell type proportions to test if certain cell types are more prevalent in one implant than the other. We further examined the association between cell type proportions and gene expression levels in the two implants through Kendall's correlation analyses. All the p- values were adjusted for multiple testing through the false discovery rate approach. The proportions of three cell types (chondrocyte, OLC, and osteocyte) had over 50 significantly positively correlated genes. For each of these cell types, we performed pathway enrichment analysis of the significantly positively correlated genes using Metascape. \(^{66}\) + +<|ref|>text<|/ref|><|det|>[[115, 297, 882, 390]]<|/det|> +Statistical analysis: The results are shown as mean \(\pm\) standard deviation using violin super plots or bar graphs. Statistical analysis was performed using Kyplot software (version 2.0 beta 15). Statistical significance was determined by Student's t- test (flat versus micropillar, two- sided). All experiments presented in the manuscript were repeated at least as two independent experiments with replicates to confirm the results are reproducible. + +<|ref|>sub_title<|/ref|><|det|>[[116, 430, 271, 448]]<|/det|> +## Acknowledgement + +<|ref|>text<|/ref|><|det|>[[115, 458, 882, 760]]<|/det|> +This work was supported by the National Science Foundation (NSF) Emerging Frontiers in Research and Innovation (EFRI) (no. 1830968 to G.A.A.), and National Institutes of Health (NIH) grants U54CA268084 and R01CA228272, NSF grant EFMA- 1830961 (to V.B.). This work was performed as a collaboration between the Center for Advanced Regenerative Engineering (CARE) and the Center for Physical Genomics and Engineering (CPGE) at Northwestern University. This work made use of the EPIC facility, the NUFAB facility, and the BioCryo facility of Northwestern University's NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS- 2025633), the International Institute for Nanotechnology (IIN) and Northwestern's MRSEC programme (NSF DMR- 1720139). Proteomics services were performed by the Northwestern Proteomics Core Facility, generously supported by NCI CCSG P30 CA060553 awarded to the Robert H Lurie Comprehensive Cancer Center, instrumentation award (S10OD025194) from NIH Office of the Director, and the National Resource for Translational and Developmental Proteomics supported by P41 GM108569. We also thank the help from Dr. Hsiu- Ming Tsai at the Department of Radiology, The University of Chicago for microCT imaging. This work also made use of the Northwestern University NUSeq Core and the Biological Imaging Facility (BIF). + +<|ref|>sub_title<|/ref|><|det|>[[115, 800, 208, 817]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 828, 882, 899]]<|/det|> +1 Rippe, K. Dynamic organization of the cell nucleus. Curr. Opin. in Genet. Dev. 17, 373- 380 (2007). +2 Kalukula, Y., Stephens, A. D., Lammerding, J. & Gabriele, S. Mechanics and functional consequences of nuclear deformations. Nat. Rev. Mol. Cell Biol. 23, 583- 602 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 90, 886, 875]]<|/det|> +3 Ramdas, N. M. & Shivashankar, G. V. Cytoskeletal Control of Nuclear Morphology and Chromatin Organization. J. Mol. Biol. 427, 695- 706 (2015).4 Heckenbach, I. et al. Nuclear morphology is a deep learning biomarker of cellular senescence. Nat. Aging 2, 742- 755 (2022).5 Seelbinder, B. et al. Nuclear deformation guides chromatin reorganization in cardiac development and disease. Nat. 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Transient nuclear deformation primes epigenetic state and promotes cell reprogramming. Nat. Mater. 21, 1191- 1199 (2022).20 Shah, P. et al. Nuclear Deformation Causes DNA Damage by Increasing Replication Stress. Curr. Biol. 31, 753- 765. e756 (2021).21 Hanson, L. et al. Vertical nanopillars for in situ probing of nuclear mechanics in adherent cells. Nat. Nanotechnol. 10, 554- 562 (2015).22 Davidson, P. M., Özçelik, H., Hasirci, V., Reiter, G. & Anselme, K. Microstructured Surfaces Cause Severe but Non- Detrimental Deformation of the Cell Nucleus. Adv. Mater. 21, 3586- 3590 (2009).23 Tusamda Wakhloo, N. et al. Actomyosin, vimentin and LINC complex pull on osteosarcoma nuclei to deform on micropillar topography. Biomaterials 234, 119746 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 90, 886, 895]]<|/det|> +24 Cao, X. et al. A Chemomechanical Model for Nuclear Morphology and Stresses during Cell Transendothelial Migration. Biophys. J. 111, 1541- 1552 (2016).25 Liu, R., Yao, X., Liu, X. & Ding, J. Proliferation of Cells with Severe Nuclear Deformation on a Micropillar Array. Langmuir : the ACS journal of surfaces and colloids 35, 284- 299 (2019).26 Carthew, J. et al. Precision Surface Microtopography Regulates Cell Fate via Changes to Actomyosin Contractility and Nuclear Architecture. Adv. Sci. 8, 2003186 (2021).27 Liu, X. et al. Subcellular cell geometry on micropillars regulates stem cell differentiation. Biomaterials 111, 27- 39 (2016).28 Long, Y., Sun, Y., Jin, L., Qin, Y. & Zeng, Y. Micropillars in Biomechanics: Role in Guiding Mesenchymal Stem Cells Differentiation and Bone Regeneration. Adv. Mater. Interfaces 11, 2300703 (2024).29 Xu, H. et al. Citric Acid: A Nexus Between Cellular Mechanisms and Biomaterial Innovations. Adv. Mater. 36, 2402871 (2024).30 Epstein, S. E., Luger, D. & Lipinski, M. J. Paracrine- Mediated Systemic Anti- Inflammatory Activity of Intravenously Administered Mesenchymal Stem Cells. Circ. Res. 121, 1044- 1046 (2017).31 Burdon, T. J., Paul, A., Noiseux, N., Prakash, S. & Shum- Tim, D. Bone Marrow Stem Cell Derived Paracrine Factors for Regenerative Medicine: Current Perspectives and Therapeutic Potential. Bone Marrow Res. 2011, 207326 (2011).32 Wang, Y., Kibbe, M. R. & Ameer, G. A. Photo- crosslinked biodegradable elastomers for controlled nitric oxide delivery. Biomater. Sci. 1, 625- 632 (2013).33 Fernando, S., McEnery, M. & Guelcher, S. A. in Advances in Polyurethane Biomaterials (eds Stuart L. Cooper & Jianjun Guan) 481- 501 (Woodhead Publishing, 2016).34 Ghibaudo, M. et al. Traction forces and rigidity sensing regulate cell functions. Soft Matter 4, 1836- 1843 (2008).35 Badique, F. et al. Directing nuclear deformation on micropillared surfaces by substrate geometry and cytoskeleton organization. Biomaterials 34, 2991- 3001 (2013).36 Ryu, H. et al. Materials and Design Approaches for a Fully Bioresorbable, Electrically Conductive and Mechanically Compliant Cardiac Patch Technology. Adv. Sci. 10, 2303429 (2023).37 Khalili, A. A. & Ahmad, M. R. A Review of Cell Adhesion Studies for Biomedical and Biological Applications. Int. J. Mol. Sci 16, 18149- 18184 (2015).38 Tomczak, A. et al. Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations. Sci. Rep. 8, 5115 (2018).39 Fabregat, A. et al. Reactome pathway analysis: a high- performance in- memory approach. BMC Bioinformatics 18, 142 (2017).40 Lu, P., Takai, K., Weaver, V. M. & Werb, Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harb Perspect Biol. 3 (2011).41 Zeng, Z., Li, Y., Li, Y. & Luo, Y. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol. 23, 83 (2022).42 Yu, M. et al. Cranial Suture Regeneration Mitigates Skull and Neurocognitive Defects in Craniosynostosis. Cell 184, 243- 256. e218 (2021).43 Dillard, L. J. et al. Single- Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population- Level scRNA- Seq Studies. J. Bone Miner. Res. 38, 1350- 1363 (2023). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 90, 886, 875]]<|/det|> +44 Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091- 1107. e1017 (2018).45 Baryawno, N. et al. A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia. Cell 177, 1915- 1932. e1916 (2019).46 Zhong, L. et al. Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment. eLife 9, e54695 (2020).47 Ma, C. et al. Citrate- based materials fuel human stem cells by metabonegenic regulation. Proc. Natl. Acad. Sci. USA 115, E11741- E11750 (2018).48 Woodard, J. R. et al. The mechanical properties and osteoconductivity of hydroxyapatite bone scaffolds with multi- scale porosity. Biomaterials 28, 45- 54 (2007).49 Wang, H., Huddleston, S., Yang, J. & Ameer, G. A. Enabling Proregenerative Medical Devices via Citrate- Based Biomaterials: Transitioning from Inert to Regenerative Biomaterials. Adv. Mater. 36, 2306326 (2024).50 Vilar, A. et al. Substrate mechanical properties bias MSC paracrine activity and therapeutic potential. Acta Biomater. 168, 144- 158 (2023).51 Li, Y. et al. 3D micropattern force triggers YAP nuclear entry by transport across nuclear pores and modulates stem cells paracrine. Natl. Sci. Rev. 10 (2023).52 Karamanos, N. K. et al. A guide to the composition and functions of the extracellular matrix. The FEBS J. 288, 6850- 6912 (2021).53 Saraswathibhatla, A., Indana, D. & Chaudhuri, O. Cell- extracellular matrix mechanotransduction in 3D. Nat. Rev. Mol. Cell Biol. 24, 495- 516 (2023).54 Cui, J. & Zhang, J. Cartilage Oligomeric Matrix Protein, Diseases, and Therapeutic Opportunities. Int. J. Mol. Sci. 23, 9253 (2022).55 Ishida, K. et al. Cartilage oligomeric matrix protein enhances osteogenesis by directly binding and activating bone morphogenetic protein- 2. Bone 55, 23- 35 (2013).56 Zheng, Z., Granado, H. S. & Li, C. Fibromodulin, a Multifunctional Matricellular Modulator. J. Dent. Res. 102, 125- 134 (2023).57 Feng, X. Chemical and Biochemical Basis of Cell- Bone Matrix Interaction in Health and Disease. Curr. Chem. Biol. 3, 189- 196 (2009).58 Alapan, Y., Younesi, M., Akkus, O. & Gurkan, U. A. Anisotropically Stiff 3D Micropillar Niche Induces Extraordinary Cell Alignment and Elongation. Adv. Healthc. Mater. 5, 1884- 1892 (2016).59 Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.- range mass accuracies and proteome- wide protein quantification. Nat. Biotech. 26, 1367- 1372 (2008).60 Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology 16, 284- 287 (2012).61 Qian, J. et al. A pan- cancer blueprint of the heterogeneous tumor microenvironment revealed by single- cell profiling. Cell Res. 30, 745- 762 (2020).62 Stuart, T. et al. Comprehensive Integration of Single- Cell Data. Cell 177, 1888- 1902. e1821 (2019).63 Li, B. et al. Benchmarking spatial and single- cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662- 670 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 884, 211]]<|/det|> +64 Chu, T., Wang, Z., Pe'er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single- cell RNA sequencing in oncology. Nat. Cancer 3, 505- 517 (2022).65 Niec, R. E. et al. Lymphatics act as a signaling hub to regulate intestinal stem cell activity. Cell Stem Cell 29, 1067- 1082. e1018 (2022).66 Zhou, Y. et al. Metascape provides a biologist- oriented resource for the analysis of systems- level datasets. Nat. Commun. 10, 1523 (2019). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 245, 63]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 80, 585, 95]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[55, 108, 955, 202]]<|/det|> +SupplementaryTable1.xlsxSupplementaryTable2.xlsxSupplementaryTable3.xlsxSupplementaryTable4.xlsxSupplementMicrotopographyinducedchangesincellnucleusmorphologyenhanceboneregenerationbymodulatingthecellularsecretome.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb_det.mmd b/preprint/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..38a607ae19c961e6491e852c707179b2e8858e59 --- /dev/null +++ b/preprint/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb_det.mmd @@ -0,0 +1,881 @@ +<|ref|>title<|/ref|><|det|>[[43, 107, 950, 177]]<|/det|> +# Thor: a platform for cell-level investigation of spatial transcriptomics and histology + +<|ref|>text<|/ref|><|det|>[[43, 196, 750, 258]]<|/det|> +Guangyu Wang gwang2@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0000- 0003- 4803- 7200 + +<|ref|>text<|/ref|><|det|>[[43, 262, 744, 320]]<|/det|> +Pengzhi Zhang pzhang@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0000- 0001- 6920- 1490 + +<|ref|>text<|/ref|><|det|>[[43, 325, 570, 383]]<|/det|> +Weiqing Chen wchen5@houstonmethodist.org Cornell University https://orcid.org/0000- 0003- 3539- 9210 + +<|ref|>text<|/ref|><|det|>[[43, 388, 388, 445]]<|/det|> +Tu Tran ttran7@houstonmethodist.org Houston Methodist Research Institute + +<|ref|>text<|/ref|><|det|>[[43, 451, 300, 507]]<|/det|> +Minghao Zhou minghaozhou01@gmail.com University of Florida + +<|ref|>text<|/ref|><|det|>[[43, 514, 750, 572]]<|/det|> +Kaylee Carter kncarter2@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0009- 0002- 8920- 3033 + +<|ref|>text<|/ref|><|det|>[[43, 577, 388, 634]]<|/det|> +Ibrahem Kandel ikandel@houstonmethodist.org Houston Methodist Research Institute + +<|ref|>text<|/ref|><|det|>[[43, 640, 290, 697]]<|/det|> +Shengyu Li sli5@houstonmethodist.org Houston Methodist + +<|ref|>text<|/ref|><|det|>[[43, 704, 750, 761]]<|/det|> +Li Lai llai@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0000- 0002- 5731- 2705 + +<|ref|>text<|/ref|><|det|>[[43, 767, 308, 823]]<|/det|> +Qianqian Song qianqian.song.66@gmail.com University of Florida + +<|ref|>text<|/ref|><|det|>[[43, 830, 750, 888]]<|/det|> +Keith Youker kayouker@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0000- 0003- 2535- 7973 + +<|ref|>text<|/ref|><|det|>[[43, 893, 230, 949]]<|/det|> +Yu Yang yangyu1@ufl.edu University of Florida + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 43, 392, 100]]<|/det|> +Keith Syon Chan kschan@houstonmethodist.org Houston Methodist Research Institute + +<|ref|>text<|/ref|><|det|>[[42, 106, 740, 165]]<|/det|> +Xen Ping Hoi xpinghoi@houstonmethodist.org Houston Methodist Research Institute https://orcid.org/0000- 0001- 7610- 7291 + +<|ref|>text<|/ref|><|det|>[[42, 170, 390, 227]]<|/det|> +Fotis Nikolo fnikolo@houstonmethodist.org Houston Methodist Research Institute + +<|ref|>sub_title<|/ref|><|det|>[[44, 269, 103, 286]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 306, 137, 325]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 344, 220, 364]]<|/det|> +DOI: https://doi.org/ + +<|ref|>text<|/ref|><|det|>[[42, 381, 914, 424]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 442, 535, 462]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[155, 89, 841, 125]]<|/det|> +# Thor: a platform for cell-level investigation of spatial transcriptomics and histology + +<|ref|>text<|/ref|><|det|>[[113, 123, 852, 175]]<|/det|> +Pengzhi Zhang \(^{1,2,3,4,\#}\) , Weiqing Chen \(^{5,\#}\) , Tu Nhi Tran \(^{1,2,3,4}\) , Minghao Zhou \(^{6}\) , Kaylee N. Carter \(^{2}\) , Ibrahem Kandel \(^{1,2,3,4}\) , Shengyu Li \(^{1,2,3,4}\) , Xen Ping Hoi \(^{7,8,9}\) , Keith Youker \(^{4,10}\) , Li Lai \(^{2}\) , Qianqian Song \(^{6}\) , Yu Yang \(^{11}\) , Fotis Nikolos \(^{7,8}\) , Keith Syson Chan \(^{7,8}\) , Guangyu Wang \(^{1,2,3,4}\) + +<|ref|>text<|/ref|><|det|>[[144, 184, 875, 512]]<|/det|> +1. Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, 77030, USA +2. Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, 77030, USA +3. Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, 77030, USA +4. Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA +5. Department of Physiology, Biophysics & Systems Biology, Weill Cornell Graduate School of Medical Science, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA +6. Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA +7. Department of Urology, Houston Methodist Research Institute, Houston, TX, 77030, USA +8. Spatial Omics Core, Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, 77030, USA +9. Graduate Program in Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90069, USA +10. Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, 77030, USA +11. Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 32608, USA +# Those authors contributed equally to the work. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 198, 106]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 106, 880, 460]]<|/det|> +Spatial transcriptomics integrates transcriptomics data with histological tissue images, offering deeper insights into cellular organization and molecular functions. However, existing computational platforms mainly focus on genomic analysis, leaving a gap in the seamless integration of genomic and image analysis. To address this, we introduce Thor, a comprehensive computational platform for multi- modal analysis of spatial transcriptomics and histological images. Thor utilizes an anti- shrinking Markov diffusion method to infer single- cell spatial transcriptomes from spot- level data, effectively integrating cell morphology with spatial transcriptomics. The platform features 10 modules designed for cell- level genomic and image analysis. Additionally, we present Mjolnir, a web- based tool for interactive tissue analysis using vivid gigapixel images that display information on histology, gene expression, pathway enrichment, and immune response. Thor's accuracy was validated through simulations and ISH, MERFISH, Xenium, and Stereo- seq datasets. To demonstrate its versatility, we applied Thor for joint genomic- histology analysis across various datasets. In in- house heart failure patient samples, Thor identified a regenerative signature in heart failure, with protein presence confirmed in blood vessels through immunofluorescence staining. Thor also revealed the layered structure of the mouse olfactory bulb, performed unbiased screening of breast cancer hallmarks, elucidated the heterogeneity of immune responses, and annotated fibrotic regions in multiple heart failure zones using a semi- supervised approach. Furthermore, Thor imputed high- resolution spatial transcriptomics data in an in- house bladder cancer sample sequenced using Visium HD, uncovering stronger spatial patterns that align more closely with histology. Bridging the gap between genomic and image analysis in spatial biology, Thor offers a powerful tool for comprehensive cellular and molecular analysis. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 232, 106]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[112, 107, 880, 444]]<|/det|> +The complex organization of cells within tissues is profoundly connected to their biological function. This underpins the widespread utility of histological images in health and disease. The development of computational methods empowered by deep learning on histological images has drastically enhanced efficiency and accuracy in tissue analysis in diverse applications1, including automated cancer diagnosis2, survival prediction3, histopathology image classification and retrieval4, tissue segmentation5, 6, nucleus and cell segmentation7- 9, and in silico staining10. Furthermore, rapid advancements in high- throughput technologies such as RNA sequencing (RNA- seq) and whole genome sequencing (WGS) are transforming the landscape of conventional histological analysis, offering unprecedented insights beyond tissue images. For example, recent research has demonstrated that the integration of histological images with genomic biomarker mutations and biological pathways leads to accurate predictions of survival across diverse conditions3, 11. In the evolving landscape of biological investigation, spatially resolved molecular technologies have become a pivotal focus for unraveling cellular diversity, tissue organization, and functions. Spatial omics data have been incorporated and routinely acquired by programs such as the human cell atlas (HCA) and the human biomolecular atlas program (HuBMAP), advancing the construction of comprehensive spatial maps featuring various biomolecules, including RNA, proteins, and metabolites12, 13. A widely adopted molecular technology is spatial transcriptomics (ST), which involves slicing tissues into thin layers for hematoxylin and eosin (H&E) staining and spatial sequencing, enabling simultaneous investigation of tissue/cellular phenotype and molecular mechanism on the same slide. + +<|ref|>text<|/ref|><|det|>[[112, 456, 880, 684]]<|/det|> +Recent efforts to advance ST analysis have focused on incorporating spatial neighborhood information14, or integrating histology images15- 17. However, these tools typically operate at subspot or superpixel spatial scales, which do not correspond to individual cells, hindering biologically relevant insights - particularly in contexts requiring cell- level data, such as analyzing ligand- receptor interactions. Another branch of ST analysis frameworks addresses cellular heterogeneity by resolving cell- type compositions within spatial spots18- 20. However, these approaches do not infer cell- level gene expression and are further restricted by the quality and availability of scRNA- seq reference data, especially for formalin- fixed paraffin- embedded (FFPE) tissues where transcriptomic data quality is often compromised. While emerging methods enable cellular- level histological structure analysis21, 22, similarly they do not generate single- cell resolution gene expression matrices, thereby excluding them from downstream functional or molecular analyses. Moreover, those platforms are mostly tailored to specific tasks (e.g. deconvolution), whereas comprehensive analysis platforms (e.g. Seurat) prioritize - omics analysis without deeply analyzing histopathological images23- 25. + +<|ref|>text<|/ref|><|det|>[[112, 696, 877, 858]]<|/det|> +To meet the urgent need for jointly analyzing genomics and histology, we present a multi- modal platform - Thor - for bridging and exploring cellular phenotypes and molecular insights. Thor enhances the incorporation of morphology and transcriptome data of individual cells by inferring cell- resolution transcriptome from spot- level ST data using an anti- shrinking Markov graph diffusion method. Moreover, Thor features extensible modules for comprehensive genomic analyses, such as immune response, functional pathway enrichment, transcription factor (TF) activity, and copy number variation (CNV), alongside tissue analyses such as semi- supervised tissue annotation and nucleus detection. Additionally, we develop Mjolnir, a user- friendly web- based platform for interactive exploration of cellular organization and pathogenesis in tissues, on a laptop, with no coding required. + +<|ref|>text<|/ref|><|det|>[[112, 873, 874, 907]]<|/det|> +We elucidated the principles of Thor and rigorously assessed its effectiveness and accuracy through simulations and various datasets, obtained from high- resolution experimental methods, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 883, 330]]<|/det|> +including in situ hybridization (ISH), multiplexed error- robust fluorescence in situ hybridization (MERFISH)26, spatio- temporal enhanced resolution omics- sequencing (Stereo- seq)27, and Xenium28. Thor outperformed state- of- the- art methods in predicting cell- level ST on a breast cancer dataset using Xenium data as the ground truth. We analyzed a mouse olfactory bulb (MOB) tissue, human breast cancer tissues, and multi- sample heart failure patient tissues. Thor revealed a refined layered structure in MOB and identified distinct gene modules. In heart failure, Thor quantified fibrotic regions across different heart zones. Furthermore, we collected in- house heart failure samples from patients who received a left ventricular assist device (LVAD) implantation to study the signature genes in vascular regeneration. We identified regenerative signatures in heart failure and validated them through immunofluorescence (IF) staining. In breast cancer, Thor conducted an unbiased screening of breast cancer hallmarks, uncovering the intricate heterogeneity of immune responses in tumor regions. In summary, Thor enables comprehensive interpretation of ST data at the single- cell and whole- transcriptome levels, delivering advanced functional insights, and providing an interactive interface for in- depth analyses. + +<|ref|>sub_title<|/ref|><|det|>[[115, 346, 188, 361]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 362, 737, 379]]<|/det|> +## Thor infers cell-resolution spatial transcriptome for multi-modal analysis + +<|ref|>text<|/ref|><|det|>[[112, 378, 880, 604]]<|/det|> +Histological images and high- throughput sequencing data are widely adopted for various applications2, 29- 31. Despite their significance, these two sources of information are often examined independently with separate tools. Sequencing- based ST and the paired histological whole slide image (WSI) capture inherent cellular structures in the tissue at different resolutions, providing complementary information. For example, in human heart tissues with MI, we observed that the projection of histological features onto principal components segregated tissues at cellular resolution (Figure 1a, Supplementary Note 1). Similarly, spatial patterns can be discerned through marker gene expression at a coarser resolution (spot level). Clustering results of spots by using either source of features were consistent and complementary, as demonstrated in the human MI samples, a human ductal carcinoma in situ (DCIS) sample, and a MOB sample (See details in Supplementary Note 1). Previous studies also indicated that spatial gene expression can be predicted or refined based on histological images15- 17. Therefore, we hypothesize that it is feasible to recover cell- level resolution transcriptomics data by learning shared patterns from both the histology and the transcriptome. + +<|ref|>text<|/ref|><|det|>[[112, 617, 881, 907]]<|/det|> +Multi- modal analysis in Thor involves two key steps. First, elevating spot- resolution ST data to single- cell resolution (Figure 1a). Second, in- depth genomics and tissue image analyses (Figure 1b- c). In the first step, Thor (i) applies deep learning methods to segment cells/nuclei from the WSI, termed in silico cells; (ii) extracts morphological and spot- level transcriptomic features into a combinatory feature space to construct a cell- cell network; (iii) creates a Markov transition matrix, representing the probabilities of transitioning from a cell to every other cell in the system in one step; (iv) infers gene expression of the in silico cells by data diffusion with the transition matrix (Figure 1a). Thor represents the cellular patterns using a nearest neighbors graph, where cells are connected according to their distances in the combinatory feature space reflecting the physical separation, and the histological and genomic complexity. The Markov transition matrix is constructed such that information from "homogeneous" spots asymmetrically corrects information from "heterogeneous" spots, where heterogeneity of a spot is determined by the enclosed cells in the combinatory feature space (Figure 1a). In the second step, we establish a standardized genomics analysis framework for in- depth research and clinical practice. The genomics analysis encompasses a wide array of insights, including cell type annotation, immune response analysis, biological functional pathway analysis, differential gene expression analysis, spatially expressed module detection, TF activity analysis, and CNV analysis (Figure 1b). Thor also includes tissue image analysis tools including nucleus segmentation, region of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 878, 155]]<|/det|> +interest (ROI) selection, and semi- supervised ROI annotation. To enhance accessibility and usability, we introduce a web- based platform Mjolnir that seamlessly visualizes both histological images and genomic analyses (Figure 1c). Altogether, Thor elevates tissue analysis by integrating image analysis and genomic insights. + +<|ref|>sub_title<|/ref|><|det|>[[115, 170, 660, 186]]<|/det|> +## Thor demonstrates accuracy and robustness in simulation data + +<|ref|>text<|/ref|><|det|>[[115, 186, 881, 300]]<|/det|> +We systematically evaluated Thor's accuracy and robustness under realistic experimental conditions. We simulated expression profiles for 1,000 genes in 6,579 cells, whose spatial positions were extracted from a mouse cerebellum tissue as the ground truth32; and based on those cells, we created "spots" by aggregating gene expression levels in cells covered by a spot (Figure S1a, see details in Methods: Simulation details). We assessed Thor's prediction accuracy by computing the normalized root mean squared error (NRMSE) between the predicted and the ground- truth gene expression values (see Methods for details). + +<|ref|>text<|/ref|><|det|>[[115, 313, 880, 473]]<|/det|> +We first evaluated Thor's performance under suboptimal histology imaging conditions. Two primary issues can impact its accuracy: (i) missed detection of cell nuclei, which commonly occurs in out- of- focus or high- density regions, and (ii) erroneous cell- cell connections resulting from poor histological features. Under ideal conditions with neither cell dropouts nor randomized connections, Thor's predicted gene expression closely matched the ground truth, yielding a median NRMSE of 0.07 (Figures S1a and S2). Introducing random "missouts" of cells (0%- 40%) lead to a slight increase in median NRMSE from 0.07 to 0.075 (Figure S1b), while introducing randomized connections in 30% - 40% of cells modestly increased the median NRMSE to 0.08 (Figure S1b). These findings suggest that Thor maintains robust prediction accuracy even in the presence of substantial missing cells and disrupted cell connections. + +<|ref|>text<|/ref|><|det|>[[113, 487, 878, 764]]<|/det|> +Next, we examined the spatial resolution, a critical factor in spatial technologies ranging from subcellular scales to \(\sim 100 \mu m\) . Larger spots lead to greater cell heterogeneity within each spot (Figure S3). When we varied the spot diameter from \(25 \mu m\) to \(150 \mu m\) , Thor accurately predicted single- cell gene expression for spots up to \(\sim 100 \mu m\) in diameter, although the median NRMSE increased to 0.08 at \(150 \mu m\) . To further highlight advantages of our algorithm, we compared Thor against three alternative methods: (a) nearest spot method – assigning gene expression based on the nearest spot; (b) k- nearest neighbors (KNN) smoothing method – assigning gene expression by averaging over the nearest twenty cells; and (c) BayesSpace – assigning gene expression based on local spatial neighborhoods of sub- spots. At \(25 \mu m\) , both the nearest spot method and Thor exhibited high accuracy (median NRMSE 0.06). The nearest spot method's performance declined sharply as spot size increased beyond \(25 \mu m\) , while Thor remained accurate with the spot size up to \(100 \mu m\) . This suggests that Thor's superior performance is not solely due to incorporating nucleus segmentation. By contrast, both the KNN smoothing method and BayesSpace performed poorly across all spot sizes, with median NRMSE values of approximately 0.2 (Figure S1c). The KNN smoothing method consistently underperformed, underscoring the benefits of Thor's shared nearest neighbors cell- cell graph and feature- preserving Markov diffusion approach. + +<|ref|>text<|/ref|><|det|>[[115, 778, 880, 891]]<|/det|> +To quantitatively evaluate Thor's performance under increasing spot complexity, we plotted the mean absolute error (MAE) of each cell against the Shannon entropy of cell type proportions. As spot heterogeneity increased, the MAE for the nearest spot method rose sharply; meanwhile, Thor accurately imputed gene expression for both low (Figure S3c, A) and high (Figure S3c, B, C) heterogeneity spots. Although a subset of cells in highly heterogeneous spots showed a slight increase in MAE (Figure S3c, C), Thor's error remained much lower than that of the nearest spot method. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 879, 300]]<|/det|> +Finally, to evaluate Thor's imputation performance under varying dropout levels, an important challenge in high- resolution spatial transcriptomics, we simulated 15 conditions with dropout ratios ranging from \(5\%\) to \(60\%\) and categorized them into three regimes: low dropout \((< 15\%)\) , moderate dropout \((15 - 40\%)\) , and high dropout \((>40\%)\) . We then measured cluster separations in principal component analysis (PCA) space using silhouette coefficients. As shown in the PCA plots (Figure S1d), introducing dropouts severely diminished cluster separations in the ground truth data, with silhouette coefficients reduced from 0.8 to near 0. In contrast, Thor- imputed data maintained the silhouette coefficient to above 0.7- 0.8 in the low- dropout regime, outperforming the KNN smoothing method and BayesSpace. When dropout ratios rose to the moderate regime, where the ground truth data's silhouette coefficients declined to 0.1- 0.4, Thor- imputed data recovered the cluster separation successfully (silhouette coefficients 0.5- 0.6). Even under high- dropout conditions \((>40\%)\) , Thor's scores remained substantially above those of KNN smoothing and BayesSpace. + +<|ref|>text<|/ref|><|det|>[[115, 312, 816, 362]]<|/det|> +Collectively, these analyses highlight Thor's accuracy and robustness in the presence of suboptimal histology or transcriptomics data, including high proportions of missed cells, disrupted cell connections, varying spot sizes, and substantial technical dropouts. + +<|ref|>sub_title<|/ref|><|det|>[[115, 377, 644, 394]]<|/det|> +## Thor infers accurate gene expression at single-cell resolution + +<|ref|>text<|/ref|><|det|>[[113, 393, 876, 730]]<|/det|> +Next, we evaluated Thor on a mouse brain receptor map data acquired by MERFISH. The MERFISH data comprised 483 RNA targets from individual cells (Figure S4a). We simulated Visium- like data within the hippocampus region by creating a grid of evenly spaced "ST spots". The RNA molecule counts in a synthetic spot were aggregated over the cells covered by the "ST spot". These synthetic spots contained a mixture of cells of different cell types, particularly within the hippocampal subregions CA1/2/3 and the dentate gyrus (DG; Figure S4a). Thor connected cells of the same cell types by proximity in the morphological feature space and the spatial space, as illustrated by the cell- cell network in CA1 and DG (Figure S5a; note the cell type information was not provided to Thor). Thor successfully predicted cell- level gene expression in these heterogeneous regions evidenced by the profiles of selected marker genes (Figures S4b and S5b). For instance, Thor recovered Adra1d expression in CA1 and DG, which was missing in the spot- level data and the BayesSpace result. Furthermore, to gain a global view of the similarity between the in silico cells and the MERFISH cells, we projected the high- dimensional gene expression matrices to a joint uniform manifold approximation and projection (UMAP) embedding. The in silico cells inferred by Thor seamlessly mixed with the MERFISH cells on UMAP, and the distribution of cell type clusters of the in silico cells matched the ground- truth cell types (Figure S4c). As a baseline, mixtures of cell types were aggregated in the spot- level data, resulting in a low silhouette coefficient and Calinski- Harabasz index when mapped to the nearest cells. Thor substantially improved the cell type separation, achieving a silhouette score of 0.45 and a high Calinski- Harabasz index of 10,000, and outperformed BayesSpace by a large margin (Figure S4d). + +<|ref|>text<|/ref|><|det|>[[114, 742, 875, 905]]<|/det|> +We further applied Thor to a Visium dataset of human breast cancer tissue and compared the result against a Xenium reference dataset of the adjacent tissue section28. Using transcriptome data from the Visium dataset and the post- Xenium H&E image as input, Thor successfully inferred in silico cell- level gene expression. Visually, the spatial patterns of gene expression align closely with Xenium data (Figure 2a). To gain a global view, we clustered the in silico cell- level gene expression using conventional single- cell RNA- seq (scRNA- seq) clustering. The same major cell types were identified from the in silico cells as from the Xenium data, as evidenced by the spatial distribution of the cell types and the mean expression heatmap of marker genes of each cell type (Figure 2b). Additionally, integrating the predicted in silico cells with the Xenium cells showed that cells from the same cell types colocalize from both datasets + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 850, 125]]<|/det|> +(Figure S6), indicating Thor's ability to predict accurate and biologically meaningful cell- level gene expressions. + +<|ref|>text<|/ref|><|det|>[[115, 137, 877, 410]]<|/det|> +For a quantitative evaluation, we benchmarked Thor with three state- of- the- art methods of enhancing ST to near- cell resolution14, 15, 17. The spatial units vary among those tools (Thor: cell, iStar: superpixel, BayesSpace: subspot, and TESLA: superpixel), therefore, we calculated both image- centric and cell- centric metrics to provide a more complete evaluation. On the one hand, by converting spatial profiles of gene expression data into images, we compared the similarities between the predicted spatial patterns with the Xenium spatial patterns using the metrics structural similarity index measure (SSIM) and root mean squared error (RMSE) of pixel values. On the other hand, by mapping the pixel expression data to the cells using the nearest neighbors approach, we compared the deviations between the resulted cell- level gene expression with the Xenium data using cell- wise RMSE as an additional metric. Thor achieved the highest similarity with the Xenium data on all the metrics (Figures 2c and S7). When using the cell- wise RMSE, the general trend remains, yet the difference between the four methods became less prominent. This is likely because all the gene expression levels including Thor needed to be mapped to the common cell positions (Xenium cells) using nearest neighbors before calculating cell- wise RMSE, which might have smoothed out some intricate details in the spatial pattern, as seen in Figure S7(c- d). Overall, Thor demonstrated significantly better agreement with the Xenium data. + +<|ref|>text<|/ref|><|det|>[[115, 422, 879, 666]]<|/det|> +To gain more insights into Thor's unique advantage, we compared the expression profiles of representative genes with second best performing tool, iStar. Thor and iStar enhanced spatial resolution to (near) cell resolution, iStar at times introduced artifacts, including excessive fusion, for instance, at segment boundaries (Figure 2d, red arrows), and in regions with sparse cells (Figure 2d, blue arrow). For example, the spatial expression of myoepithelial marker DST inferred by Thor accurately outlined the boundaries of three DCIS regions in ROI 5 (Figure 2d), as confirmed by the Xenium data and the H&E staining image. While Thor did not maintain the spatial gradient pattern due to misdetection of flat nuclei around certain region boundaries, iStar introduced excessive fusion in the tumor regions, as indicated by the red arrows in Figure 2d. Additional examples are provided in Figures S8- 9. These artifacts are likely due to that iStar predicts the expression of super- pixel patches of the whole slide image, rather than a cell. This approach may result in the omission of valuable cellular morphology information. In contrast, Thor takes a fundamentally different approach by considering a cell as the minimum biological unit and can accurately infer single- cell gene expression via a cell- cell network constructed with the integration of transcriptomics and histology data. + +<|ref|>sub_title<|/ref|><|det|>[[115, 679, 637, 696]]<|/det|> +## Thor unveils refined tissue structure in mouse olfactory bulb + +<|ref|>text<|/ref|><|det|>[[115, 696, 879, 874]]<|/det|> +Thor unveils refined tissue structure in mouse olfactory bulbWe extended our evaluation of Thor- inferred gene expression levels on a MOB dataset collected by Visium. We compared the inferred molecular patterns with those acquired from high- resolution techniques, including the ISH images33 and Stereo- seq data27. Results showed the spatial patterns of gene expression levels inferred by Thor aligned well with both ISH and Stereo- seq data (Figures S10a and S11). For example, Eomes is a marker gene of cells in the glomerular layer and mitral layer34, as observed in the ISH and Stereo- seq data. However, due to the limited spatial resolution, the spot- level Visium data failed to adequately capture the pattern in the mitral layer and exhibited discontinuities in the glomerular layer. By integrating the high- resolution H&E image with the spot- resolution ST, Thor recovered the spatial patterns marked by Eomes in glomerular and mitral layers (Figure S10a). Detailed gene expression profiles from Thor, ISH, Stereo- seq, and Visium, were provided for comparison in Figure S11. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 878, 266]]<|/det|> +At the whole- transcriptome level, the in silico cell clusters dissected six main layers in the MOB, the subependymal zone (SEZ), two granule layers, the mitral layer, the glomerular layer, and the olfactory nerve layer (Figure S10b). We further applied Cell- ID35 to infer cell types (see Method; Signature genes are provided in Supplementary Table S1). By integrating ST with spatial locations and histological features, Thor resolved refined neuron subtypes. For example, Thor distinguished granule cells (GCs) between GC- 1 and GC- 2 subtypes, with GC- 1 concentrated in the internal plexiform layer and GC- 2 predominantly in the granule cell layer. Additionally, Thor separated mitral cells (M/TCs) into M/TC- 1 and M/TC- 2 subtypes, with M/TC- 2 concentrated in the mitral layer and M/TC- 1 extending into the glomerular layer. These results demonstrated Thor's capability to refine cell type classification by leveraging histology and spatial transcriptomic data. + +<|ref|>text<|/ref|><|det|>[[113, 280, 880, 460]]<|/det|> +Leveraging the cell- resolution spatial profiles, we next identified genes with spatially dependent activation patterns and coordinated gene modules using the package Hotspot36. The genes in the in silico cells formed 8 gene modules reflecting the primary structure of MOB (Figure S10c), with modules '2', '4', '7', and '8' capturing the glomerular layer, the mitral layer, the granule layers, and the olfactory nerve layers, respectively (Figure S10d). Remarkably, module '4' captured the thin mitral layer (thickness \(< 40 \mu m\) ), indicating successful resolution- enhancement by Thor, enriching a thin layer of the M/TC- 2 mitral cell subtype. The gene ontology (GO) pathway enrichment analysis of the layer- specific gene modules suggested a cascade of activities covering odor information sensory, processing, signal transmission, and memory formation in MOB layers. Together, Thor unveiled refined layers in the MOB tissue by accurately inferring cell- level gene expression data, aligning with various experimental measurements. + +<|ref|>sub_title<|/ref|><|det|>[[115, 472, 825, 504]]<|/det|> +## Thor supports semi-supervised annotation of fibrotic regions in human myocardial infarction tissues + +<|ref|>text<|/ref|><|det|>[[115, 504, 879, 618]]<|/det|> +To better leverage the combinatory space of histological and transcriptomic features, we developed a human- in- the- loop tool for enhanced identification of tissue regions or spatial domains. The semi- supervised annotation tool operates within Mjolnir, enabling researchers to annotate small representative regions using marker gene expression and morphology of cells in gigapixel resolution images. These transcriptome- and morphology- guided annotations can then be quickly propagated across the entire tissue section based on Pearson correlation of the combinatory features, facilitating comprehensive tissue characterization. + +<|ref|>text<|/ref|><|det|>[[115, 631, 880, 793]]<|/det|> +We first quantitatively evaluated Thor's semi- supervised annotation using a cohort of heart tissue samples37, which included high- resolution H&E images, high- quality ST data, and spot- level expert annotations for key tissue types in heart including vessel, node, adipose, and fibrosis. Thor's semi- supervised annotations demonstrated strong concordance with expert annotations, achieving accuracy ranges of 0.94- 0.99 for vessels, 0.92- 0.98 for nodes, 0.84- 0.92 for adipose, and 0.92- 0.93 for fibrosis (Figure S12). In contrast, spot- level clustering, even with optimized parameters, struggled to distinguish structures such as vessels (enriched with smooth muscle cells) from certain myocardium regions (Figure S13). These results suggest Thor enhances spatial tissue annotation by integrating histology with transcriptomics, surpassing spot- level clustering. + +<|ref|>text<|/ref|><|det|>[[115, 807, 879, 905]]<|/det|> +Next, we applied Thor to analyze six myocardial infarction (MI) patient samples, comprising two necrotic zones (ischemic zone, IZ), two unaffected zones (remote zone, RZ), and two late- stage fibrotic zones (FZ), to enable granular characterization of these distinct tissue zones in heart failure. Using the Mjolnir platform, we first defined an ROI based on fibroblast marker gene expression (PDGFRA and FBLN2) and morphological patterns in an H&E image. Thor then automatically extended the curated ROIs by identifying similar cells in the entire tissue. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 870, 188]]<|/det|> +expression profiles of representative genes, including fibroblast marker genes and cardiac muscle- associated genes, displayed coherent patterns in the curated ROIs and the discovered cells (Figures 3a and S14- 17). Such semi- supervised annotation revealed dense fibrotic areas and shallow areas which were otherwise difficult to identify manually (Figures 3b, S16). The resulting fractions of fibrotic areas in the six samples increased in the order of RZ, IZ, and FZ (Figure 3c). + +<|ref|>text<|/ref|><|det|>[[113, 203, 880, 460]]<|/det|> +The precisely annotated fibrotic regions then enabled unbiased functional analysis. For each sample, we performed differential gene expression analysis between cells in the fibrotic and non- fibrotic regions (lists of differentially expressed genes are provided in Supplementary Table S2), followed by GO pathway enrichment analysis. Irrespective of sample zones, the fibrotic regions showed significant enrichment of pathways such as positive regulation of T cell proliferation, fibroblast proliferation, stress fiber assembly, and collagen fibril organization, whereas myocardium- related pathways were enriched in non- fibrotic regions (Figures 3a, d and S18a). These findings align with previous evidence of T cell proliferation and fibroblast- mediated T cell activation in cardiac settings38, 39. Interestingly, the fibrotic regions of RZ samples demonstrated more pronounced inflammation and fibrosis, likely reflecting heterogeneous progression of ischemic injury among the patient samples. After myocardial infarction, tissue in the immediate infarct area often undergoes rapid cell death and necrosis, whereas distant/remote zones may experience a delayed and prolonged inflammatory and fibrotic response40, 41. While IZ and FZ contained the largest proportions of fibrotic regions at the whole tissue level (Figure 3c), those findings demonstrate that functionally distinct fibrotic domains can exist outside necrotic regions. + +<|ref|>text<|/ref|><|det|>[[114, 473, 874, 603]]<|/det|> +To identify regulatory factors influencing those fibrotic regions, we estimated TF activities by utilizing a gene regulatory network database42. Compared to non- fibrotic regions, the most prominently activated TFs induced critical pathways, such as epithelial- mesenchymal transition (TWIST2 and SNAI2) and immune response (STAT4 and MYB; Figures 3e and S18b). The detected top regulating TFs agreed with existing studies: SMAD3 has been identified as a principal mediator of the fibrotic response to activate cardiac fibroblasts43; SP1/1 has been reported as an essential orchestrator of the pro- fibrotic gene expression program in multiple human organs44. + +<|ref|>text<|/ref|><|det|>[[115, 617, 855, 683]]<|/det|> +Overall, Thor's semi- supervised annotation provides a more nuanced view by integrating morphological features with transcriptomics. This approach refines fibrotic tissue boundaries, highlights subtle variations in fibrotic progression, and provides functional insights into the molecular drivers of post- infarction cardiac fibrosis. + +<|ref|>sub_title<|/ref|><|det|>[[115, 697, 574, 714]]<|/det|> +## Thor discovers regenerative signature in heart failure + +<|ref|>text<|/ref|><|det|>[[115, 714, 872, 795]]<|/det|> +Spot- level spatial transcriptomics often struggles to reveal intricate patterns in small or narrow regions due to limitations in spatial resolution. One such example is to identify the regenerative signatures in vessel regions. Thor allows for the exploration of gene expression in cell- resolution spatial contexts by predicting gene expression in cells detected from the histological images, thereby enhancing the ability to uncover intricate patterns. + +<|ref|>text<|/ref|><|det|>[[115, 809, 872, 906]]<|/det|> +In patients with advanced heart failure, LVADs are commonly used before heart transplantation for cardiac support which provide evident improvement in the structure and function of the heart45, 46. Thus, we applied Thor to in- house heart tissues collected from post- LVAD implantation patients to identify genes driving regenerative remodeling. As the vasculature system plays an important role in cardiac recovery47, we prioritized our analysis in the vascular regions. Blood vessels typically consist of three layers, tunica intima, tunica media, and tunica + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 90, 878, 444]]<|/det|> +adventitia, from inside to outside. The middle layer is mostly comprised of smooth muscle cells. In Mjolnir, based on the cell phenotypes and the expression levels of the smooth muscle marker MYH11, we annotated 29 and 11 vessel internal regions on two post- LVAD heart tissues (Figures 4a and S19a- c). We extracted highly expressed genes in these vessels, finding 56 genes common to both tissues (Figures 4b and S19d). Excluding smooth muscle markers (such as TAGLN, ACTA2, MYH11, and MYLK), PLA2G2A stood out. PLA2G2A was reported to promote cell proliferation, angiogenesis, and tissue regeneration48 in several tumor types. In the cardiovascular field, another study showed that the PLA2G2A is specifically expressed in donor heart fibroblasts compared with the failing heart fibroblasts49. Our previous work highlighted fibroblasts' role in revascularization50, 51, leading us to hypothesize that PLA2G2A expression is a signature of cardiovascular regeneration. To validate this hypothesis, we divided the vessel cells into PLA2G2A+ and PLA2G2A- groups based on the distribution of PLA2G2A expression (Figure 4c). We found that upregulated genes in PLA2G2A+ cells were enriched in pathways including tube morphogenesis and blood vessel morphogenesis and development (Figure 4d). The expression levels of PLA2G2A in the vessels across two patients exhibited an apparent pattern: high PLA2G2A expression was linked to vessels surrounded by connective or adipose tissues while low expression was associated with vessels surrounded by myocardium (Figure S19e- f). Follow- up immunofluorescence staining of tissues from two post- LVAD patients further confirmed PLA2G2A presence in vessels at the protein level, supporting its role in heart recovery (Figure 4e). Altogether, Thor's histology- transcriptome joint analysis revealed cell- resolution gene expression patterns and identified crucial molecules that may drive vascular regeneration in heart tissues. + +<|ref|>sub_title<|/ref|><|det|>[[115, 457, 684, 474]]<|/det|> +## Thor enables multi-layered investigation of hallmarks in DCIS data + +<|ref|>text<|/ref|><|det|>[[112, 474, 875, 631]]<|/det|> +Thor offers rich layers of information through streamlined multi- modal analyses within a unified platform. To showcase Thor's strengths and functions, we analyzed a well- validated DCIS dataset that has been used as benchmarks widely18, 52. DCIS is a potential precursor to invasive ductal carcinoma, a condition that can progress into a form requiring surgical intervention and radiotherapy. Understanding the heterogeneity of various DCIS regions is crucial for elucidating the factors driving their diverse behavior. The DCIS dataset comprises 18 pathologist- annotated major tumor regions (T1- T18; Figure 5a). Histological features of segmented cells identified distinct clusters, underscoring their ability to distinguish between tissue regions (Figure 5b; Supplementary Note 1). Through integrated histological features and ST analyses, Thor enabled a multi- layered investigation of breast cancer hallmarks. + +<|ref|>text<|/ref|><|det|>[[112, 646, 878, 760]]<|/det|> +First, Thor facilitates cell type annotation at single- cell level. The spatial distribution of annotated cell types aligned with the results from state- of- the- art methods such as CytoSPACE and RCTD18, 53 (Figures 5c and S20). The signature genes of each cell type are provided in Supplementary Table S3 for reference. While these methods require scRNA- seq reference data, Thor overcomes the limitation by integrating the underused histological features with ST. Additionally, Thor's advantage lies in providing gene expression for individual cells detected directly from the tissue image for additional analysis, maintaining cells' spatial arrangement. + +<|ref|>text<|/ref|><|det|>[[112, 774, 877, 904]]<|/det|> +Second, Mjolnir enables interactive exploration of the spatial profiles of key molecules on the gigapixel histological images seamlessly at various zoom levels spanning from the whole tissue to the cellular scale. As an example, the visualization of VEGFA, a pivotal angiogenic factor influencing tumor growth and metastasis, highlighted distinct abundance levels within tumor subpopulations at the cellular resolution (Figure 5d). Additional gene expression profiles at both spot and in silico cell levels were provided in Figure S21. A closer examination of the tumor region T1 using Thor revealed the morphological features and the nuanced expression patterns of the cancer cells. VEGFA exhibited the highest expression at the center of the tumor region + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 870, 124]]<|/det|> +T1, gradually decreasing in abundance towards the boundary; and was minimally expressed in the myeloid cell population outside of T1. + +<|ref|>text<|/ref|><|det|>[[113, 137, 877, 252]]<|/det|> +Third, Thor enables efficient search of similar cells in the combinatory space of histological and transcriptomic features. We curated a small set of tumor cells in T8 based on cell morphology and the key gene expression profiles. Cells in most tumor regions were successfully identified (Figure 5e; accuracy: 0.83). Interestingly, hardly any tumor cells in T7 matched the curated set, likely due to its distinct immune microenvironment. Instead, tumor cells in T7 were effectively identified using a separate set of curated cells within T7 (Figure S22). This demonstrates Thor's precision in identifying tumor cells through integrated analysis. + +<|ref|>text<|/ref|><|det|>[[113, 264, 878, 394]]<|/det|> +Using only the H&E image, the clustering- constrained- attention multiple- instance learning (CLAM) method2 identified high- attention regions (Figure 5e) that broadly overlapped with pathology- annotated tumor areas (Figure 5a). However, CLAM also identified adipose tissue as high- attention region, which was not directly relevant to cancer (black box in Figure 5e). These false positives happen for patterns which are not strongly represented in the negative samples2, and may require additional training of CLAM on curated datasets of labelled WSIs for more improved specificity. This demonstrated the value of tissue image analysis for tumor detection while highlighting the need for further multi- modal integration to reduce false positives. + +<|ref|>text<|/ref|><|det|>[[113, 408, 880, 585]]<|/det|> +Fourth, Thor's cell- level molecular signature and pathway enrichment analysis provided deeper insights into the heterogeneity of tumor progression. By examining spatial patterns of oncogenes and tumor suppressors, we observed a marked contrast between ERBB2 (HER2; an oncogene) and ATM (a tumor suppressor)54: ERBB2 was highly expressed across all tumor regions, whereas ATM was upregulated exclusively in region T7 (Figure S23). An unbiased investigation of cancer hallmark pathways further highlighted their complexity across different tumor regions at the cell level, including DNA repair, a crucial process for maintaining DNA integrity and preventing mutations (Figure S24). Notably, despite the low expression of ESR1 (Figure S21), the estrogen response pathway still showed significant enrichment in tumor regions (Figure S24), emphasizing the power of pathway- based analyses to refine breast cancer classification. + +<|ref|>text<|/ref|><|det|>[[113, 600, 877, 857]]<|/det|> +Lastly, genomic CNV inference from Thor's cell- level transcriptome classified tumor and normal cells. Thor successfully uncovered genome- wide CNV profiles in DCIS (Figure 5f), achieving an F1 score of 0.78 and a Jaccard index of 0.64 (Figure 5g), which closely aligned with pathology- annotated tumor regions and surpassed spot- level CNV analyses (F1 score: 0.73; Jaccard index: 0.58; Figure 5g). Unlike spot- level CNV, which averages all cells in a spot, and can misrepresent regions containing both aneuploid and diploid cells, Thor's single- cell approach accurately detected mixed populations, as exemplified by tumor region T7. Spot- level analysis labeled this entire region as aneuploid, whereas Thor- inferred and CytoSPACE- mapped single- cell data identified a mixture of aneuploid and diploid cells. Thor further revealed key copy number aberrations across all tumor cells, including gains in 1, 2q, 8q, 12p, and 18p and losses in 5, 8p, 11q, and 12q. These aberrations highlighted well- known breast cancer- associated genes, such as MDM4, ZNF595, FGFR4, HIST1H1B, TPD52, DECR1, GRB7, and JUP55. CNV analyses provide critical insights into the genomic alterations that underpin tumor heterogeneity and progression, offering potential biomarkers for prognosis and therapeutic targets. Altogether, Through Thor's unified platform of integrated analyses of histology and transcriptomics data, Thor offers an unbiased, multi- layered view of breast cancer hallmarks. + +<|ref|>text<|/ref|><|det|>[[113, 873, 744, 891]]<|/det|> +Thor reveals heterogeneity of immune response in tumor regions of DCIS + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 235]]<|/det|> +We further investigated cell- level immune responses in DCIS by computing the well- established "TLS score" to quantitively capture local immune activity around the tumor regions55. For each cell, the score was calculated by comparing the averaged RNA expression levels of 29 signature genes, including key markers of immune cells such as T cells, monocytes, macrophages, and fibroblasts56, to the average expression of randomized control genes (Figure 6a, see Methods). We then ranked tumor regions based on the median TLS scores of cells residing within each region, along with those in a narrow peritumoral layer (one spot- size outward from the tumor boundary). Regions T7, T1, and T17 exhibited the highest median TLS scores, indicative of robust immune activity (Figure 6b). + +<|ref|>text<|/ref|><|det|>[[114, 249, 877, 409]]<|/det|> +To gain deeper insight into the molecular distinctions of these high- and low- scoring regions, we performed differential gene expression analyses comparing tumor regions with the highest TLS scores (T7, T1, and T17) and those with the lowest (T11, T6, and T15). Several immune- related genes showed pronounced variation: for example, CD84 and SMAD3 were abundant in T7 but nearly undetectable in T15 (Figures 6c and S25), whereas KANK1, often relevant in cancer prognosis, was highly expressed in T6 and T15 but absent in T7. We further examined functional distinctions and interactions between tumor regions and their immediate peritumoral neighbors (Figure S25b). T7 was enriched for pathways linked to immune responses and T cell co- stimulation, whereas T15 was enriched for tumor- related pathways such as hypoxia response and cell adhesion. + +<|ref|>text<|/ref|><|det|>[[114, 423, 870, 600]]<|/det|> +Finally, we conducted unbiased region- specific pathway enrichment analysis based on upregulated genes in each tumor region (compared to the remaining tissue). As expected from the high TLS scores, T7- specific genes were linked to immune response, T cell activation, and inflammatory response pathways, while T15- specific genes were associated with hypoxia response and cell- cell adhesion (Figure 6c). A global heatmap (Figure 6d) illustrated that other tumor regions, such as T9, T14, and T13, also displayed strong enrichment for inflammatory and immune pathways. Notably, high- scoring regions like T7 and T1 showed enrichment of B cell activation pathway, suggesting more robust immune microenvironments that may be therapeutically relevant. By mapping these immune landscapes at single- cell resolution, Thor provided valuable insights into the functional heterogeneity among tumor regions, supporting a refined understanding of immune- tumor interactions in DCIS. + +<|ref|>sub_title<|/ref|><|det|>[[115, 616, 783, 633]]<|/det|> +## Thor enhances gene expression imputation in high-resolution Visium HD data + +<|ref|>text<|/ref|><|det|>[[114, 633, 880, 826]]<|/det|> +Recent advances in spatial transcriptomics technologies are pushing toward cellular or even subcellular resolution, yet these high- resolution methods still face challenges such as substantial dropout and technical noise. To demonstrate Thor's effectiveness under these conditions, we generated a high- resolution dataset from an in- house bladder cancer sample using Visium HD. In our experiment, despite the spatial resolution of up to \(2 \mu m\) square bins (aggregated into \(8 \mu m\) square bins for analyses as recommended by 10x Genomics), Visium HD data exhibited high technical noise. For example, PTPRC (a lymphoid marker) appeared sparsely distributed in immune- rich niches, while SPINK1 (a urothelium- associated gene) was erroneously detected in non- tissue regions (Figure S26a). We applied Thor to integrate the \(2 \mu m\) square bins with the histology image. Thor's cell- level imputation yielded more coherent expression patterns than \(8 \mu m\) square bins. The that correctly localized PTPRC to immune areas and SPINK1 to the tumor boundary, aligning with pathology annotations. + +<|ref|>text<|/ref|><|det|>[[115, 842, 867, 891]]<|/det|> +Beyond single- gene assessments, Thor- imputed data captured distinct cell populations more accurately. For instance, cluster 7 in Thor's results precisely matched the pathology- annotated immune cell regions (Figure S26b), whereas the raw bin- level data overestimated immune cell + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 875, 157]]<|/det|> +presence (Figure S26c). Similar overestimation of certain cell types was also reported recently in Visium HD data57. Taking together, these proof- of- concept analyses underscore Thor's ability to refine gene expression signals and enhance biological interpretability in high- resolution ST datasets. + +<|ref|>sub_title<|/ref|><|det|>[[115, 174, 475, 191]]<|/det|> +## Robustness of Thor to parameter settings + +<|ref|>text<|/ref|><|det|>[[112, 191, 877, 562]]<|/det|> +Thor is designed to be highly flexible, allowing customization of various parameters that control the preprocessing of image/transcriptome data, cell- cell graph construction, and the Markov diffusion process. To evaluate Thor's robustness, we conducted a systematic sensitivity analysis of key parameters, including the diffusion step size \(t\) , the number of cell neighbors \(k\) , and the number of principal components \(nPC\) of the transcriptome data. Thor constructs a shared nearest neighbors (SNN) cell- cell graph based on the \(k\) - nearest neighbors in the combinatory space. First, we tested a range of \(k\) values on the MOB dataset while keeping other parameters fixed ( \(t = 40\) and \(nPC = 10\) ). To reduce bias from highly expressed genes, we applied z- score normalization for each gene. We then calculated the Pearson correlation coefficients ( \(r\) ) across each pair of \(k\) settings. Thor demonstrated strong robustness for \(k\) values between 4 and 10, with a mean \(r = 0.88\) and standard deviation (std) = 0.09. However, very small \(k\) values (< 3) may produce disconnected cell graphs, whereas very large \(k\) values (40- 100) may lead to over- smoothing and weaker correlations with the results of other \(k\) values (mean \(r = 0.56\) , std = 0.27). Second, we evaluated the impact of varying \(nPC\) values while fixing \(k = 5\) and \(t = 40\) . As shown in Figure S27a, Thor remains highly robust when \(nPC \geq 8\) (mean \(r = 0.94\) , std = 0.05). In contrast, \(nPC < 4\) fails to capture sufficient complexity in the data, leading to lower correlations with high \(nPC\) values. Third, we also evaluated a range of diffusion time \(t\) while keeping \(nPC = 10\) and \(k = 5\) fixed. Thor converged after approximately 10 diffusion steps, achieving a mean \(r = 0.90\) (std = 0.10) for \(t = 10\) . However, large \(t\) values (e.g. \(t > 50\) ) may notably increase run time without significant performance gains (Figure S27b). Overall, our analyses show that Thor is robust to a broad range of \(t\) , \(k\) , and \(nPC\) values. These findings indicate that minor adjustments within reasonable parameter ranges have minimal effect on Thor's results, which justifies that we kept a common set of parameters across all case studies. + +<|ref|>text<|/ref|><|det|>[[113, 575, 870, 785]]<|/det|> +Moreover, variational autoencoder (VAE) is widely used for RNA- seq data analysis58- 60. Thor can utilize the latent representation in VAE for faster predictions. In the fast mode, the Markov diffusion is conducted on the VAE latent embeddings. The hyperparameter tuning, such as adjusting the input and latent dimensions of VAE can affect the results of Thor and contributes to generalizability. The input dimension should depend on the genes of interest, such as highly variable genes or spatially variable genes. Moreover, a proper latent dimension should sufficiently capture the biological complexity in the data. For instance, a latent dimension of 10 is set by default in scvi- tools59, with 20 or 30 being appropriate for more complex scRNA- seq datasets. We evaluated Thor's performance on the MOB dataset by varying the latent dimensions in separate VAE models (8, 16, 20, 32, 64, and 128), while keeping other parameters fixed (\(nPC = 10\), \(k = 5\), and \(t = 40\)). Thor- predicted gene expressions remained highly consistent with Pearson's \(r > 0.85\) across all settings (Figure S27c). These results indicate that Thor is robust to a broad range of parameter settings. + +<|ref|>sub_title<|/ref|><|det|>[[115, 802, 222, 818]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 819, 760, 836]]<|/det|> +Thor is an extensible and customizable platform detailed in the following aspects. + +<|ref|>text<|/ref|><|det|>[[115, 836, 880, 884]]<|/det|> +(i) The cell-level ST broadens the spectrum of downstream analyses to those originally designed for scRNA-seq data. Outputs from Thor are ready to be interfaced with a variety of existing libraries for analyses such as Squidpy24 and stLearn25 and can be easily adapted for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 857, 124]]<|/det|> +scRNA- seq tools. Currently, Thor has included submodules such as cell- specific pathway enrichment \(^{61}\) , inference of genomic CNV profiles \(^{62}\) , and ligand- receptor analysis \(^{63}\) . + +<|ref|>text<|/ref|><|det|>[[115, 123, 875, 234]]<|/det|> +(ii) Thor supports customized cell features for building the cell-cell network. In this work, we highlight Thor's performance using intensity-based morphological features such as color intensities of the staining image patches. The inclusion of more task-relevant features elevates the quality of the cell-cell network. For example, research has shown that spatial cellular graphs built from multiplexed immunofluorescence data enable the better modeling of disease-relevant microenvironments \(^{64}\) . In addition, Thor supports direct input of a cell-cell network adjacency matrix. + +<|ref|>text<|/ref|><|det|>[[115, 233, 872, 409]]<|/det|> +(iii) Beyond spatial transcriptomics, emerging omics technology such as spatial metabolomics and proteomics are increasingly adopted to capture local metabolic or protein-level processes that underlie key tissue functions and disease mechanisms. While our current work focuses on applying Thor to spatial transcriptomics, we envision that its underlying framework, which constructs a cell-cell graph from spot-level data, cell coordinates, and histological features, then refines those data through graph diffusion, could be adapted for spatial metabolomics or proteomics as well. By substituting transcriptomic values with metabolomic or proteomic intensities, Thor could enable a more comprehensive, multi-omic view of tissue biology at single-cell resolution. We anticipate that future developments will provide deeper insights into complex tissue characterizations by integrating these additional modalities. + +<|ref|>text<|/ref|><|det|>[[113, 423, 872, 633]]<|/det|> +Thor integrates histological features and transcriptomic features by inferring cell- level ST. Notably, Thor does not require any additional scRNA- seq data as a reference. This not only reduces the sequencing cost but also proves practically advantageous in FFPE tissues. FFPE tissues serve as the most abundant specimens for longitudinal studies with preserved tissue morphological details, yet RNA- seq profiling encounters hurdles due to RNA crosslinking, modifications, and degradation. The Visium platform offers a solution for profiling mRNA levels in both fresh- frozen and FFPE tissues, employing a de- crosslinking process \(^{65}\) . Nevertheless, it falls short of providing cellular- level resolution. In contrast, commonly used methods like chromogenic immunohistochemistry (IHC) for assessing in situ biomarker expression in FFPE tissues are limited by the number of analytes, non- linear staining intensity, and the subjective nature of the quantitative analysis \(^{66}\) . Thor strategically leverages the advantages of Visium and overcomes these challenges by delivering cell- level whole- transcriptome analysis, reducing the cost and workload. + +<|ref|>text<|/ref|><|det|>[[113, 647, 876, 905]]<|/det|> +Thor offers several advantages over existing frameworks for studying histological structures. PROST uses spatial relationships and transcriptomics data to identify spatially variable genes and to cluster spatial domains, but it does not enhance the resolution of the original ST data \(^{21}\) . Thus, with Visium data, PROST operates at the Visium- spot level and does not utilize histology images. By contrast, Thor integrates cell- level features from histology images with spot- level transcriptomics, enabling inference of gene expression at the single- cell level and providing a more granular analysis of histological structures from Visium data. METI, meanwhile, is an end- to- end framework tailored to cancer ST data, mapping tumor cells and the surrounding microenvironment primarily in oncology- focused contexts \(^{22}\) . Thor, on the other hand, was conceived as a more generalizable approach applicable across various tissues, disease states, and organisms. Moreover, neither PROST nor METI directly output single- cell gene expression. In contrast, Thor integrates histological and transcriptomic data in a task- agnostic manner to infer spatially resolved single- cell gene expression. This capability supports a broad range of downstream analyses and comes bundled with extensive analytical modules, including pathway enrichment, spatial gene module identification, differential gene expression, transcription factor activity estimation, and interactive whole- slide data visualization. Taken together, these features + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 866, 123]]<|/det|> +allow Thor to complement and extend the capabilities of frameworks by offering deeper spatial and molecular insights into tissue architecture. + +<|ref|>text<|/ref|><|det|>[[115, 137, 880, 331]]<|/det|> +Thor stands out as a comprehensive, user- friendly platform designed for multi- modal tissue analyses. As a model- based computational method, Thor operates efficiently on a laptop, presenting a practical advantage to existing deep learning- based approaches that demand abundant training data and intricate computational skills. The platform includes an interactive web- based tool Mjolnir, which enhances the analysis experience by allowing users to thoroughly investigate cell- level information through gigapixel histological images conveying various multimodal attributes. Its intuitive interface enhances accessibility to a broad user base. Mjolnir incorporates a tile server algorithm that dynamically loads gigapixel images for smooth navigation. This not only resolves computational resource demands for visualization but also significantly improves the overall usability and responsiveness of the platform during analysis sessions, even on a laptop. Furthermore, Mjolnir functions as a standalone tool, offering users the flexibility to upload their images and cell- level attributes. + +<|ref|>text<|/ref|><|det|>[[115, 344, 875, 697]]<|/det|> +With rapid breakthroughs in deep- learning- based computer vision algorithms \(^{7,8,67 - 69}\) , accurately detecting cell nuclei has become increasingly viable, transforming the challenge of cell detection in high- density regions \(^{70,71}\) . As an integrative spatial transcriptomics analysis platform, Thor incorporates multiple SOTA tools for cell segmentation and also supports manually/strategically added missing cells, enabling enhanced flexibility and adaptability for diverse workflows. Thor is designed to stay aligned with ongoing advancements in cell segmentation technologies, ensuring that its methods remain cutting- edge. Thor extracts tile- based image features from an image patch centered at the segmented cell nucleus centroid to capture the local environment surrounding the nucleus. These features are not limited to the nucleus itself but include the tissue context within the image tile, providing a comprehensive representation of the cell's local environment from histology. Tile- based feature extraction is a practical strategy widely adopted in histology image analysis by deep- learning models and pathology foundation models. It facilitates a wide range of downstream tasks, including cell segmentation, cell type annotation, and tumor microenvironment profiling \(^{15,17,72 - 75}\) . We recognize that certain cell types or microstructures may require more specialized descriptors. To address this, Thor provides an API (thor.pp.image.WholeSlideImage.load_external_cell_features) that supports morphological features generated by external tools (e.g., CellProfiler or CellViT). Researchers can extract customized metrics, such as cell shape, texture, or intensity profiles, then input these features into Thor, effectively augmenting or replacing the default cell detection and tile- based features. This flexible design allows Thor to accommodate a wide spectrum of histological analyses and cellular phenotyping tasks, ensuring that users can tailor the platform to their unique research objectives. + +<|ref|>text<|/ref|><|det|>[[115, 710, 869, 904]]<|/det|> +Recent advances in spatial transcriptomics technologies have pushed spatial resolution toward cellular or even subcellular level \(^{27,32,76}\) , yet each technology still faces practical hurdles that Thor can help address. For instance, although Visium HD offers sub- cellular bin sizes, it can suffer from high dropout rates, low gene coverage \(^{57}\) , and imperfect bin- to- cell alignment \(^{77}\) . Meanwhile, Slide- seq may provide sparse transcript detection and limited capture size \(^{78}\) , and image- based platforms such as Xenium and CosMX rely on predefined gene panels and may omit genes of interest. Our results show that by integrating Visium HD data with histological features, Thor reduces technical noise and reveals spatially coherent expression patterns that match pathology annotations. Beyond improving data quality, Thor functions as a comprehensive downstream analysis platform capable of handling the computational and visualization demands posed by large- scale ST datasets, where a single slide can contain millions of bins or hundreds of thousands of cells per slide. Thor's interactive visualization tool, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 870, 156]]<|/det|> +Mojlnir, renders gigapixel images and facilitates responsive exploration on standard computing hardware. Moreover, Thor remains a cost- effective option for achieving single- cell level analyses with standard Visium data (approximately \(70\%\) less expensive than Visium HD), benefiting labs with resource constraints or those seeking to reanalyze legacy ST datasets. + +<|ref|>text<|/ref|><|det|>[[112, 170, 881, 475]]<|/det|> +Thor has several limitations. First, it relies on high- resolution histology images (typically 0.25 to \(0.5 \mu m\) per pixel) for cell detection and histological feature extraction. Real- world scenarios may introduce complexities, such as loss of focus in imaging or improper staining across large tissue regions. Such conditions may lead to missed cell detection or unrepresentative image features and Thor's performance may understandably suffer. Additionally, Thor's performance may be affected in regions where nuclei are difficult to identify, such as cells in peripheral areas with flat nuclei. Incorporating higher precision imaging techniques, such as the DAPI imaging used in the Xenium data cell detection, could help address this issue. Second, Thor does not currently support multi- sample integration, as batch effects in transcriptomics data and histological variability between tissue sections introduce biases that complicate direct comparisons and spatial alignment. These challenges also limit the applicability of semi- supervised annotation across multiple samples. Third, Thor does not operate at subcellular resolution to provide further finer level analysis. Due to the light diffraction limits in standard histology imaging and complex morphological variability of subcellular structures, robust and accurate segmentation of individual organelles or subcellular structures are highly challenging \(^{79}\) and restricted for certain organelles in restricted platforms \(^{69,80,81}\) . Thor's cell- level integrated analyses of transcriptomics and histology may complement with nanoscale spatial omics technologies. Its modular architecture could, in future work, integrate with subcellular methods like Stereo- seq to bridge tissue, cellular, and subcellular- level insights. + +<|ref|>text<|/ref|><|det|>[[113, 487, 876, 632]]<|/det|> +In conclusion, Thor effectively leverages ST analysis by integrating histology and transcriptomics, refining gene expression to the single- cell level and enabling more precise characterization of tissue architecture. This approach provides a valuable foundation for future cross- modal integration, including highly multiplexed imaging techniques (e.g., CODEX or MIBI) to achieve a more comprehensive, multi- modal understanding of spatial- omics data. By enabling the exploration of cellular interactions across spatial landscapes, Thor not only facilitates discovery of biological insights but also lays the foundation for the development of novel therapeutic modalities, thereby advancing the field of precision medicine for more effective and personalized patient care. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 199, 106]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 108, 265, 123]]<|/det|> +## Overview of Thor + +<|ref|>text<|/ref|><|det|>[[115, 123, 872, 189]]<|/det|> +Thor integrates transcriptomics and histological information by faithfully inferring the whole transcriptome of in silico cells. Thor does not require training for the inference of cell- level gene expression. Instead, it operates per slide through a four- step modularized workflow (see Supplementary Note 1). + +<|ref|>text<|/ref|><|det|>[[115, 188, 877, 240]]<|/det|> +(i) Identify cells and extract locations and morphological features of each cell in their spatial neighborhood from the histological image. Meanwhile, the ST data is preprocessed, and the gene expression of the cells is initialized to their nearest spots. + +<|ref|>text<|/ref|><|det|>[[115, 238, 872, 285]]<|/det|> +(ii) Compute multi-modal distances between cells and construct the cell-cell network based on their morphological features, geometrical locations, and the transcriptome collectively. + +<|ref|>text<|/ref|><|det|>[[115, 284, 872, 336]]<|/det|> +(iii) Convert the distances to affinities using an exponential kernel, so that the similarity between two cells decreases exponentially with the multi-modal distance. (iv) Infer gene expression of the cells by transitioning information flow between similar cells and prohibiting that from cells covered by a heterogeneous spot. + +<|ref|>text<|/ref|><|det|>[[115, 345, 870, 378]]<|/det|> +Then the predicted cell- level gene expression can be applied to perform downstream analyses including interactive analysis in ROIs. The modules are described in detail as follows. + +<|ref|>sub_title<|/ref|><|det|>[[115, 393, 870, 426]]<|/det|> +## Nucleus segmentation and feature extraction from histological images (reordered for its importance) + +<|ref|>text<|/ref|><|det|>[[115, 425, 876, 491]]<|/det|> +Nucleus segmentation is critical in the analysis of histological images, enabling quantitative assessment of the number of nuclei, density, and morphological characteristics. Thor integrates several state- of- the- art tools \(^{6,7,67}\) for nucleus segmentation and supports user- supplied segmentation results to ensure adaptability across diverse platforms. + +<|ref|>text<|/ref|><|det|>[[115, 506, 872, 587]]<|/det|> +For jointly analyzing the histological image and the transcriptomics, Thor employs two filtering processes: Thor eliminates out- of- context nuclei by superimposing segmented nuclei on the aligned spatial spots and removing nuclei whose centers are beyond a cutoff distance from the nearest spots. The default cutoff distance is the diameter of the spots. Furthermore, Thor detects and removes isolated cells or artifacts located away from the tissue boundaries. + +<|ref|>text<|/ref|><|det|>[[115, 600, 870, 777]]<|/det|> +Tile- level histological features are extracted to represent the local environment surrounding each cell. This local environment is defined by extending from the nucleus centroid to a given distance, typically twice the mean distance between the nearest nuclei centroids. In this study, we included image features such as the mean and standard deviation of color intensities, as well as image entropy, within a defined radius around each nucleus on the tissue. These features have proven to be effective in constructing cell- cell networks for Thor inference across all tested datasets. Additionally, Thor supports custom functions for feature extraction and allows the integration of user- supplied nucleus or cell- specific features, as well as deep- learning- derived features, offering flexibility and extensibility in the analysis process. Thor is designed to incorporate advancements in segmentation toolkits to stay on the front of cell segmentation field. + +<|ref|>sub_title<|/ref|><|det|>[[115, 793, 406, 809]]<|/det|> +## Constructing the cell-cell network + +<|ref|>text<|/ref|><|det|>[[115, 809, 878, 874]]<|/det|> +Thor infers cell- level gene expression based on the cell- cell network. Connectivity between cells is determined by their distances in the combinatory feature space, formed by morphological features, geometrical locations, and the low- dimensional representation of the transcriptomic data. The features are standardized to normal distribution \(N(0,1)\) across all the cells. Nearest + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 870, 125]]<|/det|> +neighbors are included to construct the KNN cell- cell network based on the distance metric \(d_{ij}\) in the feature space, i.e. + +<|ref|>equation<|/ref|><|det|>[[250, 122, 881, 157]]<|/det|> +\[d_{ij} = \sqrt{\left(w^{gen\_m}d_{ij}^{gen}\right)^2 + \left(w^{geo\_m}d_{ij}^{geo}\right)^2 + \left(d_{ij}^{mor}\right)^2} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[113, 156, 870, 245]]<|/det|> +where \(d_{ij}^{gen}, d_{ij}^{geo}, d_{ij}^{mor}\) are the dimension- normalized Euclidean distances in the transcriptomic (reduced dimension), geometrical, and morphological feature space, respectively; \(w^{gen\_m}\) and \(w^{geo\_m}\) are the respective weights in relative to the morphological feature distance. Increasing the \(w^{geo\_m}\) value leads to a more localized network and increasing the \(w^{gen\_m}\) value favors the distance in the transcriptomic space. + +<|ref|>text<|/ref|><|det|>[[113, 257, 881, 339]]<|/det|> +Next, to preserve local structure and account for the non- uniform density of the cells, the KNN cell graph is converted to a shared nearest neighbors (SNN) graph. SNN prioritizes connections among cells that have multiple neighbors in common. This emphasis can unveil intricate data patterns and has demonstrated a reduced susceptibility to isolated noisy data points. Cells \(i\) and \(j\) are connected if the proportion of their shared neighbors \(w_{ij}\) is beyond a given threshold. + +<|ref|>equation<|/ref|><|det|>[[373, 338, 881, 357]]<|/det|> +\[w_{ij} = card(NN(i)\cap NN(j)) / k \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[113, 357, 810, 406]]<|/det|> +In Eqn. (2), \(NN(i)\) and \(NN(j)\) refer to the sets of nearest neighbors of cell \(i\) and cell \(j\) respectively. card refers to the cardinality of the overlap set. \(k\) is the number of nearest neighbors considered. + +<|ref|>sub_title<|/ref|><|det|>[[113, 422, 488, 439]]<|/det|> +## Feature-preserving Markov diffusion model + +<|ref|>text<|/ref|><|det|>[[113, 439, 883, 567]]<|/det|> +As the ST spot data represents the aggregate expression across enclosed cells, we hypothesize that gene expression in a homogeneous spot is more accurate compared to a heterogeneous spot. The heterogeneity of a spot is quantified by the coefficient of variation in cellular features of all cells mapped to the spot or by the Shannon entropy when cell type labels are available (e.g., from spot deconvolution methods). On the SNN cell graph, Thor ensures that more accurate gene expression data corrects the less accurate ones while inhibiting the propagation of the less accurate information through modulation of node weights and edge weights. + +<|ref|>text<|/ref|><|det|>[[113, 583, 866, 632]]<|/det|> +Cells mapped to a more homogenous spot carry a larger node weight \(G_{i}\) , thus more robust to variations. \(G_{i}\) is calculated as an exponential kernel on the heterogeneity of the corresponding spot. + +<|ref|>equation<|/ref|><|det|>[[390, 630, 881, 650]]<|/det|> +\[G_{i} = e^{-kS_{i}} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[113, 650, 881, 718]]<|/det|> +where \(k\) is the inverse kernel width that controls the shape and \(S_{i}\) is the heterogeneity of the spot enclosing cell \(i\) . The edge weight \(\epsilon_{ij}\) between two cells \(i\) and \(j\) is computed as the product of the "bandwidth" \(w_{ij}\) as shown in Eqn. (5), proportion of their shared neighbors defined in Eqn. (2), and the "latency" \(L_{ij}\) defined in Eqn. (4). + +<|ref|>equation<|/ref|><|det|>[[384, 716, 881, 760]]<|/det|> +\[\begin{array}{l}{L_{ij} = \frac{1}{1 + e^{-\alpha (G_i - G_j)}}}\\ {\epsilon_{ij} = (1 - \delta_{ij})L_{ij}w_{ij} + \delta_{ij}G_i} \end{array} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[113, 760, 613, 777]]<|/det|> +where \(\alpha\) controls the steepness of the scaled sigmoid function. + +<|ref|>text<|/ref|><|det|>[[113, 792, 477, 810]]<|/det|> +The transition matrix \(F_{ij}\) is then computed as, + +<|ref|>equation<|/ref|><|det|>[[325, 809, 881, 828]]<|/det|> +\[F_{ij} = (1 - \lambda)\delta_{ij} + \lambda \epsilon_{ij} = \delta_{ij} - \lambda (\delta_{ij} - \epsilon_{ij}) \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[113, 829, 880, 878]]<|/det|> +where the constant \(1 - \lambda \in (0,1)\) is the probability of keeping the original (self) gene expression. As shown in Eqns. (4- 6), the "latency" is a key parameter that turns the symmetric SNN into an asymmetric network in favor of incoming information flow in a connection from the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 796, 124]]<|/det|> +cell with a lower heterogeneity score, or a larger node weight, to the cell with a higher heterogeneity score. + +<|ref|>text<|/ref|><|det|>[[115, 137, 850, 202]]<|/det|> +The transition matrix takes the same form as in a Laplacian smoothing method, likewise, the diffusion causes shrinking in the transcriptome space. Therefore, we employ a well- known feature- preserving technique in the field of surface smoothing \(^{82}\) and introduce a reversed diffusion transition matrix \(R_{ij}\) after the forward diffusion to inflate the transcriptome space. + +<|ref|>equation<|/ref|><|det|>[[325, 202, 881, 220]]<|/det|> +\[R_{ij} = (1 - \mu)\delta_{ij} + \mu \epsilon_{ij} = \delta_{ij} - \mu (\delta_{ij} - \epsilon_{ij}) \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[115, 220, 800, 255]]<|/det|> +where \(\mu \in (- 1,0)\) , \(\delta_{ij}\) is the Kronecker delta. In practice, the absolute value of \(\mu\) is set marginally larger than \(\lambda\) for sufficient inflation. + +<|ref|>text<|/ref|><|det|>[[115, 272, 875, 321]]<|/det|> +The feature- preserving diffusion is composed of a forward diffusion step followed by a reversed diffusion step. Therefore, the effective transition matrix is computed as the matrix multiplication of the reversed diffusion transition matrix \(R_{ik}\) and the forward diffusion transition matrix \(F_{kj}\) , + +<|ref|>equation<|/ref|><|det|>[[390, 320, 881, 339]]<|/det|> +\[T_{ij} = \sum_k R_{ik} F_{kj} \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[115, 339, 880, 393]]<|/det|> +The resulting Markov transition matrix \(F_{ij}\) represents the probability distribution of transitioning from each cell to every other cell in a single step. The transition matrix \(T_{ij}\) is normalized by rows to ensure that the probabilities of incoming signals sum up to 1. + +<|ref|>text<|/ref|><|det|>[[115, 406, 852, 440]]<|/det|> +Lastly, after obtaining the Markov transition matrix, Thor performs graph diffusion to infer the gene expression at the cellular level. + +<|ref|>equation<|/ref|><|det|>[[390, 438, 881, 457]]<|/det|> +\[x^{n} = (T)^{n}x^{0} \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[115, 456, 880, 506]]<|/det|> +where \(x^{0}\) is the input gene expression initialized by the nearest spot- level values, \(x^{n}\) is the final inferred gene expression, \(F\) is the feature- preserving Markov transition matrix, and \(n\) is the number of diffusion steps. The Markov diffusion converges rapidly, typically within 10 steps \(^{83}\) . + +<|ref|>text<|/ref|><|det|>[[115, 519, 875, 618]]<|/det|> +Due to the substantial number of in silico cells, the diffusion can take hours. To speed up Thor, the Markov graph diffusion may be performed on the reduced- dimensional embedding, such as the latent variables of a variational autoencoder (VAE), and the transcriptome can be reconstructed from the latent variables. Finally, Thor rescales the gene expression to the same range as the input spot- level gene expression, and optionally samples cell- level gene expression considering stochasticity in scRNA- seq reads. + +<|ref|>sub_title<|/ref|><|det|>[[115, 633, 512, 649]]<|/det|> +## Advanced analyses and dynamic visualization + +<|ref|>text<|/ref|><|det|>[[115, 649, 880, 760]]<|/det|> +Technical challenges arise when analyzing and visualizing systems containing a vast number of cells. The WSIs are gigapixel- scale and typically encompass from 10,000 to 100,000 in silico cells within a \(6.5 \text{mm} \times 6.5 \text{mm}\) tissue sample. These large- scale datasets present significant difficulties in terms of computational resources and effective data visualization. To address these challenges, we adapted existing pipelines for analysis of cell- level multi- omics and imaging data, as well as developed a dedicated tool Mjolnir for interactive visualization of large biomedical images. Details for dynamic visualization and advanced analyses are as follows, + +<|ref|>text<|/ref|><|det|>[[144, 760, 857, 840]]<|/det|> +- Interactive visualization of histology and genomics. Mjolnir leverages image-tiling technologies used by Google Maps, enabling seamless navigation through gigapixel images at a range of zoom levels. Mjolnir empowers users to visualize segmented components, including spots and cells/nuclei color-coded by gene expression or additional attributes, such as copy number profiles. + +<|ref|>text<|/ref|><|det|>[[144, 840, 857, 905]]<|/det|> +- ROI selection. Mjolnir supports drawing and editing regions of any shape on the staining image. A user can export the selected ROIs in common data formats such as annData for gene expression, TIFF for image patches, and JSON for polygon coordinates, facilitating further analyses. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 875, 420]]<|/det|> +- DEG analysis. Differentially expressed genes (DEGs) are extracted between two specified groups of cells. Thor treats individual cells in a group as replicates and assesses the significance of changes in gene expression using statistical models.- Pathway enrichment analysis. A pathway is represented by a group of specific molecules that collectively carry out vital functions within cells and organisms. Thor adapts the Python package decoupler61 to compute the cellular enrichment of pathways.- TF activity analysis. The activity of a TF is inferred by the expression levels of its regulated genes. Thor adapts decoupler61 to compute cellular TF activity.- CNV analysis. Thor integrates the R package CopyKAT62 for CNV analysis with a wrapper function. Thor expedites the calculation of CNV by parallel computing.- TLS score. The TLS score is calculated based on 29 signature genes, including markers of immune cells such as T cells, monocytes, macrophages, and fibroblasts56. The TLS score in the DCIS dataset was calculated with the scanpy.tl.score_genes function in SCANPY84, as the averaged expression of a set of genes subtracted by the averaged expression of a set of randomly sampled genes.- Cell-cell communication. Thor integrates the python package COMMOT63 to analyze cell-cell communication, which accounts for competition among different ligand and receptor species as well as spatial distances between cells. Thor boosts the calculation by implementing a more efficient function to compute the cell-cell spatial distance matrix within the interaction cutoff distance in place of the original implementation. + +<|ref|>sub_title<|/ref|><|det|>[[115, 447, 480, 463]]<|/det|> +## Post-LVAD heart failure ST data collection + +<|ref|>text<|/ref|><|det|>[[115, 463, 876, 560]]<|/det|> +Sample collection and preparation Tissues were collected from patients wearing LVAD before a heart transplant. All samples were obtained under an approved IRB protocol (Pro00006097:1 Congestive Heart Failure) at Houston Methodist Hospital. FFPE heart failure tissue samples were collected using standard- of- care procedures. Tissue sections \((10\mu m)\) obtained from the FFPE tissues were mounted on Visium spatial gene expression slides (10x Genomics, 1000520). The samples were processed as described in the manufacturer's protocols. + +<|ref|>text<|/ref|><|det|>[[115, 574, 876, 735]]<|/det|> +ST by 10x Genomics Visium The tissue slides were permeabilized at \(37^{\circ}C\) for 6 min, and polyadenylated mRNA was captured by oligonucleotides bound to the slides. Reverse transcription, second- strand synthesis, complementary DNA (cDNA) amplification and library preparation proceeded using the Visium Spatial Gene Expression Slide & Reagent Kit (10x Genomics, 1000520) according to the manufacturer's protocol. After evaluation by real- time PCR, cDNA amplification included 13- 14 cycles. Indexed libraries were pooled equimolarly and sequenced on a NovaSeq X Plus instrument in a PE28/150 run (Illumina). An average of 26, 011 paired reads were generated per spot and the median genes per spot were 2,277. Tissues were stained with H&E, and slides were scanned on a Pannoramic MIDI scanner (3DHISTECH) using a \(\times 20\) , 0.8- NA objective. + +<|ref|>text<|/ref|><|det|>[[115, 750, 880, 896]]<|/det|> +Spatial profiling of vascular protein To capture the spatial expression of the candidate protein, we adapted an established protocol for spatial mapping using immunofluorescence staining. This technique provides detailed visualization of gene expression within tissue contexts, allowing for precise localization and analysis of the candidate gene's expression patterns across different tissue regions. First, paraffin- embedded sections were deparaffinized with xylene thrice for 5 minutes each. The sections were then rehydrated through a series of ethanol washes: twice in \(100\%\) ethanol for 2 minutes each, twice in \(95\%\) ethanol for 2 minutes each, and once in \(75\%\) ethanol for 2 minutes. The slides were then rinsed in ultra- pure water for 5 minutes, followed by Tris- buffered saline (TBS) containing \(0.0025\%\) TritonX- 100 for 5 minutes. For + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 880, 268]]<|/det|> +antibodies recognizing surface proteins, a rinse with 1xTBS alone was used. Subsequently, the slides were subjected to antigen retrieval by placing them in a sodium citrate solution heated to \(85^{\circ}C\) on a hot plate for 10 minutes. The sections were then encircled with a pap pen, and the primary antibody was applied overnight in a dark, humidified chamber at \(4^{\circ}C\) . The following day, the slides were washed twice with either 1xTBS containing TritonX- 100 or 1xTBS alone, depending on the nature of the protein of interest. Next, the slides were incubated with the secondary antibody in a dark chamber for 30 minutes. After incubation, the slides were washed twice and mounted using a DAPI- containing mounting medium. Microscopy images were obtained using an Olympus FV3000 Confocal microscope. A negative control slide was used to establish the threshold settings, which were consistently applied to all slides for image acquisition. + +<|ref|>sub_title<|/ref|><|det|>[[115, 281, 426, 298]]<|/det|> +## Preprocessing the histology images + +<|ref|>text<|/ref|><|det|>[[115, 297, 860, 409]]<|/det|> +In order to accurately detect cells/nuclei from histology images, preprocessing steps including image normalization and augmentation of the histology images in this study adhered to recommending settings the cell segmentation tools. For StarDist, pixel values were clipped at \(1\%\) and \(99.8\%\) for all the (red, green, blue) color channels, and the trained model '2D_versatile_he' was used. Cellpose internally included data normalization in the neural network. Following the recommendations in Squidpy, we inverted the color values of the H&E images and used the blue channel for nuclei segmentation24. + +<|ref|>text<|/ref|><|det|>[[115, 422, 866, 520]]<|/det|> +For the MOB dataset, nuclei were segmented from the H&E staining images with Cellpose7 using the parameters (min_size = 10, flow_threshold = 0.4, channel_cellpose = 0). For the human MI datasets, the 10x Genomics human breast cancer Xenium & Visium datasets, the 10x Genomics human DCIS dataset, and the post-LVAD human heart tissues, StarDist67 was used with the default parameters (prob_thresh = 0.05, nms_thresh = 0.2). For the mouse brain MERFISH dataset, the cell segmentation downloaded along with the data was used. + +<|ref|>sub_title<|/ref|><|det|>[[115, 535, 508, 552]]<|/det|> +## Preprocessing the spatial transcriptomic data + +<|ref|>text<|/ref|><|det|>[[115, 551, 866, 633]]<|/det|> +The initial preprocessing steps involved quality control and library size normalization, adhering to the SCANPY standard protocols84. Highly/spatially variable genes were identified by established protocols84- 86 for inference and following downstream analyses. A low- dimensional representation was obtained through dimension reduction methods, including PCA, UMAP, or by utilizing the latent space of a VAE. + +<|ref|>sub_title<|/ref|><|det|>[[115, 648, 430, 664]]<|/det|> +## Parameter settings in Thor inference + +<|ref|>text<|/ref|><|det|>[[115, 663, 875, 777]]<|/det|> +Thor inference demonstrated robust performance to the variations in parameter settings. Therefore, in all analyses of this study, default parameters in Thor were employed, with specific configurations as outlined below. The construction of the cell neighborhood graph utilized an initial k- nearest neighbor approach, setting the number of neighbors to 5 (n_neighbors = 5). Additionally, the probability of retaining the original (self) gene expression, denoted as \(1 - \lambda\) in Eqn. (6), was set to 0.2 (equivalently in Thor, smoothing_scale = 0.8), and the total number of diffusion steps was specified as 20 (n_iters = 20). + +<|ref|>sub_title<|/ref|><|det|>[[115, 794, 220, 809]]<|/det|> +## CytoSPACE + +<|ref|>text<|/ref|><|det|>[[115, 810, 860, 877]]<|/det|> +CytoSPACE was performed on the Human ductal carcinoma in situ by Visium dataset to map single cells from a reference scRNA- seq data. A breast cancer scRNA- seq atlas by Wu et al. was used as the reference87. Default parameters were used. The default cell type information from the original study87 was projected. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 166, 104]]<|/det|> +## RCTD + +<|ref|>text<|/ref|><|det|>[[115, 107, 876, 156]]<|/det|> +RCTD was ran on the Human ductal carcinoma in situ by Visium dataset to deconvolute the cell type proportions in the spots. An annotated breast cancer scRNA- seq atlas by Wu et al. was used as the reference data87. Default parameters were used. + +<|ref|>sub_title<|/ref|><|det|>[[115, 173, 159, 187]]<|/det|> +## iStar + +<|ref|>text<|/ref|><|det|>[[115, 189, 870, 272]]<|/det|> +For iStar prediction of superpixel level gene expression on the Human breast cancer by Visium data, default settings recommended in the documentation in the GitHub repository (https://github.com/daviddaiweizhang/istar) were used. We applied iStar to the post- Xenium H&E image and the paired Visium dataset. We set the desired pixel size to \(0.25 \mu m\) for high- resolution inference of the spatial gene expression. + +<|ref|>sub_title<|/ref|><|det|>[[115, 290, 223, 305]]<|/det|> +## BayesSpace + +<|ref|>text<|/ref|><|det|>[[115, 306, 872, 455]]<|/det|> +For enhancing spatial features on the simulation data, we set the number of clusters to the ground truth number of clusters, number of PCA components to 10, and spatial- enhancing Markov chain Monte Carlo (MCMC) rounds to 50,000. For enhancing spatial features on the mouse MERFISH- generated spot data, we set the number of clusters to 8, number of PCA components to 10, and spatial- enhancing MCMC rounds to 50,000. For enhancing spatial features on the 10x human breast cancer by Visium dataset, we set the number of clusters to 6 (the number of major clusters identified in the reference Xenium dataset), number of PCA components to 10, and spatial- enhancing MCMC rounds to 50,000. Other parameters were set to their default values. + +<|ref|>sub_title<|/ref|><|det|>[[115, 473, 377, 489]]<|/det|> +## Cell type annotation by Cell-ID + +<|ref|>text<|/ref|><|det|>[[115, 489, 863, 553]]<|/det|> +Cell- ID was used to transfer the cell type information from an annotated reference scRNA- seq data to annotate single- cell data inferred by Thor35. Cell- ID was performed by a per- cell assessment in the query dataset evaluating the replication of gene signatures extracted from the reference dataset. + +<|ref|>text<|/ref|><|det|>[[115, 568, 870, 713]]<|/det|> +We followed the Cell- ID vignette and used the default parameters. For the MOB dataset from 10x Genomics, we used the cell type signatures from the scRNA- seq data88. For the human DCIS dataset from 10x Genomics, we used the cell type signatures from the scRNA- seq data (https://drive.google.com/file/d/1G8gK4MxCmRG4JZi588wloMsP8iZIQf z/view?usp=share_link) for Cell- ID annotation18. We further refined the annotations by using expression levels of key gene signatures including EPCAM and CDH1, to distinguish between normal and tumor epithelial cells. Similarly, monocytes and macrophages were separated by using marker genes VCAN (versican) and CD14, which were upregulated in circulating monocytes and reduced upon differentiation to macrophages (Figure S20a). + +<|ref|>sub_title<|/ref|><|det|>[[115, 729, 368, 744]]<|/det|> +## Pathway enrichment analysis + +<|ref|>text<|/ref|><|det|>[[115, 745, 860, 875]]<|/det|> +Functional enrichment analysis was performed using the over- representation analysis (ORA) method implemented in the Python package decouple61, 89. For each cell, the top expressed genes were treated as the set of interest. For a given gene set (e.g. a GO term), a one- sided Fisher exact test was applied to test the significance of overlap between the gene sets. The resulting p values were log- transformed to yield enrichment scores, where higher scores indicate greater significance. For example, T cell proliferation score was calculated by overlapping the top expressed gene lists in each cell with the GO term positive regulation of T cell proliferation (GO:0042102). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 439, 106]]<|/det|> +## Transcription factor activity inference + +<|ref|>text<|/ref|><|det|>[[115, 106, 877, 219]]<|/det|> +The database CollecTRI and Python package decoupler were used for the TF activity inference. CollecTRI is a comprehensive resource comprising weighted transcriptional regulatory networks of TF- target gene interactions90. TF activities were estimated using the univariate linear model method implemented in decoupler61, by predicting gene expression levels based on the TF- Gene interaction weights from CollecTRI. The resulting TF activity scores provide directional insights: positive scores indicate active TFs driving gene expression, whereas negative scores suggest inactivity or repression. + +<|ref|>sub_title<|/ref|><|det|>[[115, 234, 348, 250]]<|/det|> +## Gene module identification + +<|ref|>text<|/ref|><|det|>[[115, 250, 877, 330]]<|/det|> +Hotspot36 was used for identification of informative genes in the single- cell level spatial transcriptome dataset. For the module assignment by Hotspot in the MOB dataset, the number of nearest neighbors was set to 30 for creating the KNN graph. A false discover rate (FDR) cutoff of 0.05 was applied, thereby grouping 1,688 out of all the 2,781 highly variable genes into 8 modules. + +<|ref|>sub_title<|/ref|><|det|>[[115, 346, 358, 362]]<|/det|> +## Datasets and preprocessing + +<|ref|>text<|/ref|><|det|>[[115, 362, 879, 507]]<|/det|> +Human breast cancer by Xenium & Visium: The Xenium gene expression matrix and the Visium raw reads were downloaded from the 10x website (https://www.10xgenomics.com/products/xenium- in- situ/preview- dataset- human- breast). We mapped the Visium reads to the post- Xenium H&E staining image (In Situ Sample 1, Replicate 1) using 10x Space Ranger software (v2.1.0) for direct comparison between Thor- inferred result and Xenium data. The processed Visium gene expression matrix of the 306 genes, found commonly in the Xenium and the Visium datasets, and the post- Xenium H&E image were utilized as input for Thor/iStar/TESLA/BayesSpace. The Xenium data was employed as a reference for assessing the performance of Thor or iStar and was excluded during prediction. + +<|ref|>text<|/ref|><|det|>[[115, 521, 872, 618]]<|/det|> +Human ductal carcinoma in situ by Visium: The gene expression matrix and the paired full- resolution H&E staining image were downloaded from the 10x website (https://www.10xgenomics.com/resources/datasets/human- breast- cancer- ductal- carcinoma- in- situ- invasive- carcinoma- ffpe- 1- standard- 1- 3- 0). The gene expression matrix was preprocessed and log- normalized expression of 2,748 highly variable genes was used to train a VAE network for accelerating Thor inference. The dimension of the latent space of VAE was set to 20. + +<|ref|>text<|/ref|><|det|>[[115, 632, 879, 778]]<|/det|> +Human healthy heart sample by Visium: The full resolution H&E staining images of 12 samples were downloaded from links (https://www.heartcellatlas.org/) in the original publication37. The gene expression matrices and spot level expert annotations were provided in the annData files. The sample IDs include "HCAHeartST11702008" (vessel: S1), "HCAHeartST12992072" (vessel: S2), "HCAHeartST9383353" (vessel: S3), "HCAHeartST11290662" (node: S4), "HCAHeartST11702008" (node: S5), "HCAHeartST11702009" (node: S6), "HCAHeartST13228106" (adipose: S7), "HCAHeartST9383354" (adipose: S8), "HCAHeartST13228103" (adipose: S9), "HCAHeartST13228106" (fibrosis: S10), "HCAHeartST11350377" (fibrosis: S11), and "HCAHeartST8795936" (fibrosis: S12). + +<|ref|>text<|/ref|><|det|>[[115, 792, 880, 888]]<|/det|> +Human myocardial infarction by Visium: The gene expression matrices and paired full- resolution H&E staining images of six samples were downloaded from links provided in the original publication40 (https://zenodo.org/records/6580069#.ZHYP9OzMK3I). Samples "10X0025" (RZ1), "ACH0019" (RZ2), "ACH0012" (IZ1), "ACH0014" (IZ2), "ACH008" (FZ1), "ACH006" (FZ2) were downloaded for analysis. To facilitate the comparison of tissues from ischaemic, fibrotic, and remote zones, where the expressed genes exhibited substantial variations, we aimed to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 881, 123]]<|/det|> +maximize the overlap of genes among the six samples. After filtering out genes not expressed in any spot, we inferred the expression of all the remaining genes. + +<|ref|>text<|/ref|><|det|>[[115, 137, 866, 202]]<|/det|> +Human post- LVAD heart failure by Visium: The gene expression matrices were obtained by using Space Ranger (v2.1.0), referencing the GRCh38 human Genome. The gene expression was preprocessed following SCANPY standard protocols. Log- normalized expression of all expressed genes was used as input. + +<|ref|>text<|/ref|><|det|>[[115, 216, 877, 299]]<|/det|> +Human bladder cancer by Visium HD: The gene expression matrices were obtained by using Space Ranger (v2.1.0), referencing the GRCh38 human Genome. Gene expression matrices of the \(2 \mu m\) square bins and \(8 \mu m\) square bins were preprocessed using SCANPY. Log- normalized expression of highly variable genes of \(2 \mu m\) square bins was used as input. Log- normalized expression of \(8 \mu m\) square bins were used for comparison. + +<|ref|>text<|/ref|><|det|>[[115, 313, 880, 432]]<|/det|> +Mouse olfactory bulb by Visium: The gene expression matrix and paired full- resolution H&E staining image were downloaded from the 10x website (https://www.10xgenomics.com/resources/datasets/adult-mouse- olfactory- bulb- 1- standard- 1). The gene expression was preprocessed following SCANPY standard protocols. Log- normalized expression of highly variable genes was used as input. A VAE network was trained to allow inference in the latent space. For evaluation, we downloaded the ISH images of selected genes in MOB from Allen brain atlas33 and the gene expression data from the Stereo- seq study27. + +<|ref|>text<|/ref|><|det|>[[115, 440, 864, 556]]<|/det|> +Mouse brain by MERFISH: We used the Vizgen MERFISH mouse brain receptor map dataset that contains a MERFISH measurement of a 483 gene panel. Sample Slice 2 Replicate 1 was used and downloaded from https://info.vizgen.com/mouse- brain- map?submissionGuid=5606514b- 5a81- 4405- 999e- 327f908281cc. The DAPI image "mosaic_DAPI_z2.tif" was used for extracting image features of single cells. 8,597 cells in the hippocampus region were extracted (Figure S4a). After preprocessing, the log- normalized expression in 535 synthetic spots and the DAPI image features were used as input for Thor. + +<|ref|>sub_title<|/ref|><|det|>[[115, 570, 271, 585]]<|/det|> +## Simulation details + +<|ref|>text<|/ref|><|det|>[[113, 585, 877, 796]]<|/det|> +Thor's accuracy, sensitivity, and limitations of Thor were evaluated on simulated ST data under conditions, including diverse sources of ground truth data, variation in spot sizes, missouts in cell identification, false connections in a cell- cell network, and technical dropouts in sequencing. We extracted the positions of 6,579 cells in a mouse cerebellum Slide- seq data32, including the Granular (Cluster 1), Oligodendrocyte (Cluster 2), and Purkinje (Cluster 3) cells. Those cell locations reliably reflect the spatial distribution of cells in the real tissue and the gene counts in the single cells were simulated using Poisson distributions. We simulated a single- cell ST dataset by generating 1,000 genes of distinct spatial expression patterns, acting as markers for the three cell types. This included 350 genes for each of the first two cell types and 300 genes for the third cell type. Specifically, for the marker genes, the mean values of the Poisson distributions ( \(\lambda\) ) were randomly sampled in the range of (100, 200); and for the non- marker genes, \(\lambda\) values were randomly sampled in the range of (10, 20). Spots were then created on a grid and the spot- resolution gene expression levels are aggregated values of the enclosed cells. + +<|ref|>text<|/ref|><|det|>[[115, 796, 881, 905]]<|/det|> +(i) To assess the effect of different spot sizes, we simulated a series of spot diameters ranging from 25 to \(150 \mu m\), with nearby spots separated by \(100 \mu m\). (ii) To assess the effect of cell missouts, we randomly dropped \(10\%\), \(20\%\), \(30\%\), and \(40\%\) of the cells. (iii) To assess the effect of the false connections in the cell-cell network, we added randomized connections in the cell-cell network, until \(10\%\), \(20\%\), \(30\%\), and \(40\%\) of the cells contained randomized connections. In this evaluation, we did not directly use an + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[172, 89, 868, 155]]<|/det|> +image, instead, the cell positions were predefined, and the cell types were converted to one- hot vectors as image features. These features, combined with the generated spot- level gene expression, constituted the input for Thor, using default parameters to infer the cell- level ST data. + +<|ref|>text<|/ref|><|det|>[[113, 168, 870, 366]]<|/det|> +Additionally, to systematically assess the effect of technical dropouts, we simulated single- cell gene expression using the R package Splatter \(^{91}\) with no dropouts and with variable levels of dropouts. Splatter models the probability of transcript dropouts using a logistic function based on the mean expression levels \(P_{\text{dropout}}(x) = 1 / (1 + e^{- k*(x - x_0)}),\) where \(x\) is the mean expression level. The probability of transcript dropouts are controlled by two parameters, the midpoint parameter \((x_0\) or dropout. mid) and the shape parameter \((k\) or dropout. shape). The former is the expression level at which \(50\%\) cells are zero, and the latter controls how quickly the probabilities change from the midpoint. To simulate a wide range of dropout conditions, we used combinations of dropout. mid values [1, 2, 3, 4, 5] and dropout. shape values [- 1, - 2, - 5], with percentages of zero reads up to \(63\%\) . This allowed comprehensive assessment of the impact of varying dropout levels on Thor's performance. Spot- level gene expressions generated from these single- cell simulations with dropout were then used as inputs for Thor. + +<|ref|>text<|/ref|><|det|>[[114, 379, 875, 462]]<|/det|> +We employed the Silhouette coefficient and Calinski- Harabasz index to measure the separation of clusters. The scores were calculated on the PCA embeddings of the corresponding gene expression arrays using the functions from the library scikit- learn. We randomly sampled 3,000 cells (without replacement out of all the 6,579 cells) 10 times in the calculation of the mean Silhouette coefficients for statistical significance. + +<|ref|>text<|/ref|><|det|>[[113, 476, 872, 684]]<|/det|> +The MERFISH data of the mouse brain receptor map consists of 83,538 cells and 483 genes. We simulated Visium- like spot- level data by creating a grid of evenly spaced "spots". The molecule counts in a synthetic spot were aggregated over all the cells covered by the "spot". The spot size was set to \(100 \mu m\) and a total of 4,870 spots were simulated. We focused on the hippocampus region (Figure S4a), which consists of 535 spots covering 8,597 cells. For visualization purposes, major Leiden cell clusters in the original data were annotated according to the cellular locations in the hippocampus components and a previous study \(^{19}\) . Minor cell clusters were merged and labeled as "Others". A DAPI image in the dataset and the generated spot- level gene expression were jointly analyzed by Thor. For comparison, the positions of cells segmented from the source were used as our cell positions. Image features including the mean and standard deviation of the grayness and the entropy of image patches surrounding the cells were calculated. The predicted transcriptome and the ground truth transcriptome were integrated by harmony \(^{92}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 700, 674, 717]]<|/det|> +## Evaluating cell-level spatial gene expression prediction accuracy + +<|ref|>text<|/ref|><|det|>[[115, 717, 867, 781]]<|/det|> +We used the root mean square error and structural similarity index to quantify the prediction accuracy for each gene. In the simulation datasets, NRMSE was employed to calculate the mean deviation of the predicted gene expression from the ground truth data in all cells, as defined in Eqn. 10. + +<|ref|>equation<|/ref|><|det|>[[375, 781, 881, 842]]<|/det|> +\[\mathrm{NRMSE}\stackrel {\mathrm{def}}{=}\frac{\sqrt{\frac{\sum_{i = 1}^{n}(x_{i}^{\mathrm{pred}} - x_{i}^{\mathrm{truth}})^{2}}{n}}}{\frac{\sum_{i = 1}^{n}x_{i}^{\mathrm{truth}}}{n}} \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[115, 844, 875, 880]]<|/det|> +where \(x_{i}^{\mathrm{pred}}(x_{i}^{\mathrm{truth}})\) is the predicted (ground truth) gene expression in cell \(i\) ; and \(n\) is the number of cells. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 877, 266]]<|/det|> +To assess the performance of predicted gene expressions by Thor and other methods, we calculated two pixel- centric metrics including SSIM, RMSE (pixels) and a cell- centric metric RMSE (cells). For pixel- centric metrics, similar to the methodology from the iStar study17, both the ground truth and predicted gene expression were treated as grayscale images. Considering spatial contexts within the images, we calculated SSIM between the spatial structures of the ground truth and predicted gene expression images. Practically, we observed slight local distortions and shifts existed between the Visium and Xenium slides. For a more reliable measure of the prediction quality prediction, we therefore calculated the Complex Wave SSIM, which is insensitive to consistent spatial translation93. SSIM values range from 0 to 1, with 1 indicating identical images and 0 indicating no similarity. For cell- centric metrics, we aggregated the nearby gene expression of super- pixels to the ground truth cells. + +<|ref|>sub_title<|/ref|><|det|>[[115, 280, 632, 297]]<|/det|> +## Comparison between Thor results and pathology annotation + +<|ref|>text<|/ref|><|det|>[[115, 297, 877, 442]]<|/det|> +To quantitatively assess Thor's semi- supervised annotation function, we compared Thor against spot- level expert annotations. Because Thor assigns labels at the single- cell level, we employed majority voting to map these cell- level annotations to each spot. This allowed a direct comparison with expert- labeled spots. We report two commonly used classification metrics, accuracy, and area under the curve (AUC) as defined below. Accuracy is defined as the ratio of correct predictions to the total number of predictions. The area under the receiver operating characteristic (ROC) curve, which plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings. A higher AUC suggests better overall classification performance. + +<|ref|>text<|/ref|><|det|>[[115, 456, 872, 586]]<|/det|> +To quantitatively assess Thor's prediction of aneuploid cells through CNV analysis, we compared against pathology- annotated tumor regions. We used two metrics F1 score and the Jaccard index. F1 score is calculated as the harmonic mean of precision and recall, and a higher F1 score indicates a better balance between precision (the proportion of predicted positives that are truly positive) and recall (the proportion of true positives that are correctly identified). Jaccard index measures the degree of overlap between predicted and reference sets by dividing the size of their intersection by the size of their union; values closer to 1 indicate a higher concordance between the two. + +<|ref|>sub_title<|/ref|><|det|>[[115, 600, 603, 617]]<|/det|> +## Human bladder cancer sample Visium HD data collection + +<|ref|>text<|/ref|><|det|>[[115, 617, 877, 745]]<|/det|> +Pre- treatment formalin- fixed paraffin- embedded (FFPE) tissue blocks were obtained from patients diagnosed with muscle- invasive bladder cancer (MIBC). All samples were collected under an approved IRB protocol (PRO00037670), and written informed consent was obtained from all participants prior to tissue collection. Standard- of- care procedures were used to preserve and process the tissues. For spatial transcriptomics, \(10 \mu m\) sections were cut from the FFPE blocks and mounted onto Visium HD Spatial Gene Expression slides (10x Genomics). Sections were prepared in accordance with the manufacturer's guidelines (10x Genomics). All subsequent steps were performed following the Visium HD sample preparation protocol. + +<|ref|>sub_title<|/ref|><|det|>[[115, 760, 272, 777]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[115, 777, 875, 810]]<|/det|> +All the source codes are attached as supplementary files. We will release them on GitHub upon acceptance of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 827, 290, 843]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[115, 844, 853, 892]]<|/det|> +This work was supported by Houston Methodist internal grant to G.W., National Institute of General Medical Sciences of the National Institutes of Health (1R35GM150460 to G.W., 1R35GM151089 to Q.S.), National Heart, Lung, and Blood Institute to L.L (HL169204- 01A1), + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 864, 155]]<|/det|> +and National Cancer Institute (U54CA274375, R01CA175397 to K.S.C). This work utilized the Houston Methodist Neal Cancer Center Spatial Omics Core. The Core was funded by Cancer Prevention and Research Institute of Texas (CPRIT: RR230010). We appreciate Drs. Qin Ma and Jordan Krull from the Ohio State University for critical reading and discussion. + +<|ref|>sub_title<|/ref|><|det|>[[115, 171, 246, 187]]<|/det|> +## Contributions + +<|ref|>text<|/ref|><|det|>[[115, 188, 880, 316]]<|/det|> +G.W. supervised the study. P.Z. and G.W. designed and developed the graph diffusion model. P.Z., W.C., T.N.T, I.K., and S.L. performed the data analyses. P.Z., T.N.T., and M.Z. developed the web platform. L.L. and K.N.C. prepared the post- LVAD patient tissues and performed the IF staining. Y.Y. annotated the bladder cancer tissue image. X.H., F.N., and K.S.C prepared the bladder cancer sample for Visium HD sequencing. P.Z., W.C., K.N.C., L.L., and G.W. wrote the manuscript. Q.S. helped supervise the development of the web platform. L.L. supervised the IF staining experiments. All authors contributed to writing the manuscript and provided approval for the submitted version. + +<|ref|>sub_title<|/ref|><|det|>[[115, 331, 297, 348]]<|/det|> +## Ethics declarations + +<|ref|>text<|/ref|><|det|>[[115, 350, 466, 381]]<|/det|> +Competing interests The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[115, 397, 222, 413]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 414, 880, 896]]<|/det|> +1. Bressan, D., Battistoni, G. & Hannon, G.J. The dawn of spatial omics. Science 381, eabq4964 (2023). +2. Lu, M.Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 5, 555-570 (2021). +3. Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 115, E2970-E2979 (2018). +4. Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T.J. & Zou, J. 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The projection of the histological features to the first principal component highlights the tissue sections at cell resolution; meanwhile, expression patterns of marker genes of the cardiac smooth muscle cells (MYH11) and the fibroblast cells (RARRES2) demonstrate consistent patterns at spot resolution. The cell- cell network is constructed according to the distances in the combinatory feature space of histology (including location) and transcriptomics. In the example and illustration of cell- cell network, the nodes represent cells, edges represent connections, and the colors indicate cell types. Thor infers single- cell spatial transcriptome by utilizing an anti- shrinking Markov graph diffusion model. The expression profile of the marker gene MYH11 in smooth muscle aligns with the texture of the H&E staining image, as visualized by the Mjolnir web platform. (b) Thor adapts and implements a diversity of modules for advanced single- cell analyses around the inferred spatially resolved whole transcriptome of the in silico cells. (c) The Mjolnir platform supports interactive multi- modal tissue analysis. + +<|ref|>text<|/ref|><|det|>[[112, 378, 880, 573]]<|/det|> +Figure 2: Thor accurately predicts single-cell spatial gene expression in human breast cancer. (a) Spatial gene expression of in silico cells inferred from the Visium data and the H&E staining image of a breast cancer tissue by Thor align closely with Xenium data from the adjacent tissue section. The numbers on the H&E staining image mark DCIS regions of interest. (b) Thor- inferred spatial transcriptome of in silico cells demonstrate consistent cell clusters with Xenium using scRNA- seq clustering. The cluster annotations were adapted from the original study of the dataset28. The mean expression levels of differentially expressed genes in each cluster were visualized using heatmaps. (c) Thor outperforms iStar in the prediction of spatial gene expression. (d) Spatial expression profiles of representative genes at the region of interest level are compared between Thor, iStar, and Xenium. Thor- inferred spatial gene expression closely aligns with the Xenium data, while iStar introduces artifacts at segment boundaries (the red arrows) and in regions with sparse cells (the blue arrow). + +<|ref|>text<|/ref|><|det|>[[112, 585, 881, 811]]<|/det|> +Figure 3: Thor detects fibrotic regions in multiple human heart tissues with MI. (a) H&E staining images of tissues from a remote zone (RZ1), an ischaemic zone (IZ1), and a fibrotic zone (FZ1). Purple and green squares mark curated ROIs and are annotated as fibrotic and non- fibrotic regions. Close- up views of the cell morphology and inferred cellular expression of the fibroblast marker gene PDGFRA are provided for the curated ROIs. (b) Mjolnir- annotated fibrotic regions (blue) are visualized on the H&E staining images. T cell proliferation pathway enrichment scores are calculated based on the top highly expressed genes in each cell. (c) Barplot the percentages of the fibrotic regions in all six samples. (d) Heatmap of the GO pathway enrichment based on the up- regulated DEGs (fold change \(> 2\) , adjusted p_value \(< 0.01\) using t- test) in the fibrotic region compared to the non- fibrotic region in each sample, and the up- regulated DEGs (fold change \(> 2\) , adjusted p_value \(< 0.01\) using t- test) in the non- fibrotic region compared to the fibrotic region in each sample. (e) TF activity is inferred from the in silico cell spatial transcriptome. We use RTN (R package) for the transcriptional network inference and Cytoscape for network visualization. + +<|ref|>text<|/ref|><|det|>[[113, 825, 876, 907]]<|/det|> +Figure 4: Thor identifies regenerative signatures in vessels in human heart failure. (a) Thor infers cell- level gene expression, expression of the smooth muscle marker MYH11 are visualized at the spot level and the cell level on sample I tissue. Utilizing Mjolnir, vessel regions are annotated. The expression of MYH11 in selected vessels (labelled 1- 4) is recovered by Thor, where there exhibits low expression of MYH11 at the spot resolution. (b) The upregulated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 878, 188]]<|/det|> +genes in the vessels shared by two samples are ranked according to the gene scores. (c) Cells in the vessel regions are divided into two groups according to PLA2G2A expression levels. A cutoff of 0.5 is used, where the first and the second Gaussian distributions overlap. (d) GO pathway enrichment using the top 500 upregulated DEGs (with the lowest adjusted \(p\) - values) in the PLA2G2A \(^+\) cells. (e) IF staining views of protein level PLA2G2A expression in post- LVAD patient tissues. + +<|ref|>text<|/ref|><|det|>[[112, 202, 878, 426]]<|/det|> +Figure 5: Thor provides unbiased screening of hallmarks in cancer. (a) H&E staining image of the DCIS tissue. The annotation of eighteen major tumor regions (T1- T18) in the DCIS tissue is adapted from the annotation by pathology experts (Agoko NV, Belgium). (b) Leiden clusters of the segmented cells using morphological features. The list of image features and details of Leiden clustering are provided in Supplementary Note 1. Colors represent cell clusters. (c) The spatial distribution of cell types. Cell types are obtained by Cell- ID using the Thor- inferred spatial transcriptome of the in silico cells and refined with cell type markers. (d) VEGFA gene expression pattern at tissue and cell scales in tumor region T1. (e) The tumor regions identified by high attention values in CLAM and semi- supervised annotation in Mjolnir. The black dotted square marks a high- attention region where adipocytes are predominantly located. (f) Heatmap of the copy number profiles inferred by CopyKAT based on the in silico cell- level transcriptome predicted by Thor. A selected list of breast cancer- related genes is provided. (g) Aneuploid (tumor) and diploid (non- tumor) regions inferred by CopyKAT show consistent results between in silico cell- level transcriptome and spot data. + +<|ref|>text<|/ref|><|det|>[[112, 439, 880, 617]]<|/det|> +Figure 6: Thor reveals mechanistic insights into the immune response of DCIS. (a) Cell- level TLS scores. The TLS score is calculated based on 29 genes. (b) Boxplot of the TLS scores in the 18 tumor regions. The tumor regions are ranked according to the median TLS score. The middle line in the box plot, median; box boundary, interquartile range; whiskers, 5- 95 percentile; minimum and maximum, not indicated in the boxplot. (c) Zoom- in view of the tumor regions with highest/lowest (T7/T15) TLS scores. The expression level of one DEG, CD84, is visualized in the inner and perimetral parts of the tumor regions. GO pathway enrichment is based on 300 up- regulated (fold change \(> 2\) , adjusted \(p\) - value \(< 0.01\) using t- test) and 300 down- regulated (fold change \(< 0.5\) , adjusted \(p\) - value \(< 0.01\) using t- test) DEGs between T7 and T15. (d) Heatmap of the GO pathway enrichment based on the up- regulated DEGs in each tumor region compared to the rest (fold change \(> 1.5\) , adjusted \(p\) - value \(< 0.05\) using t- test). + +<|ref|>text<|/ref|><|det|>[[112, 630, 880, 877]]<|/det|> +Figure S1: Thor inference has a reliable performance on simulation data. (a) Expression profile of a gene in simulated spot- resolution data (spot separation: \(100 \mu m\) ), the ground truth, and Thor- predicted single- cell data. (b) Thor shows robust accuracy with cell- misouts or perturbations in the cell- cell network. NRMSE values provide a quantitative measure of the normalized deviation of Thor- inferred gene expression from the ground- truth gene expression. (c) Thor's accuracy in different spot sizes. The nearest spot method maps the expression of the closest spot to the cell; KNN smoothing takes the average of the twenty nearest neighbors; the subspot- level gene expression from BayesSpace is mapped to the identified cells using nearest cell neighbors. (d) Thor imputes gene expression with technical dropouts and recovers cluster separation. The error bars for the mean Silhouette coefficients are omitted as they are too small to visualize. The colors in the PCA plots represent the ground truth cell type information. "Drop %" in the table is calculated as the ratio of zeros in the count matrix of the simulated scRNA- seq data. For the box plots, the middle line in the box plot, median; box boundary, interquartile range; whiskers, 5- 95 percentile; minimum and maximum, not indicated in the boxplot; gray dots, individual data points. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 853, 140]]<|/det|> +Figure S2: Gene expression profiles in the simulated dataset. Spot-resolution data (spot separation: \(100 \mu m\) ), cell- resolution, and Thor- predicted single- cell data are visualized. Each row shows two marker genes for the same cell type. + +<|ref|>text<|/ref|><|det|>[[115, 154, 870, 283]]<|/det|> +Figure S3: Visualization of cell missouts and different spot sizes. (a) In the single- cell spatial transcriptome data, the miss- detection of cells from \(10\%\) to \(40\%\) randomly leads to sparser cell distribution. (b) The proportion of homogeneous spots (characterized by low spot heterogeneity scores) decreases as spot size increases. Spot heterogeneity is quantified using the Shannon entropy of cell type proportions within a spot. (c) Thor demonstrates robust performance across varying levels of spot heterogeneity. Mean absolute error (MAE) is calculated between Thor- predicted and ground- truth gene expression levels. A, B, C mark one low- heterogeneity, and two high- heterogeneity clusters of the Thor data. + +<|ref|>text<|/ref|><|det|>[[115, 297, 880, 490]]<|/det|> +Figure S4: Thor accurately predicts single- cell spatial gene expression from simulated spot- resolution gene expression in mouse hippocampus. (a) Cell- type distribution in the mouse hippocampus region from the MERFISH data (ground truth; left panel) and cell- type population in simulated spots (right panel). Spots in regions including the CA and DG, are composed of cells of diverse cell types. (b) Expression patterns of representative genes inferred by Thor (left panel), alongside the ground- truth MERFISH data (second panel), spot data (third panel), and subspot data (right panel). Pearson correlation coefficients are provided. (c) Clusters of the Thor- inferred in silico cells overlap well with the ground- truth cell clusters. (d) Quantitative evaluation of Thor clusters. The Silhouette coefficient and Calinski- Harabasz index are calculated based on the embeddings and the ground truth cell annotations. For BayesSpace, as the native output is sub- spot level gene expression, both the sub- spot level and cell- level metrics are considered (mapping the closest sub- spots to the cells). + +<|ref|>text<|/ref|><|det|>[[115, 504, 881, 633]]<|/det|> +Figure S5: Visualization of cell- cell network and predicted gene expression in the simulated mouse cerebellum data. (a) In the analysis of this simulated dataset, Thor connects similar cells based on location and image features. Cells of similar types are interconnected (note: cell type information is not used in constructing the cell- cell network). (b) Thor refines gene expression in various regions of the mouse hippocampus, demonstrated by a few selected genes inferred by Thor (left panel), alongside the ground- truth MERFISH data (second panel), spot data (third panel), and subspot data (right panel). Pearson correlation coefficients are provided. + +<|ref|>text<|/ref|><|det|>[[115, 648, 813, 700]]<|/det|> +Figure S6: UMAP embeddings of the Thor and Xenium integrated cells colored by modalities and by cell types. The embeddings were obtained from the PCA space by integration with harmonypy. + +<|ref|>text<|/ref|><|det|>[[115, 714, 880, 858]]<|/det|> +Figure S7: Quantitative comparison between Thor and ST spatial resolution- enhancement tools. (a) Using image- level metrics, and (b) Using cell- level metrics. One- sided Mann- Whitney tests are performed between Thor and other two best- performing tools. For the box plots, the middle line in the box plot, median; box boundary, interquartile range; whiskers, 5- 95 percentile. (c) Spatial profiles of representative genes inferred by Thor and other tools. Xenium gene expression profiles are provided for reference. The CW- SSIM scores are included. (d) Spatial profiles of representative genes inferred by Thor and other tools. The RMSE of Min- Max normalized cell level expressions are provided. Nearest cell/superpixel/subspot expression levels are mapped to the Xenium cell positions. + +<|ref|>text<|/ref|><|det|>[[115, 873, 855, 906]]<|/det|> +Figure S8: Expression profiles of genes in the human breast cancer tissues. Data predicted by Thor and iStar, along with Xenium and Visium measurement are compared. For + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 857, 123]]<|/det|> +visualization, gene expression levels predicted by Thor and iStar are normalized in the whole tissue. + +<|ref|>text<|/ref|><|det|>[[113, 137, 880, 203]]<|/det|> +Figure S9: Expression profiles of genes in ROIs of the human breast cancer tissues. Data predicted by Thor and iStar, along with measured by Xenium and Visium are compared in ROIs. H&E images of the ROIs are provided for reference. For visualization, gene expression levels predicted by Thor and iStar are normalized in the ROI. + +<|ref|>text<|/ref|><|det|>[[112, 216, 872, 409]]<|/det|> +Figure S10: Thor reveals detailed mouse olfactory bulb layers. (a) Spatial expression profiles of genes in the mouse olfactory bulb using the ISH (first row), Stereo- seq (second row), Visium (third row), and Thor- inferred (fourth row) data. Selected regions (black boxes) are zoomed in for detailed inspection of gene expression in glomerular, mitral, and SEZ layers. (b) Spatial distribution of cell clusters and neuron subtypes based on the Thor- inferred single- cell gene expression. OSN: Olfactory sensory neuron, PGC: Periglomerular cell, GC: Granule cell, M/TC: Mitral/Tufted cell. (c) Genes are grouped into 8 modules based on pairwise local correlation using the package Hotspot. Marker genes for MOB layers are shown along corresponding gene modules. (d) Module scores of four gene modules are visualized with spatial context, as well as the Thor- inferred single- cell expression profiles of representative genes in the four modules. (e) Heatmap of the GO pathway enrichment of the four representative gene modules. + +<|ref|>text<|/ref|><|det|>[[113, 423, 880, 505]]<|/det|> +Figure S11: Comparison between predicted expression profiles and gene expression patterns measured from other sources in MOB. The top row is the ISH images downloaded from Allen brain atlas; the second row is the predicted gene expression using Thor; the third row is the gene expression measured by Stereo- seq; the fourth row is the spot- level gene expression sequenced by Visium from 10x Genomics. + +<|ref|>text<|/ref|><|det|>[[113, 519, 877, 616]]<|/det|> +Figure S12: Quantitative assessment of Thor's semi- supervised annotation against spot- level expert annotations. (left panel) H&E image, (second panel) spot- level pathology annotations, (third panel) Thor's cell- level annotations, and (right panel) Thor's cell- level annotation mapped to the spots using majority voting (>50%) of representative samples in each tissue type including vessel, node, adipose, and fibrosis. The metrics are calculated based on spot- level annotations. + +<|ref|>text<|/ref|><|det|>[[113, 630, 880, 761]]<|/det|> +Figure S13: Comparison between Thor's semi- supervised annotation and the spot- level clustering. (a) H&E staining image and expert annotations on the heart tissue sample. (b) Cells annotated via Thor's semi- supervised annotation. Annotation of cells are mapped to corresponding spots according to majority voting for quantitative evaluation against expert annotations. (c) K- means clusters solely based on Visium ST data. Regardless of the number of clusters used, Visium ST data alone fails to accurately distinguish vessel- associated spots (enriched with smooth muscle and endothelial cells) from some myocardium spots with high TAGLN (a smooth muscle marker) expression. + +<|ref|>text<|/ref|><|det|>[[113, 774, 860, 840]]<|/det|> +Figure S14: Curated fibrotic ROIs in human myocardial infarction tissues. H&E staining images of all the tissues from remote, ischaemic, and fibrotic zones. The predicted single- cell expression of the fibroblast marker gene (FBLN2) and cardiac muscle- associated gene (MEF2A) are visualized in the curated ROIs. + +<|ref|>text<|/ref|><|det|>[[113, 854, 878, 904]]<|/det|> +Figure S15: DEGs between the curated fibrotic and non- fibrotic regions in the RZ1, IZ1, and FZ1 tissues. H&E staining images of RZ1, IZ1, and FZ1 tissues and Thor- predicted single- cell expression of the fibroblast marker gene (FBLN2) and cardiac muscle- associated genes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 861, 124]]<|/det|> +(CASQ2 and PKP2) are visualized in the curated ROIs. F and NF stand for fibrotic region and non- fibrotic region, respectively. + +<|ref|>text<|/ref|><|det|>[[113, 137, 882, 220]]<|/det|> +Figure S16: Semi- supervised annotation selects fibrotic regions in shallow areas in human myocardial infarction tissues. Thor- annotated fibrotic regions (blue) are visualized on the H&E staining images of RZ2, IZ2, and FZ2 tissues. Thor- predicted cell- level expression profiles of fibroblast marker genes (PDGFRA and FBLN2) and cardiac muscle- associated genes (CASQ2 and PKP2) are visualized in the curated ROIs. + +<|ref|>text<|/ref|><|det|>[[113, 232, 881, 299]]<|/det|> +Figure S17: Expression patterns of fibrotic- specific and cardiac muscle- associated genes in human myocardial infarction tissues. Fibroblast marker genes (PDGFRA and FBLN2), and cardiac muscle- associated genes (CASQ2 and PKP2) are visualized on top of the whole tissues. + +<|ref|>text<|/ref|><|det|>[[113, 312, 878, 394]]<|/det|> +Figure S18: GO pathway enrichment and TF activity in human heart tissues with MI. (a) The GO pathway enrichment scores are calculated based on the gene expression in each in silico cell using the Python library decoupler. (b) The TF activity scores are calculated based on the gene expression in each in silico cell. The database CollectRI and Python library decoupler are used for inferring the TF activity. + +<|ref|>text<|/ref|><|det|>[[113, 407, 881, 521]]<|/det|> +Figure S19: Gene expression patterns in vessels in human heart failure. (a) Thor infers cell- level gene expression, expression of the smooth muscle marker MYH11 are visualized at the spot level and the cell level on sample II tissue. (b) The expression profiles of MYH11 in selected vessels are visualized at the spot resolution and the cell resolution. (c) Vessel regions are annotated utilizing Mjolnir. (d) Venn diagram of the upregulated genes in the vessels from two samples. (e) Spatial context and morphology of cells with high / low PLA2G2A expression in post- LVAD patient tissues. + +<|ref|>text<|/ref|><|det|>[[113, 534, 870, 601]]<|/det|> +Figure S20: The inferred cell type distribution in DCIS by (a) Thor, (b) CytoSPACE, and (c) RCTD. Expression profiles of tumor and macrophage marker genes are provided for reference. The aneuploid (tumor) and diploid (non- tumor) cell distributions inferred by CopyKAT using CytoSPACE- mapped gene expression is comparable to Thor result. + +<|ref|>text<|/ref|><|det|>[[113, 614, 875, 649]]<|/det|> +Figure S21: Expression profiles of genes in DCIS. (a) Thor- predicted single- cell spatial gene expression; (b) Spot- resolution spatial gene expression from the Visium data. + +<|ref|>text<|/ref|><|det|>[[113, 662, 866, 697]]<|/det|> +Figure S22: Semi- supervised annotation selects tumor cells in tumor region T7 in DCIS. The curated rectangular region is marked with a black box. + +<|ref|>text<|/ref|><|det|>[[113, 710, 875, 810]]<|/det|> +Figure S23: 2D density plots of the expression of oncogene and tumor suppressor gene in DCIS. (a) Expressions of the oncogene ERBB2 and tumor suppressor gene ATM are plotted in tumor regions with highest TLS scores (T7, T1, and T14) and lowest TLS scores (T11, T6, and T15), where cells are colored according to the density. (b) The predicted spatial expression levels of the two genes by Thor and the fold changes between the oncogene and the tumor suppressor. + +<|ref|>text<|/ref|><|det|>[[113, 822, 875, 873]]<|/det|> +Figure S24: Enrichment of hallmark pathways in DCIS. The hallmark pathway enrichment is performed using decoupler with Thor- predicted cell- level transcriptome and the MSigDB hallmark gene sets as input. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 880, 186]]<|/det|> +Figure S25: Comparison of tumor regions with highest and lowest TLS scores and their interactions with surrounding environments. (a) Zoom- in view of the tumor regions of highest (T7 and T1) and lowest (T6 and T15) TLS scores. Thor- predicted expression of DEGs is visualized in the inner and perimetral parts of the tumor regions. (b) Interaction between tumor regions and the surrounding environments. The orange dashed lines mark the pathology- annotated tumor region boundaries. + +<|ref|>text<|/ref|><|det|>[[113, 201, 880, 298]]<|/det|> +Figure S26: Thor imputes Visium HD data and reconstructs gene expression patterns that align with pathology annotations in a bladder cancer sample. (a) Pathology annotations (left) highlight immune cells (ROI 1), invasive carcinoma (ROI 2), and a tumor fragment (ROI 3). Gene expression patterns inferred by Thor (middle) and from Visium HD (right). The orange arrow points to a region with no cell. (b) Cell clusters by Thor with a zoomed- in view of ROI 1. (c) Bin clusters by Visium HD 8 \(\mu m\) bin data with a zoomed- in view of ROI 1. + +<|ref|>text<|/ref|><|det|>[[113, 312, 870, 394]]<|/det|> +Figure S27: Sensitivity analyses of Thor inference on (a) graph construction parameters, (b) the diffusion steps, and (c) VAE latent dimensions. Mean Pearson correlation coefficients between every pair of parameter settings for all genes are plotted. Spatial distributions of a representative gene Penk are provided to illustrate the influence of diffusion steps and latent dimension on interference. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 44, 142, 70]]<|/det|> +## Figures + +<|ref|>text<|/ref|><|det|>[[43, 92, 68, 110]]<|/det|> +图 + +<|ref|>text<|/ref|><|det|>[[43, 131, 115, 150]]<|/det|> +Figure 1 + +<|ref|>text<|/ref|><|det|>[[43, 174, 189, 192]]<|/det|> +Revised Figure 4 + +<|ref|>text<|/ref|><|det|>[[43, 235, 115, 253]]<|/det|> +Figure 2 + +<|ref|>text<|/ref|><|det|>[[43, 277, 189, 295]]<|/det|> +Revised Figure 6 + +<|ref|>text<|/ref|><|det|>[[43, 335, 115, 353]]<|/det|> +Figure 3 + +<|ref|>text<|/ref|><|det|>[[43, 378, 189, 396]]<|/det|> +Revised Figure 5 + +<|ref|>text<|/ref|><|det|>[[43, 438, 115, 456]]<|/det|> +Figure 4 + +<|ref|>text<|/ref|><|det|>[[43, 480, 189, 499]]<|/det|> +Revised Figure 3 + +<|ref|>text<|/ref|><|det|>[[43, 541, 115, 559]]<|/det|> +Figure 5 + +<|ref|>text<|/ref|><|det|>[[43, 584, 115, 602]]<|/det|> +Figure 1 + +<|ref|>text<|/ref|><|det|>[[43, 644, 115, 662]]<|/det|> +Figure 6 + +<|ref|>text<|/ref|><|det|>[[43, 686, 189, 704]]<|/det|> +Revised Figure 2 + +<|ref|>sub_title<|/ref|><|det|>[[43, 728, 312, 756]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 779, 768, 799]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 817, 336, 943]]<|/det|> +- nreditorialpolicychecklist.pdf- SupplementaryTables.xlsx- SupplementaryNote1.docx- sourcecode.zip- websitehtmls.zip + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[59, 46, 395, 145]]<|/det|> +- Notebooksall.zip- supplementaryfigurescombined.pdf- nrreportingsummary.pdf- NCOMMS2453068Ars.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e.mmd b/preprint/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d89780eda4f01542e495bdb1fdb562b02bd64887 --- /dev/null +++ b/preprint/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e/preprint__030d32ff9b730af3688e796beaceb960542869b68e0c2941b54bdcec394c4d7e.mmd @@ -0,0 +1,354 @@ + +# A Novel Approach for Classifying Battery and Pseudocapacitor Materials Using Capacitive Tendency and Supervised Machine Learning + +Siraprapha Deebansok VISTEC + +Jie Deng Institute for Advanced Study & College of Food and Biological Engineering, Chengdu University + +Etienne Le Calvez University of Nantes + +Yachao ZHU ICGM https://orcid.org/0000- 0001- 8057- 3754 + +Olivier Crosnier Université de Nantes + +Thierry Brousse Institut des Matériaux Jean Rouxel, CNRS UMR 6502 - Université de Nantes https://orcid.org/0000- 0002- 1715- 0377 + +Olivier Fontaine (Olivier.fontaine@vistec.ac.th) + +VISTEC (Vidyasirimedhi Institute of Science and Technology) https://orcid.org/0000- 0002- 1804- 5990 + +## Article + +Keywords: + +Posted Date: May 29th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2930525/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on February 7th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 45394-w. + +<--- Page Split ---> + +# A Novel Approach for Classifying Battery and Pseudocapacitor Materials + +# Using Capacitive Tendency and Supervised Machine Learning + +Siraprapha Deebansok,a Jie Deng,b Etienne Le Calvez,c,d Yachao Zhu,e Olivier Crosnier,c,d Thierry Brousse,c,d Olivier Fontainea,f + +a Molecular Electrochemistry for Energy laboratory, VISTEC, Institute of Science and Technology, Rayong, 21210, Thailand. + +b Institute for Advanced Study & College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China. + +c Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN, 44000 Nantes, France. + +d Réseau sur le Stockage Électrochimique de l'Énergie (RS2E), CNRS FR 3459, 33 rue Saint Leu, 80039 Amiens, France. + +e ICGM, Université de Montpellier, CNRS, 34293 Montpellier, France. + +f Institut Universitaire de France, 75005 Paris, France. + +\* Corresponding author. Email: Olivier Fontaine: olivier.fontaine@vistec.ac.th + +<--- Page Split ---> + +## Abstract + +In recent decades, there have been more than 100,000 scientific articles dedicated to developing electrode materials for supercapacitors and batteries. A heated debate nonetheless persists surrounding the standards for determining electrochemical behavior involving faradaic reactions, since the electrochemical signals produced by the various electrode materials and their different physicochemical properties often complicate matters. The difficulty lies in determining which group these materials fall into through simple binary classification as there can be an overlap between battery and pseudocapacitor signals and because both materials are faradaic in origin. To solve this conundrum, we applied supervised machine- learning toward a statistical analysis of electrochemical signals, and consequently developed a new standard which we called capacitive tendency. This predictor not only surpasses the limitations of human- based classification but also provides statistical tendencies regarding electrochemical behavior. Notably, and of particular importance to the electrochemical energy storage community publishing over a hundred articles weekly, we have created an online tool for easy classification of their data. + +<--- Page Split ---> + +## Introduction + +In the energy storage research field, batteries are one of the most studied types of devices owing to their use in a wide range of applications including electronic equipment, electric vehicles and for medical and military purposes. \(^{[1]}\) On the other hand, pseudocapacitive electrodes have attracted a considerable amount of attention due to their superior power capability. \(^{[2]}\) Both of these energy storage systems are generally composed of various types of electrode materials exhibiting electrochemical signals that may or may not resemble one another. \(^{[3]}\) + +It is common knowledge that electric double layer capacitors (EDLCs) rely on a non- faradaic process without any electron transfer, whereas batteries and pseudocapacitors are governed by faradaic reactions. \(^{[4]}\) The latter processes are generally depicted by peaks on Cyclic Voltammograms (CVs) and plateaus on Galvanostatic Charge- Discharge (GCD) curves (Figure 1). \(^{[5]}\) Nowadays, some faradaic electrode materials display electrochemical signals similar to those of EDLCs, such as the rectangular/quasi- rectangular CV and the sloping GCD curve. \(^{[6, 7]}\) This characteristic has been found in a wide variety of transition metal oxides (RuO \(_2\) , \(^{[8]}\) MnO \(_2\) \(^{[9, 10]}\) ), conducting polymers (poly(3,4- ethylenedioxythiophene) \(^{[11, 12]}\) , polyaniline \(^{[13, 14]}\) ), and carbides (MXene) \(^{[15]}\) ). Numerous studies are underway focusing on faradaic electrode materials and including the behavior of pseudocapacitors and batteries, where both involve redox reactions, in keeping with the concept proposed by Conway et al. \(^{[4]}\) . Currently, owing to the vast amounts of materials studied, guidelines for distinguishing between the two are still largely inadequate, with some studies even contradicting the conventional definition of Conway et al., as later supported by Brousse et al. and other researchers in the field. \(^{[7]}\) + +Indeed, electrochemical signals are numerous and complex, varying according to the choice of electrode materials, as shown in Figure 1, hence the difficulty in identifying and categorizing + +<--- Page Split ---> + +these materials based on electrochemical signals. Recently, Fleischmann et al. \(^{[16]}\) , in a perspective paper, postulated the importance of a unified understanding when it comes to the electrochemical signals found in capacitors and batteries. The authors proposed the concept of electrolyte confinement that could impact the electrochemical behavior as a transition as a 'spectrum' from battery- to capacitor- type signals. It depicts the continuum of the signal from one state to another by altering the degree of confinement depending on, for example, the pore size of the electrode materials or the spacing size between MXene layers. Their work highlights the significance of successful quantification in order to move away from the postulate and arrive at a quantifiable spectral variable. It is shown that understanding the overlap and transition in electrochemical signals essentially requires a clear- cut classification of electrode material types based on their electrochemical behaviors (in CV and GCD). Unfortunately, the scientific elements presented by the authors are comparable to a mathematical conjecture, meaning that the proposed continuum is not supported by any mathematical variable or formalism. It is merely the subject of a postulate. Nonetheless, stating that the continuum is necessary does not diminish its importance. However, it becomes apparent that a mathematical variable must be added to quantify and measure this variation within the continuum. In order to metric this concept of 'continuum spectrum' and to provide the quantitative value to it, we analyze for the first time to the best of our knowledge the electrochemical signals with the help of supervised machine- learning. Our method is based on data science driven- supervised machine learning for achieving the descriptor, "capacitive tendency" that allows our community to develop metric, as a next step following this postulate. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1 | Illustration of CVs and GCD curves of a pseudocapacitor without ambiguity (a and d, respectively), and a battery (c and f, respectively). CV and GCD curve with ambiguity (b and e, respectively).
+ +To date, computing techniques have been used as somewhat satisfactory tools toward ascertaining the charge storage mechanism behind various electrochemical signatures. \(^{[17 - 20]}\) It has been popular in the energy storage community that extracting the information such as electrochemical, chemical, and physical properties from literatures is essential, when big data has been generated with large number of scientific papers every year. Text mining was used to gather information of Li- ion battery research and development involving in several processes such as electrode synthesis, electrochemical performance, processing condition parameters, where the models are based on machine- learning (ML), natural language processing (NLP), Named Entity Recognition (NER). \(^{[21,22]}\) Recently, text- mining algorithms have been developed to efficiently extract various specific information of the materials from the article such as BatteryDataExtractor using bidirectional- encoder representations from transformers (BERT), \(^{[23]}\) and Li- ion battery annotated corpus (LIBAC) based on NER. \(^{[24]}\) However, using ML for electrochemical signal interpretation has not been done. + +In this work, ML approach is used to interpret the CV and GCD signals by way of a supervised ML descriptor aimed at analyzing and determining the capacitive behavior of electrode materials found in thousands of scientific papers, as illustrated in Figure 2. Since our nuanced + +<--- Page Split ---> + +classification is substantially different from the binary identification by human as only being battery or pseudocapacitor among various electrochemical signals, we propose a new definition called capacitive tendency. This tendency is not only able to classify a large majority of relevant materials, but also to depict possible behaviors of the material in question. Hence, this artificial intelligence (AI) power will be the only important tool to help transforming the information from images to accurate values based on big database available in the electrochemical energy storage community. In addition to this, we provide an online tool kit which uses supervised machine- learning to easily classify materials. Today, the large amount of literature sometimes leads to a misuse of the proposed definitions, to reduce this definitional mishap, our work will reduce these errors. Our work thus serves to put forward a new concept toward understanding and labeling the various electrochemical signatures of energy storage devices. Above all, it also offers a unique opportunity to unify the complex electrochemical signatures of more than 100,000 scientific papers through supervised ML. + +![](images/Figure_2.jpg) + +
Figure 2 | Image extraction from scientific papers followed by CV and GCD classifications based on ResNet50 architecture.
+ +<--- Page Split ---> + +## Methods + +## Dataset Construction + +In the present paper, all datasets are in the form of images extracted using PyMuPDF library in Python language from more than 3,300 scientific papers. The first dataset, or Output 1, was obtained by figures extracting using OpenCV which provides (2,979) \(GCD\) , (5,598) \(CV\) and other images such as crystal structure image (which will not be used in the further classification steps). The \(GCD\) s and \(CV\) s were then labeled as belonging to one of two classes, namely batteries or pseudocapacitors without ambiguity, to be used for model training (80% of total data), as well as for validation (20% of total data) in Process 2 and Process 3 for \(GCD\) and \(CV\) classification, respectively. From Process 3, Output 3 was obtained and categorized into three types of training sets: 100% battery, 50% battery/pseudocapacitor, and 100% pseudocapacitor. This output was then further refined in Processes 4 and 5, as illustrated in Figure 3b. Moreover, cross- validation was performed with the experts in the field with the number of meetings. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3 | (a) CV and GCD datasets obtained after classification by Process 1, splitting them into training and validation datasets for further GCD and CV classification in Process 2 and Process 3, respectively. (b) The outputs from Process 3 are used in this final classification step to obtain the capacitive tendency based on percentage confidence rating of the prediction. (c) Table of processes, inputs and outputs performed/used to obtain these results.
+ +## Validation of classification architectures + +In this work, Convolutional Neural Networks (CNNs) were selected for use as the image classification architectures.[25] Benchmarking was conducted on five different CNN models, including ResNet50,[26] MobileNetV2,[27] VGG16,[28] Xception[29] and 8- Layer CNN[25] (see Supplementary Figures ESI 1- 2), to compare model performance. It was carried out based on five metrics, including: Accuracy, Sensitivity, Specificity, Precision, and F- Score[30] (see + +<--- Page Split ---> + +Supplementary Figures ESI 3 and Eq. ESI 1- 5). During the model training cycles, the number of training and validation iterations can impact the accuracy of the prediction since this is related to the experience gained over time by the ML model. Moreover, binary cross entropy \((BCE)\) loss, \(^{[31]}\) calculated from the prediction error as shown in Eq. 1, was minimized along the number of training iterations to optimize predictor performance. + +\[L_{BCE} = -\frac{1}{n} (\sum_{i = 1}^{n}y_{i}\cdot \log (\hat{y}_{i})y_{i}\cdot \log (\hat{y}_{i}) + (1 - y_{i})\cdot \log (1 - \hat{y}_{i}))\qquad \mathrm{Eq.1}\] + +Where \(y_{i}\) is the ground truth label (0 or 1, in this case battery or pseudocapacitor), \(\hat{y}\) is the predicted value, and n is the output size. \(^{[31]}\) + +## Machine-learning for CV/GCD classification procedures + +The ML architecture displaying the best performance after the validation step (further explained in the Results and Discussion section) was selected for use in this work as will be supervised during classification processes. ResNet50 was exploited in different steps denoted as Processes 1, 2, 3, 4, and 5 (as summarized in Figure 3c) according to the types of inputs and outputs. All of the images extracted from scientific papers were then categorized by Process 1 (ResNet50 model) which yielded Output 1, comprising GCDs, CVs and other images (such as optical image). GCDs from Output 1 were then classified using Process 2, and CVs were separately classified by Process 3, thereby providing the resulting prediction (Output 2: classified GCDs, and Output 3: classified CVs) of either battery or pseudocapacitor with a percentage confidence rating of \(0 - 100\%\) , while the errors were monitored and minimized to improve the prediction. Here, the capacitive tendency \((0 - 100\%)\) was firstly defined by the percentage confidence value, indicating the probability of CV shape as peak (0% capacitive tendency) and box shape (100% capacitive tendency). In the final step (Figure 3b), the classified CVs (in Output 3) were labeled according to four percentage confidence classes — + +<--- Page Split ---> + +\(100\%\) battery, \(50\%\) battery, \(50\%\) pseudocapacitor and \(100\%\) pseudocapacitor — before being further modeled in Processes 4 and 5 to provide the capacitive tendency based on a percentage confidence of \(0 - 100\%\) . + +An alternative way to understand the definition of capacitive tendency is to analyse it as the deviation from the ideal of the purely capacitive signal (is easy to recognize). When the trained model is confident that the curve is close to a rectangle (for CV) or a triangle (for GCD), then this implies that the curve is close to an ideal capacitive signal. On the contrary, a curve whose confidence value is close to zero means that the curve has a different contribution. Basically, the capacitive tendency reflects the analysis of the signal shape. It is information based on a geometric shape. Of course, alternatives could be used. However, the use of the classical formalism, as indicated in the "ideal CVs" area in Figure 4a, is impossible when the shape of the electrochemical signal deviates from this ideal. In the purely mathematical domain, the possibility of adding a rectangle to a closed geometric shape (a CV is a closed geometric shape) is a complex mathematical situation. It is the concept of Inscribed rectangular problem in mathematics. Thus, our data science- driven by supervised deep learning approach is a suitable alternative. + +## Results and Discussion + +This section explains how the models for CV and GCD classification were established for this specific dataset through the validation of different CNN architectures. The selection was based on well- known parameters including Accuracy, Sensitivity, Specificity, Accuracy, and F- Score. Moreover, the most accurate model was developed for use as the descriptor in order to determine the capacitive tendency of the various electrochemical behaviors, by applying the experimental data of various electrode materials. Ultimately, the selected model is destined for use by electrochemists as a tool for determining the nature of their materials. + +<--- Page Split ---> + +## The issues surrounding electrochemical signal identification + +The rapidly increasing number of scientific publications involving the study of capacitive materials over the last decade points to the importance of this field of study (as shown in Figure 4). It was found that the 3,300 papers contain around 5,600 CVs and 3,000 GCDs, which generates a massive amount of data and thereby renders human- based interpretation extremely challenging. Furthermore, the CV signals measured by these experiments are mostly performed in complex situations, and thus to not produce the perfect curves obtained in theoretical demonstrations using various common types of electrode materials (Figures 4). This also holds true for GCD signals acquired from experimental measurements. The whole limitation of the analyses in the field is summarised in figure 4a, most of the electrochemical signals are too far from the ideal signal to be analysed with the tools proposed in the state- of- the- art. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4 | Illustration of a) experimental CVs and GCDs of different electrode materials including \(MnO_2\) , \(V_2C\) , \(RuO_x\) , \(LaMnO_3\) , \(Ti_3C_2T_x\) , \(H_2TiNb_6O_{18}\) , \(Ag_1\) , \(3xLa_{1-x}NbO_3\) , \(Nb_2O_5\) , \(nano-MnS_2\) , \(bulk-MoS_2\) , \(TiO_2\) , and \(NaFePO_4\) , theoretical b) CVs and c) GCDs undergoing different electrochemical processes, and d) Number of publications involving capacitive and battery electrode materials from 2012 to 2022. (Google Scholar, August 28th, 2022).
+ +In this study, these CVs and GCDs were analyzed via supervised ML trained with datasets extracted from over 4,000 scientific papers (see DOI in Supplementary Information). In the following section, various Convolutional Neural Network architectures are validated and + +<--- Page Split ---> + +selected based on the evaluations explained in the experimental section, by applying the theoretical CV and GCD curves. + +## Validation of architectures + +To select the Convolutional Neural Network architecture best suited to our datasets, the validation of a total of five models (ResNet50, MobileNetV2, VGG16, Xception, and 8- Layer CNN) was first performed using Processes 2 and 3 with different types of input and output (Table ESI 1). These architectures were chosen based on the reported accuracy ranking ascribed to the models' performance from ImageNet validation. [42, 43] In this step, the prediction was governed by binary classification to obtain only two different outputs, namely (i) battery or (ii) pseudocapacitor, since the model had been trained and supervised with CV and GCD datasets without ambiguity. ResNet50 was found to be the most accurate and precise one out of all the models (Table ESI 2) and was thus selected to further prediction in the next step. Moreover, ResNet50 is more adapted to the variety of data that will be input by the users, for example, plot with different frame and font styles and different color curves. + +To demonstrate the efficiency of the model, 5598 CVs and 2979 GCDs were randomly selected and entered into the classifier according to Processes 2 and 3. Figure ESI 9 clearly demonstrates that the majority of predicted datasets showed a 100 % confidence rating, which would suggest that our ML model displays a high level of precision and reliability with a negligible risk of error. + +<--- Page Split ---> + +## Validation of theoretical CVs and GCDs + +In this part, the simulations of CV and GCD images were done using basic equations from theoretical electrochemistry including Faradaic process with peak- shaped CV, \(^{[44]}\) and EDLC with box- shaped CV which relies on Eq. 2 and Eq. 3. The simulated images were then classified by the trained model (process 4- 5). The equation for CVs showing redox peaks is given as follows: + +\[\frac{i}{i_{max}} = \frac{e^{\frac{F}{R\cdot T}}(E - E_{peak}^{0})}{1 + \left(e^{\frac{F}{R\cdot T}}(E - E_{peak}^{0})\right)^{2}} \quad \text{Eq. 2,}\] + +where \(\frac{i}{i_{max}}\) is the normalized current of the peak current function, \(F\) is the Faraday constant, \(R\) is the gas constant, \(T\) is the temperature, \(E\) is the applied potential and \(E_{peak}^{0}\) is the peak potential. The box- shaped EDLC current function is given by: + +\[\frac{i}{i_{max}} = 1 - e^{-\frac{t}{R\cdot C}} \quad \text{Eq. 3,}\] + +where \(C\) is the capacitance, \(R\) is the resistance and \(t\) is the charging period. \(^{[45]}\) It was shown that capacitive behavior is more pronounced the further the CV shape deviates from peaked to rectangular (Figure 5a). + +Furthermore, simulating number of theoretical \(GCD\) images with the transition in curvature from straight to plateau feature could be applied with the classification model (process 2) in order to see the region of ambiguity. Using Eq. 4 by varying M parameter: + +\[E = M\cdot \left(\frac{R\cdot T}{n\cdot F}\right)\log \left(\frac{\sqrt{t} - \sqrt{t}}{\sqrt{t}}\right) + E_{\tau /4} \quad \text{Eq. 4,}\] + +where \(E\) is the potential, \(n\) is the number of electron transfers, \(t\) is the charging/discharging time, \(\tau\) is the time constant, \(E_{\tau /4}\) is the quarter- wave potential and \(M\) is the mathematical factor permitting the manipulation of the galvanostatic curve to show either a plateau feature (as + +<--- Page Split ---> + +found in battery material measurements) or straight line (as in supercapacitor material measurements), the continuum GCD curves were obtained, as shown in Figure 5b (blue, grey, and purple lines). + +![](images/Figure_5.jpg) + +
Figure 5 | The illustration of (a) classified theoretical CVs with Gaussian and box shapes as the components, and (b) classified theoretical galvanostatic charge (I) and discharge (II) curves obtained by using Eq 4. with a varying M parameter. The color of each curve is related to the probability of being battery (purple gradient bar) or capacitive material (blue gradient bar).
+ +Figure 5b(I) shows that a battery- type signature was found to apply for an \(M\) value range of between 1.6 and 7 (purple zone, with a 90- 100% confidence rating), whereas the prediction point to a pseudocapacitor- type for \(M\) values of between 7.1 and 19.6 (blue zone, with a 70- 100% confidence rating). Similarly, this result was also observed for theoretical discharging profiles, as shown in Figure 5b(II). However, in the grey zone when M is around + +<--- Page Split ---> + +7.0 during charge and 9.4 during discharge, respectively, the predictor was hesitant to define the signal type, suggesting that a certain ambiguity occurs when the curvature of the \(GCD\) signal is somewhere between a straight line and a plateau, as has already been observed and which is consistent with experimental measurements related to pseudocapacitive materials (Figure 6c). The most pertinent conclusion that can be drawn from this calculation is that our model demonstrated the transition region of \(GCD\) signals in accordance with the continuum transition concept as proposed by Fleischmann et al. \(^{[16]}\) . Our model clearly demonstrates the source of the confusion for both humans and computers, which stems from the fact that these behaviors all originate from faradaic processes where electron transfer is the elementary step. This explains why the results of theoretical studies only hold true for basic scenarios. More complex behaviors, however, are frequently observed in experimental measurements and account for vast amounts of data, as depicted in Figure 4. + +## Revealing the nature of electrode materials through supervised machine-learning + +In accordance with the main purpose of this study, namely overcoming human limitations when it comes to understanding electrochemical signals, the objective in this section concerned clarifying the behavior of faradaic electrode materials. To this end, experimental \(CVs\) from Figure 4 were applied to the model to predict the capacitive tendency behavior of various electrode materials that conventionally can be calculated from \(\mathrm{dQ / dV} =\) constant in only simple cases such as supercapacitor materials but could be too complex to apply for pseudocapacitors. Well- known pseudocapacitive and battery materials from the literature, such as \(MnO_2\) and \(NMC\) , were compared not only to separate the signals produced by Processes 2 and 3 according to the conventional binary classification, but also to establish a new standard that we called capacitive tendency. Processes 4 and 5 broadened the classification range to create a statistical tendency representing an interpretable value: in the range of \(0\%\) denoting a + +<--- Page Split ---> + +battery, to \(100\%\) being a pseudocapacitor. Finally, we were able to predict the capacitive behavior of various electrode materials from experimental data, as demonstrated in Figure 6. + +![](images/Figure_6.jpg) + +
Figure 6 | Capacitive tendency prediction of experimental voltammograms of (a) the well-known pseudocapacitor and battery electrode materials \(MnO_2\) [46] and \(NMC\) [47] respectively, compared with the ambiguous CVs of \(Ag_{1 - 3x}La_{x}N_{2}NbO_{3}\) [37] and \(H_2TiNbO_{18}\) [36], respectively. Predicted (b) CVs and (c) GCDs of other electrode materials from the literature, as per Figure 4.
+ +As previously mentioned, the exemplary rectangular and peak shapes are unfortunately not often present when it comes to systems exhibiting fast charge/discharge behavior or when pseudocapacitive materials are investigated. Electrochemists thus find it difficult to analyze the voltammograms correctly in the face of such a variety of shapes, with even the \(CVs\) of \(V_2C\) , \(Nb_2O_5\) and nano- \(MoS_2\) electrode materials (Figure 6b) displaying a similar capacitive tendency of around \(52 - 53\%\) . This finding served to emphasize the necessity of using machine- learning + +<--- Page Split ---> + +as a decisive tool for interpreting CV signals displaying a complexity that is beyond human discernment. + +## The limitation of the binary classification battery vs. pseudocapacitor + +During this phase of our research, numerous scientific articles containing the keyword "battery" (2011 articles) or "pseudocapacitor" (1346 articles) (see Supplementary Information for DOI) were analyzed using our supervised ML model to provide a statistical analysis of the number of papers containing a keyword that was in contradiction to their signals. Briefly, the articles were randomly selected and their relevant CV and GCD signals were extracted and then simply classified into either battery or pseudocapacitive type using only Processes 2 and 3. The outputs in Figure 7 depict that around \(67\%\) of the papers with a "pseudocapacitor" keyword are consistent with their experimental observations. Unexpectedly, however, nearly \(50\%\) of the articles with a "battery" keyword displayed contradicting signals. These results serve to reinforce the fact that human- based interpretation could greatly benefit from being replaced with computing techniques such as ML. Apparently, our machine- learning classification technique showed the significant portion of the articles using binary keywords (battery or pseudocapacitor) that contradict (mismatched) with their electrochemical signal (Supporting Information Section 8.1- 8.6). + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 7 | (a) The methodology behind the title classification of papers as either a battery or pseudocapacitor, followed by (b) CV and GCD extraction and then (c) the matched/mismatched outputs using our classifiers (Processes 1, 2 and 3). The percentage correlation between titles for pseudocapacitor and battery materials vs. correctly classified CVs and GCDs.
+ +This result perfectly shows the limit of the binary approach in the field. Because analysing a binary classification leads to this misclassification by the authors. Our approach, using capacitive tendency, allows a unification of the measurements, by including them in a "spectrum" as proposed by Frieshman et al[16], in a mathematical conjecture. + +<--- Page Split ---> + +## Online tool kit for CV/GCD classification + +Online tool kit for CV/GCD classificationIn order to facilitate the task of users worldwide when it comes to classifying the electrochemical behaviors (battery or pseudocapacitor) of their experimental data (CVs and GCDs), we have launched an online tool for analyzing these signals and providing an output in the form of a capacitive trend (or percentage confidence rating). It is publicly available at http://supercapacitor- battery- artificialintelligence.vistec.ac.th, and details are also provided in the Supporting Information. + +![](images/Figure_8.jpg) + +
Figure 8 | The online tool kit for CV and GCD classification based on our model.
+ +## Conclusion + +ConclusionThe research presented herein has successfully managed to resolve the decades- old conundrum concerning the interpretation of electrochemical signals from CVs and GCDs by making full use of advanced computing technology in order to classify the behavior of + +<--- Page Split ---> + +materials as battery- like or pseudocapacitor- like. Specifically, we demonstrated that supervised ML is a powerful and accurate way to distinguish between these often complex signals. Our study also highlights the recurrent issue of the titles of scientific papers often contradicting the results of their own data, especially when it comes to those articles with “battery” in the title. This emphasizes the importance of using computer- based modelling for prediction as opposed to human- based analysis, which is far slower and more subjective and that leads to much unnecessary disagreement and debate. As a major contribution to our peers in the electrochemical energy storage community, we are delighted to announce a unique online tool based on our model toward simple online classification via our distinguishing marker, called capacitance tendency, affording them the possibility of a quick and easy standard to refer to when attempting to determine the nature of their new materials. Last but not least, featuring text- mining of material information with our classification tool could be an ultimate strategy for future perspectives on artificial intelligence for energy storage technology. + +## Data and code availability + +Machine- learning models and datasets are made publicly available at GitHub repository: https://github.com/ice555mee/TB- robot_code- data or contact the author (olivier.fontaine@vistec.ac.th) for more information. The instruction is provided in both supporting information and on Github repository. The website is available via the link: http://supercapacitor- battery- artificialintelligence.vistec.ac.th/ + +## References + +1. Mahmud, S., et al., Recent advances in lithium-ion battery materials for improved electrochemical performance: A review. Results in Engineering, 2022. 15: p. 100472. + +<--- Page Split ---> + +2. Choi, C., et al., Achieving high energy density and high power density with pseudocapacitive materials. Nature Reviews Materials, 2020. 5(1): p. 5-19. + +3. Brousse, T., D. Bélanger, and J.W. Long, To Be or Not To Be Pseudocapacitive? Journal of The Electrochemical Society, 2015. 162(5): p. A5185-A5189. + +4. Ardizzone, S., G. Fregonara, and S. Trasatti, "Inner" and "outer" active surface of RuO2 electrodes. Electrochimica Acta, 1990. 35(1): p. 263-267. + +5. Mathis, T.S., et al., Energy Storage Data Reporting in Perspective—Guidelines for Interpreting the Performance of Electrochemical Energy Storage Systems. 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Zisserman Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014. arXiv:1409.1556. + +29. Chollet, F. Xception: Deep Learning with Depthwise Separable Convolutions. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. + +30. Lever, J., M. Krzywinski, and N. Altman, Classification evaluation. Nature Methods, 2016. 13(8): p. 603-604. + +31. Zhang, Z. and M.R. Sabuncu. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. in NeurIPS. 2018. + +32. Zhao, X., et al., Prepared MnO2 with different crystal forms as electrode materials for supercapacitors: experimental research from hydrothermal crystallization process to electrochemical performances. RSC Advances, 2017. 7(64): p. 40286-40294. + +33. Shan, Q., et al., Two-dimensional vanadium carbide (V2C) MXene as electrode for supercapacitors with aqueous electrolytes. Electrochemistry Communications, 2018. 96: p. 103-107. + +34. Zhang, J., et al., Template Synthesis of Tubular Ruthenium Oxides for Supercapacitor Applications. The Journal of Physical Chemistry C, 2010. 114(32): p. 13608-13613. + +35. Mefford, J.T., et al., Anion charge storage through oxygen intercalation in LaMnO3 perovskite pseudocapacitor electrodes. Nature Materials, 2014. 13(7): p. 726-732. + +36. Miranda, J., et al., Revisiting Rb2TiNb6O18 as electrode materials for energy storage devices. Electrochemistry Communications, 2022. 137: p. 107249. + +37. Le Calvez, E., et al., Investigating the Perovskite Ag1-3xLaxNbO3 as a High-Rate Negative Electrode for Li-Ion Batteries. Frontiers in Chemistry, 2022. 10. + +38. Lian, Y., et al., Optimization Design and Application of Niobium-Based Materials in Electrochemical Energy Storage. Advanced Energy and Sustainability Research, 2020. 1(1): p. 2000038. + +<--- Page Split ---> + +39. Cook, J.B., et al., Suppression of Electrochemically Driven Phase Transitions in Nanostructured MoS2 Pseudocapacitors Probed Using Operando X-ray Diffraction. ACS Nano, 2019. 13(2): p. 1223-1231. +40. Li, X., et al., Orderly integration of porous TiO2(B) nanosheets into bunchy hierarchical structure for high-rate and ultralong-lifespan lithium-ion batteries. Nano Energy, 2017. 31: p. 1-8. +41. Karthik, M., et al., Design and fabrication of NaFePO4/MWCNTs hybrid electrode material for sodium-ion battery. Journal of Materials Science: Materials in Electronics, 2020. 31(23): p. 21792-21801. +42. Zhang, D., et al., Knowledge Graph-Based Image Classification Refinement. IEEE Access, 2019. 7: p. 57678-57690. +43. Zhang, D. and T. Zhou, Deep Convolutional Neural Network Using Transfer Learning for Fault Diagnosis. IEEE Access, 2021. 9: p. 43889-43897. +44. Waelder, J., et al., A Description of the Faradaic Current in Cyclic Voltammetry of Adsorbed Redox Species on Semiconductor Electrodes. 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International Journal of Energy Research, 2020. 44(11): p. 9082-9092. + +## Acknowledgements + +Website hosting is supported by Vidyasirimedhi Institute of Science and Technology server. + +This work is supported by funding from Thailand Science Research and Innovation (TSRI) + +(Grant No. FRB660004/0457). + +## Competing interests + +The authors declare no competing interests. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- ESISUB1.docx + +<--- Page Split ---> diff --git a/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73.mmd b/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73.mmd new file mode 100644 index 0000000000000000000000000000000000000000..db87cddbc7fbbc13a19a24007897e2eefb05ca08 --- /dev/null +++ b/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73.mmd @@ -0,0 +1,188 @@ + +# Responsive nucleus accumbens deep brain stimulation restores eating control in severe obesity + +Casey Halpern ( \(\boxed{ \begin{array}{r l} \end{array} }\) casey.halpern@pennmedicine.upenn.edu ) + +University of Pennsylvania + +Rajat Shivacharan Stanford + +Cammie Rolle University of Pennsylvania + +Daniel Barbosa University of Pennsylvania + +Tricia Cunningham Stanford + +Austin Feng Stanford + +Noriah Johnson Stanford + +Debra Safer Stanford + +Cara Bohen Stanford + +Corey Keller Stanford + +Vivek Buch Stanford + +Jonathan Parker Stanford + +Dan Azagury Stanford + +Peter Tass Stanford + +Mahendra Bhati Stanford + +Robert Malenka Stanford University + +James Lock + +<--- Page Split ---> + +## Brief Communication + +# Keywords: + +Posted Date: March 15th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1432380/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Medicine on August 29th, 2022. See the published version at https://doi.org/10.1038/s41591- 022- 01941- w. + +<--- Page Split ---> + +## Abstract + +Craving that precede loss of control (LOC) over food consumption present an opportunity for intervention in patients suffering from binge eating disorder (BED). Here, we used responsive deep brain stimulation (DBS) to record NAc electrophysiology during food cravings preceding LOC eating in two patients with BED and severe obesity (NCT03868670). Increased NAc low- frequency oscillations prominent during food cravings were used to guide DBS delivery. Over 6 months, we observed improved self- control of food intake and weight loss. These findings provide early support for restoring inhibitory control with electrophysiologically- guided NAc DBS. Further work is required to determine scalability of this approach. Trial Registration # NCT03868670. + +## Introduction + +Loss of control (LOC) eating, or the subjective sense that one cannot stop eating, is associated with binge eating - defined by the consumption of an objectively large amount of food in a short period of time accompanied by a sense of LOC.1 LOC eating is often characterized by the loss of inhibitory control in response to appetitive cues and cravings leading to binge eating2. Recurrent and distressing episodes of binge eating are the key features of binge eating disorder (BED). BED is the most common eating disorder, affecting up to 3 percent of U.S. adults, and is the most severe form of LOC eating based on volume of food consumed'. It is associated with obesity, decreased quality of life and premature mortality.3 + +Most treatments for obesity fail to address LOC eating directly, limiting the efficacy of even the most aggressive interventions such as bariatric surgery.4,5 Clinical evidence supports a role of cravings for preferred food, or intense desires to consume specific palatable foods, prior to the onset of LOC and binge eating.6,7 Particularly in individuals who are overweight or obese, food cravings have been linked with LOC among those diagnosed with BED.8 Given this, recent studies have examined neural signals associated with food craving in the pursuit of identifying a biomarker used to trigger deep brain stimulation (i.e., responsive DBS or rDBS) and inhibit onset of LOC eating when patients may be most atrisk. + +In the effort to identify such a craving biomarker, previous work in mice found that anticipation of a high- fat food reward was associated with increased low- frequency oscillatory power in the NAc.9 This work supported a growing body of evidence across species reporting electrophysiological, neurochemical, and functional neuroimaging activities within circuits involving the NAc that correlate to reward anticipation,10- 13 and that predict consequential behavioral outcomes.14 Using low- frequency delta- band power as a biomarker to trigger delivery of a brief train of high- frequency electrical stimulation to the NAc (here after referred to as rDBS) resulted in significant and lasting attenuation of binge- like eating in mice sensitized to high fat food,9 while conventional, continuous DBS appeared to lose efficacy over time.15,16 + +<--- Page Split ---> + +Here, we report the proof of concept in this first- in- human study designed to characterize human NAC electrophysiology of craving as it relates to LOC eating. We sought to identify changes in NAC electrophysiology associated with moments of food craving and LOC eating during controlled in- clinic behavioral tasks and to assess the generalization of this effect to LOC eating events in a naturalistic setting and outside the behavioral laboratory. Finally, we implemented rDBS triggered by NAC electrophysiology identified in behavioral and naturalistic assessments, and report here initial results on the potential efficacy of this novel intervention. This study was performed under a U.S. Food and Drug Administration Investigational Device Exemption (G180079) using the NeuroPace Responsive Neurostimulation (RNS) System17. + +## Methods + +## PRESTUDY PROCEDURES + +Two adult women with BED and treatment- refractory severe (grade III) obesity, despite bariatric surgery were recruited for this study, approved by Stanford's Institutional Review Board (IRB- 46563) (see appendix for participant characteristics). Designed with a staggered enrollment, each subject progressed through the study stages shown in Fig. 1A. Both subjects underwent stereotactic implantation of bilateral depth electrodes, each with four contacts.18 The two distal contacts were positioned in the NAC and the two proximal contacts traversed the anterior limb of the internal capsule (Fig. 1B).19 + +## RECORDING PHASE + +Immediately following implantation, subjects entered a 6- month recording phase, during which naturalistic in- lab assessments and ambulatory real- world assessments were performed to identify an electrophysiological biomarker for rDBS in the consecutive stimulation phase. From each hemisphere, activity was recorded from the ventral and dorsal NAC (see appendix for details). Subjects underwent two assessments to evaluate NAC electrophysiology during: 1) anticipation (pre- consumption) of food during standard meals and LOC eating (i.e., Multi- Item Buffet assessment; in- lab naturalistic testing); and 2) states of hunger and craving (pre- consumption) (i.e., ambulatory assessment; real- world testing). + +## STIMULATION PHASE + +Following the recording phase, both subjects underwent single- blinded stimulation survey testing in which they received brief bursts of electrical stimulation across all electrode contacts to screen for acute effects. This was followed by a single- blinded, staged, on- off stimulation safety testing period to assess for possible side effects of rDBS. Subjects then entered the 10–12 month open- label stimulation phase of the study. In this phase, rDBS was delivered using a bipolar montage of the two NAC electrode contacts. Both subjects received bilateral NAC rDBS via depth electrodes connected to a NeuroPace RNS system to detect and inhibit LOC eating events. Stimulation was delivered at 125 Hz in two 5 second bursts with a charge density of 0.5–1.5 μC/cm.17 Detections and stimulations occurred approximately 400 times/day with a stimulation limit set to 700 bouts (or approximately 117 min) per day in order to limit unnecessary + +<--- Page Split ---> + +stimulation at night. Based on the recording phase, each subject's device was programmed to detect brief increases in low- frequency activity in both the left and right ventral NAc (see appendix). These detections of low- frequency activity triggered bilateral NAc rDBS ( \(\sim 1\mu \mathrm{C} / \mathrm{cm}^2\) charge density, 10s duration). Low- frequency triggered bilateral stimulation has been well tolerated by both subjects. Neither subject 1 nor 2 experienced a serious adverse event, and all reported events were self- limited (Table S4). Examination of sensitivity and specificity can be found in the appendix (Figures S1, S2). + +## Results + +RECORDING PHASE + +MULTI- ITEM BUFFET: NAC ELECTROPHYSIOLOGY DURING IN- LAB LOC EATING. In this assessment, we investigated each subject's LOC by modeling the at- risk environment in a controlled setting \(^{20}\) . Using mood provocation (see appendix), we assessed LOC (1–5 Likert severity scale) during presentation of a high calorie buffet of the subject's preferred foods while recording synchronized video- NAc LFP (Local Field Potential) activity. Analogous to our pre- clinical work, we analyzed and compared bite onset during the buffet to standard meals. Results showed low- frequency power increases immediately prior to LOC eating. Specifically, increases in left ventral NAc low- frequency (2–8 Hz) power were observed for both subjects during LOC immediately preceding (within 2 seconds) the videoed bite onset (see appendix) (mean ± s.e. dB power \([V^2 /\mathrm{Hz}]\) : Subject 1, \(2.4 \pm 1.5, \mathrm{n} = 16\) bites; Subject 2, \(5.6 \pm 3.1, \mathrm{n} = 12\) bites). In contrast, increases in low- frequency power were not observed immediately prior to bites during standard meals (Subject 1, \(0.6 \pm 1.0, \mathrm{n} = 15\) bites; Subject 2, \(0.3 \pm 0.9, \mathrm{n} = 11\) bites) (Fig. 1C, Student's t- test, \(p < 0.05\) ). There were no statistical changes in any of the other recorded frequency bands in either subject (Student's t- test, \(p > 0.05\) ). + +AMBULATORY ASSESSMENT: NAC ELECTROPHYSIOLOGY DURING REAL- WORLD LOC EATING EVENTS. We analyzed electrophysiology acquired during real- world behavioral states (see appendix) to validate the lab findings. Low- frequency power increases during LOC eating were corroborated with real- world assessments. Specifically, significantly higher low- frequency oscillatory power (Fig. 2A) in bilateral ventral NAc was found during subject- reported LOC eating events (craving- red trace, mean ± s.e. power \([V^2 /\mathrm{Hz}]\) : Subject 1, left NAc: \(0.21 \pm 0.11\) , right NAc: \(0.16 \pm 0.06, \mathrm{n} = 10\) events; Subject 2, left NAc: \(0.58 \pm 0.14\) , right NAc: \(0.21 \pm 0.07, \mathrm{n} = 71\) events) when compared to control periods (control- black trace, Subject 1, left NAc: \(0.1 \pm 0.04\) , right NAc: \(0.04 \pm 0.01, \mathrm{n} = 9\) events; Subject 2, left NAc: \(0.19 \pm 0.04\) , right NAc: \(0.09 \pm 0.04, \mathrm{n} = 80\) events) and periods of hunger (hunger- blue trace, Subject 1, left NAc: \(0.06 \pm 0.01\) , right NAc: \(0.03 \pm 0.01, \mathrm{n} = 13\) events; Subject 2, left NAc: \(0.27 \pm 0.11\) , right NAc: \(0.11 \pm 0.03, \mathrm{n} = 37\) events) (Fig. 2A, one- way ANOVA, Subject 1, left NAc: \(f = 3.50, \mathrm{P} = 0.04\) , right NAc: \(f = 4.95, \mathrm{P} = 0.03\) ; Subject 2, left NAc: \(f = 5.14, \mathrm{P} = 0.02\) , right NAc: \(f = 0.07, \mathrm{P} = 0.93\) ). Consistent with the in- clinic tasks, there were no differences in any other frequency band during at- risk moments in the ambulatory setting. + +SIGNAL DETECTION: BILATERAL NAC DETECTION. For each subject, we programmed the device to detect brief increases in low- frequency activity in both the left and right ventral NAc. To confirm that the signal + +<--- Page Split ---> + +being detected was in the low- frequency range, we analyzed the power spectra of the NAc LFP activity in the 5 seconds prior to a detection and found that the Area detectors (see appendix) were detecting low- frequency activity in the left and right ventral NAc (Fig. 2B). For this analysis, we compared detection made in stored LFPs during reported LOC eating events and awake events. For Subject 1, increased low- frequency power compared to baseline NAc LFP signal (average 2- minute window) was identified in \(74.4\%\) (67/90) of reported LOC eating event detections and \(63.2\%\) (84/133) of the awake detections \((X2(1,N = 223) = 24.54,p< 0.05)\) . For Subject 2, increased low- frequency power was identified in \(76.9\%\) (30/39) reported LOC eating event detections and \(45.8\%\) (22/48) awake detections \((X2(1,N = 87) = 14.82,p< 0.05)\) . + +## STIMULATION PHASE + +CHANGE in LOC EATING and Weight. Both subjects reported an increased sense of self- regulation and control over food intake specific to cravings and related eating behavior. Further, both subjects showed a decrease in the reported frequency of LOC eating events from baseline to 6- months post- stimulation (i.e. the primary endpoint), as assessed by the Eating Disorder Examination (EDE), and LOC severity, as assessed by the Eating Loss of Control Scale, across the 28- day period during the baseline month compared to 6- months post- stimulation month (LOC Frequency: Subject 1 = 80% decrease; Subject 2 = 87% decrease; LOC episode severity: Subject 1: 9- point improvement \((p = 0.09)\) ; Subject 2: 15- point improvement \((p = 0.05)\) ) (Fig. 3A,B). Notably, by the end of the 6- month follow- up period, Subject 1 exhibited substantial improvement in BED severity, while Subject 2 no longer met criteria for BED (i.e., fewer than average of 4 binge eating events per- month over the prior consecutive 3 months for no more BE diagnosis), which met our primary endpoint (Fig. 3C). Corroborating their subjective reports (Fig. 3), 6- month outcomes showed a decrease in body weight (kg and % reduction) and BMI for both subjects: Subject 1 = - 5.9 kg, - 4.5%, and - 2.2 kg/m², respectively; Subject 2 = - 8.2 kg, - 5.8%, and - 2.9 kg/m², respectively) (Fig. 3D,E). + +## Discussion + +In summary, this study identified NAc low- frequency oscillatory power as a signal associated with LOC craving, and then implemented this biomarker to guide rDBS delivery in two subjects with BED and severe obesity. In the recording phase, in- lab assessments implicated NAc low- frequency signalling during naturalistic LOC eating. The generalizability of this signal to real- world settings was then corroborated by our finding that low- frequency oscillatory power was increased during real- world LOC eating events compared to non- LOC events. In the stimulation phase, 6 months of bilateral NAc rDBS triggered by low- frequency power was found to improve LOC eating, as well as reduce body weight and BMI. Optimization of stimulation parameters is still ongoing in both subjects, and four additional subjects are expected to be implanted following a supplement approval to our investigational device exemption. We encountered early challenges when capturing LOC eating events in the real world. A training period was necessary prior to surgery for both subjects to learn to identify and document their LOC eating behaviors. This involved having a psychiatrist (DS) with expertise in obesity and eating disorders discuss with each patient her + +<--- Page Split ---> + +personal understanding of LOC eating. As we report (see appendix), while sensitivity of low- frequency detections to LOC eating was high, low- frequency oscillations in the NAC were not always specific to food craving and LOC eating compared to non- LOC eating events. Ongoing work seeks to optimize detection algorithms and improve the sensitivity and specificity of rDBS for LOC eating. Further, real- world LOC electrophysiology detected from ambulatory recordings was specific to bilateral, ventral NAC delta (2- 4Hz), whereas in- lab experiments found effects in both delta and theta (2- 8Hz) and were limited to the left ventral NAC. In addition, because real- world data capture was not time- locked to specific bite events during LOC and standard meals, the ambulatory and multi- item buffet data reflect different time windows respective to the LOC events. We also note that while the frequencies within which we found our effects here contained the delta signal identified in mice9, the effects from in- lab testing were broader and inclusive of theta frequencies. Importantly, one difficulty with the low- frequency biomarker signal is its presence during normal physiological processes such as sleep21,22. To account for detection and stimulation during sleep, we limited rDBS delivery to awake hours (7am- 10pm). Finally, the upfront cost of implantable devices is high; thus long- term follow- up of LOC eating as well as BMI beyond the study period will be necessary to assess societal cost- effectiveness of this intervention based on our decision analyses23. + +In conclusion, NAC rDBS improved LOC eating frequency and severity in two patients with BED and severe obesity. These findings were associated with weight loss even during this early follow- up period, suggesting patients can lose weight without instruction to change their diet or physical activity (efforts which are often unsuccessful). This is a testament to the potential clinical significance of this novel intervention and supports continued study in this FDA- guided first- in- human, early feasibility trial. + +## Declarations + +## Acknowledgments + +This work was supported by the National Institute of Health (5UH3NS103446- 02). The authors thank the study subjects' for their dedication and commitment to this novel, first- in- human exploratory trial; the members of the Stanford Clinical and Translational Research Unit and the Departments of Neurosurgery and Psychiatry at Stanford Medicine for space to conduct in clinic assessments; the Suthana laboratory for in- clinic tool support; Ian Kratter, Tom Prieto, Vyvian Ngo, Bharati Sanjanwala for support during surgery and intraoperative testing; Emily Mirro, Tara L. Skarpaas, Nick Hasulak, Tom Tcheng for providing technical support for the NeuroPace RNS System. + +## Competing Interests + +No funding from NeuroPace was received for this study nor were data analyses reported here conducted by NeuroPace employees. CHH, RSS, and CER have patents related to sensing and brain stimulation for the treatment of neuropsychiatric disorders. + +<--- Page Split ---> + +## References + +1. Association., A.P. Diagnostic and statistical manual of mental disorders (5th ed.). (2013). +2. Reents, J. & Pedersen, A. Differences in Food Craving in Individuals With Obesity With and Without Binge Eating Disorder. Front Psychol 12, 660880 (2021). +3. Hudson, J.I., et al. Longitudinal study of the diagnosis of components of the metabolic syndrome in individuals with binge-eating disorder. Am J Clin Nutr 91, 1568–1573 (2010). +4. White, M.A., Kalarchian, M.A., Masheb, R.M., Marcus, M.D. & Grilo, C.M. Loss of control over eating predicts outcomes in bariatric surgery patients: a prospective, 24-month follow-up study. J Clin Psychiatry 71, 175–184 (2010). +5. Chao, A.M., et al. Binge-eating disorder and the outcome of bariatric surgery in a prospective, observational study: Two-year results. Obesity (Silver Spring) 24, 2327–2333 (2016). +6. Grucza, R.A., Przybeck, T.R. & Cloninger, C.R. Prevalence and correlates of binge eating disorder in a community sample. Compr Psychiatry 48, 124–131 (2007). +7. McCuen-Wurst, C., Ruggieri, M. & Allison, K.C. Disordered eating and obesity: associations between binge-eating disorder, night-eating syndrome, and weight-related comorbidities. Ann N Y Acad Sci 1411, 96–105 (2018). +8. Bohon, C., Stice, E. & Spoor, S. Female emotional eaters show abnormalities in consummatory and anticipatory food reward: a functional magnetic resonance imaging study. Int J Eat Disord 42, 210–221 (2009). +9. Wu, H., et al. Closing the loop on impulsivity via nucleus accumbens delta-band activity in mice and man. Proc Natl Acad Sci U S A 115, 192–197 (2018). +10. Roitman, M.F., Stuber, G.D., Phillips, P.E., Wightman, R.M. & Carelli, R.M. Dopamine operates as a subsecond modulator of food seeking. J Neurosci 24, 1265–1271 (2004). +11. Smith, C.T., et al. Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults. J Neurophysiol 115, 1146–1156 (2016). +12. Taha, S.A. & Fields, H.L. Inhibitions of nucleus accumbens neurons encode a gating signal for reward-directed behavior. J Neurosci 26, 217–222 (2006). +13. Christoffel, D.J., et al. Input-specific modulation of murine nucleus accumbens differentially regulates hedonic feeding. Nat Commun 12, 2135 (2021). +14. Demos, K.E., Heatherton, T.F. & Kelley, W.M. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci 32, 5549–5552 (2012). +15. Wu, H., et al. Local accumbens in vivo imaging during deep brain stimulation reveals a strategy-dependent amelioration of hedonic feeding. Proc Natl Acad Sci U S A 118(2021). +16. Halpern, C.H., et al. Amelioration of binge eating by nucleus accumbens shell deep brain stimulation in mice involves D2 receptor modulation. J Neurosci 33, 7122–7129 (2013). + +<--- Page Split ---> + +17. Wu, H., et al. Brain-Responsive Neurostimulation for Loss of Control Eating: Early Feasibility Study. Neurosurgery 87, 1277-1288 (2020). +18. Parker, J.J., et al. First-in-human implantation protocol and ambulatory nucleus accumbens region electrophysiologic surveillance paradigm for patient-tailored responsive closed-loop deep brain stimulation for loss of control eating disorder. Neuron (2021). +19. Barbosa, D., et al. The obese state is associated with a perturbed impulsivity circuit in binge-prone females. Under Review (2021). +20. Telch, C.F. & Agras, W.S. Do emotional states influence binge eating in the obese? Int J Eat Disord 20, 271-279 (1996). +21. Adamantidis, A.R., Gutierrez Herrera, C. & Gent, T.C. Oscillating circuitries in the sleeping brain. Nat Rev Neurosci 20, 746-762 (2019). +22. Oishi, Y., et al. Slow-wave sleep is controlled by a subset of nucleus accumbens core neurons in mice. Nat Commun 8, 734 (2017). +23. Mahajan, U.V., et al. Can responsive deep brain stimulation be a cost-effective treatment for severe obesity? Clinical Trials and Investigations 0, 1-9 (2021). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Legend not included with this version + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Legend not included with this version + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Legend not included with this version + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- BITESAppendix.docx + +<--- Page Split ---> diff --git a/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73_det.mmd b/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8f3cb12f4e07c86e5bf4eb6a23542d3ec7c3a0bf --- /dev/null +++ b/preprint/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73/preprint__032a07311eb9bb5f9f4a079bca8cd4decf50b0f2c1fa36d909d6d9fbdf969e73_det.mmd @@ -0,0 +1,246 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 952, 177]]<|/det|> +# Responsive nucleus accumbens deep brain stimulation restores eating control in severe obesity + +<|ref|>text<|/ref|><|det|>[[44, 196, 583, 216]]<|/det|> +Casey Halpern ( \(\boxed{ \begin{array}{r l} \end{array} }\) casey.halpern@pennmedicine.upenn.edu ) + +<|ref|>text<|/ref|><|det|>[[45, 219, 290, 237]]<|/det|> +University of Pennsylvania + +<|ref|>text<|/ref|><|det|>[[44, 244, 207, 281]]<|/det|> +Rajat Shivacharan Stanford + +<|ref|>text<|/ref|><|det|>[[44, 289, 290, 328]]<|/det|> +Cammie Rolle University of Pennsylvania + +<|ref|>text<|/ref|><|det|>[[44, 335, 290, 374]]<|/det|> +Daniel Barbosa University of Pennsylvania + +<|ref|>text<|/ref|><|det|>[[44, 381, 210, 419]]<|/det|> +Tricia Cunningham Stanford + +<|ref|>text<|/ref|><|det|>[[44, 427, 150, 465]]<|/det|> +Austin Feng Stanford + +<|ref|>text<|/ref|><|det|>[[44, 473, 185, 510]]<|/det|> +Noriah Johnson Stanford + +<|ref|>text<|/ref|><|det|>[[44, 518, 150, 556]]<|/det|> +Debra Safer Stanford + +<|ref|>text<|/ref|><|det|>[[44, 564, 148, 601]]<|/det|> +Cara Bohen Stanford + +<|ref|>text<|/ref|><|det|>[[44, 610, 148, 647]]<|/det|> +Corey Keller Stanford + +<|ref|>text<|/ref|><|det|>[[44, 655, 144, 692]]<|/det|> +Vivek Buch Stanford + +<|ref|>text<|/ref|><|det|>[[44, 700, 190, 737]]<|/det|> +Jonathan Parker Stanford + +<|ref|>text<|/ref|><|det|>[[44, 745, 157, 783]]<|/det|> +Dan Azagury Stanford + +<|ref|>text<|/ref|><|det|>[[44, 791, 138, 828]]<|/det|> +Peter Tass Stanford + +<|ref|>text<|/ref|><|det|>[[44, 836, 186, 873]]<|/det|> +Mahendra Bhati Stanford + +<|ref|>text<|/ref|><|det|>[[44, 881, 184, 918]]<|/det|> +Robert Malenka Stanford University + +<|ref|>text<|/ref|><|det|>[[44, 925, 152, 943]]<|/det|> +James Lock + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 106, 230, 125]]<|/det|> +## Brief Communication + +<|ref|>title<|/ref|><|det|>[[44, 144, 135, 163]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 181, 315, 201]]<|/det|> +Posted Date: March 15th, 2022 + +<|ref|>text<|/ref|><|det|>[[42, 220, 475, 240]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1432380/v1 + +<|ref|>text<|/ref|><|det|>[[42, 257, 910, 300]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 335, 950, 378]]<|/det|> +Version of Record: A version of this preprint was published at Nature Medicine on August 29th, 2022. See the published version at https://doi.org/10.1038/s41591- 022- 01941- w. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 159, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[41, 82, 940, 264]]<|/det|> +Craving that precede loss of control (LOC) over food consumption present an opportunity for intervention in patients suffering from binge eating disorder (BED). Here, we used responsive deep brain stimulation (DBS) to record NAc electrophysiology during food cravings preceding LOC eating in two patients with BED and severe obesity (NCT03868670). Increased NAc low- frequency oscillations prominent during food cravings were used to guide DBS delivery. Over 6 months, we observed improved self- control of food intake and weight loss. These findings provide early support for restoring inhibitory control with electrophysiologically- guided NAc DBS. Further work is required to determine scalability of this approach. Trial Registration # NCT03868670. + +<|ref|>sub_title<|/ref|><|det|>[[44, 287, 207, 313]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[41, 325, 953, 515]]<|/det|> +Loss of control (LOC) eating, or the subjective sense that one cannot stop eating, is associated with binge eating - defined by the consumption of an objectively large amount of food in a short period of time accompanied by a sense of LOC.1 LOC eating is often characterized by the loss of inhibitory control in response to appetitive cues and cravings leading to binge eating2. Recurrent and distressing episodes of binge eating are the key features of binge eating disorder (BED). BED is the most common eating disorder, affecting up to 3 percent of U.S. adults, and is the most severe form of LOC eating based on volume of food consumed'. It is associated with obesity, decreased quality of life and premature mortality.3 + +<|ref|>text<|/ref|><|det|>[[41, 530, 956, 715]]<|/det|> +Most treatments for obesity fail to address LOC eating directly, limiting the efficacy of even the most aggressive interventions such as bariatric surgery.4,5 Clinical evidence supports a role of cravings for preferred food, or intense desires to consume specific palatable foods, prior to the onset of LOC and binge eating.6,7 Particularly in individuals who are overweight or obese, food cravings have been linked with LOC among those diagnosed with BED.8 Given this, recent studies have examined neural signals associated with food craving in the pursuit of identifying a biomarker used to trigger deep brain stimulation (i.e., responsive DBS or rDBS) and inhibit onset of LOC eating when patients may be most atrisk. + +<|ref|>text<|/ref|><|det|>[[41, 732, 953, 918]]<|/det|> +In the effort to identify such a craving biomarker, previous work in mice found that anticipation of a high- fat food reward was associated with increased low- frequency oscillatory power in the NAc.9 This work supported a growing body of evidence across species reporting electrophysiological, neurochemical, and functional neuroimaging activities within circuits involving the NAc that correlate to reward anticipation,10- 13 and that predict consequential behavioral outcomes.14 Using low- frequency delta- band power as a biomarker to trigger delivery of a brief train of high- frequency electrical stimulation to the NAc (here after referred to as rDBS) resulted in significant and lasting attenuation of binge- like eating in mice sensitized to high fat food,9 while conventional, continuous DBS appeared to lose efficacy over time.15,16 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 940, 250]]<|/det|> +Here, we report the proof of concept in this first- in- human study designed to characterize human NAC electrophysiology of craving as it relates to LOC eating. We sought to identify changes in NAC electrophysiology associated with moments of food craving and LOC eating during controlled in- clinic behavioral tasks and to assess the generalization of this effect to LOC eating events in a naturalistic setting and outside the behavioral laboratory. Finally, we implemented rDBS triggered by NAC electrophysiology identified in behavioral and naturalistic assessments, and report here initial results on the potential efficacy of this novel intervention. This study was performed under a U.S. Food and Drug Administration Investigational Device Exemption (G180079) using the NeuroPace Responsive Neurostimulation (RNS) System17. + +<|ref|>sub_title<|/ref|><|det|>[[44, 272, 163, 298]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[44, 313, 271, 332]]<|/det|> +## PRESTUDY PROCEDURES + +<|ref|>text<|/ref|><|det|>[[42, 350, 956, 490]]<|/det|> +Two adult women with BED and treatment- refractory severe (grade III) obesity, despite bariatric surgery were recruited for this study, approved by Stanford's Institutional Review Board (IRB- 46563) (see appendix for participant characteristics). Designed with a staggered enrollment, each subject progressed through the study stages shown in Fig. 1A. Both subjects underwent stereotactic implantation of bilateral depth electrodes, each with four contacts.18 The two distal contacts were positioned in the NAC and the two proximal contacts traversed the anterior limb of the internal capsule (Fig. 1B).19 + +<|ref|>sub_title<|/ref|><|det|>[[44, 508, 218, 526]]<|/det|> +## RECORDING PHASE + +<|ref|>text<|/ref|><|det|>[[42, 544, 955, 701]]<|/det|> +Immediately following implantation, subjects entered a 6- month recording phase, during which naturalistic in- lab assessments and ambulatory real- world assessments were performed to identify an electrophysiological biomarker for rDBS in the consecutive stimulation phase. From each hemisphere, activity was recorded from the ventral and dorsal NAC (see appendix for details). Subjects underwent two assessments to evaluate NAC electrophysiology during: 1) anticipation (pre- consumption) of food during standard meals and LOC eating (i.e., Multi- Item Buffet assessment; in- lab naturalistic testing); and 2) states of hunger and craving (pre- consumption) (i.e., ambulatory assessment; real- world testing). + +<|ref|>sub_title<|/ref|><|det|>[[44, 719, 238, 737]]<|/det|> +## STIMULATION PHASE + +<|ref|>text<|/ref|><|det|>[[41, 755, 958, 960]]<|/det|> +Following the recording phase, both subjects underwent single- blinded stimulation survey testing in which they received brief bursts of electrical stimulation across all electrode contacts to screen for acute effects. This was followed by a single- blinded, staged, on- off stimulation safety testing period to assess for possible side effects of rDBS. Subjects then entered the 10–12 month open- label stimulation phase of the study. In this phase, rDBS was delivered using a bipolar montage of the two NAC electrode contacts. Both subjects received bilateral NAC rDBS via depth electrodes connected to a NeuroPace RNS system to detect and inhibit LOC eating events. Stimulation was delivered at 125 Hz in two 5 second bursts with a charge density of 0.5–1.5 μC/cm.17 Detections and stimulations occurred approximately 400 times/day with a stimulation limit set to 700 bouts (or approximately 117 min) per day in order to limit unnecessary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 952, 182]]<|/det|> +stimulation at night. Based on the recording phase, each subject's device was programmed to detect brief increases in low- frequency activity in both the left and right ventral NAc (see appendix). These detections of low- frequency activity triggered bilateral NAc rDBS ( \(\sim 1\mu \mathrm{C} / \mathrm{cm}^2\) charge density, 10s duration). Low- frequency triggered bilateral stimulation has been well tolerated by both subjects. Neither subject 1 nor 2 experienced a serious adverse event, and all reported events were self- limited (Table S4). Examination of sensitivity and specificity can be found in the appendix (Figures S1, S2). + +<|ref|>sub_title<|/ref|><|det|>[[44, 204, 144, 229]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[44, 245, 219, 264]]<|/det|> +RECORDING PHASE + +<|ref|>text<|/ref|><|det|>[[40, 281, 951, 580]]<|/det|> +MULTI- ITEM BUFFET: NAC ELECTROPHYSIOLOGY DURING IN- LAB LOC EATING. In this assessment, we investigated each subject's LOC by modeling the at- risk environment in a controlled setting \(^{20}\) . Using mood provocation (see appendix), we assessed LOC (1–5 Likert severity scale) during presentation of a high calorie buffet of the subject's preferred foods while recording synchronized video- NAc LFP (Local Field Potential) activity. Analogous to our pre- clinical work, we analyzed and compared bite onset during the buffet to standard meals. Results showed low- frequency power increases immediately prior to LOC eating. Specifically, increases in left ventral NAc low- frequency (2–8 Hz) power were observed for both subjects during LOC immediately preceding (within 2 seconds) the videoed bite onset (see appendix) (mean ± s.e. dB power \([V^2 /\mathrm{Hz}]\) : Subject 1, \(2.4 \pm 1.5, \mathrm{n} = 16\) bites; Subject 2, \(5.6 \pm 3.1, \mathrm{n} = 12\) bites). In contrast, increases in low- frequency power were not observed immediately prior to bites during standard meals (Subject 1, \(0.6 \pm 1.0, \mathrm{n} = 15\) bites; Subject 2, \(0.3 \pm 0.9, \mathrm{n} = 11\) bites) (Fig. 1C, Student's t- test, \(p < 0.05\) ). There were no statistical changes in any of the other recorded frequency bands in either subject (Student's t- test, \(p > 0.05\) ). + +<|ref|>text<|/ref|><|det|>[[39, 595, 958, 895]]<|/det|> +AMBULATORY ASSESSMENT: NAC ELECTROPHYSIOLOGY DURING REAL- WORLD LOC EATING EVENTS. We analyzed electrophysiology acquired during real- world behavioral states (see appendix) to validate the lab findings. Low- frequency power increases during LOC eating were corroborated with real- world assessments. Specifically, significantly higher low- frequency oscillatory power (Fig. 2A) in bilateral ventral NAc was found during subject- reported LOC eating events (craving- red trace, mean ± s.e. power \([V^2 /\mathrm{Hz}]\) : Subject 1, left NAc: \(0.21 \pm 0.11\) , right NAc: \(0.16 \pm 0.06, \mathrm{n} = 10\) events; Subject 2, left NAc: \(0.58 \pm 0.14\) , right NAc: \(0.21 \pm 0.07, \mathrm{n} = 71\) events) when compared to control periods (control- black trace, Subject 1, left NAc: \(0.1 \pm 0.04\) , right NAc: \(0.04 \pm 0.01, \mathrm{n} = 9\) events; Subject 2, left NAc: \(0.19 \pm 0.04\) , right NAc: \(0.09 \pm 0.04, \mathrm{n} = 80\) events) and periods of hunger (hunger- blue trace, Subject 1, left NAc: \(0.06 \pm 0.01\) , right NAc: \(0.03 \pm 0.01, \mathrm{n} = 13\) events; Subject 2, left NAc: \(0.27 \pm 0.11\) , right NAc: \(0.11 \pm 0.03, \mathrm{n} = 37\) events) (Fig. 2A, one- way ANOVA, Subject 1, left NAc: \(f = 3.50, \mathrm{P} = 0.04\) , right NAc: \(f = 4.95, \mathrm{P} = 0.03\) ; Subject 2, left NAc: \(f = 5.14, \mathrm{P} = 0.02\) , right NAc: \(f = 0.07, \mathrm{P} = 0.93\) ). Consistent with the in- clinic tasks, there were no differences in any other frequency band during at- risk moments in the ambulatory setting. + +<|ref|>text<|/ref|><|det|>[[42, 909, 956, 954]]<|/det|> +SIGNAL DETECTION: BILATERAL NAC DETECTION. For each subject, we programmed the device to detect brief increases in low- frequency activity in both the left and right ventral NAc. To confirm that the signal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 941, 248]]<|/det|> +being detected was in the low- frequency range, we analyzed the power spectra of the NAc LFP activity in the 5 seconds prior to a detection and found that the Area detectors (see appendix) were detecting low- frequency activity in the left and right ventral NAc (Fig. 2B). For this analysis, we compared detection made in stored LFPs during reported LOC eating events and awake events. For Subject 1, increased low- frequency power compared to baseline NAc LFP signal (average 2- minute window) was identified in \(74.4\%\) (67/90) of reported LOC eating event detections and \(63.2\%\) (84/133) of the awake detections \((X2(1,N = 223) = 24.54,p< 0.05)\) . For Subject 2, increased low- frequency power was identified in \(76.9\%\) (30/39) reported LOC eating event detections and \(45.8\%\) (22/48) awake detections \((X2(1,N = 87) = 14.82,p< 0.05)\) . + +<|ref|>sub_title<|/ref|><|det|>[[44, 266, 238, 285]]<|/det|> +## STIMULATION PHASE + +<|ref|>text<|/ref|><|det|>[[40, 302, 949, 621]]<|/det|> +CHANGE in LOC EATING and Weight. Both subjects reported an increased sense of self- regulation and control over food intake specific to cravings and related eating behavior. Further, both subjects showed a decrease in the reported frequency of LOC eating events from baseline to 6- months post- stimulation (i.e. the primary endpoint), as assessed by the Eating Disorder Examination (EDE), and LOC severity, as assessed by the Eating Loss of Control Scale, across the 28- day period during the baseline month compared to 6- months post- stimulation month (LOC Frequency: Subject 1 = 80% decrease; Subject 2 = 87% decrease; LOC episode severity: Subject 1: 9- point improvement \((p = 0.09)\) ; Subject 2: 15- point improvement \((p = 0.05)\) ) (Fig. 3A,B). Notably, by the end of the 6- month follow- up period, Subject 1 exhibited substantial improvement in BED severity, while Subject 2 no longer met criteria for BED (i.e., fewer than average of 4 binge eating events per- month over the prior consecutive 3 months for no more BE diagnosis), which met our primary endpoint (Fig. 3C). Corroborating their subjective reports (Fig. 3), 6- month outcomes showed a decrease in body weight (kg and % reduction) and BMI for both subjects: Subject 1 = - 5.9 kg, - 4.5%, and - 2.2 kg/m², respectively; Subject 2 = - 8.2 kg, - 5.8%, and - 2.9 kg/m², respectively) (Fig. 3D,E). + +<|ref|>sub_title<|/ref|><|det|>[[44, 644, 191, 669]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[40, 683, 956, 955]]<|/det|> +In summary, this study identified NAc low- frequency oscillatory power as a signal associated with LOC craving, and then implemented this biomarker to guide rDBS delivery in two subjects with BED and severe obesity. In the recording phase, in- lab assessments implicated NAc low- frequency signalling during naturalistic LOC eating. The generalizability of this signal to real- world settings was then corroborated by our finding that low- frequency oscillatory power was increased during real- world LOC eating events compared to non- LOC events. In the stimulation phase, 6 months of bilateral NAc rDBS triggered by low- frequency power was found to improve LOC eating, as well as reduce body weight and BMI. Optimization of stimulation parameters is still ongoing in both subjects, and four additional subjects are expected to be implanted following a supplement approval to our investigational device exemption. We encountered early challenges when capturing LOC eating events in the real world. A training period was necessary prior to surgery for both subjects to learn to identify and document their LOC eating behaviors. This involved having a psychiatrist (DS) with expertise in obesity and eating disorders discuss with each patient her + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 960, 412]]<|/det|> +personal understanding of LOC eating. As we report (see appendix), while sensitivity of low- frequency detections to LOC eating was high, low- frequency oscillations in the NAC were not always specific to food craving and LOC eating compared to non- LOC eating events. Ongoing work seeks to optimize detection algorithms and improve the sensitivity and specificity of rDBS for LOC eating. Further, real- world LOC electrophysiology detected from ambulatory recordings was specific to bilateral, ventral NAC delta (2- 4Hz), whereas in- lab experiments found effects in both delta and theta (2- 8Hz) and were limited to the left ventral NAC. In addition, because real- world data capture was not time- locked to specific bite events during LOC and standard meals, the ambulatory and multi- item buffet data reflect different time windows respective to the LOC events. We also note that while the frequencies within which we found our effects here contained the delta signal identified in mice9, the effects from in- lab testing were broader and inclusive of theta frequencies. Importantly, one difficulty with the low- frequency biomarker signal is its presence during normal physiological processes such as sleep21,22. To account for detection and stimulation during sleep, we limited rDBS delivery to awake hours (7am- 10pm). Finally, the upfront cost of implantable devices is high; thus long- term follow- up of LOC eating as well as BMI beyond the study period will be necessary to assess societal cost- effectiveness of this intervention based on our decision analyses23. + +<|ref|>text<|/ref|><|det|>[[42, 428, 953, 541]]<|/det|> +In conclusion, NAC rDBS improved LOC eating frequency and severity in two patients with BED and severe obesity. These findings were associated with weight loss even during this early follow- up period, suggesting patients can lose weight without instruction to change their diet or physical activity (efforts which are often unsuccessful). This is a testament to the potential clinical significance of this novel intervention and supports continued study in this FDA- guided first- in- human, early feasibility trial. + +<|ref|>sub_title<|/ref|><|det|>[[45, 563, 212, 588]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[45, 605, 207, 624]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[42, 642, 953, 799]]<|/det|> +This work was supported by the National Institute of Health (5UH3NS103446- 02). The authors thank the study subjects' for their dedication and commitment to this novel, first- in- human exploratory trial; the members of the Stanford Clinical and Translational Research Unit and the Departments of Neurosurgery and Psychiatry at Stanford Medicine for space to conduct in clinic assessments; the Suthana laboratory for in- clinic tool support; Ian Kratter, Tom Prieto, Vyvian Ngo, Bharati Sanjanwala for support during surgery and intraoperative testing; Emily Mirro, Tara L. Skarpaas, Nick Hasulak, Tom Tcheng for providing technical support for the NeuroPace RNS System. + +<|ref|>sub_title<|/ref|><|det|>[[45, 817, 220, 836]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[42, 854, 944, 920]]<|/det|> +No funding from NeuroPace was received for this study nor were data analyses reported here conducted by NeuroPace employees. CHH, RSS, and CER have patents related to sensing and brain stimulation for the treatment of neuropsychiatric disorders. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 196, 68]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[53, 80, 945, 920]]<|/det|> +1. Association., A.P. Diagnostic and statistical manual of mental disorders (5th ed.). (2013). +2. Reents, J. & Pedersen, A. Differences in Food Craving in Individuals With Obesity With and Without Binge Eating Disorder. Front Psychol 12, 660880 (2021). +3. Hudson, J.I., et al. Longitudinal study of the diagnosis of components of the metabolic syndrome in individuals with binge-eating disorder. Am J Clin Nutr 91, 1568–1573 (2010). +4. White, M.A., Kalarchian, M.A., Masheb, R.M., Marcus, M.D. & Grilo, C.M. Loss of control over eating predicts outcomes in bariatric surgery patients: a prospective, 24-month follow-up study. J Clin Psychiatry 71, 175–184 (2010). +5. Chao, A.M., et al. Binge-eating disorder and the outcome of bariatric surgery in a prospective, observational study: Two-year results. Obesity (Silver Spring) 24, 2327–2333 (2016). +6. Grucza, R.A., Przybeck, T.R. & Cloninger, C.R. Prevalence and correlates of binge eating disorder in a community sample. Compr Psychiatry 48, 124–131 (2007). +7. McCuen-Wurst, C., Ruggieri, M. & Allison, K.C. Disordered eating and obesity: associations between binge-eating disorder, night-eating syndrome, and weight-related comorbidities. Ann N Y Acad Sci 1411, 96–105 (2018). +8. Bohon, C., Stice, E. & Spoor, S. Female emotional eaters show abnormalities in consummatory and anticipatory food reward: a functional magnetic resonance imaging study. Int J Eat Disord 42, 210–221 (2009). +9. Wu, H., et al. Closing the loop on impulsivity via nucleus accumbens delta-band activity in mice and man. Proc Natl Acad Sci U S A 115, 192–197 (2018). +10. Roitman, M.F., Stuber, G.D., Phillips, P.E., Wightman, R.M. & Carelli, R.M. Dopamine operates as a subsecond modulator of food seeking. J Neurosci 24, 1265–1271 (2004). +11. Smith, C.T., et al. Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults. J Neurophysiol 115, 1146–1156 (2016). +12. Taha, S.A. & Fields, H.L. Inhibitions of nucleus accumbens neurons encode a gating signal for reward-directed behavior. J Neurosci 26, 217–222 (2006). +13. Christoffel, D.J., et al. Input-specific modulation of murine nucleus accumbens differentially regulates hedonic feeding. Nat Commun 12, 2135 (2021). +14. Demos, K.E., Heatherton, T.F. & Kelley, W.M. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci 32, 5549–5552 (2012). +15. Wu, H., et al. Local accumbens in vivo imaging during deep brain stimulation reveals a strategy-dependent amelioration of hedonic feeding. Proc Natl Acad Sci U S A 118(2021). +16. Halpern, C.H., et al. Amelioration of binge eating by nucleus accumbens shell deep brain stimulation in mice involves D2 receptor modulation. J Neurosci 33, 7122–7129 (2013). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[47, 44, 936, 405]]<|/det|> +17. Wu, H., et al. Brain-Responsive Neurostimulation for Loss of Control Eating: Early Feasibility Study. Neurosurgery 87, 1277-1288 (2020). +18. Parker, J.J., et al. First-in-human implantation protocol and ambulatory nucleus accumbens region electrophysiologic surveillance paradigm for patient-tailored responsive closed-loop deep brain stimulation for loss of control eating disorder. Neuron (2021). +19. Barbosa, D., et al. The obese state is associated with a perturbed impulsivity circuit in binge-prone females. Under Review (2021). +20. Telch, C.F. & Agras, W.S. Do emotional states influence binge eating in the obese? Int J Eat Disord 20, 271-279 (1996). +21. Adamantidis, A.R., Gutierrez Herrera, C. & Gent, T.C. Oscillating circuitries in the sleeping brain. Nat Rev Neurosci 20, 746-762 (2019). +22. Oishi, Y., et al. Slow-wave sleep is controlled by a subset of nucleus accumbens core neurons in mice. Nat Commun 8, 734 (2017). +23. Mahajan, U.V., et al. Can responsive deep brain stimulation be a cost-effective treatment for severe obesity? Clinical Trials and Investigations 0, 1-9 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[44, 430, 143, 456]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 70, 825, 710]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 737, 115, 756]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[44, 781, 368, 800]]<|/det|> +Legend not included with this version + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 66, 765, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 800, 118, 820]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[44, 843, 367, 862]]<|/det|> +Legend not included with this version + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[57, 49, 880, 216]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 245, 116, 264]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[44, 288, 368, 307]]<|/det|> +Legend not included with this version + +<|ref|>sub_title<|/ref|><|det|>[[44, 330, 311, 357]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 380, 765, 400]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 419, 266, 437]]<|/det|> +- BITESAppendix.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f.mmd b/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..49142e4e084362eb126ffbf28e56a0a80168bf84 --- /dev/null +++ b/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f.mmd @@ -0,0 +1,246 @@ + +# Red Light-Mediated Photoredex Catalysis Promotes Regioselective Switch in the Difunctionalization of Alkenes + +Shoubhik Das + +shoubhik.das@uni- bayreuth.de + +University of Bayreuth https://orcid.org/0000- 0002- 4577- 438X Tong Zhang University of Antwerp + +## Article + +Keywords: + +Posted Date: February 12th, 2024 + +DOI: https://doi.org/10.21203/rs.3. rs- 3910735/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on June 18th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 49514- 4. + +<--- Page Split ---> + +# Red Light-Mediated Photoredox Catalysis Promotes Regioselective Switch in the Difunctionalization of Alkenes + +Tong Zhanga, and Shoubhik Das\\*a,b + +## AFFILIATIONS: + +a. Department of Chemistry, University of Antwerp, 2020 Antwerp, Belgium +b. Department of Chemistry, University of Bayreuth, 95447 Bayreuth, Germany Corresponding author: shoubhik.das@uni-bayreuth.de + +## Abstract: + +Controlling regioselectivity during difunctionalization of alkenes represents significant challenges, particularly when the installation of both functional groups is involved in radical processes. In this aspect, several functionalized trifluoromethylated \((- CF_3)\) compounds have been accomplished via difunctionalization reactions due to their wide importance in the pharmaceutical sectors, however, all these existing reports are limited to afford the corresponding \(\beta\) - trifluoromethylated products. The main reason for this limitation arises from the fact that \(- CF_3\) group served as an initiator in those reactions and predominantly preferred to be installed at the terminal \((\beta)\) position of an alkene. In contrary, functionalization of the \(- CF_3\) group at the internal \((\alpha)\) position of alkenes provides valuable products but a meticulous approach is necessary to win this regioselectivity switch. Intrigued by this challenge, we have developed an efficient and highly regioselective strategy where \(- CF_3\) group is installed at the \(\alpha\) - position of an alkene and at the end, molecular complexity is achieved via the simultaneous insertion of a sulfonyl fragment \((- SO_2R)\) at the \(\beta\) - position. This strategy provides the simultaneous installation of two important functional groups such as \(- CF_3\) and \(- SO_2R\) groups and both of these functional groups are the key units to attain or to enhance the bioactivity in organic molecules. A precisely regulated sequence of radical generation using red light-mediated photocatalysis facilitates this regioselective switch from the terminal \((\beta)\) position to the internal \((\alpha)\) position. Furthermore, this approach demonstrates distinctive regioselectivity, broad substrate scope and industrial potential for the synthesis of pharmaceuticals under mild reaction conditions. + +## Introduction + +Recently, photoredox catalysis has gained tremendous attention in achieving unique synthetic targets under mild reaction conditions. In most of these cases, short- wavelength light regions \((\lambda_{\max}< 460 \text{nm})\) were utilized to achieve these reactions successfully, however, short- wavelength light regions have severe limitations of potential health risk such as photooxidative damage to the retina and furthermore, they can lead to generate undesired side products and thereby, lower the atom economy of that reaction. Additionally, lower penetration power of short- wavelength light regions causes concern for the scale up of that particular reaction. All these limitations have encouraged scientists to move forward to the longer- wavelength regions such as red light or near- infrared (NIR) regions since these are associated with low health risk factor, generate less side products due to their lower energy and have high penetration power in the solution which in turn assist to scale up the reaction. In longer- wavelength regions as the photocatalysts will be activated by the low- energy, their corresponding redox windows are consequently narrower and that in turn assists to exercise finer control in chemical processes, permitting only specific reactions to take place under defined conditions. Inspired by this, the groups of MacMillan and Rovis have independently developed inspiring photocatalytic strategies for the activation of aryl azide via red light- mediated photoredox catalysis which have been utilized for proximity labeling. Additionally, the utilization of red light- mediated photocatalysis has been increasingly applied across multiple domains to enhance the control of chemical reactions. Thus, it is very clear that the red light- mediated photoredox catalysis can uniquely attain many unsolved + +<--- Page Split ---> + +processes which were impossible by the irradiation of ultraviolet (UV) or blue light and that leads to the growing surge of interest in this field, however, it is imperative to acknowledge that still the applications of red light-mediated strategies in organic synthesis are in the early stage of development. + +![](images/Figure_1.jpg) + +
Figure 1. Design of the sulfonyltrifluoromethylation of olefins via red light-mediated photocatalysis.
+ +Difenoxidation of alkenes is a powerful synthetic strategy to attain molecular complexity from readily available starting materials. \(^{15 - 21}\) In this approach, simultaneously two different functional groups are installed across an olefin by the introduction of two new C - C or C - X bonds. Along this direction, tremendous catalytic efforts have been paid to attain molecular complexity to design pharmaceutically relevant compounds. \(^{22 - 48}\) However, the simultaneous introduction of the trifluoromethyl (- CF₃) and the sulfonyl fragment (- SO₂R) via difunctionalization is highly challenging due to the intricate difficulty in circumventing undesired side reactions, therefore, rarely this challenge has been solved in organic synthesis. On the other hand, these two functional groups (- CF₃ and - SO₂R) are highly demanding due to their intrinsic capability to enhance the stability, membrane permeability, and metabolism in bioactive molecules and that is reflected in their wide presence as common pharmaceuticals such as CJ- 17493 and eletriptan which are served as an NK- 1 receptor antagonist, and as a medication for migraine headaches respectively (Figure 1a). \(^{49 - 54}\) To the best of our knowledge, only a single report has been published for the simultaneous introduction of these two functional groups across the alkene moiety, however, the position of the - CF₃ group was always in the + +<--- Page Split ---> + +terminal position \((\beta\) - position).49 Along the same direction, it should be clearly noted that the difunctionalization of alkenes via the introduction of a \(\mathsf{- CF}_3\) group has frequently been employed, however, \(\mathsf{- CF}_3\) group mainly acted as an initiator via the formation of a radical and was always installed to the terminal \((\beta)\) position of an alkene (as depicted by the solid frame in Figure 1b). Followed by this terminal addition, subsequent coupling with other functional groups such as - chloro, - chlorosulfonyl, - amino, - carboxylic acid groups were performed to achieve the difunctionalized products.55- 60 In contrary, reverse regioselectivity of the \(\mathsf{- CF}_3\) group at the internal position \((\alpha)\) in the difunctionalized olefins (indicated by the dashed frame in Figure 1b) is very rare, although this will allow to achieve important pharmaceuticals such as CJ- 17493, apinocaltamide and many more. To the best of our knowledge, only the group of Li presented an elegant thermocatalytic strategy by involving copper/N- fluorobenzenesulfonimide (NFSI) for the introduction of \(\mathsf{- CF}_3\) group at the internal position of an alkene (Figure 1b).30 In this approach, the \(N\) - centered radical, derived from an electrophilic NFSI, served as an initiator to facilitate the addition to the \(\beta\) position of the olefin and the (bpy)Zn(CF3)2 complex was employed as a nucleophilic \(\mathsf{- CF}_3\) reagent. + +Inspired by all these information, we became interested to design a photoredox system for the first time that should install both the \(\mathsf{- CF}_3\) and \(\mathsf{- SO}_2\mathsf{R}\) groups simultaneously in alkenes where the \(\mathsf{- CF}_3\) group should be positioned at the internal position \((\alpha)\) in the difunctionalized product. To achieve a success in this site selectivity, meticulous designing of the photoredox strategy during the coupling of two different functional groups is inevitable. This was absolutely orthogonal in the case of Li's protocol where they worked with only one radical ( \(N\) - centered radical) in attaining the difunctionalized products.30 Specifically, when both the \(\mathsf{- CF}_3\) and \(\mathsf{- SO}_2\mathsf{R}\) radicals coexist, the \(\mathsf{- CF}_3\) radical demonstrates higher propensity to attach to the olefin first.37,57 To overcome this obstacle, we argued to ensure: (1) the formation of the \(\mathsf{- CF}_3\) radical should occur to the subsequent formation of \(\mathsf{- SO}_2\mathsf{R}\) radical which will readily initiate the addition to olefins; (2) we also argued to utilize a copper salt as a catalyst to capture the free \(\mathsf{- CF}_3\) radical since copper- based salts are well known for simultaneous cross- coupling reactions by involving \(\mathsf{- CF}_3\) radical.25- 26 To fulfill these requirements, we attempted to employ a photocatalyst which should be activated by the red light to attain the sulfonyltrifluoromethylated product (Figure 1c).61- 62 The reason behind our rationale to use the red light in our reaction was due to the lower energy of the red light compared to the blue light, photocatalysts activated by the red light are expected to exhibit a narrower redox window, enabling a precisely control of radical generation, thereby should facilitate regioselectivity during the addition of two distinct radicals on alkenes. Owing to the narrower redox window of the red light- activated photocatalyst, it was essential to ensure that the excited state of the photocatalyst \((\mathsf{PC}^*)\) should undergo reduction solely through the sulfinate salts via reductive quenching pathway.44,62 The resulting sulfonyl radical should then be added to the alkene, leading to the formation of the desired carbon- centered radical. At last, the desired product will be achieved by the carbon- centered radical and \(\mathsf{Cu} - \mathsf{CF}_3\) complex via Cu- catalyzed cross- coupling reaction.25- 26 In contrast, we rationalized to avoid the oxidative quenching pathway of the \(\mathsf{PC}^*\) since this would have generated free \(\mathsf{- CF}_3\) radical which would result to the undesired trifluoromethylated side products ( \(\mathsf{- CF}_3\) group at the terminal \((\beta)\) position).37,57 To accomplish this, the photocatalyst was carefully selected based on the redox potentials of sulfinate salts and \(\mathsf{- CF}_3\) reagents and the redox potentials should have fulfilled: \(E_{\mathrm{ox}}(\mathrm{RSO}_2^- )< E(\mathrm{PC}^* /\mathrm{PC}^- ),E_{\mathrm{red}}(\mathrm{CF}_3^+ )< E(\mathrm{PC}^* /\mathrm{PC}^+)\) and \(E(\mathrm{PC}^0 /\mathrm{PC}^- )< E_{\mathrm{red}}(\mathrm{CF}_3^+ )\) (Figure 1c). + +## Results + +## Reaction optimization + +At the outset of the reaction, 4- vinyl- 1,1'- biphenyl (1 equiv.), \(\mathrm{Os(bptpy)_2(PF_6)_2}\) (0.8 mol%), \(\mathrm{NaSO_2Ph}\) (3 equiv.) and \(\mathrm{TTCF_3^+OTF^- }\) (2 equiv.) were employed as the model substrate, photocatalyst, sulfinate salt and \(\mathsf{- CF}_3\) reagent in the presence of copper chloride ( \(\mathrm{CuCl}_2\) , 20 mol%) in dichloromethane (DCM, 0.1 M) to afford the sulfonyltrifluoromethylated product (Figure 1d).5,61- 62 We carefully chosen these reagents ( \(\mathrm{Os(bptpy)_2(PF_6)_2}\) , sodium benzenesulfinate \(\mathrm{(NaSO_2Ph)}\) and trifluoromethyl thianthrenium triflate \(\mathrm{(TTCF_3^+OTF^- )}\) ) based on their redox potential values to match with our scientific rationale: \(E([\mathrm{Os}]^{1\dagger \dagger \dagger}) = +0.93\mathrm{V}\) vs. \(\mathrm{AgCl}\) (3 M KCl), \(E([\mathrm{Os}]^{1\dagger \dagger \dagger}) = -0.67\mathrm{V}\) vs. \(\mathrm{AgCl}\) (3 M KCl)5, \(E_{\mathrm{ox}}(\mathrm{NaSO_2Ph}) = +0.6\mathrm{V}\) vs. \(\mathrm{Ag/AgCl}\) (3 M KCl)57- 58, \(E_{\mathrm{red}}(\mathrm{TTCF_3^+OTF^- }) = -0.69\mathrm{V}\) vs. \(\mathrm{Ag/AgCl}\) (3 M KCl))63. As expected, the performance of the reaction under these conditions did not generate any trifluoromethylated side products (at the terminal position) and only provided the desired product with \(73\%\) of yield. It was also observed that reducing the quantities of \(\mathrm{NaSO_2Ph}\) and \(\mathrm{TTCF_3^+OTF^- }\) , led to a decrease in the yield of the final product (Figure 1d, entries 2- 3). It was necessary to use the excess quantity of sulfinate salts to ensure the faster oxidation of + +<--- Page Split ---> + +sulfinate salt to the \(\cdot \mathrm{SO}_2\mathrm{R}\) radical. In addition, due to the lower solubility in DCM, the use of the excess quantity of sulfinate salts was highly necessary as well as the presence of excess quantity of \(\cdot \mathrm{CF}_3\) reagent accelerated the reaction rate.23,61- 62 Furthermore, the addition of ligands such as \(2,2^{\prime}\) - bipyridine (bpy) and 1,10- phenanthroline (1,10- phen) exerted deleterious effects in the reaction, giving no product under this conditions (Figure 1d, entries 4- 5). We assumed that the presence of ligands occupied the coordination sites for \(\cdot \mathrm{CF}_3\) radical or hindered the binding of \(\cdot \mathrm{CF}_3\) radical to the Cu- center.25 To verify the importance of the appropriate \(\cdot \mathrm{CF}_3\) reagent, alternative electrophilic \(\cdot \mathrm{CF}_3\) sources such as Togni's reagent, Umemoto's reagent, and \(\mathrm{Cu(CF_3)_3bpy}\) were also applied, albeit substantially lower or negligible yield of the desired product was obtained (Figure 1d, entries 6- 10). The rationale behind this could be ascribed to their unsuitable redox potentials, which did not align with \(\mathrm{Os(bptpy)_2(PF_6)_2}\) and consequently, failed to meet the requirements. Furthermore, alternative Cu- salts and solvents were also investigated, but lower or negligible yields of the products were obtained (Figure 1d, entries 11- 13). Finally, control experiments revealed that the presence of the photocatalyst, Cu- salts and red light were essential for this reaction (Figure 1d, entries 14- 16). + +In order to exhibit the red light- mediated regioselective gain for this reaction, reaction conditions under the irradiation of blue light were also compared. Similar to the 'red light system', the crucial combination of the photocatalyst, sulfinate salt and \(\cdot \mathrm{CF}_3\) reagent was determined, namely \([\mathrm{Ru(bpz)_3(PF_6)_2}\) , \(\mathrm{NaSO_2Ph}\) and 5- (trifluoromethyl) dibenzothiophenium triflate (Figure 2b). However, after extensive optimizations via the investigation of each crucial component of this reaction, the highest yield of the desired product reached to \(42\%\) and this could be due to the fact that free \(\cdot \mathrm{CF}_3\) radical was generated faster under these conditions. (See SI 1.3.2). Subsequently, this \(\cdot \mathrm{CF}_3\) radical underwent an addition reaction with styrene, resulted the formation of the undesired \(\beta\) - substituted trifluoromethylated byproduct and the contrast was notably evident in the \(^{19}\mathrm{F}\) NMR spectra (Figure 2c). The 'blue light system' exhibited numerous peaks of side products while the spectrum of the 'red light system' appeared significantly cleaner and mainly contained the \(\cdot \mathrm{CF}_3\) reagent and the desired product. This significant difference highlighted the pronounced regioselectivity gain in the sulfonyltrifluoromethylation of alkenes via the red light- mediated photocatalysis. + +![](images/Figure_2.jpg) + +
Figure 2. Initial investigation of the reaction under blue and red light with respective photocatalysts.
+ +## Substrate scope + +With this optimized reaction conditions in hand, we started to evaluate the scope of the sulfonyltrifluoromethylation of alkenes. As shown in the Figure 3, an array of para- substituted styrenes containing diverse electron- donating groups (EDGs) like - methyl, - acetoxy, and - tert- butyl, as well as electron- withdrawing groups (EWGs) such as - halogens provided the corresponding sulfonyltrifluoromethylated products in moderate to excellent yield (Figure 3, 1- 8). Specifically, 4- bromostyrene and 4- chlorostyrene were tolerant under our optimized conditions to provide the desired products (6 and 7), thereby, demonstrated the potential for subsequent functionalization via cross coupling + +<--- Page Split ---> + +reactions. \(^{30}\) Furthermore, the reaction demonstrated compatibility with 2- and 3- substituted styrenes (10- 13), leading to the formation of products in satisfactory yield, regardless of the presence of - EDGs or - EWGs. In comparison, electron- deficient alkenes (9 and 14) exhibited decreased efficiency, however, the use of \(p\) - chlorophenyl sulfinate led to an improvement in the reaction. In general, the difunctionalization of \(\beta\) - substituted styrenes represents increased difficulty due to the hindrance caused by these \(\beta\) - substituents and this hindrance can impede the addition of initiators, such as sulfonyl radicals in this work. \(^{30}\) However, under our optimized reaction conditions, \((E)\) - \(\beta\) - methylstyrene (15) and indene (16) underwent the difunctionalization reaction smoothly and provided the yield of \(46\%\) and \(78\%\) , respectively. + +![](images/Figure_3.jpg) + +
Figure 3. Scope of the sulfonyltrifluoromethylation of olefins \(^{a}\) . \(^{a}\) Yields are reported as isolated yield. \(^{b}\) dr value was determined by \(^{1}\) H NMR.
+ +Encouraged by these results, an extensive exploration of sulfinate salts was conducted within the optimized reaction conditions. To our delight, a diverse array of \(p\) - substituted phenyl sulfinates, encompassing - methyl, - chloro, - bromo, - nitro, and - cyano groups, demonstrated excellent tolerance, yielding the desired products in yields from + +<--- Page Split ---> + +good to excellent (17- 21). Furthermore, aliphatic sulfinates (22 and 23) also proved to be compatible which exhibited strong application potentials in pharmaceutical area such as the modification of azidothymidine which is known as an anti- HIV drug.64 The adaptability of our methodology extended further to sulfinates bearing biphenyl-, cyclopropane-, and thiophene- groups. These substrates smoothly underwent difunctionalization reactions under the irradiation of red light, yielding products in the range of \(35 - 93\%\) (24- 26). This exhibited wide generality of our system to afford various sulfones- containing chemicals, thereby making significant contributions to the field of pharmaceuticals, agrochemicals, and it should be also noted that the synthesis of sulfones- containing chemicals is of paramount importance in organic chemistry.44- 46 + +Recently, the focus on late- stage modification has garnered significant interest due to its direct and efficient approach in synthesizing functionalized complex molecules.65- 69 The expedite synthesis of highly- functionalized molecules holds strong promise for its potential utility in various scientific disciplines including drug discovery, materials science, and molecular imaging.69 To evaluate the application of our method on complex molecules, a series of drug molecules and natural products derivatives such as estrone, (S)- (+)- naproxen, dexibuprofen, (1S)- (- )- camphanic acid, indomethacin and adapalene were applied (27- 32). Under our experimental conditions, these diverse drug derivatives, encompassing a variety of functional groups, exhibited excellent tolerance and compatibility. The resulting products were obtained in yields from \(66\%\) to \(88\%\) , indicating high reaction efficiency. This demonstrated the potential of our methodology in facilitating the synthesis of more complex sulfonyltrifluoromethylated molecules. We strongly believe that the - trifluoromethyl and - sulfonyl groups in functionalized drug molecules and natural products should not only improve their inherent properties but should also provide the opportunity for further transformation. + +![](images/Figure_4.jpg) + +
Figure 4. Post-functionalization of the sulfonyltrifluoromethylated product.
+ +## Application potentials + +To further examine the application potential, a 4 mmol- scale reaction was carried out which proceeded smoothly in 4 hours and yielded 0.85 grams of the desired product (Figure 4a). Due to the superior light penetration of red light, it became feasible to directly conduct the upscaling of the reaction within a batch reaction system.5 To further demonstrate the synthetic utility of our strategy, the elimination of the - sulfonyl group was achieved through a straightforward strategy by using a mixture of \(\mathrm{Cs_2CO_3}\) and 7- methyl- 1,5,7- triazabicyclo(4.4.0)dec- 5- ene (MTBD), resulting in the production of \(\alpha\) - trifluoromethyl styrene (33) with a yield of \(90\%\) (Figure 4b).62 The mixture of base facilitated the deprotonation and desulfonylation of the sulfonyltrifluoromethylated styrenes to form the \(\alpha\) - trifluoromethyl styrenes. In general, \(\alpha\) - trifluoromethyl styrene derivatives are highly important as versatile synthetic intermediates for the construction of complex fluorinated compounds which are synthesized through methylation of trifluoromethylketones (Wittig reaction) or via transition metal- catalyzed cross- coupling reactions.70- 71 However, compared to these approaches, our strategy enabled the direct synthesis of \(\alpha\) - trifluoromethyl styrene derivatives from styrene, eliminating the requirement of Wittig reagents as well as - borylated or - halide reagents in the processes to improve the atom economy. Additionally, the obtained \(\alpha\) - trifluoromethyl styrene was further transformed into gem- difluorolalkenes (34) in \(86\%\) yield and these fluorinated compounds have strong potential to act as a + +<--- Page Split ---> + +ketone mimic in pharmaceuticals. \(^{72 - 74}\) In fact, substitution of the carbonyl group by the gem- difluoroalkene moiety has shown to enhance the oral bioavailability of therapeutic agents. \(^{72}\) Furthermore, our strategy generated a key intermediate (35) for the synthesis of apinocaltamide (37), T- type calcium channel blocker from 4- bromostyrene (Figure 4c). \(^{75 - 76}\) All these approaches clearly demonstrate the strong potential of our strategy for further applications in designing or modifying pharmaceuticals. + +![](images/Figure_5.jpg) + +
Figure 5. Mechanistic studies.
+ +## Mechanistic investigations + +Inspired by all these outcomes, we became interested to validate the reaction mechanism of this unique reaction strategy and a series of mechanistic experiments were conducted to validate our mechanistic proposal (Figure 5). At first, (2,2,6,6- Tetramethylpiperidin- 1- yl)oxyl (TEMPO) was added as a radical quenching reagent under the optimized reaction conditions. As expected, trace quantity of the product was obtained and a carbon- centered radical (III) was captured by TEMPO which was detected by the high- resolution mass spectrometry (HRMS) (Figure 5a), indicating that the radical process was involved. To further support the involvement of radicals during the addition of the sulfonyl radical, a radical probe experiment was conducted where the model styrene (39) yielded the ring- opening product 40 (Figure 5b). Upon the addition of sulfonyl radical to 39, a cyclopropylmethyl radical moiety was formed, followed by the rapid ring opening rearrangement relieved the ring strain and finally, resulted the final ring- opening product (40). Additionally, Stern- Volmer fluorescence quenching experiments were conducted, revealing that the sodium sulfinate salt exhibited the highest potential as a quencher for the excited state of the Os- photocatalyst, which was also corroborated by the electrochemical measurements for redox potentials (Figure 5c, see SI 1.4.1). \(^{5}\) In Figure 5c, it demonstrated that as the concentration of sulfinate salt was increased, there was a notable reduction in fluorescence intensity. However, minimal alterations were detected in the case of the - CF₃ reagent, styrene, and CuCl₂. This observation was aligned with the anticipated reductive quenching pathway and supported our design that the generation of - sulfonyl radical was prior than the generation of - CF₃ radical in the reaction, indicating that no free - CF₃ radical was generated and ensured the high regioselectivity switch in this reaction. Furthermore, the form of Cu- CF₃ active species was also investigated and to analyze the possible Cu- CF₃ + +<--- Page Split ---> + +active species, various control experiments were carried out (Figure 5d). Initially, we attempted to detect the active species in the absence of styrene under model reaction conditions, while no new peak corresponding to \(\mathrm{Cu^{II} - CF_3}\) was observed in \(1 - 4h\) , however, we observed the presence of the \(\mathrm{Cu^{III}(CF_3)_4}\) anion peak (Experiment A in Figure 6). Due to the potential instability of the \(\mathrm{Cu^{II} - CF_3}\) complex, we further attempted the addition of the bpy ligand to detect the potential existence of the \(\mathrm{Cu^{II} - CF_3}\) in Experiment A. However, only peak of \(\mathrm{TTCF_3^+OTF^-}\) was observed in \(^{19}\mathrm{F}\) NMR (Experiment B in Figure 6). The presence of ligands either occupied the available coordination sites of \(\mathrm{- CF_3}\) radical or impeded the binding of \(\mathrm{- CF_3}\) radical to the \(\mathrm{Cu}\) - center. \(^{25}\) To further verify the \(\mathrm{Cu^{III}(CF_3)_4}\) anionic complex, we synthesized stable \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex by following the reference article. \(^{77}\) However, no product was obtained by using \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex instead of \(\mathrm{CuCl_2}\) under our optimized reaction conditions (Experiment C in Figure 6). Similarly, to verify the possibility of \(\mathrm{Cu^{I} - CF_3}\) complex as active species, the model reaction was carried out by replacing \(\mathrm{CuCl_2}\) with fresh copper powder \((\mathrm{Cu^0})\) and as expected, no product was obtained under this condition (Experiment D in Figure 6). By analyzing all these experiments, we could assume that the active species \(\mathrm{Cu - CF_3}\) were not in the form of \(\mathrm{Cu^{II} - CF_3}\) or \(\mathrm{Cu^{I} - CF_3}\) complexes but possibly were in the form of \(\mathrm{Cu^{II} - CF_3}\) complex. + +![](images/Figure_6.jpg) + +
Figure 6. NMR spectra of the analysis for \(\mathrm{Cu - CF_3}\) complex. Experiment A: Model reaction in the absence of styrene after \(1h\) and \(4h\) . Experiment B: Experiment A with the addition of bpy (0.5 or 1.5 equiv.) as ligand. Experiment C: Model reaction by replacing \(\mathrm{CuCl_2}\) with \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex. Experiment D: Model reaction by replacing \(\mathrm{CuCl_2}\) with fresh \(\mathrm{Cu}\) powder.
+ +Based on all these mechanistic studies, we proposed a possible mechanism for the overall reaction system (Figure 5e). The excited state of the photocatalyst \([\mathrm{Os^{II}}]^*\) \((E^{1*/1} = +0.93\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl), \(E^{1*/1} = - 0.67\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{5}\) was activated by the red light and exclusively underwent reduction by the sulfinate salts, \(\mathrm{I}\) \((E_{\mathrm{ox}} = +0.4 - 0.6\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{61 - 62}\) to form the sulfonyl radical \(\mathrm{II}\) (Path A) rather than oxidation by \(\mathrm{TTCF_3^+OTF^-}\) IV \((E_{\mathrm{red}} = - 0.69\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{63}\) to generate the free \(\mathrm{- CF_3}\) radical \(\mathbf{V}\) (Path B), which was consistent with + +<--- Page Split ---> + +the result of fluorescence quenching experiments. The formed sulfonyl radical II was added to the alkene to generate a carbon- centered radical III which was verified by the TEMPO quenching experiment and the radical probe experiment. Later, the \(\mathsf{Cu}^{1}\) - species captured the free - \(\mathsf{CF}_3\) radical V, generated through the reduction of IV by [Os] \((E^{III} = - 0.82 \text{V}\) vs. Ag/AgCl (3 M KCl)) \(^5\) , resulted the formation of the \(\mathsf{Cu}^{II} - \mathsf{CF}_3\) complex VI. At last, the final product VII was delivered via the cross- coupling reaction between III and VI. + +## Conclusions + +In summary, we have developed a unique protocol where red light- mediated photocatalysis triggered a regioselective switch during the sulfonyltrifluoromethylation of olefins. This strategy has effectively addressed the challenges associated with regioselective addition of radicals onto alkenes. The broad substrate scope and late- stage transformation demonstrated the high efficiency of these reactions and also proved the excellent tolerance of functional groups. Furthermore, post- functionalization studies highlighted the significant industrial potential of the sulfonyltrifluoromethylated product. Additionally, detailed mechanistic investigations revealed a sequential generation of radicals, followed by Cu- catalyzed cross- coupling reactions. We believe that this strategy will strongly contribute to the regioselective functionalizations and will further inspire the development of additional methods in this field. + +## Methods + +General procedure for sulfonyltrifluoromethylation of olefins. A dried reaction vial with a magnetic stirring bar was charged with \(\mathsf{Os(bptpy)_2(PF_6)_2}\) (0.0008 mmol, 0.8 mol%), \(\mathsf{CuCl_2}\) (0.02 mmol, 20 mol%), \(\mathsf{TT - CF_3^+OTF^- }\) (0.2 mmol, 2 equiv.) and sodium sulfinate (0.3 mmol, 3 equiv.). After charging all these reagents, the vessel was evacuated by using Schlenk techniques and flushed with \(\mathsf{N}_2\) for three times. Under nitrogen gas flow, olefin (0.1 mmol, 1 equiv.) (if liquid, otherwise added before flushing cycle) and dry DCM (0.1 M) were added by using a syringe which was flushed with inert gas. The resulting mixture was stirred for 3 - 4 h under the irradiation of red LED light (EvoluChem™ LED 650PF HCK1012- XX- 014 650 nm 20 mW/cm²) in the EvoluChem PhotoRedOx Box. After the completion of the reaction, the reaction mixture was quenched by adding distilled water (2 mL). The organic phase was extracted and concentrated in vacuo. 1,1,1- Trifluorotoluene was added as internal standard to determine the NMR yield of the functionalized product through \(^{19}\mathrm{F}\) NMR. Purification proceeded via flash column chromatography. + +## Data availability + +All of the data supporting the findings of this study are available within the paper and its Supplementary Information file. + +## Additional information + +Optimization of reactions, Mechanism investigation, General procedure of reactions, characterization of substrates and products and spectra of products could be found in Supporting Information. + +## Author Contributions + +T.Z. and S.D. designed the project. T.Z. developed the reaction, investigated the substrate scope, examined the applications, and studied the reaction mechanism. Finally, T.Z. and S.D. wrote the manuscript. + +## Competing interests + +The authors declare no competing financial interest. + +## Acknowledgement + +S.D. thanks the Francqui start up grant from the University of Antwerp, Belgium, for the financial support. T.Z. thanks FWO SB PhD fellowship for their financial assistance to finish this work. 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Click to download. + +Supportinginformation5. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f_det.mmd b/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7d442d11ad4e40e90e371969eda9eac1e9b2de04 --- /dev/null +++ b/preprint/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/preprint__0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f_det.mmd @@ -0,0 +1,295 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 944, 207]]<|/det|> +# Red Light-Mediated Photoredex Catalysis Promotes Regioselective Switch in the Difunctionalization of Alkenes + +<|ref|>text<|/ref|><|det|>[[44, 230, 165, 247]]<|/det|> +Shoubhik Das + +<|ref|>text<|/ref|><|det|>[[52, 257, 366, 274]]<|/det|> +shoubhik.das@uni- bayreuth.de + +<|ref|>text<|/ref|><|det|>[[45, 303, 608, 368]]<|/det|> +University of Bayreuth https://orcid.org/0000- 0002- 4577- 438X Tong Zhang University of Antwerp + +<|ref|>sub_title<|/ref|><|det|>[[44, 409, 103, 426]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 447, 135, 465]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 485, 336, 503]]<|/det|> +Posted Date: February 12th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 523, 475, 542]]<|/det|> +DOI: https://doi.org/10.21203/rs.3. rs- 3910735/v1 + +<|ref|>text<|/ref|><|det|>[[42, 560, 914, 601]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 621, 533, 640]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 677, 916, 720]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 18th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 49514- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[88, 118, 912, 170]]<|/det|> +# Red Light-Mediated Photoredox Catalysis Promotes Regioselective Switch in the Difunctionalization of Alkenes + +<|ref|>text<|/ref|><|det|>[[90, 181, 349, 198]]<|/det|> +Tong Zhanga, and Shoubhik Das\\*a,b + +<|ref|>sub_title<|/ref|><|det|>[[88, 207, 208, 223]]<|/det|> +## AFFILIATIONS: + +<|ref|>text<|/ref|><|det|>[[88, 234, 673, 292]]<|/det|> +a. Department of Chemistry, University of Antwerp, 2020 Antwerp, Belgium +b. Department of Chemistry, University of Bayreuth, 95447 Bayreuth, Germany Corresponding author: shoubhik.das@uni-bayreuth.de + +<|ref|>sub_title<|/ref|><|det|>[[88, 311, 168, 327]]<|/det|> +## Abstract: + +<|ref|>text<|/ref|><|det|>[[87, 338, 914, 608]]<|/det|> +Controlling regioselectivity during difunctionalization of alkenes represents significant challenges, particularly when the installation of both functional groups is involved in radical processes. In this aspect, several functionalized trifluoromethylated \((- CF_3)\) compounds have been accomplished via difunctionalization reactions due to their wide importance in the pharmaceutical sectors, however, all these existing reports are limited to afford the corresponding \(\beta\) - trifluoromethylated products. The main reason for this limitation arises from the fact that \(- CF_3\) group served as an initiator in those reactions and predominantly preferred to be installed at the terminal \((\beta)\) position of an alkene. In contrary, functionalization of the \(- CF_3\) group at the internal \((\alpha)\) position of alkenes provides valuable products but a meticulous approach is necessary to win this regioselectivity switch. Intrigued by this challenge, we have developed an efficient and highly regioselective strategy where \(- CF_3\) group is installed at the \(\alpha\) - position of an alkene and at the end, molecular complexity is achieved via the simultaneous insertion of a sulfonyl fragment \((- SO_2R)\) at the \(\beta\) - position. This strategy provides the simultaneous installation of two important functional groups such as \(- CF_3\) and \(- SO_2R\) groups and both of these functional groups are the key units to attain or to enhance the bioactivity in organic molecules. A precisely regulated sequence of radical generation using red light-mediated photocatalysis facilitates this regioselective switch from the terminal \((\beta)\) position to the internal \((\alpha)\) position. Furthermore, this approach demonstrates distinctive regioselectivity, broad substrate scope and industrial potential for the synthesis of pharmaceuticals under mild reaction conditions. + +<|ref|>sub_title<|/ref|><|det|>[[88, 648, 195, 665]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[86, 677, 912, 946]]<|/det|> +Recently, photoredox catalysis has gained tremendous attention in achieving unique synthetic targets under mild reaction conditions. In most of these cases, short- wavelength light regions \((\lambda_{\max}< 460 \text{nm})\) were utilized to achieve these reactions successfully, however, short- wavelength light regions have severe limitations of potential health risk such as photooxidative damage to the retina and furthermore, they can lead to generate undesired side products and thereby, lower the atom economy of that reaction. Additionally, lower penetration power of short- wavelength light regions causes concern for the scale up of that particular reaction. All these limitations have encouraged scientists to move forward to the longer- wavelength regions such as red light or near- infrared (NIR) regions since these are associated with low health risk factor, generate less side products due to their lower energy and have high penetration power in the solution which in turn assist to scale up the reaction. In longer- wavelength regions as the photocatalysts will be activated by the low- energy, their corresponding redox windows are consequently narrower and that in turn assists to exercise finer control in chemical processes, permitting only specific reactions to take place under defined conditions. Inspired by this, the groups of MacMillan and Rovis have independently developed inspiring photocatalytic strategies for the activation of aryl azide via red light- mediated photoredox catalysis which have been utilized for proximity labeling. Additionally, the utilization of red light- mediated photocatalysis has been increasingly applied across multiple domains to enhance the control of chemical reactions. Thus, it is very clear that the red light- mediated photoredox catalysis can uniquely attain many unsolved + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 44, 912, 94]]<|/det|> +processes which were impossible by the irradiation of ultraviolet (UV) or blue light and that leads to the growing surge of interest in this field, however, it is imperative to acknowledge that still the applications of red light-mediated strategies in organic synthesis are in the early stage of development. + +<|ref|>image<|/ref|><|det|>[[95, 110, 904, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 682, 861, 700]]<|/det|> +
Figure 1. Design of the sulfonyltrifluoromethylation of olefins via red light-mediated photocatalysis.
+ +<|ref|>text<|/ref|><|det|>[[87, 709, 914, 912]]<|/det|> +Difenoxidation of alkenes is a powerful synthetic strategy to attain molecular complexity from readily available starting materials. \(^{15 - 21}\) In this approach, simultaneously two different functional groups are installed across an olefin by the introduction of two new C - C or C - X bonds. Along this direction, tremendous catalytic efforts have been paid to attain molecular complexity to design pharmaceutically relevant compounds. \(^{22 - 48}\) However, the simultaneous introduction of the trifluoromethyl (- CF₃) and the sulfonyl fragment (- SO₂R) via difunctionalization is highly challenging due to the intricate difficulty in circumventing undesired side reactions, therefore, rarely this challenge has been solved in organic synthesis. On the other hand, these two functional groups (- CF₃ and - SO₂R) are highly demanding due to their intrinsic capability to enhance the stability, membrane permeability, and metabolism in bioactive molecules and that is reflected in their wide presence as common pharmaceuticals such as CJ- 17493 and eletriptan which are served as an NK- 1 receptor antagonist, and as a medication for migraine headaches respectively (Figure 1a). \(^{49 - 54}\) To the best of our knowledge, only a single report has been published for the simultaneous introduction of these two functional groups across the alkene moiety, however, the position of the - CF₃ group was always in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[87, 44, 914, 246]]<|/det|> +terminal position \((\beta\) - position).49 Along the same direction, it should be clearly noted that the difunctionalization of alkenes via the introduction of a \(\mathsf{- CF}_3\) group has frequently been employed, however, \(\mathsf{- CF}_3\) group mainly acted as an initiator via the formation of a radical and was always installed to the terminal \((\beta)\) position of an alkene (as depicted by the solid frame in Figure 1b). Followed by this terminal addition, subsequent coupling with other functional groups such as - chloro, - chlorosulfonyl, - amino, - carboxylic acid groups were performed to achieve the difunctionalized products.55- 60 In contrary, reverse regioselectivity of the \(\mathsf{- CF}_3\) group at the internal position \((\alpha)\) in the difunctionalized olefins (indicated by the dashed frame in Figure 1b) is very rare, although this will allow to achieve important pharmaceuticals such as CJ- 17493, apinocaltamide and many more. To the best of our knowledge, only the group of Li presented an elegant thermocatalytic strategy by involving copper/N- fluorobenzenesulfonimide (NFSI) for the introduction of \(\mathsf{- CF}_3\) group at the internal position of an alkene (Figure 1b).30 In this approach, the \(N\) - centered radical, derived from an electrophilic NFSI, served as an initiator to facilitate the addition to the \(\beta\) position of the olefin and the (bpy)Zn(CF3)2 complex was employed as a nucleophilic \(\mathsf{- CF}_3\) reagent. + +<|ref|>text<|/ref|><|det|>[[86, 269, 914, 670]]<|/det|> +Inspired by all these information, we became interested to design a photoredox system for the first time that should install both the \(\mathsf{- CF}_3\) and \(\mathsf{- SO}_2\mathsf{R}\) groups simultaneously in alkenes where the \(\mathsf{- CF}_3\) group should be positioned at the internal position \((\alpha)\) in the difunctionalized product. To achieve a success in this site selectivity, meticulous designing of the photoredox strategy during the coupling of two different functional groups is inevitable. This was absolutely orthogonal in the case of Li's protocol where they worked with only one radical ( \(N\) - centered radical) in attaining the difunctionalized products.30 Specifically, when both the \(\mathsf{- CF}_3\) and \(\mathsf{- SO}_2\mathsf{R}\) radicals coexist, the \(\mathsf{- CF}_3\) radical demonstrates higher propensity to attach to the olefin first.37,57 To overcome this obstacle, we argued to ensure: (1) the formation of the \(\mathsf{- CF}_3\) radical should occur to the subsequent formation of \(\mathsf{- SO}_2\mathsf{R}\) radical which will readily initiate the addition to olefins; (2) we also argued to utilize a copper salt as a catalyst to capture the free \(\mathsf{- CF}_3\) radical since copper- based salts are well known for simultaneous cross- coupling reactions by involving \(\mathsf{- CF}_3\) radical.25- 26 To fulfill these requirements, we attempted to employ a photocatalyst which should be activated by the red light to attain the sulfonyltrifluoromethylated product (Figure 1c).61- 62 The reason behind our rationale to use the red light in our reaction was due to the lower energy of the red light compared to the blue light, photocatalysts activated by the red light are expected to exhibit a narrower redox window, enabling a precisely control of radical generation, thereby should facilitate regioselectivity during the addition of two distinct radicals on alkenes. Owing to the narrower redox window of the red light- activated photocatalyst, it was essential to ensure that the excited state of the photocatalyst \((\mathsf{PC}^*)\) should undergo reduction solely through the sulfinate salts via reductive quenching pathway.44,62 The resulting sulfonyl radical should then be added to the alkene, leading to the formation of the desired carbon- centered radical. At last, the desired product will be achieved by the carbon- centered radical and \(\mathsf{Cu} - \mathsf{CF}_3\) complex via Cu- catalyzed cross- coupling reaction.25- 26 In contrast, we rationalized to avoid the oxidative quenching pathway of the \(\mathsf{PC}^*\) since this would have generated free \(\mathsf{- CF}_3\) radical which would result to the undesired trifluoromethylated side products ( \(\mathsf{- CF}_3\) group at the terminal \((\beta)\) position).37,57 To accomplish this, the photocatalyst was carefully selected based on the redox potentials of sulfinate salts and \(\mathsf{- CF}_3\) reagents and the redox potentials should have fulfilled: \(E_{\mathrm{ox}}(\mathrm{RSO}_2^- )< E(\mathrm{PC}^* /\mathrm{PC}^- ),E_{\mathrm{red}}(\mathrm{CF}_3^+ )< E(\mathrm{PC}^* /\mathrm{PC}^+)\) and \(E(\mathrm{PC}^0 /\mathrm{PC}^- )< E_{\mathrm{red}}(\mathrm{CF}_3^+ )\) (Figure 1c). + +<|ref|>sub_title<|/ref|><|det|>[[88, 696, 155, 712]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[88, 718, 260, 733]]<|/det|> +## Reaction optimization + +<|ref|>text<|/ref|><|det|>[[86, 738, 914, 923]]<|/det|> +At the outset of the reaction, 4- vinyl- 1,1'- biphenyl (1 equiv.), \(\mathrm{Os(bptpy)_2(PF_6)_2}\) (0.8 mol%), \(\mathrm{NaSO_2Ph}\) (3 equiv.) and \(\mathrm{TTCF_3^+OTF^- }\) (2 equiv.) were employed as the model substrate, photocatalyst, sulfinate salt and \(\mathsf{- CF}_3\) reagent in the presence of copper chloride ( \(\mathrm{CuCl}_2\) , 20 mol%) in dichloromethane (DCM, 0.1 M) to afford the sulfonyltrifluoromethylated product (Figure 1d).5,61- 62 We carefully chosen these reagents ( \(\mathrm{Os(bptpy)_2(PF_6)_2}\) , sodium benzenesulfinate \(\mathrm{(NaSO_2Ph)}\) and trifluoromethyl thianthrenium triflate \(\mathrm{(TTCF_3^+OTF^- )}\) ) based on their redox potential values to match with our scientific rationale: \(E([\mathrm{Os}]^{1\dagger \dagger \dagger}) = +0.93\mathrm{V}\) vs. \(\mathrm{AgCl}\) (3 M KCl), \(E([\mathrm{Os}]^{1\dagger \dagger \dagger}) = -0.67\mathrm{V}\) vs. \(\mathrm{AgCl}\) (3 M KCl)5, \(E_{\mathrm{ox}}(\mathrm{NaSO_2Ph}) = +0.6\mathrm{V}\) vs. \(\mathrm{Ag/AgCl}\) (3 M KCl)57- 58, \(E_{\mathrm{red}}(\mathrm{TTCF_3^+OTF^- }) = -0.69\mathrm{V}\) vs. \(\mathrm{Ag/AgCl}\) (3 M KCl))63. As expected, the performance of the reaction under these conditions did not generate any trifluoromethylated side products (at the terminal position) and only provided the desired product with \(73\%\) of yield. It was also observed that reducing the quantities of \(\mathrm{NaSO_2Ph}\) and \(\mathrm{TTCF_3^+OTF^- }\) , led to a decrease in the yield of the final product (Figure 1d, entries 2- 3). It was necessary to use the excess quantity of sulfinate salts to ensure the faster oxidation of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[87, 44, 914, 263]]<|/det|> +sulfinate salt to the \(\cdot \mathrm{SO}_2\mathrm{R}\) radical. In addition, due to the lower solubility in DCM, the use of the excess quantity of sulfinate salts was highly necessary as well as the presence of excess quantity of \(\cdot \mathrm{CF}_3\) reagent accelerated the reaction rate.23,61- 62 Furthermore, the addition of ligands such as \(2,2^{\prime}\) - bipyridine (bpy) and 1,10- phenanthroline (1,10- phen) exerted deleterious effects in the reaction, giving no product under this conditions (Figure 1d, entries 4- 5). We assumed that the presence of ligands occupied the coordination sites for \(\cdot \mathrm{CF}_3\) radical or hindered the binding of \(\cdot \mathrm{CF}_3\) radical to the Cu- center.25 To verify the importance of the appropriate \(\cdot \mathrm{CF}_3\) reagent, alternative electrophilic \(\cdot \mathrm{CF}_3\) sources such as Togni's reagent, Umemoto's reagent, and \(\mathrm{Cu(CF_3)_3bpy}\) were also applied, albeit substantially lower or negligible yield of the desired product was obtained (Figure 1d, entries 6- 10). The rationale behind this could be ascribed to their unsuitable redox potentials, which did not align with \(\mathrm{Os(bptpy)_2(PF_6)_2}\) and consequently, failed to meet the requirements. Furthermore, alternative Cu- salts and solvents were also investigated, but lower or negligible yields of the products were obtained (Figure 1d, entries 11- 13). Finally, control experiments revealed that the presence of the photocatalyst, Cu- salts and red light were essential for this reaction (Figure 1d, entries 14- 16). + +<|ref|>text<|/ref|><|det|>[[87, 286, 914, 490]]<|/det|> +In order to exhibit the red light- mediated regioselective gain for this reaction, reaction conditions under the irradiation of blue light were also compared. Similar to the 'red light system', the crucial combination of the photocatalyst, sulfinate salt and \(\cdot \mathrm{CF}_3\) reagent was determined, namely \([\mathrm{Ru(bpz)_3(PF_6)_2}\) , \(\mathrm{NaSO_2Ph}\) and 5- (trifluoromethyl) dibenzothiophenium triflate (Figure 2b). However, after extensive optimizations via the investigation of each crucial component of this reaction, the highest yield of the desired product reached to \(42\%\) and this could be due to the fact that free \(\cdot \mathrm{CF}_3\) radical was generated faster under these conditions. (See SI 1.3.2). Subsequently, this \(\cdot \mathrm{CF}_3\) radical underwent an addition reaction with styrene, resulted the formation of the undesired \(\beta\) - substituted trifluoromethylated byproduct and the contrast was notably evident in the \(^{19}\mathrm{F}\) NMR spectra (Figure 2c). The 'blue light system' exhibited numerous peaks of side products while the spectrum of the 'red light system' appeared significantly cleaner and mainly contained the \(\cdot \mathrm{CF}_3\) reagent and the desired product. This significant difference highlighted the pronounced regioselectivity gain in the sulfonyltrifluoromethylation of alkenes via the red light- mediated photocatalysis. + +<|ref|>image<|/ref|><|det|>[[90, 513, 905, 758]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[90, 766, 866, 784]]<|/det|> +
Figure 2. Initial investigation of the reaction under blue and red light with respective photocatalysts.
+ +<|ref|>sub_title<|/ref|><|det|>[[89, 794, 218, 810]]<|/det|> +## Substrate scope + +<|ref|>text<|/ref|><|det|>[[88, 822, 914, 924]]<|/det|> +With this optimized reaction conditions in hand, we started to evaluate the scope of the sulfonyltrifluoromethylation of alkenes. As shown in the Figure 3, an array of para- substituted styrenes containing diverse electron- donating groups (EDGs) like - methyl, - acetoxy, and - tert- butyl, as well as electron- withdrawing groups (EWGs) such as - halogens provided the corresponding sulfonyltrifluoromethylated products in moderate to excellent yield (Figure 3, 1- 8). Specifically, 4- bromostyrene and 4- chlorostyrene were tolerant under our optimized conditions to provide the desired products (6 and 7), thereby, demonstrated the potential for subsequent functionalization via cross coupling + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 44, 914, 179]]<|/det|> +reactions. \(^{30}\) Furthermore, the reaction demonstrated compatibility with 2- and 3- substituted styrenes (10- 13), leading to the formation of products in satisfactory yield, regardless of the presence of - EDGs or - EWGs. In comparison, electron- deficient alkenes (9 and 14) exhibited decreased efficiency, however, the use of \(p\) - chlorophenyl sulfinate led to an improvement in the reaction. In general, the difunctionalization of \(\beta\) - substituted styrenes represents increased difficulty due to the hindrance caused by these \(\beta\) - substituents and this hindrance can impede the addition of initiators, such as sulfonyl radicals in this work. \(^{30}\) However, under our optimized reaction conditions, \((E)\) - \(\beta\) - methylstyrene (15) and indene (16) underwent the difunctionalization reaction smoothly and provided the yield of \(46\%\) and \(78\%\) , respectively. + +<|ref|>image<|/ref|><|det|>[[108, 191, 919, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[86, 800, 912, 834]]<|/det|> +
Figure 3. Scope of the sulfonyltrifluoromethylation of olefins \(^{a}\) . \(^{a}\) Yields are reported as isolated yield. \(^{b}\) dr value was determined by \(^{1}\) H NMR.
+ +<|ref|>text<|/ref|><|det|>[[88, 847, 912, 898]]<|/det|> +Encouraged by these results, an extensive exploration of sulfinate salts was conducted within the optimized reaction conditions. To our delight, a diverse array of \(p\) - substituted phenyl sulfinates, encompassing - methyl, - chloro, - bromo, - nitro, and - cyano groups, demonstrated excellent tolerance, yielding the desired products in yields from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 44, 912, 179]]<|/det|> +good to excellent (17- 21). Furthermore, aliphatic sulfinates (22 and 23) also proved to be compatible which exhibited strong application potentials in pharmaceutical area such as the modification of azidothymidine which is known as an anti- HIV drug.64 The adaptability of our methodology extended further to sulfinates bearing biphenyl-, cyclopropane-, and thiophene- groups. These substrates smoothly underwent difunctionalization reactions under the irradiation of red light, yielding products in the range of \(35 - 93\%\) (24- 26). This exhibited wide generality of our system to afford various sulfones- containing chemicals, thereby making significant contributions to the field of pharmaceuticals, agrochemicals, and it should be also noted that the synthesis of sulfones- containing chemicals is of paramount importance in organic chemistry.44- 46 + +<|ref|>text<|/ref|><|det|>[[88, 191, 912, 390]]<|/det|> +Recently, the focus on late- stage modification has garnered significant interest due to its direct and efficient approach in synthesizing functionalized complex molecules.65- 69 The expedite synthesis of highly- functionalized molecules holds strong promise for its potential utility in various scientific disciplines including drug discovery, materials science, and molecular imaging.69 To evaluate the application of our method on complex molecules, a series of drug molecules and natural products derivatives such as estrone, (S)- (+)- naproxen, dexibuprofen, (1S)- (- )- camphanic acid, indomethacin and adapalene were applied (27- 32). Under our experimental conditions, these diverse drug derivatives, encompassing a variety of functional groups, exhibited excellent tolerance and compatibility. The resulting products were obtained in yields from \(66\%\) to \(88\%\) , indicating high reaction efficiency. This demonstrated the potential of our methodology in facilitating the synthesis of more complex sulfonyltrifluoromethylated molecules. We strongly believe that the - trifluoromethyl and - sulfonyl groups in functionalized drug molecules and natural products should not only improve their inherent properties but should also provide the opportunity for further transformation. + +<|ref|>image<|/ref|><|det|>[[90, 409, 911, 616]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 633, 667, 650]]<|/det|> +
Figure 4. Post-functionalization of the sulfonyltrifluoromethylated product.
+ +<|ref|>sub_title<|/ref|><|det|>[[90, 660, 261, 676]]<|/det|> +## Application potentials + +<|ref|>text<|/ref|><|det|>[[88, 688, 912, 921]]<|/det|> +To further examine the application potential, a 4 mmol- scale reaction was carried out which proceeded smoothly in 4 hours and yielded 0.85 grams of the desired product (Figure 4a). Due to the superior light penetration of red light, it became feasible to directly conduct the upscaling of the reaction within a batch reaction system.5 To further demonstrate the synthetic utility of our strategy, the elimination of the - sulfonyl group was achieved through a straightforward strategy by using a mixture of \(\mathrm{Cs_2CO_3}\) and 7- methyl- 1,5,7- triazabicyclo(4.4.0)dec- 5- ene (MTBD), resulting in the production of \(\alpha\) - trifluoromethyl styrene (33) with a yield of \(90\%\) (Figure 4b).62 The mixture of base facilitated the deprotonation and desulfonylation of the sulfonyltrifluoromethylated styrenes to form the \(\alpha\) - trifluoromethyl styrenes. In general, \(\alpha\) - trifluoromethyl styrene derivatives are highly important as versatile synthetic intermediates for the construction of complex fluorinated compounds which are synthesized through methylation of trifluoromethylketones (Wittig reaction) or via transition metal- catalyzed cross- coupling reactions.70- 71 However, compared to these approaches, our strategy enabled the direct synthesis of \(\alpha\) - trifluoromethyl styrene derivatives from styrene, eliminating the requirement of Wittig reagents as well as - borylated or - halide reagents in the processes to improve the atom economy. Additionally, the obtained \(\alpha\) - trifluoromethyl styrene was further transformed into gem- difluorolalkenes (34) in \(86\%\) yield and these fluorinated compounds have strong potential to act as a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 44, 912, 128]]<|/det|> +ketone mimic in pharmaceuticals. \(^{72 - 74}\) In fact, substitution of the carbonyl group by the gem- difluoroalkene moiety has shown to enhance the oral bioavailability of therapeutic agents. \(^{72}\) Furthermore, our strategy generated a key intermediate (35) for the synthesis of apinocaltamide (37), T- type calcium channel blocker from 4- bromostyrene (Figure 4c). \(^{75 - 76}\) All these approaches clearly demonstrate the strong potential of our strategy for further applications in designing or modifying pharmaceuticals. + +<|ref|>image<|/ref|><|det|>[[108, 140, 900, 545]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 562, 322, 578]]<|/det|> +
Figure 5. Mechanistic studies.
+ +<|ref|>sub_title<|/ref|><|det|>[[88, 589, 299, 605]]<|/det|> +## Mechanistic investigations + +<|ref|>text<|/ref|><|det|>[[86, 616, 912, 919]]<|/det|> +Inspired by all these outcomes, we became interested to validate the reaction mechanism of this unique reaction strategy and a series of mechanistic experiments were conducted to validate our mechanistic proposal (Figure 5). At first, (2,2,6,6- Tetramethylpiperidin- 1- yl)oxyl (TEMPO) was added as a radical quenching reagent under the optimized reaction conditions. As expected, trace quantity of the product was obtained and a carbon- centered radical (III) was captured by TEMPO which was detected by the high- resolution mass spectrometry (HRMS) (Figure 5a), indicating that the radical process was involved. To further support the involvement of radicals during the addition of the sulfonyl radical, a radical probe experiment was conducted where the model styrene (39) yielded the ring- opening product 40 (Figure 5b). Upon the addition of sulfonyl radical to 39, a cyclopropylmethyl radical moiety was formed, followed by the rapid ring opening rearrangement relieved the ring strain and finally, resulted the final ring- opening product (40). Additionally, Stern- Volmer fluorescence quenching experiments were conducted, revealing that the sodium sulfinate salt exhibited the highest potential as a quencher for the excited state of the Os- photocatalyst, which was also corroborated by the electrochemical measurements for redox potentials (Figure 5c, see SI 1.4.1). \(^{5}\) In Figure 5c, it demonstrated that as the concentration of sulfinate salt was increased, there was a notable reduction in fluorescence intensity. However, minimal alterations were detected in the case of the - CF₃ reagent, styrene, and CuCl₂. This observation was aligned with the anticipated reductive quenching pathway and supported our design that the generation of - sulfonyl radical was prior than the generation of - CF₃ radical in the reaction, indicating that no free - CF₃ radical was generated and ensured the high regioselectivity switch in this reaction. Furthermore, the form of Cu- CF₃ active species was also investigated and to analyze the possible Cu- CF₃ + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 44, 914, 279]]<|/det|> +active species, various control experiments were carried out (Figure 5d). Initially, we attempted to detect the active species in the absence of styrene under model reaction conditions, while no new peak corresponding to \(\mathrm{Cu^{II} - CF_3}\) was observed in \(1 - 4h\) , however, we observed the presence of the \(\mathrm{Cu^{III}(CF_3)_4}\) anion peak (Experiment A in Figure 6). Due to the potential instability of the \(\mathrm{Cu^{II} - CF_3}\) complex, we further attempted the addition of the bpy ligand to detect the potential existence of the \(\mathrm{Cu^{II} - CF_3}\) in Experiment A. However, only peak of \(\mathrm{TTCF_3^+OTF^-}\) was observed in \(^{19}\mathrm{F}\) NMR (Experiment B in Figure 6). The presence of ligands either occupied the available coordination sites of \(\mathrm{- CF_3}\) radical or impeded the binding of \(\mathrm{- CF_3}\) radical to the \(\mathrm{Cu}\) - center. \(^{25}\) To further verify the \(\mathrm{Cu^{III}(CF_3)_4}\) anionic complex, we synthesized stable \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex by following the reference article. \(^{77}\) However, no product was obtained by using \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex instead of \(\mathrm{CuCl_2}\) under our optimized reaction conditions (Experiment C in Figure 6). Similarly, to verify the possibility of \(\mathrm{Cu^{I} - CF_3}\) complex as active species, the model reaction was carried out by replacing \(\mathrm{CuCl_2}\) with fresh copper powder \((\mathrm{Cu^0})\) and as expected, no product was obtained under this condition (Experiment D in Figure 6). By analyzing all these experiments, we could assume that the active species \(\mathrm{Cu - CF_3}\) were not in the form of \(\mathrm{Cu^{II} - CF_3}\) or \(\mathrm{Cu^{I} - CF_3}\) complexes but possibly were in the form of \(\mathrm{Cu^{II} - CF_3}\) complex. + +<|ref|>image<|/ref|><|det|>[[95, 290, 905, 740]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[86, 753, 912, 815]]<|/det|> +
Figure 6. NMR spectra of the analysis for \(\mathrm{Cu - CF_3}\) complex. Experiment A: Model reaction in the absence of styrene after \(1h\) and \(4h\) . Experiment B: Experiment A with the addition of bpy (0.5 or 1.5 equiv.) as ligand. Experiment C: Model reaction by replacing \(\mathrm{CuCl_2}\) with \(\mathrm{Me_4NCu^{III}(CF_3)_4}\) complex. Experiment D: Model reaction by replacing \(\mathrm{CuCl_2}\) with fresh \(\mathrm{Cu}\) powder.
+ +<|ref|>text<|/ref|><|det|>[[86, 825, 912, 910]]<|/det|> +Based on all these mechanistic studies, we proposed a possible mechanism for the overall reaction system (Figure 5e). The excited state of the photocatalyst \([\mathrm{Os^{II}}]^*\) \((E^{1*/1} = +0.93\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl), \(E^{1*/1} = - 0.67\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{5}\) was activated by the red light and exclusively underwent reduction by the sulfinate salts, \(\mathrm{I}\) \((E_{\mathrm{ox}} = +0.4 - 0.6\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{61 - 62}\) to form the sulfonyl radical \(\mathrm{II}\) (Path A) rather than oxidation by \(\mathrm{TTCF_3^+OTF^-}\) IV \((E_{\mathrm{red}} = - 0.69\mathrm{V}\) vs. \(\mathrm{Ag / AgCl}\) (3 M KCl)) \(^{63}\) to generate the free \(\mathrm{- CF_3}\) radical \(\mathbf{V}\) (Path B), which was consistent with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 44, 912, 128]]<|/det|> +the result of fluorescence quenching experiments. The formed sulfonyl radical II was added to the alkene to generate a carbon- centered radical III which was verified by the TEMPO quenching experiment and the radical probe experiment. Later, the \(\mathsf{Cu}^{1}\) - species captured the free - \(\mathsf{CF}_3\) radical V, generated through the reduction of IV by [Os] \((E^{III} = - 0.82 \text{V}\) vs. Ag/AgCl (3 M KCl)) \(^5\) , resulted the formation of the \(\mathsf{Cu}^{II} - \mathsf{CF}_3\) complex VI. At last, the final product VII was delivered via the cross- coupling reaction between III and VI. + +<|ref|>sub_title<|/ref|><|det|>[[88, 142, 198, 159]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[88, 171, 912, 306]]<|/det|> +In summary, we have developed a unique protocol where red light- mediated photocatalysis triggered a regioselective switch during the sulfonyltrifluoromethylation of olefins. This strategy has effectively addressed the challenges associated with regioselective addition of radicals onto alkenes. The broad substrate scope and late- stage transformation demonstrated the high efficiency of these reactions and also proved the excellent tolerance of functional groups. Furthermore, post- functionalization studies highlighted the significant industrial potential of the sulfonyltrifluoromethylated product. Additionally, detailed mechanistic investigations revealed a sequential generation of radicals, followed by Cu- catalyzed cross- coupling reactions. We believe that this strategy will strongly contribute to the regioselective functionalizations and will further inspire the development of additional methods in this field. + +<|ref|>sub_title<|/ref|><|det|>[[88, 320, 164, 336]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[87, 348, 912, 516]]<|/det|> +General procedure for sulfonyltrifluoromethylation of olefins. A dried reaction vial with a magnetic stirring bar was charged with \(\mathsf{Os(bptpy)_2(PF_6)_2}\) (0.0008 mmol, 0.8 mol%), \(\mathsf{CuCl_2}\) (0.02 mmol, 20 mol%), \(\mathsf{TT - CF_3^+OTF^- }\) (0.2 mmol, 2 equiv.) and sodium sulfinate (0.3 mmol, 3 equiv.). After charging all these reagents, the vessel was evacuated by using Schlenk techniques and flushed with \(\mathsf{N}_2\) for three times. Under nitrogen gas flow, olefin (0.1 mmol, 1 equiv.) (if liquid, otherwise added before flushing cycle) and dry DCM (0.1 M) were added by using a syringe which was flushed with inert gas. The resulting mixture was stirred for 3 - 4 h under the irradiation of red LED light (EvoluChem™ LED 650PF HCK1012- XX- 014 650 nm 20 mW/cm²) in the EvoluChem PhotoRedOx Box. After the completion of the reaction, the reaction mixture was quenched by adding distilled water (2 mL). The organic phase was extracted and concentrated in vacuo. 1,1,1- Trifluorotoluene was added as internal standard to determine the NMR yield of the functionalized product through \(^{19}\mathrm{F}\) NMR. Purification proceeded via flash column chromatography. + +<|ref|>sub_title<|/ref|><|det|>[[88, 528, 226, 545]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[88, 558, 911, 592]]<|/det|> +All of the data supporting the findings of this study are available within the paper and its Supplementary Information file. + +<|ref|>sub_title<|/ref|><|det|>[[88, 605, 281, 622]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[88, 635, 911, 670]]<|/det|> +Optimization of reactions, Mechanism investigation, General procedure of reactions, characterization of substrates and products and spectra of products could be found in Supporting Information. + +<|ref|>sub_title<|/ref|><|det|>[[88, 682, 271, 700]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[88, 712, 911, 747]]<|/det|> +T.Z. and S.D. designed the project. T.Z. developed the reaction, investigated the substrate scope, examined the applications, and studied the reaction mechanism. Finally, T.Z. and S.D. wrote the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[88, 759, 264, 777]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[88, 790, 465, 806]]<|/det|> +The authors declare no competing financial interest. + +<|ref|>sub_title<|/ref|><|det|>[[88, 819, 250, 836]]<|/det|> +## Acknowledgement + +<|ref|>text<|/ref|><|det|>[[88, 850, 911, 917]]<|/det|> +S.D. thanks the Francqui start up grant from the University of Antwerp, Belgium, for the financial support. T.Z. thanks FWO SB PhD fellowship for their financial assistance to finish this work. We thank Dr. Rakesh Maiti from University of Bayreuth for helpful discussions. We also thank Mr. Glenn Van Haesendonck from UAntwerpen, Belgium for HRMS measurements. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[88, 46, 187, 62]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[84, 60, 914, 920]]<|/det|> +1. Twilton, J., Le, C., Zhang, P., Shaw, M. H., Evans, R. W. & MacMillan, D. W. C. 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Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 328, 150]]<|/det|> +Supportinginformation5. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/images_list.json b/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..adbfaaa84c6cb513a861a99f92f2c90e8dc645f6 --- /dev/null +++ b/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1: Atomic structure of phosphorus chains on Ag(111). a, STM topographic image after sublimation of phosphorus atoms on Ag(111) leading to P chains (1) and cyclo-\\(P_{5}\\) domains (2), \\((I_{\\mathrm{T}} = 1 \\mathrm{pA}, V = 0.15 \\mathrm{mV})\\) . The inset shows a STM image of the single, double and triple chains, respectively. b-d, Series of AFM images with CO-terminated tip revealing the armchair structure of single, double and triple P chains, \\((f_{0} = 26 \\mathrm{kHz}, A = 50 \\mathrm{pm})\\) . Scale bars are \\(1 \\mathrm{nm}\\) . e, Atomic configurations of the triple armchair chains obtained by DFT calculations. Phosphorus and silver atoms are shown in orang and gray, respectively. f, Corresponding AFM simulation using the DFT coordinates.", + "footnote": [], + "bbox": [ + [ + 112, + 195, + 884, + 604 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2: Atomic structure of the self-assembled cyclo-\\(P_{5}\\) molecules. a, STM image of the self-assembled pentamers on Ag(111), \\((I_{\\mathrm{T}} = 1 \\mathrm{pA}\\) , \\(V = 0.15 \\mathrm{mV}\\) ). Islands systematically shows a superlattice of bright lines rotated by \\(19^{\\circ}\\) with respect to the \\([110]\\) directions of Ag(111). b, Close-up STM topography showing the \\(P_{5}\\) molecules depicted by dashed pentagons. c, Corresponding AFM image revealing the \\(P_{5}\\) chemical structure, \\((f_{0} = 26 \\mathrm{kHz}\\) , \\(A = 50 \\mathrm{pm}\\) ). d, Atomic configurations of the pentamer assembly on Ag(111) obtained by DFT. Phosphorus and silver atoms are shown in orange and gray, respectively. e, Corresponding AFM simulation using the DFT coordinates. f, Site-dependent \\(\\Delta f(Z)\\) spectroscopic curves acquired at one P atoms of a \\(P_{5}\\) molecule (orange), between two \\(P_{5}\\) molecules (brown) and on Ag(111) (black), respectively. The local minima of the \\(\\Delta f(Z)\\) curves indicate the relative height of the phosphorus atoms.", + "footnote": [], + "bbox": [ + [ + 111, + 216, + 884, + 528 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3: Charge redistribution at the cyclo-\\(P_{5}\\) /Ag(111) interface. a, Frequency shift \\(\\Delta f\\) as a function of sample bias voltage \\(V_{\\mathrm{s}}\\) , measured across a pentamer domain shown in the STM image (top), (parameters : \\(f_{0} = 26 \\mathrm{kHz}\\) , \\(A = 80 \\mathrm{pm}\\) ). b, Single \\(\\Delta f(V)\\) curves at the pentamer assembly (orange) as compared to the Ag(111) (black). Dashed lines mark the top of the parabola allowing to extract a LCPD shift \\(\\Delta V^{*} = 0.22 \\mathrm{V}\\) . c, Top and side views of the charge redistribution between pentamers and Ag(111). Blue areas show electron accumulation, red areas electron depletion. The isosurface level of the plot is set to \\(\\pm 13 \\times 10^{-3} \\mathrm{e} / \\mathrm{\\AA}^{3}\\) . d, Schematic illustration of the charge redistribution at the \\(P_{5}\\) /Ag(111) interface leading to an inward surface dipole (D) moment and a local work function change \\((\\phi_{\\mathrm{P}_{5} / \\mathrm{Ag}})\\) . The cyclo-\\(P_{5}\\) layer is colored in orange. \\(\\Delta V^{*}\\) refers to the LCPD change.", + "footnote": [], + "bbox": [ + [ + 243, + 179, + 750, + 585 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4: Tunneling spectroscopy of the \\(P_{5} / \\mathrm{Ag}\\) interface. a, \\(\\mathrm{d}I / \\mathrm{d}V\\) point-spectra acquired above the \\(P_{5}\\) assembly (orange) and on Ag(111) (black), where precise locations are shown in the STM inset. (parameters: \\(I_{\\mathrm{t}} = 1 \\mathrm{pA}\\) , \\(V_{\\mathrm{s}} = 500 \\mathrm{mV}\\) , \\(A_{\\mathrm{mod}} = 10 \\mathrm{mV}\\) , \\(f = 511 \\mathrm{Hz}\\) ). b, \\(\\mathrm{d}I / \\mathrm{d}V\\) maps at \\(V_{\\mathrm{s}} = -1.25\\) and \\(2.5 \\mathrm{V}\\) corresponding to the valence band energy and the IS interface state, respectively. c, STM topographic image of three \\(P_{5}\\) domains and the corresponding \\(\\mathrm{d}I / \\mathrm{d}V\\) maps of the IS modulation. d, Scheme of the band alignment and the formation of Stark-shifted IPS (orange lines). e, Field-effect resonance tunneling (FERT cross-section acquired across the \\(P_{5}\\) assembly along the dashed line in a, (Set-points: \\(I_{\\mathrm{t}} = 1 \\mathrm{pA}\\) , \\(V_{\\mathrm{s}} = 500 \\mathrm{mV}\\) , \\(A_{\\mathrm{mod}} = 35 \\mathrm{mV}\\) , \\(f = 511 \\mathrm{Hz}\\) ). f, Single FERT spectra of the \\(P_{5}\\) assembly and the Ag(111) substrate, showing the series of \\(\\mathrm{n}^{th}\\) IPS. g, Extracted IPS peak voltages as a function of \\(n^{2 / 3}\\) .", + "footnote": [], + "bbox": [ + [ + 110, + 163, + 884, + 580 + ] + ], + "page_idx": 26 + } +] \ No newline at end of file diff --git a/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290_det.mmd b/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..c55c4681e96b93b7b7adfac99957cb3c5db542cc --- /dev/null +++ b/preprint/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290/preprint__035e55afc9c40b2a32b10bb61cbaf9c417c4c43287f20e12b4733b13052ac290_det.mmd @@ -0,0 +1,436 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 955, 178]]<|/det|> +# Probing charge redistribution at the interface of self-assembled cyclo-P5 pentamers on Ag(111) + +<|ref|>text<|/ref|><|det|>[[44, 195, 164, 214]]<|/det|> +Rémy Pawlak + +<|ref|>text<|/ref|><|det|>[[54, 222, 290, 240]]<|/det|> +remy.pawl ak@unibas.ch + +<|ref|>text<|/ref|><|det|>[[50, 268, 576, 288]]<|/det|> +University of Basel https://orcid.org/0000- 0001- 8295- 7241 + +<|ref|>text<|/ref|><|det|>[[44, 293, 220, 333]]<|/det|> +Outhmane Chahib University of Basel + +<|ref|>text<|/ref|><|det|>[[44, 339, 321, 380]]<|/det|> +Yulin Yin Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 385, 579, 427]]<|/det|> +Jung-Ching Liu University of Basel https://orcid.org/0000- 0002- 9472- 3343 + +<|ref|>text<|/ref|><|det|>[[44, 431, 787, 473]]<|/det|> +Chao Li Department of Physics, University of Basel https://orcid.org/0000- 0003- 2125- 9989 + +<|ref|>text<|/ref|><|det|>[[44, 477, 579, 519]]<|/det|> +Thilo Glatzel University of Basel https://orcid.org/0000- 0002- 3533- 4217 + +<|ref|>text<|/ref|><|det|>[[44, 524, 928, 588]]<|/det|> +Feng Ding Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences https://orcid.org/0000- 0001- 9153- 9279 + +<|ref|>text<|/ref|><|det|>[[44, 593, 670, 635]]<|/det|> +Qinghong Yuan East China Normal University https://orcid.org/0000- 0003- 4683- 2112 + +<|ref|>text<|/ref|><|det|>[[44, 640, 400, 681]]<|/det|> +Ernst Meyer https://orcid.org/0000- 0001- 6385- 3412 + +<|ref|>sub_title<|/ref|><|det|>[[44, 722, 103, 740]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 759, 952, 804]]<|/det|> +Keywords: cyclo- P- 5 pentamer, work function, atomic force microscopy, scanning tunneling microscopy, field- emission resonance spectroscopy, density functional theory + +<|ref|>text<|/ref|><|det|>[[44, 820, 330, 840]]<|/det|> +Posted Date: January 30th, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 858, 475, 878]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3777510/v1 + +<|ref|>text<|/ref|><|det|>[[42, 895, 914, 939]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 930, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 2nd, 2024. See the published version at https://doi.org/10.1038/s41467-024-50862-4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[115, 85, 885, 141]]<|/det|> +# Probing charge redistribution at the interface of self-assembled cyclo- \(P_{5}\) pentamers on Ag(111) + +<|ref|>text<|/ref|><|det|>[[114, 166, 884, 227]]<|/det|> +Outhmane Chahib, \(^{1}\) Yulin Yin, \(^{2}\) Jung- Ching Liu, \(^{1}\) Chao Li, \(^{1}\) Thilo Glatzel, \(^{1}\) Feng Ding, \(^{2}\) Qinghong Yuan, \(^{3}\) Ernst Meyer \(^{1,*}\) & Rémy Pawlak \(^{1,*}\) + +<|ref|>text<|/ref|><|det|>[[113, 262, 886, 410]]<|/det|> +\(^{1}\) Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland \(^{2}\) Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China + +<|ref|>text<|/ref|><|det|>[[114, 409, 885, 469]]<|/det|> +\(^{3}\) State Key Laboratory of Precision Spectroscopy School of Physics and Electronic Science, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China + +<|ref|>sub_title<|/ref|><|det|>[[115, 535, 209, 556]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 595, 886, 875]]<|/det|> +Phosphorus pentamer (cyclo- \(P_{5}^{- }\) ) ions are unstable in nature but can be synthesized at the Ag(111) surface. Unlike monolayer black phosphorous, little is known about their electronic properties when in contact with metal electrodes, although this is crucial for future applications. Here we characterize the atomic structure of cyclo- \(P_{5}\) assembled on Ag(111) using atomic force microscopy with functionalized tips and density functional theory. Combining force and tunneling spectroscopy, we find that a strong charge transfer induces an inward dipole moment at the cyclo- \(P_{5}\) /Ag interface as well as the formation of an interface state. We probe the image potential states by field- effect resonant tunneling + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 220]]<|/det|> +and quantify the increase of the local change of work function of \(0.46 \mathrm{eV}\) at the \(cyclo - P_{5}\) assembly. Our results suggest that the high- quality of the \(cyclo - P_{5} / \mathrm{Ag}\) interface might serve as a prototypical system for electric contacts in phosphorus- based semiconductor devices. + +<|ref|>text<|/ref|><|det|>[[113, 280, 886, 338]]<|/det|> +Keywords: \(cyclo - P_{5}^{- }\) pentamer, work function, atomic force microscopy, scanning tunneling microscopy, field- emission resonance spectroscopy, density functional theory + +<|ref|>sub_title<|/ref|><|det|>[[114, 392, 250, 412]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[112, 450, 886, 877]]<|/det|> +Elemental phosphorus (P) is not only ubiquitous in human life, it is also one of the most fascinating areas of chemistry as it can exist in a large diversity of allotropes, \(^{1 - 3}\) in various cluster configurations \(^{4,5}\) or in organic compounds. \(^{6}\) The phosphorous polymorphism is even multiplied on the atomic- scale when using a surface to constrain the reaction in two dimensions (2D). As in the field of on- surface chemistry producing complex nanographene structures in ultra- high vacuum (UHV), \(^{7,8}\) surface- assisted phosphorus reactions on metals have synthesized blue phosphorus, \(^{9}\) P chains \(^{10}\) or even planar \(cyclo - P_{5}\) rings. \(^{11,12}\) Since then, phosphorus allotropes have emerged as a promising one- atom thick 2D material beyond graphene, due to its moderate direct band gap (0.3 to \(2.0 \mathrm{eV}\) ) \(^{13}\) suitable for nanoelectronics and nanophotonics applications. \(^{14,15}\) However, allotropic configurations, their atomic buckling, defects or potential alloy formation can be detrimental for the semiconducting character. In addition, the interaction of 2D materials with delocalized electrons of a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 220]]<|/det|> +metal, as well as the dynamical charge transfer between the two media, are key factors in fostering new gate- tunable functionalities such as superconductivity. \(^{16,17}\) Experimental study of these aspects at the fundamental level is therefore essential for future quantum applications where metallic electrical contacts are required. \(^{18}\) + +<|ref|>text<|/ref|><|det|>[[112, 293, 886, 792]]<|/det|> +Low- temperature scanning probe microscopy is an incontrovertible tool for assessing atomic structures in contact to metals and characterizing their electronic properties with high spectral resolution in UHV. Atomic force microscopy (AFM) with functionalized tips \(^{19,20}\) has opened new avenues into the real- space imaging with improved lateral and vertical resolution of aromatic molecules, cyclo- carbon \(^{21}\) and monoelemental 2D materials, \(^{22,23}\) while charge distributions and work function changes at the nanometer scale are also accessible using Kelvin probe force microscopy (KPFM). \(^{24 - 27}\) The investigation of the local density of states (LDOS) of 2D materials near the Fermi level is readily achieved by means of scanning tunneling microscopy and spectroscopy (STM/STS). Tunneling spectroscopy can also probe the IPS of 2D synthetic materials such as graphene, \(^{28}\) germanene \(^{29}\) or borophene. \(^{30,31}\) Quantifying these Stark- shifted unoccupied states lying below the vacuum level give not only access to the fundamental physical processes involved in charge carrier dynamics but also to quantify local modulations of the work function at the interface between 2D materials and metals. + +<|ref|>text<|/ref|><|det|>[[113, 828, 882, 850]]<|/det|> +By applying this in- situ methodology, we determine here the structure of phosphorus + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 477]]<|/det|> +chains and self- assembled \(cyclo - P_{5}\) pentamers on Ag(111) using low- temperature (4.5 K) AFM imaging with CO- terminated tips. KPFM spectroscopic measurements indicates the formation of an inwards dipole moment at the \(P_{5} / \mathrm{Ag}\) interface, which results from the charge transfer from the Ag substrate to the network as confirmed by DFT calculations. This charge transfer leads to a complex charge redistribution and the formation of an interfacial hybridized state (IS). Through field- emission resonance tunneling (FERT) and STS spectroscopy, we determined the energy position of the IS state and the series of IPS at the \(cyclo - P_{5}\) assembly as compared to pristine Ag, confirming an increase of the local work function of \(\sim 0.46 \mathrm{eV}\) . Given the strong interest in tailoring the physical characteristics of monoelemental 2D materials contacted to a metal, we think that the \(cyclo - P_{5} / \mathrm{Ag}\) interface might serve as a model system for future devices involving electric contacts. + +<|ref|>sub_title<|/ref|><|det|>[[115, 527, 840, 552]]<|/det|> +## Atomic-scale imaging of phosphorus chains and \(cyclo - P_{5}\) pentamers + +<|ref|>text<|/ref|><|det|>[[112, 588, 886, 866]]<|/det|> +Phosphorus atoms were sublimed in UHV onto the Ag(111) substrate kept at about \(150^{\circ} \mathrm{C}\) (see Methods). Figure 1a shows an STM overview image of the resulting structures for a relative coverage of less than 0.3 monolayer (ML). Extended 1D chains aligned along the \([1\bar{1} 0]\) directions of Ag(111) (marked 1 in Fig. 1a) coexist with domains of \(P_{5}\) molecules (2), as recently reported in references. \(^{10,11}\) The inset of Fig. 1a further shows a STM image of the double and triple chains, that depends on the P deposition rate. \(^{11}\) Each chain configuration has a relative STM height of 1.6 Å, and a width of 11 Å, 17 Å, and 26 Å for the single, double and triple chains, respectively. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 886, 404]]<|/det|> +To precisely determine their atomic configurations, we employed AFM imaging with CO- terminated tips (see Methods, Figs. 1c- d).19 A common AFM contrast is observed for all configurations assigned to an armchair structure of the chain, which resembles that of hydrocarbon chains.32 The relaxed structure of the triple chain configuration calculated from DFT is shown in Fig. 1e. Phosphorus atoms colored in orange sit on bridge sites of the Ag lattice (gray) and are aligned along one \([1\bar{1} 0]\) direction in accordance with the experimental data. Based on the DFT coordinates we simulated the AFM image (see Methods, Fig.1f). The excellent agreement with the experimental image of Fig. 1d confirms the armchair structure of the P chains on Ag(111). + +<|ref|>text<|/ref|><|det|>[[112, 475, 886, 864]]<|/det|> +Increasing the P coverage to about 0.4 ML while keeping the substrate at \(150^{\circ}\mathrm{C}\) leads to the formation of large islands of \(cyclo - P_{5}\) pentamers relative to the chains (Fig. 2a).12 In Fig. 2b, the close- up STM image reveals the structure of the self- assembled domains consisting of an hexagonal lattice with parameters \(a_{1} = b_{1} = 7.3 \AA\) . Each bright protrusion corresponds to one \(cyclo - P_{5}\) molecule as schematized by the black dashed pentagons. Domains of \(cyclo - P_{5}\) rings also exhibit a superstructure characterized by stripes separated by \(\approx 3.8 \mathrm{nm}\) (i.e. 6 \(P_{5}\) rows) as shown by black dotted lines in Fig. 2a. These lines are rotated by \(19^{\circ}\) as compared to the \([1\bar{1} 0]\) directions of the Ag(111) substrate, which agrees with previous experimental works11 as well as the relaxed structure obtained by DFT calculations (Fig. 2d).12 A deeper insights into the chemical structure of the \(cyclo - P_{5}\) molecules is provided by the AFM image of Fig. 2c. The P- P bond length within the pentagon extracted by AFM is about + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 222]]<|/det|> +2.2 Å, which is comparable to the value of 2.185 Å obtained by DFT. For comparison, we also simulated the AFM image based on the DFT coordinates, allowing us to confirm the exact position and structure of the \(P_{5}\) molecules in their self-assembly in registry with the Ag(111). + +<|ref|>text<|/ref|><|det|>[[112, 293, 886, 682]]<|/det|> +To accurately quantify the atomic corrugation within the cyclo- \(P_{5}\) structure, we acquired a series of site- dependent \(\Delta f(Z)\) spectroscopic curves (Fig. 2f), at the locations marked in the inset AFM image. The black and gray curves were obtained on Ag and between two pentamers, respectively. On top of neighboring atoms of a cyclo- \(P_{5}\) (orange and brown curve), the spectra exhibits a characteristic dip arising from the interaction between the front- end oxygen atom of the CO- terminated tip with the phosphorus atom. The dashed vertical lines indicate the \(Z\) position of their bottoms and is the signature of the relative atomic \(Z\) height. \(^{23}\) The difference \(\Delta Z\) of \(\approx 20 - 30\) pm thus represents the intrinsic atomic corrugation within the cyclo- \(P_{5}\) pentagonal structure, \(^{5}\) which is comparable with atomic corrugations in graphene \(^{33}\) or planar molecules. \(^{34}\) Thus, this confirms the planarity of the cyclo- \(P_{5}\) structure, \(^{5}\) as reflected in the constant- height AFM image of Fig. 2e. + +<|ref|>sub_title<|/ref|><|det|>[[115, 732, 634, 756]]<|/det|> +## Charge distribution at the cyclo- \(P_{5}\) /Ag interface + +<|ref|>text<|/ref|><|det|>[[114, 794, 886, 852]]<|/det|> +The binding energy of \(P_{5}\) pentamers has been calculated by DFT to be strong on Ag(111), allowing the stabilization of the cyclo- \(P_{5}\) structure through a charge transfer. \(^{12}\) To provide + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 550]]<|/det|> +insights into the charge distribution at the \(cyclo - P_{5} / Ag\) interface, we performed force versus voltage spectroscopic measurements (see Methods). Experimentally, the frequency shift \(\Delta f\) as a function of the sample bias \(V_{\mathrm{s}}\) is measured at a constant tip height \(Z\) , providing in the \(\Delta f(V)\) curve a parabola due to the electric force acting between tip and sample. The voltage \(V^{*}\) at top of the parabola represents the local contact potential difference (LCPD) between tip and sample, which allows one to image charge distributions and work function changes with nanoscale resolution. \(^{24 - 27}\) Figure 3a shows a \(\Delta f(V)\) cross- section acquired across a \(P_{5}\) domain (see STM inset of Fig. 3a). Single \(\Delta f(V)\) point- spectra on top of the \(P_{5}\) network (orange) and on Ag(111) (black) are plotted in Fig. 3b, respectively. Dashed lines in both figures refers to the \(V^{*}\) position. The LCPD value systematically shifts towards positive values ( \(\Delta V^{*} \approx 0.22 \mathrm{V}\) ) for the pentamer assembly as compared to the pristine Ag substrate. This indicates the accumulation of charges at the \(P_{5}\) network as compared to the Ag substrate. + +<|ref|>text<|/ref|><|det|>[[112, 585, 886, 862]]<|/det|> +To better rationalize this, we calculated the charge redistribution at the \(cyclo - P_{5} / Ag\) interface (see Methods), whose top and side views of isosurfaces of electron accumulation (blue, \(+13 \times 10^{- 3} \mathrm{e} / \mathrm{\AA}^{3}\) ) and depletion (red, \(- 13 \times 10^{- 3} \mathrm{e} / \mathrm{\AA}^{2}\) ) are displayed in Fig. 3c. An electron transfer from the Ag(111) substrate to the P atoms of the pentamers (is observed as a charge accumulation located at the \(cyclo - P_{5}\) ring (red). In the \(P_{5} / Ag\) gap (marked by white and black dashed lines in the side view of Fig. 3c), charge accumulation/depletion layers emerges below each \(cyclo - P_{5}\) structure, which supports the formation of an hybridized state. \(^{30,31}\) We emphasize that such an interface state is not restricted to 2D Xenes on metals + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 586, 110]]<|/det|> +since it is well- established in organic/metal systems. \(^{35}\) + +<|ref|>text<|/ref|><|det|>[[112, 144, 888, 760]]<|/det|> +Between cyclo- \(P_{5}\) rings, we note the absence of in- plane charge redistribution. Considering that the last Ag layer is depleted (red) while each cyclo- \(P_{5}\) has an excess of charges (blue), the \(P_{5}\) assembly can be approximated to a lattice of surface dipole moments of \(D = 1.42\) Debye pointing towards the substrate (see arrow in Fig. 3c). This observation is consistent with an increase of the LWF at the \(P_{5} / \mathrm{Ag}\) interface induced by an inwards dipole moment as schematized in Fig. 3d, which is in agreement with the increase of the LCPD in force spectroscopy. It is important to mention here that the LCPD value has a strong distance- dependence on metal substrate, which is good indicate of the local work function changes at the atomic scale, but prevents quantitative determination. \(^{36,37}\) Indeed, the \(\Delta V^{*}\) cannot directly account for the difference of work function \(\Delta \phi = \phi_{P_{5} / A_{S}} - \phi_{A_{S}}\) shown in Fig. 3d, due to averaging effects of the electrostatic interactions between tip and sample. Last, the presence of an interfacial state can alter the amount of charge transfer away from an integer number compared to those with weaker interactions or adsorbed on insulating layers. Indeed, the Bader charge analysis show an accumulation of electrons on P atoms (- 0.115 e) and an electron depletion (+0.057 e) of the depleted Ag layer. Thus, we conclude that the cyclo- \(P_{5}\) does not have a pure anionic character for the \(P_{5}\) molecule when adsorbed on Ag(111) (i.e. cyclo- \(P_{5}^{- }\) ), as expected by theory for its gas- phase counterpart. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 87, 746, 111]]<|/det|> +## Interface state and work function of the cyclo- \(P_{5}\) assembly + +<|ref|>text<|/ref|><|det|>[[112, 148, 886, 538]]<|/det|> +To shed more lights into the electronic properties at the \(P_{5} / \mathrm{Ag}\) interface, we next performed differential conductance measurements ( \(\mathrm{d}I / \mathrm{d}V\) ) across one \(P_{5}\) domain (see Methods). Figure 4a shows the typical \(\mathrm{d}I / \mathrm{d}V\) point- spectra spectra of the network (orange) as compared to Ag(111) (black). We assign the gap of the \(P_{5}\) assembly to about 0.9- 1.0 eV (dashed lines) similar to Ref. \(^{11}\) The spectra also shows a strong resonance at 2.5 V, which we attribute to tunneling into the interface state (IS), respectively. \(\mathrm{d}I / \mathrm{d}V\) maps (Fig. 4b) further reveal the density of states at the valence band at \(V_{\mathrm{s}} = - 0.5 \mathrm{V}\) . This atomic feature evolves to a stripe pattern at \(V_{\mathrm{s}} = +2.5 \mathrm{V}\) (Fig. 4c), revealing the spatial modulation of the IS state (Fig. 4c) similar to the superstructure shown in STM topographic image of Fig. 2a. This state, which derived from the occupied Shockley state of the clean Ag(111) surface, is upshifted by more than 2 eV and becomes unoccupied by the presence of the \(P_{5}\) assembly. \(^{35}\) + +<|ref|>text<|/ref|><|det|>[[112, 572, 886, 813]]<|/det|> +To quantify the local change of work function (LWF), we acquired field- effect resonant tunneling (FERT) spectra in order to probe IPS between the cyclo- \(P_{5}\) assembly and silver. \(^{28 - 31}\) Experimentally, FERT spectra (also called \(\mathrm{d}Z / \mathrm{d}V\) spectroscopy) are obtained by sweeping the sample voltage \(V_{\mathrm{s}}\) while measuring \(\mathrm{d}I / \mathrm{d}V\) at constant- current by adjusting the tip height \(Z\) using the STM feedback loop (see Methods). From a quasi- classical approximation (Fig. 4d), tunneling resonances spectrum occurs when the Fermi level of the tip aligns with the Stark- shifted IPS states and follow the equation : + +<|ref|>equation<|/ref|><|det|>[[398, 832, 882, 875]]<|/det|> +\[e V_{\mathrm{n}} = \phi + \left(\frac{3n\pi\hbar eE}{\sqrt{2m}}\right) \quad (1)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 146]]<|/det|> +where \(V_{\mathrm{n}}\) is the sample voltage for the \(\mathrm{n}^{th}\) IPS, \(\phi\) is the work function of the sample, \(m\) is the free electron mass and \(E\) is the electric field. + +<|ref|>text<|/ref|><|det|>[[112, 219, 886, 425]]<|/det|> +Figure 4d shows the series of IPS states obtained above the cyclo- \(P_{5}\) self- assembly as compared to Ag(111), respectively. The resonance at \(V_{\mathrm{s}} = 2.2 \mathrm{eV}\) of the orange spectrum, which is absent for the Ag one (black), corresponds to the IS state. The peaks noted \(n = 1\) to 7 of the black spectra are the IPS states of the pristine Ag substrate. On the \(P_{5}\) assembly, IPS states are clearly shifted to higher voltage when increasing the electric field (i.e. \(V_{\mathrm{s}}\) ), which is the signature of the change of LWF. \(^{30,31}\) + +<|ref|>text<|/ref|><|det|>[[112, 460, 886, 813]]<|/det|> +A quantitative estimation of the LWF can be obtained from Eq. 1. In Fig. 4e, we plot the voltage position \(V_{\mathrm{n}}\) of the IPS states as a function of \(n^{2 / 3}\) for both the cyclo- \(P_{5}\) network (orange squares) and the Ag(111) substrate (black triangles). By fitting the linear progression of each datasets, we extract the LWF value corresponding to the \(y\) - intercepts to \(\phi_{\mathrm{Ag}} = 4.49 \mathrm{eV}\) and \(\phi_{\mathrm{P}_{5}} = 4.95 \mathrm{eV}\) . Considering that our experimental estimate of \(\phi_{\mathrm{Ag}}\) is in excellent agreement with that obtained by ultraviolet photoelectron spectroscopy (UPS), \(^{38}\) we confirm the strong increase of LWF of \(\Delta \phi = 0.46 \mathrm{eV}\) induced by the cyclo- \(P_{5}\) assembly adsorbed on Ag(111). Altogether, the observation of an IS and the shift of the IPS resonances in tunneling spectroscopy point to a charge transfer from the Ag substrate to the cyclo- \(P_{5}\) network and the creation of a strong inwards electric dipole at the interface. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 88, 353, 110]]<|/det|> +## Summary and outlook + +<|ref|>text<|/ref|><|det|>[[112, 144, 886, 757]]<|/det|> +In summary, we synthesized phosphorus chains and \(cyclo - P_{5}\) pentamers by depositing phosphorus atoms on atomically flat Ag(111) in ultra- high vacuum. Using low- temperature AFM with CO- terminated tips, armchair \(P\) chains and the planar \(cyclo - P_{5}\) rings are resolved with atomic precision. Flat- lying \(cyclo - P_{5}\) pentamers self- assemble into an extended hexagonal assembly in registry with the Ag substrate. DFT calculations support a substantial charge transfer from the Ag substrate to \(P_{5}\) pentamers, which results in a complex charge redistribution at the \(P_{5} / \mathrm{Ag}\) interface and the emergence of an interface state. Using force spectroscopic measurements, the inward surface dipole moment induced by this charge transfer is confirmed as an increase of the LCPD value at the \(P_{5}\) assembly in comparison to the pristine Ag. This corresponds to an increase of LWF at the \(P_{5}\) network as compared to the bare metal substrate. We corroborated these measurement with FERT spectroscopy allowing us to quantify the LWF increase of \(0.46\mathrm{eV}\) at the \(P_{5} / \mathrm{Ag}\) interface. By exploring the fundamental characteristics of the prototypical \(cyclo - P_{5} / \mathrm{metal}\) interface, our results not only underline the importance of scanning probe microscopy (applicable to other emerging 2D materials and related quantum materials) to study structural and electronic properties at the atomic scale, but also provides new insights for improved performances of phosphorus- based devices. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 88, 212, 109]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 149, 301, 170]]<|/det|> +## Sample preparation + +<|ref|>text<|/ref|><|det|>[[113, 185, 886, 390]]<|/det|> +The Ag(111) substrate purchased from Mateck GmbH was sputtered by \(\mathrm{Ar^{+}}\) ions and annealed at \(500^{\circ}\mathrm{C}\) to eliminate any surface contaminations. Phosphorus atoms were sublimed by heating up a black phosphorus crystal contained in a Knudsen cell in ultra high vacuum (UHV). The P flux was estimated using a quartz microbalance. To obtain the phosphorus chains and \(P_{5}\) domains, we annealed the Ag(111) substrate during deposition at temperatures described in Ref. 9 + +<|ref|>sub_title<|/ref|><|det|>[[115, 426, 288, 448]]<|/det|> +## STM experiments + +<|ref|>text<|/ref|><|det|>[[112, 462, 886, 742]]<|/det|> +The STM experiments were conducted at a temperature of \(4.8\mathrm{K}\) using an Omicron GmbH low- temperature STM/AFM system operated with Nanonis RC5 electronics. Differential conductance spectroscopy \(\mathrm{d}I / \mathrm{d}V(\mathrm{V})\) spectra were acquired with the lock- in amplifier technique using a modulation of \(610\mathrm{Hz}\) and a modulation amplitude of \(10\mathrm{meV}\) . All voltages refer to the sample bias \(V_{\mathrm{s}}\) with respect to the tip. For field- emission resonance tunneling spectroscopy (FERT), the lock- in amplifier generates a \(15\mathrm{- }30\mathrm{mV}\) (RMS) bias modulation at \(650\mathrm{Hz}\) . The FERT spectra is obtained by recording the differential conductance data while the sample bias is swept with a closed feedback loop (setpoints: \(I_{\mathrm{t}} = 1\mathrm{pA}\) , \(V_{\mathrm{s}} = 500\mathrm{mV}\) ). + +<|ref|>sub_title<|/ref|><|det|>[[115, 777, 288, 799]]<|/det|> +## AFM experiments + +<|ref|>text<|/ref|><|det|>[[113, 814, 886, 872]]<|/det|> +AFM measurements were performed with commercially available tuning- fork sensors in the qPlus configuration39 equipped with a tungsten tip ( \(f_{0} = 26\mathrm{kHz}\) , \(Q = 10000\) to \(25\mathrm{000}\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 479]]<|/det|> +nominal spring constant \(k = 1800\) , N.m \(^{- 1}\) , oscillation amplitude A \(\approx 50\) pm. Constant- height AFM images were obtained using tips terminated with a single carbon monoxide (CO) in the non- contact mode (frequency- modulated AFM- FMAFM) at zero voltage. \(^{19,40}\) CO molecules were adsorbed on the sample maintained at low temperature below 20 K. Before its functionalization, the apex was sharpened by gentle indentations into the silver surface. A single CO molecule was carefully attached to the tip following the procedure of reference. \(^{41}\) Simulations of the AFM images based on the DFT coordinates were carried out using the probe- particle model. \(^{42}\) Site- dependent \(\Delta f(Z)\) spectroscopic measurements to determine the atomic buckling of phosphorus pentamers were obtained with CO- terminated tips. The \(\Delta f(V)\) cross- section of \(1 \times 85\) pixels \(^{2}\) was acquired with Ag- coated metallic tips (tunneling setpoints: \(I_{\mathrm{t}} = 1\) pA, \(V_{\mathrm{s}} = 800\) mV, \(Z_{\mathrm{offset}} = +80\) pm). + +<|ref|>sub_title<|/ref|><|det|>[[115, 512, 282, 533]]<|/det|> +## DFT calculations + +<|ref|>text<|/ref|><|det|>[[112, 549, 886, 864]]<|/det|> +All density functional theory calculations were carried out in the Vienna ab initio simulation package (VASP) \(^{43}\) with projector augmented wave (PAW) \(^{44,45}\) method. The generalized gradient approximation (GGA) in the framework of Perdew- Burke- Ernzerhof (PBE) \(^{46}\) was chosen with the plane- wave cutoff energy set at 400 eV for all calculations. The DFT- D3 \(^{47}\) method of Grimme was employed to describe the van der Waals (vdW) interactions. The geometries of the structures were relaxed until the force on each atom was less than 0.02 eV Å \(^{- 1}\) , and the energy convergence criterion of \(1 \times 10^{- 4}\) eV was met. The Brillouin zone was sampled using Gamma k- mesh with a separation criterion of 0.03. Metal slabs with 3 atomic layers was adopted as the substrate and the bottom layer was fixed to simulate + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 146]]<|/det|> +the bulk. The vacuum spacing between neighboring images was set at least 15 Åalong the non- periodic directions to avoid a periodic interaction. + +<|ref|>sub_title<|/ref|><|det|>[[114, 199, 297, 221]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[113, 261, 886, 355]]<|/det|> +The data that supports the findings of this study are available within the paper or its Supplementary Information. All STM/AFM images are raw data. The raw data of spectroscopic measurements are available from the repository ZENODO (link will be updated). + +<|ref|>sub_title<|/ref|><|det|>[[114, 408, 234, 429]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[120, 468, 886, 528]]<|/det|> +1. Liu, H., Du, Y., Deng, Y. & Ye, P. D. Semiconducting black phosphorus: synthesis, transport properties and electronic applications. Chem. Soc. Rev. 44, 2732-2743 (2015). + +<|ref|>text<|/ref|><|det|>[[121, 553, 884, 612]]<|/det|> +2. Carvalho, A. et al. Phosphorene: from theory to applications. Nat. Rev. Mater. 1, 1-16 (2016). + +<|ref|>text<|/ref|><|det|>[[121, 639, 884, 698]]<|/det|> +3. Batmunkh, M., Bat-Erdene, M. & Shapter, J. G. Phosphorene and phosphorene-based materials - prospects for future applications. Adv. Mater. 28, 8586-8617 (2016). + +<|ref|>text<|/ref|><|det|>[[121, 724, 886, 783]]<|/det|> +4. Jones, R. O. & Hohl, D. Structure of phosphorus clusters using simulated annealing - P2 to P8. J. Chem. 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Hydroxyl-induced partial charge states of single porphyrins on titania rutile. J. Phys. Chem. C 121, 3607–3614 (2017). + +<|ref|>text<|/ref|><|det|>[[112, 515, 886, 575]]<|/det|> +28. Borca, B. et al. Potential energy landscape for hot electrons in periodically nanostructured graphene. Phys. Rev. Lett. 105, 036804 (2010). + +<|ref|>text<|/ref|><|det|>[[112, 601, 852, 623]]<|/det|> +29. Borca, B. et al. Image potential states of germanene. 2D Materials 7, 035021 (2020). + +<|ref|>text<|/ref|><|det|>[[112, 649, 886, 744]]<|/det|> +30. Liu, X., Wang, L., Yakobson, B. I. & Hersam, M. C. Nanoscale probing of image-potential states and electron transfer doping in borophene polymorphs. Nano Lett. 21, 1169–1174 (2021). + +<|ref|>text<|/ref|><|det|>[[112, 772, 884, 830]]<|/det|> +31. Liu, X. et al. Borophene synthesis beyond the single-atomic-layer limit. Nat. Mater. 21, 35–40 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 884, 147]]<|/det|> +32. Giovanelli, L. et al. 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ACS Nano 5, 6349–6354 (2011). + +<|ref|>text<|/ref|><|det|>[[113, 210, 884, 269]]<|/det|> +41. Bartels, L. et al. Dynamics of electron-induced manipulation of individual CO molecules on Cu (111). Phys. Rev. Lett. 80, 2004 (1998). + +<|ref|>text<|/ref|><|det|>[[113, 296, 884, 355]]<|/det|> +42. Hapala, P. et al. Mechanism of high-resolution STM/AFM imaging with functionalized tips. Phys. Rev. B 90, 085421 (2014). + +<|ref|>text<|/ref|><|det|>[[113, 381, 884, 441]]<|/det|> +43. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996). + +<|ref|>text<|/ref|><|det|>[[113, 467, 880, 489]]<|/det|> +44. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994). + +<|ref|>text<|/ref|><|det|>[[113, 515, 884, 575]]<|/det|> +45. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. 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Phys. 132, 154104 (2010). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 88, 320, 111]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[112, 148, 888, 536]]<|/det|> +E.M. and R.P. acknowledge funding from the Swiss Nanoscience Institute (SNI), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ULTRADISS grant agreement No 834402 and supports as a part of NCCR SPIN, a National Centre of Competence (or Excellence) in Research, funded by the SNF (grant number 51NF40- 180604). E.M., T.G. and S.- X.L. acknowledge the Sinergia Project funded by the SNF (CRSII5_213533). E.M., T.G. and R.P. acknowledge the SNF grant (200020_188445). T.G. acknowledges the FET- Open program (Q- AFM grant agreement No 828966) of the European Commission. J.- C.L. acknowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement number 847471. C.L. and E.M. acknowledges the Georg H. Endress Foundation. + +<|ref|>sub_title<|/ref|><|det|>[[116, 588, 330, 610]]<|/det|> +## Author information + +<|ref|>text<|/ref|><|det|>[[115, 648, 344, 669]]<|/det|> +Authors and Affiliations + +<|ref|>text<|/ref|><|det|>[[113, 685, 886, 808]]<|/det|> +Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland + +<|ref|>text<|/ref|><|det|>[[113, 819, 886, 841]]<|/det|> +State Key Laboratory of Precision Spectroscopy School of Physics and Electronic Science, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 808, 147]]<|/det|> +East China Normal University, 500 Dongchuan Road, Shanghai 200241, ChinaYulin Yin & Qinghong Yuan, + +<|ref|>text<|/ref|><|det|>[[113, 183, 888, 280]]<|/det|> +Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China + +<|ref|>text<|/ref|><|det|>[[114, 295, 209, 315]]<|/det|> +Feng Ding + +<|ref|>sub_title<|/ref|><|det|>[[115, 352, 247, 372]]<|/det|> +## Contributions + +<|ref|>text<|/ref|><|det|>[[113, 388, 888, 521]]<|/det|> +R.P. and E.M. conceived the experiments. O.C. and R.P. performed the STM/AFM measurements. Y.Y. F.D. and Q.Y. performed DFT calculations. O.C. and R.P. analyzed the data. R.P. wrote the manuscript. All authors discussed on the results and revised the manuscript. + +<|ref|>text<|/ref|><|det|>[[115, 558, 333, 578]]<|/det|> +Corresponding authors + +<|ref|>text<|/ref|><|det|>[[115, 595, 723, 615]]<|/det|> +Correspondence to ernst.meyer@unibas.ch or remy.pawlak@unibas.ch + +<|ref|>sub_title<|/ref|><|det|>[[115, 668, 319, 689]]<|/det|> +## Ethics declarations + +<|ref|>text<|/ref|><|det|>[[115, 729, 310, 749]]<|/det|> +Competing interests + +<|ref|>text<|/ref|><|det|>[[115, 767, 493, 786]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 195, 884, 604]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 612, 884, 759]]<|/det|> +
Fig. 1: Atomic structure of phosphorus chains on Ag(111). a, STM topographic image after sublimation of phosphorus atoms on Ag(111) leading to P chains (1) and cyclo-\(P_{5}\) domains (2), \((I_{\mathrm{T}} = 1 \mathrm{pA}, V = 0.15 \mathrm{mV})\) . The inset shows a STM image of the single, double and triple chains, respectively. b-d, Series of AFM images with CO-terminated tip revealing the armchair structure of single, double and triple P chains, \((f_{0} = 26 \mathrm{kHz}, A = 50 \mathrm{pm})\) . Scale bars are \(1 \mathrm{nm}\) . e, Atomic configurations of the triple armchair chains obtained by DFT calculations. Phosphorus and silver atoms are shown in orang and gray, respectively. f, Corresponding AFM simulation using the DFT coordinates.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[111, 216, 884, 528]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 536, 884, 739]]<|/det|> +
Fig. 2: Atomic structure of the self-assembled cyclo-\(P_{5}\) molecules. a, STM image of the self-assembled pentamers on Ag(111), \((I_{\mathrm{T}} = 1 \mathrm{pA}\) , \(V = 0.15 \mathrm{mV}\) ). Islands systematically shows a superlattice of bright lines rotated by \(19^{\circ}\) with respect to the \([110]\) directions of Ag(111). b, Close-up STM topography showing the \(P_{5}\) molecules depicted by dashed pentagons. c, Corresponding AFM image revealing the \(P_{5}\) chemical structure, \((f_{0} = 26 \mathrm{kHz}\) , \(A = 50 \mathrm{pm}\) ). d, Atomic configurations of the pentamer assembly on Ag(111) obtained by DFT. Phosphorus and silver atoms are shown in orange and gray, respectively. e, Corresponding AFM simulation using the DFT coordinates. f, Site-dependent \(\Delta f(Z)\) spectroscopic curves acquired at one P atoms of a \(P_{5}\) molecule (orange), between two \(P_{5}\) molecules (brown) and on Ag(111) (black), respectively. The local minima of the \(\Delta f(Z)\) curves indicate the relative height of the phosphorus atoms.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[243, 179, 750, 585]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 592, 884, 776]]<|/det|> +
Fig. 3: Charge redistribution at the cyclo-\(P_{5}\) /Ag(111) interface. a, Frequency shift \(\Delta f\) as a function of sample bias voltage \(V_{\mathrm{s}}\) , measured across a pentamer domain shown in the STM image (top), (parameters : \(f_{0} = 26 \mathrm{kHz}\) , \(A = 80 \mathrm{pm}\) ). b, Single \(\Delta f(V)\) curves at the pentamer assembly (orange) as compared to the Ag(111) (black). Dashed lines mark the top of the parabola allowing to extract a LCPD shift \(\Delta V^{*} = 0.22 \mathrm{V}\) . c, Top and side views of the charge redistribution between pentamers and Ag(111). Blue areas show electron accumulation, red areas electron depletion. The isosurface level of the plot is set to \(\pm 13 \times 10^{-3} \mathrm{e} / \mathrm{\AA}^{3}\) . d, Schematic illustration of the charge redistribution at the \(P_{5}\) /Ag(111) interface leading to an inward surface dipole (D) moment and a local work function change \((\phi_{\mathrm{P}_{5} / \mathrm{Ag}})\) . The cyclo-\(P_{5}\) layer is colored in orange. \(\Delta V^{*}\) refers to the LCPD change.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[110, 163, 884, 580]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 586, 884, 787]]<|/det|> +
Fig. 4: Tunneling spectroscopy of the \(P_{5} / \mathrm{Ag}\) interface. a, \(\mathrm{d}I / \mathrm{d}V\) point-spectra acquired above the \(P_{5}\) assembly (orange) and on Ag(111) (black), where precise locations are shown in the STM inset. (parameters: \(I_{\mathrm{t}} = 1 \mathrm{pA}\) , \(V_{\mathrm{s}} = 500 \mathrm{mV}\) , \(A_{\mathrm{mod}} = 10 \mathrm{mV}\) , \(f = 511 \mathrm{Hz}\) ). b, \(\mathrm{d}I / \mathrm{d}V\) maps at \(V_{\mathrm{s}} = -1.25\) and \(2.5 \mathrm{V}\) corresponding to the valence band energy and the IS interface state, respectively. c, STM topographic image of three \(P_{5}\) domains and the corresponding \(\mathrm{d}I / \mathrm{d}V\) maps of the IS modulation. d, Scheme of the band alignment and the formation of Stark-shifted IPS (orange lines). e, Field-effect resonance tunneling (FERT cross-section acquired across the \(P_{5}\) assembly along the dashed line in a, (Set-points: \(I_{\mathrm{t}} = 1 \mathrm{pA}\) , \(V_{\mathrm{s}} = 500 \mathrm{mV}\) , \(A_{\mathrm{mod}} = 35 \mathrm{mV}\) , \(f = 511 \mathrm{Hz}\) ). f, Single FERT spectra of the \(P_{5}\) assembly and the Ag(111) substrate, showing the series of \(\mathrm{n}^{th}\) IPS. g, Extracted IPS peak voltages as a function of \(n^{2 / 3}\) .
+ +<--- Page Split ---> diff --git a/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/images_list.json b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..de62b20785924c3c6db7747dbaed3a13aa2d7c49 --- /dev/null +++ b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Map of African vegetation zones after ref.49. Med: Mediterranean zone; MST: Mediterranean-Saharan transitional vegetation; AM: Afro-montane vegetation zone. The Nile River catchment is marked by the blue shading. The red star indicates the study site GeoB7702-3.", + "footnote": [], + "bbox": [ + [ + 270, + 110, + 707, + 411 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Environmental changes in the Nile-River delta region during the past 18 kyrs. (a) Ice-core \\(\\mathrm{CO_2}\\) -contents from EPICA Dome \\(\\mathrm{C^{50}}\\) given as indicator for atmospheric \\(\\mathrm{CO_2}\\) concentrations. (b) Atmospheric \\(\\Delta^{14}\\mathrm{C}\\) contents according to INTCAL20. (c) Reservoir age offsets between the \\(n\\) -alkanoic acids and the atmosphere at the time of deposition at site GeoB7702-3. \\(\\mathrm{t_{soil}}\\) deduced from the reservoir age offsets of \\(n\\) -alkanoic acids. (d) Sea surface temperature reconstruction for the eastern Mediterranean based on the \\(\\mathrm{TEX_{86}}\\) proxy from core GeoB7702-3. (e) Hydrogen isotopic composition of precipitation \\((\\delta \\mathrm{Dp})\\) calculated from the \\(\\delta \\mathrm{D}\\) of \\(n\\) -alkanoic acids from core GeoB7702-3 as proxy for rainfall amount. (f) Oxygen isotopic compositions of the planktic foraminifera species Globigerinoides ruber \\((\\delta^{18}\\mathrm{O}_{G.rubber})\\) in core MS27PT (Figure 1) indicating salinity changes in the eastern Mediterranean associated with freshwater runoff from the Nile River. (g) Aminopentol abundances in core GeoB7702-3 used as proxy for the extent of methane producing wetlands in the catchment. AU: arbitrary units; dw: dry weight of extracted sediment. Additional abundance profiles from the suite of aminobacteriohanopelyols are given in Extended Data Figure 3 (h) Concentrations of \\(n\\) -alkanoic acids \\((\\Sigma n - \\mathrm{C}_{26:0}, n - \\mathrm{C}_{28:0}, n - \\mathrm{C}_{30:0}, n - \\mathrm{C}_{32:0})\\) reporting on the land-ocean transport of terrigenous organic matter. (i) Global rate of sea-level change over the last 20 kyrs. The blue bars mark the timing of the African Humid Period (AHP) and Green Sahara and their optimum. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bolling/Allerod interstadial; YD: Younger Dryas stadial.", + "footnote": [], + "bbox": [ + [ + 115, + 78, + 816, + 682 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Power-law relationships between \\(\\tau_{\\mathrm{soll}}\\) and (a) temperature and (b) hydrogen isotopic composition of precipitation (8Dp). Temperature estimates are based upon the TEX86-proxy at site GeoB7702-3 and are adopted from ref.13. 8Dp is calculated from the hydrogen isotopic composition of \\(n\\) -alkanoic acids ( \\(n\\) -C26:0 and \\(n\\) -C28:0 homologues) from core GeoB7702-3. The p-values for the regressions are \\(< 0.05\\) .8Dp is deduced from the hydrogen isotopic composition of \\(n\\) -alkanoic acids and \\(n\\) -alkanes in core GeoB7702-3. Unfortunately, mean annual air temperature estimates covering the past 18 kyrs are not available for the Nile-River catchment. Therefore, we use the TEX86-based temperature record from GeoB7702-3 interpreted to reflect sea surface temperature (SST) in the eastern Mediterranean. We assume that SST and surface air temperatures in the Nile delta region probably developed similarly due to heat exchange between the sea surface and the overlying air.", + "footnote": [], + "bbox": [ + [ + 115, + 81, + 880, + 344 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Extended Data Figure 1: Abundances of Amino-Bacteriophanepolyols in core GeoB7702-3 normalized to the dry weight of extracted sediment (dw). AU: Arbitrary units. AHP: African Humid Period. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.", + "footnote": [], + "bbox": [ + [ + 118, + 135, + 880, + 515 + ] + ], + "page_idx": 20 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Extended Data Figure 2: Reservoir age offsets of leaf-wax lipids with the atmosphere at the time of deposition at site GeoB7702-3 (a) plotted along with temperature and precipitation reconstructions from the Nile catchment. (b): Temperature reconstruction for the eastern Mediterranean based on the TEX86-proxy in core GeoB7702-313. (c): hydrogen isotope compositions of precipitation (δDp) calculated from δD of the alkanoic acids (mean of \\(n-C_{26:0}\\) and \\(n-C_{28:0}\\) homologues; purple) and \\(n-C_{31}\\) alkane (orange) in core GeoB7702-322. The blue bars mark the timing of the African Humid Period (AHP), \"Green Sahara\" and their optimum17,44. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.", + "footnote": [], + "bbox": [ + [ + 117, + 95, + 833, + 480 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Extended Data Figure 3: Recalculation of results from the Lund Potsdam Jena Dynamic Global Vegetation Model (LPJ DGVM) over the last 21 kyrs as published in ref.41. These results are identical to those LPJ results that have been forced by the Hadley center climate model as discussed in ref.41. Relative changes between the LGM and pre-industrial conditions (PI, here: 1 kyr BP) are shown. a,b) \\(\\tau_{\\mathrm{soil}}\\) calculated either based on the carbon influx (net primary production (NPP)) or on the carbon efflux (Rh), where \\(\\mathrm{Rh}\\) is the heterotrophic respiration. Large positive anomalies (red) occur on shelf areas inundated during deglacial sea-level rise, while the areas with large negative anomalies (blue) were covered by large continental ice sheets during the LGM.; c,d) relative changes in NPP and \\(\\mathrm{Rh}\\) ; e) absolute changes in soil carbon content ( \\(\\mathrm{C_{soil}}\\) ).", + "footnote": [], + "bbox": [ + [ + 113, + 80, + 884, + 666 + ] + ], + "page_idx": 22 + } +] \ No newline at end of file diff --git a/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199.mmd b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a68e748de7a0a8355a2e6cb0216ab60e1c261768 --- /dev/null +++ b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199.mmd @@ -0,0 +1,367 @@ + +# Dominant control of temperature on (sub-)tropical soil carbon turnover + +Vera Dorothee Meyer + +vmeyer@marum- alumni.de + +MARUM - Center for Marine Environmental Sciences https://orcid.org/0000- 0002- 4958- 5367 + +Peter Köhler + +Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research https://orcid.org/0000- 0003- 0904- 8484 + +Nadine Smit + +MARUM - Center for Marine Environmental Sciences, now: Bruker Daltonics GmbH & Co. KG. + +Julius Lipp + +MARUM - Center for Marine Environmental Sciences + +Bingbing Wei + +Alfred Wegener Institut, Helmholtz Zentrum für Polar- und Meeresforschung + +Gesine Mollenhauer + +Alfred Wegener Institute https://orcid.org/0000- 0001- 5138- 564X + +Enno SchefuB + +MARUM - Center for Marine Environmental Sciences, University of Bremen, Germany + +https://orcid.org/0000- 0002- 5960- 930X + +## Article + +Keywords: + +Posted Date: August 19th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4726729/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on May 15th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59013-9. + +<--- Page Split ---> + +## Dominant control of temperature on (sub-)tropical soil carbon turnover + +Vera D. Meyer \(^{1*}\) , Peter Köhler \(^{2}\) , Nadine T. Smit \(^{1,3}\) , Julius S. Lipp \(^{1}\) , Bingbing Wei \(^{2}\) , Gesine Mollenhauer \(^{1,2}\) and Enno Schefuß \(^{1*}\) + +\(^{1}\) : MARUM – Center for Marine Environmental Sciences, University of Bremen, Germany + +\(^{2}\) : Alfred Wegener Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany + +\(^{3}\) : now at: Bruker Daltonics GmbH & Co. KG., Bremen, Germany + +\(^{*}\) corresponding authors: vmeyer@marum.de; eschefuss@marum.de + +Carbon storage in soils is important in regulating atmospheric carbon dioxide ( \(\mathrm{CO_2}\) ). However, the sensitivity of the soil- carbon turnover time \((\tau_{\mathrm{soil}})\) to temperature and hydrology forcing is still not fully understood. Here, we use radiocarbon dating of plant- derived lipids in conjunction with reconstructions of temperature and rainfall from an eastern Mediterranean sediment core receiving terrigenous material from the Nile- River watershed to investigate \(\tau_{\mathrm{soil}}\) in subtropical and tropical areas during the last 18,000 years. We find that the \(\tau_{\mathrm{soil}}\) was reduced by an order of magnitude over the last deglaciation and infer that this reduction was caused from amplified soil respiration rates. Our data indicate that the deglacial warming was the major driver of these changes while the impact of hydroclimate was relatively small. We conclude that increased \(\mathrm{CO_2}\) efflux from soils into the atmosphere constituted a positive feedback to global warming. However, simulated glacial- to- interglacial changes in a dynamic global vegetation model underestimate our data- based reconstructions of soil- carbon turnover times suggesting that this climate feedback might be underestimated. + +Globally, soils store more than twice as much carbon as the atmosphere at present \(^{1,2}\) . The soil carbon cycle is sensitive to climate change and human activities \(^{1,3,4}\) . Therefore, future warming, shifts in precipitation patterns and land use might perturb the soil- carbon storage and subsequently result in positive feedbacks on global warming via \(\mathrm{CO_2}\) release into the atmosphere \(^{1,5}\) . Soil carbon storage is regulated by carbon influx (fixation through net primary production; NPP) and efflux. The latter is controlled by microbial respiration, soil erosion and fire emissions \(^{2,5}\) . These processes determine \(\tau_{\mathrm{soil}}\) defined as: + +\[\tau_{\mathrm{soil}} = \frac{C_{\mathrm{total}}}{f} \quad (\mathrm{Eq. 1}),\] + +where \(\mathrm{C_{total}}\) is the soil carbon- stock size and f either the carbon influx (NPP) or the efflux. Under steady state conditions influx and efflux are equal \(^{6}\) . Turnover times are critical components in carbon cycling for constraining the time scales of carbon exchange between different reservoirs. \(\tau_{\mathrm{soil}}\) depends on soil temperature \(^{3,4,7}\) and moisture content \(^{3,4}\) but also on chemical properties \(^{8,9,10}\) and soil fertility \(^{8,10}\) . Temperature effects on \(\tau_{\mathrm{soil}}\) are widely observed across the globe \(^{4}\) while hydroclimate may exert strong control in low latitudes where it may be even more important than temperature \(^{4,11,12}\) . However, the key controls on \(\tau_{\mathrm{soil}}\) are still debated \(^{3,9,11}\) . This forms a major open question in tropical and subtropical regions where combined effects of future warming and precipitation changes may be amplified or attenuated depending on whether warming will be accompanied by drier or wetter conditions \(^{11}\) . + +<--- Page Split ---> + +Here, we investigate how \(\tau_{\mathrm{soil}}\) changed in the Nile- River catchment during the last 18 kyrs when the global climate warmed and transitioned from the last Glacial (before 17.3 kyr BP) into the Holocene (after 11.7 kyr BP). With a length of \(6650\mathrm{km}\) the Nile River is the longest river in the world spanning \(35^{\circ}\) of latitude \((4^{\circ}\mathrm{S - 31^{\circ}N})\) in northeastern Africa. During the last deglaciation (8- 18 kyrs BP) the northern African climate warmed \(^{13,14}\) and humid conditions during the African Humid Period(AHP, 14.5- 5 kyr BP) \(^{15,16}\) allowed for plants and permanent water bodies to persist in the nowadays barren, hyperarid Sahara Desert \(^{17,18}\) . The different timing of changes in temperature \(^{13,14,19}\) and hydroclimate \(^{20,21,22}\) in northeastern Africa around the AHP allows for disentangling temperature and precipitation effects on \(\tau_{\mathrm{soil}}\) . We investigate the response of the soil carbon cycle to these climatic changes using compound- specific radiocarbon dating (CSRA) of the plant- wax biomarkers long chain \(n\) - alkanoic acids and long chain \(n\) - alkanes preserved in marine sediment core GeoB7702- 3, which was retrieved in the eastern Mediterranean from the continental margin off the Sinai Peninsula (Figure 1). Refractory plant- wax lipids deposited in marine sediments commonly are pre- aged due to transport processes and intermediate storage \(^{23,24}\) . Their pre- depositional ages are powerful recorders of changes in the terrestrial carbon cycle \(^{23,25,26,27,28}\) . + +## Environmental signals in the CSRA data + +To calculate the pre- depositional age of the leaf- wax biomarkers we use the "reservoir age offset" \(^{29}\) between the leaf- wax biomarkers and the atmosphere at the time of deposition (Table 1, Figure 2c). The reservoir age offsets of \(n\) - alkanoic acids and \(n\) - alkanes in core GeoB7702- 3 range between approximately 0 and \(8700^{14}\mathrm{C}\) yrs. Glacial offsets (7800- 8700 \(^{14}\mathrm{C}\) yrs at 18 kyr BP) are substantially higher than those during the Holocene (0- 3400 \(^{14}\mathrm{C}\) yrs; between \(\sim 2 - 11.5\) kyrs BP). A linear relationship between the \(^{14}\mathrm{C}\) ages of long chain \(n\) - alkanoic acids in marine sediments with \(\tau_{\mathrm{soil}}\) \(^{30}\) makes it possible to calculate \(\tau_{\mathrm{soil}}\) from CSRA data. However, three factors that may introduce biases to the reconstruction of \(\tau_{\mathrm{soil}}\) need to be considered beforehand. + +First, sea level rose by up to \(120\mathrm{m}\) over the deglaciation \(^{33}\) and coastal erosion during shelf flooding led to the deposition of pre- aged organic matter on continental margins \(^{26,34}\) . Such processes may mask hinterland signals in the \(^{14}\mathrm{C}\) - record of leaf- wax lipids in marine sediments. However, biases from coastal erosion during retrogradation of the Nile Delta are unlikely as the concentration profile of \(n\) - alkanoic acids in core GeoB7702- 3 differs from the global rate of sea- level change \(^{34}\) (Figure 2h,i) but resembles the oxygen isotopic composition of planktic foraminifera Globigerinoides ruber ( \(\delta^{18}\mathrm{O}_{G.rubcr}\) ) off the Nile River delta, a proxy for freshwater discharge \(^{35}\) (Figure 2f). Hence, the export of organic matter was primarily controlled by river runoff \(^{22}\) . + +Second, in addition to mineral soils peatlands need to be considered as source of pre- aged organic matter \(^{30}\) . Anaerobic conditions in wetlands hamper degradation of organic matter leading to its preservation in peat over millennia \(^{36}\) . During wetland contraction, erosion and fluvial export of this pre- aged organic matter \(^{28}\) could thus bias the calculations of mean \(\tau_{\mathrm{soil}}\) of mineral soils \(^{30}\) . This might be relevant to the Nile River catchment since wetlands occur along the basin today \(^{37}\) . To constrain wetland dynamics we analyzed a suite of amino- Bacteriohopanepolyols (amino- BHPs; Extended Data Figure 1) which are specific markers for methane oxidizing bacteria in wetlands \(^{38}\) and thus indicative of the relative extension and contraction of methane producing landcover \(^{28}\) . Low concentrations imply that between 18- 11 kyrs BP methane producing permanently flooded wetlands were barely present in the + +<--- Page Split ---> + +catchment (Figure 2g and Extended Data Figure 1) rendering it unlikely that the decrease in the reservoir age offset stemmed from wetland dynamics. A massive expansion of wetlands occurred between 11- 8 kyr BP, which probably occurred in response to maximal rainfall and river runoff during the AHP- optimum (Figure 2d,e,g). Contributions of pre- aged organic matter associated with wetland contraction at the end of the AHP were probably minor as reservoir age offsets remain constant when amino- BHP concentrations decline in our core (Figure 2c,g). + +Third, river dynamics including morphology and runoff are known controls on the ages of organic matter discharged into the ocean \(^{31,32}\) . Increased fluvial runoff may strengthen riverbank erosion and export of relatively old matter from deeper soil horizons potentially overprinting signals from \(\tau_{\mathrm{soil}}\) \(^{32}\) . Although the Nile- River runoff increased in response to intensified rainfall during the AHP \(^{21,35}\) considerable biases from deep- soil erosion are unlikely given the decrease in reservoir age offsets of \(n\) - alkanoic acids and \(n\) - alkanes during the AHP (Table 1, Extended Data Figure 2). However, intensified Nile River runoff \(^{35}\) may have increased the transport velocity hampering aging of organic matter during land- ocean transit \(^{31}\) . This speed- up would have led to smaller ages of plant waxes in core GeoB7702- 3. Although signals of the transport efficiency in our data cannot be fully ruled out we consider a predominant control of river dynamics and morphology on ages of discharged organic matter unlikely for the following reasons. River runoff decreased after 7 kyrs BP (Figure 2f) while the ages of leaf- wax biomarkers remained relatively constant (Table 1; Figure 2c). The second argument is the similarity between the ages of \(n\) - alkanoic acids and \(n\) - alkanes (Table 1 and Extended Data Figure 2). \(n\) - Alkanoic acids reflect a local signals from the Nile delta region while the \(n\) - alkanes provide a more catchment- integrating signal \(^{22}\) . The extensive Nile catchment is characterized by multiple fluvial environments that differ in geomorphology, flow regime and sedimentary processes \(^{39,40}\) . If such morphologic characteristics exerted substantial control on the ages of organic matter in the fluvial load \(^{31}\) , \(n\) - alkanoic acids and \(n\) - alkanes would show different ages and trends which is not the case (Extended Data Figure 2). + +## \(\tau_{\mathrm{soil}}\) during the past 18 kyrs + +Excluding these potential biases, we conclude that reservoir age offsets of the leaf- wax biomarkers in core GeoB7702- 3 can be used to calculate mean \(\tau_{\mathrm{soil}}\) (see Methods). For \(n\) - alkanes the relationship to mean \(\tau_{\mathrm{soil}}\) is not calibrated \(^{30}\) which is why we focus on the \(n\) - alkanoic acids. Despite the local origin of the \(n\) - alkanoic acids \(^{22}\) catchment- wide inferences on changes in \(\tau_{\mathrm{soil}}\) are justified given the strong similarity with the reservoir age offsets of the \(n\) - alkanes (Extended Data Figure 2). + +During the last 10 kyrs, \(\tau_{\mathrm{soil}}\) was 9- 22 yrs (average 16 yrs). During the late glacial \(\tau_{\mathrm{soil}}\) was 218 yrs which implies that \(\tau_{\mathrm{soil}}\) reduced by an order of magnitude across the deglaciation (Table 1, Figure 2c). According to Eq.1, changes in \(\tau_{\mathrm{soil}}\) may result from variations in the carbon stock size or the efflux. Globally, the terrestrial carbon stocks rose during the deglaciation but according to models they remained rather constant in tropical and subtropical regions \(^{41,42}\) . As for the Nile- River catchment, savannah expanded into the formerly barren Sahara during the AHP (14- 5 kyrs BP) \(^{18,43}\) which would have temporarily increased the total carbon stock in the catchment at these times. Our detected decrease in \(\tau_{\mathrm{soil}}\) together with a likely larger amount of soil carbon during the AHP thus requires a large increase in the carbon efflux from soils (Eq. 1). + +<--- Page Split ---> + +It is well constrained that microbial respiration, a key component determining the carbon efflux12, accelerates in response to warming and increased soil moisture3,9,11. Both, temperature13,14,19 and precipitation20,21,22 increased in the Nile-River catchment during the deglaciation (Figure 2d,e). To investigate the relationship of \(\tau_{\mathrm{soil}}\) to temperature and rainfall we fit the natural logarithm of \(\tau_{\mathrm{soil}}\) to temperature estimates from the eastern Mediterranean and to the hydrogen isotopic composition of paleo precipitation ( \(\delta \mathrm{Dp}\) ), a common proxy for the amount of rainfall22,44(Figure 3). \(\tau_{\mathrm{soil}}\) is strongly correlated with temperature ( \(\mathrm{R}^2 = 0.82\) ; Figure 3). The correlation with \(\delta \mathrm{Dp}\) ( \(\mathrm{R}^2 = 0.59\) ; Figure 3b) is clearly weaker indicating that temperature was a substantial control on microbial respiration rates during the past 18 kyrs (Figure 3a) while precipitation effects were relatively small. + +## Implications for the global carbon cycle + +The high glacial \(\tau_{\mathrm{soil}}\) indicates that the \(\mathrm{CO_2}\) efflux from northeastern African soils into the atmosphere was much smaller than during the Holocene because of lower respiration rates. According to ref.30, 14C- ages of \(n\) - alkanoic acids have constant offsets not only with mean \(\tau_{\mathrm{soil}}\) but also with soil mean carbon ages (see Methods). Our data show that during the last Glacial soils were much older than during the Holocene (14 000 yrs at 18 kyrs BP vs about 1000 yrs during the Holocene; Table 1). A relatively old soil- carbon pool together with high \(\tau_{\mathrm{soil}}\) agrees with previous estimates of a lower glacial global NPP45 which is congruent with a lower carbon efflux from soils assuming equilibrium conditions (Eq.1). The rejuvenation of soil organic matter accompanying the reduced \(\tau_{\mathrm{soil}}\) implies a massive loss of pre- aged organic carbon from the soils during the deglaciation once the climate warmed. Under present- day conditions, respiration constitutes the majority of the total efflux (>90%) and contributions of lateral fluxes are minor12. If this relation remained similar in the past, the decrease in our estimated \(\tau_{\mathrm{soil}}\) almost entirely reflects increased efflux of aged \(\mathrm{CO_2}\) into the atmosphere. Accordingly, the reduction of \(\tau_{\mathrm{soil}}\) by an order of magnitude implies an increase in soil- to- atmosphere \(\mathrm{CO_2}\) flux of a similar size (Eq.1). This forms a positive feedback to global warming. If widespread across the tropics and sub- tropics this process may have provided relevant contributions to rising atmospheric \(\mathrm{CO_2}\)46 and declining atmospheric radiocarbon contents47 across the deglaciation (Figure 2a,b). Soil- carbon turnover also accelerated in the Ganga- Brahmaputra River catchment as inferred from reservoir age offsets of long chain \(n\) - alkanoic acids from the Bengal Fan27. Calculating \(\tau_{\mathrm{soil}}\) from these data reveals that the range of values and the magnitude of deglacial changes ( \(\tau_{\mathrm{soil}}\) falls from \(\sim 200\) to \(\sim 20\) yrs; Extended Data Table 2) are very similar to the results from the Nile River catchment. Thus, it is very likely that changes in \(\tau_{\mathrm{soil}}\) in that order of magnitude were common across the (sub- )tropics. Interestingly, the radiocarbon data from the Bengal Fan are strongly correlated with rainfall indicating that variability of the Indian summer monsoon played a substantial role in this positive soil- carbon- climate feedback27. However, the results from the Nile River catchment do not confirm the involvement of hydroclimate suggesting a direct response of soil respiration rates to warming. + +Dynamic global vegetation models (DGVM) allow for investigating the effect of the decreasing \(\tau_{\mathrm{soil}}\) on the global carbon cycle and \(\mathrm{CO_{2atm}}\) . We revisit the analysis performed using the Lund Potsdam Jena DGVM (LPJ DGVM)41 and focus on the differences between the Last Glacial Maximum (LGM; 21 kyrs BP) and pre- industrial conditions (PI; 1 kyr BP). Details of the simulation are given in the methods and ref.41. The model suggests relatively constant carbon stocks in the tropics and sub- tropics like other modeling studies42. As for the change in + +<--- Page Split ---> + +\(\tau_{\mathrm{soil}}\) , we find pronounced discrepancies between our data- based reconstruction (decrease by 200 yrs, Table 1) and the simulated values (Extended Data Figure 4a, b). The model indicates marginal change of less than 50 yrs in the wider (sub- )tropics. Substantial changes of similar magnitude as in our reconstruction are simulated only in the northern high latitudes (Extended Data Figure 4a, b). According to Eq.1, the underestimation of changes in (sub- )tropical \(\tau_{\mathrm{soil}}\) translates into underestimated, simulated changes in microbial respiration rates, respectively \(\mathrm{CO_2}\) efflux. The discrepancies between our data- based estimates of \(\tau_{\mathrm{soil}}\) and the LPJ DGVM simulations suggest that the climate feedback from amplified (sub- ) tropical soil respiration due to warming is underestimated in models. In most recent CMIP6 models the global mean \(\tau_{\mathrm{soil}}\) decreases by up to 20 years until the year 2100 for future emission scenarios \(^{48}\) . However, a spatially resolved analysis of \(\tau_{\mathrm{soil}}\) is missing preventing an evaluation if the soil carbon cycle in the (sub- )tropics has substantially improved in the meantime. + +Providing evidence for a direct response of \(\tau_{\mathrm{soil}}\) to warming in the (sub- ) tropics during the last deglaciation, our study suggests that also the recent global warming will be associated with dominant temperature effects on \(\tau_{\mathrm{soil}}\) . Positive feedbacks from enhanced soil \(\mathrm{CO_2}\) efflux from soils into the atmosphere may thus be expected upon further warming. + +## References + +1. Lal, R. Soil carbon sequestration impact on global climate change and food security. Science 304, 1623-1627 (2004). +2. Crisp, D. et al. How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle? Rev. Geophys. 60, 1-64 (2022). +3. Wang, S. et al. Soil and vegetation carbon turnover times from tropical to boreal forests. Funct. Ecol. 32, 71-82 (2018). +4. 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Woodward, J. C., Macklin, M. G., Krom, M. D. & Williams, M. A. J. The Nile: Evolution, Quaternary River Environments and Material Fluxes. In Large Rivers: Geomorphology and Management, edited by A. Gupta, pp. 261–292, John Wiley & Sons. (2008). +40. Macklin, M. G. et al. A new model of river dynamics, hydroclimatic change and human settlement in the Nile Valley derived from meta-analysis of the Holocene fluvial archive. Quat. Sci. Rev. 130, 109–123 (2015). +41. Köhler, P., Joos, F., Gerber, S. & Knutti, R. Simulated changes in vegetation distribution, land carbon storage, and atmospheric \(\mathrm{CO_2}\) in response to a collapse of the North Atlantic thermohaline circulation. Clim. Dyn. 25, 689–708 (2005). +42. Jeltsch-Thönmes, A., Battaglia, G., Cartapanis, O., Jaccard, S. L. & Joos, F. Low terrestrial carbon storage at the Last Glacial Maximum: Constraints from multi-proxy data. Clim. Past 15, 849–879 + +<--- Page Split ---> + +43. Watrin, J. et al. Plant migration and plant communities at the time of the 'green Sahara'. Comptes Rendus - + +Geosci. 341, 656- 670 (2009). + +44. Tierney, J. E., Pausata, F. S. R. & De Menocal, P. B. Rainfall regimes of the Green Sahara. Sci. Adv. 3, 1-10 (2017). + +45. Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74-79 (2012). + +46. Marcott, S. A. et al. Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature 514, 616-619 (2014). + +47. Reimer, P. J. et al. The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBp). Radiocarbon 62, 725-757 (2020). + +48. Varney, R. M. et al. Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming. Biogeosciences 20, 3767-3790 (2023). + +49. Schefuf, E., Schouten, S., Jansen, J. H. F. & Sinninghe Damsté, J. S. African vegetation controlled by tropical sea surface temperatures in the mid-Pleistocene period. Nature 422, 418-421 (2003). + +50. Köhler P., Nehrbass-Ahles C., Schmitt J., Stocker T. F. & Fischer, H. A. 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4 and N2O and their radiative forcing Earth Syst. Sci. Data 9 363-87 (2017). + +## Methods + +## Core material and chronology + +Gravity core GeoB7702- 3 was retrieved onboard RV Meteor at the continental slope off the Sinai Peninsula during cruise M52/2 in \(2002^{51}\) . Due to the anticlockwise surface circulation in the eastern Mediterranean the fluvial load of the Nile River is transported eastward along the coast to the study site \(^{52}\) . Prior to sample preparation, the core was stored at \(4^{\circ}\mathrm{C}\) . The sample set for Bacteriohapanepolyol (BHP) quantification comprised 21 samples. Samples for compound-specific radiocarbon analysis (CSRA) were taken from 9 selected horizons ( \(\sim 2\) cm thickness). The age model of the core was previously published in ref. \(^{13}\) and updated by ref. \(^{22}\) . Age depth modeling is based upon 24 radiocarbon dates of planktic foraminifera and Bayesian modeling using the BACON software \(^{53}\) and the Marine20 calibration curve \(^{54}\) . + +## Lipid extraction + +Samples were freeze-dried and homogenized with a mortar. Samples for compound-specific radiocarbon analyses (ca. 100- 120g) were extracted with Dichloromethane (DCM):Methanol (MeOH) 9:1 (v/v) using a Soxhlet-apparatus (60°C, 48 hours) and were processed without internal standards. The samples were hydrolyzed with 0.1 N potassium hydroxide (KOH) in MeOH:H2O 9:1 (v/v) at 80°C for two hours. Neutral compounds were extracted with \(n\) - hexane, acids with DCM after acidifying the saponified solution with hydrochloric acid (HCl). Hydrocarbons were separated from polar compounds by column-chromatography using deactivated SiO2. The hydrocarbons were eluted with \(n\) - hexane, polar compounds with DCM:MeOH 1:1 (v/v). The fatty acids were derivatized to fatty acid methyl esters (FAME). The methylation was performed with MeOH of known \(\Delta^{14}\mathrm{C}\) , together with HCl at 50°C. Air in the headspace of the sample-tube was replaced by nitrogen gas (N2). FAMEs were recovered with \(n\) - hexane and were subsequently cleaned-up with column chromatography using deactivated SiO2 and NaSO4. FAMEs were eluted with DCM:Hexane 2:1 (v/v). + +Freeze-dried sediment samples dedicated for BHP analysis (ca. 3- 6 g) were extracted using a modified Bligh and Dyer extraction \(^{55,56,57}\) . The sediment samples were ultrasonically extracted (10 min) with a solvent mixture containing MeOH, DCM and phosphate buffer (2:1:0.8, + +<--- Page Split ---> + +v:v:v). After centrifugation, the solvent was collected, combined and the residues re-extracted twice. The combined solvent layers were added to separatory funnels and separated from the aqueous layer by the addition of DCM and Milli- Q water. After the layers separated, the bottom layer (DCM) was drawn off and collected, while the remaining aqueous layer was washed twice with DCM. The combined DCM layers were dried under a continuous flow of \(\mathrm{N}_2\) . Aliquots of the total lipid extracts (TLEs) were obtained and DGTS (1,2-dipalmitoyl-sn-glycero-3-O-4'-(N,N,N-trimethyl)-homoserine, Avanti Polar Lipids) was added as an internal standard before UHPLC- HRMS analysis. + +## UHPLC-HRMS analysis of non-derivatized BHPs + +Non- derivatized BHPs were quantified by injecting \(1\%\) of the TLE with \(2\mathrm{ng}\) internal standard (DGTS) dissolved in MeOH:DCM (9:1, v:v) on a Dionex Ultimate 3000RS ultra- high performance liquid chromatography (UHPLC) system connected to a Bruker maXis Plus Ultra- High Resolution quadrupole time- of- flight tandem mass spectrometer (UHR- qTOF- MS) equipped with an ESI ion source operating in positive mode (Bruker Daltonik, Bremen, Germany). The non- derivatized BHP analysis was performed according to ref.58 with a column temperature of \(30^{\circ}\mathrm{C}\) and a modified separation method. Briefly, separation was achieved on an Acquity BEH C18 column (2.1x 150 mm, \(1.7\mu \mathrm{m}\) particle size, Waters, Eschborn, Germany) and a solvent system consisting of eluent A of MeOH: \(\mathrm{H}_2\mathrm{O}\) (85:15) and eluent B MeOH:isopropanol (1:1) with both containing \(0.12\%\) (v/v) formic acid and \(0.04\%\) (v/v) aqueous ammonia. Compounds were eluted with \(5\%\) B for \(3\mathrm{min}\) , followed by a linear gradient to \(60\%\) B at \(12\mathrm{min}\) and then to \(100\%\) B at \(50\mathrm{min}\) and holding at \(100\%\) B until 80 min. The column was then equilibrated for \(20\mathrm{min}\) leading to a total run time of \(100\mathrm{min}\) . The flow rate was held constant at \(0.2\mathrm{ml}\mathrm{min}^{- 1}\) . Mass spectra were acquired in positive ion monitoring of m/z 50 to 2000 and data- dependent fragmentation of the most abundant ions (dynamically selected, typically 3- 8) for a total cycle time of \(2\mathrm{s}\) and dynamic exclusion (activation after 5 spectra, release after \(15\mathrm{s}\) ). Ion source settings and parameters for detection and fragmentation of BHPs were optimized while infusing extracts. Every analytical run was mass- calibrated by loop- injection of Agilent ESI- L tune mix and lock mass calibration (m/z 922.0098, added in ESI source) of each mass spectrum, leading to typical mass deviations of \(< 1 - 3\mathrm{ppm}\) . + +BHPs were identified based on the exact mass of the protonated or ammoniated molecular ion, relative retention time and \(\mathrm{MS}^2\) fragmentation similar to ref.58. Extracted ion chromatograms (EIC) of the most abundant molecular ion ( \(10\mathrm{mDa}\) mass accuracy window) were used to (semi- )quantify individual BHPs by peak integration. MS variability and ion suppression was controlled by the peak area of the DGTS internal standard. As no authentic standards were available for BHP quantification, abundances are reported based on peak areas of the individual BHPs normalized to the dry weight of the extracted sediments (i.e., in arbitrary units (AU)/ \(\mu \mathrm{g}\) dw). + +## Purification of leaf-wax lipids + +For CSRA the target FAMEs and \(n\) - alkanes were purified using preparative capillary gas chromatography59. The purification was performed on an Agilent 7890B gas chromatograph (GC), equipped with a temperature programmable cooled injection- system (CIS, Gerstel) and connected to a preparative fraction collector (PFC, Gerstel). Separation was performed on a Restek Rxi- 1ms fused silica capillary column ( \(30\mathrm{m}\) , \(0.53\mathrm{mm}\) i.d., \(1.5\mu \mathrm{m}\) film thickness). All + +<--- Page Split ---> + +samples were injected repeatedly with \(5\mu \mathrm{L}\) per injection from a concentration of \(1\mu \mathrm{g / \mu l}\) (FAMEs) and \(500\mu \mathrm{g / \mu l}\) ( \(n\) - alkanes) using \(n\) - hexane. The injector was operated in solvent vent mode (vent: \(100\mathrm{ml / min}\) , 0 psi until 0.12 min). The CIS temperature program was: \(60^{\circ}\mathrm{C}\) (0.05 min), \(12^{\circ}\mathrm{C / s}\) to \(320^{\circ}\mathrm{C}\) (5 min), \(12^{\circ}\mathrm{C / s}\) to \(340^{\circ}\mathrm{C}\) (5 min). The GC temperature program was set: \(60^{\circ}\mathrm{C}\) (2 min), \(20^{\circ}\mathrm{C / min}\) to \(150^{\circ}\mathrm{C}\) , \(8^{\circ}\mathrm{C / min}\) to \(320^{\circ}\mathrm{C}\) (40 min). Helium was used as carrier gas (4.0 ml/min). The transfer line and PFC were heated at \(320^{\circ}\mathrm{C}\) while the traps for collection were maintained at room temperature. The backflash system of the PFC was constantly switched off. The traps were rinsed with \(n\) - hexane to recover the purified compounds. Splits (0.1%) were analyzed by GC- FID to check for potential contaminants and to quantify the purified target compounds for CSRA. + +## CSRA + +The isotopic ratio \((^{14}\mathrm{C} / ^{12}\mathrm{C})\) of the FAMEs and \(n\) - alkanes was determined by Accelerator Mass Spectrometry (AMS). The measurements were carried out on the Ionplus MICADAS- system equipped with a gas- ion source \(^{60,61,62}\) at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven. CSRA was performed according to the protocols described in ref. \(^{63}\) . In short, the purified individual target compounds were transferred into tin capsules and packed. As for FAMEs, the \(n\) - \(\mathrm{C}_{26:0}\) and \(n\) - \(\mathrm{C}_{28:0}\) homologues were prepared individually except for two samples for which the homologues had to be combined in order to achieve adequate sample size (Extended Data Table 1). For \(n\) - alkanes we combined the \(n\) - \(\mathrm{C}_{29}\) , \(n\) - \(\mathrm{C}_{31}\) and \(n\) - \(\mathrm{C}_{33}\) homologues to obtain enough material for dating. Samples were combusted via the Elementar vario ISOTOPE EA (Elemental Analyzer) and the produced \(\mathrm{CO_2}\) was directly transferred into the coupled MICADAS. Radiocarbon contents of the samples were analyzed along with reference standards (oxalic acid II; NIST 4990c) and blanks (phthalic anhydride; Sigma- Aldrich 320064) and in- house reference sediments. Blank correction and standard normalization were performed via the BATS software \(^{64}\) . The AMS- results are reported as “fraction modern carbon” ( \(\mathrm{F}^{14}\mathrm{C}\) ) and \(\Delta^{14}\mathrm{C}^{65}\) . + +## Assessment of procedure blanks and correction + +In order to correct for carbon introduced during sample processing, procedure blanks were assessed by isolating FAs from a modern and a fossil standard material according to the methods described above. Leaves of a corn plant, collected in 2019, were used as modern standard ( \(\mathrm{F}^{14}\mathrm{C}\) : \(1.0096 \pm 0.0024\) ) while “Rekord” coal- briquette (lignite from Lusatia, Eastern Germany) served as fossil standard ( \(\mathrm{F}^{14}\mathrm{C}\) : \(0.0019 \pm 0.0002\) ). For the coal, asphaltene precipitation was performed additionally using DCM:MeOH 97:3 (v/v) and pentane. The \(\mathrm{F}^{14}\mathrm{C}\) and mass of the blank were assessed using a Bayesian approach \(^{66}\) . The procedure blank was \(3.079 \pm 0.433 \mu \mathrm{gC}\) with an \(\mathrm{F}^{14}\mathrm{C}\) of \(0.529 \pm 0.072\) . Blank- correction of the samples and error propagation was performed after ref. \(^{67}\) . The blank corrected \(\mathrm{F}^{14}\mathrm{C}\) - values of FAMEs were further corrected for the methyl- group, which had been added during the derivatization process, using isotopic mass balance. + +## Calculation of pre-depositional ages + +The age of the compounds at the time of deposition can be calculated using the “reservoir age offset” (R) \(^{29}\) which describes the age offset (in \(^{14}\mathrm{C}\) years) between two carbon reservoirs at a given time \(^{29}\) . In our case it needs to be calculated from the ratio of the radiocarbon contents of the sample and the atmosphere at the time of deposition in marine sediments (Eq. 2). + +<--- Page Split ---> + +\[\mathrm{R} = 8033^{*}\mathrm{ln}\left(\frac{\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}}}{\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}}\right)\quad (\mathrm{Eq.}2),\] + +where \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}}\) is the \(\mathrm{F}^{14}\mathrm{C}\) - value the sample had at the time of deposition at site GeoB7702- 3 and \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}\) is the radiocarbon content of the atmosphere. It can be calculated by correcting the measured \(\mathrm{F}^{14}\mathrm{C}\) - value of the sample \((\mathrm{F}^{14}\mathrm{C}_{\mathrm{sample}})\) for the decay that has taken place since the deposition (Eq. 3). + +\[\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}} = \mathrm{F}^{14}\mathrm{C}_{\mathrm{sample}}*\mathrm{e}^{\lambda t}\quad (\mathrm{Eq.}3),\] + +where t is the time of deposition and \(\lambda\) the decay constant of radiocarbon \(^{65}\) . The time of deposition was inferred from radiocarbon dates of planktic foraminifera (core chronology) \(^{22}\) . \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}\) values were adopted from INTCAL20 \(^{47}\) . In case of samples for which the \(\mathrm{F}^{14}\mathrm{C}\) values of the \(n - \mathrm{C}_{26:0}\) and \(n - \mathrm{C}_{28:0}\) homologues had been measured separately, we calculated R from the abundance weighted mean of the \(\mathrm{F}^{14}\mathrm{C}\) - values in order to keep comparability with samples for which the two homologues had been combined prior to AMS measurement (Extended Data Table 1). + +## Calculation of \(\tau_{\mathrm{soil}}\) and mean soil ages + +Soil organic matter is a complex mixture of compounds that vary in terms of their reactivity and consequently possess different turnover times \(^{68,69}\) . Due to this complexity of fast and slow cycling components in SOC, leaf- wax lipids generally exceed the mean soil turnover times by a multiple \(^{30}\) . Analyzing the ages of \(n\) - alkanoic acids in particulate organic matter from a global sample set comprising coastal sediments near river mouths, riverbeds and banks as well as suspension load, ref. \(^{30}\) identified globally constant offsets between \(^{14}\mathrm{C}\) ages of \(n\) - alkanoic acids and \(\tau_{\mathrm{soil}}\) (Eq. 4). Similarly, constant offsets between \(n\) - alkanoic acids and soil mean carbon age have been reported (Eq. 5). The soil mean carbon age here is defined as the radiocarbon age integrated over the top 100 cm depth \(^{30,70}\) . + +\[\mathrm{Age}_{n - \mathrm{alkanoic\ acid}} = 40.1*\tau_{\mathrm{soil}}\quad (\mathrm{Eq.}4)\] + +\[\mathrm{Age}_{n - \mathrm{alkanoic\ acid}} = 0.62*\mathrm{soil\ age}\quad (\mathrm{Eq.}5),\] + +where the \(\mathrm{age}_{n - \mathrm{alkanoic\ acid}}\) is given in \(^{14}\mathrm{C}\) years \(^{30}\) . Under the premise that these relationships remained constant in the past, they allow to calculate catchment- integrating mean \(\tau_{\mathrm{soil}}\) (in yrs) and mean soil carbon ages (0- 100 cm, in yrs) from the \(^{14}\mathrm{C}\) - ages of \(n\) - alkanoic acids in marine sedimentary archives and to monitor changes in the carbon cycle within a river catchment through time. The sample set of ref. \(^{30}\) covers a broad range of latitude (73 °N- 38 °S) and consequently represents different biomes and climate zones from tropical rainforest to arctic tundra. It reflects broad ranges of annual air temperature (- 16 to 27 °C) and mean annual precipitation (amount 230 mm/yr - 2200 mm/yr) \(^{30}\) . The range of \(^{14}\mathrm{C}\) ages from \(n\) - alkanoic acids covered by the dataset is recent to >10,000 yrs \(^{30}\) . The pre- depositional ages calculated + +<--- Page Split ---> + +for the \(n\) - alkanoic acids in core GeoB7702- 3 are within that range ( \(348 \pm 240 - 8723 \pm 212\) yrs; Table 1 and Extended Data Table 1). Thus, our inferred \(\tau_{\text{soil}}\) are within the calibrated range. Since the relationship between \(\tau_{\text{soil}}\) and the pre-depositional age of \(n\) - alkanes is unknown, we cannot convert our \(n\) - alkane age into \(\tau_{\text{soil}}\). + +## Dynamic Global Vegetation Model simulation + +Temperature and soil moisture effects have been implemented in dynamical global vegetation models for decades \(^{71,72}\) For this study, the Lund Potsdam Jena Dynamic Global Vegetation Model (LPJ DGVM) \(^{41,48}\) was used. We revisited the analysis performed by ref. \(^{48}\) and investigate changes in \(\tau_{\text{soil}}\) , net primary production (NPP), soil respiration ( \(\text{Rh}\) ) and soil carbon stock size between the Last Glacial Maximum (LGM; 21 kyrs BP) and pre- industrial (PI, 1 kyrs BP; Extended Data Figure 3). The global land carbon cycle was transiently simulated across Termination I subtracting the effect of \(\text{CO}_2\) fertilization and restricting the analysis to areas unaffected by rising sea level or continental ice retreat \(^{41,48}\) . \(\tau_{\text{soil}}\) is calculated according to Eq. 1 using the simulated carbon stock size and the simulated NPP and \(\text{Rh}\) , respectively. The results are shown in Extended Data Figure 3a,b. + +The model simulates a total change in the global terrestrial carbon pools of \(820 \text{ PgC}\) between the LGM and \(\text{PI}^{48}\) . This agrees well with the median of \(850 \text{ PgC}\) estimated by a recent multi- proxy approach \(^{42}\) showing that the simulated global patterns are in agreement with other studies. The model suggests a reduction of the global land carbon stock by \(200 - 250 \text{ PgC}\) for PI relative to the LGM \(^{48}\) . This represents the summed- up change in vegetation and soil carbon caused by temperature and precipitation variability \(^{48}\) . Calculating \(\tau_{\text{soil}}\) from net primary production (NPP) and respiration fluxes ( \(\text{Rh}\) ) reveals similar results indicating that NPP and Rh are in equilibrium (Extended Data Figure 3a,b,c,d). + +## Data availability + +The data generated in the study will be accessible from the PANGAEA database (www.pangaea.de). + +## References + +51. Pätzold, J., Bohrmann, G., Hübscher, C. Black Sea – Mediterranean – Red Sea. Cruise No. 52, January 2 – March 27, 2002, Istanbul – Limassol. Universität Hamburg. METEOR-Berichte 03-2, 178 pp (2003). +52. Weldeab, S., Emeis, K.-C., Hemleben, C., Siebel, W. Provenance of lithogenic surface sediments and pathways of riverine suspended matter in the Eastern Mediterranean Sea: evidence from \(^{143}\text{Nd}/^{144}\text{Nd}\) and \(^{87}\text{Sr}/^{86}\text{Sr}\) ratios. Chem. Geol. 186, 139-149 (2002). +53. Blaauw, M., & Christen, J.A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analysis 6, 457-474 (2011). +54. Heaton, T. J. et al. Marine20 - The Marine Radiocarbon Age Calibration Curve (0-55,000 cal BP). Radiocarbon 62, 779-820 (2020). +55. Sturt, H. F., Summons, R. E., Smith, K., Elvert, M., & Hinrichs, K. (2004) Intact polar membrane lipids in prokaryotes and sediments deciphered by high performance liquid chromatography / electrospray ionization multistage mass spectrometry — new biomarkers for biogeochemistry and microbial ecology. Rapid communications in mass spectrometry 18(6), 617-628 (2004). +56. Wörmer, L., Lipp, J. S. & Hinrichs, K. U. Comprehensive Analysis of Microbial Lipids in Environmental Samples Through HPLC-MS Protocols. In: McGenity, T., Timmis, K. &, Nogales, B. + +<--- Page Split ---> + +(eds) Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks. Springer, Berlin, Heidelberg (2015).57. Smit, N.T., Villanueva, L., Rush, D., Grassa, F., Witkowski, C.R., Holzheimer, M., Minnaard, A.J., Sinninghe Damsté, J.S. and Schouten, S. (2021) Novel hydrocarbon- utilizing soil mycobacteria synthesize unique mycoercosis acids at a Sicilian everlasting fire. Biogeosciences 18, 1463- 1479.58. Hopmans, E. C., et al. Analysis of nonderivatized bacteriohanopolys using UHPLC- HRMS reveals great structural diversity in environmental lipid assemblages, Org. Geochem., 160, 104285 (2021).59. Eglinton, T., Aluwihare, L., Bauer, J., Druffel, E. & McNichol, A. Gas chromatographic isolation of individual compounds from complex matrices for radiocarbon dating. Anal. Chem. 68, 904- 912 (1996).60. Ruff, M., Wacker, L., Gaggeler, H. W., Suter, M., Synal, H.- A. & Szidat S. A gas ion source for radiocarbon measurements at 200 kV. Radiocarbon 49, 307- 314 (2007).61. Synal, H.- A., Stocker, M. & Suter, M. MICADAS: a new compact radiocarbon AMS system. Nucl. Instrum. Methods Phys. Res. A 259, 7- 13 (2007).62. Wacker, L. et al. A versatile gas interface for routine radiocarbon analysis with a gas ion source. Nucl. Instrum. Methods Phys. Res. B 294, 315- 319 (2013).63. Mollenhauer, G., Grotheer, H., Gentz, T., Bonk, E. & Hefter, J. Standard operation procedures and performance of the MICADAS radiocarbon laboratory at Alfred Wegener Institute (AWI), Germany. Nucl. Instruments Methods Phys. Res. Sect. B Beam Interact. with Mater. Atoms 496, 45- 51 (2021).64. Wacker, L., Christl, M. & Synal, H. A. Bats: a new tool for AMS data reduction. Nucl. Instrum. Methods Phys. Res. B 268, 976- 979 (2010).65. Stuiver, M. & Polach, H. Discussion: reporting of \(^{14}\mathrm{C}\) data. Radiocarbon 19, 355- 363 (1977).66. Sun, S. et al. \(^{14}\mathrm{C}\) Blank Assessment in Small- Scale Compound- Specific Radiocarbon Analysis of Lipid Biomarkers and Lignin Phenols. Radiocarbon 62, 207- 218 (2020).67. Wacker, L. & Christl, M. Data Reduction for Small Samples. Error Propagation using the Model of Constant Contamination. Annual Report of Ion Beam Physics, ETH Zürich 36 (2012).68. Wiesenberg, G. L. B., Schwarzbauer, J., Schmidt, M. W. I. & Schwark, L. Source and turnover of organic matter in agricultural soils derived from n- alkane/n- carboxylic acid compositions and C- isotope signatures. Org. Geochem. 35, 1371- 1393 (2004).69. Trumbore, S. Age of soil organic matter and soil respiration: Radiocarbon constraints on belowground C dynamics. Ecol. Appl. 10, 399- 411 (2000).70. Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555- 559 (2020).71. Lloyd, J. and Taylor, J. A. On the Temperature Dependence of Soil Respiration Functional Ecology, 8, 315- 323 (1994).72. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan OJ, Lucht W, Sykes MT, Thonicke K, Venevsky S. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol 9, 161- 185 (2003). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Map of African vegetation zones after ref.49. Med: Mediterranean zone; MST: Mediterranean-Saharan transitional vegetation; AM: Afro-montane vegetation zone. The Nile River catchment is marked by the blue shading. The red star indicates the study site GeoB7702-3.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Environmental changes in the Nile-River delta region during the past 18 kyrs. (a) Ice-core \(\mathrm{CO_2}\) -contents from EPICA Dome \(\mathrm{C^{50}}\) given as indicator for atmospheric \(\mathrm{CO_2}\) concentrations. (b) Atmospheric \(\Delta^{14}\mathrm{C}\) contents according to INTCAL20. (c) Reservoir age offsets between the \(n\) -alkanoic acids and the atmosphere at the time of deposition at site GeoB7702-3. \(\mathrm{t_{soil}}\) deduced from the reservoir age offsets of \(n\) -alkanoic acids. (d) Sea surface temperature reconstruction for the eastern Mediterranean based on the \(\mathrm{TEX_{86}}\) proxy from core GeoB7702-3. (e) Hydrogen isotopic composition of precipitation \((\delta \mathrm{Dp})\) calculated from the \(\delta \mathrm{D}\) of \(n\) -alkanoic acids from core GeoB7702-3 as proxy for rainfall amount. (f) Oxygen isotopic compositions of the planktic foraminifera species Globigerinoides ruber \((\delta^{18}\mathrm{O}_{G.rubber})\) in core MS27PT (Figure 1) indicating salinity changes in the eastern Mediterranean associated with freshwater runoff from the Nile River. (g) Aminopentol abundances in core GeoB7702-3 used as proxy for the extent of methane producing wetlands in the catchment. AU: arbitrary units; dw: dry weight of extracted sediment. Additional abundance profiles from the suite of aminobacteriohanopelyols are given in Extended Data Figure 3 (h) Concentrations of \(n\) -alkanoic acids \((\Sigma n - \mathrm{C}_{26:0}, n - \mathrm{C}_{28:0}, n - \mathrm{C}_{30:0}, n - \mathrm{C}_{32:0})\) reporting on the land-ocean transport of terrigenous organic matter. (i) Global rate of sea-level change over the last 20 kyrs. The blue bars mark the timing of the African Humid Period (AHP) and Green Sahara and their optimum. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bolling/Allerod interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Power-law relationships between \(\tau_{\mathrm{soll}}\) and (a) temperature and (b) hydrogen isotopic composition of precipitation (8Dp). Temperature estimates are based upon the TEX86-proxy at site GeoB7702-3 and are adopted from ref.13. 8Dp is calculated from the hydrogen isotopic composition of \(n\) -alkanoic acids ( \(n\) -C26:0 and \(n\) -C28:0 homologues) from core GeoB7702-3. The p-values for the regressions are \(< 0.05\) .8Dp is deduced from the hydrogen isotopic composition of \(n\) -alkanoic acids and \(n\) -alkanes in core GeoB7702-3. Unfortunately, mean annual air temperature estimates covering the past 18 kyrs are not available for the Nile-River catchment. Therefore, we use the TEX86-based temperature record from GeoB7702-3 interpreted to reflect sea surface temperature (SST) in the eastern Mediterranean. We assume that SST and surface air temperatures in the Nile delta region probably developed similarly due to heat exchange between the sea surface and the overlying air.
+ +Table 1. Reservoir age offsets (R) of leaf- wax biomarkers and the atmosphere at the time of deposition in marine sediments. R is calculated from compound- specific radiocarbon dating results of the combined \(n\) - C26:0 and \(n\) - C28:0 alkanoic acid homologues and the combined \(n\) - C29, \(n\) - C31, \(n\) - C33 alkane homologues (see Methods). Mean soil carbon turnover times ( \(\tau_{\mathrm{soll}}\) ) and soil mean carbon ages were deduced from the R of \(n\) - alkanoic acids according to ref.30. The hydrogen isotopic composition of precipitation (8Dp) and TEX86 are mean values for the range of the deposition age. 8Dp is based on the 8D signature of \(n\) - alkanoic acids in core GeoB7702-3. TEX86 are sea surface temperature reconstructions at site GeoB7702-3 from ref.13. Propagated standard errors (±) are reported along with the results. \(\mathrm{F}^{14}\mathrm{C}\) and \(\Delta^{14}\mathrm{C}\) values are listed in Extended Data Table 1. + +
Sample depth [cm]Deposition age min. - max. [kyrs BP]aDeposition age mid-point [kyrs BP]aR n-alkanoic acids [14C yrs]R n-alkanes [14C yrs]τsoll [yrs]Soil mean carbon age [yrs]δDp [‰ VSMOW]bTEX86 [°C]
81.5-84.51.62 - 2.291.93348 ± 240959 ± 1469 ± 6561 ± 392-8.8 ± 2.726.9 ± 0.4
130-1333.11 - 3.693.40733 ± 4321633 ± 16718 ± 111182 ± 710-9.3 ± 2.526.3 ± 0.6
198-2015.35 - 6.015.70902 ± 3311668 ± 11622 ± 91455 ± 559-8.1 ± 1.525.1 ± 0.4
231-2347.24 - 8.147.72563 ± 24721 ± 8714 ± 6908 ± 411-19.3 ± 5.726.7 ± 0.7
251-2549.02 - 10.119.66736 ± 1963447 ± 29818 ± 51187 ± 343-27.2 ± 2.125.3 ± 2.0
278-28111.05 - 12.0511.501631 ± 1583313 ± 17841 ± 62630 ± 3915.0 ± 2.919.1 ± 0.7
297-30012.69 - 13.7313.215384 ± 6184334 ± 213134 ± 208684 ± 13991.0 ± 2.920.0 ± 0.7
359-36216.26 - 17.0716.673453 ± 1192415 ± 8186 ± 95569 ± 6578.3 ± 8.117.5 ± 1.3
393-39617.69 - 18.7318.158723 ± 2127816 ± 341218 ± 2214069 ± 16258.3 ± 2.716.3 ± 0.7
+ +a: Obtained by radiocarbon dating of planktic foraminifera22. b: calculated by correcting the 8D of the \(n\) - C26:0 and \(n\) - C28:0 alkanoic acids in core GeoB7702-3 for vegetation changes and ice volume22. + +<--- Page Split ---> + +# 583 Acknowledgements + +584 The study was funded by the DFG Cluster of Excellence: "The Ocean Floor - Earth's + +585 Uncharted Interface". We are grateful to Jürgen Pätzold for providing sample material of core + +586 GeoB7702-3. We thank Ralph Kreutz for support during sample processing for CSRA and + +587 GC-maintenance. Julia Cordes is acknowledged for technical support during BHP analysis. + +588 Pushpak Nadar is thanked for assistance during sample processing for BHP analysis and core sampling for CSRA. Hendrik Grotheer is thanked for support during the AMS measurements at AWI. + +# 592 Author Contributions + +593 VDM developed the concept of the study supported by BW, ES, GM and PK. VDM carried out the sample preparation and data analysis in the laboratories and performed data processing. NTS and JL conducted the analysis of Bacteriohopanepolyols. PK performed the simulations with the LPJ DGVM. All authors were involved in the interpretation and discussion of the results. VDM drafted the manuscript with contributions from all co-authors. + +# 599 Competing interests + +600 The authors declare that none of them has any competing interests. + +# 602 Extended Data + +603 **Extended Data Table 1. CSRA-results reported along with the standard errors (±) of** \(n\) -alkanoic acids and \(n\) -alkanes in core 604 GeoB7702-3. CSRA was performed at the Alfred Wegener Institute (AWI). R is the reservoir age offset between the 605 biomarkers and the atmosphere at the time of deposition in marine sediments calculated after ref.29. + +
Sample
depth
[cm]		AWI sample
identification
number		Deposition
age range
[kyrs BP]a		Deposition
age
mid-point
[kyrs BP]a		Compounds\(F^{14}C^{b}\)\(A^{14}C^{b}\)
\([\%]\)		R
\([^{14}C\) yrs]
81.5-84.55252.1.11.62-2.291.93\(n-C_{26:0}+n-C_{28:0}\)\(0.7408\pm 0.0281\)-265±28\(348\pm 240\)
81.5-84.55252.2.11.62-2.291.93\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.6866\pm 0.0158\)-319±16\(959\pm 146\)
130-1335061.2.13.11-3.693.40\(n-C_{26:0}\)\(0.6180\pm 0.0130\)-387±13\(604\pm 112\)
130-1335061.3.13.11-3.693.40\(n-C_{28:0}\)\(0.5975\pm 0.0142\)-408±14\(875\pm 126\)
130-133-3.11-3.693.40\(n-C_{26:0}+n-C_{28:0}\)c\(0.6082\pm 0.0498\)c-397±50\(733\pm 432\)
130-1335061.4.13.11-3.693.40\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.5437\pm 0.0172\)-461±17\(1633\pm 167\)
198-2015251.2.15.35-6.015.70\(n-C_{26:0}\)\(0.4795\pm 0.0131\)-525±13\(871\pm 111\)
198-2015251.1.15.35-6.015.70\(n-C_{28:0}\)\(0.4761\pm 0.0152\)-528±15\(929\pm 129\)
198-201-5.35-6.015.70\(n-C_{26:0}+n-C_{28:0}\)c\(0.4777\pm 0.0395\)c-526±40\(902\pm 331\)
198-2015251.3.15.35-6.015.70\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.4342\pm 0.0125\)-569±13\(1668\pm 116\)
231-23411116.2.17.24-8.147.72\(n-C_{26:0}\)\(0.4086\pm 0.0085\)-595±9\(202\pm 69\)
231-23411116.3.17.24-8.147.72\(n-C_{28:0}\)\(0.3705\pm 0.0092\)-633±9\(988\pm 81\)
231-234-7.24-8.147.72\(n-C_{26:0}+n-C_{28:0}\)c\(0.3906\pm 0.0307\)c-613±31\(563\pm 247\)
231-23411116.1.17.24-8.147.72\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.4179\pm 0.0112\)-586±11\(21\pm 87\)
251-2545060.2.19.02-10.119.66\(n-C_{26:0}\)\(0.3062\pm 0.0093\)-696±9\(734\pm 79\)
+ +<--- Page Split ---> + + +
251-2545060.3.19.02 - 10.119.66\(n-C_{28:0}\)0.3060 ± 0.0085-697 ± 8738 ± 79
251-254-9.02 - 10.119.66\(n-C_{26:0}+n-C_{28:0}\)c0.3061 ± 0.0240c-696 ± 24736 ± 196
251-2545060.4.19.02 - 10.119.66\(n-C_{29}+n-C_{31}+n-C_{33}\)0.2184 ± 0.0263-783 ± 263447 ± 298
278-2815059.2.111.05 - 12.0511.50\(n-C_{26:0}\)0.2183 ± 0.0082-784 ± 82126 ± 77
278-2815059.4.111.05 - 12.0511.50\(n-C_{28:0}\)0.2475 ± 0.0088-755 ± 91117 ± 74
278-281-11.05 - 12.0511.50\(n-C_{26:0}+n-C_{28:0}\)c0.2321 ± 0.0183c-770 ± 181613 ± 158
278-2815059.5.111.05 - 12.0511.50\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1883 ± 0.0167-813 ± 173313 ± 178
297-3005250.1.112.69 - 13.7313.21\(n-C_{26:0}+n-C_{28:0}\)0.1236 ± 0.0474-877 ± 475384 ± 618
297-3005250.2.112.69 - 13.7313.21\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1408 ± 0.0185-860 ± 194334 ± 213
359-3625249.2.116.26 - 17.0716.67\(n-C_{26:0}\)0.1118 ± 0.0160-889 ± 163763 ± 157
359-3625249.1.116.26 - 17.0716.67\(n-C_{28:0}\)0.1206 ± 0.0158-880 ± 163154 ± 144
359-362-16.26 - 17.0716.67\(n-C_{26:0}+n-C_{28:0}\)c0.1162 ± 0.0123c-885 ± 123453 ± 219
359-3625249.3.116.26 - 17.0716.67\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1322 ± 0.0089-869 ± 92415 ± 81
393-3965248.2.117.69 - 18.7318.15\(n-C_{26:0}\)0.0748 ± 0.0269-926 ± 276005 ± 322
393-3965248.1.117.69 - 18.7318.15\(n-C_{28:0}\)0.0372 ± 0.0403-963 ± 4011611 ± 961
393-396-17.69 - 18.7318.15\(n-C_{26:0}+n-C_{28:0}\)c0.0534 ± 0.0125c-947 ± 128723 ± 212
393-3965248.3.117.69 - 18.7318.15\(n-C_{29}+n-C_{31}+n-C_{33}\)0.0597 ± 0.0228-941 ± 237816 ± 341
+ +606 a: Obtained from radiocarbon dating of planktic foraminifera22. + +607 b: Corrected for procedure blanks. \(n\) -Alkanoic acids were additionally corrected for the carbon introduced during methylation (see Methods). + +609 c: Calculated abundance-weighted means of the \(n-C_{26:0}\) and \(n-C_{28:0}\) homologues. + +611 **Extended Data Table 2.** \(\tau _{soll}\) for the Ganga-Brahamaputra river catchment during the past 17 kyrs calculated from + +612 compound-specific radiocarbon data from \(n\) -alkanoic acids27. R is the reservoir age offset29 between the \(n\) -alkanoic acids and the atmosphere at the time of deposition in the Bengal Fan. + +
\(n\) -Alkanoic
acid
homologues		Deposition
age [kyrs BP]		Mass
weighted
mean R [14C
yrs]		\(\tau _{soll}\) [yrs]
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)0.003\(1446\pm 80\)\(36\pm 4\)
+ +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)0.004\(927\pm 87\)*\(23\pm 3\)
+ +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{34:0}\)\(3.54\pm 0.39\)\(7119\pm 1149\)\(178\pm 33\)
+ +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)\(6.57\pm 0.42\)\(1489\pm 618\)\(37\pm 16\)
+ +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)\(10.92\pm 0.48\)\(2070\pm 1116\)\(52\pm 28\)
+ +<--- Page Split ---> + + +
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{34,0}\)12.74 ± 0.423234 ± 116680 ± 30
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{32}\), \(n-C_{34,0}\)13.61 ± 0.231375 ± 83034 ± 21
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\)15.62 ± 0.378709 ± 4166217 ± 106
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{34,0}\)16.77 ± 0.396453 ± 2177116 ± 55
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{32,0}\)16.90 ± 0.104004 ± 3507100 ± 88
+ +614 *: \(^{14}\mathrm {C}\) ages of pre-1950 Bengal Fan sediments taken from ref.24 + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Extended Data Figure 1: Abundances of Amino-Bacteriophanepolyols in core GeoB7702-3 normalized to the dry weight of extracted sediment (dw). AU: Arbitrary units. AHP: African Humid Period. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Extended Data Figure 2: Reservoir age offsets of leaf-wax lipids with the atmosphere at the time of deposition at site GeoB7702-3 (a) plotted along with temperature and precipitation reconstructions from the Nile catchment. (b): Temperature reconstruction for the eastern Mediterranean based on the TEX86-proxy in core GeoB7702-313. (c): hydrogen isotope compositions of precipitation (δDp) calculated from δD of the alkanoic acids (mean of \(n-C_{26:0}\) and \(n-C_{28:0}\) homologues; purple) and \(n-C_{31}\) alkane (orange) in core GeoB7702-322. The blue bars mark the timing of the African Humid Period (AHP), "Green Sahara" and their optimum17,44. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Extended Data Figure 3: Recalculation of results from the Lund Potsdam Jena Dynamic Global Vegetation Model (LPJ DGVM) over the last 21 kyrs as published in ref.41. These results are identical to those LPJ results that have been forced by the Hadley center climate model as discussed in ref.41. Relative changes between the LGM and pre-industrial conditions (PI, here: 1 kyr BP) are shown. a,b) \(\tau_{\mathrm{soil}}\) calculated either based on the carbon influx (net primary production (NPP)) or on the carbon efflux (Rh), where \(\mathrm{Rh}\) is the heterotrophic respiration. Large positive anomalies (red) occur on shelf areas inundated during deglacial sea-level rise, while the areas with large negative anomalies (blue) were covered by large continental ice sheets during the LGM.; c,d) relative changes in NPP and \(\mathrm{Rh}\) ; e) absolute changes in soil carbon content ( \(\mathrm{C_{soil}}\) ).
+ +<--- Page Split ---> diff --git a/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199_det.mmd b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..698570c170f43e1b1452e7b5093d36f05000188d --- /dev/null +++ b/preprint/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199/preprint__039c199243e3ff42a62092628fd75c4b2179e5ca70bda3bac1533b18d7195199_det.mmd @@ -0,0 +1,485 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 912, 175]]<|/det|> +# Dominant control of temperature on (sub-)tropical soil carbon turnover + +<|ref|>text<|/ref|><|det|>[[44, 196, 237, 214]]<|/det|> +Vera Dorothee Meyer + +<|ref|>text<|/ref|><|det|>[[55, 223, 300, 240]]<|/det|> +vmeyer@marum- alumni.de + +<|ref|>text<|/ref|><|det|>[[44, 268, 872, 289]]<|/det|> +MARUM - Center for Marine Environmental Sciences https://orcid.org/0000- 0002- 4958- 5367 + +<|ref|>text<|/ref|><|det|>[[44, 294, 154, 311]]<|/det|> +Peter Köhler + +<|ref|>text<|/ref|><|det|>[[44, 315, 910, 356]]<|/det|> +Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research https://orcid.org/0000- 0003- 0904- 8484 + +<|ref|>text<|/ref|><|det|>[[44, 363, 154, 380]]<|/det|> +Nadine Smit + +<|ref|>text<|/ref|><|det|>[[50, 384, 857, 404]]<|/det|> +MARUM - Center for Marine Environmental Sciences, now: Bruker Daltonics GmbH & Co. KG. + +<|ref|>text<|/ref|><|det|>[[44, 410, 137, 428]]<|/det|> +Julius Lipp + +<|ref|>text<|/ref|><|det|>[[50, 431, 510, 450]]<|/det|> +MARUM - Center for Marine Environmental Sciences + +<|ref|>text<|/ref|><|det|>[[44, 456, 160, 474]]<|/det|> +Bingbing Wei + +<|ref|>text<|/ref|><|det|>[[50, 477, 715, 497]]<|/det|> +Alfred Wegener Institut, Helmholtz Zentrum für Polar- und Meeresforschung + +<|ref|>text<|/ref|><|det|>[[44, 502, 220, 520]]<|/det|> +Gesine Mollenhauer + +<|ref|>text<|/ref|><|det|>[[50, 523, 629, 542]]<|/det|> +Alfred Wegener Institute https://orcid.org/0000- 0001- 5138- 564X + +<|ref|>text<|/ref|><|det|>[[44, 548, 168, 565]]<|/det|> +Enno SchefuB + +<|ref|>text<|/ref|><|det|>[[50, 570, 787, 588]]<|/det|> +MARUM - Center for Marine Environmental Sciences, University of Bremen, Germany + +<|ref|>text<|/ref|><|det|>[[50, 593, 401, 611]]<|/det|> +https://orcid.org/0000- 0002- 5960- 930X + +<|ref|>sub_title<|/ref|><|det|>[[44, 653, 105, 671]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 690, 136, 709]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 728, 321, 747]]<|/det|> +Posted Date: August 19th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 766, 475, 785]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4726729/v1 + +<|ref|>text<|/ref|><|det|>[[44, 804, 912, 846]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 864, 535, 884]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 911, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 15th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59013-9. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[100, 83, 840, 103]]<|/det|> +## Dominant control of temperature on (sub-)tropical soil carbon turnover + +<|ref|>text<|/ref|><|det|>[[100, 110, 850, 147]]<|/det|> +Vera D. Meyer \(^{1*}\) , Peter Köhler \(^{2}\) , Nadine T. Smit \(^{1,3}\) , Julius S. Lipp \(^{1}\) , Bingbing Wei \(^{2}\) , Gesine Mollenhauer \(^{1,2}\) and Enno Schefuß \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[100, 156, 852, 175]]<|/det|> +\(^{1}\) : MARUM – Center for Marine Environmental Sciences, University of Bremen, Germany + +<|ref|>text<|/ref|><|det|>[[100, 184, 767, 220]]<|/det|> +\(^{2}\) : Alfred Wegener Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany + +<|ref|>text<|/ref|><|det|>[[100, 229, 653, 247]]<|/det|> +\(^{3}\) : now at: Bruker Daltonics GmbH & Co. KG., Bremen, Germany + +<|ref|>text<|/ref|><|det|>[[100, 256, 668, 274]]<|/det|> +\(^{*}\) corresponding authors: vmeyer@marum.de; eschefuss@marum.de + +<|ref|>text<|/ref|><|det|>[[113, 283, 880, 531]]<|/det|> +Carbon storage in soils is important in regulating atmospheric carbon dioxide ( \(\mathrm{CO_2}\) ). However, the sensitivity of the soil- carbon turnover time \((\tau_{\mathrm{soil}})\) to temperature and hydrology forcing is still not fully understood. Here, we use radiocarbon dating of plant- derived lipids in conjunction with reconstructions of temperature and rainfall from an eastern Mediterranean sediment core receiving terrigenous material from the Nile- River watershed to investigate \(\tau_{\mathrm{soil}}\) in subtropical and tropical areas during the last 18,000 years. We find that the \(\tau_{\mathrm{soil}}\) was reduced by an order of magnitude over the last deglaciation and infer that this reduction was caused from amplified soil respiration rates. Our data indicate that the deglacial warming was the major driver of these changes while the impact of hydroclimate was relatively small. We conclude that increased \(\mathrm{CO_2}\) efflux from soils into the atmosphere constituted a positive feedback to global warming. However, simulated glacial- to- interglacial changes in a dynamic global vegetation model underestimate our data- based reconstructions of soil- carbon turnover times suggesting that this climate feedback might be underestimated. + +<|ref|>text<|/ref|><|det|>[[113, 540, 876, 664]]<|/det|> +Globally, soils store more than twice as much carbon as the atmosphere at present \(^{1,2}\) . The soil carbon cycle is sensitive to climate change and human activities \(^{1,3,4}\) . Therefore, future warming, shifts in precipitation patterns and land use might perturb the soil- carbon storage and subsequently result in positive feedbacks on global warming via \(\mathrm{CO_2}\) release into the atmosphere \(^{1,5}\) . Soil carbon storage is regulated by carbon influx (fixation through net primary production; NPP) and efflux. The latter is controlled by microbial respiration, soil erosion and fire emissions \(^{2,5}\) . These processes determine \(\tau_{\mathrm{soil}}\) defined as: + +<|ref|>equation<|/ref|><|det|>[[420, 672, 576, 703]]<|/det|> +\[\tau_{\mathrm{soil}} = \frac{C_{\mathrm{total}}}{f} \quad (\mathrm{Eq. 1}),\] + +<|ref|>text<|/ref|><|det|>[[113, 709, 880, 887]]<|/det|> +where \(\mathrm{C_{total}}\) is the soil carbon- stock size and f either the carbon influx (NPP) or the efflux. Under steady state conditions influx and efflux are equal \(^{6}\) . Turnover times are critical components in carbon cycling for constraining the time scales of carbon exchange between different reservoirs. \(\tau_{\mathrm{soil}}\) depends on soil temperature \(^{3,4,7}\) and moisture content \(^{3,4}\) but also on chemical properties \(^{8,9,10}\) and soil fertility \(^{8,10}\) . Temperature effects on \(\tau_{\mathrm{soil}}\) are widely observed across the globe \(^{4}\) while hydroclimate may exert strong control in low latitudes where it may be even more important than temperature \(^{4,11,12}\) . However, the key controls on \(\tau_{\mathrm{soil}}\) are still debated \(^{3,9,11}\) . This forms a major open question in tropical and subtropical regions where combined effects of future warming and precipitation changes may be amplified or attenuated depending on whether warming will be accompanied by drier or wetter conditions \(^{11}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 879, 365]]<|/det|> +Here, we investigate how \(\tau_{\mathrm{soil}}\) changed in the Nile- River catchment during the last 18 kyrs when the global climate warmed and transitioned from the last Glacial (before 17.3 kyr BP) into the Holocene (after 11.7 kyr BP). With a length of \(6650\mathrm{km}\) the Nile River is the longest river in the world spanning \(35^{\circ}\) of latitude \((4^{\circ}\mathrm{S - 31^{\circ}N})\) in northeastern Africa. During the last deglaciation (8- 18 kyrs BP) the northern African climate warmed \(^{13,14}\) and humid conditions during the African Humid Period(AHP, 14.5- 5 kyr BP) \(^{15,16}\) allowed for plants and permanent water bodies to persist in the nowadays barren, hyperarid Sahara Desert \(^{17,18}\) . The different timing of changes in temperature \(^{13,14,19}\) and hydroclimate \(^{20,21,22}\) in northeastern Africa around the AHP allows for disentangling temperature and precipitation effects on \(\tau_{\mathrm{soil}}\) . We investigate the response of the soil carbon cycle to these climatic changes using compound- specific radiocarbon dating (CSRA) of the plant- wax biomarkers long chain \(n\) - alkanoic acids and long chain \(n\) - alkanes preserved in marine sediment core GeoB7702- 3, which was retrieved in the eastern Mediterranean from the continental margin off the Sinai Peninsula (Figure 1). Refractory plant- wax lipids deposited in marine sediments commonly are pre- aged due to transport processes and intermediate storage \(^{23,24}\) . Their pre- depositional ages are powerful recorders of changes in the terrestrial carbon cycle \(^{23,25,26,27,28}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 374, 469, 392]]<|/det|> +## Environmental signals in the CSRA data + +<|ref|>text<|/ref|><|det|>[[115, 402, 882, 560]]<|/det|> +To calculate the pre- depositional age of the leaf- wax biomarkers we use the "reservoir age offset" \(^{29}\) between the leaf- wax biomarkers and the atmosphere at the time of deposition (Table 1, Figure 2c). The reservoir age offsets of \(n\) - alkanoic acids and \(n\) - alkanes in core GeoB7702- 3 range between approximately 0 and \(8700^{14}\mathrm{C}\) yrs. Glacial offsets (7800- 8700 \(^{14}\mathrm{C}\) yrs at 18 kyr BP) are substantially higher than those during the Holocene (0- 3400 \(^{14}\mathrm{C}\) yrs; between \(\sim 2 - 11.5\) kyrs BP). A linear relationship between the \(^{14}\mathrm{C}\) ages of long chain \(n\) - alkanoic acids in marine sediments with \(\tau_{\mathrm{soil}}\) \(^{30}\) makes it possible to calculate \(\tau_{\mathrm{soil}}\) from CSRA data. However, three factors that may introduce biases to the reconstruction of \(\tau_{\mathrm{soil}}\) need to be considered beforehand. + +<|ref|>text<|/ref|><|det|>[[115, 570, 872, 729]]<|/det|> +First, sea level rose by up to \(120\mathrm{m}\) over the deglaciation \(^{33}\) and coastal erosion during shelf flooding led to the deposition of pre- aged organic matter on continental margins \(^{26,34}\) . Such processes may mask hinterland signals in the \(^{14}\mathrm{C}\) - record of leaf- wax lipids in marine sediments. However, biases from coastal erosion during retrogradation of the Nile Delta are unlikely as the concentration profile of \(n\) - alkanoic acids in core GeoB7702- 3 differs from the global rate of sea- level change \(^{34}\) (Figure 2h,i) but resembles the oxygen isotopic composition of planktic foraminifera Globigerinoides ruber ( \(\delta^{18}\mathrm{O}_{G.rubcr}\) ) off the Nile River delta, a proxy for freshwater discharge \(^{35}\) (Figure 2f). Hence, the export of organic matter was primarily controlled by river runoff \(^{22}\) . + +<|ref|>text<|/ref|><|det|>[[111, 738, 881, 916]]<|/det|> +Second, in addition to mineral soils peatlands need to be considered as source of pre- aged organic matter \(^{30}\) . Anaerobic conditions in wetlands hamper degradation of organic matter leading to its preservation in peat over millennia \(^{36}\) . During wetland contraction, erosion and fluvial export of this pre- aged organic matter \(^{28}\) could thus bias the calculations of mean \(\tau_{\mathrm{soil}}\) of mineral soils \(^{30}\) . This might be relevant to the Nile River catchment since wetlands occur along the basin today \(^{37}\) . To constrain wetland dynamics we analyzed a suite of amino- Bacteriohopanepolyols (amino- BHPs; Extended Data Figure 1) which are specific markers for methane oxidizing bacteria in wetlands \(^{38}\) and thus indicative of the relative extension and contraction of methane producing landcover \(^{28}\) . Low concentrations imply that between 18- 11 kyrs BP methane producing permanently flooded wetlands were barely present in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 870, 207]]<|/det|> +catchment (Figure 2g and Extended Data Figure 1) rendering it unlikely that the decrease in the reservoir age offset stemmed from wetland dynamics. A massive expansion of wetlands occurred between 11- 8 kyr BP, which probably occurred in response to maximal rainfall and river runoff during the AHP- optimum (Figure 2d,e,g). Contributions of pre- aged organic matter associated with wetland contraction at the end of the AHP were probably minor as reservoir age offsets remain constant when amino- BHP concentrations decline in our core (Figure 2c,g). + +<|ref|>text<|/ref|><|det|>[[111, 215, 884, 570]]<|/det|> +Third, river dynamics including morphology and runoff are known controls on the ages of organic matter discharged into the ocean \(^{31,32}\) . Increased fluvial runoff may strengthen riverbank erosion and export of relatively old matter from deeper soil horizons potentially overprinting signals from \(\tau_{\mathrm{soil}}\) \(^{32}\) . Although the Nile- River runoff increased in response to intensified rainfall during the AHP \(^{21,35}\) considerable biases from deep- soil erosion are unlikely given the decrease in reservoir age offsets of \(n\) - alkanoic acids and \(n\) - alkanes during the AHP (Table 1, Extended Data Figure 2). However, intensified Nile River runoff \(^{35}\) may have increased the transport velocity hampering aging of organic matter during land- ocean transit \(^{31}\) . This speed- up would have led to smaller ages of plant waxes in core GeoB7702- 3. Although signals of the transport efficiency in our data cannot be fully ruled out we consider a predominant control of river dynamics and morphology on ages of discharged organic matter unlikely for the following reasons. River runoff decreased after 7 kyrs BP (Figure 2f) while the ages of leaf- wax biomarkers remained relatively constant (Table 1; Figure 2c). The second argument is the similarity between the ages of \(n\) - alkanoic acids and \(n\) - alkanes (Table 1 and Extended Data Figure 2). \(n\) - Alkanoic acids reflect a local signals from the Nile delta region while the \(n\) - alkanes provide a more catchment- integrating signal \(^{22}\) . The extensive Nile catchment is characterized by multiple fluvial environments that differ in geomorphology, flow regime and sedimentary processes \(^{39,40}\) . If such morphologic characteristics exerted substantial control on the ages of organic matter in the fluvial load \(^{31}\) , \(n\) - alkanoic acids and \(n\) - alkanes would show different ages and trends which is not the case (Extended Data Figure 2). + +<|ref|>sub_title<|/ref|><|det|>[[115, 580, 352, 597]]<|/det|> +## \(\tau_{\mathrm{soil}}\) during the past 18 kyrs + +<|ref|>text<|/ref|><|det|>[[113, 605, 881, 712]]<|/det|> +Excluding these potential biases, we conclude that reservoir age offsets of the leaf- wax biomarkers in core GeoB7702- 3 can be used to calculate mean \(\tau_{\mathrm{soil}}\) (see Methods). For \(n\) - alkanes the relationship to mean \(\tau_{\mathrm{soil}}\) is not calibrated \(^{30}\) which is why we focus on the \(n\) - alkanoic acids. Despite the local origin of the \(n\) - alkanoic acids \(^{22}\) catchment- wide inferences on changes in \(\tau_{\mathrm{soil}}\) are justified given the strong similarity with the reservoir age offsets of the \(n\) - alkanes (Extended Data Figure 2). + +<|ref|>text<|/ref|><|det|>[[111, 721, 880, 900]]<|/det|> +During the last 10 kyrs, \(\tau_{\mathrm{soil}}\) was 9- 22 yrs (average 16 yrs). During the late glacial \(\tau_{\mathrm{soil}}\) was 218 yrs which implies that \(\tau_{\mathrm{soil}}\) reduced by an order of magnitude across the deglaciation (Table 1, Figure 2c). According to Eq.1, changes in \(\tau_{\mathrm{soil}}\) may result from variations in the carbon stock size or the efflux. Globally, the terrestrial carbon stocks rose during the deglaciation but according to models they remained rather constant in tropical and subtropical regions \(^{41,42}\) . As for the Nile- River catchment, savannah expanded into the formerly barren Sahara during the AHP (14- 5 kyrs BP) \(^{18,43}\) which would have temporarily increased the total carbon stock in the catchment at these times. Our detected decrease in \(\tau_{\mathrm{soil}}\) together with a likely larger amount of soil carbon during the AHP thus requires a large increase in the carbon efflux from soils (Eq. 1). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 860, 260]]<|/det|> +It is well constrained that microbial respiration, a key component determining the carbon efflux12, accelerates in response to warming and increased soil moisture3,9,11. Both, temperature13,14,19 and precipitation20,21,22 increased in the Nile-River catchment during the deglaciation (Figure 2d,e). To investigate the relationship of \(\tau_{\mathrm{soil}}\) to temperature and rainfall we fit the natural logarithm of \(\tau_{\mathrm{soil}}\) to temperature estimates from the eastern Mediterranean and to the hydrogen isotopic composition of paleo precipitation ( \(\delta \mathrm{Dp}\) ), a common proxy for the amount of rainfall22,44(Figure 3). \(\tau_{\mathrm{soil}}\) is strongly correlated with temperature ( \(\mathrm{R}^2 = 0.82\) ; Figure 3). The correlation with \(\delta \mathrm{Dp}\) ( \(\mathrm{R}^2 = 0.59\) ; Figure 3b) is clearly weaker indicating that temperature was a substantial control on microbial respiration rates during the past 18 kyrs (Figure 3a) while precipitation effects were relatively small. + +<|ref|>sub_title<|/ref|><|det|>[[115, 270, 460, 287]]<|/det|> +## Implications for the global carbon cycle + +<|ref|>text<|/ref|><|det|>[[112, 295, 880, 792]]<|/det|> +The high glacial \(\tau_{\mathrm{soil}}\) indicates that the \(\mathrm{CO_2}\) efflux from northeastern African soils into the atmosphere was much smaller than during the Holocene because of lower respiration rates. According to ref.30, 14C- ages of \(n\) - alkanoic acids have constant offsets not only with mean \(\tau_{\mathrm{soil}}\) but also with soil mean carbon ages (see Methods). Our data show that during the last Glacial soils were much older than during the Holocene (14 000 yrs at 18 kyrs BP vs about 1000 yrs during the Holocene; Table 1). A relatively old soil- carbon pool together with high \(\tau_{\mathrm{soil}}\) agrees with previous estimates of a lower glacial global NPP45 which is congruent with a lower carbon efflux from soils assuming equilibrium conditions (Eq.1). The rejuvenation of soil organic matter accompanying the reduced \(\tau_{\mathrm{soil}}\) implies a massive loss of pre- aged organic carbon from the soils during the deglaciation once the climate warmed. Under present- day conditions, respiration constitutes the majority of the total efflux (>90%) and contributions of lateral fluxes are minor12. If this relation remained similar in the past, the decrease in our estimated \(\tau_{\mathrm{soil}}\) almost entirely reflects increased efflux of aged \(\mathrm{CO_2}\) into the atmosphere. Accordingly, the reduction of \(\tau_{\mathrm{soil}}\) by an order of magnitude implies an increase in soil- to- atmosphere \(\mathrm{CO_2}\) flux of a similar size (Eq.1). This forms a positive feedback to global warming. If widespread across the tropics and sub- tropics this process may have provided relevant contributions to rising atmospheric \(\mathrm{CO_2}\)46 and declining atmospheric radiocarbon contents47 across the deglaciation (Figure 2a,b). Soil- carbon turnover also accelerated in the Ganga- Brahmaputra River catchment as inferred from reservoir age offsets of long chain \(n\) - alkanoic acids from the Bengal Fan27. Calculating \(\tau_{\mathrm{soil}}\) from these data reveals that the range of values and the magnitude of deglacial changes ( \(\tau_{\mathrm{soil}}\) falls from \(\sim 200\) to \(\sim 20\) yrs; Extended Data Table 2) are very similar to the results from the Nile River catchment. Thus, it is very likely that changes in \(\tau_{\mathrm{soil}}\) in that order of magnitude were common across the (sub- )tropics. Interestingly, the radiocarbon data from the Bengal Fan are strongly correlated with rainfall indicating that variability of the Indian summer monsoon played a substantial role in this positive soil- carbon- climate feedback27. However, the results from the Nile River catchment do not confirm the involvement of hydroclimate suggesting a direct response of soil respiration rates to warming. + +<|ref|>text<|/ref|><|det|>[[115, 808, 881, 913]]<|/det|> +Dynamic global vegetation models (DGVM) allow for investigating the effect of the decreasing \(\tau_{\mathrm{soil}}\) on the global carbon cycle and \(\mathrm{CO_{2atm}}\) . We revisit the analysis performed using the Lund Potsdam Jena DGVM (LPJ DGVM)41 and focus on the differences between the Last Glacial Maximum (LGM; 21 kyrs BP) and pre- industrial conditions (PI; 1 kyr BP). Details of the simulation are given in the methods and ref.41. The model suggests relatively constant carbon stocks in the tropics and sub- tropics like other modeling studies42. As for the change in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 880, 292]]<|/det|> +\(\tau_{\mathrm{soil}}\) , we find pronounced discrepancies between our data- based reconstruction (decrease by 200 yrs, Table 1) and the simulated values (Extended Data Figure 4a, b). The model indicates marginal change of less than 50 yrs in the wider (sub- )tropics. Substantial changes of similar magnitude as in our reconstruction are simulated only in the northern high latitudes (Extended Data Figure 4a, b). According to Eq.1, the underestimation of changes in (sub- )tropical \(\tau_{\mathrm{soil}}\) translates into underestimated, simulated changes in microbial respiration rates, respectively \(\mathrm{CO_2}\) efflux. The discrepancies between our data- based estimates of \(\tau_{\mathrm{soil}}\) and the LPJ DGVM simulations suggest that the climate feedback from amplified (sub- ) tropical soil respiration due to warming is underestimated in models. In most recent CMIP6 models the global mean \(\tau_{\mathrm{soil}}\) decreases by up to 20 years until the year 2100 for future emission scenarios \(^{48}\) . However, a spatially resolved analysis of \(\tau_{\mathrm{soil}}\) is missing preventing an evaluation if the soil carbon cycle in the (sub- )tropics has substantially improved in the meantime. + +<|ref|>text<|/ref|><|det|>[[115, 310, 880, 381]]<|/det|> +Providing evidence for a direct response of \(\tau_{\mathrm{soil}}\) to warming in the (sub- ) tropics during the last deglaciation, our study suggests that also the recent global warming will be associated with dominant temperature effects on \(\tau_{\mathrm{soil}}\) . Positive feedbacks from enhanced soil \(\mathrm{CO_2}\) efflux from soils into the atmosphere may thus be expected upon further warming. + +<|ref|>sub_title<|/ref|><|det|>[[115, 418, 228, 436]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[112, 444, 880, 907]]<|/det|> +1. Lal, R. Soil carbon sequestration impact on global climate change and food security. Science 304, 1623-1627 (2004). +2. Crisp, D. et al. How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle? Rev. Geophys. 60, 1-64 (2022). +3. Wang, S. et al. Soil and vegetation carbon turnover times from tropical to boreal forests. Funct. Ecol. 32, 71-82 (2018). +4. Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213-217 (2014). +5. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165-173 (2006) +6. Raich, J. W. and Schlesinger, W. H. 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Hydroclimate variability in the Nile River Basin during the past 28,000 years. Earth Planet. Sci. Lett. 438, 47–56 (2016). +22. Meyer, V. D. et al. Evolution of winter precipitation in the Nile river watershed since the last glacial. Clim. Past 20, 523–546 (2024). +23. Kusch, S. et al. Controls on the age of plant waxes in marine sediments – A global synthesis. Org. Geochem. 157, 104259 (2021). +24. French, K. L. et al. Millennial soil retention of terrestrial organic matter deposited in the Bengal Fan. Sci. Rep. 8, 1–8 (2018). +25. Kusch, S., Rethemeyer, J., Schefuß, E. & Mollenhauer, G. Controls on the age of vascular plant biomarkers in Black Sea sediments. Geochim. Cosmochim. Acta 74, 7031–7047 (2010). +26. Meyer, V. D. et al. Permafrost-carbon mobilization in Beringia caused by deglacial meltwater runoff, sea-level rise and warming. Environ. Res. Lett. 14, 085003 (2019). +27. Hein, C. J., Usman, M., Eglinton, T. I., Haghipour, N. & Galy, V. V. Millennial-scale hydroclimate control of tropical soil carbon storage. Nature 581, 63–66 (2020). +28. Schefuß, E. et al. Hydrologic control of carbon cycling and aged carbon discharge in the Congo River basin. Nat. Geosci. 9, 687–690 (2016). +29. Soulet, G., Skinner, L. C., Beaupré, S. R. & Galy, V. A note on reporting of reservoir \(^{14}\mathrm{C}\) disequilibria and age offsets. Radiocarbon 58, 205–211 (2016). +30. Eglinton, T. et al. Climate Control on Terrestrial Biospheric Carbon Turnover. PNAS 118, 781–781 (2021). +31. Repasch, M. et al. Fluvial organic carbon cycling regulated by sediment transit time and mineral protection. Nat. Geosci. 14, 842–848 (2021). +32. Chen, M., Li, D. W., Zhang, H., Wang, Z. & Zhao, M. Distinct variations and mechanisms for terrestrial OC \(^{14}\mathrm{C}\) -ages in the Eastern China marginal sea sediments since the last deglaciation. Quat. Sci. Rev. 315, 108235 (2023). +33. Lambeck, K., Rouby, H., Purcell, A., Sun, Y. & Sambridge, M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proc. Natl. Acad. Sci. 111, 15296–15303 (2014). +34. Winterfeld, M. et al. Deglacial mobilization of pre-aged terrestrial carbon from degrading permafrost. Nat. Commun. 9, (2018). +35. Revel, M. et al. 100,000 Years of African monsoon variability recorded in sediments of the Nile margin. Quat. Sci. Rev. 29, 1342–1362 (2010). +36. Kayranli, B., Scholz, M., Mustafa, A. & Hedmark, Å. Carbon storage and fluxes within freshwater wetlands: A critical review. Wetlands 30, 111–124 (2010). +37. Rebelo, L. M. & McCartney, M. P. Wetlands of the Nile Basin Distribution, functions and contribution to livelihoods. Nile River Basin Water, Agric. Gov. Livelihoods, 9780203128, 212–228 (2013). +38. Spencer-Jones, C. L. et al. Bacteriohapanepolyols in tropical soils and sediments from the Congo River catchment area. Org. Geochem. 89–90, 1–13 (2015). +39. Woodward, J. C., Macklin, M. G., Krom, M. D. & Williams, M. A. J. The Nile: Evolution, Quaternary River Environments and Material Fluxes. In Large Rivers: Geomorphology and Management, edited by A. Gupta, pp. 261–292, John Wiley & Sons. (2008). +40. Macklin, M. G. et al. A new model of river dynamics, hydroclimatic change and human settlement in the Nile Valley derived from meta-analysis of the Holocene fluvial archive. Quat. Sci. Rev. 130, 109–123 (2015). +41. Köhler, P., Joos, F., Gerber, S. & Knutti, R. Simulated changes in vegetation distribution, land carbon storage, and atmospheric \(\mathrm{CO_2}\) in response to a collapse of the North Atlantic thermohaline circulation. Clim. Dyn. 25, 689–708 (2005). +42. Jeltsch-Thönmes, A., Battaglia, G., Cartapanis, O., Jaccard, S. L. & Joos, F. Low terrestrial carbon storage at the Last Glacial Maximum: Constraints from multi-proxy data. Clim. Past 15, 849–879 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 872, 97]]<|/det|> +43. Watrin, J. et al. Plant migration and plant communities at the time of the 'green Sahara'. Comptes Rendus - + +<|ref|>text<|/ref|><|det|>[[113, 98, 344, 111]]<|/det|> +Geosci. 341, 656- 670 (2009). + +<|ref|>text<|/ref|><|det|>[[113, 112, 875, 142]]<|/det|> +44. Tierney, J. E., Pausata, F. S. R. & De Menocal, P. B. Rainfall regimes of the Green Sahara. Sci. Adv. 3, 1-10 (2017). + +<|ref|>text<|/ref|><|det|>[[113, 143, 850, 171]]<|/det|> +45. Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74-79 (2012). + +<|ref|>text<|/ref|><|det|>[[113, 172, 870, 201]]<|/det|> +46. Marcott, S. A. et al. Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature 514, 616-619 (2014). + +<|ref|>text<|/ref|><|det|>[[113, 202, 866, 231]]<|/det|> +47. Reimer, P. J. et al. The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBp). Radiocarbon 62, 725-757 (2020). + +<|ref|>text<|/ref|><|det|>[[113, 232, 860, 261]]<|/det|> +48. Varney, R. M. et al. Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming. Biogeosciences 20, 3767-3790 (2023). + +<|ref|>text<|/ref|><|det|>[[113, 262, 830, 290]]<|/det|> +49. Schefuf, E., Schouten, S., Jansen, J. H. F. & Sinninghe Damsté, J. S. African vegetation controlled by tropical sea surface temperatures in the mid-Pleistocene period. Nature 422, 418-421 (2003). + +<|ref|>text<|/ref|><|det|>[[113, 290, 870, 334]]<|/det|> +50. Köhler P., Nehrbass-Ahles C., Schmitt J., Stocker T. F. & Fischer, H. A. 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4 and N2O and their radiative forcing Earth Syst. Sci. Data 9 363-87 (2017). + +<|ref|>sub_title<|/ref|><|det|>[[115, 372, 206, 390]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 401, 377, 418]]<|/det|> +## Core material and chronology + +<|ref|>text<|/ref|><|det|>[[115, 427, 880, 585]]<|/det|> +Gravity core GeoB7702- 3 was retrieved onboard RV Meteor at the continental slope off the Sinai Peninsula during cruise M52/2 in \(2002^{51}\) . Due to the anticlockwise surface circulation in the eastern Mediterranean the fluvial load of the Nile River is transported eastward along the coast to the study site \(^{52}\) . Prior to sample preparation, the core was stored at \(4^{\circ}\mathrm{C}\) . The sample set for Bacteriohapanepolyol (BHP) quantification comprised 21 samples. Samples for compound-specific radiocarbon analysis (CSRA) were taken from 9 selected horizons ( \(\sim 2\) cm thickness). The age model of the core was previously published in ref. \(^{13}\) and updated by ref. \(^{22}\) . Age depth modeling is based upon 24 radiocarbon dates of planktic foraminifera and Bayesian modeling using the BACON software \(^{53}\) and the Marine20 calibration curve \(^{54}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 596, 258, 612]]<|/det|> +## Lipid extraction + +<|ref|>text<|/ref|><|det|>[[115, 622, 870, 852]]<|/det|> +Samples were freeze-dried and homogenized with a mortar. Samples for compound-specific radiocarbon analyses (ca. 100- 120g) were extracted with Dichloromethane (DCM):Methanol (MeOH) 9:1 (v/v) using a Soxhlet-apparatus (60°C, 48 hours) and were processed without internal standards. The samples were hydrolyzed with 0.1 N potassium hydroxide (KOH) in MeOH:H2O 9:1 (v/v) at 80°C for two hours. Neutral compounds were extracted with \(n\) - hexane, acids with DCM after acidifying the saponified solution with hydrochloric acid (HCl). Hydrocarbons were separated from polar compounds by column-chromatography using deactivated SiO2. The hydrocarbons were eluted with \(n\) - hexane, polar compounds with DCM:MeOH 1:1 (v/v). The fatty acids were derivatized to fatty acid methyl esters (FAME). The methylation was performed with MeOH of known \(\Delta^{14}\mathrm{C}\) , together with HCl at 50°C. Air in the headspace of the sample-tube was replaced by nitrogen gas (N2). FAMEs were recovered with \(n\) - hexane and were subsequently cleaned-up with column chromatography using deactivated SiO2 and NaSO4. FAMEs were eluted with DCM:Hexane 2:1 (v/v). + +<|ref|>text<|/ref|><|det|>[[115, 862, 883, 915]]<|/det|> +Freeze-dried sediment samples dedicated for BHP analysis (ca. 3- 6 g) were extracted using a modified Bligh and Dyer extraction \(^{55,56,57}\) . The sediment samples were ultrasonically extracted (10 min) with a solvent mixture containing MeOH, DCM and phosphate buffer (2:1:0.8, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 877, 222]]<|/det|> +v:v:v). After centrifugation, the solvent was collected, combined and the residues re-extracted twice. The combined solvent layers were added to separatory funnels and separated from the aqueous layer by the addition of DCM and Milli- Q water. After the layers separated, the bottom layer (DCM) was drawn off and collected, while the remaining aqueous layer was washed twice with DCM. The combined DCM layers were dried under a continuous flow of \(\mathrm{N}_2\) . Aliquots of the total lipid extracts (TLEs) were obtained and DGTS (1,2-dipalmitoyl-sn-glycero-3-O-4'-(N,N,N-trimethyl)-homoserine, Avanti Polar Lipids) was added as an internal standard before UHPLC- HRMS analysis. + +<|ref|>sub_title<|/ref|><|det|>[[115, 250, 547, 268]]<|/det|> +## UHPLC-HRMS analysis of non-derivatized BHPs + +<|ref|>text<|/ref|><|det|>[[112, 275, 880, 675]]<|/det|> +Non- derivatized BHPs were quantified by injecting \(1\%\) of the TLE with \(2\mathrm{ng}\) internal standard (DGTS) dissolved in MeOH:DCM (9:1, v:v) on a Dionex Ultimate 3000RS ultra- high performance liquid chromatography (UHPLC) system connected to a Bruker maXis Plus Ultra- High Resolution quadrupole time- of- flight tandem mass spectrometer (UHR- qTOF- MS) equipped with an ESI ion source operating in positive mode (Bruker Daltonik, Bremen, Germany). The non- derivatized BHP analysis was performed according to ref.58 with a column temperature of \(30^{\circ}\mathrm{C}\) and a modified separation method. Briefly, separation was achieved on an Acquity BEH C18 column (2.1x 150 mm, \(1.7\mu \mathrm{m}\) particle size, Waters, Eschborn, Germany) and a solvent system consisting of eluent A of MeOH: \(\mathrm{H}_2\mathrm{O}\) (85:15) and eluent B MeOH:isopropanol (1:1) with both containing \(0.12\%\) (v/v) formic acid and \(0.04\%\) (v/v) aqueous ammonia. Compounds were eluted with \(5\%\) B for \(3\mathrm{min}\) , followed by a linear gradient to \(60\%\) B at \(12\mathrm{min}\) and then to \(100\%\) B at \(50\mathrm{min}\) and holding at \(100\%\) B until 80 min. The column was then equilibrated for \(20\mathrm{min}\) leading to a total run time of \(100\mathrm{min}\) . The flow rate was held constant at \(0.2\mathrm{ml}\mathrm{min}^{- 1}\) . Mass spectra were acquired in positive ion monitoring of m/z 50 to 2000 and data- dependent fragmentation of the most abundant ions (dynamically selected, typically 3- 8) for a total cycle time of \(2\mathrm{s}\) and dynamic exclusion (activation after 5 spectra, release after \(15\mathrm{s}\) ). Ion source settings and parameters for detection and fragmentation of BHPs were optimized while infusing extracts. Every analytical run was mass- calibrated by loop- injection of Agilent ESI- L tune mix and lock mass calibration (m/z 922.0098, added in ESI source) of each mass spectrum, leading to typical mass deviations of \(< 1 - 3\mathrm{ppm}\) . + +<|ref|>text<|/ref|><|det|>[[115, 655, 880, 792]]<|/det|> +BHPs were identified based on the exact mass of the protonated or ammoniated molecular ion, relative retention time and \(\mathrm{MS}^2\) fragmentation similar to ref.58. Extracted ion chromatograms (EIC) of the most abundant molecular ion ( \(10\mathrm{mDa}\) mass accuracy window) were used to (semi- )quantify individual BHPs by peak integration. MS variability and ion suppression was controlled by the peak area of the DGTS internal standard. As no authentic standards were available for BHP quantification, abundances are reported based on peak areas of the individual BHPs normalized to the dry weight of the extracted sediments (i.e., in arbitrary units (AU)/ \(\mu \mathrm{g}\) dw). + +<|ref|>sub_title<|/ref|><|det|>[[115, 806, 373, 823]]<|/det|> +## Purification of leaf-wax lipids + +<|ref|>text<|/ref|><|det|>[[115, 832, 880, 920]]<|/det|> +For CSRA the target FAMEs and \(n\) - alkanes were purified using preparative capillary gas chromatography59. The purification was performed on an Agilent 7890B gas chromatograph (GC), equipped with a temperature programmable cooled injection- system (CIS, Gerstel) and connected to a preparative fraction collector (PFC, Gerstel). Separation was performed on a Restek Rxi- 1ms fused silica capillary column ( \(30\mathrm{m}\) , \(0.53\mathrm{mm}\) i.d., \(1.5\mu \mathrm{m}\) film thickness). All + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 872, 260]]<|/det|> +samples were injected repeatedly with \(5\mu \mathrm{L}\) per injection from a concentration of \(1\mu \mathrm{g / \mu l}\) (FAMEs) and \(500\mu \mathrm{g / \mu l}\) ( \(n\) - alkanes) using \(n\) - hexane. The injector was operated in solvent vent mode (vent: \(100\mathrm{ml / min}\) , 0 psi until 0.12 min). The CIS temperature program was: \(60^{\circ}\mathrm{C}\) (0.05 min), \(12^{\circ}\mathrm{C / s}\) to \(320^{\circ}\mathrm{C}\) (5 min), \(12^{\circ}\mathrm{C / s}\) to \(340^{\circ}\mathrm{C}\) (5 min). The GC temperature program was set: \(60^{\circ}\mathrm{C}\) (2 min), \(20^{\circ}\mathrm{C / min}\) to \(150^{\circ}\mathrm{C}\) , \(8^{\circ}\mathrm{C / min}\) to \(320^{\circ}\mathrm{C}\) (40 min). Helium was used as carrier gas (4.0 ml/min). The transfer line and PFC were heated at \(320^{\circ}\mathrm{C}\) while the traps for collection were maintained at room temperature. The backflash system of the PFC was constantly switched off. The traps were rinsed with \(n\) - hexane to recover the purified compounds. Splits (0.1%) were analyzed by GC- FID to check for potential contaminants and to quantify the purified target compounds for CSRA. + +<|ref|>sub_title<|/ref|><|det|>[[115, 270, 173, 286]]<|/det|> +## CSRA + +<|ref|>text<|/ref|><|det|>[[113, 295, 880, 560]]<|/det|> +The isotopic ratio \((^{14}\mathrm{C} / ^{12}\mathrm{C})\) of the FAMEs and \(n\) - alkanes was determined by Accelerator Mass Spectrometry (AMS). The measurements were carried out on the Ionplus MICADAS- system equipped with a gas- ion source \(^{60,61,62}\) at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven. CSRA was performed according to the protocols described in ref. \(^{63}\) . In short, the purified individual target compounds were transferred into tin capsules and packed. As for FAMEs, the \(n\) - \(\mathrm{C}_{26:0}\) and \(n\) - \(\mathrm{C}_{28:0}\) homologues were prepared individually except for two samples for which the homologues had to be combined in order to achieve adequate sample size (Extended Data Table 1). For \(n\) - alkanes we combined the \(n\) - \(\mathrm{C}_{29}\) , \(n\) - \(\mathrm{C}_{31}\) and \(n\) - \(\mathrm{C}_{33}\) homologues to obtain enough material for dating. Samples were combusted via the Elementar vario ISOTOPE EA (Elemental Analyzer) and the produced \(\mathrm{CO_2}\) was directly transferred into the coupled MICADAS. Radiocarbon contents of the samples were analyzed along with reference standards (oxalic acid II; NIST 4990c) and blanks (phthalic anhydride; Sigma- Aldrich 320064) and in- house reference sediments. Blank correction and standard normalization were performed via the BATS software \(^{64}\) . The AMS- results are reported as “fraction modern carbon” ( \(\mathrm{F}^{14}\mathrm{C}\) ) and \(\Delta^{14}\mathrm{C}^{65}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 571, 526, 588]]<|/det|> +## Assessment of procedure blanks and correction + +<|ref|>text<|/ref|><|det|>[[113, 598, 880, 792]]<|/det|> +In order to correct for carbon introduced during sample processing, procedure blanks were assessed by isolating FAs from a modern and a fossil standard material according to the methods described above. Leaves of a corn plant, collected in 2019, were used as modern standard ( \(\mathrm{F}^{14}\mathrm{C}\) : \(1.0096 \pm 0.0024\) ) while “Rekord” coal- briquette (lignite from Lusatia, Eastern Germany) served as fossil standard ( \(\mathrm{F}^{14}\mathrm{C}\) : \(0.0019 \pm 0.0002\) ). For the coal, asphaltene precipitation was performed additionally using DCM:MeOH 97:3 (v/v) and pentane. The \(\mathrm{F}^{14}\mathrm{C}\) and mass of the blank were assessed using a Bayesian approach \(^{66}\) . The procedure blank was \(3.079 \pm 0.433 \mu \mathrm{gC}\) with an \(\mathrm{F}^{14}\mathrm{C}\) of \(0.529 \pm 0.072\) . Blank- correction of the samples and error propagation was performed after ref. \(^{67}\) . The blank corrected \(\mathrm{F}^{14}\mathrm{C}\) - values of FAMEs were further corrected for the methyl- group, which had been added during the derivatization process, using isotopic mass balance. + +<|ref|>sub_title<|/ref|><|det|>[[115, 803, 428, 820]]<|/det|> +## Calculation of pre-depositional ages + +<|ref|>text<|/ref|><|det|>[[115, 830, 880, 900]]<|/det|> +The age of the compounds at the time of deposition can be calculated using the “reservoir age offset” (R) \(^{29}\) which describes the age offset (in \(^{14}\mathrm{C}\) years) between two carbon reservoirs at a given time \(^{29}\) . In our case it needs to be calculated from the ratio of the radiocarbon contents of the sample and the atmosphere at the time of deposition in marine sediments (Eq. 2). + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[366, 81, 630, 115]]<|/det|> +\[\mathrm{R} = 8033^{*}\mathrm{ln}\left(\frac{\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}}}{\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}}\right)\quad (\mathrm{Eq.}2),\] + +<|ref|>text<|/ref|><|det|>[[115, 159, 878, 230]]<|/det|> +where \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}}\) is the \(\mathrm{F}^{14}\mathrm{C}\) - value the sample had at the time of deposition at site GeoB7702- 3 and \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}\) is the radiocarbon content of the atmosphere. It can be calculated by correcting the measured \(\mathrm{F}^{14}\mathrm{C}\) - value of the sample \((\mathrm{F}^{14}\mathrm{C}_{\mathrm{sample}})\) for the decay that has taken place since the deposition (Eq. 3). + +<|ref|>equation<|/ref|><|det|>[[350, 243, 644, 266]]<|/det|> +\[\mathrm{F}^{14}\mathrm{C}_{\mathrm{initial}} = \mathrm{F}^{14}\mathrm{C}_{\mathrm{sample}}*\mathrm{e}^{\lambda t}\quad (\mathrm{Eq.}3),\] + +<|ref|>text<|/ref|><|det|>[[115, 280, 877, 406]]<|/det|> +where t is the time of deposition and \(\lambda\) the decay constant of radiocarbon \(^{65}\) . The time of deposition was inferred from radiocarbon dates of planktic foraminifera (core chronology) \(^{22}\) . \(\mathrm{F}^{14}\mathrm{C}_{\mathrm{atm}}\) values were adopted from INTCAL20 \(^{47}\) . In case of samples for which the \(\mathrm{F}^{14}\mathrm{C}\) values of the \(n - \mathrm{C}_{26:0}\) and \(n - \mathrm{C}_{28:0}\) homologues had been measured separately, we calculated R from the abundance weighted mean of the \(\mathrm{F}^{14}\mathrm{C}\) - values in order to keep comparability with samples for which the two homologues had been combined prior to AMS measurement (Extended Data Table 1). + +<|ref|>sub_title<|/ref|><|det|>[[115, 419, 441, 437]]<|/det|> +## Calculation of \(\tau_{\mathrm{soil}}\) and mean soil ages + +<|ref|>text<|/ref|><|det|>[[115, 445, 880, 605]]<|/det|> +Soil organic matter is a complex mixture of compounds that vary in terms of their reactivity and consequently possess different turnover times \(^{68,69}\) . Due to this complexity of fast and slow cycling components in SOC, leaf- wax lipids generally exceed the mean soil turnover times by a multiple \(^{30}\) . Analyzing the ages of \(n\) - alkanoic acids in particulate organic matter from a global sample set comprising coastal sediments near river mouths, riverbeds and banks as well as suspension load, ref. \(^{30}\) identified globally constant offsets between \(^{14}\mathrm{C}\) ages of \(n\) - alkanoic acids and \(\tau_{\mathrm{soil}}\) (Eq. 4). Similarly, constant offsets between \(n\) - alkanoic acids and soil mean carbon age have been reported (Eq. 5). The soil mean carbon age here is defined as the radiocarbon age integrated over the top 100 cm depth \(^{30,70}\) . + +<|ref|>equation<|/ref|><|det|>[[350, 640, 648, 660]]<|/det|> +\[\mathrm{Age}_{n - \mathrm{alkanoic\ acid}} = 40.1*\tau_{\mathrm{soil}}\quad (\mathrm{Eq.}4)\] + +<|ref|>equation<|/ref|><|det|>[[333, 697, 663, 718]]<|/det|> +\[\mathrm{Age}_{n - \mathrm{alkanoic\ acid}} = 0.62*\mathrm{soil\ age}\quad (\mathrm{Eq.}5),\] + +<|ref|>text<|/ref|><|det|>[[115, 754, 875, 916]]<|/det|> +where the \(\mathrm{age}_{n - \mathrm{alkanoic\ acid}}\) is given in \(^{14}\mathrm{C}\) years \(^{30}\) . Under the premise that these relationships remained constant in the past, they allow to calculate catchment- integrating mean \(\tau_{\mathrm{soil}}\) (in yrs) and mean soil carbon ages (0- 100 cm, in yrs) from the \(^{14}\mathrm{C}\) - ages of \(n\) - alkanoic acids in marine sedimentary archives and to monitor changes in the carbon cycle within a river catchment through time. The sample set of ref. \(^{30}\) covers a broad range of latitude (73 °N- 38 °S) and consequently represents different biomes and climate zones from tropical rainforest to arctic tundra. It reflects broad ranges of annual air temperature (- 16 to 27 °C) and mean annual precipitation (amount 230 mm/yr - 2200 mm/yr) \(^{30}\) . The range of \(^{14}\mathrm{C}\) ages from \(n\) - alkanoic acids covered by the dataset is recent to >10,000 yrs \(^{30}\) . The pre- depositional ages calculated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 870, 153]]<|/det|> +for the \(n\) - alkanoic acids in core GeoB7702- 3 are within that range ( \(348 \pm 240 - 8723 \pm 212\) yrs; Table 1 and Extended Data Table 1). Thus, our inferred \(\tau_{\text{soil}}\) are within the calibrated range. Since the relationship between \(\tau_{\text{soil}}\) and the pre-depositional age of \(n\) - alkanes is unknown, we cannot convert our \(n\) - alkane age into \(\tau_{\text{soil}}\). + +<|ref|>sub_title<|/ref|><|det|>[[115, 163, 513, 180]]<|/det|> +## Dynamic Global Vegetation Model simulation + +<|ref|>text<|/ref|><|det|>[[115, 190, 879, 368]]<|/det|> +Temperature and soil moisture effects have been implemented in dynamical global vegetation models for decades \(^{71,72}\) For this study, the Lund Potsdam Jena Dynamic Global Vegetation Model (LPJ DGVM) \(^{41,48}\) was used. We revisited the analysis performed by ref. \(^{48}\) and investigate changes in \(\tau_{\text{soil}}\) , net primary production (NPP), soil respiration ( \(\text{Rh}\) ) and soil carbon stock size between the Last Glacial Maximum (LGM; 21 kyrs BP) and pre- industrial (PI, 1 kyrs BP; Extended Data Figure 3). The global land carbon cycle was transiently simulated across Termination I subtracting the effect of \(\text{CO}_2\) fertilization and restricting the analysis to areas unaffected by rising sea level or continental ice retreat \(^{41,48}\) . \(\tau_{\text{soil}}\) is calculated according to Eq. 1 using the simulated carbon stock size and the simulated NPP and \(\text{Rh}\) , respectively. The results are shown in Extended Data Figure 3a,b. + +<|ref|>text<|/ref|><|det|>[[115, 377, 879, 517]]<|/det|> +The model simulates a total change in the global terrestrial carbon pools of \(820 \text{ PgC}\) between the LGM and \(\text{PI}^{48}\) . This agrees well with the median of \(850 \text{ PgC}\) estimated by a recent multi- proxy approach \(^{42}\) showing that the simulated global patterns are in agreement with other studies. The model suggests a reduction of the global land carbon stock by \(200 - 250 \text{ PgC}\) for PI relative to the LGM \(^{48}\) . This represents the summed- up change in vegetation and soil carbon caused by temperature and precipitation variability \(^{48}\) . Calculating \(\tau_{\text{soil}}\) from net primary production (NPP) and respiration fluxes ( \(\text{Rh}\) ) reveals similar results indicating that NPP and Rh are in equilibrium (Extended Data Figure 3a,b,c,d). + +<|ref|>sub_title<|/ref|><|det|>[[115, 547, 284, 564]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 575, 768, 610]]<|/det|> +The data generated in the study will be accessible from the PANGAEA database (www.pangaea.de). + +<|ref|>sub_title<|/ref|><|det|>[[115, 650, 228, 666]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[135, 676, 880, 899]]<|/det|> +51. Pätzold, J., Bohrmann, G., Hübscher, C. Black Sea – Mediterranean – Red Sea. Cruise No. 52, January 2 – March 27, 2002, Istanbul – Limassol. Universität Hamburg. METEOR-Berichte 03-2, 178 pp (2003). +52. Weldeab, S., Emeis, K.-C., Hemleben, C., Siebel, W. Provenance of lithogenic surface sediments and pathways of riverine suspended matter in the Eastern Mediterranean Sea: evidence from \(^{143}\text{Nd}/^{144}\text{Nd}\) and \(^{87}\text{Sr}/^{86}\text{Sr}\) ratios. Chem. Geol. 186, 139-149 (2002). +53. Blaauw, M., & Christen, J.A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analysis 6, 457-474 (2011). +54. Heaton, T. J. et al. Marine20 - The Marine Radiocarbon Age Calibration Curve (0-55,000 cal BP). Radiocarbon 62, 779-820 (2020). +55. Sturt, H. F., Summons, R. E., Smith, K., Elvert, M., & Hinrichs, K. (2004) Intact polar membrane lipids in prokaryotes and sediments deciphered by high performance liquid chromatography / electrospray ionization multistage mass spectrometry — new biomarkers for biogeochemistry and microbial ecology. Rapid communications in mass spectrometry 18(6), 617-628 (2004). +56. Wörmer, L., Lipp, J. S. & Hinrichs, K. U. Comprehensive Analysis of Microbial Lipids in Environmental Samples Through HPLC-MS Protocols. In: McGenity, T., Timmis, K. &, Nogales, B. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[135, 81, 880, 630]]<|/det|> +(eds) Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks. Springer, Berlin, Heidelberg (2015).57. Smit, N.T., Villanueva, L., Rush, D., Grassa, F., Witkowski, C.R., Holzheimer, M., Minnaard, A.J., Sinninghe Damsté, J.S. and Schouten, S. (2021) Novel hydrocarbon- utilizing soil mycobacteria synthesize unique mycoercosis acids at a Sicilian everlasting fire. Biogeosciences 18, 1463- 1479.58. Hopmans, E. C., et al. Analysis of nonderivatized bacteriohanopolys using UHPLC- HRMS reveals great structural diversity in environmental lipid assemblages, Org. Geochem., 160, 104285 (2021).59. Eglinton, T., Aluwihare, L., Bauer, J., Druffel, E. & McNichol, A. Gas chromatographic isolation of individual compounds from complex matrices for radiocarbon dating. Anal. Chem. 68, 904- 912 (1996).60. Ruff, M., Wacker, L., Gaggeler, H. W., Suter, M., Synal, H.- A. & Szidat S. A gas ion source for radiocarbon measurements at 200 kV. Radiocarbon 49, 307- 314 (2007).61. Synal, H.- A., Stocker, M. & Suter, M. MICADAS: a new compact radiocarbon AMS system. Nucl. Instrum. Methods Phys. Res. A 259, 7- 13 (2007).62. Wacker, L. et al. A versatile gas interface for routine radiocarbon analysis with a gas ion source. Nucl. Instrum. Methods Phys. Res. B 294, 315- 319 (2013).63. Mollenhauer, G., Grotheer, H., Gentz, T., Bonk, E. & Hefter, J. Standard operation procedures and performance of the MICADAS radiocarbon laboratory at Alfred Wegener Institute (AWI), Germany. Nucl. Instruments Methods Phys. Res. Sect. B Beam Interact. with Mater. Atoms 496, 45- 51 (2021).64. Wacker, L., Christl, M. & Synal, H. A. Bats: a new tool for AMS data reduction. Nucl. Instrum. Methods Phys. Res. B 268, 976- 979 (2010).65. Stuiver, M. & Polach, H. Discussion: reporting of \(^{14}\mathrm{C}\) data. Radiocarbon 19, 355- 363 (1977).66. Sun, S. et al. \(^{14}\mathrm{C}\) Blank Assessment in Small- Scale Compound- Specific Radiocarbon Analysis of Lipid Biomarkers and Lignin Phenols. Radiocarbon 62, 207- 218 (2020).67. Wacker, L. & Christl, M. Data Reduction for Small Samples. Error Propagation using the Model of Constant Contamination. Annual Report of Ion Beam Physics, ETH Zürich 36 (2012).68. Wiesenberg, G. L. B., Schwarzbauer, J., Schmidt, M. W. I. & Schwark, L. Source and turnover of organic matter in agricultural soils derived from n- alkane/n- carboxylic acid compositions and C- isotope signatures. Org. Geochem. 35, 1371- 1393 (2004).69. Trumbore, S. Age of soil organic matter and soil respiration: Radiocarbon constraints on belowground C dynamics. Ecol. Appl. 10, 399- 411 (2000).70. Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555- 559 (2020).71. Lloyd, J. and Taylor, J. A. On the Temperature Dependence of Soil Respiration Functional Ecology, 8, 315- 323 (1994).72. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan OJ, Lucht W, Sykes MT, Thonicke K, Venevsky S. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol 9, 161- 185 (2003). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[270, 110, 707, 411]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 426, 872, 465]]<|/det|> +
Figure 1: Map of African vegetation zones after ref.49. Med: Mediterranean zone; MST: Mediterranean-Saharan transitional vegetation; AM: Afro-montane vegetation zone. The Nile River catchment is marked by the blue shading. The red star indicates the study site GeoB7702-3.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 78, 816, 682]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 688, 875, 863]]<|/det|> +
Figure 2: Environmental changes in the Nile-River delta region during the past 18 kyrs. (a) Ice-core \(\mathrm{CO_2}\) -contents from EPICA Dome \(\mathrm{C^{50}}\) given as indicator for atmospheric \(\mathrm{CO_2}\) concentrations. (b) Atmospheric \(\Delta^{14}\mathrm{C}\) contents according to INTCAL20. (c) Reservoir age offsets between the \(n\) -alkanoic acids and the atmosphere at the time of deposition at site GeoB7702-3. \(\mathrm{t_{soil}}\) deduced from the reservoir age offsets of \(n\) -alkanoic acids. (d) Sea surface temperature reconstruction for the eastern Mediterranean based on the \(\mathrm{TEX_{86}}\) proxy from core GeoB7702-3. (e) Hydrogen isotopic composition of precipitation \((\delta \mathrm{Dp})\) calculated from the \(\delta \mathrm{D}\) of \(n\) -alkanoic acids from core GeoB7702-3 as proxy for rainfall amount. (f) Oxygen isotopic compositions of the planktic foraminifera species Globigerinoides ruber \((\delta^{18}\mathrm{O}_{G.rubber})\) in core MS27PT (Figure 1) indicating salinity changes in the eastern Mediterranean associated with freshwater runoff from the Nile River. (g) Aminopentol abundances in core GeoB7702-3 used as proxy for the extent of methane producing wetlands in the catchment. AU: arbitrary units; dw: dry weight of extracted sediment. Additional abundance profiles from the suite of aminobacteriohanopelyols are given in Extended Data Figure 3 (h) Concentrations of \(n\) -alkanoic acids \((\Sigma n - \mathrm{C}_{26:0}, n - \mathrm{C}_{28:0}, n - \mathrm{C}_{30:0}, n - \mathrm{C}_{32:0})\) reporting on the land-ocean transport of terrigenous organic matter. (i) Global rate of sea-level change over the last 20 kyrs. The blue bars mark the timing of the African Humid Period (AHP) and Green Sahara and their optimum. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bolling/Allerod interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 81, 880, 344]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 353, 870, 465]]<|/det|> +
Figure 3: Power-law relationships between \(\tau_{\mathrm{soll}}\) and (a) temperature and (b) hydrogen isotopic composition of precipitation (8Dp). Temperature estimates are based upon the TEX86-proxy at site GeoB7702-3 and are adopted from ref.13. 8Dp is calculated from the hydrogen isotopic composition of \(n\) -alkanoic acids ( \(n\) -C26:0 and \(n\) -C28:0 homologues) from core GeoB7702-3. The p-values for the regressions are \(< 0.05\) .8Dp is deduced from the hydrogen isotopic composition of \(n\) -alkanoic acids and \(n\) -alkanes in core GeoB7702-3. Unfortunately, mean annual air temperature estimates covering the past 18 kyrs are not available for the Nile-River catchment. Therefore, we use the TEX86-based temperature record from GeoB7702-3 interpreted to reflect sea surface temperature (SST) in the eastern Mediterranean. We assume that SST and surface air temperatures in the Nile delta region probably developed similarly due to heat exchange between the sea surface and the overlying air.
+ +<|ref|>text<|/ref|><|det|>[[114, 504, 875, 603]]<|/det|> +Table 1. Reservoir age offsets (R) of leaf- wax biomarkers and the atmosphere at the time of deposition in marine sediments. R is calculated from compound- specific radiocarbon dating results of the combined \(n\) - C26:0 and \(n\) - C28:0 alkanoic acid homologues and the combined \(n\) - C29, \(n\) - C31, \(n\) - C33 alkane homologues (see Methods). Mean soil carbon turnover times ( \(\tau_{\mathrm{soll}}\) ) and soil mean carbon ages were deduced from the R of \(n\) - alkanoic acids according to ref.30. The hydrogen isotopic composition of precipitation (8Dp) and TEX86 are mean values for the range of the deposition age. 8Dp is based on the 8D signature of \(n\) - alkanoic acids in core GeoB7702-3. TEX86 are sea surface temperature reconstructions at site GeoB7702-3 from ref.13. Propagated standard errors (±) are reported along with the results. \(\mathrm{F}^{14}\mathrm{C}\) and \(\Delta^{14}\mathrm{C}\) values are listed in Extended Data Table 1. + +<|ref|>table<|/ref|><|det|>[[115, 614, 960, 831]]<|/det|> + +
Sample depth [cm]Deposition age min. - max. [kyrs BP]aDeposition age mid-point [kyrs BP]aR n-alkanoic acids [14C yrs]R n-alkanes [14C yrs]τsoll [yrs]Soil mean carbon age [yrs]δDp [‰ VSMOW]bTEX86 [°C]
81.5-84.51.62 - 2.291.93348 ± 240959 ± 1469 ± 6561 ± 392-8.8 ± 2.726.9 ± 0.4
130-1333.11 - 3.693.40733 ± 4321633 ± 16718 ± 111182 ± 710-9.3 ± 2.526.3 ± 0.6
198-2015.35 - 6.015.70902 ± 3311668 ± 11622 ± 91455 ± 559-8.1 ± 1.525.1 ± 0.4
231-2347.24 - 8.147.72563 ± 24721 ± 8714 ± 6908 ± 411-19.3 ± 5.726.7 ± 0.7
251-2549.02 - 10.119.66736 ± 1963447 ± 29818 ± 51187 ± 343-27.2 ± 2.125.3 ± 2.0
278-28111.05 - 12.0511.501631 ± 1583313 ± 17841 ± 62630 ± 3915.0 ± 2.919.1 ± 0.7
297-30012.69 - 13.7313.215384 ± 6184334 ± 213134 ± 208684 ± 13991.0 ± 2.920.0 ± 0.7
359-36216.26 - 17.0716.673453 ± 1192415 ± 8186 ± 95569 ± 6578.3 ± 8.117.5 ± 1.3
393-39617.69 - 18.7318.158723 ± 2127816 ± 341218 ± 2214069 ± 16258.3 ± 2.716.3 ± 0.7
+ +<|ref|>table_footnote<|/ref|><|det|>[[114, 831, 861, 870]]<|/det|> +a: Obtained by radiocarbon dating of planktic foraminifera22. b: calculated by correcting the 8D of the \(n\) - C26:0 and \(n\) - C28:0 alkanoic acids in core GeoB7702-3 for vegetation changes and ice volume22. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[63, 85, 315, 101]]<|/det|> +# 583 Acknowledgements + +<|ref|>text<|/ref|><|det|>[[63, 115, 811, 128]]<|/det|> +584 The study was funded by the DFG Cluster of Excellence: "The Ocean Floor - Earth's + +<|ref|>text<|/ref|><|det|>[[63, 133, 876, 147]]<|/det|> +585 Uncharted Interface". We are grateful to Jürgen Pätzold for providing sample material of core + +<|ref|>text<|/ref|><|det|>[[63, 151, 856, 164]]<|/det|> +586 GeoB7702-3. We thank Ralph Kreutz for support during sample processing for CSRA and + +<|ref|>text<|/ref|><|det|>[[63, 169, 856, 182]]<|/det|> +587 GC-maintenance. Julia Cordes is acknowledged for technical support during BHP analysis. + +<|ref|>text<|/ref|><|det|>[[63, 187, 872, 236]]<|/det|> +588 Pushpak Nadar is thanked for assistance during sample processing for BHP analysis and core sampling for CSRA. Hendrik Grotheer is thanked for support during the AMS measurements at AWI. + +<|ref|>title<|/ref|><|det|>[[63, 268, 340, 282]]<|/det|> +# 592 Author Contributions + +<|ref|>text<|/ref|><|det|>[[63, 297, 860, 381]]<|/det|> +593 VDM developed the concept of the study supported by BW, ES, GM and PK. VDM carried out the sample preparation and data analysis in the laboratories and performed data processing. NTS and JL conducted the analysis of Bacteriohopanepolyols. PK performed the simulations with the LPJ DGVM. All authors were involved in the interpretation and discussion of the results. VDM drafted the manuscript with contributions from all co-authors. + +<|ref|>title<|/ref|><|det|>[[63, 421, 323, 437]]<|/det|> +# 599 Competing interests + +<|ref|>text<|/ref|><|det|>[[63, 451, 658, 465]]<|/det|> +600 The authors declare that none of them has any competing interests. + +<|ref|>title<|/ref|><|det|>[[63, 505, 270, 520]]<|/det|> +# 602 Extended Data + +<|ref|>text<|/ref|><|det|>[[63, 533, 872, 569]]<|/det|> +603 **Extended Data Table 1. CSRA-results reported along with the standard errors (±) of** \(n\) -alkanoic acids and \(n\) -alkanes in core 604 GeoB7702-3. CSRA was performed at the Alfred Wegener Institute (AWI). R is the reservoir age offset between the 605 biomarkers and the atmosphere at the time of deposition in marine sediments calculated after ref.29. + +<|ref|>table<|/ref|><|det|>[[63, 585, 890, 910]]<|/det|> + +
Sample
depth
[cm]		AWI sample
identification
number		Deposition
age range
[kyrs BP]a		Deposition
age
mid-point
[kyrs BP]a		Compounds\(F^{14}C^{b}\)\(A^{14}C^{b}\)
\([\%]\)		R
\([^{14}C\) yrs]
81.5-84.55252.1.11.62-2.291.93\(n-C_{26:0}+n-C_{28:0}\)\(0.7408\pm 0.0281\)-265±28\(348\pm 240\)
81.5-84.55252.2.11.62-2.291.93\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.6866\pm 0.0158\)-319±16\(959\pm 146\)
130-1335061.2.13.11-3.693.40\(n-C_{26:0}\)\(0.6180\pm 0.0130\)-387±13\(604\pm 112\)
130-1335061.3.13.11-3.693.40\(n-C_{28:0}\)\(0.5975\pm 0.0142\)-408±14\(875\pm 126\)
130-133-3.11-3.693.40\(n-C_{26:0}+n-C_{28:0}\)c\(0.6082\pm 0.0498\)c-397±50\(733\pm 432\)
130-1335061.4.13.11-3.693.40\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.5437\pm 0.0172\)-461±17\(1633\pm 167\)
198-2015251.2.15.35-6.015.70\(n-C_{26:0}\)\(0.4795\pm 0.0131\)-525±13\(871\pm 111\)
198-2015251.1.15.35-6.015.70\(n-C_{28:0}\)\(0.4761\pm 0.0152\)-528±15\(929\pm 129\)
198-201-5.35-6.015.70\(n-C_{26:0}+n-C_{28:0}\)c\(0.4777\pm 0.0395\)c-526±40\(902\pm 331\)
198-2015251.3.15.35-6.015.70\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.4342\pm 0.0125\)-569±13\(1668\pm 116\)
231-23411116.2.17.24-8.147.72\(n-C_{26:0}\)\(0.4086\pm 0.0085\)-595±9\(202\pm 69\)
231-23411116.3.17.24-8.147.72\(n-C_{28:0}\)\(0.3705\pm 0.0092\)-633±9\(988\pm 81\)
231-234-7.24-8.147.72\(n-C_{26:0}+n-C_{28:0}\)c\(0.3906\pm 0.0307\)c-613±31\(563\pm 247\)
231-23411116.1.17.24-8.147.72\(n-C_{29}+n-C_{31}+n-C_{33}\)\(0.4179\pm 0.0112\)-586±11\(21\pm 87\)
251-2545060.2.19.02-10.119.66\(n-C_{26:0}\)\(0.3062\pm 0.0093\)-696±9\(734\pm 79\)
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[90, 84, 886, 386]]<|/det|> + +
251-2545060.3.19.02 - 10.119.66\(n-C_{28:0}\)0.3060 ± 0.0085-697 ± 8738 ± 79
251-254-9.02 - 10.119.66\(n-C_{26:0}+n-C_{28:0}\)c0.3061 ± 0.0240c-696 ± 24736 ± 196
251-2545060.4.19.02 - 10.119.66\(n-C_{29}+n-C_{31}+n-C_{33}\)0.2184 ± 0.0263-783 ± 263447 ± 298
278-2815059.2.111.05 - 12.0511.50\(n-C_{26:0}\)0.2183 ± 0.0082-784 ± 82126 ± 77
278-2815059.4.111.05 - 12.0511.50\(n-C_{28:0}\)0.2475 ± 0.0088-755 ± 91117 ± 74
278-281-11.05 - 12.0511.50\(n-C_{26:0}+n-C_{28:0}\)c0.2321 ± 0.0183c-770 ± 181613 ± 158
278-2815059.5.111.05 - 12.0511.50\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1883 ± 0.0167-813 ± 173313 ± 178
297-3005250.1.112.69 - 13.7313.21\(n-C_{26:0}+n-C_{28:0}\)0.1236 ± 0.0474-877 ± 475384 ± 618
297-3005250.2.112.69 - 13.7313.21\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1408 ± 0.0185-860 ± 194334 ± 213
359-3625249.2.116.26 - 17.0716.67\(n-C_{26:0}\)0.1118 ± 0.0160-889 ± 163763 ± 157
359-3625249.1.116.26 - 17.0716.67\(n-C_{28:0}\)0.1206 ± 0.0158-880 ± 163154 ± 144
359-362-16.26 - 17.0716.67\(n-C_{26:0}+n-C_{28:0}\)c0.1162 ± 0.0123c-885 ± 123453 ± 219
359-3625249.3.116.26 - 17.0716.67\(n-C_{29}+n-C_{31}+n-C_{33}\)0.1322 ± 0.0089-869 ± 92415 ± 81
393-3965248.2.117.69 - 18.7318.15\(n-C_{26:0}\)0.0748 ± 0.0269-926 ± 276005 ± 322
393-3965248.1.117.69 - 18.7318.15\(n-C_{28:0}\)0.0372 ± 0.0403-963 ± 4011611 ± 961
393-396-17.69 - 18.7318.15\(n-C_{26:0}+n-C_{28:0}\)c0.0534 ± 0.0125c-947 ± 128723 ± 212
393-3965248.3.117.69 - 18.7318.15\(n-C_{29}+n-C_{31}+n-C_{33}\)0.0597 ± 0.0228-941 ± 237816 ± 341
+ +<|ref|>text<|/ref|><|det|>[[90, 386, 500, 398]]<|/det|> +606 a: Obtained from radiocarbon dating of planktic foraminifera22. + +<|ref|>text<|/ref|><|det|>[[90, 400, 880, 423]]<|/det|> +607 b: Corrected for procedure blanks. \(n\) -Alkanoic acids were additionally corrected for the carbon introduced during methylation (see Methods). + +<|ref|>text<|/ref|><|det|>[[90, 425, 603, 437]]<|/det|> +609 c: Calculated abundance-weighted means of the \(n-C_{26:0}\) and \(n-C_{28:0}\) homologues. + +<|ref|>text<|/ref|><|det|>[[90, 465, 812, 476]]<|/det|> +611 **Extended Data Table 2.** \(\tau _{soll}\) for the Ganga-Brahamaputra river catchment during the past 17 kyrs calculated from + +<|ref|>text<|/ref|><|det|>[[90, 477, 876, 500]]<|/det|> +612 compound-specific radiocarbon data from \(n\) -alkanoic acids27. R is the reservoir age offset29 between the \(n\) -alkanoic acids and the atmosphere at the time of deposition in the Bengal Fan. + +<|ref|>table<|/ref|><|det|>[[115, 510, 540, 580]]<|/det|> + +
\(n\) -Alkanoic
acid
homologues		Deposition
age [kyrs BP]		Mass
weighted
mean R [14C
yrs]		\(\tau _{soll}\) [yrs]
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)0.003\(1446\pm 80\)\(36\pm 4\)
+ +<|ref|>table<|/ref|><|det|>[[115, 600, 540, 670]]<|/det|> + +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)0.004\(927\pm 87\)*\(23\pm 3\)
+ +<|ref|>table<|/ref|><|det|>[[115, 690, 540, 760]]<|/det|> + +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{34:0}\)\(3.54\pm 0.39\)\(7119\pm 1149\)\(178\pm 33\)
+ +<|ref|>table<|/ref|><|det|>[[115, 778, 540, 848]]<|/det|> + +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)\(6.57\pm 0.42\)\(1489\pm 618\)\(37\pm 16\)
+ +<|ref|>table<|/ref|><|det|>[[115, 866, 540, 911]]<|/det|> + +
\(n-C_{24:0},\) \(n-C_{26:0},\) \(n-C_{28:0},\) \(n-C_{30:0},\) \(n-C_{32:0}\)\(10.92\pm 0.48\)\(2070\pm 1116\)\(52\pm 28\)
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 80, 540, 355]]<|/det|> + +
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{34,0}\)12.74 ± 0.423234 ± 116680 ± 30
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{32}\), \(n-C_{34,0}\)13.61 ± 0.231375 ± 83034 ± 21
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\)15.62 ± 0.378709 ± 4166217 ± 106
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{34,0}\)16.77 ± 0.396453 ± 2177116 ± 55
\(n-C_{24,0}\), \(n-C_{26,0}\), \(n-C_{28,0}\), \(n-C_{30,0}\), \(n-C_{32,0}\)16.90 ± 0.104004 ± 3507100 ± 88
+ +<|ref|>text<|/ref|><|det|>[[60, 355, 504, 370]]<|/det|> +614 *: \(^{14}\mathrm {C}\) ages of pre-1950 Bengal Fan sediments taken from ref.24 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 135, 880, 515]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 522, 875, 561]]<|/det|> +
Extended Data Figure 1: Abundances of Amino-Bacteriophanepolyols in core GeoB7702-3 normalized to the dry weight of extracted sediment (dw). AU: Arbitrary units. AHP: African Humid Period. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 95, 833, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 488, 877, 573]]<|/det|> +
Extended Data Figure 2: Reservoir age offsets of leaf-wax lipids with the atmosphere at the time of deposition at site GeoB7702-3 (a) plotted along with temperature and precipitation reconstructions from the Nile catchment. (b): Temperature reconstruction for the eastern Mediterranean based on the TEX86-proxy in core GeoB7702-313. (c): hydrogen isotope compositions of precipitation (δDp) calculated from δD of the alkanoic acids (mean of \(n-C_{26:0}\) and \(n-C_{28:0}\) homologues; purple) and \(n-C_{31}\) alkane (orange) in core GeoB7702-322. The blue bars mark the timing of the African Humid Period (AHP), "Green Sahara" and their optimum17,44. LGM: Last Glacial Maximum; HS1: Heinrich Stadial 1; B/A: Bølling/Allerød interstadial; YD: Younger Dryas stadial.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 80, 884, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 677, 872, 764]]<|/det|> +
Extended Data Figure 3: Recalculation of results from the Lund Potsdam Jena Dynamic Global Vegetation Model (LPJ DGVM) over the last 21 kyrs as published in ref.41. These results are identical to those LPJ results that have been forced by the Hadley center climate model as discussed in ref.41. Relative changes between the LGM and pre-industrial conditions (PI, here: 1 kyr BP) are shown. a,b) \(\tau_{\mathrm{soil}}\) calculated either based on the carbon influx (net primary production (NPP)) or on the carbon efflux (Rh), where \(\mathrm{Rh}\) is the heterotrophic respiration. Large positive anomalies (red) occur on shelf areas inundated during deglacial sea-level rise, while the areas with large negative anomalies (blue) were covered by large continental ice sheets during the LGM.; c,d) relative changes in NPP and \(\mathrm{Rh}\) ; e) absolute changes in soil carbon content ( \(\mathrm{C_{soil}}\) ).
+ +<--- Page Split ---> diff --git a/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/images_list.json b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..64ab379bf0bf6170ae7a7d16c478111d30c5098c --- /dev/null +++ b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/images_list.json @@ -0,0 +1,100 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1: Transient upregulation of SEL1L-HRD1 ERAD expression in the hypothalamus in response to high fat diet (HFD) feeding.", + "footnote": [], + "bbox": [ + [ + 120, + 108, + 876, + 655 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2: Hypothalamic POMC-specific ERAD deficiency leads to early-set DIO and its pathologies.", + "footnote": [], + "bbox": [ + [ + 118, + 95, + 856, + 595 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3: Hypothalamic ERAD deficiency triggers hyperphagia and leptin resistance. (A) Daily food intake of male Sel1Lf and Sel1LOMC mice at 1w- and 8w-HFD (n=9-11 mice per group). (B) Growth curve of male Sel1LOMC mice fed with either NCD or HFD under ad libitum or pair feeding as indicated (n=3 mice per group, blue solid circles). Male Sel1Lf mice fed ad libitum with the same diets were included as controls (n=3 mice per group, black open circles) (C) Growth of Sel1LOMC male mice with either ad libitum or pair-feeding of HFD starting at 5 weeks of age (n=3-5 mice per group). (D) Body weights of 12-week-old mice put on HFD (at day 0) followed by daily i.p. injected with vehicle (PBS) and leptin (2 mg/kg body weight) for 3 days (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice, indicated in squares). (E-F) Percentage of body weight change (E), average daily food intake (F) following 3 daily vehicle and leptin injections of the mice (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice). % Body weight is calculated based on the body weights at the end point over those at the starting point for each treatment. (G) Serum leptin levels in mice fed on NCD, 1w- and 8w-HFD (n=5-13 mice per group). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (A-G).", + "footnote": [], + "bbox": [ + [ + 121, + 95, + 800, + 536 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4: Hypothalamic SEL1L-HRD1 deficiency leads to DIO via leptin signaling. (A) Schematic diagram for parabiosis and pictures (right) of Sel1L// and Sel1L^POMC female mice after parabiosis HFD for 8 weeks (n=3 pairs in group I, n=1 pair in group II, n=5 pairs in group III). (B-C) Body weights (B) of mice before and after parabiosis and body composition (C) after parabiosis following 8-week HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). (D-E) Serum leptin (D) and insulin (E) levels of mice after parabiosis HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (B-E).", + "footnote": [], + "bbox": [ + [ + 130, + 98, + 853, + 450 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5: Hypothalamic SEL1L-HRD1 ERAD deficiency impairs leptin-pSTAT3 signaling.", + "footnote": [], + "bbox": [ + [ + 113, + 90, + 857, + 861 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6: The effect of POMC-specific ERAD in DIO is likely uncoupled from UPR and inflammation.", + "footnote": [], + "bbox": [ + [ + 128, + 90, + 850, + 777 + ] + ], + "page_idx": 33 + }, + { + "type": "image", + "img_path": "images/Figure_9.jpg", + "caption": "Fig.9: Proposed models for SEL1L-HRD1 ERAD degradation of wildtype LepRb and C604S disease mutant.", + "footnote": [], + "bbox": [], + "page_idx": 35 + } +] \ No newline at end of file diff --git a/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f.mmd b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1498f8337ec397d965f73121c6e332ad3e5701b3 --- /dev/null +++ b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f.mmd @@ -0,0 +1,386 @@ + +# SEL1L-HRD1 ER-associated degradation regulates leptin receptor maturation and signaling in POMC neurons in diet-induced obesity + +Ling Qi xvr2hm@virginia.edu + +University of Virginia https://orcid.org/0000- 0001- 8229- 0184 + +Hancheng Mao Department of Molecular & Integrative Physiology, University of Michigan Medical School https://orcid.org/0000- 0003- 2546- 6774 + +Geun Hyang Kim Regeneron Pharmaceuticals, Inc. + +## Article + +Keywords: SEL1L- HRD1 ERAD, POMC, diet- induced obesity, leptin signaling, leptin receptor, parabiosis + +Posted Date: January 12th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 3768472/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on September 29th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52743- 2. + +<--- Page Split ---> + +# SEL1L-HRD1 ER-associated degradation regulates leptin receptor maturation and signaling in POMC neurons in diet-induced obesity + +Hancheng Mao1, Geun Hyang Kim1,3, Ling Qi2\* + +1Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann + +Arbor, MI 48105, USA + +2 Department of Molecular Physiology and Biological Physics, University of Virginia, School of + +Medicine, Charlottesville, VA 22903, USA + +3 Present address: Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, + +New York 10591, USA + +2 Correspondence: xvr2hm@virginia.edu + +The authors have declared that no conflict of interest exists. + +Short title: Regulation of leptin receptor maturation and signaling by SEL1L- HRD1 ERAD + +Summary: Here we report that POMC- specific SEL1L- HRD1 ER- associated degradation is indispensable for leptin signaling in diet- induced obesity by controlling the turnover and maturation of nascent leptin receptor in the ER. + +Keywords: SEL1L- HRD1 ERAD, POMC, diet- induced obesity, leptin signaling, leptin receptor, + +parabiosis + +<--- Page Split ---> + +## 24 ABSTRACT + +Endoplasmic reticulum (ER) homeostasis in the hypothalamus has been implicated in the pathogenesis of certain patho- physiological conditions such as diet- induced obesity (DIO) and type 2 diabetes; however, the significance of ER quality control mechanism(s) and its underlying mechanism remain largely unclear and highly controversial in some cases. Moreover, how the biogenesis of nascent leptin receptor in the ER is regulated remains largely unexplored. Here we report that the SEL1L- HRD1 protein complex of the highly conserved ER- associated protein degradation (ERAD) machinery in POMC neurons is indispensable for leptin signaling in diet- induced obesity. SEL1L- HRD1 ERAD is constitutively expressed in hypothalamic POMC neurons. Loss of SEL1L in POMC neurons attenuates leptin signaling and predisposes mice to HFD- associated pathologies including leptin resistance. Mechanistically, newly synthesized leptin receptors, both wildtype and disease- associated human mutant Cys604Ser (Cys602Ser in mice), are misfolding prone and bona fide substrates of SEL1L- HRD1 ERAD. Indeed, defects in SEL1L- HRD1 ERAD markedly impair the maturation of these receptors and causes their ER retention. This study not only uncovers a new role of SEL1L- HRD1 ERAD in the pathogenesis of diet- induced obesity and central leptin resistance, but a new regulatory mechanism for leptin signaling. + +<--- Page Split ---> + +Hypothalamic neurons play important roles in the adaptation and mal- adaptation to pathophysiological conditions such as diet- induced obesity (DIO) and type- 2 diabetes 1- 7. Homeostasis in the endoplasmic reticulum (ER) regulates many physiological processes such as systemic inflammation, inter- organellar crosstalk and mitochondrial dynamics 8- 14. It has been proposed that hypothalamic ER stress or unfolded protein response (UPR) may play a causal role in inflammation and leptin resistance in DIO and type- 2 diabetes 15- 21. However, others have reported a protective role of UPR in similar experimental settings 22,23. Hence, the significance of ER quality control pathways and its underlying mechanisms remain controversial. + +In addition to UPR that respond to misfolded proteins in the ER, ER- associated protein degradation (ERAD) is a constitutively active and highly conserved process responsible for recruiting unfolded or misfolded proteins in ER for cytosolic proteasomal degradation 24- 31. Among over a dozen of putative ERAD complexes, the SEL1L- HRD1 protein complex represents the most evolutionarily conserved ERAD branch where SEL1L/Hrd3p is an obligatory cofactor for the E3 ligase HRD1 28- 30,32- 34. Recent studies using cell type- specific SEL1L or HRD1 knockout mouse models have revealed the patho- physiological importance of SEL1L- HRD1 ERAD in a substrate- specific manner 35- 42. Particularly relevant to this study, SEL1L- HRD1 ERAD has been reported as indispensable for AVP and POMC neurons to control water balance and food intake via the maturation of prohormones, proAVP and POMC, respectively 39,40. POMC neuron- specific Sel1L deletion leads to hyperphagia and age- associated obesity starting around 13 weeks of age when fed a low- fat chow diet 39. Given the importance of POMC neurons in maintaining energy homeostasis under various nutritional status, one outstanding question is the relevance and significance of SEL1L- HRD1 ERAD in POMC neurons under pathophysiological conditions, including DIO. + +<--- Page Split ---> + +Here, we show that SEL1L- HRD1 ERAD in POMC neurons at the arcuate nucleus (ARC) of the hypothalamus, a key group of metabolic neurons that control food intake and energy expenditure \(^{43}\) , controls DIO pathogenesis and leptin sensitivity via the regulation of leptin receptor biogenesis and signaling. Soon after weaning, POMC- specific Sel1L deficient (Sel1LPOMC) mice are hypersensitive to DIO. Much to our surprise, SEL1L- HRD1 ERAD is indispensable for the maturation of nascent leptin receptor to reach to the cell surface. Hence, SEL1L- HRD1 ERAD is a critical regulator of the maturation of leptin receptor in the ER and thereby leptin signaling in POMC neurons. + +## RESULTS + +## Transient upregulation of SEL1L-HRD1 ERAD expression in the hypothalamus in response to high fat diet (HFD) feeding. + +We previously showed that the SEL1L- HRD1 protein complex is constitutively expressed in the ARC of the hypothalamus \(^{39}\) . Here we first asked whether its expression in the ARC region is regulated in response to overnutrition by placing the mice on \(60\%\) HFD ( \(60\%\) calories derived from fat) for 1 or 8 weeks. HFD feeding expectedly reduced the expression of Pomc, Npy and Agrp (Supplementary Fig. 1A), while enhance the protein levels of POMC derivatives \(\beta\) - Endorphin and \(\alpha\) - MSH (Supplementary Fig. 1B- E). Moreover, HFD feeding enhanced neuronal activity in PVH region as measured by nuclear c- FOS following both 1- and 8- week HFD (Supplementary Fig. 1D, E). One- week HFD significantly induced Hrd1 mRNA level, but not Sel1L mRNA level, while 8- week HFD feeding had no such effect (Fig. 1A). At the protein levels, both SEL1L and HRD1 proteins, were elevated at 1- week HFD, and returned to the basal levels after 8- week HFD (Fig. 1B, C), pointing to the transient response to SEL1L- HRD1 expression in the hypothalamus in response to HFD challenge. We next performed confocal microscopy to visualize SEL1L- HRD1 expression in the ARC regions in response to HFD. To visualize POMC neurons, we used POMC- eGFP transgenic mice where eGFP is under the control of POMC + +<--- Page Split ---> + +promoter \(^{39,44}\) . SEL1L protein level was increased specifically in POMC neurons upon 1- week HFD, and returned to the basal level with prolonged HFD feeding (Fig. 1D, E). Similar observation was obtained for HRD1 protein levels in POMC neurons, but unlike SEL1L, HRD1 protein level was transiently upregulated in non POMC neurons as well (Fig. 1F, G). Hence, SEL1L- HRD1 expression in POMC neurons are responsive to acute, but not chronic, nutrient overload. + +Hypothalamic POMC- specific ERAD deficiency leads to early- set DIO and its pathologies. To delineate the significance of hypothalamic ERAD in DIO, we next characterized the phenotypes of Sel1L \(^{POMC}\) mice, generated by crossing Sel1L \(^{f/f}\) with the Pome- Cre mouse line \(^{39}\) , following 8- week HFD feeding from 5 weeks of age. While, in line with our previous report, Sel1L \(^{POMC}\) mice appeared comparably to WT littermates in terms of body weight on chow diet for the first 13 weeks of age \(^{39}\) (Fig. 2A), Sel1L \(^{POMC}\) mice, both sexes, gained significantly more body weight soon after HFD feeding (Fig. 2A). Body composition analysis showed that fat content was significantly increased in Sel1L \(^{POMC}\) mice, reaching over 50% of body mass after 8- week HFD (Fig. 2B and Supplementary Fig. 2A) with more lipid deposition in the livers, as well as both white and brown adipose tissues (WAT and BAT) (Fig. 2C). Sel1L \(^{POMC}\) mice became highly glucose intolerant and insulin resistant following 8- week HFD (Fig. 2D, E), with elevated ad libitum and fasting blood glucose (Fig. 2F) and ad libitum insulin levels (Fig. 2G). In addition, glucagon and corticosterone levels were elevated in Sel1L \(^{POMC}\) mice (Supplementary Fig. 2B, C), while rectal temperature in Sel1L \(^{POMC}\) mice was decreased by 2 degrees compared to that of WT littermates (Supplementary Fig. 2D). Hence, we concluded that mice with POMC- specific ERAD defects exhibit early onset DIO and its pathologies including glucose and insulin resistance. + +Hypothalamic ERAD deficiency triggers hyperphagia and leptin resistance. + +<--- Page Split ---> + +We next explored the possible mechanism underlying the susceptibility to DIO in Sel1L POMC mice. Sel1L POMC mice consumed \(\sim 40\%\) more food daily, i.e., hyperphagia, upon both 1- and 8- week HFD feeding (Fig. 3A). To directly demonstrate the direct causal link between food intake and weight gain, we performed pair feeding (giving the same amount of the food as WT littermates consume) following 8- week ad libitum HFD feeding. Sel1L POMC mice gained weight quite rapidly under ad libitum feeding of HFD; however, their weight gain was significantly slowed down following pair- feeding and recovered when placed on ad libitum HFD feeding again (Fig. 3B). Indeed, weight gain of Sel1L POMC mice was comparable to that of WT littermates if pair- feeding was performed at the beginning of HFD feeding (Fig. 3C). We then tested whether hyperphagia of Sel1L POMC mice is caused by leptin resistance by leptin injection (Fig. 3D). Leptin injection was expected to induce body weight loss in WT mice, but not Sel1L POMC mice. Indeed, unlike WT mice, Sel1L POMC mice continued to gain body weight following leptin injection (Fig. 3D, E). This difference in body weight gain was likely due to the differences in food intake in response to leptin injection (Fig. 3F), pointing to a significant leptin resistance in Sel1L POMC mice. Sel1L POMC mice exhibited progressively marked hyperleptinemia with HFD feeding (Fig. 3G). Hence, we concluded that hypothalamic POMC neurons- specific ERAD deficiency triggers hyperphagia and leptin resistance. + +## The effect of hypothalamic SEL1L-HRD1 ERAD in DIO is mediated by leptin resistance. + +To further establish the effect of leptin resistance in ERAD deficiency- associated DIO, we next performed parabiosis where two littermates were surgically stitched together to allow the sharing of the circulation (Fig. 4A). Following two weeks of recovery on chow diet, the parabions WT: Sel1L POMC (Group III) were placed on HFD for 8 weeks (Fig. 4A). Two control parabions, WT: WT (Group I) and Sel1L POMC: Sel1L POMC (Group II), gained weight as expected with the latter pair becoming obese (Fig. 4B). However, in WT: Sel1L POMC (Group III) parabions, body weight gain for WT mice was attenuated compared to WT mice in WT: WT (Group I) + +<--- Page Split ---> + +control parabionts (P=0.08), while body weight gain for Sel1L \(^{POMC}\) mice was comparable to that of Sel1L \(^{POMC}\) : Sel1L \(^{POMC}\) parabionts (Group II) (Fig. 4B). Body compositions (i.e., lean vs. fat) in parabionts were not affected by the partner (Fig. 4C). Moreover, serum leptin and insulin levels were highly elevated in the Sel1L \(^{POMC}\) mice, but unaltered in WT mice regardless of the partners (Fig. 4D, E). Hence, these data suggested that hypothalamic SEL1L- HRD1 ERAD controls DIO pathogenesis via hyperleptinemia. + +## Hypothalamic SEL1L-HRD1 ERAD deficiency impairs leptin-pSTAT3 signaling. + +We next asked how POMC- specific SEL1L- HRD1 ERAD regulates leptin sensitivity. As leptin signaling induces phosphorylation of STAT3 (pSTAT3), we next examined the levels of pSTAT3 in POMC neurons following leptin challenge. To visualize POMC neurons, we generated Sel1L \(^{POMC}\) mice on the POMC- eGFP background (Sel1L \(^{POMC}\) : POMC- eGFP) \(^{39,44}\) . HFD feeding progressively blunted leptin- induced pSTAT3 in the POMC neurons of the ARC region of WT mice, but to a much greater extent, in Sel1L \(^{POMC}\) mice (Fig. 5A- D and Supplementary Fig. 3). In keeping with the notion that pSTAT3 a critical transcription factor for the POMC gene \(^{45}\) , hypothalamic POMC mRNA expression was markedly decreased in Sel1L \(^{POMC}\) mice with HFD (Fig. 5E). Moreover, Western blot analysis of pSTAT3 of the ARC region also showed a greater reduction of the percent of STAT3 being phosphorylated following HFD feeding (Fig. 5F, G). Thus, our data suggested that SEL1L- HRD1 ERAD in POMC neurons is vital for maintaining central leptin sensitivity during DIO pathogenesis. + +The effect of POMC- specific ERAD in DIO is likely uncoupled from UPR or inflammation. As ERAD deficiency expectedly causes the accumulation of unfolded/misfolded proteins in the ER that can potentially trigger UPR and given the reported role of UPR in DIO pathogenesis, we next tested whether ERAD deficiency activates UPR and if so, to what extent. There was no detectable activation of the PERK pathway as measured by phosphorylation of PERK and its + +<--- Page Split ---> + +downstream phosphorylation of elF2α (Fig. 6A and Supplementary Fig. 4A). Phosphorylation of IRE1α, on the other hand, was moderately elevated in the ARC of Sel1LPOMC mice, so was the splicing of Xbp1 mRNA (a downstream effector of IRE1α) (Fig. 6B, C and Supplementary Fig. 4B, C). Consistently, ER chaperons BiP (an XBP1 target) was mildly elevated in the ARC of Sel1LPOMC mice (Fig. 6A and Supplementary Fig. 4A, D). In vitro, treatment with an ER stress inducer thapsigargin (Tg) induced strong ER stress, but failed to affect leptin signaling in WT HEK293T cells transfected with long isoform of mouse Leptin receptors (mLepRb) (Fig. 6D and Supplementary Fig. 4E), indicating that UPR is not sufficient to induce leptin resistance. Importantly, we found no significant POMC neuronal loss in the ARC of Sel1LPOMC;POMC- eGFP mice (Fig. 6E). Inflammatory markers were largely comparable in the ARC of Sel1LPOMC mice compared to those in WT littermates as measured by phosphorylation and protein levels of c-Jun N-terminal Kinase (JNK) as well as protein levels of I kappa B alpha (IkBa) (Fig. 6F, G). Chronic HFD feeding mildly increased astrogliosis in the ARC regions of both Sel1LPOMC and Sel1LPOMC mice as measured by both Western blot and immunofluorescence staining of astrocyte marker Glial Fibrillary acidic protein (GFAP) and/or microglia marker Ionized calcium- binding adaptor molecule 1 (IBA1) (Fig. 6F- H and Supplementary Fig. 4F). Taken together, these data demonstrate that Sel1L deficiency in POMC neurons triggers leptin resistance, independently of UPR, neuronal cell death and inflammation. + +## SEL1L-HRD1 is required for the maturation of nascent leptin receptor (LepR). + +The forementioned data suggested that SEL1L- HRD1 ERAD regulates leptin sensitivity upstream of STAT3. To further explore the underlying mechanism, we generated leptin- responsive HEK293T cell system expressing the long isoform of LepR (LepRb) responsible for leptin- induced JAK2- STAT3 signaling46-49. Indeed, in line with decreased leptin sensitivity in vivo, mLepRb- positive HRD1-/- HEK293T cells exhibited impaired phosphorylation of JAK2 and STAT3 compared to those in transfected WT cells in response to leptin stimulation (Fig. 7A, B). + +<--- Page Split ---> + +Surprisingly, the protein level of mLepRb was significantly higher in HRD1- HEK293T cells compared to that of WT cells, under both serum- deprived and - supplemented conditions (Fig. 7B, C). Moreover, SEL1L interaction with in LepRb- transfected cells was markedly enhanced in HRD1- cells where substrate- SEL1L interaction is known to be stabilized \(^{29,50,51}\) (Fig. 7D, E). LepRb was ubiquitinated in an HRD1- dependent manner (Fig. 7F) and was significantly stabilized in HRD1- cells compared to that in WT cells (Fig. 7G). + +We next assess the consequence of ERAD deficiency on LepRb maturation in the ER. Endoglycosidase H (EndoH) digestion, which cleaves asparagine- linked high mannose or hybrid glycans of the immature glycoproteins predominantly in ER \(^{52}\) , revealed significantly lower fraction of EndoH resistant form of LepRb that were able to exit the ER for complete maturation in HRD1- HEK293T cells (Fig. 7H). This was further confirmed by surface biotinylation assay followed by immunoprecipitation with streptavidin- beads, which indicated reduced proportion of surface LepRb in HRD1- cells (Fig. 7I and Supplementary Fig. 5A). Moreover, confocal microscopy following immunofluorescence staining further demonstrated an altered distribution of LepRb with increased intracellular, but decreased surface, expression in ERAD- deficient cells (Fig. 7J and Supplementary Fig. 5B). In the absence of SEL1L- HRD1, LepRb protein was prone to form high molecular weight aggregates via disulfide bonds (Fig. 7K) Taken together, our data show that SEL1L- HRD1 ERAD is required for the maturation of LepRb by targeting the misfolding- prone or misfolded LepRb for proteasomal degradation. + +## SEL1L-HRD1 ERAD degrades and limits the pathogenicity of human LepRb Cys604Ser (C604S) mutant. + +To demonstrate the clinical relevance of our findings, we asked whether human LepRb (hLepRb) mutants \(^{53,54}\) are SEL1L- HRD1 ERAD substrates. Here, we focused on hLepRb mutant C604S, a recessive point mutation due to missense homozygous substitution T > A at position 1810, identified in two brothers at 1- and 5- years old with severely early onset obesity + +<--- Page Split ---> + +54,55. C604- C674 forms a disulfide bond in human LepRb corresponding to C602- C672 in mouse LepRb (Fig. 8A, B) 56- 58. This mutation has been predicted as loss- of- function likely due to defects in folding 54,56- 58. C602S mLepRb significantly impaired leptin response compared to WT mLepRb in WT cells, which was further diminished in HRD1- /- cells (Fig. 8C). Similar to WT mLepRb, C602S mLepRb was stabilized in the absence of HRD1 (Fig. 8D). Notably, C602S mLepRb readily formed HMW aggregates in WT HEK293T cells, and to much greater extent, in HRD1- /- cells (Fig. 8E). Such aggregates likely formed in the ER as demonstrated by their colocalization with the ER chaperone BiP based on immunostaining (Fig. 8F- I). Hence, SEL1L- HRD1 ERAD is indispensable for the degradation of nascent WT and, at least a subset of, disease mutant LepRb, which ensures the maturation, trafficking and membrane display of functional LepRb. + +## DISCUSSION + +This study not only identifies a novel regulatory mechanism for leptin receptor and signaling, but also reports a key role of hypothalamic ERAD in maintaining energy homeostasis under nutrient overload conditions. SEL1L- HRD1 ERAD defects in POMC neurons predispose mice to DIO and its pathologies, due to hyperphagia and hypothalamic leptin resistance. Our mechanistic studies establish LepRb as a bona fide endogenous substrate of SEL1L- HRD1 ERAD. Pointing to the clinical relevance of our findings, human recessive LepRb C604S variant is trapped in the ER and degraded by SEL1L- HRD1 ERAD (Fig. 9). In the absence of SEL1L- HRD1 ERAD, both WT and C604S LepRb are trapped in the ER in the form of HMW aggregates, with attenuated cell surface expression (Fig. 8E- I and Fig. 9). While this reported effect of ERAD in POMC neurons is in keeping with recent studies demonstrating the profound physiological importance of SEL1L- HRD1 ERAD in vivo 39,40, it uncovers a novel function of SEL1L- HRD1 ERAD in leptin signaling and a novel regulatory mechanism for leptin biology. + +<--- Page Split ---> + +Our data show that hypothalamic SEL1L deficiency markedly increases the progression and pathogenesis of DIO in mice. Sel1L- deficient POMC neurons exhibit mild alterations in ER homeostasis including elevated activation of the IRE1α - XBP1 pathway and expression of ER chaperones, but without any detectable cell death. As previous studies have shown that deficiency of Ire1a or Xbp1 in POMC neurons predispose mice to DIO \(^{21}\) , while gain- of- function of XBP1s in POMC neurons had an opposite effect \(^{23}\) , we conclude that the effect of SEL1L- HRD1 ERAD is uncoupled from IRE1α - XBP1 pathway of the UPR and cell death, which is in line with many recent studies of various tissue- specific Sel1L- or Hrd1- deficient models \(^{37,39- 42,59}\) . These findings point to the cellular adaption in response to ERAD deficiency \(^{25}\) . Such mild UPR activation and chaperone expression are potentially cyto- protective in response to the accumulation of misfolding proteins in the ER. + +Previous reports have suggested that UPR may play a causal role in leptin resistance due to impaired leptin signaling \(^{15,17,60}\) . These studies were performed via the administration of ER stress inducers tunicamycin and thapsigargin which can be fraught with artefacts. Indeed, tunicamycin can inhibit glycosylation of the glycoproteins \(^{61}\) including LepRb, and thus the impaired leptin signaling can be directly due to defective glycosylation and concomitant functionality of LepRb instead of UPR activation as a general outcome of numerous dysregulation of glycoproteins. Further, high dosage of ER stress inducers included in previous studies may fall far from any physiological relevance \(^{15,17,60}\) . In our study, thapsigargin treatment induced a range of ER stress response in a dose dependent manner, but failed to alter leptin signaling in WT HEK293T cells transfected with mLepRb even at the high level of UPR. Hence, collective evidence suggests that UPR is likely uncoupled from leptin signaling. The reason for these discrepancies remains unknown. Careful future studies are needed to validate either model. + +<--- Page Split ---> + +This study demonstrates an important role of SEL1L- HRD1 ERAD in leptin signaling, at least in part via the regulation of the maturation of nascent LepRb protein. We previously showed that SEL1L- HRD1 ERAD is required for the posttranslational maturation of POMC prohormone in mice on chow diet and that Sel1L deficiency in POMC neurons cause age- associate obesity in mice on chow diet due to the ER retention of POMC prohormone 39. In DIO mouse models, we found defects in Sel1LPOMC mice occurring upstream of POMC transcription as leptin- induced STAT3 phosphorylation is impaired in the absence of SEL1L- HRD1 ERAD 45- 49. Further mechanistic studies identify partial loss- of- function of LepRb resulted from attenuated ER exit of nascent LepRb in SEL1L- HRD1 ERAD deficient cells. This study suggests that nascent LepRb protein is likely misfolding prone in the ER, likely due to multiple glycosylation and the formation of disulfide bonds, and hence relies on SEL1L- HRD1 ERAD to generate an ER environment conducive for the proper folding and conformation of bystander LepRb. + +Several human mutants have also been identified as SEL1L- HRD1 ERAD substrates that readily form aggregates and become resistant to and bypassing the quality control mediated by ERAD, leading to loss- of- function disease phenotype. These misfolded substrates with highly reactive cysteine thiols accumulate and promote the formation of inter- or intra- molecular disulfide- bonded aggregates 39- 41. Hence, SEL1L- HRD1 ERAD- mediated degradation of nascent unfolded and misfolded substrates, including LepRb in this study, may effectively prevent protein aggregation and maintain the folding environment in the ER. Efforts to target SEL1L- HRD1 ERAD function may represent a viable means for the treatment of certain diseases caused by a dominant- negative disease allele or a general collapse of the folding environment in the ER. + +## METHODS + +<--- Page Split ---> + +Mice. As described previously \(^{39}\) , POMC- specific \(Sel1L\) - deficient mice ( \(Sel1L^{POMC}\) ) and control littermates ( \(Sel1L^{//}\) ) were generated. The mice were further crossed with Pomc- eGFP reporter mice to generate \(Sel1L^{POMC}\) ; POMC- eGFP and control littermates \(Sel1L^{//}\) ; POMC- eGFP. WT B6 mice were purchased from JAX and bred in our mouse facility. Mice were fed a chow diet (13% fat, 57% carbohydrate and 30% protein, PicoLab Rodent Diet 5L0D) and placed on a high- fat diet (HFD, calories provided by 60% fat, 20% carbohydrate and 20% protein, Research Diet D12492) from 5 weeks of age for 1 week or 8 weeks. All mice were housed in a temperature- controlled room with a 12- hour light/12- hour dark cycle. + +Food intake measurement and pair- feeding. Food intake were measured as previously described \(^{39}\) . Briefly, to perform daily food intake measurement, mice were first acclimatized to single housing 24 hours before the experiment. Daily food intake was measured 1 hour before the onset of the dark cycle each day. For the pair- feeding at later stage of HFD feeding, \(Sel1L^{POMC}\) and WT littermates had continuous free access to HFD for eight weeks and were then single housed and fed \(\sim 2.5 \text{g}\) , which was determined by the average of daily food intake of WT littermates, at the start of the dark cycle. For the pair- feeding at early stage of HFD feeding, 5- week- old \(Sel1L^{POMC}\) mice were split into two groups: One group of \(Sel1L^{POMC}\) and WT littermates had continuous free access to food; the other group of \(Sel1L^{POMC}\) mice (pair- fed) was fed \(\sim 2.5 \text{g}\) at the start of dark hours. Weekly bodyweight gains were monitored. + +Leptin treatment in mice. Twelve- week- old mice were intraperitoneally (i.p.) injected PBS followed by leptin (2 mg/kg body weight, R&D systems; catalog 498- OB- 05M) 1 hour before the onset of dark cycle for three consecutive days as described \(^{39}\) . Body weight and food intake were monitored daily during the treatment period. For phosphorylated STAT3 staining, 2 mg/kg leptin were i.p. injected to mice, followed by overnight fasting. Mice were anesthetized by isoflurane for fixation- perfusion 30 min after injection. + +<--- Page Split ---> + +Tissue and blood collection. These procedures were carried out as previously described39. Briefly, blood was collected from anesthetized mice via cardiac puncture, transferred to 1.5ml microcentrifuge tubes, kept at room temperature for 30 minutes prior to centrifugation at 2,000 \(g\) for 15 minutes. Serum was aliquoted and stored at \(- 80^{\circ}C\) until analysis. For brain microdissection, Adult Mouse Brain Slicer Matrix (BSMAA001- 1, Zivic Instruments) was used to collect coronal brain slices containing ARC region with further microdissection to obtain ARC- enriched region. All tissues were snap- frozen in liquid nitrogen and stored at \(- 80^{\circ}C\) before use. + +Preparation of brain sections. Mice were anesthetized with isoflurane, perfused with PBS followed by \(4\%\) paraformaldehyde (PFA) (Electron Microscopy Sciences; catalog 19210) for fixation. Brains were then postfixed in \(4\%\) PFA for overnight at \(4^{\circ}C\) , dehydrated in \(15\%\) sucrose and then \(30\%\) sucrose consecutively overnights at \(4^{\circ}C\) , and sectioned (30 \(\mu m\) ) on a cryostat (Microm HM550 Cryostat, Thermo Fisher Scientific). The sections were stored in DEPC- containing anti- freezing media ( \(50\%\) 0.05 M sodium phosphate pH 7.3, \(30\%\) ethylene glycol, \(20\%\) glycerol) at \(- 20^{\circ}C\) . Different brain regions were identified using the Paxinos and Franklin atlas. Counted as distance from bregma, the following coordinates were used: PVN (- 0.82 mm to - 0.94 mm) and ARC (- 1.58 mm to - 1.7 mm). + +Western blot and antibodies. Frozen tissue or cells were homogenized by sonication in lysis buffer [150mM NaCl, 50mM Tris pH 7.5, 10 mM EDTA, \(1\%\) Triton X- 100] with freshly added protease inhibitors (Sigma; catalog P8340), phosphatase inhibitors (Sigma; catalog P5726) and 10 mM N- ethylmaleimide (Thermo Scientific; catalog 23030). Lysates were incubated on ice for 30 min followed by centrifugation (13,000 g, 10 min at \(4^{\circ}C\) ). Supernatants were collected and analyzed for protein concentration using Bradford assay (Bio- Rad; catalog 5000006). For + +<--- Page Split ---> + +denaturing SDS- PAGE, samples were further supplied with 1mM DTT and denatured at \(95^{\circ}C\) for 5 min in 5x SDS sample buffer (250 mM Tris- HCl pH 6.8, \(10\%\) sodium dodecyl sulfate, \(0.05\%\) Bromophenol blue, \(50\%\) glycerol, and 1.44 M \(\beta\) - mercaptoethanol). For non- reducing SDAPAGE, samples were prepared in 5x non- denaturing sample buffer (250 mM Tris- HCl pH 6.8, \(10\%\) sodium dodecyl sulfate, \(0.05\%\) bromophenol blue, \(50\%\) glycerol). For phostag gel analysis based on phos- tag system as described \(^{62,63}\) , SDS- PAGE gel was supplemented by \(50\mu \mathrm{M}\) MnCl2 (Sigma) and \(25\mu \mathrm{M}\) phostag reagent (NARD Institute; catalog AAL- 107) and must be protected from light until finishing running. Protein isolated from the liver of mice treated with tunicamycin (TM, \(1\mathrm{mg / kg}\) , i.p.) for 24 hours was used as a positive control to indicate the position of phosphorylated PERK and IRE1a. For phosphatase treatment, \(100\mu \mathrm{g}\) tissue lysates were incubated with \(1\mu \mathrm{l}\) lambda phosphatase (APPase, New England BioLabs; catalog P0753S) in \(1\times \mathrm{PMP}\) buffer (New England BioLabs; catalog B0761S) with \(1\mathrm{mM}\mathrm{MnCl}_2\) (New England BioLabs; catalog B1761S) at \(30^{\circ}C\) for 30 min. Reaction was stopped by adding \(5\times\) SDS sample buffer and incubated at \(90^{\circ}C\) for 5 min. + +All samples were incubated in \(65^{\circ}C\) for 10min and run with 15- 30 \(\mu \mathrm{g}\) total lysate on SDS- PAGE gel for separation followed by electrophoretic transfer to PVDF membrane (0.45um, Millipore; catalog IPFL00010). The blots were incubated in \(2\%\) BSA/Tri- buffered saline tween- 20 (TBST) with primary antibodies overnight at \(4^{\circ}C\) , washed with TBST followed by 1hr incubation with goat anti- rabbit or mouse IgG HRP at room temperature. Band density was quantitated using the Image Lab software on the ChemiDOC XRS+ system (Bio- Rad). + +Antibodies for Western blot were as follows: SEL1L (rabbit, 1:8000, Abclonal; catalog E112049), HRD1 (rabbit, 1:2000, ABclonal; catalog E15102), GRP78 BiP (rabbit, 1:5000, Abcam; catalog ab21685), HSP90 (rabbit, 1:5,000, Santa Cruz Biotechnology Inc.; catalog sc- 7947), FLAG (mouse, 1:2000, Sigma- Aldrich; catalog F- 1804), IRE1α (rabbit, 1:2,000, Cell Signaling Technology; catalog 3294), p- elF2α (rabbit, 1:2000, Cell Signaling Technology; catalog 3597), elF2α (rabbit, 1:2000, Cell Signaling Technology; catalog S722), p- JNK (mouse, 1:2000, Cell + +<--- Page Split ---> + +Signaling Technology; catalog 9255), JNK (rabbit, 1:1000, Cell Signaling Technology; catalog 9252), PERK (Rabbit, 1:1000, Cell Signaling Technology; catalog 3192), pSTAT3 (Tyr705) (rabbit, 1:1000, catalog 9131, Cell Signaling Technology), STAT3 (rabbit, 1:1000, Cell Signaling Technology; catalog 9132), pJAK2 (Tyr1007/1008) (rabbit, 1:1000, Cell Signaling Technology; catalog 3771), JAK2 (rabbit, 1:1000, ABclonal; catalog A19629), Tubulin (mouse, 1:5000, Santa Cruz Biotechnology Inc.; catalog sc- 5286), IkBa (rabbit, 1:1000, Cell Signaling Technology; catalog 9242) and IBA1 (rabbit, 1:1000, Proteintech; catalog 10904- 1- AP) Secondary antibodies for Western blot were goat anti- rabbit IgG HRP and goat anti- mouse IgG HRP at 1:5,000, both from Bio- Rad. + +Immunostaining and antibodies. For fluorescent immunostaining in free- floating brain sections, samples were picked out of anti- freezing buffer followed by 3 washes with PBS. Free- floating sections were simultaneously incubated with primary antibodies in blocking buffer (0.3% donkey serum and 0.25% Triton X- 100 in 0.1 M PBS) overnight at 4°C. Following 3 washes with PBS, sections were incubated with secondary antibodies for 2 hours at room temperature. Brain sections were then mounted on gelatin- coated slides (Southern Biotech; catalog SLD01- CS). Counterstaining and mounting were performed with mounting medium containing DAPI (Vector Laboratories; catalog H- 1200) and Fisherfinest Premium Cover Glasses (Fisher Scientific; catalog 12- 548- 5P). For immunostaining in cells, 24 hours after transfection of LepRb- 3xFLAG constructs, cells were placed on Poly- L- Lysine (Advanced Biomatrix; catalog 5048) coated Millicell EZ SLIDE 8- well glasses (Millipore; catalog PEZGS0816) for 24 hours before treatment and fixation. For staining surface bound leptin, samples were washed by ice cold PBS for 5 times and fixed by 4% formaldehyde (VWR; catalog 89370- 094) for 15 minutes on ice followed by 3 washes with PBS. No permeabilization reagents were involved. For staining other markers, permeabilization was included and the overall process were the same as described above. To quantify immunoreactivity, identical acquisition settings were used for imaging each brain + +<--- Page Split ---> + +section from all groups within an experiment. The numbers of immunoreactivity- positive soma analysis and intensity of immunoreaction were quantified in 3D stack volumes after uniform background subtraction using the NIS Elements AR software (Nikon) and FIJI (National Institute of Health, USA). + +Antibodies for immunostaining were as follows: HRD1 (rabbit, 1:500, homemade), GRP78 BiP (rabbit, 1:500, Abcam; catalog ab21685), \(\alpha\) - MSH (sheep, 1:2,000, Millipore; catalog AB5087), \(\beta\) - endorphin (rabbit, 1:2,000, Phoenix Pharmaceuticals; catalog H- 022- 33, provided by Carol Elisa), and GFP (chicken IgY, 1:300, Abcam; catalog ab13970), p- Y705 STAT3 (rabbit, 1:200, Cell Signaling Technology; catalog 9145), GFAP (rabbit, 1:500, Agilent; Z033429- 2), FLAG (mouse, 1:500, Sigma- Aldrich; catalog F- 1804), KDEL (rabbit, 1:500, Novus Biologicals; catalog NBP2- 75549), eIF3n (goat, 1:500, Santa Cruz Biotechnology; catalog sc- 16377). Secondary antibodies for fluorescent immunostaining (all 1:500) were as follows: Anti- rabbit IgG Alexa Fluor 647; anti- goat IgG Alexa Fluor 488 & 647; anti- sheep IgG Cy5 were from Jackson ImmunoResearch. Donkey anti- mouse IgG Alexa fluor 555 was from Invitrogen (catalog A32773) and goat anti- chicken IgY FITC was from Aves Labs (catalog F- 1005). + +Plasmids. Mouse LepRb cDNA was provided by Dr. Martin Myer at University of Michigan Medical School. The LepRb coding region was amplified by PCR using a primer set containing HindIII and XbaI restriction site at 5' and 3' respectively. + +F: 5'- CCG AAGCTT ATGATGTGTCAGAAATTCTATGTGGTT- 3' + +R: 5'- TGC TCTAGA CACAGTTAAGTCACACATCTTATT- 3' + +Both PCR products and the backbone vector p3xFLAG- CMV14 were digested using HindIII and XbaI restriction enzymes in the double digestion system from New England BioLabs. For construction of LepRb point mutants, quick change mutagenesis was performed using PFU + +<--- Page Split ---> + +DNA polymerase (600140, Agilent). The following primers were used for mutagenesis to + +construct LepRb- C602S: + +F: 5'- CCTGCTGGTGTCAGACCTCAGTCGACTCTATG- 3' + +R: 5'- CATAGACTGCACTGAGGCTCTGACACCAGCAGG- 3' + +CRISPR/Cas9- based knockout (KO) in HEK293T cells. HEK293T cells were cultured at \(37^{\circ}C\) + +with \(5\% \text{CO}_2\) in DMEM with \(10\%\) fetal bovine serum (Fisher Scientific). To generate HRD1- + +deficient HEK293T cells, sgRNA oligonucleotides designed for human HRD1 (5'- + +GGACAAAGGCCCTGGATGTAC- 3') was inserted into lentICRISPR v2 (plasmid 52961, + +Addgene). Cells transfected with empty plasmids without sgRNA were used as wild type control. + +Cells grown in \(10 \text{cm}\) petri dishes were transfected with indicated plasmids using 5μl \(1 \text{mg/ml}\) + +polyethylenimine (PEI, Sigma) per \(1 \mu \text{g}\) of plasmids for HEK293T cells. Cells were cultured 24 + +hours after transfection in medium containing \(2 \mu \text{g/ml}\) puromycin for 48 hours and then in + +normal growth media. + +Statistics. Results are expressed as the mean \(\pm\) SEM unless otherwise stated. Statistical + +analyses were performed in GraphPad Prism version 8.0 (GraphPad Software Inc.). + +Comparisons between the groups were made by unpaired two- tailed Student's t test for two groups, or one- way ANOVA or two- way ANOVA followed by multiple comparisons test for more than two groups. \(P\) value \(< 0.05\) was considered as statistically significant. All experiments were repeated at least twice and/or performed with several independent biological samples, and representative data are shown. + +Study Approval. All experiments performed with mice were in compliance with University of + +Michigan (Ann Arbor, MI) Institutional Animal Care and Use Committee (#PRO00006888) + +guidelines. + +<--- Page Split ---> + +452 Data and material availability. The materials and reagents used are either commercially available or available upon the request, with detailed information included in Methods. The predicted structure of mLepRb is available at AlphaFold ID AF- P48356- F1. All data supporting the findings and materials for the manuscript are available within the article and the Supplementary Information. + +<--- Page Split ---> + +## AUTHOR CONTRIBUTION + +H.M. and G.H.K. designed the most of experiments and H.M., with the help of G.H.K., performed most of the experiments and data analysis. H.M., with the help of G.H.K., wrote the methods and figure legends. L.Q. and H.M. wrote the manuscript. All authors have approved the manuscript. + +<--- Page Split ---> + +## 464 ACKNOWLEDGEMENTS + +465 We thank Drs. Richard Wojcikiewicz and Martin Myers for reagents; Drs. Peter Arvan, Carol Elias and Daniel Klionsky for critical comments and suggestions, and members of the Qi and Arvan laboratories for comments and technical assistance. This work was supported by NIH grants 1R01DK11174 (to P.A. and L.Q.), 1R01DK105393, 1R01DK120047, and American Diabetes Association (ADA) 1- 19- IBS- 235 (to L.Q.). + +<--- Page Split ---> + +471 1 McLean, F. H. et al. A high- fat diet induces rapid changes in the mouse 472 hypothalamic proteome. Nutr Metab (Lond) 16, 26, doi:10.1186/s12986- 019- 473 0352- 9 (2019). 474 2 Dalvi, P. S. et al. High fat induces acute and chronic inflammation in the 475 hypothalamus: effect of high- fat diet, palmitate and TNF- alpha on appetite- 476 regulating NPY neurons. Int J Obes (Lond) 41, 149- 158, 477 doi:10.1038/ijo.2016.183 (2017). 478 3 Beutler, L. R. et al. 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Structural and mechanistic paradigm of leptin receptor 634 activation revealed by complexes with wild- type and antagonist leptins. Structure 635 22, 866- 877, doi:10.1016/j.str.2014.04.012 (2014). 636 58 Tsirigotaki, A. et al. Mechanism of receptor assembly via the pleiotropic 637 adipokine Leptin. Nat Struct Mol Biol 30, 551- 563, doi:10.1038/s41594- 023- 638 00941- 9 (2023). 639 59 Zhou, Z. et al. Endoplasmic reticulum- associated degradation regulates 640 mitochondrial dynamics in brown adipocytes. Science 368, 54- 60, 641 doi:10.1126/science.aay2494 (2020). 642 60 Hosoi, T. et al. Endoplasmic reticulum stress induces leptin resistance. Mol 643 Pharmacol 74, 1610- 1619, doi:10.1124/mol.108.050070 (2008). 644 61 Heifetz, A., Keenan, R. W. & Elbein, A. D. Mechanism of action of tunicamycin on 645 the UDP- GlcNAc: doliclryl- phosphate Glc- NAc- 1- phosphate transferase. 646 Biochemistry 18, 2186- 2192, doi:10.1021/bi00578a008 (1979). 647 62 Qi, L., Yang, L. & Chen, H. Detecting and quantitating physiological endoplasmic 648 reticulum stress. Methods Enzymol 490, 137- 146, doi:10.1016/B978- 0- 12- 649 385114- 7.00008- 8 (2011). + +<--- Page Split ---> + +650 63 Yang, L. et al. A Phos- tag- based approach reveals the extent of physiological 651 endoplasmic reticulum stress. PLoS One 5, e11621, 652 doi:10.1371/journal.pone.0011621 (2010). 653 + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1: Transient upregulation of SEL1L-HRD1 ERAD expression in the hypothalamus in response to high fat diet (HFD) feeding.
+ +(A) Quantitative PCR (qPCR) analysis of Sel1L and Hrd1 mRNA levels in the arcuate nucleus (ARC) of the C57BL/6J male mice fed on normal chow diet (NCD), 1w- and 8w-HFD (n=3-4 mice per group). + +(B-C) Representative Western blot of SEL1L and HRD1 in the ARC of the C57BL/6J male mice fed on NCD, 1w- and 8w-HFD, with quantitation shown on the right (n=13-15 mice per group). + +(D-E, F-G) Representative images and quantitation of IF staining of SEL1L (D-E) and HRD1 (F-G) in the ARC of POMC-eGFP mice fed NCD, or HFD for 1-week or 8-week (n=3-4 mice per group, 70-100 POMC and non-POMC cells respectively per mice). Yellow arrows, GFP-positive POMC neurons; White arrows, GFP-negative non-POMC neurons. + +Values, mean ± SEM. ns., not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by one-way ANOVA followed by Tukey's multiple comparisons test (A, C, E, G). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2: Hypothalamic POMC-specific ERAD deficiency leads to early-set DIO and its pathologies.
+ +(A) Growth curve of Sel1Lf and Sel1LPOmC mice, male (left) and female (right), fed on NCD (open symbols/dotted lines) or HFD (solid symbols/lines) \((n = 18 - 24\) per group for male mice, \(n = 10 - 16\) per group for female mice). +(B) Body composition of Sel1Lf and Sel1LPOmC male mice after 8w-HFD \((n = 4 - 7\) mice per group). +(C) H&E images of peripheral tissues from male mice fed HFD for 8 weeks \((n = 3\) mice per group). iWAT and gWAT, inguinal and gonadal white adipose tissues; BAT, brown adipose tissues. +(D-E) Glucose tolerance (D) and insulin tolerance tests (E) in male mice fed HFD for 8 weeks. Mice were fasted for 16 or 6 hours prior to glucose (2 g/kg body weight) or insulin (1 unit/kg body weight) injection, respectively \((n = 6\) mice per group). +(F) Serum glucose in 8w-HFD male mice, either ad-lib or after 6h-fasting \((n = 7 - 10\) mice per group). +(G) Insulin levels in 8w-HFD male mice under ad-lib condition \((n = 5 - 6\) mice per group). Values, mean \(\pm\) SEM. ns, not significant; \(^{*}p< 0.05\) , \(^{**}p< 0.01\) , \(^{***}p< 0.001\) and \(^{****}p< 0.0001\) by two-way ANOVA followed by multiple comparisons test (A-B, D-F) or two-tailed Student's t-test (G). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3: Hypothalamic ERAD deficiency triggers hyperphagia and leptin resistance. (A) Daily food intake of male Sel1Lf and Sel1LOMC mice at 1w- and 8w-HFD (n=9-11 mice per group). (B) Growth curve of male Sel1LOMC mice fed with either NCD or HFD under ad libitum or pair feeding as indicated (n=3 mice per group, blue solid circles). Male Sel1Lf mice fed ad libitum with the same diets were included as controls (n=3 mice per group, black open circles) (C) Growth of Sel1LOMC male mice with either ad libitum or pair-feeding of HFD starting at 5 weeks of age (n=3-5 mice per group). (D) Body weights of 12-week-old mice put on HFD (at day 0) followed by daily i.p. injected with vehicle (PBS) and leptin (2 mg/kg body weight) for 3 days (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice, indicated in squares). (E-F) Percentage of body weight change (E), average daily food intake (F) following 3 daily vehicle and leptin injections of the mice (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice). % Body weight is calculated based on the body weights at the end point over those at the starting point for each treatment. (G) Serum leptin levels in mice fed on NCD, 1w- and 8w-HFD (n=5-13 mice per group). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (A-G).
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4: Hypothalamic SEL1L-HRD1 deficiency leads to DIO via leptin signaling. (A) Schematic diagram for parabiosis and pictures (right) of Sel1L// and Sel1L^POMC female mice after parabiosis HFD for 8 weeks (n=3 pairs in group I, n=1 pair in group II, n=5 pairs in group III). (B-C) Body weights (B) of mice before and after parabiosis and body composition (C) after parabiosis following 8-week HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). (D-E) Serum leptin (D) and insulin (E) levels of mice after parabiosis HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (B-E).
+ +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5: Hypothalamic SEL1L-HRD1 ERAD deficiency impairs leptin-pSTAT3 signaling.
+ +<--- Page Split ---> + +721 (A-D) Representative immunofluorescence (IF) staining of pSTAT3 in Sel1Lfl/fl; POMC- eGFP and Sel1LPOMC; POMC- eGFP mice at NCD (A), 1w- HFD (B) and 8w- HFD (C), with quantitation 723 shown in D. Mice were fasted for overnight (16hrs) and administrated with leptin (i.p., 2 mg/kg 724 body weight) for 30 min (n=3- 4 mice per group). Yellow arrows, pSTAT3 positive POMC 725 neurons; White arrows, pSTAT3 negative POMC neurons. PBS- injected mice were included as 726 negative controls and shown in Supplementary Fig. 3. 727 (E) Quantitative PCR (qPCR) analysis of Pomc mRNA expression levels in ARC of Sel1Lfl/fl and Sel1LPOMC mice at 8w- HFD (n=3 mice per group). 728 (F- G) Representative Western blot for pSTAT3 in ARC of Sel1Lfl/fl and Sel1LPOMC mice at NCD or 730 8w- HFD, injected with leptin or PBS for 30 min (n=4 male mice per group), with quantitation 731 shown in G. 732 Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by 733 two- way ANOVA followed by multiple comparisons test (D, E, G). + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Fig. 6: The effect of POMC-specific ERAD in DIO is likely uncoupled from UPR and inflammation.
+ +(A) Representative Western blot for the PERK pathway of UPR in the ARC of Sel1Lff and Sel1LPOMC mice fed on 8w-HFD (n=6 mice per group with 3 male mice and 3 female), with quantitation shown on the right. Livers of mice treated with tunicamycin (TM, 1 mg/kg, i.p.) for 24 hours (Liver_TM) or not (Liver_CON), as well as lysates treated with Lambda protein phosphatase, included as controls. + +<--- Page Split ---> + +(B) Phostag gel (P-T)-based Western blot for IRE1α phosphorylation in the ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD, with quantitation shown on the right (n=3 mice per group with 2 male and 1 female). + +(C) Reverse transcriptase PCR (RT-PCR) analysis of Xbp1 mRNA splicing (u, unspliced; s, spliced) in ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD (n=2-3 male mice and n=2-3 female mice per group), with quantitation shown on the right. ARC of mice treated with + +female mice per group), with quantitation shown on the right. ARC of mice treated with tunicamycin (TM, 1 mg/kg, i.p.) for 24 hours (ARC_TM) included as a positive control. + +(D) Representative assays for UPR and pSTAT3 in mLepRb-transfected HEK293T treated with leptin with/without Thapsigargin (Tg) (n=5 independent cell samples for SDS-PAGE gel, n=3 for P-T gel, two independent repeats for RT-PCR). + +(E) Representative confocal images of the number of GFP-expressing POMC neurons in Sel1Lf/f;POMC-eGFP and Sel1LPOMC;POMC-eGFP mice after 8w-HFD, with quantitation shown on the right (n=6-9 mice per group). + +(F-G) Representative Western blot analysis of inflammatory markers in the ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD, with quantitation shown in G (n=3 mice per group). + +(H) Representative confocal images of GFAP, a marker of astrocytes, in the ARC of male Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD (n=3 mice per group). + +Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (D) or two-tailed Student's t-test (A-C, E, G). + +<--- Page Split ---> +![](images/Figure_9.jpg) + + +<--- Page Split ---> + +## Fig. 7: SEL1L-HRD1 is required for the maturation of nascent LepRb. + +(A- B) Representative Western blot analysis for pJAK2, pSTAT3 and LepRb in HEK293T transfected with or without mLepR, treated with or without leptin (A), with quantitation shown in (B) (n=4- 7 individual cell samples per group). + +(C) Representative Western blot analysis of mLepRb protein levels in mLepRb-transfected HEK293T in complete medium (DMEM w/ 10% FBS), with quantitation shown on the right (n=8 individual cell samples per group). + +(D- E) Representative Western blot analysis of interaction between SEL1L- HRD and mLepRb following immunoprecipitation (IP) of Flag (D) or SEL1L (E) from lysates of mLepRb-transfected HEK293T (n=2- 3 individual cell samples). + +(F) Representative Western blot analysis of Ub following denaturing immunoprecipitation (IP) of Flag from lysates of mLepRb-transfected HEK293T, with quantitation shown on the right (n=3 individual cell samples per group). + +(G) Representative Western blot analysis of LepRb protein decay in LepRb-transfected HEK293T cells co-treated with protein trafficking inhibitor Brefeldin-A and translation inhibitor cycloheximide (CHX) for the 0, 2 and 4 hours, with quantitation shown below (n=4 individual cell samples per group). + +(H) Representative Western blot analysis of LepRb glycosylation in LepRb-transfected HEK293T with EndoH and PNGase treatment, with quantitation shown on the right (n=3 individual cell samples per group). + +(I) Representative Western blot analysis of mLepRb membrane display by surface biotinylation and streptavidin-bead pull down assay in mLepRb-transfected HEK293T treated with leptin. T, total lysate; S, surface fraction. (n=2 individual cell samples per group). + +(J) Representative IF images of LepRb in mLepRb-transfected HEK293T treated with leptin, with quantitation of %surface signals over total shown on the right (n=28 cells per genotype from 3 independent repeats). + +(K) Reducing and non-reducing SDS-PAGE and Western blot analysis of LepRb high molecular-weight aggregates of LepRb in mLepRb-transfected WT and HRD1-/- HEK293T, with quantitation shown on the right (n=3 individual cell samples per group). + +Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by two-tailed Student's t-test (A, C, F, H, J, K) or two-way ANOVA followed by multiple comparisons test (B, G). + +<--- Page Split ---> +![PLACEHOLDER_37_0] + + +<--- Page Split ---> + +# Fig. 8: SEL1L-HRD1 ERAD degrads and limits the pathogenicity of LepRb Cys602Ser disease mutant. + +796 (A) Schematic diagram of mouse LepRb. "SP", Signal Peptide; "TM", Transmembrane. Star 797 symbols, N-glycosylation sites; Green lines, disulfide bonds. 800 (B) Structural modeling of mouse LepRb by AlphaFold2. Red arrow, location of human mutation 801 C604S (mouse C602S). 802 (C) Representative Western blot analysis for pSTAT3 in HEK293T transfected with mLepRb-WT 803 or mLepRb-C602S with or without leptin treatment, with quantitation shown below (n=3 804 individual cell samples per group). 805 (D) Representative Western blot analysis of LepRb protein decay in WT and HRD1-/- HEK293T 806 transfected with mLepRb-WT or -C602S, treated with brefeldin-A and cycloheximide (CHX) for 807 the 0, 1 and 2 hours, with quantitation shown below (n=4 individual cell samples per group). 808 (E) Reducing and non-reducing SDS-PAGE and Western blot analysis of LepRb high molecular- 809 weight (HMW) aggregates of LepRb in WT and HRD1-/- HEK293T transfected with mLepRb-WT 810 or -C602S, with quantitation shown on the right (n=6 individual cell samples per group). 811 (F-I) Representative IF images of mLepRb-WT and -C602S in transfected WT and HRD1-/- 812 HEK293T cells (F) with quantitation %surface signals over total (G) (n=11-17 cells per group) 813 and analysis of co-localization of LepRb with BiP signals by Pearson correlation coefficient (H) 814 and Manders overlap coefficient (I) (n=10-14 cells per group). 815 Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by 816 two-way ANOVA followed by multiple comparisons test (C, D, E, G, H, I). 817 + +<--- Page Split ---> +![PLACEHOLDER_39_0] + +
Fig.9: Proposed models for SEL1L-HRD1 ERAD degradation of wildtype LepRb and C604S disease mutant.
+ +In the basal conditions, SEL1L- HRD1 ERAD constitutively degrades misfolded LepRb and ensures the proper folding, maturation and surface expression of the LepRb. In the absence of ERAD, the accumulation of misfolded receptors forms aggregates, interferes with the folding and maturation of the nascent LepRb with attenuated surface display. In the context of recessive LepRb C604S mutant, though degraded by SEL1L- HRD1 ERAD, C604S LepRb readily forms aggregates to the extent beyond the capacity of ERAD, resulting in impaired maturation and surface display of the receptors. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- HFDPKOSupplementaryNC.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f_det.mmd b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1e4d9dfaae685dbc6c463ad210cf8cee2ef4ebe4 --- /dev/null +++ b/preprint/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/preprint__03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f_det.mmd @@ -0,0 +1,489 @@ +<|ref|>title<|/ref|><|det|>[[42, 106, 914, 209]]<|/det|> +# SEL1L-HRD1 ER-associated degradation regulates leptin receptor maturation and signaling in POMC neurons in diet-induced obesity + +<|ref|>text<|/ref|><|det|>[[42, 230, 270, 275]]<|/det|> +Ling Qi xvr2hm@virginia.edu + +<|ref|>text<|/ref|><|det|>[[50, 302, 596, 323]]<|/det|> +University of Virginia https://orcid.org/0000- 0001- 8229- 0184 + +<|ref|>text<|/ref|><|det|>[[42, 328, 830, 390]]<|/det|> +Hancheng Mao Department of Molecular & Integrative Physiology, University of Michigan Medical School https://orcid.org/0000- 0003- 2546- 6774 + +<|ref|>text<|/ref|><|det|>[[42, 396, 344, 438]]<|/det|> +Geun Hyang Kim Regeneron Pharmaceuticals, Inc. + +<|ref|>sub_title<|/ref|><|det|>[[42, 479, 103, 496]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[40, 515, 925, 537]]<|/det|> +Keywords: SEL1L- HRD1 ERAD, POMC, diet- induced obesity, leptin signaling, leptin receptor, parabiosis + +<|ref|>text<|/ref|><|det|>[[42, 554, 330, 574]]<|/det|> +Posted Date: January 12th, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 592, 475, 612]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3768472/v1 + +<|ref|>text<|/ref|><|det|>[[42, 630, 914, 673]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 690, 535, 710]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 745, 920, 789]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 29th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52743- 2. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[84, 88, 864, 154]]<|/det|> +# SEL1L-HRD1 ER-associated degradation regulates leptin receptor maturation and signaling in POMC neurons in diet-induced obesity + +<|ref|>text<|/ref|><|det|>[[310, 209, 685, 230]]<|/det|> +Hancheng Mao1, Geun Hyang Kim1,3, Ling Qi2\* + +<|ref|>text<|/ref|><|det|>[[111, 272, 872, 291]]<|/det|> +1Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann + +<|ref|>text<|/ref|><|det|>[[112, 304, 295, 322]]<|/det|> +Arbor, MI 48105, USA + +<|ref|>text<|/ref|><|det|>[[111, 335, 863, 355]]<|/det|> +2 Department of Molecular Physiology and Biological Physics, University of Virginia, School of + +<|ref|>text<|/ref|><|det|>[[111, 369, 450, 388]]<|/det|> +Medicine, Charlottesville, VA 22903, USA + +<|ref|>text<|/ref|><|det|>[[111, 400, 870, 421]]<|/det|> +3 Present address: Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, + +<|ref|>text<|/ref|><|det|>[[111, 433, 299, 452]]<|/det|> +New York 10591, USA + +<|ref|>text<|/ref|><|det|>[[111, 466, 435, 485]]<|/det|> +2 Correspondence: xvr2hm@virginia.edu + +<|ref|>text<|/ref|><|det|>[[111, 530, 635, 551]]<|/det|> +The authors have declared that no conflict of interest exists. + +<|ref|>text<|/ref|><|det|>[[111, 596, 838, 617]]<|/det|> +Short title: Regulation of leptin receptor maturation and signaling by SEL1L- HRD1 ERAD + +<|ref|>text<|/ref|><|det|>[[111, 660, 857, 744]]<|/det|> +Summary: Here we report that POMC- specific SEL1L- HRD1 ER- associated degradation is indispensable for leptin signaling in diet- induced obesity by controlling the turnover and maturation of nascent leptin receptor in the ER. + +<|ref|>text<|/ref|><|det|>[[111, 788, 870, 808]]<|/det|> +Keywords: SEL1L- HRD1 ERAD, POMC, diet- induced obesity, leptin signaling, leptin receptor, + +<|ref|>text<|/ref|><|det|>[[111, 821, 201, 839]]<|/det|> +parabiosis + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[65, 88, 217, 107]]<|/det|> +## 24 ABSTRACT + +<|ref|>text<|/ref|><|det|>[[110, 115, 884, 620]]<|/det|> +Endoplasmic reticulum (ER) homeostasis in the hypothalamus has been implicated in the pathogenesis of certain patho- physiological conditions such as diet- induced obesity (DIO) and type 2 diabetes; however, the significance of ER quality control mechanism(s) and its underlying mechanism remain largely unclear and highly controversial in some cases. Moreover, how the biogenesis of nascent leptin receptor in the ER is regulated remains largely unexplored. Here we report that the SEL1L- HRD1 protein complex of the highly conserved ER- associated protein degradation (ERAD) machinery in POMC neurons is indispensable for leptin signaling in diet- induced obesity. SEL1L- HRD1 ERAD is constitutively expressed in hypothalamic POMC neurons. Loss of SEL1L in POMC neurons attenuates leptin signaling and predisposes mice to HFD- associated pathologies including leptin resistance. Mechanistically, newly synthesized leptin receptors, both wildtype and disease- associated human mutant Cys604Ser (Cys602Ser in mice), are misfolding prone and bona fide substrates of SEL1L- HRD1 ERAD. Indeed, defects in SEL1L- HRD1 ERAD markedly impair the maturation of these receptors and causes their ER retention. This study not only uncovers a new role of SEL1L- HRD1 ERAD in the pathogenesis of diet- induced obesity and central leptin resistance, but a new regulatory mechanism for leptin signaling. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 120, 884, 368]]<|/det|> +Hypothalamic neurons play important roles in the adaptation and mal- adaptation to pathophysiological conditions such as diet- induced obesity (DIO) and type- 2 diabetes 1- 7. Homeostasis in the endoplasmic reticulum (ER) regulates many physiological processes such as systemic inflammation, inter- organellar crosstalk and mitochondrial dynamics 8- 14. It has been proposed that hypothalamic ER stress or unfolded protein response (UPR) may play a causal role in inflammation and leptin resistance in DIO and type- 2 diabetes 15- 21. However, others have reported a protective role of UPR in similar experimental settings 22,23. Hence, the significance of ER quality control pathways and its underlying mechanisms remain controversial. + +<|ref|>text<|/ref|><|det|>[[110, 404, 884, 875]]<|/det|> +In addition to UPR that respond to misfolded proteins in the ER, ER- associated protein degradation (ERAD) is a constitutively active and highly conserved process responsible for recruiting unfolded or misfolded proteins in ER for cytosolic proteasomal degradation 24- 31. Among over a dozen of putative ERAD complexes, the SEL1L- HRD1 protein complex represents the most evolutionarily conserved ERAD branch where SEL1L/Hrd3p is an obligatory cofactor for the E3 ligase HRD1 28- 30,32- 34. Recent studies using cell type- specific SEL1L or HRD1 knockout mouse models have revealed the patho- physiological importance of SEL1L- HRD1 ERAD in a substrate- specific manner 35- 42. Particularly relevant to this study, SEL1L- HRD1 ERAD has been reported as indispensable for AVP and POMC neurons to control water balance and food intake via the maturation of prohormones, proAVP and POMC, respectively 39,40. POMC neuron- specific Sel1L deletion leads to hyperphagia and age- associated obesity starting around 13 weeks of age when fed a low- fat chow diet 39. Given the importance of POMC neurons in maintaining energy homeostasis under various nutritional status, one outstanding question is the relevance and significance of SEL1L- HRD1 ERAD in POMC neurons under pathophysiological conditions, including DIO. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 875, 330]]<|/det|> +Here, we show that SEL1L- HRD1 ERAD in POMC neurons at the arcuate nucleus (ARC) of the hypothalamus, a key group of metabolic neurons that control food intake and energy expenditure \(^{43}\) , controls DIO pathogenesis and leptin sensitivity via the regulation of leptin receptor biogenesis and signaling. Soon after weaning, POMC- specific Sel1L deficient (Sel1LPOMC) mice are hypersensitive to DIO. Much to our surprise, SEL1L- HRD1 ERAD is indispensable for the maturation of nascent leptin receptor to reach to the cell surface. Hence, SEL1L- HRD1 ERAD is a critical regulator of the maturation of leptin receptor in the ER and thereby leptin signaling in POMC neurons. + +<|ref|>sub_title<|/ref|><|det|>[[115, 377, 203, 395]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 408, 808, 458]]<|/det|> +## Transient upregulation of SEL1L-HRD1 ERAD expression in the hypothalamus in response to high fat diet (HFD) feeding. + +<|ref|>text<|/ref|><|det|>[[110, 470, 880, 907]]<|/det|> +We previously showed that the SEL1L- HRD1 protein complex is constitutively expressed in the ARC of the hypothalamus \(^{39}\) . Here we first asked whether its expression in the ARC region is regulated in response to overnutrition by placing the mice on \(60\%\) HFD ( \(60\%\) calories derived from fat) for 1 or 8 weeks. HFD feeding expectedly reduced the expression of Pomc, Npy and Agrp (Supplementary Fig. 1A), while enhance the protein levels of POMC derivatives \(\beta\) - Endorphin and \(\alpha\) - MSH (Supplementary Fig. 1B- E). Moreover, HFD feeding enhanced neuronal activity in PVH region as measured by nuclear c- FOS following both 1- and 8- week HFD (Supplementary Fig. 1D, E). One- week HFD significantly induced Hrd1 mRNA level, but not Sel1L mRNA level, while 8- week HFD feeding had no such effect (Fig. 1A). At the protein levels, both SEL1L and HRD1 proteins, were elevated at 1- week HFD, and returned to the basal levels after 8- week HFD (Fig. 1B, C), pointing to the transient response to SEL1L- HRD1 expression in the hypothalamus in response to HFD challenge. We next performed confocal microscopy to visualize SEL1L- HRD1 expression in the ARC regions in response to HFD. To visualize POMC neurons, we used POMC- eGFP transgenic mice where eGFP is under the control of POMC + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 865, 266]]<|/det|> +promoter \(^{39,44}\) . SEL1L protein level was increased specifically in POMC neurons upon 1- week HFD, and returned to the basal level with prolonged HFD feeding (Fig. 1D, E). Similar observation was obtained for HRD1 protein levels in POMC neurons, but unlike SEL1L, HRD1 protein level was transiently upregulated in non POMC neurons as well (Fig. 1F, G). Hence, SEL1L- HRD1 expression in POMC neurons are responsive to acute, but not chronic, nutrient overload. + +<|ref|>text<|/ref|><|det|>[[110, 310, 884, 844]]<|/det|> +Hypothalamic POMC- specific ERAD deficiency leads to early- set DIO and its pathologies. To delineate the significance of hypothalamic ERAD in DIO, we next characterized the phenotypes of Sel1L \(^{POMC}\) mice, generated by crossing Sel1L \(^{f/f}\) with the Pome- Cre mouse line \(^{39}\) , following 8- week HFD feeding from 5 weeks of age. While, in line with our previous report, Sel1L \(^{POMC}\) mice appeared comparably to WT littermates in terms of body weight on chow diet for the first 13 weeks of age \(^{39}\) (Fig. 2A), Sel1L \(^{POMC}\) mice, both sexes, gained significantly more body weight soon after HFD feeding (Fig. 2A). Body composition analysis showed that fat content was significantly increased in Sel1L \(^{POMC}\) mice, reaching over 50% of body mass after 8- week HFD (Fig. 2B and Supplementary Fig. 2A) with more lipid deposition in the livers, as well as both white and brown adipose tissues (WAT and BAT) (Fig. 2C). Sel1L \(^{POMC}\) mice became highly glucose intolerant and insulin resistant following 8- week HFD (Fig. 2D, E), with elevated ad libitum and fasting blood glucose (Fig. 2F) and ad libitum insulin levels (Fig. 2G). In addition, glucagon and corticosterone levels were elevated in Sel1L \(^{POMC}\) mice (Supplementary Fig. 2B, C), while rectal temperature in Sel1L \(^{POMC}\) mice was decreased by 2 degrees compared to that of WT littermates (Supplementary Fig. 2D). Hence, we concluded that mice with POMC- specific ERAD defects exhibit early onset DIO and its pathologies including glucose and insulin resistance. + +<|ref|>text<|/ref|><|det|>[[112, 886, 757, 907]]<|/det|> +Hypothalamic ERAD deficiency triggers hyperphagia and leptin resistance. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 874, 620]]<|/det|> +We next explored the possible mechanism underlying the susceptibility to DIO in Sel1L POMC mice. Sel1L POMC mice consumed \(\sim 40\%\) more food daily, i.e., hyperphagia, upon both 1- and 8- week HFD feeding (Fig. 3A). To directly demonstrate the direct causal link between food intake and weight gain, we performed pair feeding (giving the same amount of the food as WT littermates consume) following 8- week ad libitum HFD feeding. Sel1L POMC mice gained weight quite rapidly under ad libitum feeding of HFD; however, their weight gain was significantly slowed down following pair- feeding and recovered when placed on ad libitum HFD feeding again (Fig. 3B). Indeed, weight gain of Sel1L POMC mice was comparable to that of WT littermates if pair- feeding was performed at the beginning of HFD feeding (Fig. 3C). We then tested whether hyperphagia of Sel1L POMC mice is caused by leptin resistance by leptin injection (Fig. 3D). Leptin injection was expected to induce body weight loss in WT mice, but not Sel1L POMC mice. Indeed, unlike WT mice, Sel1L POMC mice continued to gain body weight following leptin injection (Fig. 3D, E). This difference in body weight gain was likely due to the differences in food intake in response to leptin injection (Fig. 3F), pointing to a significant leptin resistance in Sel1L POMC mice. Sel1L POMC mice exhibited progressively marked hyperleptinemia with HFD feeding (Fig. 3G). Hence, we concluded that hypothalamic POMC neurons- specific ERAD deficiency triggers hyperphagia and leptin resistance. + +<|ref|>sub_title<|/ref|><|det|>[[115, 664, 858, 684]]<|/det|> +## The effect of hypothalamic SEL1L-HRD1 ERAD in DIO is mediated by leptin resistance. + +<|ref|>text<|/ref|><|det|>[[111, 694, 880, 907]]<|/det|> +To further establish the effect of leptin resistance in ERAD deficiency- associated DIO, we next performed parabiosis where two littermates were surgically stitched together to allow the sharing of the circulation (Fig. 4A). Following two weeks of recovery on chow diet, the parabions WT: Sel1L POMC (Group III) were placed on HFD for 8 weeks (Fig. 4A). Two control parabions, WT: WT (Group I) and Sel1L POMC: Sel1L POMC (Group II), gained weight as expected with the latter pair becoming obese (Fig. 4B). However, in WT: Sel1L POMC (Group III) parabions, body weight gain for WT mice was attenuated compared to WT mice in WT: WT (Group I) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 880, 270]]<|/det|> +control parabionts (P=0.08), while body weight gain for Sel1L \(^{POMC}\) mice was comparable to that of Sel1L \(^{POMC}\) : Sel1L \(^{POMC}\) parabionts (Group II) (Fig. 4B). Body compositions (i.e., lean vs. fat) in parabionts were not affected by the partner (Fig. 4C). Moreover, serum leptin and insulin levels were highly elevated in the Sel1L \(^{POMC}\) mice, but unaltered in WT mice regardless of the partners (Fig. 4D, E). Hence, these data suggested that hypothalamic SEL1L- HRD1 ERAD controls DIO pathogenesis via hyperleptinemia. + +<|ref|>sub_title<|/ref|><|det|>[[115, 311, 784, 332]]<|/det|> +## Hypothalamic SEL1L-HRD1 ERAD deficiency impairs leptin-pSTAT3 signaling. + +<|ref|>text<|/ref|><|det|>[[111, 342, 880, 715]]<|/det|> +We next asked how POMC- specific SEL1L- HRD1 ERAD regulates leptin sensitivity. As leptin signaling induces phosphorylation of STAT3 (pSTAT3), we next examined the levels of pSTAT3 in POMC neurons following leptin challenge. To visualize POMC neurons, we generated Sel1L \(^{POMC}\) mice on the POMC- eGFP background (Sel1L \(^{POMC}\) : POMC- eGFP) \(^{39,44}\) . HFD feeding progressively blunted leptin- induced pSTAT3 in the POMC neurons of the ARC region of WT mice, but to a much greater extent, in Sel1L \(^{POMC}\) mice (Fig. 5A- D and Supplementary Fig. 3). In keeping with the notion that pSTAT3 a critical transcription factor for the POMC gene \(^{45}\) , hypothalamic POMC mRNA expression was markedly decreased in Sel1L \(^{POMC}\) mice with HFD (Fig. 5E). Moreover, Western blot analysis of pSTAT3 of the ARC region also showed a greater reduction of the percent of STAT3 being phosphorylated following HFD feeding (Fig. 5F, G). Thus, our data suggested that SEL1L- HRD1 ERAD in POMC neurons is vital for maintaining central leptin sensitivity during DIO pathogenesis. + +<|ref|>text<|/ref|><|det|>[[111, 758, 880, 905]]<|/det|> +The effect of POMC- specific ERAD in DIO is likely uncoupled from UPR or inflammation. As ERAD deficiency expectedly causes the accumulation of unfolded/misfolded proteins in the ER that can potentially trigger UPR and given the reported role of UPR in DIO pathogenesis, we next tested whether ERAD deficiency activates UPR and if so, to what extent. There was no detectable activation of the PERK pathway as measured by phosphorylation of PERK and its + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 880, 655]]<|/det|> +downstream phosphorylation of elF2α (Fig. 6A and Supplementary Fig. 4A). Phosphorylation of IRE1α, on the other hand, was moderately elevated in the ARC of Sel1LPOMC mice, so was the splicing of Xbp1 mRNA (a downstream effector of IRE1α) (Fig. 6B, C and Supplementary Fig. 4B, C). Consistently, ER chaperons BiP (an XBP1 target) was mildly elevated in the ARC of Sel1LPOMC mice (Fig. 6A and Supplementary Fig. 4A, D). In vitro, treatment with an ER stress inducer thapsigargin (Tg) induced strong ER stress, but failed to affect leptin signaling in WT HEK293T cells transfected with long isoform of mouse Leptin receptors (mLepRb) (Fig. 6D and Supplementary Fig. 4E), indicating that UPR is not sufficient to induce leptin resistance. Importantly, we found no significant POMC neuronal loss in the ARC of Sel1LPOMC;POMC- eGFP mice (Fig. 6E). Inflammatory markers were largely comparable in the ARC of Sel1LPOMC mice compared to those in WT littermates as measured by phosphorylation and protein levels of c-Jun N-terminal Kinase (JNK) as well as protein levels of I kappa B alpha (IkBa) (Fig. 6F, G). Chronic HFD feeding mildly increased astrogliosis in the ARC regions of both Sel1LPOMC and Sel1LPOMC mice as measured by both Western blot and immunofluorescence staining of astrocyte marker Glial Fibrillary acidic protein (GFAP) and/or microglia marker Ionized calcium- binding adaptor molecule 1 (IBA1) (Fig. 6F- H and Supplementary Fig. 4F). Taken together, these data demonstrate that Sel1L deficiency in POMC neurons triggers leptin resistance, independently of UPR, neuronal cell death and inflammation. + +<|ref|>sub_title<|/ref|><|det|>[[112, 694, 777, 714]]<|/det|> +## SEL1L-HRD1 is required for the maturation of nascent leptin receptor (LepR). + +<|ref|>text<|/ref|><|det|>[[111, 725, 874, 907]]<|/det|> +The forementioned data suggested that SEL1L- HRD1 ERAD regulates leptin sensitivity upstream of STAT3. To further explore the underlying mechanism, we generated leptin- responsive HEK293T cell system expressing the long isoform of LepR (LepRb) responsible for leptin- induced JAK2- STAT3 signaling46-49. Indeed, in line with decreased leptin sensitivity in vivo, mLepRb- positive HRD1-/- HEK293T cells exhibited impaired phosphorylation of JAK2 and STAT3 compared to those in transfected WT cells in response to leptin stimulation (Fig. 7A, B). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 880, 268]]<|/det|> +Surprisingly, the protein level of mLepRb was significantly higher in HRD1- HEK293T cells compared to that of WT cells, under both serum- deprived and - supplemented conditions (Fig. 7B, C). Moreover, SEL1L interaction with in LepRb- transfected cells was markedly enhanced in HRD1- cells where substrate- SEL1L interaction is known to be stabilized \(^{29,50,51}\) (Fig. 7D, E). LepRb was ubiquitinated in an HRD1- dependent manner (Fig. 7F) and was significantly stabilized in HRD1- cells compared to that in WT cells (Fig. 7G). + +<|ref|>text<|/ref|><|det|>[[110, 277, 884, 686]]<|/det|> +We next assess the consequence of ERAD deficiency on LepRb maturation in the ER. Endoglycosidase H (EndoH) digestion, which cleaves asparagine- linked high mannose or hybrid glycans of the immature glycoproteins predominantly in ER \(^{52}\) , revealed significantly lower fraction of EndoH resistant form of LepRb that were able to exit the ER for complete maturation in HRD1- HEK293T cells (Fig. 7H). This was further confirmed by surface biotinylation assay followed by immunoprecipitation with streptavidin- beads, which indicated reduced proportion of surface LepRb in HRD1- cells (Fig. 7I and Supplementary Fig. 5A). Moreover, confocal microscopy following immunofluorescence staining further demonstrated an altered distribution of LepRb with increased intracellular, but decreased surface, expression in ERAD- deficient cells (Fig. 7J and Supplementary Fig. 5B). In the absence of SEL1L- HRD1, LepRb protein was prone to form high molecular weight aggregates via disulfide bonds (Fig. 7K) Taken together, our data show that SEL1L- HRD1 ERAD is required for the maturation of LepRb by targeting the misfolding- prone or misfolded LepRb for proteasomal degradation. + +<|ref|>sub_title<|/ref|><|det|>[[112, 727, 857, 777]]<|/det|> +## SEL1L-HRD1 ERAD degrades and limits the pathogenicity of human LepRb Cys604Ser (C604S) mutant. + +<|ref|>text<|/ref|><|det|>[[112, 789, 865, 907]]<|/det|> +To demonstrate the clinical relevance of our findings, we asked whether human LepRb (hLepRb) mutants \(^{53,54}\) are SEL1L- HRD1 ERAD substrates. Here, we focused on hLepRb mutant C604S, a recessive point mutation due to missense homozygous substitution T > A at position 1810, identified in two brothers at 1- and 5- years old with severely early onset obesity + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 881, 430]]<|/det|> +54,55. C604- C674 forms a disulfide bond in human LepRb corresponding to C602- C672 in mouse LepRb (Fig. 8A, B) 56- 58. This mutation has been predicted as loss- of- function likely due to defects in folding 54,56- 58. C602S mLepRb significantly impaired leptin response compared to WT mLepRb in WT cells, which was further diminished in HRD1- /- cells (Fig. 8C). Similar to WT mLepRb, C602S mLepRb was stabilized in the absence of HRD1 (Fig. 8D). Notably, C602S mLepRb readily formed HMW aggregates in WT HEK293T cells, and to much greater extent, in HRD1- /- cells (Fig. 8E). Such aggregates likely formed in the ER as demonstrated by their colocalization with the ER chaperone BiP based on immunostaining (Fig. 8F- I). Hence, SEL1L- HRD1 ERAD is indispensable for the degradation of nascent WT and, at least a subset of, disease mutant LepRb, which ensures the maturation, trafficking and membrane display of functional LepRb. + +<|ref|>sub_title<|/ref|><|det|>[[115, 473, 230, 490]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[111, 503, 883, 876]]<|/det|> +This study not only identifies a novel regulatory mechanism for leptin receptor and signaling, but also reports a key role of hypothalamic ERAD in maintaining energy homeostasis under nutrient overload conditions. SEL1L- HRD1 ERAD defects in POMC neurons predispose mice to DIO and its pathologies, due to hyperphagia and hypothalamic leptin resistance. Our mechanistic studies establish LepRb as a bona fide endogenous substrate of SEL1L- HRD1 ERAD. Pointing to the clinical relevance of our findings, human recessive LepRb C604S variant is trapped in the ER and degraded by SEL1L- HRD1 ERAD (Fig. 9). In the absence of SEL1L- HRD1 ERAD, both WT and C604S LepRb are trapped in the ER in the form of HMW aggregates, with attenuated cell surface expression (Fig. 8E- I and Fig. 9). While this reported effect of ERAD in POMC neurons is in keeping with recent studies demonstrating the profound physiological importance of SEL1L- HRD1 ERAD in vivo 39,40, it uncovers a novel function of SEL1L- HRD1 ERAD in leptin signaling and a novel regulatory mechanism for leptin biology. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 875, 430]]<|/det|> +Our data show that hypothalamic SEL1L deficiency markedly increases the progression and pathogenesis of DIO in mice. Sel1L- deficient POMC neurons exhibit mild alterations in ER homeostasis including elevated activation of the IRE1α - XBP1 pathway and expression of ER chaperones, but without any detectable cell death. As previous studies have shown that deficiency of Ire1a or Xbp1 in POMC neurons predispose mice to DIO \(^{21}\) , while gain- of- function of XBP1s in POMC neurons had an opposite effect \(^{23}\) , we conclude that the effect of SEL1L- HRD1 ERAD is uncoupled from IRE1α - XBP1 pathway of the UPR and cell death, which is in line with many recent studies of various tissue- specific Sel1L- or Hrd1- deficient models \(^{37,39- 42,59}\) . These findings point to the cellular adaption in response to ERAD deficiency \(^{25}\) . Such mild UPR activation and chaperone expression are potentially cyto- protective in response to the accumulation of misfolding proteins in the ER. + +<|ref|>text<|/ref|><|det|>[[110, 470, 870, 875]]<|/det|> +Previous reports have suggested that UPR may play a causal role in leptin resistance due to impaired leptin signaling \(^{15,17,60}\) . These studies were performed via the administration of ER stress inducers tunicamycin and thapsigargin which can be fraught with artefacts. Indeed, tunicamycin can inhibit glycosylation of the glycoproteins \(^{61}\) including LepRb, and thus the impaired leptin signaling can be directly due to defective glycosylation and concomitant functionality of LepRb instead of UPR activation as a general outcome of numerous dysregulation of glycoproteins. Further, high dosage of ER stress inducers included in previous studies may fall far from any physiological relevance \(^{15,17,60}\) . In our study, thapsigargin treatment induced a range of ER stress response in a dose dependent manner, but failed to alter leptin signaling in WT HEK293T cells transfected with mLepRb even at the high level of UPR. Hence, collective evidence suggests that UPR is likely uncoupled from leptin signaling. The reason for these discrepancies remains unknown. Careful future studies are needed to validate either model. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 881, 460]]<|/det|> +This study demonstrates an important role of SEL1L- HRD1 ERAD in leptin signaling, at least in part via the regulation of the maturation of nascent LepRb protein. We previously showed that SEL1L- HRD1 ERAD is required for the posttranslational maturation of POMC prohormone in mice on chow diet and that Sel1L deficiency in POMC neurons cause age- associate obesity in mice on chow diet due to the ER retention of POMC prohormone 39. In DIO mouse models, we found defects in Sel1LPOMC mice occurring upstream of POMC transcription as leptin- induced STAT3 phosphorylation is impaired in the absence of SEL1L- HRD1 ERAD 45- 49. Further mechanistic studies identify partial loss- of- function of LepRb resulted from attenuated ER exit of nascent LepRb in SEL1L- HRD1 ERAD deficient cells. This study suggests that nascent LepRb protein is likely misfolding prone in the ER, likely due to multiple glycosylation and the formation of disulfide bonds, and hence relies on SEL1L- HRD1 ERAD to generate an ER environment conducive for the proper folding and conformation of bystander LepRb. + +<|ref|>text<|/ref|><|det|>[[110, 503, 886, 811]]<|/det|> +Several human mutants have also been identified as SEL1L- HRD1 ERAD substrates that readily form aggregates and become resistant to and bypassing the quality control mediated by ERAD, leading to loss- of- function disease phenotype. These misfolded substrates with highly reactive cysteine thiols accumulate and promote the formation of inter- or intra- molecular disulfide- bonded aggregates 39- 41. Hence, SEL1L- HRD1 ERAD- mediated degradation of nascent unfolded and misfolded substrates, including LepRb in this study, may effectively prevent protein aggregation and maintain the folding environment in the ER. Efforts to target SEL1L- HRD1 ERAD function may represent a viable means for the treatment of certain diseases caused by a dominant- negative disease allele or a general collapse of the folding environment in the ER. + +<|ref|>sub_title<|/ref|><|det|>[[115, 856, 208, 872]]<|/det|> +## METHODS + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 872, 333]]<|/det|> +Mice. As described previously \(^{39}\) , POMC- specific \(Sel1L\) - deficient mice ( \(Sel1L^{POMC}\) ) and control littermates ( \(Sel1L^{//}\) ) were generated. The mice were further crossed with Pomc- eGFP reporter mice to generate \(Sel1L^{POMC}\) ; POMC- eGFP and control littermates \(Sel1L^{//}\) ; POMC- eGFP. WT B6 mice were purchased from JAX and bred in our mouse facility. Mice were fed a chow diet (13% fat, 57% carbohydrate and 30% protein, PicoLab Rodent Diet 5L0D) and placed on a high- fat diet (HFD, calories provided by 60% fat, 20% carbohydrate and 20% protein, Research Diet D12492) from 5 weeks of age for 1 week or 8 weeks. All mice were housed in a temperature- controlled room with a 12- hour light/12- hour dark cycle. + +<|ref|>text<|/ref|><|det|>[[111, 375, 880, 683]]<|/det|> +Food intake measurement and pair- feeding. Food intake were measured as previously described \(^{39}\) . Briefly, to perform daily food intake measurement, mice were first acclimatized to single housing 24 hours before the experiment. Daily food intake was measured 1 hour before the onset of the dark cycle each day. For the pair- feeding at later stage of HFD feeding, \(Sel1L^{POMC}\) and WT littermates had continuous free access to HFD for eight weeks and were then single housed and fed \(\sim 2.5 \text{g}\) , which was determined by the average of daily food intake of WT littermates, at the start of the dark cycle. For the pair- feeding at early stage of HFD feeding, 5- week- old \(Sel1L^{POMC}\) mice were split into two groups: One group of \(Sel1L^{POMC}\) and WT littermates had continuous free access to food; the other group of \(Sel1L^{POMC}\) mice (pair- fed) was fed \(\sim 2.5 \text{g}\) at the start of dark hours. Weekly bodyweight gains were monitored. + +<|ref|>text<|/ref|><|det|>[[112, 712, 880, 891]]<|/det|> +Leptin treatment in mice. Twelve- week- old mice were intraperitoneally (i.p.) injected PBS followed by leptin (2 mg/kg body weight, R&D systems; catalog 498- OB- 05M) 1 hour before the onset of dark cycle for three consecutive days as described \(^{39}\) . Body weight and food intake were monitored daily during the treatment period. For phosphorylated STAT3 staining, 2 mg/kg leptin were i.p. injected to mice, followed by overnight fasting. Mice were anesthetized by isoflurane for fixation- perfusion 30 min after injection. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 118, 880, 350]]<|/det|> +Tissue and blood collection. These procedures were carried out as previously described39. Briefly, blood was collected from anesthetized mice via cardiac puncture, transferred to 1.5ml microcentrifuge tubes, kept at room temperature for 30 minutes prior to centrifugation at 2,000 \(g\) for 15 minutes. Serum was aliquoted and stored at \(- 80^{\circ}C\) until analysis. For brain microdissection, Adult Mouse Brain Slicer Matrix (BSMAA001- 1, Zivic Instruments) was used to collect coronal brain slices containing ARC region with further microdissection to obtain ARC- enriched region. All tissues were snap- frozen in liquid nitrogen and stored at \(- 80^{\circ}C\) before use. + +<|ref|>text<|/ref|><|det|>[[111, 391, 875, 670]]<|/det|> +Preparation of brain sections. Mice were anesthetized with isoflurane, perfused with PBS followed by \(4\%\) paraformaldehyde (PFA) (Electron Microscopy Sciences; catalog 19210) for fixation. Brains were then postfixed in \(4\%\) PFA for overnight at \(4^{\circ}C\) , dehydrated in \(15\%\) sucrose and then \(30\%\) sucrose consecutively overnights at \(4^{\circ}C\) , and sectioned (30 \(\mu m\) ) on a cryostat (Microm HM550 Cryostat, Thermo Fisher Scientific). The sections were stored in DEPC- containing anti- freezing media ( \(50\%\) 0.05 M sodium phosphate pH 7.3, \(30\%\) ethylene glycol, \(20\%\) glycerol) at \(- 20^{\circ}C\) . Different brain regions were identified using the Paxinos and Franklin atlas. Counted as distance from bregma, the following coordinates were used: PVN (- 0.82 mm to - 0.94 mm) and ARC (- 1.58 mm to - 1.7 mm). + +<|ref|>text<|/ref|><|det|>[[111, 712, 876, 893]]<|/det|> +Western blot and antibodies. Frozen tissue or cells were homogenized by sonication in lysis buffer [150mM NaCl, 50mM Tris pH 7.5, 10 mM EDTA, \(1\%\) Triton X- 100] with freshly added protease inhibitors (Sigma; catalog P8340), phosphatase inhibitors (Sigma; catalog P5726) and 10 mM N- ethylmaleimide (Thermo Scientific; catalog 23030). Lysates were incubated on ice for 30 min followed by centrifugation (13,000 g, 10 min at \(4^{\circ}C\) ). Supernatants were collected and analyzed for protein concentration using Bradford assay (Bio- Rad; catalog 5000006). For + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 881, 528]]<|/det|> +denaturing SDS- PAGE, samples were further supplied with 1mM DTT and denatured at \(95^{\circ}C\) for 5 min in 5x SDS sample buffer (250 mM Tris- HCl pH 6.8, \(10\%\) sodium dodecyl sulfate, \(0.05\%\) Bromophenol blue, \(50\%\) glycerol, and 1.44 M \(\beta\) - mercaptoethanol). For non- reducing SDAPAGE, samples were prepared in 5x non- denaturing sample buffer (250 mM Tris- HCl pH 6.8, \(10\%\) sodium dodecyl sulfate, \(0.05\%\) bromophenol blue, \(50\%\) glycerol). For phostag gel analysis based on phos- tag system as described \(^{62,63}\) , SDS- PAGE gel was supplemented by \(50\mu \mathrm{M}\) MnCl2 (Sigma) and \(25\mu \mathrm{M}\) phostag reagent (NARD Institute; catalog AAL- 107) and must be protected from light until finishing running. Protein isolated from the liver of mice treated with tunicamycin (TM, \(1\mathrm{mg / kg}\) , i.p.) for 24 hours was used as a positive control to indicate the position of phosphorylated PERK and IRE1a. For phosphatase treatment, \(100\mu \mathrm{g}\) tissue lysates were incubated with \(1\mu \mathrm{l}\) lambda phosphatase (APPase, New England BioLabs; catalog P0753S) in \(1\times \mathrm{PMP}\) buffer (New England BioLabs; catalog B0761S) with \(1\mathrm{mM}\mathrm{MnCl}_2\) (New England BioLabs; catalog B1761S) at \(30^{\circ}C\) for 30 min. Reaction was stopped by adding \(5\times\) SDS sample buffer and incubated at \(90^{\circ}C\) for 5 min. + +<|ref|>text<|/ref|><|det|>[[110, 535, 875, 714]]<|/det|> +All samples were incubated in \(65^{\circ}C\) for 10min and run with 15- 30 \(\mu \mathrm{g}\) total lysate on SDS- PAGE gel for separation followed by electrophoretic transfer to PVDF membrane (0.45um, Millipore; catalog IPFL00010). The blots were incubated in \(2\%\) BSA/Tri- buffered saline tween- 20 (TBST) with primary antibodies overnight at \(4^{\circ}C\) , washed with TBST followed by 1hr incubation with goat anti- rabbit or mouse IgG HRP at room temperature. Band density was quantitated using the Image Lab software on the ChemiDOC XRS+ system (Bio- Rad). + +<|ref|>text<|/ref|><|det|>[[110, 725, 880, 907]]<|/det|> +Antibodies for Western blot were as follows: SEL1L (rabbit, 1:8000, Abclonal; catalog E112049), HRD1 (rabbit, 1:2000, ABclonal; catalog E15102), GRP78 BiP (rabbit, 1:5000, Abcam; catalog ab21685), HSP90 (rabbit, 1:5,000, Santa Cruz Biotechnology Inc.; catalog sc- 7947), FLAG (mouse, 1:2000, Sigma- Aldrich; catalog F- 1804), IRE1α (rabbit, 1:2,000, Cell Signaling Technology; catalog 3294), p- elF2α (rabbit, 1:2000, Cell Signaling Technology; catalog 3597), elF2α (rabbit, 1:2000, Cell Signaling Technology; catalog S722), p- JNK (mouse, 1:2000, Cell + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 350]]<|/det|> +Signaling Technology; catalog 9255), JNK (rabbit, 1:1000, Cell Signaling Technology; catalog 9252), PERK (Rabbit, 1:1000, Cell Signaling Technology; catalog 3192), pSTAT3 (Tyr705) (rabbit, 1:1000, catalog 9131, Cell Signaling Technology), STAT3 (rabbit, 1:1000, Cell Signaling Technology; catalog 9132), pJAK2 (Tyr1007/1008) (rabbit, 1:1000, Cell Signaling Technology; catalog 3771), JAK2 (rabbit, 1:1000, ABclonal; catalog A19629), Tubulin (mouse, 1:5000, Santa Cruz Biotechnology Inc.; catalog sc- 5286), IkBa (rabbit, 1:1000, Cell Signaling Technology; catalog 9242) and IBA1 (rabbit, 1:1000, Proteintech; catalog 10904- 1- AP) Secondary antibodies for Western blot were goat anti- rabbit IgG HRP and goat anti- mouse IgG HRP at 1:5,000, both from Bio- Rad. + +<|ref|>text<|/ref|><|det|>[[111, 406, 882, 909]]<|/det|> +Immunostaining and antibodies. For fluorescent immunostaining in free- floating brain sections, samples were picked out of anti- freezing buffer followed by 3 washes with PBS. Free- floating sections were simultaneously incubated with primary antibodies in blocking buffer (0.3% donkey serum and 0.25% Triton X- 100 in 0.1 M PBS) overnight at 4°C. Following 3 washes with PBS, sections were incubated with secondary antibodies for 2 hours at room temperature. Brain sections were then mounted on gelatin- coated slides (Southern Biotech; catalog SLD01- CS). Counterstaining and mounting were performed with mounting medium containing DAPI (Vector Laboratories; catalog H- 1200) and Fisherfinest Premium Cover Glasses (Fisher Scientific; catalog 12- 548- 5P). For immunostaining in cells, 24 hours after transfection of LepRb- 3xFLAG constructs, cells were placed on Poly- L- Lysine (Advanced Biomatrix; catalog 5048) coated Millicell EZ SLIDE 8- well glasses (Millipore; catalog PEZGS0816) for 24 hours before treatment and fixation. For staining surface bound leptin, samples were washed by ice cold PBS for 5 times and fixed by 4% formaldehyde (VWR; catalog 89370- 094) for 15 minutes on ice followed by 3 washes with PBS. No permeabilization reagents were involved. For staining other markers, permeabilization was included and the overall process were the same as described above. To quantify immunoreactivity, identical acquisition settings were used for imaging each brain + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 880, 205]]<|/det|> +section from all groups within an experiment. The numbers of immunoreactivity- positive soma analysis and intensity of immunoreaction were quantified in 3D stack volumes after uniform background subtraction using the NIS Elements AR software (Nikon) and FIJI (National Institute of Health, USA). + +<|ref|>text<|/ref|><|det|>[[111, 246, 884, 590]]<|/det|> +Antibodies for immunostaining were as follows: HRD1 (rabbit, 1:500, homemade), GRP78 BiP (rabbit, 1:500, Abcam; catalog ab21685), \(\alpha\) - MSH (sheep, 1:2,000, Millipore; catalog AB5087), \(\beta\) - endorphin (rabbit, 1:2,000, Phoenix Pharmaceuticals; catalog H- 022- 33, provided by Carol Elisa), and GFP (chicken IgY, 1:300, Abcam; catalog ab13970), p- Y705 STAT3 (rabbit, 1:200, Cell Signaling Technology; catalog 9145), GFAP (rabbit, 1:500, Agilent; Z033429- 2), FLAG (mouse, 1:500, Sigma- Aldrich; catalog F- 1804), KDEL (rabbit, 1:500, Novus Biologicals; catalog NBP2- 75549), eIF3n (goat, 1:500, Santa Cruz Biotechnology; catalog sc- 16377). Secondary antibodies for fluorescent immunostaining (all 1:500) were as follows: Anti- rabbit IgG Alexa Fluor 647; anti- goat IgG Alexa Fluor 488 & 647; anti- sheep IgG Cy5 were from Jackson ImmunoResearch. Donkey anti- mouse IgG Alexa fluor 555 was from Invitrogen (catalog A32773) and goat anti- chicken IgY FITC was from Aves Labs (catalog F- 1005). + +<|ref|>text<|/ref|><|det|>[[111, 630, 870, 712]]<|/det|> +Plasmids. Mouse LepRb cDNA was provided by Dr. Martin Myer at University of Michigan Medical School. The LepRb coding region was amplified by PCR using a primer set containing HindIII and XbaI restriction site at 5' and 3' respectively. + +<|ref|>text<|/ref|><|det|>[[111, 725, 636, 746]]<|/det|> +F: 5'- CCG AAGCTT ATGATGTGTCAGAAATTCTATGTGGTT- 3' + +<|ref|>text<|/ref|><|det|>[[111, 758, 598, 778]]<|/det|> +R: 5'- TGC TCTAGA CACAGTTAAGTCACACATCTTATT- 3' + +<|ref|>text<|/ref|><|det|>[[111, 790, 880, 875]]<|/det|> +Both PCR products and the backbone vector p3xFLAG- CMV14 were digested using HindIII and XbaI restriction enzymes in the double digestion system from New England BioLabs. For construction of LepRb point mutants, quick change mutagenesis was performed using PFU + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 819, 108]]<|/det|> +DNA polymerase (600140, Agilent). The following primers were used for mutagenesis to + +<|ref|>text<|/ref|><|det|>[[112, 120, 316, 138]]<|/det|> +construct LepRb- C602S: + +<|ref|>text<|/ref|><|det|>[[112, 152, 586, 172]]<|/det|> +F: 5'- CCTGCTGGTGTCAGACCTCAGTCGACTCTATG- 3' + +<|ref|>text<|/ref|><|det|>[[112, 185, 590, 204]]<|/det|> +R: 5'- CATAGACTGCACTGAGGCTCTGACACCAGCAGG- 3' + +<|ref|>text<|/ref|><|det|>[[112, 240, 880, 260]]<|/det|> +CRISPR/Cas9- based knockout (KO) in HEK293T cells. HEK293T cells were cultured at \(37^{\circ}C\) + +<|ref|>text<|/ref|><|det|>[[112, 272, 840, 292]]<|/det|> +with \(5\% \text{CO}_2\) in DMEM with \(10\%\) fetal bovine serum (Fisher Scientific). To generate HRD1- + +<|ref|>text<|/ref|><|det|>[[112, 304, 760, 323]]<|/det|> +deficient HEK293T cells, sgRNA oligonucleotides designed for human HRD1 (5'- + +<|ref|>text<|/ref|><|det|>[[112, 336, 800, 355]]<|/det|> +GGACAAAGGCCCTGGATGTAC- 3') was inserted into lentICRISPR v2 (plasmid 52961, + +<|ref|>text<|/ref|><|det|>[[112, 368, 880, 388]]<|/det|> +Addgene). Cells transfected with empty plasmids without sgRNA were used as wild type control. + +<|ref|>text<|/ref|><|det|>[[112, 400, 860, 420]]<|/det|> +Cells grown in \(10 \text{cm}\) petri dishes were transfected with indicated plasmids using 5μl \(1 \text{mg/ml}\) + +<|ref|>text<|/ref|><|det|>[[112, 432, 860, 452]]<|/det|> +polyethylenimine (PEI, Sigma) per \(1 \mu \text{g}\) of plasmids for HEK293T cells. Cells were cultured 24 + +<|ref|>text<|/ref|><|det|>[[112, 464, 825, 484]]<|/det|> +hours after transfection in medium containing \(2 \mu \text{g/ml}\) puromycin for 48 hours and then in + +<|ref|>text<|/ref|><|det|>[[112, 497, 290, 515]]<|/det|> +normal growth media. + +<|ref|>text<|/ref|><|det|>[[112, 560, 840, 580]]<|/det|> +Statistics. Results are expressed as the mean \(\pm\) SEM unless otherwise stated. Statistical + +<|ref|>text<|/ref|><|det|>[[112, 593, 783, 612]]<|/det|> +analyses were performed in GraphPad Prism version 8.0 (GraphPad Software Inc.). + +<|ref|>text<|/ref|><|det|>[[112, 624, 880, 744]]<|/det|> +Comparisons between the groups were made by unpaired two- tailed Student's t test for two groups, or one- way ANOVA or two- way ANOVA followed by multiple comparisons test for more than two groups. \(P\) value \(< 0.05\) was considered as statistically significant. All experiments were repeated at least twice and/or performed with several independent biological samples, and representative data are shown. + +<|ref|>text<|/ref|><|det|>[[112, 817, 857, 837]]<|/det|> +Study Approval. All experiments performed with mice were in compliance with University of + +<|ref|>text<|/ref|><|det|>[[112, 849, 827, 869]]<|/det|> +Michigan (Ann Arbor, MI) Institutional Animal Care and Use Committee (#PRO00006888) + +<|ref|>text<|/ref|><|det|>[[112, 881, 202, 899]]<|/det|> +guidelines. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 118, 866, 268]]<|/det|> +452 Data and material availability. The materials and reagents used are either commercially available or available upon the request, with detailed information included in Methods. The predicted structure of mLepRb is available at AlphaFold ID AF- P48356- F1. All data supporting the findings and materials for the manuscript are available within the article and the Supplementary Information. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[63, 88, 339, 107]]<|/det|> +## AUTHOR CONTRIBUTION + +<|ref|>text<|/ref|><|det|>[[61, 118, 884, 235]]<|/det|> +H.M. and G.H.K. designed the most of experiments and H.M., with the help of G.H.K., performed most of the experiments and data analysis. H.M., with the help of G.H.K., wrote the methods and figure legends. L.Q. and H.M. wrote the manuscript. All authors have approved the manuscript. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[58, 88, 326, 106]]<|/det|> +## 464 ACKNOWLEDGEMENTS + +<|ref|>text<|/ref|><|det|>[[58, 118, 856, 268]]<|/det|> +465 We thank Drs. Richard Wojcikiewicz and Martin Myers for reagents; Drs. Peter Arvan, Carol Elias and Daniel Klionsky for critical comments and suggestions, and members of the Qi and Arvan laboratories for comments and technical assistance. This work was supported by NIH grants 1R01DK11174 (to P.A. and L.Q.), 1R01DK105393, 1R01DK120047, and American Diabetes Association (ADA) 1- 19- IBS- 235 (to L.Q.). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 120, 880, 910]]<|/det|> +471 1 McLean, F. H. et al. A high- fat diet induces rapid changes in the mouse 472 hypothalamic proteome. Nutr Metab (Lond) 16, 26, doi:10.1186/s12986- 019- 473 0352- 9 (2019). 474 2 Dalvi, P. S. et al. High fat induces acute and chronic inflammation in the 475 hypothalamus: effect of high- fat diet, palmitate and TNF- alpha on appetite- 476 regulating NPY neurons. Int J Obes (Lond) 41, 149- 158, 477 doi:10.1038/ijo.2016.183 (2017). 478 3 Beutler, L. R. et al. Obesity causes selective and long- lasting desensitization of 479 AgRP neurons to dietary fat. Elife 9, doi:10.7554/eLife.55909 (2020). 480 4 Horvath, T. L. et al. 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Detecting and quantitating physiological endoplasmic 648 reticulum stress. Methods Enzymol 490, 137- 146, doi:10.1016/B978- 0- 12- 649 385114- 7.00008- 8 (2011). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 88, 848, 157]]<|/det|> +650 63 Yang, L. et al. A Phos- tag- based approach reveals the extent of physiological 651 endoplasmic reticulum stress. PLoS One 5, e11621, 652 doi:10.1371/journal.pone.0011621 (2010). 653 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 108, 876, 655]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 658, 868, 693]]<|/det|> +
Fig. 1: Transient upregulation of SEL1L-HRD1 ERAD expression in the hypothalamus in response to high fat diet (HFD) feeding.
+ +<|ref|>text<|/ref|><|det|>[[113, 691, 876, 739]]<|/det|> +(A) Quantitative PCR (qPCR) analysis of Sel1L and Hrd1 mRNA levels in the arcuate nucleus (ARC) of the C57BL/6J male mice fed on normal chow diet (NCD), 1w- and 8w-HFD (n=3-4 mice per group). + +<|ref|>text<|/ref|><|det|>[[113, 738, 876, 785]]<|/det|> +(B-C) Representative Western blot of SEL1L and HRD1 in the ARC of the C57BL/6J male mice fed on NCD, 1w- and 8w-HFD, with quantitation shown on the right (n=13-15 mice per group). + +<|ref|>text<|/ref|><|det|>[[113, 783, 876, 831]]<|/det|> +(D-E, F-G) Representative images and quantitation of IF staining of SEL1L (D-E) and HRD1 (F-G) in the ARC of POMC-eGFP mice fed NCD, or HFD for 1-week or 8-week (n=3-4 mice per group, 70-100 POMC and non-POMC cells respectively per mice). Yellow arrows, GFP-positive POMC neurons; White arrows, GFP-negative non-POMC neurons. + +<|ref|>text<|/ref|><|det|>[[113, 830, 860, 864]]<|/det|> +Values, mean ± SEM. ns., not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by one-way ANOVA followed by Tukey's multiple comparisons test (A, C, E, G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 95, 856, 595]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 604, 830, 639]]<|/det|> +
Fig. 2: Hypothalamic POMC-specific ERAD deficiency leads to early-set DIO and its pathologies.
+ +<|ref|>text<|/ref|><|det|>[[111, 636, 880, 896]]<|/det|> +(A) Growth curve of Sel1Lf and Sel1LPOmC mice, male (left) and female (right), fed on NCD (open symbols/dotted lines) or HFD (solid symbols/lines) \((n = 18 - 24\) per group for male mice, \(n = 10 - 16\) per group for female mice). +(B) Body composition of Sel1Lf and Sel1LPOmC male mice after 8w-HFD \((n = 4 - 7\) mice per group). +(C) H&E images of peripheral tissues from male mice fed HFD for 8 weeks \((n = 3\) mice per group). iWAT and gWAT, inguinal and gonadal white adipose tissues; BAT, brown adipose tissues. +(D-E) Glucose tolerance (D) and insulin tolerance tests (E) in male mice fed HFD for 8 weeks. Mice were fasted for 16 or 6 hours prior to glucose (2 g/kg body weight) or insulin (1 unit/kg body weight) injection, respectively \((n = 6\) mice per group). +(F) Serum glucose in 8w-HFD male mice, either ad-lib or after 6h-fasting \((n = 7 - 10\) mice per group). +(G) Insulin levels in 8w-HFD male mice under ad-lib condition \((n = 5 - 6\) mice per group). Values, mean \(\pm\) SEM. ns, not significant; \(^{*}p< 0.05\) , \(^{**}p< 0.01\) , \(^{***}p< 0.001\) and \(^{****}p< 0.0001\) by two-way ANOVA followed by multiple comparisons test (A-B, D-F) or two-tailed Student's t-test (G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[121, 95, 800, 536]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 538, 866, 825]]<|/det|> +
Fig. 3: Hypothalamic ERAD deficiency triggers hyperphagia and leptin resistance. (A) Daily food intake of male Sel1Lf and Sel1LOMC mice at 1w- and 8w-HFD (n=9-11 mice per group). (B) Growth curve of male Sel1LOMC mice fed with either NCD or HFD under ad libitum or pair feeding as indicated (n=3 mice per group, blue solid circles). Male Sel1Lf mice fed ad libitum with the same diets were included as controls (n=3 mice per group, black open circles) (C) Growth of Sel1LOMC male mice with either ad libitum or pair-feeding of HFD starting at 5 weeks of age (n=3-5 mice per group). (D) Body weights of 12-week-old mice put on HFD (at day 0) followed by daily i.p. injected with vehicle (PBS) and leptin (2 mg/kg body weight) for 3 days (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice, indicated in squares). (E-F) Percentage of body weight change (E), average daily food intake (F) following 3 daily vehicle and leptin injections of the mice (n=2 per group for male mice, indicated in dots; n=2-3 per group for female mice). % Body weight is calculated based on the body weights at the end point over those at the starting point for each treatment. (G) Serum leptin levels in mice fed on NCD, 1w- and 8w-HFD (n=5-13 mice per group). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (A-G).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 98, 853, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 451, 872, 625]]<|/det|> +
Fig. 4: Hypothalamic SEL1L-HRD1 deficiency leads to DIO via leptin signaling. (A) Schematic diagram for parabiosis and pictures (right) of Sel1L// and Sel1L^POMC female mice after parabiosis HFD for 8 weeks (n=3 pairs in group I, n=1 pair in group II, n=5 pairs in group III). (B-C) Body weights (B) of mice before and after parabiosis and body composition (C) after parabiosis following 8-week HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). (D-E) Serum leptin (D) and insulin (E) levels of mice after parabiosis HFD for 8 weeks (n=6 mice in group I, n=2 mice in group II, n=5 mice per genotype in group III). Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (B-E).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 90, 857, 861]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 861, 842, 880]]<|/det|> +
Fig. 5: Hypothalamic SEL1L-HRD1 ERAD deficiency impairs leptin-pSTAT3 signaling.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 90, 880, 300]]<|/det|> +721 (A-D) Representative immunofluorescence (IF) staining of pSTAT3 in Sel1Lfl/fl; POMC- eGFP and Sel1LPOMC; POMC- eGFP mice at NCD (A), 1w- HFD (B) and 8w- HFD (C), with quantitation 723 shown in D. Mice were fasted for overnight (16hrs) and administrated with leptin (i.p., 2 mg/kg 724 body weight) for 30 min (n=3- 4 mice per group). Yellow arrows, pSTAT3 positive POMC 725 neurons; White arrows, pSTAT3 negative POMC neurons. PBS- injected mice were included as 726 negative controls and shown in Supplementary Fig. 3. 727 (E) Quantitative PCR (qPCR) analysis of Pomc mRNA expression levels in ARC of Sel1Lfl/fl and Sel1LPOMC mice at 8w- HFD (n=3 mice per group). 728 (F- G) Representative Western blot for pSTAT3 in ARC of Sel1Lfl/fl and Sel1LPOMC mice at NCD or 730 8w- HFD, injected with leptin or PBS for 30 min (n=4 male mice per group), with quantitation 731 shown in G. 732 Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by 733 two- way ANOVA followed by multiple comparisons test (D, E, G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 90, 850, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 777, 825, 811]]<|/det|> +
Fig. 6: The effect of POMC-specific ERAD in DIO is likely uncoupled from UPR and inflammation.
+ +<|ref|>text<|/ref|><|det|>[[113, 810, 859, 890]]<|/det|> +(A) Representative Western blot for the PERK pathway of UPR in the ARC of Sel1Lff and Sel1LPOMC mice fed on 8w-HFD (n=6 mice per group with 3 male mice and 3 female), with quantitation shown on the right. Livers of mice treated with tunicamycin (TM, 1 mg/kg, i.p.) for 24 hours (Liver_TM) or not (Liver_CON), as well as lysates treated with Lambda protein phosphatase, included as controls. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 877, 125]]<|/det|> +(B) Phostag gel (P-T)-based Western blot for IRE1α phosphorylation in the ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD, with quantitation shown on the right (n=3 mice per group with 2 male and 1 female). + +<|ref|>text<|/ref|><|det|>[[111, 137, 844, 164]]<|/det|> +(C) Reverse transcriptase PCR (RT-PCR) analysis of Xbp1 mRNA splicing (u, unspliced; s, spliced) in ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD (n=2-3 male mice and n=2-3 female mice per group), with quantitation shown on the right. ARC of mice treated with + +<|ref|>text<|/ref|><|det|>[[111, 165, 800, 195]]<|/det|> +female mice per group), with quantitation shown on the right. ARC of mice treated with tunicamycin (TM, 1 mg/kg, i.p.) for 24 hours (ARC_TM) included as a positive control. + +<|ref|>text<|/ref|><|det|>[[111, 196, 875, 247]]<|/det|> +(D) Representative assays for UPR and pSTAT3 in mLepRb-transfected HEK293T treated with leptin with/without Thapsigargin (Tg) (n=5 independent cell samples for SDS-PAGE gel, n=3 for P-T gel, two independent repeats for RT-PCR). + +<|ref|>text<|/ref|><|det|>[[111, 249, 870, 297]]<|/det|> +(E) Representative confocal images of the number of GFP-expressing POMC neurons in Sel1Lf/f;POMC-eGFP and Sel1LPOMC;POMC-eGFP mice after 8w-HFD, with quantitation shown on the right (n=6-9 mice per group). + +<|ref|>text<|/ref|><|det|>[[111, 298, 870, 330]]<|/det|> +(F-G) Representative Western blot analysis of inflammatory markers in the ARC of Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD, with quantitation shown in G (n=3 mice per group). + +<|ref|>text<|/ref|><|det|>[[111, 331, 877, 363]]<|/det|> +(H) Representative confocal images of GFAP, a marker of astrocytes, in the ARC of male Sel1Lf/f and Sel1LPOMC mice fed on 8w-HFD (n=3 mice per group). + +<|ref|>text<|/ref|><|det|>[[111, 364, 880, 410]]<|/det|> +Values, mean ± SEM. ns, not significant; *p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001 by two-way ANOVA followed by multiple comparisons test (D) or two-tailed Student's t-test (A-C, E, G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 87, 850, 899]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 702, 107]]<|/det|> +## Fig. 7: SEL1L-HRD1 is required for the maturation of nascent LepRb. + +<|ref|>text<|/ref|><|det|>[[111, 106, 875, 173]]<|/det|> +(A- B) Representative Western blot analysis for pJAK2, pSTAT3 and LepRb in HEK293T transfected with or without mLepR, treated with or without leptin (A), with quantitation shown in (B) (n=4- 7 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 153, 875, 201]]<|/det|> +(C) Representative Western blot analysis of mLepRb protein levels in mLepRb-transfected HEK293T in complete medium (DMEM w/ 10% FBS), with quantitation shown on the right (n=8 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 200, 877, 250]]<|/det|> +(D- E) Representative Western blot analysis of interaction between SEL1L- HRD and mLepRb following immunoprecipitation (IP) of Flag (D) or SEL1L (E) from lysates of mLepRb-transfected HEK293T (n=2- 3 individual cell samples). + +<|ref|>text<|/ref|><|det|>[[111, 248, 877, 300]]<|/det|> +(F) Representative Western blot analysis of Ub following denaturing immunoprecipitation (IP) of Flag from lysates of mLepRb-transfected HEK293T, with quantitation shown on the right (n=3 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 298, 877, 364]]<|/det|> +(G) Representative Western blot analysis of LepRb protein decay in LepRb-transfected HEK293T cells co-treated with protein trafficking inhibitor Brefeldin-A and translation inhibitor cycloheximide (CHX) for the 0, 2 and 4 hours, with quantitation shown below (n=4 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 362, 820, 410]]<|/det|> +(H) Representative Western blot analysis of LepRb glycosylation in LepRb-transfected HEK293T with EndoH and PNGase treatment, with quantitation shown on the right (n=3 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 408, 870, 460]]<|/det|> +(I) Representative Western blot analysis of mLepRb membrane display by surface biotinylation and streptavidin-bead pull down assay in mLepRb-transfected HEK293T treated with leptin. T, total lysate; S, surface fraction. (n=2 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 457, 850, 506]]<|/det|> +(J) Representative IF images of LepRb in mLepRb-transfected HEK293T treated with leptin, with quantitation of %surface signals over total shown on the right (n=28 cells per genotype from 3 independent repeats). + +<|ref|>text<|/ref|><|det|>[[111, 504, 870, 555]]<|/det|> +(K) Reducing and non-reducing SDS-PAGE and Western blot analysis of LepRb high molecular-weight aggregates of LepRb in mLepRb-transfected WT and HRD1-/- HEK293T, with quantitation shown on the right (n=3 individual cell samples per group). + +<|ref|>text<|/ref|><|det|>[[111, 552, 855, 600]]<|/det|> +Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by two-tailed Student's t-test (A, C, F, H, J, K) or two-way ANOVA followed by multiple comparisons test (B, G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 840, 904]]<|/det|> + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[112, 90, 850, 123]]<|/det|> +# Fig. 8: SEL1L-HRD1 ERAD degrads and limits the pathogenicity of LepRb Cys602Ser disease mutant. + +<|ref|>text<|/ref|><|det|>[[111, 123, 880, 430]]<|/det|> +796 (A) Schematic diagram of mouse LepRb. "SP", Signal Peptide; "TM", Transmembrane. Star 797 symbols, N-glycosylation sites; Green lines, disulfide bonds. 800 (B) Structural modeling of mouse LepRb by AlphaFold2. Red arrow, location of human mutation 801 C604S (mouse C602S). 802 (C) Representative Western blot analysis for pSTAT3 in HEK293T transfected with mLepRb-WT 803 or mLepRb-C602S with or without leptin treatment, with quantitation shown below (n=3 804 individual cell samples per group). 805 (D) Representative Western blot analysis of LepRb protein decay in WT and HRD1-/- HEK293T 806 transfected with mLepRb-WT or -C602S, treated with brefeldin-A and cycloheximide (CHX) for 807 the 0, 1 and 2 hours, with quantitation shown below (n=4 individual cell samples per group). 808 (E) Reducing and non-reducing SDS-PAGE and Western blot analysis of LepRb high molecular- 809 weight (HMW) aggregates of LepRb in WT and HRD1-/- HEK293T transfected with mLepRb-WT 810 or -C602S, with quantitation shown on the right (n=6 individual cell samples per group). 811 (F-I) Representative IF images of mLepRb-WT and -C602S in transfected WT and HRD1-/- 812 HEK293T cells (F) with quantitation %surface signals over total (G) (n=11-17 cells per group) 813 and analysis of co-localization of LepRb with BiP signals by Pearson correlation coefficient (H) 814 and Manders overlap coefficient (I) (n=10-14 cells per group). 815 Values, mean ± SEM. ns, not significant; \*p<0.05, \*\*p<0.01, \*\*\*p<0.001 and \*\*\*\*p<0.0001 by 816 two-way ANOVA followed by multiple comparisons test (C, D, E, G, H, I). 817 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 95, 725, 360]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 368, 830, 401]]<|/det|> +
Fig.9: Proposed models for SEL1L-HRD1 ERAD degradation of wildtype LepRb and C604S disease mutant.
+ +<|ref|>text<|/ref|><|det|>[[115, 401, 870, 515]]<|/det|> +In the basal conditions, SEL1L- HRD1 ERAD constitutively degrades misfolded LepRb and ensures the proper folding, maturation and surface expression of the LepRb. In the absence of ERAD, the accumulation of misfolded receptors forms aggregates, interferes with the folding and maturation of the nascent LepRb with attenuated surface display. In the context of recessive LepRb C604S mutant, though degraded by SEL1L- HRD1 ERAD, C604S LepRb readily forms aggregates to the extent beyond the capacity of ERAD, resulting in impaired maturation and surface display of the receptors. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 354, 150]]<|/det|> +- HFDPKOSupplementaryNC.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/images_list.json b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..73f2c57c459d2d1475abd42e817004b4339f7c9f --- /dev/null +++ b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figures:", + "footnote": [], + "bbox": [ + [ + 130, + 120, + 850, + 544 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Characterization of the organic neuromorphic circuit. (a) Circuit schematic of the organic neuromorphic circuit following a two-branch (+ and -) architecture. The gate of each organic electrochemical device is connected to specified sensory stimulus. The sum \\(\\sum V\\) over the output voltages \\(V_{+}\\) and \\(V_{- }\\) branches is forwarded to the robotic system via an activation function. (b) Biological representation of dendritic summation involving two presynaptic signals. (c) A sigmoidal activation function translates the stimulus intensity \\(\\sum V\\) into a neural response (probability for a certain behavior). (d) Output characteristics of the volatile synaptic device that displays short-term memory. (e) Transfer characteristics of the volatile synaptic device that displays short-term memory. (f) Long-term memory of the non-volatile synaptic device.", + "footnote": [], + "bbox": [ + [ + 130, + 85, + 860, + 652 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Behavioral change of the robotic manipulator upon adaptive processing of multimodal stimuli. (a) Explorative behavior of the robot before any adaptation. (b) Adaptation of the \\(V_{+}\\) branch to the pressure stimuli when incidentally grabbing a cup. (c) After training, the robotic manipulator consistently grabs the cup if it is close by, when detected by the proximity sensor. The inset image depicts the robot holding a (dark) cup. (d) Established behavior from the \\(V_{+}\\) branch is maintained. (e) Adaptation to the new temperature stimuli in the \\(V_{-}\\) branch. (f) After training, the robotic manipulator only grabs cups that are white and cold, but not those that are dark and hot. The inset image pictures the robot holding a white cup.", + "footnote": [], + "bbox": [ + [ + 125, + 87, + 874, + 686 + ] + ], + "page_idx": 14 + } +] \ No newline at end of file diff --git a/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7.mmd b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b6c43d68dee54a1c486c320294095e793a5677e3 --- /dev/null +++ b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7.mmd @@ -0,0 +1,232 @@ + +# Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics + +Yoeri van de Burgt y.b.v.d.burgt@tue.nl + +Eindhoven University of Technology https://orcid.org/0000- 0003- 3472- 0148 Imke Krauhausen Eindhoven University of Technology https://orcid.org/0000- 0001- 5633- 389X Sophie Griggs Department of Chemistry, University of Oxford Iain McCulloch University of Oxford https://orcid.org/0000- 0002- 6340- 7217 Jaap Toonder Eindhoven University of Technology https://orcid.org/0000- 0002- 5923- 4456 Paschalis Gkoupidenis Max Planck Institute for Polymer Research https://orcid.org/0000- 0002- 0139- 0851 + +## Article + +## Keywords: + +Posted Date: January 30th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 3878146/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on June 4th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 48881- 2. + +<--- Page Split ---> + +# Title: Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics + +## Authors: + +Imke Krauhausen, \(^{1,2,3}\) Sophie Griggs, \(^{4}\) Iain McCulloch, \(^{4}\) Jaap M. J. den Toonder, \(^{1,2}\) Paschalis Gkoupidenis, \(^{3*}\) Yoeri van de Burgt \(^{1,2*}\) + +## Affiliations: + +\(^{1}\) Institute for Complex Molecular Systems, Eindhoven University of Technology, The Netherlands. \(^{2}\) Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, The Netherlands. \(^{3}\) Max Planck Institute for Polymer Research, Mainz, Germany. \(^{4}\) Department of Chemistry, University of Oxford, United Kingdom. + +\*Corresponding author. Email: gkoupidenis@mpip-mainz.mpg.de, Y.B.v.d.Burgt@tue.nl + +Abstract: Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small- scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real- time handling of sensory stimuli via low- voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. + +This work demonstrates that adaptive neuro- inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio- inspired learning for advancing intelligent robotics. + +<--- Page Split ---> + +## INTRODUCTION + +Advancements in the field of robotics have witnessed a notable shift towards bio- inspiration, motivated by the remarkable capabilities of biological nervous systems 1 2 3. Bio- inspired robotics introduces novel ways for robots to interact with and be integrated into the physical world. Achieving this goal often necessitates the use of functional materials chosen for their ability to provide the desired flexibility, deformability, or adaptability 4 5. + +At the same time, artificial intelligence (AI) is already demonstrating its proficiency for highly complex tasks in various domains such as data analysis, decision making and computer vision 6. AI systems mostly utilize large- scale (deep) neural networks for learning, pattern recognition, classification and language processing inside a static environment 7 8. These systems are based on gradient- based algorithms that require high computing power and memory storage as well as a large amount of labeled training data. Although these systems are highly effective, their biological plausibility is limited 9, and they can be power hungry 10. Hence, there is a desire to explore alternative bio- inspired algorithms, such as spiking neural networks, genetic and evolutionary algorithms, and swarm strategies, and to further enhance the development of specialized neuromorphic hardware platforms 11 12 13. Such innovations in algorithms and hardware have proven to be powerful tools for simulating neural processes, accelerating the training of artificial neural networks, and leading to increasingly sophisticated hardware for artificial neural systems. However, essential adaptive neuronal processes, including associative learning and behavioral conditioning, exist in primitive organisms like the box jellyfish which even lack centralized nervous systems 14. This raises the question of whether complexity in algorithms and architectures is always imperative for achieving cognitive functions and intelligent behavior. The relatively simple neural circuits of primitive species still exhibit significant capabilities, suggesting that emulating fundamental biological learning principles locally with functional materials and devices could be equally important as complexity while gaining in efficiency 4 15. + +Primitive biological organisms employ fundamental strategies for learning such as exploration, multimodal processing, and behavioral conditioning. From early developmental stages, living beings instinctively start to learn from experience and through trial and error, by interacting with their surroundings 16. During this initial exploration phase behaviors tend to be somewhat random and lack a specific goal, while the organism is engaged with the environment via a wide range of sensory modalities (touch, vision, olfaction etc.). The randomness of certain behaviors, such as bumping into an object, leads to the discovery of new sensations and consequently learning opportunities. Through this physical interaction of organisms with their surroundings, behavioral randomness develops gradually into consistency 17. In this context, multimodal sensing enables the collection of various sensations describing the same event. These concurrent multimodal observations are synchronized in time and, as a result, become correlated, establishing autonomic connections across different sensory modalities, and enabling behaviors such as respondent (Pavlovian) conditioning and associative learning. Indeed, a recent study of the complete connectome of a Drosophila brain reveals that the majority of neurons process multimodal signals 18. Adaptivity and plasticity in function and behavior - essentials for biological development - are especially effective if previous experiences and memory are taken into account as well 19. For instance, behaviors are associated with consequences through affirmative (rewards or reinforcement) and adverse (punishment) stimuli to strengthen or weaken a specific behavior (operant conditioning). By providing diverse sensory feedback and abundant opportunities to learn from the environment, explorative behavior and multimodal processing allow for instruction- free processes that converge into optimal behavioral conditions via adaptivity. + +Emerging functional materials and devices can offer unique properties that go beyond what conventional systems and electronics could achieve 20. Organic mixed ionic- electronic (semi)conductors have recently experienced a notable upswing for neuromorphic engineering 21 22 23. They are able to replicate bio- inspired functionalities such as synaptic plasticity 24 25, neural processing 26, high connectivity and recurrence 27 28 and even forgetfulness 29 just by material- inherent adaptivity. + +<--- Page Split ---> + +The key for this adaptivity stems from the fact that organic synaptic devices display linear, symmetric and analogue tuning of electrical conductance and operate at low voltage with high energy efficiency. The compatibility of organics with solution- based processes and large- area integration into flexible or stretchable substrates, can enable the merging of organic neuromorphic electronics in unconventional form factors (body, robotics, buildings, etc.)30. Indeed, significant steps have been made using conductive polymers regarding localized handling of data via on- chip training31,32, real- time operation with online learning33 and spiking circuits for bio- integration34,35. Despite these significant demonstrations, applications are often limited to abstracted and conceptual demonstrations in well- defined laboratory settings or mock environments, enabled by simple binary decisions. Robotic setups offer a realistic platform for interaction- rich, real- life set- ups36. Robotic manipulators for example are crucial for a variety of applications serving in versatile and dynamic environments, ranging from industrial assembly lines to neural prosthesis. Highly adaptive and localized control close to the sensory nodes, can drastically improve performance, and can also warrant operational safety which is essential for human- oriented purposes such as neuroprosthetics37,38. + +In this work, we present a robotic system that uses multimodal sensory stimuli to explore and interact with a real- world environment in real- time while adapting to it using bio- inspired mechanisms. At the core for adaptivity and learning of the robotic system is an organic neuromorphic circuit that consists of organic electrochemical transistors (OECTs) and organic neuromorphic devices (also called electrochemical random- access memories, ECRAms). This bio- inspired approach enables the robotic agent to incrementally learn and perform a complex behavioral task, showcasing its adaptability and distributed intelligence in responding locally to dynamic and multimodal environmental cues. More specifically, the robotic system gains the ability to distinguish between safe and potentially harmful objects through local adaptation of neuromorphic circuitry. This work demonstrates that highly functional organic materials can reform neuromorphic hardware, rethinking adaptive intelligent systems as small(er)- scale local circuitry that interacts with the environment with bio- inspired learning mechanisms. + +## RESULTS + +The robotic system is based on the Arduino Braccio Kit (Fig. 1a), with five degrees of freedom and an additional movement option for opening and closing a gripper. The gripper acts as hand of the robotic manipulator and is equipped with four sensors that are continuously collecting multimodal sensory stimuli of pressure, distance, temperature, and color tone when manipulating objects (Fig. 1a and 1b). A custom gripper setup is realized to accommodate the collection of multimodal sensory signals in a hand- like shape (Fig. S1 and Methods section). Different cups (dark/hot, white/cold) are placed sequentially near the robotic system, so that it is able to either pick them up or refuse them. Each movement of the robot follows an autonomic sequence of specified moves that provides a behavioral baseline for any action taken. The movements vary between a pick- up action with a grab or no- grab option in the end, a drop action that concludes a successful grab and a pull- back action to avoid the cup that functions as no- grab. These actions are driven via an Arduino Uno that operates the motors of the robotic setup. The motor commands are continuously modulated by sensory stimuli from the environment, i.e. a detection of a cup in close proximity with the hand or a pressure applied due to a successful grab, creating a real- time response of the robot to its surroundings (that is, the object of interest). Without any prior external influence, the robot is in an explorative state in which it incidentally picks a cup or not with the grab or no- grab actions initially taken randomly (Fig. 1b). Whenever a cup is discovered (grabbed) by chance, it inherently leads to new sensory sensations. An analogue trainable neuromorphic circuit (Fig. 1a and 1c) interacts locally with the sensory signals and allows learning via adaptive associative connections necessary for behavioral conditioning (Fig. 1b, right). The organic neuromorphic circuit comprises of organic electrochemical devices, OECTs and ECRAms, that are either volatile or non- volatile respectively (Fig. 1d). The output voltage \(\sum V\) of the + +<--- Page Split ---> + +organic neuromorphic circuit depends on the conductance state of each organic electrochemical device and reflects the sensory signals in an event- driven nature. \(\sum V\) merges the input branches of electrical circuitry similar to the dendritic summation of multiple neurons via the synapses (Fig. 1b, right). + +The organic neuromorphic circuit consists of four micrometer- scale organic electrochemical devices (Fig. S2 and Methods section), mimicking synaptic plasticity and therefore exhibiting neuro- emulating functionality. Two of these devices function as OECT and operate in a volatile, short- term manner (indicated as ST). The other two devices operate in a non- volatile manner as ECRAM with long- term effects (referenced as LT, Fig. 2a). The four devices are arranged in two branches (+ and - ) that each contain a volatile and a non- volatile element in series. The combined output voltage is the sum over both branches: \(\sum V = V_{+} + V_{- }\) . This closely resembles the dendritic summation of multiple presynaptic signals at the synapses of a postsynaptic neuron (Fig. 2b). Each branch also displays an intrinsic associative adaptation due to the interplay of OECT and ECRAM. If loaded with a (adaptive) resistive load the OECT changes its operating regime and thus its transconductance (Fig. S3 and S4). The transconductance represents a tunable sensitivity towards the sensory stimuli that can be strengthened or weakened via the ECRAM leading to an inherent association between the two stimuli at OECT and ECRAM. + +The output voltage \(\sum V\) is translated into a motor action through an activation function which relates the signal to a behavioral probability (Fig. 2c). The activation function is sigmoidal and proportional to the widely used activation function hyperbolic tangent (tanh). It is executed on the Arduino Uno to reduce circuit complexity though hardware implementations are feasible. The output voltage is interpreted in terms of probability. The non- deterministic and fail- prone behavior in biological systems causing new sensations is one of the reasons for their remarkable adaptability in unknown situations \(^{39}\) . While the Arduino Uno relays signals from the organic neuromorphic circuit to the robotic setup, it operates solely as translator/mediator and has no agency on the behavior of the robotic agent. In order to react to the environment, the neuromorphic circuit handles optical, thermal, and mechanical stimuli. A color and proximity sensor are used for gaining information on objects (i.e. a cup) from afar/without contact and drive the gates and thus (trans- )conductance of the volatile devices, \(G_{ST + }\) and \(G_{ST - }\) . A pressure and temperature sensor feed a signal on contact to the non- volatile gates of the neuromorphic circuit, \(G_{LT + }\) and \(G_{LT - }\) providing the necessary impulses for learning and conditioning. Via the series connection in the circuit layout, the (+)- branch then combines the sensory input of pressure and proximity in a single information stream leading to the output voltage \(V_{+}\) . This functionality is mirrored in the (- )- branch coupling temperature and color resulting in signal stream \(V_{- }\) . We employ off- the- shelf sensors for collecting sensory input which provides lifelike, noise- containing data (Fig. S5, see Methods). The sensory signals undergo basic pretreatment through an additional analog hardware unit to align with the low operating voltages (≤1.0V) of the neuromorphic devices (Fig. S6). + +The robotic system follows its movement patterns remaining in an explorative state until it starts interacting with the environment and receives new sensory stimuli. These stimuli change the output voltage \(\sum V\) momentarily or permanently leading to an event- driven and adaptive behavior. + +The neuromorphic circuit consists of volatile (OECTs) and non- volatile (ECRAMs) organic electrochemical devices. These devices utilize the semiconducting polymer poly(2- (3,3'- bis(2- (2- (2- methoxyethoxy)ethoxy)ethoxy)- [2,2'- bithiophen]- 5- yl) thieno [3,2- b] thiophene) [p(g2T- TT)] as the channel material and are controlled through an electrolyte. The modulation of the electronic current within the channel, specifically the conductance state, is achieved through the application of an ionic gate current \(^{40}\) . The polymer p(g2T- TT) displays mixed ionic- electronic conduction by supporting the transport of both holes and ions. This polymer serves as a versatile platform for various functionalities and is suitable for both short- and long- term devices depending on the probing conditions \(^{33,41}\) . Hence, the organic neuromorphic circuit allows for monolithic integration of both volatile and non- volatile + +<--- Page Split ---> + +functionalities with the same polymer as the channel material of the transistors. It exhibits a wide range of well- defined conductance states (with a \(>100\) on/off ratio), high linearity, sensitivity to gate pulses (ranging from \(\mu \mathrm{S}\) to \(\mathrm{mS}\) ), and stability ( \(>10^{9}\) write- read operations) \(^{41,42}\) . The low- voltage operation ( \(\leq \pm 1\mathrm{V}\) ) and compatibility with solution- based processing methods contribute to high energy efficiency and cost- effectiveness. While short- term (volatile) and long- term (non- volatile) synaptic devices share a similar device architecture, their primary distinction lies in the device configuration. For the short- term effect, the gates are directly linked to the sensor signal. Conversely, in non- volatile devices, a switch with a current- limiting resistance of \(100M\Omega\) is connected in series to the gate, inducing an open- circuit potential when no sensor signal is applied (see Methods). This induces a lasting change in conductance, inducing long- term (non- volatile) synaptic memory phenomena. We adopt a side- gate device architecture with a solid- state electrolyte comprised of the ionic liquid [1- ethyl- 3- methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM: TFSI) embedded in a polyvinylidene fluoride- co- hexafluoropropylene (PVDF- HFP) polymer matrix (see Methods). + +The device characteristics of the neuromorphic circuit are shown in Figures 2d to 2f in the face of the volatile and non- volatile synaptic devices respectively. We attain low voltage operation for all components of the organic neuromorphic circuit. We achieve stable performance with a minimal hysteresis for the volatile synaptic device as shown in the output ( \(I_{D}\) over \(V_{D}\) ) and transfer ( \(I_{D}\) over \(V_{G}\) ) characteristics (Fig. 2d and 2e, respectively). The transconductance \(g_{m}\) (Fig. 2e), also described as the device sensitivity, depends on the gate voltage but can also be influenced via the drain voltage. An OECT switched in series with a resistive load \(R_{L}\) moves its operation from linear to saturation depending on \(R_{L}\) as detailed in \(^{43}\) . The ratio of resistances between load and OECT is critical and a substantial ratio change \(\left(\frac{R_{L}}{R_{OECT}} = 1 \rightarrow 50\right)\) is necessary to achieve a significant change in the output voltage \((V_{OUT} = \frac{V_{SUPP}}{2} \rightarrow 0V)\) and in the amplification of the gate voltage through the transconductance (Fig. S3). An additional measurement of the voltage output for an OECT loaded with different resistances is provided in Fig. S4. Replacing the resistive load \(R_{L}\) with the non- volatile synaptic device (LT), as in our circuit topology, prompts similar changes in voltage level for the branch voltages \(V_{+}\) and \(V_{- }\) and in the transconductance of the OECTs. Figure 2f shows the programming characteristics of the non- volatile synaptic device, which displays high on- off ratio across orders of magnitude with linear switching behavior and stable state retention (zoom- ins) for long- term plasticity at very low programming voltage ( \(V \leq |0.2V|\) ). The conductance states are adjusted reversibly by applying gate pulses of opposite polarity. These long- term conductance changes in the artificial synapses create the memory effect needed for learning and adaptive behavior. + +Overall, the learning process of the robotic manipulator is shown in Figure 3. The organic neuromorphic circuit combines the collection of multimodal sensory stimuli with neuronal processing leading to associative connections and behavioral consequences. Therefore, the robot learns to avoid potentially harmful objects like a hot cup. Initially, the robotic system is an explorative state in which it experiments with different behaviors, in this case grabbing or non- grabbing action (Fig. 3a). As a baseline behavior, the robotic system is already able to grab a cup, but this occurs at random and is unrelated to any external stimuli (i.e., the trait of a cup). It operates undirected and associative conditioning is latent and thus yet to be formed. Sensory cues are already present but lead to no change in behavior via the activation function. Initially, only standard (cold) cups are used as objects which render the (- )- branch (Fig. 2a and 2b, orange bolt) of the neuromorphic circuit reacting to temperature inactive for now. An object (i.e., a cup) gets registered by the proximity sensor causing a short- term peak of \(V_{+}\) and subsequently of \(\sum V\) (Fig. 3a). A longer peak in this context means that the cup is picked up (checkmark) and held until the follow- up drop action, a shorter peak indicates that the cup is indeed detected but not grabbed (cross) (Fig. 3a and Movie S1). To showcase the random behavior of the robotic agent over time without learning, the training signals are disconnected from the non- volatile synaptic device for this experiment to prevent any adaptation. With all sensor connections restored, the organic neuromorphic circuit adapts to the sensory cues from its environment. Whenever the robot successfully grabs a cup, the pressure sensor on the gripper directly + +<--- Page Split ---> + +forwards a signal to the non- volatile synaptic device \((V_{G,LT} = \pm 0.5V)\) . This happens in addition to the peak shown before which was provoked by a pulse from the proximity sensor at the gate of the OECT \((V_{G,ST + } = - 0.25V)\) . The activation leads to an increase in voltage \(V_{+}\) (Fig. 3b). The probability for a grab behavior therefore changes represented as the background color (light to darker blue) in Figure 3 and consequently the overall behavior shifts from random to systemic (Movie S2). A darker blue tone indicates a high probability of grabbing a cup. From Figure 3b, it is apparent that a certainty in behavior develops only for the simultaneous occurrence of long- term synaptic change (increase in general voltage level of \(V_{+}\) ) and the short- term change during the detection of an object (peak in \(V_{+}\) ). In between peaks (that is, in between object detections) the probability declines again (lighter blue), so an inherent associative link between object proximity and the grabbing action (the training pressure signal) is formed, similar to biological associative learning or respondent conditioning (Pavlovian response). Complete adaptation is achieved after 14 training steps and the robotic manipulator consistently grabs the cup if it is close (Fig. 3c, checkmarks and Movie S3). This behavior is also resistant to noise and imperfect sensor signals that can be caused by non- optimal grip and/or shifting of the object during grasping (seen in the last peak of the measurement) and maintained stably over time. + +Complex tasks can often be broken down into smaller components that are learned separately and incrementally. This technique is called chaining and is well- known in research fields like behavioral psychology and deep learning \(^{44,45}\) . Chaining involves teaching a series of behaviors in a specific sequence. Each behavior serves as a cue for the next one. After completing the first cycle of learning, a second behavioral change is built on top (chained) concluding in the fulfillment of a more complex task: The robotic system now faces cups of different temperature (cold and hot) which are mirrored in their color: a cold cup is white, and a hot cup is dark. Introducing this new thermal stimulus, the (- )- branch connected to the related sensor signals (temperature and grayscale/color) is also active. In the initial state, the previously learned behavior is maintained (Fig. 3d and Movie S4). The (+)- branch \((V_{+}\) in blue) follows the adapted behavior from before. The (- )- branch yields a small voltage \(V_{- }\) (in orange) and a peak reaction to the color of the dark (hot) cups. The probability output of the activation function is depicted as orange hue in the background. Cold and hot cups are handed alternately. Initially, the robot again grabs the cup every time it comes close disregarding the temperature or color (Fig. 3d, checkmarks) as it has learned to do previously. However, the new thermal stimulus induces a gate voltage at the second non- volatile device \((V_{G,LT} = \pm 0.5V)\) causing a change in voltage level \(V_{- }\) and increasing the response in output voltage (peak height) towards a color stimulus. Like in the first training process, an association between the temperature and color is formed resulting in an associative link (Fig. 3b and Movie S5). Color is thus coupled to temperature. After 4 training steps, the activation function with \(\sum V\) as input reaches a very high stimulus intensity (Fig. 2c, probability \(>100\%\) ) forcing a protective reaction of the robotic hand. It draws back and avoids the object. This overstimulation – noxious behavior – only occurs when a hot (and dark) cup is detected, highlighted in Fig. 3e and 3f in dark orange. This progresses our initial adaptation from respondent/Pavlovian learning to a more complex behavior of operant conditioning by learning from positive (pressure) and negative (temperature) consequences of different stimuli. At the end of the whole training process, by including both branches \((V_{+},V_{- }\) and \(\sum V)\) , the robotic system is able to distinguish between two types of cups, essentially classifying dangerous and non- dangerous objects. More specifically, by following and adapting to the dynamic cues of the environment, the robot learns to avoid potentially harmful objects like a hot cup while actively engaging with other safe objects. Fig. 3f and Movie S6 present the final output signals and behavior. Both color and temperature sensor are more sensitive to positioning (seen as noisy signals in the measurements) demonstrating a high tolerance for stimulus variations in the learning scheme. + +## DISCUSSION + +Taking inspiration in the versatile capabilities of biological systems, we combine bio- inspired processing, learning and control paradigms with the development of organic neuromorphic circuitry, and we demonstrate a standalone robotic system that interacts intelligently with a non- static + +<--- Page Split ---> + +environment. Through the integration of an organic neuromorphic circuit, the system adapts its behavior based on multimodal sensory feedback from environmental cues. The synaptic devices in the circuit enable associative learning, leading to both respondent (Pavlovian) conditioning and the more complex operant conditioning. The robotic agent learns to associate positive and negative consequences with multimodal stimuli, showcasing adaptability and the ability to distinguish between safe and potentially harmful objects. The use of functional materials, such as organic (semi- )conducting polymers, in the neuromorphic circuit is elemental to the system's capabilities, replicating bio- inspired functionalities like synaptic plasticity, dendritic summation and neural processing. This is possible using small- scale, locally integrated, and low- voltage monolithic polymer electronics. Moreover, due to the modular- like structure of the neuromorphic circuit, the concept can be extended into multiple branches in order to handle sensory signals of arbitrary complexity and multimodality. The presented robotic system serves as a tangible example of how combining bio- inspired principles with localized organic neuromorphic circuitry can lead to the development of highly adaptive, intelligent, and efficient systems for real- world applications. + +## MATERIALS AND METHODS + +## Device fabrication + +Standard microscope glass slides (75 mm by 25 mm) are cleaned in a sonicated bath, first in soap solution (Micro- 90) and then in a 1:1 (v/v) solvent mixture of acetone and isopropanol. Gold electrodes for source, drain, and gates are photolithographically patterned [with negative photoresist AZ nLof2035 (MicroChemicals) and AZ 726MIF Developer (MicroChemicals)] on the cleaned glass slides. A chromium layer is deposited to achieve better adhesion of the gold. The photolithography foil masks are designed using Klayout \(^{46}\) and the complementary pypi- package koala \(^{47}\) . Each glass slide contains twelve devices with fixed dimensions. The channel dimensions of the non- volatile devices (LT) are as follows: \(\mathrm{W} / \mathrm{L} = 1 / 3\) with \(\mathrm{L} = 250 \mu \mathrm{m}\) with a lateral gate of the \(1000 \mu \mathrm{m}\) by \(1000 \mu \mathrm{m}\) and \(150 - \mu \mathrm{m}\) distance between the gate and the channel. The volatile device (ST) has the following dimensions: \(\mathrm{W} / \mathrm{L} = 1 / 6\) with \(\mathrm{L} = 500 \mu \mathrm{m}\) with a lateral gate of \(1000 \mu \mathrm{m}\) by \(1000 \mu \mathrm{m}\) and \(150 - \mu \mathrm{m}\) distance between the gate and the channel. The complete layouts are depicted in Fig. S2. Two layers of parylene C (Specialty Coating Systems) are deposited. Soap solution (Micro- 90 soap solution, \(2\%\) (v/v) in deionized water) is used for separation between the layers, allowing the peel- off of the upper layer. An adhesion promoter (silane A- 174, Specialty Coating Systems) is added to the lower layer of parylene C to prevent detachment. This layer insulates the gold electrodes. In a second photolithography step using positive photoresist AZ 10XT (MicroChemicals) and AZ Developer (MicroChemicals), the channel and lateral gate dimensions of the devices are defined. Reactive ion etching with O2 plasma is used to carve out the channel and corresponding gates. The semiconducting polymer p(g2T- TT) is synthesized according to (41) and prepared and applied following the procedure in \(^{41,42}\) . p(g2T- TT) is solved in chloroform (3 mg/ml) inside an N2- filled glove box and spin- cast inside the N2- filled glove box at \(1000 \mathrm{rpm}\) for 1 min. The devices are baked at \(60^{\circ} \mathrm{C}\) for 1 min. + +In ambient, the sacrificial upper parylene C is peeled off to confine the polymer inside the gate and channel regions. Excess soap is rinsed off with de- ionized water. An ionic gel is prepared as electrolyte according to \(^{48}\) . An ionic liquid 1- Ethyl- 3- methylimidazoliumbis(trifluoromethylsulfonyl)imide (EMIM: TFSI, Merck) and the copolymer poly(vinylidene fluoride)- co- hexafluoropropylene (PVDF- HFP) are solved in acetone inside an N2- filled glove box in the following proportions: \(17.6 \mathrm{weight\%}\) (wt%) ionic liquid, \(4.4 \mathrm{wt\%}\) copolymer, and \(78 \mathrm{wt\%}\) acetone. The solution is stirred for at least 2 hours at \(40^{\circ} \mathrm{C}\) inside the glove box. The ionic gel is drop- cast with a pipette onto each channel and gate under ambient conditions and dried overnight (Fig. S2). + +<--- Page Split ---> + +## Measurements + +For measurements of the electrical characteristics of volatile and non- volatile devices, a Keithley 2602B SourceMeter is used. The measurements of the volatile device (ST), the source measure units at the three device terminals are directly connected with the measurement system. For non- volatile measurements (LT), a mechanical switch in series with a resistance \(R_{G} = 100M\Omega\) is added between the gate of the device and the measurement system and enhance the analog memory phenomena. The switch forces open- circuit potential condition between the gate and channel, while the gate resistor \(R_{G}\) downscales and limits the gate current in the range of nanoamperes. + +## Sensors + +The robotic sensors are off- the- shelf components, operate in the analog domain and are Arduino- compatible. The proximity (URM09 ultrasonic distance), grayscale and temperature (LM35 temperature) sensor are from the DFRobot Gravity line. The pressure sensor uses the Grove force sensor module with a rectangular Taiwan Alpha force sensor pad (MF02- N- 221- A01). The sensors and detailed specifications are depicted in Fig. S5. + +## 3D-printed parts + +The custom robotic gripper is designed using Autodesk Inventor and is then 3D- printed using a Formlabs SLA resin printer, model 3. For the gripper, Tough1500 resin is used to allow for slight flexibility and bend. To attach the ultrasonic sensor in front of the gripper, clear resin is used for the printed fixture. All fixtures are shown in Fig. S1 and S5. + +## Supplementary Materials + +Materials and Methods + +Figs. S1 to S6 + +Movies S1 to S6 + +## References + +1. Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., Nelson, B. J., Scassellati, B., Taddeo, M., Taylor, R., Veloso, M., Wang, Z. L. & Wood, R. The grand challenges of Science Robotics. Sci. Robot. 3, eaar7650 (2018). +2. Hopfield, J. J. Artificial neural networks. IEEE Circuits Devices Mag. 4, 3-10 (1988). +3. Neftci, E. O. & Averbeck, B. B. 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'Cut and stick' rubbery ion gels as high capacitance gate dielectrics. Adv. Mater. 24, 4457-4462 (2012). + +Acknowledgments: We acknowledge Paul Beijer from the Equipment and Prototype Center of TU/e for his contribution in the design and realization of additional electronics. We also acknowledge René Janssen for opening his laboratory facilities to us and Irene Dobbelaer- Bosboom, Jaap de Hullu and Katherine Pacheco Morillo for their assistance in the Microfablab at TU/e. We also acknowledge Paul W. M. Blom for hosting part of the research at the Department of Molecular Electronics, Max Plank Institute for Polymer Research and Michelle Beuchel and Christian Bauer for their assistance in the + +<--- Page Split ---> + +clean room facilities of the Max Planck Institute for Polymer Research. We recognize the support of Charles- Théophile Coen and Simone Spolaor. + +## Funding: + +Joint project between the Max Planck Institute for Polymer Research and the Institute for Complex Molecular Systems, TU Eindhoven, grant MPIPICMS2019001 (IK, PG, YB) + +European Union's Horizon 2020 Research and Innovation Programme, grant agreement 802615 (YB) + +Carl- Zeiss Foundation (PG) + +Author contributions: IK, JDT, YB, PG conceived the project and designed the experiments. IK investigated materials and tuned their properties. IK designed, fabricated, and characterized the devices and the neuromorphic circuit. IK designed and implemented the setup, robot, electronics, and control algorithms. SG and IM synthesized and provided the semiconducting material. IK, JDT, YB, PG prepared the manuscript with input from all the authors. JDT, YB, PG supervised the project and acquired the financial support. + +Competing interests: Authors declare that they have no competing interests. + +Data and materials availability: All data is available in the manuscript or the supplementary materials. Code and additional software (robotic control, measurement control, mask design) is available upon request. Arduino® is a trademark of Arduino SA. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figures:
+ +Fig. 1. Robotic manipulator with an organic neuromorphic circuit using bio- inspired learning. (a) A robotic manipulator with custom- made gripper is equipped with four multimodal sensors. The sensory stimuli are processed adaptively via specialized hardware and condition the grasp behavior of the robotic system. (b) The robot employs the following bio- inspired principles for learning: exploration of its environment through random movement, collection of multimodal sensory inputs and adaptive processing leading to behavioral conditioning. (c) The robotic system is connected to a local organic neuromorphic circuit that emulates neuronal processing such as short- term and long- term synaptic plasticity and dendritic summation. The neuromorphic circuit consists of organic electrochemical devices. (d) Schematic architecture of an organic electrochemical device based on the semiconducting polymer p(g2T- TT) and a solid- state electrolyte based on the ionic liquid EMIM: TFSI. The device is defined by three electrodes (grey): source (left), drain (right) and gate (top). The polymer is distributed between the source and drain terminals (blue) and exhibits mixed electronic- ionic conduction. Anions (dark blue) from the electrolyte can penetrate into the polymer bulk leading to the formation of holes (white) along the polymer backbone and changing its conductivity. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Characterization of the organic neuromorphic circuit. (a) Circuit schematic of the organic neuromorphic circuit following a two-branch (+ and -) architecture. The gate of each organic electrochemical device is connected to specified sensory stimulus. The sum \(\sum V\) over the output voltages \(V_{+}\) and \(V_{- }\) branches is forwarded to the robotic system via an activation function. (b) Biological representation of dendritic summation involving two presynaptic signals. (c) A sigmoidal activation function translates the stimulus intensity \(\sum V\) into a neural response (probability for a certain behavior). (d) Output characteristics of the volatile synaptic device that displays short-term memory. (e) Transfer characteristics of the volatile synaptic device that displays short-term memory. (f) Long-term memory of the non-volatile synaptic device.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3. Behavioral change of the robotic manipulator upon adaptive processing of multimodal stimuli. (a) Explorative behavior of the robot before any adaptation. (b) Adaptation of the \(V_{+}\) branch to the pressure stimuli when incidentally grabbing a cup. (c) After training, the robotic manipulator consistently grabs the cup if it is close by, when detected by the proximity sensor. The inset image depicts the robot holding a (dark) cup. (d) Established behavior from the \(V_{+}\) branch is maintained. (e) Adaptation to the new temperature stimuli in the \(V_{-}\) branch. (f) After training, the robotic manipulator only grabs cups that are white and cold, but not those that are dark and hot. The inset image pictures the robot holding a white cup.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- supplementary.docx- movieS1.mp4- MovieS2.mp4- movieS3.mp4- movieS4.mp4- movieS5.mp4- movieS6.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7_det.mmd b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..0d5a08d2e8268c8bffc3bcef8d2a2de18ab7a270 --- /dev/null +++ b/preprint/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7/preprint__03c25f68f3d0c96b8fec3add764c54f1099c116629752a6e11fde084cee976b7_det.mmd @@ -0,0 +1,310 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 833, 208]]<|/det|> +# Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics + +<|ref|>text<|/ref|><|det|>[[44, 229, 275, 275]]<|/det|> +Yoeri van de Burgt y.b.v.d.burgt@tue.nl + +<|ref|>text<|/ref|><|det|>[[44, 301, 728, 560]]<|/det|> +Eindhoven University of Technology https://orcid.org/0000- 0003- 3472- 0148 Imke Krauhausen Eindhoven University of Technology https://orcid.org/0000- 0001- 5633- 389X Sophie Griggs Department of Chemistry, University of Oxford Iain McCulloch University of Oxford https://orcid.org/0000- 0002- 6340- 7217 Jaap Toonder Eindhoven University of Technology https://orcid.org/0000- 0002- 5923- 4456 Paschalis Gkoupidenis Max Planck Institute for Polymer Research https://orcid.org/0000- 0002- 0139- 0851 + +<|ref|>sub_title<|/ref|><|det|>[[44, 595, 104, 613]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 633, 135, 651]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 670, 330, 690]]<|/det|> +Posted Date: January 30th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 708, 475, 728]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3878146/v1 + +<|ref|>text<|/ref|><|det|>[[44, 745, 912, 789]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 807, 535, 826]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[44, 862, 905, 905]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 4th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 48881- 2. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[142, 90, 853, 123]]<|/det|> +# Title: Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics + +<|ref|>sub_title<|/ref|><|det|>[[117, 146, 185, 160]]<|/det|> +## Authors: + +<|ref|>text<|/ref|><|det|>[[175, 160, 850, 193]]<|/det|> +Imke Krauhausen, \(^{1,2,3}\) Sophie Griggs, \(^{4}\) Iain McCulloch, \(^{4}\) Jaap M. J. den Toonder, \(^{1,2}\) Paschalis Gkoupidenis, \(^{3*}\) Yoeri van de Burgt \(^{1,2*}\) + +<|ref|>sub_title<|/ref|><|det|>[[117, 210, 208, 224]]<|/det|> +## Affiliations: + +<|ref|>text<|/ref|><|det|>[[175, 225, 877, 320]]<|/det|> +\(^{1}\) Institute for Complex Molecular Systems, Eindhoven University of Technology, The Netherlands. \(^{2}\) Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, The Netherlands. \(^{3}\) Max Planck Institute for Polymer Research, Mainz, Germany. \(^{4}\) Department of Chemistry, University of Oxford, United Kingdom. + +<|ref|>text<|/ref|><|det|>[[177, 336, 836, 353]]<|/det|> +\*Corresponding author. Email: gkoupidenis@mpip-mainz.mpg.de, Y.B.v.d.Burgt@tue.nl + +<|ref|>text<|/ref|><|det|>[[115, 390, 882, 536]]<|/det|> +Abstract: Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small- scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real- time handling of sensory stimuli via low- voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. + +<|ref|>text<|/ref|><|det|>[[115, 536, 880, 569]]<|/det|> +This work demonstrates that adaptive neuro- inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio- inspired learning for advancing intelligent robotics. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 240, 99]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[117, 108, 881, 188]]<|/det|> +Advancements in the field of robotics have witnessed a notable shift towards bio- inspiration, motivated by the remarkable capabilities of biological nervous systems 1 2 3. Bio- inspired robotics introduces novel ways for robots to interact with and be integrated into the physical world. Achieving this goal often necessitates the use of functional materials chosen for their ability to provide the desired flexibility, deformability, or adaptability 4 5. + +<|ref|>text<|/ref|><|det|>[[116, 198, 881, 484]]<|/det|> +At the same time, artificial intelligence (AI) is already demonstrating its proficiency for highly complex tasks in various domains such as data analysis, decision making and computer vision 6. AI systems mostly utilize large- scale (deep) neural networks for learning, pattern recognition, classification and language processing inside a static environment 7 8. These systems are based on gradient- based algorithms that require high computing power and memory storage as well as a large amount of labeled training data. Although these systems are highly effective, their biological plausibility is limited 9, and they can be power hungry 10. Hence, there is a desire to explore alternative bio- inspired algorithms, such as spiking neural networks, genetic and evolutionary algorithms, and swarm strategies, and to further enhance the development of specialized neuromorphic hardware platforms 11 12 13. Such innovations in algorithms and hardware have proven to be powerful tools for simulating neural processes, accelerating the training of artificial neural networks, and leading to increasingly sophisticated hardware for artificial neural systems. However, essential adaptive neuronal processes, including associative learning and behavioral conditioning, exist in primitive organisms like the box jellyfish which even lack centralized nervous systems 14. This raises the question of whether complexity in algorithms and architectures is always imperative for achieving cognitive functions and intelligent behavior. The relatively simple neural circuits of primitive species still exhibit significant capabilities, suggesting that emulating fundamental biological learning principles locally with functional materials and devices could be equally important as complexity while gaining in efficiency 4 15. + +<|ref|>text<|/ref|><|det|>[[116, 493, 881, 813]]<|/det|> +Primitive biological organisms employ fundamental strategies for learning such as exploration, multimodal processing, and behavioral conditioning. From early developmental stages, living beings instinctively start to learn from experience and through trial and error, by interacting with their surroundings 16. During this initial exploration phase behaviors tend to be somewhat random and lack a specific goal, while the organism is engaged with the environment via a wide range of sensory modalities (touch, vision, olfaction etc.). The randomness of certain behaviors, such as bumping into an object, leads to the discovery of new sensations and consequently learning opportunities. Through this physical interaction of organisms with their surroundings, behavioral randomness develops gradually into consistency 17. In this context, multimodal sensing enables the collection of various sensations describing the same event. These concurrent multimodal observations are synchronized in time and, as a result, become correlated, establishing autonomic connections across different sensory modalities, and enabling behaviors such as respondent (Pavlovian) conditioning and associative learning. Indeed, a recent study of the complete connectome of a Drosophila brain reveals that the majority of neurons process multimodal signals 18. Adaptivity and plasticity in function and behavior - essentials for biological development - are especially effective if previous experiences and memory are taken into account as well 19. For instance, behaviors are associated with consequences through affirmative (rewards or reinforcement) and adverse (punishment) stimuli to strengthen or weaken a specific behavior (operant conditioning). By providing diverse sensory feedback and abundant opportunities to learn from the environment, explorative behavior and multimodal processing allow for instruction- free processes that converge into optimal behavioral conditions via adaptivity. + +<|ref|>text<|/ref|><|det|>[[117, 823, 881, 902]]<|/det|> +Emerging functional materials and devices can offer unique properties that go beyond what conventional systems and electronics could achieve 20. Organic mixed ionic- electronic (semi)conductors have recently experienced a notable upswing for neuromorphic engineering 21 22 23. They are able to replicate bio- inspired functionalities such as synaptic plasticity 24 25, neural processing 26, high connectivity and recurrence 27 28 and even forgetfulness 29 just by material- inherent adaptivity. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 306]]<|/det|> +The key for this adaptivity stems from the fact that organic synaptic devices display linear, symmetric and analogue tuning of electrical conductance and operate at low voltage with high energy efficiency. The compatibility of organics with solution- based processes and large- area integration into flexible or stretchable substrates, can enable the merging of organic neuromorphic electronics in unconventional form factors (body, robotics, buildings, etc.)30. Indeed, significant steps have been made using conductive polymers regarding localized handling of data via on- chip training31,32, real- time operation with online learning33 and spiking circuits for bio- integration34,35. Despite these significant demonstrations, applications are often limited to abstracted and conceptual demonstrations in well- defined laboratory settings or mock environments, enabled by simple binary decisions. Robotic setups offer a realistic platform for interaction- rich, real- life set- ups36. Robotic manipulators for example are crucial for a variety of applications serving in versatile and dynamic environments, ranging from industrial assembly lines to neural prosthesis. Highly adaptive and localized control close to the sensory nodes, can drastically improve performance, and can also warrant operational safety which is essential for human- oriented purposes such as neuroprosthetics37,38. + +<|ref|>text<|/ref|><|det|>[[115, 316, 881, 507]]<|/det|> +In this work, we present a robotic system that uses multimodal sensory stimuli to explore and interact with a real- world environment in real- time while adapting to it using bio- inspired mechanisms. At the core for adaptivity and learning of the robotic system is an organic neuromorphic circuit that consists of organic electrochemical transistors (OECTs) and organic neuromorphic devices (also called electrochemical random- access memories, ECRAms). This bio- inspired approach enables the robotic agent to incrementally learn and perform a complex behavioral task, showcasing its adaptability and distributed intelligence in responding locally to dynamic and multimodal environmental cues. More specifically, the robotic system gains the ability to distinguish between safe and potentially harmful objects through local adaptation of neuromorphic circuitry. This work demonstrates that highly functional organic materials can reform neuromorphic hardware, rethinking adaptive intelligent systems as small(er)- scale local circuitry that interacts with the environment with bio- inspired learning mechanisms. + +<|ref|>sub_title<|/ref|><|det|>[[117, 543, 184, 557]]<|/det|> +## RESULTS + +<|ref|>text<|/ref|><|det|>[[115, 568, 881, 903]]<|/det|> +The robotic system is based on the Arduino Braccio Kit (Fig. 1a), with five degrees of freedom and an additional movement option for opening and closing a gripper. The gripper acts as hand of the robotic manipulator and is equipped with four sensors that are continuously collecting multimodal sensory stimuli of pressure, distance, temperature, and color tone when manipulating objects (Fig. 1a and 1b). A custom gripper setup is realized to accommodate the collection of multimodal sensory signals in a hand- like shape (Fig. S1 and Methods section). Different cups (dark/hot, white/cold) are placed sequentially near the robotic system, so that it is able to either pick them up or refuse them. Each movement of the robot follows an autonomic sequence of specified moves that provides a behavioral baseline for any action taken. The movements vary between a pick- up action with a grab or no- grab option in the end, a drop action that concludes a successful grab and a pull- back action to avoid the cup that functions as no- grab. These actions are driven via an Arduino Uno that operates the motors of the robotic setup. The motor commands are continuously modulated by sensory stimuli from the environment, i.e. a detection of a cup in close proximity with the hand or a pressure applied due to a successful grab, creating a real- time response of the robot to its surroundings (that is, the object of interest). Without any prior external influence, the robot is in an explorative state in which it incidentally picks a cup or not with the grab or no- grab actions initially taken randomly (Fig. 1b). Whenever a cup is discovered (grabbed) by chance, it inherently leads to new sensory sensations. An analogue trainable neuromorphic circuit (Fig. 1a and 1c) interacts locally with the sensory signals and allows learning via adaptive associative connections necessary for behavioral conditioning (Fig. 1b, right). The organic neuromorphic circuit comprises of organic electrochemical devices, OECTs and ECRAms, that are either volatile or non- volatile respectively (Fig. 1d). The output voltage \(\sum V\) of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 881, 147]]<|/det|> +organic neuromorphic circuit depends on the conductance state of each organic electrochemical device and reflects the sensory signals in an event- driven nature. \(\sum V\) merges the input branches of electrical circuitry similar to the dendritic summation of multiple neurons via the synapses (Fig. 1b, right). + +<|ref|>text<|/ref|><|det|>[[115, 156, 881, 363]]<|/det|> +The organic neuromorphic circuit consists of four micrometer- scale organic electrochemical devices (Fig. S2 and Methods section), mimicking synaptic plasticity and therefore exhibiting neuro- emulating functionality. Two of these devices function as OECT and operate in a volatile, short- term manner (indicated as ST). The other two devices operate in a non- volatile manner as ECRAM with long- term effects (referenced as LT, Fig. 2a). The four devices are arranged in two branches (+ and - ) that each contain a volatile and a non- volatile element in series. The combined output voltage is the sum over both branches: \(\sum V = V_{+} + V_{- }\) . This closely resembles the dendritic summation of multiple presynaptic signals at the synapses of a postsynaptic neuron (Fig. 2b). Each branch also displays an intrinsic associative adaptation due to the interplay of OECT and ECRAM. If loaded with a (adaptive) resistive load the OECT changes its operating regime and thus its transconductance (Fig. S3 and S4). The transconductance represents a tunable sensitivity towards the sensory stimuli that can be strengthened or weakened via the ECRAM leading to an inherent association between the two stimuli at OECT and ECRAM. + +<|ref|>text<|/ref|><|det|>[[115, 372, 881, 692]]<|/det|> +The output voltage \(\sum V\) is translated into a motor action through an activation function which relates the signal to a behavioral probability (Fig. 2c). The activation function is sigmoidal and proportional to the widely used activation function hyperbolic tangent (tanh). It is executed on the Arduino Uno to reduce circuit complexity though hardware implementations are feasible. The output voltage is interpreted in terms of probability. The non- deterministic and fail- prone behavior in biological systems causing new sensations is one of the reasons for their remarkable adaptability in unknown situations \(^{39}\) . While the Arduino Uno relays signals from the organic neuromorphic circuit to the robotic setup, it operates solely as translator/mediator and has no agency on the behavior of the robotic agent. In order to react to the environment, the neuromorphic circuit handles optical, thermal, and mechanical stimuli. A color and proximity sensor are used for gaining information on objects (i.e. a cup) from afar/without contact and drive the gates and thus (trans- )conductance of the volatile devices, \(G_{ST + }\) and \(G_{ST - }\) . A pressure and temperature sensor feed a signal on contact to the non- volatile gates of the neuromorphic circuit, \(G_{LT + }\) and \(G_{LT - }\) providing the necessary impulses for learning and conditioning. Via the series connection in the circuit layout, the (+)- branch then combines the sensory input of pressure and proximity in a single information stream leading to the output voltage \(V_{+}\) . This functionality is mirrored in the (- )- branch coupling temperature and color resulting in signal stream \(V_{- }\) . We employ off- the- shelf sensors for collecting sensory input which provides lifelike, noise- containing data (Fig. S5, see Methods). The sensory signals undergo basic pretreatment through an additional analog hardware unit to align with the low operating voltages (≤1.0V) of the neuromorphic devices (Fig. S6). + +<|ref|>text<|/ref|><|det|>[[117, 701, 880, 749]]<|/det|> +The robotic system follows its movement patterns remaining in an explorative state until it starts interacting with the environment and receives new sensory stimuli. These stimuli change the output voltage \(\sum V\) momentarily or permanently leading to an event- driven and adaptive behavior. + +<|ref|>text<|/ref|><|det|>[[117, 758, 881, 902]]<|/det|> +The neuromorphic circuit consists of volatile (OECTs) and non- volatile (ECRAMs) organic electrochemical devices. These devices utilize the semiconducting polymer poly(2- (3,3'- bis(2- (2- (2- methoxyethoxy)ethoxy)ethoxy)- [2,2'- bithiophen]- 5- yl) thieno [3,2- b] thiophene) [p(g2T- TT)] as the channel material and are controlled through an electrolyte. The modulation of the electronic current within the channel, specifically the conductance state, is achieved through the application of an ionic gate current \(^{40}\) . The polymer p(g2T- TT) displays mixed ionic- electronic conduction by supporting the transport of both holes and ions. This polymer serves as a versatile platform for various functionalities and is suitable for both short- and long- term devices depending on the probing conditions \(^{33,41}\) . Hence, the organic neuromorphic circuit allows for monolithic integration of both volatile and non- volatile + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 291]]<|/det|> +functionalities with the same polymer as the channel material of the transistors. It exhibits a wide range of well- defined conductance states (with a \(>100\) on/off ratio), high linearity, sensitivity to gate pulses (ranging from \(\mu \mathrm{S}\) to \(\mathrm{mS}\) ), and stability ( \(>10^{9}\) write- read operations) \(^{41,42}\) . The low- voltage operation ( \(\leq \pm 1\mathrm{V}\) ) and compatibility with solution- based processing methods contribute to high energy efficiency and cost- effectiveness. While short- term (volatile) and long- term (non- volatile) synaptic devices share a similar device architecture, their primary distinction lies in the device configuration. For the short- term effect, the gates are directly linked to the sensor signal. Conversely, in non- volatile devices, a switch with a current- limiting resistance of \(100M\Omega\) is connected in series to the gate, inducing an open- circuit potential when no sensor signal is applied (see Methods). This induces a lasting change in conductance, inducing long- term (non- volatile) synaptic memory phenomena. We adopt a side- gate device architecture with a solid- state electrolyte comprised of the ionic liquid [1- ethyl- 3- methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM: TFSI) embedded in a polyvinylidene fluoride- co- hexafluoropropylene (PVDF- HFP) polymer matrix (see Methods). + +<|ref|>text<|/ref|><|det|>[[115, 299, 881, 618]]<|/det|> +The device characteristics of the neuromorphic circuit are shown in Figures 2d to 2f in the face of the volatile and non- volatile synaptic devices respectively. We attain low voltage operation for all components of the organic neuromorphic circuit. We achieve stable performance with a minimal hysteresis for the volatile synaptic device as shown in the output ( \(I_{D}\) over \(V_{D}\) ) and transfer ( \(I_{D}\) over \(V_{G}\) ) characteristics (Fig. 2d and 2e, respectively). The transconductance \(g_{m}\) (Fig. 2e), also described as the device sensitivity, depends on the gate voltage but can also be influenced via the drain voltage. An OECT switched in series with a resistive load \(R_{L}\) moves its operation from linear to saturation depending on \(R_{L}\) as detailed in \(^{43}\) . The ratio of resistances between load and OECT is critical and a substantial ratio change \(\left(\frac{R_{L}}{R_{OECT}} = 1 \rightarrow 50\right)\) is necessary to achieve a significant change in the output voltage \((V_{OUT} = \frac{V_{SUPP}}{2} \rightarrow 0V)\) and in the amplification of the gate voltage through the transconductance (Fig. S3). An additional measurement of the voltage output for an OECT loaded with different resistances is provided in Fig. S4. Replacing the resistive load \(R_{L}\) with the non- volatile synaptic device (LT), as in our circuit topology, prompts similar changes in voltage level for the branch voltages \(V_{+}\) and \(V_{- }\) and in the transconductance of the OECTs. Figure 2f shows the programming characteristics of the non- volatile synaptic device, which displays high on- off ratio across orders of magnitude with linear switching behavior and stable state retention (zoom- ins) for long- term plasticity at very low programming voltage ( \(V \leq |0.2V|\) ). The conductance states are adjusted reversibly by applying gate pulses of opposite polarity. These long- term conductance changes in the artificial synapses create the memory effect needed for learning and adaptive behavior. + +<|ref|>text<|/ref|><|det|>[[115, 626, 881, 913]]<|/det|> +Overall, the learning process of the robotic manipulator is shown in Figure 3. The organic neuromorphic circuit combines the collection of multimodal sensory stimuli with neuronal processing leading to associative connections and behavioral consequences. Therefore, the robot learns to avoid potentially harmful objects like a hot cup. Initially, the robotic system is an explorative state in which it experiments with different behaviors, in this case grabbing or non- grabbing action (Fig. 3a). As a baseline behavior, the robotic system is already able to grab a cup, but this occurs at random and is unrelated to any external stimuli (i.e., the trait of a cup). It operates undirected and associative conditioning is latent and thus yet to be formed. Sensory cues are already present but lead to no change in behavior via the activation function. Initially, only standard (cold) cups are used as objects which render the (- )- branch (Fig. 2a and 2b, orange bolt) of the neuromorphic circuit reacting to temperature inactive for now. An object (i.e., a cup) gets registered by the proximity sensor causing a short- term peak of \(V_{+}\) and subsequently of \(\sum V\) (Fig. 3a). A longer peak in this context means that the cup is picked up (checkmark) and held until the follow- up drop action, a shorter peak indicates that the cup is indeed detected but not grabbed (cross) (Fig. 3a and Movie S1). To showcase the random behavior of the robotic agent over time without learning, the training signals are disconnected from the non- volatile synaptic device for this experiment to prevent any adaptation. With all sensor connections restored, the organic neuromorphic circuit adapts to the sensory cues from its environment. Whenever the robot successfully grabs a cup, the pressure sensor on the gripper directly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 323]]<|/det|> +forwards a signal to the non- volatile synaptic device \((V_{G,LT} = \pm 0.5V)\) . This happens in addition to the peak shown before which was provoked by a pulse from the proximity sensor at the gate of the OECT \((V_{G,ST + } = - 0.25V)\) . The activation leads to an increase in voltage \(V_{+}\) (Fig. 3b). The probability for a grab behavior therefore changes represented as the background color (light to darker blue) in Figure 3 and consequently the overall behavior shifts from random to systemic (Movie S2). A darker blue tone indicates a high probability of grabbing a cup. From Figure 3b, it is apparent that a certainty in behavior develops only for the simultaneous occurrence of long- term synaptic change (increase in general voltage level of \(V_{+}\) ) and the short- term change during the detection of an object (peak in \(V_{+}\) ). In between peaks (that is, in between object detections) the probability declines again (lighter blue), so an inherent associative link between object proximity and the grabbing action (the training pressure signal) is formed, similar to biological associative learning or respondent conditioning (Pavlovian response). Complete adaptation is achieved after 14 training steps and the robotic manipulator consistently grabs the cup if it is close (Fig. 3c, checkmarks and Movie S3). This behavior is also resistant to noise and imperfect sensor signals that can be caused by non- optimal grip and/or shifting of the object during grasping (seen in the last peak of the measurement) and maintained stably over time. + +<|ref|>text<|/ref|><|det|>[[115, 330, 881, 828]]<|/det|> +Complex tasks can often be broken down into smaller components that are learned separately and incrementally. This technique is called chaining and is well- known in research fields like behavioral psychology and deep learning \(^{44,45}\) . Chaining involves teaching a series of behaviors in a specific sequence. Each behavior serves as a cue for the next one. After completing the first cycle of learning, a second behavioral change is built on top (chained) concluding in the fulfillment of a more complex task: The robotic system now faces cups of different temperature (cold and hot) which are mirrored in their color: a cold cup is white, and a hot cup is dark. Introducing this new thermal stimulus, the (- )- branch connected to the related sensor signals (temperature and grayscale/color) is also active. In the initial state, the previously learned behavior is maintained (Fig. 3d and Movie S4). The (+)- branch \((V_{+}\) in blue) follows the adapted behavior from before. The (- )- branch yields a small voltage \(V_{- }\) (in orange) and a peak reaction to the color of the dark (hot) cups. The probability output of the activation function is depicted as orange hue in the background. Cold and hot cups are handed alternately. Initially, the robot again grabs the cup every time it comes close disregarding the temperature or color (Fig. 3d, checkmarks) as it has learned to do previously. However, the new thermal stimulus induces a gate voltage at the second non- volatile device \((V_{G,LT} = \pm 0.5V)\) causing a change in voltage level \(V_{- }\) and increasing the response in output voltage (peak height) towards a color stimulus. Like in the first training process, an association between the temperature and color is formed resulting in an associative link (Fig. 3b and Movie S5). Color is thus coupled to temperature. After 4 training steps, the activation function with \(\sum V\) as input reaches a very high stimulus intensity (Fig. 2c, probability \(>100\%\) ) forcing a protective reaction of the robotic hand. It draws back and avoids the object. This overstimulation – noxious behavior – only occurs when a hot (and dark) cup is detected, highlighted in Fig. 3e and 3f in dark orange. This progresses our initial adaptation from respondent/Pavlovian learning to a more complex behavior of operant conditioning by learning from positive (pressure) and negative (temperature) consequences of different stimuli. At the end of the whole training process, by including both branches \((V_{+},V_{- }\) and \(\sum V)\) , the robotic system is able to distinguish between two types of cups, essentially classifying dangerous and non- dangerous objects. More specifically, by following and adapting to the dynamic cues of the environment, the robot learns to avoid potentially harmful objects like a hot cup while actively engaging with other safe objects. Fig. 3f and Movie S6 present the final output signals and behavior. Both color and temperature sensor are more sensitive to positioning (seen as noisy signals in the measurements) demonstrating a high tolerance for stimulus variations in the learning scheme. + +<|ref|>sub_title<|/ref|><|det|>[[117, 837, 213, 852]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[117, 862, 880, 910]]<|/det|> +Taking inspiration in the versatile capabilities of biological systems, we combine bio- inspired processing, learning and control paradigms with the development of organic neuromorphic circuitry, and we demonstrate a standalone robotic system that interacts intelligently with a non- static + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 83, 881, 306]]<|/det|> +environment. Through the integration of an organic neuromorphic circuit, the system adapts its behavior based on multimodal sensory feedback from environmental cues. The synaptic devices in the circuit enable associative learning, leading to both respondent (Pavlovian) conditioning and the more complex operant conditioning. The robotic agent learns to associate positive and negative consequences with multimodal stimuli, showcasing adaptability and the ability to distinguish between safe and potentially harmful objects. The use of functional materials, such as organic (semi- )conducting polymers, in the neuromorphic circuit is elemental to the system's capabilities, replicating bio- inspired functionalities like synaptic plasticity, dendritic summation and neural processing. This is possible using small- scale, locally integrated, and low- voltage monolithic polymer electronics. Moreover, due to the modular- like structure of the neuromorphic circuit, the concept can be extended into multiple branches in order to handle sensory signals of arbitrary complexity and multimodality. The presented robotic system serves as a tangible example of how combining bio- inspired principles with localized organic neuromorphic circuitry can lead to the development of highly adaptive, intelligent, and efficient systems for real- world applications. + +<|ref|>sub_title<|/ref|><|det|>[[118, 341, 329, 356]]<|/det|> +## MATERIALS AND METHODS + +<|ref|>sub_title<|/ref|><|det|>[[118, 367, 254, 381]]<|/det|> +## Device fabrication + +<|ref|>text<|/ref|><|det|>[[115, 392, 881, 727]]<|/det|> +Standard microscope glass slides (75 mm by 25 mm) are cleaned in a sonicated bath, first in soap solution (Micro- 90) and then in a 1:1 (v/v) solvent mixture of acetone and isopropanol. Gold electrodes for source, drain, and gates are photolithographically patterned [with negative photoresist AZ nLof2035 (MicroChemicals) and AZ 726MIF Developer (MicroChemicals)] on the cleaned glass slides. A chromium layer is deposited to achieve better adhesion of the gold. The photolithography foil masks are designed using Klayout \(^{46}\) and the complementary pypi- package koala \(^{47}\) . Each glass slide contains twelve devices with fixed dimensions. The channel dimensions of the non- volatile devices (LT) are as follows: \(\mathrm{W} / \mathrm{L} = 1 / 3\) with \(\mathrm{L} = 250 \mu \mathrm{m}\) with a lateral gate of the \(1000 \mu \mathrm{m}\) by \(1000 \mu \mathrm{m}\) and \(150 - \mu \mathrm{m}\) distance between the gate and the channel. The volatile device (ST) has the following dimensions: \(\mathrm{W} / \mathrm{L} = 1 / 6\) with \(\mathrm{L} = 500 \mu \mathrm{m}\) with a lateral gate of \(1000 \mu \mathrm{m}\) by \(1000 \mu \mathrm{m}\) and \(150 - \mu \mathrm{m}\) distance between the gate and the channel. The complete layouts are depicted in Fig. S2. Two layers of parylene C (Specialty Coating Systems) are deposited. Soap solution (Micro- 90 soap solution, \(2\%\) (v/v) in deionized water) is used for separation between the layers, allowing the peel- off of the upper layer. An adhesion promoter (silane A- 174, Specialty Coating Systems) is added to the lower layer of parylene C to prevent detachment. This layer insulates the gold electrodes. In a second photolithography step using positive photoresist AZ 10XT (MicroChemicals) and AZ Developer (MicroChemicals), the channel and lateral gate dimensions of the devices are defined. Reactive ion etching with O2 plasma is used to carve out the channel and corresponding gates. The semiconducting polymer p(g2T- TT) is synthesized according to (41) and prepared and applied following the procedure in \(^{41,42}\) . p(g2T- TT) is solved in chloroform (3 mg/ml) inside an N2- filled glove box and spin- cast inside the N2- filled glove box at \(1000 \mathrm{rpm}\) for 1 min. The devices are baked at \(60^{\circ} \mathrm{C}\) for 1 min. + +<|ref|>text<|/ref|><|det|>[[116, 736, 881, 864]]<|/det|> +In ambient, the sacrificial upper parylene C is peeled off to confine the polymer inside the gate and channel regions. Excess soap is rinsed off with de- ionized water. An ionic gel is prepared as electrolyte according to \(^{48}\) . An ionic liquid 1- Ethyl- 3- methylimidazoliumbis(trifluoromethylsulfonyl)imide (EMIM: TFSI, Merck) and the copolymer poly(vinylidene fluoride)- co- hexafluoropropylene (PVDF- HFP) are solved in acetone inside an N2- filled glove box in the following proportions: \(17.6 \mathrm{weight\%}\) (wt%) ionic liquid, \(4.4 \mathrm{wt\%}\) copolymer, and \(78 \mathrm{wt\%}\) acetone. The solution is stirred for at least 2 hours at \(40^{\circ} \mathrm{C}\) inside the glove box. The ionic gel is drop- cast with a pipette onto each channel and gate under ambient conditions and dried overnight (Fig. S2). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 232, 99]]<|/det|> +## Measurements + +<|ref|>text<|/ref|><|det|>[[117, 108, 881, 220]]<|/det|> +For measurements of the electrical characteristics of volatile and non- volatile devices, a Keithley 2602B SourceMeter is used. The measurements of the volatile device (ST), the source measure units at the three device terminals are directly connected with the measurement system. For non- volatile measurements (LT), a mechanical switch in series with a resistance \(R_{G} = 100M\Omega\) is added between the gate of the device and the measurement system and enhance the analog memory phenomena. The switch forces open- circuit potential condition between the gate and channel, while the gate resistor \(R_{G}\) downscales and limits the gate current in the range of nanoamperes. + +<|ref|>sub_title<|/ref|><|det|>[[117, 231, 177, 245]]<|/det|> +## Sensors + +<|ref|>text<|/ref|><|det|>[[117, 255, 881, 335]]<|/det|> +The robotic sensors are off- the- shelf components, operate in the analog domain and are Arduino- compatible. The proximity (URM09 ultrasonic distance), grayscale and temperature (LM35 temperature) sensor are from the DFRobot Gravity line. The pressure sensor uses the Grove force sensor module with a rectangular Taiwan Alpha force sensor pad (MF02- N- 221- A01). The sensors and detailed specifications are depicted in Fig. S5. + +<|ref|>sub_title<|/ref|><|det|>[[117, 345, 243, 360]]<|/det|> +## 3D-printed parts + +<|ref|>text<|/ref|><|det|>[[117, 370, 881, 433]]<|/det|> +The custom robotic gripper is designed using Autodesk Inventor and is then 3D- printed using a Formlabs SLA resin printer, model 3. For the gripper, Tough1500 resin is used to allow for slight flexibility and bend. To attach the ultrasonic sensor in front of the gripper, clear resin is used for the printed fixture. All fixtures are shown in Fig. S1 and S5. + +<|ref|>sub_title<|/ref|><|det|>[[117, 469, 312, 484]]<|/det|> +## Supplementary Materials + +<|ref|>text<|/ref|><|det|>[[176, 495, 353, 510]]<|/det|> +Materials and Methods + +<|ref|>text<|/ref|><|det|>[[176, 521, 277, 536]]<|/det|> +Figs. 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Organic mixed conductors for bioinspired electronics. Nat. Rev. Mater. 1-16 (2023). doi:10.1038/s41578-023-00622-5 + +<|ref|>text<|/ref|><|det|>[[115, 576, 880, 609]]<|/det|> +24. Gkoupidenis, P., Schaefer, N., Strakosas, X., Fairfield, J. A. & Malliaras, G. G. Synaptic plasticity functions in an organic electrochemical transistor. Appl. Phys. Lett. 107, 263302 (2015). + +<|ref|>text<|/ref|><|det|>[[115, 609, 870, 656]]<|/det|> +25. Van De Burgt, Y., Lubberman, E., Fuller, E. J., Keene, S. T., Faria, G. C., Agarwal, S., Marinella, M. J., Alec Talin, A. & Salleo, A. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414-418 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 656, 870, 703]]<|/det|> +26. Kim, Y., Chortos, A., Xu, W., Liu, Y., Oh, J. Y., Son, D., Kang, J., Foudeh, A. M., Zhu, C., Lee, Y., Niu, S., Liu, J., Pfattner, R., Bao, Z. & Lee, T. W. A bioinspired flexible organic artificial afferent nerve. Science 360, 998-1003 (2018). + +<|ref|>text<|/ref|><|det|>[[115, 704, 875, 736]]<|/det|> +27. Gkoupidenis, P., Koutsouras, D. A. & Malliaras, G. G. Neuromorphic device architectures with global connectivity through electrolyte gating. Nat. Commun. 8, 15448 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 736, 870, 797]]<|/det|> +28. Cucchi, M., Gruener, C., Petrauskas, L., Steiner, P., Tseng, H., Fischer, A., Penkovsky, B., Matthus, C., Birkholz, P., Kleemann, H. & Leo, K. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. Sci. Adv. 7, eabh0693 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 797, 870, 829]]<|/det|> +29. Felder, D., Muche, K., Linkhorst, J. & Wessling, M. Reminding forgetful organic neuromorphic device networks. Neuromorphic Comput. Eng. 2, 044014 (2022). + +<|ref|>text<|/ref|><|det|>[[115, 829, 848, 860]]<|/det|> +30. Liu, F., Deswal, S., Christou, A., Sandamirskaya, Y., Kaboli, M. & Dahiya, R. Neuro-inspired electronic skin for robots. Sci. Robot. 7, eabl734 (2022). + +<|ref|>text<|/ref|><|det|>[[115, 850, 870, 912]]<|/det|> +31. Dai, S., Dai, Y., Zhao, Z., Xia, F., Li, Y., Liu, Y., Cheng, P., Strzalka, J., Li, S., Li, N., Su, Q., Wai, S., Liu, W., Zhang, C., Zhao, R., Yang, J. J., Stevens, R., Xu, J., Huang, J. & Wang, S. Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence. Matter 5, (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 83, 880, 770]]<|/det|> +32. van Doremaele, E. R. W., Ji, X., Rivnay, J. & van de Burgt, Y. A retrainable neuromorphic biosensor for on-chip learning and classification. Nat. Electron. 6, 765-770 (2023).33. Krauhausen, I., Koutsouras, D. A., Melianas, A., Keene, S. T., Lieberth, K., Ledanseur, H., Sheelamanthula, R., Giovannitti, A., Torricelli, F., Mcculloch, I., Blom, P. W. M., Salleo, A., Burgt, Y. van de & Gkoupidenis, P. Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Sci. Adv. 7, (2021).34. Harikesh, P. C., Yang, C.-Y., Tu, D., Gerasimov, J. Y., Dar, A. M., Armada-Moreira, A., Massetti, M., Kroon, R., Bliman, D., Olsson, R., Stavrinidou, E., Berggren, M. & Fabiano, S. Organic electrochemical neurons and synapses with ion mediated spiking. Nat. Commun. 13, 901 (2022).35. Sarkar, T., Lieberth, K., Pavlou, A., Frank, T., Mailaender, V., McCulloch, I., Blom, P. W. M., Torricelli, F. & Gkoupidenis, P. An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat. Electron. 5, 774-783 (2022).36. Cheng, G., Ehrlich, S. K., Lebedev, M. & Nicolelis, M. A. L. Neuroengineering challenges of fusing robotics and neuroscience. Sci. Robot. 5, eabd1911 (2020).37. Seminara, L., Dosen, S., Mastrogiovanni, F., Bianchi, M., Watt, S., Beckerle, P., Nanayakkara, T., Drewing, K., Moscatelli, A., Klatzky, R. I. & Loeb, G. E. A hierarchical sensorimotor control framework for human-in-the-loop robotic hands. Sci. Robot. 8, eadd5434 (2023).38. Iberite, F., Muheim, J., Akouissi, O., Gallo, S., Rognini, G., Morosato, F., Clerc, A., Kalff, M., Gruppioni, E., Micera, S. & Shokur, S. Restoration of natural thermal sensation in upper-limb amputees. Science 380, 731-735 (2023).39. Honegger, K. & De Bivort, B. Stochasticity, individuality and behavior. Curr. Biol. 28, R8-R12 (2018).40. Rivnay, J., Inal, S., Salleo, A., Owens, R. M., Berggren, M. & Malliaras, G. G. Organic electrochemical transistors. Nat. Rev. Mater. 3, 1-14 (2018).41. Melianas, A., Quill, T. J., LeCroy, G., Tuchman, Y., Loo, H. v., Keene, S. T., Giovannitti, A., Lee, H. R., Maria, I. P., McCulloch, I. & Salleo, A. Temperature-resilient solid-state organic artificial synapses for neuromorphic computing. Sci. Adv. 6, eabb2958 (2020).42. Giovannitti, A., Sbirca, D. T., Inal, S., Nielsen, C. B., Bandiello, E., Hanifi, D. A., Sessolo, M., Malliaras, G. G., McCulloch, I. & Rivnay, J. Controlling the mode of operation of organic transistors through side-chain engineering. Proc. Natl. Acad. Sci. U. S. A. 113, 12017-12022 (2016).43. Bernards, D. A. & Malliaras, G. G. Steady-state and transient behavior of organic electrochemical transistors. Adv. Funct. Mater. 17, 3538-3544 (2007).44. Torelli, J. N. & Pickren, S. E. Using Chained or Tandem Schedules With Functional Communication Training: A Systematic Review. Behav. Modif. 47, 185-218 (2023).45. Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P. & Rajendra Acharya, U. Transfer learning techniques for medical image analysis: A review. Biocybern. Biomed. Eng. 42, 79-107 (2022).46. Köfferlein, M. Klayout - chip mask layout viewing, editing and more. at 47. Coen, C.-T., Krauhausen, I. & Spolaor, S. koala: KlayOut mAsk Layout Automation. at 48. Lee, K. H., Kang, M. S., Zhang, S., Gu, Y., Lodge, T. P. & Frisbie, C. D. 'Cut and stick' rubbery ion gels as high capacitance gate dielectrics. Adv. Mater. 24, 4457-4462 (2012). + +<|ref|>text<|/ref|><|det|>[[115, 794, 883, 890]]<|/det|> +Acknowledgments: We acknowledge Paul Beijer from the Equipment and Prototype Center of TU/e for his contribution in the design and realization of additional electronics. We also acknowledge René Janssen for opening his laboratory facilities to us and Irene Dobbelaer- Bosboom, Jaap de Hullu and Katherine Pacheco Morillo for their assistance in the Microfablab at TU/e. We also acknowledge Paul W. M. Blom for hosting part of the research at the Department of Molecular Electronics, Max Plank Institute for Polymer Research and Michelle Beuchel and Christian Bauer for their assistance in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 115]]<|/det|> +clean room facilities of the Max Planck Institute for Polymer Research. We recognize the support of Charles- Théophile Coen and Simone Spolaor. + +<|ref|>sub_title<|/ref|><|det|>[[117, 150, 185, 165]]<|/det|> +## Funding: + +<|ref|>text<|/ref|><|det|>[[174, 175, 850, 207]]<|/det|> +Joint project between the Max Planck Institute for Polymer Research and the Institute for Complex Molecular Systems, TU Eindhoven, grant MPIPICMS2019001 (IK, PG, YB) + +<|ref|>text<|/ref|><|det|>[[174, 216, 836, 248]]<|/det|> +European Union's Horizon 2020 Research and Innovation Programme, grant agreement 802615 (YB) + +<|ref|>text<|/ref|><|det|>[[175, 257, 377, 273]]<|/det|> +Carl- Zeiss Foundation (PG) + +<|ref|>text<|/ref|><|det|>[[116, 308, 882, 404]]<|/det|> +Author contributions: IK, JDT, YB, PG conceived the project and designed the experiments. IK investigated materials and tuned their properties. IK designed, fabricated, and characterized the devices and the neuromorphic circuit. IK designed and implemented the setup, robot, electronics, and control algorithms. SG and IM synthesized and provided the semiconducting material. IK, JDT, YB, PG prepared the manuscript with input from all the authors. JDT, YB, PG supervised the project and acquired the financial support. + +<|ref|>text<|/ref|><|det|>[[117, 414, 696, 430]]<|/det|> +Competing interests: Authors declare that they have no competing interests. + +<|ref|>text<|/ref|><|det|>[[117, 439, 881, 488]]<|/det|> +Data and materials availability: All data is available in the manuscript or the supplementary materials. Code and additional software (robotic control, measurement control, mask design) is available upon request. Arduino® is a trademark of Arduino SA. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 120, 850, 544]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 85, 179, 99]]<|/det|> +
Figures:
+ +<|ref|>text<|/ref|><|det|>[[115, 565, 883, 790]]<|/det|> +Fig. 1. Robotic manipulator with an organic neuromorphic circuit using bio- inspired learning. (a) A robotic manipulator with custom- made gripper is equipped with four multimodal sensors. The sensory stimuli are processed adaptively via specialized hardware and condition the grasp behavior of the robotic system. (b) The robot employs the following bio- inspired principles for learning: exploration of its environment through random movement, collection of multimodal sensory inputs and adaptive processing leading to behavioral conditioning. (c) The robotic system is connected to a local organic neuromorphic circuit that emulates neuronal processing such as short- term and long- term synaptic plasticity and dendritic summation. The neuromorphic circuit consists of organic electrochemical devices. (d) Schematic architecture of an organic electrochemical device based on the semiconducting polymer p(g2T- TT) and a solid- state electrolyte based on the ionic liquid EMIM: TFSI. The device is defined by three electrodes (grey): source (left), drain (right) and gate (top). The polymer is distributed between the source and drain terminals (blue) and exhibits mixed electronic- ionic conduction. Anions (dark blue) from the electrolyte can penetrate into the polymer bulk leading to the formation of holes (white) along the polymer backbone and changing its conductivity. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 85, 860, 652]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 661, 883, 803]]<|/det|> +
Fig. 2. Characterization of the organic neuromorphic circuit. (a) Circuit schematic of the organic neuromorphic circuit following a two-branch (+ and -) architecture. The gate of each organic electrochemical device is connected to specified sensory stimulus. The sum \(\sum V\) over the output voltages \(V_{+}\) and \(V_{- }\) branches is forwarded to the robotic system via an activation function. (b) Biological representation of dendritic summation involving two presynaptic signals. (c) A sigmoidal activation function translates the stimulus intensity \(\sum V\) into a neural response (probability for a certain behavior). (d) Output characteristics of the volatile synaptic device that displays short-term memory. (e) Transfer characteristics of the volatile synaptic device that displays short-term memory. (f) Long-term memory of the non-volatile synaptic device.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 87, 874, 686]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 692, 883, 829]]<|/det|> +
Fig. 3. Behavioral change of the robotic manipulator upon adaptive processing of multimodal stimuli. (a) Explorative behavior of the robot before any adaptation. (b) Adaptation of the \(V_{+}\) branch to the pressure stimuli when incidentally grabbing a cup. (c) After training, the robotic manipulator consistently grabs the cup if it is close by, when detected by the proximity sensor. The inset image depicts the robot holding a (dark) cup. (d) Established behavior from the \(V_{+}\) branch is maintained. (e) Adaptation to the new temperature stimuli in the \(V_{-}\) branch. (f) After training, the robotic manipulator only grabs cups that are white and cold, but not those that are dark and hot. The inset image pictures the robot holding a white cup.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 131, 262, 310]]<|/det|> +- supplementary.docx- movieS1.mp4- MovieS2.mp4- movieS3.mp4- movieS4.mp4- movieS5.mp4- movieS6.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/images_list.json b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a228df07e69ab74b0cb6cdbfd6226d9d63560e0c --- /dev/null +++ b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/images_list.json @@ -0,0 +1,100 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Abundance and microdiversity of Campylobacteraceae from the rumen epithelia.", + "footnote": [], + "bbox": [ + [ + 115, + 156, + 884, + 393 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. The two populations have highly similar gene content but divergent pgl operons.", + "footnote": [], + "bbox": [ + [ + 112, + 88, + 880, + 380 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Gene-specific sweeps involving pilin biogenesis genes in “Ca. C. stinkeria”.", + "footnote": [], + "bbox": [ + [ + 111, + 125, + 884, + 468 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. In vivo expression and analysis of evolution of an acetate acetyl-CoA transferase (AarC) that differentiates \"Ca. C. stinkeria\" and \"Ca. C. noahi\".", + "footnote": [], + "bbox": [ + [ + 115, + 88, + 884, + 303 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. An apparent trade-off in acetate-utilization and propionate-resistance in vitro and", + "footnote": [], + "bbox": [ + [ + 115, + 87, + 882, + 280 + ] + ], + "page_idx": 32 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_1.jpg", + "caption": "Supplementary Figure 1. The 16S rRNA gene is conserved across the genomes sampled. The", + "footnote": [], + "bbox": [ + [ + 112, + 155, + 888, + 600 + ] + ], + "page_idx": 33 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_3.jpg", + "caption": "Supplementary Figure 3. The ratio of \"Ca. C. stinkeria\" to \"Ca. C.noahi\" at two different locations along the papillae. A) The two sections that were dissected from papillae biopsies. From", + "footnote": [], + "bbox": [], + "page_idx": 35 + } +] \ No newline at end of file diff --git a/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a.mmd b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..575ce2d0d3927e600fabc5dc3c1feab3959b3b7b --- /dev/null +++ b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a.mmd @@ -0,0 +1,353 @@ + +# Differential partitioning of key carbon substrates at the rumen wall by recently diverged Campylobacteraceae populations + +Cameron Strachan University of Veterinary Medicine Vienna + +Xiaoqian Yu University of Vienna + +Viktoria Neubauer University of Veterinary Medicine Vienna + +Anna Mueller Division of Microbial Ecology, University of Vienna https://orcid.org/0000- 0002- 9939- 5633 + +Martin Wagner University of Veterinary medicine + +Qendrim Zebeli University of Veterinary Medicine Vienna + +Evelyne Selberherr University of Veterinary Medicine Vienna + +Martin Polz ( martin.f.polz@univie.ac.at) University of Vienna https://orcid.org/0000- 0001- 9296- 3733 + +## Article + +# Keywords: + +Posted Date: July 14th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1832745/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +1 Differential partitioning of key carbon substrates at the rumen wall by recently diverged + +2 Campylobacteraceae populations + +3 + +4 Cameron R. Strachan \(^{1,2}\) , Xiaoqian Yu \(^{3}\) , Viktoria Neubauer \(^{1,2}\) , Anna J. Mueller \(^{3,4}\) , Martin + +5 Wagner \(^{1,2}\) , Qendrim Zebeli \(^{5,6}\) , Evelyne Selberherr \(^{1*}\) , Martin F. Polz \(^{3*}\) + +6 + +7 'Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm + +8 Animals and Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 + +9 Vienna, Austria + +10 'Austrian Competence Centre for Feed and Food Quality, Safety and Innovation FFOQSI GmbH, + +11 Technopark 1C, 3430, Tulln, Austria + +12 'Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, + +13 University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria + +14 'University of Vienna, Doctoral School in Microbiology and Environmental Science, + +15 Djerassiplatz 1, 1030 Vienna, Austria + +16 'Institute of Animal Nutrition and Functional Plant Compounds, Department for Farm Animals + +17 and Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, + +18 Austria + +19 'Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Veterinärplatz + +20 1, 1210 Vienna, Austria + +21 + +22 + +23 \*Correspondence to: martin.f.polz@univie.ac.at, evelyne.selberherr@vetmeduni.ac.at + +<--- Page Split ---> + +## Abstract + +While the activities of different microbes in the rumen have been shown to modulate the host's ability to utilize plant biomass, microbes colonizing the host- rumen interface have received little attention. Here, we show that highly abundant Campylobacteraceae on the rumen epithelia have recently diverged into two populations, one of which has become a sink for acetate, the main carbon source for the host. Genomic comparisons suggest that the populations were structured by genome- wide selective sweeps after which they acquired several specific adaptations. These include differential expression and domain duplication in acetate utilization genes, which led to the ability to grow on acetate but also to inhibition by propionate in one population. This metabolic trade- off also manifests itself in differential dynamics of the two populations in vivo. By exploring population- level adaptations that otherwise remain cryptic in culture independent analyses, our results highlight recent evolutionary dynamics with unexpected consequences for ruminant nutrition. + +<--- Page Split ---> + +## Introduction + +Ruminants depend on their microbiome's remarkable metabolic capacity to digest diverse plant matter, but inefficiencies in feed conversion represent an enormous environmental burden. In addition to demanding over a quarter of earth's land and crop mass, current ruminant- based farming practices have a particularly large impact on terrestrial acidification, eutrophication, freshwater usage, and methane emissions1-4. In fact, many of these consequences go hand- in- hand and involve interconnected aspects of host and microbial metabolism. For instance, the host's feed efficiency, the portion of plant biomass that is used for physiological processes such as growth and lactation, is reduced by microbial activities that divert carbon away from the animal5. Understanding and potentially limiting these activities while simultaneously promoting those that funnel biomass into metabolites that are readily absorbed by the host is a major goal of rumen microbiome research6,7. There have been extensive community- level analyses aimed at understanding the microbial sinks and sources of key metabolites8. Complementary work has determined how these metabolites influence host nutrition, with a strong focus on acetate, which provides the host with most of the carbon used for de novo lipogenesis, as evidenced by a dose- dependent relationship between acetate and milk fat9. Yet, we only have a detailed understanding of the metabolic roles of microbes residing in the lumen, while those attached to the epithelial wall have received comparatively little attention10-12. With that said, the microbial groups that are specific to the epithelia have been identified in several 16S rRNA gene surveys and those that are both dominant and metabolically active have the potential to act as 'gatekeepers' of nutrient exchange with the host. One group of microbes that stands out as being particularly abundant and + +<--- Page Split ---> + +active are previously uncultivated Campylobacteraceae11,13,14. We focus on members of this group and ask whether they have distinct abundance distributions and metabolic adaptations with consequences for ruminant nutrition. + +To determine how the epithelial Campylobacteriaceae are genotypically structured and to relate this structure to function, we reasoned that the fine- scale genetic and gene flow analysis would allow us to predict niche specific adaptations and their potential consequences. A recently proposed framework for making such predictions is reverse ecology, which begins by predicting ecologically differentiated populations from genomic information15. Such populations are defined as groups of closely related co- occurring bacteria that are characterized by specific adaptations, so that they differ in at least some niche dimensions from their most closely related sister populations16. There are two principal modes by which adaptations can spread through populations and differentiate them17: The first mode is via gene- specific sweeps where a novel adaptive gene or allele spreads within a bacterial population that represents a cohesive gene flow unit due to higher recombination within than between other such units18,19. The second mode is via genome- wide sweeps where the entire genome hitchhikes with an adaptive gene or allele resulting in a highly clonal population structure20. Detecting populations via these evolutionary modes is thus useful for resolving units with niche differentiating adaptations, which can be subsequently quantified and linked to specific functional roles and dynamics within the microbiome21. + +Following the above logic, we explored the population structure of Campylobacteraceae on the cow rumen epithelia by applying approaches of increasing resolution. First, the host- attached bacteria were demarcated into 16S rRNA gene amplicon sequence variants (ASVs), then metagenome- assembled genomes (MAGs), and finally fine- scale clusters of isolate genomes. This strategy revealed that a single dominant Campylobacteraceae ASV was shared by two distantly + +<--- Page Split ---> + +related MAGs. Isolate genomes further split the dominant MAG into two closely related populations that bear the hallmarks of genome- wide selective sweeps. Searching the genomes for population specific, potentially adaptive features revealed extensive similarity in terms of genomic synteny and core metabolic potential, while the few genetic differences present were suggestive of differential colonization strategies. However, no spatial pattern in colonization was discernible, suggesting the populations co- occur, leading us to further explore whether gene expression differed among the Campylobacteraceae populations. We leveraged in vivo transcriptomes, which predicted modifications in the regulation of acetate- utilization and led us to notice a duplication event within the underlying genes. Growth and fitness assays with representative strains then exposed a metabolic trade- off where one population can grow better on acetate but is inhibited by propionate while the other population showed no detectable growth advantage with either substrate. Being diet- dependant cornerstones of ruminant nutrition, acetate and propionate are commonly measured in feeding trials, which allowed us to detect correlations with individual populations that are consistent with the observed trade- off. Taken together, the results highlight how metabolic differences resulting from micro- evolutionary processes structuring populations may significantly impact the availability of short- chain fatty acids (SCFAs) to the animal. + +## Results + +As several 16S rRNA amplicon surveys of the rumen epithelial microbiome reported a particularly dominant operational taxonomic unit (OTU) classified as Campylobacteraceae, we first explored the diversity within this OTU and its relative abundance across individual cows11. By merging amplicon sequence data from two recent studies22,23, we saw that a single ASV belonging to the + +<--- Page Split ---> + +Campylobacteraceae was indeed most abundant, but highly variable across individual cows, with a median relative abundance and coefficient of variance (CV) of \(15\%\) and \(50\%\) , respectively (Fig. 1A). This ASV was detected in all 48 epithelial samples we analyzed, which were taken from animals representing the control group in multiple feeding trials (eight animals in total, see Material and Methods for details). To further characterize the diversity and abundance of these Campylobacteraceae, we sequenced metagenomes from 6 of the same animals and calculated the coverage of MAGs (Fig. 1B). Consistent with the amplicon sequence data, one of the MAGs classified as Campylobacteraceae (MAG 73) was overall most abundant but varied substantially between the different animals (CV of \(58\%\) ). A second MAG was also classified as Campylobacteraceae (MAG 61) but recruited nearly 3- fold less reads. Although bacteria represented by the two MAGs presumably share an identical segment of their 16S rRNA gene, as only a single Campylobacteraceae ASV was detected, their genome- wide average nucleotide identity was only \(77\%\) (Fig. S2), suggesting a horizontal gene transfer event involving the 16S rRNA gene. We next asked whether these MAGs represent two cohesive populations or whether further population- level differentiation exists within a MAG. + +Metatranscriptomics informed cultivation recovered isolates that represented the two MAGs. Namely, we noticed a highly expressed nitrate reductase, which could be assigned to the genus Campylobacteraceae \(^{13}\) . We therefore supplemented anaerobic media with nitrate as a terminal electron acceptor, leading to the isolation of 34 strains, 31 of which share an identical 16S rRNA gene sequence, which encompasses the Campylobacteraceae ASV, while two alternative variants were detected in the remaining sequences. Within one of the three closed genomes, multiple 16S rRNA operons were present and included one of the two alternative 16S rRNA variants, which can + +<--- Page Split ---> + +therefore be accounted for by within genome variation, while the remaining variant consists of a single polymorphism (Supplementary Fig. 1). Pairwise ANI comparisons showed that the genomes from these strains cluster tightly with the two MAGs and that these two groups share only \(80\%\) ANI on average, similar to the observed MAG divergence (Supplementary Fig. 2). As the MAGs are co- assemblies of related bacteria that effectively collapse the sampled genome diversity, we refer to genomes that cluster with one of the two MAGs as belonging within that MAG. + +Phylogenetic analysis of the isolates' genomes suggested further fine- scale differentiation of the MAGs. Within the more abundant MAG 73, two well sampled and approximately equally represented clusters sharing \(96.6\%\) ANI were observed (Fig. 1C, Supplementary Fig. 2). We considered these as candidate populations because they fall below what is often considered species- level differentiation (< \(95\%\) ANI) and the within population divergence is very low (< \(0.4\%\) ) ("Ca. C. stinkeris" and "Ca. C noahi" in Supplementary Fig. 2) \(^{24}\) . Although some strains within MAG 61 are also closely related, the majority of the sampled isolates consist of single or pairs of genomes that are approximately equidistant from each other (Supplementary Fig. 2). In either case, comparing isolate genomes with the MAGs suggested that there is additional population structure, based on sampling multiple genomes from microdiverse clusters within each group, but that this structure was less diverse and better sampled within MAG 73. Considering this and that MAG 73 is the dominant group in vivo, we carried out a detailed analysis with the genomes from the 13 sampled members therein to assess their mode of diversification and differentiating features by comparative genomics. + +<--- Page Split ---> + +We hypothesize the two clusters within MAG 73 to represent populations that have been differentially optimized by selection. This hypothesis is based on the observation that the two groups consist of very closely related genomes that are connected to each other by long branches (Fig. 1C). Such structure is consistent with relatively recent genome- wide selective sweeps where selection favors a genome carrying an adaptive mutation over its immediate kin allowing it to outcompete other genomes occupying the same niche over time \(^{20,25}\) . Eventually, this process leads to highly clonal population structure, evident as the brush- like structure observed in the phylogeny. As a corollary, for sister populations differentiated in such a way to co- occur in samples, they must have sufficiently reduced niche overlap, either manifesting as differential spatial associations or metabolic differences and trade- offs that allow overlapping coexistence \(^{26 - 29}\) . We reasoned that the two abundant populations from MAG 73 ("Ca. C. stinkeria" and "Ca. C. noahi" in Fig. 1C) represent a tractable model to test these theoretical expectations and applied a reverse ecology approach \(^{15}\) . By leveraging both genomics and metatranscriptomics, we aimed to test the hypothesized population structure and further predict differentiating features that could be tested experimentally. + +To explore potential mechanisms of differentiation between "Ca. C. stinkeria" and "Ca. C. noahi", we first compared the two populations in terms of shared gene content. We aligned open readings frames at the nucleotide level and detected 'core', 'flexible' and 'population specific' genes. By plotting the percent identity of genes shared between the complete reference genomes obtained for "Ca. C. stinkeria" and "Ca. C. noahi", we observed a large syntenic core genome that comprised approximately \(90\%\) of the total gene content (Fig. 2A). The majority of the flexible genome fraction was specific for one of the populations (Fig. 2B), but these were largely annotated as + +<--- Page Split ---> + +hypothetical (77%, Supplementary Table 1). Among the remaining 65 annotations were no apparent core metabolic pathways (Supplementary Table 1). With that said, 5 of the genes in "Ca. C. noahi" were annotated as being glycosyl transferases and fell into a cluster adjacent to the pgl (protein glycosylation) operon (Fig. 2C) \(^{30}\) . These genes included 4 eps (exopolysaccharide) genes that utilize the same substrates as the pgl operon to synthesize poly- N- acetylgalactosamine (PNAG), a biofilm component involved in bacterial adhesion \(^{31}\) . Within the corresponding region of "Ca. C. stinkeris", however, there are several variants of pglH, a key gene in protein glycosylation (Fig. 2C) \(^{32}\) . The corresponding pglH proteins are highly divergent and were likely all acquired by horizontal gene transfer (Fig. 2D) \(^{33}\) . Together the differences in gene content in "Ca. C. noahi" and "Ca. C. stinkeris" suggest adaptions for colonization strategies that differ in the specifics of biofilm formation. + +Because adaptive genes have also been shown to spread within populations by homologous recombination (gene- specific sweeps), leading to reduced nucleotide diversity compared to the rest of the genome within the affected loci, we analyzed the intrapopulation SNP distribution across genomes of both populations \(^{18,21}\) . To best capture genome- wide diversity, SNPs were called by competitively mapping metagenomic reads to the reference genomes from each population, revealing a large (>7kB) SNP- free region in "Ca. C. stinkeris" (Fig. 3A). No such region was detected in "Ca. C. noahi". Aligning the SNP- free region from "Ca. C. stinkeris" to "Ca. C. noahi" showed a sharp decrease in alignment identity that falls within two genes annotated as being involved in pilin glycosylation and biogenesis (Fig. 3B) \(^{18,27,34}\) . This pattern is consistent with the recent acquisition of the SNP- free region, i.e., after the last genome- wide sweep, from a distant but unknown source followed by a population- specific sweep within "Ca. C. stinkeris", and overall + +<--- Page Split ---> + +suggests that two pilin biogenesis genes are under differential selection in the two populations. Further one of the two genes, \(pilO\) , has also become truncated in " \(Ca\) . C. noahi" (Fig. 3B). Altogether, population- specific gene content and population- specific signatures of selection support the proposed population structure and predicted modifications in pili and biofilm formation, which led us to hypothesize divergent colonization strategies and thereby spatial separation on the epithelial wall. + +To test for potential spatial separation of the two populations, we used digital PCR (dPCR) to measure their distribution across papillae samples. This was done by dissecting the apex of the papillae and the crypts (Supplementary Fig. 3A), representing the furthest and nearest tissue connected to the epithelial wall, respectively, which would be expected to harbour different ratios of the populations when assuming a gradient of opposite relative abundances across the papillae. However, the ratio of the two populations appeared similar in the two dissected samples, despite large differences between animals (Supplementary Fig. 3B). Of course, it is still possible that smaller scale spatial associations exist, beyond the resolution of our dissection strategy. But considering that we don't detect differences at opposite ends of single papillae and that the outer layer of the papillae are gradually sloughed off in vivo, interaction between the populations seem likely. We therefore reasoned that some other mechanism might be present to minimize competition. Such a mechanism would need to support co- existence of the two populations by preventing overlap in their growth dynamics via differentiation in some metabolic niche dimension. + +<--- Page Split ---> + +Because comparative genomics suggested that metabolic genes are shared among the two populations, we hypothesized that the metabolism might be rewired in ways that are not easily predicted by annotation35. We therefore aimed at comparing “\(Ca. C. stinkeris”\) and “\(Ca. C. noahi”\) on the regulatory level. Leveraging in vivo transcriptomes, we carried out competitive mapping between the representative genomes of each population. Among the most differentially expressed genes in “\(Ca. C. noahi”\) was the pilin biogenesis gene, \(mshL\), corroborating that this gene, hypothesized to be under differential selection in the analysis of gene-specific sweeps (Fig. 3, Supplementary Table 2), is involved in population-specific ecology in vivo. Additionally, two of the most highly differentially expressed loci in “\(Ca. C. stinkeris”\) implicated two variants of the metabolic gene, \(aarC\) (V1 and V2 in Fig. 4A), which code for an enzyme that assimilates acetate via the TCA cycle36,37. On average, the variants are quite divergent from each other when compared across the two populations, with the more divergent of the two variants falling two standard deviations outside of the mean gene identity (90.9 vs 96.6%, Supplementary Table 3). By comparing the variants in detail, we noticed that a segment coding for the C-terminal end (the last 500 of the total 1500 base pairs) was highly similar in the two copies of the “\(Ca. C. noahi”\) \(aarCs\). Gene trees including the variants from both populations were therefore constructed using the two segments of the gene, the larger of which implied an ancestral duplication of \(aarC\). Yet, the shorter, C-terminal segment appears to have been recently transferred within the “\(Ca. C. noahi”\) (Fig. 4B). Overall, these observations suggest that finetuning at both the regulatory and allelic level has occurred since population divergence. Such finetuning may be an indicator of metabolic differentiation, as the most related AarC is a CoA transferase acting primarily on acetate but also to a degree on propionate (Fig. 4C)38. As these two metabolites are the most abundant products of + +<--- Page Split ---> + +rumen fermentation and main energy substrates for the host7, we wanted to test whether the two populations interact differently with acetate and propionate. + +Growth assays with the representative strains showed that "Ca. C. stinkeris" accumulated biomass when provided with acetate as the main carbon source but appeared to be inhibited by propionate (Fig. 5A), suggesting a trade- off between acetate- utilization and propionate- resistance. In contrast, "Ca. C. noahi" did not accumulate any measurable biomass with either of the SCFAs (Fig. 5A). A relative fitness advantage afforded by acetate and propionate to "Ca. C. stinkeris" and "Ca. C. noahi", respectively, was also observed when the strains were competed against each other. Specifically, acetate led to "Ca. C. stinkeris" outcompeting "Ca. C. noahi" by over 3- fold, while the same concentration of propionate led to complete dominance by "Ca. C. noahi" (Fig. 5A). We then tested whether these effects were supported by in vivo relative abundances by analyzing population dynamics in a recent feeding trial during which a time- course of epithelial samples were collected and SCFA concentrations were measured in the rumen23. We used the population- specific dPCR assay and correlated "Ca. C. stinkeris" and "Ca. C. noahi" abundance in the epithelial samples with SCFAs. In line with the trade- off observed in vitro, the "Ca. C. stinkeris" population correlated positively with acetate and negatively with propionate, while "Ca. C. noahi" showed no significant correlation with either fatty acid (Fig. 5B). Considering that the two populations are otherwise predicted to utilize the same types of electron acceptors and donors based on the overlap in metabolic gene annotations, our data show that the two populations possess fine- scale adaptations, involving changes in regulation and structure of the AarC enzyme. These adaptations appear to have lessened the ability of "Ca. C. noahi" to utilize acetate but made it more resistant to propionate. Altogether, the changes thus involve a metabolic trade- off in acetate + +<--- Page Split ---> + +utilization and propionate resistance that in turn may lead to different dynamics depending on the flux of acetate and propionate. + +## Discussion + +Microbes attached to the rumen epithelia have received little attention compared to those in the lumen, but by forming a biofilm at the host- rumen interface, they may have a disproportionate influence on the flux of metabolites entering the host. By characterizing one of the most abundant epithelia- attached microbes, we shed light on a novel sink for acetate, the main fermentative end- product of the rumen microbiota and essential carbon source for the animal9. We were further able to show that the underlying metabolic trait, acetate utilization, is partitioned among closely related sister populations. This difference was not predictable by annotation alone, as both populations have the genomic potential to use the same electron donors and acceptors. If this pattern of fine- scale metabolic differentiation observed in SCFA utilization is more general to other metabolic traits, then current cultivation- independent approaches, such as the analysis of MAGs, may be insufficient to describe other important functional roles. + +Acyl- CoA transferases like the AarC enzyme appear to be more generally involved in differentiating important metabolic roles of closely related bacteria in the microbiome. A recent comprehensive study of Bacteroidetes in the human microbiome uncovered a trade- off similar to the one described here that involves acyl- CoA transferases acting on butyrate39. This has the exciting implication that there may be generalizable trade- offs involving acyl- CoA transferases that act on SCFAs. Since SCFAs have been found to be involved in several microbiome- host + +<--- Page Split ---> + +interactions40- 42, understanding the specifics of these trade- offs will provide a more mechanistic framework for designing microbiome- targeted interventions. In the rumen, these could aim to modulate the ratio of acetate to propionate without requiring dietary modifications. Further, microbial populations that potentially lessen epithelial SCFA absorption by the host, such as "Ca. C. stinkeris", could be preferentially inhibited. A particularly promising means of intervention and re- emerging technology is phage therapy since phage generally display high specificity for clonal bacterial populations43. + +The clonal structure of the populations observed here suggests that they are the result of recurrent genome- wide selective sweeps. This view is supported by the relatively high divergence between and low diversity within the populations. Practically, this structure provides a convenient means to define populations, fundamental units that have been differently optimized by selection and therefore can be hypothesized to have differential associations or dynamics. Pinpointing the underlying adaptions that initiated the differentiation, however, is complicated by the fact that genome- wide sweeps purge diversity in the population, a form of hitchhiking involving the entire genome44,45. In fact, genome- wide sweeps are expected if selection is strong relative to recombination46. Conversely, if selection is relatively weak in recombining populations, genetic specific sweeps may happen and are evident as reduced diversity within a locus in a population. Indeed, our analysis provides evidence that the populations have also been optimized by genetic specific sweeps relatively recently, i.e., after the last genome- wide sweep. These have likely involved several differentiating loci, including the homogenization of the C- terminal end of the two variants of \(aarC\) in "Ca. C. noahi". This may indicate that shifting feeding strategies, such as increased reliance on grain, that elevate SCFA production in the gut have rapid selective feedback + +<--- Page Split ---> + +on the evolution of microbial populations. Although it is impossible to know the triggers of the initial population differentiation, we speculate that it occurred during the agricultural revolution with dramatic changes in feeding practices as cattle were more intensively reared. Supporting this idea is the fact that "Ca. C. stinkeris" and "Ca. C. noahi" are more closely related than the clade 1 and 2 Campylobacter coli, which were shown to have diverged from each other approximately 1000 years ago47. It is therefore possible that shifts in feed composition initiated the process of divergence, which is still ongoing today as cattle production is increasingly industrialized. + +There is an urgent need to reduce the impact of intensive ruminant- based agriculture. While several inefficiencies in these systems will need to be tackled simultaneously, a promising means of intervention is the rumen microbiome5,6,8. Our work demonstrates that microbial adaptation to shifts in feed may be rapid and have unexpected consequences for the nutritional supply of the animal. These findings may help us to understand and influence the flux of nutrients into the host with the goal of decreasing the extent to which food crops are required by high- performance ruminants. + +## Material and Methods + +Amplicon data reanalysis + +Rumen epithelial amplicon data was downloaded from two studies22,23 that amplified the same variable region of the 16S rRNA (V3- V5) and the data were reprocessed using the qiime2 environment (v. 2021.4.0)48. The forward reads from both datasets were denoised by implementing dada2 with trimming from positions 25 to 225 and 42 to 242 for the Neubauer et al.23 and Wetzels + +<--- Page Split ---> + +et al. \(^{22}\) data, respectively. The resulting count tables were merged by shared, identical ASV sequences, which classified using the rRDP package in R (v. 1.20.0) \(^{49}\) . The relative abundances for each ASV in each sample were also calculated in R, where the top 10 most abundant ASVs based on their median relative abundance across all samples were plotted. + +# Metagenomic sequencing and analysis + +Papillae biopsies were taken as described in Pacifico et al \(^{22}\) . from six rumen cannulated Holstein cows at the start of a recent feeding trial \(^{50}\) , before the animals were administered any specific diet. All procedures involving animal handling and treatment were approved by the institutional ethics committee of the University of Veterinary Medicine (Vetmeduni) Vienna and the national authority according to §26 of the Law for Animal Experiments, Tierversuchsgesetz 2012- TVG (GZ: BMWFW- 68.205/0023- WF/V/3b/2015 and BMNWF- 68.205/0003- V/3b/2019). In the lab, biopsies were thawed on sterile microscope slides and the surface keratinous layer was scrapped off using a scalpel. This was done to enrich for bacterial DNA relative to the DNA from host epithelial cells. DNA extraction was then carried out using the PowerSoil Pro kit (Qiagen), paired- end libraries were prepared using the Westburg NGS DNA Library Prep Kit, and metagenomic sequencing was carried out on an Illumina Novoseq 6000 instrument with a 250 bp read length at the Vienna BioCenter Core facilities. The reads were trimmed using Trimmomatic (v. 0.39) \(^{51}\) and mapped against a BosTorus reference genome (GCF_000003055.6) to filter out any reads obtained from the host. The remaining reads were assembled using SPAdes (v. 3.15.2) \(^{52}\) , reads were mapped using BWA- MEM \(^{53}\) , and MAGs were generated using Metabat2 (v. 2.12.2) \(^{52}\) with a minimum contig size of 1500 bp. The MAGs were assessed for completeness and contamination using checkM (v. 1.1.3) \(^{54}\) , and then classified using the classify workflow from the genome taxonomy + +<--- Page Split ---> + +database toolkit (GTDB- Tk, v. 2.1.0) \(^{54}\) . Filtered reads were re- mapped to the MAGs using BWA- MEM \(^{53}\) and the reads per kb (RPKB) were calculated in R. The median RPKB for the top 10 most abundant MAGs, based on total reads per kb, with over 50% completeness and less than 10% contamination were then plotted. + +Cultivation approach and isolate screening + +All strains were cultivated using a tryptic soy broth (TSB) agar with the addition of 0.5 g/L L- Cystein HCL. The agar media was always prepared and used on the same day. After autoclaving, 5mM sodium nitrate, 5mM sodium fumarate and 2.5 mM sodium formate were added to the media. Further, 20 μg/mL Nalidixic acid and 5μg/mL Vancomycin were added to select for the Campylobacteraceae over other rumen microbes, which was based on recent work to enrich for Campylobacter ureolyticus \(^{55}\) . For bacterial isolation, papillae samples were thawed on ice, rinsed with sterile PBS, and transferred to a 1.5 mL Eppendorf tube with a single 6.35 mm ceramic bead (MP Biomedical, 116540424- CF). The tubes were shaken on a vortex at full speed for 10 minutes using a Qiagen Vortex adapter (13000- V1- 24). Six serial dilutions (1/10) were made using 1xPBS and 15 μL of the resulting dilutions were spread on agar plates, which were incubated anaerobically in a 2.5 L anaerobic jar and atmosphere generation sachet (Thermo Scientific, R685025, Biomerieux, 96124). After a week of incubation at 39°C in the dark, single colonies were picked and re- streaked 3 times to ensure purity. To identify Campylobacteriaceae among these colonies, scraped biomass was transferred into 20 μL of 10 g/L Chelex 100 (Biorad, 1421253), which was boiled for 10 minutes on a hot plate. From this, 1μL was added to a PCR reaction using primers that targeted the originally observed ASV (Forward; 5'- GGAGGACAACAGTTAGAAATGAC- 3', Reverse; 5'- + +<--- Page Split ---> + +CGTGAGATTTCACAAGAGACTTGAT- 3'). Sequences were confirmed to be identical to the ASV of interest by Sanger sequencing. + +Genome sequencing and diversity analysis + +Genome sequencing and diversity analysisBiomass was scraped from agar plates and DNA extraction was then carried out using the PowerSoil Pro kit. A total of 36 isolates were sequenced in two batches, the first containing 10 isolates. For the first batch, paired- end libraries were prepared using the Westburg NGS DNA Library Prep Kit, and genomic sequencing was carried out on a MiSeq instrument with a 300 bp read length at the Vienna BioCenter Core facilities. Reads were trimmed using Trimmomatic (v. 0.39) \(^{53}\) , assembled using SPAdes (v. 3.15.2) \(^{51}\) and contigs smaller than 1 kB were removed using PRINSEQ- lite (v. 0.20.4) \(^{56}\) . For the second batch, paired- end libraries were prepared using the NEBNext FS II DNA Library Prep Kit, and genomic sequencing was carried out on an Illumina Novaseq 6000 instrument with a 100 bp read length at the Joint Microbiome Facility, Vienna. These reads were trimmed using cutadapt (v. 2.10) and assembled using SPAdes (v. 3.14.1) \(^{52}\) . Contigs shorter than 1 kbp were removed using seqtk (v. 1.3). The genomes with \(>1\%\) contamination were filtered using the mmgenome2 package (v. 2.1.2) \(^{57}\) . + +To obtain complete genomes, DNA from three reference genomes (C. stinkeria NA3, C. noahi NE2 and VBCF_01 NA2) was also sequenced using the Oxford Nanopore platform. Libraries were prepared using the Nanopore Native Barcoding Genomic DNA by Ligation (EXP- NBD196, SQK- LSK109) protocol and sequenced on the MinION Mk1C instrument using a FLO- MIN106 flowcell. The resulting reads were basecalled using Guppy (v. 3.0.3+7e7b7d0, Oxford Nanopore Technologies), assembled using pomoxis (v. 0.3.1, Oxford Nanopore Technologies), polished + +<--- Page Split ---> + +using metadata (v. 1.4.3, Oxford Nanopore Technologies) and finally co- assembled with Illumina data using SPAdes (v. 3.15.2) \(^{52}\) . The 16S rRNA gene was extracted from all genomes using prokka (v. 1.14.6) \(^{58}\) , and aligned and visualized using MUSCLE (v. 5) \(^{59}\) in Genious (v.9.1.8, https://www.geneious.com). To assess genome wide diversity, MAGs and genomes were compared pairwise using fastANI (v. 1.33) \(^{60}\) . The resulting ANI values were hierarchically clustered using a complete linkage algorithm and plotted in R. Based on the observed clustering, genomes clustering with the more abundant MAG (MAG 73) were aligned using progressiveMAUVE (v. 2015.02.05) \(^{61}\) and a phylogenetic tree was constructed with the JC69 \(^{61}\) model using phyML (v. 2.2.3) \(^{62}\) . + +## Gene content and SNP analysis + +Open reading frames (ORFs) from the two complete reference genomes were predicted and annotated using prokka (v. 1.14.6) \(^{58}\) . The resulting gene sequences were compared to all other genome assemblies using blastn and only alignments with both a percent identity and percent alignment of 70% were kept for classifying core, flexible and population specific genes in R. For the SNP analysis, metagenomes were first competitively mapped with BWA- MEM \(^{53}\) to the complete genomes and a set of genomes representing those clustering with MAG 61. For the genomes clustering with MAG 61, a single representative genome was used for each of the clusters that were within 1% divergence. SNPs were called and filtered using bcfools (1.12) \(^{63}\) and VCFtools (0.1.16) \(^{64}\) and then counted in 1 kB windows in R. The largest SNP free region was in the C. stinkeria NA3, and this region was aligned with the corresponding region in C. noahi NE2 in Genious (v.9.1.8, https://www.geneious.com) with MUSCLE (v. 5) \(^{59}\) and the identity was calculated over 1 kB windows and plotted in R. + +<--- Page Split ---> + +Digital PCR assay and papillae dissection + +To quantify the two populations in vivo, papillae biopsies were taken as described in Pacifico et al22 from five animals. After thawing on ice, three crypts and apex sections were taken from each and placed in a 1.5 mL Eppendorf tube. To these, \(200~\mu \mathrm{L}\) of \(10\mathrm{g / L}\) Chelex 100 (Biorad, 1421253) was added the tubes were placed at \(99^{\circ}\mathrm{C}\) on a hot plate shaking at \(900~\mathrm{rpm}\) . The tubes were then spun down briefly and \(1\mu \mathrm{L}\) was sampled for digital PCR, which was conducted with chips on the. Stilla Naica Crystal DigitalTM PCR System. The mastermix contained Stilla Naica? multiplex PCR Mix and \(10~\mu \mathrm{M}\) of each primer and probe. A "Ca. C. stinkeris" specific region was targeted with a fluorescein containing probe (Forward; 5'- TGGGCGCAATGCTATTAT G- 3', Reverse; 5'- CATTTCACGCCTAAACATAAC C- 3', Probe; 5'- 56- FAM/CTGGTTTTG/ZEN/GCATAGATAAAAGCGGAGA/3IABkFQ/- 3'), while the "Ca. C. noahi" specific region was targeted by a Phosphoramidite containing probe (Forward; 5'- CAC AAC GAC CAT TGT AAC GAT AAT- 3', Reverse; 5'- CCT ACA ACC AGC CAC AGT C- 3', Probe; 5'- 5HEX/TG GTT TGA A/ZEN/A CTA AAT GGC GAG TTG CA/3IABkFQ/- 3'). Probes were designed and provided by integrated DNA technologies (IDT). After droplet generation, the following protocol was used to amplify the population specific targets: \(95^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) , 45 cycles of \(95^{\circ}\mathrm{C}\) for \(10\mathrm{s}\) and \(62^{\circ}\mathrm{C}\) for \(40~\mathrm{s}\) . A Silla Naica Prism 3 reader was the used to detect droplets, which were analysed by Crystal Miner software (v. 2.4.0.3) to export the copy numbers for the two targets based on the default settings. + +Comparative metatranscriptomics and aarC analysis + +<--- Page Split ---> + +Transcriptomics were mapped using the same approach as described for the metagenomic mapping above. Reads mapping to ORFs predicted with prokka (v. 1.14.6) \(^{58}\) were counted with htseq-count (v. 0.11.3) \(^{65}\) . To be able to compare the expression of genes across populations, we aligned ORFs with blastn and compared genes with over \(80\%\) alignment identity in terms of the number of mapped reads. To ensure that genes could be clearly distinguished from each other during the competitive mapping, genes that were over \(97.5\%\) similar were not compared. We then carried out the statistical analysis of differential expression using using the R package DESeq2 (1.26.0) \(^{66}\) . To assess the diversity of the aarC genes, those predicted by prokka (v. 1.14.6) \(^{58}\) were taken from the reference genomes, and aligned using MUSCLE (v. 5) \(^{59}\) in Genious (v.9.1.8, https://www.geneious.com). Gene trees were constructed with the JC69 \(^{61}\) model using phyML (v. 2.2.3) \(^{62}\) . + +## Growth and fitness assays + +We compared the growth representative strains on agar in the presence of acetate and propionate using dPCR as the strains did not grow on liquid media. This was only true for the “Ca. C. stinkeris” and “Ca. C. noahi” strains, as all others grew in the cultivation media in liquid form. We further reasoned that improvements in growth on solid media may be more representative of the in vivo growth conditions than liquid, as the bacteria are attached to the epithelial wall. Using the same agar media as for cultivation, strains were streaked out and allowed to grow anaerobically for 1 week at \(39^{\circ}\mathrm{C}\) . On the day of the experiment, fresh TSB agar media with 0.5 g/L L-Cystein HCL was prepared. After autoclaving, the media without any further supplementation was used as a control. To the media representing the two treatments, 5 mM sodium acetate or sodium propionate was added. For the 3 different media (control, acetate and propionate), 1 mL was added + +<--- Page Split ---> + +to the wells of a sterile 24- well cell culture plate. Biomass was then collected from the agar plates by scraping and resuspending it in \(2\mathrm{mL}\) of freshly prepared peptone broth containing \(0.5\mathrm{g / L}\) L- cystein HCL. The optical density of the two suspensions was standardised to 0.075 at \(570\mathrm{nm}\) and an equal mixture of the two re- suspended strains was prepared for the co- culture experiments. Cell culture plates containing agar were inoculated with either \(20\mu \mathrm{L}\) single strain or \(40\mu \mathrm{L}\) co- culture mixture and then incubated at \(39^{\circ}\mathrm{C}\) anaerobically (as described above). After \(48\mathrm{h}\) and \(72\mathrm{h}\) in the case of the single strains and co- culture mixture, respectively, the cells were harvested by cutting out each agar circle from a well with a scalpel and placing it in a \(15\mathrm{mL}\) falcon tube. To the falcon tube, \(2\mathrm{mL}\) of \(10\mathrm{g / L}\) Chelex 100 (Biorad, 1421253) was added, and the mixture was boiled at \(100^{\circ}\mathrm{C}\) in a water bath for 45 minutes. The samples were then the diluted 1/5 in sterile, DNA- free water, before \(2\mu \mathrm{L}\) were used for dPCR, as described above. Copy numbers for the acetate and propionate treatments were compared to the base media for calculating the fold change and standard deviation in R. + +## Population tracking in vivo + +The DNA extracted by Neubauer et al. \(^{23}\) was used to monitor populations using the digital PCR assay and method described above. This study tested the effects of feed additives using 8 cows in a change- over design where 2 cows were assigned to the control group for each of the 4 experimental runs. Each experimental run consisted of two periods where a high- grain diet was fed, which induced changes in ruminal SCFA concentrations, and papillae samples were taken at 3 time points (1 before and 2 after the high- grain periods). From 96 samples, \(2\mu \mathrm{L}\) of the extracted DNA was added to the mastermix. The resulting count data were merged from the rumen SCFA + +<--- Page Split ---> + +502 data measured in Neubauer et al. \(^{23}\) and Pearson correlations were calculated using the cor function in R. Correlations with a p value lower than 0.01 were considered significant. + +## Code availability + +Scripts with all the custom analysis and commands described above can be found at [https://github.com/cameronstrachan/RumenCampylobacter2022]. + +## Data availability + +The publicly available data that we reanalysed here were generated by Wetzels et al. \(^{22}\) , Neubauer et al. \(^{23}\) , and Mann et al. \(^{13}\) . The metagenomic sequencing data from the rumen papillae samples are available on the NCBI SRA under the accession number PRJNAXXXX. The genomes are available on NCBI under the accession numbers PRJNAXXXX. + +## References + +1. Humpenöder, F. et al. Projected environmental benefits of replacing beef with microbial protein. Nature 605, 90–96 (2022). +2. Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014). +3. Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science (1979) 370, 705–708 (2020). +4. Eisler, M. C. et al. Agriculture: Steps to sustainable livestock. Nature 507, 32–34 (2014). +5. Kamke, J. et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome 4, (2016). +6. Kruger Ben Shabat, S. et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME Journal 10, 2958–2972 (2016). + +<--- Page Split ---> + +7. Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Animal Feed Science and Technology 160, 1–22 (2010).8. Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances 5, (2019).9. Urrutia, N. L. & Harvatine, K. J. Acetate Dose-Dependently Stimulates Milk Fat Synthesis in Lactating Dairy Cows. The Journal of Nutrition 147, 763–769 (2017).10. Seshadri, R. et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nature Biotechnology vol. 36 359–367 Preprint at https://doi.org/10.1038/nbt.4110 (2018).11. Anderson, C. J., Koester, L. R. & Schmitz-Esser, S. Rumen Epithelial Communities Share a Core Bacterial Microbiota: A Meta-Analysis of 16S rRNA Gene Illumina MiSeq Sequencing Datasets. Frontiers in Microbiology 12, (2021).12. Wallace, R. J., Cheng, K.-J., Dinsdale, D. & Ørskov, E. R. An independent microbial flora of the epithelium and its role in the ecomicrobiology of the rumen. Nature 279, 424–426 (1979).13. Mann, E., Wetzels, S. U., Wagner, M., Zebeli, Q. & Schmitz-Esser, S. Metatranscriptome Sequencing Reveals Insights into the Gene Expression and Functional Potential of Rumen Wall Bacteria. Frontiers in Microbiology 9, (2018).14. Pacifico, C. et al. Unveiling the Bovine Epimural Microbiota Composition and Putative Function. Microorganisms 9, 342 (2021).15. VanInsberghe, D., Arevalo, P., Chien, D. & Polz, M. F. How can microbial population genomics inform community ecology? Philosophical Transactions of the Royal Society B: Biological Sciences 375, 20190253 (2020).16. Hunt, D. E. et al. Resource Partitioning and Sympatric Differentiation Among Closely Related Bacterioplankton. Science (1979) 320, 1081–1085 (2008).17. Fraser, C., Hanage, W. P. & Spratt, B. G. Recombination and the Nature of Bacterial Speciation. Science (1979) 315, 476–480 (2007).18. Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science (1979) 335, 48–51 (2012).19. Cadillo-Quiroz, H. et al. Patterns of gene flow define species of thermophilic Archaea. PLoS Biology 10, (2012).20. Koeppel, A. et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc Natl Acad Sci U S A 105, 2504–9 (2008).21. Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J. & Polz, M. F. A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations. Cell 178, 820–834.e14 (2019).22. Wetzels, S. U. et al. Epimural bacterial community structure in the rumen of Holstein cows with different responses to a long-term subacute ruminal acidosis diet challenge. Journal of Dairy Science 100, 1829–1844 (2017).23. Neubauer, V. et al. Effects of clay mineral supplementation on particle-associated and epimural microbiota, and gene expression in the rumen of cows fed high-concentrate diet. Anaerobe 59, 38–48 (2019).24. Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing Cultivation To Identify Bacterial Species. Microbe Magazine 9, 111–118 (2014). + +<--- Page Split ---> + +576 25. Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural 577 bacterial populations. ISME Journal 10, 1589-1601 (2016). 578 26. Tilman, D. Resource Competition between Plankton Algae: An Experimental and 579 Theoretical Approach. Ecology 58, 338-348 (1977). 580 27. Yawata, Y. et al. Competition-dispersal tradeoff ecologically differentiates recently 581 speciated marine bacterioplankton populations. Proceedings of the National Academy of 582 Sciences 111, 5622-5627 (2014). 583 28. Basan, M. et al. A universal trade-off between growth and lag in fluctuating 584 environments. Nature 584, 470-474 (2020). 585 29. Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W. & Milo, R. Glycolytic strategy as 586 a tradeoff between energy yield and protein cost. Proceedings of the National Academy of 587 Sciences 110, 10039-10044 (2013). 588 30. Szymanski, C. M., Yao, R., Ewing, C. P., Trust, T. J. & Guerry, P. Evidence for a system 589 of general protein glycosylation in Campylobacter jejuni. Molecular Microbiology 32, 1022-1030 (1999). 590 31. Roux, D. et al. Identification of Poly-N-acetylglucosamine as a Major Polysaccharide 591 Component of the Bacillus subtilis Biofilm Matrix. Journal of Biological Chemistry 290, 592 19621-19272 (2015). 593 32. Troutman, J. M. & Imperiali, B. Campylobacter jejuni PglH Is a Single Active Site 594 Processive Polymerase that Utilizes Product Inhibition to Limit Sequential Glycosyl 595 Transfer Reactions. Biochemistry 48, 2807-2816 (2009). 596 33. Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale 597 resource partitioning in marine microbes. Nature Communications 7, (2016). 598 34. Castric, P. pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. 599 Microbiology (N Y) 141, 1247-1254 (1995). 600 35. Lawson, C. E. et al. Autotrophic and mixotrophic metabolism of an anammox bacterium 601 revealed by in vivo 13C and 2H metabolic network mapping. The ISME Journal 15, 673- 602 687 (2021). 603 36. Kwong, W. K., Zheng, H. & Moran, N. A. Convergent evolution of a modified, acetate- 604 driven TCA cycle in bacteria. Nature Microbiology 2, 17067 (2017). 605 37. Kather, B., Stingl, K., van der Rest, M. E., Altendorf, K. & Molenaar, D. Another Unusual 606 Type of Citric Acid Cycle Enzyme in Helicobacter pylori : the Malate:Quinone 607 Oxidoreductase. Journal of Bacteriology 182, 3204-3209 (2000). 608 38. Mullins, E. A. & Kappock, T. J. Crystal Structures of Acetobacter aceti Succinyl- 609 Coenzyme A (CoA):Acetate CoA-Transferase Reveal Specificity Determinants and 610 Illustrate the Mechanism Used by Class I CoA-Transferases. Biochemistry 51, 8422-8434 611 (2012). 612 39. Park, S. Y. et al. Strain-level fitness in the gut microbiome is an emergent property of 613 glycans and a single metabolite. Cell 185, 513-529.e21 (2022). 614 40. Kim, C. H. Control of lymphocyte functions by gut microbiota-derived short-chain fatty 615 acids. Cellular & Molecular Immunology 18, 1161-1171 (2021). 616 41. Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota 617 and their impact on human metabolism. Gut Microbes 7, 189-200 (2016). 618 42. Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as 619 potential regulators of skeletal muscle metabolism and function. Nature Metabolism 2, 620 840-848 (2020). + +<--- Page Split ---> + +668 Guindon, S. et al. New Algorithms and Methods to Estimate Maximum- Likelihood 669 Phylogenies: Assessing the Performance of PhyML 3.0. Systematic Biology 59, 307–321 670 (2010). 671 62. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, (2021). 672 64. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 673 (2011). 674 65. Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high- throughput 675 sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022). 676 66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion 677 for RNA- seq data with DESeq2. Genome Biology 15, 550 (2014). 678 67. Le, S. Q. & Gascuel, O. An Improved General Amino Acid Replacement Matrix. 679 Molecular Biology and Evolution 25, 1307–1320 (2008). + +## Acknowledgements + +We first thank Sara Ricci for providing papillae samples remaining from recent feeding trials. We further thank the Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna (JMF) for technical assistance in sample preparation and processing for sequencing. All quantitative and digital PCR experiments were further supported using resources of the VetCore Facility (Genomics) of the University of Veterinary Medicine Vienna. C.R.S. and A.J.M. were partially supported by a Fellowship from the Natural Science and Engineering Council of Canada Postgraduate Scholarship-Doctoral (NSERC PGS-D). The competence centre FFoQSI is funded by the Austrian ministries BMVIT, BMDW and the Austrian provinces Niederoesterreich, Upper Austria and Vienna within the scope of COMET - Competence Centers for Excellent Technologies. The programme COMET is handled by the Austrian Research Promotion Agency FFG. The research of Q.Z. was funded by Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, through the Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock. + +<--- Page Split ---> + +## Author contributions + +The concept was developed by C.R.S., A.Y., E.S., and M.F.P. C.R.S conducted all experiments and analysis. A.Y. guided the population genomic analysis. V.N. carried out all in vivo sampling and dissections, and compiled data from previous feeding trials. A.J.M. assisted with cultivation, genome annotation and data presentation. M.W. and Q.Z. acquired funding and provided valuable feedback. Q.Z. designed all cow experiments and provided access to papillae samples obtained during recent feeding trials. C.R.S. and M.F.P. prepared the manuscript. All authors read and approved the final version of the manuscript. + +## Competing interests + +The authors declare no competing interests. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Abundance and microdiversity of Campylobacteraceae from the rumen epithelia.
+ +A) Relative abundance of the top 10 16S rRNA gene ASVs based on the merging and re-analysis of two recent studies22,23. The points represent baseline samples (no feed additives were applied) taken from 8 cows at different times. B) Metagenomes were sequenced from 6 baseline samples, each from a different cow, and assembled into metagenome-assembled genomes (MAGs). The reads mapped per kilobase are shown for the 10 most abundant MAGs with a completeness of over 50% and under 10% contamination. C) Phylogenetic tree reconstructed with isolate genomes that correspond to MAG 73 in panel B (Supplementary Fig. 2). Whole-genome alignments at the nucleotide-level spanned 1.3 Mb. The two closely related populations are named “Ca. C. stinkeria” and “Ca. C noahi” for convenience. The names given in brackets are the names of the animals from which we obtained the isolate genome. The genomes of the two marked isolates were closed and are used as reference genomes in subsequent analysis. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. The two populations have highly similar gene content but divergent pgl operons.
+ +A) Percent identity of shared genes between “\(Ca. C. stinkeris” NA3 and “\(Ca. C. noahi” NE2 numbered by their order in the reference genomes starting with the same gene. The remaining 11 genomes were further leveraged to label genes as core, flexible or population specific. Core genes are those found in all 13 genomes. Lines plotted directly on the axes are flexible genes, defined as genes missing from 2 or more of any of the “\(Ca. C. stinkeris” or “\(Ca. C. noahi” genomes. Those flexible genomes that are unique to and found in all genomes of one of the populations are marked as ‘population specific’. B) Summary of the number of core, flexible and population-specific genes. C) Cluster of annotated “\(Ca. C. noahi” genes that are adjacent to the pgl operon and include 4 eps (exopolysaachride) genes. The predicted role of these genes in producing poly-N-acetylgalactosamine is shown to the right. Below is the corresponding region in “\(Ca. C. stinkeris”, which in contrast has variants of the pglH gene. This gene is predicted to code for an enzyme involved in polymerizing N-acetylgalactosamine for protein glycosylation. D) A protein tree constructed using the LG model\(^{67}\) and amino acid sequences for each of the pglH genes. The average pairwise amino acid identity is 40%. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Gene-specific sweeps involving pilin biogenesis genes in “Ca. C. stinkeria”.
+ +A) Single-nucleotide polymorphisms (SNPs) were called in “Ca. C. stinkeria” after competitively mapping of metagenomic reads to the reference genomes from both populations. The percentage of detected SNPs were calculated in 1000 bp windows and plotted. No SNPs were observed in a region spanning over 7kB, the largest SNP-free region. B) The region identified in panel A was aligned between “Ca. C. stinkeria” NA2 and “Ca. C. noahi” NE3. The region included two genes annotated to be involved in pilin biogenesis, pilO and mshL. The pilO gene has been interrupted by a stop codon. Alignment identity of the region was calculated in 1000 bp windows and plotted as a moving average. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4. In vivo expression and analysis of evolution of an acetate acetyl-CoA transferase (AarC) that differentiates "Ca. C. stinkeria" and "Ca. C. noahi".
+ +A) Volcano plot showing the range of fold changes and corresponding p-values from differential expression analysis comparing the two populations. The horizontal line indicates significant genes with a p-value cut-off of 0.00001 (Supplementary Table 2). The two variants of aarC are marked. mshL is also marked as it corroborates the analysis in Fig. 3. B) Phylogenetic trees at the gene-level for different regions of the two variants of aarC. The two trees were reconstructed using different segments of the 4 aarC variants (2 per genome), either the first 1000 bp or the last 500 bp. C) Graphical summary of results and hypothesis from the analysis in panel A and B. The gene diagrams show that two different variants of the aarC gene are highly expressed in "Ca. C. stinkeria" relative to "Ca. C. noahi" wherein a segment of the aarC genes has been homogenized. AarC is CoA transferase predicted to assimilate acetate via the TCA cycle, but has also been shown to have activity on propionate, leading to propionyl-CoA38. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5. An apparent trade-off in acetate-utilization and propionate-resistance in vitro and
+ +in vivo. A) Growth experiments assessing the effect of adding 5 mM acetate or propionate on biomass accumulation by population- specific dPCR. Growth differences were assessed on agar since the strains did not grow in liquid (see Materials and Methods). Fold change is shown relative to control (no SCFA added). Experiments with individual strains (left, monoculture) and in competition (right, coculture) were performed. Error bars show standard deviation (n=6). B) Correlations (Pearson) between population-specific dPCR data and rumen SCFA concentrations (n=96) \(^{23}\) . Coefficients are indicated for significant correlations (p value < 0.01). Data were obtained from samples taken during a previously performed feeding trial \(^{23}\) that used the same cows (n=8) from which the strains in this study were cultivated. + +<--- Page Split ---> + +## Supplementary Figures + +![](images/Supplementary_Figure_1.jpg) + +
Supplementary Figure 1. The 16S rRNA gene is conserved across the genomes sampled. The
+ +16S rRNA gene was extracted from all genomes and aligned. The topmost sequence is the ASV found to be most abundant in our reanalysis of two rumen epithelial amplicon studies22,23. Only the first 600 of 1508 positions are shown, as there was no variation observed across the rest of the alignment. On the left had side, sequences that were extracted from the same genome are shown (indicated with brackets). Two variants were detected in 4 genomes and are indicated by the genome names in red. The most divergent of the two variants (9 bp changes) is represented as one + +<--- Page Split ---> + +793 of the copies of the rRNA gene within the closed reference genome VBCF_01 NA2. A single base 794 pair change defines the remaining variant detected in genomes JMF_06 NA1 and JMF_09 ED2. 795 + +<--- Page Split ---> +![](images/Supplementary_Figure_3.jpg) + + +Supplementary Figure 2. Pairwise ANI and clustering of genomes with MAGs suggests population structure. Genomes were compared pairwise using FastANI \(^{60}\) and the resulting ANI + +<--- Page Split ---> + +values were hierarchically clustered using a complete linkage algorithm. The resulting clusters are represented by a dendrogram on the top and are named, as referred to throughout the paper, on the side of the heatmap. Values representing comparisons of genomes from the two major clusters (containing MAG 61 and 73) are coloured in grey. + +<--- Page Split ---> +![PLACEHOLDER_37_0] + +
Supplementary Figure 3. The ratio of "Ca. C. stinkeria" to "Ca. C.noahi" at two different locations along the papillae. A) The two sections that were dissected from papillae biopsies. From
+ +these, DNA was extracted, and the populations were measured using dPCR. B) The ratios of the populations are plotted as we hypothesized divergent distributions of the two populations across the papillae. The names on the x- axis are the names of the different animals from which we obtained the papillae samples. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryTable1. pdf SupplementaryTable2. pdf SupplementaryTable3. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a_det.mmd b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6b569c401b1d062a79020b3239ebe8c5142f73e6 --- /dev/null +++ b/preprint/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a/preprint__03c31296ef578267e0681ad7fcc6fa64be79421e7b799bf5f8abf78e1cf3cf5a_det.mmd @@ -0,0 +1,443 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 933, 210]]<|/det|> +# Differential partitioning of key carbon substrates at the rumen wall by recently diverged Campylobacteraceae populations + +<|ref|>text<|/ref|><|det|>[[44, 230, 409, 272]]<|/det|> +Cameron Strachan University of Veterinary Medicine Vienna + +<|ref|>text<|/ref|><|det|>[[44, 277, 232, 316]]<|/det|> +Xiaoqian Yu University of Vienna + +<|ref|>text<|/ref|><|det|>[[44, 323, 409, 363]]<|/det|> +Viktoria Neubauer University of Veterinary Medicine Vienna + +<|ref|>text<|/ref|><|det|>[[44, 370, 844, 410]]<|/det|> +Anna Mueller Division of Microbial Ecology, University of Vienna https://orcid.org/0000- 0002- 9939- 5633 + +<|ref|>text<|/ref|><|det|>[[44, 415, 344, 455]]<|/det|> +Martin Wagner University of Veterinary medicine + +<|ref|>text<|/ref|><|det|>[[44, 461, 409, 501]]<|/det|> +Qendrim Zebeli University of Veterinary Medicine Vienna + +<|ref|>text<|/ref|><|det|>[[44, 507, 409, 547]]<|/det|> +Evelyne Selberherr University of Veterinary Medicine Vienna + +<|ref|>text<|/ref|><|det|>[[44, 553, 590, 594]]<|/det|> +Martin Polz ( martin.f.polz@univie.ac.at) University of Vienna https://orcid.org/0000- 0001- 9296- 3733 + +<|ref|>sub_title<|/ref|><|det|>[[44, 637, 102, 654]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 675, 135, 693]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 712, 296, 731]]<|/det|> +Posted Date: July 14th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 750, 473, 769]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1832745/v1 + +<|ref|>text<|/ref|><|det|>[[44, 787, 910, 830]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 886, 110]]<|/det|> +1 Differential partitioning of key carbon substrates at the rumen wall by recently diverged + +<|ref|>text<|/ref|><|det|>[[70, 123, 399, 144]]<|/det|> +2 Campylobacteraceae populations + +<|ref|>text<|/ref|><|det|>[[70, 160, 88, 175]]<|/det|> +3 + +<|ref|>text<|/ref|><|det|>[[70, 193, 886, 214]]<|/det|> +4 Cameron R. Strachan \(^{1,2}\) , Xiaoqian Yu \(^{3}\) , Viktoria Neubauer \(^{1,2}\) , Anna J. Mueller \(^{3,4}\) , Martin + +<|ref|>text<|/ref|><|det|>[[70, 229, 668, 250]]<|/det|> +5 Wagner \(^{1,2}\) , Qendrim Zebeli \(^{5,6}\) , Evelyne Selberherr \(^{1*}\) , Martin F. Polz \(^{3*}\) + +<|ref|>text<|/ref|><|det|>[[70, 266, 88, 281]]<|/det|> +6 + +<|ref|>text<|/ref|><|det|>[[70, 298, 886, 320]]<|/det|> +7 'Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm + +<|ref|>text<|/ref|><|det|>[[70, 333, 886, 355]]<|/det|> +8 Animals and Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 + +<|ref|>text<|/ref|><|det|>[[70, 369, 244, 388]]<|/det|> +9 Vienna, Austria + +<|ref|>text<|/ref|><|det|>[[70, 402, 884, 425]]<|/det|> +10 'Austrian Competence Centre for Feed and Food Quality, Safety and Innovation FFOQSI GmbH, + +<|ref|>text<|/ref|><|det|>[[70, 438, 410, 458]]<|/det|> +11 Technopark 1C, 3430, Tulln, Austria + +<|ref|>text<|/ref|><|det|>[[70, 472, 884, 494]]<|/det|> +12 'Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, + +<|ref|>text<|/ref|><|det|>[[70, 508, 600, 529]]<|/det|> +13 University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria + +<|ref|>text<|/ref|><|det|>[[70, 543, 786, 565]]<|/det|> +14 'University of Vienna, Doctoral School in Microbiology and Environmental Science, + +<|ref|>text<|/ref|><|det|>[[70, 579, 415, 599]]<|/det|> +15 Djerassiplatz 1, 1030 Vienna, Austria + +<|ref|>text<|/ref|><|det|>[[70, 613, 867, 635]]<|/det|> +16 'Institute of Animal Nutrition and Functional Plant Compounds, Department for Farm Animals + +<|ref|>text<|/ref|><|det|>[[70, 648, 857, 670]]<|/det|> +17 and Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, + +<|ref|>text<|/ref|><|det|>[[70, 684, 176, 702]]<|/det|> +18 Austria + +<|ref|>text<|/ref|><|det|>[[70, 717, 870, 739]]<|/det|> +19 'Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Veterinärplatz + +<|ref|>text<|/ref|><|det|>[[70, 752, 306, 772]]<|/det|> +20 1, 1210 Vienna, Austria + +<|ref|>text<|/ref|><|det|>[[70, 788, 88, 803]]<|/det|> +21 + +<|ref|>text<|/ref|><|det|>[[70, 828, 88, 844]]<|/det|> +22 + +<|ref|>text<|/ref|><|det|>[[70, 859, 792, 880]]<|/det|> +23 \*Correspondence to: martin.f.polz@univie.ac.at, evelyne.selberherr@vetmeduni.ac.at + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 126, 192, 143]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[111, 193, 886, 597]]<|/det|> +While the activities of different microbes in the rumen have been shown to modulate the host's ability to utilize plant biomass, microbes colonizing the host- rumen interface have received little attention. Here, we show that highly abundant Campylobacteraceae on the rumen epithelia have recently diverged into two populations, one of which has become a sink for acetate, the main carbon source for the host. Genomic comparisons suggest that the populations were structured by genome- wide selective sweeps after which they acquired several specific adaptations. These include differential expression and domain duplication in acetate utilization genes, which led to the ability to grow on acetate but also to inhibition by propionate in one population. This metabolic trade- off also manifests itself in differential dynamics of the two populations in vivo. By exploring population- level adaptations that otherwise remain cryptic in culture independent analyses, our results highlight recent evolutionary dynamics with unexpected consequences for ruminant nutrition. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 225, 143]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[111, 180, 886, 880]]<|/det|> +Ruminants depend on their microbiome's remarkable metabolic capacity to digest diverse plant matter, but inefficiencies in feed conversion represent an enormous environmental burden. In addition to demanding over a quarter of earth's land and crop mass, current ruminant- based farming practices have a particularly large impact on terrestrial acidification, eutrophication, freshwater usage, and methane emissions1-4. In fact, many of these consequences go hand- in- hand and involve interconnected aspects of host and microbial metabolism. For instance, the host's feed efficiency, the portion of plant biomass that is used for physiological processes such as growth and lactation, is reduced by microbial activities that divert carbon away from the animal5. Understanding and potentially limiting these activities while simultaneously promoting those that funnel biomass into metabolites that are readily absorbed by the host is a major goal of rumen microbiome research6,7. There have been extensive community- level analyses aimed at understanding the microbial sinks and sources of key metabolites8. Complementary work has determined how these metabolites influence host nutrition, with a strong focus on acetate, which provides the host with most of the carbon used for de novo lipogenesis, as evidenced by a dose- dependent relationship between acetate and milk fat9. Yet, we only have a detailed understanding of the metabolic roles of microbes residing in the lumen, while those attached to the epithelial wall have received comparatively little attention10-12. With that said, the microbial groups that are specific to the epithelia have been identified in several 16S rRNA gene surveys and those that are both dominant and metabolically active have the potential to act as 'gatekeepers' of nutrient exchange with the host. One group of microbes that stands out as being particularly abundant and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 180]]<|/det|> +active are previously uncultivated Campylobacteraceae11,13,14. We focus on members of this group and ask whether they have distinct abundance distributions and metabolic adaptations with consequences for ruminant nutrition. + +<|ref|>text<|/ref|><|det|>[[111, 194, 886, 704]]<|/det|> +To determine how the epithelial Campylobacteriaceae are genotypically structured and to relate this structure to function, we reasoned that the fine- scale genetic and gene flow analysis would allow us to predict niche specific adaptations and their potential consequences. A recently proposed framework for making such predictions is reverse ecology, which begins by predicting ecologically differentiated populations from genomic information15. Such populations are defined as groups of closely related co- occurring bacteria that are characterized by specific adaptations, so that they differ in at least some niche dimensions from their most closely related sister populations16. There are two principal modes by which adaptations can spread through populations and differentiate them17: The first mode is via gene- specific sweeps where a novel adaptive gene or allele spreads within a bacterial population that represents a cohesive gene flow unit due to higher recombination within than between other such units18,19. The second mode is via genome- wide sweeps where the entire genome hitchhikes with an adaptive gene or allele resulting in a highly clonal population structure20. Detecting populations via these evolutionary modes is thus useful for resolving units with niche differentiating adaptations, which can be subsequently quantified and linked to specific functional roles and dynamics within the microbiome21. + +<|ref|>text<|/ref|><|det|>[[112, 716, 886, 876]]<|/det|> +Following the above logic, we explored the population structure of Campylobacteraceae on the cow rumen epithelia by applying approaches of increasing resolution. First, the host- attached bacteria were demarcated into 16S rRNA gene amplicon sequence variants (ASVs), then metagenome- assembled genomes (MAGs), and finally fine- scale clusters of isolate genomes. This strategy revealed that a single dominant Campylobacteraceae ASV was shared by two distantly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 636]]<|/det|> +related MAGs. Isolate genomes further split the dominant MAG into two closely related populations that bear the hallmarks of genome- wide selective sweeps. Searching the genomes for population specific, potentially adaptive features revealed extensive similarity in terms of genomic synteny and core metabolic potential, while the few genetic differences present were suggestive of differential colonization strategies. However, no spatial pattern in colonization was discernible, suggesting the populations co- occur, leading us to further explore whether gene expression differed among the Campylobacteraceae populations. We leveraged in vivo transcriptomes, which predicted modifications in the regulation of acetate- utilization and led us to notice a duplication event within the underlying genes. Growth and fitness assays with representative strains then exposed a metabolic trade- off where one population can grow better on acetate but is inhibited by propionate while the other population showed no detectable growth advantage with either substrate. Being diet- dependant cornerstones of ruminant nutrition, acetate and propionate are commonly measured in feeding trials, which allowed us to detect correlations with individual populations that are consistent with the observed trade- off. Taken together, the results highlight how metabolic differences resulting from micro- evolutionary processes structuring populations may significantly impact the availability of short- chain fatty acids (SCFAs) to the animal. + +<|ref|>sub_title<|/ref|><|det|>[[115, 683, 180, 699]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[111, 752, 886, 876]]<|/det|> +As several 16S rRNA amplicon surveys of the rumen epithelial microbiome reported a particularly dominant operational taxonomic unit (OTU) classified as Campylobacteraceae, we first explored the diversity within this OTU and its relative abundance across individual cows11. By merging amplicon sequence data from two recent studies22,23, we saw that a single ASV belonging to the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 600]]<|/det|> +Campylobacteraceae was indeed most abundant, but highly variable across individual cows, with a median relative abundance and coefficient of variance (CV) of \(15\%\) and \(50\%\) , respectively (Fig. 1A). This ASV was detected in all 48 epithelial samples we analyzed, which were taken from animals representing the control group in multiple feeding trials (eight animals in total, see Material and Methods for details). To further characterize the diversity and abundance of these Campylobacteraceae, we sequenced metagenomes from 6 of the same animals and calculated the coverage of MAGs (Fig. 1B). Consistent with the amplicon sequence data, one of the MAGs classified as Campylobacteraceae (MAG 73) was overall most abundant but varied substantially between the different animals (CV of \(58\%\) ). A second MAG was also classified as Campylobacteraceae (MAG 61) but recruited nearly 3- fold less reads. Although bacteria represented by the two MAGs presumably share an identical segment of their 16S rRNA gene, as only a single Campylobacteraceae ASV was detected, their genome- wide average nucleotide identity was only \(77\%\) (Fig. S2), suggesting a horizontal gene transfer event involving the 16S rRNA gene. We next asked whether these MAGs represent two cohesive populations or whether further population- level differentiation exists within a MAG. + +<|ref|>text<|/ref|><|det|>[[111, 648, 886, 876]]<|/det|> +Metatranscriptomics informed cultivation recovered isolates that represented the two MAGs. Namely, we noticed a highly expressed nitrate reductase, which could be assigned to the genus Campylobacteraceae \(^{13}\) . We therefore supplemented anaerobic media with nitrate as a terminal electron acceptor, leading to the isolation of 34 strains, 31 of which share an identical 16S rRNA gene sequence, which encompasses the Campylobacteraceae ASV, while two alternative variants were detected in the remaining sequences. Within one of the three closed genomes, multiple 16S rRNA operons were present and included one of the two alternative 16S rRNA variants, which can + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 283]]<|/det|> +therefore be accounted for by within genome variation, while the remaining variant consists of a single polymorphism (Supplementary Fig. 1). Pairwise ANI comparisons showed that the genomes from these strains cluster tightly with the two MAGs and that these two groups share only \(80\%\) ANI on average, similar to the observed MAG divergence (Supplementary Fig. 2). As the MAGs are co- assemblies of related bacteria that effectively collapse the sampled genome diversity, we refer to genomes that cluster with one of the two MAGs as belonging within that MAG. + +<|ref|>text<|/ref|><|det|>[[111, 333, 886, 808]]<|/det|> +Phylogenetic analysis of the isolates' genomes suggested further fine- scale differentiation of the MAGs. Within the more abundant MAG 73, two well sampled and approximately equally represented clusters sharing \(96.6\%\) ANI were observed (Fig. 1C, Supplementary Fig. 2). We considered these as candidate populations because they fall below what is often considered species- level differentiation (< \(95\%\) ANI) and the within population divergence is very low (< \(0.4\%\) ) ("Ca. C. stinkeris" and "Ca. C noahi" in Supplementary Fig. 2) \(^{24}\) . Although some strains within MAG 61 are also closely related, the majority of the sampled isolates consist of single or pairs of genomes that are approximately equidistant from each other (Supplementary Fig. 2). In either case, comparing isolate genomes with the MAGs suggested that there is additional population structure, based on sampling multiple genomes from microdiverse clusters within each group, but that this structure was less diverse and better sampled within MAG 73. Considering this and that MAG 73 is the dominant group in vivo, we carried out a detailed analysis with the genomes from the 13 sampled members therein to assess their mode of diversification and differentiating features by comparative genomics. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 600]]<|/det|> +We hypothesize the two clusters within MAG 73 to represent populations that have been differentially optimized by selection. This hypothesis is based on the observation that the two groups consist of very closely related genomes that are connected to each other by long branches (Fig. 1C). Such structure is consistent with relatively recent genome- wide selective sweeps where selection favors a genome carrying an adaptive mutation over its immediate kin allowing it to outcompete other genomes occupying the same niche over time \(^{20,25}\) . Eventually, this process leads to highly clonal population structure, evident as the brush- like structure observed in the phylogeny. As a corollary, for sister populations differentiated in such a way to co- occur in samples, they must have sufficiently reduced niche overlap, either manifesting as differential spatial associations or metabolic differences and trade- offs that allow overlapping coexistence \(^{26 - 29}\) . We reasoned that the two abundant populations from MAG 73 ("Ca. C. stinkeria" and "Ca. C. noahi" in Fig. 1C) represent a tractable model to test these theoretical expectations and applied a reverse ecology approach \(^{15}\) . By leveraging both genomics and metatranscriptomics, we aimed to test the hypothesized population structure and further predict differentiating features that could be tested experimentally. + +<|ref|>text<|/ref|><|det|>[[111, 647, 886, 876]]<|/det|> +To explore potential mechanisms of differentiation between "Ca. C. stinkeria" and "Ca. C. noahi", we first compared the two populations in terms of shared gene content. We aligned open readings frames at the nucleotide level and detected 'core', 'flexible' and 'population specific' genes. By plotting the percent identity of genes shared between the complete reference genomes obtained for "Ca. C. stinkeria" and "Ca. C. noahi", we observed a large syntenic core genome that comprised approximately \(90\%\) of the total gene content (Fig. 2A). The majority of the flexible genome fraction was specific for one of the populations (Fig. 2B), but these were largely annotated as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 460]]<|/det|> +hypothetical (77%, Supplementary Table 1). Among the remaining 65 annotations were no apparent core metabolic pathways (Supplementary Table 1). With that said, 5 of the genes in "Ca. C. noahi" were annotated as being glycosyl transferases and fell into a cluster adjacent to the pgl (protein glycosylation) operon (Fig. 2C) \(^{30}\) . These genes included 4 eps (exopolysaccharide) genes that utilize the same substrates as the pgl operon to synthesize poly- N- acetylgalactosamine (PNAG), a biofilm component involved in bacterial adhesion \(^{31}\) . Within the corresponding region of "Ca. C. stinkeris", however, there are several variants of pglH, a key gene in protein glycosylation (Fig. 2C) \(^{32}\) . The corresponding pglH proteins are highly divergent and were likely all acquired by horizontal gene transfer (Fig. 2D) \(^{33}\) . Together the differences in gene content in "Ca. C. noahi" and "Ca. C. stinkeris" suggest adaptions for colonization strategies that differ in the specifics of biofilm formation. + +<|ref|>text<|/ref|><|det|>[[110, 506, 886, 877]]<|/det|> +Because adaptive genes have also been shown to spread within populations by homologous recombination (gene- specific sweeps), leading to reduced nucleotide diversity compared to the rest of the genome within the affected loci, we analyzed the intrapopulation SNP distribution across genomes of both populations \(^{18,21}\) . To best capture genome- wide diversity, SNPs were called by competitively mapping metagenomic reads to the reference genomes from each population, revealing a large (>7kB) SNP- free region in "Ca. C. stinkeris" (Fig. 3A). No such region was detected in "Ca. C. noahi". Aligning the SNP- free region from "Ca. C. stinkeris" to "Ca. C. noahi" showed a sharp decrease in alignment identity that falls within two genes annotated as being involved in pilin glycosylation and biogenesis (Fig. 3B) \(^{18,27,34}\) . This pattern is consistent with the recent acquisition of the SNP- free region, i.e., after the last genome- wide sweep, from a distant but unknown source followed by a population- specific sweep within "Ca. C. stinkeris", and overall + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 285]]<|/det|> +suggests that two pilin biogenesis genes are under differential selection in the two populations. Further one of the two genes, \(pilO\) , has also become truncated in " \(Ca\) . C. noahi" (Fig. 3B). Altogether, population- specific gene content and population- specific signatures of selection support the proposed population structure and predicted modifications in pili and biofilm formation, which led us to hypothesize divergent colonization strategies and thereby spatial separation on the epithelial wall. + +<|ref|>text<|/ref|><|det|>[[111, 333, 886, 808]]<|/det|> +To test for potential spatial separation of the two populations, we used digital PCR (dPCR) to measure their distribution across papillae samples. This was done by dissecting the apex of the papillae and the crypts (Supplementary Fig. 3A), representing the furthest and nearest tissue connected to the epithelial wall, respectively, which would be expected to harbour different ratios of the populations when assuming a gradient of opposite relative abundances across the papillae. However, the ratio of the two populations appeared similar in the two dissected samples, despite large differences between animals (Supplementary Fig. 3B). Of course, it is still possible that smaller scale spatial associations exist, beyond the resolution of our dissection strategy. But considering that we don't detect differences at opposite ends of single papillae and that the outer layer of the papillae are gradually sloughed off in vivo, interaction between the populations seem likely. We therefore reasoned that some other mechanism might be present to minimize competition. Such a mechanism would need to support co- existence of the two populations by preventing overlap in their growth dynamics via differentiation in some metabolic niche dimension. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 886, 848]]<|/det|> +Because comparative genomics suggested that metabolic genes are shared among the two populations, we hypothesized that the metabolism might be rewired in ways that are not easily predicted by annotation35. We therefore aimed at comparing “\(Ca. C. stinkeris”\) and “\(Ca. C. noahi”\) on the regulatory level. Leveraging in vivo transcriptomes, we carried out competitive mapping between the representative genomes of each population. Among the most differentially expressed genes in “\(Ca. C. noahi”\) was the pilin biogenesis gene, \(mshL\), corroborating that this gene, hypothesized to be under differential selection in the analysis of gene-specific sweeps (Fig. 3, Supplementary Table 2), is involved in population-specific ecology in vivo. Additionally, two of the most highly differentially expressed loci in “\(Ca. C. stinkeris”\) implicated two variants of the metabolic gene, \(aarC\) (V1 and V2 in Fig. 4A), which code for an enzyme that assimilates acetate via the TCA cycle36,37. On average, the variants are quite divergent from each other when compared across the two populations, with the more divergent of the two variants falling two standard deviations outside of the mean gene identity (90.9 vs 96.6%, Supplementary Table 3). By comparing the variants in detail, we noticed that a segment coding for the C-terminal end (the last 500 of the total 1500 base pairs) was highly similar in the two copies of the “\(Ca. C. noahi”\) \(aarCs\). Gene trees including the variants from both populations were therefore constructed using the two segments of the gene, the larger of which implied an ancestral duplication of \(aarC\). Yet, the shorter, C-terminal segment appears to have been recently transferred within the “\(Ca. C. noahi”\) (Fig. 4B). Overall, these observations suggest that finetuning at both the regulatory and allelic level has occurred since population divergence. Such finetuning may be an indicator of metabolic differentiation, as the most related AarC is a CoA transferase acting primarily on acetate but also to a degree on propionate (Fig. 4C)38. As these two metabolites are the most abundant products of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 145]]<|/det|> +rumen fermentation and main energy substrates for the host7, we wanted to test whether the two populations interact differently with acetate and propionate. + +<|ref|>text<|/ref|><|det|>[[111, 194, 886, 880]]<|/det|> +Growth assays with the representative strains showed that "Ca. C. stinkeris" accumulated biomass when provided with acetate as the main carbon source but appeared to be inhibited by propionate (Fig. 5A), suggesting a trade- off between acetate- utilization and propionate- resistance. In contrast, "Ca. C. noahi" did not accumulate any measurable biomass with either of the SCFAs (Fig. 5A). A relative fitness advantage afforded by acetate and propionate to "Ca. C. stinkeris" and "Ca. C. noahi", respectively, was also observed when the strains were competed against each other. Specifically, acetate led to "Ca. C. stinkeris" outcompeting "Ca. C. noahi" by over 3- fold, while the same concentration of propionate led to complete dominance by "Ca. C. noahi" (Fig. 5A). We then tested whether these effects were supported by in vivo relative abundances by analyzing population dynamics in a recent feeding trial during which a time- course of epithelial samples were collected and SCFA concentrations were measured in the rumen23. We used the population- specific dPCR assay and correlated "Ca. C. stinkeris" and "Ca. C. noahi" abundance in the epithelial samples with SCFAs. In line with the trade- off observed in vitro, the "Ca. C. stinkeris" population correlated positively with acetate and negatively with propionate, while "Ca. C. noahi" showed no significant correlation with either fatty acid (Fig. 5B). Considering that the two populations are otherwise predicted to utilize the same types of electron acceptors and donors based on the overlap in metabolic gene annotations, our data show that the two populations possess fine- scale adaptations, involving changes in regulation and structure of the AarC enzyme. These adaptations appear to have lessened the ability of "Ca. C. noahi" to utilize acetate but made it more resistant to propionate. Altogether, the changes thus involve a metabolic trade- off in acetate + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 144]]<|/det|> +utilization and propionate resistance that in turn may lead to different dynamics depending on the flux of acetate and propionate. + +<|ref|>sub_title<|/ref|><|det|>[[115, 197, 207, 214]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[111, 262, 886, 633]]<|/det|> +Microbes attached to the rumen epithelia have received little attention compared to those in the lumen, but by forming a biofilm at the host- rumen interface, they may have a disproportionate influence on the flux of metabolites entering the host. By characterizing one of the most abundant epithelia- attached microbes, we shed light on a novel sink for acetate, the main fermentative end- product of the rumen microbiota and essential carbon source for the animal9. We were further able to show that the underlying metabolic trait, acetate utilization, is partitioned among closely related sister populations. This difference was not predictable by annotation alone, as both populations have the genomic potential to use the same electron donors and acceptors. If this pattern of fine- scale metabolic differentiation observed in SCFA utilization is more general to other metabolic traits, then current cultivation- independent approaches, such as the analysis of MAGs, may be insufficient to describe other important functional roles. + +<|ref|>text<|/ref|><|det|>[[111, 682, 886, 876]]<|/det|> +Acyl- CoA transferases like the AarC enzyme appear to be more generally involved in differentiating important metabolic roles of closely related bacteria in the microbiome. A recent comprehensive study of Bacteroidetes in the human microbiome uncovered a trade- off similar to the one described here that involves acyl- CoA transferases acting on butyrate39. This has the exciting implication that there may be generalizable trade- offs involving acyl- CoA transferases that act on SCFAs. Since SCFAs have been found to be involved in several microbiome- host + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 319]]<|/det|> +interactions40- 42, understanding the specifics of these trade- offs will provide a more mechanistic framework for designing microbiome- targeted interventions. In the rumen, these could aim to modulate the ratio of acetate to propionate without requiring dietary modifications. Further, microbial populations that potentially lessen epithelial SCFA absorption by the host, such as "Ca. C. stinkeris", could be preferentially inhibited. A particularly promising means of intervention and re- emerging technology is phage therapy since phage generally display high specificity for clonal bacterial populations43. + +<|ref|>text<|/ref|><|det|>[[111, 368, 886, 878]]<|/det|> +The clonal structure of the populations observed here suggests that they are the result of recurrent genome- wide selective sweeps. This view is supported by the relatively high divergence between and low diversity within the populations. Practically, this structure provides a convenient means to define populations, fundamental units that have been differently optimized by selection and therefore can be hypothesized to have differential associations or dynamics. Pinpointing the underlying adaptions that initiated the differentiation, however, is complicated by the fact that genome- wide sweeps purge diversity in the population, a form of hitchhiking involving the entire genome44,45. In fact, genome- wide sweeps are expected if selection is strong relative to recombination46. Conversely, if selection is relatively weak in recombining populations, genetic specific sweeps may happen and are evident as reduced diversity within a locus in a population. Indeed, our analysis provides evidence that the populations have also been optimized by genetic specific sweeps relatively recently, i.e., after the last genome- wide sweep. These have likely involved several differentiating loci, including the homogenization of the C- terminal end of the two variants of \(aarC\) in "Ca. C. noahi". This may indicate that shifting feeding strategies, such as increased reliance on grain, that elevate SCFA production in the gut have rapid selective feedback + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 319]]<|/det|> +on the evolution of microbial populations. Although it is impossible to know the triggers of the initial population differentiation, we speculate that it occurred during the agricultural revolution with dramatic changes in feeding practices as cattle were more intensively reared. Supporting this idea is the fact that "Ca. C. stinkeris" and "Ca. C. noahi" are more closely related than the clade 1 and 2 Campylobacter coli, which were shown to have diverged from each other approximately 1000 years ago47. It is therefore possible that shifts in feed composition initiated the process of divergence, which is still ongoing today as cattle production is increasingly industrialized. + +<|ref|>text<|/ref|><|det|>[[111, 368, 886, 597]]<|/det|> +There is an urgent need to reduce the impact of intensive ruminant- based agriculture. While several inefficiencies in these systems will need to be tackled simultaneously, a promising means of intervention is the rumen microbiome5,6,8. Our work demonstrates that microbial adaptation to shifts in feed may be rapid and have unexpected consequences for the nutritional supply of the animal. These findings may help us to understand and influence the flux of nutrients into the host with the goal of decreasing the extent to which food crops are required by high- performance ruminants. + +<|ref|>sub_title<|/ref|><|det|>[[115, 648, 306, 667]]<|/det|> +## Material and Methods + +<|ref|>text<|/ref|><|det|>[[115, 717, 320, 736]]<|/det|> +Amplicon data reanalysis + +<|ref|>text<|/ref|><|det|>[[111, 750, 886, 876]]<|/det|> +Rumen epithelial amplicon data was downloaded from two studies22,23 that amplified the same variable region of the 16S rRNA (V3- V5) and the data were reprocessed using the qiime2 environment (v. 2021.4.0)48. The forward reads from both datasets were denoised by implementing dada2 with trimming from positions 25 to 225 and 42 to 242 for the Neubauer et al.23 and Wetzels + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 214]]<|/det|> +et al. \(^{22}\) data, respectively. The resulting count tables were merged by shared, identical ASV sequences, which classified using the rRDP package in R (v. 1.20.0) \(^{49}\) . The relative abundances for each ASV in each sample were also calculated in R, where the top 10 most abundant ASVs based on their median relative abundance across all samples were plotted. + +<|ref|>title<|/ref|><|det|>[[115, 264, 423, 283]]<|/det|> +# Metagenomic sequencing and analysis + +<|ref|>text<|/ref|><|det|>[[111, 293, 886, 880]]<|/det|> +Papillae biopsies were taken as described in Pacifico et al \(^{22}\) . from six rumen cannulated Holstein cows at the start of a recent feeding trial \(^{50}\) , before the animals were administered any specific diet. All procedures involving animal handling and treatment were approved by the institutional ethics committee of the University of Veterinary Medicine (Vetmeduni) Vienna and the national authority according to §26 of the Law for Animal Experiments, Tierversuchsgesetz 2012- TVG (GZ: BMWFW- 68.205/0023- WF/V/3b/2015 and BMNWF- 68.205/0003- V/3b/2019). In the lab, biopsies were thawed on sterile microscope slides and the surface keratinous layer was scrapped off using a scalpel. This was done to enrich for bacterial DNA relative to the DNA from host epithelial cells. DNA extraction was then carried out using the PowerSoil Pro kit (Qiagen), paired- end libraries were prepared using the Westburg NGS DNA Library Prep Kit, and metagenomic sequencing was carried out on an Illumina Novoseq 6000 instrument with a 250 bp read length at the Vienna BioCenter Core facilities. The reads were trimmed using Trimmomatic (v. 0.39) \(^{51}\) and mapped against a BosTorus reference genome (GCF_000003055.6) to filter out any reads obtained from the host. The remaining reads were assembled using SPAdes (v. 3.15.2) \(^{52}\) , reads were mapped using BWA- MEM \(^{53}\) , and MAGs were generated using Metabat2 (v. 2.12.2) \(^{52}\) with a minimum contig size of 1500 bp. The MAGs were assessed for completeness and contamination using checkM (v. 1.1.3) \(^{54}\) , and then classified using the classify workflow from the genome taxonomy + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 214]]<|/det|> +database toolkit (GTDB- Tk, v. 2.1.0) \(^{54}\) . Filtered reads were re- mapped to the MAGs using BWA- MEM \(^{53}\) and the reads per kb (RPKB) were calculated in R. The median RPKB for the top 10 most abundant MAGs, based on total reads per kb, with over 50% completeness and less than 10% contamination were then plotted. + +<|ref|>text<|/ref|><|det|>[[112, 263, 460, 283]]<|/det|> +Cultivation approach and isolate screening + +<|ref|>text<|/ref|><|det|>[[111, 295, 886, 895]]<|/det|> +All strains were cultivated using a tryptic soy broth (TSB) agar with the addition of 0.5 g/L L- Cystein HCL. The agar media was always prepared and used on the same day. After autoclaving, 5mM sodium nitrate, 5mM sodium fumarate and 2.5 mM sodium formate were added to the media. Further, 20 μg/mL Nalidixic acid and 5μg/mL Vancomycin were added to select for the Campylobacteraceae over other rumen microbes, which was based on recent work to enrich for Campylobacter ureolyticus \(^{55}\) . For bacterial isolation, papillae samples were thawed on ice, rinsed with sterile PBS, and transferred to a 1.5 mL Eppendorf tube with a single 6.35 mm ceramic bead (MP Biomedical, 116540424- CF). The tubes were shaken on a vortex at full speed for 10 minutes using a Qiagen Vortex adapter (13000- V1- 24). Six serial dilutions (1/10) were made using 1xPBS and 15 μL of the resulting dilutions were spread on agar plates, which were incubated anaerobically in a 2.5 L anaerobic jar and atmosphere generation sachet (Thermo Scientific, R685025, Biomerieux, 96124). After a week of incubation at 39°C in the dark, single colonies were picked and re- streaked 3 times to ensure purity. To identify Campylobacteriaceae among these colonies, scraped biomass was transferred into 20 μL of 10 g/L Chelex 100 (Biorad, 1421253), which was boiled for 10 minutes on a hot plate. From this, 1μL was added to a PCR reaction using primers that targeted the originally observed ASV (Forward; 5'- GGAGGACAACAGTTAGAAATGAC- 3', Reverse; 5'- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 145]]<|/det|> +CGTGAGATTTCACAAGAGACTTGAT- 3'). Sequences were confirmed to be identical to the ASV of interest by Sanger sequencing. + +<|ref|>text<|/ref|><|det|>[[112, 195, 454, 213]]<|/det|> +Genome sequencing and diversity analysis + +<|ref|>text<|/ref|><|det|>[[111, 228, 886, 633]]<|/det|> +Genome sequencing and diversity analysisBiomass was scraped from agar plates and DNA extraction was then carried out using the PowerSoil Pro kit. A total of 36 isolates were sequenced in two batches, the first containing 10 isolates. For the first batch, paired- end libraries were prepared using the Westburg NGS DNA Library Prep Kit, and genomic sequencing was carried out on a MiSeq instrument with a 300 bp read length at the Vienna BioCenter Core facilities. Reads were trimmed using Trimmomatic (v. 0.39) \(^{53}\) , assembled using SPAdes (v. 3.15.2) \(^{51}\) and contigs smaller than 1 kB were removed using PRINSEQ- lite (v. 0.20.4) \(^{56}\) . For the second batch, paired- end libraries were prepared using the NEBNext FS II DNA Library Prep Kit, and genomic sequencing was carried out on an Illumina Novaseq 6000 instrument with a 100 bp read length at the Joint Microbiome Facility, Vienna. These reads were trimmed using cutadapt (v. 2.10) and assembled using SPAdes (v. 3.14.1) \(^{52}\) . Contigs shorter than 1 kbp were removed using seqtk (v. 1.3). The genomes with \(>1\%\) contamination were filtered using the mmgenome2 package (v. 2.1.2) \(^{57}\) . + +<|ref|>text<|/ref|><|det|>[[111, 683, 886, 876]]<|/det|> +To obtain complete genomes, DNA from three reference genomes (C. stinkeria NA3, C. noahi NE2 and VBCF_01 NA2) was also sequenced using the Oxford Nanopore platform. Libraries were prepared using the Nanopore Native Barcoding Genomic DNA by Ligation (EXP- NBD196, SQK- LSK109) protocol and sequenced on the MinION Mk1C instrument using a FLO- MIN106 flowcell. The resulting reads were basecalled using Guppy (v. 3.0.3+7e7b7d0, Oxford Nanopore Technologies), assembled using pomoxis (v. 0.3.1, Oxford Nanopore Technologies), polished + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 390]]<|/det|> +using metadata (v. 1.4.3, Oxford Nanopore Technologies) and finally co- assembled with Illumina data using SPAdes (v. 3.15.2) \(^{52}\) . The 16S rRNA gene was extracted from all genomes using prokka (v. 1.14.6) \(^{58}\) , and aligned and visualized using MUSCLE (v. 5) \(^{59}\) in Genious (v.9.1.8, https://www.geneious.com). To assess genome wide diversity, MAGs and genomes were compared pairwise using fastANI (v. 1.33) \(^{60}\) . The resulting ANI values were hierarchically clustered using a complete linkage algorithm and plotted in R. Based on the observed clustering, genomes clustering with the more abundant MAG (MAG 73) were aligned using progressiveMAUVE (v. 2015.02.05) \(^{61}\) and a phylogenetic tree was constructed with the JC69 \(^{61}\) model using phyML (v. 2.2.3) \(^{62}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 438, 366, 457]]<|/det|> +## Gene content and SNP analysis + +<|ref|>text<|/ref|><|det|>[[111, 470, 886, 876]]<|/det|> +Open reading frames (ORFs) from the two complete reference genomes were predicted and annotated using prokka (v. 1.14.6) \(^{58}\) . The resulting gene sequences were compared to all other genome assemblies using blastn and only alignments with both a percent identity and percent alignment of 70% were kept for classifying core, flexible and population specific genes in R. For the SNP analysis, metagenomes were first competitively mapped with BWA- MEM \(^{53}\) to the complete genomes and a set of genomes representing those clustering with MAG 61. For the genomes clustering with MAG 61, a single representative genome was used for each of the clusters that were within 1% divergence. SNPs were called and filtered using bcfools (1.12) \(^{63}\) and VCFtools (0.1.16) \(^{64}\) and then counted in 1 kB windows in R. The largest SNP free region was in the C. stinkeria NA3, and this region was aligned with the corresponding region in C. noahi NE2 in Genious (v.9.1.8, https://www.geneious.com) with MUSCLE (v. 5) \(^{59}\) and the identity was calculated over 1 kB windows and plotted in R. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 124, 454, 144]]<|/det|> +Digital PCR assay and papillae dissection + +<|ref|>text<|/ref|><|det|>[[111, 155, 886, 789]]<|/det|> +To quantify the two populations in vivo, papillae biopsies were taken as described in Pacifico et al22 from five animals. After thawing on ice, three crypts and apex sections were taken from each and placed in a 1.5 mL Eppendorf tube. To these, \(200~\mu \mathrm{L}\) of \(10\mathrm{g / L}\) Chelex 100 (Biorad, 1421253) was added the tubes were placed at \(99^{\circ}\mathrm{C}\) on a hot plate shaking at \(900~\mathrm{rpm}\) . The tubes were then spun down briefly and \(1\mu \mathrm{L}\) was sampled for digital PCR, which was conducted with chips on the. Stilla Naica Crystal DigitalTM PCR System. The mastermix contained Stilla Naica? multiplex PCR Mix and \(10~\mu \mathrm{M}\) of each primer and probe. A "Ca. C. stinkeris" specific region was targeted with a fluorescein containing probe (Forward; 5'- TGGGCGCAATGCTATTAT G- 3', Reverse; 5'- CATTTCACGCCTAAACATAAC C- 3', Probe; 5'- 56- FAM/CTGGTTTTG/ZEN/GCATAGATAAAAGCGGAGA/3IABkFQ/- 3'), while the "Ca. C. noahi" specific region was targeted by a Phosphoramidite containing probe (Forward; 5'- CAC AAC GAC CAT TGT AAC GAT AAT- 3', Reverse; 5'- CCT ACA ACC AGC CAC AGT C- 3', Probe; 5'- 5HEX/TG GTT TGA A/ZEN/A CTA AAT GGC GAG TTG CA/3IABkFQ/- 3'). Probes were designed and provided by integrated DNA technologies (IDT). After droplet generation, the following protocol was used to amplify the population specific targets: \(95^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) , 45 cycles of \(95^{\circ}\mathrm{C}\) for \(10\mathrm{s}\) and \(62^{\circ}\mathrm{C}\) for \(40~\mathrm{s}\) . A Silla Naica Prism 3 reader was the used to detect droplets, which were analysed by Crystal Miner software (v. 2.4.0.3) to export the copy numbers for the two targets based on the default settings. + +<|ref|>text<|/ref|><|det|>[[113, 832, 536, 851]]<|/det|> +Comparative metatranscriptomics and aarC analysis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 456]]<|/det|> +Transcriptomics were mapped using the same approach as described for the metagenomic mapping above. Reads mapping to ORFs predicted with prokka (v. 1.14.6) \(^{58}\) were counted with htseq-count (v. 0.11.3) \(^{65}\) . To be able to compare the expression of genes across populations, we aligned ORFs with blastn and compared genes with over \(80\%\) alignment identity in terms of the number of mapped reads. To ensure that genes could be clearly distinguished from each other during the competitive mapping, genes that were over \(97.5\%\) similar were not compared. We then carried out the statistical analysis of differential expression using using the R package DESeq2 (1.26.0) \(^{66}\) . To assess the diversity of the aarC genes, those predicted by prokka (v. 1.14.6) \(^{58}\) were taken from the reference genomes, and aligned using MUSCLE (v. 5) \(^{59}\) in Genious (v.9.1.8, https://www.geneious.com). Gene trees were constructed with the JC69 \(^{61}\) model using phyML (v. 2.2.3) \(^{62}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 508, 325, 527]]<|/det|> +## Growth and fitness assays + +<|ref|>text<|/ref|><|det|>[[111, 541, 886, 880]]<|/det|> +We compared the growth representative strains on agar in the presence of acetate and propionate using dPCR as the strains did not grow on liquid media. This was only true for the “Ca. C. stinkeris” and “Ca. C. noahi” strains, as all others grew in the cultivation media in liquid form. We further reasoned that improvements in growth on solid media may be more representative of the in vivo growth conditions than liquid, as the bacteria are attached to the epithelial wall. Using the same agar media as for cultivation, strains were streaked out and allowed to grow anaerobically for 1 week at \(39^{\circ}\mathrm{C}\) . On the day of the experiment, fresh TSB agar media with 0.5 g/L L-Cystein HCL was prepared. After autoclaving, the media without any further supplementation was used as a control. To the media representing the two treatments, 5 mM sodium acetate or sodium propionate was added. For the 3 different media (control, acetate and propionate), 1 mL was added + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 540]]<|/det|> +to the wells of a sterile 24- well cell culture plate. Biomass was then collected from the agar plates by scraping and resuspending it in \(2\mathrm{mL}\) of freshly prepared peptone broth containing \(0.5\mathrm{g / L}\) L- cystein HCL. The optical density of the two suspensions was standardised to 0.075 at \(570\mathrm{nm}\) and an equal mixture of the two re- suspended strains was prepared for the co- culture experiments. Cell culture plates containing agar were inoculated with either \(20\mu \mathrm{L}\) single strain or \(40\mu \mathrm{L}\) co- culture mixture and then incubated at \(39^{\circ}\mathrm{C}\) anaerobically (as described above). After \(48\mathrm{h}\) and \(72\mathrm{h}\) in the case of the single strains and co- culture mixture, respectively, the cells were harvested by cutting out each agar circle from a well with a scalpel and placing it in a \(15\mathrm{mL}\) falcon tube. To the falcon tube, \(2\mathrm{mL}\) of \(10\mathrm{g / L}\) Chelex 100 (Biorad, 1421253) was added, and the mixture was boiled at \(100^{\circ}\mathrm{C}\) in a water bath for 45 minutes. The samples were then the diluted 1/5 in sterile, DNA- free water, before \(2\mu \mathrm{L}\) were used for dPCR, as described above. Copy numbers for the acetate and propionate treatments were compared to the base media for calculating the fold change and standard deviation in R. + +<|ref|>sub_title<|/ref|><|det|>[[115, 588, 335, 606]]<|/det|> +## Population tracking in vivo + +<|ref|>text<|/ref|><|det|>[[112, 620, 886, 852]]<|/det|> +The DNA extracted by Neubauer et al. \(^{23}\) was used to monitor populations using the digital PCR assay and method described above. This study tested the effects of feed additives using 8 cows in a change- over design where 2 cows were assigned to the control group for each of the 4 experimental runs. Each experimental run consisted of two periods where a high- grain diet was fed, which induced changes in ruminal SCFA concentrations, and papillae samples were taken at 3 time points (1 before and 2 after the high- grain periods). From 96 samples, \(2\mu \mathrm{L}\) of the extracted DNA was added to the mastermix. The resulting count data were merged from the rumen SCFA + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 88, 884, 145]]<|/det|> +502 data measured in Neubauer et al. \(^{23}\) and Pearson correlations were calculated using the cor function in R. Correlations with a p value lower than 0.01 were considered significant. + +<|ref|>sub_title<|/ref|><|det|>[[115, 196, 260, 214]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[115, 230, 884, 285]]<|/det|> +Scripts with all the custom analysis and commands described above can be found at [https://github.com/cameronstrachan/RumenCampylobacter2022]. + +<|ref|>sub_title<|/ref|><|det|>[[115, 334, 257, 352]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 404, 884, 528]]<|/det|> +The publicly available data that we reanalysed here were generated by Wetzels et al. \(^{22}\) , Neubauer et al. \(^{23}\) , and Mann et al. \(^{13}\) . The metagenomic sequencing data from the rumen papillae samples are available on the NCBI SRA under the accession number PRJNAXXXX. The genomes are available on NCBI under the accession numbers PRJNAXXXX. + +<|ref|>sub_title<|/ref|><|det|>[[115, 579, 209, 596]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[111, 647, 872, 858]]<|/det|> +1. Humpenöder, F. et al. Projected environmental benefits of replacing beef with microbial protein. Nature 605, 90–96 (2022). +2. Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014). +3. Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science (1979) 370, 705–708 (2020). +4. Eisler, M. C. et al. Agriculture: Steps to sustainable livestock. Nature 507, 32–34 (2014). +5. Kamke, J. et al. 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Identification of Poly-N-acetylglucosamine as a Major Polysaccharide 591 Component of the Bacillus subtilis Biofilm Matrix. Journal of Biological Chemistry 290, 592 19621-19272 (2015). 593 32. Troutman, J. M. & Imperiali, B. Campylobacter jejuni PglH Is a Single Active Site 594 Processive Polymerase that Utilizes Product Inhibition to Limit Sequential Glycosyl 595 Transfer Reactions. Biochemistry 48, 2807-2816 (2009). 596 33. Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale 597 resource partitioning in marine microbes. Nature Communications 7, (2016). 598 34. Castric, P. pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. 599 Microbiology (N Y) 141, 1247-1254 (1995). 600 35. Lawson, C. E. et al. Autotrophic and mixotrophic metabolism of an anammox bacterium 601 revealed by in vivo 13C and 2H metabolic network mapping. The ISME Journal 15, 673- 602 687 (2021). 603 36. Kwong, W. K., Zheng, H. & Moran, N. A. 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Formation of short chain fatty acids by the gut microbiota 617 and their impact on human metabolism. Gut Microbes 7, 189-200 (2016). 618 42. Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as 619 potential regulators of skeletal muscle metabolism and function. Nature Metabolism 2, 620 840-848 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 90, 876, 308]]<|/det|> +668 Guindon, S. et al. New Algorithms and Methods to Estimate Maximum- Likelihood 669 Phylogenies: Assessing the Performance of PhyML 3.0. Systematic Biology 59, 307–321 670 (2010). 671 62. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, (2021). 672 64. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 673 (2011). 674 65. Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high- throughput 675 sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022). 676 66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion 677 for RNA- seq data with DESeq2. Genome Biology 15, 550 (2014). 678 67. Le, S. Q. & Gascuel, O. An Improved General Amino Acid Replacement Matrix. 679 Molecular Biology and Evolution 25, 1307–1320 (2008). + +<|ref|>sub_title<|/ref|><|det|>[[116, 335, 280, 353]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[111, 401, 886, 842]]<|/det|> +We first thank Sara Ricci for providing papillae samples remaining from recent feeding trials. We further thank the Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna (JMF) for technical assistance in sample preparation and processing for sequencing. All quantitative and digital PCR experiments were further supported using resources of the VetCore Facility (Genomics) of the University of Veterinary Medicine Vienna. C.R.S. and A.J.M. were partially supported by a Fellowship from the Natural Science and Engineering Council of Canada Postgraduate Scholarship-Doctoral (NSERC PGS-D). The competence centre FFoQSI is funded by the Austrian ministries BMVIT, BMDW and the Austrian provinces Niederoesterreich, Upper Austria and Vienna within the scope of COMET - Competence Centers for Excellent Technologies. The programme COMET is handled by the Austrian Research Promotion Agency FFG. The research of Q.Z. was funded by Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, through the Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 296, 109]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[112, 156, 886, 390]]<|/det|> +The concept was developed by C.R.S., A.Y., E.S., and M.F.P. C.R.S conducted all experiments and analysis. A.Y. guided the population genomic analysis. V.N. carried out all in vivo sampling and dissections, and compiled data from previous feeding trials. A.J.M. assisted with cultivation, genome annotation and data presentation. M.W. and Q.Z. acquired funding and provided valuable feedback. Q.Z. designed all cow experiments and provided access to papillae samples obtained during recent feeding trials. C.R.S. and M.F.P. prepared the manuscript. All authors read and approved the final version of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 440, 287, 458]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[115, 510, 460, 528]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 156, 884, 393]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 408, 883, 429]]<|/det|> +
Figure 1. Abundance and microdiversity of Campylobacteraceae from the rumen epithelia.
+ +<|ref|>text<|/ref|><|det|>[[111, 440, 886, 812]]<|/det|> +A) Relative abundance of the top 10 16S rRNA gene ASVs based on the merging and re-analysis of two recent studies22,23. The points represent baseline samples (no feed additives were applied) taken from 8 cows at different times. B) Metagenomes were sequenced from 6 baseline samples, each from a different cow, and assembled into metagenome-assembled genomes (MAGs). The reads mapped per kilobase are shown for the 10 most abundant MAGs with a completeness of over 50% and under 10% contamination. C) Phylogenetic tree reconstructed with isolate genomes that correspond to MAG 73 in panel B (Supplementary Fig. 2). Whole-genome alignments at the nucleotide-level spanned 1.3 Mb. The two closely related populations are named “Ca. C. stinkeria” and “Ca. C noahi” for convenience. The names given in brackets are the names of the animals from which we obtained the isolate genome. The genomes of the two marked isolates were closed and are used as reference genomes in subsequent analysis. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 88, 880, 380]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 394, 882, 415]]<|/det|> +
Figure 2. The two populations have highly similar gene content but divergent pgl operons.
+ +<|ref|>text<|/ref|><|det|>[[111, 428, 886, 904]]<|/det|> +A) Percent identity of shared genes between “\(Ca. C. stinkeris” NA3 and “\(Ca. C. noahi” NE2 numbered by their order in the reference genomes starting with the same gene. The remaining 11 genomes were further leveraged to label genes as core, flexible or population specific. Core genes are those found in all 13 genomes. Lines plotted directly on the axes are flexible genes, defined as genes missing from 2 or more of any of the “\(Ca. C. stinkeris” or “\(Ca. C. noahi” genomes. Those flexible genomes that are unique to and found in all genomes of one of the populations are marked as ‘population specific’. B) Summary of the number of core, flexible and population-specific genes. C) Cluster of annotated “\(Ca. C. noahi” genes that are adjacent to the pgl operon and include 4 eps (exopolysaachride) genes. The predicted role of these genes in producing poly-N-acetylgalactosamine is shown to the right. Below is the corresponding region in “\(Ca. C. stinkeris”, which in contrast has variants of the pglH gene. This gene is predicted to code for an enzyme involved in polymerizing N-acetylgalactosamine for protein glycosylation. D) A protein tree constructed using the LG model\(^{67}\) and amino acid sequences for each of the pglH genes. The average pairwise amino acid identity is 40%. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[111, 125, 884, 468]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 483, 818, 504]]<|/det|> +
Figure 3. Gene-specific sweeps involving pilin biogenesis genes in “Ca. C. stinkeria”.
+ +<|ref|>text<|/ref|><|det|>[[111, 515, 886, 782]]<|/det|> +A) Single-nucleotide polymorphisms (SNPs) were called in “Ca. C. stinkeria” after competitively mapping of metagenomic reads to the reference genomes from both populations. The percentage of detected SNPs were calculated in 1000 bp windows and plotted. No SNPs were observed in a region spanning over 7kB, the largest SNP-free region. B) The region identified in panel A was aligned between “Ca. C. stinkeria” NA2 and “Ca. C. noahi” NE3. The region included two genes annotated to be involved in pilin biogenesis, pilO and mshL. The pilO gene has been interrupted by a stop codon. Alignment identity of the region was calculated in 1000 bp windows and plotted as a moving average. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 88, 884, 303]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 323, 884, 377]]<|/det|> +
Figure 4. In vivo expression and analysis of evolution of an acetate acetyl-CoA transferase (AarC) that differentiates "Ca. C. stinkeria" and "Ca. C. noahi".
+ +<|ref|>text<|/ref|><|det|>[[112, 390, 886, 760]]<|/det|> +A) Volcano plot showing the range of fold changes and corresponding p-values from differential expression analysis comparing the two populations. The horizontal line indicates significant genes with a p-value cut-off of 0.00001 (Supplementary Table 2). The two variants of aarC are marked. mshL is also marked as it corroborates the analysis in Fig. 3. B) Phylogenetic trees at the gene-level for different regions of the two variants of aarC. The two trees were reconstructed using different segments of the 4 aarC variants (2 per genome), either the first 1000 bp or the last 500 bp. C) Graphical summary of results and hypothesis from the analysis in panel A and B. The gene diagrams show that two different variants of the aarC gene are highly expressed in "Ca. C. stinkeria" relative to "Ca. C. noahi" wherein a segment of the aarC genes has been homogenized. AarC is CoA transferase predicted to assimilate acetate via the TCA cycle, but has also been shown to have activity on propionate, leading to propionyl-CoA38. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 87, 882, 280]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 296, 884, 317]]<|/det|> +
Figure 5. An apparent trade-off in acetate-utilization and propionate-resistance in vitro and
+ +<|ref|>text<|/ref|><|det|>[[111, 330, 886, 630]]<|/det|> +in vivo. A) Growth experiments assessing the effect of adding 5 mM acetate or propionate on biomass accumulation by population- specific dPCR. Growth differences were assessed on agar since the strains did not grow in liquid (see Materials and Methods). Fold change is shown relative to control (no SCFA added). Experiments with individual strains (left, monoculture) and in competition (right, coculture) were performed. Error bars show standard deviation (n=6). B) Correlations (Pearson) between population-specific dPCR data and rumen SCFA concentrations (n=96) \(^{23}\) . Coefficients are indicated for significant correlations (p value < 0.01). Data were obtained from samples taken during a previously performed feeding trial \(^{23}\) that used the same cows (n=8) from which the strains in this study were cultivated. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[63, 91, 315, 110]]<|/det|> +## Supplementary Figures + +<|ref|>image<|/ref|><|det|>[[112, 155, 888, 600]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 614, 884, 636]]<|/det|> +
Supplementary Figure 1. The 16S rRNA gene is conserved across the genomes sampled. The
+ +<|ref|>text<|/ref|><|det|>[[111, 650, 886, 844]]<|/det|> +16S rRNA gene was extracted from all genomes and aligned. The topmost sequence is the ASV found to be most abundant in our reanalysis of two rumen epithelial amplicon studies22,23. Only the first 600 of 1508 positions are shown, as there was no variation observed across the rest of the alignment. On the left had side, sequences that were extracted from the same genome are shown (indicated with brackets). Two variants were detected in 4 genomes and are indicated by the genome names in red. The most divergent of the two variants (9 bp changes) is represented as one + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 88, 885, 179]]<|/det|> +793 of the copies of the rRNA gene within the closed reference genome VBCF_01 NA2. A single base 794 pair change defines the remaining variant detected in genomes JMF_06 NA1 and JMF_09 ED2. 795 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[111, 120, 880, 820]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[55, 830, 884, 886]]<|/det|> +Supplementary Figure 2. Pairwise ANI and clustering of genomes with MAGs suggests population structure. Genomes were compared pairwise using FastANI \(^{60}\) and the resulting ANI + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 214]]<|/det|> +values were hierarchically clustered using a complete linkage algorithm. The resulting clusters are represented by a dendrogram on the top and are named, as referred to throughout the paper, on the side of the heatmap. Values representing comparisons of genomes from the two major clusters (containing MAG 61 and 73) are coloured in grey. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 157, 884, 416]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 433, 884, 480]]<|/det|> +
Supplementary Figure 3. The ratio of "Ca. C. stinkeria" to "Ca. C.noahi" at two different locations along the papillae. A) The two sections that were dissected from papillae biopsies. From
+ +<|ref|>text<|/ref|><|det|>[[111, 492, 886, 628]]<|/det|> +these, DNA was extracted, and the populations were measured using dPCR. B) The ratios of the populations are plotted as we hypothesized divergent distributions of the two populations across the papillae. The names on the x- axis are the names of the different animals from which we obtained the papillae samples. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 312, 203]]<|/det|> +SupplementaryTable1. pdf SupplementaryTable2. pdf SupplementaryTable3. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6.mmd b/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6.mmd new file mode 100644 index 0000000000000000000000000000000000000000..66e2a5f421328e0b517c8ca85f8decb2125de794 --- /dev/null +++ b/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6.mmd @@ -0,0 +1,544 @@ + +# Vanishing weekly hydropeaking cycles in American and Canadian rivers + +Stephen J. Dery (sdery@unbc.ca) University of Northern British Columbia https://orcid.org/0000- 0002- 3553- 8949 + +Marco A. Hernández- Henríquez University of Northern British Columbia + +Tricia A. Stadnyk University of Calgary https://orcid.org/0000- 0002- 2145- 4963 + +Tara J. Troy University of Victoria https://orcid.org/0000- 0001- 5366- 0633 + +## Research Article + +Keywords: Canada, United States of America, Flow Regulation, Human Intervention, Hydropeaking, Hydropower, Streamflow + +Posted Date: April 20th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 441563/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on December 1st, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 27465- 4. + +<--- Page Split ---> + +# Vanishing weekly hydropeaking cycles in American and Canadian rivers + +Stephen J. Dery1,\*, Marco A. Hernández- Henríquez1, Tricia A. Stadnyk2, and Tara J. Troy3 + +1Department of Geography, Earth and Environmental Sciences, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada + +2Department of Geography, University of Calgary, Calgary, Alberta, T2N 1N4, Canada + +3Department of Civil Engineering, University of Victoria, Victoria, British Columbia, V8W 2Y2, Canada + +4Corresponding author: Stephen Dery (sdery@unbc.ca), ORCID # 0000- 0002- 3553- 8949 + +Email address for Marco Hernández- Henríquez: hernandezhenriquez.m@gmail.com + +Email address for Tricia Stadnyk: Tricia.Stadnyk@ucalgary.ca, ORCID # 0000- 0002- 2145- 4963 + +Email address for Tara Troy: tjtroy@uvic.ca, ORCID # 0000- 0001- 5366- 0633 + +# CONFIDENTIAL MANUSCRIPT - FOR PEER REVIEW ONLY + +31 March 2021 + +Running head: Vanishing weekly hydropeaking cycles in American and Canadian rivers + +Keywords: Canada, United States of America, Flow Regulation, Human Intervention, Hydropeaking, Hydropower, Streamflow + +<--- Page Split ---> + +## Abstract + +Sub- daily and weekly flow cycles termed 'hydropeaking' are common features in regulated rivers worldwide. Weekly flow periodicity arises from fluctuating hydropower demand and production tied to socioeconomic activity, typically with higher consumption during weekdays followed by reductions on weekends. Here, we propose a novel weekly hydropeaking index to quantify the 1920- 2019 intensity and prevalence of weekly hydropeaking cycles at 400 sites across the United States of America and Canada. A robust weekly hydropeaking signal exists at \(1.1\%\) of sites starting in 1920, peaking at \(17.0\%\) in 1963, and diminishing to \(3.2\%\) in 2019, marking a \(21^{\text{st}}\) century decline in hydropeaking intensity. We propose this decline may be tied to recent, above- average precipitation, socioeconomic shifts, alternative energy production, and legislative and policy changes impacting water management in regulated systems. Vanishing weekly hydropeaking cycles may offset some of the prior deleterious ecohydrological impacts from hydropeaking in highly regulated rivers. + +## Introduction + +In 2019, the United States of America (USA) and Canada generated a combined 674 TWh of hydroelectricity from a total 184 GW of installed capacity, ranking them with China and Brazil in the four largest global producers of hydroelectricity \(^{1}\) . With the proliferation of dam and reservoir construction during the \(20^{\text{th}}\) and early \(21^{\text{st}}\) centuries \(^{2}\) , many of the two countries' main rivers are now moderately or strongly affected by fragmentation, regulation and/or diversions \(^{4 - 6}\) . With increasing demands for renewable sources of energy, additional generating capacity is being developed or planned across Canada. This includes the 1,100 MW Site C Dam on the Peace River in northeastern + +<--- Page Split ---> + +British Columbia (BC), the 824 MW Muskrat Falls development on the lower Churchill River in Labrador, and the 695 MW Keeyask Generating Station on the Nelson River in northern Manitoba1, with its first of seven units becoming operational in February 2021. + +While overall demand for electricity continues to increase, consumption patterns vary depending on socioeconomic activity, short- term weather conditions, seasonal climate fluctuations and long- term climate trends7, 8. In the northern USA and Canada, the winter season usually incurs peak hydroelectric demand due to domestic, commercial and industrial heating and lighting requirements9. With climate change, winter cold waves subside while summer heat waves intensify10, 11, shifting some of the demand from winter heating to summer cooling12- 14. Apart from seasonality shifts, day- to- day activities influence hydroelectricity demand as well. Similar to many other industrialized countries, North American educational, industrial and commercial activity intensifies on weekdays (Monday through Friday) but abates on weekends, particularly on Sundays9. This weekly rhythm of socioeconomic activity can thus impact water retention and releases in regulated rivers15. These rapid, frequent and periodic flow fluctuations downstream of regulation points are commonly termed ‘hydropacking’ events and are known to disrupt a range of ecohydrological processes16, 17. Yet the characteristics and trends in weekly hydropacking cycles due to daily variation in hydropower demands remain largely unknown. This is despite the general availability of discharge data at a daily time scale and the distinct weekly rhythm of socioeconomic activity including hydropower production, and hence water releases in regulated waterways, which impact ecohydrological processes. + +<--- Page Split ---> + +To address that knowledge gap and a demand for global attention to hydropeaking rivers \(^{18}\) , we assess here the prevalence of weekly hydropeaking cycles for 400 gauging sites along rivers of the USA and Canada spanning a wide range of basin characteristics, regulation, hydrological and climatic regimes. Specifically, we develop a scale- independent and dynamic weekly hydropeaking index (WHI) with both time and frequency domain terms, allowing quantification of weekly flow periodicity. Application of the novel WHI to 1920- 2019 time series of river discharge provides evidence of vanishing weekly hydropeaking cycles in many regulated rivers of the USA and Canada with the 2010s comparable to the 1920s for hydropeaking prevalence. We conclude that increased commercial and industrial activity on weekends, a shift towards other modes of energy production, policy changes altering water management practices, electrical grid interconnectivity and deregulation of electricity generation, plus a relatively wet decade in the 2010s are contributing factors to waning weekly hydropeaking cycles. + +## Results + +Overall WHI statistics. The 1980- 2019 mean, median, and standard deviation of WHI for the 400 sites reach 0.097, 0.005 and 1.115, respectively (Supplementary Table 1). An application of the Shapiro- Wilk test to the WHI data suggests the distribution is not Gaussian ( \(W = 0.974\) , \(p = 1.32 \times 10^{- 6}\) , \(n = 400\) ); yet, the low skewness (0.157) and excess kurtosis (0.754) along with a Cullen and Frey graph (Supplementary Fig. 1) infer a reasonable fit. Twenty- five sites attain a mean annual WHI \(\geq 2.0\) for 1980- 2019 with another 49 sites achieving WHI \(\geq 1.0\) . A list of sites with the top ten ranking WHI values reveals their wide regional distribution with foci in the Chattahoochee, Colorado, Columbia, Great Lakes- St. Lawrence, Nelson and upper Tennessee drainage basins + +<--- Page Split ---> + +(Table 1), all of which are heavily dammed. The Chattahoochee River at Buford Dam claims the top WHI score of 3.299 while BC's Stuart River shows the lowest score of - 3.469. Some highly regulated systems such as Manitoba's Burntwood River, which funnels water diverted from the Churchill River into the Nelson River, exhibit large negative WHI values (- 1.892) as Notigi (the upstream point of regulation) is a control structure for a large reservoir operated in a longer term (e.g., seasonal) manner. Similarly, while several large dams impound the Missouri River, they are managed not only for hydropower production but also for flood control, irrigation, navigation and recreational values. As such, the three sites along the Missouri River used in this study exhibit an average WHI = - 0.492 revealing an absence of significant weekly hydropeaking cycles. + +Spatial analyses. A map of the 1980- 2019 WHI values reveals that weekly hydropeaking rivers abound across the USA and Canada. Clusters of high WHI values emerge in the Alabama, Chattahoochee, and Tennessee river basins of the southeastern USA, in waterways draining the Ozark Mountains, the Colorado River and in northern Ontario rivers draining into the Great Lakes (Fig. 1). The Columbia River has several major points of regulation (WHI \(\geq 1.5\) ) from its headwaters in BC to its outlet in the Pacific Ocean. Highly hydropeaking sites (WHI \(\geq 2.0\) ) appear in both small (e.g., Alberta's Kananaskis River, \(A = 899 \text{km}^2\) ) and large (Manitoba's Nelson River, \(A = 1.1 \times 10^6 \text{km}^2\) ) systems. In contrast to their adjacent regulated rivers, free- flowing rivers of northern Canada, particularly those draining into Hudson Bay, exhibit large, negative WHI values. These unregulated rivers manifest strong annual cycles dominated by snowmelt- driven freshets and contain large natural storage capacity in the form of + +<--- Page Split ---> + +extensive lakes, ponds and wetlands. Free- flowing, pluvial rivers of the southeastern USA (e.g. the Choctawhatchee, Ogeechee, Pascagoula, Satilla and Suwanee rivers) also exhibit negative, albeit \(> - 1.5\) , WHI scores. WHI values diminish moving downstream from a point of regulation. For instance, \(\mathrm{WHI} = 1.437\) on the Peace River just downstream of BC's WAC Bennett and Peace Canyon dams where minimum flows arise on weekends; 400 km downstream from the dams \(^{19}\) , however, WHI declines to 0.929 at the community of Peace River in Alberta where minimum flows occur on Mondays/Tuesdays, indicating a 2- day delay in signal propagation. A cascade of dams and reservoirs can amplify or sustain the hydropeaking signals along waterways (e.g., the Colorado, Columbia, and Tennessee rivers) or attenuate them (e.g., Ottawa River). + +Sites with high values of WHI \((\geq 1.5)\) also show a preponderance of flow reductions on the weekends (Saturdays/Sundays) as identified by the larger symbols in Fig. 1. Of the 44 sites with \(\mathrm{WHI} \geq 1.5\) , 39 experience the two consecutive days with low flows on weekends. In contrast, sites with negative WHI values show a range of low flow days with no distinct pattern emerging. No less than \(30.8\%\) of all sites used in this study exhibit low flows on Saturdays/Sundays, more than twice the expected value (Fig. 2). This disproportionate amount of weekend low flows occurs mainly in hydropeaking rivers \((\mathrm{WHI} > 0)\) . Weekday combinations show frequencies at, or lower than, the expected value with the Friday/Saturday sequence appearing at only \(6.0\%\) of sites. A Chi- Square test applied to the frequency of two consecutive low flow days reveals that the results differ significantly from the expected value of \(0.143\) \((\chi^2 = 109.95, p < 2.2 \times 10^{- 16}, n = 7\) with six degrees of freedom). The mean WHI equals 0.292 for 123 sites with + +<--- Page Split ---> + +low flows on weekends while it remains near zero or slightly negative for the six other two- day combinations. The distribution of mean WHI for the two- day combinations differs significantly from a uniform distribution based on a Chi- Square test ( \(\chi^{2} = 8.43\) , \(p = 0.05\) based on 10,000 replicates with \(n = 7\) ). + +Temporal evolution and trend analysis. The temporal evolution of the mean and median WHI shows a rapid increase in hydropeaking intensity from the 1920s to the 1950s at which point they level off and fluctuate near zero (Supplementary Fig. 2). Starting in the 1990s, though, there is a gradual decline in both the mean and median WHI values with a return in the 2010s to statistics first seen in the 1930s (largely pre- regulation), a pattern observed both in the USA and Canada (not shown). The discharge- weighted WHI₀ emphasizes the increasing volumes of regulated flows starting from the 1920s through the 1980s; however, WHI₀ also declines markedly thereafter into the 21st century. In 1920, only 1.1% of available sites rank in the top decile of 1920- 2019 WHI values (WHI ≥ 2.021). This fraction peaks at 17.0% of available sites in 1963 but thereafter diminishes consistently. In 2000, 50 or 13.2% of available sites score in the top decile of 1920- 2019 WHI values but these counts fall precipitously to just 12 or 3.2% of the available sites by 2019, marking a 21st century declining pattern in weekly hydropeaking intensity. Trend analysis applied to the overall mean annual WHI reveals a statistically- significant decline of - 0.40 over 1980- 2019 (Supplementary Fig. 3). These temporal results, however, rely on the availability of discharge data, as the record length averages 78.4 years, ranging from a minimum of 24 years at one site to a full century at 87 sites (Supplementary Fig. 4). The number of available sites increases steadily from 1920 into the early 1990s and peaks at 393 sites + +<--- Page Split ---> + +in 1985 and 1992 but then declines to 373 sites by 1996 thereafter averaging 383±6 sites until 2019. Notable gaps appear in the discharge records starting in the 1990s, particularly for regulated rivers in Ontario and Québec; however, adjusting the time series of mean annual WHI for unavailable sites reveals little difference in the overall pattern and trend of WHI during 1980- 2019 (Supplementary Fig. 3). + +Data availability also factors in the appraisal of the decadal evolution of hydropeaking intensity across the USA and Canada (Fig. 3a-j). Nevertheless, this shows the gradual inception of hydropeaking cycles during the 1920s and 1930s, particularly in the north- central, northeastern, and southeastern USA and in northern Ontario. The 1940s show an expansion of weekly hydropeaking rivers into the western USA including within the Colorado, Columbia and Sacramento river basins. The 1940s and 1950s mark an intensification of regulation in the Tennessee and Alabama river basins as well as rivers of northern Ontario draining to Lakes Superior and Huron. A pronounced expansion and amplification of the hydropeaking signal appears in the 1960s, particularly across the Great Lakes- St. Lawrence river basin in Ontario and Québec. Some stabilization of the hydropeaking pattern marks the 1970s but a resurgence follows in the 1980s and 1990s when additional hydropeaking rivers emerge in western Canada. The 2000s retain a wide distribution of hydropeaking rivers across both countries; yet, by the 2010s, the number of highly hydropeaking rivers diminishes considerably, particularly in parts of the Great Lakes- St. Lawrence and Tennessee river basins. The decadal distribution of the 10 WHI bins (Fig. 3k) further highlights the peak fraction of sites with \(\mathrm{WHI} \geq 1.5\) attained in the 1960s (19.6%), with nearly matching minimum values in the 1920s + +<--- Page Split ---> + +(6.8%) and 2010s (6.7%). After the 1960s, there is a steady decline in the relative number of sites with low flows either on the Saturday/Sunday or Sunday/Monday combinations, indicating waning differences between weekday and weekend flows across the USA and Canada (Fig. 3l). + +The temporal evolution of the annual maximum WHI value shows a rapid increase from \(\sim 3.0\) in the 1920s to \(>4.0\) in the 1930s onward (Supplementary Fig. 2d). Annual peak WHI values \(>4.0\) are generally sustained for the remainder of the \(20^{\text{th}}\) century but then fall below that threshold starting in 2003 until 2019. The peak WHI value each year over the study period is distributed among 19 sites, with the Winnipeg River at the outlet of the Lake of the Woods capturing the top spot 12 times in the 1920s to early 1960s (Supplementary Fig. 5). The Colorado River at Hoover Dam dominates the list 25 times between the 1940s into the early 1980s. From the 1960s to 2010s, the Chattahoochee River at Buford Dam ranks first 12 times while in the 1990s and 2000s, the Montreal River that drains to Lake Superior tops the list 10 times. The overall maximum WHI score of 4.587 arises in 1961 at the Winnipeg River at the outlet of Lake of the Woods. + +Further statistical analysis reveals an abundance of strong, negative WHI trends interspersed with positive ones for the 380 sites with \(n_{y} \geq 30\) years over 1980- 2019 (Fig. 4). A total of 104 sites show locally statistically- significant \((p < 0.05)\) declines in WHI while 26 show locally statistically- significant inclines. Of the 130 locally- significant trends, 81 remain globally significant. Significant negative WHI trends abound in the southeastern and northeastern USA, the Great Lakes- St. Lawrence basin, and the + +<--- Page Split ---> + +Pacific Northwest while a cluster of positive trends arises in Québec's Saguenay watershed. While regulated rivers of Newfoundland show increasing WHI values, their unregulated counterparts show similar tendencies. Similarly, in New Brunswick, the regulated St. John River shows a decreasing trend in WHI while the proximal, unregulated Southwest Miramichi River shows an increasing trend. Sixty- four percent of the locally- significant WHI trends arise in hydropeaking rivers (WHI \(>0\) ) with fewer locally- significant trends in non- hydropeaking rivers (WHI \(< 0\) ; Supplementary Fig. 6). + +Interannual and interdecadal variability. Water management practices and climate variability, among other factors, yield significant interannual variation in hydropeaking intensity. For example, the Colorado River at Lees Ferry shows marked declines in WHI during high flow years (Supplementary Fig. 7a). Indeed, heavy precipitation during strong El Niño events in the early 1980s induced high flows in the Colorado River including at Lees Ferry. Due to the unusually wet weather, the bypass tubes and spillway at Glen Canyon Dam were used to release additional water downstream, thereby moderating hydropeaking signals from 1983 to 198620. Similar declines in WHI appear in 1997 and 2011 when flows exceed the recent annual average. Computing the Pearson correlation coefficient between the 1980- 2019 annual river discharge and the corresponding WHI yields 81 statistically- significant negative correlations and only 16 statistically- significant positive correlations (Supplementary Fig. 7b). Thus high flows over extended periods attenuate weekly periodicity even in heavily regulated rivers such as the Colorado. + +<--- Page Split ---> + +This analysis suggests that sustained wet periods may attenuate hydropeaking intensity while dry periods may accentuate it. Binned distributions of decadal standardized anomalies in river discharge reveal the contrasting dry 1930s vs. the wet 1970s, the latter coinciding with a suppression of hydropeaking across the USA and Canada (Supplementary Fig. 8). Yet, while the 2010s experienced relatively high flows, \(6.7\%\) of sites have \(\mathrm{WHI} \geq 1.5\) whereas in the similarly wet 1990s, \(15.6\%\) of sites achieve \(\mathrm{WHI} \geq 1.5\) . Of 20 sites with large \((>1)\) , positive standardized discharge anomalies during the 2010s, only three (the Betsiamites, La Grande and Nelson rivers) have \(\mathrm{WHI} > 1\) , which are likely more in response to enhanced diverted flows rather than high precipitation. Thus it is unlikely interdecadal climate variations alone account for recent WHI declines. + +Dispersion of daily flows. Apart from climate variations, changes in day- of- the- week flows may influence WHI trends. Sites with \(\mathrm{WHI} > 0\) generally observe greater dispersion of day- of- the- week flows although pluvial and intermittent rivers, particularly in the southern USA, also experience greater day- to- day flow variations (Supplementary Fig. 9a). A trend analysis reveals significant declines in the dispersion of flows across the seven days of the week, concomitant with diminishing WHI values from 1980 to 2019 (Supplementary Fig. 9b). As an example, an abrupt reduction in dispersion of day- of- the- week flows in Labrador's Churchill River appears in 1997 and is then sustained, suggesting factors other than climate variations are altering daily flows (Supplementary Fig. 10). + +<--- Page Split ---> + +## Discussion + +Possible factors leading to recent WHI declines. The recent decline in weekly hydropeaking cycles in the USA and Canada emerges as a key finding in this study. Several possible factors may be contributing to this general pattern observed over the study area. Firstly, hydropower demand, production and consumption may have shifted in recent years, thereby diminishing differences between weekdays vs. weekends. For instance, there has been a gradual shift towards more commercial (including e- commerce) and industrial activity on weekends that could alter the weekly discharge patterns in regulated rivers21, 22. A shifting manufacturing sector, globalization, and lifestyle changes are all socioeconomic factors modifying electricity demand. Another possible factor is the development and expansion of other modes of energy production such as dispatchable combustion turbines and non- dispatchable solar and wind energy. Solar and wind energy production activate during favourable weather conditions with hydropower otherwise matching the demand, which may disrupt the typical weekly pattern in regulated flows. Regulatory bodies and changing governmental policies may also be altering how utilities manage regulated waterways. Indeed, there is renewed interest for environmental, ecological and cultural (e.g., from a First Nations perspective) flows in human- influenced systems, with emerging regulations and policies supporting their implementation23. For instance, regulatory changes in the operation of the Prickett hydroelectric facility from a peaking to run- of- river site to assist spawning lake sturgeon24 induced a significant WHI decline (of - 0.216 decade- 1) along the Sturgeon River in the upper peninsula of Michigan starting in the 1990s. Indeed, + +<--- Page Split ---> + +changes in operation away from peaking hydropower generating stations, whether mandated or voluntary, could influence hydropeaking patterns. + +Additionally, the increasing interconnectivity of the North American power grid, deregulation, and centralization of electricity dispatching may further contribute to a recent reduction of hydropeaking intensity. Finally, climate variations may also play a role in hydropower production as wet periods may require greater spillage of water from reservoirs thereby diminishing hydropeaking intensity. The relatively wet climate of the 2010s could account for part of the recent declines in WHI across the USA and Canada. Thus a combination of factors including changing hydropower demand patterns tied to lifestyle factors and socioeconomic activity, the emergence of alternative modes of energy production plus power grid interconnectivity, implementation of regulations and policies, and climate variations may be influencing the day- to- day hydrology of many regulated waterways across the USA and Canada. + +Spatio- temporal patterns within and across jurisdictions. Given the vast territory of the USA and Canada, their waterways often drain multiple jurisdictions including international transboundary watersheds (e.g., the Rio Grande, Great Lakes- St. Lawrence, Winnipeg and Columbia rivers). Regional water authorities, interjurisdictional water boards, federal, provincial, and state legislation, and international water treaties and commissions all affect how waterways are managed. Furthermore, synchronous inter- jurisdictional power grids (e.g., interconnections) can also affect hydropower generation and hence regulated flows, leading to distinct spatio- temporal + +<--- Page Split ---> + +patterns in hydropeaking intensity. Decadal maps of WHI values reveal the progression of weekly hydropeaking systems from the eastern and central USA to the Pacific Northwest in the 1960s when development in the Columbia River Basin expanded rapidly. The international Columbia River Treaty implemented in 1961 led to the construction of three major dams along the Columbia River (Duncan, Keenleyside and Mica Dams in Canada) plus another on the Kootenai River (Libby Dam in the USA) \(^{25}\) . These dams and generating stations expanded the presence of hydropeaking cycles from the lower to the upper Columbia Basin in the 1970s and 1980s (Fig. 3). As such, regulation in the Canadian portion of the Columbia Basin now leads to downstream propagation of hydropeaking into the northern USA where it is regenerated at multiple points of regulation including Grand Coulee Dam and the Dalles. + +Another noticeable pattern in the decadal results is the WHI decline in many rivers of southern Québec in the 1970s and 1980s. As the 5,428 MW Churchill Falls generating station in Labrador came online in late 1971 (with hydropower sold mainly to the provincial utility Hydro- Québec) \(^{26}\) , followed a decade later by the 17,418 MW James Bay Hydroelectric Complex in northern Québec \(^{15}\) , a northward shift in hydropower generation abated the weekly hydropeaking cycles in more southern waterways. Simultaneous reductions in WHI in the northeastern USA (e.g., Hudson and Connecticut Rivers) may also be tied to transboundary power grid interconnections and Hydro- Québec's large export capacity (7,974 MW in 2019 \(^{27}\) ). Similar to regional climate trends \(^{28}\) , synchronous power grids thus have the capacity to shift the intensity of hydropeaking signals 1000s of kms away from points where hydropower is consumed, + +<--- Page Split ---> + +thereby creating hydropeaking teleconnections with potential for far- reaching social and ecohydrological effects. + +Ecohydrological implications. Ecohydrological impacts of hydropeaking are site- specific and may include rapid changes in water temperature (i.e., 'thermo- peaking'), increases in soil erosion and suspended matter, and habitat degradation, which affect ecosystems, reduce species abundance, and limit biodiversity (e.g., fish, riparian plants, macroinvertebrates) \(^{16,29,30}\) . Across the USA and southern Canada, hydropeaking emerged relatively early in the \(20^{\text{th}}\) century with the proliferation of dams and flow regulation in these regions. Starting in the 1960s, hydropower infrastructure expanded northwards into regions previously devoid of any significant flow regulation and hydropeaking. This includes major waterways like BC's Peace River, Manitoba's Nelson River, Ontario's Moose and Abitibi rivers, and Québec's La Grande Rivière. On these systems, major dams and reservoirs were built from the 1960s to early 1980s, vastly expanding the northern reach of hydropeaking rivers (Supplementary Fig. 11). This shifted potential ecohydrological impacts of hydropeaking to areas also undergoing rapid climate change through Arctic amplification of global warming \(^{31}\) . As such, sub- Arctic species of fish (e.g., brook trout, lake sturgeon, northern pike, and walleye), insects and riparian plants may now be exposed to the cumulative impacts of these environmental stressors \(^{17}\) . Additionally, winter frazil ice production and ice jams may be precipitated and accentuated downstream of hydroelectric facilities with persistent hydropeaking signals such as in the Peace River \(^{19}\) . + +<--- Page Split ---> + +Despite their recent northward expansion, weekly hydropeaking cycles are generally waning across the USA and southern Canada, suggesting a \(21^{\text{st}}\) century hydropeaking recovery in some of these river systems. Indeed, prior ecohydrological impacts of hydropeaking may be partially offset, benefiting local biota and ecosystem biodiversity32. For instance, recovery of lake sturgeon in the northern peninsula of Michigan demonstrates some of the benefits of shifting away from peaking hydropower operations24. This is particularly important as evidence is also mounting that hydropeaking influences aquatic species in rivers of Canada33- 36. Other aspects of flow regulation, such as sub- daily flow fluctuations and associated ramping up and down cycles not investigated in this study, may negate this hydropeaking recovery16, 17. Additional research is thus needed to explore hydropeaking cycles at other temporal scales to establish their site- specific ecohydrological impacts. + +Advantages and limitations of the WHI relative to other metrics. The proposed index to infer weekly hydropeaking signals provides a complementary metric to those developed in other studies5, 37, 38. Advantages of our approach include its scale independence, dynamic response, and relatively simple implementation. The WHI can be applied from small \((< 1 \times 10^{3} \text{km}^{2})\) to large \((> 1 \times 10^{6} \text{km}^{2})\) river basins with available daily discharge data (whether observed, reconstructed or simulated). The WHI responds to interannual variability in climate (e.g., wet/dry periods), changes in water management practices and policies, commissioning of new hydroelectric facilities or decommissioning of old ones, and other factors that affect flows. The use of daily discharge data also avoids the need for extensive databases on dams, reservoirs and + +<--- Page Split ---> + +other infrastructure that influence flows. Its possible implementation for short- term flow predictions emerges as another distinct advantage of the WHI. As an example, a running value of the WHI can be computed on the past year's daily flows and used to infer the possible deviations in daily flows over a given week based on recent historical patterns. Its computational simplicity, coded in our study in Fortran, allows processing of results for the 400 sites in \(< 2.5\) minutes. As such, it is feasible to implement a version of the code for short- term flow predictions so long as up- to- date daily flow records remain available. It would also be relatively straightforward to adapt the code to explore sub- daily hydropeaking cycles9 if appropriate discharge data are available. + +One challenge in implementing the WHI is access to daily discharge records. While considerable gauging stations exist in most of the USA and southern Canada, other waterways are not necessarily well monitored. A late \(20^{\text{th}}\) century decline in hydrometric stations due to budget restraints39 and the Water Survey of Canada's curtailment of data collection combined with stricter quality standards from third parties have exacerbated hydrological data accessibility. As well, private industry and government- owned corporations often record discharge at or near their hydroelectric facilities but may consider these data as sensitive such that they are not released publicly. Thus, acquisition of daily discharge data in regulated systems, particularly as the number of small, private firms operating run- of- river hydroelectric facilities expands3, yields a distinct challenge in accessing flow data. Therefore, remote sensing40, data reconstructions (e.g. from statistical models or machine learning methods41) and + +<--- Page Split ---> + +numerical simulations that incorporate regulation42 are key in filling spatio- temporal gaps where and when in situ observations are lacking. + +Concluding remarks. As hydropower generation and infrastructure development continues to expand across the USA and Canada, it is important to establish how water management practices affect downstream river flows and ecosystems. A common feature in regulated rivers are discharge periodicities associated with hydropower production ebbs and flows including weekly cycles. In this study, a new measure of this weekly rhythm in flows, the weekly hydropeaking index (WHI), is formulated and applied to 400 sites over parts of North America. Our findings reveal vanishing weekly hydropeaking cycles across the USA and Canada in the 2010s, suggesting diminishing differences between discharge on weekends vs. weekdays. Factors possibly yielding this result include increased commercial and industrial activity on weekends, a shift towards other modes of energy production during peak demand hours or days, and policy changes altering water management practices including for ecological and environmental flows. This reduction in weekly hydropeaking also may benefit aquatic species, insects and riparian vegetation that otherwise are susceptible to rapid shifts in flows and water levels. Future efforts should therefore establish the ecohydrological implications of waning hydropeaking cycles. The application of the WHI to other regions over the globe would provide broader perspectives on the commonality of this feature in regulated rivers. + +<--- Page Split ---> + +## Methods + +Study area. The USA and Canada harbor abundant freshwater resources that include some of the world's largest rivers (by annual volumetric flows) including the Mississippi, St. Lawrence, Mackenzie, Ohio and Columbia rivers \(^{43}\) . Many of these rivers and/or their tributaries have been impounded for hydropower generation, flood control, irrigation, potable water supply, navigation and recreation, leading to fragmented river networks and regulated flows \(^{4,6}\) . Indeed, numerous dams have been built across the USA and Canada in the \(20^{\text{th}}\) and early \(21^{\text{st}}\) centuries \(^{2,3}\) . While many dams in North America have multiple purposes, hydropower generation remains a principal function. Distinct weekly patterns mark hydropower production except perhaps at run- of- river facilities and those supplying industries continuously in operation such as aluminum smelters or pulp and paper mills \(^{8,9}\) . As such, this study focuses on both regulated and unregulated waterways of the USA and Canada to explore the prevalence and intensity of weekly periodicity in discharge. + +Site selection. A total of 400 sites across the USA and Canada ranging 480- 1,805,222 \(\mathrm{km}^2\) in gauged area (A), 25- 60°N in latitude, 54- 132°W in longitude, and 0.11- 268.28 \(\mathrm{km}^3\) in mean annual discharge are selected for this study (Supplementary Fig. 12 and Supplementary Table 2). A primary site selection criterion is discharge data availability for \(\geq 24\) years between 1920- 2019, with \(\geq 14\) years during the focus period of 1980- 2019. The chosen sites span a wide range of hydrological regimes from pluvial rivers in warmer climates (e.g., BC's Yakoun River) to nival and glacial systems at higher elevations or latitudes in cooler climates (e.g., BC's Lillooet River) \(^{44}\) . Thus, the study area spans regions with little to no snowmelt where sub- annual scales govern temporal + +<--- Page Split ---> + +variability while others are mainly snowmelt- driven with predominant annual cycles45. + +The database also includes intermittent streams in warmer, drier climates such as + +California's Santa Ana River and Arizona's Little Colorado River. Regulated and + +unregulated rivers are selected (using guidance from Benke and Cushing43) to allow + +comparisons between sites. Some sites such as Lees Ferry on the Colorado River + +include extended records that cover pre- and post- regulation effects on flows. + +Data. Data and metadata (station ID, gauge coordinates, and gauged area) are + +extracted from various sources including publicly accessible databases maintained by + +federal, provincial and state agencies in addition to proprietary data from private + +industry, government- owned utilities and international commissions. For most + +unregulated rivers, daily discharge data are sourced partly from the Water Survey of + +Canada's Hydrometric Database (HYDAT), the Centre d'Expertise Hydrique du Québec + +(CEHQ) and the United States Geological Survey (USGS). For regulated rivers, though, + +daily discharge data are not necessarily available from these sources or other public + +repositories as they are partially or entirely collected, quality controlled and archived by + +government- controlled utilities or private industry (see Supplementary Tables 2 and 3). + +This includes: Nalcor Energy for the Salmon and Exploits rivers plus the Churchill Falls + +(Labrador) Corporation Limited for the Churchill River at Churchill Falls Powerhouse in + +Newfoundland and Labrador; NB Power for the St. John River in New Brunswick; Rio + +Tinto for the Kemano Powerhouse in BC and the Saguenay and Péribonca rivers in + +Québec; Hydro- Québec for La Grande Rivière, Betsiamites, Manicouagan, des + +Outaouais, des Outardes and St- Maurice rivers; Evolugen by Brookfield Renewable for + +the Coulonge, Lièvre, and Noire rivers in Québec and Mississagi and Aux Sables rivers + +<--- Page Split ---> + +in Ontario; Ontario Power Generation for the Abitibi, English, Kaministiquia, Madawaska, Mattagami (tributary to the Moose River), Montreal and Ottawa rivers; H2O Power for the Abitibi River; Manitoba Hydro for the Nelson and Winnipeg rivers; Transalta for the North Saskatchewan and Kananaskis rivers; and BC Hydro for the Columbia River at Mica Dam. Additional data for gauges along the Rio Grande on the border between the USA and Mexico and the Pecos River are provided by the International Boundary and Water Commission. Data at six sites in the Tennessee River Basin are provided by the Tennessee Valley Authority. Recent records of daily discharge from the US Bureau of Reclamation supplement those from the USGS for sites on the Colorado and upper Rio Grande rivers. Finally, the 1 October to 31 December 2019 daily discharge data for the Snake River at Hells Canyon Dam are sourced from Idaho Power. Potential errors associated with discharge measurements and implications to our results are discussed in the Supplementary Methods. + +Time series construction. The overall study period spans 1 January 1920 to 31 December 2019 for which at least partial, extended (≥ 24 years) records of daily discharge are available at all sites. Time series of daily streamflow (in m³ s⁻¹) are constructed based on data availability for each site of interest (Supplementary Table 2) and follows Déry et al.⁴⁶ in its approach. Daily discharge data sourced from the USGS, US Bureau of Reclamation, Tennessee Valley Authority, Idaho Power, Nalcor Energy (Exploits River) and NB Power are converted to metric units prior to analysis. For several waterways (e.g., the Nelson and Saguenay Rivers), data furthest downstream are first used, but when unavailable (prior to construction of dams and hydroelectric facilities), are replaced with those from the closest upstream gauging station while + +<--- Page Split ---> + +adjusting the data for the missing contributing area as necessary46, 47. Gaps are in- filled with the mean daily discharge over the period of record; however, any calendar year with \(\geq 10\%\) missing records is excluded from analysis. Supplementary Table 2 lists the percentage of in- filled data at each site (average: \(0.02\%\) , maximum: \(0.55\%\) ) omitting years when \(\geq 10\%\) of the data remain unavailable. Uncertainty in the results associated with data homogeneity and the gap- filling strategy is evaluated and discussed in the Supplementary Methods. + +Development of the WHI. Various approaches are commonly used to explore flow alterations in regulated rivers including comparisons of hydrographs pre- and postregulation9, 48, 49, trends in peak and/or low flows50 or of naturalized versus observed (regulated) flows51- 53. A broader approach employs a set of multiple (up to 33) indicators of hydrologic alteration (IHA) to quantify changes over the water year arising from regulation54- 56. Another method combines hydrological data, reservoir information and a database of large dams in developing river regulation and fragmentation indices with a matrix of impact for application to all major global watersheds4, 5. Apart from time domain analyses, Discrete Fourier Transforms (DFTs) or wavelet analyses offer additional insights on impacts of flow alterations from human interventions15, 20, 45, 57. Consult Jumani et al.38 for a review of river regulation and fragmentation indices including their applications, advantages and limitations. + +While various approaches exist to infer hydrologic alterations from diversions, dam and reservoir operations including sub- daily hydropeaking cycles58, 59, none focuses on the weekly timescale, a primary periodicity of socioeconomic activity. Therefore, we develop a novel WHI that combines time and frequency domain terms to quantify weekly + +<--- Page Split ---> + +periodicity in river discharge. The time domain term ( \(T_{T}\) , %) counts the number of weeks ( \(D_{w}\) ) in a given calendar year when two consecutive days exhibit flows lower than the corresponding weekly average ( \(Q_{1 - 7}\) ), followed by five sequential days above the corresponding weekly average: + +\[T_{T} = \max \left\{\frac{100}{52}\sum_{w = 1}^{52}D_{w},0.001\right\} \mathrm{and~where}\] + +\[D_{w} = \left\{ \begin{array}{ll}1 & \mathrm{if} Q_{1,2}< \overline{Q_{1 - 7}} \mathrm{and if} Q_{3,\dots,7} > \overline{Q_{1 - 7}} \\ 0.25 & \mathrm{if} Q_{1}< \overline{Q_{1 - 7}} \mathrm{and if} Q_{2,\dots,7} > \overline{Q_{1 - 7}} \\ 0 & \mathrm{if~otherwise} \end{array} \right\}\] + +This sequence of daily flows is chosen to emphasize the typical weekly rhythm observed in hydropeaking rivers: low flows on weekends when hydropower demand wanes, followed by high flows on weekdays when hydropower demand waxes \(^{9}\) . A partial score of 0.25 is ascribed to sites where six consecutive days above the weekly average follow a single low flow day for that week. As some gauging sites lie downstream from points of regulation such that low flows are shifted later in the week rather than occurring on Saturdays and Sundays, we test all seven possible combinations of two consecutive days (e.g., Saturday/Sunday, Sunday/Monday, ..., Friday/Saturday) and select the one that maximizes WHI at each site over the period of record. This approach for the time domain term attenuates the effects of cyclical (rather than periodic) variations from synoptic- scale storm activity, which otherwise leads to marked weekly cycles in pluvial rivers \(^{45}\) . + +<--- Page Split ---> + +An application of DFTs to the daily discharge data provides the frequency domain term. Here we follow Wilks \(^{60}\) in partitioning the daily discharge time series into sine and cosine waves of amplitude \(C_{k}\) for harmonic \(k\) . DFTs are computed for each calendar year with the \(52^{\text{nd}}\) harmonic representing the weekly timescale of interest here. Then we compute the explained variance of the \(52^{\text{nd}}\) harmonic \((T_{F})\) : + +\[T_{F} = \frac{\left(\frac{n}{2}\right)C_{52}^{2}}{(n - 1)s_{Q}^{2}} \quad (2)\] + +where \(n\) is the number of days in a given year (365 or 366 for a leap year), \(C_{52}\) is the amplitude of the \(52^{\text{nd}}\) harmonic, and \(s_{Q}\) is the standard deviation in discharge. + +After expressing \(T_{T}\) and \(T_{F}\) as percentages, we take the base 10 logarithm of their product to obtain an annual WHI: + +\[\mathrm{WHI} = \log_{10}[B(T_{T}\times T_{F})] \quad (3)\] + +in which \(B (= 10)\) is a coefficient chosen so that the median \(\mathrm{WHI} \approx 0\) among all 400 sites. Annual WHI values range typically from about - 4 to +4 (although WHI values have no theoretical upper or lower bounds), with large positive values indicating strong weekly periodicity attributed to flow regulation at hydropower stations. In contrast, rivers with robust annual cycles with flows dominated by potent snowmelt- driven freshets and/or large (natural) storage capacity within abundant lakes, ponds and wetlands exhibit large negative WHI values. The transition between negative to positive WHI values marks a shift from annual to weekly dominant time scales of variability in flow. The 1980- 2019 mean daily flows (considering the day of the week) for the Stuart River (BC), Mohawk River (New York), and Chattahoochee River at Buford Dam (Georgia) + +<--- Page Split ---> + +illustrate the WHI ranging from the minimum, median, and maximum values (Supplementary Fig. 13). WHI values remain site- specific and must be interpreted with care, particularly moving away (both upstream and downstream) from measurement sites with an intervening body of water, a confluence or another point of regulation altering hydropeaking intensity. + +Statistical analyses. We first compute WHI time series at all 400 sites and develop a 'climatology' of index values for 1980- 2019, with 14 years \(\leq n_{y} \leq 40\) years depending on data availability at each site. Summary statistics (mean, median, standard deviation, etc.) of the 1980- 2019 WHI data are tabulated and their distribution tested for normality using the Shapiro- Wilk test. Similar climatological analyses are developed for each decade (1920s to 2010s) with results reported when \(n_{y} \geq 5\) years at a given site. The Mann- Kendall test (MKT61, 62) applied to all WHI time series with \(n_{y} \geq 30\) years over 1980- 2019 yields linear, monotonic trends in hydropeaking intensity, with \(p < 0.05\) considered locally statistically- significant. The field (or global) significance of the individual (or local) trend tests is assessed following Wilks60. The approach minimizes the false discovery rate (FDR) by first ranking \(p\) - values in ascending order for all trend tests with \(n_{y} \geq 30\) years. Trends are then globally significant if \(p < p_{\text{FDR}}\) depending on the distribution of sorted \(p\) - values as: + +\[p_{FDR} = \max_{i = 1,2,\dots,N}\{p_i; p_i \leq (i / N) \alpha_{\text{global}}\} \quad (4)\] + +in which we set \(\alpha_{\text{global}} = 0.10\) . Trend analysis sensitivity to autocorrelation is tested in the Supplementary Methods. + +<--- Page Split ---> + +We assess the 1920 to 2019 annual mean, median and maximum WHI across all sites with available data in a given year to track the overall evolution of hydropeaking intensity across the USA and Canada. We also count the annual number and percentage of sites that fall in the top decile of all 1920- 2019 WHI scores. An additional metric reported is the discharge- weighted WHIQI computed each calendar year (index \(j\) ) as: + +\[\mathrm{WHI}_{Q_i} = \sum_{i = 1}^{n = 400}\mathrm{WHI}_{i,j}\times Q_{i,j} / \sum_{i = 1}^{n = 400}Q_{i,j} \quad (5)\] + +where \(\mathrm{Q}_{i,j}\) (km3 yr- 1) denotes the annual discharge and \(i\) is the site index. This yields a relative measure of annual volumetric flows affected by weekly hydropeaking cycles rather than just the number of sites. For monotonic trend analysis, the MKT is applied to time series of overall mean annual WHI over the 1980- 2019 focus period. The potential influence of missing data on the evolution of average WHI over 1980- 2019 is assessed by substituting incomplete time series with each missing site's average WHI computed over the remainder of the focus period. This yields an adjusted mean annual WHI time series for a first order assessment of the influence of incomplete data. + +A histogram illustrates the distribution of two consecutive days when low flows emerge relative to the expected value of \(1 / 7 = 0.143\) were these randomly distributed. Fractions of the seven possible two- day combinations are partitioned according to \(\mathrm{WHI} \gtrsim 0\) . The histogram also includes the corresponding mean WHI across all rivers for a given two- day combination of low flows. A Chi- Square goodness- of- fit test63 verifies the hypothesis of whether the distribution of low flow days differs significantly from the expected value with threshold \(p = 0.05\) . Similarly, we test if the corresponding mean WHI values for the + +<--- Page Split ---> + +two- day pairs with low flows follow a uniform distribution using a Chi- Square test. The relationship between annual WHI values and mean annual flows over 1980- 2019 is evaluated using Pearson's correlation coefficient with \(p < 0.05\) considered statistically- significant values. Next, we transform annual discharge time series to standardized anomalies over the period of record at each site (with \(< 10\%\) missing data in a calendar year). Decadal mean standardized anomalies for all available sites are then computed when \(n_y \geq 5\) years in a given decade. These decadal average anomalies are binned in increments of 0.25 standard anomaly for comparison with WHI decadal distributions. + +To explore possible factors contributing to WHI trends we assess whether the dispersion of flows across the seven days of the week is changing over time. Here we first compile total annual flows (in \(m^3 s^{- 1}\) ) for each of the seven days of the week, as well as the overall average, over each calendar year. Then we quantify departures (as a percentage) for each day of the week relative to the annual mean. Next, we calculate standard deviations (\(\sigma\)) in the percentage departures for the seven days of the week each year, creating \(\sigma\) time series for all 400 sites over 1980- 2019. Finally, application of the MKT on the \(\sigma\) time series (when \(n_y \geq 30\) years) yields 1980- 2019 dispersion trends. + +## Data availability + +Data related to this article can be found in the Supplementary Information and Supplementary Data files. Discharge data used in this study are available in the following publicly accessible databases: Centre d'Expertise Hydrique du Québec (http://www.cehq.gouv.qc.ca/hydrometrie/historiqne_donnees/info_validite.htm), US Bureau of Reclamation (https://data.usbr.gov/), United States Geological Survey + +<--- Page Split ---> + +(https://waterdata.usgs.gov/nwis), Water Survey of Canada's Hydrometric Database + +(https://wateroffice.ec.gc.ca), Idaho Power (https://idastream.idahopower.com/Data/) + +and the International Boundary and Water Commission + +(https://www.ibwc.gov/Water_Data/). For some regulated rivers, proprietary discharge + +data can be requested from the following data providers: BC Hydro, Evolugen, H2O + +Power, Hydro- Québec, International Boundary and Water Commission, Manitoba + +Hydro, Nalcor Energy, NB Power, Ontario Power Generation, Rio Tinto, Tennessee + +Valley Authority, and TransAlta (see Supplementary Table 3). 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Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J. Hydrol. 564, 266-282 (2018). + +42. Tefs, A. A. G. et al. Simulating river regulation and reservoir performance in a continental-scale hydrologic model. Environ. Modell. Softw., in press (2021). + +43. Benke, A. C. & Cushing, C. E. (Eds.) Rivers of North America. Elsevier Academic Press, Amsterdam, 1144 pp. (2005). + +44. Déry, S. J. et al. Detection of runoff timing changes in pluvial, rival and glacial rivers of western Canada. Water Resour. Res. 45, W04426 (2009). + +45. Smith, L. C., Turcotte, D. L. & Isacks, B. L. Stream flow characterization and feature detection using a discrete wavelet transform. Hydrol. Process. 12, 233-249 (1998). + +<--- Page Split ---> + +46. Dery, S. J., Stadnyk, T. A., MacDonald, M. K. & Gauli-Sharma, B. Recent trends and variability in river discharge across northern Canada. Hydrol. Earth Syst. Sci. 20, 4801-4818 (2016). + +47. Dery, S. J., Stieglitz, M., McKenna, E. C. & Wood, E. F. Characteristics and trends of river discharge into Hudson, James, and Ungava Bays, 1964-2000. J. Clim. 18, 2540-2557 (2005). + +48. Peters, D. L. & Buttle, J. M. The effects of flow regulation and climatic variability on obstructed drainage and reverse flow contribution in a northern river-lake-delta complex, Mackenzie Basin headwaters. River Res. Appl. 26, 1065-1089 (2010). + +49. Woo, M., Thorne, R., Szeto, K. & Yang, D. Streamflow hydrology in the boreal region under the influence of climate and human interference. Philos. T. R. Soc. B. 363, 2251-2260 (2008). + +50. Assani, A. A., Stichelbout, E., Roy, A. G. & Petit, F. Comparison of impacts of dams on the annual maximum flow characteristics in three regulated hydrologic regimes in Québec (Canada). Hydrol. Process. 20, 3485-3501 (2006). + +51. Naik, P. K. & Jay, D. A. Distinguishing human and climate influences on the Columbia River: Changes in mean flow and sediment transport. J. Hydrol. 404, 259-277 (2011). + +52. St. Jacques, J.-M., Sauchyn, D. J. & Zhao, Y. Northern Rocky Mountain streamflow records: Global warming trends, human impacts or natural variability? Geophys. Res. Lett. 37, L06407 (2010). + +53. Ye, B., Yang, D. & Kane, D. L. Changes in Lena River streamflow hydrology: Human impacts versus natural variations. Water Resour. Res. 39, 1200 (2003). + +54. Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, D. P. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10, 1163-1174 (1996). + +55. Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Sci. Adv. 3, e1700611 (2017). + +56. Zhou, X., Huang, X., Zhao, H. & Ma, K. Development of a revised method for indicators of hydrologic alteration for analyzing the cumulative impacts of cascading reservoirs on flow regime. Hydrol. Earth Syst. Sci. 24, 4091-4107 (2020). + +57. Tongal, H., Demirel, M. C. & Moradkhani, H. Analysis of dam-induced patterns on river flow dynamics. Hydrol. Sci. J. 62, 626-641 (2017). + +<--- Page Split ---> + +58. Carolli, M. et al. A simple procedure for the assessment of hydropeaking flow alterations applied to several European streams. Aquat. Sci. 77, 639-653 (2015). + +59. Ashraf, F. B. H. et al. Changes in short term river flow regulation and hydropeaking in Nordic rivers. Sci. Rep. 8, 17232 (2018). + +60. Wilks, D. S. Statistical Methods in the Atmospheric Sciences, Elsevier, Amsterdam, 4th edition, 818 pp. (2019). + +61. Mann, H. B. Non-parametric test against trend. Econometrika 13, 245–259 (1945). + +62. Kendall, M. G. Rank Correlation Methods. 202 pp., Oxford Univ. Press, New York (1975). + +63. McCuen, R. H. Modeling Hydrologic Change: Statistical Methods. Lewis Publishers, Boca Raton, 433 pp. (2003). + +Acknowledgements. Thanks to the Water Survey of Canada and its provincial and + +territorial partners, the Centre d’Expertise Hydrique du Québec, USGS, BC Hydro, + +Evolugen by Brookfield Renewable, Transalta, Manitoba Hydro, Ontario Power + +Generation, H2O Power, Rio Tinto, Hydro-Québec, NB Power, Nalcor Energy, Idaho + +Power, the Tennessee Valley Authority, the International Boundary and Water + +Commission and the US Bureau of Reclamation for providing hydrometric data. Thanks + +to Aseem Sharma (UNBC/NRCan) for preparing the spatial plots, Clyde McLean and + +Joanna Barnard (Nalcor Energy), Jim Samms (NB Power), Marie Broesky, Kevin + +Gawne, Kristina Koenig, Phil Slota, Kevin Sydor, Efrem Teklemariam, Mike Vieira and + +Shane Wruth (Manitoba Hydro), Matt MacDonald (Ontario Power Generation), Erik + +Richards and Marc Mantha (H2O Power), Samer Alghabra and Mokhtar Moujahid + +(Hydro-Québec), Bruno Larouche and Richard Loubier (Rio Tinto), Michael Smilski + +(Transalta), Jim Li, Debbie Rinvold and Stephanie Smith (BC Hydro), Adrian Cortez and + +<--- Page Split ---> + +Delbert Humberson (International Boundary and Water Commission), Kelly Withers and Matti Hanninen (Evolugen) for providing comments on this work and for additional data for regulated rivers, Dwayne Akerman, Amber Brown, Michel Desjardins, Matt Falcone, Samantha Hussey, Lyssa Maurer, Angus Pippy, Melanie Taylor, and Frank Weber (Water Survey of Canada) for sharing supplemental hydrometric data, and Huilin Gao (Texas A&M), John Zhu (Texas Water Development Board), Julie Thériault (UQAM) and Mike Vieira and Kristina Koenig (Manitoba Hydro) for logistical support. This research was supported by the Natural Sciences and Engineering Research Council of Canada, Manitoba Hydro, and partners through funding of the BaySys project. + +Author contributions. S.J.D. designed the study, extracted hydrometric data and constructed time series of daily discharge for all rivers, formulated the weekly hydropeaking index, developed the codes, performed the statistical and computational analyses, and drafted line graphs with support from M.A.H.H., T.A.S., and T.J.T. S.J.D. wrote the manuscript with contributions from all co- authors and all contributed to manuscript refinement and revisions. + +Competing interests. The authors declare no competing interests. + +<--- Page Split ---> + +## Figure Legends + +Fig. 1 Map of the 1980- 2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). + +Fig. 2 Histogram of the 1980- 2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980- 2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two- day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites. + +Fig. 3 Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n_{y} \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate WHI \(\geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +Fig. 4 Map of the 1980- 2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically- significant trends \((p < 0.05)\) . Results are shown only when \(n_{y} \geq 30\) years. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 Map of the 1980-2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols).
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Histogram of the 1980-2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980-2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two-day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + + +<--- Page Split ---> +![](images/Figure_1.jpg) + + +<--- Page Split ---> + +Fig. 3 Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n_{y} \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate WHI \(\geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 4 Map of the 1980-2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically-significant trends \((p < 0.05)\) . Results are shown only when \(n_{y} \geq 30\) years.
+ +<--- Page Split ---> + +Table 1 List of sites with the top ten ranking WHI values, 1980-2019. + +
RankSiteWHI
1Chattahoochee R. at Buford Dam (GA)3.299
2Chattahoochee R. at West Point (GA)3.276
3Colorado R. at Hoover Dam (AZ/NV)3.222
4Nelson R. (MB)2.916
5Niagara R. (ON/NY)2.900
6Colorado R. at Lees Ferry (AZ)2.844
7Montreal R. (Lake Superior, ON)2.790
8Montreal R. (Ottawa Basin, ON)2.716
9Holston R. at Cherokee Dam (TN)2.675
10Columbia R. at Grand Coulee Dam (WA)2.662
+ +AZ: Arizona, GA: Georgia, MB: Manitoba, NV: Nevada, NY: New York,ON: Ontario, TN: Tennessee, WA: Washington + +<--- Page Split ---> + +## Figures + +![](images/Figure_3.jpg) + +
Figure 1
+ +Map of the 1980- 2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 2
+ +Histogram of the 1980- 2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980- 2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two- day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites. + +<--- Page Split ---> +![PLACEHOLDER_45_0] + +
Figure 3
+ +Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate \(\mathrm{WHI} \geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +<--- Page Split ---> +![PLACEHOLDER_46_0] + +
Figure 4
+ +Map of the 1980- 2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically- significant trends \((p < 0.05)\) . Results are shown only when \(n \geq 30\) years. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupplementaryInformation.pdf- SupplementaryTable2.xlsx- SupplementaryTable3.xlsx- WHITimeSeries.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6_det.mmd b/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..256d0bcd4ee2357126bf7f8fcd4c13226411d8cc --- /dev/null +++ b/preprint/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6/preprint__03cad287deb044c05daa550c40716d2819e5af19adf18b7b6e72878c97733fe6_det.mmd @@ -0,0 +1,716 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 936, 175]]<|/det|> +# Vanishing weekly hydropeaking cycles in American and Canadian rivers + +<|ref|>text<|/ref|><|det|>[[44, 195, 752, 238]]<|/det|> +Stephen J. Dery (sdery@unbc.ca) University of Northern British Columbia https://orcid.org/0000- 0002- 3553- 8949 + +<|ref|>text<|/ref|><|det|>[[44, 243, 395, 283]]<|/det|> +Marco A. Hernández- Henríquez University of Northern British Columbia + +<|ref|>text<|/ref|><|det|>[[44, 290, 595, 332]]<|/det|> +Tricia A. Stadnyk University of Calgary https://orcid.org/0000- 0002- 2145- 4963 + +<|ref|>text<|/ref|><|det|>[[44, 337, 595, 377]]<|/det|> +Tara J. Troy University of Victoria https://orcid.org/0000- 0001- 5366- 0633 + +<|ref|>sub_title<|/ref|><|det|>[[44, 417, 186, 436]]<|/det|> +## Research Article + +<|ref|>text<|/ref|><|det|>[[44, 455, 895, 499]]<|/det|> +Keywords: Canada, United States of America, Flow Regulation, Human Intervention, Hydropeaking, Hydropower, Streamflow + +<|ref|>text<|/ref|><|det|>[[44, 515, 297, 536]]<|/det|> +Posted Date: April 20th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 554, 463, 574]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 441563/v1 + +<|ref|>text<|/ref|><|det|>[[44, 591, 909, 634]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 669, 944, 713]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on December 1st, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 27465- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[85, 103, 861, 161]]<|/det|> +# Vanishing weekly hydropeaking cycles in American and Canadian rivers + +<|ref|>text<|/ref|><|det|>[[84, 210, 860, 232]]<|/det|> +Stephen J. Dery1,\*, Marco A. Hernández- Henríquez1, Tricia A. Stadnyk2, and Tara J. Troy3 + +<|ref|>text<|/ref|><|det|>[[85, 277, 860, 315]]<|/det|> +1Department of Geography, Earth and Environmental Sciences, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada + +<|ref|>text<|/ref|><|det|>[[85, 328, 803, 349]]<|/det|> +2Department of Geography, University of Calgary, Calgary, Alberta, T2N 1N4, Canada + +<|ref|>text<|/ref|><|det|>[[85, 363, 857, 401]]<|/det|> +3Department of Civil Engineering, University of Victoria, Victoria, British Columbia, V8W 2Y2, Canada + +<|ref|>text<|/ref|><|det|>[[85, 447, 828, 468]]<|/det|> +4Corresponding author: Stephen Dery (sdery@unbc.ca), ORCID # 0000- 0002- 3553- 8949 + +<|ref|>text<|/ref|><|det|>[[85, 481, 790, 502]]<|/det|> +Email address for Marco Hernández- Henríquez: hernandezhenriquez.m@gmail.com + +<|ref|>text<|/ref|><|det|>[[85, 515, 875, 536]]<|/det|> +Email address for Tricia Stadnyk: Tricia.Stadnyk@ucalgary.ca, ORCID # 0000- 0002- 2145- 4963 + +<|ref|>text<|/ref|><|det|>[[85, 549, 730, 570]]<|/det|> +Email address for Tara Troy: tjtroy@uvic.ca, ORCID # 0000- 0001- 5366- 0633 + +<|ref|>title<|/ref|><|det|>[[246, 682, 748, 703]]<|/det|> +# CONFIDENTIAL MANUSCRIPT - FOR PEER REVIEW ONLY + +<|ref|>text<|/ref|><|det|>[[435, 718, 558, 736]]<|/det|> +31 March 2021 + +<|ref|>text<|/ref|><|det|>[[85, 784, 812, 805]]<|/det|> +Running head: Vanishing weekly hydropeaking cycles in American and Canadian rivers + +<|ref|>text<|/ref|><|det|>[[85, 818, 790, 857]]<|/det|> +Keywords: Canada, United States of America, Flow Regulation, Human Intervention, Hydropeaking, Hydropower, Streamflow + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 199, 108]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[110, 120, 884, 567]]<|/det|> +Sub- daily and weekly flow cycles termed 'hydropeaking' are common features in regulated rivers worldwide. Weekly flow periodicity arises from fluctuating hydropower demand and production tied to socioeconomic activity, typically with higher consumption during weekdays followed by reductions on weekends. Here, we propose a novel weekly hydropeaking index to quantify the 1920- 2019 intensity and prevalence of weekly hydropeaking cycles at 400 sites across the United States of America and Canada. A robust weekly hydropeaking signal exists at \(1.1\%\) of sites starting in 1920, peaking at \(17.0\%\) in 1963, and diminishing to \(3.2\%\) in 2019, marking a \(21^{\text{st}}\) century decline in hydropeaking intensity. We propose this decline may be tied to recent, above- average precipitation, socioeconomic shifts, alternative energy production, and legislative and policy changes impacting water management in regulated systems. Vanishing weekly hydropeaking cycles may offset some of the prior deleterious ecohydrological impacts from hydropeaking in highly regulated rivers. + +<|ref|>sub_title<|/ref|><|det|>[[115, 593, 252, 614]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[110, 628, 872, 896]]<|/det|> +In 2019, the United States of America (USA) and Canada generated a combined 674 TWh of hydroelectricity from a total 184 GW of installed capacity, ranking them with China and Brazil in the four largest global producers of hydroelectricity \(^{1}\) . With the proliferation of dam and reservoir construction during the \(20^{\text{th}}\) and early \(21^{\text{st}}\) centuries \(^{2}\) , many of the two countries' main rivers are now moderately or strongly affected by fragmentation, regulation and/or diversions \(^{4 - 6}\) . With increasing demands for renewable sources of energy, additional generating capacity is being developed or planned across Canada. This includes the 1,100 MW Site C Dam on the Peace River in northeastern + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 872, 180]]<|/det|> +British Columbia (BC), the 824 MW Muskrat Falls development on the lower Churchill River in Labrador, and the 695 MW Keeyask Generating Station on the Nelson River in northern Manitoba1, with its first of seven units becoming operational in February 2021. + +<|ref|>text<|/ref|><|det|>[[110, 255, 872, 909]]<|/det|> +While overall demand for electricity continues to increase, consumption patterns vary depending on socioeconomic activity, short- term weather conditions, seasonal climate fluctuations and long- term climate trends7, 8. In the northern USA and Canada, the winter season usually incurs peak hydroelectric demand due to domestic, commercial and industrial heating and lighting requirements9. With climate change, winter cold waves subside while summer heat waves intensify10, 11, shifting some of the demand from winter heating to summer cooling12- 14. Apart from seasonality shifts, day- to- day activities influence hydroelectricity demand as well. Similar to many other industrialized countries, North American educational, industrial and commercial activity intensifies on weekdays (Monday through Friday) but abates on weekends, particularly on Sundays9. This weekly rhythm of socioeconomic activity can thus impact water retention and releases in regulated rivers15. These rapid, frequent and periodic flow fluctuations downstream of regulation points are commonly termed ‘hydropacking’ events and are known to disrupt a range of ecohydrological processes16, 17. Yet the characteristics and trends in weekly hydropacking cycles due to daily variation in hydropower demands remain largely unknown. This is despite the general availability of discharge data at a daily time scale and the distinct weekly rhythm of socioeconomic activity including hydropower production, and hence water releases in regulated waterways, which impact ecohydrological processes. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 884, 530]]<|/det|> +To address that knowledge gap and a demand for global attention to hydropeaking rivers \(^{18}\) , we assess here the prevalence of weekly hydropeaking cycles for 400 gauging sites along rivers of the USA and Canada spanning a wide range of basin characteristics, regulation, hydrological and climatic regimes. Specifically, we develop a scale- independent and dynamic weekly hydropeaking index (WHI) with both time and frequency domain terms, allowing quantification of weekly flow periodicity. Application of the novel WHI to 1920- 2019 time series of river discharge provides evidence of vanishing weekly hydropeaking cycles in many regulated rivers of the USA and Canada with the 2010s comparable to the 1920s for hydropeaking prevalence. We conclude that increased commercial and industrial activity on weekends, a shift towards other modes of energy production, policy changes altering water management practices, electrical grid interconnectivity and deregulation of electricity generation, plus a relatively wet decade in the 2010s are contributing factors to waning weekly hydropeaking cycles. + +<|ref|>sub_title<|/ref|><|det|>[[115, 550, 201, 570]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[110, 602, 879, 904]]<|/det|> +Overall WHI statistics. The 1980- 2019 mean, median, and standard deviation of WHI for the 400 sites reach 0.097, 0.005 and 1.115, respectively (Supplementary Table 1). An application of the Shapiro- Wilk test to the WHI data suggests the distribution is not Gaussian ( \(W = 0.974\) , \(p = 1.32 \times 10^{- 6}\) , \(n = 400\) ); yet, the low skewness (0.157) and excess kurtosis (0.754) along with a Cullen and Frey graph (Supplementary Fig. 1) infer a reasonable fit. Twenty- five sites attain a mean annual WHI \(\geq 2.0\) for 1980- 2019 with another 49 sites achieving WHI \(\geq 1.0\) . A list of sites with the top ten ranking WHI values reveals their wide regional distribution with foci in the Chattahoochee, Colorado, Columbia, Great Lakes- St. Lawrence, Nelson and upper Tennessee drainage basins + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 870, 459]]<|/det|> +(Table 1), all of which are heavily dammed. The Chattahoochee River at Buford Dam claims the top WHI score of 3.299 while BC's Stuart River shows the lowest score of - 3.469. Some highly regulated systems such as Manitoba's Burntwood River, which funnels water diverted from the Churchill River into the Nelson River, exhibit large negative WHI values (- 1.892) as Notigi (the upstream point of regulation) is a control structure for a large reservoir operated in a longer term (e.g., seasonal) manner. Similarly, while several large dams impound the Missouri River, they are managed not only for hydropower production but also for flood control, irrigation, navigation and recreational values. As such, the three sites along the Missouri River used in this study exhibit an average WHI = - 0.492 revealing an absence of significant weekly hydropeaking cycles. + +<|ref|>text<|/ref|><|det|>[[110, 485, 883, 892]]<|/det|> +Spatial analyses. A map of the 1980- 2019 WHI values reveals that weekly hydropeaking rivers abound across the USA and Canada. Clusters of high WHI values emerge in the Alabama, Chattahoochee, and Tennessee river basins of the southeastern USA, in waterways draining the Ozark Mountains, the Colorado River and in northern Ontario rivers draining into the Great Lakes (Fig. 1). The Columbia River has several major points of regulation (WHI \(\geq 1.5\) ) from its headwaters in BC to its outlet in the Pacific Ocean. Highly hydropeaking sites (WHI \(\geq 2.0\) ) appear in both small (e.g., Alberta's Kananaskis River, \(A = 899 \text{km}^2\) ) and large (Manitoba's Nelson River, \(A = 1.1 \times 10^6 \text{km}^2\) ) systems. In contrast to their adjacent regulated rivers, free- flowing rivers of northern Canada, particularly those draining into Hudson Bay, exhibit large, negative WHI values. These unregulated rivers manifest strong annual cycles dominated by snowmelt- driven freshets and contain large natural storage capacity in the form of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 875, 422]]<|/det|> +extensive lakes, ponds and wetlands. Free- flowing, pluvial rivers of the southeastern USA (e.g. the Choctawhatchee, Ogeechee, Pascagoula, Satilla and Suwanee rivers) also exhibit negative, albeit \(> - 1.5\) , WHI scores. WHI values diminish moving downstream from a point of regulation. For instance, \(\mathrm{WHI} = 1.437\) on the Peace River just downstream of BC's WAC Bennett and Peace Canyon dams where minimum flows arise on weekends; 400 km downstream from the dams \(^{19}\) , however, WHI declines to 0.929 at the community of Peace River in Alberta where minimum flows occur on Mondays/Tuesdays, indicating a 2- day delay in signal propagation. A cascade of dams and reservoirs can amplify or sustain the hydropeaking signals along waterways (e.g., the Colorado, Columbia, and Tennessee rivers) or attenuate them (e.g., Ottawa River). + +<|ref|>text<|/ref|><|det|>[[110, 501, 872, 905]]<|/det|> +Sites with high values of WHI \((\geq 1.5)\) also show a preponderance of flow reductions on the weekends (Saturdays/Sundays) as identified by the larger symbols in Fig. 1. Of the 44 sites with \(\mathrm{WHI} \geq 1.5\) , 39 experience the two consecutive days with low flows on weekends. In contrast, sites with negative WHI values show a range of low flow days with no distinct pattern emerging. No less than \(30.8\%\) of all sites used in this study exhibit low flows on Saturdays/Sundays, more than twice the expected value (Fig. 2). This disproportionate amount of weekend low flows occurs mainly in hydropeaking rivers \((\mathrm{WHI} > 0)\) . Weekday combinations show frequencies at, or lower than, the expected value with the Friday/Saturday sequence appearing at only \(6.0\%\) of sites. A Chi- Square test applied to the frequency of two consecutive low flow days reveals that the results differ significantly from the expected value of \(0.143\) \((\chi^2 = 109.95, p < 2.2 \times 10^{- 16}, n = 7\) with six degrees of freedom). The mean WHI equals 0.292 for 123 sites with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 867, 214]]<|/det|> +low flows on weekends while it remains near zero or slightly negative for the six other two- day combinations. The distribution of mean WHI for the two- day combinations differs significantly from a uniform distribution based on a Chi- Square test ( \(\chi^{2} = 8.43\) , \(p = 0.05\) based on 10,000 replicates with \(n = 7\) ). + +<|ref|>text<|/ref|><|det|>[[110, 240, 864, 897]]<|/det|> +Temporal evolution and trend analysis. The temporal evolution of the mean and median WHI shows a rapid increase in hydropeaking intensity from the 1920s to the 1950s at which point they level off and fluctuate near zero (Supplementary Fig. 2). Starting in the 1990s, though, there is a gradual decline in both the mean and median WHI values with a return in the 2010s to statistics first seen in the 1930s (largely pre- regulation), a pattern observed both in the USA and Canada (not shown). The discharge- weighted WHI₀ emphasizes the increasing volumes of regulated flows starting from the 1920s through the 1980s; however, WHI₀ also declines markedly thereafter into the 21st century. In 1920, only 1.1% of available sites rank in the top decile of 1920- 2019 WHI values (WHI ≥ 2.021). This fraction peaks at 17.0% of available sites in 1963 but thereafter diminishes consistently. In 2000, 50 or 13.2% of available sites score in the top decile of 1920- 2019 WHI values but these counts fall precipitously to just 12 or 3.2% of the available sites by 2019, marking a 21st century declining pattern in weekly hydropeaking intensity. Trend analysis applied to the overall mean annual WHI reveals a statistically- significant decline of - 0.40 over 1980- 2019 (Supplementary Fig. 3). These temporal results, however, rely on the availability of discharge data, as the record length averages 78.4 years, ranging from a minimum of 24 years at one site to a full century at 87 sites (Supplementary Fig. 4). The number of available sites increases steadily from 1920 into the early 1990s and peaks at 393 sites + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 850, 250]]<|/det|> +in 1985 and 1992 but then declines to 373 sites by 1996 thereafter averaging 383±6 sites until 2019. Notable gaps appear in the discharge records starting in the 1990s, particularly for regulated rivers in Ontario and Québec; however, adjusting the time series of mean annual WHI for unavailable sites reveals little difference in the overall pattern and trend of WHI during 1980- 2019 (Supplementary Fig. 3). + +<|ref|>text<|/ref|><|det|>[[110, 325, 884, 910]]<|/det|> +Data availability also factors in the appraisal of the decadal evolution of hydropeaking intensity across the USA and Canada (Fig. 3a-j). Nevertheless, this shows the gradual inception of hydropeaking cycles during the 1920s and 1930s, particularly in the north- central, northeastern, and southeastern USA and in northern Ontario. The 1940s show an expansion of weekly hydropeaking rivers into the western USA including within the Colorado, Columbia and Sacramento river basins. The 1940s and 1950s mark an intensification of regulation in the Tennessee and Alabama river basins as well as rivers of northern Ontario draining to Lakes Superior and Huron. A pronounced expansion and amplification of the hydropeaking signal appears in the 1960s, particularly across the Great Lakes- St. Lawrence river basin in Ontario and Québec. Some stabilization of the hydropeaking pattern marks the 1970s but a resurgence follows in the 1980s and 1990s when additional hydropeaking rivers emerge in western Canada. The 2000s retain a wide distribution of hydropeaking rivers across both countries; yet, by the 2010s, the number of highly hydropeaking rivers diminishes considerably, particularly in parts of the Great Lakes- St. Lawrence and Tennessee river basins. The decadal distribution of the 10 WHI bins (Fig. 3k) further highlights the peak fraction of sites with \(\mathrm{WHI} \geq 1.5\) attained in the 1960s (19.6%), with nearly matching minimum values in the 1920s + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 830, 214]]<|/det|> +(6.8%) and 2010s (6.7%). After the 1960s, there is a steady decline in the relative number of sites with low flows either on the Saturday/Sunday or Sunday/Monday combinations, indicating waning differences between weekday and weekend flows across the USA and Canada (Fig. 3l). + +<|ref|>text<|/ref|><|det|>[[111, 240, 880, 614]]<|/det|> +The temporal evolution of the annual maximum WHI value shows a rapid increase from \(\sim 3.0\) in the 1920s to \(>4.0\) in the 1930s onward (Supplementary Fig. 2d). Annual peak WHI values \(>4.0\) are generally sustained for the remainder of the \(20^{\text{th}}\) century but then fall below that threshold starting in 2003 until 2019. The peak WHI value each year over the study period is distributed among 19 sites, with the Winnipeg River at the outlet of the Lake of the Woods capturing the top spot 12 times in the 1920s to early 1960s (Supplementary Fig. 5). The Colorado River at Hoover Dam dominates the list 25 times between the 1940s into the early 1980s. From the 1960s to 2010s, the Chattahoochee River at Buford Dam ranks first 12 times while in the 1990s and 2000s, the Montreal River that drains to Lake Superior tops the list 10 times. The overall maximum WHI score of 4.587 arises in 1961 at the Winnipeg River at the outlet of Lake of the Woods. + +<|ref|>text<|/ref|><|det|>[[110, 690, 880, 887]]<|/det|> +Further statistical analysis reveals an abundance of strong, negative WHI trends interspersed with positive ones for the 380 sites with \(n_{y} \geq 30\) years over 1980- 2019 (Fig. 4). A total of 104 sites show locally statistically- significant \((p < 0.05)\) declines in WHI while 26 show locally statistically- significant inclines. Of the 130 locally- significant trends, 81 remain globally significant. Significant negative WHI trends abound in the southeastern and northeastern USA, the Great Lakes- St. Lawrence basin, and the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 880, 320]]<|/det|> +Pacific Northwest while a cluster of positive trends arises in Québec's Saguenay watershed. While regulated rivers of Newfoundland show increasing WHI values, their unregulated counterparts show similar tendencies. Similarly, in New Brunswick, the regulated St. John River shows a decreasing trend in WHI while the proximal, unregulated Southwest Miramichi River shows an increasing trend. Sixty- four percent of the locally- significant WHI trends arise in hydropeaking rivers (WHI \(>0\) ) with fewer locally- significant trends in non- hydropeaking rivers (WHI \(< 0\) ; Supplementary Fig. 6). + +<|ref|>text<|/ref|><|det|>[[111, 395, 881, 872]]<|/det|> +Interannual and interdecadal variability. Water management practices and climate variability, among other factors, yield significant interannual variation in hydropeaking intensity. For example, the Colorado River at Lees Ferry shows marked declines in WHI during high flow years (Supplementary Fig. 7a). Indeed, heavy precipitation during strong El Niño events in the early 1980s induced high flows in the Colorado River including at Lees Ferry. Due to the unusually wet weather, the bypass tubes and spillway at Glen Canyon Dam were used to release additional water downstream, thereby moderating hydropeaking signals from 1983 to 198620. Similar declines in WHI appear in 1997 and 2011 when flows exceed the recent annual average. Computing the Pearson correlation coefficient between the 1980- 2019 annual river discharge and the corresponding WHI yields 81 statistically- significant negative correlations and only 16 statistically- significant positive correlations (Supplementary Fig. 7b). Thus high flows over extended periods attenuate weekly periodicity even in heavily regulated rivers such as the Colorado. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 876, 424]]<|/det|> +This analysis suggests that sustained wet periods may attenuate hydropeaking intensity while dry periods may accentuate it. Binned distributions of decadal standardized anomalies in river discharge reveal the contrasting dry 1930s vs. the wet 1970s, the latter coinciding with a suppression of hydropeaking across the USA and Canada (Supplementary Fig. 8). Yet, while the 2010s experienced relatively high flows, \(6.7\%\) of sites have \(\mathrm{WHI} \geq 1.5\) whereas in the similarly wet 1990s, \(15.6\%\) of sites achieve \(\mathrm{WHI} \geq 1.5\) . Of 20 sites with large \((>1)\) , positive standardized discharge anomalies during the 2010s, only three (the Betsiamites, La Grande and Nelson rivers) have \(\mathrm{WHI} > 1\) , which are likely more in response to enhanced diverted flows rather than high precipitation. Thus it is unlikely interdecadal climate variations alone account for recent WHI declines. + +<|ref|>text<|/ref|><|det|>[[111, 470, 883, 807]]<|/det|> +Dispersion of daily flows. Apart from climate variations, changes in day- of- the- week flows may influence WHI trends. Sites with \(\mathrm{WHI} > 0\) generally observe greater dispersion of day- of- the- week flows although pluvial and intermittent rivers, particularly in the southern USA, also experience greater day- to- day flow variations (Supplementary Fig. 9a). A trend analysis reveals significant declines in the dispersion of flows across the seven days of the week, concomitant with diminishing WHI values from 1980 to 2019 (Supplementary Fig. 9b). As an example, an abrupt reduction in dispersion of day- of- the- week flows in Labrador's Churchill River appears in 1997 and is then sustained, suggesting factors other than climate variations are altering daily flows (Supplementary Fig. 10). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 240, 111]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[111, 120, 880, 850]]<|/det|> +Possible factors leading to recent WHI declines. The recent decline in weekly hydropeaking cycles in the USA and Canada emerges as a key finding in this study. Several possible factors may be contributing to this general pattern observed over the study area. Firstly, hydropower demand, production and consumption may have shifted in recent years, thereby diminishing differences between weekdays vs. weekends. For instance, there has been a gradual shift towards more commercial (including e- commerce) and industrial activity on weekends that could alter the weekly discharge patterns in regulated rivers21, 22. A shifting manufacturing sector, globalization, and lifestyle changes are all socioeconomic factors modifying electricity demand. Another possible factor is the development and expansion of other modes of energy production such as dispatchable combustion turbines and non- dispatchable solar and wind energy. Solar and wind energy production activate during favourable weather conditions with hydropower otherwise matching the demand, which may disrupt the typical weekly pattern in regulated flows. Regulatory bodies and changing governmental policies may also be altering how utilities manage regulated waterways. Indeed, there is renewed interest for environmental, ecological and cultural (e.g., from a First Nations perspective) flows in human- influenced systems, with emerging regulations and policies supporting their implementation23. For instance, regulatory changes in the operation of the Prickett hydroelectric facility from a peaking to run- of- river site to assist spawning lake sturgeon24 induced a significant WHI decline (of - 0.216 decade- 1) along the Sturgeon River in the upper peninsula of Michigan starting in the 1990s. Indeed, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 833, 144]]<|/det|> +changes in operation away from peaking hydropower generating stations, whether mandated or voluntary, could influence hydropeaking patterns. + +<|ref|>text<|/ref|><|det|>[[110, 192, 880, 563]]<|/det|> +Additionally, the increasing interconnectivity of the North American power grid, deregulation, and centralization of electricity dispatching may further contribute to a recent reduction of hydropeaking intensity. Finally, climate variations may also play a role in hydropower production as wet periods may require greater spillage of water from reservoirs thereby diminishing hydropeaking intensity. The relatively wet climate of the 2010s could account for part of the recent declines in WHI across the USA and Canada. Thus a combination of factors including changing hydropower demand patterns tied to lifestyle factors and socioeconomic activity, the emergence of alternative modes of energy production plus power grid interconnectivity, implementation of regulations and policies, and climate variations may be influencing the day- to- day hydrology of many regulated waterways across the USA and Canada. + +<|ref|>text<|/ref|><|det|>[[110, 610, 875, 877]]<|/det|> +Spatio- temporal patterns within and across jurisdictions. Given the vast territory of the USA and Canada, their waterways often drain multiple jurisdictions including international transboundary watersheds (e.g., the Rio Grande, Great Lakes- St. Lawrence, Winnipeg and Columbia rivers). Regional water authorities, interjurisdictional water boards, federal, provincial, and state legislation, and international water treaties and commissions all affect how waterways are managed. Furthermore, synchronous inter- jurisdictional power grids (e.g., interconnections) can also affect hydropower generation and hence regulated flows, leading to distinct spatio- temporal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 870, 459]]<|/det|> +patterns in hydropeaking intensity. Decadal maps of WHI values reveal the progression of weekly hydropeaking systems from the eastern and central USA to the Pacific Northwest in the 1960s when development in the Columbia River Basin expanded rapidly. The international Columbia River Treaty implemented in 1961 led to the construction of three major dams along the Columbia River (Duncan, Keenleyside and Mica Dams in Canada) plus another on the Kootenai River (Libby Dam in the USA) \(^{25}\) . These dams and generating stations expanded the presence of hydropeaking cycles from the lower to the upper Columbia Basin in the 1970s and 1980s (Fig. 3). As such, regulation in the Canadian portion of the Columbia Basin now leads to downstream propagation of hydropeaking into the northern USA where it is regenerated at multiple points of regulation including Grand Coulee Dam and the Dalles. + +<|ref|>text<|/ref|><|det|>[[111, 504, 876, 877]]<|/det|> +Another noticeable pattern in the decadal results is the WHI decline in many rivers of southern Québec in the 1970s and 1980s. As the 5,428 MW Churchill Falls generating station in Labrador came online in late 1971 (with hydropower sold mainly to the provincial utility Hydro- Québec) \(^{26}\) , followed a decade later by the 17,418 MW James Bay Hydroelectric Complex in northern Québec \(^{15}\) , a northward shift in hydropower generation abated the weekly hydropeaking cycles in more southern waterways. Simultaneous reductions in WHI in the northeastern USA (e.g., Hudson and Connecticut Rivers) may also be tied to transboundary power grid interconnections and Hydro- Québec's large export capacity (7,974 MW in 2019 \(^{27}\) ). Similar to regional climate trends \(^{28}\) , synchronous power grids thus have the capacity to shift the intensity of hydropeaking signals 1000s of kms away from points where hydropower is consumed, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 877, 144]]<|/det|> +thereby creating hydropeaking teleconnections with potential for far- reaching social and ecohydrological effects. + +<|ref|>text<|/ref|><|det|>[[110, 193, 880, 844]]<|/det|> +Ecohydrological implications. Ecohydrological impacts of hydropeaking are site- specific and may include rapid changes in water temperature (i.e., 'thermo- peaking'), increases in soil erosion and suspended matter, and habitat degradation, which affect ecosystems, reduce species abundance, and limit biodiversity (e.g., fish, riparian plants, macroinvertebrates) \(^{16,29,30}\) . Across the USA and southern Canada, hydropeaking emerged relatively early in the \(20^{\text{th}}\) century with the proliferation of dams and flow regulation in these regions. Starting in the 1960s, hydropower infrastructure expanded northwards into regions previously devoid of any significant flow regulation and hydropeaking. This includes major waterways like BC's Peace River, Manitoba's Nelson River, Ontario's Moose and Abitibi rivers, and Québec's La Grande Rivière. On these systems, major dams and reservoirs were built from the 1960s to early 1980s, vastly expanding the northern reach of hydropeaking rivers (Supplementary Fig. 11). This shifted potential ecohydrological impacts of hydropeaking to areas also undergoing rapid climate change through Arctic amplification of global warming \(^{31}\) . As such, sub- Arctic species of fish (e.g., brook trout, lake sturgeon, northern pike, and walleye), insects and riparian plants may now be exposed to the cumulative impacts of these environmental stressors \(^{17}\) . Additionally, winter frazil ice production and ice jams may be precipitated and accentuated downstream of hydroelectric facilities with persistent hydropeaking signals such as in the Peace River \(^{19}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 880, 495]]<|/det|> +Despite their recent northward expansion, weekly hydropeaking cycles are generally waning across the USA and southern Canada, suggesting a \(21^{\text{st}}\) century hydropeaking recovery in some of these river systems. Indeed, prior ecohydrological impacts of hydropeaking may be partially offset, benefiting local biota and ecosystem biodiversity32. For instance, recovery of lake sturgeon in the northern peninsula of Michigan demonstrates some of the benefits of shifting away from peaking hydropower operations24. This is particularly important as evidence is also mounting that hydropeaking influences aquatic species in rivers of Canada33- 36. Other aspects of flow regulation, such as sub- daily flow fluctuations and associated ramping up and down cycles not investigated in this study, may negate this hydropeaking recovery16, 17. Additional research is thus needed to explore hydropeaking cycles at other temporal scales to establish their site- specific ecohydrological impacts. + +<|ref|>text<|/ref|><|det|>[[111, 540, 880, 877]]<|/det|> +Advantages and limitations of the WHI relative to other metrics. The proposed index to infer weekly hydropeaking signals provides a complementary metric to those developed in other studies5, 37, 38. Advantages of our approach include its scale independence, dynamic response, and relatively simple implementation. The WHI can be applied from small \((< 1 \times 10^{3} \text{km}^{2})\) to large \((> 1 \times 10^{6} \text{km}^{2})\) river basins with available daily discharge data (whether observed, reconstructed or simulated). The WHI responds to interannual variability in climate (e.g., wet/dry periods), changes in water management practices and policies, commissioning of new hydroelectric facilities or decommissioning of old ones, and other factors that affect flows. The use of daily discharge data also avoids the need for extensive databases on dams, reservoirs and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 881, 388]]<|/det|> +other infrastructure that influence flows. Its possible implementation for short- term flow predictions emerges as another distinct advantage of the WHI. As an example, a running value of the WHI can be computed on the past year's daily flows and used to infer the possible deviations in daily flows over a given week based on recent historical patterns. Its computational simplicity, coded in our study in Fortran, allows processing of results for the 400 sites in \(< 2.5\) minutes. As such, it is feasible to implement a version of the code for short- term flow predictions so long as up- to- date daily flow records remain available. It would also be relatively straightforward to adapt the code to explore sub- daily hydropeaking cycles9 if appropriate discharge data are available. + +<|ref|>text<|/ref|><|det|>[[110, 435, 880, 842]]<|/det|> +One challenge in implementing the WHI is access to daily discharge records. While considerable gauging stations exist in most of the USA and southern Canada, other waterways are not necessarily well monitored. A late \(20^{\text{th}}\) century decline in hydrometric stations due to budget restraints39 and the Water Survey of Canada's curtailment of data collection combined with stricter quality standards from third parties have exacerbated hydrological data accessibility. As well, private industry and government- owned corporations often record discharge at or near their hydroelectric facilities but may consider these data as sensitive such that they are not released publicly. Thus, acquisition of daily discharge data in regulated systems, particularly as the number of small, private firms operating run- of- river hydroelectric facilities expands3, yields a distinct challenge in accessing flow data. Therefore, remote sensing40, data reconstructions (e.g. from statistical models or machine learning methods41) and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 838, 144]]<|/det|> +numerical simulations that incorporate regulation42 are key in filling spatio- temporal gaps where and when in situ observations are lacking. + +<|ref|>text<|/ref|><|det|>[[110, 170, 880, 789]]<|/det|> +Concluding remarks. As hydropower generation and infrastructure development continues to expand across the USA and Canada, it is important to establish how water management practices affect downstream river flows and ecosystems. A common feature in regulated rivers are discharge periodicities associated with hydropower production ebbs and flows including weekly cycles. In this study, a new measure of this weekly rhythm in flows, the weekly hydropeaking index (WHI), is formulated and applied to 400 sites over parts of North America. Our findings reveal vanishing weekly hydropeaking cycles across the USA and Canada in the 2010s, suggesting diminishing differences between discharge on weekends vs. weekdays. Factors possibly yielding this result include increased commercial and industrial activity on weekends, a shift towards other modes of energy production during peak demand hours or days, and policy changes altering water management practices including for ecological and environmental flows. This reduction in weekly hydropeaking also may benefit aquatic species, insects and riparian vegetation that otherwise are susceptible to rapid shifts in flows and water levels. Future efforts should therefore establish the ecohydrological implications of waning hydropeaking cycles. The application of the WHI to other regions over the globe would provide broader perspectives on the commonality of this feature in regulated rivers. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 212, 111]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[111, 125, 877, 570]]<|/det|> +Study area. The USA and Canada harbor abundant freshwater resources that include some of the world's largest rivers (by annual volumetric flows) including the Mississippi, St. Lawrence, Mackenzie, Ohio and Columbia rivers \(^{43}\) . Many of these rivers and/or their tributaries have been impounded for hydropower generation, flood control, irrigation, potable water supply, navigation and recreation, leading to fragmented river networks and regulated flows \(^{4,6}\) . Indeed, numerous dams have been built across the USA and Canada in the \(20^{\text{th}}\) and early \(21^{\text{st}}\) centuries \(^{2,3}\) . While many dams in North America have multiple purposes, hydropower generation remains a principal function. Distinct weekly patterns mark hydropower production except perhaps at run- of- river facilities and those supplying industries continuously in operation such as aluminum smelters or pulp and paper mills \(^{8,9}\) . As such, this study focuses on both regulated and unregulated waterways of the USA and Canada to explore the prevalence and intensity of weekly periodicity in discharge. + +<|ref|>text<|/ref|><|det|>[[111, 594, 875, 896]]<|/det|> +Site selection. A total of 400 sites across the USA and Canada ranging 480- 1,805,222 \(\mathrm{km}^2\) in gauged area (A), 25- 60°N in latitude, 54- 132°W in longitude, and 0.11- 268.28 \(\mathrm{km}^3\) in mean annual discharge are selected for this study (Supplementary Fig. 12 and Supplementary Table 2). A primary site selection criterion is discharge data availability for \(\geq 24\) years between 1920- 2019, with \(\geq 14\) years during the focus period of 1980- 2019. The chosen sites span a wide range of hydrological regimes from pluvial rivers in warmer climates (e.g., BC's Yakoun River) to nival and glacial systems at higher elevations or latitudes in cooler climates (e.g., BC's Lillooet River) \(^{44}\) . Thus, the study area spans regions with little to no snowmelt where sub- annual scales govern temporal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 852, 110]]<|/det|> +variability while others are mainly snowmelt- driven with predominant annual cycles45. + +<|ref|>text<|/ref|><|det|>[[110, 123, 840, 147]]<|/det|> +The database also includes intermittent streams in warmer, drier climates such as + +<|ref|>text<|/ref|><|det|>[[110, 158, 850, 181]]<|/det|> +California's Santa Ana River and Arizona's Little Colorado River. Regulated and + +<|ref|>text<|/ref|><|det|>[[110, 193, 848, 216]]<|/det|> +unregulated rivers are selected (using guidance from Benke and Cushing43) to allow + +<|ref|>text<|/ref|><|det|>[[110, 228, 833, 251]]<|/det|> +comparisons between sites. Some sites such as Lees Ferry on the Colorado River + +<|ref|>text<|/ref|><|det|>[[110, 263, 789, 285]]<|/det|> +include extended records that cover pre- and post- regulation effects on flows. + +<|ref|>text<|/ref|><|det|>[[110, 312, 808, 335]]<|/det|> +Data. Data and metadata (station ID, gauge coordinates, and gauged area) are + +<|ref|>text<|/ref|><|det|>[[110, 346, 864, 370]]<|/det|> +extracted from various sources including publicly accessible databases maintained by + +<|ref|>text<|/ref|><|det|>[[110, 381, 810, 404]]<|/det|> +federal, provincial and state agencies in addition to proprietary data from private + +<|ref|>text<|/ref|><|det|>[[110, 416, 790, 439]]<|/det|> +industry, government- owned utilities and international commissions. For most + +<|ref|>text<|/ref|><|det|>[[110, 450, 854, 473]]<|/det|> +unregulated rivers, daily discharge data are sourced partly from the Water Survey of + +<|ref|>text<|/ref|><|det|>[[110, 485, 877, 508]]<|/det|> +Canada's Hydrometric Database (HYDAT), the Centre d'Expertise Hydrique du Québec + +<|ref|>text<|/ref|><|det|>[[110, 520, 877, 543]]<|/det|> +(CEHQ) and the United States Geological Survey (USGS). For regulated rivers, though, + +<|ref|>text<|/ref|><|det|>[[110, 555, 856, 578]]<|/det|> +daily discharge data are not necessarily available from these sources or other public + +<|ref|>text<|/ref|><|det|>[[110, 590, 872, 613]]<|/det|> +repositories as they are partially or entirely collected, quality controlled and archived by + +<|ref|>text<|/ref|><|det|>[[110, 625, 868, 648]]<|/det|> +government- controlled utilities or private industry (see Supplementary Tables 2 and 3). + +<|ref|>text<|/ref|><|det|>[[110, 660, 870, 683]]<|/det|> +This includes: Nalcor Energy for the Salmon and Exploits rivers plus the Churchill Falls + +<|ref|>text<|/ref|><|det|>[[110, 695, 868, 718]]<|/det|> +(Labrador) Corporation Limited for the Churchill River at Churchill Falls Powerhouse in + +<|ref|>text<|/ref|><|det|>[[110, 730, 857, 752]]<|/det|> +Newfoundland and Labrador; NB Power for the St. John River in New Brunswick; Rio + +<|ref|>text<|/ref|><|det|>[[110, 764, 840, 787]]<|/det|> +Tinto for the Kemano Powerhouse in BC and the Saguenay and Péribonca rivers in + +<|ref|>text<|/ref|><|det|>[[110, 799, 802, 822]]<|/det|> +Québec; Hydro- Québec for La Grande Rivière, Betsiamites, Manicouagan, des + +<|ref|>text<|/ref|><|det|>[[110, 834, 872, 857]]<|/det|> +Outaouais, des Outardes and St- Maurice rivers; Evolugen by Brookfield Renewable for + +<|ref|>text<|/ref|><|det|>[[110, 869, 872, 892]]<|/det|> +the Coulonge, Lièvre, and Noire rivers in Québec and Mississagi and Aux Sables rivers + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 883, 530]]<|/det|> +in Ontario; Ontario Power Generation for the Abitibi, English, Kaministiquia, Madawaska, Mattagami (tributary to the Moose River), Montreal and Ottawa rivers; H2O Power for the Abitibi River; Manitoba Hydro for the Nelson and Winnipeg rivers; Transalta for the North Saskatchewan and Kananaskis rivers; and BC Hydro for the Columbia River at Mica Dam. Additional data for gauges along the Rio Grande on the border between the USA and Mexico and the Pecos River are provided by the International Boundary and Water Commission. Data at six sites in the Tennessee River Basin are provided by the Tennessee Valley Authority. Recent records of daily discharge from the US Bureau of Reclamation supplement those from the USGS for sites on the Colorado and upper Rio Grande rivers. Finally, the 1 October to 31 December 2019 daily discharge data for the Snake River at Hells Canyon Dam are sourced from Idaho Power. Potential errors associated with discharge measurements and implications to our results are discussed in the Supplementary Methods. + +<|ref|>text<|/ref|><|det|>[[111, 556, 872, 892]]<|/det|> +Time series construction. The overall study period spans 1 January 1920 to 31 December 2019 for which at least partial, extended (≥ 24 years) records of daily discharge are available at all sites. Time series of daily streamflow (in m³ s⁻¹) are constructed based on data availability for each site of interest (Supplementary Table 2) and follows Déry et al.⁴⁶ in its approach. Daily discharge data sourced from the USGS, US Bureau of Reclamation, Tennessee Valley Authority, Idaho Power, Nalcor Energy (Exploits River) and NB Power are converted to metric units prior to analysis. For several waterways (e.g., the Nelson and Saguenay Rivers), data furthest downstream are first used, but when unavailable (prior to construction of dams and hydroelectric facilities), are replaced with those from the closest upstream gauging station while + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 874, 319]]<|/det|> +adjusting the data for the missing contributing area as necessary46, 47. Gaps are in- filled with the mean daily discharge over the period of record; however, any calendar year with \(\geq 10\%\) missing records is excluded from analysis. Supplementary Table 2 lists the percentage of in- filled data at each site (average: \(0.02\%\) , maximum: \(0.55\%\) ) omitting years when \(\geq 10\%\) of the data remain unavailable. Uncertainty in the results associated with data homogeneity and the gap- filling strategy is evaluated and discussed in the Supplementary Methods. + +<|ref|>text<|/ref|><|det|>[[111, 346, 883, 753]]<|/det|> +Development of the WHI. Various approaches are commonly used to explore flow alterations in regulated rivers including comparisons of hydrographs pre- and postregulation9, 48, 49, trends in peak and/or low flows50 or of naturalized versus observed (regulated) flows51- 53. A broader approach employs a set of multiple (up to 33) indicators of hydrologic alteration (IHA) to quantify changes over the water year arising from regulation54- 56. Another method combines hydrological data, reservoir information and a database of large dams in developing river regulation and fragmentation indices with a matrix of impact for application to all major global watersheds4, 5. Apart from time domain analyses, Discrete Fourier Transforms (DFTs) or wavelet analyses offer additional insights on impacts of flow alterations from human interventions15, 20, 45, 57. Consult Jumani et al.38 for a review of river regulation and fragmentation indices including their applications, advantages and limitations. + +<|ref|>text<|/ref|><|det|>[[111, 779, 884, 907]]<|/det|> +While various approaches exist to infer hydrologic alterations from diversions, dam and reservoir operations including sub- daily hydropeaking cycles58, 59, none focuses on the weekly timescale, a primary periodicity of socioeconomic activity. Therefore, we develop a novel WHI that combines time and frequency domain terms to quantify weekly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 216]]<|/det|> +periodicity in river discharge. The time domain term ( \(T_{T}\) , %) counts the number of weeks ( \(D_{w}\) ) in a given calendar year when two consecutive days exhibit flows lower than the corresponding weekly average ( \(Q_{1 - 7}\) ), followed by five sequential days above the corresponding weekly average: + +<|ref|>equation<|/ref|><|det|>[[320, 243, 675, 301]]<|/det|> +\[T_{T} = \max \left\{\frac{100}{52}\sum_{w = 1}^{52}D_{w},0.001\right\} \mathrm{and~where}\] + +<|ref|>equation<|/ref|><|det|>[[275, 353, 720, 420]]<|/det|> +\[D_{w} = \left\{ \begin{array}{ll}1 & \mathrm{if} Q_{1,2}< \overline{Q_{1 - 7}} \mathrm{and if} Q_{3,\dots,7} > \overline{Q_{1 - 7}} \\ 0.25 & \mathrm{if} Q_{1}< \overline{Q_{1 - 7}} \mathrm{and if} Q_{2,\dots,7} > \overline{Q_{1 - 7}} \\ 0 & \mathrm{if~otherwise} \end{array} \right\}\] + +<|ref|>text<|/ref|><|det|>[[111, 464, 850, 870]]<|/det|> +This sequence of daily flows is chosen to emphasize the typical weekly rhythm observed in hydropeaking rivers: low flows on weekends when hydropower demand wanes, followed by high flows on weekdays when hydropower demand waxes \(^{9}\) . A partial score of 0.25 is ascribed to sites where six consecutive days above the weekly average follow a single low flow day for that week. As some gauging sites lie downstream from points of regulation such that low flows are shifted later in the week rather than occurring on Saturdays and Sundays, we test all seven possible combinations of two consecutive days (e.g., Saturday/Sunday, Sunday/Monday, ..., Friday/Saturday) and select the one that maximizes WHI at each site over the period of record. This approach for the time domain term attenuates the effects of cyclical (rather than periodic) variations from synoptic- scale storm activity, which otherwise leads to marked weekly cycles in pluvial rivers \(^{45}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 879, 250]]<|/det|> +An application of DFTs to the daily discharge data provides the frequency domain term. Here we follow Wilks \(^{60}\) in partitioning the daily discharge time series into sine and cosine waves of amplitude \(C_{k}\) for harmonic \(k\) . DFTs are computed for each calendar year with the \(52^{\text{nd}}\) harmonic representing the weekly timescale of interest here. Then we compute the explained variance of the \(52^{\text{nd}}\) harmonic \((T_{F})\) : + +<|ref|>equation<|/ref|><|det|>[[405, 278, 881, 327]]<|/det|> +\[T_{F} = \frac{\left(\frac{n}{2}\right)C_{52}^{2}}{(n - 1)s_{Q}^{2}} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[111, 355, 850, 412]]<|/det|> +where \(n\) is the number of days in a given year (365 or 366 for a leap year), \(C_{52}\) is the amplitude of the \(52^{\text{nd}}\) harmonic, and \(s_{Q}\) is the standard deviation in discharge. + +<|ref|>text<|/ref|><|det|>[[111, 440, 828, 496]]<|/det|> +After expressing \(T_{T}\) and \(T_{F}\) as percentages, we take the base 10 logarithm of their product to obtain an annual WHI: + +<|ref|>equation<|/ref|><|det|>[[405, 525, 880, 548]]<|/det|> +\[\mathrm{WHI} = \log_{10}[B(T_{T}\times T_{F})] \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[110, 557, 881, 895]]<|/det|> +in which \(B (= 10)\) is a coefficient chosen so that the median \(\mathrm{WHI} \approx 0\) among all 400 sites. Annual WHI values range typically from about - 4 to +4 (although WHI values have no theoretical upper or lower bounds), with large positive values indicating strong weekly periodicity attributed to flow regulation at hydropower stations. In contrast, rivers with robust annual cycles with flows dominated by potent snowmelt- driven freshets and/or large (natural) storage capacity within abundant lakes, ponds and wetlands exhibit large negative WHI values. The transition between negative to positive WHI values marks a shift from annual to weekly dominant time scales of variability in flow. The 1980- 2019 mean daily flows (considering the day of the week) for the Stuart River (BC), Mohawk River (New York), and Chattahoochee River at Buford Dam (Georgia) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 868, 249]]<|/det|> +illustrate the WHI ranging from the minimum, median, and maximum values (Supplementary Fig. 13). WHI values remain site- specific and must be interpreted with care, particularly moving away (both upstream and downstream) from measurement sites with an intervening body of water, a confluence or another point of regulation altering hydropeaking intensity. + +<|ref|>text<|/ref|><|det|>[[111, 276, 880, 718]]<|/det|> +Statistical analyses. We first compute WHI time series at all 400 sites and develop a 'climatology' of index values for 1980- 2019, with 14 years \(\leq n_{y} \leq 40\) years depending on data availability at each site. Summary statistics (mean, median, standard deviation, etc.) of the 1980- 2019 WHI data are tabulated and their distribution tested for normality using the Shapiro- Wilk test. Similar climatological analyses are developed for each decade (1920s to 2010s) with results reported when \(n_{y} \geq 5\) years at a given site. The Mann- Kendall test (MKT61, 62) applied to all WHI time series with \(n_{y} \geq 30\) years over 1980- 2019 yields linear, monotonic trends in hydropeaking intensity, with \(p < 0.05\) considered locally statistically- significant. The field (or global) significance of the individual (or local) trend tests is assessed following Wilks60. The approach minimizes the false discovery rate (FDR) by first ranking \(p\) - values in ascending order for all trend tests with \(n_{y} \geq 30\) years. Trends are then globally significant if \(p < p_{\text{FDR}}\) depending on the distribution of sorted \(p\) - values as: + +<|ref|>equation<|/ref|><|det|>[[264, 746, 881, 768]]<|/det|> +\[p_{FDR} = \max_{i = 1,2,\dots,N}\{p_i; p_i \leq (i / N) \alpha_{\text{global}}\} \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[111, 796, 883, 852]]<|/det|> +in which we set \(\alpha_{\text{global}} = 0.10\) . Trend analysis sensitivity to autocorrelation is tested in the Supplementary Methods. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 875, 280]]<|/det|> +We assess the 1920 to 2019 annual mean, median and maximum WHI across all sites with available data in a given year to track the overall evolution of hydropeaking intensity across the USA and Canada. We also count the annual number and percentage of sites that fall in the top decile of all 1920- 2019 WHI scores. An additional metric reported is the discharge- weighted WHIQI computed each calendar year (index \(j\) ) as: + +<|ref|>equation<|/ref|><|det|>[[320, 295, 881, 355]]<|/det|> +\[\mathrm{WHI}_{Q_i} = \sum_{i = 1}^{n = 400}\mathrm{WHI}_{i,j}\times Q_{i,j} / \sum_{i = 1}^{n = 400}Q_{i,j} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[110, 375, 880, 644]]<|/det|> +where \(\mathrm{Q}_{i,j}\) (km3 yr- 1) denotes the annual discharge and \(i\) is the site index. This yields a relative measure of annual volumetric flows affected by weekly hydropeaking cycles rather than just the number of sites. For monotonic trend analysis, the MKT is applied to time series of overall mean annual WHI over the 1980- 2019 focus period. The potential influence of missing data on the evolution of average WHI over 1980- 2019 is assessed by substituting incomplete time series with each missing site's average WHI computed over the remainder of the focus period. This yields an adjusted mean annual WHI time series for a first order assessment of the influence of incomplete data. + +<|ref|>text<|/ref|><|det|>[[110, 670, 883, 902]]<|/det|> +A histogram illustrates the distribution of two consecutive days when low flows emerge relative to the expected value of \(1 / 7 = 0.143\) were these randomly distributed. Fractions of the seven possible two- day combinations are partitioned according to \(\mathrm{WHI} \gtrsim 0\) . The histogram also includes the corresponding mean WHI across all rivers for a given two- day combination of low flows. A Chi- Square goodness- of- fit test63 verifies the hypothesis of whether the distribution of low flow days differs significantly from the expected value with threshold \(p = 0.05\) . Similarly, we test if the corresponding mean WHI values for the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 875, 355]]<|/det|> +two- day pairs with low flows follow a uniform distribution using a Chi- Square test. The relationship between annual WHI values and mean annual flows over 1980- 2019 is evaluated using Pearson's correlation coefficient with \(p < 0.05\) considered statistically- significant values. Next, we transform annual discharge time series to standardized anomalies over the period of record at each site (with \(< 10\%\) missing data in a calendar year). Decadal mean standardized anomalies for all available sites are then computed when \(n_y \geq 5\) years in a given decade. These decadal average anomalies are binned in increments of 0.25 standard anomaly for comparison with WHI decadal distributions. + +<|ref|>text<|/ref|><|det|>[[110, 382, 884, 650]]<|/det|> +To explore possible factors contributing to WHI trends we assess whether the dispersion of flows across the seven days of the week is changing over time. Here we first compile total annual flows (in \(m^3 s^{- 1}\) ) for each of the seven days of the week, as well as the overall average, over each calendar year. Then we quantify departures (as a percentage) for each day of the week relative to the annual mean. Next, we calculate standard deviations (\(\sigma\)) in the percentage departures for the seven days of the week each year, creating \(\sigma\) time series for all 400 sites over 1980- 2019. Finally, application of the MKT on the \(\sigma\) time series (when \(n_y \geq 30\) years) yields 1980- 2019 dispersion trends. + +<|ref|>sub_title<|/ref|><|det|>[[115, 677, 267, 697]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[110, 725, 850, 888]]<|/det|> +Data related to this article can be found in the Supplementary Information and Supplementary Data files. Discharge data used in this study are available in the following publicly accessible databases: Centre d'Expertise Hydrique du Québec (http://www.cehq.gouv.qc.ca/hydrometrie/historiqne_donnees/info_validite.htm), US Bureau of Reclamation (https://data.usbr.gov/), United States Geological Survey + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 853, 109]]<|/det|> +(https://waterdata.usgs.gov/nwis), Water Survey of Canada's Hydrometric Database + +<|ref|>text<|/ref|><|det|>[[110, 123, 850, 144]]<|/det|> +(https://wateroffice.ec.gc.ca), Idaho Power (https://idastream.idahopower.com/Data/) + +<|ref|>text<|/ref|><|det|>[[110, 158, 595, 179]]<|/det|> +and the International Boundary and Water Commission + +<|ref|>text<|/ref|><|det|>[[110, 193, 860, 214]]<|/det|> +(https://www.ibwc.gov/Water_Data/). For some regulated rivers, proprietary discharge + +<|ref|>text<|/ref|><|det|>[[110, 228, 844, 249]]<|/det|> +data can be requested from the following data providers: BC Hydro, Evolugen, H2O + +<|ref|>text<|/ref|><|det|>[[110, 263, 825, 283]]<|/det|> +Power, Hydro- Québec, International Boundary and Water Commission, Manitoba + +<|ref|>text<|/ref|><|det|>[[110, 297, 841, 318]]<|/det|> +Hydro, Nalcor Energy, NB Power, Ontario Power Generation, Rio Tinto, Tennessee + +<|ref|>text<|/ref|><|det|>[[110, 332, 877, 353]]<|/det|> +Valley Authority, and TransAlta (see Supplementary Table 3). Source data are provided + +<|ref|>text<|/ref|><|det|>[[110, 368, 250, 387]]<|/det|> +with this paper. + +<|ref|>sub_title<|/ref|><|det|>[[115, 418, 272, 437]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[115, 456, 755, 496]]<|/det|> +The Fortran code used in this study is available online with explanation at http://web.unbc.ca/\~sdery/NatComm.zip. + +<|ref|>sub_title<|/ref|><|det|>[[115, 531, 223, 549]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 565, 774, 625]]<|/det|> +1. International Hydropower Association. 2020 Hydropower Status Report, https://hydropower-assets.s3.eu-west-2.amazonaws.com/publications-docs/2020_hydropower_status_report.pdf (2020). + +<|ref|>text<|/ref|><|det|>[[115, 638, 848, 679]]<|/det|> +2. Graf, W. L. Dam nation: A geographic census of American dams and their large-scale hydrologic impacts. Water Resour. Res. 35, 1305-1311 (1999). + +<|ref|>text<|/ref|><|det|>[[115, 692, 825, 732]]<|/det|> +3. Couto, T. B. A. & Olden, J. D. 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D., Jansson, R. & Nilsson, C. The effects of hydropeaking on riverine plants: a review. Biol. Rev. 93, 658-673 (2018). + +<|ref|>text<|/ref|><|det|>[[112, 699, 864, 740]]<|/det|> +30. Moreira, M. et al. Ecologically-based criteria for hydropeaking mitigation: A review. Sci. Total Environ. 657, 1508-1522 (2019). + +<|ref|>text<|/ref|><|det|>[[112, 755, 856, 796]]<|/det|> +31. Serreze, M. C. & Francis, J. A. The Arctic amplification debate. Clim. Change 76, 241-265 (2006). + +<|ref|>text<|/ref|><|det|>[[112, 812, 841, 852]]<|/det|> +32. Poff, N. L. & Schmidt, J. C. How dams can go with the flow. Science 353, 1099-1100 (2016). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 800, 148]]<|/det|> +33. Harvey-Lavoie, S., Cooke, S. J., Guénard, G. & Boisclair, D. Differences in movements of northern pike inhabiting rivers with contrasting flow regimes, Ecohydrology 9, 1687-1699 (2016). + +<|ref|>text<|/ref|><|det|>[[115, 163, 830, 204]]<|/det|> +34. Jones, N. E. & Petreman, I. C. Environmental influences on fish migration in a hydropeaking river. River Res. Appl. 31, 1109-1118 (2015). + +<|ref|>text<|/ref|><|det|>[[115, 218, 860, 279]]<|/det|> +35. Kelly, B., Smokorowski, K. E. E. & Power, M. Downstream effects of hydroelectric dam operation on thermal habitat use by Brook Trout (Salvelinus fontinalis) and Slimy Sculpin (Cottus cognatus). Ecol. Freshw. Fish 26, 552-562 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 293, 881, 354]]<|/det|> +36. Taylor, M. K. et al. Reach-scale movements of bull trout (Salvelinus confluentus) relative to hydropeaking operations in the Columbia River, Canada. Ecohydrology 7, 1079-1086 (2014). + +<|ref|>text<|/ref|><|det|>[[115, 369, 880, 409]]<|/det|> +37. Haghighi, A. T., Marttila, H. & Klove, B. Development of a new index to assess river regime impacts after dam construction. Global Planet. Change 122, 186-196 (2014). + +<|ref|>text<|/ref|><|det|>[[115, 423, 866, 464]]<|/det|> +38. Jumani, S. et al. River fragmentation and flow alteration metrics: a review of methods and directions for future research. Environ. Res. Lett. 15, 123009 (2020). + +<|ref|>text<|/ref|><|det|>[[115, 479, 870, 540]]<|/det|> +39. Mlynowski, T. J., Hernández-Henríquez, M. A. & Déry, S. J. An evaluation of hydrometric monitoring across the Canadian pan-Arctic region, 1950-2008. Hydrol. Res. 42, 479-490 (2011). + +<|ref|>text<|/ref|><|det|>[[115, 555, 840, 615]]<|/det|> +40. Gleason, C. J. & Smith, L. C. Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proc. Natl Acad. Sci. USA 111, 4788-4791 (2014). + +<|ref|>text<|/ref|><|det|>[[115, 630, 870, 671]]<|/det|> +41. Tongal, H. & Booij, M. J. Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J. Hydrol. 564, 266-282 (2018). + +<|ref|>text<|/ref|><|det|>[[115, 685, 825, 726]]<|/det|> +42. Tefs, A. A. G. et al. Simulating river regulation and reservoir performance in a continental-scale hydrologic model. Environ. Modell. Softw., in press (2021). + +<|ref|>text<|/ref|><|det|>[[115, 740, 860, 781]]<|/det|> +43. Benke, A. C. & Cushing, C. E. (Eds.) Rivers of North America. Elsevier Academic Press, Amsterdam, 1144 pp. (2005). + +<|ref|>text<|/ref|><|det|>[[115, 795, 880, 836]]<|/det|> +44. Déry, S. J. et al. Detection of runoff timing changes in pluvial, rival and glacial rivers of western Canada. Water Resour. Res. 45, W04426 (2009). + +<|ref|>text<|/ref|><|det|>[[115, 850, 880, 891]]<|/det|> +45. Smith, L. C., Turcotte, D. L. & Isacks, B. L. Stream flow characterization and feature detection using a discrete wavelet transform. Hydrol. Process. 12, 233-249 (1998). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 850, 148]]<|/det|> +46. Dery, S. J., Stadnyk, T. A., MacDonald, M. K. & Gauli-Sharma, B. Recent trends and variability in river discharge across northern Canada. Hydrol. Earth Syst. Sci. 20, 4801-4818 (2016). + +<|ref|>text<|/ref|><|det|>[[115, 163, 866, 223]]<|/det|> +47. Dery, S. J., Stieglitz, M., McKenna, E. C. & Wood, E. F. Characteristics and trends of river discharge into Hudson, James, and Ungava Bays, 1964-2000. J. Clim. 18, 2540-2557 (2005). + +<|ref|>text<|/ref|><|det|>[[115, 238, 870, 299]]<|/det|> +48. Peters, D. L. & Buttle, J. M. The effects of flow regulation and climatic variability on obstructed drainage and reverse flow contribution in a northern river-lake-delta complex, Mackenzie Basin headwaters. River Res. Appl. 26, 1065-1089 (2010). + +<|ref|>text<|/ref|><|det|>[[115, 313, 870, 373]]<|/det|> +49. Woo, M., Thorne, R., Szeto, K. & Yang, D. Streamflow hydrology in the boreal region under the influence of climate and human interference. Philos. T. R. Soc. B. 363, 2251-2260 (2008). + +<|ref|>text<|/ref|><|det|>[[115, 388, 880, 449]]<|/det|> +50. Assani, A. A., Stichelbout, E., Roy, A. G. & Petit, F. Comparison of impacts of dams on the annual maximum flow characteristics in three regulated hydrologic regimes in Québec (Canada). Hydrol. Process. 20, 3485-3501 (2006). + +<|ref|>text<|/ref|><|det|>[[115, 463, 879, 524]]<|/det|> +51. Naik, P. K. & Jay, D. A. Distinguishing human and climate influences on the Columbia River: Changes in mean flow and sediment transport. J. Hydrol. 404, 259-277 (2011). + +<|ref|>text<|/ref|><|det|>[[115, 539, 875, 599]]<|/det|> +52. St. Jacques, J.-M., Sauchyn, D. J. & Zhao, Y. Northern Rocky Mountain streamflow records: Global warming trends, human impacts or natural variability? Geophys. Res. Lett. 37, L06407 (2010). + +<|ref|>text<|/ref|><|det|>[[115, 614, 840, 654]]<|/det|> +53. Ye, B., Yang, D. & Kane, D. L. Changes in Lena River streamflow hydrology: Human impacts versus natural variations. Water Resour. Res. 39, 1200 (2003). + +<|ref|>text<|/ref|><|det|>[[115, 669, 880, 709]]<|/det|> +54. Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, D. P. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10, 1163-1174 (1996). + +<|ref|>text<|/ref|><|det|>[[115, 724, 875, 764]]<|/det|> +55. Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Sci. Adv. 3, e1700611 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 780, 876, 840]]<|/det|> +56. Zhou, X., Huang, X., Zhao, H. & Ma, K. Development of a revised method for indicators of hydrologic alteration for analyzing the cumulative impacts of cascading reservoirs on flow regime. Hydrol. Earth Syst. Sci. 24, 4091-4107 (2020). + +<|ref|>text<|/ref|><|det|>[[115, 856, 861, 896]]<|/det|> +57. Tongal, H., Demirel, M. C. & Moradkhani, H. Analysis of dam-induced patterns on river flow dynamics. Hydrol. Sci. J. 62, 626-641 (2017). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 850, 130]]<|/det|> +58. Carolli, M. et al. A simple procedure for the assessment of hydropeaking flow alterations applied to several European streams. Aquat. Sci. 77, 639-653 (2015). + +<|ref|>text<|/ref|><|det|>[[115, 145, 870, 185]]<|/det|> +59. Ashraf, F. B. H. et al. Changes in short term river flow regulation and hydropeaking in Nordic rivers. Sci. Rep. 8, 17232 (2018). + +<|ref|>text<|/ref|><|det|>[[115, 201, 872, 241]]<|/det|> +60. Wilks, D. S. Statistical Methods in the Atmospheric Sciences, Elsevier, Amsterdam, 4th edition, 818 pp. (2019). + +<|ref|>text<|/ref|><|det|>[[115, 256, 864, 275]]<|/det|> +61. Mann, H. B. Non-parametric test against trend. Econometrika 13, 245–259 (1945). + +<|ref|>text<|/ref|><|det|>[[115, 293, 857, 333]]<|/det|> +62. Kendall, M. G. Rank Correlation Methods. 202 pp., Oxford Univ. Press, New York (1975). + +<|ref|>text<|/ref|><|det|>[[115, 350, 875, 390]]<|/det|> +63. McCuen, R. H. Modeling Hydrologic Change: Statistical Methods. Lewis Publishers, Boca Raton, 433 pp. (2003). + +<|ref|>text<|/ref|><|det|>[[115, 440, 840, 459]]<|/det|> +Acknowledgements. Thanks to the Water Survey of Canada and its provincial and + +<|ref|>text<|/ref|><|det|>[[115, 475, 830, 494]]<|/det|> +territorial partners, the Centre d’Expertise Hydrique du Québec, USGS, BC Hydro, + +<|ref|>text<|/ref|><|det|>[[115, 510, 800, 529]]<|/det|> +Evolugen by Brookfield Renewable, Transalta, Manitoba Hydro, Ontario Power + +<|ref|>text<|/ref|><|det|>[[115, 545, 850, 564]]<|/det|> +Generation, H2O Power, Rio Tinto, Hydro-Québec, NB Power, Nalcor Energy, Idaho + +<|ref|>text<|/ref|><|det|>[[115, 580, 792, 599]]<|/det|> +Power, the Tennessee Valley Authority, the International Boundary and Water + +<|ref|>text<|/ref|><|det|>[[115, 615, 872, 634]]<|/det|> +Commission and the US Bureau of Reclamation for providing hydrometric data. Thanks + +<|ref|>text<|/ref|><|det|>[[115, 650, 852, 669]]<|/det|> +to Aseem Sharma (UNBC/NRCan) for preparing the spatial plots, Clyde McLean and + +<|ref|>text<|/ref|><|det|>[[115, 685, 816, 704]]<|/det|> +Joanna Barnard (Nalcor Energy), Jim Samms (NB Power), Marie Broesky, Kevin + +<|ref|>text<|/ref|><|det|>[[115, 720, 860, 739]]<|/det|> +Gawne, Kristina Koenig, Phil Slota, Kevin Sydor, Efrem Teklemariam, Mike Vieira and + +<|ref|>text<|/ref|><|det|>[[115, 755, 834, 774]]<|/det|> +Shane Wruth (Manitoba Hydro), Matt MacDonald (Ontario Power Generation), Erik + +<|ref|>text<|/ref|><|det|>[[115, 790, 825, 809]]<|/det|> +Richards and Marc Mantha (H2O Power), Samer Alghabra and Mokhtar Moujahid + +<|ref|>text<|/ref|><|det|>[[115, 825, 835, 844]]<|/det|> +(Hydro-Québec), Bruno Larouche and Richard Loubier (Rio Tinto), Michael Smilski + +<|ref|>text<|/ref|><|det|>[[115, 860, 877, 879]]<|/det|> +(Transalta), Jim Li, Debbie Rinvold and Stephanie Smith (BC Hydro), Adrian Cortez and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 880, 389]]<|/det|> +Delbert Humberson (International Boundary and Water Commission), Kelly Withers and Matti Hanninen (Evolugen) for providing comments on this work and for additional data for regulated rivers, Dwayne Akerman, Amber Brown, Michel Desjardins, Matt Falcone, Samantha Hussey, Lyssa Maurer, Angus Pippy, Melanie Taylor, and Frank Weber (Water Survey of Canada) for sharing supplemental hydrometric data, and Huilin Gao (Texas A&M), John Zhu (Texas Water Development Board), Julie Thériault (UQAM) and Mike Vieira and Kristina Koenig (Manitoba Hydro) for logistical support. This research was supported by the Natural Sciences and Engineering Research Council of Canada, Manitoba Hydro, and partners through funding of the BaySys project. + +<|ref|>text<|/ref|><|det|>[[110, 450, 872, 647]]<|/det|> +Author contributions. S.J.D. designed the study, extracted hydrometric data and constructed time series of daily discharge for all rivers, formulated the weekly hydropeaking index, developed the codes, performed the statistical and computational analyses, and drafted line graphs with support from M.A.H.H., T.A.S., and T.J.T. S.J.D. wrote the manuscript with contributions from all co- authors and all contributed to manuscript refinement and revisions. + +<|ref|>text<|/ref|><|det|>[[110, 676, 700, 697]]<|/det|> +Competing interests. The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 264, 109]]<|/det|> +## Figure Legends + +<|ref|>text<|/ref|><|det|>[[113, 144, 866, 224]]<|/det|> +Fig. 1 Map of the 1980- 2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). + +<|ref|>text<|/ref|><|det|>[[112, 275, 880, 455]]<|/det|> +Fig. 2 Histogram of the 1980- 2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980- 2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two- day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites. + +<|ref|>text<|/ref|><|det|>[[112, 490, 877, 729]]<|/det|> +Fig. 3 Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n_{y} \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate WHI \(\geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +<|ref|>text<|/ref|><|det|>[[112, 771, 872, 870]]<|/det|> +Fig. 4 Map of the 1980- 2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically- significant trends \((p < 0.05)\) . Results are shown only when \(n_{y} \geq 30\) years. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 170, 880, 632]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 664, 866, 744]]<|/det|> +
Fig. 1 Map of the 1980-2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[100, 214, 875, 664]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 685, 884, 864]]<|/det|> +
Fig. 2 Histogram of the 1980-2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980-2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two-day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[80, 88, 875, 870]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[80, 92, 875, 860]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 120, 876, 362]]<|/det|> +Fig. 3 Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n_{y} \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate WHI \(\geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 188, 883, 655]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 690, 872, 789]]<|/det|> +
Fig. 4 Map of the 1980-2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically-significant trends \((p < 0.05)\) . Results are shown only when \(n_{y} \geq 30\) years.
+ +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[182, 150, 815, 168]]<|/det|> +Table 1 List of sites with the top ten ranking WHI values, 1980-2019. + +<|ref|>table<|/ref|><|det|>[[211, 207, 785, 544]]<|/det|> + +
RankSiteWHI
1Chattahoochee R. at Buford Dam (GA)3.299
2Chattahoochee R. at West Point (GA)3.276
3Colorado R. at Hoover Dam (AZ/NV)3.222
4Nelson R. (MB)2.916
5Niagara R. (ON/NY)2.900
6Colorado R. at Lees Ferry (AZ)2.844
7Montreal R. (Lake Superior, ON)2.790
8Montreal R. (Ottawa Basin, ON)2.716
9Holston R. at Cherokee Dam (TN)2.675
10Columbia R. at Grand Coulee Dam (WA)2.662
+ +<|ref|>text<|/ref|><|det|>[[192, 564, 805, 600]]<|/det|> +AZ: Arizona, GA: Georgia, MB: Manitoba, NV: Nevada, NY: New York,ON: Ontario, TN: Tennessee, WA: Washington + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 69]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[42, 90, 955, 644]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 658, 115, 678]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 700, 953, 767]]<|/det|> +Map of the 1980- 2019 mean WHI values for 400 sites across the USA and Canada. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 50, 944, 586]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 610, 118, 629]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[40, 651, 940, 808]]<|/det|> +Histogram of the 1980- 2019 frequency distribution of low flow days and corresponding WHI values. Black bars denote the two consecutive days with low flows while red bars represent the WHI values for 400 sites across the USA and Canada, 1980- 2019. Fractions of the two consecutive days with low flows are partitioned according to positive (solid) and negative (hatched) WHI values. The days of the week begin with the Saturday/Sunday (SS) combination and end with the Friday/Saturday (FS) combination. The horizontal black line denotes the expected value if the two- day low flows were distributed randomly while the horizontal red line marks the mean WHI across the 400 sites. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[44, 40, 950, 500]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 523, 117, 543]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 565, 950, 790]]<|/det|> +Maps of the decadal mean WHI values for 400 sites across the USA and Canada. Maps are shown for a 1920- 1929, b 1930- 1939, c 1940- 1949, d 1950- 1959, e 1960- 1969, f 1970- 1979, g 1980- 1989, h 1990- 1999, i 2000- 2009, and j 2010- 2019. Circle size corresponds to the two consecutive days with low flows beginning with the Saturday/Sunday (SS, largest symbols) combination and ending with the Friday/Saturday (FS, smallest symbols). Results are shown only when \(n \geq 5\) years in a given decade. Panels k and l represent the cumulative percentage of sites falling within one of 10 WHI bins and one of seven two- day combinations of low flows, respectively. In k, WHI bins match those used in the spatial plots a- j with a similar color palette (e.g., the maroon bars indicate \(\mathrm{WHI} \geq 3.0\) starting at a zero cumulative percentage). In l, the two- day combinations with low flows start on Friday/Saturday at a zero cumulative percentage (maroon bars) and end on Saturday/Sunday at 100% (black bars). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[42, 42, 959, 597]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 610, 118, 630]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 652, 955, 740]]<|/det|> +Map of the 1980- 2019 monotonic trends in WHI at 380 sites across the USA and Canada. Red upward (blue downward) pointing triangles indicate positive (negative) trends. Trend magnitudes are proportional to the triangle sizes and green circles (pink outlines) indicate locally (globally) statistically- significant trends \((p < 0.05)\) . Results are shown only when \(n \geq 30\) years. + +<|ref|>sub_title<|/ref|><|det|>[[44, 764, 311, 791]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 814, 765, 834]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 852, 353, 950]]<|/det|> +- SupplementaryInformation.pdf- SupplementaryTable2.xlsx- SupplementaryTable3.xlsx- WHITimeSeries.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/images_list.json b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1f0cf5046df9c8427d21d0d4dace3c2ec05d8041 --- /dev/null +++ b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/images_list.json @@ -0,0 +1,122 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 Device fabrication and characteristics. a, An illustration of the VAC-treatment. b, Schematic of the PeLED structure and the HAADF cross-sectional device image. The scale bar is 100 nm. c, Histograms of peak EQEs extracted from control (top) and VAC-treated devices (bottom) with varying chloride contents (30%, 35%, 40%). d-f, Spectral stability for control and VAC-treated devices with 40% Cl loading. The representative plots of \\(\\mathrm{CIE}_y\\) versus applied voltages (top) and current densities (bottom) (d); EL spectra at low and high voltage/current density for control (left) and VAC-treated devices (right) (e); EL spectra of VAC-treated devices with varying chloride content (30\\~57%) at maximum luminance (f). The points labelled as \\(L_{\\mathrm{max}}\\) in Fig. 1d represent the operational condition for peak luminance.", + "footnote": [], + "bbox": [ + [ + 100, + 99, + 888, + 520 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Understanding superior spectral stability of VAC-treated devices. a-d, Photophysical characterizations for control and VAC-treated perovskite films: Fluence-dependent PLQYs (a); PL decay measured by TCSPC (b). PL spectra (c); Transient absorption of control (top) and VAC-treated films (bottom) after excitation at 400 nm (d). e, f, Derivations of temperature-dependent capacitance versus frequency plots for control (e) and VAC-treated (f) devices. The blue arrows indicate temperature change from 350 K to 200 K. Here, two mobile ions marked as \\(\\beta\\) and \\(\\epsilon\\) are visible.", + "footnote": [], + "bbox": [ + [ + 95, + 95, + 900, + 410 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 Understanding the halide redistribution during VAC-process. a, b, PL evolution of the precursor films kept in the glovebox atmosphere (a) and DMF atmosphere (b) with time. c, the evolution of emission linewidth and the proportion of P2 (Ar2) to the respective total area of the emission band (A) in VAC-treated films with time. d, Schematic illustration of the proposed mechanism for halide redistribution. Here, the purple \\(\\mathrm{Pb(Br / Cl)_6^{4 - }}\\) octahedra represent chloride-rich phases in respect to that with stoichiometric bromide/chloride distribution (blue octahedra). The khaki represents the liquid phase within the films and the blue arrows represent ion exchange process. The excessive ions within the dried films are not illustrated for clarity. e, f, The evolution of CIE coordinates upon bias (e) and peak EQEs of the devices with varying duration of VAC-treatment (0, 1, 2, 5, 20 minutes) (f). The data were extracted from 4 to 6 devices.", + "footnote": [], + "bbox": [ + [ + 95, + 98, + 904, + 556 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 The device performance of Rb-passivated perovskites with 40% and 45% Cl contents. a,", + "footnote": [], + "bbox": [ + [ + 167, + 95, + 825, + 423 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 75, + 100, + 920, + 600 + ] + ], + "page_idx": 34 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 63, + 52, + 933, + 422 + ] + ], + "page_idx": 35 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 50, + 49, + 940, + 600 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 75, + 58, + 940, + 528 + ] + ], + "page_idx": 37 + } +] \ No newline at end of file diff --git a/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e.mmd b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9550c36176fab5eb0ac0b2ae3ea9a9bb9b3e9742 --- /dev/null +++ b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e.mmd @@ -0,0 +1,441 @@ + +# Mixed Halide Perovskites for High-Efficiency and Spectrally Stable Blue Light-Emitting Diodes + +Max Karlsson Linköping University https://orcid.org/0000- 0003- 2750- 552X + +Ziyue Yi Linköping University + +Sebastian Reichert Chemnitz University of Technology https://orcid.org/0000- 0003- 3214- 7114 + +Xiyu Luo Linköping University + +Weihua Lin Lund University + +Zeyu Lin Beijing University of Technology + +Chunxiong Bao Linköping University https://orcid.org/0000- 0001- 7076- 7635 + +Rui Zhang Linköping University + +Sai Bai Linköping University https://orcid.org/0000- 0001- 7623- 686X + +Guanhaojie Zheng Linköping University + +Pengpeng Teng Linköping University + +Lian Duan Tsinghua University + +Yue Lu Beijing University of Technology + +Kaibo Zheng Lund University + +Tonu Pullerits Lund University https://orcid.org/0000- 0003- 1428- 5564 + +Carsten Deibel Chemnitz University of Technology https://orcid.org/0000- 0002- 3061- 7234 + +weidong xu + +<--- Page Split ---> + +linköping university https://orcid.org/0000- 0002- 0767- 3086 + +Richard FriendUniversity of Cambridge https://orcid.org/0000- 0001- 6565- 6308Feng Gao (feng.gao@liu.se)Linköping University https://orcid.org/0000- 0002- 2582- 1740 + +## Article + +Keywords: light- emitting diodes, mixed halide perovskites, blue emission + +Posted Date: December 1st, 2020 + +DOI: https://doi.org/10.21203/rs.3.rs- 92649/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on January 13th, 2021. See the published version at https://doi.org/10.1038/s41467- 020- 20582- 6. + +<--- Page Split ---> + +# Mixed Halide Perovskites for Spectrally Stable and High-Efficiency Blue Light-Emitting + +## Diodes + +Max Karlsson \(^{1,8}\) , Ziyue Yi \(^{1,2,8}\) , Sebastian Reichert \(^{3}\) , Xiyu Luo \(^{1,4}\) , Weihua Lin \(^{5}\) , Zeyu Zhang \(^{6}\) , Chunxiong Bao \(^{1}\) , Rui Zhang \(^{1}\) , Sai Bai \(^{1}\) , Guanhaojie Zheng \(^{1}\) , Pengpeng Teng \(^{1}\) , Lian Duan \(^{4}\) , Yue Lu \(^{6}\) , Kaibo Zheng \(^{5,7}\) , Tönu Pullerits \(^{5}\) , Carsten Deibel \(^{3}\) , Weidong Xu \(^{1}\) , \(*\) Richard Friend \(^{2}\) , and Feng Gao \(*^{1}\) + +\(^{1}\) Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden. \(^{2}\) Cavendish Laboratory, University of Cambridge, Cambridge, UK. \(^{3}\) Institut für Physik, Technische Universität Chemnitz, Chemnitz, Germany. \(^{4}\) Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University Beijing, China. \(^{5}\) Chemical Physics and NanoLund, Lund University, Box 124, 22100 Lund, Sweden. \(^{6}\) Institute of Microstructure and Properties of Advanced Materials, Beijing University of Technology, Beijing, China. \(^{7}\) Department of Chemistry, Technical University of Denmark, DK- 2800 Kongens Lyngby, Denmark. \(^{8}\) These authors contributed equally: M. K. and Z. Y. + +<--- Page Split ---> + +## Abstract + +AbstractBright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content that is desired to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes (PeLEDs) over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue PeLEDs based on three-dimensional perovskites show high EQE values of \(11.0\%\) and \(5.5\%\) with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion migration. + +<--- Page Split ---> + +Blue light- emitting diode, with the Commission Internationale de l'Eclairage (CIE) y coordinate value below 0.15 along with the \((x + y)\) value below 0.30, is of critical importance for display and energy- saving lighting applications'. Similar to preceding light- emitting technologies, achieving efficient blue emission in metal halide perovskite light- emitting diodes (PeLEDs) has proven to be very challenging, with performance lagging far behind their green, red and near- infrared counterparts2- 7. Current efforts in blue PeLEDs largely take advantage of quantum confinement effects for bandgap engineering, i.e. using mixed dimensional perovskites or colloidal perovskite nanocrystals8- 10. Although impressive progress has been achieved in developing sky- blue PeLEDs (with \(\mathrm{CIEy} > 0.15\) ) (Supplementary Table 1)11, there are increasing difficulties to realize blue emission using these strategies. For example, state- of- the- art blue perovskite emitters achieved by strong quantum confinement commonly suffer from deteriorated electronic properties due to an excess of large- size organic cations and/or over- capped ligands. These issues lead to problematic charge injection and hence low brightness, as well as a big gap between photoluminescence quantum yields (PLQYs) of thin films and external quantum efficiencies (EQEs) of devices12- 14. + +Compared with enhancing quantum confinement, modulating the halide anions is a more straightforward way to tune the bandgap of perovskites15. However, implementation of this facile approach in blue PeLEDs is largely hindered by poor colour stability of the resultant blue perovskite emitters (mixed bromide/chloride perovskites), due to anion segregation under electric bias16- 18. In addition, it has been widely observed that the PLQYs decrease with increasing chloride content since chloride perovskites are less defect-tolerant compared to their bromide and iodide counterparts19,20. Both issues are particularly pronounced in perovskites with high chloride content that are desired for producing blue and deep- blue emssion19- 22. Very recently, strategies on mitigating photo- induced + +<--- Page Split ---> + +phase segregation in perovskite solar cells (e.g. defect passivation) have been borrowed to improve the spectral stability of mixed bromide/chloride blue PeLEDs \(^{23,24}\) . These strategies were so far demonstrated to be feasible only in the cases where the chloride content is low (<30%) \(^{22,25}\) . Unfortunately, even by combining the strategies of mixed bromide/chloride perovskites with the advantages of enhanced quantum- confinement, device performance of spectrally stable blue PeLEDs is still far from practical applications (Supplementary Table 2) \(^{11,14,20,25}\) . + +Here, we demonstrate that blue PeLEDs based on mixed- halide perovskites can be highly efficient and their colour instability issues can be substantially eliminated across a large range of the blue spectral region spanning 490\~451 nm (with a high chloride content ranging from 30% to 57%), without any assistance from enhanced quantum confinement. We show that not only halide ion migration, but also compositional heterogeneity is critical for triggering phase segregation. Both factors can be remarkably suppressed through depositing the perovskite films via a vapour- assisted crystallization (VAC) technique. As a result, we demonstrate spectrally stable blue PeLEDs presenting a high EQE value of 11.0% and a peak brightness of 2180 cd m \(^{-2}\) , with an emission peak at 477 nm and CIE coordinates of (0.107, 0.115). In addition, we fabricate a PeLED exhibiting ideal deep- blue emission at 467 nm and a decent EQE of 5.5%, which is the highest efficiency with an emission peak below 470 nm. The CIE coordinates of our deep- blue PeLEDs are (0.130, 0.059), approaching that of Rec. 2020 specified primary blue. + +## Results and discussion + +Device fabrication and spectral stability. We prepare perovskites from precursors with a stoichiometry of \(\mathrm{Cs^{+}}\) : \(\mathrm{FA^{+}}\) : \(\mathrm{Pb^{2 + }}\) : \([\mathrm{Br_{1 - x} + Cl_{x}}]^{- } = 1.2\) : 0.3: 1: 3.5 \((x = 30\% \sim 57\%)\) , where \(\mathrm{FA^{+}}\) is formamidinium. In the cases of \(x\) below 20%, we do not observe any colour instability issues + +<--- Page Split ---> + +(Supplementary Fig. 1). We focus our discussions on the perovskites from a precursor solution with \(x = 40\%\) , which is the most representative case due to its high chloride content, decent device performance and emission within the blue region. The chloride content in this film accounts for \(42\%\) of total halide anions as determined by X- ray photoelectron spectroscopy (XPS) (Supplementary Fig. 2). We introduce 4,7,10- trioxa- 1,13- tridecanediamin (TTDDA) into the perovskite precursors as a passivating agent to reduce defects5. + +We show an illustration of the VAC- treatment for preparation of perovskite films in Fig. 1a. In brief, the as- casted films are directly moved into a petri- dish with dimethylformamide (DMF) atmosphere, followed by a typical thermal annealing process. Control samples, annealed directly after spin- coating, are prepared for comparison. The different film processing techniques result in distinct variations of the film morphology, i.e. a discontinuous network of large grains for the VAC- treated films and full coverage of small nano- grains for the control ones, as shown in scanning electron microscope (SEM) images (Supplementary Fig. 3). We observe no change in the 3D crystal structure but a more preferential crystalline orientation along the (110) direction and slight enhancement of crystallinity with VAC- treatment, as demonstrated by grazing incidence wide- angle X- ray scattering (GIWAXS) and X- ray diffraction (XRD) measurements (Supplementary Fig. 4). + +We fabricate the PeLEDs based on a device structure of indium tin oxide (ITO)/nickel oxide (NiOx, 10 nm)/ poly(9- vinylcarbazole) (PVK): polyvinylpyrrolidone (PVP) (10 nm)/ perovskite/ 2,2',2''-(1,3,5- benzinetriyl)tris(1- phenyl- 1H- benzimidazole) (TPBi)/ lithium fluoride (1 nm)/aluminium (100 nm) (Fig. 1b). The employment of NiOx/PVK bilayer facilitates the hole injection due to the cascade energy level alignment26, which contributes to improve PeLED performance (Supplementary Figs. 5 and 6). The PVP layer is used to improve the wettability of the precursor + +<--- Page Split ---> + +solution on the PVK surface, as demonstrated by the reduced water contact angle after PVP deposition (Supplementary Fig. 7). The high- angle annular dark- field scanning transmission electron microscope (HAADF- STEM) and energy- dispersive X- ray spectroscopy (EDX) device cross- sectional images indicate higher TPBi layer thickness ( \(\sim 50 \mathrm{nm}\) ) on the bottom hole injection layer with respect to that on perovskite grains ( \(\sim 35 \mathrm{nm}\) ) (Fig. 1b and Supplementary Fig. 8 (carbon distribution)), leading to enhanced local resistance at the TPBi/PVK:PVP interface. Combined with the large injection barrier caused by the energy level mismatch between TPBi and PVK, the discontinuous morphology in VAC- treated devices does not necessarily lead to strong electrical shunts under normal PeLED operational conditions \(^{3,5}\) (Supplementary Fig. 9). Notably, the VAC- treated devices with varying chloride content ( \(30 - 40\%\) ) show considerable enhancement of EQE values compared to the respective control samples (Fig. 1c). We show the characteristics of the representative devices in Supplementary Fig. 10. We notice that, as expected, our device performance also benefits from the efficient defect passivation ability of TTDDA (Supplementary Fig. 11). + +The VAC- treated devices show stable electroluminescence (EL) with a negligible shift of the CIE coordinates up to \(\sim 400 \mathrm{mA cm^{- 2}}\) ( \(6 - 6.5 \mathrm{V}\) ), which is far above maximum light output ( \(L_{\mathrm{max}}\) ) conditions at \(100 \mathrm{mA cm^{- 2} / 5.0 \mathrm{V}}\) (Fig. 1d). The EL spectrum obtained at a high voltage (or current density) of 6 \(\mathrm{V} (\sim 400 \mathrm{mA cm^{- 2}})\) is only slightly broader than that at \(3.5 \mathrm{V} (\sim 3 \mathrm{mA cm^{- 2}})\) (Fig. 1e). We attribute this slight EL broadening to charge carrier/phonon interaction due to Joule heating \(^{27}\) , as similar behaviour is observed in pure- bromide PeLEDs (Supplementary Fig. 12). In contrast, the control devices undergo distinct emission colour changes starting at very low bias and current density of around \(3.0 - 3.5 \mathrm{V}\) and \(1 - 5 \mathrm{mA cm^{- 2}}\) (Figs. 1d and e), analogous to previous reports on spectrally unstable mixed bromide/chloride PeLEDs \(^{16,21}\) . Under the harsh operational condition, that is, with the bias larger than + +<--- Page Split ---> + +6.5 V, distinct colour change is observable even in VAC- treated devices. We emphasize that in this case the current density is over \(400 \mathrm{mA cm}^{-2}\) , which is far above normal working conditions of reported blue \(\mathrm{PeLEDs}^{11,14,20,25}\) . Notably, we observe abnormal plateau- like \(J - V\) characteristics during the voltage sweep at 6\~6.5 V and severe PL quenching after the operation (Supplementary Fig. 13), indicating severe device damage due to perovskite and/or interfacial degradation. Given the high current density, Joule heating could be a critical reason \(^{28}\) . + +With VAC- treatment, we demonstrate spectrally stable \(\mathrm{PeLEDs}\) with emission colours from sky- blue to deep- blue (emission peaks at 490 to 451 nm) by simply varying the chloride content (30%\~57%) (Fig. 1f and Supplementary Fig. 14). We also examine the spectral stability of our devices at a constant current density of \(5 \mathrm{mA cm}^{-2}\) (with initial luminance ranging from \(\sim 200\) to \(\sim 600 \mathrm{cd m}^{-2}\) for different devices). Although the operational lifetime is no better than those in previously reported blue \(\mathrm{PeLEDs}\) (with \(\mathrm{T}_{50}\) around \(1 \sim 2 \mathrm{min}\) ) \(^{8,11,12,20}\) , we observe no spectral shift even after 10 minutes of operation (Supplementary Fig. 15). Previous reports on photoinduced phase segregation in mixed halide perovskites indicate that it is only triggered when the excitation density is above a certain threshold, below which little to no effects are present \(^{29,30}\) . Our results are consistent with these observations, indicating that employing mixed halide anions is a feasible approach for blue \(\mathrm{PeLEDs}\) as long as we can control the phase segregation threshold to be far above working conditions. + +The origin of improved spectral stability. Although the underlying reason for phase segregation is complicated, previous investigations on perovskite solar cells propose that three factors may be collectively contributing to this phenomenon. These three factors include a polaron induced strain effect \(^{31}\) , a thermodynamic process as driven by free energy differences associated with composition + +<--- Page Split ---> + +and band offsets32, and field- dependent anion motion33. We carry out a series of characterizations to understand the origin of the spectral stability of PeLEDs based on VAC- treated perovskite films. + +We first measure PL properties of our perovskite films. We observe obviously enhanced PLQYs in the VAC- treated films across a wide range of excitation fluences, with a peak PLQY of 12% compared to 3% for the control sample (Fig. 2a). Time correlated single photon counting (TCSPC) measurements demonstrate a prolonged PL lifetime for VAC- treated samples compared to the control one (Fig. 2b). These results suggest fewer defects and much suppressed non- radiative recombination in the VAC- treated films, consistent with the higher EQEs of the devices. We believe that the enhanced spectral stability in our PeLEDs is partially ascribed to the reduced defects, as defects are generally believed to act as channels for anion hopping and hence facilitate phase segregation34. + +In addition to the reduced defect density, we also observe significantly improved local compositional homogeneity in the VAC- treated films compared to the control sample. It is first evidenced by a steeper edge of the absorption spectrum (Supplementary Fig. 16a) and a much- narrowed PL linewidth (with full- width at half- maximum (FWHM) of \(\sim 18 \mathrm{nm}\) ) of VAC- treated films compared to that of control samples (FWHM of \(\sim 25 \mathrm{nm}\) ) (Fig. 2c). To gain further understanding of electronic states in the films, we conducted transient absorption (TA) spectroscopy measurements. The control film displays a broad photobleaching peak that shifts from \(455 \mathrm{nm}\) to \(465 \mathrm{nm}\) (Fig. 2d and Supplementary Figs. 16b- c), which is consistent with the coexistence of different phases. As there is no sign of low- dimensional phases from GIWAXS and XRD patterns, we assign the compositional heterogeneity in the control films to a non- uniform distribution of halide anions, which has been widely reported in bromide/iodide mixed perovskites35,36. In contrast, the VAC- treated film shows a single + +<--- Page Split ---> + +narrow ground state photobleaching situated at 473 nm, indicating a high compositional homogeneity. According to current polaronic and thermodynamic models for rationalizing phase segregation in perovskite solar cells, a high compositional heterogeneity can contribute to the phase segregation31,32,37- 39. In specific, fluctuations in halide compositions can yield heterogeneous regions in the perovskites where polarons tend to localize at lower bandgap areas, leading to enhanced local lattice strain which drives de-mixing of halide anions31,38,39. A system with initially high free energy due to severe compositional disorder might be energetically unfavourable for phase stability as indicated by the thermodynamic model37. Lattice mismatch and discrepancy of band offsets between different phases are also believed to be the driving forces for phase segregation32,37. Our observations are in line with these previous investigations, demonstrating the critical role of high homogeneity in improving phase stability of VAC-treated devices. + +Assured about the reduced defects and improved compositional homogeneity in VAC- treated films, we then evaluate field- dependent ion migration in our devices. We perform temperature- dependent admittance spectroscopy, from which we can determine ion migration activation energy \((E_{\mathrm{A}})\) , ion diffusion coefficient \((D)\) , and concentration of mobile ions \((N_{\mathrm{i}})^{40}\) . The capacitance (C) response of mobile ionic species can be probed by varying the frequency \((\omega)\) of an applied alternating voltage and the temperature. We show the admittance spectra in Supplementary Fig. 17 and the plots of derivations (- odC/do versus \(\omega\) ) in Figs. 2e and 2f. Two distinct signatures from mobile ionic species are visible, which are labelled \(\epsilon\) and \(\beta\) . We confirm that the charge transport layers are not responsible for these signatures by characterizing devices with only charge transport layers (Supplementary Fig. 18)41. In particular, we observe that the response peaks at the low- frequency region \((\epsilon)\) in the VAC- treated devices are much less prominent than those in the control devices, suggesting a much lower + +<--- Page Split ---> + +mobile ion concentration. We show the deduced Arrhenius plots in Supplementary Fig. 19 and summarize all the obtained parameters \((E_{\mathrm{A}}, D\) , and \(N_{\mathrm{i}}\) ) in Supplementary Table 3. The \(E_{\mathrm{A}}\) values of ion diffusion for both \(\epsilon\) and \(\beta\) are very close in the two samples, implying that the mode of ion migration is not significantly altered. Both the concentration of mobile ions and ion diffusion coefficient are reduced in VAC- treated devices compared to the control devices. The most striking difference occurs to \(N_{\mathrm{i}}(\epsilon)\) , which is decreased from \(5.4 \times 10^{16} \mathrm{~cm}^{- 3}\) to \(1.6 \times 10^{16} \mathrm{~cm}^{- 3}\) . Considering the small \(E_{\mathrm{A}}\) of \(\epsilon\) ( \(\sim 0.2 \mathrm{eV}\) ), we assign them to mobile halide anions \(^{33,40}\) . The mitigated halide migration can be a result of reduced ionic defects, as confirmed by PLQYs and TCSPC results \(^{34}\) . + +Based on the analysis above, we conclude that the excellent spectral stability in VAC- treated devices originates from a synergistic effect of less ionic defects, mitigated ion migration and a higher compositional homogeneity. + +Understanding the effect of the VAC- treatment process. Having understood the origin of high colour stability and excellent device performance, the question that remains is how VAC- treatment brings about these effects. We conduct SEM measurements to track the grain growth and morphological evolution of the films during the VAC treatment. We clearly observe two stages. The first stage happens within the first minute of vapour treatment, showing a crystal growth from initially formed small grains into large ones, accompanied by the morphological evolution from dense films into discontinuous network (Supplementary Fig. 20). Considering the presence of crystalline perovskites within the pristine films and the diffusive vapour atmosphere, we assign the process of grain growth to Ostwald ripening. The wet films preserved by DMF vapour can be regarded as a sol system, with the solvent as the dispersing medium and perovskites as the dispersed phases. The ripening process occurs because large grains are more energetically favoured to smaller grains, leading + +<--- Page Split ---> + +to reduced grain boundaries and hence fewer defects. The second stage happens during the prolonged duration of treatment, which only has a slight impact on the morphology. + +We also employ in- situ PL and transmittance measurements to monitor the crystal growth with and without DMF vapour. The measurement setups are illustrated in Supplementary Fig. 21. Initially, both films show broad emission bands with the main peak at the low- energy region and a distinguishable shoulder at the high- energy region, which are labelled as P1 and P2, respectively (Figs. 3a and 3b). We speculate that the high- energy emission originates from initially formed Cl- rich perovskite phases due to their fast nucleation and crystallization, as governed by their poor solubility compared to Br- rich counterparts. By following the PL spectral evolution with time, we observe a gradual disappearance of P2 and a continuous red- shift of P1 in VAC- treated samples, leading to a narrow and single- emission peak eventually (Fig. 3b). To further clarify the spectral evolution of VAC- treated films in different time scales, we show the changes of emission bandwidth as well as the proportion of P2 (AP2) to a total area of emission band (A) with time in Fig. 3c. We find that the most striking changes occur within the first five minutes of treatment, while prolonged duration of up to 20 minutes results in only a small difference (Fig. 3c). This PL evolution is consistent with the results of in- situ transmittance measurements, i.e. a red- shift of absorption onset and steeper absorption edge after VAC- treatment (Supplementary Fig. 22a). In contrast, keeping the pristine films in the glovebox atmosphere does not change the PL (Fig. 3a) and transmittance (Supplementary Fig. 22b) spectra to any significant degree over time. + +Based on the in- situ spectroscopic measurement results, we can now rationalize the effect of the VAC- treatment. It provides a favourable diffusive environment for halide rearrangement within the films (Fig. 3d), which undergo an equilibrating crystallization process that homogenises local chemical + +<--- Page Split ---> + +composition and reduces disorder. Initially, the as- casted films are composed of various Cl- rich solid phases and Br- rich components in liquid phases due to nonequilibrium grain growth during spin- casting. For the films with no vapour atmosphere, quick solvent evaporation and following fast crystallization result in immediate freezing of the perovskite composition. The post- annealing could mitigate phase heterogeneity to some extent, as indicated by the weakened emission shoulder at short wavelength (P2) after annealing (Fig. 2c). However, the initially formed heterogeneous phases are still partially preserved in the resulting films. In contrast, with the presence of DMF vapour, the liquid phase can be preserved for a long duration. This facilitates and prolongs the following halide exchange process as driven and modulated by the chemical potential difference between solid (Cl- rich) and liquid phases (Br- rich), resulting in a rearranged composition that gradually approaches chemical equilibrium and homogeneous distribution of constituents. The following annealing procedure has little impact on the PL spectra of VAC- treated films, further confirming that the high homogeneous composition has already been achieved during the VAC- treatment. + +We then tune the duration of DMF vapour- treatment to assess the impact on spectral stability and device efficiency in different timescale (Fig. 3e and 3f), further supporting our understanding of this technique. We observe distinct batch to batch variations in EL spectra and dispersion of CIE coordinates in control devices (Fig. 3e and Supplementary Fig. 23a), ascribed to nonequilibrium crystal growth and hence uncontrollable local film composition. In contrast, EL spectra and CIE coordinates of VAC- treated devices are highly reproducible between batches, resulting from self- moderated halide rearrangement during the VAC- treatment (Fig. 3e and Supplementary Fig. 23b). When comparing the devices processed with different duration of VAC treatment, we observe a remarkable EQE enhancement in one minute of treatment, that is, with averaged peak EQE values improved from \(\sim 0.6\%\) + +<--- Page Split ---> + +to \(\sim 3.8\%\) (Fig. 3f), which well corresponds to the dramatical morphological variations in the same time scale from SEM results (Supplementary Fig. 20). We believe that perovskite re-crystallization, enlarged grain size and improved local homogeneity collectively help to reduce the defect density and hence reduce non- radiative recombination. In addition, the isolated nano- structures may also contribute to the efficiency improvement due to enhanced light- out coupling3. With increasing the processing duration, the EQE values gradually approach saturation. We assign this to a slow diffusion- mediated defect healing process from the gradually improved homogeneity that reduces local lattice mismatch and strain- induced interfacial defects42,43. Notably, one minute of VAC- treatment is sufficient for improving the efficiency but not the spectral stability (Fig. 3e), indicating that discontinuous morphology has little impact on improving phase stability. In other words, a large perovskite grain with size scale of hundreds of nanometres in our samples can hardly be the reason for the suppression of phase segregation within the grain, as probed in previous reports showing that the phase segregated domain can be as small as \(\sim 8 \mathrm{nm}^{30,32}\). We also notice that the devices with five- minute treatment show comparable colour stability to those with twenty- minute treatment (Fig. 3e), corresponding well to the time scale of the disappearance of high energy phases as observed in Fig. 3c. It further confirms the critical role of high compositional homogeneity in improving phase stability. Given the critical role of diffusive environment on retarding crystallization for halide rearrangement, a proper solubility of perovskite precursors in the solvent vapour might be the key to achieving high compositional homogeneity. We thus perform additional experiments using dimethyl sulfoxide (DMSO) or chloroform as the alternative vapour for further understanding the VAC treatment. DMSO is another commonly used solvent for perovskite precursors, while chloroform is a well- known “anti- solvent” that is widely used to accelerate perovskite crystallization44. Considering + +<--- Page Split ---> + +that the vapour residues in the glovebox may affect the results, we also prepare the samples without introducing any vapour on purpose, that is, leaving the as- casted films in the glovebox for the same duration. As shown in Supplementary Fig. 24, the introduction of chloroform vapour has no positive effect on either device efficiency or spectral stability, which can be attributed to the poor solubility of perovskite precursors in chloroform, leading to a fast crystallization and freezing of the composition, and hence resulting in high heterogeneity. In contrast, DMSO treatment gives comparable improvement as DMF vapour, further rationalising our understanding of the effect of the vapour treatment. + +The general applicability of VAC- treatment and device optimization. We proceed to explore the VAC- treatment in other material systems, aiming to further improve the device performance and validate the general applicability. We incorporate a small amount of rubidium ions \((\mathrm{Rb}^{+})\) in our perovskites, that is, using a precursor composition of \(\mathrm{Rb^{+}}\) : \(\mathrm{Cs^{+}}\) : \(\mathrm{FA^{+}}\) : \(\mathrm{Pb^{2 + }}\) : \([\mathrm{Br}_{0.6} + \mathrm{Cl}_{0.4}]^{- } = 0.1:1.2:0.2\) : 1: 3.5. Consistent with the previous reports in perovskite solar cells \(^{45,46}\) , the incorporation of \(\mathrm{Rb^{+}}\) effectively suppresses non- radiative recombination as indicated by a considerable enhancement of peak external PLQY (25%) and a prolonged PL lifetime (Supplementary Figs. 25a and b). The small amount of \(\mathrm{Rb^{+}}\) addition has little impact on the film morphology (Supplementary Fig. 25c). + +We show the characteristics of the best- performing VAC- treated Rb- device using 40% Cl content in Fig. 4. The device exhibits blue emission peaking at 477 nm with FWHM of 18 nm. The corresponding CIE coordinates are (0.107, 0.115), approaching the primary blue (0.14, 0.08) specified by the National Television System Committee (NTSC). Compared to the device without using VAC- treatment (Supplementary Fig. 26), the treated device shows a significant enhancement of EQE value up to 11.0%. The luminance rises rapidly after the device turns on at a low voltage of 2.6 V, reaching + +<--- Page Split ---> + +a peak value of 2,180 cd cm \(^{- 2}\) at 5.0 V (106 mA cm \(^{- 2}\) ). The low turn-on voltage and high brightness indicate efficient charge injection, which is usually very challenging in strongly confined perovskites \(^{14}\) . We observe no peak shift during voltage scans until reaching a high bias at 6.0 V (\~300 mA cm \(^{- 2}\) ) (Supplementary Fig. 27a), analogous to the device without Rb \(^+\) incorporation, further indicating that phase segregation in VAC-treated devices is mainly mediated by the device damage at harsh operating conditions. In addition, we demonstrate that no EL shift can be observed even after 75 min of operation at 3 V (\~0.1 mA cm \(^{- 2}\) , with initial luminance of \~10 cd m \(^2\) ) (Supplementary Fig. 27b). Although Rb \(^+\) addition significantly improves the device efficiency, we have not observed any distinct effect on operational stability (\~3 min, Supplementary Fig. 27c and d). The short operational lifetime could be a result of Joule heating and ion-migration induced material and/or interfacial degradation under the bias \(^{9,28}\) , as well as Al diffusion and relevant redox reaction between Pb \(^{2 + }\) and Al \(^{0.47}\) . An EQE histogram for 40 devices shows an average peak EQE of 9.3% with a low standard deviation of 0.67%, indicating high reproducibility of the VAC-treatment. + +Further increasing Cl content to 45% results in deep- blue emission, whose device characteristics are also summarized in Fig. 4. The corresponding CIE coordinates are (0.130, 0.059), very close to Rec. 2020 specified blue standards (0.131, 0.046). The deep- blue PeLEDs achieves a peak EQE of 5.5% and an average peak EQE of 3.9% with a standard deviation of 0.76%, which are among the best for PeLEDs with ideal deep- blue emission. + +We demonstrate that the VAC- treatment is also applicable for improving the colour stability and device performance of low- dimensional perovskites with mixed bromide/chloride anions, e.g. the typical phenethylammonium (PEA \(^+\) )- modified CsPb(Bro \(_7\) Cl \(_{0.3}\) ) \(^3\) (Supplementary Fig. 28). These results indicate that the wavelength of the previously reported high- performance sky- blue PeLEDs + +<--- Page Split ---> + +based on quasi- 2D perovskites could be pushed to a bluer region without any negative impacts on their colour stability and device efficiency. + +## Conclusion + +In summary, we have demonstrated that the notorious colour instability issues in mixed halide blue PeLEDs can be substantially mitigated across a wide range of emission colour from sky blue to deep blue region (490 to 451 nm). The excellent phase stability is mainly achieved by the development of a vapour- assisted crystallization technique that effectively suppresses the ion migration and compositional heterogeneity. Particularly, for the first time, we show high- efficiency and spectrally stable blue and deep- blue PeLEDs based on mixed halide 3D perovskites, with respective peak EQEs of \(11.0\%\) and \(5.5\%\) , presenting two of the most efficient blue PeLEDs to date. Our findings are also applicable to the prevailing low- dimensional blue perovskite emitters, indicating a bright future for further improvement of blue PeLEDs by combining these two strategies. Our research thus provides a broad avenue for future development of blue perovskite emitters, representing another milestone towards practical implementation of perovskite light- emitting diodes in full- colour displays and lighting applications. Beyond that, stabilized mixed halide perovskites are also of great interest for a wide range of perovskite applications where the bandgap needs to be finely controlled, for instance, lasing and tandem solar cells. + +## Acknowledgement + +We thank D. Egger, X. Zhu, C. Yin, H. Tian and J. Li for valuable discussions, and X. Liu for help with the XPS measurements. We acknowledge the support from the ERC Starting Grant (No. 717026), the Swedish Energy Agency Energimyndigheten (No. 48758- 1 and 44651- 1), Swedish Research + +<--- Page Split ---> + +Council VR, NanoLund and the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linköping University (Faculty Grant SFO- Mat- LiU No. 2009- 00971). C.D. and S.R. acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF Hyper project, contract no. 03SF0514C) and the DFG (no. DE 830/22- 1) within the framework of SPP 2196 programme. Y. L. acknowledge financial support from the National Key Research and Development Program of China (2016YFB0700700), the National Natural Science Foundation of China (11704015, 51621003, 12074016), the Scientific Research Key Program of Beijing Municipal Commission of Education, China (KZ201310005002), and the Beijing Innovation Team Building Program, China (IDHT20190503). F.G. is a Wallenberg Academy Fellow. + +## Author Contributions + +F.G. and W.X. conceived the idea and supervised the project; M.K. performed the experiments and analysed the data; Z.Y. developed Rb- doped devices and low- dimensional perovskite- based devices; R.S. performed admittance spectroscopy and analysed the data under the supervision of C.D.; X.L. and P.T. contributed to device fabrication and measurements; W.L. performed transient absorption under the supervision of K.Z. and T.P.; Z.L. performed transmission electron microscopy under the supervision of Y.L.; R. Z. and G. Z. performed GIWAXS measurements and analysed the data; C.B., S.B., L.D. and R.F. contributed the interpretation of results; M.K., W.X. and F.G. wrote the manuscript; S.B. provided revisions to the manuscript; All authors discussed the results and commented on the manuscript. + +## Additional information + +<--- Page Split ---> + +Supplementary information is available in the online version of the paper. Correspondence to W. X. and F.G. + +## Data availability + +The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +## Competing interests + +The authors declare no competing interests. + +## Reference + +1. Zhu, M. & Yang, C. Blue fluorescent emitters: Design tactics and applications in organic light-emitting diodes. Chem. Soc. Rev. 42, 4963-4976 (2013). + +2. Tan, Z. K. et al. Bright light-emitting diodes based on organometal halide perovskite. Nat. Nanotechnol. 9, 687-692 (2014). + +3. Cao, Y. et al. Perovskite light-emitting diodes based on spontaneously formed submicrometre-scale structures. Nature 562, 249-253 (2018). + +4. Lin, K. et al. Perovskite light-emitting diodes with external quantum efficiency exceeding 20 per cent. Nature 562, 245-248 (2018). + +5. Xu, W. et al. Rational molecular passivation for high-performance perovskite light-emitting diodes. Nat. 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ACS Energy Lett. 3, 2671-2678 (2018). + +47. Zhao, L. et al. Redox chemistry dominates the degradation and decomposition of metal halide perovskite optoelectronic devices. ACS Energy Lett. 1, 595-602 (2016). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 Device fabrication and characteristics. a, An illustration of the VAC-treatment. b, Schematic of the PeLED structure and the HAADF cross-sectional device image. The scale bar is 100 nm. c, Histograms of peak EQEs extracted from control (top) and VAC-treated devices (bottom) with varying chloride contents (30%, 35%, 40%). d-f, Spectral stability for control and VAC-treated devices with 40% Cl loading. The representative plots of \(\mathrm{CIE}_y\) versus applied voltages (top) and current densities (bottom) (d); EL spectra at low and high voltage/current density for control (left) and VAC-treated devices (right) (e); EL spectra of VAC-treated devices with varying chloride content (30\~57%) at maximum luminance (f). The points labelled as \(L_{\mathrm{max}}\) in Fig. 1d represent the operational condition for peak luminance.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Understanding superior spectral stability of VAC-treated devices. a-d, Photophysical characterizations for control and VAC-treated perovskite films: Fluence-dependent PLQYs (a); PL decay measured by TCSPC (b). PL spectra (c); Transient absorption of control (top) and VAC-treated films (bottom) after excitation at 400 nm (d). e, f, Derivations of temperature-dependent capacitance versus frequency plots for control (e) and VAC-treated (f) devices. The blue arrows indicate temperature change from 350 K to 200 K. Here, two mobile ions marked as \(\beta\) and \(\epsilon\) are visible.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 Understanding the halide redistribution during VAC-process. a, b, PL evolution of the precursor films kept in the glovebox atmosphere (a) and DMF atmosphere (b) with time. c, the evolution of emission linewidth and the proportion of P2 (Ar2) to the respective total area of the emission band (A) in VAC-treated films with time. d, Schematic illustration of the proposed mechanism for halide redistribution. Here, the purple \(\mathrm{Pb(Br / Cl)_6^{4 - }}\) octahedra represent chloride-rich phases in respect to that with stoichiometric bromide/chloride distribution (blue octahedra). The khaki represents the liquid phase within the films and the blue arrows represent ion exchange process. The excessive ions within the dried films are not illustrated for clarity. e, f, The evolution of CIE coordinates upon bias (e) and peak EQEs of the devices with varying duration of VAC-treatment (0, 1, 2, 5, 20 minutes) (f). The data were extracted from 4 to 6 devices.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 The device performance of Rb-passivated perovskites with 40% and 45% Cl contents. a,
+ +EQE- current density \((J)\) curves \((J\) - EQE). b, Current density- voltage- luminance \((J - V - L)\) characteristics. + +c, EL spectra and CIE colour coordinates. The square and pentagram in the CIE 1931 (x, y) chromaticity diagram represent the colour coordinates of primary blue specified in NTSC and + +Rec.2020, respectively. d, Histograms of the peak EQEs extracted from 40 devices for each case. + +<--- Page Split ---> + +## Methods + +Materials. Caesium bromide (CsBr, 99.999%), lead bromide (PbBr2, 99.999%), lead chloride (PbCl2, 99.999%) was purchased from Alfa Aesar. Formamidinium bromide (FABr) and phenethylammonium bromide (PEABr) were purchased from Greatcell Solar. Rubidium bromide (RbBr, 99.99%), polyvinylpyrrolidone (PVP, average Mw \(\sim 55000\) ), 4,7,10- trioxa- 1,13- tridecanediamin (TTDDA), poly(9- vinylcarbazole) (PVK, average Mn 25,000- 50,000) were purchased from Sigma Aldrich. The \(\mathrm{NiO_x}\) nano- crystals were purchased from Avantama AG and were used without additional treatment. 1,3,5- tris(1- phenyl- 1H- benzimidazol- 2- yl)benzene (TPBi) was purchased from Luminescence Technology corp. Other materials for device fabrication were all purchased from Sigma- Aldrich. + +Preparation of the perovskite solution. Perovskite precursors (CsBr: FABr: PbBr2: PbCl2: TTDDA) with a molar ratio of 1.2: 0.3: x: y: 0.1 (where x + y = 1) were mixed and dissolved in dimethyl sulfoxide (DMSO). The precursor concentration as determined by \(\mathrm{Pb^{2 + }}\) is 0.15 M for 30\~40% Cl, 0.13 M for 45% Cl, 0.11 M for 50% Cl, and 0.09 M for 57% Cl, respectively. The precursor solutions were stirred at 80°C for 4h before use. For the low- dimensional perovskites, precursors (PEABr: CsBr PbBr2: PbCl2) with a molar ratio of 0.9: 1.1: 0.4: 0.6 mixed and dissolved in DMSO to make a solution with 30% Cl- content. The precursor concentration determined by \(\mathrm{Pb^{2 + }}\) is 0.15 M. + +PeLED fabrication. Glass substrates with patterned Indium tin oxide (ITO) were sequentially cleaned by detergent and TL- 1 (a mixture of water, ammonia (25%) and hydrogen peroxide (28%) (5:1:1 by volume)). The clean substrates were then treated by ultraviolet- ozone for 10 min. \(\mathrm{NiO_x}\) was spin- coated in air at 4,000 r.p.m. for 30 s, followed by baking at 150 °C for 10 min in air. The substrates were then transferred into a nitrogen- filled glovebox (< 0.1 ppm \(\mathrm{H_2O}\) , < 0.1 ppm \(\mathrm{O_2}\) ). PVK (4 mg ml⁻¹ in chloro benzene) was deposited at 3000 r.p.m. followed by thermal annealing at 150°C for 10 min. Next, + +<--- Page Split ---> + +a thin layer of PVP (2.0 mg mL-1 in isopropyl alcohol (IPA)) was deposited at 3000 r.p.m. and baked at \(100^{\circ}\mathrm{C}\) for 5 min. After cooling down to room temperature, the perovskite solutions with varying bromide/chloride ratios were deposited at 3000 r.p.m. Directly after spin-coating, the films were put in an unsealed \(\varnothing 60 \mathrm{mm}\) petri- dish (with lid) at room temperature, where \(20 \mu \mathrm{l}\) of dimethylformamide had been dropped 10 min prior to the film placement. After 20 min of vapour assisted crystallisation (VAC) treatment, the films were annealed at \(80^{\circ}\mathrm{C}\) for 8 min. For low- dimensional perovskite films with mixed halides, the treatment duration is 10 min and the annealing condition is \(80^{\circ}\mathrm{C}\) for 5 min. Finally, the electron transport layer TPBi and top contacts LiF/Al (1 nm / 100 nm) were deposited by thermal evaporation through shadow masks at a base pressure of \(\sim 10^{-7}\) torr. The device area was 7.25 \(\mathrm{mm}^2\) . + +PeLED characterization. All PeLED device characterizations were performed at room temperature in a nitrogen- filled glovebox without encapsulation. A Keithley 2400 source- meter and a fibre integration sphere (FOIS- 1) coupled with a QE Pro spectrometer (Ocean Optics) were utilized. The absolute radiance was calibrated by a standard Vis- NIR light source (HL- 3P- INT- CAL plus, Ocean Optics). The PeLED devices were measured on top of the integration sphere and only forward light emission can be collected. The devices were swept from zero bias to forward bias with a step voltage of 0.05 V, lasting for 100 ms at each voltage step for stabilisation. The sweep duration from 1 to 7 V is 70 seconds (with a scan rate of 86 mV S-1). The EQE and spectral evolution with time was measured using the same system. + +Perovskite film characterization. Top- view scanning electron microscope (SEM) images were tested by LEO 1550 Gemini. Steady- state PL spectra of the perovskite films were recorded by a fluorescent spectrophotometer (F- 4600, HITACHI) with a 200 W Xe lamp as an excitation source. UV- Vis + +<--- Page Split ---> + +absorbance spectra were collected using a PerkinElmer model Lambda 900. X- ray diffraction patterns were measured using a Panalytical X'Pert Pro with an X- ray tube (Cu Kα, \(\lambda = 1.5406 \mathring{\mathrm{A}}\) ). + +X- ray photoelectron spectroscopy (XPS) tests were performed by a Scienta ESCA 200 spectrometer in ultrahigh vacuum ( \(\sim 1 \times 10^{- 10}\) mbar) with a monochromatic Al (Ka) X- ray source providing photons with 1,486.6 eV. The experimental was set so that the full- width at half- maximum of clean Au 4f 7/2 line (at the binding energy of 84.00 eV) was 0.65 eV. All spectra were characterized at a photoelectron take- off angle of \(0^{\circ}\) . Ultraviolet photoelectron spectroscopy (UPS) was carried out using a Kratos AXIS Supra on perovskite samples spun- cast on ITO/NiOₓ/PVK/PVP. He I (21.22eV) radiation was generated from a helium discharge lamp. Samples were biased at 9.1V. + +In- situ PL of the crystallisation process was collected using the integrating sphere and the QE Pro spectrometer as described above, and a 365 nm UV laser as excitation source. In- situ transmittance tests were performed using the same spectrometer but with a solar simulator (AM 1.5G) as the light source. A ND filter was used to decrease the light intensity. The systems were illustrated in Supplementary Fig. 21. + +Time- correlated single photon counting (TCSPC) measurements were carried out by using an Edinburgh Instruments FL1000 with a 405 nm pulsed picosecond laser (EPL- 405). Fluence dependent PLQY was measured using a 405 nm continuous wave laser, an integrating sphere and the same spectrometer. The perovskite films were deposited on ITO/NiOₓ/PVK/PVP substrates under identical conditions as for the PeLEDs, and encapsulated using glass slides and UV- curable resin. + +Grazing- incidence wide- angle X- ray scattering (GIWAXS) was recorded in Shanghai Synchrotron Radiation Facility. The diffraction patterns were collected by two dimensional MarCCD 225 detector with 234 mm from samples to the detector. All the samples were protected with N₂ gas + +<--- Page Split ---> + +during the measurements. To assure the diffraction intensity, an exposure time of \(15\mathrm{s}\) was adopted with an incidence angle of \(0.5^{\circ}\) , and the wavelength of the X- ray was \(1.24\mathrm{\AA}\) (10 KeV). For all these tests, the perovskite films were deposited on ITO/NiOx/PVK/PVP substrates under identical conditions as device fabrication. + +Scanning transmission electron microscopy (STEM) and Energy- dispersive X- ray spectroscopy (EDX). The STEM samples were fabricated by using the FEI Focused Ion Beam (FIB) system (Helios Nanolab 600i). A FEI Titan- G2 Cs- corrected transmission electron microscope with 300 KV accelerating voltage was used to get the high angle angular dark field (HAADF) images of the samples. The STEM elemental mapping images were collected by four silicon drift windowless detectors (Super- EDX) in the FEI Titan- G2 Cs- corrected transmission electron microscope. The energy resolution of the Super- EDX was \(137\mathrm{eV}\) . + +Transient absorption. A femtosecond oscillator (Mai Tai, Spectra Physics) is used as a seed laser for a regenerative amplifier (Spitfire XP Pro, Spectra Physics) which generates well collimated beam of femtosecond pulses (800 nm, 80 fs pulse duration, 1 kHz repetition rate). The second harmonic generated by a BBO crystal was used as pump (400 nm). White light continuum (WLC) as the probe was produced by focusing the 800 nm fs pulse on a thin \(\mathrm{CaF_2}\) plate. Polarization between the pump and probe was set to the magic angle \((54.7^{\circ})\) . Both pump and probe pulses are monitored to compensate for the laser fluctuations during the measurements. + +Admittance spectroscopy. For the defect studies we used a setup consisting of a Zurich Instruments MFLI lock- in amplifier with MF- IA and MF- MD options, a Keysight Technologies 33600A function generator and a cryo probe station Janis ST500 with a Lakeshore 336 temperature controller. For determining the ion signature using admittance spectroscopy we varied the sample temperature from + +<--- Page Split ---> + +200 K to 350 K in 5 K steps, controlled accurately within 0.01 K and using liquid nitrogen for cooling. The capacitance in term of a C||R equivalence model was measured by applying an ac voltage with amplitude of \(V_{\mathrm{ac}} = 20 \mathrm{mV}\) and varying the frequency from 0.6 Hz to 3.2 MHz. The rates \(e_{\mathrm{t}}\) are obtained from the peak maxima of the derivative of the capacitance. These rates are linked to the diffusion coefficient \(D\) in terms of the underlying hopping process of the mobile ions \(^{48,49}\) , + +\[e_{\mathrm{t}} = \frac{e^{2}N_{\mathrm{eff}}D}{k_{\mathrm{B}}T\epsilon_{0}\epsilon_{\mathrm{R}}},\] + +(1) + +where \(N_{\mathrm{eff}}\) refers to the effective doping density, \(e\) is the elementary charge, \(k_{\mathrm{B}}\) is the Boltzmann constant, \(T\) the temperature, \(\epsilon_{0}\) the dielectric constant and \(\epsilon_{\mathrm{R}}\) the relative permittivity. For the calculation of \(D_{300\mathrm{K}}\) we used a dielectric permittivity of \(19.2^{50}\) . Since ion migration is a thermally activated process, the diffusion coefficient depends on the temperature, + +\[D = D_{0}exp\left(-\frac{E_{A}}{k_{B}T}\right)\] + +(2) + +with the activation energy for ion migration \(E_{\mathrm{A}}\) and the diffusion coefficient at infinite temperatures \(D_{0}\) . Subsequently, \(E_{\mathrm{A}}\) and \(D_{0}\) can be extracted from the slope and the cross section with the emission rate axis using Eqns. (1) and (2). By taking into account the surface polarization caused by the accumulation of mobile ions at the interfaces of the perovskite layer, the ion concentration \(N_{\mathrm{i}}\) is determined as \(^{51}\) , + +\[N_{\mathrm{i}} = \frac{k_{\mathrm{B}}T\Delta C^{2}}{e^{2}\epsilon_{0}\epsilon_{\mathrm{R}}}\] + +(3) + +Here, \(\Delta \mathrm{C}\) refers to the capacitance step in the admittance spectra of the contributing ions. + +<--- Page Split ---> + +## Reference + +48. Heiser, T. & Mesli, A. Determination of the copper diffusion coefficient in silicon from transient ion-drift. Appl. Phys. A 57, 325-328 (1993). + +49. Futscher, M. H. et al. Quantification of ion migration in \(\mathrm{CH_3NH_3PbI_3}\) perovskite solar cells by transient capacitance measurements. Mater. Horiz. 6, 1497-1503 (2019). + +50. Schlaus, A. P. et al. How lasing happens in CsPbBr₃ perovskite nanowires. Nat. Commun. 10, 265 (2019). + +51. Almora, O. et al. Capacitive dark currents, hysteresis, and electrode polarization in lead halide perovskite solar cells. J. Phys. Chem. Lett. 6, 1645-1652 (2015). + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +Device fabrication and characteristics. a, An illustration of the VAC- treatment. b, Schematic of the PeLED structure and the HAADF cross- sectional device image. The scale bar is \(100~\mathrm{nm}\) . c, Histograms of peak EQEs extracted from control (top) and VAC- treated devices (bottom) with varying chloride contents (30%, 35%, 40%). d- f, Spectral stability for control and VAC- treated devices with 40% Cl loading. The representative plots of ClEy versus applied voltages (top) and current densities (bottom) (d); EL spectra at low and high voltage/current density for control (left) and VAC- treated devices (right) (e); EL spectra of VAC- treated devices with varying chloride content (30\~57%) at maximum luminance (f). The points labelled as Lmax in Fig. 1d represent the operational condition for peak luminance. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Understanding superior spectral stability of VAC- treated devices. a- d, Photophysical characterizations for control and VAC- treated perovskite films: Fluence- dependent PLQYs (a); PL decay measured by TCSPC (b). PL spectra (c); Transient absorption of control (top) and VAC- treated films (bottom) after excitation at 400 nm (d). e, f, Derivations of temperature- dependent capacitance versus frequency plots for control (e) and VAC- treated (f) devices. The blue arrows indicate temperature change from 350 K to 200 K. Here, two mobile ions marked as \(\beta\) and \(\epsilon\) are visible. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Understanding the halide redistribution during VAC- process. a, b, PL evolution of the precursor films kept in the glovebox atmosphere (a) and DMF atmosphere (b) with time. c, the evolution of emission linewidth and the proportion of P2 (AP2) to the respective total area of the emission band (A) in VAC- treated films with time. d, Schematic illustration of the proposed mechanism for halide redistribution. Here, the purple Pb(Br/Cl)64- octahedra represent chloride- rich phases in respect to that with stoichiometric bromide/chloride distribution (blue octahedra). The khaki represents the liquid phase within the films and the blue arrows represent ion exchange process. The excessive ions within the dried films are not illustrated for clarity. e, f, The evolution of CIE coordinates upon bias (e) and peak EQEs of the devices with varying duration of VAC- treatment (0, 1, 2, 5, 20 minutes) (f). The data were extracted from 4 to 6 devices. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +The device performance of Rb- passivated perovskites with \(40\%\) and \(45\%\) Cl contents. a, EQE- current density (J) curves (J- EQE). b, Current density- voltage- luminance (J- V- L) characteristics. c, EL spectra and CIE colour coordinates. The square and pentagram in the CIE 1931 (x, y) chromaticity diagram represent the colour coordinates of primary blue specified in NTSC and Rec.2020, respectively. d, Histograms of the peak EQEs extracted from 40 devices for each case. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SIJrevision10.14.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e_det.mmd b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..e2ed8b89421d7ab3d74ea62412076e6202233895 --- /dev/null +++ b/preprint/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e/preprint__03d26da9a3f9742a5c4379adc0b43ed786e4c88c352b20688af2045cfd953d4e_det.mmd @@ -0,0 +1,580 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 895, 178]]<|/det|> +# Mixed Halide Perovskites for High-Efficiency and Spectrally Stable Blue Light-Emitting Diodes + +<|ref|>text<|/ref|><|det|>[[44, 196, 666, 238]]<|/det|> +Max Karlsson Linköping University https://orcid.org/0000- 0003- 2750- 552X + +<|ref|>text<|/ref|><|det|>[[44, 243, 234, 284]]<|/det|> +Ziyue Yi Linköping University + +<|ref|>text<|/ref|><|det|>[[44, 290, 715, 331]]<|/det|> +Sebastian Reichert Chemnitz University of Technology https://orcid.org/0000- 0003- 3214- 7114 + +<|ref|>text<|/ref|><|det|>[[44, 336, 234, 377]]<|/det|> +Xiyu Luo Linköping University + +<|ref|>text<|/ref|><|det|>[[44, 383, 194, 424]]<|/det|> +Weihua Lin Lund University + +<|ref|>text<|/ref|><|det|>[[44, 430, 336, 471]]<|/det|> +Zeyu Lin Beijing University of Technology + +<|ref|>text<|/ref|><|det|>[[44, 476, 592, 517]]<|/det|> +Chunxiong Bao Linköping University https://orcid.org/0000- 0001- 7076- 7635 + +<|ref|>text<|/ref|><|det|>[[44, 522, 234, 563]]<|/det|> +Rui Zhang Linköping University + +<|ref|>text<|/ref|><|det|>[[44, 568, 592, 609]]<|/det|> +Sai Bai Linköping University https://orcid.org/0000- 0001- 7623- 686X + +<|ref|>text<|/ref|><|det|>[[44, 614, 234, 655]]<|/det|> +Guanhaojie Zheng Linköping University + +<|ref|>text<|/ref|><|det|>[[44, 660, 234, 701]]<|/det|> +Pengpeng Teng Linköping University + +<|ref|>text<|/ref|><|det|>[[44, 707, 230, 747]]<|/det|> +Lian Duan Tsinghua University + +<|ref|>text<|/ref|><|det|>[[44, 753, 336, 794]]<|/det|> +Yue Lu Beijing University of Technology + +<|ref|>text<|/ref|><|det|>[[44, 799, 194, 840]]<|/det|> +Kaibo Zheng Lund University + +<|ref|>text<|/ref|><|det|>[[44, 845, 556, 886]]<|/det|> +Tonu Pullerits Lund University https://orcid.org/0000- 0003- 1428- 5564 + +<|ref|>text<|/ref|><|det|>[[44, 891, 716, 932]]<|/det|> +Carsten Deibel Chemnitz University of Technology https://orcid.org/0000- 0002- 3061- 7234 + +<|ref|>text<|/ref|><|det|>[[44, 937, 145, 955]]<|/det|> +weidong xu + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 585, 65]]<|/det|> +linköping university https://orcid.org/0000- 0002- 0767- 3086 + +<|ref|>text<|/ref|><|det|>[[44, 70, 625, 159]]<|/det|> +Richard FriendUniversity of Cambridge https://orcid.org/0000- 0001- 6565- 6308Feng Gao (feng.gao@liu.se)Linköping University https://orcid.org/0000- 0002- 2582- 1740 + +<|ref|>sub_title<|/ref|><|det|>[[44, 199, 102, 216]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 236, 667, 256]]<|/det|> +Keywords: light- emitting diodes, mixed halide perovskites, blue emission + +<|ref|>text<|/ref|><|det|>[[44, 275, 335, 294]]<|/det|> +Posted Date: December 1st, 2020 + +<|ref|>text<|/ref|><|det|>[[44, 313, 452, 333]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 92649/v1 + +<|ref|>text<|/ref|><|det|>[[42, 350, 910, 393]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 428, 940, 471]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on January 13th, 2021. See the published version at https://doi.org/10.1038/s41467- 020- 20582- 6. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[117, 88, 880, 108]]<|/det|> +# Mixed Halide Perovskites for Spectrally Stable and High-Efficiency Blue Light-Emitting + +<|ref|>sub_title<|/ref|><|det|>[[466, 116, 528, 134]]<|/det|> +## Diodes + +<|ref|>text<|/ref|><|det|>[[87, 139, 911, 202]]<|/det|> +Max Karlsson \(^{1,8}\) , Ziyue Yi \(^{1,2,8}\) , Sebastian Reichert \(^{3}\) , Xiyu Luo \(^{1,4}\) , Weihua Lin \(^{5}\) , Zeyu Zhang \(^{6}\) , Chunxiong Bao \(^{1}\) , Rui Zhang \(^{1}\) , Sai Bai \(^{1}\) , Guanhaojie Zheng \(^{1}\) , Pengpeng Teng \(^{1}\) , Lian Duan \(^{4}\) , Yue Lu \(^{6}\) , Kaibo Zheng \(^{5,7}\) , Tönu Pullerits \(^{5}\) , Carsten Deibel \(^{3}\) , Weidong Xu \(^{1}\) , \(*\) Richard Friend \(^{2}\) , and Feng Gao \(*^{1}\) + +<|ref|>text<|/ref|><|det|>[[87, 242, 910, 512]]<|/det|> +\(^{1}\) Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden. \(^{2}\) Cavendish Laboratory, University of Cambridge, Cambridge, UK. \(^{3}\) Institut für Physik, Technische Universität Chemnitz, Chemnitz, Germany. \(^{4}\) Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University Beijing, China. \(^{5}\) Chemical Physics and NanoLund, Lund University, Box 124, 22100 Lund, Sweden. \(^{6}\) Institute of Microstructure and Properties of Advanced Materials, Beijing University of Technology, Beijing, China. \(^{7}\) Department of Chemistry, Technical University of Denmark, DK- 2800 Kongens Lyngby, Denmark. \(^{8}\) These authors contributed equally: M. K. and Z. Y. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[88, 95, 180, 113]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[85, 140, 912, 568]]<|/det|> +AbstractBright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content that is desired to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes (PeLEDs) over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue PeLEDs based on three-dimensional perovskites show high EQE values of \(11.0\%\) and \(5.5\%\) with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion migration. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 912, 595]]<|/det|> +Blue light- emitting diode, with the Commission Internationale de l'Eclairage (CIE) y coordinate value below 0.15 along with the \((x + y)\) value below 0.30, is of critical importance for display and energy- saving lighting applications'. Similar to preceding light- emitting technologies, achieving efficient blue emission in metal halide perovskite light- emitting diodes (PeLEDs) has proven to be very challenging, with performance lagging far behind their green, red and near- infrared counterparts2- 7. Current efforts in blue PeLEDs largely take advantage of quantum confinement effects for bandgap engineering, i.e. using mixed dimensional perovskites or colloidal perovskite nanocrystals8- 10. Although impressive progress has been achieved in developing sky- blue PeLEDs (with \(\mathrm{CIEy} > 0.15\) ) (Supplementary Table 1)11, there are increasing difficulties to realize blue emission using these strategies. For example, state- of- the- art blue perovskite emitters achieved by strong quantum confinement commonly suffer from deteriorated electronic properties due to an excess of large- size organic cations and/or over- capped ligands. These issues lead to problematic charge injection and hence low brightness, as well as a big gap between photoluminescence quantum yields (PLQYs) of thin films and external quantum efficiencies (EQEs) of devices12- 14. + +<|ref|>text<|/ref|><|det|>[[85, 612, 912, 891]]<|/det|> +Compared with enhancing quantum confinement, modulating the halide anions is a more straightforward way to tune the bandgap of perovskites15. However, implementation of this facile approach in blue PeLEDs is largely hindered by poor colour stability of the resultant blue perovskite emitters (mixed bromide/chloride perovskites), due to anion segregation under electric bias16- 18. In addition, it has been widely observed that the PLQYs decrease with increasing chloride content since chloride perovskites are less defect-tolerant compared to their bromide and iodide counterparts19,20. Both issues are particularly pronounced in perovskites with high chloride content that are desired for producing blue and deep- blue emssion19- 22. Very recently, strategies on mitigating photo- induced + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 912, 300]]<|/det|> +phase segregation in perovskite solar cells (e.g. defect passivation) have been borrowed to improve the spectral stability of mixed bromide/chloride blue PeLEDs \(^{23,24}\) . These strategies were so far demonstrated to be feasible only in the cases where the chloride content is low (<30%) \(^{22,25}\) . Unfortunately, even by combining the strategies of mixed bromide/chloride perovskites with the advantages of enhanced quantum- confinement, device performance of spectrally stable blue PeLEDs is still far from practical applications (Supplementary Table 2) \(^{11,14,20,25}\) . + +<|ref|>text<|/ref|><|det|>[[85, 315, 912, 744]]<|/det|> +Here, we demonstrate that blue PeLEDs based on mixed- halide perovskites can be highly efficient and their colour instability issues can be substantially eliminated across a large range of the blue spectral region spanning 490\~451 nm (with a high chloride content ranging from 30% to 57%), without any assistance from enhanced quantum confinement. We show that not only halide ion migration, but also compositional heterogeneity is critical for triggering phase segregation. Both factors can be remarkably suppressed through depositing the perovskite films via a vapour- assisted crystallization (VAC) technique. As a result, we demonstrate spectrally stable blue PeLEDs presenting a high EQE value of 11.0% and a peak brightness of 2180 cd m \(^{-2}\) , with an emission peak at 477 nm and CIE coordinates of (0.107, 0.115). In addition, we fabricate a PeLED exhibiting ideal deep- blue emission at 467 nm and a decent EQE of 5.5%, which is the highest efficiency with an emission peak below 470 nm. The CIE coordinates of our deep- blue PeLEDs are (0.130, 0.059), approaching that of Rec. 2020 specified primary blue. + +<|ref|>sub_title<|/ref|><|det|>[[88, 760, 315, 780]]<|/det|> +## Results and discussion + +<|ref|>text<|/ref|><|det|>[[85, 797, 912, 891]]<|/det|> +Device fabrication and spectral stability. We prepare perovskites from precursors with a stoichiometry of \(\mathrm{Cs^{+}}\) : \(\mathrm{FA^{+}}\) : \(\mathrm{Pb^{2 + }}\) : \([\mathrm{Br_{1 - x} + Cl_{x}}]^{- } = 1.2\) : 0.3: 1: 3.5 \((x = 30\% \sim 57\%)\) , where \(\mathrm{FA^{+}}\) is formamidinium. In the cases of \(x\) below 20%, we do not observe any colour instability issues + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 912, 300]]<|/det|> +(Supplementary Fig. 1). We focus our discussions on the perovskites from a precursor solution with \(x = 40\%\) , which is the most representative case due to its high chloride content, decent device performance and emission within the blue region. The chloride content in this film accounts for \(42\%\) of total halide anions as determined by X- ray photoelectron spectroscopy (XPS) (Supplementary Fig. 2). We introduce 4,7,10- trioxa- 1,13- tridecanediamin (TTDDA) into the perovskite precursors as a passivating agent to reduce defects5. + +<|ref|>text<|/ref|><|det|>[[85, 315, 912, 670]]<|/det|> +We show an illustration of the VAC- treatment for preparation of perovskite films in Fig. 1a. In brief, the as- casted films are directly moved into a petri- dish with dimethylformamide (DMF) atmosphere, followed by a typical thermal annealing process. Control samples, annealed directly after spin- coating, are prepared for comparison. The different film processing techniques result in distinct variations of the film morphology, i.e. a discontinuous network of large grains for the VAC- treated films and full coverage of small nano- grains for the control ones, as shown in scanning electron microscope (SEM) images (Supplementary Fig. 3). We observe no change in the 3D crystal structure but a more preferential crystalline orientation along the (110) direction and slight enhancement of crystallinity with VAC- treatment, as demonstrated by grazing incidence wide- angle X- ray scattering (GIWAXS) and X- ray diffraction (XRD) measurements (Supplementary Fig. 4). + +<|ref|>text<|/ref|><|det|>[[85, 686, 911, 890]]<|/det|> +We fabricate the PeLEDs based on a device structure of indium tin oxide (ITO)/nickel oxide (NiOx, 10 nm)/ poly(9- vinylcarbazole) (PVK): polyvinylpyrrolidone (PVP) (10 nm)/ perovskite/ 2,2',2''-(1,3,5- benzinetriyl)tris(1- phenyl- 1H- benzimidazole) (TPBi)/ lithium fluoride (1 nm)/aluminium (100 nm) (Fig. 1b). The employment of NiOx/PVK bilayer facilitates the hole injection due to the cascade energy level alignment26, which contributes to improve PeLED performance (Supplementary Figs. 5 and 6). The PVP layer is used to improve the wettability of the precursor + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[82, 92, 912, 560]]<|/det|> +solution on the PVK surface, as demonstrated by the reduced water contact angle after PVP deposition (Supplementary Fig. 7). The high- angle annular dark- field scanning transmission electron microscope (HAADF- STEM) and energy- dispersive X- ray spectroscopy (EDX) device cross- sectional images indicate higher TPBi layer thickness ( \(\sim 50 \mathrm{nm}\) ) on the bottom hole injection layer with respect to that on perovskite grains ( \(\sim 35 \mathrm{nm}\) ) (Fig. 1b and Supplementary Fig. 8 (carbon distribution)), leading to enhanced local resistance at the TPBi/PVK:PVP interface. Combined with the large injection barrier caused by the energy level mismatch between TPBi and PVK, the discontinuous morphology in VAC- treated devices does not necessarily lead to strong electrical shunts under normal PeLED operational conditions \(^{3,5}\) (Supplementary Fig. 9). Notably, the VAC- treated devices with varying chloride content ( \(30 - 40\%\) ) show considerable enhancement of EQE values compared to the respective control samples (Fig. 1c). We show the characteristics of the representative devices in Supplementary Fig. 10. We notice that, as expected, our device performance also benefits from the efficient defect passivation ability of TTDDA (Supplementary Fig. 11). + +<|ref|>text<|/ref|><|det|>[[85, 575, 912, 892]]<|/det|> +The VAC- treated devices show stable electroluminescence (EL) with a negligible shift of the CIE coordinates up to \(\sim 400 \mathrm{mA cm^{- 2}}\) ( \(6 - 6.5 \mathrm{V}\) ), which is far above maximum light output ( \(L_{\mathrm{max}}\) ) conditions at \(100 \mathrm{mA cm^{- 2} / 5.0 \mathrm{V}}\) (Fig. 1d). The EL spectrum obtained at a high voltage (or current density) of 6 \(\mathrm{V} (\sim 400 \mathrm{mA cm^{- 2}})\) is only slightly broader than that at \(3.5 \mathrm{V} (\sim 3 \mathrm{mA cm^{- 2}})\) (Fig. 1e). We attribute this slight EL broadening to charge carrier/phonon interaction due to Joule heating \(^{27}\) , as similar behaviour is observed in pure- bromide PeLEDs (Supplementary Fig. 12). In contrast, the control devices undergo distinct emission colour changes starting at very low bias and current density of around \(3.0 - 3.5 \mathrm{V}\) and \(1 - 5 \mathrm{mA cm^{- 2}}\) (Figs. 1d and e), analogous to previous reports on spectrally unstable mixed bromide/chloride PeLEDs \(^{16,21}\) . Under the harsh operational condition, that is, with the bias larger than + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 912, 300]]<|/det|> +6.5 V, distinct colour change is observable even in VAC- treated devices. We emphasize that in this case the current density is over \(400 \mathrm{mA cm}^{-2}\) , which is far above normal working conditions of reported blue \(\mathrm{PeLEDs}^{11,14,20,25}\) . Notably, we observe abnormal plateau- like \(J - V\) characteristics during the voltage sweep at 6\~6.5 V and severe PL quenching after the operation (Supplementary Fig. 13), indicating severe device damage due to perovskite and/or interfacial degradation. Given the high current density, Joule heating could be a critical reason \(^{28}\) . + +<|ref|>text<|/ref|><|det|>[[85, 325, 914, 715]]<|/det|> +With VAC- treatment, we demonstrate spectrally stable \(\mathrm{PeLEDs}\) with emission colours from sky- blue to deep- blue (emission peaks at 490 to 451 nm) by simply varying the chloride content (30%\~57%) (Fig. 1f and Supplementary Fig. 14). We also examine the spectral stability of our devices at a constant current density of \(5 \mathrm{mA cm}^{-2}\) (with initial luminance ranging from \(\sim 200\) to \(\sim 600 \mathrm{cd m}^{-2}\) for different devices). Although the operational lifetime is no better than those in previously reported blue \(\mathrm{PeLEDs}\) (with \(\mathrm{T}_{50}\) around \(1 \sim 2 \mathrm{min}\) ) \(^{8,11,12,20}\) , we observe no spectral shift even after 10 minutes of operation (Supplementary Fig. 15). Previous reports on photoinduced phase segregation in mixed halide perovskites indicate that it is only triggered when the excitation density is above a certain threshold, below which little to no effects are present \(^{29,30}\) . Our results are consistent with these observations, indicating that employing mixed halide anions is a feasible approach for blue \(\mathrm{PeLEDs}\) as long as we can control the phase segregation threshold to be far above working conditions. + +<|ref|>text<|/ref|><|det|>[[85, 741, 912, 872]]<|/det|> +The origin of improved spectral stability. Although the underlying reason for phase segregation is complicated, previous investigations on perovskite solar cells propose that three factors may be collectively contributing to this phenomenon. These three factors include a polaron induced strain effect \(^{31}\) , a thermodynamic process as driven by free energy differences associated with composition + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 911, 150]]<|/det|> +and band offsets32, and field- dependent anion motion33. We carry out a series of characterizations to understand the origin of the spectral stability of PeLEDs based on VAC- treated perovskite films. + +<|ref|>text<|/ref|><|det|>[[85, 177, 913, 457]]<|/det|> +We first measure PL properties of our perovskite films. We observe obviously enhanced PLQYs in the VAC- treated films across a wide range of excitation fluences, with a peak PLQY of 12% compared to 3% for the control sample (Fig. 2a). Time correlated single photon counting (TCSPC) measurements demonstrate a prolonged PL lifetime for VAC- treated samples compared to the control one (Fig. 2b). These results suggest fewer defects and much suppressed non- radiative recombination in the VAC- treated films, consistent with the higher EQEs of the devices. We believe that the enhanced spectral stability in our PeLEDs is partially ascribed to the reduced defects, as defects are generally believed to act as channels for anion hopping and hence facilitate phase segregation34. + +<|ref|>text<|/ref|><|det|>[[85, 483, 913, 875]]<|/det|> +In addition to the reduced defect density, we also observe significantly improved local compositional homogeneity in the VAC- treated films compared to the control sample. It is first evidenced by a steeper edge of the absorption spectrum (Supplementary Fig. 16a) and a much- narrowed PL linewidth (with full- width at half- maximum (FWHM) of \(\sim 18 \mathrm{nm}\) ) of VAC- treated films compared to that of control samples (FWHM of \(\sim 25 \mathrm{nm}\) ) (Fig. 2c). To gain further understanding of electronic states in the films, we conducted transient absorption (TA) spectroscopy measurements. The control film displays a broad photobleaching peak that shifts from \(455 \mathrm{nm}\) to \(465 \mathrm{nm}\) (Fig. 2d and Supplementary Figs. 16b- c), which is consistent with the coexistence of different phases. As there is no sign of low- dimensional phases from GIWAXS and XRD patterns, we assign the compositional heterogeneity in the control films to a non- uniform distribution of halide anions, which has been widely reported in bromide/iodide mixed perovskites35,36. In contrast, the VAC- treated film shows a single + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 912, 486]]<|/det|> +narrow ground state photobleaching situated at 473 nm, indicating a high compositional homogeneity. According to current polaronic and thermodynamic models for rationalizing phase segregation in perovskite solar cells, a high compositional heterogeneity can contribute to the phase segregation31,32,37- 39. In specific, fluctuations in halide compositions can yield heterogeneous regions in the perovskites where polarons tend to localize at lower bandgap areas, leading to enhanced local lattice strain which drives de-mixing of halide anions31,38,39. A system with initially high free energy due to severe compositional disorder might be energetically unfavourable for phase stability as indicated by the thermodynamic model37. Lattice mismatch and discrepancy of band offsets between different phases are also believed to be the driving forces for phase segregation32,37. Our observations are in line with these previous investigations, demonstrating the critical role of high homogeneity in improving phase stability of VAC-treated devices. + +<|ref|>text<|/ref|><|det|>[[85, 501, 912, 892]]<|/det|> +Assured about the reduced defects and improved compositional homogeneity in VAC- treated films, we then evaluate field- dependent ion migration in our devices. We perform temperature- dependent admittance spectroscopy, from which we can determine ion migration activation energy \((E_{\mathrm{A}})\) , ion diffusion coefficient \((D)\) , and concentration of mobile ions \((N_{\mathrm{i}})^{40}\) . The capacitance (C) response of mobile ionic species can be probed by varying the frequency \((\omega)\) of an applied alternating voltage and the temperature. We show the admittance spectra in Supplementary Fig. 17 and the plots of derivations (- odC/do versus \(\omega\) ) in Figs. 2e and 2f. Two distinct signatures from mobile ionic species are visible, which are labelled \(\epsilon\) and \(\beta\) . We confirm that the charge transport layers are not responsible for these signatures by characterizing devices with only charge transport layers (Supplementary Fig. 18)41. In particular, we observe that the response peaks at the low- frequency region \((\epsilon)\) in the VAC- treated devices are much less prominent than those in the control devices, suggesting a much lower + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 913, 372]]<|/det|> +mobile ion concentration. We show the deduced Arrhenius plots in Supplementary Fig. 19 and summarize all the obtained parameters \((E_{\mathrm{A}}, D\) , and \(N_{\mathrm{i}}\) ) in Supplementary Table 3. The \(E_{\mathrm{A}}\) values of ion diffusion for both \(\epsilon\) and \(\beta\) are very close in the two samples, implying that the mode of ion migration is not significantly altered. Both the concentration of mobile ions and ion diffusion coefficient are reduced in VAC- treated devices compared to the control devices. The most striking difference occurs to \(N_{\mathrm{i}}(\epsilon)\) , which is decreased from \(5.4 \times 10^{16} \mathrm{~cm}^{- 3}\) to \(1.6 \times 10^{16} \mathrm{~cm}^{- 3}\) . Considering the small \(E_{\mathrm{A}}\) of \(\epsilon\) ( \(\sim 0.2 \mathrm{eV}\) ), we assign them to mobile halide anions \(^{33,40}\) . The mitigated halide migration can be a result of reduced ionic defects, as confirmed by PLQYs and TCSPC results \(^{34}\) . + +<|ref|>text<|/ref|><|det|>[[85, 389, 912, 485]]<|/det|> +Based on the analysis above, we conclude that the excellent spectral stability in VAC- treated devices originates from a synergistic effect of less ionic defects, mitigated ion migration and a higher compositional homogeneity. + +<|ref|>text<|/ref|><|det|>[[85, 500, 913, 892]]<|/det|> +Understanding the effect of the VAC- treatment process. Having understood the origin of high colour stability and excellent device performance, the question that remains is how VAC- treatment brings about these effects. We conduct SEM measurements to track the grain growth and morphological evolution of the films during the VAC treatment. We clearly observe two stages. The first stage happens within the first minute of vapour treatment, showing a crystal growth from initially formed small grains into large ones, accompanied by the morphological evolution from dense films into discontinuous network (Supplementary Fig. 20). Considering the presence of crystalline perovskites within the pristine films and the diffusive vapour atmosphere, we assign the process of grain growth to Ostwald ripening. The wet films preserved by DMF vapour can be regarded as a sol system, with the solvent as the dispersing medium and perovskites as the dispersed phases. The ripening process occurs because large grains are more energetically favoured to smaller grains, leading + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 911, 150]]<|/det|> +to reduced grain boundaries and hence fewer defects. The second stage happens during the prolonged duration of treatment, which only has a slight impact on the morphology. + +<|ref|>text<|/ref|><|det|>[[85, 168, 912, 784]]<|/det|> +We also employ in- situ PL and transmittance measurements to monitor the crystal growth with and without DMF vapour. The measurement setups are illustrated in Supplementary Fig. 21. Initially, both films show broad emission bands with the main peak at the low- energy region and a distinguishable shoulder at the high- energy region, which are labelled as P1 and P2, respectively (Figs. 3a and 3b). We speculate that the high- energy emission originates from initially formed Cl- rich perovskite phases due to their fast nucleation and crystallization, as governed by their poor solubility compared to Br- rich counterparts. By following the PL spectral evolution with time, we observe a gradual disappearance of P2 and a continuous red- shift of P1 in VAC- treated samples, leading to a narrow and single- emission peak eventually (Fig. 3b). To further clarify the spectral evolution of VAC- treated films in different time scales, we show the changes of emission bandwidth as well as the proportion of P2 (AP2) to a total area of emission band (A) with time in Fig. 3c. We find that the most striking changes occur within the first five minutes of treatment, while prolonged duration of up to 20 minutes results in only a small difference (Fig. 3c). This PL evolution is consistent with the results of in- situ transmittance measurements, i.e. a red- shift of absorption onset and steeper absorption edge after VAC- treatment (Supplementary Fig. 22a). In contrast, keeping the pristine films in the glovebox atmosphere does not change the PL (Fig. 3a) and transmittance (Supplementary Fig. 22b) spectra to any significant degree over time. + +<|ref|>text<|/ref|><|det|>[[85, 798, 911, 890]]<|/det|> +Based on the in- situ spectroscopic measurement results, we can now rationalize the effect of the VAC- treatment. It provides a favourable diffusive environment for halide rearrangement within the films (Fig. 3d), which undergo an equilibrating crystallization process that homogenises local chemical + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 912, 560]]<|/det|> +composition and reduces disorder. Initially, the as- casted films are composed of various Cl- rich solid phases and Br- rich components in liquid phases due to nonequilibrium grain growth during spin- casting. For the films with no vapour atmosphere, quick solvent evaporation and following fast crystallization result in immediate freezing of the perovskite composition. The post- annealing could mitigate phase heterogeneity to some extent, as indicated by the weakened emission shoulder at short wavelength (P2) after annealing (Fig. 2c). However, the initially formed heterogeneous phases are still partially preserved in the resulting films. In contrast, with the presence of DMF vapour, the liquid phase can be preserved for a long duration. This facilitates and prolongs the following halide exchange process as driven and modulated by the chemical potential difference between solid (Cl- rich) and liquid phases (Br- rich), resulting in a rearranged composition that gradually approaches chemical equilibrium and homogeneous distribution of constituents. The following annealing procedure has little impact on the PL spectra of VAC- treated films, further confirming that the high homogeneous composition has already been achieved during the VAC- treatment. + +<|ref|>text<|/ref|><|det|>[[85, 575, 912, 892]]<|/det|> +We then tune the duration of DMF vapour- treatment to assess the impact on spectral stability and device efficiency in different timescale (Fig. 3e and 3f), further supporting our understanding of this technique. We observe distinct batch to batch variations in EL spectra and dispersion of CIE coordinates in control devices (Fig. 3e and Supplementary Fig. 23a), ascribed to nonequilibrium crystal growth and hence uncontrollable local film composition. In contrast, EL spectra and CIE coordinates of VAC- treated devices are highly reproducible between batches, resulting from self- moderated halide rearrangement during the VAC- treatment (Fig. 3e and Supplementary Fig. 23b). When comparing the devices processed with different duration of VAC treatment, we observe a remarkable EQE enhancement in one minute of treatment, that is, with averaged peak EQE values improved from \(\sim 0.6\%\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[84, 90, 912, 900]]<|/det|> +to \(\sim 3.8\%\) (Fig. 3f), which well corresponds to the dramatical morphological variations in the same time scale from SEM results (Supplementary Fig. 20). We believe that perovskite re-crystallization, enlarged grain size and improved local homogeneity collectively help to reduce the defect density and hence reduce non- radiative recombination. In addition, the isolated nano- structures may also contribute to the efficiency improvement due to enhanced light- out coupling3. With increasing the processing duration, the EQE values gradually approach saturation. We assign this to a slow diffusion- mediated defect healing process from the gradually improved homogeneity that reduces local lattice mismatch and strain- induced interfacial defects42,43. Notably, one minute of VAC- treatment is sufficient for improving the efficiency but not the spectral stability (Fig. 3e), indicating that discontinuous morphology has little impact on improving phase stability. In other words, a large perovskite grain with size scale of hundreds of nanometres in our samples can hardly be the reason for the suppression of phase segregation within the grain, as probed in previous reports showing that the phase segregated domain can be as small as \(\sim 8 \mathrm{nm}^{30,32}\). We also notice that the devices with five- minute treatment show comparable colour stability to those with twenty- minute treatment (Fig. 3e), corresponding well to the time scale of the disappearance of high energy phases as observed in Fig. 3c. It further confirms the critical role of high compositional homogeneity in improving phase stability. Given the critical role of diffusive environment on retarding crystallization for halide rearrangement, a proper solubility of perovskite precursors in the solvent vapour might be the key to achieving high compositional homogeneity. We thus perform additional experiments using dimethyl sulfoxide (DMSO) or chloroform as the alternative vapour for further understanding the VAC treatment. DMSO is another commonly used solvent for perovskite precursors, while chloroform is a well- known “anti- solvent” that is widely used to accelerate perovskite crystallization44. Considering + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 914, 373]]<|/det|> +that the vapour residues in the glovebox may affect the results, we also prepare the samples without introducing any vapour on purpose, that is, leaving the as- casted films in the glovebox for the same duration. As shown in Supplementary Fig. 24, the introduction of chloroform vapour has no positive effect on either device efficiency or spectral stability, which can be attributed to the poor solubility of perovskite precursors in chloroform, leading to a fast crystallization and freezing of the composition, and hence resulting in high heterogeneity. In contrast, DMSO treatment gives comparable improvement as DMF vapour, further rationalising our understanding of the effect of the vapour treatment. + +<|ref|>text<|/ref|><|det|>[[85, 390, 914, 670]]<|/det|> +The general applicability of VAC- treatment and device optimization. We proceed to explore the VAC- treatment in other material systems, aiming to further improve the device performance and validate the general applicability. We incorporate a small amount of rubidium ions \((\mathrm{Rb}^{+})\) in our perovskites, that is, using a precursor composition of \(\mathrm{Rb^{+}}\) : \(\mathrm{Cs^{+}}\) : \(\mathrm{FA^{+}}\) : \(\mathrm{Pb^{2 + }}\) : \([\mathrm{Br}_{0.6} + \mathrm{Cl}_{0.4}]^{- } = 0.1:1.2:0.2\) : 1: 3.5. Consistent with the previous reports in perovskite solar cells \(^{45,46}\) , the incorporation of \(\mathrm{Rb^{+}}\) effectively suppresses non- radiative recombination as indicated by a considerable enhancement of peak external PLQY (25%) and a prolonged PL lifetime (Supplementary Figs. 25a and b). The small amount of \(\mathrm{Rb^{+}}\) addition has little impact on the film morphology (Supplementary Fig. 25c). + +<|ref|>text<|/ref|><|det|>[[85, 686, 914, 891]]<|/det|> +We show the characteristics of the best- performing VAC- treated Rb- device using 40% Cl content in Fig. 4. The device exhibits blue emission peaking at 477 nm with FWHM of 18 nm. The corresponding CIE coordinates are (0.107, 0.115), approaching the primary blue (0.14, 0.08) specified by the National Television System Committee (NTSC). Compared to the device without using VAC- treatment (Supplementary Fig. 26), the treated device shows a significant enhancement of EQE value up to 11.0%. The luminance rises rapidly after the device turns on at a low voltage of 2.6 V, reaching + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 914, 560]]<|/det|> +a peak value of 2,180 cd cm \(^{- 2}\) at 5.0 V (106 mA cm \(^{- 2}\) ). The low turn-on voltage and high brightness indicate efficient charge injection, which is usually very challenging in strongly confined perovskites \(^{14}\) . We observe no peak shift during voltage scans until reaching a high bias at 6.0 V (\~300 mA cm \(^{- 2}\) ) (Supplementary Fig. 27a), analogous to the device without Rb \(^+\) incorporation, further indicating that phase segregation in VAC-treated devices is mainly mediated by the device damage at harsh operating conditions. In addition, we demonstrate that no EL shift can be observed even after 75 min of operation at 3 V (\~0.1 mA cm \(^{- 2}\) , with initial luminance of \~10 cd m \(^2\) ) (Supplementary Fig. 27b). Although Rb \(^+\) addition significantly improves the device efficiency, we have not observed any distinct effect on operational stability (\~3 min, Supplementary Fig. 27c and d). The short operational lifetime could be a result of Joule heating and ion-migration induced material and/or interfacial degradation under the bias \(^{9,28}\) , as well as Al diffusion and relevant redox reaction between Pb \(^{2 + }\) and Al \(^{0.47}\) . An EQE histogram for 40 devices shows an average peak EQE of 9.3% with a low standard deviation of 0.67%, indicating high reproducibility of the VAC-treatment. + +<|ref|>text<|/ref|><|det|>[[85, 575, 913, 742]]<|/det|> +Further increasing Cl content to 45% results in deep- blue emission, whose device characteristics are also summarized in Fig. 4. The corresponding CIE coordinates are (0.130, 0.059), very close to Rec. 2020 specified blue standards (0.131, 0.046). The deep- blue PeLEDs achieves a peak EQE of 5.5% and an average peak EQE of 3.9% with a standard deviation of 0.76%, which are among the best for PeLEDs with ideal deep- blue emission. + +<|ref|>text<|/ref|><|det|>[[85, 760, 913, 890]]<|/det|> +We demonstrate that the VAC- treatment is also applicable for improving the colour stability and device performance of low- dimensional perovskites with mixed bromide/chloride anions, e.g. the typical phenethylammonium (PEA \(^+\) )- modified CsPb(Bro \(_7\) Cl \(_{0.3}\) ) \(^3\) (Supplementary Fig. 28). These results indicate that the wavelength of the previously reported high- performance sky- blue PeLEDs + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 94, 910, 150]]<|/det|> +based on quasi- 2D perovskites could be pushed to a bluer region without any negative impacts on their colour stability and device efficiency. + +<|ref|>sub_title<|/ref|><|det|>[[88, 168, 203, 188]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[85, 203, 912, 707]]<|/det|> +In summary, we have demonstrated that the notorious colour instability issues in mixed halide blue PeLEDs can be substantially mitigated across a wide range of emission colour from sky blue to deep blue region (490 to 451 nm). The excellent phase stability is mainly achieved by the development of a vapour- assisted crystallization technique that effectively suppresses the ion migration and compositional heterogeneity. Particularly, for the first time, we show high- efficiency and spectrally stable blue and deep- blue PeLEDs based on mixed halide 3D perovskites, with respective peak EQEs of \(11.0\%\) and \(5.5\%\) , presenting two of the most efficient blue PeLEDs to date. Our findings are also applicable to the prevailing low- dimensional blue perovskite emitters, indicating a bright future for further improvement of blue PeLEDs by combining these two strategies. Our research thus provides a broad avenue for future development of blue perovskite emitters, representing another milestone towards practical implementation of perovskite light- emitting diodes in full- colour displays and lighting applications. Beyond that, stabilized mixed halide perovskites are also of great interest for a wide range of perovskite applications where the bandgap needs to be finely controlled, for instance, lasing and tandem solar cells. + +<|ref|>sub_title<|/ref|><|det|>[[89, 732, 277, 752]]<|/det|> +## Acknowledgement + +<|ref|>text<|/ref|><|det|>[[85, 769, 912, 864]]<|/det|> +We thank D. Egger, X. Zhu, C. Yin, H. Tian and J. Li for valuable discussions, and X. Liu for help with the XPS measurements. We acknowledge the support from the ERC Starting Grant (No. 717026), the Swedish Energy Agency Energimyndigheten (No. 48758- 1 and 44651- 1), Swedish Research + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 92, 912, 410]]<|/det|> +Council VR, NanoLund and the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linköping University (Faculty Grant SFO- Mat- LiU No. 2009- 00971). C.D. and S.R. acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF Hyper project, contract no. 03SF0514C) and the DFG (no. DE 830/22- 1) within the framework of SPP 2196 programme. Y. L. acknowledge financial support from the National Key Research and Development Program of China (2016YFB0700700), the National Natural Science Foundation of China (11704015, 51621003, 12074016), the Scientific Research Key Program of Beijing Municipal Commission of Education, China (KZ201310005002), and the Beijing Innovation Team Building Program, China (IDHT20190503). F.G. is a Wallenberg Academy Fellow. + +<|ref|>sub_title<|/ref|><|det|>[[88, 437, 312, 456]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[85, 472, 914, 789]]<|/det|> +F.G. and W.X. conceived the idea and supervised the project; M.K. performed the experiments and analysed the data; Z.Y. developed Rb- doped devices and low- dimensional perovskite- based devices; R.S. performed admittance spectroscopy and analysed the data under the supervision of C.D.; X.L. and P.T. contributed to device fabrication and measurements; W.L. performed transient absorption under the supervision of K.Z. and T.P.; Z.L. performed transmission electron microscopy under the supervision of Y.L.; R. Z. and G. Z. performed GIWAXS measurements and analysed the data; C.B., S.B., L.D. and R.F. contributed the interpretation of results; M.K., W.X. and F.G. wrote the manuscript; S.B. provided revisions to the manuscript; All authors discussed the results and commented on the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[88, 807, 323, 826]]<|/det|> +## Additional information + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 910, 150]]<|/det|> +Supplementary information is available in the online version of the paper. Correspondence to W. X. and F.G. + +<|ref|>sub_title<|/ref|><|det|>[[86, 167, 258, 188]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[85, 205, 911, 261]]<|/det|> +The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[86, 279, 293, 299]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[86, 307, 443, 325]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[86, 349, 192, 368]]<|/det|> +## Reference + +<|ref|>text<|/ref|><|det|>[[85, 385, 880, 440]]<|/det|> +1. Zhu, M. & Yang, C. Blue fluorescent emitters: Design tactics and applications in organic light-emitting diodes. Chem. Soc. Rev. 42, 4963-4976 (2013). + +<|ref|>text<|/ref|><|det|>[[85, 450, 840, 503]]<|/det|> +2. Tan, Z. K. et al. Bright light-emitting diodes based on organometal halide perovskite. Nat. 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Thermal management enables bright and stable perovskite light-emitting diodes. Adv. Mater. 32, 2000752 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 85, 888, 135]]<|/det|> +29. Braly, I. L. et al. Current-induced phase segregation in mixed halide hybrid perovskites and its impact on two-terminal tandem solar cell design. ACS Energy Lett. 2, 1841-1847 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 150, 870, 200]]<|/det|> +30. Kuno, M. & Brennan, M. C. What exactly causes light-induced halide segregation in mixed-halide perovskites? Matter 2, 21-23 (2020). + +<|ref|>text<|/ref|><|det|>[[85, 215, 884, 264]]<|/det|> +31. Bischak, C. G. et al. Origin of reversible photoinduced phase separation in hybrid perovskites. Nano Lett. 17, 1028-1033 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 280, 858, 330]]<|/det|> +32. Draguta, S. et al. Rationalizing the light-induced phase separation of mixed halide organic-inorganic perovskites. Nat. Commun. 8, 200 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 345, 904, 396]]<|/det|> +33. Brennan, M. C., Draguta, S., Kamat, P. V. & Kuno, M. Light-induced anion phase segregation in mixed halide perovskites. ACS Energy Lett. 3, 204-213 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 410, 888, 461]]<|/det|> +34. Barker, A. J. et al. Defect-assisted photoinduced halide segregation in mixed-halide perovskite thin films. ACS Energy Lett. 2, 1416-1424 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 476, 897, 526]]<|/det|> +35. Gratia, P. et al. Intrinsic halide segregation at nanometer scale determines the high efficiency of mixed cation/mixed halide perovskite solar cells. J. Am. Chem. Soc. 138, 15821-15824 (2016). + +<|ref|>text<|/ref|><|det|>[[85, 541, 887, 592]]<|/det|> +36. Tennyson, E. M., Doherty, T. A. S. & Stranks, S. D. Heterogeneity at multiple length scales in halide perovskite semiconductors. Nat. Rev. Mater. 4, 573-587 (2019). + +<|ref|>text<|/ref|><|det|>[[85, 607, 858, 657]]<|/det|> +37. Brivio, F., Caetano, C. & Walsh, A. Thermodynamic origin of photoinstability in the CH3NH3Pb(I1-xBrx)3 hybrid halide perovskite alloy. J. Phys. Chem. Lett. 7, 1083-1087 (2016). + +<|ref|>text<|/ref|><|det|>[[85, 672, 874, 722]]<|/det|> +38. Zhu, C. et al. Strain engineering in perovskite solar cells and its impacts on carrier dynamics. Nat. Commun. 10, 815 (2019). + +<|ref|>text<|/ref|><|det|>[[85, 737, 896, 788]]<|/det|> +39. Wang, X. et al. Suppressed phase separation of mixed-halide perovskites confined in endotaxial matrices. Nat. Commun. 10, 695 (2019). + +<|ref|>text<|/ref|><|det|>[[85, 803, 844, 854]]<|/det|> +40. Reichert, S. et al. Ionic-defect distribution revealed by improved evaluation of deep-level transient spectroscopy on perovskite solar cells. Phys. Rev. Appl. 13, 034018 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 82, 840, 135]]<|/det|> +41. Awni, R. A. et al. Influence of charge transport layers on capacitance measured in halide perovskite solar cells. Joule 4, 644-657 (2020). + +<|ref|>text<|/ref|><|det|>[[85, 149, 892, 201]]<|/det|> +42. Doherty, T. A. S. et al. Performance-limiting nanoscale trap clusters at grain junctions in halide perovskites. Nature 580, 360-366 (2020). + +<|ref|>text<|/ref|><|det|>[[85, 215, 890, 267]]<|/det|> +43. Jariwala, S. et al. Local crystal misorientation influences non-radiative recombination in halide perovskites. Joule 3, 3048-3060 (2019). + +<|ref|>text<|/ref|><|det|>[[85, 280, 856, 333]]<|/det|> +44. Wu, C. et al. Improved performance and stability of all-inorganic perovskite light-emitting diodes by antisolvent vapour treatment. Adv. Funct. Mater. 27, 1700338 (2017). + +<|ref|>text<|/ref|><|det|>[[85, 346, 822, 399]]<|/det|> +45. Saliba, M. et al. Incorporation of rubidium cations into perovskite solar cells improves photovoltaic performance. Science 354, 206-209 (2016). + +<|ref|>text<|/ref|><|det|>[[85, 411, 800, 464]]<|/det|> +46. Abdi-Jalebi, M. et al. Potassium- and rubidium-passivated alloyed perovskite films: Optoelectronic properties and moisture stability. ACS Energy Lett. 3, 2671-2678 (2018). + +<|ref|>text<|/ref|><|det|>[[85, 477, 880, 530]]<|/det|> +47. Zhao, L. et al. Redox chemistry dominates the degradation and decomposition of metal halide perovskite optoelectronic devices. ACS Energy Lett. 1, 595-602 (2016). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[100, 99, 888, 520]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[90, 535, 911, 775]]<|/det|> +
Fig. 1 Device fabrication and characteristics. a, An illustration of the VAC-treatment. b, Schematic of the PeLED structure and the HAADF cross-sectional device image. The scale bar is 100 nm. c, Histograms of peak EQEs extracted from control (top) and VAC-treated devices (bottom) with varying chloride contents (30%, 35%, 40%). d-f, Spectral stability for control and VAC-treated devices with 40% Cl loading. The representative plots of \(\mathrm{CIE}_y\) versus applied voltages (top) and current densities (bottom) (d); EL spectra at low and high voltage/current density for control (left) and VAC-treated devices (right) (e); EL spectra of VAC-treated devices with varying chloride content (30\~57%) at maximum luminance (f). The points labelled as \(L_{\mathrm{max}}\) in Fig. 1d represent the operational condition for peak luminance.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 95, 900, 410]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 433, 911, 590]]<|/det|> +
Fig. 2 Understanding superior spectral stability of VAC-treated devices. a-d, Photophysical characterizations for control and VAC-treated perovskite films: Fluence-dependent PLQYs (a); PL decay measured by TCSPC (b). PL spectra (c); Transient absorption of control (top) and VAC-treated films (bottom) after excitation at 400 nm (d). e, f, Derivations of temperature-dependent capacitance versus frequency plots for control (e) and VAC-treated (f) devices. The blue arrows indicate temperature change from 350 K to 200 K. Here, two mobile ions marked as \(\beta\) and \(\epsilon\) are visible.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 98, 904, 556]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 580, 913, 850]]<|/det|> +
Fig. 3 Understanding the halide redistribution during VAC-process. a, b, PL evolution of the precursor films kept in the glovebox atmosphere (a) and DMF atmosphere (b) with time. c, the evolution of emission linewidth and the proportion of P2 (Ar2) to the respective total area of the emission band (A) in VAC-treated films with time. d, Schematic illustration of the proposed mechanism for halide redistribution. Here, the purple \(\mathrm{Pb(Br / Cl)_6^{4 - }}\) octahedra represent chloride-rich phases in respect to that with stoichiometric bromide/chloride distribution (blue octahedra). The khaki represents the liquid phase within the films and the blue arrows represent ion exchange process. The excessive ions within the dried films are not illustrated for clarity. e, f, The evolution of CIE coordinates upon bias (e) and peak EQEs of the devices with varying duration of VAC-treatment (0, 1, 2, 5, 20 minutes) (f). The data were extracted from 4 to 6 devices.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[167, 95, 825, 423]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 451, 911, 480]]<|/det|> +
Fig. 4 The device performance of Rb-passivated perovskites with 40% and 45% Cl contents. a,
+ +<|ref|>text<|/ref|><|det|>[[85, 480, 911, 512]]<|/det|> +EQE- current density \((J)\) curves \((J\) - EQE). b, Current density- voltage- luminance \((J - V - L)\) characteristics. + +<|ref|>text<|/ref|><|det|>[[85, 504, 911, 550]]<|/det|> +c, EL spectra and CIE colour coordinates. The square and pentagram in the CIE 1931 (x, y) chromaticity diagram represent the colour coordinates of primary blue specified in NTSC and + +<|ref|>text<|/ref|><|det|>[[85, 559, 875, 579]]<|/det|> +Rec.2020, respectively. d, Histograms of the peak EQEs extracted from 40 devices for each case. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[87, 94, 180, 112]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[85, 130, 912, 410]]<|/det|> +Materials. Caesium bromide (CsBr, 99.999%), lead bromide (PbBr2, 99.999%), lead chloride (PbCl2, 99.999%) was purchased from Alfa Aesar. Formamidinium bromide (FABr) and phenethylammonium bromide (PEABr) were purchased from Greatcell Solar. Rubidium bromide (RbBr, 99.99%), polyvinylpyrrolidone (PVP, average Mw \(\sim 55000\) ), 4,7,10- trioxa- 1,13- tridecanediamin (TTDDA), poly(9- vinylcarbazole) (PVK, average Mn 25,000- 50,000) were purchased from Sigma Aldrich. The \(\mathrm{NiO_x}\) nano- crystals were purchased from Avantama AG and were used without additional treatment. 1,3,5- tris(1- phenyl- 1H- benzimidazol- 2- yl)benzene (TPBi) was purchased from Luminescence Technology corp. Other materials for device fabrication were all purchased from Sigma- Aldrich. + +<|ref|>text<|/ref|><|det|>[[85, 425, 912, 670]]<|/det|> +Preparation of the perovskite solution. Perovskite precursors (CsBr: FABr: PbBr2: PbCl2: TTDDA) with a molar ratio of 1.2: 0.3: x: y: 0.1 (where x + y = 1) were mixed and dissolved in dimethyl sulfoxide (DMSO). The precursor concentration as determined by \(\mathrm{Pb^{2 + }}\) is 0.15 M for 30\~40% Cl, 0.13 M for 45% Cl, 0.11 M for 50% Cl, and 0.09 M for 57% Cl, respectively. The precursor solutions were stirred at 80°C for 4h before use. For the low- dimensional perovskites, precursors (PEABr: CsBr PbBr2: PbCl2) with a molar ratio of 0.9: 1.1: 0.4: 0.6 mixed and dissolved in DMSO to make a solution with 30% Cl- content. The precursor concentration determined by \(\mathrm{Pb^{2 + }}\) is 0.15 M. + +<|ref|>text<|/ref|><|det|>[[85, 686, 912, 891]]<|/det|> +PeLED fabrication. Glass substrates with patterned Indium tin oxide (ITO) were sequentially cleaned by detergent and TL- 1 (a mixture of water, ammonia (25%) and hydrogen peroxide (28%) (5:1:1 by volume)). The clean substrates were then treated by ultraviolet- ozone for 10 min. \(\mathrm{NiO_x}\) was spin- coated in air at 4,000 r.p.m. for 30 s, followed by baking at 150 °C for 10 min in air. The substrates were then transferred into a nitrogen- filled glovebox (< 0.1 ppm \(\mathrm{H_2O}\) , < 0.1 ppm \(\mathrm{O_2}\) ). PVK (4 mg ml⁻¹ in chloro benzene) was deposited at 3000 r.p.m. followed by thermal annealing at 150°C for 10 min. Next, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 92, 912, 448]]<|/det|> +a thin layer of PVP (2.0 mg mL-1 in isopropyl alcohol (IPA)) was deposited at 3000 r.p.m. and baked at \(100^{\circ}\mathrm{C}\) for 5 min. After cooling down to room temperature, the perovskite solutions with varying bromide/chloride ratios were deposited at 3000 r.p.m. Directly after spin-coating, the films were put in an unsealed \(\varnothing 60 \mathrm{mm}\) petri- dish (with lid) at room temperature, where \(20 \mu \mathrm{l}\) of dimethylformamide had been dropped 10 min prior to the film placement. After 20 min of vapour assisted crystallisation (VAC) treatment, the films were annealed at \(80^{\circ}\mathrm{C}\) for 8 min. For low- dimensional perovskite films with mixed halides, the treatment duration is 10 min and the annealing condition is \(80^{\circ}\mathrm{C}\) for 5 min. Finally, the electron transport layer TPBi and top contacts LiF/Al (1 nm / 100 nm) were deposited by thermal evaporation through shadow masks at a base pressure of \(\sim 10^{-7}\) torr. The device area was 7.25 \(\mathrm{mm}^2\) . + +<|ref|>text<|/ref|><|det|>[[85, 464, 912, 781]]<|/det|> +PeLED characterization. All PeLED device characterizations were performed at room temperature in a nitrogen- filled glovebox without encapsulation. A Keithley 2400 source- meter and a fibre integration sphere (FOIS- 1) coupled with a QE Pro spectrometer (Ocean Optics) were utilized. The absolute radiance was calibrated by a standard Vis- NIR light source (HL- 3P- INT- CAL plus, Ocean Optics). The PeLED devices were measured on top of the integration sphere and only forward light emission can be collected. The devices were swept from zero bias to forward bias with a step voltage of 0.05 V, lasting for 100 ms at each voltage step for stabilisation. The sweep duration from 1 to 7 V is 70 seconds (with a scan rate of 86 mV S-1). The EQE and spectral evolution with time was measured using the same system. + +<|ref|>text<|/ref|><|det|>[[85, 798, 912, 890]]<|/det|> +Perovskite film characterization. Top- view scanning electron microscope (SEM) images were tested by LEO 1550 Gemini. Steady- state PL spectra of the perovskite films were recorded by a fluorescent spectrophotometer (F- 4600, HITACHI) with a 200 W Xe lamp as an excitation source. UV- Vis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 911, 150]]<|/det|> +absorbance spectra were collected using a PerkinElmer model Lambda 900. X- ray diffraction patterns were measured using a Panalytical X'Pert Pro with an X- ray tube (Cu Kα, \(\lambda = 1.5406 \mathring{\mathrm{A}}\) ). + +<|ref|>text<|/ref|><|det|>[[85, 168, 913, 410]]<|/det|> +X- ray photoelectron spectroscopy (XPS) tests were performed by a Scienta ESCA 200 spectrometer in ultrahigh vacuum ( \(\sim 1 \times 10^{- 10}\) mbar) with a monochromatic Al (Ka) X- ray source providing photons with 1,486.6 eV. The experimental was set so that the full- width at half- maximum of clean Au 4f 7/2 line (at the binding energy of 84.00 eV) was 0.65 eV. All spectra were characterized at a photoelectron take- off angle of \(0^{\circ}\) . Ultraviolet photoelectron spectroscopy (UPS) was carried out using a Kratos AXIS Supra on perovskite samples spun- cast on ITO/NiOₓ/PVK/PVP. He I (21.22eV) radiation was generated from a helium discharge lamp. Samples were biased at 9.1V. + +<|ref|>text<|/ref|><|det|>[[85, 426, 913, 595]]<|/det|> +In- situ PL of the crystallisation process was collected using the integrating sphere and the QE Pro spectrometer as described above, and a 365 nm UV laser as excitation source. In- situ transmittance tests were performed using the same spectrometer but with a solar simulator (AM 1.5G) as the light source. A ND filter was used to decrease the light intensity. The systems were illustrated in Supplementary Fig. 21. + +<|ref|>text<|/ref|><|det|>[[85, 611, 913, 781]]<|/det|> +Time- correlated single photon counting (TCSPC) measurements were carried out by using an Edinburgh Instruments FL1000 with a 405 nm pulsed picosecond laser (EPL- 405). Fluence dependent PLQY was measured using a 405 nm continuous wave laser, an integrating sphere and the same spectrometer. The perovskite films were deposited on ITO/NiOₓ/PVK/PVP substrates under identical conditions as for the PeLEDs, and encapsulated using glass slides and UV- curable resin. + +<|ref|>text<|/ref|><|det|>[[85, 797, 913, 890]]<|/det|> +Grazing- incidence wide- angle X- ray scattering (GIWAXS) was recorded in Shanghai Synchrotron Radiation Facility. The diffraction patterns were collected by two dimensional MarCCD 225 detector with 234 mm from samples to the detector. All the samples were protected with N₂ gas + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 94, 911, 223]]<|/det|> +during the measurements. To assure the diffraction intensity, an exposure time of \(15\mathrm{s}\) was adopted with an incidence angle of \(0.5^{\circ}\) , and the wavelength of the X- ray was \(1.24\mathrm{\AA}\) (10 KeV). For all these tests, the perovskite films were deposited on ITO/NiOx/PVK/PVP substrates under identical conditions as device fabrication. + +<|ref|>text<|/ref|><|det|>[[85, 241, 911, 483]]<|/det|> +Scanning transmission electron microscopy (STEM) and Energy- dispersive X- ray spectroscopy (EDX). The STEM samples were fabricated by using the FEI Focused Ion Beam (FIB) system (Helios Nanolab 600i). A FEI Titan- G2 Cs- corrected transmission electron microscope with 300 KV accelerating voltage was used to get the high angle angular dark field (HAADF) images of the samples. The STEM elemental mapping images were collected by four silicon drift windowless detectors (Super- EDX) in the FEI Titan- G2 Cs- corrected transmission electron microscope. The energy resolution of the Super- EDX was \(137\mathrm{eV}\) . + +<|ref|>text<|/ref|><|det|>[[85, 500, 912, 742]]<|/det|> +Transient absorption. A femtosecond oscillator (Mai Tai, Spectra Physics) is used as a seed laser for a regenerative amplifier (Spitfire XP Pro, Spectra Physics) which generates well collimated beam of femtosecond pulses (800 nm, 80 fs pulse duration, 1 kHz repetition rate). The second harmonic generated by a BBO crystal was used as pump (400 nm). White light continuum (WLC) as the probe was produced by focusing the 800 nm fs pulse on a thin \(\mathrm{CaF_2}\) plate. Polarization between the pump and probe was set to the magic angle \((54.7^{\circ})\) . Both pump and probe pulses are monitored to compensate for the laser fluctuations during the measurements. + +<|ref|>text<|/ref|><|det|>[[85, 760, 911, 891]]<|/det|> +Admittance spectroscopy. For the defect studies we used a setup consisting of a Zurich Instruments MFLI lock- in amplifier with MF- IA and MF- MD options, a Keysight Technologies 33600A function generator and a cryo probe station Janis ST500 with a Lakeshore 336 temperature controller. For determining the ion signature using admittance spectroscopy we varied the sample temperature from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 93, 911, 260]]<|/det|> +200 K to 350 K in 5 K steps, controlled accurately within 0.01 K and using liquid nitrogen for cooling. The capacitance in term of a C||R equivalence model was measured by applying an ac voltage with amplitude of \(V_{\mathrm{ac}} = 20 \mathrm{mV}\) and varying the frequency from 0.6 Hz to 3.2 MHz. The rates \(e_{\mathrm{t}}\) are obtained from the peak maxima of the derivative of the capacitance. These rates are linked to the diffusion coefficient \(D\) in terms of the underlying hopping process of the mobile ions \(^{48,49}\) , + +<|ref|>equation<|/ref|><|det|>[[436, 268, 560, 310]]<|/det|> +\[e_{\mathrm{t}} = \frac{e^{2}N_{\mathrm{eff}}D}{k_{\mathrm{B}}T\epsilon_{0}\epsilon_{\mathrm{R}}},\] + +<|ref|>text<|/ref|><|det|>[[750, 318, 779, 334]]<|/det|> +(1) + +<|ref|>text<|/ref|><|det|>[[85, 353, 911, 484]]<|/det|> +where \(N_{\mathrm{eff}}\) refers to the effective doping density, \(e\) is the elementary charge, \(k_{\mathrm{B}}\) is the Boltzmann constant, \(T\) the temperature, \(\epsilon_{0}\) the dielectric constant and \(\epsilon_{\mathrm{R}}\) the relative permittivity. For the calculation of \(D_{300\mathrm{K}}\) we used a dielectric permittivity of \(19.2^{50}\) . Since ion migration is a thermally activated process, the diffusion coefficient depends on the temperature, + +<|ref|>equation<|/ref|><|det|>[[409, 491, 589, 531]]<|/det|> +\[D = D_{0}exp\left(-\frac{E_{A}}{k_{B}T}\right)\] + +<|ref|>text<|/ref|><|det|>[[750, 540, 779, 556]]<|/det|> +(2) + +<|ref|>text<|/ref|><|det|>[[85, 574, 911, 742]]<|/det|> +with the activation energy for ion migration \(E_{\mathrm{A}}\) and the diffusion coefficient at infinite temperatures \(D_{0}\) . Subsequently, \(E_{\mathrm{A}}\) and \(D_{0}\) can be extracted from the slope and the cross section with the emission rate axis using Eqns. (1) and (2). By taking into account the surface polarization caused by the accumulation of mobile ions at the interfaces of the perovskite layer, the ion concentration \(N_{\mathrm{i}}\) is determined as \(^{51}\) , + +<|ref|>equation<|/ref|><|det|>[[439, 748, 559, 789]]<|/det|> +\[N_{\mathrm{i}} = \frac{k_{\mathrm{B}}T\Delta C^{2}}{e^{2}\epsilon_{0}\epsilon_{\mathrm{R}}}\] + +<|ref|>text<|/ref|><|det|>[[750, 800, 779, 816]]<|/det|> +(3) + +<|ref|>text<|/ref|><|det|>[[85, 834, 808, 855]]<|/det|> +Here, \(\Delta \mathrm{C}\) refers to the capacitance step in the admittance spectra of the contributing ions. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[80, 94, 193, 113]]<|/det|> +## Reference + +<|ref|>text<|/ref|><|det|>[[80, 130, 904, 180]]<|/det|> +48. Heiser, T. & Mesli, A. Determination of the copper diffusion coefficient in silicon from transient ion-drift. Appl. Phys. A 57, 325-328 (1993). + +<|ref|>text<|/ref|><|det|>[[80, 196, 884, 246]]<|/det|> +49. Futscher, M. H. et al. Quantification of ion migration in \(\mathrm{CH_3NH_3PbI_3}\) perovskite solar cells by transient capacitance measurements. Mater. Horiz. 6, 1497-1503 (2019). + +<|ref|>text<|/ref|><|det|>[[80, 262, 880, 311]]<|/det|> +50. Schlaus, A. P. et al. How lasing happens in CsPbBr₃ perovskite nanowires. Nat. Commun. 10, 265 (2019). + +<|ref|>text<|/ref|><|det|>[[80, 327, 884, 378]]<|/det|> +51. Almora, O. et al. Capacitive dark currents, hysteresis, and electrode polarization in lead halide perovskite solar cells. J. Phys. Chem. Lett. 6, 1645-1652 (2015). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 144, 68]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[75, 100, 920, 600]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 628, 118, 647]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[40, 668, 953, 850]]<|/det|> +Device fabrication and characteristics. a, An illustration of the VAC- treatment. b, Schematic of the PeLED structure and the HAADF cross- sectional device image. The scale bar is \(100~\mathrm{nm}\) . c, Histograms of peak EQEs extracted from control (top) and VAC- treated devices (bottom) with varying chloride contents (30%, 35%, 40%). d- f, Spectral stability for control and VAC- treated devices with 40% Cl loading. The representative plots of ClEy versus applied voltages (top) and current densities (bottom) (d); EL spectra at low and high voltage/current density for control (left) and VAC- treated devices (right) (e); EL spectra of VAC- treated devices with varying chloride content (30\~57%) at maximum luminance (f). The points labelled as Lmax in Fig. 1d represent the operational condition for peak luminance. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[63, 52, 933, 422]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 451, 117, 471]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[40, 493, 953, 628]]<|/det|> +Understanding superior spectral stability of VAC- treated devices. a- d, Photophysical characterizations for control and VAC- treated perovskite films: Fluence- dependent PLQYs (a); PL decay measured by TCSPC (b). PL spectra (c); Transient absorption of control (top) and VAC- treated films (bottom) after excitation at 400 nm (d). e, f, Derivations of temperature- dependent capacitance versus frequency plots for control (e) and VAC- treated (f) devices. The blue arrows indicate temperature change from 350 K to 200 K. Here, two mobile ions marked as \(\beta\) and \(\epsilon\) are visible. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 49, 940, 600]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 636, 117, 655]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[39, 677, 953, 904]]<|/det|> +Understanding the halide redistribution during VAC- process. a, b, PL evolution of the precursor films kept in the glovebox atmosphere (a) and DMF atmosphere (b) with time. c, the evolution of emission linewidth and the proportion of P2 (AP2) to the respective total area of the emission band (A) in VAC- treated films with time. d, Schematic illustration of the proposed mechanism for halide redistribution. Here, the purple Pb(Br/Cl)64- octahedra represent chloride- rich phases in respect to that with stoichiometric bromide/chloride distribution (blue octahedra). The khaki represents the liquid phase within the films and the blue arrows represent ion exchange process. The excessive ions within the dried films are not illustrated for clarity. e, f, The evolution of CIE coordinates upon bias (e) and peak EQEs of the devices with varying duration of VAC- treatment (0, 1, 2, 5, 20 minutes) (f). The data were extracted from 4 to 6 devices. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 58, 940, 528]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 560, 116, 579]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[41, 600, 949, 714]]<|/det|> +The device performance of Rb- passivated perovskites with \(40\%\) and \(45\%\) Cl contents. a, EQE- current density (J) curves (J- EQE). b, Current density- voltage- luminance (J- V- L) characteristics. c, EL spectra and CIE colour coordinates. The square and pentagram in the CIE 1931 (x, y) chromaticity diagram represent the colour coordinates of primary blue specified in NTSC and Rec.2020, respectively. d, Histograms of the peak EQEs extracted from 40 devices for each case. + +<|ref|>sub_title<|/ref|><|det|>[[44, 736, 310, 763]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 787, 764, 807]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 825, 265, 843]]<|/det|> +- SIJrevision10.14.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/images_list.json b/preprint/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..556fb88e5c3f568c4aad16afbe7bc209df05af34 --- /dev/null +++ b/preprint/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Crystalline structure and microstructure of the KNN-M thin film grown on a LSSO-buffered STO (001) substrate. a, XRD \\(\\theta -2\\theta\\) pattern. b, Rocking curves around STO (002), LSSO (002) and KNN-M (002). c, d, Reciprocal space maps around the STO (002) and (f03) reflections. e, Cross-sectional low magnification HAADF image. f, SEAD pattern of the KNN-M thin film in the out-of-plane direction. g, Atomic resolution HAADF image of the KNN-M thin film. h, Planar-view low-magnification ABF image. i, Corresponding EDS mapping of Mn element for the KNN-M thin film. j, Plan-view atomic resolution HAADF image of the KNN-M thin film. The scale bars in (g) and (j) are 2 nm.", + "footnote": [], + "bbox": [ + [ + 130, + 128, + 863, + 744 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig.2 | In-plane atomic structure of Mn-inlaid antiphase boundaries in the KNN-M thin films. a, In-plane atomic resolution HAADF image of Type-I antiphase boundaries. b, Line-profile of the single antiphase atomic columns along the direction of the antiphase boundary in (A). c, iDPC image of Type-I antiphase boundaries. d, HAADF image of Type-I antiphase boundaries with in-situ electron irradiations. e, In-plane HAADF image of the KNN-M thin film containing Type-I antiphase boundaries. f-j, Corresponding color-coded atomic resolution EDS mapping of the K, Na, O, Mn and Nb elements, respectively. K, Composite elemental map with Nb (in red) and Mn (in green). I, Schematic structural models of Type-I Mn-inlaid antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. m, In-plane atomic resolution HAADF image of Type-II antiphase boundaries. n, Line-profile of the single antiphase atomic columns along the direction of the transition boundary in (m). o, iDPC image of Type-II antiphase boundaries. p, Schematic structural model of Type-II antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. The scale bar is 1nm.", + "footnote": [], + "bbox": [ + [ + 132, + 85, + 867, + 666 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Ferroelectric properties of the KNN-M thin films. a, Out-of-plane polarization switching of the KNN-M film captured using PFM imaging technique, under a dc bias of \\(\\pm 8\\mathrm {V}\\) . b, Room-temperature polarization–electric field \\((P - E)\\) hysteresis loops displayed under different electric fields. c, \\(P - E\\) hysteresis loops under different electric field for the KNN-M thin films at a low-temperature of \\(80\\mathrm {K}\\) . d, Comparison of the polarization \\((2P_{r})\\) of the present KNN-M films with previously reported values of the KNN-based films.", + "footnote": [], + "bbox": [ + [ + 140, + 370, + 857, + 808 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Strains and atom displacement polarizations in the KNN-M thin film. a, Planar-view atomic-resolution HAADF image of the KNN-M thin film. b-d, Maps depicting the relative lattice strains: (b) \\(\\epsilon_{xx}\\) (in-plane strain), (c) \\(\\epsilon_{yy}\\) (out-of-plane strain) and (d) \\(\\epsilon_{xy}\\) (shear strain). e, Colored arrows map of polarizations \\((\\partial_{Nb-KNa})\\) , indicating the polarization orientation of the KNN nanocolumns in (a). f, Polarization of KNN-M obtained from phase-field simulation. g, Mapping of the piezoelectric response of the KNN-M film under an applied electric field obtained from phase-field simulation. h, Calculated P-E curves with different local strains.", + "footnote": [], + "bbox": [ + [ + 128, + 85, + 876, + 430 + ] + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e.mmd b/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..99b755241d44dc9e60ae455253613a1e9ce1d016 --- /dev/null +++ b/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e.mmd @@ -0,0 +1,241 @@ + +# Ultra-rapid and highly efficient enrichment of organic pollutants via magnetic nanoparticles/mesoporous nanosponge compounds for ultrasensitive nanosensors + +Lingling Zhang Xi'an Jiaotong University https://orcid.org/0000- 0002- 6272- 7171Rui Hao Xi'an Jiaotong UniversityHongjun You School of Science, Xi'an Jiaotong University https://orcid.org/0000- 0002- 9389- 3076Hu Nan Xi'an Jiaotong UniversityYanzhu Dai School of ScienceJixiang Fang ( jxfang@mail.xjtu.edu.cn) Xi'an Jiaotong University https://orcid.org/0000- 0003- 3618- 2144 + +## Article + +Keywords: nanosensors, SERS technique + +Posted Date: January 6th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 127668/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on November 25th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 27100- 2. + +<--- Page Split ---> + +## Abstract + +AbstractDeveloping advanced sensing and detection technologies to effectively monitor organic micropollutants in water is under urgent demand in both scientific and industrial communities. Currently, owing to the ultrahigh sensitivity on the single- molecule level with highly informative spectra characteristics, SERS technique is regarded as the most direct and effective detection technique. However, some weakly adsorbed molecules, such as most of persistent organic pollutants, cannot exhibit strong SERS signals, which is a long- standing key challenge that has not been solved. Here, we show an enrichment- typed sensing strategy based on a powerful porous composite material, call mesoporous nanosponge. The nanosponge consists of magnetic nanoparticles immobilized porous \(\beta\) - cyclodextrin polymers, demonstrating remarkable capability of effective and fast removal of organic micropollutants, e.g. \(\sim 90\%\) removal efficiency within \(\sim 1\) min. With the anchoring of magnetic nanoparticles, the current new polymer adsorbent can be easily recycled from water and re- dispersed in ethanol so that the target molecules in the cavity of adsorbent is concentrated, with an enrichment factor up to \(\sim 103\) . By means of the current enrichment strategy, the limit of detection (LOD) of the typical organic pollutants can be significantly improved, i.e. increasing \(2\sim 3\) orders of magnitude, compared with the detection without molecule enrichment protocol. Consequently, the current enrichment strategy is proved to be applicable in a variety of fields for portable and fast detection, such as Raman and fluorescent. + +## Introduction + +The Stockholm Convention on Persistent Organic Pollutants (POPs) was endorsed by 131 nations in 2004 to eliminate most persistent bioaccumulative and toxic substances in the world.1 Organic micropollutants of ground and surface water resources, such as pesticides and plastic components, have aroused great concerns about potential negative effects on aquatic ecosystems and human health.2,3 Therefore, parallel to the researches of adsorbent materials to remove organic pollutants from water, the ultrasensitive detection of organic pollutants is another crucial field, since the solubility of organic micropollutants in water is always at the trace level.4 Among diverse detection approaches, surface- enhanced Raman scattering (SERS), which achieved significant breakthroughs in 1997 and became the first vibrational spectroscopy technique that could provide delicate information on molecular fingerprints with potential single- molecule level of sensitivity,5- 8 is regarded as the most simple, fast, flexible and portable detection technique. + +However, up to date, the superiority of single- molecule SERS in the detection of diverse molecules with intrinsic small cross- sections or low affinity for the plasmonic surface has not been into full play, particularly in real complex situation.9 As is well known, the SERS process involves complicated coupled three- body interactions among photons, molecules, and nanostructures.10,11 Besides the interaction between light and nanostructures, the investigation on the interaction between molecule and plasmonic surface is of importance.12 On the one hand, SERS is an optical near- field effect.12- 14 A high activity can be obtained only when the target molecule is very close to the plasmonic surface. On the other hand, + +<--- Page Split ---> + +most organic pollutants in water can not be effectively adsorbed onto a metallic surface because of their low affinity toward the metal.\(^{7}\) Therefore, recently, some strategies, such as selective enrichment and spatial localization of target molecules,\(^{15- 17}\) are suggested to solve this long- standing challenge. + +In this work, we propose a new sensing strategy based on the efficient enrichment and rapid separation of POPs by means of the magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP), called mesoporous nanosponge. The microporous \(\beta\) - cyclodextrin ( \(\beta\) - CD) material, an inexpensive and renewable carbohydrate, which is featured by small pores and high surface areas,\(^{18,19}\) was used in this work as an excellent adsorbent. In fact, microporous \(\beta\) - CD material has been widely studied because of outstanding adsorption efficiency through forming host- guest inclusion with many hydrophobic organic pollutants.\(^{20}\) \(^{21}\) The magnetic nanoparticles are introduced into the MN- PCDP compounds to rapidly separate the adsorbent from water. The current strategy (the schematic description of the protocol is shown in Fig. 1) demonstrates several remarkable advantages. Firstly, as shown in Fig. 1a, when the MN- PCDP adsorbent (shown in Fig. 1b) is dispersed into water in the beaker, e.g. \(\sim 1000 \text{ml}\) , containing organic pollutants, ultra- rapid adsorption and magnetic separation can be accomplished, i.e., totally within \(\sim 1 \text{min}\) . Secondly, the adsorbed pollutant in MN- PCDP from water can be desorbed in ethanol with a volume of \(\sim 1 \text{ml}\) , for further analysis such as UV- vis, Raman and fluorescent spectroscopy. Thus, an ultra- high enrichment efficiency with an enrichment factor up to \(\sim 10^{3}\) times can be obtained (Fig. 1c). With current enrichment strategy, the limit of detection (LOD) in a variety of sensing applications, such as SERS and fluorescent, can be lowered by \(2 \sim 3\) orders of magnitude. Furthermore, through the magnetic separation, the MN- PCDP mesoporous nanosponge can selectively adsorb the target organic pollutants, avoiding the disturbance of complex matrix. The current sensing strategy can be believed to be applicable to a wider range of sensing areas for an economical, simple, fast, flexible, and portable detection. + +## Results + +Synthesis and characterization. The MN- PCDP was prepared by cross- linking polymerization of \(\beta\) - CD and cross- linking agent (tetrafluoroterephthalmonitrile (TFT)), with magnetic nanoparticles ( \(\mathrm{Fe_3O_4}\) ) in one- step solvothermal reaction. Fig. 2a- c show the transmission electron microscope (TEM) images of magnetic nanoparticles (MN, \(\mathrm{Fe_3O_4}\) ), porous \(\beta\) - CD polymer (PCDP) and MN- PCDP, respectively. As shown in Fig. 2a, the synthesized MN exhibits regular spheres with good dispersibility and uniform size (average size \(\sim 200 \text{nm}\) ). The Fourier transform- infrared spectroscopy (FT- IR) spectrum of MN is displayed in Fig. 2d. The absorption bands at \(1652 \text{cm}^{- 1}\) and \(1396 \text{cm}^{- 1}\) of the MN can be associated with carboxylate group\(^{22}\) and that also appear in the MN- PCDP. Fig. 2b and Supplementary Fig. 1 exhibit that the PCDP is porous network structure. After the immobilization of MN, as shown in Fig. 2c, the porous network structure of MN- PCDP is not disrupted. The FT- IR spectrum of the MN- PCDP not only obviously combines the characteristic peaks of the TFT and the \(\beta\) - CD but also displays a new peak at \(1265 \text{cm}^{- 1}\) in relation to the newly formed C- F group, implying that the \(\beta\) - CD has been crosslinked with TFT.\(^{23,24}\) Fig. 2e indicates that the Brunauer- Emmett- Teller surface areas ( \(\mathrm{S_{BET}}\) ) of MN- PCDP is about \(66 \text{m}^2 \text{g}^{- 1}\) . The pores with diameter + +<--- Page Split ---> + +of 1.7- 3.0 nm comprise the majority of the free volume of MN- PCDP and its average pore diameter is 2.12 nm. + +Adsorption of MN- PCDP nanosponges. The high surface area and permanent porosity of MN- PCDP mesoporous nanosponge enable the rapid removal of organic micropollutants from water. \(^{25}\) As shown in Supplementary Fig. 2, the PCDP and MN- PCDP displays the same properties in time- dependent adsorptions of bisphenol A (BPA), illustrating the immobilization of magnetic nanoparticles has no remarkable influence on the adsorption performance of PCDP. The time- dependent adsorptions of various organic micropollutants adsorbed by MN- PCDP, including plastic components, pesticide and aromatic model compounds (Fig. 3a), are shown in Fig. 3b, Supplementary Fig. 3 and Supplementary Table S1. The removal rate of the above organic micropollutants is very fast, which tends to be constant within 1 min. The removal efficiencies of BPA, parathion, carbendazim and 2- naphthol (2- NO) are more than 80% in 30 sec, which is much higher than the Norit ROW 0.8 supra extruded activated carbon (NAC) as presented in Fig. 3c, Supplementary Fig. 4- 5 and Supplementary Table S2. We further probe the readily accessible binding sites of MN- PCDP by determining the flow- through uptake of different organic micropollutants. In these experiments, the adsorbent ( \(\sim 5\) mg) was trapped as a thin layer on a 0.22 μm syringe filter, and aqueous organic pollutants (5 ml, 0.1 mM) passed rapidly through the filter at a flow rate of 10 ml min \(^{- 1}\) (Supplementary Fig. 6). Under these conditions, for example, 76% of the BPA is removed from the solution, corresponding to more than 84% of its equilibrium adsorption, confirming that the host- guest interaction plays a major role in the filtration process by syringe. \(^{26}\) The superior performance of MN- PCDP can be further indicated that its \(\beta\) - CD moieties are easily accessed by most of organic micropollutants. Furthermore, these molecules are rapidly trapped. In addition, the influence of the concentrations of adsorbent on the BPA adsorption efficiency is studied as shown in Fig. 4b, Supplementary Fig. 7 and Supplementary Table S3. When the concentration of adsorbent increases from 0.1 mg L \(^{- 1}\) to 1.0 mg L \(^{- 1}\) , the adsorption efficiency of BPA is enhanced from 25.12% to 87.09% within 1 min and from 35.07% to 89.82% within 10 min. + +Desorption and enrichment of MN- PCDP nanosponges. As we all know, organic micropollutants exhibit good solubility in organic solution, such as ethanol and methanol. \(^{18}\) Hence, after adsorption process, we utilized ethanol to desorb the organic micropollutants from MN- PCDP mesoporous nanosponges, and then obtained the enriched pollutant solution through magnetic nanoparticle separation. In order to obtain higher concentration of desorbed micropollutant solution, in this work, we chose 1 mL ethanol to desorb organic micropollutants adsorbed in 100 mg MN- PCDP adsorbent. As shown in Supplementary Fig. 8, using current enrichment processes, the concentration of BPA can be increased to 88.5 times of its initial concentration with a recipe of 100 mL organic pollutant (BPA) solution and 100 mg MN- PCDP adsorbent. This result reveals that more than 98% of the adsorbed organic micropollutants are desorbed into the ethanol solution. That is to say, for 100 mL organic pollutant solution, we realize \(\sim 10^{2}\) times enrichment of target molecule. As the amount of adsorbent increases, the adsorption efficiency tends to reach equilibrium. Considering the cost increase of sample preparation and the operation in the + +<--- Page Split ---> + +desorption process (with 1 mL ethanol) resulting from the increase of adsorbent dosage, 100 mg of adsorbent is selected as the amount of material for subsequent experiments. + +In order to further improve the enrichment effect of 100 mg adsorbent in total 1000 mL organic micropollutants, herein, we attempted three methods, including 100 mL×10 times, 250 mL×4 times and 500 mL×2 times, to optimize the adsorption and desorption processes. Importantly, the adsorbent can be simply separated by magnet in every adsorption cycle, and desorbed in ethanol in the last adsorption cycle. As shown in Fig. 4c, Supplementary Fig. 9 and Supplementary Table S4, with adsorption times increasing, the removal efficiencies of these three methods gradually decrease. The removal efficiencies of these three methods (100 mL×10 times, 250 mL×4 times and 500 mL×2 times) are 50.78%, 62.58% and 41.22%, respectively. Meanwhile, the enrichment efficiencies of these three methods are 485, 605 and 396 times of the initial concentration (Fig. 4d), respectively. Therefore, we achieve over 600 times' enrichment of organic pollutants (with 1000 mL of initial micropollutants) in the optimized adsorption and desorption processes. Here, the selected parameters (100 mg adsorbent in 250 mL organic solution for 4 cycle times) were used for the succedent experiments. Meanwhile, it is also worth pointing out that the separation process by magnet is very fast and facile, such as 90 sec for 250 mL solution (Fig. 4a and Supplementary Fig. 10b), 60 sec for 100 mL (Supplementary Fig. 10a), and 150 sec for in 500 mL (Supplementary Fig. 10c), fully meeting the requirement of immediate- pretreatment detection application. + +SERS and fluorescence measurement of pollutants based on the enrichment of MN- PCDP nanosponges. Many POPs are mutagenic, carcinogenic and not degradable by direct biological treatment, some of which damage nerve, endocrine systems of human body, and the ecological balance due to their toxicity in nature. \(^{27}\) Based on the above experimental researches, the MN- PCDP nanosponges were used to absorb organic micropollutants and then were collected from water (Fig. 5a), so as to realize the rapid removal and enrichment of POPs. Moreover, we evaluated fluorescence and enhanced Raman spectra of POPs (carbendazim and BPA) to demonstrate the enrichment effect of current enrichment strategy. The enhanced Raman spectra of carbendazim molecular with \(\sim 55 \text{nm}\) Au nanoparticles (Supplementary Fig. 11) were measured under 785 nm laser. As shown in Figure 5b- c, without the enrichment process, the LOD of SERS for carbendazim is 1 nM, but after the enrichment using MN- PCDP adsorbent, this value reaches to \(\sim 5 \text{pM}\) , which shows an increase of \(10^{2} \sim 10^{3}\) . In Supplementary Fig. 12, for BPA molecule, with the help of adsorbent, the LOD of fluorescence is also greatly improved. In Fig. 5d- e and Supplementary Fig. 13, based on the current enrichment sensing strategy, the LODs of fluorescence detections for the pure solution of carbendazim and BPA are lower by \(2 \sim 3\) orders of magnitude. In this study, the enrichment strategy based on the adsorption and desorption processes of MN- PCDP adsorbent may significantly increase the sensitivity of plasmonic sensors, compared with the LOD for similar molecules, \(^{28- 30}\) illustrating its wide applicability. + +Owing to the excellent enrichment and easily- separated features, the current strategy was believed that the mesoporous nanosponges could be served as a preprocessing for direct, rapid and ultrasensitive detection of contaminants in complex situations. After adsorption process, the MN- PCDP adsorbent was easily collected on the wall of beaker (Fig. 5a) with a magnet, avoiding the interference of complex + +<--- Page Split ---> + +matrix, such as mud and microorganism. Fig. 5f reveals that both the characteristic peaks of BPA (830 and \(1179 \text{cm}^{- 1}\) ) and carbendazim (1008, 1244 and \(1263 \text{cm}^{- 1}\) ) evidently appear in the Raman spectrum of mixture solution, including \(1 \mu \text{M BPA}\) and \(10 \text{nM carbendazim}\) . Furthermore, the MN- PCDP demonstrates a superior reusability as shown in Supplementary Fig. 14. Six consecutive BPA adsorption/desorption cycles were performed and the regenerated MN- PCDP exhibited almost no decrease (90.2% to 87.5%) in performance compared to the as- synthesized polymer. + +## Discussion + +In summary, we have developed a robust and rapid sensing strategy based on the MN- PCDP mesoporous nanoponges to capture and enrich organic pollutants from water. In this strategy, the MN- PCDP adsorbent exhibits excellent adsorption capacity for various kinds of pollutants owing to the unique cavity structures. Moreover, the adsorbed pollutant in MN- PCDP can be desorbed in ethanol with a very fast and facile operation. In SERS detection of organic pollutants, i.e. carbendazim and BPA, in this work, the current sensing strategy may significantly increase the sensitivity of plasmonic sensors with 2\~3 orders of magnitude. Therefore, the current robust sensing strategy with the ultra- rapid and highly efficient sample pretreatment and molecule enrichment is believed to be applicable to a wider range of sensing devices, such as fluorescent, Raman and infrared spectroscopes for a cost- effective, simple, fast, flexible and portable detection. + +## Methods + +Preparation of magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) . The carboxyl- functionalized magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) with highly water- dispersibility were synthesized by a modified solvothermal reaction approach. Typically, \(\mathrm{FeCl}_3\cdot 6\mathrm{H}_2\mathrm{O}\) (1.08 g, 4.0 mmol) and trisodium citrate (0.20 g, 0.68 mmol) were dissolved in ethylene glycol (20 mL) with stirring at 500 rpm. Afterward, sodium acetate trihydrate (2.0 g, 15 mmol) was added and the mixture was stirred for 30 min. Then, the mixture was sealed in a Teflon- lined stainless- steel autoclave (50 mL). The autoclave was heated at \(200 ^{\circ}\mathrm{C}\) for 12 h, and then allowed to cool to room temperature. The black products were washed with ethanol and ultrapure water for several times. Finally, the carboxyl- functionalized magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) were separated by magnet, re- dispersed in ethanol and dried in vacuum drying oven at \(30^{\circ}\mathrm{C}\) . + +Preparation of magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP). The MN- PCDP composites were then prepared by modification of nucleophilic aromatic substitution method of hydroxyl groups of \(\beta\) - CD. A dried \(100 \text{mL Shrek reaction vial with a magnetic stir bar was charged with} \beta\) - CD (0.82 g, 0.724 mmol), TFT (0.40 g, 1.03 mmol), and \(\mathrm{K}_2\mathrm{CO}_3\) (1.28 g, 9.28 mmol) and dried \(\mathrm{Fe}_3\mathrm{O}_4\) (0.041 g). The vial was flushed with \(\mathrm{N}_2\) gas for 10 min, then an anhydrous THF/DMF mixture (9:1 v/v, 40 mL) was added and the vial was purged with \(\mathrm{N}_2\) for additional 5 min. After that, the \(\mathrm{N}_2\) inlet was removed. The mixture was stirred at 500 rpm and refluxed at \(85^{\circ}\mathrm{C}\) for 36 h under nitrogen protection. The brown suspension was cooled to room temperature and magnetically separated the supernatant by magnet. The + +<--- Page Split ---> + +precipitate was washed twice with an appropriate amount of distilled water, THF, ethanol and \(\mathrm{CH}_2\mathrm{Cl}_2\) , respectively. The final precipitate was vacuum dried at \(77 \text{K}\) in a liquid nitrogen bath for \(24 \text{h}\) and then the magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP) was obtained. + +Batch adsorption kinetic studies. In studies, the dried polymer (MN- PCDP, \(20 \text{mg}\) ) was initially washed with \(\mathrm{H}_2\mathrm{O}\) for 2 times and then separated by magnet. Adsorption kinetic studies for different pollutants were performed in \(30 \text{mL}\) scintillation vials with \(20 \text{mL}\) organic pollutant solution and \(20 \text{mg}\) adsorbent, at ambient temperature on a hot plate at \(25^{\circ}\text{C}\) . Then the sample was shaken at \(250 \text{rpm}\) until the adsorption equilibrium was reached. The mixture was immediately stirred and \(1 \text{mL}\) aliquots of the suspension were taken at certain intervals via syringe and filtered immediately by a \(0.22 \mu \text{m} \text{PTFE}\) membrane filter. The residual concentration of the pollutant in each sample was determined by UV- vis spectroscopy. + +Calculation of removal efficiency. The removal efficiency of pollutant removal by the adsorbent was determined by the following equation: + +\[\mathrm{Removal~efficiency(\%) = \frac{C_0 - C_t}{C_0}\times 100}\] + +where \(C_0\) and \(C_t\) are the initial and residual concentration of pollutant in the stock solution and filtrate, respectively. + +Flow- through adsorption experiments. Individual pollutants were at high concentrations (mM). \(5.0 \text{mg}\) of the MN- PCDP adsorbent was washed with deionized \(\mathrm{H}_2\mathrm{O}\) for 2 times, then the precipitate was pushed by a syringe through a \(0.22 \mu \text{m} \text{PTFE}\) membrane filter to form a thin layer of the adsorbent on the filter membrane. \(5 \text{mL}\) of the pollutant stock solution was then pushed through the adsorbent in \(\sim 30 \text{s}\) ( \(10 \text{mL} \text{min}^{- 1}\) flow rate). The filtrate was then measured by UV- vis spectroscopy to determine the pollutant removal efficiency. + +MN- PCDP desorption studies. \(100.0 \text{mg}\) of the adsorbent was washed with deionized \(\mathrm{H}_2\mathrm{O}\) for 2 times, and then added to the organic pollutant stock solution ( \(0.01 \text{mM}\) ) with determine volume ( \(100 \text{mL}\) , \(250 \text{mL}\) , \(500 \text{mL}\) ). The mixture was shaken at \(250 \text{rpm}\) for \(1 \text{min}\) at \(25^{\circ}\text{C}\) . After separating the supernatant and the adsorbent by an external magnet, the supernatant was filtered through a \(0.22 \mu \text{m} \text{filter}\) membrane and determined by UV- vis spectroscopy. Meanwhile the precipitate was evaporated to dryness with a gentle nitrogen stream, then the residue was dissolved in \(1 \text{mL}\) of ethanol to desorb the adsorbed organic pollutant. The desorption solution was measured by UV- vis spectroscopy and compared with the initial concentration of pollutant in the stock solution. + +Calculation of enrichment efficiency. The enrichment efficiency of pollutant adsorbed by the adsorbent was determined by the following equation: + +<--- Page Split ---> + +\[\mathrm{Enrichment~efficiency} = \frac{c}{c_0}\] + +Where \(C_0\) and \(C\) are the initial and desorbed solution concentration of pollutant, respectively. + +Fluorescence measurement. The fluorescence spectra of pure solution were directly measured by fluorescence spectrophotometer. + +Preparation of SERS active Au NPs. The Au NPs with different size in diameter were synthesized based on a modified citrate reduction approach. The growth process of gold nanoparticles with different size included three steps. For step 1, \(100~\mathrm{mL}\) of ultrapure water was added into a conical flask and heated to boiling. Then, \(4\mathrm{ml}\) of \(1\mathrm{wt}\%\) sodium citrate (SC) solution was injected immediately, and \(3.2\mathrm{mL}\) of \(10\mathrm{mM}\) \(\mathrm{HAuCl_4}\) was added after 3 min. Kept the reaction for 25 minutes and made it natural cooling, then the Au seeds were obtained. For step 2, \(80~\mathrm{mL}\) of ultrapure water and \(20~\mathrm{mL}\) of Au seeds were mixed into the conical flask and heated to boiling. Then, \(2\mathrm{mL}\) of \(1\mathrm{wt}\%\) sodium citrate solution was injected immediately, and \(0.2\mathrm{mL}\) of \(\mathrm{HAuCl_4}\) was added 3 min later. Then additional \(0.2\mathrm{mL}\times 9\) dosage of \(\mathrm{HAuCl_4}\) was injected every 8 minutes. After the last precursor was added, the reaction was kept for 25 min, and Au NPs- 25 nm were obtained. For step 3, Au NPs prepared in step 2 were used as the seed solution, and the growth process was repeated as growth steps 2, and then Au NPs- 55 nm were obtained in this step. + +SERS measurement. SERS measurement is based on the hydrophobic slippery surface. Concentrated molecules and Au NPs were prepared on a hydrophobic slippery Teflon membrane as follows: First, a Teflon membrane was attached on a flat glass slide ( \(5\mathrm{cm}\times 5\mathrm{cm}\) ) by using a double- sided adhesive. Then, \(0.5\mathrm{mL}\) of perfluorinated fluid was dispersed by spin coating. The low speed was \(300\mathrm{rpm}\) for \(30\mathrm{s}\) and the high speed was \(1500\mathrm{rpm}\) for 1 min. After the excess lubricating liquid was removed by centrifugal force, and the infused membrane was heated for 30 min. Lastly, \(50\mu \mathrm{L}\) of probe molecules and \(10\mu \mathrm{L}\) of Au colloids were simultaneously dropped onto the slippery surface. During drying, the contact line shrunk because of the low friction of the lubricated Teflon surface. As a result, the initial droplet could be concentrated into a small area less than \(0.5\mathrm{mm}\) in diameter. + +## Declarations + +## Data availability + +The data that support the findings of this study are available within the paper and its Supplementary Information or from the corresponding authors on reasonable request. + +## Acknowledgements + +This work was supported by the programs supported by the National Natural Science Foundation of China (No. 21675122, 21874104, 22074115), the Key Research Program in Shaanxi (2017NY- 114), Basic + +<--- Page Split ---> + +Public Welfare Research Project of Zhejiang Province (No. LY20E010007), and Natural Science Foundation of Shaanxi Province (No. 2019JLP- 19), the World- Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities. + +## Author contributions + +L.L.Z. synthesized the materials, carried out the characterizations and performance, analyzed the data, and wrote the manuscript. R. H., H. N., Y. Z. D. contributed in part of the TEM, Raman and fluorescence characterizations. H.J.Y. and J.X.F., supervised the project, designed the experiments, contributed in discussions, comments and writing of manuscript. All authors discussed the results and commented on the manuscript. + +## Additional information + +Supplementary Information accompanies this paper at + +competing of Interest: The authors declare no competing of interest. + +Reprints and permission information is available online at + +Journal peer review information: + +Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. + +## References + +1 Kelly, B. C., Ikonomou, M. G., Blair, J. D., Morin, A. E. & F. A. P. C. Gobas. Food web- specific biomagnification of persistent organic pollutants. Science 317, 236- 239 (2007). + +2 Schwarzenbach, R. P. et al. The challenge of micropollutants in aquatic systems. Science 313, 1072- 1077, doi:10.1126/science.1127291 (2006). + +3 Jones, K. C. & Voogt, P. De. Persistent organic pollutants (POPs): state of the science. Environ. Pollut. 100, 209- 221 (1999). + +4 Pi, Y. et al. Adsorptive and photocatalytic removal of Persistent Organic Pollutants (POPs) in water by metal- organic frameworks (MOFs). Chem. Eng. J 337, 351- 371, doi:10.1016/j.cej.2017.12.092 (2018). + +5 Xu, H., Bjerneld, E. J., Käll, M. & Börjesson, L. Spectroscopy of Single Hemoglobin Molecules by Surface Enhanced Raman Scattering. Phys. Rev. Lett. 83, 4357- 4360, doi:10.1103/PhysRevLett.83.4357 (1999). + +<--- Page Split ---> + +6 Xu, H., Aizpurua, J., Kall, M. & Apell, P. Electromagnetic contributions to single-molecule sensitivity in surface- enhanced raman scattering. Phys. Rev. E 62, 4318- 4324, doi:10.1103/physreve.62.4318 (2000). + +7 Pieczonka, N. P. & Aroca, R. F. Single molecule analysis by surfaced- enhanced Raman scattering. Chem. Soc. 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An accurate electromagnetic theory study of surface enhancement factors for silver, gold, copper, lithium, sodium, aluminum, gallium, indium, zinc, and cadmium. J. Phys. Chem. 91, 634- 643 (1987). + +15 Zhang, D. et al. Buoyant particulate strategy for few- to- single particle- based plasmonic enhanced nanosensors. Nat. Commun. 11, 2603, doi:10.1038/s41467- 020- 16329- y (2020). + +16 Hao, R., You, H., Zhu, J., Chen, T. & Fang, J. "Burning Lamp"- like Robust Molecular Enrichment for Ultrasensitive Plasmonic Nanosensors. ACS Sens. 5, 781- 788, doi:10.1021/acssensors.9b02423 (2020). + +17 De Angelis, F. et al. Breaking the diffusion limit with super- hydrophobic delivery of molecules to plasmonic nanofocusing SERS structures. Nat. Photonics 5, 682- 687, doi:10.1038/nphoton.2011.222 (2011). + +18 Alsbaiee, A. et al. Rapid removal of organic micropollutants from water by a porous beta- cyclodextrin polymer. Nature 529, 190- 194, doi:10.1038/nature16185 (2016). + +19 Xiao, L. et al. Beta- Cyclodextrin Polymer Network Sequesters Perfluorooctanoic Acid at Environmentally Relevant Concentrations. J. Am. Chem. Soc. 139, 7689- 7692, doi:10.1021/jacs.7b02381 (2017). + +<--- Page Split ---> + +20 Liu, X. et al. A magnetic graphene hybrid functionalized with beta- cyclodextrins for fast and efficient removal of organic dyes. J. Mater. Chem. A 2, doi:10.1039/c4ta00753k (2014). + +21 Huang, D. et al. Fabrication of water-compatible molecularly imprinted polymer based on β- cyclodextrin modified magnetic chitosan and its application for selective removal of bisphenol A from aqueous solution. J. Taiwan Inst. Chem. E 77, 113- 121, doi:10.1016/j.jtice.2017.04.030 (2017). + +22 Liu, J. et al. Highly water- dispersible biocompatible magnetite particles with low cytotoxicity stabilized by citrate groups. Angew. Chem. Int. Ed. 48, 5875- 5879, doi:10.1002/anie.200901566 (2009). + +23 Jiang, H.- L. et al. A novel crosslinked β- cyclodextrin- based polymer for removing methylene blue from water with high efficiency. Colloid. Surfaces. A 560, 59- 68, doi:10.1016/j.colsurfa.2018.10.004 (2019). + +24 Huang, D., Zhang, Y. & Zhang, H. A novel synthesis of ethyl carbonate derivatives of beta- cyclodextrin. Carbohyd. Res. 370, 82- 85, doi:10.1016/j.carres.2013.01.022 (2013). + +25 Huang, Q., Chai, K., Zhou, L. & Ji, H. A phenyl- rich β- cyclodextrin porous crosslinked polymer for efficient removal of aromatic pollutants: Insight into adsorption performance and mechanism. Chem. Eng. J 387, doi:10.1016/j.cej.2020.124020 (2020). + +26 Wang, Z., Zhang, P., Hu, F., Zhao, Y. & Zhu, L. A crosslinked beta- cyclodextrin polymer used for rapid removal of a broad- spectrum of organic micropollutants from water. Carbohyd. Polym. 177, 224- 231, doi:10.1016/j.carbpol.2017.08.059 (2017). + +27 Rusiecki, J. A. et al. Global DNA hypomethylation is associated with high serum- persistent organic pollutants in Greenland Inuit. Environ. Health. Persp. 116, 1547- 1552, doi:10.1289/ehp.11338 (2008). + +28 Chen, X. et al. Detection and quantification of carbendazim in Oolong tea by surface- enhanced Raman spectroscopy and gold nanoparticle substrates. Food Chem 293, 271- 277, doi:10.1016/j.foodchem.2019.04.085 (2019). + +29 Zhu, X. et al. A novel graphene- like titanium carbide MXene/Au- Ag nanoshuttles bifunctional nanosensor for electrochemical and SERS intelligent analysis of ultra- trace carbendazim coupled with machine learning. Ceram. Int. doi:10.1016/j.ceramint.2020.08.121 (2020). + +30 Zhai, Y. et al. Metal- organic- frameworks- enforced surface enhanced Raman scattering chip for elevating detection sensitivity of carbendazim in seawater. Sensors Actuat. B- Chem. 326, doi:10.1016/j.snb.2020.128852 (2021). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Schematic of the current enrichment and detection based on the porous \(\beta\) - CD polymer. a Adsorption and c desorption processes using magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP) with \(\sim 1000\) times enrichment. b Optical photograph of MN- PCDP. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Characterizations of magnetic nanoparticle (MN), porous \(\beta\) - CD polymer (PCDP) and magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP). TEM images of a MN, b PCDP, and c MN- PCDP. d FT- IR spectra of MN (black), TFT (red), \(\beta\) - CD (blue), PCDP (orange) and MN- PCDP (green). e N2 adsorption isotherms and cumulative pore volume of MN- PCDP. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +MN- PCDP rapidly adsorbs a broad range of organic pollutants. a Structures and of each tested organic pollutant. b Time- dependent adsorption of each pollutant (0.1 mM) by MN- PCDP (1 mg mL- 1). c Percentage removal efficiency of each pollutant obtained by stirring NAC (blue), stirring MN- PCDP (red) and rapidly flowing the through a thin MN- PCDP layer (green). The data are reported as the average uptake of triplicate experiments. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Rapid enrichment performance of MN- PCDP. a Optical photographs of MN- PCDP separation process by magnet in continuous time. b Time- dependent adsorption of BPA (0.1 mM) using MN- PCDP with different dosage (0.1, 0.25, 0.5, 0.75 and 1 mg L- 1). c Removal efficiency of BPA (0.01 mM) using MN- PCDP (100 mg) in three methods (100 mL for 10 times, 250 mL for 4 times and 500 mL for 2 times). d Average removal (black) and enrichment (red) efficiency of the three methods in c. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5
+ +Application in Raman and fluorescence detection used this enrichment strategy. A Optical photographs about the enrichment process of MN- PCDP in mud water. Fluorescence spectra of carbendazim b before and c after enrichment process of MN- PCDP. Raman spectrum of carbendazim d before and e after enrichment process of MN- PCDP. f Raman spectrum of mixture after the enrichment process of MN- PCDP in real samples. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupportingInformationNC.docx + +<--- Page Split ---> diff --git a/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e_det.mmd b/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a55bf50167eae9c207850ee922048acda29daf10 --- /dev/null +++ b/preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e_det.mmd @@ -0,0 +1,327 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 820, 243]]<|/det|> +# Ultra-rapid and highly efficient enrichment of organic pollutants via magnetic nanoparticles/mesoporous nanosponge compounds for ultrasensitive nanosensors + +<|ref|>text<|/ref|><|det|>[[44, 263, 787, 547]]<|/det|> +Lingling Zhang Xi'an Jiaotong University https://orcid.org/0000- 0002- 6272- 7171Rui Hao Xi'an Jiaotong UniversityHongjun You School of Science, Xi'an Jiaotong University https://orcid.org/0000- 0002- 9389- 3076Hu Nan Xi'an Jiaotong UniversityYanzhu Dai School of ScienceJixiang Fang ( jxfang@mail.xjtu.edu.cn) Xi'an Jiaotong University https://orcid.org/0000- 0003- 3618- 2144 + +<|ref|>sub_title<|/ref|><|det|>[[44, 579, 102, 597]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 616, 395, 636]]<|/det|> +Keywords: nanosensors, SERS technique + +<|ref|>text<|/ref|><|det|>[[44, 654, 317, 674]]<|/det|> +Posted Date: January 6th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 692, 463, 712]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 127668/v1 + +<|ref|>text<|/ref|><|det|>[[42, 729, 910, 772]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 808, 956, 852]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on November 25th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 27100- 2. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 159, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[40, 82, 950, 445]]<|/det|> +AbstractDeveloping advanced sensing and detection technologies to effectively monitor organic micropollutants in water is under urgent demand in both scientific and industrial communities. Currently, owing to the ultrahigh sensitivity on the single- molecule level with highly informative spectra characteristics, SERS technique is regarded as the most direct and effective detection technique. However, some weakly adsorbed molecules, such as most of persistent organic pollutants, cannot exhibit strong SERS signals, which is a long- standing key challenge that has not been solved. Here, we show an enrichment- typed sensing strategy based on a powerful porous composite material, call mesoporous nanosponge. The nanosponge consists of magnetic nanoparticles immobilized porous \(\beta\) - cyclodextrin polymers, demonstrating remarkable capability of effective and fast removal of organic micropollutants, e.g. \(\sim 90\%\) removal efficiency within \(\sim 1\) min. With the anchoring of magnetic nanoparticles, the current new polymer adsorbent can be easily recycled from water and re- dispersed in ethanol so that the target molecules in the cavity of adsorbent is concentrated, with an enrichment factor up to \(\sim 103\) . By means of the current enrichment strategy, the limit of detection (LOD) of the typical organic pollutants can be significantly improved, i.e. increasing \(2\sim 3\) orders of magnitude, compared with the detection without molecule enrichment protocol. Consequently, the current enrichment strategy is proved to be applicable in a variety of fields for portable and fast detection, such as Raman and fluorescent. + +<|ref|>sub_title<|/ref|><|det|>[[44, 468, 208, 495]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[41, 508, 952, 764]]<|/det|> +The Stockholm Convention on Persistent Organic Pollutants (POPs) was endorsed by 131 nations in 2004 to eliminate most persistent bioaccumulative and toxic substances in the world.1 Organic micropollutants of ground and surface water resources, such as pesticides and plastic components, have aroused great concerns about potential negative effects on aquatic ecosystems and human health.2,3 Therefore, parallel to the researches of adsorbent materials to remove organic pollutants from water, the ultrasensitive detection of organic pollutants is another crucial field, since the solubility of organic micropollutants in water is always at the trace level.4 Among diverse detection approaches, surface- enhanced Raman scattering (SERS), which achieved significant breakthroughs in 1997 and became the first vibrational spectroscopy technique that could provide delicate information on molecular fingerprints with potential single- molecule level of sensitivity,5- 8 is regarded as the most simple, fast, flexible and portable detection technique. + +<|ref|>text<|/ref|><|det|>[[41, 780, 945, 944]]<|/det|> +However, up to date, the superiority of single- molecule SERS in the detection of diverse molecules with intrinsic small cross- sections or low affinity for the plasmonic surface has not been into full play, particularly in real complex situation.9 As is well known, the SERS process involves complicated coupled three- body interactions among photons, molecules, and nanostructures.10,11 Besides the interaction between light and nanostructures, the investigation on the interaction between molecule and plasmonic surface is of importance.12 On the one hand, SERS is an optical near- field effect.12- 14 A high activity can be obtained only when the target molecule is very close to the plasmonic surface. On the other hand, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 951, 115]]<|/det|> +most organic pollutants in water can not be effectively adsorbed onto a metallic surface because of their low affinity toward the metal.\(^{7}\) Therefore, recently, some strategies, such as selective enrichment and spatial localization of target molecules,\(^{15- 17}\) are suggested to solve this long- standing challenge. + +<|ref|>text<|/ref|><|det|>[[39, 130, 958, 570]]<|/det|> +In this work, we propose a new sensing strategy based on the efficient enrichment and rapid separation of POPs by means of the magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP), called mesoporous nanosponge. The microporous \(\beta\) - cyclodextrin ( \(\beta\) - CD) material, an inexpensive and renewable carbohydrate, which is featured by small pores and high surface areas,\(^{18,19}\) was used in this work as an excellent adsorbent. In fact, microporous \(\beta\) - CD material has been widely studied because of outstanding adsorption efficiency through forming host- guest inclusion with many hydrophobic organic pollutants.\(^{20}\) \(^{21}\) The magnetic nanoparticles are introduced into the MN- PCDP compounds to rapidly separate the adsorbent from water. The current strategy (the schematic description of the protocol is shown in Fig. 1) demonstrates several remarkable advantages. Firstly, as shown in Fig. 1a, when the MN- PCDP adsorbent (shown in Fig. 1b) is dispersed into water in the beaker, e.g. \(\sim 1000 \text{ml}\) , containing organic pollutants, ultra- rapid adsorption and magnetic separation can be accomplished, i.e., totally within \(\sim 1 \text{min}\) . Secondly, the adsorbed pollutant in MN- PCDP from water can be desorbed in ethanol with a volume of \(\sim 1 \text{ml}\) , for further analysis such as UV- vis, Raman and fluorescent spectroscopy. Thus, an ultra- high enrichment efficiency with an enrichment factor up to \(\sim 10^{3}\) times can be obtained (Fig. 1c). With current enrichment strategy, the limit of detection (LOD) in a variety of sensing applications, such as SERS and fluorescent, can be lowered by \(2 \sim 3\) orders of magnitude. Furthermore, through the magnetic separation, the MN- PCDP mesoporous nanosponge can selectively adsorb the target organic pollutants, avoiding the disturbance of complex matrix. The current sensing strategy can be believed to be applicable to a wider range of sensing areas for an economical, simple, fast, flexible, and portable detection. + +<|ref|>sub_title<|/ref|><|det|>[[44, 592, 144, 618]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[39, 631, 956, 939]]<|/det|> +Synthesis and characterization. The MN- PCDP was prepared by cross- linking polymerization of \(\beta\) - CD and cross- linking agent (tetrafluoroterephthalmonitrile (TFT)), with magnetic nanoparticles ( \(\mathrm{Fe_3O_4}\) ) in one- step solvothermal reaction. Fig. 2a- c show the transmission electron microscope (TEM) images of magnetic nanoparticles (MN, \(\mathrm{Fe_3O_4}\) ), porous \(\beta\) - CD polymer (PCDP) and MN- PCDP, respectively. As shown in Fig. 2a, the synthesized MN exhibits regular spheres with good dispersibility and uniform size (average size \(\sim 200 \text{nm}\) ). The Fourier transform- infrared spectroscopy (FT- IR) spectrum of MN is displayed in Fig. 2d. The absorption bands at \(1652 \text{cm}^{- 1}\) and \(1396 \text{cm}^{- 1}\) of the MN can be associated with carboxylate group\(^{22}\) and that also appear in the MN- PCDP. Fig. 2b and Supplementary Fig. 1 exhibit that the PCDP is porous network structure. After the immobilization of MN, as shown in Fig. 2c, the porous network structure of MN- PCDP is not disrupted. The FT- IR spectrum of the MN- PCDP not only obviously combines the characteristic peaks of the TFT and the \(\beta\) - CD but also displays a new peak at \(1265 \text{cm}^{- 1}\) in relation to the newly formed C- F group, implying that the \(\beta\) - CD has been crosslinked with TFT.\(^{23,24}\) Fig. 2e indicates that the Brunauer- Emmett- Teller surface areas ( \(\mathrm{S_{BET}}\) ) of MN- PCDP is about \(66 \text{m}^2 \text{g}^{- 1}\) . The pores with diameter + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 955, 88]]<|/det|> +of 1.7- 3.0 nm comprise the majority of the free volume of MN- PCDP and its average pore diameter is 2.12 nm. + +<|ref|>text<|/ref|><|det|>[[39, 100, 955, 636]]<|/det|> +Adsorption of MN- PCDP nanosponges. The high surface area and permanent porosity of MN- PCDP mesoporous nanosponge enable the rapid removal of organic micropollutants from water. \(^{25}\) As shown in Supplementary Fig. 2, the PCDP and MN- PCDP displays the same properties in time- dependent adsorptions of bisphenol A (BPA), illustrating the immobilization of magnetic nanoparticles has no remarkable influence on the adsorption performance of PCDP. The time- dependent adsorptions of various organic micropollutants adsorbed by MN- PCDP, including plastic components, pesticide and aromatic model compounds (Fig. 3a), are shown in Fig. 3b, Supplementary Fig. 3 and Supplementary Table S1. The removal rate of the above organic micropollutants is very fast, which tends to be constant within 1 min. The removal efficiencies of BPA, parathion, carbendazim and 2- naphthol (2- NO) are more than 80% in 30 sec, which is much higher than the Norit ROW 0.8 supra extruded activated carbon (NAC) as presented in Fig. 3c, Supplementary Fig. 4- 5 and Supplementary Table S2. We further probe the readily accessible binding sites of MN- PCDP by determining the flow- through uptake of different organic micropollutants. In these experiments, the adsorbent ( \(\sim 5\) mg) was trapped as a thin layer on a 0.22 μm syringe filter, and aqueous organic pollutants (5 ml, 0.1 mM) passed rapidly through the filter at a flow rate of 10 ml min \(^{- 1}\) (Supplementary Fig. 6). Under these conditions, for example, 76% of the BPA is removed from the solution, corresponding to more than 84% of its equilibrium adsorption, confirming that the host- guest interaction plays a major role in the filtration process by syringe. \(^{26}\) The superior performance of MN- PCDP can be further indicated that its \(\beta\) - CD moieties are easily accessed by most of organic micropollutants. Furthermore, these molecules are rapidly trapped. In addition, the influence of the concentrations of adsorbent on the BPA adsorption efficiency is studied as shown in Fig. 4b, Supplementary Fig. 7 and Supplementary Table S3. When the concentration of adsorbent increases from 0.1 mg L \(^{- 1}\) to 1.0 mg L \(^{- 1}\) , the adsorption efficiency of BPA is enhanced from 25.12% to 87.09% within 1 min and from 35.07% to 89.82% within 10 min. + +<|ref|>text<|/ref|><|det|>[[39, 650, 951, 926]]<|/det|> +Desorption and enrichment of MN- PCDP nanosponges. As we all know, organic micropollutants exhibit good solubility in organic solution, such as ethanol and methanol. \(^{18}\) Hence, after adsorption process, we utilized ethanol to desorb the organic micropollutants from MN- PCDP mesoporous nanosponges, and then obtained the enriched pollutant solution through magnetic nanoparticle separation. In order to obtain higher concentration of desorbed micropollutant solution, in this work, we chose 1 mL ethanol to desorb organic micropollutants adsorbed in 100 mg MN- PCDP adsorbent. As shown in Supplementary Fig. 8, using current enrichment processes, the concentration of BPA can be increased to 88.5 times of its initial concentration with a recipe of 100 mL organic pollutant (BPA) solution and 100 mg MN- PCDP adsorbent. This result reveals that more than 98% of the adsorbed organic micropollutants are desorbed into the ethanol solution. That is to say, for 100 mL organic pollutant solution, we realize \(\sim 10^{2}\) times enrichment of target molecule. As the amount of adsorbent increases, the adsorption efficiency tends to reach equilibrium. Considering the cost increase of sample preparation and the operation in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[43, 45, 904, 87]]<|/det|> +desorption process (with 1 mL ethanol) resulting from the increase of adsorbent dosage, 100 mg of adsorbent is selected as the amount of material for subsequent experiments. + +<|ref|>text<|/ref|><|det|>[[39, 105, 955, 445]]<|/det|> +In order to further improve the enrichment effect of 100 mg adsorbent in total 1000 mL organic micropollutants, herein, we attempted three methods, including 100 mL×10 times, 250 mL×4 times and 500 mL×2 times, to optimize the adsorption and desorption processes. Importantly, the adsorbent can be simply separated by magnet in every adsorption cycle, and desorbed in ethanol in the last adsorption cycle. As shown in Fig. 4c, Supplementary Fig. 9 and Supplementary Table S4, with adsorption times increasing, the removal efficiencies of these three methods gradually decrease. The removal efficiencies of these three methods (100 mL×10 times, 250 mL×4 times and 500 mL×2 times) are 50.78%, 62.58% and 41.22%, respectively. Meanwhile, the enrichment efficiencies of these three methods are 485, 605 and 396 times of the initial concentration (Fig. 4d), respectively. Therefore, we achieve over 600 times' enrichment of organic pollutants (with 1000 mL of initial micropollutants) in the optimized adsorption and desorption processes. Here, the selected parameters (100 mg adsorbent in 250 mL organic solution for 4 cycle times) were used for the succedent experiments. Meanwhile, it is also worth pointing out that the separation process by magnet is very fast and facile, such as 90 sec for 250 mL solution (Fig. 4a and Supplementary Fig. 10b), 60 sec for 100 mL (Supplementary Fig. 10a), and 150 sec for in 500 mL (Supplementary Fig. 10c), fully meeting the requirement of immediate- pretreatment detection application. + +<|ref|>text<|/ref|><|det|>[[39, 460, 955, 853]]<|/det|> +SERS and fluorescence measurement of pollutants based on the enrichment of MN- PCDP nanosponges. Many POPs are mutagenic, carcinogenic and not degradable by direct biological treatment, some of which damage nerve, endocrine systems of human body, and the ecological balance due to their toxicity in nature. \(^{27}\) Based on the above experimental researches, the MN- PCDP nanosponges were used to absorb organic micropollutants and then were collected from water (Fig. 5a), so as to realize the rapid removal and enrichment of POPs. Moreover, we evaluated fluorescence and enhanced Raman spectra of POPs (carbendazim and BPA) to demonstrate the enrichment effect of current enrichment strategy. The enhanced Raman spectra of carbendazim molecular with \(\sim 55 \text{nm}\) Au nanoparticles (Supplementary Fig. 11) were measured under 785 nm laser. As shown in Figure 5b- c, without the enrichment process, the LOD of SERS for carbendazim is 1 nM, but after the enrichment using MN- PCDP adsorbent, this value reaches to \(\sim 5 \text{pM}\) , which shows an increase of \(10^{2} \sim 10^{3}\) . In Supplementary Fig. 12, for BPA molecule, with the help of adsorbent, the LOD of fluorescence is also greatly improved. In Fig. 5d- e and Supplementary Fig. 13, based on the current enrichment sensing strategy, the LODs of fluorescence detections for the pure solution of carbendazim and BPA are lower by \(2 \sim 3\) orders of magnitude. In this study, the enrichment strategy based on the adsorption and desorption processes of MN- PCDP adsorbent may significantly increase the sensitivity of plasmonic sensors, compared with the LOD for similar molecules, \(^{28- 30}\) illustrating its wide applicability. + +<|ref|>text<|/ref|><|det|>[[42, 869, 947, 958]]<|/det|> +Owing to the excellent enrichment and easily- separated features, the current strategy was believed that the mesoporous nanosponges could be served as a preprocessing for direct, rapid and ultrasensitive detection of contaminants in complex situations. After adsorption process, the MN- PCDP adsorbent was easily collected on the wall of beaker (Fig. 5a) with a magnet, avoiding the interference of complex + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 937, 181]]<|/det|> +matrix, such as mud and microorganism. Fig. 5f reveals that both the characteristic peaks of BPA (830 and \(1179 \text{cm}^{- 1}\) ) and carbendazim (1008, 1244 and \(1263 \text{cm}^{- 1}\) ) evidently appear in the Raman spectrum of mixture solution, including \(1 \mu \text{M BPA}\) and \(10 \text{nM carbendazim}\) . Furthermore, the MN- PCDP demonstrates a superior reusability as shown in Supplementary Fig. 14. Six consecutive BPA adsorption/desorption cycles were performed and the regenerated MN- PCDP exhibited almost no decrease (90.2% to 87.5%) in performance compared to the as- synthesized polymer. + +<|ref|>sub_title<|/ref|><|det|>[[44, 204, 191, 229]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[41, 243, 955, 470]]<|/det|> +In summary, we have developed a robust and rapid sensing strategy based on the MN- PCDP mesoporous nanoponges to capture and enrich organic pollutants from water. In this strategy, the MN- PCDP adsorbent exhibits excellent adsorption capacity for various kinds of pollutants owing to the unique cavity structures. Moreover, the adsorbed pollutant in MN- PCDP can be desorbed in ethanol with a very fast and facile operation. In SERS detection of organic pollutants, i.e. carbendazim and BPA, in this work, the current sensing strategy may significantly increase the sensitivity of plasmonic sensors with 2\~3 orders of magnitude. Therefore, the current robust sensing strategy with the ultra- rapid and highly efficient sample pretreatment and molecule enrichment is believed to be applicable to a wider range of sensing devices, such as fluorescent, Raman and infrared spectroscopes for a cost- effective, simple, fast, flexible and portable detection. + +<|ref|>sub_title<|/ref|><|det|>[[44, 492, 163, 518]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[41, 531, 950, 742]]<|/det|> +Preparation of magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) . The carboxyl- functionalized magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) with highly water- dispersibility were synthesized by a modified solvothermal reaction approach. Typically, \(\mathrm{FeCl}_3\cdot 6\mathrm{H}_2\mathrm{O}\) (1.08 g, 4.0 mmol) and trisodium citrate (0.20 g, 0.68 mmol) were dissolved in ethylene glycol (20 mL) with stirring at 500 rpm. Afterward, sodium acetate trihydrate (2.0 g, 15 mmol) was added and the mixture was stirred for 30 min. Then, the mixture was sealed in a Teflon- lined stainless- steel autoclave (50 mL). The autoclave was heated at \(200 ^{\circ}\mathrm{C}\) for 12 h, and then allowed to cool to room temperature. The black products were washed with ethanol and ultrapure water for several times. Finally, the carboxyl- functionalized magnetite nanoparticles \(\left(\mathrm{Fe}_3\mathrm{O}_4\right)\) were separated by magnet, re- dispersed in ethanol and dried in vacuum drying oven at \(30^{\circ}\mathrm{C}\) . + +<|ref|>text<|/ref|><|det|>[[41, 758, 955, 946]]<|/det|> +Preparation of magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP). The MN- PCDP composites were then prepared by modification of nucleophilic aromatic substitution method of hydroxyl groups of \(\beta\) - CD. A dried \(100 \text{mL Shrek reaction vial with a magnetic stir bar was charged with} \beta\) - CD (0.82 g, 0.724 mmol), TFT (0.40 g, 1.03 mmol), and \(\mathrm{K}_2\mathrm{CO}_3\) (1.28 g, 9.28 mmol) and dried \(\mathrm{Fe}_3\mathrm{O}_4\) (0.041 g). The vial was flushed with \(\mathrm{N}_2\) gas for 10 min, then an anhydrous THF/DMF mixture (9:1 v/v, 40 mL) was added and the vial was purged with \(\mathrm{N}_2\) for additional 5 min. After that, the \(\mathrm{N}_2\) inlet was removed. The mixture was stirred at 500 rpm and refluxed at \(85^{\circ}\mathrm{C}\) for 36 h under nitrogen protection. The brown suspension was cooled to room temperature and magnetically separated the supernatant by magnet. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 953, 113]]<|/det|> +precipitate was washed twice with an appropriate amount of distilled water, THF, ethanol and \(\mathrm{CH}_2\mathrm{Cl}_2\) , respectively. The final precipitate was vacuum dried at \(77 \text{K}\) in a liquid nitrogen bath for \(24 \text{h}\) and then the magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP) was obtained. + +<|ref|>text<|/ref|><|det|>[[41, 129, 936, 313]]<|/det|> +Batch adsorption kinetic studies. In studies, the dried polymer (MN- PCDP, \(20 \text{mg}\) ) was initially washed with \(\mathrm{H}_2\mathrm{O}\) for 2 times and then separated by magnet. Adsorption kinetic studies for different pollutants were performed in \(30 \text{mL}\) scintillation vials with \(20 \text{mL}\) organic pollutant solution and \(20 \text{mg}\) adsorbent, at ambient temperature on a hot plate at \(25^{\circ}\text{C}\) . Then the sample was shaken at \(250 \text{rpm}\) until the adsorption equilibrium was reached. The mixture was immediately stirred and \(1 \text{mL}\) aliquots of the suspension were taken at certain intervals via syringe and filtered immediately by a \(0.22 \mu \text{m} \text{PTFE}\) membrane filter. The residual concentration of the pollutant in each sample was determined by UV- vis spectroscopy. + +<|ref|>text<|/ref|><|det|>[[42, 328, 907, 373]]<|/det|> +Calculation of removal efficiency. The removal efficiency of pollutant removal by the adsorbent was determined by the following equation: + +<|ref|>equation<|/ref|><|det|>[[66, 404, 383, 437]]<|/det|> +\[\mathrm{Removal~efficiency(\%) = \frac{C_0 - C_t}{C_0}\times 100}\] + +<|ref|>text<|/ref|><|det|>[[42, 472, 925, 518]]<|/det|> +where \(C_0\) and \(C_t\) are the initial and residual concentration of pollutant in the stock solution and filtrate, respectively. + +<|ref|>text<|/ref|><|det|>[[41, 533, 949, 673]]<|/det|> +Flow- through adsorption experiments. Individual pollutants were at high concentrations (mM). \(5.0 \text{mg}\) of the MN- PCDP adsorbent was washed with deionized \(\mathrm{H}_2\mathrm{O}\) for 2 times, then the precipitate was pushed by a syringe through a \(0.22 \mu \text{m} \text{PTFE}\) membrane filter to form a thin layer of the adsorbent on the filter membrane. \(5 \text{mL}\) of the pollutant stock solution was then pushed through the adsorbent in \(\sim 30 \text{s}\) ( \(10 \text{mL} \text{min}^{- 1}\) flow rate). The filtrate was then measured by UV- vis spectroscopy to determine the pollutant removal efficiency. + +<|ref|>text<|/ref|><|det|>[[41, 689, 958, 871]]<|/det|> +MN- PCDP desorption studies. \(100.0 \text{mg}\) of the adsorbent was washed with deionized \(\mathrm{H}_2\mathrm{O}\) for 2 times, and then added to the organic pollutant stock solution ( \(0.01 \text{mM}\) ) with determine volume ( \(100 \text{mL}\) , \(250 \text{mL}\) , \(500 \text{mL}\) ). The mixture was shaken at \(250 \text{rpm}\) for \(1 \text{min}\) at \(25^{\circ}\text{C}\) . After separating the supernatant and the adsorbent by an external magnet, the supernatant was filtered through a \(0.22 \mu \text{m} \text{filter}\) membrane and determined by UV- vis spectroscopy. Meanwhile the precipitate was evaporated to dryness with a gentle nitrogen stream, then the residue was dissolved in \(1 \text{mL}\) of ethanol to desorb the adsorbed organic pollutant. The desorption solution was measured by UV- vis spectroscopy and compared with the initial concentration of pollutant in the stock solution. + +<|ref|>text<|/ref|><|det|>[[42, 889, 930, 932]]<|/det|> +Calculation of enrichment efficiency. The enrichment efficiency of pollutant adsorbed by the adsorbent was determined by the following equation: + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[75, 58, 298, 90]]<|/det|> +\[\mathrm{Enrichment~efficiency} = \frac{c}{c_0}\] + +<|ref|>text<|/ref|><|det|>[[43, 126, 839, 147]]<|/det|> +Where \(C_0\) and \(C\) are the initial and desorbed solution concentration of pollutant, respectively. + +<|ref|>text<|/ref|><|det|>[[43, 166, 882, 209]]<|/det|> +Fluorescence measurement. The fluorescence spectra of pure solution were directly measured by fluorescence spectrophotometer. + +<|ref|>text<|/ref|><|det|>[[41, 225, 955, 479]]<|/det|> +Preparation of SERS active Au NPs. The Au NPs with different size in diameter were synthesized based on a modified citrate reduction approach. The growth process of gold nanoparticles with different size included three steps. For step 1, \(100~\mathrm{mL}\) of ultrapure water was added into a conical flask and heated to boiling. Then, \(4\mathrm{ml}\) of \(1\mathrm{wt}\%\) sodium citrate (SC) solution was injected immediately, and \(3.2\mathrm{mL}\) of \(10\mathrm{mM}\) \(\mathrm{HAuCl_4}\) was added after 3 min. Kept the reaction for 25 minutes and made it natural cooling, then the Au seeds were obtained. For step 2, \(80~\mathrm{mL}\) of ultrapure water and \(20~\mathrm{mL}\) of Au seeds were mixed into the conical flask and heated to boiling. Then, \(2\mathrm{mL}\) of \(1\mathrm{wt}\%\) sodium citrate solution was injected immediately, and \(0.2\mathrm{mL}\) of \(\mathrm{HAuCl_4}\) was added 3 min later. Then additional \(0.2\mathrm{mL}\times 9\) dosage of \(\mathrm{HAuCl_4}\) was injected every 8 minutes. After the last precursor was added, the reaction was kept for 25 min, and Au NPs- 25 nm were obtained. For step 3, Au NPs prepared in step 2 were used as the seed solution, and the growth process was repeated as growth steps 2, and then Au NPs- 55 nm were obtained in this step. + +<|ref|>text<|/ref|><|det|>[[41, 494, 958, 696]]<|/det|> +SERS measurement. SERS measurement is based on the hydrophobic slippery surface. Concentrated molecules and Au NPs were prepared on a hydrophobic slippery Teflon membrane as follows: First, a Teflon membrane was attached on a flat glass slide ( \(5\mathrm{cm}\times 5\mathrm{cm}\) ) by using a double- sided adhesive. Then, \(0.5\mathrm{mL}\) of perfluorinated fluid was dispersed by spin coating. The low speed was \(300\mathrm{rpm}\) for \(30\mathrm{s}\) and the high speed was \(1500\mathrm{rpm}\) for 1 min. After the excess lubricating liquid was removed by centrifugal force, and the infused membrane was heated for 30 min. Lastly, \(50\mu \mathrm{L}\) of probe molecules and \(10\mu \mathrm{L}\) of Au colloids were simultaneously dropped onto the slippery surface. During drying, the contact line shrunk because of the low friction of the lubricated Teflon surface. As a result, the initial droplet could be concentrated into a small area less than \(0.5\mathrm{mm}\) in diameter. + +<|ref|>sub_title<|/ref|><|det|>[[45, 720, 213, 745]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 761, 187, 780]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[44, 799, 912, 842]]<|/det|> +The data that support the findings of this study are available within the paper and its Supplementary Information or from the corresponding authors on reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[45, 860, 216, 878]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[44, 897, 949, 940]]<|/det|> +This work was supported by the programs supported by the National Natural Science Foundation of China (No. 21675122, 21874104, 22074115), the Key Research Program in Shaanxi (2017NY- 114), Basic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 916, 110]]<|/det|> +Public Welfare Research Project of Zhejiang Province (No. LY20E010007), and Natural Science Foundation of Shaanxi Province (No. 2019JLP- 19), the World- Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities. + +<|ref|>sub_title<|/ref|><|det|>[[44, 128, 223, 147]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[42, 165, 940, 276]]<|/det|> +L.L.Z. synthesized the materials, carried out the characterizations and performance, analyzed the data, and wrote the manuscript. R. H., H. N., Y. Z. D. contributed in part of the TEM, Raman and fluorescence characterizations. H.J.Y. and J.X.F., supervised the project, designed the experiments, contributed in discussions, comments and writing of manuscript. All authors discussed the results and commented on the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[44, 294, 241, 313]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[44, 333, 520, 352]]<|/det|> +Supplementary Information accompanies this paper at + +<|ref|>text<|/ref|><|det|>[[44, 370, 630, 390]]<|/det|> +competing of Interest: The authors declare no competing of interest. + +<|ref|>text<|/ref|><|det|>[[44, 408, 546, 427]]<|/det|> +Reprints and permission information is available online at + +<|ref|>text<|/ref|><|det|>[[44, 446, 325, 465]]<|/det|> +Journal peer review information: + +<|ref|>text<|/ref|><|det|>[[42, 484, 792, 540]]<|/det|> +Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. + +<|ref|>sub_title<|/ref|><|det|>[[44, 565, 196, 590]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[42, 605, 860, 650]]<|/det|> +1 Kelly, B. C., Ikonomou, M. G., Blair, J. 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An accurate electromagnetic theory study of surface enhancement factors for silver, gold, copper, lithium, sodium, aluminum, gallium, indium, zinc, and cadmium. J. Phys. Chem. 91, 634- 643 (1987). + +<|ref|>text<|/ref|><|det|>[[39, 611, 935, 656]]<|/det|> +15 Zhang, D. et al. Buoyant particulate strategy for few- to- single particle- based plasmonic enhanced nanosensors. Nat. Commun. 11, 2603, doi:10.1038/s41467- 020- 16329- y (2020). + +<|ref|>text<|/ref|><|det|>[[39, 672, 940, 717]]<|/det|> +16 Hao, R., You, H., Zhu, J., Chen, T. & Fang, J. "Burning Lamp"- like Robust Molecular Enrichment for Ultrasensitive Plasmonic Nanosensors. ACS Sens. 5, 781- 788, doi:10.1021/acssensors.9b02423 (2020). + +<|ref|>text<|/ref|><|det|>[[39, 733, 923, 800]]<|/det|> +17 De Angelis, F. et al. Breaking the diffusion limit with super- hydrophobic delivery of molecules to plasmonic nanofocusing SERS structures. Nat. Photonics 5, 682- 687, doi:10.1038/nphoton.2011.222 (2011). + +<|ref|>text<|/ref|><|det|>[[39, 817, 864, 861]]<|/det|> +18 Alsbaiee, A. et al. Rapid removal of organic micropollutants from water by a porous beta- cyclodextrin polymer. Nature 529, 190- 194, doi:10.1038/nature16185 (2016). + +<|ref|>text<|/ref|><|det|>[[39, 877, 945, 944]]<|/det|> +19 Xiao, L. et al. Beta- Cyclodextrin Polymer Network Sequesters Perfluorooctanoic Acid at Environmentally Relevant Concentrations. J. Am. Chem. Soc. 139, 7689- 7692, doi:10.1021/jacs.7b02381 (2017). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 888, 90]]<|/det|> +20 Liu, X. et al. A magnetic graphene hybrid functionalized with beta- cyclodextrins for fast and efficient removal of organic dyes. J. Mater. Chem. A 2, doi:10.1039/c4ta00753k (2014). + +<|ref|>text<|/ref|><|det|>[[42, 105, 927, 173]]<|/det|> +21 Huang, D. et al. Fabrication of water-compatible molecularly imprinted polymer based on β- cyclodextrin modified magnetic chitosan and its application for selective removal of bisphenol A from aqueous solution. J. Taiwan Inst. Chem. E 77, 113- 121, doi:10.1016/j.jtice.2017.04.030 (2017). + +<|ref|>text<|/ref|><|det|>[[42, 189, 933, 234]]<|/det|> +22 Liu, J. et al. Highly water- dispersible biocompatible magnetite particles with low cytotoxicity stabilized by citrate groups. Angew. Chem. Int. Ed. 48, 5875- 5879, doi:10.1002/anie.200901566 (2009). + +<|ref|>text<|/ref|><|det|>[[42, 249, 926, 316]]<|/det|> +23 Jiang, H.- L. et al. A novel crosslinked β- cyclodextrin- based polymer for removing methylene blue from water with high efficiency. Colloid. Surfaces. A 560, 59- 68, doi:10.1016/j.colsurfa.2018.10.004 (2019). + +<|ref|>text<|/ref|><|det|>[[42, 332, 865, 377]]<|/det|> +24 Huang, D., Zhang, Y. & Zhang, H. A novel synthesis of ethyl carbonate derivatives of beta- cyclodextrin. Carbohyd. Res. 370, 82- 85, doi:10.1016/j.carres.2013.01.022 (2013). + +<|ref|>text<|/ref|><|det|>[[42, 392, 919, 461]]<|/det|> +25 Huang, Q., Chai, K., Zhou, L. & Ji, H. A phenyl- rich β- cyclodextrin porous crosslinked polymer for efficient removal of aromatic pollutants: Insight into adsorption performance and mechanism. Chem. Eng. J 387, doi:10.1016/j.cej.2020.124020 (2020). + +<|ref|>text<|/ref|><|det|>[[42, 476, 945, 544]]<|/det|> +26 Wang, Z., Zhang, P., Hu, F., Zhao, Y. & Zhu, L. A crosslinked beta- cyclodextrin polymer used for rapid removal of a broad- spectrum of organic micropollutants from water. Carbohyd. Polym. 177, 224- 231, doi:10.1016/j.carbpol.2017.08.059 (2017). + +<|ref|>text<|/ref|><|det|>[[42, 559, 944, 605]]<|/det|> +27 Rusiecki, J. A. et al. Global DNA hypomethylation is associated with high serum- persistent organic pollutants in Greenland Inuit. Environ. Health. Persp. 116, 1547- 1552, doi:10.1289/ehp.11338 (2008). + +<|ref|>text<|/ref|><|det|>[[42, 620, 914, 689]]<|/det|> +28 Chen, X. et al. Detection and quantification of carbendazim in Oolong tea by surface- enhanced Raman spectroscopy and gold nanoparticle substrates. Food Chem 293, 271- 277, doi:10.1016/j.foodchem.2019.04.085 (2019). + +<|ref|>text<|/ref|><|det|>[[42, 703, 925, 773]]<|/det|> +29 Zhu, X. et al. A novel graphene- like titanium carbide MXene/Au- Ag nanoshuttles bifunctional nanosensor for electrochemical and SERS intelligent analysis of ultra- trace carbendazim coupled with machine learning. Ceram. Int. doi:10.1016/j.ceramint.2020.08.121 (2020). + +<|ref|>text<|/ref|><|det|>[[42, 788, 911, 856]]<|/det|> +30 Zhai, Y. et al. Metal- organic- frameworks- enforced surface enhanced Raman scattering chip for elevating detection sensitivity of carbendazim in seawater. Sensors Actuat. B- Chem. 326, doi:10.1016/j.snb.2020.128852 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[44, 878, 143, 904]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[61, 100, 940, 360]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 412, 115, 432]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 455, 950, 523]]<|/det|> +Schematic of the current enrichment and detection based on the porous \(\beta\) - CD polymer. a Adsorption and c desorption processes using magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP) with \(\sim 1000\) times enrichment. b Optical photograph of MN- PCDP. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[68, 75, 925, 685]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 722, 117, 741]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 762, 931, 851]]<|/det|> +Characterizations of magnetic nanoparticle (MN), porous \(\beta\) - CD polymer (PCDP) and magnetic nanoparticles immobilized porous \(\beta\) - CD polymer (MN- PCDP). TEM images of a MN, b PCDP, and c MN- PCDP. d FT- IR spectra of MN (black), TFT (red), \(\beta\) - CD (blue), PCDP (orange) and MN- PCDP (green). e N2 adsorption isotherms and cumulative pore volume of MN- PCDP. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[78, 65, 900, 580]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 612, 116, 631]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 654, 932, 765]]<|/det|> +MN- PCDP rapidly adsorbs a broad range of organic pollutants. a Structures and of each tested organic pollutant. b Time- dependent adsorption of each pollutant (0.1 mM) by MN- PCDP (1 mg mL- 1). c Percentage removal efficiency of each pollutant obtained by stirring NAC (blue), stirring MN- PCDP (red) and rapidly flowing the through a thin MN- PCDP layer (green). The data are reported as the average uptake of triplicate experiments. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[72, 66, 911, 440]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 466, 117, 485]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[41, 507, 955, 619]]<|/det|> +Rapid enrichment performance of MN- PCDP. a Optical photographs of MN- PCDP separation process by magnet in continuous time. b Time- dependent adsorption of BPA (0.1 mM) using MN- PCDP with different dosage (0.1, 0.25, 0.5, 0.75 and 1 mg L- 1). c Removal efficiency of BPA (0.01 mM) using MN- PCDP (100 mg) in three methods (100 mL for 10 times, 250 mL for 4 times and 500 mL for 2 times). d Average removal (black) and enrichment (red) efficiency of the three methods in c. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[68, 61, 925, 519]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 553, 117, 572]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 593, 950, 707]]<|/det|> +Application in Raman and fluorescence detection used this enrichment strategy. A Optical photographs about the enrichment process of MN- PCDP in mud water. Fluorescence spectra of carbendazim b before and c after enrichment process of MN- PCDP. Raman spectrum of carbendazim d before and e after enrichment process of MN- PCDP. f Raman spectrum of mixture after the enrichment process of MN- PCDP in real samples. + +<|ref|>sub_title<|/ref|><|det|>[[44, 729, 311, 756]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 779, 764, 799]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 817, 356, 836]]<|/det|> +- SupportingInformationNC.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a.mmd b/preprint/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d8c119835dacc04f0c313cb48094075e747fcc8e --- /dev/null +++ b/preprint/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a.mmd @@ -0,0 +1,179 @@ + +# Hyperuniformity and phase enrichment in vortex and rotor assemblies + +Naomi Oppenheimer ( naomiop@gmail.com ) Tel Aviv University https://orcid.org/0000- 0002- 8212- 3404 + +David Stein Flatiron Institute Matan Yah Ben Zion New York University https://orcid.org/0000- 0002- 9876- 787X Michael Shelley Flatiron Institute https://orcid.org/0000- 0002- 4835- 0339 + +## Article + +Keywords: Particle Ensembles, Two- dimensional Fluid, Spontaneous Self- assembly, Hamiltonian Structure, Topological Defects + +Posted Date: April 26th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 385285/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on February 10th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28375- 9. + +<--- Page Split ---> + +# Hyperuniformity and phase enrichment in vortex and rotor assemblies + +Naomi Oppenheimer, \(^{1,*}\) David B. Stein, \(^{2}\) Matan Yah Ben Zion, \(^{3}\) and Michael J. Shelley \(^{2,4,}\) \(^{1}\) School of Physics, and the Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel \(^{2}\) Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA \(^{3}\) Laboratoire Gulliver, UMR CNRS 7083, ESPCI Paris, PSL Research University, 75005 Paris, France \(^{4}\) Courant Institute, New York University, New York, NY 10012, USA(Dated: April 13, 2021) + +Ensembles of particles rotating in a two- dimensional fluid can exhibit chaotic dynamics yet develop signatures of hidden order. Such "rotors" are found in the natural world spanning vastly disparate length scales — from the rotor proteins in cellular membranes to models of atmospheric dynamics. Here we show that an initially random distribution of either ideal vortices in an inviscid fluid, or driven rotors in a viscous membrane, spontaneously self assembles. Despite arising from drastically different physics, these systems share a Hamiltonian structure that sets geometrical conservation laws resulting in distinct structural states. We find that the rotationally invariant interactions isotropically suppress long wavelength fluctuations — a hallmark of a disordered hyperuniform material. With increasing area fraction, the system orders into a hexagonal lattice. In mixtures of two co- rotating populations, the stronger population will gain order from the other and both will become phase enriched. Finally, we show that classical 2D point vortex systems arise as exact limits of the experimentally accessible microscopic membrane rotors, yielding a new system through which to study topological defects. + +Two- dimensional (or nearly so) fluid flows show rich and complex vortical dynamics. These can arise from flow interactions with boundaries (1, 2), the inverse cascades of 2D turbulence (3- 5), from Coriolis force dominated atmospheric flows (6), and from quantization effects in super fluid He- II (7, 8). Point vortices have long been staples for the modeling of such inertially dominated inviscid flows. Kirchoff (9) was the first to describe point vortices using a Hamiltonian framework and his work was extended by many others [e.g. (10- 13)], notably, Onsager (14) in his statistical mechanics treatment of 2D turbulence as clouds of point vortices. + +Remarkably, structurally identical Hamiltonian and moment constraints can arise in the microscopic viscously- dominated realm from a strict balance of dissipation with drive on immersed rotating objects. These objects include models of interacting transmembrane ATP- synthase "rotor- proteins" (15- 17), and the planar interactions of rotors — microscopic particles driven to rotate by an external torque (18, 19). We refer to such systems as BDD systems, as in balanced drive and dissipation. In modeling rotational BDD systems other physical effects may also come into play, such as steric interactions, that can yield interesting complexities (17). Interacting assemblies of driven- to- rotate particles has become an area of intensifying interest in the active matter community (18- 26) + +Here we study both point vortices and a BDD rotor system of rotationally- driven microscopic particles — membrane rotors — immersed in a flat membrane. We show that in both systems, their Hamiltonian conservation laws lead to distinct structural states — hyperuniformity, phase enrichment and crystallization (see Fig. 1), not yet observed for either system. We use the Hamiltonian to derive a bound for spatial correlations requiring hyperuniformity. We demonstrate numerically that rotational dynamics robustly self- assembles particles into a disordered hyperuniform 2D material; This self- assembly is insensitive to the details of the hydrodynamic interactions, steric repulsion, or the presence of impurities in the form of different rotation rates. At steady state, the long wavelength configuration is characterized by an isotropically vanishing structure factor, \(S(\mathbf{q} \to 0) \to 0\) (where \(\mathbf{q}\) is the wavevector), leading to an isotropic band- gap (27- 29). + +mity, phase enrichment and crystallization (see Fig. 1), not yet observed for either system. We use the Hamiltonian to derive a bound for spatial correlations requiring hyperuniformity. We demonstrate numerically that rotational dynamics robustly self- assembles particles into a disordered hyperuniform 2D material; This self- assembly is insensitive to the details of the hydrodynamic interactions, steric repulsion, or the presence of impurities in the form of different rotation rates. At steady state, the long wavelength configuration is characterized by an isotropically vanishing structure factor, \(S(\mathbf{q} \to 0) \to 0\) (where \(\mathbf{q}\) is the wavevector), leading to an isotropic band- gap (27- 29). + +In classical mechanics, symmetries of the Hamiltonian \(\mathcal{H}\) restrict the phase- space of the conjugate variables, position and momentum. However, in 2D point vortex or BDD point rotor systems, the conjugate variables are the actual spatial coordinates of the ensemble \(\{x_{i}\}\) and \(\{y_{i}\}\) . The conservation laws are therefore geometrical in nature, bounding the proximity and distribution of the particles. For both point vortices and membrane rotors, as well as for a myriad of other 2D rotating systems (18- 21, 24, 30), the dynamics are dictated by Hamilton's equations, + +\[\Gamma_{i}\mathbf{v}_{1} = \partial_{i}^{\perp}\mathcal{H}, \quad (1)\] + +where \(\partial_{i}^{\perp} = (\partial y_{i}, - \partial x_{i})\) , \(\mathbf{v}_{1}\) is the velocity of rotor \(i\) , and \(\Gamma_{i}\) is the circulation (proportional to the magnitude of the torque for rotors). Our finding, as we will show, is that the spatial arrangements of point vortices, as measured by \(S(\mathbf{q})\) , are dictated by the Hamiltonian, + +\[\mathcal{H}[\rho (\mathbf{r})] = \frac{N\Gamma^{2}}{4\pi}\int \mathrm{d}\mathbf{q}\frac{S(\mathbf{q})}{q^{2}}. \quad (2)\] + +To derive Eq. 2 and to find the Hamiltonian of \(N\) particles, we first describe the flow due to a single vortex in + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIG. 1. Three different structural states of 2D vortices/rotors - hyperuniformity for Euler point vortices (A) and QG rotors/surface rotors (B), (C) phase enrichment induced by circulation differences where green (black) represents vortices of high (low) circulation, and (D) crystallization arising from hydrosteric interactions. The insets of (A), (B) and (C) show the structure factor, \(S(q)\) . In (A) and (B) \(S(q)\) decays to zero at small \(q\) , indicating that the distribution is hyperuniform. In (C) the structure factor shows the six distinct peaks of a hexagonal lattice.
+ +an ideal Euler fluid and show its equivalence to a point rotor in a viscous membrane. We then use the linearity of the equations to extend the result to the many- body case. An ideal point vortex is given by a singular vorticity, \(\omega = \nabla \times \mathbf{v} = \delta (\mathbf{r})\) . A 2D incompressible fluid can be described using a stream function \(\Psi\) such that the velocity, \(\mathbf{v}\) , is given by \(\mathbf{v} = \partial^{\perp}\Psi\) . This equation, combined with the equation above gives, \(\Psi = - \frac{1}{2\pi}\log r\) (12). The flow, \(\mathbf{v}(r)\) , therefore, scales as \(1 / r\) , where \(r = |\mathbf{r}|\) . + +We switch now to a point rotor in a viscous membrane, driven by an external torque \(\tau\) . Following Saffman and Delbruck's seminal work (31), and many others that followed (32- 34), we assume that the membrane is incompressible \((\nabla \cdot \mathbf{v} = 0)\) , and that inertia is negligible. Under these assumptions, the Stokes momentum conservation equation for the membrane reads, + +\[0 = \eta_{2D}\nabla^{2}\mathbf{v} + \eta_{3D}\frac{\partial\mathbf{u}^{\pm}}{\partial z}\bigg|_{z = 0^{\pm}} + \tau \partial^{\perp}\delta (\mathbf{r}), \quad (3)\] + +where \(\mathbf{v}\) is the 2D velocity in the plane of the membrane, \(\mathbf{u}^{\pm}\) is the 3D flow in the outer fluids, \(\eta_{2D}\) is the 2D viscosity, and \(\eta_{3D}\) is the viscosity of the outer fluids. The second term on the right hand side is the surface shear stress of the outer fluids, and the third term is the force due to a rotating point object. There is no pressure contribution when the motion is purely rotational. This equation is coupled to the equations of the outer fluids. It is easy to solve the above equations using a 2D Fourier Transform \((\tilde{F} (\mathbf{q}) = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}F(\mathbf{r})e^{-i\mathbf{q}\cdot \mathbf{r}}d^{2}r)\) , giving: + +\[\tilde{\mathbf{v}} (\mathbf{q}) = \Gamma \partial^{\perp}\tilde{\Psi} \quad ; \quad \tilde{\Psi} = \frac{1}{q(q + \lambda^{-1})}, \quad (4)\] + +where \(\Gamma = \tau /\eta_{2D}\) , and \(\lambda = \eta_{2D} / 2\eta_{3D}\) is the Saffman Delbruck length. At small distances ( \(r \ll \lambda\) ) momentum travels in the plane of the membrane. At large distances ( \(r \gg \lambda\) ) momentum travels through the outer fluid as well (35, 36). In real space \(\Psi (\mathbf{r}) = 1 / 4(H_{0}(r / \lambda) - Y_{0}(r / \lambda))\) , where \(H_{0}\) and \(Y_{0}\) are zeroth order Struve function and Bessel function of the second kind respectively. + +In the limit of small distances, \(r \ll \lambda\) , the stream function is, \(\Psi \approx - \frac{1}{2\pi}\log r\) , i.e. exactly the same as for an ideal point vortex. In the opposite limit, \(r \gg \lambda\) , the stream function becomes \(\Psi = \frac{1}{2\pi r}\) as in quasigeostrophic (QG) flows — atmospheric or oceanic flows coming from gradients in pressure coupled to the Coriolis force (37), or driven rotors on the surface of a fluid (22). A membrane rotor, therefore, transitions from a point vortex for Euler at small distances to that of QG flow at large distances. The velocity is given by derivatives of \(\Psi\) and is thus proportional to \(1 / r\) ( \(1 / r^{2}\) ) in the limit of small (large) distances (see Fig. 2B). For simplicity, we work primarily in the limit of small distances, \(r \ll \lambda\) , since in this limit the dynamics in a membrane converge with those of point vortices (many results still apply to the more general case as shown in the SI). In what follows, we will use "point vortices" when there are only hydrodynamic interactions and "rotors" when the particles have steric interactions in addition to hydrodynamic ones. + +The dynamics of \(N\) point vortices follows from the Hamiltonian \(\mathcal{H} = \frac{1}{2}\sum_{i \neq j}\Gamma_{i}\Gamma_{j}\Psi (|\mathbf{r}_{i} - \mathbf{r}_{j}|)\) , where \(\Gamma_{i}\) is the circulation of vortex \(i\) (in a membrane \(\Gamma_{i} = \tau_{i} / \eta_{2D}\) ). The Hamiltonian depends on the conjugate variables \(\mathbf{r}_{i} = (x_{i}, y_{i})\) , [normalized by the circulation \(\sqrt{|\Gamma_{i}|} \operatorname{sgn}(\Gamma_{i})\) ], i.e. the positions of the vortices (12). The symmetries of the Hamiltonian correspond to conservation laws (39). In this case, we have symmetries with respect to translation in time, space, and rotation, corresponding to conservation of the Hamiltonian itself, and of the first and second moments of the distribution, \(\mathbf{L} = \sum_{i}\Gamma_{i}\mathbf{r}_{i} (= \mathbf{0}\) wlog), and \(M = \sum_{i,j}\Gamma_{i}r_{i}^{2}\) . Thus, the initial area cannot change dramatically, particles cannot drift to infinity since the second moment is fixed, nor can they collapse to a point since the Hamiltonian is conserved. These properties are readily observed in simulations. Figure 2D shows typical trajectories of 200 membrane rotors. The initial distribution is random in a predefined finite area, and the dynamics are chaotic (40). The final configuration occupies nearly the same region of space as the initial configuration does, and the conservation laws hold + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
FIG. 2. (A) A representation of a membrane rotor — a disk rotating due to a torque \(\tau\) in the plane of the membrane. (B) The velocity field due to a membrane rotor (solid line) which scales as a point vortex \(v \sim 1 / r\) at small distances (dotted), \(r / \lambda \ll 1\) , transitioning to a QG behavior at large distances \(v \sim 1 / r^2\) (dashed). (C) Contour dynamics of an ellipse with radii ratios \(r_l / r_s \leq 3\) , where \(r_l\) ( \(r_s\) ) is the major (minor) axis. Starting from the same contour, the dynamics differ according to the radius relative to the SD length. Blue is in the limit \(r_l \ll \lambda\) . In this limit the ellipse is rotating as a rigid body, as predicted by Kelvin (38) for an elliptic patch in an Euler fluid. Black is in the limit \(r_l \gg \lambda\) , no longer conserving its shape since the large distance flow is in the quasigeostrophic regime. (D) 200 point membrane rotors, blue is the initial random configuration, black is the final configuration. Solid line shows typical trajectory of an individual vortex. Note that the area did not change considerably since the system of vortices is self-bounding. (E) the relative error in \(\mathcal{H}\) and \(M\) over a few cycle times, \(t_c\) .
+ +to high precision in our simulations, as shown in Fig. 2E. This self confining property of vortex dynamics has further consequences, as we now show. + +Hyperuniformity. Hyperuniformity is the suppression of density- density fluctuations at small wavenumbers (or correspondingly, at large distances) (41- 43). Disordered hyperuniformity can emerge due to short ranged interactions such as those that arise in sheared suspensions (30, 44, 45), jammed materials (46), and for spinning particles (47). Here we will show hyperuniformity emerging from long ranged interactions, similar to its emergence in sedimentation of irregular objects (48). A good way to characterize hyperuniformity is the structure factor, defined as \(S(\mathbf{q}) = N^{- 1}|\tilde{\rho} (\mathbf{q})|^2\) , where \(\rho (\mathbf{r}) = \sum_i \delta (\mathbf{r} - \mathbf{r}_i)\) is the coarse grained density. In a hyperuniform material, \(S(q)\) goes to zero as a power law at small wavenumbers. We argue that point vortices must be hyperuniform due to the conservation of the Hamiltonian. For a density of rotors, the Hamiltonian is given by \(\mathcal{H}[\rho (\mathbf{r})] \sim \frac{\Gamma^2}{2} \int \mathrm{d}\mathbf{r} \int \mathrm{d}\mathbf{r}' \rho (\mathbf{r}) \rho (\mathbf{r}') \psi (|\mathbf{r} - \mathbf{r}'|)\) . Using the convolution theorem, we find a general relation between the Hamiltonian and the structure factor + +\[\mathcal{H}[\rho (\mathbf{r})] = \frac{N\Gamma^2}{4\pi} \int \mathrm{d}\mathbf{q} S(\mathbf{q}) \tilde{\Psi} (\mathbf{q}). \quad (5)\] + +In the case of point vortices, \(\tilde{\Psi} (\mathbf{q}) = 1 / q^2\) , which gives Eq. 2. For the integral of Eq. 2 to converge in 2D, \(S(\mathbf{q}) \sim q^\alpha\) near the origin, and we must have \(\alpha > 0\) . In other words, an ensemble of point vortices is hyperuniform (a similar calculation in the QG limit, where \(\tilde{\Psi} = \lambda /q\) , yields \(\alpha > - 1\) ). Figures 3B and 4C, show an apparent \(\alpha \sim 1.3\) scaling for point vortices, consistent with the above argument. + +Using simulations we show that a set of \(N\) vortices, uniformly distributed within a radius \(R\) , evolves to a disordered steady- state with a hidden order visible to the naked eye (compare Figures 3A left and right). We quantitatively characterize the system in steady- state in three + +ways: (1) The structure factor. At steady- state \(S(\mathbf{q})\) shows a distinct cavity, at \(q \approx 0\) , \(S(\mathbf{q}) \to 0\) , for both points vortices (Fig. 3A) and rotors (Fig. 3C). All simulations produce a hyperuniform arrangement. (2) Perturbations. We demonstrate that hyperuniformity is robust under different perturbations, be it in the form of numerical errors, repulsive interactions, or impurities (in the next section). For point vortices, the steady state appears later and later as the timestep is decreased, suggesting that perturbations are necessary for convergence, here very small but persistent timestepping errors (49). Adding steric interactions, hyperuniformity appears on a timescale that is independent of the timestep. Moreover, with steric interactions, as the area fraction \(\phi\) of the particles is increased, the system transitions from disordered hyperuniform, to an ordered hyperuniform hexagonal lattice at \(\phi \sim 0.5\) , as can be seen in Fig. 3C. The inset of Fig. 3B shows the averaged structure factor where at intermediate area fractions we see Percus- Yevick type features for the structure factor of disks (50). (3) The returnity. We observe that at late times the ensemble of point vortices rotates almost as a rigid body and each particle goes back to its position at the previous cycle. We measure particle deviations by what we term the "returnity" (see Fig. 3D for details). The system may seem to have reached an absorbing state, but the motion of vortices over many cycles is still chaotic. + +Rotation induced phase enrichment. We now show that for mixed populations of fast and slow rotating particles, there is phase enrichment of both populations and hyperuniformity of the fast ones. Consider a mixture of two equally numbered populations ( \(\rho_l = \rho_h\) at \(t = 0\) ) initially placed within the same radius \(R\) . \(\rho_l\) rotates slowly with \(\Gamma_l \ll \Gamma_h\) , where \(\Gamma_h\) is the circulation of the second population. Figure 4A shows long- time simulation results for 10,000 point vortices. The two populations behave very differently. The fast vortices remain in + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 3. Hyperuniformity in ensembles of point vortices and rotors. (A) Snapshots of 10,000 point vortices initially (left) and at steady-state (right). Insets show the structure factor, \(S(\mathbf{q})\) showing a distinct cavity at steady-state. (B) Angular average of the structure factor shown in A, in a log-log scale with solid line showing a \(q^{1.3}\) scaling. Error bars are standard deviation over 10 well separated timesteps. Inset shows the structure factor of the rotors shown in (C) with increasing hue corresponding to increased concentration \(\phi = (0.14, 0.24, 0.37, 0.54)\) . Solid line is the same \(\alpha \sim 1.3\) scaling. (C) Steady state configurations of 2,000 membrane rotors with the corresponding structure factors, showing a transition from disordered hyperuniformity to a hexagonal lattice. (D) A plot of the returnity measuring the deviation of particle \(i\) at position \(r_i\) from its position at the previous cycle, \(returnity = \Delta r_i(t_{\mathrm{cyc}}) / R\) , where \(R\) is the initial radius of the ensemble. The cycle time, \(t_{\mathrm{cyc}}\) , is defined at steady state as the distance between two adjacent minima of the function \(f = \sum_i^N \Delta r_i(\Delta t)\) , where \(\Delta t\) is the time difference. Color scheme is from blue to yellow with increasing deviation.
+ +a disk of only slightly smaller size than their initial area (Fig. 4B). The slow particle distribution shows a significant expansion. In addition, there is a striking difference when comparing the independently computed structure factors of these two populations, the fast vortices are hyperuniform with the same scaling as before, \(S(q) \sim q^{1.3}\) , whereas the slow ones show no signs of hyperuniformity (Fig. 4C). This difference is dramatic enough to be visible in a cursory examination of the separate distributions; see Fig. 4A. + +Using a heuristic model, we show that the conservation laws allow two solutions at steady- state. In one solution, the two populations remain confined to a circle of the same radius. In the second solution, the radius of the slower population expands, while the radius of the faster + +population contracts. We then show that the segregated solution is the one that maximizes the number of states in the system. For simplicity, we assume that the final steady states are uniform (not true for the slow vortices as is clear from Fig. 4B). There are two possible solutions where \(\mathcal{H}\) and \(M\) are conserved — in the first, the initial radius, \(R\) , does not change; in the second, the radius of the fast vortices slightly decreases to \(R_h\) , allowing the slow vortices to expand to a larger radius \(R_l\) given by \(R_l^2 = (\gamma + 1)R^2 - R_h^2 \gamma\) , where \(\gamma = \Gamma_h / \Gamma_l\) (see Fig. 4D). Linearly expanding in \(1 / \gamma\) , we find that \(R_h \simeq R(1 - \beta / \gamma)\) for the high circulation vortices, where \(\beta\) is a positive prefactor of order 1. The slow vortices asymptote to \(R_l \simeq R\sqrt{1 + 2\beta} + O(1 / \gamma)\) . The simulation results indicate that the outer radius indeed asymptotes to a larger valued + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 4. Two populations of vortices with different circulations showing phase enrichment, \(\Gamma_{l} = 2\pi\) in black and \(\Gamma_{h} = 256\pi\) in green. (A) Steady-state configuration for ten thousand point vortices of a circulation ratio \(\gamma = \Gamma_{h} / \Gamma_{l} = 128\) . Each inset shows a close-up view of one of the populations within the same physical region. (B) Density of the configuration in (A), \(\rho (r)\) , averaged over angle as a function of distance from the center. Note how density fluctuations are suppressed for the high circulation vortices, as is more clearly observed by the averaged structure factor, \(S(q)\) , in (C), where the solid green line shows a \(\sim q^{1.3}\) power law. (D) The second moment for \(N = 10,000\) vortices. Plotted separately for the high (in green) and low (in black) vortices at steady state as a function of \(\gamma\) (i.e. increasing \(\Gamma_{h}\) ). (E) LOSSLESS compression for the two populations showing an increase (decrease) in file size (an estimate of entropy) for the low (high) circulation vortices over a couple of cycles. In blue is the file size for the total system. Solid line is a moving average, time is normalized by an average cycle time \(t_{c}\) .
+ +constant as \(\gamma\) increases and does not increase indefinitely (see Fig. 4D and SI). + +A solution with two different radii is therefore possible and is indeed observed at large circulation ratios. Such a solution is favored entropically since it maximizes the available states. Asymptotically at large \(\gamma\) , the main entropical contribution is volumetric, \(\Delta S_{\mathrm{volume}} = 2N \log (R_{\mathrm{final}} / R_{\mathrm{initial}})\) . Since the high circulation vortices hardly change radius, \(R_{h} \xrightarrow{\gamma \to \infty} R\) , the change in entropy is coming mainly from the expansion of the low circulation vortices and is given by \(\Delta S_{\mathrm{total}} \sim N \log (1 + 2\beta) > 0\) . Coupling the two populations allows one population to expand where before it was bounded (51). The situation is analogous to depletion interactions, where the net entropy of a system increases by condensing the large particles allowing for the small particles to explore a larger volume (52). + +A simple way to estimate the entropy in a system is by using LOSSLESS compression, as suggested by Refs. (53, 54). Compressing plots of particle positions in a system of 10,000 point vortices with circulation ratio \(\Gamma_{h} / \Gamma_{l} = 128\) shows an increase in file size for \(\rho_{l}\) and a decrease for \(\rho_{h}\) , while the combined system is increasing, see Fig. 4E. + +Discussion. We have shown that driven particles in a membrane or a soap film, as well as point vortices in an ideal 2D fluid, have geometrical conservation laws which limit their distribution. These conservation laws dictate different possible structural states — namely hyperuniformity and phase enrichment. We have shown that hy + +peruniformity is robust to several forms of perturbations whether arising due to numerical errors, steric interactions, or impurities in the form of low circulation vortices. For rotors with steric interactions, the unbounded ensemble crystallizes into a hexagonal lattice when the area fraction \(\phi \gtrsim 0.5\) (see also (17)). We have limited the discussion to membrane rotors and vortices, but the results hold for other settings in which mass is conserved in the 2D plane, e.g. particles at the surface of a fluid (see SI). + +What is especially interesting about our particular BDD system is its potential for experimental realizability, its moment and Hamiltonian structure, and that its near- field interactions (i.e. below the Saffman- Delbruck length) are identical to those of Euler point vortices. Further, the far- field interactions of membrane rotors are identical to those of point vortices of the semiquasigeostrophic equations (37, 55, 56) used to model atmospheric flows. Thus, to observe the interesting dynamical features we describe, one does not need to go to the atmospheric scale, or cool a fluid to near- zero temperature. In principle, one can simply observe microscopic particles on a soap film, in smectic films, a membrane, or even at the surface of a fluid (19, 22, 57, 58). + +Methods. Simulations. Simulations were performed in Python. Random initial configurations within the unit disk were found by rejection sampling (points in the unit rectangle were sampled uniformly, transformed to the rectangle \([- 1,1]^{2}\) , and those with \(r > 1\) were discarded). The initial Hamiltonian \(H_{0}\) is computed at \(t = 0\) , and the relative error \(\epsilon (t) = |H_{t} - H_{0}| / H_{0}\) is + +<--- Page Split ---> + +monitored as a measure of fidelity. For simulations of rotors (i.e. with steric repulsion), a 5th order explicit Runge- Kutta method based on the Dormand- Prince scheme (59) with a fixed timestep size of \(\delta t = 10^{- 7}\) was used. Long integration times were required for simulations of point vortices, and for these simulations an explicit eighth- order adaptive method based on the Dormand- Prince scheme (60, 61) was used, with both relative and absolute tolerances set to \(10^{- 6}\) . The specific implementation of the scheme used was the \(DOP853\) method of scipy.integrate (62). For simulations of 10,000 point vortices with \(\Gamma = 2\pi\) , \(\epsilon (t) < 1.6 \times 10^{- 3}\) up to \(t \approx 16,000\) cycles, while for simulations with 5,000 vortices with \(\Gamma = 2\pi\) and 5,000 vortices with \(\Gamma = 256\pi\) , \(\epsilon (t) < 5 \cdot 10^{- 3}\) up to \(t \approx 10\) cycles. Time is normalized by the average cycle time, \(t_c \approx 4\pi^2 R^2 / \sum_i \Gamma_i\) , where \(R\) is the initial radius. + +Steric interactions were taken as the repulsive part of a harmonic potential, i.e. for two particles whose centers are distance \(r_i\) apart, \(F = - kr_{ij}\) if \(r_{ij} < 2a\) and zero otherwise. The use of a harmonic potential, rather than a sharp step function for hard core particles, provided improved numerical stability and convergence. A large \(k\) value was chosen to ensure no overlap between particles, \(k = 1 \cdot 10^6\) , for particles of size \(a = 0.01\) . + +Structure factor. To accurately compute the structure factor \(S(\mathbf{q})\) we use a type- 1 non- uniform fast- Fourier transform (63). Explicitly, points are restricted to a windowing region which is confined entirely within the unit disk. The frequencies \(\tilde{\rho} (\mathbf{q})\) are com + +puted for the first 512 modes in each direction, and the average value (i.e. \(\tilde{\rho} (0)\) ) is set to 0. This results in structure factors in the plane, such as those shown in Fig. 3. Except in those cases where crystallization occurs, these structure factors are azimuthally isotropic. To summarize this information, the angular average over the structure factor was calculated by slicing the result to 1000 equal bins between \(q_{\mathrm{min}}\) and \(q_{\mathrm{max}}\) and taking the mean of the results that fell within each slice. + +Compression. A plot of the positions of the point vortices was compressed using PNG with AGG backend. Each vortex was plotted by a single pixel. The total size of the plots was kept fixed in time. The figure size was chosen to minimize overlap between neighboring vortices but maintaining a computationally accessible file size. + +Acknowledgment We thank Haim Diamant for insightful discussions regarding the emergence of hyperuniformity from the conservation laws, to Martin Lenz for suggesting a simple heuristic model of the phase enrichment, and to Enkeleida Lushi. N.O. acknowledges supported by the Israel Science Foundation (grant No. 1752/20). M.J.S. acknowledges support by the National Science Foundation under Awards Nos. DMR- 1420073 (NYU MRSEC), DMS- 1620331, and DMR- 2004469. + +[1] R. King, Ocean Engineering 4, 141 (1977). [2] M. J. Shelley and J. Zhang, Annual Review of Fluid Mechanics 43, 449 (2011). [3] R. Fjortoft, Tellus 5, 225 (1953). [4] R. H. Kraichnan, Physics of Fluids 10, 1417 (1967). [5] D. Bernard, G. Boffetta, A. Celani, and G. Falkovich, Nature Physics 2, 124 (2006). [6] R. P. Behringer, S. D. Meyers, and H. L. 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Prost, The European Physical Journal E 13, 379 (2004). [17] N. Oppenheimer, D. B. Stein, and M. J. Shelley, Phys. Rev. Lett. 123, 148101 (2019), arXiv:1903.00940. [18] B. a. Grzybowski, H. a. Stone, and G. M. Whitesides, Nature 405, 1033 (2000). [19] V. Soni, E. S. Bililign, S. Magkiriadou, S. Sacanna, D. Bartolo, M. J. Shelley, and W. T. M. Irvine, Nature Physics (2019). + +[20] N. H. P. Nguyen, D. Klotsa, M. Engel, and S. C. Glotzer, Physical Review Letters 112, 075701 (2014). [21] E. Lushi, H. Wioland, and R. E. Goldstein, Proceedings of the National Academy of Sciences 111, 9733 (2014), arXiv:1407.3633. [22] K. Yeo, E. Lushi, and P. M. Vlahovska, Physical Review Letters 114, 188301 (2015). [23] E. Lushi and P. M. Vlahovska, Journal of Nonlinear Science 25, 1111 (2015). [24] Y. Goto and H. Tanaka, Nature Communications 6, 5994 (2015). [25] Z. Shen and J. S. Lintuvuori, Physical Review Letters 125, 228002 (2020), arXiv:2007.16142. [26] E. S. Bililign, F. B. Usabiaga, Y. A. Ganan, V. Soni, S. Magkiriadou, M. J. Shelley, D. Bartolo, and W. T. M. Irvine, 1 (2021), arXiv:2102.03263. [27] S. John, Phys. Rev. Lett. 58, 2486 (1987). [28] E. Yablonovitch, Phys. Rev. Lett. 58, 2059 (1987). [29] W. Man, M. Florescu, E. P. Williamson, Y. He, S. R. Hashemizad, B. Y. C. Leung, D. R. Liner, S. Torquato, P. M. Chaikin, and P. J. Steinhardt, Proceedings of the National Academy of Sciences 110, 15886 (2013). [30] J. H. Weijs, R. Jeanneret, R. Dreyfus, and D. Bartolo, Phys. Rev. Lett. 115, 1 (2015). [31] P. G. Saffman and M. Delbruck, Proceedings of the National Academy of Sciences 72, 3111 (1975). [32] A. J. Levine, T. B. Liverpool, and F. C. MacKintosh, Physical Review E 69, 021503 (2004). [33] K. Seki, S. Mogre, and S. Komura, Physical Review E 89, 022713 (2014). [34] B. A. Camley and F. L. H. Brown, Physical Review Letters 105, 148102 (2010), arXiv:1105.4898. [35] N. Oppenheimer and H. Diamant, Biophysical Journal 96, 3041 (2009), arXiv:0809.4163. [36] N. Oppenheimer and H. A. Stone, Biophysical Journal 113, 440 (2017). [37] I. M. Held, R. T. Pierrehubert, S. T. Garner, and K. L. + +<--- Page Split ---> + +Swanson, J. Fluid Mech. 282, 1 (1995). [38] W. Thomson, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 10, 155 (1880). [39] E. Noether, Nachr D Konig Gesellschaft D Wiss Zu Gottingen Mathphys Klasse (1918). [40] H. Aref and N. Pomphrey, Proc. Natl. Acad. Sci. 380, 359 (1982). [41] S. Torquato, Physical Review E 94, 022122 (2016). [42] D. Hexner and D. Levine, Physical Review Letters 114, 110602 (2015). [43] G. Ariel and H. Diamant, Physical Review E 102, 022110 (2020), arXiv:2004.10588. [44] S. Wilken, R. E. Guerra, D. J. Pine, and P. M. Chaikin, arXiv, 0 (2020), arXiv:2002.04499. [45] J. Wang, J. M. Schwarz, and J. D. Paulsen, Nature Communications 9, 2836 (2018), arXiv:1711.06731. [46] S. Torquato, Physics Reports 745, 1 (2018). [47] Q.- L. Lei and R. Ni, Proceedings of the National Academy of Sciences 116, 22983 (2019), arXiv:1904.07514. [48] T. Goldfriend, H. Diamant, and T. A. Witten, Physical Review Letters 118, 158005 (2017), arXiv:1612.08632. [49] W. Dai and M. J. Shelley, Physics of Fluids A: Fluid Dynamics 5, 2131 (1993). [50] J. K. Percus and G. J. Yevick, Physical Review 110, 1 (1958). [51] On a side note, as Onsager first suggested [Onsager1949], in a bound system, configurational entropy must have a maximum. Entropy therefore decreases beyond a critical energy, and the system has a negative temperature. Negative temperature manifests itself as an increase in order with an increase in the energy. When coupling two vortical systems of negative temperature, there is a tendency + +to segregate. [52] M. Kardar, Statistical Physics of Particles (Cambridge University Press, Cambridge, 2007). [53] S. Martiniani, P. M. Chaikin, and D. Levine, Physical Review X 9, 011031 (2019), arXiv:1708.04993. [54] R. Avinery, M. Kornreich, and R. Beck, Physical Review Letters 123, 178102 (2019), arXiv:1709.10164. [55] G. Falkovich, Journal of Physics A: Mathematical and Theoretical 42, 123001 (2009). [56] D. Córdoba, C. Fefferman, and J. L. Rodrigo, Proc. Natal. Acad. Sci. 101, 2687 (2004). [57] Z. H. Nguyen, M. Atkinson, C. S. Park, J. Maclennan, M. Glaser, and N. Clark, Physical Review Letters 105, 268304 (2010). [58] G. Lumay, N. Obara, F. Weyer, and N. Vandewalle, Soft Matter 9, 2420 (2013). [59] J. R. Dormand and P. J. Prince, Journal of computational and applied mathematics 6, 19 (1980). [60] P. J. Prince and J. R. Dormand, Journal of computational and applied mathematics 7, 67 (1981). [61] E. Hairer, S. P. Norsett, and G. Wanner, Solving Ordinary Differential Equations I (2nd Revised. Ed.): Nons- tiff Problems (Springer- Verlag, Berlin, Heidelberg, 1993). [62] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, Nature Methods 17, 261 (2020). [63] A. H. Barnett, J. Magland, and L. af Klinteberg, SIAM Journal on Scientific Computing 41, C479 (2019). + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +Three different structural states of 2D vortices/rotors - hyperuniformity for Euler point vortices (A) and QG ro- tors/surface rotors (B), (C) phase enrichment induced by circulation differences where green (black) represents vortices of high (low) circulation, and (D) crystallization arising from hydrosteric interactions. The insets of (A), (B) and (C) show the structure factor, S(q). In (A) and (B) S(q) decays to zero at small q, indicating that the distribution is hyperuniform. In (C) the structure factor shows the six distinct peaks of a hexagonal lattice. + +![](images/Figure_2.jpg) + +
Figure 2
+ +(A) A representation of a membrane rotor - a disk rotating due to a torque \(\tau\) in the plane of the membrane. (B) The velocity field due to a membrane rotor (solid line) which scales as a point vortex \(\nu \mathbb{Q} /1 / r\) at small distances (dotted), \(r / \lambda \ll 1\) , transitioning to a QG behavior at large distances \(\nu \mathbb{Q} /1 / r2\) (dashed). (C) Contour dynamics of an ellipse with radii ratios \(\mathrm{rl} / \mathrm{rs} \leq 3\) , where \(\mathrm{rl}\) (rs) is the major (minor) axis. Starting from the same contour, the dynamics differ according to the radius relative to the SD length. Blue is in the limit \(\mathrm{rl} \ll \lambda\) . In this limit the ellipse is rotating as a rigid body, as predicted by Kelvin (38) for an elliptic patch in an Euler fluid. Black is in the limit \(\mathrm{rl} \gg \lambda\) , no longer conserving its shape since the large distance flow is in the quasigeostrophic regime. (D) 200 point membrane rotors, blue is the initial random configuration, black is the final configuration. Solid line shows typical trajectory of an individual vortex. + +<--- Page Split ---> + +Note that the area did not change considerably since the system of vortices is self- bounding. (E) the relative error in H and M over a few cycle times, tc. + +![](images/Figure_3.jpg) + +
Figure 3
+ +Hyperuniformity in ensembles of point vortices and rotors. Please see manuscript .pdf for full figure caption + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Two populations of vortices with different circulations showing phase enrichment, \(\Gamma 1 = 2\pi\) in black and \(\Gamma h = 256\pi\) in green. (A) Steady- state configuration for ten thousand point vortices of a circulation ratio \(\gamma = \Gamma h / \Gamma 1 = 128\) . Each inset shows a close- up view of one of the populations within the same physical region. (B) Density of the configuration in (A), \(\rho (r)\) , averaged over angle as a function of distance from the center. Note how density fluctuations are suppressed for the high circulation vortices, as is more clearly observed by the averaged structure factor, S(q), in (C), where the solid green line shows a \(\mathbb{Q}1.3\) power law. (D) The second moment for \(\mathrm{N} = 10,000\) vortices. Plotted separately for the high (in green) and low (in black) vortices at steady state as a function of \(\gamma\) (i.e. increasing \(\Gamma h\) ). (E) LOSSLESS compression for the two populations showing an increase (decrease) in file size (an estimate of entropy) for the low (high) circulation vortices over a couple of cycles. In blue is the file size for the total system. Solid line is a moving average, time is normalized by an average cycle time tc. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SI.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397_det.mmd b/preprint/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..920a219589d3c7a9fb2d7416cc5d7b5836c83710 --- /dev/null +++ b/preprint/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397_det.mmd @@ -0,0 +1,534 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 922, 208]]<|/det|> +# Mapping Intrapatient Response Heterogeneity and Lesion-specific Relapse Dynamics in Metastatic Colorectal Cancer + +<|ref|>text<|/ref|><|det|>[[44, 230, 770, 505]]<|/det|> +Jiawei Zhou University of North Carolina at Chapel Hill Amber Cipriani University of North Carolina at Chapel Hill https://orcid.org/0000- 0003- 3596- 0581 Yutong Liu University of North Carolina at Chapel Hill Gang Fang University of North Carolina at Chapel Hill Quefeng Li University of North Carolina at Chapel Hill Yanguang Cao ( yanguang@unc.edu ) University of North Carolina at Chapel Hill https://orcid.org/0000- 0002- 3974- 9073 + +<|ref|>text<|/ref|><|det|>[[44, 544, 102, 562]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 582, 135, 600]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 620, 314, 639]]<|/det|> +Posted Date: March 28th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 658, 475, 678]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1447896/v1 + +<|ref|>text<|/ref|><|det|>[[44, 696, 910, 738]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[72, 89, 880, 108]]<|/det|> +# Mapping Intrapatient Response Heterogeneity and Lesion-specific Relapse Dynamics in Metastatic + +<|ref|>title<|/ref|><|det|>[[425, 120, 570, 138]]<|/det|> +# Colorectal Cancer + +<|ref|>text<|/ref|><|det|>[[70, 161, 880, 383]]<|/det|> +Jiawei Zhou \(^{1}\) , Amber Cipriani \(^{1,2}\) , Yutong Liu \(^{3}\) , Gang Fang \(^{4}\) , Quefeng Li \(^{3}\) , Yanguang Cao \(^{1,5*}\) \(^{1}\) Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA; \(^{2}\) UNC Health Medical Center, Department of Pharmacy, Chapel Hill, NC 27514; \(^{3}\) School of Public Health, University of North Carolina at Chapel Hill, NC 27599, USA; \(^{4}\) Division of Pharmaceutical Outcomes and Policy, School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA; \(^{5}\) Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA + +<|ref|>title<|/ref|><|det|>[[68, 406, 295, 425]]<|/det|> +# Corresponding author: + +<|ref|>text<|/ref|><|det|>[[68, 449, 760, 679]]<|/det|> +Yanguang Cao, Ph.D. Division of Pharmacotherapy and Experimental Therapeutics, UNC School of Pharmacy 2318 Kerr Hall, UNC Eshelman School of Pharmacy Chapel Hill, NC 27599- 7569 E- mail: yanguang@unc.edu Phone: +1- 919- 966- 4040. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[67, 91, 185, 107]]<|/det|> +## 17 Abstract + +<|ref|>text<|/ref|><|det|>[[110, 130, 884, 500]]<|/det|> +Achieving systemic tumor control across metastases is vital for long- term patient survival but remains intractable in many patients. High intrapatient heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying intrapatient heterogeneity. However, our understanding of intrapatient heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and relapse during treatment, remains rudimentary. This study investigated intrapatient heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and relapse dynamics, metastatic lesions converged on four phenotypes that varied with anatomical site. Importantly, we found that organ- level relapse sequence was closely associated with patient survival, and that patients with the first relapses in the liver often had worse survival. In conclusion, our study provides insights into intrapatient response heterogeneity in mCRC and creates impetus for metastasis- specific therapeutics. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 130, 881, 342]]<|/det|> +Metastasis is the leading cause of cancer mortality1. Unfortunately, antitumor therapies are still designed mostly based on the biology of primary tumors, with little consideration of metastases2,3. Achieving systemic tumor control across metastases is critical for long- term survival but remains intractable in many patients. Some metastases respond highly to treatment while others do not at all, resulting in many dissociated and heterogeneous responses within patients4-7. Lesion- level response and relapse heterogeneity are common in many cancer types, but our understanding of such intrapatient heterogeneity and its relevance to prognosis remains rudimentary. + +<|ref|>text<|/ref|><|det|>[[112, 364, 864, 639]]<|/det|> +Most investigations of intrapatient lesion heterogeneity focus on tumor genetic mutations, clonal compositions, or transcriptomics8-10. These molecular and cellular characterizations are critical to elucidating the underlying mechanisms of intrapatient response heterogeneity11,12. However, it is equivalently critical to study intrapatient heterogeneity on the macroscopic level, such as distinct lesion dynamics in growth, response, and relapse during treatment, as well as their potential phenotypic convergence anatomically. These phenotypes would complement molecular and cellular analyses for a holistic view of intrapatient heterogeneity. This study sought to investigate intrapatient response heterogeneity through mapping lesion- specific response and relapse dynamics in metastatic CRC (mCRC). + +<|ref|>text<|/ref|><|det|>[[112, 660, 880, 905]]<|/det|> +Colorectal cancer (CRC) is the third leading cause of cancer- related death13. About \(20\%\) of CRC patients have distant metastases at diagnosis; the five- year relative survival rate is only \(14\%\) for these patients14,15. Intrapatient response heterogeneity is common in CRC patients treated with either standard chemotherapy alone or in combination with targeted therapy16. We, along with others, have found that high intrapatient response heterogeneity is associated with worse survival16-19. Importantly, we also found favorable responses in liver metastases predicted longer patient survival, compared to lesions in the lungs and lymph nodes (LN)16. Characterizing intrapatient response heterogeneity in mCRC is valuable for prognosis and therapies. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 397]]<|/det|> +The local microenvironment selects tumor phenotypes in response to treatment, leading to heterogeneity across anatomically distinct lesions in terms of response and relapse dynamics20,21. Characterizing their phenotypic differences (divergence) or similarities (convergence) could yield insights into tumor ecological features and systemic resistance. To map the lesion- level response and relapse patterns in mCRC, we first applied a mathematical model to capture tumor growth dynamics in 4,308 mCRC patients. Next, individual lesion- specific response and relapse probabilities were mapped to predict their phenotypic divergence and convergence across anatomical sites. Last, we applied a machine learning approach to analyze the relapse sequence across lesions and its relevance to long- term patient survival. Our study provides insights into intrapatient phenotypic heterogeneity in mCRC and yields substantial implications for designing metastasis- specific therapeutics. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 92, 174, 108]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[113, 132, 332, 149]]<|/det|> +## Data Sources and Structure + +<|ref|>text<|/ref|><|det|>[[111, 171, 874, 545]]<|/det|> +To evaluate lesion- level response and relapse dynamics in mCRC, we collected longitudinal radiographic measurements of metastatic lesions in colorectal cancer (CRC) patients from Project Data Sphere. In total, 4,308 patients with 40,612 lesions from eight Phase III trials were included. The inclusion and exclusion criteria are presented in Fig. 1a. The distribution of lesion number across organs is shown in Fig. 1b. The total target lesions were 19,180 with 94,174 radiographic measurements, and there were 18,594 nontarget lesions and 2,838 new lesions with response status over time. Additional information including patients' demographic and clinical characteristics (e.g., age, gender, race, body mass index [BMI], tumor type, treatment history, RECIST response, and KRAS status), progression- free survival (PFS) and overall survival (OS) are reported in Table 1. We also included the tumor longitudinal measurements in a head and neck squamous cell carcinomas (mHNSCC) trial for external validation. The data was also from Project Data Sphere with similar criteria as CRC data (Supplementary Fig. 3a). + +<|ref|>sub_title<|/ref|><|det|>[[113, 567, 620, 586]]<|/det|> +## Model recapitulated tumor growth dynamics of individual lesions + +<|ref|>text<|/ref|><|det|>[[111, 607, 864, 852]]<|/det|> +The tumor growth dynamics of 19,180 target lesions with 94,174 radiographical measurements were recapitulated with a widely adopted growth model22. The three dynamic parameters in the model are the regression rate \(Kd\) , the fraction of non- responding cells \(F\) , and the progression rate \(Kg\) (Fig. 2a). The model was optimized using a nonlinear mixed effect (NLME) modeling approach, which allows the estimation of three dynamic parameters at the individual level and their inter- lesion variance in the population. Overall, the model adequately recapitulated the longitudinal profiles of tumor radiographic measurements for each lesion. The goodness- of- fit and model visual predictive check plots, as well as representative individual fittings, show good model predictive performance (Supplementary Fig. 1). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 870, 302]]<|/det|> +Population estimates and inter- lesion variances in tumor dynamic parameters are summarized in Supplementary Table 1. The parameters for individual lesions significantly differed across organs ( \(\mathrm{p}<\) 0.0001, Fig. 2b). Among all metastases, lesions in the bone exhibited the lowest response depth (1- \(F\) ), while lesions in the genitourinary and reproductive (GR) system had the fastest progression rates ( \(Kg\) ), and kidney lesions showed the lowest regression rates ( \(Kd\) ). Among three most abundant metastatic sites (liver, lung, and LN), lesions in the liver showed the highest response depth but the fastest progression rates, suggesting the unique growth feature of liver lesions. + +<|ref|>text<|/ref|><|det|>[[111, 323, 881, 629]]<|/det|> +Higher treatment- resistant cell fraction \(F\) is associated with slower rates of regression ( \(Kd\) , \(\mathrm{r} = - 0.69\) , \(\mathrm{p}< 0.005\) ) and faster rates of progression ( \(Kg\) , \(\mathrm{r} = 0.53\) , \(\mathrm{p}< 0.05\) , Fig. 2c). Progression rates seemed to be independent of regression rates (Fig. 2c). Remarkably, no significant correlations were observed between baseline tumor burden and all tumor dynamic parameters (Fig. 2d). Large tumor burden, on the individual lesion level, did not necessarily confer slow regression rates, high treatment- resistant fractions, or slow progression rates, implying that tumor burden at baseline is not a robust prognostic factor in mCRC \(^{23 - 25}\) . Notably, metastatic lesions under antibody targeted therapy (bevacizumab and/or panitumumab) plus chemotherapy (FOLFOX or FOLFIRI), compared to standard chemotherapy alone, showed significantly deeper response (effect size = 0.43) and lower progression rates (effect size = 0.26), but had a moderate effect on tumor regression rates (effect size = 0.06, Supplementary Fig. 2). + +<|ref|>sub_title<|/ref|><|det|>[[112, 652, 722, 671]]<|/det|> +## Response and relapse dynamics suggest phenotypic convergence on organ level + +<|ref|>text<|/ref|><|det|>[[111, 692, 879, 904]]<|/det|> +The tumor growth model predicted the longitudinal profiles of response and relapse for each target lesion. Response and relapse times were then derived as the duration from the start of treatment to the time of response or relapse per RECIST \(\mathrm{v}1.1^{26}\) , respectively. We integrated the response time for both target and non- target lesions and the relapse time for all lesions, including the new ones, into random effect Cox proportional models \(^{27}\) . The Cox model predicted the relative probabilities of lesion response or relapse at the organ level. Of note, treatment effects from either chemotherapy or combination therapy were included as a confounding factor in the Cox regression model. With that, we could focus on the organ + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 140]]<|/det|> +intrinsic response and relapse characteristics. The hazard ratios for the response and relapse across organs are shown in Fig. 3a and Fig. 3b. + +<|ref|>text<|/ref|><|det|>[[111, 162, 884, 374]]<|/det|> +With abdominal lesions as the reference, metastatic lesions in the liver were most likely to respond to treatments, whereas lesions in the brain/central nervous system (CNS) were least likely (Fig. 3a). Lesions in the gastrointestinal (GI) system, skin, and bone were significantly less likely to respond than abdominal lesions. Lesions in the spleen, lung, and peritoneum showed comparable responses. The probability of relapse also differed greatly across anatomical sites (Fig. 3b). The metastatic lesions with the highest likelihood of relapse were those in the brain/CNS, GR system, and liver, while lesions in the GI system, and regional and distal LNs were least likely. + +<|ref|>text<|/ref|><|det|>[[110, 395, 880, 770]]<|/det|> +We then integrated organ- specific response and relapse probabilities to investigate their potential phenotypic convergence across anatomical sites. As in Fig. 3c, an anatomical chart of organ- specific response and relapse probabilities was created based on their relative hazards in the Cox model. Four types of phenotypic features emerge in CRC- metastatic organs defined by their associated lesions' likelihood of response and relapse. Notably, bone and brain lesions had low response and high relapse probabilities (low- high phenotype), while liver lesions had high probabilities of both response and relapse (high- high phenotype). Patients with these metastases, particularly those with low- high phenotype, had much worse survival outcomes (OS median 378 days vs. 561 days, p<0.0001, Supplementary Fig. 3a). On the other side, metastatic lesions in the lung and LN showed high response and low relapse probabilities (high- low phenotypes). Patients who have metastases in high- low phenotype organs only tend to have a better prognosis than patients with other phenotypic metastases do (OS median 770 days vs. 524 days, p<0.0001, Supplementary Fig. 3b). + +<|ref|>text<|/ref|><|det|>[[111, 789, 872, 905]]<|/det|> +Interestingly, most metastatic lesions with high relapse probabilities tend to occur in organs known to have immunosuppressive microenvironments, such as the liver, bone, and brain/CNS28- 31. To discern the influence of local tissue environment on tumor response phenotype, we performed the same analyses in head and neck squamous cell carcinomas (mHNSCC) to see whether a similar anatomical chart exists + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 875, 330]]<|/det|> +(Fig. 3d). A total of 393 patients with 1,892 lesions were analyzed, including eleven metastatic organs (Supplementary Fig. 4a and 4b). Patients' demographics are reported in Supplementary Table 2. The organ-specific hazard ratios for relapse and response were ranked, as we did in mCRC (Supplementary Fig. 4c and 4d). In mHNSCC, metastases in the liver, bone, and brain also showed high relapse potential, in line with what we observed in mCRC. Metastatic lesions in the LNs exhibit a high-low phenotype, consistent with mCRC. Similar anatomical charts across cancer types suggest that organ-intrinsic microenvironmental factors, such as the local physical and immunological components, could be key modulators to the mechanisms underlying the probabilities of tumor response and relapse. + +<|ref|>text<|/ref|><|det|>[[111, 353, 875, 691]]<|/det|> +Treatment effects on organ- specific responses were also investigated. For simplicity, treatments were divided into two groups, chemotherapy alone and in combination with antibody targeted therapy. The combined antibody targeted therapies are either panitumumab or bevacizumab, or both. Surprisingly, combination with the antibody targeted therapies did not significantly influence organ- specific response probabilities (Fig. 3e), suggesting low direct cytotoxic effects of antibody- based therapies. Notably, the primary therapeutic benefit of antibody targeted therapies was to decrease relapse potential (Fig. 3f). Relapse hazards significantly decreased in most metastatic organs except for the skin, brain/CNS, spleen, and kidney. Taken together, antibody targeted therapies had the primary effect of decreasing lesion relapse probability but had limited influence on the lesion response probability. Interestingly, high- relapse organs in Fig. 3c also had high relapse probability during cytotoxic chemotherapies in Fig. 3f, suggesting a critical role for local tissue environments in long- term tumor control. + +<|ref|>sub_title<|/ref|><|det|>[[113, 716, 551, 735]]<|/det|> +## Relapse sequence across organs predicts patient survival + +<|ref|>text<|/ref|><|det|>[[112, 757, 860, 807]]<|/det|> +We built a k- means unsupervised clustering model to cluster patients based on their organ- level lesion relapse sequence to investigate their relevance to patient survival. Elbow sum of square \(^{32}\) + +<|ref|>text<|/ref|><|det|>[[112, 820, 876, 904]]<|/det|> +(Supplementary Fig. 5a) and Silhouette score \(^{33}\) (Supplementary Fig. 5b) were calculated to determine the optimal \(\mathrm{k} (= 5)\) in the final classification. Five groups of patients were identified with distinct patterns of organ- specific relapse sequences and were stratified by relapsing organ number and first- relapsing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 171]]<|/det|> +organ: Mono- Organ (n=1,425), Hetero- Organ (n=801), Lung- First (n=577), Liver- First (n=1,194), and the Other- First (n=888) groups. The clinical demographics and baseline information of each group are summarized in Supplementary Table 3. + +<|ref|>text<|/ref|><|det|>[[112, 195, 872, 305]]<|/det|> +Organ- level relapse sequence is significantly correlated with long- term patient survival (p < 0.0001, Fig. 4b). As expected, patients with multiple organ relapses had worse survival than patients with only one organ relapse (OS median Hetero- Organ 385 days vs. Mono- Organ 653 days). Remarkably, despite comparable number of baseline metastases, patients whose first relapses were in the liver had a much worse prognosis than those whose first relapses were in lungs or other sites (OS median Liver- First 450 days vs. Lung- First 679 days vs. Other- First 581 days, Fig. 4b). This is consistent with earlier + +<|ref|>text<|/ref|><|det|>[[111, 315, 884, 533]]<|/det|> +observations (Fig. 3c) that lesions in the lung had high- low phenotype that is often associated with good patient prognosis. Patients with relapse first in the liver had faster subsequent relapses than patients whose relapses occurred in lungs or other sites, suggesting that relapsing lesions in the liver have high systemic consequences (p<0.0001, Fig. 4c). It also aligns with our previous finding that the response of liver lesions to treatments strongly predicted patient survival16. + +<|ref|>text<|/ref|><|det|>[[112, 556, 881, 767]]<|/det|> +Next, we performed k- means unsupervised clustering in the Hetero- Organ group to further investigate relapse patterns in patients with extensive metastases and relapses. Four groups of patients were optimally clustered (Supplementary Fig. 5c and 5d), and one distinctive feature among these clusters was the relapse order of liver lesions (Supplementary Fig. 6a). Despite similar metastases, patients with first or second relapse occurring in the liver had worse survival than those with early relapses occurring in other organs (Supplementary Fig. 6b and 6c). This observation further underlines the importance of liver lesions to systemic response and resistance. + +<|ref|>sub_title<|/ref|><|det|>[[113, 790, 673, 809]]<|/det|> +## Targeted antibody therapies minimally influence lesion relapse sequence + +<|ref|>text<|/ref|><|det|>[[112, 832, 830, 881]]<|/det|> +We compared the relapse sequence in patients under different treatments (chemotherapy alone vs. combination with antibody targeted therapy). In patients with Liver- First, Lung- First or Other- First + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 876, 333]]<|/det|> +relapse patterns, antibody targeted therapies significantly improved patient overall survival (p < 0.0001, Fig. 5a). However, neither the proportion of patients with each relapsing pattern (Fig. 5b) nor the sequence of relapse across metastatic organs were significantly different (Fig. 5c- 5e). Relapses in the GR and pancreas occurred slightly earlier in antibody targeted therapy, which did not seem to translate meaningful difference in patient survival. Despite the similar sequence, patients under antibody targeted therapies had significantly slower first and second relapses, but had non- significant difference in the third or later relapses (Fig. 5f- 5g). The average relapse times were much longer in combination therapy compared to chemotherapy alone. + +<|ref|>text<|/ref|><|det|>[[111, 354, 870, 570]]<|/det|> +In patients with the Hetero- Organ pattern, antibody targeted therapies did not meaningfully improve overall survival (Supplementary Fig. 7a) compared to chemotherapy alone, and the proportions of patients in each subcluster were similar between the two treatment groups (Supplementary Fig. 7b). Patients' relapse patterns and lesion relapse time were largely comparable, especially for those who had early liver lesion relapse (Supplementary Fig. 7c- h). Similarly, antibody targeted therapies did not influence lesion relapse sequence. Overall, the primary therapeutic benefit of antibody targeted therapies was to delay relapse in patients with few (< 4) metastatic organs, but not in those with broad metastases. + +<|ref|>sub_title<|/ref|><|det|>[[113, 589, 556, 608]]<|/det|> +## Machine learning model predicts lesion relapse sequence. + +<|ref|>text<|/ref|><|det|>[[111, 629, 865, 844]]<|/det|> +In order to predict patient relapse sequence at the time of diagnosis, we built a gradient boosting model using patient baseline characteristics and metastases profiles34. The model parameters are in Supplementary Table 4. The area under the receiver operating characteristic (ROC) curve of the testing data was 0.91, which indicated fair performance (Supplementary Fig. 8a). The model could predict Mono- Organ and Hetero- Organ groups better than Lung- First, Liver- First, and Other- First groups with higher area under the ROC curve. This indicates that more follow- up information is imperative to accurately predict the relapse sequences of the latter three groups (Supplementary Fig. 8b). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 91, 199, 107]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[111, 130, 877, 567]]<|/det|> +Metastasis is responsible for the majority of cancer- related mortality. Unfortunately, systemic tumor control across metastases remains intractable in many patients. This study investigated inter- lesion heterogeneity by analyzing response dynamics of 40,612 lesions in 4,308 mCRC patients. Unlike most molecular characterizations of metastases, we focused on the phenotypic features associated with lesion response and relapse dynamics as well as the anatomical divergence and convergence of these features. Our analyses yielded several intriguing findings. First, metastases differed considerably in their response to treatment, with depth of response positively correlating with regression rate and negatively correlating with progression rate. Second, metastatic lesions within the same organ exhibited congruent response and relapse dynamics, converging upon four organ- level phenotypes. Metastatic lesions in the liver exhibited high response and high relapse probabilities (high- high phenotype), while lesions in the bone and brain/CNS had low response and high relapse probabilities (low- high phenotype). These phenotypes appear to be consistent across cancers. Third, we found that organ- level relapse sequence was closely associated with patient survival, and patients with the first relapse in the liver had worse survival outcomes compared to patients with first relapse in other sites. + +<|ref|>text<|/ref|><|det|>[[111, 588, 865, 865]]<|/det|> +This study quantified the degree of inter- lesion heterogeneity by modeling tumor regression and progression dynamics. By assuming first- order regression of drug- sensitive cancer cells (log- kill hypothesis), the empirical model adequately recapitulated the longitudinal size measurements on the lesion level. The first- order regression implies that drug- sensitive cancer cells may have only one rate- limiting step on the path to cell death35. Baseline tumor burden did not correlate with regression rates in our analyses, restating the first- order regression. Large tumors are often expected to have tumor regression potentially deviating from strict first- order kinetics because of their non- uniform drug distributions inside the tumor or only the surface tumor cells being actively proliferating and sensitive to treatment36- 38. Our analyses did not find evidence to support these speculations. In contrast, despite large + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 140]]<|/det|> +sizes, metastatic lesions in the liver had relatively high regression rates compared to lesions at other organ sites. + +<|ref|>text<|/ref|><|det|>[[111, 162, 870, 343]]<|/det|> +The progression rates of drug- resistant tumor cells varied more between lesions than their associated regression rates and accounted the majority of intrapatient heterogeneity. Lesion relapse time was more closely associated with the progression rates than with the regression rates, in line with Stein et al., who reported that progression rates were a stronger predictor of patient survival39. If validated prospectively, the progression rates would offer more appropriate efficacy endpoints in clinical trials than the current ones that focus on the early response and regression, such as response rate and best of response. + +<|ref|>text<|/ref|><|det|>[[111, 364, 884, 799]]<|/det|> +Antibody therapies significantly increased response depths and decreased progression rates, but did not considerably affect regression rates. These observations indicate that the primary therapeutic benefit of combined antibody therapies is from growth suppression rather than direct cytotoxicity. In renal cell carcinomas, bevacizumab significantly reduced the growth rate constants, and the effect could become more apparent after relapse, in line with our observations in mCRC40. Interestingly, despite the broad evidence of its antibody- dependent cellular cytotoxicity (ADCC)41 or complement- dependent cytotoxicity in vitro systems42, the other antibody panitumumab in our analyses did not significantly affect tumor regression rates either, suggesting its low direct cytotoxicity in patients. In fact, the magnitude of the ADCC elicited by EGFR- targeting antibodies in patient remains hard to define, especially considering the restricted and highly varying infiltrations of effector cells in tumor beds43,44. Panitumumab (IgG2), compared to another EGFR- targeting antibody cetuximab (IgG1) showed reduced ADCC- dependent therapeutic effect, probably related to the reduced avidity of IgG2 for CD16, as compared to IgG145,46. Unfortunately, our analyses did not include patients under cetuximab treatment, precluding direct comparison. + +<|ref|>text<|/ref|><|det|>[[112, 821, 878, 904]]<|/det|> +Metastatic lesions with lower fractions of resistant cells also had slower progression rates, suggesting consistent fitness of resistant cells before treatment and after relapse. However, metastatic lesions in the liver appear to behave differently; they had higher probability to respond, but also faster progression rates + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 878, 365]]<|/det|> +than lesions in the LN and lungs, suggesting unique ecological properties of liver lesions. Our analyses highlight the importance of tissue microenvironments to metastatic phenotypes. Metastatic lesions with higher responses were typically found in the liver, spleen, LN, and lungs. These organs have discontinuous or fenestrated endothelial membranes, which may lead to higher drug exposure, potentially conferring high treatment responses47,48. In contrast, the organs bearing poorly- responding lesions are usually those with continuous endothelial membranes and thus more limited drug distribution, such as the muscle and brain/CNS49- 52. Some organs that bear poorly- responding metastatic lesions, such as kidney and muscle, have relatively dense tissue matrices. This could limit the growth rate of metastatic lesions within these organs53,54 and render them less responsive to cytotoxic chemotherapy55,56. + +<|ref|>text<|/ref|><|det|>[[112, 387, 876, 629]]<|/det|> +On the other hand, organ- specific relapse probabilities seem to closely relate to the local immune microenvironments. Metastatic lesions with higher relapse potentials were found in the liver, bone, and brain/CNS, which either are immune- privileged or tolerogenic organs20,21,28- 31. Interestingly, high relapses in these organs also occurred during cytotoxic chemotherapies that primarily work through DNA damage- induced cell death (Fig. 3f). Higher containment of tumor relapses in immunocompetent organs highlights the critical role anticancer immunity plays in long- term tumor control. Patients with highly relapsing lesions, such as lesions in the liver and bones, had much worse survival outcomes and likely require more effective and targeted therapeutics. + +<|ref|>text<|/ref|><|det|>[[112, 652, 875, 896]]<|/det|> +Tumor relapse is a serious impediment to cancer treatment, but organ- level relapse patterns remain poorly characterized. We found that early relapses in the liver, compared to early relapses in other sites, predicts worse patient survival and more rapid subsequent relapses. The liver's anatomical location, as a trafficking hub for CRC cells to spread to other organs, possibly underlies this finding57. By modeling large autopsy data sets in mCRC, Newton et al. highlighted that liver metastases could serve as tumor "spreaders"58, and that there are multidirectional paths of tumor spread during progression58,59. Although we did not estimate transit probabilities from site to site, we speculate it is likely that early relapses in liver metastases could lead to more resistant cells spreading throughout the body and cause more frequent + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 815, 140]]<|/det|> +subsequent relapses. Our population- level analysis supports this speculation and shows that liver metastases were often associated with a more pronounced tumor spread in the body. + +<|ref|>text<|/ref|><|det|>[[112, 161, 870, 341]]<|/det|> +The primary therapeutic benefit of antibody targeted therapies was to delay tumor progression and systemic relapses, without strong preferential effect on any organ- specific metastases. As such, antibody therapies did not affect relapse sequences, and the fraction of patients with the first relapse in the liver were largely comparable to chemotherapy alone. Unfortunately, in patients with multiple relapsed metastases ( \(>4\) relapsed organs), the therapeutic benefit of antibody therapies is minimal, and more effective treatments remain sorely needed to treat patients with broad metastases. + +<|ref|>text<|/ref|><|det|>[[112, 363, 867, 510]]<|/det|> +In conclusion, we quantified intrapatient heterogeneity by modeling the longitudinal size measurement of metastatic lesions. This study provided a broad characterization of the phenotypic heterogeneity across metastatic lesions in mCRC, which could complement conventional molecular and cellular analyses to promote a more comprehensive view of intrapatient heterogeneity and yield substantial implications for metastasis- targeting therapies. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 91, 185, 107]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[113, 133, 155, 148]]<|/det|> +## Data + +<|ref|>text<|/ref|><|det|>[[111, 172, 870, 386]]<|/det|> +Multiple mCRC and mHNSCC studies with longitudinal measurements of individual metastatic tumor information were included for the analyses. All datasets are accessible in Project Data Sphere (https://www.projectdatasphere.org/). Patients under one of the following conditions were excluded: (1) no target lesion longitudinal measurements; (2) baseline tumor size measured more than 12 weeks before the treatment. Patients' demographics and survival information were collected if applicable. The size and anatomical site about target/non- target lesion and occurring time and anatomical sites of new lesions were all recorded and analyzed if any. + +<|ref|>text<|/ref|><|det|>[[112, 407, 860, 490]]<|/det|> +All study protocols were approved by institutional review boards at each participating center. All patients have been provided written informed consent before study- related procedures were performed. All data sharing plans have been approved by the data sponsors. + +<|ref|>sub_title<|/ref|><|det|>[[113, 513, 422, 532]]<|/det|> +## Lesion-specific tumor growth dynamics + +<|ref|>text<|/ref|><|det|>[[112, 555, 883, 638]]<|/det|> +The longest diameter was converted to volume assuming the ellipsoidal shape of tumor (Equation 1) and the ratio of the tumor long versus short axis as \(1.31^{60}\) . An empirical tumor growth model (Equation 2) was used to recapitulate lesion- specific tumor growth dynamics. + +<|ref|>equation<|/ref|><|det|>[[112, 660, 413, 691]]<|/det|> +\[V = \frac{(\log a x i s)\times(\mathrm{short} a x i s)^{2}}{2} (E q u a t i o n I)\] + +<|ref|>equation<|/ref|><|det|>[[112, 712, 513, 735]]<|/det|> +\[V = V0\cdot [F\cdot e^{Kg\cdot t} + (1 - F)\cdot e^{-Kd\cdot t}](Equation 2)\] + +<|ref|>text<|/ref|><|det|>[[112, 755, 880, 904]]<|/det|> +\(V\) is the tumor volume, \(V0\) is the tumor baseline volume, \(t\) is the time. The model has three parameters for estimation: \(F\) is the fraction of non- responding tumor cells, with \(1 - F\) as the response depth; \(Kg\) is the progression rate and \(Kd\) is the regression rate. We fitted the model for all target lesions simultaneously using the Non- Linear Mixed Effect (NLME) method in Monolix2020R1. Stochastic approximation expectation- maximization (SAEM) algorithm was applied to search global optimum in the estimation. M3 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 870, 140]]<|/det|> +method \(^{61}\) was applied for quantifying size below the quantification of limit ( \(< 200 \mathrm{mm}^{3})^{62}\) . In the NLME method, the model parameters are described in Equation 3- 5. + +<|ref|>equation<|/ref|><|det|>[[112, 163, 416, 186]]<|/det|> +\[\ln \left(K g^{j}\right) = \ln \left(\theta_{K g}\right) + \eta_{K g j}\left(\text{Equation 3}\right)\] + +<|ref|>equation<|/ref|><|det|>[[112, 210, 415, 232]]<|/det|> +\[\ln \left(K d^{j}\right) = \ln \left(\theta_{K d}\right) + \eta_{K d j}\left(\text{Equation 4}\right)\] + +<|ref|>equation<|/ref|><|det|>[[112, 255, 421, 277]]<|/det|> +\[\mathrm{logit}\big(F^{j}\big) = \mathrm{logit}\big(\theta_{F}\big) + \eta_{F j}\left(\text{Equation 5}\right)\] + +<|ref|>text<|/ref|><|det|>[[112, 300, 817, 320]]<|/det|> +where \(\theta\) is the population typical value, and \(\eta\) is the random effect with a log- normal distribution + +<|ref|>text<|/ref|><|det|>[[112, 331, 876, 352]]<|/det|> +describing the difference between individuals and population average for each lesion \(j\) . Proportional error + +<|ref|>text<|/ref|><|det|>[[112, 364, 850, 384]]<|/det|> +model was assumed. The initial values of \(K g\) , \(K d\) and \(F\) were 0.01 day \(^{- 1}\) , 0.01 day \(^{- 1}\) , and 0.1 (unitless). + +<|ref|>sub_title<|/ref|><|det|>[[113, 408, 382, 426]]<|/det|> +## Tumor response and relapse times + +<|ref|>text<|/ref|><|det|>[[111, 448, 877, 690]]<|/det|> +Tumor growth dynamic parameters were further taken to predict the longitudinal profiles of response and relapses for each target lesions. The longitudinal response and relapse status for each target or non- target lesion were determined per RECIST V1.1 \(^{26}\) . Target lesion response time (when the lesion size decreases \(\geq 20\%\) from baseline) and relapse time (when the lesion size increases \(\geq 30\%\) from tumor nadir or at least \(200 \mathrm{mm}^{3}\) increase from nadir) were derived using tumor growth model with NLME- estimated parameters on the individual lesion level. Non- target lesions responded when “partial response” or “complete response” was firstly observed during the treatment and relapsed when “progressive disease” appeared in tumor evaluation. The relapse time for new lesions were defined as the detection time. + +<|ref|>sub_title<|/ref|><|det|>[[113, 714, 386, 732]]<|/det|> +## Cox proportional regression model + +<|ref|>text<|/ref|><|det|>[[112, 755, 881, 901]]<|/det|> +Cox proportional models were built to estimate lesion response and relapse probabilities across organs and treatments in R- 4.1.0 and RStudio “coxme” package. Inter- patient variability was adjusted in the Cox models as random effect. Lesions without relapse or response during the treatment were labeled as censored by the last day of that patient in the trial. New lesions were considered only in the relapse hazard estimation. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 460, 108]]<|/det|> +## Relapse pattern classification and prediction + +<|ref|>text<|/ref|><|det|>[[112, 130, 872, 247]]<|/det|> +We used the k- means machine learning algorithm to classify all the patients based on their organ relapse sequence in Spyder (Python 3.8) in Anaconda using the SCIKIT- LEARN 1.0.2 software package. Elbow method and Silhouette score were applied to find optimal k. The relapse patterns of patients clustered with different k were compared to help determine the choice of k in the final classification. + +<|ref|>text<|/ref|><|det|>[[112, 268, 870, 576]]<|/det|> +Gradient Boosting algorithm was applied to build a relapse pattern predictive model in Spyder (Python 3.8) in Anaconda using the SCIKIT- LEARN 1.0.2 software package. The research samples were randomly divided into a training and testing groups at a ratio of 4:1. The initial value of the hyperparameters used in this model was determined by parameter grid search, using 5- fold cross- validation and F1- score as a metric (Supplementary Table 4). The model outcome is the patient relapse sequence classified in k- means algorithm. Model predictors included patient clinical and demographic characteristics, as well as the baseline metastatic profiles, including the metastatic organs, metastatic numbers, metastatic target lesion baseline volume. Continuous predictors were normalized and categorical predictors were transformed to dummy variables. Performance index accuracy, precision, recall rate and area ROC curves were used to evaluate model performance. + +<|ref|>sub_title<|/ref|><|det|>[[113, 599, 260, 616]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[112, 639, 872, 787]]<|/det|> +Comparisons of continuous variables were performed using the two- tailed Mann- Whitney test or Kruskal- Wallis test. Multiple comparisons were adjusted by Dunn's test. PFS (defined as the start of therapies until RECIST- defined progression or death) and OS (defined as the start of therapies until patient death) among the groups were depicted using Kaplan- Meier curves and compared using log- rank tests. All the statistical tests were performed in GraphPad Prism 9. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[63, 90, 202, 107]]<|/det|> +364 References + +<|ref|>text<|/ref|><|det|>[[60, 129, 858, 183]]<|/det|> +365 1. Welch, D. R. & Hurst, D. R. Defining the hallmarks of metastasis. Cancer Res. 79, 3011- 3027 (2019). + +<|ref|>text<|/ref|><|det|>[[60, 203, 880, 255]]<|/det|> +367 2. Eccles, S. A. & Welch, D. R. Metastasis: recent discoveries and novel treatment strategies. Lancet 369, 1742- 1757 (2007). + +<|ref|>text<|/ref|><|det|>[[60, 277, 864, 329]]<|/det|> +369 3. Anderson, R. L. et al. A framework for the development of effective anti-metastatic agents. Nat. Rev. Clin. 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Sensing the microenvironment of the 428 central nervous system: immune cells in the central nervous system and their pharmacological 429 manipulation. Curr. Opin. Pharmacol. 8, 496- 507 (2008). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 87, 879, 245]]<|/det|> +32. Syakur, M. A., Khotimah, B. K., Rochman, E. M. S. & Satoto, B. D. Integration k-means clustering method and elbow method for identification of the best customer profile cluster. in IOP conference series: materials science and engineering vol. 336 12017 (IOP Publishing, 2018). + +<|ref|>text<|/ref|><|det|>[[60, 298, 875, 420]]<|/det|> +34. Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013). + +<|ref|>text<|/ref|><|det|>[[60, 447, 875, 547]]<|/det|> +36. Norton, L. The norton-simon hypothesis revisited. Cancer Treat Rep 70, 163-169 (1986). + +<|ref|>text<|/ref|><|det|>[[60, 565, 844, 616]]<|/det|> +37. Fu, F., Nowak, M. A. & Bonhoeffer, S. Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy. PLoS Comput. Biol. 11, e1004142 (2015). + +<|ref|>text<|/ref|><|det|>[[60, 639, 880, 720]]<|/det|> +39. Stein, W. D. et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin. Cancer Res. 17, 907-917 (2011). + +<|ref|>text<|/ref|><|det|>[[60, 744, 870, 828]]<|/det|> +40. Stein, W. D., Yang, J., Bates, S. E. & Fojo, T. Bevacizumab reduces the growth rate constants of renal carcinomas: a novel algorithm suggests early discontinuation of bevacizumab resulted in a lack of survival advantage. Oncologist 13, 1055-1062 (2008). + +<|ref|>text<|/ref|><|det|>[[60, 850, 840, 901]]<|/det|> +41. Kurai, J. et al. Antibody-dependent cellular cytotoxicity mediated by cetuximab against lung cancer cell lines. Clin. Cancer Res. 13, 1552-1561 (2007). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 88, 833, 140]]<|/det|> +Bleeker, W. K. et al. Dual mode of action of a human anti- epidermal growth factor receptor monoclonal antibody for cancer therapy. J. Immunol. 173, 4699–4707 (2004). + +<|ref|>text<|/ref|><|det|>[[57, 162, 839, 213]]<|/det|> +Weiner, L. M. Building better magic bullets—improving unconjugated monoclonal antibody therapy for cancer. Nat. Rev. Cancer 7, 701–706 (2007). + +<|ref|>text<|/ref|><|det|>[[57, 235, 884, 287]]<|/det|> +Wheeler, D. L., Dunn, E. F. & Harari, P. M. Understanding resistance to EGFR inhibitors—impact on future treatment strategies. Nat. Rev. Clin. Oncol. 7, 493–507 (2010). + +<|ref|>text<|/ref|><|det|>[[57, 309, 879, 393]]<|/det|> +Lee, S. C., Srivastava, R. M., López-Albaitero, A., Ferrone, S. & Ferris, R. L. Natural killer (NK): dendritic cell (DC) cross talk induced by therapeutic monoclonal antibody triggers tumor antigen-specific T cell immunity. Immunol. Res. 50, 248–254 (2011). + +<|ref|>text<|/ref|><|det|>[[57, 415, 881, 499]]<|/det|> +Monteverde, M. et al. The relevance of ADCC for EGFR targeting: a review of the literature and a clinically-applicable method of assessment in patients. Crit. Rev. Oncol. Hematol. 95, 179–190 (2015). + +<|ref|>text<|/ref|><|det|>[[57, 521, 884, 573]]<|/det|> +Claesson-Welsh, L., Dejana, E. & McDonald, D. M. Permeability of the endothelial barrier: identifying and reconciling controversies. Trends Mol. Med. 27, 314–331 (2021). + +<|ref|>text<|/ref|><|det|>[[57, 595, 879, 679]]<|/det|> +Gifre-Renom, L., Daems, M., Luttun, A. & Jones, E. A. V. Organ-Specific Endothelial Cell Differentiation and Impact of Microenvironmental Cues on Endothelial Heterogeneity. Int. J. Mol. Sci. 23, 1477 (2022). + +<|ref|>text<|/ref|><|det|>[[57, 701, 856, 784]]<|/det|> +Sarin, H. Physiologic upper limits of pore size of different blood capillary types and another perspective on the dual pore theory of microvascular permeability. J. Angiogenes. Res. 2, 1–19 (2010). + +<|ref|>text<|/ref|><|det|>[[57, 807, 852, 858]]<|/det|> +Parton, R. G., Schrotz, P., Bucci, C. & Gruenberg, J. Plasticity of early endosomes. J. Cell Sci. 103, 335–348 (1992). + +<|ref|>text<|/ref|><|det|>[[57, 881, 864, 901]]<|/det|> +Augustin, H. G., Kozian, D. H. & Johnson, R. C. Differentiation of endothelial cells: analysis of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[163, 88, 812, 108]]<|/det|> +the constitutive and activated endothelial cell phenotypes. Bioessays 16, 901- 906 (1994). + +<|ref|>text<|/ref|><|det|>[[163, 129, 878, 214]]<|/det|> +Cao, Y., Balthasar, J. P. & Jusko, W. J. Second- generation minimal physiologically- based pharmacokinetic model for monoclonal antibodies. J. Pharmacokinet. Pharmacodyn. 40, 597- 607 (2013). + +<|ref|>text<|/ref|><|det|>[[163, 235, 825, 288]]<|/det|> +Kay, K., Dolcy, K., Bies, R. & Shah, D. K. Estimation of solid tumor doubling times from progression- free survival plots using a novel statistical approach. AAPS J. 21, 1- 12 (2019). + +<|ref|>text<|/ref|><|det|>[[163, 310, 845, 362]]<|/det|> +Zharinov, G. M. et al. Prognostic value of tumor growth kinetic parameters in prostate cancer patients. Oncotarget 10, 5020 (2019). + +<|ref|>text<|/ref|><|det|>[[163, 383, 861, 436]]<|/det|> +Lin, Z., Fan, Z., Zhang, X., Wan, J. & Liu, T. Cellular plasticity and drug resistance in sarcoma. Life Sci. 263, 118589 (2020). + +<|ref|>text<|/ref|><|det|>[[163, 457, 855, 509]]<|/det|> +Amato, R. J. Chemotherapy for renal cell carcinoma. in Seminars in oncology vol. 27 177- 186 (2000). + +<|ref|>text<|/ref|><|det|>[[163, 530, 877, 583]]<|/det|> +Clark, A. M., Ma, B., Taylor, D. L., Griffith, L. & Wells, A. Liver metastases: microenvironment and ex- vivo models. Exp. Biol. Med. 241, 1639- 1652 (2016). + +<|ref|>text<|/ref|><|det|>[[163, 604, 839, 656]]<|/det|> +Newton, P. K. et al. Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model. Cancer Res. 73, 2760- 2769 (2013). + +<|ref|>text<|/ref|><|det|>[[163, 678, 864, 731]]<|/det|> +Quinn, J. J. et al. Single- cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science (80-. ). 371, eabc1944 (2021). + +<|ref|>text<|/ref|><|det|>[[163, 752, 866, 837]]<|/det|> +Zhou, J., Liu, Y., Zhang, Y., Li, Q. & Cao, Y. Modeling tumor evolutionary dynamics to predict clinical outcomes for patients with metastatic colorectal cancer: a retrospective analysis. Cancer Res. 80, 591- 601 (2020). + +<|ref|>text<|/ref|><|det|>[[163, 858, 876, 879]]<|/det|> +Ahn, J. E., Karlsson, M. O., Dunne, A. & Ludden, T. M. Likelihood based approaches to handling + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 88, 852, 140]]<|/det|> +498 data below the quantification limit using NONMEM VI. J. Pharmacokinet. Pharmacodyn. 35, 499 401–421 (2008). + +<|ref|>text<|/ref|><|det|>[[57, 161, 861, 214]]<|/det|> +500 62. Erdi, Y. E. Limits of tumor detectability in nuclear medicine and PET. Mol. Imaging Radionucl. 501 Ther. 21, 23 (2012). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 250, 108]]<|/det|> +## Data Availability + +<|ref|>text<|/ref|><|det|>[[113, 130, 872, 247]]<|/det|> +The clinical data that support the findings of this study are available in the Project Data Sphere, https://data.projectdatasphere.org/projectdatasphere/html/access. The machine learning algorithms codes were deposited at https://github.com/zhoujw14/Mapping- Metastasis.git. All source data for our model development and plotting will be provided upon request. + +<|ref|>sub_title<|/ref|><|det|>[[115, 270, 267, 288]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[113, 310, 830, 390]]<|/det|> +We thank Mr. Timothy Qi and Dr. Tyler Dunlap from University of North Carolina at Chapel Hill, Eshelman School of Pharmacy for providing valuable suggestions and edits for the manuscript. Funding Source: National Institute of Health, R35GM119661 + +<|ref|>sub_title<|/ref|><|det|>[[115, 407, 286, 425]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[113, 447, 868, 531]]<|/det|> +Conceptualizations: J.Z., and Y.C.; methodology: J.Z., A.C., G.F., Q.L., and Y.C.; formal analysis: J.Z.; investigation: J.Z., Y.L., Q.L., and Y.C.; writing- original draft: J.Z., and Y.C.; writing- reviewing and editing: J.Z., A.C., G.F., Y.L., Q.L., and Y.C.; supervision: Y.C. + +<|ref|>sub_title<|/ref|><|det|>[[115, 555, 275, 573]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[115, 596, 453, 614]]<|/det|> +All the authors declare no competing interests. + +<|ref|>text<|/ref|><|det|>[[115, 638, 639, 657]]<|/det|> +Correspondence and requests for materials should be addressed to Y.C. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[58, 92, 170, 106]]<|/det|> +# 519 Tables + +<|ref|>table_caption<|/ref|><|det|>[[58, 135, 609, 149]]<|/det|> +520 Table 1. Demographic information of colorectal cancer patients. + +<|ref|>table<|/ref|><|det|>[[115, 171, 881, 894]]<|/det|> + +
Variable
Age, years (mean, sd)60.2 (10.8)
Gender (n, %)
Male2538 (58.9)
Female1770 (41.1)
Race (n, %)
White/Caucasian3883 (90.1)
Black/African American104 (2.4)
Asian142 (3.3)
Other179 (4.2)
Body Mass Index, \(\mathrm {kg}/\mathrm {m}^{2}\) (mean, sd)26.2 (5.1)
Tumor Type (n, %)
Colon2581 (59.9)
Rectal1359 (31.5)
Unspecified368 (8.5)
Prior Surgery (n, %)
Yes2993 (69.5)
No1315 (30.5)
Prior Radiation (n, %)
Yes445 (10.3)
No3345 (77.6)
Unknown518 (12.1)
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 87, 882, 840]]<|/det|> +
Treatment1 (n, %)
Bevacizumab plus chemotherapy376 (8.7)
Bevacizumab plus FOLFOX640 (14.9)
FOLFIRI alone1303 (30.2)
FOLFOX alone762 (17.7)
Panitumumab plus Bevacizumab plus chemotherapy372 (8.6)
Panitumumab plus FOLFOX441 (10.2)
Panitumumab plus FOLFIRI424 (9.8)
Response (n, %)
Complete Response118 (2.7)
Partial Response1473 (34.2)
Progressive Disease781 (18.1)
Stable Disease1806 (41.9)
Not Evaluable130 (3)
Metastatic organ number (n, %)
1553 (12.8)
21159 (26.9)
31146 (26.6)
≥41450 (33.7)
KRAS status (n, %)
Wild-Type795 (18.4)
Mutant593 (13.8)
Unknown2920 (67.8)
+ +<|ref|>text<|/ref|><|det|>[[57, 843, 877, 888]]<|/det|> +521 'FOLFOX is the combination of folinic acid, fluorouracil and oxaliplatin. FOLFIRI is the combination of folinic acid, fluorouracil and irinotecan. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 144, 850, 432]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[60, 444, 88, 457]]<|/det|> +
524
+ +<|ref|>text<|/ref|><|det|>[[60, 476, 864, 590]]<|/det|> +525 Fig. 1 Data source. a. CONSORT diagram of metastatic colorectal cancer data inclusion and exclusion 526 criteria. b. The number of all lesions (target, non- target and new) and target lesions across organs. GR, 527 Genitourinary and Reproductive; CNS, central nervous system; GI, Gastrointestinal tract; LN, lymph 528 nodes. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 115, 845, 680]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 700, 872, 911]]<|/det|> +
Fig. 2 Tumor response dynamics were recapitulated by modeling. a. Schematic plot of tumor growth model. b. Box plots of model parameters \(Kd\) , \(F\) and \(Kg\) across organs. Significance was calculated using Kruskal-Wallis tests. The box extends from the 25th to 75th percentiles and the line in the middle is plotted as the median. The whiskers are drawn down to the 10th percentile and up to the 90th percentile. Points below and above the whiskers represent individual lesions. c. The correlations between model parameters. d. The correlations between model parameters and tumor baseline volume. The size of the dots represents lesion number (reported in panel b). The dashed lines with gray area are the linear
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 88, 857, 139]]<|/det|> +537 regression with \(95\%\) confidence interval. The correlation coefficients and significance were calculated using two- tailed Pearson correlation tests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 103, 770, 850]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 870, 330]]<|/det|> +Fig. 3 Organ- level tumor response and relapse probabilities suggest phenotypic convergence. a and b rank the hazard ratio estimates with \(95\%\) confidence interval by organs on lesion response and relapse in colorectal cancer patients. c and d are the anatomical charts of organ- specific response and relapse hazard ratios in metastatic colorectal cancer (mCRC) and metastatic head and neck squamous cell carcinomas (mHNSCC). e and f are response and relapse hazard ratio with \(95\%\) confidence interval by organs stratified on treatments in mCRC. P- values were calculated by comparing the hazard ratios in antibody targeted therapies plus chemotherapy (TAR+Chemo) vs. chemotherapy alone (Chemo Alone) within each organ. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 100, 863, 456]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 495, 872, 514]]<|/det|> +
Fig. 4 Patient relapse sequence association with patient survival. a. Patients were clustered into five
+ +<|ref|>text<|/ref|><|det|>[[111, 527, 879, 707]]<|/det|> +groups based on their lesion relapse sequence. The column labels are the relapse sequence. Color of the heatmap represents the log10 scale of patient number (all plus one to avoid zero values). b. Kaplan- Meier curves of clustered patients overall survival. c. The mean and standard deviation of the first lesion relapse time (1st), time between first and second relapse (2nd- 1st), time between second and third relapse (3rd- 2nd), time between third and fourth relapse (4th- 3rd), and the average relapse time in Lung- First (n=577), Other- First (n=639), and Liver- First (n=930). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[131, 108, 777, 889]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 875, 333]]<|/det|> +Fig. 5. Targeted therapy decreases average time to relapse but has minimal effect on relapse sequence. a. Lung- First, Other- First and Liver- First patients overall survival stratified by treatments. b. Lung- First, Other- First and Liver- First patient proportions by treatments. c, d, and e are patient relapse sequences stratified by treatments. f, g, and h are the mean and standard deviation of the first lesion relapse time (1st), time between first and second relapse (2nd- 1st), time between second and third relapse (3rd- 2nd), time between third and fourth relapse (4th- 3rd), and the average relapse time by treatments of the groups in c, d, and e. TAR+Chemo, antibody targeted therapies plus chemotherapy; Chemo Alone, chemotherapy alone. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 492, 177]]<|/det|> +Supplementary3.13. pdf ClinicalTrialInformationNCOMMS2209853. xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/images_list.json b/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..40ba572cecbf6932fe1cf1c16c3a5c6be79009f2 --- /dev/null +++ b/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/images_list.json @@ -0,0 +1,251 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig 1. Infant adipocytes are immune privileged for mitochondria", + "footnote": [], + "bbox": [], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Infant adipocytes cast away mtRNA to induce beige adipose tissue development (A) Clathrin-coated pits, endosome budding [1-4] and multivesicular bodies (MVBs) [5] in P6 adipocyte. Pm: plasma membrane, En: endosome, Aly: autolysosome, Cav: caveolae, arrowhead: EVs in MVBs. Scale: \\(1 \\mu \\mathrm{m}\\) . (B) P6/P56 iAT comparison of transcripts associated with endosomes, MVBs, lysosomes and exocytosis. (C) TEM image of EVs released by P6 adipocytes. Scale: \\(0.1 \\mu \\mathrm{m}\\) . FACS analysis of nucleic acids in EVs of P6 adipocytes. N-St: non-stained; St: stained with SytoxGreen. Amount of EV-bound nucleic acids in cell culture media of P6 adipocytes. (D) Level of mtDNA in EVs of P6 adipocytes. \\(l\\) : light chain; \\(h\\) : heavy chain. (E) Labeling of dsRNA with J2 antibody in P6 adipocytes; scale: \\(10 \\mu \\mathrm{m}\\) . Quantification of RNA species released by EVs of P6 adipocytes. Effect of P6 EVs on mitochondrial content, mitobiogenesis (F), UCP1 level (G) and beige gene expression (H) in P56 adipocytes. -EVs: cells cultured in EV-free media, +EVs: cells treated with EVs. Scale: \\(10 \\mu \\mathrm{m}\\) (MTR); \\(50 \\mu \\mathrm{m}\\) (UCP1), MFI: mean fluorescence intensity. (I) Cytosolic delivery of mtDNA and mtRNA into 3T3-L1 cells, and their effect on beige gene transcription and mitobiogenesis. (J) Effect of P6 EVs on mitobiogenesis of adipocytes. \\(Ddx58^{- / - }\\) : RIG-I (DDX58)-deficient adipocytes, \\(Mda5^{- / - }\\) : MDA5-deficient adipocytes. \\(*P< 0.05\\) , \\(**P< 0.01\\) , \\(***P< 0.001\\) . Student's 2-tailed unpaired \\(t\\) -test or one-way ANOVA with Dunnett's post-hoc test. (K) Scheme of mtRNA-activated signal transduction in infant adipocytes.", + "footnote": [], + "bbox": [ + [ + 123, + 90, + 872, + 562 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. VDR abrogates IRF7 expression in the infant adipocytes", + "footnote": [], + "bbox": [ + [ + 125, + 88, + 868, + 528 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. Effect of cytosolic mRNA combined vit Vit-D3 treatment in diet-induced obesity (A) VDR-controlled gene expression in iAT of children. (B) Nursing mice received high-fat diet (HFD) or normal chow diet (NCD) between postnatal day 6 and 9 of the offspring. Mice nursed by NCD-fed or HFD-fed dams were analyzed on postnatal day 10 (P10). (C) \\(Vdr\\) and \\(Irf7\\) expression in iAT. (D) Histology of iAT. H&E: hematoxylin and eosin staining, UCP1: UCP1 immunostaining, Scale: \\(50 \\mu \\mathrm{m}\\) . Note the lack of multilocular adipocytes in mice nursed by HFD-fed dams. (E) Ratio of iAT and body weight, and inflammasome caspase 1 (CASP1) activity of the adipocytes. (F) Mitochondrial network and the expression of AIM2, DDX41, p204 and ZBP1 in adipocytes. MTR: MitoTracker Red. Scale \\(50 \\mu \\mathrm{m}\\) . (G) Mice were nursed by HFD-fed dams, and treated with vehicle or Vit-D3 from P6 to P9. Histology of iAT on P10. Scale: 100 \\(\\mu \\mathrm{m}\\) (H) Ratio of iAT and body weight, and CASP1 activity of the adipocytes on P10. (I) In adult HFD-fed mice the iAT was transfected with vehicle or mtRNA, and IRF7 protein level was measured in adipocytes. (J) Histology of iAT of vehicle- or mtRNA-transfected mice. (K) Adipose tissue weight/body weight ratio, and CASP1 activity of adipocytes. eAT: epididymal adipose tissue (L) Mitochondrial network of adipocytes isolated from vehicle- or mtRNA-transfected mice. Scale: \\(10 \\mu \\mathrm{m}\\) . Note the expansion of the mitochondrial network after mtRNA treatment. (M) Mitochondrial mass (relative MTR fluorescent intensity) and mitochondrial temperature change (Mito-ΔT) in adipocytes isolated from vehicle- or mtRNA-transfected mice. (L) CASP1 activity of adipocytes isolated from vehicle- or mtRNA-transfected mice, and treated with vehicle of cGAMP for 4h. \\(**P< 0.01\\) , \\(***P< 0.001\\) . Student’s 2-tailed unpaired \\(t\\) -test or one-way ANOVA with Dunnett’s post-hoc test.", + "footnote": [], + "bbox": [ + [ + 128, + 90, + 875, + 521 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5. Role of mtRNA signaling in beige adipocytes", + "footnote": [], + "bbox": [ + [ + 196, + 405, + 800, + 705 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Supplemental Figure 1. Characterization of mouse inguinal adipose tissue at P6 and P56, and human inguinal adipose tissue in infancy", + "footnote": [], + "bbox": [ + [ + 120, + 70, + 880, + 496 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Supplemental Figure 2. Expression of the STING/AIM2 pathways in P6 and P56 iAT", + "footnote": [], + "bbox": [ + [ + 123, + 72, + 875, + 560 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Supplemental Figure 3. Cytosolic DNA sensing in adipocytes", + "footnote": [], + "bbox": [ + [ + 120, + 74, + 870, + 468 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Supplemental Figure 5. Endosomal DNA/RNA sensing in adipocytes", + "footnote": [], + "bbox": [], + "page_idx": 32 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Supplemental Figure 6. STING/AIM2 pathways in human adipose tissue", + "footnote": [], + "bbox": [ + [ + 120, + 81, + 878, + 473 + ] + ], + "page_idx": 33 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Supplemental Figure 7. STING-mediated mitophagy in P6 adipocytes", + "footnote": [], + "bbox": [ + [ + 123, + 74, + 872, + 476 + ] + ], + "page_idx": 34 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Supplemental Figure 8. Biogenesis of EVs by P6 adipocytes", + "footnote": [], + "bbox": [ + [ + 120, + 72, + 880, + 400 + ] + ], + "page_idx": 35 + }, + { + "type": "image", + "img_path": "images/Figure_9.jpg", + "caption": "Supplemental Figure 9. Effect of adipocyte EV cargo on mitochondrial morphology, and predicted secondary structure of mtRNA species found in adipocyte EVs (A) Mitochondrial morphometry of 3T3-L1 cells without extracellular vesicles (-EVs) or with P6 EVs (+EVs). \\*\\*\\*P<0.001. Student's 2-tailed unpaired \\(t\\) -test. (B) Predicted minimum free energy (MFE) secondary structures of mtRNA species found in P6 EVs. Results were computed using ViennaRNA Package 2.0 and RNAfold 2.2.18, as described (32, 33).", + "footnote": [], + "bbox": [ + [ + 117, + 73, + 875, + 435 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_11.jpg", + "caption": "Supplemental Figure 11. IL-6/STAT3 and RIG-I/MDA5 signaling and mitoagonesis (A) Mitobigenesis was assessed by measuring SDH-A (succinate dehydrogenase complex, subunit A) and COX-1 (cyclooxygenase 1) by FACS. SDH-A is encoded by genomic DNA (gDNA), COX-I by mtDNA. Representative FACS histograms of COX-I and SDH-A in 3T3-L1 cells after P6 EV treatment. Histochemical staining of SDH-A activity of 3T3-L1 cells cultured without EVs (-EVs), with P6 EVs (+EVs) or with 0.2 ng/ml IL-6 for 18 h. Scale: 10 μm. (B) Effect of 200 pg/ml IL-6 on the net mitochondrial mass labeled with MitoTracker Red (MTR), and on the amount of newly synthesized (GFP-expressing) mitochondria. Scale: 50 μm. (C) FACS analysis of IL-6 content of P6 EVs. Iso: isotype control; IgG: labeling with anti-IL-6 IgG. Effect of P6 EVs on adipocyte Il6 expression and IL-6 release. Effect of P6 EVs on Cox7a1 expression (D) Effect of 200 pg/ml IL-6 on the Mitothermo-Yellow (MTY) signal in 3T3-L1 cells. Correlation of Il6 and Ucp1 relative expression in adipocytes. Heat map showing expression levels of beige adipocyte genes in 3T3-L1 cells treated with P6 EVs for 18 h. (E) MTR signal in 3T3-L1 cells treated with P6 EVs for 18 h. RXL: cells were simultaneously treated with the JAK2/STAT3 inhibitor ruxolitinib; BAY11-7082: cells were treated with an NFkB inhibitor to abrogate the effect of IL-6. (F) Histology of iAT from wild-type (wt), RIG-I-deficient (Ddx58-/-) and MDA5-deficient (Mda5-/-) mice. Note the absence of beige (multicolour) adipocytes in Ddx58-/- and Mda5-/- mice. Scale 50 μm. (G) Mitobigenesis (relative COX-I and SDH-A levels) in wt, Ddx58-/- and Mda5-/- adipocytes. (H) Heat map showing expression levels of beige adipocyte genes in wt or Ddx58-/- adipocytes treated with vehicle or mtRNA for 18 h. *P<0.05, **P<0.01, ***P<0.001. Student’s 2-tailed unpaired t-test.", + "footnote": [], + "bbox": [], + "page_idx": 37 + }, + { + "type": "image", + "img_path": "images/Figure_12.jpg", + "caption": "Supplemental Figure 12. Cytosolic DNA/RNA effects on mitobiogenesis", + "footnote": [], + "bbox": [ + [ + 120, + 73, + 870, + 340 + ] + ], + "page_idx": 38 + }, + { + "type": "image", + "img_path": "images/Figure_13.jpg", + "caption": "Supplemental Figure 13. IFN-response to EV cargo in adipocytes", + "footnote": [], + "bbox": [ + [ + 120, + 72, + 880, + 435 + ] + ], + "page_idx": 39 + }, + { + "type": "image", + "img_path": "images/Figure_14.jpg", + "caption": "Supplemental Figure 14. Metabolic role of mtRNA-mediated signaling", + "footnote": [], + "bbox": [ + [ + 128, + 75, + 864, + 320 + ] + ], + "page_idx": 40 + }, + { + "type": "image", + "img_path": "images/Figure_15.jpg", + "caption": "Supplemental Figure 15. Technical information on next-generation sequencing and image analysis", + "footnote": [], + "bbox": [ + [ + 255, + 95, + 744, + 393 + ] + ], + "page_idx": 41 + } +] \ No newline at end of file diff --git a/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5_det.mmd b/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..13c33aa1bb2f62b3c2306641520f8f6818e6d7b6 --- /dev/null +++ b/preprint/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5/preprint__04370ec5a4accd2d6e3e1f489050ff616fcd50df7e181fd6b1932f46cd42e1a5_det.mmd @@ -0,0 +1,905 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 816, 177]]<|/det|> +# Immune privilege of adipocyte mitochondria protects from obesity + +<|ref|>text<|/ref|><|det|>[[44, 196, 204, 238]]<|/det|> +Anh Cuong Hoang Ulm University + +<|ref|>text<|/ref|><|det|>[[44, 243, 184, 284]]<|/det|> +Haidong Yu Ulm University + +<|ref|>text<|/ref|><|det|>[[44, 290, 614, 331]]<|/det|> +Ya- Tin Lin Chang Gung University https://orcid.org/0000- 0001- 7910- 1223 + +<|ref|>text<|/ref|><|det|>[[44, 336, 255, 377]]<|/det|> +Jin- Chung Chen Chang Gung University + +<|ref|>text<|/ref|><|det|>[[44, 382, 255, 423]]<|/det|> +Chia- Chun Chen Chang Gung University + +<|ref|>text<|/ref|><|det|>[[44, 428, 195, 469]]<|/det|> +Victoria Diedrich Ulm University + +<|ref|>text<|/ref|><|det|>[[44, 475, 184, 515]]<|/det|> +Annika Herwig Ulm University + +<|ref|>text<|/ref|><|det|>[[44, 521, 195, 541]]<|/det|> +Kathrin Landgraf + +<|ref|>text<|/ref|><|det|>[[50, 544, 828, 585]]<|/det|> +University of Leipzig, Pediatric Research Centre, Department of Women's and Child Health https://orcid.org/0000- 0002- 6878- 6033 + +<|ref|>text<|/ref|><|det|>[[44, 590, 152, 608]]<|/det|> +Antje Körner + +<|ref|>text<|/ref|><|det|>[[44, 611, 950, 653]]<|/det|> +Center for Pediatric Research Leipzig (CPL), University Hospital for Children & Adolescents, University of Leipzig, Leipzig https://orcid.org/0000- 0001- 6001- 0356 + +<|ref|>text<|/ref|><|det|>[[44, 658, 459, 700]]<|/det|> +Tamas Roszer (tamas.roeszer@uni- ulm.de) Ulm University + +<|ref|>sub_title<|/ref|><|det|>[[44, 742, 101, 759]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 779, 595, 799]]<|/det|> +Keywords: innate immunity, obesity, interferons, IFI16, vitamin D + +<|ref|>text<|/ref|><|det|>[[44, 817, 334, 836]]<|/det|> +Posted Date: November 4th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 854, 463, 874]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 988599/v1 + +<|ref|>text<|/ref|><|det|>[[44, 892, 909, 935]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 912, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Metabolism on November 28th, 2022. See the published version at https://doi.org/10.1038/s42255-022-00683-w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[113, 90, 673, 108]]<|/det|> +# Immune privilege of adipocyte mitochondria protects from obesity + +<|ref|>text<|/ref|><|det|>[[113, 156, 860, 214]]<|/det|> +Anh Cuong Hoang \(^{1}\) ; Haidong Yu \(^{1}\) , Ya- Tin Lin \(^{2}\) , Jin- Chung Chen \(^{2}\) , Chia- Chun Chen \(^{3}\) , Victoria Diedrich \(^{1}\) , Annika Herwig \(^{1}\) , Kathrin Landgraf \(^{4}\) , Antje Körner \(^{4}\) , Tamás Röszer \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[113, 262, 833, 460]]<|/det|> +\(^{1}\) Institute of Neurobiology, Ulm University, Ulm, Germany \(^{2}\) Department of Physiology and Pharmacology, Graduate Institute of Biomedical Sciences, School of Medicine; Healthy Aging Research Center, Chang Gung University, Taiwan \(^{3}\) Molecular Medicine Research Center, Chang Gung Memorial Hospital at Linkou, Taiwan \(^{4}\) Center for Pediatric Research, University Hospital for Children and Adolescents, University of Leipzig, Germany + +<|ref|>text<|/ref|><|det|>[[113, 473, 721, 492]]<|/det|> +\*Correspondence to: tamas.roeszer@uni- ulm.de, Fax: +49 (0) 731- 50- 22629 + +<|ref|>text<|/ref|><|det|>[[113, 508, 658, 527]]<|/det|> +Short title: Anti- mitochondrial immune response aggravates obesity + +<|ref|>text<|/ref|><|det|>[[113, 543, 691, 562]]<|/det|> +Key words: innate immunity – obesity – interferons – IFI16 – vitamin D + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 191, 108]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[111, 123, 886, 530]]<|/det|> +Infant nutrition is rich in lipids, and the adipose tissue has been adapted to properly break down neutral lipids and oxidize fatty acids in infancy. Accordingly, infant adipose tissue contains so- called beige adipocytes, which burn off lipids to heat, and impede fat storage and obesity. We show here that infant adipocytes are immune privileged sites for mitochondria due to a blockade in interferon regulatory factor 7 (IRF7)- signaling, which allows mitochondrial RNA to trigger beige adipocyte differentiation through mitochondria- to- nucleus signaling. These mechanisms serve to maintain an extensive mitochondrial network in beige adipocytes and protect against obesity. By contrast, fat storing white adipocytes lack these mechanisms and respond to their mitochondrial content with inflammation. We show that obesity subverts the immune privilege for mitochondria in adipocytes, which reduces mitochondrial mass and abrogates beige adipocyte development. In turn, suppressing IRF7 signaling and restoring the RNA- mediated mitochondria- to- nucleus signaling in adipocytes effectively reduces obesity. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[133, 108, 848, 275]]<|/det|> + +<|ref|>sub_title<|/ref|><|det|>[[115, 305, 282, 323]]<|/det|> +## Graphical Abstract + +<|ref|>text<|/ref|><|det|>[[113, 346, 884, 437]]<|/det|> +Infant adipocytes have a suppressed IRF7 expression and a mitochondria- to- nucleus signaling through mitochondrial RNA (mtRNA), which stimulates the transcription of beige adipocyte genes, and is key for mitobiogenesis and burning off fat as heat. + +<|ref|>sub_title<|/ref|><|det|>[[115, 502, 250, 520]]<|/det|> +## Video summary + +<|ref|>text<|/ref|><|det|>[[115, 544, 480, 562]]<|/det|> +https://figshare.com/s/36e7ca6a4953471fba42 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 530]]<|/det|> +Childhood obesity is a serious public health crisis and is associated with an increased risk of obesity and diabetes in adulthood, which is projected to affect \(\sim 58\%\) of the world's adult population by 2030 (1- 3). Obesity is an excessive accumulation of white adipose tissue (WAT) mediated by a mismatch between energy supply and utilization. Infant nutrition is rich in lipids, and adipocytes in the infant WAT break down lipids to free fatty acids, and generate energy and heat from lipids in their extensive mitochondrial network (4- 6). These fat oxidizing and thermogenic fat cells are termed as beige adipocytes (7, 8). In adults however, adipocytes of the subcutaneous fat depots are scarce in mitochondria and accumulate fat (9, 10). WAT is necessary for metabolic and endocrine health in adulthood, however its excess expansion accounts for metabolic diseases (1- 3). Previous studies have suggested that the premature loss of fat oxidizing and thermogenic potential in infant WAT is accelerated in childhood obesity (2, 3, 10), and delaying or reverting the metabolic shift of WAT from fat catabolism to storage has therapeutic potential in the prevention of obesity (7, 8). + +<|ref|>text<|/ref|><|det|>[[112, 541, 886, 808]]<|/det|> +Cell metabolism of fat into ATP and heat requires an extensive mitochondrial network and mitochondrial uncoupling (8), which increases the abundance of "misplaced" mitochondria- associated danger signals in the cytoplasm, such as prokaryote- type mitochondrial DNA (mtDNA) and virus- like double stranded RNA (dsRNA). These signals trigger inflammasome activation and interferon (IFN) response (11), which abrogate the expansion of the mitochondrial network and the capacity of fat oxidation, and cause metabolic inflammation (12). Obesity is a hyper- inflammatory disorder, and IFNs trigger obesity- associated metabolic diseases (13, 14), especially in children with insufficient breastfeeding (15), who are prone for premature WAT expansion (7). + +<|ref|>text<|/ref|><|det|>[[113, 821, 884, 876]]<|/det|> +These observations prompted us to question whether infant adipocytes have a unique nucleic acid immunity that supports their mitochondrial network. We found that the infant + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 424]]<|/det|> +subcutaneous adipocytes were immune privileged towards mitochondria due to the suppression of cytosolic mtDNA recognition and interferon regulatory factor 7 (IRF7). Mitochondrial RNA (mtRNA) eventually activated a mitochondria- to- nucleus signaling which stimulated mitobiogenesis and beige adipocyte development without provoking an IFN- response against mitochondrial content. These mechanisms were lacking from the adult subcutaneous adipocytes, which responded with IFN- burst to mitochondrial content and were hostile for mitochondria. Obesity subverted mitochondrial immune privilege in adipocytes, and in turn, restoring mtRNA- mediated signaling effectively reduced obesity. Innate immune sensing of mitochondrial nucleic acids is hence a novel mechanism which controls early adipose tissue development and protects against obesity. + +<|ref|>sub_title<|/ref|><|det|>[[115, 473, 179, 490]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 507, 645, 527]]<|/det|> +## Infant subcutaneous fat is immune privileged for mitochondria + +<|ref|>text<|/ref|><|det|>[[111, 541, 886, 877]]<|/det|> +After birth, subcutaneous adipose tissue is a relevant fat depot in mouse and human (6), hence we surveyed the transcriptional landscape of mouse inguinal adipose tissue (iAT) at postnatal day 6 (P6) and P56 by next- generation sequencing (NGS) (Fig. S1A, Fig. S2A,B). P6 iAT was rich in beige adipocytes and mtDNA, and expressed beige adipocyte- associated transcripts together with Prdm16, encoding PR domain containing 16, a key transcriptional regulator of thermogenic fat development (Fig. S1B- E) (10, 16). By contrast, P56 iAT lacked beige adipocytes, contained significantly lower amounts of mtDNA, and expressed transcripts associated with white adipocytes (Fig. S1B- E). Thus, infant but not adult mouse fat is rich in thermogenic, fat- oxidizing adipocytes (6). Beige adipocytes have been reported in the subcutaneous adipose tissue of human infants and children (7, 10), and we found that the level of \(UCP1\) , encoding uncoupling protein 10, in human + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 883, 144]]<|/det|> +infant iAT correlated positively with the level of beige adipocyte genes and negatively with white adipocyte markers (Fig. S1F). + +<|ref|>text<|/ref|><|det|>[[112, 157, 886, 496]]<|/det|> +IFN- stimulated genes (ISGs) were suppressed in adipocytes at P6 (Fig. 1A- C), and the under- represented ISGs belonged to one network (Fig. S2B), and included the stimulator of interferon genes (STING) and IFN- inducible protein absent in melanoma 2 (AIM2) pathways (Fig. 1B). These pathways trigger DNA- inflammasome assembly, inflammasome activation and IFN- response to cytosolic DNA (17). Cytosolic B- DNA is recognized by DDX41 (DEAD- box helicase 41) and p204, also known as IFN- - inducible protein 204 (IFI204) in BALB/C mice, IFI205 in C57/BL6 mice, and IFI16 in human (17) (for details see Fig. S2C). Cytosolic Z- DNA, which is prevalent in transcriptionally active cells (18), is recognized by ZBP1 (Z- DNA- binding protein 1, also termed DAI (19)). Transcription of these cytosolic DNA sensors was low at P6, specifically in adipocytes (Fig. 1B,C, Fig. S2D- H). + +<|ref|>text<|/ref|><|det|>[[112, 508, 886, 846]]<|/det|> +The STING and AIM2 pathways converge on interferon regulatory factor 3 and 7 (IRF3, IRF7), and the adipocyte level of Irf7 was significantly lower at P6 than at P56 (Fig. 1B,C). Accordingly, P6 adipocytes were protected from inflammasome activation by cytosolic DNA (Fig. 1B, Fig. S3A- D, S4A- C) or endosomal DNA (Fig. S5A- F), and genetic ablation of IRF7 protected adipocytes from IFN- response against mtDNA (Fig. S4E). IRF7 activation triggered the expression of the STING/AIM2 pathway in P56 adipocytes (Fig. S4F), and consistently, the STING/AIM2 pathway proteins were lacking in P6 adipocytes (Fig. 1D). In turn, AIM2, DDX41, p204 and ZBP1 were present in the perinuclear region and in the cytoplasm of P56 adipocytes, which distribution is consistent with their known tasks to monitor DNA fragments in specific subcellular compartments (Fig. 1D) (19, 20). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 459]]<|/det|> +We next examined the expression of the STING/AIM2 pathways and IRF7 in the inguinal adipose tissue (iAT) of human infants and children (0.3–6.9 years of age, N=26). Overweight (BMI- SDS>1.28) and obesity (BMI- SDS>1.88) strongly increased the expression of IFI16, ZBP1, and IRF7, and moderately increased TMEM173 level (Fig. 1E, S6A- C), which was coherent with the loss of beige adipocytes in childhood obesity (3, 7, 21). IFI16 protein level positively correlated with adipocyte size (Fig. 1E). IRF7 and IFI16 expression was triggered by in vitro white adipogenesis (Fig. 1E, Fig. S6D), and TMEM173 expression positively correlated with IFI16 and IRF7 levels and was increased by premature loss of beige fat (Fig. 1E, Fig. S6D). We next extended the age group of our analysis (7.0–11.0 years, N=73; 11.1–20.5 years, N=155) and found that in lean subjects the STING/AIM2 pathways moderately increased with age, matching the time scale of the physiological WAT expansion (Fig. S6E). + +<|ref|>text<|/ref|><|det|>[[112, 472, 886, 701]]<|/det|> +In summary, immune response to cytosolic mtDNA and mtRNA was lacking in P6 adipocytes and was dependent on IRF7 (Fig. 1F, Fig. S3D, Fig. S4C- E). Moreover, STING had opposing functions in P6 and P56 adipocytes: activation of STING with its natural activator 2'3'- cyclic- GMP- AMP (cGAMP), increased autophagosome number and mitophagy in P6 adipocytes (Fig. 1G,H,I, Fig. S7A- C), while STING inhibition compromised mitophagy, reduced mitochondrial mass and led to inflammation in P6 adipocytes (Fig S7D- G). On the contrary, cGAMP triggered IFN- response in P56 adipocytes (Fig. 1F, Fig. S3D). + +<|ref|>text<|/ref|><|det|>[[112, 715, 886, 876]]<|/det|> +STING stimulates IFN- response against mtDNA (22), however it is known that the STING signaling may also induce autophagy (23, 24). Mitophagy is a form of autophagy and protects the cytosol from leaking mtDNA (25). Our data show that an autophagy- inducer effect of STING protects from cytosolic mtDNA accumulation in infant adipocytes (Fig. S7D- G), and infant adipocytes are also protected against the STING- induced IFN- response (Fig. 1I). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[131, 95, 866, 567]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 572, 660, 590]]<|/det|> +
Fig 1. Infant adipocytes are immune privileged for mitochondria
+ +<|ref|>text<|/ref|><|det|>[[123, 590, 875, 890]]<|/det|> +(A) NGS analysis of mouse iAT, DEGs: differentially expressed genes, ISGs: interferon stimulated genes. (B) Excerpt of the interactome-, and heat map of genes underrepresented in P6 iAT. Scheme of STING signaling, and inflammasome-associated caspase 1 (CASP1) activity in P6 and P56 iAT in response to 18h cGAMP treatment. Cyt-B-DNA: cytosolic B-DNA; Cyt-Z-DNA: cytosolic Z-DNA. (C) Transcription of the STING/AIM2 pathways in mouse iAT at P6 and P56. (D) Expression of DNA sensors in adipocytes. (E) Transcription of the STING/AIM2 pathway in iAT of human infants and children. Correlation of IFI16 level and adipocyte (AC) size in human. IRF7 level in human preadipocytes (Pre-ACs) and white ACs. Correlation of IFI16 and IRF7 with TMEM173 levels in human infant iAT. (F) Response of P6 and P56 adipocytes to 18h cGAMP treatment. (G) Mito-Tracker-Red (MTR) staining of P6 adipocytes after 2h cGAMP treatment. (H) Top: Labeling of autophagosomes (APh) in P6 adipocytes. nc: nucleus, Bottom: APh number in P6 adipocytes and in 3T3-L1 cells following vehicle or 2h cGAMP treatment. TEM image of a P6 adipocyte showing autophagosome formation. Php: phagophore, Phs: phagosome, Phl: phagolysosome, Mt: mitochondrion. Scale: 10 μm (D,G,H); 0.1 μm (TEM). \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student's 2-tailed unpaired \(t\) -test or one-way ANOVA with Dunnett's post-hoc test. (I) Opposing effects of STING activation in P6 and P56 iAT. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 88, 795, 110]]<|/det|> +## Infant adipocytes employ mtRNA as a paracrine signal for beige fat development + +<|ref|>text<|/ref|><|det|>[[111, 120, 886, 670]]<|/det|> +We found that P6 adipocytes secreted mitochondrial contents in extracellular vesicles (EVs). Adipocyte EVs were generated in the endosomal pathway, by inverse budding of endosomes, leading to the formation of multivesicular bodies (MVBs) (Fig. 2A,B; S8A- G). In line with this, transcripts necessary for inverse budding of endosomes and the generation of MVBs were overrepresented in iAT at P6 (Fig. 2B). Inverse budding allows cytosolic nucleic acids to be delivered to MVBs, and this process is a form of micro- autophagy (26). Endosomal content can be further targeted for degradation in the lysosomes; however lysosomal genes were underrepresented in P6 iAT and by contrast, transcripts required for exocytosis were over- represented in P6 iAT (Fig. 2B). P6 EVs were packed with mtDNA molecules and mitochondrial mRNA and rRNA species (Fig. 2C- E). Some of the EV cargo mRNAs, including Nds, Col and Cytb, are known to generate non- coding mtRNA species (27, 28). The adipose tissue mesenchymal stem cell EV- specific microRNA miR29a- 5p was absent in P6 EVs (Fig. S8H). P6 EVs also contained minimal amounts of circular- RNA, piwi- RNAs and the adipocyte- specific microRNA miR34a, together with traces of Ucp1 mRNA (Fig. S8I). P6 adipocytes released more EVs than their P56 counterparts (Fig. S8J), and inhibitors of EV generation suppressed both DNA and RNA release from adipocytes (Fig. S8K). + +<|ref|>text<|/ref|><|det|>[[112, 681, 886, 876]]<|/det|> +P6 EVs increased mitochondrial content, mitobiogenesis, uncoupling protein- 1 (UCP1) expression and thermogenesis in recipient adipocytes, without inducing unfavorable mitochondrial swelling (Fig. 2F,G; Fig. S9A). P6 EVs also triggered the transcription of beige adipocyte genes (Fig. 2H). Adipocyte EVs carried dsRNA (Fig. 2E, S9B), which may activate Toll- like receptor 3 (TLR3), or the retinoic acid- inducible gene- I (RIG- I) and RIG- I- like melanoma differentiation- associated protein 5 (MDA5) signaling (29, 30). Cytosolic single stranded RNA, or stimulation of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 145]]<|/det|> +TLR3 did not mirror the effects of EVs (Fig. S10A- C), unlike the activation of RIG- I/MDA5 which induced strong beige adipocyte gene transcription (Fig. 2I, S10D- H). + +<|ref|>text<|/ref|><|det|>[[113, 158, 884, 283]]<|/det|> +Beige- inducing effect of EVs was dependent on IL- 6/STAT3 and RIG- I/MDA5 signaling (Fig. 2J, Fig. S11A- E), and the lack of RIG- I or MDA5 led to the loss of beige adipocytes and compromised mitobiogenesis, and compromised the expression of the nucleus- encoded mitochondrial succinate dehydrogenase complex (Fig. 11F,G). + +<|ref|>text<|/ref|><|det|>[[112, 297, 885, 528]]<|/det|> +Nucleic acids in EVs are protected from extracellular nucleases by the surrounding membrane and they may function as intercellular messengers (31). Accordingly, delivery of total mRNA into the cytosol induced beige adipocyte gene expression, mitobiogenesis and mitochondrial thermogenesis (Fig. 2I, Fig. S10G,H) in a RIG- I/MDA5- dependent manner (Fig. 2J,K, Fig. S11H). Cytosolic mtDNA stimulated mitophagy in infant adipocytes (Fig. S12A,B). In summary, EVs of infant adipocytes conveyed mtRNA and mtDNA to recipient adipocytes and triggered beige adipocyte differentiation and mitophagy, respectively. + +<|ref|>text<|/ref|><|det|>[[112, 540, 884, 666]]<|/det|> +Breast milk is a known beige- inducing signal (7), and we found that human breast milk EVs were rich in mtRNA (Fig. S12C). Eventually, breast milk EVs – unlike formula milk EVs – induced beige adipocyte gene expression, mitobiogenesis and mitochondrial thermogenesis, and in turn reduced IRF7 abundance in human adipocytes (Fig. S12D,E). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 90, 872, 562]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 567, 872, 660]]<|/det|> +
Fig. 2. Infant adipocytes cast away mtRNA to induce beige adipose tissue development (A) Clathrin-coated pits, endosome budding [1-4] and multivesicular bodies (MVBs) [5] in P6 adipocyte. Pm: plasma membrane, En: endosome, Aly: autolysosome, Cav: caveolae, arrowhead: EVs in MVBs. Scale: \(1 \mu \mathrm{m}\) . (B) P6/P56 iAT comparison of transcripts associated with endosomes, MVBs, lysosomes and exocytosis. (C) TEM image of EVs released by P6 adipocytes. Scale: \(0.1 \mu \mathrm{m}\) . FACS analysis of nucleic acids in EVs of P6 adipocytes. N-St: non-stained; St: stained with SytoxGreen. Amount of EV-bound nucleic acids in cell culture media of P6 adipocytes. (D) Level of mtDNA in EVs of P6 adipocytes. \(l\) : light chain; \(h\) : heavy chain. (E) Labeling of dsRNA with J2 antibody in P6 adipocytes; scale: \(10 \mu \mathrm{m}\) . Quantification of RNA species released by EVs of P6 adipocytes. Effect of P6 EVs on mitochondrial content, mitobiogenesis (F), UCP1 level (G) and beige gene expression (H) in P56 adipocytes. -EVs: cells cultured in EV-free media, +EVs: cells treated with EVs. Scale: \(10 \mu \mathrm{m}\) (MTR); \(50 \mu \mathrm{m}\) (UCP1), MFI: mean fluorescence intensity. (I) Cytosolic delivery of mtDNA and mtRNA into 3T3-L1 cells, and their effect on beige gene transcription and mitobiogenesis. (J) Effect of P6 EVs on mitobiogenesis of adipocytes. \(Ddx58^{- / - }\) : RIG-I (DDX58)-deficient adipocytes, \(Mda5^{- / - }\) : MDA5-deficient adipocytes. \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student's 2-tailed unpaired \(t\) -test or one-way ANOVA with Dunnett's post-hoc test. (K) Scheme of mtRNA-activated signal transduction in infant adipocytes.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 89, 666, 110]]<|/det|> +## Suppressed IRF7 signaling permits beige adipogenesis by mtRNA + +<|ref|>text<|/ref|><|det|>[[112, 123, 886, 425]]<|/det|> +P56 adipocytes expressed IRF7, unlike P6 adipocytes. Activation of the STING/AIM2 and RIG- I/MDA5 pathway was strong in P56 adipocytes with synthetic ligands, with mtRNA or with mtDNA, leading to Ifnb expression (Fig. S13A). Ultimately, IFNβ damaged adipocyte mitochondria (Fig. S13B,C). In turn, IRF7- deficient adipocytes were immune privileged for mitochondria (Fig. 3A, S4E), and mice lacking IRF7 retained their beige adipocytes to adulthood (Fig. 3B). This is coherent with the protection of IRF7- deficient mice from obesity (32). Moreover, P6 EVs reduced Irf7 mRNA and IRF7 protein levels in adipocytes (Fig. 3C) and did not induce IFN- response (Fig. S13D). On the contrary, P56 EVs induced IFN- response and triggered Irf7 expression, and reduced mitochondrial content in adipocytes (Fig. S13D,E). + +<|ref|>text<|/ref|><|det|>[[112, 438, 886, 880]]<|/det|> +Vitamin D receptor (VDR)- controlled gene networks were highly expressed in P6 iAT (Fig. S2A). The known VDR- target Camp, encoding cathelicidin, an adipose tissue enriched antimicrobial peptide (33), was highly expressed at P6. In turn, the VDR- repressed gene Corola had a low transcript level at P6 (Fig. 3D). Corola encodes coronin A1, also known as tryptophan- aspartate containing coat protein (TACO), which inhibits autophagosome formation (34). Low levels of coronin A1 allow autophagy (34), which is in accordance with the prominent autophagy we found in P6 iAT (Fig. 1H). The transcription of vitamin D metabolizing enzymes favored the storage of vitamin D3 (Vit- D3) and the synthesis of the potent VDR- agonist calcitriol in P6 iAT (Fig. 3D). Moreover, miR434- 3p, a VDR- controlled miRNA which had complementarity to Irf7 mRNA (35) was also highly expressed in P6 iAT (Fig. 3E). IRF7 level and inflammasome activation was effectively reduced by miR434- 3p in adipocytes (Fig. 3E). Moreover, P6 EVs were rich in Vit- D3, and cytosolic mRNA increased the transcription of the calcitriol synthesis gene Cyp27b1 in adipocytes (Fig. 3F). VDR protein expression was higher in P6 than in P56 iAT, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 283]]<|/det|> +Vit- D3 effectively suppressed Irf7 transcription in a VDR- dependent manner in adipocytes (Fig. 3F). Diet- induced obesity diminished adipocyte Vdr expression, and concomitantly upregulated Irf7 in mice (Fig. 3G). Accordingly, inhibition of VDR signaling in young mice led to the loss of beige adipocytes in iAT, along with increased IRF7 level in adipocytes (Fig. 3H). In turn, suppression of IRF7 level with miR434- 3p protected from inflammasome activation in adipocytes of HFD- fed mice (Fig. 3I). + +<|ref|>text<|/ref|><|det|>[[112, 298, 885, 597]]<|/det|> +IRF7 is a hub for the transcription of AIM2/STING pathway (Fig. 13F,G), and thus repression of IRF7 expression is a potential mechanism that protects infant adipocytes from an IFN- response to cytosolic mtDNA/mtRNA (Fig. 13H). We found that VDR signaling suppressed IRF7 expression and abolished immune response towards cytosolic mtDNA/mtRNA in mouse and human adipocytes (Fig. 3J- L), but did not affect Il6 transcription and IL- 6 release (Fig. 3K,L). VDR thus did not block the beige adipocyte- inducing IL- 6 production, however suppressed IRF7- dependent inflammatory signaling. This allowed cytosolic mtRNA to induce mitobiogenesis and beige gene expression, and mtDNA to trigger mitophagy, without unfavorable induction of an IFN- response (Fig. 3M). + +<|ref|>sub_title<|/ref|><|det|>[[113, 646, 857, 668]]<|/det|> +## Obesity in early postnatal life compromises immune privilege of adipocyte mitochondria + +<|ref|>text<|/ref|><|det|>[[112, 680, 885, 876]]<|/det|> +We found that childhood obesity compromised VDR- controlled gene networks and decreased the expression of the calcitriol producing CYP27A1 (Fig. 4A), and increased IRF7 expression in the iAT (Fig. 1E). Similarly, diet induced obesity compromised Vdr and increased Irf7 expression in mouse, and inhibition of VDR signaling in infant mice led to the loss of beige fat cells (Fig. 3G,H). Next, we studied a mouse model of childhood obesity, using infant mice which were nursed by dams fed with HFD (Fig. 4B) (36). In the offspring of HFD- fed dams adipocytes had a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 494]]<|/det|> +compromised Vdr, and a robust Irf7 expression (Fig. 4C), and beige adipocytes were lacking from the iAT (Fig. 4D). Eventually obesity developed and the adipocytes had a sustained inflammasome activation (Fig. 4E). Moreover, the mitochondrial network was compromised in adipocytes (Fig. 4F), and AIM2/STING pathway proteins were expressed in the cytosol and in the nuclei of adipocytes of mice nursed by HFD-fed dams (Fig. 4F). In turn, Vit-D3 reverted these adverse effects and protected the beige adipocyte content in infant mice (Fig. 4G), reduced obesity and adipocyte inflammation (Fig. 4H). In adult HFD-fed mice, cytosolic delivery of mtRNA into the iAT, combined with Vit-D3 treatment, reduced IRF7 level and increased beige adipocyte content in the iAT (Fig. 4I,J), reduced obesity and adipocyte inflammation, increased mitochondrial mass, thermogenesis and energy expenditure, inhibited inflammasome activation following STING activation, and induced adipocyte expression of calcitriol forming Cyp27b1 (Fig. 4K-N, Fig. S14A-C). + +<|ref|>text<|/ref|><|det|>[[112, 507, 886, 877]]<|/det|> +Altogether, the immune privilege of mitochondrial content was dependent on the suppression of adipocyte IRF7 level by VDR. In human, childhood obesity compromised VDR signaling and increased IRF7 expression in the adipose tissue. Adipocyte maturation increased IRF7 level, and in turn, Vit-D3 reduced IRF7 expression and immune response to cytosolic mtRNA and mtDNA in human adipocytes. Similarly, diet induced obesity compromised Vdr and triggered Irf7 transcription in both adult and infant mice and triggered immune response against cytosolic mtDNA and mtRNA. These data show that beige adipocytes lack an immune response against mtDNA/mtRNA at least in part due to VDR signaling (Fig. 5A). When VDR sustains this immune privilege of mitochondria, cytosolic mtRNA stimulates the expression of nucleus- encoded mitochondrial genes and promotes beige adipocyte development (Fig. 5B). This mitochondria- to- nucleus signaling protects against obesity. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 88, 868, 528]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 531, 655, 549]]<|/det|> +
Fig. 3. VDR abrogates IRF7 expression in the infant adipocytes
+ +<|ref|>text<|/ref|><|det|>[[123, 549, 875, 830]]<|/det|> +(A) Response of P56 adipocytes to cGAMP, CCCP (carbonyl cyanide m-chlorophenyl hydrazone)-induced mitochondrial damage, cytosolic mtDNA and mtRNA. Irf7-/-: IRF7-deficient adipocytes. (B) iAT of adult wt and Irf7-/- mice. Scale: \(25 \mu \mathrm{m}\) . H&E: hematoxylin-eosin (C) Effect or P6 EVs on Irf7 and IRF7 level in mouse adipocytes (D) Structure of miR434-3p, its level in P6 and P56 iAT, and its effect on IRF7 level and inflammasome activity in mouse adipocytes. (E) P6/P56 transcript level of Vdr, VDR-controlled genes and vitamin D metabolism genes in iAT. (F) Top: Level of Vit-D3 in P6 EVs, effect of cytosolic mtRNA on the expression of calcitriol synthesizing Cyp27b1, and the ratio of VitD3/VDR in P6 iAT. Bottom: Effect of 48h Vit-D3 treatment on Irf7 level in mouse adipocytes. PS121912: VDR inhibitor. (G) Level of Vdr and Irf7 in iAT of HFD-fed mice. (H) Left: Histology of iAT at P10 of mice treated with vehicle or PS12912. Scale: \(25 \mu \mathrm{m}\) . Right: Adipocyte IRF7 protein level of the same mice. (I) Effect of overexpression of miR434-3p on HFD-induced inflammasome activation in adipocytes. (J,K) Response of \(1 \mu \mathrm{M}\) Vit-D3 pretreated mouse adipocytes to cytosolic mtDNA, mtRNA, or cGAMP. (L) IRF7 level and cGAMP response of human adipocytes treated with vehicle or Vit-D3. \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student's 2-tailed unpaired \(t\) -test or one-way ANOVA with Dunnett's post-hoc test. (M) Scheme of VDR function in infant adipocytes. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 90, 875, 521]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 523, 875, 896]]<|/det|> +
Fig. 4. Effect of cytosolic mRNA combined vit Vit-D3 treatment in diet-induced obesity (A) VDR-controlled gene expression in iAT of children. (B) Nursing mice received high-fat diet (HFD) or normal chow diet (NCD) between postnatal day 6 and 9 of the offspring. Mice nursed by NCD-fed or HFD-fed dams were analyzed on postnatal day 10 (P10). (C) \(Vdr\) and \(Irf7\) expression in iAT. (D) Histology of iAT. H&E: hematoxylin and eosin staining, UCP1: UCP1 immunostaining, Scale: \(50 \mu \mathrm{m}\) . Note the lack of multilocular adipocytes in mice nursed by HFD-fed dams. (E) Ratio of iAT and body weight, and inflammasome caspase 1 (CASP1) activity of the adipocytes. (F) Mitochondrial network and the expression of AIM2, DDX41, p204 and ZBP1 in adipocytes. MTR: MitoTracker Red. Scale \(50 \mu \mathrm{m}\) . (G) Mice were nursed by HFD-fed dams, and treated with vehicle or Vit-D3 from P6 to P9. Histology of iAT on P10. Scale: 100 \(\mu \mathrm{m}\) (H) Ratio of iAT and body weight, and CASP1 activity of the adipocytes on P10. (I) In adult HFD-fed mice the iAT was transfected with vehicle or mtRNA, and IRF7 protein level was measured in adipocytes. (J) Histology of iAT of vehicle- or mtRNA-transfected mice. (K) Adipose tissue weight/body weight ratio, and CASP1 activity of adipocytes. eAT: epididymal adipose tissue (L) Mitochondrial network of adipocytes isolated from vehicle- or mtRNA-transfected mice. Scale: \(10 \mu \mathrm{m}\) . Note the expansion of the mitochondrial network after mtRNA treatment. (M) Mitochondrial mass (relative MTR fluorescent intensity) and mitochondrial temperature change (Mito-ΔT) in adipocytes isolated from vehicle- or mtRNA-transfected mice. (L) CASP1 activity of adipocytes isolated from vehicle- or mtRNA-transfected mice, and treated with vehicle of cGAMP for 4h. \(**P< 0.01\) , \(***P< 0.001\) . Student’s 2-tailed unpaired \(t\) -test or one-way ANOVA with Dunnett’s post-hoc test.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 91, 206, 108]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[112, 123, 884, 353]]<|/det|> +Adipose tissue inflammation is considered deleterious for metabolism (37). However, various lines of evidence show that differentiation of thermogenic adipose tissue requires JAK/STAT3 signaling (7, 38, 39), and an autocrine IL- 6/STAT3 signaling loop is sustained by breast milk- derived lipid signaling in the newborn adipose tissue (7). Some inflammatory signal mechanisms that cause obesity- associated metabolic impairment also sustain beige adipocytes (40, 41). Here we report the unexpected finding that beige adipocyte development is promoted by a potentially inflammation- evoking cytosolic RNA signal, released by the mitochondria of infant adipocytes. + +<|ref|>image<|/ref|><|det|>[[196, 405, 800, 705]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 710, 556, 729]]<|/det|> +
Fig. 5. Role of mtRNA signaling in beige adipocytes
+ +<|ref|>text<|/ref|><|det|>[[123, 728, 874, 905]]<|/det|> +(A) Under physiological conditions infant adipocytes release cytosolic mtRNA and mtDNA in extracellular vesicles (EVs). Eventually, mtRNA serves as endogenous signal for beige adipogenesis in neighboring cells through the RIG-I/MDA5/IL-6/STAT3 pathway. In turn, mtDNA content of the EVs triggers mitophagy through STING signaling. (B) Albeit cytosolic mtRNA and mtRNA are noxious signals, they can act as metabolically beneficial mitochondria-to-nucleus signals when IRF7 expression is suppressed. VDR is an effective suppressor of IRF7 and abrogates IFN-response to cytosolic mtRNA and mtDNA in infant adipocytes. Infant adipocytes are hence immune privileged sites for mitochondria, allowing a retrograde mitochondria-to-nucleus signaling through mtRNA, which is key for mitobiogenesis and beige fat development. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 565]]<|/det|> +The endosymbiotic origin of mitochondria has led to a metabolic co- dependence of the mitochondria and the host cell (42). This is driven by a retrograde, mitochondria- to- nucleus signaling pathway, as the majority of genes required for the maintenance of mitochondria are encoded in the nuclear genome. We show that, analogous to a parasite- host interaction, mitochondrial nucleic acids are released by EVs, and are taken up by surrounding adipocytes to activate cytosolic RNA sensors that stimulate an autocrine IL- 6/STAT3 signaling loop, ultimately triggering the nuclear expression of beige adipocyte genes (Fig. 2K, Fig. 5A). Non- coding RNA species of mitochondria are known to increase the transcription of mitochondrial genome- encoded genes (27). As an equivalent mechanism, we show that mtRNA species boost the transcription of nuclear genome- encoded genes for mitochondrial biogenesis and thermogenesis. This is key for mitobiogenesis since the majority of the mitochondrial genes are encoded in the nuclear genome (42). The release of EVs containing mitochondrial nucleic acids resembles the recently explored mechanism that allows nucleic acid delivery from bacteria to host cells in membrane microvesicles (43, 44). + +<|ref|>text<|/ref|><|det|>[[112, 577, 886, 842]]<|/det|> +The primary sensors of cytoplasmic mtRNA are RIG- I and MDA5. RIG- I detects dsRNAs with or without a 5'- triphosphate end; MDA5 binds uncapped RNA; and RIG- I and MDA5 selectively recognize short and long dsRNAs, respectively (29, 30). Given the prokaryote origin of mitochondria, various mtRNA species such as mitochondrial ribosomal RNAs, uncapped mitochondrial mRNA, and non- coding mtRNAs, can potentially stimulate the cytoplasmic RNA sensor system (45, 46). Beige adipocyte gene transcription was achievable by indirect RIG- I activation using cytosolic p(dA:dT), and also by MDA5 activation using cytosolic high molecular weight p(I:C), but not with cytosolic ssRNA. Coherently, lack of RIG- I and MDA5 signaling + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 145]]<|/det|> +compromised the mtRNA- mediated beige adipocyte development, and abrogated nucleus- encoded SDH- A expression and mitobiogenesis, and promoted the loss of beige adipocytes in mice. + +<|ref|>text<|/ref|><|det|>[[112, 157, 886, 706]]<|/det|> +Nevertheless, excessive release of mitochondrial content is a danger signal, and activates an IFN- response, which is detrimental for thermogenic fat development (16, 47, 48), triggers obesity, mitochondrial dysfunction and the mitochondrial pathway of adipocyte apoptosis (49, 50), and may aggravate obesity- associated metabolic diseases (51, 52). We show here that beige adipocytes lack cytosolic DNA sensors and show suppressed expression of IRF7. Consequently, cytosolic mtDNA and mtRNA do not stimulate an IFN- response in beige adipocytes. Instead, beige adipocytes respond by activating mitophagy to cytoplasmic mtDNA, allowing the removal of damaged mitochondria and curtailing inflammation. Moreover, cytosolic mtRNA stimulates mitobiogenesis. The key protective mechanism – i.e., compromised IRF7 signaling – is a trait of the infant adipocytes, and is lost in the course of adipocyte maturation. While the activation of STAT1 and NFκB signaling may account for the increasing IRF7 expression during adipocyte maturation (6), we show that VDR signaling contributes to the suppression of IRF7 level in infant adipocytes, and cytosolic mtRNA stimulates mitochondrial calcitriol synthesis – hence supplies a VDR ligand – in infant adipocytes. However, diet- induced obesity in mouse, and obesity in children were associated with robust expression of the cytosolic DNA sensor system and IRF7, leading to the loss of the immune privilege of mitochondria. + +<|ref|>text<|/ref|><|det|>[[113, 718, 886, 878]]<|/det|> +VDR signaling is involved in the innate immune response in the adipose tissue (33), and VDR may also skew IFN- response and IRF7 expression (53, 54). Vit- D3 supplementation is today routine in postnatal care, however, Vit- D3 deficiency is prevalent among obese children and adolescents and is a risk factor for metabolic diseases (55- 57). Vit- D3/VDR is proposed to inhibit weight gain by activating UCP3 in the muscles (58), albeit VDR overexpression promotes weight + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 319]]<|/det|> +gain in mouse (59). Indeed, promotion of formula feeding originally served to increase Vit- D3 supply and induce weight gain (60). Formula milk lacks maternal lipid species that maintain beige fat and has obesogenic effects (7). We also show here that formula milk lacks beige- inducing mRNA signals. Moreover, VDR signaling was impaired in the adipose tissue of obese children, therefore despite its increased Vit- D3 level, formula milk is not sufficient to trigger beige adipogenesis. However, when Vit- D3 supplementation is combined with stimulation of cytosolic mRNA signaling, beige adipocytes develop and obesity is reduced. + +<|ref|>text<|/ref|><|det|>[[113, 333, 884, 423]]<|/det|> +In summary, beige adipocyte development is dependent on a mtRNA- mediated signaling and the suppression of IFN- response. Restoring the mtRNA- mediated mitochondria- to- nucleus signaling may represent a novel and effective mechanism to increase beige fat and reduce obesity. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 191, 108]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 266, 144]]<|/det|> +## Animals and cells + +<|ref|>text<|/ref|><|det|>[[113, 158, 884, 320]]<|/det|> +We used wt male C57BL/6 (Charles River Laboratories, Wilmington, MA), Irf7- (RIKEN, Wako, Japan), Ddx58- and Mda5- (kindly provided by Gunther Hartmann, University of Bonn, Germany) mice. All mouse lines were housed under SPF conditions. Animal experiments were approved by the local ethics committees. Primary mouse adipocytes were isolated by collagenase digestion and separation of cell fractions and subsequently analyzed or cultured, as described (7). + +<|ref|>sub_title<|/ref|><|det|>[[115, 334, 253, 353]]<|/det|> +## Human samples + +<|ref|>text<|/ref|><|det|>[[113, 367, 884, 529]]<|/det|> +Subcutaneous adipose tissue from human infants, adolescents and young adults were collected in the Leipzig Childhood Adipose Tissue cohort during elective surgery (3). For all children included in the study written informed consent was obtained from the parents. The study protocol was approved by the local ethics committee of the Medical Faculty, University of Leipzig (#265- 08- ff; NCT02208141). Adult adipocytes samples were collected in our previous study (7). + +<|ref|>sub_title<|/ref|><|det|>[[115, 542, 520, 562]]<|/det|> +## mRNA analysis and next-generation sequencing + +<|ref|>text<|/ref|><|det|>[[113, 576, 884, 842]]<|/det|> +Extraction of total RNA was performed as described (6). qPCR assays were carried out on the Quantabio platform (Beverly, MA), using Bactin, Gapdh and Pgia as references. Primer sequences are provided in Supplemental Table 1. NGS analysis was carried out on the BGISEQ- 500 platform by BGI Genomics Inc. (Cambridge, MA), generating about 26.20M reads per sample (Fig. S15). EnrichR, Panther and Interferome- 2.0 were used for annotation of transcripts; clustered image maps (CIMs, heat- maps) were rendered by CIM- Miner and Heatmapper. Gene expression in human samples was quantified by ILLUMINA HT12v4 Gene Expression BeadChip arrays and data were background corrected and quantile normalized (6). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 90, 310, 108]]<|/det|> +## Supplemental methods + +<|ref|>text<|/ref|><|det|>[[113, 124, 884, 215]]<|/det|> +Cytosolic delivery of RNA/DNA, viral infections, ELISA assays, overexpression studies, autophagosome/lysosome labeling, EV collection, FACS, histology, image analysis, and TEM analysis are provided in the Supplemental Information. + +<|ref|>sub_title<|/ref|><|det|>[[114, 230, 399, 248]]<|/det|> +## Data representation and statistics + +<|ref|>text<|/ref|><|det|>[[113, 262, 885, 422]]<|/det|> +Data are represented as mean \(\pm\) s.e.m, along with each individual data point. When data are represented as CIMs to visualize gene transcription differences between experimental conditions, we indicate fold changes or Z- scores of the relative abundance. Statistical significance is indicated as \(^{*}P< 0.01\) , \(^{**}P< 0.01\) ; \(^{***}P< 0.001\) , Student's 2- tailed unpaired \(t\) - test, or 1- way ANOVA with Dunnett's post hoc test. + +<|ref|>sub_title<|/ref|><|det|>[[114, 437, 377, 457]]<|/det|> +## Data and materials availability + +<|ref|>text<|/ref|><|det|>[[113, 471, 885, 631]]<|/det|> +Materials and data are available for secondary use upon request. Flow Repository identifiers of FACS data are as follows: #FR- FCM- Z236, #FR- FCM- Z2R6, #FR- FCM- ZYPU, #FR- FCM- ZYUU. NGS data are deposited at GEO with the accession number #GSE185317. For secondary analysis, we used our previously published DNA Chip and NGS datasets, with accession numbers #GSE125405, #GSE90658, #GSE154925 and #GSE133500. + +<|ref|>sub_title<|/ref|><|det|>[[115, 655, 281, 673]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 689, 590, 708]]<|/det|> +We thank Dr. Kenneth McCreath for editing the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 724, 190, 742]]<|/det|> +## Funding + +<|ref|>text<|/ref|><|det|>[[113, 757, 884, 882]]<|/det|> +This study was supported by the German Research Fund (DFG, RO 4856- 1, to TR; DFG, CRC1052 C05, to AK), the European Foundation for the Study of Diabetes on New Targets for Type 2 Diabetes, Supported by MSD (No. 96403, to TR), by the Federal Ministry of Education and Research (BMBF), Germany (FKZ: 01EO1501 IFB Adiposity Diseases, to AK. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 288, 108]]<|/det|> +## Author contribution + +<|ref|>text<|/ref|><|det|>[[115, 124, 883, 180]]<|/det|> +ACH, HY, YTL, CCC, VD carried out experiments, AK, AH, JC designed experiments, TR conceived the project, designed experiments and wrote the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 228, 209, 247]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 260, 880, 833]]<|/det|> +1. 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IFNα Impairs Autophagic Degradation of mtDNA Promoting Autoreactivity of SLE Monocytes in a STING-Dependent Fashion. Cell reports. 2018;25(4):921-33.e5. + +<|ref|>text<|/ref|><|det|>[[111, 483, 800, 528]]<|/det|> +51. Tian Y, Jennings J, Gong Y, and Sang Y. Viral Infections and Interferons in the Development of Obesity. Biomolecules. 2019;9(11). + +<|ref|>text<|/ref|><|det|>[[111, 535, 880, 580]]<|/det|> +52. Birk RZ, and Rubinstein M. IFN-alpha induces apoptosis of adipose tissue cells. Biochem Biophys Res Commun. 2006;345(2):669-74. + +<|ref|>text<|/ref|><|det|>[[111, 587, 870, 632]]<|/det|> +53. Cippitelli M, and Santoni A. Vitamin D3: a transcriptional modulator of the interferon-γ gene. European Journal of Immunology. 1998;28(10):3017-30. + +<|ref|>text<|/ref|><|det|>[[111, 639, 880, 712]]<|/det|> +54. Stoppelenburg AJ, von Hegedus JH, Huis in't Veld R, Bont L, and Boes M. Defective control of vitamin D receptor-mediated epithelial STAT1 signalling predisposes to severe respiratory syncytial virus bronchiolitis. The Journal of Pathology. 2014;232(1):57-64. + +<|ref|>text<|/ref|><|det|>[[111, 718, 875, 763]]<|/det|> +55. Roth CL, Elfers C, Kratz M, and Hoofnagle AN. Vitamin d deficiency in obese children and its relationship to insulin resistance and adipokines. J Obes. 2011;2011:495101-. + +<|ref|>text<|/ref|><|det|>[[111, 770, 875, 841]]<|/det|> +56. de Oliveira LF, de Azevedo LG, da Mota Santana J, de Sales LPC, and Pereira-Santos M. Obesity and overweight decreases the effect of vitamin D supplementation in adults: systematic review and meta-analysis of randomized controlled trials. 2020;21(1):67-76. + +<|ref|>text<|/ref|><|det|>[[111, 848, 820, 893]]<|/det|> +57. Pramono A, Jocken JWE, and Blaak EE. Vitamin D deficiency in the aetiology of obesity-related insulin resistance. 2019;35(5):e3146. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 875, 293]]<|/det|> +58. Fan Y, Futawaka K, Koyama R, Fukuda Y, Hayashi M, Imamoto M, et al. Vitamin D3/VDR resists diet-induced obesity by modulating UCP3 expression in muscles. J Biomed Sci. 2016;23(1):56-. +59. Xu Y, Lou Y, and Kong J. VDR regulates energy metabolism by modulating remodeling in adipose tissue. European journal of pharmacology. 2019;865:172761. +60. Biggs K, Hurrell K, Matthews E, Khaleva E, Munblit D, and Boyle R. Formula Milk Supplementation on the Postnatal Ward: A Cross-Sectional Analytical Study. Nutrients. 2018;10(5):608. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 70, 880, 496]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 496, 878, 529]]<|/det|> +
Supplemental Figure 1. Characterization of mouse inguinal adipose tissue at P6 and P56, and human inguinal adipose tissue in infancy
+ +<|ref|>text<|/ref|><|det|>[[117, 528, 880, 891]]<|/det|> +(A) Scheme of NGS analysis. For RNA sequencing we obtained inguinal fat depots (iAT) of 3-3 mice at postnatal day 6 (P6) and P56 and compared their transcriptional profiles. The differentially expressed genes (DEGs) were analyzed further in this study. (B) Hematoxylin and eosin (H&E) staining, and immunostaining of UCP1 in mouse iAT at P6 and P56. Scale: 50 μm. (C) Abundance of mtDNA (16S and Ndl genes) relative to genomic DNA in mouse iAT at P6 and P56. (D) Gene network associated with PR/SET domain 16 (PRDM16), a key regulator of brown adipocyte development (1-3). Red symbols indicate DEGs overrepresented in P6 iAT. Beige/brown adipocyte-associated genes were overrepresented in P6 iAT. (E) Transcription of Prdm16 in mouse adipocytes at P6 and P56. Heat map summarizing the transcription level of beige/brown adipocyte marker genes and white adipocyte marker genes in P6 and P56 iAT. Ucp1 is necessary for thermogenesis; Ppargc1a for mitochondrial biogenesis; Cidea, Cox7a1, Dio2, Zic1 are associated with brown/beige adipocytes; Tmem26 and Tbx1 are beige adipocyte markers; Evala is a brown adipocyte marker (4-9); Myf5 is expressed by progenitors of brown adipocytes (10). Levels of Hoxc8 and Hoxc9 increase along white adipocyte development (4), although Hoxc9 may also be a marker of beige adipocytes (9). Lep, Fabp4, Plin2, Adipoq, Gpd1, Slc2a4 and Pparg are associated with white adipocyte maturation (11). See also (12). (F) Correlation of UCP1 levels with beige/brown adipocyte-associated transcripts (PPARGC1A, TMEM26, CIDEA, LHX8) and white adipocyte markers (HOXC8, HOXC9) in the iAT of human male infants (4, 13). P values were determined with linear regression analysis. Age 0.2-3.5 years. Further details regarding beige adipocyte content in mouse and human fat depots are provided in (4, 12-14), and reviewed in the introduction section of (3). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 72, 875, 560]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 562, 850, 579]]<|/det|> +
Supplemental Figure 2. Expression of the STING/AIM2 pathways in P6 and P56 iAT
+ +<|ref|>text<|/ref|><|det|>[[116, 579, 880, 874]]<|/det|> +(A) Gene ontology and STRING protein-protein association network of DEGs overrepresented in P6 iAT. Further analysis is available in (12). Vdr and its gene network were overrepresented at P6. (B) Gene ontology and protein-protein association network of underrepresented DEGs at P6 (15). (C) Structure of the DNA-sensor p204. The three DNA-binding domains are labeled A, B and C. p204 is encoded by Ifi204 in BALB/C mice. In C57/BL6, however, Ifi204 has a frameshift mutation and its function is taken over by Ifi205 (16-18). In 3T3-L1 cells, which have a BALB/C origin, we measured Ifi204, whereas we measured Ifi205 in adipocytes from C57/BL6 mice. Level of Ifi204 in P6 and P56-derived adipocytes mirrored that of Ifi205, shown in Figure 1. (D) Expression of Tmem173 and Mb21d in metabolic organs at P56. Note their prominent expression in iAT and in the epididymal adipose tissue (eAT). (E) Level of Tmem173 and Mb21d in iAT of mice fed normal chow diet (NCD) or high-fat diet (HFD). Amount of STING-expressing ATMs in iAT following NCD or HFD. STING expression was not influenced by HFD. (F) FACS plot of adipose tissue macrophages (ATMs) and adipocytes (ACs) from iAT. ATMs were defined as F4/80+, CD11b+. (G) Single cell sequencing data retrieved from the TabulaMuris consortium (19), showing that the STING pathway is expressed in both ATMs and in adipocytes. There is a marked expression of Ifi205 in adipocytes. (H) FACS analysis of the STING pathway in ATMs at P6 and P56. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 74, 870, 468]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 471, 640, 488]]<|/det|> +
Supplemental Figure 3. Cytosolic DNA sensing in adipocytes
+ +<|ref|>text<|/ref|><|det|>[[115, 485, 880, 740]]<|/det|> +(A) Left: Possible routes of DNA and RNA release into the cytosol: membrane fusion with EVs [1]; release of mtDNA and mtRNA into the cytosol [2]. Both mechanisms can activate RIG-I/MDA5 or STING signaling. Middle: Scheme of RIG-I/MDA5 signaling. RNA Pol III: RNA polymerase III, which can generate dsRNA from DNA templates, ultimately activating the RIG-I/MDA5 pathway. Right: Expression of RNA Pol III and RIG-I/MDA5 pathway genes in P6 iAT. As a comparison, genes of the STING signaling pathway are also shown. See also the heatmap in Figure 1. (B) Left: Scheme of LyoVec-encapsulated dsDNA. The LyoVec lipid carrier fuses with the cell membrane and dsDNA is released into the cytosol of the recipient cell. Right: Responsiveness of P6 and P56 adipocytes to the synthetic dsDNA poly dA:dT (pdA:dT) packed in LyoVec (5 μg/ml, 2 h). (C) Left: Scheme of LyoVec encapsulated VACV-70 (Vaccinia virus DNA sequence), a ligand for IFI16 in human and p204/p205 in mouse. Responsiveness of P6 and P56 adipocytes to 1 μg/ml VACV-70 (18 h). (D) Left: Structure of cGAMP and scheme of its entry into the cytosol mediated by the solute carrier SLC19a (20). Transcript level of Slc19a1 was equivalent in P6 and P56 iAT. Right: IFN-response of P6 and P56 iAT after cGAMP treatment (10 μg/ml, 18 h). *P<0.05, **P<0.01, ***P<0.001. Student's 2-tailed unpaired t-test or one-way ANOVA with Dunnett's post-hoc test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 73, 880, 296]]<|/det|> + +<|ref|>sub_title<|/ref|><|det|>[[118, 301, 737, 319]]<|/det|> +## Supplemental Figure 4. Cytosolic mtDNA/mtRNA sensing in adipocytes + +<|ref|>text<|/ref|><|det|>[[117, 319, 880, 518]]<|/det|> +(A) Scheme of lipofectamine-encapsulated total mtDNA and its delivery into the cytosol of adipocytes. Cytosolic mtDNA is recognized by p204 (IFI16 in humans) and AIM2, and ultimately activates inflammasome and STING signaling. (B) Scheme of lipofectamine-encapsulated total mtRNA and its delivery into the cytosol of adipocytes. Cytosolic mtRNA activates RIG-I and MDA5 signaling. (C) Inflammasome activation of P56 and P6 adipocytes after 4-h challenge with cytoplasmic mtDNA or mtRNA. CASP1: caspase-1 of the inflammasome (D) IFN-response of P56 and P6 adipocytes following transfection with mtDNA or mtRNA (2 µg/ml, 18 h). (E) Ifnb transcription of wild-type (wt) and Irf7-/- adipocytes following transfection with vehicle, mtDNA or mtRNA (2 µg/ml, 4 h). (F) Transcription of the STING/AIM2 pathway, Ddx58 and Irf7 following 18-h activation of IRF7 signaling with LyoVec-encapsulated p(I:C). *P<0.05, **P<0.01, ***P<0.001. Student's 2-tailed unpaired t-test or one-way ANOVA with Dunnett's post-hoc test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 74, 875, 339]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 342, 707, 358]]<|/det|> +
Supplemental Figure 5. Endosomal DNA/RNA sensing in adipocytes
+ +<|ref|>text<|/ref|><|det|>[[117, 358, 880, 526]]<|/det|> +(A) Scheme of DNA sensing pathways activated by endosomal uptake of DNA. (B) Rhodamine-conjugated naked DNA molecules (p(dA:dT) and CpG) were readily taken up by P6 adipocytes. Scale: \(10 \mu \mathrm{m}\) . (C) Effect of naked CpG on inflammatory gene expression in P6 and P56 adipocytes. (D) TEM image of two adjacent adipocytes in vitro. The cell membranes form numerous endosomes allowing the interchange of EV cargos. en: endosomes; mt: mitochondria; scale: \(1 \mu \mathrm{m}\) . (E) Transcript level of TLRs in P6 and P56 iAT. Respective ligands (dsRNA, ssRNA, DNA and rRNA) of the receptors are indicated. Mitochondrial RNA stimulates human TLR8 (21) and triggers inflammation in mouse macrophages mediated by TLR9 (22). (F) J2 antibody labeling of dsRNA at the lamellipodia of adipocytes, in an active region of endocytosis (23). nc: nucleus, scale: \(10 \mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 81, 878, 473]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 472, 744, 490]]<|/det|> +
Supplemental Figure 6. STING/AIM2 pathways in human adipose tissue
+ +<|ref|>text<|/ref|><|det|>[[116, 489, 880, 787]]<|/det|> +(A) Anatomical sites of human inguinal adipose tissue (iAT) samples used in this study. Left: in infants, and Right: in children and adults. For proper comparison we used equivalent fat depots in all age groups, from the region bordered by the inguinal ligament, the fundiform ligament of the penis, and the linea alba. (B) Scheme of human STING/AIM2 pathways and the relative abundance of their gene products in the iAT collected from human infants (0.2–1.0 years of age, N=24), toddlers (1.1–2.0 years, N=29), children (3.0–11.0 years, N=99), adolescents and young adults (11.1–20.5 years, N=155). (C) Top: transcript level of adipose tissue AIM2, DDX41, MB21D (encoding cGAS) and IRF3 in lean (BMI-SDS<1.28) and overweight or obese (BMI-SDS>1.28) infants and children; Illumina HT12v4 assay. Bottom: Correlation of age in years (y) and the transcript level of adipose tissue STING/AIM2 pathway genes in human infants. (D) Top: Immunostaining of IFI16 in a human preadipocyte (Pre-AC) and white adipocyte (AC). Samples from studies (14) and (24). Scale: 50 μm. Bottom: Level of adipose tissue TMEM173 in breastfed and formula-fed infants. Formula-fed infants show premature loss of beige adipocytes in the subcutaneous fat depot (14). (E) Transcript level of the human adipose tissue STING/AIM2 pathway genes at various age groups. Correlation between TMEM173 expression and the level of various DNA sensors. Age group: 0.1–20.5 years. Gender, gestational age, maternal age, maternal diabetes were not correlated with the above parameters. Linear regression analyses with Pearson’s correlation. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 74, 872, 476]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 488, 718, 506]]<|/det|> +
Supplemental Figure 7. STING-mediated mitophagy in P6 adipocytes
+ +<|ref|>text<|/ref|><|det|>[[116, 506, 881, 867]]<|/det|> +(A) Autophagosome (APh) number and size in P6 adipocytes and in 3T3-L1 adipocytes treated with vehicle or \(5\mu \mathrm{g / ml}\) cGAMP for 6h. (B) Top: Western blotting of LC3 in P6 adipocytes and 3T3-L1 cells treated with vehicle or cGAMP (5 \(\mu \mathrm{g / ml}\) , 6 h). Bottom: GFP-labeled mitochondrial remnants accumulate in autophagosomes after cGAMP treatment. Scale: \(10\mu \mathrm{m}\) . (C) Autophagosomes and lysosomes (labeled with Lyso-View) in 3T3-L1 cells cultured in \(10\%\) fetal calf serum (FCS) or in \(1\%\) FCS-containing medium for \(18\mathrm{h}\) . (D) Effect of STING inhibition with \(0.5\mu \mathrm{M}\) H151 on mitochondrial content and morphology in P6 adipocytes. MTR: MitoTracker Red labeling, GFP: GFP labeling of newly synthesized mitochondria with the BacMan 0.2 labeling system, nc: nucleus; scale: \(10\mu \mathrm{m}\) . (E) FACS analysis of MTR labeling of P6 adipocytes, and transcription of inflammatory genes and DNA sensors after \(18\mathrm{h}\) H151 treatment. H151 covalently binds to STING (25). Ddx58 encodes RIG-I. (F) Autophagosomes (arrows) containing GFP-labeled mitochondrial remnants in P6 adipocytes. Scale: \(10\mu \mathrm{m}\) . (G) TEM image of an autophagosome containing mitochondria. mt: mitochondria, MVB: multivesicular body, arrow indicates autophagosome with mitochondrial remnants. Scale: \(500\mathrm{nm}\) . (H) Western blotting of LC3 in P6 adipocytes following 6-h serum deprivation. Cells were treated with vehicle or H151 during serum deprivation. (I) GFP-labeled mitochondrial remnants in autophagosomes of P6 adipocytes following 6-h serum deprivation. Cells were treated with vehicle or H151 during serum deprivation. Scale: \(10\mu \mathrm{m}\) . \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student's 2-tailed unpaired \(t\) - test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 72, 880, 400]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 411, 627, 428]]<|/det|> +
Supplemental Figure 8. Biogenesis of EVs by P6 adipocytes
+ +<|ref|>text<|/ref|><|det|>[[116, 428, 880, 909]]<|/det|> +(A) TEM image of EVs released by P6 adipocytes in vitro. Scale: 1 μm. Three distinct EV morphologies were recognized: electron-lucent or clear (Cl), electron-dense (Ds) and complex (Cp) EVs. Electron-lucent appearance is typical for EVs (26). Electron-dense EVs may be frequent in EVs within multivesicular bodies (MVBs) (27). Complex EVs contain remnants of intracellular membranes. Size distribution of P6 EVs; inset showing negative TEM staining of EVs. Scale: 100 nm. EVs were classified in small and large categories according to a recent study (28). (B) FACS analysis of EVs secreted by P6 adipocytes. Free beads: remainder of capture beads used to enrich EVs. (C) TEM image of an MVB; scale: 1 μm. (D) Endosomal pathway of EV generation was tested by incubating P6 adipocytes with FITC-conjugated dextran, a marker of fluid-phase endocytosis (pinocytosis). Dextran is taken up by endosomes and may later accumulate in MVBs or in lysosomes. (E) Left: FACS analysis of P6 adipocytes cultured without FITC-conjugated dextran (-Dextran) or after incubation with dextran (+Dextran). Right: P6 adipocytes readily endocytosed FITC-dextran, as confirmed with fluorescence microscopy. nc: nucleus; scale: 10 μm. EVs secreted by the dextran-incubated adipocytes were collected and analyzed further with FACS. Dextran was present in the EVs, showing that the endosomal pathway contributed to EV generation. (F) Adipocytes were incubated without EVs (-EVs) or with FITC dextran-labeled EVs (+EVs) for 4h. Mean fluorescence intensity (MFI) of the adipocytes was measured by FACS, confirming the uptake of EV cargo by adipocytes. (G) Phagocytosis activity of P6 adipocytes was tested with using 50-nm large latex beads. Adipocytes failed to phagocytose these particles, showing that EVs were not taken up by phagocytosis. (H) Level of an adipose tissue mesenchymal stem cell-specific microRNA (miR-29a-5p) in P6 and P56 EVs (29). Effect of miR-29a-5p overexpression of the mitochondrial content (MTR fluorescence intensity). (I) Ucp1 and small non-coding RNA species in the EV cargo of P6 adipocytes. As a comparison, the level of the mitochondrionally-encoded 12S ribosomal RNA (Rn12s) is shown. (J) FACS plots of EVs secreted by P6 and P56 adipocytes. (K) Inhibitors of EV generation reduced the DNA and RNA content in the culture medium of P6 adipocytes. Isoproterenol (1 μM) inhibits EV release (30), and fumonisin B1 (30 μM) inhibits ceramide synthase, a key enzyme of negative budding of MVBs (31). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 73, 875, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 444, 880, 543]]<|/det|> +
Supplemental Figure 9. Effect of adipocyte EV cargo on mitochondrial morphology, and predicted secondary structure of mtRNA species found in adipocyte EVs (A) Mitochondrial morphometry of 3T3-L1 cells without extracellular vesicles (-EVs) or with P6 EVs (+EVs). \*\*\*P<0.001. Student's 2-tailed unpaired \(t\) -test. (B) Predicted minimum free energy (MFE) secondary structures of mtRNA species found in P6 EVs. Results were computed using ViennaRNA Package 2.0 and RNAfold 2.2.18, as described (32, 33).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 75, 880, 551]]<|/det|> + +<|ref|>sub_title<|/ref|><|det|>[[117, 566, 816, 584]]<|/det|> +## Supplemental Figure 10. Cytosolic and endosomal RNA effects on mitobiogenesis + +<|ref|>text<|/ref|><|det|>[[116, 584, 881, 853]]<|/det|> +(A) Secondary and schematic structures of the synthetic ligands used to activate cytosolic RNA sensors. ssRNA41: single-stranded RNA, 3p-hp-RNA: 5' triphosphate hairpin RNA, is an RIG-I ligand (34), 5'ppp-dsRNA: 5' triphosphate dsRNA, a ligand for RIG-I, cytosolic p(I:C) activates MDA5 and RIG-I (35), and cytosolic p(dA:dT) is transcribed into RNA and ultimately activates RIG-I (36). (B) Adipocytes were transfected with 2 µg/ml ssRNA41 using the LyoVec transfection system for cytosol delivery. Levels of beige marker genes was measured 18 h after transfection. (C) 3T3-L1 cells were treated with 5 µg/ml naked pI:pC to stimulate TLR3 and beige adipocyte gene transcription was then measured 18h after treatment. (D,E,F) Adipocytes were transfected with RIG-I/MDA5 ligands: 5'ppp-dsRNA, 3p-hairpin-RNA, p(dA:dT) and p(I:C) in LyoVec. Levels of beige marker genes was measured 18 h after transfection. (G) Transcript level of beige adipocyte genes in P56 adipocytes transfected with mtRNA for 18h. (H) Mitochondrial temperature change (Mito-ΔT) measured with the heat-sensitive probe Mitothermoy-Yellow (MTY) in mouse and human primary adipocytes. Adipocytes were transfected with vehicle, mtDNA or mtRNA for 18 h. *P<0.05, **P<0.01, ***P<0.001. Student's 2-tailed unpaired t-test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 100, 876, 455]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 464, 880, 830]]<|/det|> +
Supplemental Figure 11. IL-6/STAT3 and RIG-I/MDA5 signaling and mitoagonesis (A) Mitobigenesis was assessed by measuring SDH-A (succinate dehydrogenase complex, subunit A) and COX-1 (cyclooxygenase 1) by FACS. SDH-A is encoded by genomic DNA (gDNA), COX-I by mtDNA. Representative FACS histograms of COX-I and SDH-A in 3T3-L1 cells after P6 EV treatment. Histochemical staining of SDH-A activity of 3T3-L1 cells cultured without EVs (-EVs), with P6 EVs (+EVs) or with 0.2 ng/ml IL-6 for 18 h. Scale: 10 μm. (B) Effect of 200 pg/ml IL-6 on the net mitochondrial mass labeled with MitoTracker Red (MTR), and on the amount of newly synthesized (GFP-expressing) mitochondria. Scale: 50 μm. (C) FACS analysis of IL-6 content of P6 EVs. Iso: isotype control; IgG: labeling with anti-IL-6 IgG. Effect of P6 EVs on adipocyte Il6 expression and IL-6 release. Effect of P6 EVs on Cox7a1 expression (D) Effect of 200 pg/ml IL-6 on the Mitothermo-Yellow (MTY) signal in 3T3-L1 cells. Correlation of Il6 and Ucp1 relative expression in adipocytes. Heat map showing expression levels of beige adipocyte genes in 3T3-L1 cells treated with P6 EVs for 18 h. (E) MTR signal in 3T3-L1 cells treated with P6 EVs for 18 h. RXL: cells were simultaneously treated with the JAK2/STAT3 inhibitor ruxolitinib; BAY11-7082: cells were treated with an NFkB inhibitor to abrogate the effect of IL-6. (F) Histology of iAT from wild-type (wt), RIG-I-deficient (Ddx58-/-) and MDA5-deficient (Mda5-/-) mice. Note the absence of beige (multicolour) adipocytes in Ddx58-/- and Mda5-/- mice. Scale 50 μm. (G) Mitobigenesis (relative COX-I and SDH-A levels) in wt, Ddx58-/- and Mda5-/- adipocytes. (H) Heat map showing expression levels of beige adipocyte genes in wt or Ddx58-/- adipocytes treated with vehicle or mtRNA for 18 h. *P<0.05, **P<0.01, ***P<0.001. Student’s 2-tailed unpaired t-test.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 73, 870, 340]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 344, 733, 361]]<|/det|> +
Supplemental Figure 12. Cytosolic DNA/RNA effects on mitobiogenesis
+ +<|ref|>text<|/ref|><|det|>[[117, 361, 880, 543]]<|/det|> +(A) Autophagosomes (APh) in P6 adipocytes treated with vehicle or transfected with \(2\mu \mathrm{g / ml}\) total mtDNA for \(18\mathrm{h}\) . Scale: \(10\mu \mathrm{m}\) . (B) Scheme of LyoVec-encapsulated pCMV6 plasmid – an activator of the c-GAS/STING pathway – and its effect on beige adipocyte gene expression in P6 adipocytes. (C) Relative abundance of mtRNA species in human breast milk EVs and commercially available formula milk EVs. (D) Effect of breast milk EVs on beige adipocyte gene expression in P56 adipocytes. As a comparison, adipocytes were treated with formula milk-derived EVs (FM). (E) Effect of breast milk EVs on the mitobiogenesis of human subcutaneous adipocytes, Irf7 mRNA levels in mouse adipocytes, and IRF7 protein levels of human adipocytes. Adipocytes were treated with breast milk-derived EVs for \(18\mathrm{h}\) . COX-I: cytochrome oxidase, SDH-A: succinate dehydrogenase, \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student’s 2-tailed unpaired \(t\)-test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 72, 880, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 440, 680, 457]]<|/det|> +
Supplemental Figure 13. IFN-response to EV cargo in adipocytes
+ +<|ref|>text<|/ref|><|det|>[[117, 456, 880, 723]]<|/det|> +(A) Effect of cytosolic DNA/RNA on Ifnb expression in P56 adipocytes. pCMV6: transfection with pCMV6 plasmid (circular cytosolic DNA), pCMV6 EVs: treatment with extracellular vesicles released by pCMV6 plasmid-transfected adipocytes) (B) Effect of IFNβ on the mitochondrial network in P56 adipocytes. Scale: \(20 \mu \mathrm{m}\) . (C) Effect of IFNβ and IFNα on mitochondrial mass measured by MitoTracker Red (MTR) staining intensity. Cells were treated with vehicle, \(1 \mathrm{pg / ml}\) IFNβ or \(1 \mathrm{pg / ml}\) IFNα for \(18 \mathrm{h}\) . (D) EVs of P6 adipocytes were collected and added to cultures of P56 adipocytes. Similarly, EVs of P56 adipocytes were collected and added to P56 or P6 adipocytes. Levels of Ifnb and Tnfa were then measured. P6 EVs did not induce IFN-response, whereas P56 EVs triggered a robust IFN-response. (E) Transcript level of Irf7, and MTR staining intensity in P6 adipocytes treated with P56 EVs. Unlike P6 EVs, which suppressed Irf7, P56 EVs stimulated robust Irf7 expression (see Figure 3C) and reduced mitochondrial content. (F) Relative position and percentage of transcription factor binding sites in the promoters of the AIM2/STING pathway and Irf7. (G) Effect of LPS on the transcription of AIM2/STING pathway and Irf7 in adipocytes. (H) Scheme of the VDR-suppressed signal path which control the expression of Irf7, AIM2/STING pathway and IFN-response to cytosolic DNA/RNA (37-40). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 75, 864, 320]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 336, 725, 353]]<|/det|> +
Supplemental Figure 14. Metabolic role of mtRNA-mediated signaling
+ +<|ref|>text<|/ref|><|det|>[[117, 352, 880, 536]]<|/det|> +(A) Indirect calorimetry assay of HFD-fed adult male C57BL/6 mice. The inguinal fat depot was transfected with vehicle or with \(0.6 \mu \mathrm{g / g}\) body weight (BW) per day mtRNA for 14 days. The mtRNA was delivered into the adipocyte cytoplasm using magnetofection. Both groups received \(4 \mathrm{ng / g}\) BW Vit-D3 daily. MR: metabolic rate, EE: energy expenditure, RER: respiratory exchange rate (B) BW, daily food intake normalized to BW, and liver weight normalized to BW. Plasma level of TNFα and IL-6 (% of vehicle) from vehicle- or mtRNA-transfected mice, and the level of Irf7 in quadriceps muscle and liver. (C) Left: Transcription of Cyp27b1 (encoding a Vit-D3/calcitriol converting mitochondrial enzyme) in adipocytes treated with vehicle or transfected with mtRNA for 18h. Middle: Rate of Vit-D3/calcitriol conversion in the same cells. Right: Effect of calcitriol on the transcription of Vdr in adipocytes. \(*P< 0.05\) , \(**P< 0.01\) , \(***P< 0.001\) . Student's 2-tailed unpaired \(t\) -test. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[255, 95, 744, 393]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 397, 877, 430]]<|/det|> +
Supplemental Figure 15. Technical information on next-generation sequencing and image analysis
+ +<|ref|>text<|/ref|><|det|>[[118, 429, 879, 510]]<|/det|> +(A) Work flow of the next-generation sequencing analysis. (B) Steps of image analysis in histomorphometry. (C) Negative control specimens. Left: Adipocytes in vitro, stained with secondary antibodies only; nuclei are labeled with DAPI. Scale: \(10 \mu \mathrm{m}\) . Middle: Brown adipose tissue section labeled with secondary antibody only. Scale: \(20 \mu \mathrm{m}\) . Right: human adipose tissue labeled with secondary antibody only. Scale: \(20 \mu \mathrm{m}\) . + +<|ref|>image<|/ref|><|det|>[[0, 0, 997, 100]]<|/det|> + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 87, 870, 912]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 73, 739, 91]]<|/det|> +Supplemental Table 1. Mouse qPCR primer sequences used in the study + +
BactinfwGCACCAGGGTGTGATGGTG
revCCAGATCTTCTCCATGTCGTCC
PpiafwATTTCTTTTGACTTGCGGGC
revAGACTTGAAGGGGAATG
GapdhfwTGACGTGCCGCCTGGAGAAA
revAGTGTAGCCCAAGATGCCCTTCAG
Aim2fwGATTCAAAGTGCAGGTGCGG
revTCTGAGGGTTAGCTTGAGGAC
Ddx41fwACAGGAGAAGCGGTTGCCTTTC
revGACGGCAGTAATACTCCAGGATG
Ifi204fwCAGGGAAAATGGGAAGTGGTG
revCAGAGAGGTTCTCCCGACTG
Zbp1fwAACCCTCAATCAAGTCCTTTACCGC
revTCTTCCACGTCTGTCGTCATAGCT
Mb21dfwAGGAAGCCCTGCTGTAACACTTCT
revAGCCAGCCTTGAATAGGTAGGTAGTCCT
Tmem173fwGGGCCCTGTCACTTTGGTC
revGAGTATGGCATCAGCAGCCAC
Irf3fwGGCTTGTGATGGTCAAGGTT
revCATGTCCTCCACCAAGTCCT
Irf7fwCGACTTCAGCACTTTCTTCGGAGA
revAGATGGTGTAGTGCTGGTGCACCTT
If6fwGCTACCAAACTGGATATAATCAGGA
revCCAGGTAGCTATGGTACTCCAGAA
TnfafwTGCCTATGTCTCAGCCTCTTC
revGAGGCCATTTGGGAACTTCT
IfnbfwCCAGCTCCAAGAAAGGACGA
revCGCCCTGTAGGTGAGGTTGAT
IfnafwTGAAGGACAGGAAGGACTTTG
revGAATGAGTCTAGGAGGGTTGT
28SfwCAGGGGAATCCGACTGTTTA
revATGACGAGGCATTTGGCTAC
18SfwCGCGGTTCTATTTTGTTGGT
revAGTCGGCATCGTTTATGGTC
16SfwACACCGGAATGCCCTAAAGGA
revATACCGCGGCGTTAAACTT
12SfwACACCTTGCCATAGCCACACC
revGTGGCTGGCACGAAATTTACCA
Nd1fwGCTTTACGAGCCGTAGCCCA
revGGGTCAGGCCTGGCAGAAGTAA
CytbfwTCCTTCATGTCGGACGAGGC
revAATGCTGTGGCTATGACTGCG
Nd5fwGCCCTACACCAGTTTCAGC
revAGGGCTCCGAGGCAAGATAT
Co1fwTCAACATGAAACCCCCAGCCA
revGCGGCTAGCACTGGTAGTGA
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 69, 870, 755]]<|/det|> +
Ucp1fwCCTGCTCTCTCGGAACAA
revCTGTAGGCTGCCCAATGAAC
Ppargc1fwGACTCAGTGTcACCACCGAAA
revTGAACGAGAGCGCATCCTT
Cox7a1fwATGAGGCCCTACGGGTCTC
revCATTGTCGGCCTGGAAAGAG
CideafwTACTACCCGGTGTCCATTTCT
revATCACAACTGGCCTGGTTACG
Dio2fwGTCCGCAAATGACCCCTTT
revCCCACCCACTCTCTGACTTTC
Ifi205fwCAAGCAGGCCACTTCTGTG
revTCAAACGGGTCTGTGTCAGT
Ddx58fwCAAACCGGGCAACAGGAATG
revATCTCCGCTGGCTCTGAATG
Ifi202bfwAAGTTCCCGGTGTCAGAAC
revTCCAGGAGAGGCTTGAGGTT
MndalfwGACAGCACACTAGAAACCCC
revCTTGTCTCCTACTCAGTCCG
miR34afwTCTTTGGCAGTGTCTTAGCTGG
revACAATGTGCAGCACTTCTAGGG
circRNAfwCTGCTCCTCCAGCTCTT
revAGTGATCTTGAACCCCAAAG
piRNA 6464.1fwGGCAAGCTTAGGAGGTGTCC
revCGTGGGTCCACTGTATCACC
piRNA 6463.1fwTAAAGCCCTAAAGCCCACGG
revAGGTGTAATGCCAGCCAGTC
Pnp1fwCTTGGACATGGTGCTCTTGC
revGCCAAACTTCCACCACATGC
Adrb3fwGTCGTCTTCTGTGTAGCTACGGT
revCATAGCCATCAAACCTGTTGAG
Lipe (Hsl)fwAGCCTCATGGACCCTCTTCT
revAGCGAAATGTCTCTCTGCAC
Atg (Pnpla2)fwACTGAACCAACCCAACCCTT
revCGCACTGGTAGCATGTTGGA
Cyp27b1fwAGCTCCTGCGACAAGAAAGT
revATTCTTCACCATCCGCCGTTA
VdrfwACTTTGACCGGAATGTGCCT
revCATGCTCCGCCTGAAGAAAC
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 99, 870, 422]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 73, 763, 92]]<|/det|> +Supplemental Table 1. (cont.) qPCR primers for measuring mouse mtDNA + +
Nd1fwGCTTTACGAGCCGTAGCCCA
revGGGTCAGGCTGGCAGAAGTAA
16SfwACACCGGAATGCTCAAAGGA
revATACCGCGGCCGTTAACTT
12SfwACACCTTGCCTAGCCACACC
revGTGGCTGGCACGAAATTTACCA
D-loopfwAATCTACCATCCTCCGTGAAACC
revTCAGTTTAGCTACCCCAAGTTTAA
CytbfwTCCTTCATGTCGGACGAGGC
revAATGCTGTGGCTATGACTGCG
Atp6fwAGCTCACTTGCCCACTTCCT
revAAGCCGGACTGCTAATGCCA
Nd5fwGGCCCTACACCAGTTTCAGC
revAGGGCTCCGAGGCAAGATAT
Co1fwTCAACATGAAACCCCCAGCCA
revGCGGCTAGCACTGGTAGTGA
HK2fwGCCAGCCTCTCCTGATTTTAGTGT
revGGGAACACAAAAGACCTCTTCTGG
+ +<|ref|>table<|/ref|><|det|>[[115, 460, 870, 664]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 448, 832, 466]]<|/det|> +Supplemental Table 1. (cont.) qPCR primers for measuring bovine/human mtRNA + +
16SfwGACTTCACCAGTCAAGACGA
revACATCGAGGTCGTAAACCCT
12SfwACTGCTCGCCAGAACACTAC
revGGTGAGGTTGATCGGGGTTT
ND1fwGCAGCCGCTATTAAAGGTTCG
revTATCATTTACGGGGGAAGGCG
ND5fwTATGTGCTCCGGGTCCTCA
revCTGCTAATGCTAGGCTGCCA
CO1fwTCAGGCTACACCCTAGACCA
revCCGGATAGGCCGAGAAAGTG
CYTBfwAACTTCGGCTCACTCCTTG
revCTCAGAGTGATGTGGGCGATT
+ +<|ref|>table<|/ref|><|det|>[[115, 696, 870, 895]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 678, 832, 696]]<|/det|> +Supplemental Table 1. (cont.) qPCR primers for measuring bovine/human mtDNA + +
16SfwGACTTCACCAGTCAAGACGA
revACATCGAGGTCGTAAACCCT
12SfwACTGCTCGCCAGAACACTAC
revGGTGAGGTTGATCGGGGTTT
ND1fwGCAGCCGCTATTAAAGGTTCG
revTATCATTTACGGGGGAAGGCG
ND5fwTATGTGCTCCGGGTCCTCA
revCTGCTAATGCTAGGCTGCCA
CO1fwTCAGGCTACACCCTAGACCA
revCCGGATAGGCCGAGAAAGTG
CYTBfwAACTTCGGCTCACTCCTTG
revCTCAGAGTGATGTGGGCGATT
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[111, 88, 870, 618]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 74, 755, 90]]<|/det|> +Supplemental Table 2. Antibodies used in the study (h, human; m, mouse) + +
TargetCat. No.IgG type, source
h/m STINGNBP2-24683Rabbit polyclonal
Novus Biologicals, Denver, CO
h/m AIM2201708-T10Rabbit polyclonal
Sino Biological, Eschborn, Germany
h/m DDX41102459-T32Rabbit polyclonal
Sino Biological, Eschborn, Germany
h/m p204 (IFI16)NBP2-27153Rabbit Polyclonal
Novus Biologicals, Denver, CO
h/m ZBP1207744-T08Rabbit polyclonal
Sino Biological, Eschborn, Germany
h/m LC3L8918Rabbit polyclonal, Merck Sigma-Aldrich, St. Louis, MO, Darmstadt, Germany
h/m UCP1PA1-24894Rabbit polyclonal
ThermoFisher Scientific, Rockford, IL
m NPFFab10352Rabbit polyclonal
Abcam, Cambridge, UK
β-actinNB600-532SSRabbit polyclonal
Novus Biologicals, Denver, CO
h/m DDX41102459-T32Rabbit polyclonal
Invitrogen, Carlsbad, CA
h/m Tmem150bPA5-71527Rabbit polyclonal
Invitrogen, Carlsbad, CA
J2 (dsRNA)Anti-dsRNA [J2]Mouse monoclonal
Absolute Antibody, Wilton, UK
m IRF712-5829-82PE-conjugated monoclonal IgG, and matching isotype IgG, ThermoFisher, Waltham, MA
m F4/80 antigensc-377009F4/80 APC, CD45 PerCy5.5, CD11b APC or PE or AF700 (FACS analysis), eBioscience,
ThermoFisher, Waltham, MA, Santa Cruz Biotech (for IHC)
m CD11bE-AB-F1081EH+L, cross-Adsorbed, FITC, polyclonal, secondary antibody, Invitrogen, Carlsbad, CA
Rabbit anti-goat IgGF-2765Goat anti-Rabbit IgG (H+L), HRP-conjugated
Invitrogen, Carlsbad, CA
Goat anti-rabbit IgGA16096
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 74, 320, 90]]<|/det|> +## Supplemental Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 107, 604, 123]]<|/det|> +## Activation and inhibition of cytosolic DNA/RNA sensors + +<|ref|>text<|/ref|><|det|>[[117, 123, 880, 337]]<|/det|> +Activation and inhibition of cytosolic DNA/RNA sensorsTo activate STING, we treated adipocytes or 3T3- L1 cells with cGAMP (InvivoGene, Toulouse, France) for 6- 18 h, or overexpressed the pCMV6 plasmid (OriGene Technologies, Rockville, MD). In the latter case, \(1 \mu \mathrm{g}\) of DNA was transfected into 300,000 cells using TurboFect Transfection Reagent (Fisher Scientific, Hampton, NH). Control cells received transfection reagent only. Analyses were performed 18- h after transfection. To stimulate RIG- IMDA5, we transfected 3T3- L1 cells at \(80\%\) confluency with high molecular weight polyinosine- polycytidylic acid (p(I:C)) or poly(deoxaydenylic- deoxythymidylic) acid (p(dA:dT)) using the LyoVec cationic lipid- based transfection reagent (InvivoGene, Toulouse, France). Control cells were treated with LyoVec transfection reagent only. We used \(2.5 - 5 \mu \mathrm{g / ml}\) p(dA:dT) or p(I:C), and cells were analyzed 2- 24 h after transfection. IFI16/p204 was activated with \(1 \mu \mathrm{g / ml}\) VACV- 70 conjugated to LyoVec transfection reagent (InvivoGene; 18 h) (41). Treatments are summarized in the table below. + +<|ref|>table<|/ref|><|det|>[[108, 350, 909, 690]]<|/det|> + +
Activation of cytosolic nucleic acid sensors with various ligands
ReceptorLigandEC50Applied concentration
STING2'3 cGAMP20 nM10 μg/ml
poly(dA:dT) 2h40-200 ng/ml2.5-5 μg/ml
human/mouse mtDNA-2 μg/ml
cGASpCMV6 circular DNA-1 μg/well
3p-hpRNA5 ng/ml0.5 μg/ml
5'ppp-dsRNA1.2 nM1 μg/ml
RIG-Ipoly(I:C) HMW70±10 ng/ml0.5 μg/ml
poly(dA:dT) 18-24h40-200 ng/ml2.5-5 μg/ml
low molecular weight poly(I:C)82±8 ng/ml1 μg/ml
human/mouse mtRNA-2 μg/well
AIM2poly(dA:dT) 2h40-200 ng/ml2.5-5 μg/ml
DDX41poly(dA:dT) 2h40-200 ng/ml2.5-5 μg/ml
dsDNA (VACV-70)-1 μg/ml
IFI16 (p204 or Ifi204)poly(dA:dT) 2h40-200 ng/ml2.5-5 μg/ml
dsDNA (VACV-70)-1 μg/ml
ZBP1poly(dA:dT) 2h40-200 ng/ml2.5-5 μg/ml
+ +<|ref|>text<|/ref|><|det|>[[117, 705, 880, 852]]<|/det|> +TLR3 was stimulated with naked p(I:C) (Sigma- Aldrich, \(10 \mathrm{ng / ml}\) , \(18 \mathrm{h}\) ) and TLR8/9 with naked p(dA:dT) or CpG ( \(1 \mu \mathrm{g / ml}\) synthetic oligonucleotides that contain unmethylated CpG dinucleotides; InvivoGene) for \(8 \mathrm{h}\) . STING was inhibited with the irreversible STING inhibitor H- 151 (0.5 \(\mu \mathrm{M}\) , InvivoGene) (25). As a negative control we used ssRNA (InvivoGene). NFκB was inhibited with \(5 \mu \mathrm{M}\) BAY 11- 7082 and JAK2/STAT3 with \(280 \mathrm{nM}\) ruxolitinib (Cayman Chemical Company, Ann Arbor, MI). Mitochondrial damage was induced with \(10 \mathrm{ng / ml}\) LPS or with CCCP (carbonyl cyanide m- chlorophenyl hydrazone, \(1 \mu \mathrm{M}\) , \(15 \mathrm{min}\) treatment). + +<|ref|>text<|/ref|><|det|>[[118, 853, 878, 905]]<|/det|> +Vit- D3 and calcitriol were purchased from Sigma- Aldrich; IL- 6, IFNα and IFNβ from ImmunoTools (Friesoythe, Germany), NPVF, human and mouse NPFF from Tocris Bioscience (Bristol, UK). Isoproterenol and fumonisin B1 were purchased from Sigma + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 73, 879, 174]]<|/det|> +Aldrich and from Cayman Chemical Company, respectively. To test the inhibitory effect of Vit- D3 on IRF7 signaling, 3T3- L1 cells were treated with \(1\mu \mathrm{M}\) Vit- D3 for \(48\mathrm{h}\) , and treated further with vehicle or \(5\mu \mathrm{g / ml}\) cGAMP for \(6\mathrm{h}\) , or were transfected with mtRNA for \(18\mathrm{h}\) . VDR was inhibited with PS121912, as described (42). Cellular uptake of cGAMP is dependent on the transporter Slc19a1 (20), whose level was similar in P6 and P56 adipocytes (GEO submission #GSE154925). + +<|ref|>text<|/ref|><|det|>[[117, 191, 880, 468]]<|/det|> +Isolation of extracellular vesicles from cell culture media, breast milk and formula milk Extracellular vesicles (EVs) were collected from adipocyte culture media, human breast milk, or from commercially available cattle milk- based infant formula. Human breast milk was collected from healthy volunteers. For cell culture, to avoid contamination with bovine EVs, we used EV- depleted fetal calf serum throughout the study (Gibco). EVs were precipitated with the EPStep exosome precipitation solution (Immunostep, Centro de Investigación del Cáncer, Campus Miguel de Unamuno, Salamanca, Spain) and concentrated by centrifugation. EVs were analyzed with FACS using capture beads and labeling for CD63 (Immunostep). EV pellets were used for treating recipient cells, to extract DNA/RNA, or were processed for FACS. Fractions of EV pellets and adipocytes were also fixed in paraformaldehyde/glutaraldehyde, and processed for transmission electron microscopy (TEM) analysis, as described (43). Morphology of EVs was analyzed with conventional TEM, and with negative staining for TEM (44). EV diameter and area was measured with ImageJ (NIH) with manual annotation, and EVs were classified according to their morphology and electron density, as described (26, 27). + +<|ref|>sub_title<|/ref|><|det|>[[119, 490, 431, 506]]<|/det|> +## Phagocytosis and endocytosis assays + +<|ref|>text<|/ref|><|det|>[[118, 508, 880, 618]]<|/det|> +Uptake of naked nucleic acids was assessed microscopically by incubating adipocytes with rhodamine- conjugated p(dA:dT) or FITC- conjugated ODN 1668 CpG (both from InvivoGene) for \(1\mathrm{h}\) . Endocytosis by means of pinocytosis was assessed by incubating adipocytes with FITC- conjugated dextran, followed by FACS analysis or fluorescence microscopy. Uptake of solid particles was assessed with the use of fluorescent latex beads (Sigma- Aldrich) and FACS analysis (BD LSR II). + +<|ref|>sub_title<|/ref|><|det|>[[118, 641, 240, 656]]<|/det|> +## ELISA assays + +<|ref|>text<|/ref|><|det|>[[118, 659, 880, 770]]<|/det|> +Tissue samples were weighed and homogenized in RIPA buffer using a Roche bead mill homogenizer at \(6,500\mathrm{rpm}\) for \(1\mathrm{min}\) . Cell culture supernatants and plasma samples were centrifuged at \(0.8\mathrm{g}\) for \(10\mathrm{min}\) to remove cell debris, and supernatants were used for analysis. We used commercial ELISA kits to measure the levels of IL- 6, TNF \(\alpha\) (Fisher Scientific), Vit- D3, calcitriol and VDR (MBS268259- 48, MBS2701844- 24, MyBioSource). All samples were stored at \(- 80^{\circ}\mathrm{C}\) until analysis. + +<|ref|>sub_title<|/ref|><|det|>[[118, 793, 476, 808]]<|/det|> +## mtRNA isolation and in vitro transfection + +<|ref|>text<|/ref|><|det|>[[118, 811, 880, 883]]<|/det|> +Adipocyte mitochondria were isolated with a commercial mitochondrial isolation kit (Thermo Fisher Scientific, Waltham, MA). Mitochondrial RNA (mtRNA) was isolated by lysing the mitochondrial pellet with TRI Reagent (Sigma- Aldrich), as described (1). 3T3L1 cells were transfected with \(2\mu \mathrm{g}\) of mtRNA in 6- or 24- well plates with cells at \(80–90\%\) confluency. As + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 73, 878, 108]]<|/det|> +a transfection reagent we used Lipofectamine 3000 (Invitrogen) at a 1:3 ratio. Control cells received transfection reagent only. Cells were analyzed 18 h after transfection. + +<|ref|>sub_title<|/ref|><|det|>[[118, 130, 411, 146]]<|/det|> +## mtDNA isolation and transfection + +<|ref|>text<|/ref|><|det|>[[118, 149, 880, 242]]<|/det|> +Mitochondrial DNA (mtDNA) was isolated from mitochondria pellets using TRI Reagent (Merck Sigma- Aldrich) and reconstituted in TE buffer (10 mM Tris- HCL, 1 mM EDTA, pH 8.0). 3T3L1 cells were transfected for 18 h with \(1 \mu \mathrm{g / ml}\) mtDNA using the TurboFect Transfection Reagent. Control cells received transfection reagent only. Agarose gel electrophoresis was used to examine mtDNA integrity. + +<|ref|>sub_title<|/ref|><|det|>[[118, 263, 350, 279]]<|/det|> +## Cytosolic mtRNA isolation + +<|ref|>text<|/ref|><|det|>[[118, 281, 880, 391]]<|/det|> +Cytosolic mtRNA isolationCytosol fractions of 3T3- L1 preadipocytes were collected by subcellular fractionation of the cytoplasm and the cell organelles using digitonin, as described (45). Digitonin buffer contained \(150 \mathrm{mM NaCl}\) , \(50 \mathrm{mM HEPES}\) (pH 7.4) and \(25 \mu \mathrm{g / ml}\) digitonin (D141, Merck Sigma- Aldrich). Treated cells were processed until the step in which cytoplasm was obtained as described (1). 3T3- L1 cytoplasm ( \(250 \mu \mathrm{l}\) ) was added to \(750 \mu \mathrm{l}\) TRI Reagent (T3934, Merck Sigma- Aldrich) and total RNA extraction was performed as described (24). + +<|ref|>sub_title<|/ref|><|det|>[[118, 412, 370, 428]]<|/det|> +## Histology and image analysis + +<|ref|>text<|/ref|><|det|>[[118, 429, 880, 659]]<|/det|> +Histology and image analysisTissues were fixed with \(4\%\) paraformaldehyde and embedded in paraffin, as described (1). Sections were stained with hematoxylin and eosin (Carl Roth, Karlsruhe, Germany). Antibodies are listed in Supplemental Table 2. UCP1, IF116, AIM2 and NPFFR1 immunohistochemistry was performed on paraffin- embedded tissue sections. For histomorphometry of fat cells we used Image J, with an image- processing algorithm that incorporated the Euclidean distance- based Watershed transformation to segment the images. Briefly, binarized images were generated using Otsu's method for thresholding; enhanced images were generated using contrast limited adaptive histogram equalization (CLAHE), and finally segmented images were generated using the Watershed transformation (Supplemental Figure 20). Negative control specimens of our fluorescent imaging and immunostaining are shown in Supplemental Figure 15. Mitochondrial content and morphology was analyzed with ImageJ, as described (14). Beige adipose area was measured with our custom- developed image analysis software (BeAR©, (14)). + +<|ref|>sub_title<|/ref|><|det|>[[118, 677, 599, 694]]<|/det|> +## Oil Red-O staining and quantification of UCP1 staining + +<|ref|>text<|/ref|><|det|>[[118, 696, 880, 800]]<|/det|> +The triglyceride content of cultured adipocytes was examined by Oil Red- O using a commercial kit from BioOptica (Milan, Italy), as described (24). In vitro UCP1 immunostaining was performed in 6- well culture plates, and samples were imaged and the optical density was measured using digital image analysis. Original images are available upon request through Figshare. Mitochondria were also labeled using an SDH- A histochemistry assay (BioOptica). + +<|ref|>sub_title<|/ref|><|det|>[[118, 819, 337, 835]]<|/det|> +## Adipocyte differentiation + +<|ref|>text<|/ref|><|det|>[[118, 837, 880, 909]]<|/det|> +Mouse preadipocytes of the stromal vascular fraction (SVF) were isolated and maintained as described (24, 43, 46). To ensure the depletion of adipose tissue macrophages (ATMs) from the harvested preadipocytes, we used magnetic bead cell purification of the SVF with an antibody against the F4/80 antigen (Miltenyi Biotec, Bergisch Gladbach, Germany) (47). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 74, 880, 170]]<|/det|> +Human subcutaneous adipose tissue preadipocytes were harvested as described (24, 43). Preadipocytes were maintained in cell culture medium supplemented with \(20 \mu \mathrm{g / mL}\) insulin. To induce white differentiation of preadipocytes of the SVF, we treated the cells with \(50 \mu \mathrm{M}\) IBMX, \(1 \mu \mathrm{M}\) dexamethasone, \(1 \mu \mathrm{M}\) rosiglitazone and \(20 \mu \mathrm{g / ml}\) insulin (all from Merck Sigma- Aldrich), as described (14). + +<|ref|>sub_title<|/ref|><|det|>[[118, 190, 878, 226]]<|/det|> +## Flow cytometry analysis of DNA sensors, mitochondrial biogenesis, mitochondrial content and mitochondrial uncoupling + +<|ref|>text<|/ref|><|det|>[[117, 228, 880, 492]]<|/det|> +Mitochondrial content was analyzed with MitoTracker dyes (Thermo Fisher Scientific). Mitochondrial biogenesis was detected with the MitoBiogenesis™ Flow Cytometry Kit (Abcam, Cambridge, UK). MitoThermo Yellow (MTY), a temperature- sensitive fluorescent probe (48) was used to assess mitochondrial thermogenesis and uncoupling, as described (49, 50). Temperature difference between the control and the test groups was expressed as Mito- \(\Delta \mathrm{T}\) , and shown in the respective figures. MTY was developed and provided by Dr. Y- T. Chang (Center for Self- Assembly and Complexity, Institute for Basic Science & Department of Chemistry, Pohang University of Science and Technology, Pohang 37673, Republic of Korea). We used MTY for FACS analysis at \(0.1 \mathrm{ng / ml}\) to label \(10^{6} / \mathrm{ml}\) cells. Cells were maintained at \(37^{\circ} \mathrm{C}\) throughout the assay. DNA sensors (STING, p204, AIM2, DDX41) were detected with unconjugated antibodies (listed in Supplemental Table 2) and labeled with an FITC- conjugated secondary antibody for FACS analysis. Nucleic acids were labeled with Sytox Green (Thermo Fisher). Flow Repository identifiers of raw FACS data are as follows: #FR- FCM- Z236, #FR- FCM- Z2R6, #FR- FCM- ZYPU, #FR- FCM- ZYUU. + +<|ref|>sub_title<|/ref|><|det|>[[118, 513, 639, 530]]<|/det|> +## Imaging of mitochondrial content, autophagy and lysosomes + +<|ref|>text<|/ref|><|det|>[[118, 532, 880, 737]]<|/det|> +For fluorescent microscopy of mitochondrial content and morphology preadipocytes or 3T3- L1 cells were grown on optical transparent glass- bottom plates (Greiner Bio- One GmbH, Frickenhausen, Germany) or glass coverslips. Functional mitochondria were labeled with MitoTracker Red. Mitochondria were also labeled with GFP using the BacMan 2.0 transfection system (Fisher Scientific). Oxygen consumption was assayed with the Extracellular \(\mathrm{O}_2\) Consumption Reagent (Abcam) for 30–120 min. Mitochondrial respiration was evaluated with the WST- 81 assay (Carl Roth), as described (51). Autophagosomes and lysosomes were labeled with Cell Meter Autophagy Fluorescence Imaging kit (AAT Bioquest, Sunnyvale, CA), Lyso Brite Orange (Bertin Bioreagent, Montigny le Bretonneux, France) and Lyso View 405 (Biotium, Inc. Fremont, C). Inflammasome activity was measured with the Caspase- Glo 1 Inflammasome Assay (Promega Co., Madison, WI). + +<|ref|>sub_title<|/ref|><|det|>[[118, 757, 512, 774]]<|/det|> +## High fat diet feeding and indirect calorimetry + +<|ref|>text<|/ref|><|det|>[[118, 776, 880, 887]]<|/det|> +Respiratory exchange rate (RER), oxygen consumption \((\mathrm{VO}_2)\) and energy expenditure (EE) were measured in each individual mouse for \(24 \mathrm{~h}\) using a small animal indirect calorimetry system (CaloBox, Phenosys, Germany). Mean RER, \(\mathrm{VO}_2\) and EE values were determined over \(7 \mathrm{~h}\) in the middle of both the day and the night phases. Basal glucose levels and glucose tolerance were measured as described (24). For HFD feeding of mice (dams with litters P6 to P9, or mice at P28 for 12 weeks) we used a rodent HFD from SSNIFF Spezialdiäten (Soest, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 73, 878, 108]]<|/det|> +Germany) (24). Vit- D3 was supplemented in diet, mtRNA was transfected with magnetofection for 14 days. + +<|ref|>sub_title<|/ref|><|det|>[[118, 130, 270, 146]]<|/det|> +## miRNA detection + +<|ref|>text<|/ref|><|det|>[[117, 149, 880, 411]]<|/det|> +Total RNA was extracted by TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions and was quantified using the NanoDrop™ 8000 Fluorospectrometer (Thermo Fisher Scientific). In total, 50 ng of purified RNA was subjected to reverse transcription using a TaqMan miRNA Reverse Transcription Kit and TaqMan® MicroRNA Assays (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions (Assay ID: mmu- miR- 434- 3p, 002604; mmu- miR- 29a- 5p, 002447; RUN6B, 001973). Quantification of individual miRNAs was using a QuantStudio™ 12K flex real- time PCR system (Applied Biosystems) and the relative expression values were calculated by using the 2- ΔΔCt method and normalized to RUN6B. miRNA 434- 3p was overexpressed using a custom- synthesized RNA (Sigma- Aldrich) and transfected with Turbofect transfection reagent (Fisher Scientific). To identify potential Irf7- interacting miRNA species, we searched the TargetScan database for miRNAs with complementarity to Irf7 mRNA. In the next step, we used miRNA to identify precursor-, and mature sequences of the candidate miRNA species (52). + +<|ref|>sub_title<|/ref|><|det|>[[118, 432, 281, 448]]<|/det|> +## Cell viability assay + +<|ref|>text<|/ref|><|det|>[[118, 451, 878, 486]]<|/det|> +We used the Presto Blue Cell Viability Assay (Thermo Fisher Scientific) and the Rotitest Vital (Carl Roth) assays according to the manufacturers' instructions. + +<|ref|>sub_title<|/ref|><|det|>[[118, 507, 265, 523]]<|/det|> +## Western blotting + +<|ref|>text<|/ref|><|det|>[[117, 525, 880, 668]]<|/det|> +Cells were lysed in ice- cold RIPA buffer supplemented with Pierce™ protease and phosphatase inhibitor mini tablets (Thermo Scientific). Protein concentration was measured by the Pierce™ Rapid Gold BCA Protein Assay Kit and 30- 40 μg protein samples were run on \(16\%\) SDS gels for protein separation, followed by blotting the gels on \(0.2 - \mu \mathrm{m}\) nitrocellulose blotting membrane (Amersham, Freiberg, Germany) at \(300\mathrm{mA}\) for \(1\mathrm{h}\) in a cold room. After blotting, membranes were blocked with \(5\%\) skimmed milk for \(1\mathrm{h}\) . Providers of the \(\beta\) - actin and LC3 antibodies are listed in Supplement Table 2. Antibody concentrations used were as follows: \(\beta\) - actin, 1:10,000, LC3, \(0.2\mu \mathrm{g / ml}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 687, 588, 703]]<|/det|> +## Quantification of nucleic acids in extracellular vesicles + +<|ref|>text<|/ref|><|det|>[[117, 705, 880, 862]]<|/det|> +We collected EV pellets from cells, from formula milk or infant formula in a clean Eppendorf tube, which was centrifuged at \(0.8\mathrm{g}\) to remove cell debris. To isolate the EV- associated DNA from the pellets or from the cell culture media, we used the Zymo Quick DNA Microprep Kit (Zymo Research, Irvine, CA). After determination of the DNA concentration, we used \(5\mathrm{ng}\) for qPCR assays. EV- depleted cell culture media was used as a reference. For comparison between groups, we used the \(\Delta \Delta \mathrm{Ct}\) method to determine relative changes in mtDNA levels. For extraction of mtRNA and other EV- associated RNA species from cell EV pellets and culture media, we used Trizol Reagent. After determination of the RNA concentration, we used \(50\mathrm{ng}\) of RNA to generate cDNA. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 73, 560, 90]]<|/det|> +## mtDNA copy number in the inguinal adipose tissue + +<|ref|>text<|/ref|><|det|>[[118, 91, 880, 161]]<|/det|> +We used Trizol Reagent DNA isolation from iAT at P6 and P56. DNA was reconstituted in TE buffer and adjusted to \(10\mathrm{ng / \mu l}\) . We performed qPCR using \(HK2\) as a reference nuclear genome- encoded gene, and measured the DNA copy number of mtDNA- encoded 16S and Nd1. We calculated the copy number according to the formula: + +<|ref|>equation<|/ref|><|det|>[[281, 180, 803, 215]]<|/det|> +\[\Delta \mathrm{Ct} = \mathrm{Ct}_{\mathrm{Target~gene}} - \mathrm{Ct}_{\mathrm{Reference~gene}} \quad (1)\] + +<|ref|>sub_title<|/ref|><|det|>[[118, 250, 348, 267]]<|/det|> +## Magnetofection of mtRNA + +<|ref|>text<|/ref|><|det|>[[118, 269, 880, 380]]<|/det|> +In vivo delivery of mtRNA into the cytosol of adipocytes was achieved with magnetofection, using mtRNA- magnetic nanoparticle complexes (DogtorMag, OzBiosciences, San Diego, CA). Briefly, mtRNA- nanoparticle complexes were injected into the inguinal adipose tissue of mice, and enrichment of the magnetic nanoparticles was ensured by magnetic exposure of the fat depot, as described (53). MicroRNA was transfected using Lipofectamine 3000 (Thermo Fisher). + +<|ref|>sub_title<|/ref|><|det|>[[118, 399, 446, 415]]<|/det|> +## Institutional Review Board Statement + +<|ref|>text<|/ref|><|det|>[[118, 417, 850, 453]]<|/det|> +Research involving animals was approved by the regional governmental ethics and animal welfare committee in Tübingen, Germany (#1511; #1557; #1492; #1546; #0.232- 1,2,4,5). + +<|ref|>sub_title<|/ref|><|det|>[[118, 474, 579, 490]]<|/det|> +## Acknowledgements for the supplemental information + +<|ref|>text<|/ref|><|det|>[[117, 492, 881, 660]]<|/det|> +The VDR inhibitor was provided by Prof. Dr. Leggy A. Arnold, University of Wisconsin, USA. MTY was developed and provided by Dr. Y- T. Chang (Center for Self- assembly and Complexity, Institute for Basic Science & Department of Chemistry, Pohang University of Science and Technology, Republic of Korea. The authors thank Prof. Hartmut Geiger (Ulm University) for providing access to the FACS equipment. The assistance of Katharina Schormair and Burak Yildiz in image analysis is much appreciated. The contribution of Vincent Pflüger, Yun Chen, Antonia Stubenvoll, Angelika Bauer are acknowledged. Elements of the 3D artwork used in the graphical abstract was provided by Dreamstime Stock Photography. + +<|ref|>sub_title<|/ref|><|det|>[[118, 690, 214, 705]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 716, 858, 880]]<|/det|> +1. Seale P, Bjork B, Yang W, Kajimura S, Chin S, Kuang S, et al. PRDM16 controls a brown fat/skeletal muscle switch. Nature. 2008;454(7207):961-7. +2. Kissig M, Ishibashi J, Harms MJ, Lim H-W, Stine RR, Won K-J, et al. PRDM16 represses the type I interferon response in adipocytes to promote mitochondrial and thermogenic programing. The EMBO Journal. 2017;36(11):1528-42. +3. Seale P, Kajimura S, Yang W, Chin S, Rohas LM, Uldry M, et al. Transcriptional control of brown fat determination by PRDM16. Cell metabolism. 2007;6(1):38-54. +4. 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