Biology subjects

Shou, W.

Publications and source records attributed to Shou, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Resource supply dynamics control stability and chaos in complex ecosystems

Ecological interactions are often mediated by feedbacks between organisms and their resource environments. Yet, how resource supply dynamics dictate collective dynamical phases of an ecosystem remains unclear. Here, we analyse a generalised consumer--resource model with non-reciprocal interactions to demonstrate that self-renewing versus externally-supplied resources yield fundamentally different dynamical phase diagrams. As interactions become increasingly non-reciprocal, ecosystems relying on self-renewing resources transition from stable dynamics to chaos and ultimately to infeasibility. By contrast, ecosystems with externally-supplied resources remain stable over a broader parameter range and transition to infeasibility without experiencing an intervening chaotic phase. Using the cavity method, we derive a unified stability condition applicable to a broad class of resource supply functions, explaining why externally-supplied resources can expand the stable region. We show that stability hinges crucially on the susceptibility of resources to perturbations, which depends strongly on their supply. Further, we show that external resource supply suppresses chaos in the unstable region by drastically reducing the susceptibility of resources closest to extinction. Our findings demonstrate that resource dynamics fundamentally reshape the accessible dynamical behaviours of an ecosystem, with implications for interpreting microbial community experiments.

ecology↗

bioRxiv2025

Resource diversity begets stability in complex ecosystems

A fundamental paradox in ecology is the relationship between species diversity and ecosystem stability: Mays stability condition predicts that species diversity destabilises communities, yet many diverse ecosystems in nature are stable. Here, we show that this paradox can be resolved by explicitly considering resources, which May neglects. Specifically, Mays framework and the competitive exclusion principle jointly predict that resource diversity, which promotes species diversity, should destabilise communities. However, from computer simulations and analytical calculations using the finite-size cavity method, we find the opposite: resource diversity consistently generates stable, species-rich communities. Importantly, this stabilising effect disappears when resource dynamics is neglected (set to steady state). We also show that, contrary to the prevailing belief that interaction heterogeneity is always destabilising, different biological sources of heterogeneity have opposing effects on stability. Our work provides a solution to Mays paradox and demonstrates that resource dynamics are not just negligible background but are central drivers of ecosystem stability.

ecology↗

bioRxiv2025

Co-substrate induced nonlinear flux switching at branch-points can explain overflow metabolism

Metabolic overflow is pervasive from microbes to tumors: as glucose consumption increases, cells switch from respiration to respirofermentation with excretion of organic acids or ethanol. How do cells regulate their fluxes to implement this switch? Using simplified models of a branch point, we found that three independent "built-in" mechanisms can each enable flux switching at a branch point: differential enzyme kinetics at the two branches, allosteric regulation of a branch enzyme, and asymmetric co-substrate (e.g. NADH/NAD+) usage across the two branch point re-actions. Notably, the co-substrate mechanism achieves several fold higher sensitivity, which can be further enhanced by the other two mechanisms. We then applied this theory to the yeast pyruvate branch point underpinning overflow metabolism by developing a compartmentalized model with or without NADH dynamics in cytoplasm and in mitochondria. Crucially, only models that included NADH dynamics recapitulated experimental data: (a) NADH level increases nonlinearly with increasing glucose influx; (b) this increase occurs at a lower glucose threshold than that for overflow; and (c) glucose threshold for overflow increases when NADH oxidase is expressed in mitochondria but not in cytosol. Thus, cosubstrates are not merely passive carriers of chemical groups; their asymmetric utilization at a branch point offers an intrinsic, network-embedded regulatory layer that synergises with enzyme-based control. Beyond explaining overflow metabolism in yeast, this theory offers a metabolic engineering strategy for re-routing flux through manipulating co-substrates.

systems biology↗

bioRxiv2025

Balanced Permeability Index is a Strong Predictor of Intestinal Absorption and Oral Bioavailability for Heterobifunctional Ligand-Directed Degraders

Ligand-directed degraders (LDDs) are heterobifunctional molecules that degrade proteins by engaging the ubiquitin-protein ligase (E3) system. LDDs consist of a target-engaging moiety, an E3 ligase-binding moiety and a bridging linker. Due to their size and physicochemical complexity these molecules do not adhere to well-established rules of lead optimization. The optimization of passive permeability remains a key challenge to develop orally bioavailable LDDs. To overcome this challenge, in this study we demonstrate that the Balanced Permeability Index (BPI)--a new metric that combines size, polarity and lipophilicity--is highly predictive of oral bioavailability for LDDs. Here, we introduce an additional parameter--called smallest maximum intramo-lecular distance (SMID)--to the original BPI index to account for cross sectional area of LDDs, termed BPILDD. With this new parameter, BPILDD can differentiate oral bioavailability of LDDs in our dataset more effectively than polarity, lipophilicity, or size separately. In addition, BPILDD is also more effective at identifying orally bioavailable LDDs than some in vitro measurements of cell permeability that traditionally inform bioavailability. This finding opens the possibility of employing BPILDD for the design and optimization of orally bioavailable LDDs to improve their drug metabolism and pharmacokinetics properties.

pharmacology and toxicology↗

bioRxiv2025

Wearable Photonic Device for Multiple Biomarker Sampling and Detection Without Blood Draws

Blood draws or phlebotomy using needles have been practiced in clinics for several centuries and it often causes pain, discomfort, and inconvenience. Herein, we create a wearable photonic device by integrating a microlens array (MLA) and an optic microneedle array (OMNA) functionalized with immunobinding capacity for safe and needle-free biomarker sampling and detection. The MLA-integrated OMNA amplified and transmitted LED light at 595 nm into the skin through the OMNA, bypassing the light-absorbing melanin in the epidermal layer, and evenly distributing it in the capillary-enriched dermis. The 595 nm light was preferably absorbed by hemoglobin (Hb) and oxygen-Hb within the capillaries, triggering thermal dilation of capillaries without damaging them or causing petechiae. The light illumination resulted in remarkable increases in the concentrations of various blood biomarkers in the skin owing to capillary dilation and biomarker extravasation. These extravasated biomarkers bound specifically to the OMNA, which was covalently decorated with capture antibodies, each microneedle immobilized with one specific antibody. The multi-functional OMNA was extensively modified to amplify the immunobinding signals and achieve sensitivity superior to that of enzyme-linked immunosorbent assay (ELISA) kits. As a proof of concept, we validated the functionality of the prototype for minimally invasive sampling and precise multiplexed blood biomarker detection in two mouse models to quantify acute inflammation and specific antibody production. This cost-efficient device provides a promising platform for blood-free, multiplexed detection of blood biomarkers.

bioengineering↗

bioRxiv2024

Subtle methodological variations substantially impact correlation test results in ecological time series

Correlation analyses using ecological time series can indicate phenomena such as interspecific interactions or an environmental factor that affects several populations. However, methodological choices in these analyses can significantly impact the results, potentially leading to spurious correlations or missed true associations. In this study, we explore how different decisions affect the performance of statistical tests for correlations between pairs of time series in simulated two-species ecosystems. We show that when performing nonparametric "surrogate data" tests, both the choice of statistic and the method of generating the null distribution can affect true positive and false positive rates. We also show how seemingly closely related methods of accounting for lagged correlation produce vastly different false positive rates. For methods that establish a null model by simulating the dynamics of one of the two species, we show that the choice of species simulated can influence test behavior. Additionally, we identify scenarios where the outcomes of analyses can be highly sensitive to the initial conditions of an ecosystem, even under simple mathematical models. Our results indicate the importance of thoughtful consideration and documentation of the statistical choices investigated here. To make this work broadly accessible, we include visual explanations of most methods tested in an appendix.

bioinformatics↗

bioRxiv2024

Desmoplakin mutations in cardiac fibroblasts cause TGF?1-mediated pathological fibrogenesis in desmoplakin cardiomyopathy via beclin-1 regulation

BackgroundPathological fibrosis is a major finding in cardiovascular diseases and can result in arrhythmia and heart failure. Desmosome gene mutations can lead to arrhythmogenic cardiomyopathy (ACM). Among ACM, pathogenic desmoplakin (DSP) variants cause a distinctive cardiomyopathy with excessive cardiac fibrosis that could precede ventricular dysfunction. DSP variants are also linked to other fibrotic diseases. Whether DSP plays any role in pathological fibrosis remain unknown. MethodsMesenchymal stromal cells (MSCs) are resident fibroblast-like cells that are responsible for fibrogenesis in most organs, including hearts. We first used unbiased genome-wide analyses to generate cardiac fibroblasts-like, induced pluripotent stem cell-derived MSCs from normal donors and ACM patients with DSP mutations. We then studied the fibrogenic responses of cardiac MSCs to transforming growth factor beta-1 (TGF-{beta}1) using Western/Co-IP, autophagy assay, gene knockdowns/over-expressions, genomic analyses, mouse DSP knockdown models, immunostaining, and qPCR. ResultsTGF{beta}1 induced excessive accumulations of vimentin (VIM)/fibrillar collagens, and over-activated fibrotic genes in DSP-mutant MSCs when compared to normal MSCs. In normal MSCs, VIMs bind to wild-type DSP during normal fibrogenesis after TGF{beta}1. DSP-mutant MSCs exhibited a haplo-insufficient phenotype with increased DSP-unbound VIMs that sequestered beclin-1 (BECN1) from activating autophagy and caveolin-1 (CAV1)-mediated endocytosis. Decreased autophagy caused collagen accumulations and diminished CAV1 endocytosis resulted in abnormal CAV1 plaque formation that over-activated fibrotic genes [COL1A1, COL3A1, and fibronectin (FN)] via heightened p38 activities after TGF{beta}1. Genome-wide analysis and DSP knockdown in mouse fibroblasts confirmed this novel role of DSP mutations in pathological fibrosis. Overexpression of VIM-binding domains of DSP could suppress pathological fibrosis by increasing collagen autophagic degradation and decreasing fibrotic gene expressions. ConclusionsOur data reveal that DSP deficiency in MSCs/fibroblasts leads to exaggerated fibrogenesis in DSP-cardiomyopathy by decreasing BECN1 availability for autophagy and CAV1-endocytosis. Overexpression of VIM binding domains of DSP could be a new strategy to treat pathological fibrosis.

cell biology↗

bioRxiv2023

A quantitative genetics framework for understanding the selection response of microbial communities

Heritability, a quantity that reflects the degree of resemblance between parent and offspring traits, is measured during plant and animal breeding because it predicts selection success during artificial selection of individuals. However, when whole microbial communities are under artificial selection to improve their traits, high heritability of the community trait does not necessarily predict selection success. To better understand the relationship between heritability and success during community selection, we establish a quantitative genetics framework, and in doing so, we obtain practical recommendations. Specifically, we decompose a community trait into "trait determinants": genotype compositions and species compositions that impact the community trait and that vary among communities. This allows us to interpret heritability of a community trait in terms of the heritability of its determinants. We then use the Price equation to partition the selection response of a community trait into three phenomena: inter-community selection (heritability multiplied by selection intensity), transmission infidelity (the change in community trait from parent to offspring), and nonlinearity (due to a nonlinear relationship between parent and offspring traits). We illustrate that evolution within a community can cause the three terms to covary: in addition to the known effect of worsening transmission infidelity, intra-community evolution can lead to inflated heritability values greater than one (through an effect whereby "the poor get poorer"), and simultaneously magnify nonlinearity. As a consequence of these effects, heritability no longer predicts the selection response of a community trait. We propose effective selection strategies that improve heritability without accelerating intra-community evolution.

evolutionary biology↗

bioRxiv2023

When is an auxotroph not an auxotroph: how budding yeast lacking MET17 collectively overcome their metabolic defect

Assimilation of sulfur is vital to all organisms. In S. cerevisiae, inorganic sulfate is first reduced to sulfide, which is then reacted to an organic carbon backbone by the Met17 enzyme. The resulting homocysteine can then be converted to all other essential organosulfurs such as methionine, cysteine, and glutathione. This pathway has been known for nearly half a century, and met17 mutants have long been classified as organosulfur auxotrophs which are unable to grow on sulfate as the sole sulfur source. Surprisingly, we found that met17{Delta} could grow on sulfate, albeit only at sufficiently high cell densities. We show that the accumulation of hydrogen sulfide gas underpins this density-dependent growth of met17{Delta} on sulfate, and that the locus YLL058W (HSU1) enables met17{Delta} cells to assimilate hydrogen sulfide. Hsu1 protein is induced during sulfur starvation and under exposure to high sulfide in wildtype cells, suggesting multiple functions of this gene. In a mathematical model, the low efficiency of sulfide assimilation in met17{Delta} can explain the observed density-dependent growth of met17{Delta} on sulfate. Thus, having uncovered and explained the paradoxical growth of a commonly used "auxotroph", our findings may impact the design of future studies in yeast genetics, metabolism, and volatile-mediated microbial interactions.

microbiology↗

bioRxiv2023

Testing nonparametrically for dependence between nonstationary time series with very few replicates

Researchers frequently analyze correlations between pairs of time series by determining whether an observed correlation is stronger than expected under the null hypothesis of independence. However, the time series are often nonstationary, with statistical properties that change over time, thereby making standard tests invalid. If sufficient replicates exist, a trial-swapping permutation test can be performed that handles nonstationarity by comparing within-replicate correlations to between-replicate correlations. Although largely assumption-free, this test is fundamentally limited by the number of replicates (n) because its minimum p-value is 1/n!. With n = 3, this minimum is 1/6, rendering thresholds like 0.05 unattainable. This limits its use considerably in animal experiments, where n may be as low as 3. We propose permute-match tests -- modified permutation tests that can report lower p-values of 2/nn or 1/nn under strong evidence of dependence. Permute-match tests guarantee a false positive rate at or below the significance level when replicates are independent and identically distributed. The bound of 1/nn is not gratuitously conservative, since it cannot be further lowered without additional assumptions. We demonstrate our approach using synthetic data and apply it to an existing dataset with 3 independent groups of zebrafish, confirming the observation that zebrafish swim faster when directionally aligned. Data availabilitySource code 1 contains all the simulation and analysis code used to prepare this manuscript. The experimental data analyzed in this paper can be obtained at the following web address: https://drive.google.com/drive/folders/1UmzlX-yJhzQ5KX5rGry8wZgXvcz6HefD.

bioinformatics↗

bioRxiv2023

Condition for successful artificial selection on microbial collectives and optimal experimental setup

Collectives, such as microbial communities, can perform functions beyond the capability of individual members. Enhancing these collective functions through artificial selection, however, presents significant challenges. Here, we explore the rafting-a-waterfall phenomenon, a metaphor illustrating how the success in achieving a target population composition in microbial collectives depends on both the target characteristics and initial conditions. Specifically, collectives comprising fast-growing (F) and slow-growing (S) individuals were grown for a period of "maturation" time, and the collective with S-frequency closest to the target value is chosen to "reproduce" (inoculate) offspring collectives. Such collective selection is dictated by two opposing forces: during collective maturation, intra-collective selection acts like a waterfall, relentlessly driving the S-frequency to lower values, while during collective reproduction, inter-collective selection resembles a rafter striving to reach the target frequency. Due to this model structure, maintaining a target frequency requires the continued action of inter-collective selection. Using simulations and analytical calculations, we show that intermediate target S frequencies are the most challenging, akin to a target within the vertical drop of a waterfall, rather than above or below it. This arises because intra-collective selection is the strongest at intermediate S-frequencies, which can overpower inter-collective selection. While achieving low target S frequencies is consistently feasible, attaining high target S-frequencies requires an initially high S-frequency -- much like a raft that can descend but not ascend a waterfall. The range of attainable target frequencies depends on the initial population size of the collectives: as the population size in Newborn collectives increases, the region of achievable target frequency is reduced until no frequency is achievable. In contrast, the number of collectives under selection plays a less critical role. In scenarios involving more than two populations, the evolutionary trajectory must navigate entirely away from the metaphorical waterfall drop. Our findings illustrate that the strength of intra-collective evolution is frequency-dependent, with implications in experimental planning.

evolutionary biology↗

bioRxiv2022

Integrated transcriptome and lineage analyses reveal novel catecholaminergic cardiomyocytes contributing to the cardiac conduction system in murine heart

Cardiac conduction system (CCS) morphogenesis is essential for correct heart function yet is incompletely understood. Here we established the transcriptional landscape of cell types populating the developing heart by integrating single-cell RNA sequencing and spatial enhanced resolution omics-sequencing (Stereo-seq). Stereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. This led to the identification of a previously unrecognized cardiomyocyte population expressing dopamine beta-hydroxylase (Dbh+-CMs), which is closely associated with the CCS in transcriptomic analyses. To confirm this finding, genetic fate mapping by using DbhCre/Rosa26-tdTomato reporter mouse line was performed with Stereo-seq, RNAscope, and immunohistology. We revealed that Dbh+-derived CMs first emerged in the sinus venosus at E12.5, then populated the atrial and ventricular CCS components at E14.5, with increasing abundance towards perinatal stages. Further tracing by using DbhCFP reporter and DbhCreERT/Rosa26-tdTomato inducible reporter, we confirmed that Dbh+-CMs are mostly abundant in the AVN and ventricular CCS and this persists in the adult heart. By using DbhCre/Rosa26-tdTomato/Cx40-eGFP compound reporter line, we validated a clear co-localization of tdTomato and eGFP signals in both left and right ventricular Purkinje fibre networks. Finally, electrophysiological optogenetic study using cell-type specific Channelrhodopsin2 (ChR2) expression further elucidated that Dbh+-derived CMs form a functional part of the ventricular CCS and display similar photostimulation-induced electrophysiological characteristics to Cx40CreERT/ChR2-tdTomato CCS components. Thus, by utilizing advanced transcriptomic, mouse genetic, and optogenetic functional analyses, our study provides new insights into mammalian CCS development and heterogeneity by revealing novel Dbh+-CMs. HighlightsO_LIStereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. C_LIO_LIEstablished the transcriptional landscape of cell types populating the developing murine heart. C_LIO_LIRevealed previously unreported catecholaminergic cardiomyocyte populations contributing to the developing and mature murine cardiac conduction system. C_LI

cell biology↗

bioRxiv2022

An exactly valid and distribution-free statistical significance test for correlations between time series

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series, and determine whether the correlation is statistically significant (i.e. unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation, which cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. Among nonparametric tests, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between two time series. We prove that this test is valid as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to data sets from climatology, animal behavior and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.

bioinformatics↗

bioRxiv2022

SARS-CoV-2 Viral Genes Compromise Survival and Functions of Human Pluripotent Stem Cell-derived Cardiomyocytes via Reducing Cellular ATP Level

Cardiac manifestations are commonly observed in COVID-19 patients and prominently contributed to overall mortality. Human myocardium could be infected by SARS-CoV-2, and human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are susceptible to SARS-CoV-2 infection. However, molecular mechanisms of SARS-CoV-2 gene-induced injury and dysfunction of human CMs remain elusive. Here, we find overexpression of three SARS-CoV-2 coding genes, Nsp6, Nsp8 and M, could globally compromise transcriptome of hPSC-CMs. Integrated transcriptomic analyses of hPSC-CMs infected by SARS-CoV-2 with hPSC-CMs of Nsp6, Nsp8 or M overexpression identified concordantly activated genes enriched into apoptosis and immune/inflammation responses, whereas reduced genes related to heart contraction and functions. Further, Nsp6, Nsp8 or M overexpression induce prominent apoptosis and electrical dysfunctions of hPSC-CMs. Global interactome analysis find Nsp6, Nsp8 and M all interact with ATPase subunits, leading to significantly reduced cellular ATP level of hPSC-CMs. Finally, we find two FDA-approved drugs, ivermectin and meclizine, could enhance the ATP level, and ameliorate cell death and dysfunctions of hPSC-CMs overexpressing Nsp6, Nsp8 or M. Overall, we uncover the global detrimental impacts of SARS-CoV-2 genes Nsp6, Nsp8 and M on the whole transcriptome and interactome of hPSC-CMs, define the crucial role of ATP level reduced by SARS-CoV-2 genes in CM death and functional abnormalities, and explore the potentially pharmaceutical approaches to ameliorate SARS-CoV-2 genes-induced CM injury and abnormalities.

molecular biology↗

bioRxiv2021

Molecular mechanism of the anti-lung cancer effect of Jin Ning Fang based on network pharmacology and experimental verification

BackgroundJin Ning Fang (JNF) is widely used as an adjuvant therapy for lung cancer. However, its molecular mechanism against lung cancer is still unclear. MethodsThe chemical compounds JNF were screened from the TCMSP database and its target proteins were then predicted. The genes related to lung cancer were collected from the CTD and DisGeNET databases. Next, targets were integrated with disease-related genes to obtain candidate genes. Functional enrichment and protein-protein interaction (PPI) analysis were also performed, followed by construction of pharmacological network. Meanwhile, Autodock was used to assess the affinity between targets and compound. Finally, the anti-cancer effect of JNF on lung cancer cells was detected and some predicted key genes was validated by using real-time PCR. ResultsTwenty-five overlapping targets were obtained, and pathway analysis showed that JNF might exert its anti-cancer function by regulating some biological pathways, such as apoptosis pathway. PPI and pharmacological network revealed several core targets (such as AKT1, AR, and ESR1) and three compounds (quercetin, calcium carbonate, and beta-sitosterol). Then, beta-sitosterol had a high affinity with AKT1, AR, and ESR1. Further in vitro experiments confirmed that JNF could inhibit proliferation and promote apoptosis of A549 cells. The expression of FDPS, PIM1, VCAM1, SLC29A1, NQO1, and ESR1 were significantly decreased, while mRNA level of AR and ANPEP were markedly increased after JNF treatment. ConclusionJNF may exert anti-lung cancer effect through multiple targets and pathways, and identified genes may be used as potential biomarkers for diagnosis and treatment of lung cancer.

cancer biology↗

bioRxiv2020

Data-driven causal analysis of observational time series: a synthesis

Complex ecosystems are challenging to understand as they often defy manipulative experiments for practical or ethical reasons. In response, several fields have developed parallel approaches to infer causal relations from observational time series. Yet these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal inference approaches popular in ecological time series analysis: pairwise correlation, Granger causality, and state space reconstruction. For each, we ask what a method tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of causal inference methods, and point out how so-called "model-free" causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of causal inference approaches and encourage explicit statements of assumptions.

bioinformatics↗

bioRxiv2020

Pleiotropic win-win mutations can rapidly evolve in a nascent cooperative community despite unfavorable conditions

Cooperation, paying a cost to benefit other individuals, is widespread. Cooperation can be promoted by pleiotropic "win-win" mutations which directly benefit self and partner. Previously, we showed that "partner-serving" should be defined as increased benefit supply rate per intake benefit (Hart & Pineda et al., 2019). Here, we report that "win-win" mutations can rapidly evolve even in nascent cooperation under conditions unfavorable for cooperation. Specifically, in a well-mixed environment we evolved engineered yeast cooperative communities where two strains exchanged costly metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, ecm21 mutations repeatedly arose. ecm21 is "self-serving", improving selfs growth rate in limiting lysine. ecm21 is also "partner-serving", increasing hypoxanthine release rate per lysine consumption and the steady state growth rate of partner. ecm21 also arose in monocultures evolving in lysine-limited chemostats. Thus, even without any pressure to maintain cooperation, pleiotropic win-win mutations may readily evolve.

evolutionary biology↗

bioRxiv2020

Genome-wide Studies Reveal the Essential and Opposite Roles of ARID1A in Controlling Human Cardiogenesis and Neurogenesis from Pluripotent Stem Cells

BackgroundEarly human heart and brain development simultaneously occur during embryogenesis. Notably, in human newborns, congenital heart defects strongly associate with neurodevelopmental abnormalities, suggesting a common gene/complex underlying both cardiogenesis and neurogenesis. However, due to lack of in vivo studies, the molecular mechanisms that govern both early human heart and brain development remain elusive. ResultsHere, we report ARID1A, which is a DNA-binding-subunit of the SWI/SNF epigenetic complex, controls both neurogenesis and cardiogenesis from human embryonic stem cells (hESCs) via employing distinct mechanisms. Knockout of ARID1A (ARID1A-/-) led to spontaneous differentiation of neural cells together with globally enhanced expression of neurogenic genes in undifferentiated hESCs. Additionally, when compared with WT hESCs, cardiac differentiation from ARID1A-/- hESCs was prominently suppressed, whereas neural differentiation was significantly promoted. Whole genome-wide scRNA-seq, ATAC-seq, and ChIP-seq analyses revealed that ARID1A was required to open chromatin accessibility on promoters of essential cardiogenic genes, and temporally associated with key cardiogenic transcriptional factors T and MEF2C during early cardiac development. However, during early neural development, transcription of most essential neurogenic genes was dependent on ARID1A, which could interact with a known neural restrictive silencer factor REST/NRSF. ConclusionsWe uncovered the opposite roles by ARID1A to govern both early cardiac and neural development from pluripotent stem cells. Global chromatin accessibility on cardiogenic genes is dependent on ARID1A, whereas transcriptional activity of neurogenic genes is under control by ARID1A, possibly through ARID1A-REST/NRSF interaction.

cell biology↗