Biology subjects

Saucerman, J. J.

Publications and source records attributed to Saucerman, J. J..

At least 19 recordsLinked to original sources

bioRxiv2026

SP1 tethers NR2F2 to drive atrial cardiomyocyte identity

Human heart development is guided by complex gene regulatory networks (GRNs), which are difficult to unravel and differ from that in animal models. In this study, we use a systematic approach to identify gene interactions driving human fetal atrial cardiomyocyte identity. We inferred GRNs for atrial and ventricular cardiomyocytes from human fetal single-cell RNA-seq and ATAC-seq data, predicting transcription factors that differentially regulate atrial vs. ventricular cardiomyocyte identity. Comparison of GRNs constrained by ATAC-seq vs. promoter annotations revealed that during human heart development, the canonical transcription factor for atrial specification, NR2F2, drives atrial genes not directly but by interacting with SP1. The predicted NR2F2-SP1 relationship at atrial gene promoters was supported with AlphaFold predictions of biophysical tethering and comparison with previous ChIP-seq data. These predictions were experimentally validated in human iPSC-derived atrial cardiomyocyte differentiation, which was suppressed by SP1 knockdown. Further, proximity ligation experiments validated the physical interaction of NR2F2 and SP1 in the nucleus, coincident with atrial identity. This study reveals SP1 as a transcription factor necessary for atrial cardiomyocyte identity in human development, helping to coordinate the effects of NR2F2. Furthermore, comparing GRNs based on current and potential epigenetic state offers a novel systematic approach to study interactions between transcription factors, which is key to understanding complex GRNs.

developmental biology↗

bioRxiv2026

Network-enabled drug repurposing identifies lithium to promote cardiomyocyte proliferation

The adult mammalian heart has minimal regenerative capacity, limited in part by endogenous cardiomyocyte (CM) renewal. Although CM proliferation is a promising target in both congenital and adult heart disease, no current therapies specifically target this process. Progress has been hindered by the complexity of CM proliferation signaling networks, which may require systems-level approaches. The overall goal of this study was to leverage systems modeling to identify FDA approved drugs that have the potential to be repurposed towards CM proliferation. To achieve this goal, we developed a computational drug repurposing framework that integrates drug-target interaction data with a previously developed logic-based network model of CM proliferation signaling. Using this framework, we identified 81 drugs that directly target nodes within the CM proliferation network, corresponding to 32 unique drug-target interactions. Virtual screening predicted 5 unique drug-target interactions that increase CM proliferation and 14 that decrease CM proliferation outputs. Lithium, a GSK3{beta} inhibitor widely prescribed for bipolar disorder, was predicted and experimentally validated to promote CM proliferation through a Cyclin D/CDK4 pathway. In vivo, treatment of pregnant mice with lithium increased DNA synthesis in the CMs of their embryos. A retrospective analysis of the FDA Adverse Events Reporting System confirmed that relative to other mood stabilizers, in utero exposure to lithium was associated with an increased incidence of congenital heart defects, in males but not females. Together, this work provides a computational-experimental framework for repurposing FDA-approved drugs to modulate CM proliferation, which highlights therapeutic potential of lithium for adult CM regeneration but congenital risks to developing embryos.

systems biology↗

bioRxiv2026

Mapping a genome-scale in vivo knockout screen to a mechanistic network model identifies VAV2, RASA1, and LEPR as regulators of cardiomyocyte hypertrophy

Cardiomyocyte hypertrophy is a leading clinical predictor of heart failure, yet newly identified candidate genes often remain disconnected from the signaling mechanisms that govern cardiomyocyte growth. We developed a computational-experimental pipeline that integrates genome-scale mouse knockout phenotypes with a logic-based differential equation model of hypertrophic signaling. Among 9,605 genes evaluated by the International Mouse Phenotyping Consortium, 939 knockout lines induced abnormal heart morphology. Directional curation of hypertrophy-related sub-phenotypes followed by interaction-based network expansion mapped 37 genes to the signaling model. Virtual knockdown screening identified five candidates with concordant in vivo and in silico effects: LRIG1 and CBL as predicted negative regulators and VAV2, RASA1, and LEPR as predicted positive regulators. Mechanistic subnetwork analysis linked these candidates to distinct receptor-proximal, Ras, PI3K-AKT, and MAPK signaling axes. In neonatal rat cardiomyocytes, siRNA-mediated depletion of VAV2, RASA1, or LEPR reduced phenylephrine-induced cell growth, supporting their cell-autonomous contribution to hypertrophy. Quantitative phenotyping further validated the predicted decreased cardiac hypertrophy for VAV2 and LEPR knockouts but identified potential age-dependent mechanisms for RASA1 knockout. Overall, this study establishes the application of network models to translate from in vivo phenotypic screens into pathway mechanisms.

systems biology↗

bioRxiv2026

Therapeutic targeting of MYBPC3 mutation-specific hypertrophic cardiomyopathy guided by network modeling

Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death with genotype positive cases most commonly associated with mutations of cardiac myosin binding protein-C (MYBPC3). Recently approved drugs for HCM target the myofilaments rather than the aberrant molecular signaling pathways that drive long-term remodeling. Here, we identified a patient with familial HCM that was associated with a MYBPC3 W1078 truncation mutation. A CRISPR knock-in mouse model of the orthologous mutation MYBPC3 W1082 exhibited marked cardiac hypertrophy including wall thickening, reduced ejection fraction, and decreased survival. To identify pathways, mechanisms, and potential candidate therapeutics, we integrated the MYBPC3 mutation into a computational network model of the signaling underlying familial cardiomyopathy. The network model predicted that the MYBPC3 mutation drove hypertrophy through mTOR/PI3K pathways, consistent with the results of RNA sequencing of cardiomyocytes of MYBPC3W1082*/W1082* mice. A virtual drug screen using FDA-approved drugs predicted that the mTOR inhibitor, Rapamycin, could mitigate mutation-induced hypertrophy. We then experimentally validated the effects of Rapamycin on hypertrophic responses using cultured cardiomyocytes. Further, Rapamycin attenuated cardiac hypertrophy and fibrosis of MYBPC3W1082*/W1082* mice in vivo. mTOR inhibitors (rapamycin and everolimus) were associated with a decreased incidence of cardiac hypertrophy associated diagnostic codes in patients in the FDA Adverse Events Reporting System. Query of electronic health records and echocardiograms from a University of Virginia cohort of patients treated with mTOR or calcineurin inhibitors showed that patients prescribed everolimus or tacrolimus had reduced LV wall thicknesses. Together, these studies suggest that targeting mTOR as a translationally relevant target for a mutation-induced hypertrophic cardiomyopathy, as well as demonstrating the utility of guiding precision therapies by iterating between network models and experimental validation.

physiology↗

bioRxiv2026

Computational Analysis of Fibroblast Subpopulation Dynamics as a Driver of Fibrotic Foci Formation

Fibroblasts maintain the extracellular matrix (ECM) to support tissue homeostasis and wound healing. In fibrotic diseases, fibroblasts are a primary driver of disease progression through excess collagen secretion and enhanced contractility. Replacing native tissue with a collagen rich fibrotic scar leads to a decline in tissue function. Recent research into idiopathic pulmonary fibrosis (IPF) has identified fibroblast subpopulations that may be primed for the hyper-activation that leads to increased progression of fibrotic disease. Understanding the contribution of these subpopulations to disease progression requires integrating experimental and computational techniques to understand their dynamic contributions to tissue phenotype. Herein, we introduce a framework for modeling subpopulations using a multiscale mechanistic computational model to understand differences within subpopulations, at the intracellular level and how these differences contribute to cell-and tissue-level pathology. We build and validate this framework using two well-defined subpopulations of fibroblasts in IPF. The subpopulations are defined by the presence or absence of Thy-1, a cell-surface protein that regulates fibroblast mechanosensing. We first developed a logic-based network model of a fibroblast. We then applied this model to identify sub-networks that regulate myofibroblast marker expression in the two subpopulations. Coupling this with an agent-based model (ABM) of the lung microenvironment, we observed how different rules regulating cell fate decisions in each subpopulation affected collagen content. Computational image outputs were analyzed with the open-source biological image analysis software QuPath to quantify how changes in subpopulation dynamics change model-predicted foci characteristics such as size and collagen density. We find that the ability for Thy-1+ fibroblasts to transition to Thy-1- fibroblasts significantly increases total collagen content, as well as influences fibrotic foci characteristics. Overall, we present a combined experimental and computational framework for studying how dynamic changes in fibroblast subpopulations lead to tissue-level disease phenotypes. Author SummaryIn wound healing fibroblasts are responsible for rebuilding the scaffolding, called the extracellular matrix (ECM), of the damaged tissue to aid in regeneration. In fibrosis, the normal wound healing processes are hijacked leading to overproduction of ECM proteins, such as collagen, by fibroblasts leading to fibrosis. In fibrotic diseases with no known cause, such as idiopathic pulmonary fibrosis (IPF), subpopulations of fibroblasts have been identified as possible drivers of disease. Herein, we use a multiscale computational model that represents intracellular, cellular, and tissue level signaling to study how the presence or absence of a single protein on a fibroblasts surface, Thy-1, affects the formation of fibrotic scar in IPF. Thy-1 regulates how a fibroblast senses the stiffness of the microenvironment. The absence of Thy-1 impacted intracellular signaling and when combined across many cells led to fibrosis at the tissue level. Additionally, if normal Thy-1+ fibroblasts could dynamically lose Thy-1 expression the amount of collagen they produced correlated with that in end stage IPF lungs. This model presents a framework for studying different fibroblast subpopulations using multiscale computational modeling to understand how changes in a single protein in one cell can, over an entire population, affect the dynamics of disease progression.

bioengineering↗

bioRxiv2026

Paired single-cell imaging of calcium and expres-sion to map niches of identity and function

Cellular identity is often inferred from molecular markers, while function is measured independently, obscuring how these dimensions align at single-cell resolution. In cardiomyocytes, this disconnect is especially limiting, as calcium dynamics and subtype markers are typically assessed in bulk or separate cells then averaged across populations. In human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), this gap has limited our ability to determine whether heterogeneity in electrophysiology and calcium handling reflects noise, maturation, or structured biological states. This lack of clarity is due in part to the lack of methods that directly link live functional measurements with molecular identity at single-cell resolution. Here, we introduce CARBONITE (Calcium Recordings Before Identification by Expression), a scalable single-cell imaging framework that pairs live calcium dynamics with protein expression and spatial phenotyping. Using high-content imaging in 96-well plates, CARBONITE integrates per-cell calcium transient features with immunofluorescent identification applied to cardiomyocyte subtype markers, enabling quantitative functional-molecular mapping within the same cells. Applying CARBONITE to mixed human induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) populations reveals heterogeneity in calcium transient dynamics and marker expression within individual wells. Canonical atrial and ventricular markers capture only a subset of functional variability. Notably, calcium transient shape segregates cells into two discrete functional states that exhibit perinuclear ANP (atrial marker) enrichment and nucleation state (i.e., mono- vs. binucleation). Notably, these groups show know relationship to MYL2 (ventricular marker) expression. Binucleated cells are more likely to exhibit a spike-like calcium transient, identifying nucleation as a dominant and previously underappreciated axis of cardiomyocyte identity influencing calcium function. Together, these results establish CARBONITE as a functional multimodal single-cell platform that reveals organizational principles of cell identity, providing a foundation for dissecting functional niches in development and disease.

bioengineering↗

bioRxiv2025

Systems analysis reveals neuregulin-1 control of cardiomyocyte size and shape mediated by distinct PI3K and p38 pathways

Pathological and physiological stresses induce diverse forms of cardiac hypertrophy, with distinct manifestations in cardiomyocyte size and shape regulated by still poorly understood signaling networks. Here, we combined high-content morphological profiling, phospho-protein arrays, and systems modeling to characterize the diverse forms of hypertrophy induced by angiotensin II, endothelin-1, insulin growth factor-1, and neuregulin-1. Reverse-phase protein array profiling of neonatal rat cardiomyocytes and partial least squares regression modeling revealed that Akt, GSK3, and MAPK signaling are differentially regulated by hypertrophic agonists and are predictive of distinct phenotypic outcomes. Among these agonists, neuregulin-1 uniquely induced cardiomyocyte elongation in both neonatal rat and human iPSC-derived cardiomyocytes, in addition to increasing cell area. Pharmacological perturbations in neonatal rat cardiomyocytes demonstrated that neuregulin1-induced elongation and area expansion both require PI3K activity, whereas p38 selectively mediates cell area. A logic-based network model incorporating dual-specificity phosphatases were sufficient to capture the amplifying PI3K and transient p38 signaling dynamics driving phenotypic changes. Together, these results identify distinct signaling cascades by which neuregulin-1 coordinates cardiomyocyte size and shape, providing mechanistic insight into how hypertrophic remodeling can be differentially regulated. This systems approach provides new insight into the pathways that drive distinct forms of cardiomyocyte hypertrophy, highlighting opportunities to selectively target maladaptive remodeling in heart failure.

systems biology↗

bioRxiv2025

Network Modeling Predicts How DYRK1A Inhibition Promotes Cardiomyocyte Cycling after Ischemic/Reperfusion Injury

The adult mammalian heart has a limited ability to regenerate lost myocardium following myocardial infarction (MI), largely due to the poor proliferative capacity of cardiomyocytes (CMs). Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a known regulator of cell quiescence, though the mechanisms underlying its function remain unclear. Previous studies have shown that pharmacological inhibition of DYRK1A using harmine induces CM cell cycle re-entry after ischemia/reperfusion (I/R) MI. Here, we developed a computational network model of DYRK1A-mediated regulation of the cell cycle, which predicts how DYRK1A inhibition promotes CM re-entry. To validate these predictions, we tested selective DYRK1A inhibitors and observed robust induction of cell cycle activity in neonatal rat cardiomyocytes (NRCMs). Integrating our network model with bulk RNA-sequencing data from DYRK1A inhibitor-treated NRCMs, we identified E2F1 as a key transcriptional driver of cell cycle gene expression. Finally, we demonstrate that both pharmacological and post-developmental inhibition of DYRK1A enhances heart function and increases CM cycling following I/R MI. Our findings suggest that functional recovery induced by small molecule inhibitor of DYRK1A is mediated by the induction of cycling CMs. One Sentence SummaryInhibition of DYRK1A through LCTB-92 induces cardiomyocyte cycling and improved heart function in a mouse model of ischemic/reperfusion injury.

pharmacology and toxicology↗

bioRxiv2025

Reduced TBX5 dosage undermines developmental control of atrial cardiomyocyte identity in a model of human atrial disease

While atrial septal defects (ASDs) and atrial fibrillation (AF) present differently, there is evidence that they share some genetic basis. Here, we used directed differentiation of human induced pluripotent stem cells into atrial or ventricular cardiomyocytes (CMs) to delineate gene regulatory networks (GRNs) that define each identity. We uncovered accessible chromatin regions, transcription factor motifs and key regulatory nodes specific to, or shared by, both CM types, including the transcription factor TBX5, which is linked to genetic susceptibility of ASDs and AF in humans. Complete TBX5 loss resulted in a near absence of atrial CMs with a concomitant increase in the abundance of other cell types. Reduced dosage of TBX5 in human atrial CMs caused cellular, electrophysiologic and molecular phenotypes consistent with features of atrial CM dysfunction. This included dose-dependent aberrant accessibility of many chromatin regions and perturbation of gene regulatory networks of atrial CM identity. These results suggest that, in addition to stemming from ion channel or extracellular matrix dysfunction, atrial diseases such as ASDs or AF may result from disruptions of atrial CM identity.

developmental biology↗

bioRxiv2025

Benchmarking of signaling networks generated by large language models

Computational models of biochemical networks provide frameworks for predicting how molecular cues guide cell decisions. These models are typically limited by the time-intensive manual curation required to extract network mechanisms from incomplete literature. Here, we test whether general-purpose large language models (LLMs) can generate accurate models of signaling and metabolic networks. We find that general-purpose LLMs generate 24-65% of the reactions of literature-curated signaling networks for cardiomyocyte hypertrophy, myofibroblast activation, and mechanosignaling. Further, logic-based models based on these networks predict responses to perturbations with accuracies of 6-33%. In the context of metabolic modeling, LLMs are able to generate 64-91% of the reactions within the core Escherichia coli metabolic network and demonstrate highly variable accuracies in predicting substrate utilization. Current general-purpose LLMs generate biochemical networks with moderate accuracy, and this study provides a pipeline and benchmarks to guide future improvements.

systems biology↗

bioRxiv2024

Logic-based machine learning predicts how escitalopram inhibits cardiomyocyte hypertrophy

IntroductionCardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. MethodsHere we developed LogiRx, a logic-based mechanistic machine learning method that predicts drug-induced pathways. We applied LogiRx to discover how drugs discovered in a previous compound screen attenuate cardiomyocyte hypertrophy. We experimentally validated LogiRx predictions in neonatal cardiomyocytes, adult mice, and two patient databases. ResultsUsing LogiRx, we predicted anti-hypertrophic pathways for 7 drugs currently used to treat non-cardiac disease. We experimentally validated that escitalopram (Lexapro) and mifepristone inhibit hypertrophy of cultured cardiomyocytes in two contexts. The LogiRx model predicted that escitalopram prevents hypertrophy through an "off-target" serotonin receptor/PI3K{gamma} pathway, mechanistically validated using additional investigational drugs. Further, escitalopram reduced cardiomyocyte hypertrophy in a mouse model of hypertrophy and fibrosis. Finally, mining of both FDA and University of Virginia databases showed that patients with depression on escitalopram have a lower incidence of cardiac hypertrophy than those prescribed other serotonin reuptake inhibitors that do not target the serotonin receptor. ConclusionMechanistic machine learning by LogiRx discovers drug pathways that perturb cell states, which may enable repurposing of escitalopram and other drugs to limit cardiac remodeling through "off-target" pathways.

systems biology↗

bioRxiv2024

Heart tube morphogenesis is regulated by segment-specific gene regulatory networks controlled by MEF2C

The gene regulatory networks (GRNs) that control early heart formation are beginning to be understood, but lineage-specific GRNs remain largely undefined. We investigated networks controlled by the vital transcription factor MEF2C, with a time course of single-nucleus RNA- and ATAC-sequencing in wild-type and Mef2c-null embryos. We identified a "posteriorized" cardiac gene signature and chromatin landscape in the absence of MEF2C. Integrating our multiomics data in a deep learning-based model, we constructed developmental trajectories for each of the outflow tract, ventricular, and inflow tract segments, and alterations of these in Mef2c-null embryos. We computationally identified segment-specific MEF2C-dependent enhancers, with activity in the developing zebrafish heart. Finally, using inferred GRNs we discovered that the Mef2c-null heart malformations are partly driven by increased activity of the nuclear hormone receptor NR2F2. Our results delineate lineage-specific GRNs in the early heart tube and provide a generalizable framework for dissecting transcriptional networks governing developmental processes.

developmental biology↗

bioRxiv2024

Myocardial Endoglin Regulates Cardiomyocyte Proliferation and Cardiac Regeneration

The mammalian heart loses almost all its regenerative potential in the first week of life due to the cessation of the ability of cardiomyocytes to proliferate. In recent years, a number of regulators of cardiomyocyte proliferation have been identified. Despite this, a clear understanding of the regulatory pathways that control cardiomyocyte proliferation and cardiac regeneration is lacking, and there are likely additional regulators to be discovered. Here, we performed a genome-wide screen on fetal murine cardiomyocytes to identify potential novel regulators of cardiomyocyte proliferation. Endoglin was identified as an inhibitor of cardiomyocyte proliferation in vitro. Endoglin knock-down resulted in enhanced DNA synthesis, cardiomyocyte mitosis and cytokinesis in mouse, rat and human cardiomyocytes. Using gene-targeted mice, we confirmed myocardial Endoglin to be important in cardiomyocyte proliferation and cardiac regeneration using gene-targeted mice. Mechanistically, we show that Smad signaling is required for the endoglin-mediated anti-proliferative effects. Our results identify the TGF-{beta} coreceptor Endoglin as a regulator of cardiac regeneration and cardiomyocyte proliferation. SummaryHigh-content function screening is used to identify a novel inhibitor of cardiomyocyte proliferation which can promote mammalian cardiac regeneration.

genetics↗

bioRxiv2024

Contributions of mechanical loading and hormonal changes to eccentric hypertrophy during volume overload: a Bayesian analysis using logic-based network models.

Primary mitral regurgitation (MR) is a pathology that alters mechanical loading on the left ventricle, triggers an array of compensatory neurohormonal responses, and induces a distinctive ventricular remodeling response known as eccentric hypertrophy. Drug therapies may alleviate symptoms, but only mitral valve repair or replacement can provide significant recovery of cardiac function and dimensions. Questions remain about the optimal timing of surgery, with 20% of patients developing systolic dysfunction post-operatively despite being treated according to the current guidelines. Thus, better understanding of the hypertrophic process in the setting of ventricular volume overload (VO) is needed to improve and better personalize the management of MR. To address this knowledge gap, we employ a Bayesian approach to combine data from 70 studies on experimental volume overload in dogs and rats and use it to calibrate a logic-based network model of hypertrophic signaling in myocytes. The calibrated model predicts that growth in experimental VO is mostly driven by the neurohormonal response, with an initial increase in myocardial tissue stretch being compensated by subsequent remodeling fairly early in the time course of VO. This observation contrasts with a common perception that volume-overload hypertrophy is driven primarily by increased myocyte strain. The model reproduces many aspects of 43 studies not used in its calibration, including infusion of individual hypertrophic agonists alone or in combination with various drugs commonly employed to treat heart failure, as well as administration of some of those drugs in the setting of experimental volume overload. We believe this represents a promising approach to using the known structure of an intracellular signaling network to integrate information from multiple studies into quantitative predictions of the range of expected responses to potential interventions in the complex setting of cardiac hypertrophy driven by a combination of hormonal and mechanical factors. Author SummaryMitral valve regurgitation is a common heart disease in which a malfunctioning valve allows part of the blood pumped by the heart to flow in the wrong direction. This condition overloads the heart by making it pump more blood volume than normal; the heart temporarily adapts by growing in mass and volume, but if untreated the condition can ultimately lead to heart failure and death. The most effective treatment is to surgically repair the valve; however, in many patients heart function deteriorates even after a successful surgery. Many researchers have studied this condition by experimentally overloading the hearts of dogs and rats, producing large amounts of data on the resulting geometric, mechanical, and biologic changes. Yet it has been difficult to translate those studies into effective selection and timing of treatment in all patients. In this work we integrate experimental data reported from 70 research articles on experimental volume overload through a simple model of heart mechanics and a more complex model of the molecular signaling pathways inside heart cells. We use a statistical approach to calibrate the computational model, so that it can predict not only average responses but also the degree of expected uncertainty for each prediction. We then use the model to explore how the heart responds to different combinations of drugs and hormones, and potential treatments during overload.

bioengineering↗

bioRxiv2024

Multi-Scale Computational Model of Microvascular Remodeling in Idiopathic Pulmonary Fibrosis

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.

bioengineering↗

bioRxiv2024

Logic-based modeling of biological networks with Netflux

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

bioengineering↗

bioRxiv2023

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGF{beta} and/or IL-1{beta}, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis. SignificanceCardiac fibrosis is a dysregulation of the normal wound healing response, resulting in excessive scarring and cardiac dysfunction. As cardiac fibroblasts primarily regulate this process, we explored how candidate anti-fibrotic drugs alter the fibroblast phenotype. We identify a set of 137 phenotypic features that change in response to drug treatments. Using a new computational modeling approach termed logic-based mechanistic machine learning, we predict how pirfenidone and Src inhibition affect the regulation of the phenotypic features actin filament assembly and actin-myosin stress fiber formation. We also show that inhibition of PI3K reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts, supporting a role for PI3K as a mechanism by which Src inhibition may suppress fibrosis.

systems biology↗

bioRxiv2022

Benchmarking of protein interaction databases for integration with manually reconstructed signaling network models

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath, and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signaling, and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signaling (85 of 125 interactions, 68%), and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of PKC autophosphorylation and CaMKII phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signaling.

systems biology↗