Biology subjects

Srivastava, D.

Publications and source records attributed to Srivastava, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Chromatin Remodeling Drives Immune-Fibroblast Crosstalk in Heart Failure Pathogenesis

Chronic inflammation and tissue fibrosis are common stress responses that worsen organ function, yet the molecular mechanisms governing their crosstalk are poorly understood. In diseased organs, stress-induced changes in gene expression fuel maladaptive cell state transitions and pathological interaction between diverse cellular compartments. Although chronic fibroblast activation worsens dysfunction of lung, liver, kidney, and heart, and exacerbates many cancers, the stress-sensing mechanisms initiating the transcriptional activation of fibroblasts are not well understood. Here, we show that conditional deletion of the transcription co-activator Brd4 in Cx3cr1-positive myeloid cells ameliorates heart failure and is associated with a dramatic reduction in fibroblast activation. Analysis of single-cell chromatin accessibility and BRD4 occupancy in vivo in Cx3cr1-positive cells identified a large enhancer proximal to Interleukin-1 beta (Il1b), and a series of CRISPR deletions revealed the precise stress-dependent regulatory element that controlled expression of Il1b in disease. Secreted IL1B functioned non-cell autonomously to activate a p65/RELA-dependent enhancer near the transcription factor MEOX1, resulting in a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL1B neutralization prevented stress-induced expression of MEOX1, inhibited fibroblast activation, and improved cardiac function in heart failure. The elucidation of BRD4-dependent crosstalk between a specific immune cell subset and fibroblasts through IL1B provides new therapeutic strategies for heart disease and other disorders of chronic inflammation and maladaptive tissue remodeling.

molecular biology↗

bioRxiv2022

A Mesp1-dependent developmental breakpoint in transcriptional and epigenomic specification of early cardiac precursors

Transcriptional networks governing cardiac precursor cell (CPC) specification are incompletely understood due in part to limitations in distinguishing CPCs from non-cardiac mesoderm in early gastrulation. We leveraged detection of early cardiac lineage transgenes within a granular single cell transcriptomic time course of mouse embryos to identify emerging CPCs and describe their transcriptional profiles. Mesp1, a transiently-expressed mesodermal transcription factor (TF), is canonically described as an early regulator of cardiac specification. However, we observed perdurance of CPC transgene-expressing cells in Mesp1 mutants, albeit mis-localized, prompting us to investigate the scope of Mesp1s role in CPC emergence and differentiation. Mesp1 mutant CPCs failed to robustly activate markers of cardiomyocyte maturity and critical cardiac TFs, yet they exhibited transcriptional profiles resembling cardiac mesoderm progressing towards cardiomyocyte fates. Single cell chromatin accessibility analysis defined a Mesp1-dependent developmental breakpoint in cardiac lineage progression at a shift from mesendoderm transcriptional networks to those necessary for cardiac patterning and morphogenesis. These results reveal Mesp1-independent aspects of early CPC specification and underscore a Mesp1-dependent regulatory landscape required for progression through cardiogenesis.

developmental biology↗

bioRxiv2022

Single Cell Epigenetics Reveal Cell-Cell Communication Networks in Normal and Abnormal Cardiac Morphogenesis

Communication between myriad cell types during organ formation underlies proper morphogenesis1. In cardiac development, reciprocal signaling between mesoderm progenitors and neural crest cells is essential, and its disruption leads to congenital heart malformations, the most common human birth defect. However, mechanistic interrogation of temporal gene networks and cis regulatory elements in this crosstalk is limited2,3. Here, we integrated single cell chromatin accessibility and transcriptomics to establish an unbiased and temporal epigenomic map of the embryonic mouse heart over multiple stages and developed machine learning models to predict enhancers for heart and neural crest. We leveraged these advances to determine the consequences of dysregulated signaling at single cell resolution caused by deletion of TBX1, a transcription factor that causes morphogenetic defects of the cardiac outflow tract in humans and functions non-cell autonomously in cardiac mesodermal progenitors to direct pharyngeal neural crest differentiation4-6. Loss of Tbx1 in mice led to broad closure of chromatin regions enriched in cardiac progenitor transcription factor motifs within a narrow subset of cardiac mesodermal progenitors and correlated with diminished expression of numerous members of the fibroblast growth factor, retinoic acid, Notch and Semaphorin pathways. In affected progenitors, ectopic accessibility and expression of posterior heart field factors in the anterior heart field suggested impaired axial patterning. In response, a subset of cardiac neural crest cells displayed epigenomic and transcriptional defects, indicating a failure of differentiation corresponding to dysregulation of the anterior-posterior gradient of pharyngeal Hox gene expression. This study demonstrates that single-cell genomics and machine learning can generate a mechanistic model for how disruptions in cell communication selectively affect spatiotemporally dynamic regulatory networks in cardiogenesis.

developmental biology↗

bioRxiv2022

Single Cell Multimodal Analyses Reveal Epigenomic and Transcriptomic Basis for Birth Defects in Maternal Diabetes

Birth defects occur in [~]6% of all live births and can be caused by combinations of genetic and environmental influences1. Large-scale DNA sequencing efforts are revealing genetic influences2,3, but investigations into the contributions of environmental factors have largely been limited to association studies with limited mechanistic insight. Hyperglycemia present in pre-gestational diabetic mothers is among the most frequent environmental contributor to congenital defects and results in an increased incidence of congenital heart defects and craniofacial anomalies4. However, the cell types involved and underlying mechanisms by which maternal hyperglycemia affects these regions are unknown. Here, we utilized multi-modal single cell analyses to reveal that maternal diabetes affects the epigenomic and transcriptomic state of specific subsets of cardiac and craniofacial progenitors during embryogenesis. A previously unrecognized subpopulation of anterior heart field progenitors expressing Alx3 acquired a more posterior identity in response to maternal hyperglycemia, based on gene expression and chromatin status. Similarly, a sub-population of neural crest-derived cells in the second pharyngeal arch, which contributes to craniofacial structures, also displayed abnormalities in cell specification and patterning. Analysis of differentially accessible chromatin regions demonstrated that disrupted patterning was associated with increased intrinsic retinoic acid signaling in affected cell types in response to maternal diabetes and hyperglycemia. This work demonstrates how an environmental insult can have highly selective epigenomic consequences on discrete cell types leading to developmental patterning defects.

developmental biology↗

bioRxiv2022

An oligogenic inheritance test detects risk genes and their interactions in congenital heart defects and developmental comorbidities

Exome sequencing of thousands of families has revealed many individual risk genes for congenital heart defects (CHD), yet most cases cannot be explained by a single causal mutation. Further, those who carry de novo and inherited mutations in known risk genes often demonstrate variable phenotypes even within the same family, indicating the presence of genetic modifiers. To explore oligogenic causes of CHD without assessing billions of variant combinations, we developed an efficient, simulation-based method to detect gene sets that carry damaging variants in probands at a higher rate than expected given parental genotypes. We implemented this approach in software called Gene Combinations in Oligogenic Disease (GCOD) and applied it to a cohort of 3382 trios with exome sequencing. This analysis detected 353 high-confidence risk genes in 202 pairs that appear together in multiple probands but rarely or never appear in combination in their unaffected parents. Stratifying analyses by specific CHD diagnosis and considering gene combinations of higher orders yielded an additional 244 gene sets. The oligogenic genes we discovered cluster in pathways specific to heart development and suggest new molecular disease mechanisms, such as arylsulfatase activity and de novo nucleotide biosynthesis. Finally, by combining CHD families with an autism spectrum disorder cohort, we were able to detect 925 oligogenic sets transmitted in renal disease, a known co-morbidity of both conditions. As genome sequencing is applied to more families and other disorders, GCOD will enable detection of increasingly large, novel gene combinations, shedding light on combinatorial causes of genetic diseases.

genetics↗

bioRxiv2022

The maternal X chromosome impairs cognition and accelerates brain aging through epigenetic modulation in female mice

Female mammalian cells harbor two X chromosomes, one of maternal and one of paternal origin. During development, one X randomly inactivates1-4. This renders either the maternal or paternal X active, causing X mosaicism that varies among individual females, with some showing considerable or complete skew in the general female population5-7. Parent-of-X-origin can modify epigenetics via DNA methylation8, 9 and possibly gene expression; thus, mosaicism could buffer dysregulated processes in aging and disease. However, whether X skewing - or its mosaicism - alters functions in females is largely unknown. Here we tested whether skew toward the maternal X (Xm) influences key functions of the body. Among cardiac, bone, metabolic, and brain functions, Xm selectively impaired cognition in female mice throughout the lifespan. Cognitive deficits were accompanied by Xm-mediated acceleration of biologic or epigenetic aging of the female hippocampus, a key center for learning and memory. Xm showed epigenetic imprinting of several genes within hippocampal neurons, suggesting silenced cognitive loci. Thus, the maternal X chromosome impaired cognition, accelerated brain aging, and silenced genes. Understanding how the maternal X impairs brain function could lead to new understanding of female heterogeneity in cognitive heath and to X chromosome-derived pathways against cognitive deficits and brain aging.

neuroscience↗

bioRxiv2021

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosis

MotivationContinuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as big data records to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of disease outcomes provide an excellent ground for identifying important mutations by developing machine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2. ResultsWe describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a temporal modeling approach to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on past data in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern. Contactsharmila.mande@tcs.com Supplementary informationSupplementary data are enclosed.

bioinformatics↗

bioRxiv2021

Domain adaptive neural networks improvecross-species prediction of transcription factor binding

The intrinsic DNA sequence preferences and cell-type specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell-type specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species-specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results demonstrate that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.

bioinformatics↗

bioRxiv2021

Integration of Protein Interactome Networks with Congenital Heart Disease Variants Reveals Candidate Disease Genes

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains a challenge despite large-scale genomic sequencing efforts. We hypothesized that genetic determinants for CHDs may lie in protein interactomes of GATA4 and TBX5, two transcription factors that cause CHDs. Defining their interactomes in human cardiac progenitors via affinity purification-mass spectrometry and integrating results with genetic data from the Pediatric Cardiac Genomic Consortium revealed an enrichment of de novo variants among proteins that interact with GATA4 or TBX5. A consolidative score that prioritized interactome members based on variant, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied cardiac developmental genes, resulting in co-activation, and the GLYR1 missense variant associated with CHD disrupted interaction with GATA4. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating the contribution of genetic variants in disease.

genetics↗

bioRxiv2021

Graph Signal Processing on protein residue networks helps in studying its biophysical properties

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on nodes are decomposed on networks Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

biophysics↗

bioRxiv2020

What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes

Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a Bag of Words to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported recurrent mutations and phylogenetic clades for genomes. Additionally, we report the geographical distribution of the identified mutation signatures in SARS-CoV-2 genomes on the global map. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).

bioinformatics↗

bioRxiv2020

A Transcriptional Switch Governing Fibroblast Plasticity Underlies Reversibility of Chronic Heart Disease

In diseased organs, stress-activated signaling cascades alter chromatin, triggering broad shifts in transcription and cell state that exacerbate pathology. Fibroblast activation is a common stress response that worsens lung, liver, kidney and heart disease, yet its mechanistic basis remains poorly understood1,2. Pharmacologic inhibition of the BET family of transcriptional coactivators alleviates cardiac dysfunction and associated fibrosis, providing a tool to mechanistically interrogate maladaptive fibroblast states and modulate their plasticity as a potential therapeutic approach3-8. Here, we leverage dynamic single cell transcriptomic and epigenomic interrogation of heart tissue with and without BET inhibition to reveal a reversible transcriptional switch underlying stress-induced fibroblast activation. Transcriptomes of resident cardiac fibroblasts demonstrated robust and rapid toggling between the quiescent fibroblast and activated myofibroblast state in a manner that directly correlated with BET inhibitor exposure and cardiac function. Correlation of single cell chromatin accessibility with cardiac function revealed a novel set of reversibly accessible DNA elements that correlated with disease severity. Among the most dynamic elements was an enhancer regulating the transcription factor MEOX1, which was specifically expressed in activated myofibroblasts, occupied putative regulatory elements of a broad fibrotic gene program, and was required for TGF{beta}-induced myofibroblast activation. CRISPR interference of the most dynamic cis-element within the enhancer, marked by nascent transcription, prevented TGF{beta}-induced activation of Meox1. These findings identify MEOX1 as a central regulator of stress-induced myofibroblast activation associated with cardiac dysfunction. The plasticity and specificity of the BET-dependent regulation of MEOX1 in endogenous tissue fibroblasts provides new trans- and cis- targets for treating fibrotic disease.

molecular biology↗

bioRxiv2020

BRD4 Interacts with GATA4 to Govern Mitochondrial Homeostasis in Adult Cardiomyocytes

Gene regulatory networks control tissue plasticity during basal homeostasis and disease in a cell-type specific manner. Ubiquitously expressed chromatin regulators modulate these networks, yet the mechanisms governing how tissue-specificity of their function is achieved are poorly understood. BRD4, a member of the BET (Bromo- and Extra-Terminal domain) family of ubiquitously expressed acetyl-lysine reader proteins, plays a pivotal role as a coactivator of enhancer signaling across diverse tissue types in both health and disease, and has been implicated as a pharmacologic target in heart failure. However, the cell-specific role of BRD4 in adult cardiomyocytes remains unknown. Here, we show that cardiomyocyte-specific deletion of BRD4 in adult mice leads to acute deterioration of cardiac contractile function with mutant animals demonstrating a transcriptomic signature enriched for decreased expression of genes critical for mitochondrial energy production. Genome-wide occupancy data show that BRD4 enriches at many downregulated genes and preferentially co-localizes with GATA4, a lineage determining cardiac transcription factor not previously implicated in regulation of adult cardiac metabolism. Co-immunoprecipitation assays demonstrate that BRD4 and GATA4 form a complex in a bromodomain-independent manner, revealing a new interaction partner for BRD4 that has functional consequences for target transactivation and may allow for locus and tissue specificity. These results highlight a novel role for a BRD4-GATA4 module in cooperative regulation of a cardiomyocyte specific gene program governing bioenergetic homeostasis in the adult heart.

molecular biology↗

bioRxiv2019

Hox binding specificity is directed by DNA sequence preferences and differential abilities to engage inaccessible chromatin.

While Hox genes encode for conserved transcription factors (TFs), they are further divided into anterior, central, and posterior groups based on their DNA-binding domain similarity. The posterior Hox group expanded in the deuterostome clade and patterns caudal and distal structures. We aim to address how similar HOX TFs diverge to induce different positional identities. We studied HOX TF DNA-binding and regulatory activity during an in vitro motor neuron differentiation system that recapitulates embryonic development. We find diversity in the genomic binding profiles of different HOX TFs, even among the posterior group paralogs that share similar DNA binding domains. These differences in genomic binding are explained by differing abilities to bind to previously inaccessible sites. For example, the posterior group HOXC9 has a greater ability to bind occluded sites than the posterior HOXC10, producing different binding patterns and driving differential gene expression programs. From these results, we propose that the differential abilities of posterior HOX TFs to bind to previously inaccessible chromatin drive patterning diversification.

developmental biology↗

bioRxiv2019

Modulation of Yorkie activity by alternative splicing is required for developmental stability

The mechanisms that contribute to developmental stability are barely known. Here we show that alternative splicing of yorkie (yki) is required for developmental stability in Drosophila. Yki encodes the effector of the Hippo pathway that has a central role in controlling organ growth and regeneration. We identify the splicing factor B52 as necessary for inclusion of yki alternative exon 3 that encodes one of the two WW domains of Yki protein. B52 depletion favors expression of Yki1 isoform carrying a single WW domain, and reduces growth in part through modulation of yki alternative splicing. Compared to the canonical Yki2 isoform containing two WW domains, Yki1 isoform has reduced transcriptional and growth-promoting activities, decreased binding to PPxY-containing partners, and lacks the ability to bridge two proteins containing PPxY motifs. Yet, Yki1 and Yki2 interact similarly with transcription factors and can thus compete in vivo. Strikingly, flies deprived from Yki1 isoform exhibit increased fluctuating wing asymmetry, a signal of increased developmental noise. Our results identify yki alternative splicing as a new level of control of the Hippo pathway and provide the first experimental evidence that alternative splicing participates in developmental robustness.

developmental biology↗

bioRxiv2019

Early post-zygotic mutations contribute to congenital heart disease

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.\n\nResultsWe developed a computational method, Expectation-Maximization-based detection of Mosaicism (EM-mosaic), to analyze mosaicism in exome sequences of 2530 CHD proband-parent trios. EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The frequency of mosaic variants above 10% mosaicism was 0.13/person in blood and 0.14/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.\n\nConclusionsWe estimate that ~1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants expressed at higher allele fraction compared to benign variants. Although blood is a readily-available DNA source, cardiac tissues analyzed contributed ~5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

bioinformatics↗

bioRxiv2019

Characterizing the sequence and prior chromatin determinants of induced TF binding with bimodal neural networks

Transcription factor (TF) binding specificity is determined via a complex interplay between the TFs DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with TF binding in a given cell type have been well characterized. For instance, the binding sites for a majority of TFs display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the TF itself, and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of TF binding specificity, we therefore need to examine how newly activated TFs interact with sequence and preexisting chromatin landscapes. Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of TFs that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced TFs. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some TFs substantially, but not others. Furthermore, by analyzing site-level predictors, we show that TF binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences. Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

bioinformatics↗

bioRxiv2019

Unique Transcription Factor Functions Regulate Epigenetic and Transcriptional Dynamics During Cardiac Reprogramming

Direct lineage conversion, whereby a somatic cell assumes a new cellular identity, can be driven by ectopic expression of combinations of lineage-enriched transcription factors. To determine the molecular mechanisms by which expression of Gata4, Mef2c, and Tbx5 (GMT) induces direct reprogramming from a cardiac fibroblast toward an induced cardiomyocyte, we performed a comprehensive transcriptomic and epigenomic interrogation of the reprogramming process. Single cell RNA sequencing indicated that a reprogramming trajectory was acquired within 48 hours of GMT introduction, did not require cell division, and was limited mainly by successful expression of GMT. Evaluation of chromatin accessibility by ATAC-seq supported the expression dynamics and revealed widespread chromatin remodeling at early stages of the reprogramming process. Chromatin immunoprecipitation followed by sequencing of each factor alone or in combinations revealed that GMT bind DNA individually and in combination, and that ectopic expression of either Mef2c or Tbx5 is sufficient in some contexts to increase accessibility. We also find evidence for cooperative facilitation and refinement of each factors binding in a combinatorial setting. A random-forest classifier that integrated the observed gene expression dynamics with regions of dynamic chromatin accessibility suggested Tbx5 binding is a primary driver of gene expression changes and revealed additional transcription factor motifs co-segregating with reprogramming factor motifs, suggesting new factors that may be involved in the reprogramming process. These results begin to explain the mechanisms by which transcription factors normally expressed in multiple germ layers can function combinatorially to direct lineage conversion.

molecular biology↗