Biology subjects

Epstein, C. B.

Publications and source records attributed to Epstein, C. B..

9 recordsLinked to original sources

bioRxiv2025

The 9p21.3 Coronary Artery Disease Risk Locus Modulates Vascular Cell-State Transitions via Enhancer-Driven Regulation of MTAP

The 9p21.3 locus is the strongest genetic association with coronary artery disease (CAD), yet its causal mechanisms remain unresolved. We map the regulatory architecture of 9p21.3 in disease-relevant vascular cells, identifying 12 enhancers within the CAD risk haplotype that respond dynamically to inflammatory and metabolic stress in fibroblasts and smooth muscle cells. These activated states are enriched for CAD heritability, implicating stress-responsive vascular wall cells in disease pathophysiology. Dense CRISPRi tiling integrated with fine-mapping and genomic constraint across >500,000 individuals nominates MTAP as the effector gene, with rs1537371 as a likely causal variant. Perturbation and multi-modal analyses show that MTAP loss induces pro-fibrotic and angiogenic programs and sensitizes vascular cells to TGF-{beta}-driven pathological transitions. Our findings reveal a vascular-specific enhancer network through which noncoding variation at 9p21.3 modulates CAD risk via MTAP--a previously unrecognized regulator of vascular remodeling located 269 kb from the risk haplotype. HighlightsO_LI9p21.3 CAD risk locus harbors 12 functional enhancers active in vascular wall cell types C_LIO_LICAD heritability is enriched in stressed vascular fibroblasts and smooth muscle cells C_LIO_LIHigh-resolution CRISPRi-MAC-seq maps 9p21.3 enhancer-gene and SNP-gene interactions C_LIO_LIMTAP identified as a causal effector gene regulated by 9p21.3 fine-mapped and constrained variants C_LIO_LIMTAP modulates TGF-{beta}-driven pathological vascular cell transitions C_LI

genomics↗

bioRxiv2024

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions1. We have expanded the Registry to include 2.35 million human and 927 thousand mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded Registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays like STARR-seq, MPRA, CRISPR perturbation, and transgenic mouse assays now cover over 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer/silencer roles in different cellular contexts. Integrating the Registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by discovering KLF1 as a novel causal gene for red blood cell traits. This expanded Registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

genomics↗

bioRxiv2023

Multi-locus CRISPRi targeting with a single truncated guide RNA

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi- based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach is valuable for elucidating functional transcription factor binding motifs or other repeated genomic sequences and is easily implementable with existing tools.

molecular biology↗

bioRxiv2023

Single-cell multi-scale footprinting reveals the modular organization of DNA regulatory elements

Cis-regulatory elements control gene expression and are dynamic in their structure, reflecting changes to the composition of diverse effector proteins over time1-3. Here we sought to connect the structural changes at cis-regulatory elements to alterations in cellular fate and function. To do this we developed PRINT, a computational method that uses deep learning to correct sequence bias in chromatin accessibility data and identifies multi-scale footprints of DNA-protein interactions. We find that multi-scale footprints enable more accurate inference of TF and nucleosome binding. Using PRINT with single-cell multi-omics, we discover wide-spread changes to the structure and function of candidate cis-regulatory elements (cCREs) across hematopoiesis, wherein nucleosomes slide, expose DNA for TF binding, and promote gene expression. Activity segmentation using the co-variance across cell states identifies "sub-cCREs" as modular cCRE subunits of regulatory DNA. We apply this single-cell and PRINT approach to characterize the age-associated alterations to cCREs within hematopoietic stem cells (HSCs). Remarkably, we find a spectrum of aging alterations among HSCs corresponding to a global gain of sub-cCRE activity while preserving cCRE accessibility. Collectively, we reveal the functional importance of cCRE structure across cell states, highlighting changes to gene regulation at single-cell and single-base-pair resolution.

genomics↗

bioRxiv2022

An Autoimmune Transcriptional Circuit Driving Foxp3+ Regulatory T cell Dysfunction

Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. While CD4+Foxp3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify central transcriptional programs regulating human autoimmunity. We discovered that upregulation of a primate-specific short PRDM1 isoform (PRDM1-S) induces SGK1 independent from evolutionally conserved long PRDM1, leading to destabilization of Foxp3 and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, by chromatin landscape profiling in MS Tregs we identified a PRDM1-S specific cis-regulatory element associated with enriched binding of AP-1/IRF transcription factors. Our study identifies evolutionally emerged PRDM1-S and epigenetic priming of AP-1/IRF as key drivers of pathogenic Treg programs leading to human autoimmune disease.

immunology↗

bioRxiv2022

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Functional genomics experiments are invaluable for understanding mechanisms of gene regulation. However, comprehensively performing all such experiments, even across a fixed set of sample and assay types, is often infeasible in practice. A promising alternative to performing experiments exhaustively is to, instead, perform a core set of experiments and subsequently use machine learning methods to impute the remaining experiments. However, questions remain as to the quality of the imputations, the best approaches for performing imputations, and even what performance measures meaningfully evaluate performance of such models. In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures. Our systematic analyses suggest several steps that are necessary, but also simple, for fairly evaluating the performance of such models, as well as promising directions for more robust research in this area.

bioinformatics↗

bioRxiv2022

A resource for generating and manipulating human microglial states in vitro

Microglia have emerged as key players in the pathogenesis of neurodegenerative conditions such as Alzheimers disease (AD). In response to CNS stimuli, these cells adopt distinct transcriptional and functional subtypes known as states. However, an understanding of the function of these states has been elusive, especially in human microglia, due to lack of tools to model and manipulate this cell-type. Here, we provide a platform for modeling human microglia transcriptional states in vitro. Using single-cell RNA sequencing, we found that exposure of human stem-cell differentiated microglia (iMGLs) to brain-related challenges generated extensive transcriptional diversity which mapped to gene signatures identified in human brain microglia. We identified two in vitro transcriptional clusters that were analogous to human and mouse disease-associated microglia (DAMs), a state enriched in neurodegenerative disease contexts. To facilitate scalable functional analyses, we established a lentiviral approach enabling broad and highly efficient genetic transduction of microglia in vitro. Using this new technology, we demonstrated that MITF (Melanocyte Inducing Transcription Factor), an AD-enriched transcription factor in microglia, drives both a disease-associated transcriptional signature and a highly phagocytic state. Finally, we confirmed these results across iMGLs differentiated from multiple iPSC lines demonstrating the broad utility of this platform. Together, these tools provide a comprehensive resource that enables the manipulation and functional interrogation of human microglial states in both homeostatic and disease-relevant contexts.

neuroscience↗

bioRxiv2020

Genome-wide maps of enhancer regulation connect risk variants to disease genes

Genome-wide association studies have now identified tens of thousands of noncoding loci associated with human diseases and complex traits, each of which could reveal insights into biological mechanisms of disease. Many of the underlying causal variants are thought to affect enhancers, but we have lacked genome-wide maps of enhancer-gene regulation to interpret such variants. We previously developed the Activity-by-Contact (ABC) Model to predict enhancer-gene connections and demonstrated that it can accurately predict the results of CRISPR perturbations across several cell types. Here, we apply this ABC Model to create enhancer-gene maps in 131 cell types and tissues, and use these maps to interpret the functions of fine-mapped GWAS variants. For inflammatory bowel disease (IBD), causal variants are >20-fold enriched in enhancers in particular cell types, and ABC outperforms other regulatory methods at connecting noncoding variants to target genes. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes via variants in enhancers that act in different cell types. Guided by these variant-to-function maps, we show that an enhancer containing an IBD risk variant regulates the expression of PPIF to tune mitochondrial membrane potential. Together, our study reveals insights into principles of genome regulation, illuminates mechanisms that influence IBD, and demonstrates a generalizable strategy to connect common disease risk variants to their molecular and cellular functions.

genetics↗

bioRxiv2020

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing [~]1% of ENCODE datasets.

bioinformatics↗