Biology subjects

Reddy, T. E.

Publications and source records attributed to Reddy, T. E..

At least 19 recordsLinked to original sources

bioRxiv2026

The Encyclopedia of DNA Elements

We present the Encyclopedia of DNA Elements (ENCODE), a reference map of the genomic basis of gene regulation. A product of more than two decades of systematic interrogation of genome function, ENCODE encompasses more than 16,000 genome-wide experiments, predominantly in primary cells and tissues, focused on three core layers of genome function. First, ENCODE now provides a catalog of gene regulatory elements. The catalog is based on a foundation of 5.3 million DNase I hypersensitive sites that delineate essentially all chromatin-accessible regulatory DNA in the human genome, as well as extensive maps of chromatin states, transcription factor occupancy, and nascent transcription, and systematic predictions of the functional consequences of non-coding genetic variants on regulatory element activity. Second, ENCODE expands the catalog of genes and transcripts, which now includes nearly 18,000 novel human long noncoding RNA genes, nearly 150,000 novel transcript isoforms, and genome-wide maps of transcript stability across cell types and time. Third, ENCODE now maps physical and functional interactions among regulatory elements and genes across more than 100 human tissues and cell lines at up to 10 bp resolution. Those studies reveal a vast network of interactions among millions of loop anchors across and links those interactions to gene expression. Through parallel studies in mice, ENCODE also provides extensive maps of gene regulatory elements, transcripts, and their interactions across the mouse postnatal development. Together, the Encyclopedia of DNA Elements provides a foundational framework for genome-focused studies of human and mouse biology.

genomics↗

bioRxiv2026

Enhancer hubs govern chromatin topology and Th17 identity

Between one and two million noncoding regulatory elements have been described across mammalian genomes, but determining their functional role remains a challenge. To address this gap, we measure the regulatory potential of open chromatin regions (OCRs) in five closely related mouse CD4+ T cell subsets with ATAC-STARR-seq, then map endogenous enhancer function and three-dimensional contacts in Th17 cells with pooled CRISPR-based noncoding screens and high-resolution Micro-C. Of all CD4+ T cell OCRs, approximately 25% demonstrate largely subset-shared regulatory activity, though we identify subset-restricted active elements distinguishable by their sequence features. In Th17 cells, we reveal a set of core regulatory OCRs essential for subset polarization at the Batf, Rorc(t) and Il17a/f loci, and confirm their requirement for cell identity in vivo. At these three loci, we resolve nested yet selective enhancer-to-enhancer and enhancer-to-promoter physical interactions that converge into multi-enhancer hubs. Importantly, these hubs contain many of the strongest functional elements. Perturbation of a single enhancer within the Batf locus selectively disrupts hub contacts and Batf expression in Th17 cells, recapitulating the genome-wide transcriptional and chromatin accessibility signatures of a germline knockout. Together, this work assigns regulatory activity to accessible chromatin across CD4+ T cell subsets and couples the function of a set of essential elements to selective three-dimensional contacts in Th17 cells.

genomics↗

bioRxiv2026

Modeling gene regulatory perturbations via deep learning from high-throughput reporter assays

Assessing likely variant effects on phenotypes is of critical importance in diagnostic settings, and while much progress has been made in interpreting genic mutations based on our understanding of coding sequence, noncoding variants can be much more challenging to reliably interpret based on DNA sequence alone. High-throughput reporter assays such as STARR-seq and MPRA have shown utility in experimentally measuring regulatory effects of noncoding variants present in samples but provide no readout for variants not present in the assay inputs. However, whole-genome reporter assays provide copious data that can be used to train predictive models for prioritizing variants not directly observed in the experiment. We describe a retrainable predictive modeling framework, BlueSTARR, for this task, and present results of training several models with this framework on whole-genome STARR-seq data from two cell lines and one drug treatment. Using these models, we uncover a global signature across the human genome consistent with purifying selection against both loss-of-function and gain-of-function regulatory variants, with the latter showing a significant bias consistent with selection against gains of cis regulatory function in closed chromatin proximal to genes. By testing the model on synthetic enhancers with binding motifs for transcription factors GR and AP-1, we find that when trained on drug perturbation data, the model is able to learn distance-dependent and treatment-dependent binding patterns and their resulting reporter gene activation. These results demonstrate that lightweight, easily retrainable models such as ours have utility in probing latent signals present in novel experimental data. Finally, we find only modest differences in performance between different deep-learning architectures when trained on this single data modality, and while somewhat greater predictive accuracy can be achieved with much larger models trained at great expense on many terabytes of data, there is still copious room for improvement even for industrial strength, state-of-the-art models.

bioinformatics↗

bioRxiv2026

Structured Pooling Improves Detection of Rare Regulatory Mutations in Population-Scale Reporter Assays

Identifying genetic variants in noncoding DNA that impact gene expression and thereby contribute to disease risk remains a difficult but important challenge in genomic medicine. Modern reporter assays such as STARR-seq and MPRA provide an efficient and effective means of testing, in very high throughput, millions of variants captured directly from patient genomes. While these assays have previously been scaled to whole genomes and, separately, to populations, we report findings from the first whole-genome population-scale STARR-seq experiment performed on 100 individuals. In order to achieve that scale we devised a novel experimental design that partitions samples into pools so as to increase allele frequencies within pools and thereby reduce expected dropout and increase signal-to-noise ratio in experimental readouts. We show that this design produces more accurate estimates of variant effect sizes, and we provide a Bayesian model for robust estimation of those effect sizes that also reports full posterior distributions for assessment of confidence in estimates. Together, these methodological innovations facilitate the detection of functional regulatory variants, particularly rare variants, with much higher accuracy and at greater scale than previously possible. We demonstrate the utility of this approach on the task of functional annotation of quantitative trait loci such as eQTLs and caQTLs, and show concordance with patterns of constraint in transcription factor binding profiles.

bioinformatics↗

bioRxiv2026

Identifying Inheritance Patterns of Allelic Imbalance, using Integrative Modeling and Bayesian Inference

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis-regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis-regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

bioinformatics↗

bioRxiv2026

Functional Annotation of the Major Histocompatibility Complex Locus

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis-regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

genomics↗

bioRxiv2025

The architecture of allele-specific regulatory variant effects across five human genomes

The overwhelming majority of genetic associations with complex traits and disease involve non-coding genetic variants. Those variants are substantially enriched in gene regulatory elements, indicating that allelic impacts on gene regulatory element activity is a major contributor to those associations. As a step towards fine mapping the mechanisms within those associations, several studies have identified variants associated with gene expression and chromatin accessibility within those phenotypic associations. However, connecting those associations with functional impacts on gene regulatory element activity remains a major challenge. Here, we functionally measured allele-specific regulatory element activity across five human genomes using the genome-wide reporter assay STARR-seq. We identified tens of thousands of gene regulatory elements and estimated allele effects at [~]200,000 genetic variants therein, including [~]10,000 indels in our study population. Allelic effects on regulatory element activity correspond closely with predicted impacts on transcription factor binding motifs. The measured variant effects also allow us to fine map potential causal variants within eQTLs and chromatin QTLs from the same population. Together, these results provide an initial atlas of genome-wide variant effects across the human genome and demonstrate the potential for such approaches to prioritize causal variants for future mechanistic investigation.

genomics↗

bioRxiv2025

SMARCA2 is an essential and potent cofactor for a specific subset of the glucocorticoid response in A549 cells

Glucocorticoids are a widely used, potent class of anti-inflammatory drugs that modulate the expression of hundreds of genes across the genome. Although the glucocorticoid response is primarily carried out by the glucocorticoid receptor (NR3C1, a.k.a. GR), there are many glucocorticoid receptor co-factors that are also essential to the downstream effects. To identify novel factors necessary for the glucocorticoid gene expression response, we used a genome-wide CRISPR screen in A549 lung adenocarcinoma cells. In that screen, we knocked out every gene in the human genome, and measured the effect of expression of the glucocorticoid-induced leucine zipper (GILZ), a classic glucocorticoid-response gene. We identified two chromatin remodeling proteins, SMARCA2 and BPTF, that are essential for GILZ expression. We then evaluated the genome-wide effects of SMARCA2 and BPTF on glucocorticoid-mediated gene expression. BPTF had a highly specific role in the glucocorticoid response, affecting the expression of only a handful of genes, and having virtually no effect on dexamethasone-induced changes in chromatin accessibility. However, SMARCA2 was necessary for 27% of dexamethasone-induced transcriptional changes (152 genes), and [~]7% of dexamethasone-induced changes in chromatin accessibility (586 regions of the genome). Genomic regions with SMARCA2-dependent changes in chromatin accessibility were characterized by high dexamethasone-induced regulatory activity in a massively parallel reporter assay, and dexamethasone-induced increases in transcription factor binding and chromatin states. Taken together, these data suggest that SMARCA2 is critical for chromatin remodeling at a specific set of genomic regions with high regulatory activity, which in turn drive changes in expression for many glucocorticoid-responsive genes.

genomics↗

bioRxiv2025

The gene regulatory effects of selective glucocorticoid receptor ligands

Synthetic glucocorticoids (GCs), which induce the transcription factor activity of the glucocorticoid receptor (GR), are frequently prescribed anti-inflammatory therapeutics that have been in use for over 70 years. Despite their broad immunosuppressive utility, sustained use of GCs is often intolerable due to the prevalence of adverse side effects. A longstanding goal has been to make synthetic GCs safer by developing selective GR ligands that have similar anti-inflammatory activity but without the burden of side effects. To evaluate the ability of synthetic GCs to target specific subsets of the GC response, we completed a genome-wide comparative analysis of changes in gene expression and gene regulatory element activity in response to ten ligands with various evidence of dissociated adverse side effects. We measured the gene expression response using mRNA-seq and the gene regulatory element response using genome-wide STARR-seq. Effects associated with each ligand were highly correlated with and linearly related to the response to dexamethasone, a strong, non-selective GR agonist used as a positive control for this study. Furthermore, 93% of the variation in regulatory element activity responses could be explained by the efficacy of each ligand alone. We also found limited evidence of differential enrichment of chromatin context-specific markers of regulatory activity with each ligand. Based on those findings, we developed a simulation framework to evaluate selectivity of GR ligands. We conclude that the ligands we tested elicit attenuated molecular responses according to their respective efficacies, and do not selectively target subsets of the molecular GC response.

genomics↗

bioRxiv2025

Cell modeling and rescue of a novel non-coding genetic cause of Glycogen Storage Disease IX

Delayed diagnosis of Mendelian disease substantially prevents early therapeutic intervention that could improve symptoms and prognosis. One major contributing challenge is the functional interpretation of non-coding variants that cause disease by altering splicing and/or gene expression. We identified two siblings with glycogen storage disease (GSD) type IX {gamma}2, both of whom had a classic clinical presentation, enzyme deficiency, and a known pathogenic splice acceptor variant on one allele of PHKG2. Despite the autosomal recessive nature of the disease, no variant on the second allele was identified by gene panel sequencing. To identify a potential missing second pathogenic variant, we completed whole genome sequencing (WGS) and detected putative deep intronic splicing variant in PHKG2 in both siblings. We confirmed the functional splicing effects of this variant using short-read and long-read RNA-seq on patient blood and a HEK293T cell model in which we installed the variant using CRISPR editing. Using the cell model, we demonstrated multiple biochemical and cellular impacts that are consistent with GSD IX {gamma}2, and a reversal of aberrant splicing using antisense splice-switching oligonucleotides. In doing so, we demonstrate a novel and robust pathway for detecting, validating, and reversing the impacts of novel non-coding causes of rare disease.

genetics↗

bioRxiv2025

Comprehensive Evaluation of Diverse Massively Parallel Reporter Assays to Functionally Characterize Human Enhancers Genome-wide

Massively parallel reporter assays (MPRAs) and self-transcribing active regulatory region sequencing (STARR-seq) have revolutionized enhancer characterization by enabling high-throughput functional assessment of regulatory sequences. Here, we systematically evaluated six MPRA and STARR-seq datasets generated in the human K562 cell line and found substantial inconsistencies in enhancer calls from different labs that are primarily due to technical variations in data processing and experimental workflows. To address these variations, we implemented a uniform enhancer call pipeline, which significantly improved cross-assay agreement. While increasing sequence overlap thresholds enhanced concordance in STARR-seq assays, cross-assay consistency in LentiMPRA was strongly influenced by assay-specific factors. Notably, our results show that LentiMPRA exhibits a strong preference for promoter-associated sequences rather than enhancers. Functional validation using candidate cis-regulatory elements (cCREs) confirmed that epigenomic features such as chromatin accessibility and histone modifications are strong predictors of enhancer activity. Importantly, our study validated transcription as a critical hallmark of active enhancers, demonstrating that highly transcribed regions exhibit significantly higher active rates across assays. Furthermore, we show that transcription enhances the predictive power of epigenomic features, enabling more accurate and refined enhancer annotation. Our study provides a comprehensive framework for integrating different enhancer datasets and underscores the importance of accounting for assay-specific biases when interpreting enhancer activity. These findings refine enhancer identification using massively parallel reporter assays and improve the functional annotation of the human genome.

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↗

bioRxiv2024

A consensus variant-to-function score to functionally prioritize variants for disease

Identifying and functionally characterizing causal disease variants in genome-wide association studies remains a pressing challenge. Here, we construct a consensus variant-to-function (cV2F) score that assigns a single value to each common single-nucleotide variant in the genome, and helps to predict and characterize causal disease variants. The cV2F score leverages features reflecting variant-level experimentally and computationally predicted function (e.g. allelic imbalance and sequence-based deep learning models) and element-level function (e.g. predicted enhancers), and learns optimal combinations of features by training a gradient boosting model on GWAS fine-mapping results. The cV2F-annotated variants attained an AUPRC of 0.822 at identifying held-out fine-mapped variants. Variants with high cV2F scores are highly enriched for heritability (14.2x, s.e. 0.5) across 66 diseases/traits, are uniquely informative for disease heritability, and are highly predictive of variants implicated by reporter assays; cV2F substantially outperforms previous variant-to-function scores using all of these metrics. GWAS fine-mapping of 110 diseases/traits informed by cV2F identified 14.3% more confidently fine-mapped (PIP > 0.95) variants than non-functionally informed fine-mapping. We further constructed tissue/cell line-specific cV2F scores that prioritize variants based on regulatory potential in specific tissues/cell lines, attaining high heritability enrichment for tissue-related diseases/traits (15.6x, s.e. 2.3) while providing independent information (average correlation of 0.27 with the primary cV2F score). We highlight examples of GWAS loci for which cV2F pinpoints causal variants with high confidence and elucidates their functional role.

genetics↗

bioRxiv2024

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

MotivationAllele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. ResultsWe developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. Availability and ImplementationThe software is freely available from https://github.com/x811zou/BEASTIE. BEASTIE is available as Python source code and as a Docker image. Supplementary informationAdditional information is available online.

bioinformatics↗

bioRxiv2024

HyperCas12a enables highly-multiplexed epigenome editing screens

Interactions between multiple genes or cis-regulatory elements (CREs) underlie a wide range of biological processes in both health and disease. High-throughput screens using dCas9 fused to epigenome editing domains have allowed researchers to assess the impact of activation or repression of both coding and non-coding genomic regions on a phenotype of interest, but assessment of genetic interactions between those elements has been limited to pairs. Here, we combine a hyper-efficient version of Lachnospiraceae bacterium dCas12a (dHyperLbCas12a) with RNA Polymerase II expression of long CRISPR RNA (crRNA) arrays to enable efficient highly-multiplexed epigenome editing. We demonstrate that this system is compatible with several activation and repression domains, including the P300 histone acetyltransferase domain and SIN3A interacting domain (SID). We further show that the system can be used in cultured primary immune cells and to drive differentiation of induced pluripotent stem cells. We also developed new approaches to use the dCas12a platform for simultaneous activation and repression from a single crRNA array via co-expression of multiple dCas12a orthologues. Lastly, we demonstrate that the dHyperLbCas12a effectors are highly effective for multiple modalities of high-throughput screens, namely proliferation screens and screens to dissect the independent and combinatorial contributions of CREs on gene expression. The tools and methods introduced here create new possibilities for highly multiplexed control of gene expression in a wide variety of biological systems.

genomics↗

bioRxiv2024

Gene regulatory activity associated with PCOS revealed DENND1A-dependent testosterone production

Polycystic ovary syndrome (PCOS) is among the most common disorders affecting up to 15% of the menstruating population globally. It is the leading cause of anovulatory infertility and a major risk factor for type 2 diabetes. Elevated testosterone levels are a core endophenotype. Despite that prevalence, the underlying causes remain unknown. PCOS genome-wide association studies (GWAS) have reproducibly mapped a number of susceptibility loci, including one encompassing a gene regulating androgen biosynthesis, DENND1A. Identifying the causal variants within these loci will provide fundamental insight into the precise biological pathways that are disrupted in PCOS. We report the discovery of gene regulatory mechanisms that help explain genetic association with PCOS in the GATA4, FSHB and DENND1A loci using a combination of high throughput reporter assays, CRISPR-based epigenome editing, and genetic association analysis from PCOS case and control populations. In addition, we found that increased endogenous DENND1A expression causes elevated testosterone levels in an adrenal cell model, specifically by perturbing candidate regulatory elements. These results further highlight the potential for combining genetic variant analyses with experimental approaches to fine map genetic associations with disease risk.

genomics↗

bioRxiv2023

Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function

The clinical response to adoptive T cell therapies is strongly associated with transcriptional and epigenetic state. Thus, technologies to discover regulators of T cell gene networks and their corresponding phenotypes have great potential to improve the efficacy of T cell therapies. We developed pooled CRISPR screening approaches with compact epigenome editors to systematically profile the effects of activation and repression of 120 transcription factors and epigenetic modifiers on human CD8+ T cell state. These screens nominated known and novel regulators of T cell phenotypes with BATF3 emerging as a high confidence gene in both screens. We found that BATF3 overexpression promoted specific features of memory T cells such as increased IL7R expression and glycolytic capacity, while attenuating gene programs associated with cytotoxicity, regulatory T cell function, and T cell exhaustion. In the context of chronic antigen stimulation, BATF3 overexpression countered phenotypic and epigenetic signatures of T cell exhaustion. CAR T cells overexpressing BATF3 significantly outperformed control CAR T cells in both in vitro and in vivo tumor models. Moreover, we found that BATF3 programmed a transcriptional profile that correlated with positive clinical response to adoptive T cell therapy. Finally, we performed CRISPR knockout screens with and without BATF3 overexpression to define co-factors and downstream factors of BATF3, as well as other therapeutic targets. These screens pointed to a model where BATF3 interacts with JUNB and IRF4 to regulate gene expression and illuminated several other novel targets for further investigation.

genomics↗

bioRxiv2022

Multi-center integrated analysis of non-coding CRISPR screens

The ENCODE Consortiums efforts to annotate non-coding, cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes which play a major role in health and disease. Pooled, non-coding CRISPR screens are a promising approach for systematically investigating gene regulatory mechanisms. Here, the ENCODE Functional Characterization Centers report 109 screens comprising 346,970 individual perturbations across 13.3Mb of the genome, using a variety of methods, readouts, and statistical analyses. Across 332 functionally confirmed CRE-gene links, we identify principles for screening endogenous, non-coding elements for causal regulatory mechanisms. Nearly all CREs show strong evidence of open chromatin, and targeting accessibility peak summits is a critical component of our proposed sgRNA design rules. We provide experimental guidelines to accurately detect CREs with variable, often low, transcriptional effects. We discover a previously undescribed DNA strand-bias for CRISPRi in transcribed regions with implications for screen design and analysis. Benchmarking five screen analysis tools, we find CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity sgRNAs. Together, we provide an accessible data resource, predesigned sgRNAs targeting 3,275,697 ENCODE SCREEN candidate CREs, and screening guidelines to accelerate functional characterization of the non-coding genome.

genomics↗