Biology subjects

Kundaje, A.

Publications and source records attributed to Kundaje, A..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Dissecting the cis-regulatory syntax of transcription initiation with deep learning

Despite extensive characterization of mammalian Pol II transcription, the DNA sequence determinants of transcription initiation at a third of human promoters and most enhancers remain poorly understood. We trained and interpreted a neural network called ProCapNet that accurately models base-resolution initiation profiles from PRO-cap experiments using local DNA sequence. ProCapNet learns sequence motifs with distinct effects on initiation rates and TSS positioning and uncovers context-specific cryptic initiator elements intertwined within other TF motifs. ProCapNet annotates predictive motifs in nearly all actively transcribed regulatory elements across multiple cell-lines, revealing a shared cis-regulatory logic across promoters and enhancers and a highly epistatic sequence syntax of cooperative and competitive motif interactions. ProCapNet models of steady-state RAMPAGE profiles distill initiation signals on par with models trained directly on PRO-cap profiles. ProCapNet learns a largely cell-type-agnostic cis-regulatory code of initiation complementing sequence drivers of cell-type-specific chromatin state critical for accurate prediction of cell-type-specific transcription initiation.

genomics↗

bioRxiv2024

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage

Deep learning approaches have made significant advances in predicting cell type-specific chromatin patterns from the identity and arrangement of transcription factor (TF) binding motifs. However, most models have been applied in unperturbed contexts, precluding a predictive understanding of how chromatin state responds to TF perturbation. Here, we used transfer learning to train and interpret deep learning models that use DNA sequence to predict, with accuracy approaching experimental reproducibility, how the concentration of two dosage-sensitive TFs (TWIST1, SOX9) affects regulatory element (RE) chromatin accessibility in facial progenitor cells. High-affinity motifs that allow for heterotypic TF co-binding and are concentrated at the center of REs buffer against quantitative changes in TF dosage and strongly predict unperturbed accessibility. In contrast, motifs with low-affinity or homotypic binding distributed throughout REs lead to sensitive responses with minimal contributions to unperturbed accessibility. Both buffering and sensitizing features show signatures of purifying selection. We validated these predictive sequence features using reporter assays and showed that a biophysical model of TF-nucleosome competition can explain the sensitizing effect of low-affinity motifs. Our approach of combining transfer learning and quantitative measurements of the chromatin response to TF dosage therefore represents a powerful method to reveal additional layers of the cis-regulatory code.

genomics↗

bioRxiv2024

Predicting chromatin conformation contact maps

Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets--each represented as a 2D contact map-- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.

bioinformatics↗

bioRxiv2023

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants ([≤]10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

genetics↗

bioRxiv2023

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

genomics↗

bioRxiv2023

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

genomics↗

bioRxiv2023

The landscape of the histone-organized chromatin of Bdellovibrionota bacteria

Histone proteins have traditionally been thought to be restricted to eukaryotes and most archaea, with eukaryotic nucleosomal histones deriving from their archaeal ancestors. In contrast, bacteria lack histones as a rule. However, histone proteins have recently been identified in a few bacterial clades, most notably the phylum Bdellovibrionota, and these histones have been proposed to exhibit a range of divergent features compared to histones in archaea and eukaryotes. However, no functional genomic studies of the properties of Bdellovibrionota chromatin have been carried out. In this work, we map the landscape of chromatin accessibility, active transcription and three-dimensional genome organization in a member of Bdellovibrionota (a Bacteriovorax strain). We find that, similar to what is observed in some archaea and in eukaryotes with compact genomes such as yeast, Bacteriovorax chromatin is characterized by preferential accessibility around promoter regions. Similar to eukaryotes, chromatin accessibility in Bacteriovorax positively correlates with gene expression. Mapping active transcription through single-strand DNA (ssDNA) profiling revealed that unlike in yeast, but similar to the state of mammalian and fly promoters, Bacteriovorax promoters exhibit very strong polymerase pausing. Finally, similar to that of other bacteria without histones, the Bacteriovorax genome exists in a three-dimensional (3D) configuration organized by the parABS system along the axis defined by replication origin and termination regions. These results provide a foundation for understanding the chromatin biology of the unique Bdellovibrionota bacteria and the functional diversity in chromatin organization across the tree of life.

genomics↗

bioRxiv2023

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 {+/-} 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 {+/-} 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

biochemistry↗

bioRxiv2023

Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency

The concentration and stoichiometry of transcription factors (TFs) determine cellular identity and can be manipulated to drive cell state transitions. Understanding how changes in TF concentration regulate chromatin state and expression across cell state transitions remains a challenge. We investigated this relationship by profiling chromatin accessibility and gene expression at single-cell resolution across a densely sampled time course of reprogramming human fibroblasts to induced pluripotent stem cells via ectopic expression of OCT4, SOX2, KLF4, and MYC (OSKM). Using deep learning sequence models of base-resolution chromatin accessibility profiles across cell states, we deciphered predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of TF footprints, linked peaks to putative target genes, and elucidated rewiring of cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution. We establish a quantitative, predictive framework that links TF stoichiometry, motif syntax, and somatic silencing to provide new perspectives on the control of cell identity by TFs during fate transitions.

genetics↗

bioRxiv2023

SLC12A9 is a lysosome-detoxifying ammonium - chloride co-transporter

Ammonia is a ubiquitous, toxic by-product of cell metabolism. Its high membrane permeability and proton affinity causes ammonia to accumulate inside acidic lysosomes in its poorly membrane-permeant form: ammonium (NH4+). Ammonium buildup compromises lysosomal function, suggesting the existence of mechanisms that protect cells from ammonium toxicity. Here, we identified SLC12A9 as a lysosomal ammonium exporter that preserves lysosomal homeostasis. SLC12A9 knockout cells showed grossly enlarged lysosomes and elevated ammonium content. These phenotypes were reversed upon removal of the metabolic source of ammonium or dissipation of the lysosomal pH gradient. Lysosomal chloride increased in SLC12A9 knockout cells and chloride binding by SLC12A9 was required for ammonium transport. Our data indicate that SLC12A9 is a chloride-driven ammonium co-transporter that is central in an unappreciated, fundamental mechanism of lysosomal physiology that may have special relevance in tissues with elevated ammonia, such as tumors.

cell biology↗

bioRxiv2023

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

genomics↗

bioRxiv2023

Codon affinity in mitochondrial DNA shapes evolutionary and somatic fitness

Summary ParagraphSomatic variation contributes to biological heterogeneity by modulating cellular proclivity to differentiate, expand, adapt, or die. While large-scale sequencing efforts have revealed the foundational role of somatic variants to drive human tumor evolution, our understanding of the contribution of mutations to modulate cellular fitness in non-malignant contexts remains understudied. Here, we identify a mosaic synonymous variant (m.7076A>G) in the mitochondrial DNA (mtDNA) encoded cytochrome c-oxidase subunit 1 gene (MT-CO1, p.Gly391=), which was present at homoplasmy in 47% of immune cells from a healthy donor. Using single-cell multi-omics, we discover highly specific selection against the m.7076G mutant allele in the CD8+ effector memory T cell compartment in vivo, reminiscent of selection observed for pathogenic mtDNA alleles1, 2 and indicative of lineage-specific metabolic requirements. While the wildtype m.7076A allele is translated via Watson-Crick-Franklin base-pairing, the anticodon diversity of the mitochondrial transfer RNA pool is limited, requiring wobble-dependent translation of the m.7076G mutant allele. Notably, mitochondrial ribosome profiling revealed altered codon-anticodon affinity at the wobble position as evidenced by stalled translation of the synonymous m.7076G mutant allele encoding for glycine. Generalizing this observation, we provide a new ontogeny of the 8,482 synonymous variants in the human mitochondrial genome that enables interpretation of functional mtDNA variation. Specifically, via inter- and intra-species evolutionary analyses, population-level complex trait associations, and the occurrence of germline and somatic mtDNA mutations from large-scale sequencing studies, we demonstrate that synonymous variation impacting codon:anticodon affinity is actively evolving across the entire mitochondrial genome and has broad functional and phenotypic effects. In summary, our results introduce a new ontogeny for mitochondrial genetic variation and support a model where organismal principles can be discerned from somatic evolution via single-cell genomics.

genetics↗

bioRxiv2023

Aberrant phase separation is a common killing strategy of positively charged peptides in biology and human disease

Positively charged repeat peptides are emerging as key players in neurodegenerative diseases. These peptides can perturb diverse cellular pathways but a unifying framework for how such promiscuous toxicity arises has remained elusive. We used mass-spectrometry-based proteomics to define the protein targets of these neurotoxic peptides and found that they all share similar sequence features that drive their aberrant condensation with these positively charged peptides. We trained a machine learning algorithm to detect such sequence features and unexpectedly discovered that this mode of toxicity is not limited to human repeat expansion disorders but has evolved countless times across the tree of life in the form of cationic antimicrobial and venom peptides. We demonstrate that an excess in positive charge is necessary and sufficient for this killer activity, which we name polycation poisoning. These findings reveal an ancient and conserved mechanism and inform ways to leverage its design rules for new generations of bioactive peptides.

cell biology↗

bioRxiv2023

CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3 scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a cis-regulatory code that predicts responsiveness to perturbation and reveals interactions between distinct regulatory complexes. Finally, we leverage our framework to re-analyze published scRNA-seq datasets, identifying new regulators that affect the relative abundance of alternatively polyadenylated transcripts, and characterizing extensive cellular heterogeneity in 3 UTR length amongst antibody-producing cells. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation in vitro and in vivo.

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↗

bioRxiv2022

Chromatin accessibility is a two-tier process regulated by transcription factor pioneering and enhancer activation

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in the Drosophila embryo as a model, we generated high-resolution TF binding and chromatin accessibility data, analyzed the data with interpretable deep learning, and performed genetic experiments for validation. We uncover a clear hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, while patterning TFs augment chromatin accessibility in sequence contexts in which they mediate enhancer activation. We conclude that chromatin accessibility occurs in two phases: one through pioneering, which makes enhancers accessible but not necessarily active, and a second when the correct combination of transcription factors leads to enhancer activation.

genomics↗

bioRxiv2022

Single-cell multi-omics reveals dynamics of purifying selection of pathogenic mitochondrial DNA across human immune cells

Cells experience intrinsic and extrinsic pressures that affect their proclivity to expand and persist in vivo. In congenital disorders caused by loss-of-function mutations in mitochondrial DNA (mtDNA), metabolic vulnerabilities may result in cell-type specific phenotypes and depletion of pathogenic alleles, contributing to purifying selection. However, the impact of pathogenic mtDNA mutations on the cellular hematopoietic landscape is not well understood. Here, we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from Pearson syndrome patients. We resolve the interdependence between pathogenic mtDNA and lineage, including purifying selection against deletions in effector/memory CD8 T-cell populations and recent thymic emigrants and dynamics in other hematopoietic populations. Our mapping of lineage-specific purifying selection dynamics in primary cells from patients carrying pathogenic heteroplasmy provides a new perspective on recurrent clinical phenotypes in mitochondrial disorders, including cancer and infection, with potential broader relevance to age-related immune dysfunction.

genetics↗

bioRxiv2022

Global loss of fine-scale chromatin architecture and rebalancing of gene expression during early colorectal cancer development

Although 3D genome architecture can be essential for gene regulation, the biological implications of long-range chromatin interactions in disease remain elusive. In this study, we traced the early evolution and malignant transformation of colorectal cancer by generating high-resolution chromatin conformation maps of 33 colon samples spanning different stages of early neoplastic growth from polyps of Familial Adenomatous Polyposis (FAP) patients. Our analysis reveals a substantial progressive loss of genome-wide cis-regulatory connectivity at early stages of malignancy, which correlates with a non-linear effect on gene regulation. Genes with high promoter-enhancer (P-E) connectivity in unaffected mucosa are not correlated with elevated baseline expression, but instead tend to be up-regulated at advanced stages. Inhibition of highly connected promoters preferentially represses gene expression in colorectal cancer cells relative to normal colonic epithelial cells. Our results suggest a two-phase model whereby neoplastic transformation reduces P-E connectivity from a redundant state to a rate-limiting one for transcriptional levels. Overall, our study illuminates the intricate interplay between 3D genome architecture and gene regulation during early colorectal cancer progression, and provides valuable insights for potential therapeutic interventions targeting the connectivity of cis-regulatory elements.

genomics↗