Biology subjects

Kundaje, A.

Publications and source records attributed to Kundaje, A..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Latent human herpesvirus 6 is reactivated in chimeric antigen receptor T cells

Cell therapies have yielded durable clinical benefits for patients with cancer, but the risks associated with the development of therapies from manipulated human cells are still being understood. For example, we currently lack a comprehensive understanding of the mechanisms of neurotoxicity observed in patients receiving T cell therapies, including recent reports of encephalitis caused by human herpesvirus 6 (HHV-6) reactivation1. Here, via petabase-scale viral RNA data mining, we examine the landscape of human latent viral reactivation and demonstrate that HHV-6B can become reactivated in human CD4+ T cells in standard in vitro cultures. Using single-cell sequencing, we identify a rare population of HHV-6 super-expressors (~1 in 300-10,000 cells) that possess high viral transcriptional activity in chimeric antigen receptor (CAR) T cell culture before spreading to infect other cells in vitro. Through the analysis of single-cell sequencing data from patients receiving cell therapy products that are FDA-approved2 or used in clinical studies3,4, we identify the presence of CAR+, HHV-6 super-expressor T cells in vivo. Together, our study implicates cell therapy products as a source of lytic HHV-6 reported in clinical trials1,5-7 and has broad implications for the design, production, and monitoring of cell therapies.

immunology↗

bioRxiv2022

The chromatin landscape of the euryarchaeon Haloferax volcanii

Archaea, together with Bacteria, represent the two main divisions of life on Earth, with many of the defining characteristics of the more complex eukaryotes tracing their origin to evolutionary innovations first made in their archaeal ancestors. One of the most notable such features is nucleosomal chromatin, although archaeal histones and chromatin differ significantly from those of eukaryotes. Despite increased interest in archaeal histones in recent years, the properties of archaeal chromatin have been little studied using genomic tools. Here, we adapt the ATAC-seq assay to archaea and use it to map the accessible landscape of the genome of the euryarchaeote Haloferax volcanii. We integrate the resulting datasets with genome-wide maps of active transcription and single-stranded DNA (ssDNA) and find that while H. volcanii promoters exist in a preferentially accessible state, unlike most eukaryotes, modulation of transcriptional activity is not associated with changes in promoter accessibility. Applying orthogonal single-molecule footprinting methods, we quantify the absolute levels of physical protection of H. volcanii, and find that Haloferax chromatin is similarly or only slightly more accessible, in aggregate, than that of eukaryotes. We also evaluate the degree of coordination of transcription within archaeal operons and make the unexpected observation that some CRISPR arrays are associated with highly prevalent ssDNA structures. These results provide a foundation for the future functional studies of archaeal chromatin.

genomics↗

bioRxiv2022

Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease

Congenital heart defects, the most common birth disorders, are the clinical manifestation of anomalies in fetal heart development - a complex process involving dynamic spatiotemporal coordination among various precursor cell lineages. This complexity underlies the incomplete understanding of the genetic architecture of congenital heart diseases (CHDs). To define the multi-cellular epigenomic and transcriptional landscape of cardiac cellular development, we generated single-cell chromatin accessibility maps of human fetal heart tissues. We identified eight major differentiation trajectories involving primary cardiac cell types, each associated with dynamic transcription factor (TF) activity signatures. We identified similarities and differences of regulatory landscapes of iPSC-derived cardiac cell types and their in vivo counterparts. We interpreted deep learning models that predict cell-type resolved, base-resolution chromatin accessibility profiles from DNA sequence to decipher underlying TF motif lexicons and infer the regulatory impact of non-coding variants. De novo mutations predicted to affect chromatin accessibility in arterial endothelium were enriched in CHD cases versus controls. We used CRISPR-based perturbations to validate an enhancer harboring a nominated regulatory CHD mutation, linking it to effects on the expression of a known CHD gene JARID2. Together, this work defines the cell-type resolved cis-regulatory sequence determinants of heart development and identifies disruption of cell type-specific regulatory elements as a component of the genetic etiology of CHD.

genomics↗

bioRxiv2022

A single cell spatial temporal atlas of skeletal muscle reveals cellular neighborhoods that orchestrate regeneration and become disrupted in aging

Our mobility requires muscle regeneration throughout life. Yet our knowledge of the interplay of cell types required to rebuild injured muscle is lacking, because most single cell assays require tissue dissociation. Here we use multiplexed spatial proteomics and neural network analyses to resolve a single cell spatiotemporal atlas of 34 cell types during muscle regeneration and aging. This atlas maps interactions of immune, fibrogenic, vascular, nerve, and myogenic cells at sites of injury in relation to tissue architecture and extracellular matrix. Spatial pseudotime mapping reveals sequential cellular neighborhoods that mediate repair and a nodal role for immune cells. We confirm this role by macrophage depletion, which triggers formation of aberrant neighborhoods that obstruct repair. In aging, immune dysregulation is chronic, cellular neighborhoods are disrupted, and an autoimmune response is evident at sites of denervation. Our findings highlight the spatial cellular ecosystem that orchestrates muscle regeneration, and is altered in aging. HighlightsO_LISingle cell resolution spatial atlas resolves a cellular ecosystem of 34 cell types in multicellular neighborhoods that mediate efficient skeletal muscle repair C_LIO_LIHighly multiplexed spatial proteomics, neural network and machine learning uncovers temporal dynamics in the spatial crosstalk between immune, fibrogenic, vascular, nerve, and muscle stem cells and myofibers during regeneration C_LIO_LISpatial pseudotime mapping reveals coherent formation of multicellular neighborhoods during efficacious repair and the nodal role of immune cells in coordinating muscle repair C_LIO_LIIn aged muscle, cellular neighborhoods are disrupted by a chronically inflamed state and autoimmunity C_LI

cell biology↗

bioRxiv2022

The dynseq genome browser track enables visualization of context-specific, dynamic DNA sequence features at single nucleotide resolution

We introduce the dynseq genome browser track, which displays DNA nucleotide characters scaled by user-specified, base-resolution scores provided in the BigWig file format. The dynseq track enables visualization of context-specific, informative genomic sequence features. We demonstrate its utility in three popular genome browsers for interpreting cis-regulatory sequence syntax and regulatory variant interpretation by visualizing nucleotide importance scores derived from machine learning models of regulatory DNA trained on protein-DNA binding and chromatin accessibility experiments.

genomics↗

bioRxiv2022

Short tandem repeats bind transcription factors to tune eukaryotic gene expression

Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and their polymorphisms alter gene expression, yet how they regulate transcription remains unknown. We find that STRs can modulate transcription factor (TF)-DNA affinities and on rates by up to 70-fold by directly binding TF DNA-binding domains, with energetic impacts approaching or exceeding mutations to consensus sites. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density near motifs to speed target search. Confirming that STRs also impact TF binding in cells, neural networks trained only on in vivo occupancies predict identical effects to those observed in vitro. Approximately 90% of TFs preferentially bind STRs that need not resemble known motifs, providing a novel cis-regulatory mechanism to target TFs to cognate sites.

biophysics↗

bioRxiv2022

Deciphering the Impact of Genetic Variation on Human Polyadenylation

Genetic variants that disrupt polyadenylation can cause or contribute to genetic disorders. Yet, due to the complex cis-regulation of polyadenylation, variant interpretation remains challenging. Here, we introduce a residual neural network model, APARENT2, that can infer 3-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2s performance on several variant datasets, including functional reporter data and human 3 aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. Finally, we perform in-silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of >44 million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, using APARENT2s predictions we detect an association between gain-of-function mutations in the 3-end and Autism Spectrum Disorder.

genomics↗

bioRxiv2022

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

bioRxiv2022

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Genome-wide association studies (GWAS) of eye disorders have identified hundreds of genetic variants associated with ocular disease. However, the vast majority of these variants are noncoding, making it challenging to interpret their function. Here, we present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina with >50,000 cells, which we used to analyze noncoding single-nucleotide polymorphisms (SNPs) implicated by GWAS of age-related macular degeneration, glaucoma, diabetic retinopathy, myopia, and type 2 macular telangiectasia. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding SNPs with causal roles in eye disease, assess SNP impact on transcription factor binding, and define their known and novel target genes. Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders and provide a potentially powerful resource for interpreting noncoding variation in the eye.

genomics↗

bioRxiv2021

Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics

Deep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence features without prior assumptions. Several methods such as in-silico mutagenesis, GradCAM, DeepLIFT, Integrated Gradients and Gkm-Explain have been developed to reveal these learned features. However, the behavior of these methods on regulatory genomic data remains an area of active research. Although prior work has benchmarked these methods on simulated datasets with known ground-truth motifs, these simulations employed highly simplified regulatory logic that is not representative of the genome. In this work, we propose a novel pipeline for designing simulated data that comes closer to modeling the complexity of regulatory genomic DNA. We apply the pipeline to build simulated datasets based on publicly-available chromatin accessibility experiments and use these datasets to bench-mark different interpretation methods based on their ability to identify ground-truth motifs. We find that a GradCAM-based method, which was reported to perform well on a more simplified dataset, does not do well on this dataset (particularly when using an architecture with shorter convolutional kernels in the first layer), and we theoretically show that this is expected based on the nature of regulatory genomic data. We also show that Integrated Gradients sometimes performs worse than gradient-times-input, likely owing to its linear interpolation path. We additionally explore the impact of user-defined settings on the interpretation methods, such as the choice of "reference"/"baseline", and identify recommended settings for genomics. Our analysis suggests several promising directions for future research on these model interpretation methods. Code and links to data are available at https://github.com/kundajelab/interpret-benchmark.

bioinformatics↗

bioRxiv2021

Accelerating in-silico saturation mutagenesis using compressed sensing

In-silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined. In this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. We have made this tool available at https://github.com/kundajelab/yuzu.

bioinformatics↗

bioRxiv2021

Direct induction of human neurons from fibroblasts carrying the neuropsychiatric 22q11.2 microdeletion reveals transcriptome- and epigenome-wide alterations

Standard methods for the creation of neuronal cells via direct induction from primary tissue use perinatal fibroblasts, which hinders the important study of patient specific genetic lesions such as those underlying neuropsychiatric disorders. To address this we developed a novel method for the direct induction of neuronal cells (induced neuronal cells, iN cells) from adult human fibroblast cells. Reprogramming fibroblasts into iN cells via recombinant virus resulted in cells that stain for markers such as MAP2 and PSA-NCAM and exhibit electrophysiological properties such as action potentials and voltage dependent sodium- and potassium currents that reveal a neuronal phenotype. Transcriptome and chromatin analysis using RNA-Seq, microRNA-Seq and ATAC-Seq, respectively, further confirm neuronal character. 22q11.2 Deletion-Syndrome (22q11DS) is caused by a large 3 million base-pair heterozygous deletion on human chromosome 22 and is strongly associated with neurodevelopmental, neuropsychiatric phenotypes such as schizophrenia and autism. We leverage the direct-iN cell model for the study of genetic neurodevelopmental conditions by presenting gene-by-gene as well as networkwide effects of the 22q11DS deletion on gene expression in human neuronal cells, on several levels of functional genomics analysis. Some of the genes within the 22q11DS deletion boundary exhibit unexpected cell-type-specific changes in transcript levels, and genome-wide we can detect dysregulation of calcium channel subunit genes and other genes known to be involved in autism or schizophrenia, such as NRXN1, as well synaptic pathways. This genome-wide effect on gene expression can also be observed at the microRNA and chromatin levels, showing that the iN cells have indeed converted to a neuronal phenotype at several regulatory levels: chromatin, protein-coding RNAs and microRNAs, revealing relevant disease pathways and genes. We present this model of inducing neurons from fibroblasts as a useful general resource to study the genetic and molecular basis of normal and abnormal brain development and brain function.

genomics↗

bioRxiv2021

RNA polymerase II dynamics and mRNA stability feedback determine mRNA scaling with cell size

A fundamental feature of cellular growth is that protein and RNA amounts scale with cell size so that concentrations remain constant. A key component to this is that global transcription rates increase in larger cells, but the underlying mechanism has remained unknown. Here, we identify RNAPII as the major limiting factor increasing transcription with cell size in budding yeast as transcription is highly sensitive to the dosage of RNAPII but not to other components of the general transcriptional machinery. Our experiments support a dynamic equilibrium model where global transcription at a given size is set by the mass-action recruitment kinetics of unengaged nucleoplasmic RNAPII, and DNA content. This drives a sub-linear increase in transcription with size, which is precisely compensated for by a decrease in mRNA decay rates as cells enlarge. Thus, limiting RNAPII and feedback on mRNA stability work in concert to ensure mRNA concentration homeostasis in growing cells.

molecular biology↗

bioRxiv2021

The chromatin organization of a chlorarachniophyte nucleomorph genome

Nucleomoprhs are remnants of secondary endosymbiotic events between two eukaryote cells wherein the endosymbiont has retained its eukaryotic nucleus. Nucleomorphs have evolved at least twice independently, in chlorarachniophytes and cryptophytes, yet they have converged on a remarkably similar genomic architecture, characterized by the most extreme compression and miniaturization among all known eukaryotic genomes. Previous computational studies have suggested that nucleomorph chromatin likely exhibits a number of divergent features. In this work, we provide the first maps of open chromatin, active transcription, and three-dimensional organization for the nucleomorph genome of the chlorarachniophyte Bigelowiella natans. We find that the B. natans nucleomorph genome exists in a highly accessible state, akin to that of ribosomal DNA in some other eukaryotes, and that it is highly transcribed over its entire length, with few signs of polymerase pausing at transcription start sites (TSSs). At the same time, most nucleomorph TSSs show very strong nucleosome positioning. Chromosome conformation (Hi-C) maps reveal that nucleomorph chromosomes interact with one other at their telomeric regions, and show the relative contact frequencies between the multiple genomic compartments of distinct origin that B. natans cells contain.

genomics↗

bioRxiv2021

Direct profiling of genome-wide dCas9 and Cas9 specificity using ssDNA mapping (CasKAS)

Detecting and mitigating off-target activity is critical to the practical application of CRISPR-mediated genome and epigenome editing. While numerous methods have been developed to map Cas9 binding specificity genome-wide, they are generally time-consuming and/or expensive, and not applicable to catalytically dead CRISPR enzymes. We have developed a rapid, inexpensive, and facile assay for identifying off-target CRISPR enzyme binding and cleavage by chemically mapping the unwound single-stranded DNA structures formed upon binding of a sgRNA-loaded Cas9 protein ("CasKAS"). We demonstrate this method in both in vitro and in vivo contexts.

genomics↗

bioRxiv2021

Chromatin accessibility dynamics of neurogenic niche cells reveal a reversible decline in neural stem cell migration during aging

Aging is accompanied by a deterioration in the regenerative and repair potential of stem cell niches in the brain1-5. However, the mechanisms underlying this decline are largely unknown. Here we characterize genome-wide chromatin accessibility in young and old neurogenic niche cells in vivo, revealing defects in neural stem cell (NSC) adhesion and migration during aging. Interestingly, chromatin accessibility at cell adhesion and migration genes decreases with age in quiescent NSCs but increases with age in activated (proliferative) NSCs, and this is accompanied by corresponding expression changes in these genes. We experimentally validate that quiescent and activated NSCs exhibit opposing adhesion and migration behaviors with age: quiescent NSCs become less adhesive (and more migratory) whereas activated NSCs and progeny become more adhesive (and less migratory) during aging. We also show that the ability of activated NSCs and progeny to mobilize out of the niche during in vivo neurogenesis diminishes during aging. Using tension sensors with single molecule resolution, we find that one of the cellular mechanisms by which aging impairs the migration of old activated NSCs and progeny involves increased force-producing adhesions. We identify inhibition of the cytoskeletal-regulating kinase ROCK6, 7 as a way to reduce force-producing adhesions and restore migration in old activated NSCs in vitro. Interestingly, inhibition of ROCK in the neurogenic niche of old mice boosts neurogenesis to the olfactory bulb in vivo. These results have important implications for restoring the migratory potential of NSCs and progeny and for improving neurogenesis in the aged brain.

neuroscience↗

bioRxiv2021

Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated 451,886 single-cell chromatin accessibility profiles and 208,557 single-cell transcriptomes from 48 polyps, 27 normal tissues, and 6 CRCs collected from patients with and without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from normal to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T-cells, and a subtype of FOX-regulated pre-cancer associated fibroblasts. In the cancerous state, we observe T-cell exhaustion, RUNX1-regulated cancer associated fibroblasts, and increasing accessibility associated with HNF4A motifs in epithelia. Methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

genomics↗

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↗