Biology subjects

Vierstra, J.

Publications and source records attributed to Vierstra, J..

7 recordsLinked to original sources

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↗

bioRxiv2023

Statistical framework for calling allelic imbalance in high-throughput sequencing data

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene expression. Compared to classic association studies, allelic imbalance at heterozygous variants captures the functional effects of the regulatory genome variation with smaller sample sizes and higher sensitivity. Yet, the identification of allele-specific events from allelic read counts remains non-trivial due to multiple sources of technical and biological variability, which induce data-dependent biases and overdispersion. Here we present MIXALIME, a novel computational framework for calling allele-specific events in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy-number variation. We benchmark MIXALIME against existing tools and demonstrate its practical usage by constructing an atlas of allele-specific chromatin accessibility, UDACHA, from thousands of available datasets obtained from diverse cell types. Availabilityhttps://github.com/autosome-ru/MixALime, https://udacha.autosome.org

bioinformatics↗

bioRxiv2023

Large-scale discovery of potent, compact and lineage specific enhancers for gene therapy vectors

Regulation of gene expression during cell development and differentiation is chiefly orchestrated by distal noncoding regulatory elements that precisely modulate cell selective gene activity. Gene therapy vectors rely on the cellular and context specificity of regulatory DNA elements to express therapeutic transgenes in the correct location and time. Here, we develop a straight-forward, one-shot approach to screen putative regulatory sequences identified in large-scale epigenomics profiling experiments for precise and programmable control of transgenes encoded within gene therapy viral vectors. We designed a library of 15,000 short sequences ([~]200bp) derived from a set of developmentally active DHS elements during human ex vivo erythropoiesis and cloned them into a GFP reporter lentiviral vector. In an erythroid progenitor cell line, these elements display a gradient of transcriptional enhancer activity, with some demonstrating equivalent activity to the canonical {beta}-globin LCR despite a 9-fold smaller size. We show that these elements are both highly cell type restricted and developmental stage specific both in vitro and in vivo. Finally, we replace the LCR element with one of the novel short enhancers in a {beta}-thalassemia lentiviral therapeutic vector and efficiently correct the thalassemic phenotype in patient-derived HSPCs. More broadly, our approach provides further insights into enhancer biology with wider implications into the development of highly cell type specific and efficacious viral vectors for human gene therapy.

genomics↗

bioRxiv2023

GATA1-defective immune-megakaryocytes as possible drivers of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disorder with limited therapeutic options. Insufficient understanding of driver mutations and poor fidelity of currently available animal models has limited the development of effective therapies. Since GATA1 deficient megakaryocytes sustain myelofibrosis, we hypothesized that they may also induce fibrosis in lungs. We discovered that lungs from IPF patients and Gata1lowmice contain numerous GATA1negative immune-poised megakaryocytes that, in mice, have defective RNA-seq profiling and increased TGF-{beta}1, CXCL1 and P-selectin content. With age, Gata1low mice develop fibrosis in lungs. Development of lung fibrosis in this model is prevented by P-selectin deletion and rescued by P-selectin, TGF-{beta}1 or CXCL1 inhibition. Mechanistically, P-selectin inhibition decreases TGF-{beta}1 and CXCL1 content and increases GATA1positive megakaryocytes while TGF-{beta}1 or CXCL1 inhibition decreased CXCL1 only. In conclusion, Gata1low mice are a novel genetic-driven model for IPF and provide a link between abnormal immune-megakaryocytes and lung fibrosis.

immunology↗

bioRxiv2020

Chromatin dynamics during hematopoiesis reveal discrete regulatory modules instructing differentiation

Lineage commitment and differentiation is driven by the concerted action of master transcriptional regulators at their target chromatin sites. Multiple efforts have characterized the key transcription factors (TFs) that determine the various hematopoietic lineages. However, the temporal interactions between individual TFs and their chromatin targets during differentiation and how these interactions dictate lineage commitment remains poorly understood. We performed dense, daily, temporal profiling of chromatin accessibility (DNase I-seq) and gene expression changes (total RNA-seq) along ex vivo human erythropoiesis to comprehensively define developmentally regulated DNase I hypersensitive sites (DHSs) and transcripts. We link both distal DHSs to their target gene promoters and individual TFs to their target DHSs, revealing that the regulatory landscape is organized in distinct sequential regulatory modules that regulate lineage restriction and maturation. Finally, direct comparison of transcriptional dynamics (bulk and single-cell) and lineage potential between erythropoiesis and megakaryopoiesis uncovers differential fate commitment dynamics between the two lineages as they exit pluripotency. Collectively, these data provide novel insights into the global regulatory landscape during hematopoiesis.

developmental biology↗

bioRxiv2020

Global reference mapping and dynamics of human transcription factor footprints

Combinatorial binding of transcription factors to regulatory DNA underpins gene regulation in all organisms. Genetic variation in regulatory regions has been connected with diseases and diverse phenotypic traits1, yet it remains challenging to distinguish variants that impact regulatory function2. Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin3-5. However, to date only a small fraction of such sites have been precisely resolved on the human genome sequence5. To enable comprehensive mapping of transcription factor footprints, we produced high-density DNase I cleavage maps from 243 human cell and tissue types and states and integrated these data to delineate at nucleotide resolution ~4.5 million compact genomic elements encoding transcription factor occupancy. We map the fine-scale structure of ~1.6 million DHS and show that the overwhelming majority is populated by well-spaced sites of single transcription factor:DNA interaction. Cell context-dependent cis-regulation is chiefly executed by wholesale actuation of accessibility at regulatory DNA versus by differential transcription factor occupancy within accessible elements. We show further that the well-described enrichment of disease- and phenotypic trait-associated genetic variants in regulatory regions1,6 is almost entirely attributable to variants localizing within footprints, and that functional variants impacting transcription factor occupancy are nearly evenly partitioned between loss- and gain-of-function alleles. Unexpectedly, we find that the global density of human genetic variation is markedly increased within transcription factor footprints, revealing an unappreciated driver of cis-regulatory evolution. Our results provide a new framework for both global and nucleotide-precision analyses of gene regulatory mechanisms and functional genetic variation.

genomics↗

bioRxiv2019

Index and biological spectrum of accessible DNA elements in the human genome

DNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA and harbor disease- and phenotypic trait-associated genetic variation. We established high-precision maps of DNase I hypersensitive sites from 733 human biosamples encompassing 439 cell and tissue types and states, and integrated these to precisely delineate and numerically index ~3.6 million DHSs encoded within the human genome, providing a common coordinate system for regulatory DNA. Here we show that the expansive scale of cell and tissue states sampled exposes an unprecedented degree of stereotyped actuation of large sets of elements, signaling the operation of distinct genome-scale regulatory programs. We show further that the complex actuation patterns of individual elements can be captured comprehensively by a simple regulatory vocabulary reflecting their dominant cellular manifestation. This vocabulary, in turn, enables comprehensive and quantitative regulatory annotation of both protein-coding genes and the vast array of well-defined but poorly-characterized non-coding RNA genes. Finally, we show that the combination of high-precision DHSs and regulatory vocabularies markedly concentrate disease- and trait-associated non-coding genetic signals both along the genome and across cellular compartments. Taken together, our results provide a common and extensible coordinate system and vocabulary for human regulatory DNA, and a new global perspective on the architecture of human gene regulation.

genomics↗