Biology subjects

Abnousi, A.

Publications and source records attributed to Abnousi, A..

11 recordsLinked to original sources

bioRxiv2023

Combinatorial transcription factor binding encodes cis-regulatory wiring of forebrain GABAergic neurogenesis

Transcription factors (TFs) bind combinatorially to genomic cis-regulatory elements (cREs), orchestrating transcription programs. While studies of chromatin state and chromosomal interactions have revealed dynamic neurodevelopmental cRE landscapes, parallel understanding of the underlying TF binding lags. To elucidate the combinatorial TF-cRE interactions driving mouse basal ganglia development, we integrated ChIP-seq for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and transcriptional state, and transgenic enhancer assays. We identified TF-cREs modules with distinct chromatin features and enhancer activity that have complementary roles driving GABAergic neurogenesis and suppressing other developmental fates. While the majority of distal cREs were bound by one or two TFs, a small proportion were extensively bound, and these enhancers also exhibited exceptional evolutionary conservation, motif density, and complex chromosomal interactions. Our results provide new insights into how modules of combinatorial TF-cRE interactions activate and repress developmental expression programs and demonstrate the value of TF binding data in modeling gene regulatory wiring.

developmental biology↗

bioRxiv2022

Integrative analysis of the 3D genome and epigenome in mouse embryonic tissues

While a rich set of putative cis-regulatory sequences involved in mouse fetal development has been annotated recently based on chromatin accessibility and histone modification patterns, delineating their role in developmentally regulated gene expression continues to be challenging. To fill this gap, we mapped chromatin contacts between gene promoters and distal sequences genome-wide in seven mouse fetal tissues, and for one of them, across six developmental stages. We identified 248,620 long-range chromatin interactions centered at 14,138 protein-coding genes and characterized their tissue-to-tissue variations as well as developmental dynamics. Integrative analysis of the interactome with previous epigenome and transcriptome datasets from the same tissues revealed a strong correlation between the chromatin contacts and chromatin state at distal enhancers, as well as gene expression patterns at predicted target genes. We predicted target genes of 15,098 candidate enhancers, and used them to annotate target genes of homologous candidate enhancers in the human genome that harbor risk variants of human diseases. We present evidence that schizophrenia and other adult disease risk variants are frequently found in fetal enhancers, providing support for the hypothesis of fetal origins of adult diseases.

developmental biology↗

bioRxiv2021

Parallel Characterization of cis-Regulatory Elements for Multiple Genes UsingCRISPRpath

Current pooled CRISPR screens for cis-regulatory elements (CREs) can only accommodate one gene based on its expression level. Here, we describe CRISPRpath, a scalable screening strategy for parallelly characterizing CREs of genes linked to the same biological pathway and converging phenotypes. We demonstrate the ability of CRISPRpath for simultaneously identifying functional enhancers of six genes in the 6-thioguanine-induced DNA mismatch repair pathway using both CRISPR interference (CRISPRi) and CRISPR nuclease (CRISPRn) approaches. 60% of the identified enhancers are known promoters with distinct epigenomic features compared to other active promoters, including increased chromatin accessibility and interactivity. Furthermore, by imposing different levels of selection pressure, CRISPRpath can distinguish enhancers exerting strong impact on gene expression from those exerting weak impact. Our results offer a nuanced view of cis-regulation and demonstrate that CRISPRpath can be leveraged for understanding the complex gene regulatory program beyond transcriptional output at scale.

genomics↗

bioRxiv2020

SnapHiC: a computational pipeline to map chromatin contacts from single cell Hi-C data

Single cell Hi-C (scHi-C) analysis has been increasingly used to map the chromatin architecture in diverse tissue contexts, but computational tools to define chromatin contacts at high resolution from scHi-C data are still lacking. Here, we describe SnapHiC, a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. We benchmark SnapHiC against HiCCUPS, a common tool for mapping chromatin contacts in bulk Hi-C data, using scHi-C data from 742 mouse embryonic stem cells. We further demonstrate its utility by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells. We uncover cell-type-specific chromatin loops and predict putative target genes for non-coding sequence variants associated with neuropsychiatric disorders. Our results suggest that SnapHiC could facilitate the analysis of cell-type-specific chromatin architecture and gene regulatory programs in complex tissues.

bioinformatics↗

bioRxiv2020

HPRep: Quantifying reproducibility in HiChIP and PLAC-seq datasets

HiChIP and PLAC-seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-seq data from mouse embryonic stem cells and mouse brain tissues, as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrates expected clustering of data which could not be achieved by existing methods developed for Hi-C data, highlighting the need of a reproducibility metric tailored to HiChIP and PLAC-seq data.

genomics↗

bioRxiv2020

MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple sample UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting sample-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.

genetics↗

bioRxiv2020

HiCAR: a robust and sensitive multi-omic co-assay for simultaneous measurement of transcriptome, chromatin accessibility, and cis-regulatory chromatin contacts

The long-range interactions of cis-regulatory elements (cREs) play a central role in regulating the spatial-temporal gene expression program of multi-cellular organism. cREs are characterized by the presence of accessible (or open) chromatin, which can be identified at genome-wide scale with assays such as ATAC-seq, DHS-seq, and FAIRE-seq. However, it remains technically challenging to comprehensively identify the long-range physical interactions that occur between cREs, especially in a cost effective manner using low-input samples. Here, we report HiCAR (High-throughput Chromosome conformation capture on Accessible DNA with mRNA-seq co-assay), a method that enables simultaneous assessment of cis-regulatory chromatin interactions and chromatin accessibility, as well as evaluation of the transcriptome, which represents the functional output of chromatin structure and accessibility. Unlike immunoprecipitation-based methods such as HiChIP, PLAC-seq, and ChIA-PET, HiCAR does not require target-specific antibodies and thus can comprehensively capture the cis-regulatory chromatin contacts anchored at accessible regulatory DNA regions and associated with diverse epigenetic modifications and transcription factor binding. Compared to Trac-looping, another method designed to capture interactions between accessible chromatin regions, HiCAR produced a 17-fold greater yield of informative long-range cis- reads at a similar sequencing depth and required 1,000-fold fewer cells as input. Applying HiCAR to H1 human embryonic stem cells (hESCs) revealed 46,792 cis-regulatory chromatin interactions at 5kb resolution. Interestingly, we found that epigenetically poised, bivalent, and repressed cREs exhibit comparable spatial interaction activity to those transcriptionally activated cREs. Using machine learning approaches, we predicated 22 epigenome features that are potentially important for the spatial interaction activity of cREs in H1 hESC. Lastly, we also identified long-range cis-regulatory chromatin interactions in GM12878 and mouse embryonic stem cells with HiCAR. Our results demonstrate that HiCAR is a robust and cost-effective multi-omics assay, which is broadly applicable for simultaneous analysis of genome architecture, chromatin accessibility, and the transcriptome using low-input samples.

genomics↗

bioRxiv2020

Transcriptional Network Orchestrating Regional Patterning of Cortical Progenitors

We uncovered a transcription factor (TF) network that regulates cortical regional patterning. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1 and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer-gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning.

neuroscience↗

bioRxiv2020

HiC-ACT: Improved Detection of Chromatin Interactions from Hi-C Data via Aggregated Cauchy Test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence, in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT) based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line GM12878 and mouse embryonic stem cell line (mESC). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type-I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal to noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks, in both GM12878 and mESC. HiC-ACT, effectively a summary statistic based approach, is computationally efficient (~6 minutes and ~2GB memory to process 25,000 pairwise interactions).

bioinformatics↗

bioRxiv2020

3D Epigenomic Characterization Reveals Insights Into Gene Regulation and Lineage Specification During Corticogenesis

Lineage-specific epigenomic changes during human corticogenesis have previously remained elusive due to challenges with tissue heterogeneity and sample availability. Here, we analyze cis-regulatory chromatin interactions, open chromatin regions, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational human brain samples. We show that chromatin looping underlies transcriptional regulation for lineage-specific genes, with transcription factor motifs, families of transposable elements, and disease-associated variants enriched at distal interacting regions in a cell type-specific manner. A subset of promoters exhibit unusually high degrees of chromatin interactivity, which we term super interactive promoters. Super interactive promoters are enriched for critical lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of cell type-specific transcription. Finally, we present CRISPRview, a novel approach for validating distal interacting regions in primary cells. Our study presents the first characterization of cell type-specific 3D epigenomic landscapes during human corticogenesis, advancing our understanding of gene regulation and lineage specification during human brain development.

genomics↗

bioRxiv2018

MAPS: model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting the information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in analysis of chromatin interactions centered on cohesin, CTCF and H3K4me3 associated regions in multiple cell types. MAPS is freely available at https://github.com/ijuric/MAPS.

bioinformatics↗