Genome-wide screens identify specific drivers of mutant hTERT promoters

Cancer-specific hTERT promoter mutations reported in 19% of cancers result in enhanced telomerase activity. Understanding the distinctions between transcriptional regulation of wild-type (WT) and mutant (Mut) hTERT promoters may open up avenues for development of inhibitors which specially block hTERT expression in cancer cells. To comprehensively identify physiological regulators of WT- or Mut-hTERT promoters, we generated several isogenic reporter cells driven by endogenous hTERT loci. Genome-wide CRISPR-Cas9 and small interfering RNA screens using these isogenic reporter lines identified specific regulators of Mut-hTERT promoters. We validate and characterize one of these hits, namely, MED12, a kinase subunit of mediator complex. We demonstrate that MED12 specifically drives expression of hTERT from the Mut-hTERT promoter by mediating long-range chromatin interaction between the proximal Mut-hTERT promoter and T-INT1 distal regulatory region 260 kb upstream. Several hits identified in our screens could serve as potential therapeutic targets, inhibition of which may specifically block Mut-hTERT promoter driven telomerase reactivation in cancers.

Computational Inference of DNA Folding Principles: From Data Management to Machine Learning

DNA is the molecular basis of life and would total about three meters if linearly untangled. To fit in the cell nucleus at the micrometer scale, DNA has, therefore, to fold itself into several layers of hierarchical structures, which are thought to be associated with functional compartmentalization of genomic features like genes and their regulatory elements. For this reason, understanding the mechanisms of genome folding is a major biological research problem. Studying chromatin conformation requires high computational resources and complex data analyses pipelines. In this chapter, we first present the PyGMQL software for interactive and scalable data exploration for genomic data. PyGMQL allows the user to inspect genomic datasets and design complex analysis pipelines. The software presents itself as a easy-to-use Python library and interacts seamlessly with other data analysis packages. We then use the software for the study of chromatin conformation data. We focus on the epigenetic determinants of Topologically Associating Domains (TADs), which are region of high self chromatin interaction. The results of this study highlight the existence of a “grammar of genome folding” which dictates the formation of TADs and boundaries, which is based on the CTCF insulator protein. Finally we focus on the relationship between chromatin conformation and gene expression, designing a graph representation learning model for the prediction of gene co-expression from gene topological features obtained from chromatin conformation data. We demonstrate a correlation between chromatin topology and co-expression, shedding a new light on this debated topic and providing a novel computational framework for the study of co-expression networks.

Myeloid leukemia vulnerabilities at CTCF-enriched long noncoding RNA loci

The noncoding genome presents a largely untapped source of biological insights, including thousands of long noncoding RNA (lncRNA) loci. While some produce bona fide lncRNAs, others exert transcript-independent cis-regulatory effects, and a lack of predictive features renders mechanistic dissection challenging. Here, we describe MYNRL15, a CTCF-enriched lncRNA locus and pan-myeloid leukemia dependency initially identified by expression-guided CRISPR interference screens. We show that accessibility and integrity of the MYNRL15 locus is required for myeloid leukemia maintenance; its perturbation selectively impairs acute myeloid leukemia (AML) cells compared to hematopoietic stem and progenitor cells in vitro, and depletes AML xenografts in vivo. While the MYNRL15 transcript and neighboring protein-coding genes appear dispensable, dense CRISPR tiling of the locus revealed two crucial candidate cis-regulatory DNA elements which drive the perturbation phenotype. Disruption of these elements triggers the formation of a tumor-suppressive, long-range chromatin interaction. By integrating transcriptome profiling with a CRISPR-Cas9 knockout screen of genes from the gained interaction region, we pinpointed two downregulated, potent cancer dependency genes as effectors of MYNRL15 disruption: WDR61 and IMP3. Finally, guided by distinctive features of the MYNRL15 locus, we find that elevated CTCF density characterizes a set of lncRNA loci enriched in leukemia vulnerabilities (22.6-24.2% essentiality rate). A catalog of CTCF-enriched lncRNA loci (C-LNCs) in 18 cell types representing different cancer entities and tissues is provided with this study, towards refining the search for noncoding oncogenic vulnerabilities in leukemia and other malignancies.

Efficient pre-processing of Single-cell ATAC-seq data

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

A novel RNA-mediated mechanism causing down-regulation of insulating promoter interactions in human embryonic stem cells

The genome-wide promoter interactome is primarily maintained and regulated by architectural proteins such as CTCF and cohesin. However, some studies suggest a role for non-coding RNAs (ncRNAs) in this process. We aimed to characterise the regulatory role of RNA-mediated promoter interactions in the control of gene expression. We integrated genome-wide datasets of RNA-chromatin and promoter-genome interactions in human embryonic stem cells (hESCs) to identify putative RNA-mediated promoter interactions. We discovered that CTCF sites were enriched in RNA-PIRs (promoter interacting regions co-localising with RNA-chromatin interaction sites) and genes interacting with RNA-PIRs containing CTCF sites showed higher expression levels. One of the long noncoding RNAs (lncRNAs) expressed in hESCs, Syntaxin 18-Antisense 1 (STX18-AS1), appeared to be involved in an insulating promoter interaction with the neighbouring gene, MSX1. By knocking down STX18-AS1, the MSX1 promoter-PIR interaction was intensified and the target gene (MSX1) expression was down-regulated. Conversely, reduced MSX1 promoter-PIR interactions, resulting from CRISPR-Cas9 deletion of the PIR, increased the expression of MSX1. We conclude that STX18-AS1 RNA antagonised local CTCF-mediated insulating promoter interactions to augment gene expression. Such down-regulation of the insulating promoter interactions by this novel mechanism may explain the higher expression of genes interacting with RNA-PIRs linked to CTCF sites.

KDM6B promotes activation of the oncogenic CDK4/6-pRB-E2F pathway by maintaining enhancer activity in MYCN-amplified neuroblastoma

The H3K27me2/me3 histone demethylase KDM6B is essential to neuroblastoma cell survival. However, the mechanism of KDM6B action remains poorly defined. We demonstrate that inhibition of KDM6B activity 1) reduces the chromatin accessibility of E2F target genes and MYCN, 2) selectively leads to an increase of H3K27me3 but a decrease of the enhancer mark H3K4me1 at the CTCF and BORIS binding sites, which may, consequently, disrupt the long-range chromatin interaction of MYCN and E2F target genes, and 3) phenocopies the transcriptome induced by the specific CDK4/6 inhibitor palbociclib. Overexpression of CDK4/6 or Rb1 knockout confers neuroblastoma cell resistance to both palbociclib and the KDM6 inhibitor GSK-J4. These data indicate that KDM6B promotes an oncogenic CDK4/6-pRB-E2F pathway in neuroblastoma cells via H3K27me3-dependent enhancer-promoter interactions, providing a rationale to target KDM6B for high-risk neuroblastoma.

Chromatin Interaction Responds to Breast Muscle Development and Intramuscular Fat Deposition Between Chinese Indigenous Chicken and Fast-Growing Broiler

Skeletal muscle development and intramuscular fat (IMF) content, which positively contribute to meat production and quality, are regulated by precisely orchestrated processes. However, changes in three-dimensional chromatin structure and interaction, a newly emerged mediator of gene expression, during the skeletal muscle development and IMF deposition have remained unclear. In the present study, we analyzed the differences in muscle development and IMF content between one-day-old commercial Arbor Acres broiler (AA) and Chinese indigenous Lushi blue-shelled-egg chicken (LS) and performed Hi-C analysis on their breast muscles. Our results indicated that significantly higher IMF content, however remarkably lower muscle fiber diameter was detected in breast muscle of LS chicken compared to that of AA broiler. The chromatin intra-interaction was prior to inter-interaction in both AA and LS chicken, and chromatin inter-interaction was heavily focused on the small and gene-rich chromosomes. For genomic compartmentalization, no significant difference in the number of B type compartments was found, but AA had more A type compartments versus LS. The A/B compartment switching of AA versus LS showed more A to B switching than B to A switching. There were no significant differences in the average sizes and distributions of topologically associating domains (TAD). Additionally, approximately 50% of TAD boundaries were overlapping. The reforming and disappearing events of TAD boundaries were identified between AA and LS chicken breast muscles. Among these, the HMGCR gene was located in the TAD-boundary regions in AA broilers, but in TAD-interior regions in LS chickens, and the IGF2BP3 gene was located in the AA-unique TAD boundaries. Both HMGCR and IGF2BP3 genes exhibited increased mRNA expression in one-day-old AA broiler breast muscles. It was demonstrated that the IGF2BP3 and HMGCR genes regulated by TAD boundary sliding were potential biomarkers for chicken breast muscle development and IMF deposition. Our data not only provide a valuable understanding of higher-order chromatin dynamics during muscle development and lipid accumulation but also reveal new insights into the regulatory mechanisms of muscle development and IMF deposition in chicken.

COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types

Common genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in TH2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.