Poly-Enrich: count-based methods for gene set enrichment testing with genomic regions

SMARCA4 controls state plasticity in small cell lung cancer through regulation of neuroendocrine transcription factors and REST splicing

INTRODUCTION

Small Cell Lung Cancer (SCLC) can be classified into transcriptional subtypes with distinct degrees of neuroendocrine (NE) differentiation. Recent evidence supports plasticity among subtypes with a bias toward adoption of low-NE states during disease progression or upon acquired chemotherapy resistance. Here, we identify a role for SMARCA4, the catalytic subunit of the SWI/SNF complex, as a regulator of subtype shift in SCLC.

METHODS

ATACseq and RNAseq experiments were performed in SCLC cells after pharmacological inhibition of SMARCA4. DNA binding of SMARCA4 was characterized by ChIPseq in high-NE SCLC patient derived xenografts (PDXs). Enrichment analyses were applied to transcriptomic data. Combination of FHD-286 and afatinib was tested in vitro and in a set of chemo-resistant SCLC PDXs in vivo.

RESULTS

SMARCA4 expression positively correlates with that of NE genes in both SCLC cell lines and patient tumors. Pharmacological inhibition of SMARCA4 with FHD-286 induces the loss of NE features and downregulates neuroendocrine and neuronal signaling pathways while activating non-NE factors. SMARCA4 binds to gene loci encoding NE-lineage transcription factors ASCL1 and NEUROD1 and alters chromatin accessibility, enhancing NE programs. Enrichment analysis applied to high-confidence SMARCA4 targets confirmed neuron related pathways as the top GO Biological processes regulated by SMARCA4 in SCLC. In parallel, SMARCA4 also controls REST, a known suppressor of the NE phenotype, by regulating SRRM4-dependent REST transcript splicing. Furthermore, SMARCA4 inhibition drives ERBB pathway activation in SCLC, rendering SCLC tumors sensitive to afatinib.

CONCLUSIONS

This study nominates SMARCA4 as a key regulator of the NE state plasticity and defines a novel therapeutic strategy for SCLC.

MUFFIN: a suite of tools for the analysis of functional sequencing data

The large diversity of functional genomic assays allows for the characterization of non-coding and coding events at the tissue level or at a single-cell resolution. However, this diversity also leads to protocol differences, widely varying sequencing depths, substantial disparities in sample sizes, and number of features. In this work, we have built a Python package, MUFFIN, which offers a wide variety of tools suitable for a broad range of genomic assays and brings many tools that were missing from the Python ecosystem. First, MUFFIN has specialized tools for the exploration of the non-coding regions of genomes, such as a function to identify consensus peaks in peak-called assays, as well as linking genomic regions to genes and performing Gene Set Enrichment Analyses. MUFFIN also possesses a robust and flexible count table processing pipeline, comprising normalization, count transformation, dimensionality reduction, Differential Expression, and clustering. Our tools were tested on three widely different scRNA-seq, ChIP-seq and ATAC-seq datasets. MUFFIN integrates with the popular Scanpy ecosystem and is available on Conda and at https://github.com/pdelangen/Muffin.

Genetic variation is a key determinant of chromatin accessibility and drives differences in the regulatory landscape of C57BL/6J and 129S1/SvImJ mice

Most common genetic variants associated with disease are located in non-coding regions of the genome. One mechanism by which they function is through altering transcription factor (TF) binding. In this study, we explore how genetic variation is connected to differences in the regulatory landscape of livers from C57BL/6J and 129S1/SvImJ mice fed either chow or a high-fat diet. To identify sites where regulatory variation affects TF binding and nearby gene expression, we employed an integrative analysis of H3K27ac ChIP-seq (active enhancers), ATAC-seq (chromatin accessibility) and RNA-seq (gene expression). We show that, across all these assays, the genetically driven (i.e. strain-specific) differences in the regulatory landscape are more pronounced than those modified by diet. Most notably, our analysis revealed that differentially accessible regions (DARs, N = 29635, FDR < 0.01 and fold change > 50%) are almost always strain-specific and enriched with genetic variation. Moreover, proximal DARs are highly correlated with differentially expressed genes. We also show that TF binding is affected by genetic variation, which we validate experimentally using ChIP-seq for TCF7L2 and CTCF. This study provides detailed insights into how non-coding genetic variation alters the gene regulatory landscape, and demonstrates how this can be used to study the regulatory variation influencing TF binding.

A complex with poly(A)-binding protein and EWS facilitates the transcriptional function of oncogenic ETS transcription factors in prostate cells

The ETS transcription factor ERG is aberrantly expressed in approximately 50% of prostate tumors due to chromosomal rearrangements such as TMPRSS2/ERG. The ability of ERG to drive oncogenesis in prostate epithelial cells requires interaction with distinct coactivators, such as the RNA-binding protein EWS. Here, we find that ERG has both direct and indirect interactions with EWS, and the indirect interaction is mediated by the poly-A RNA-binding protein PABPC1. PABPC1 directly bound both ERG and EWS. ERG expression in prostate cells promoted PABPC1 localization to the nucleus and recruited PABPC1 to ERG/EWS-binding sites in the genome. Knockdown of PABPC1 in prostate cells abrogated ERG-mediated phenotypes and decreased the ability of ERG to activate transcription. These findings define a complex including ERG and the RNA-binding proteins EWS and PABPC1 that represents a potential therapeutic target for ERG-positive prostate cancer and identify a novel nuclear role for PABPC1.

An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance

The Hippo pathway is a key growth control pathway that is conserved across species. The downstream effectors of the Hippo pathway, YAP (Yes-associated protein) and TAZ (transcriptional coactivator with PDZ-binding motif), are frequently activated in cancers to drive proliferation and survival. Based on the premise that sustained interactions between YAP/TAZ and TEADs (transcriptional enhanced associate domain) are central to their transcriptional activities, we discovered a potent small-molecule inhibitor (SMI), GNE-7883, that allosterically blocks the interactions between YAP/TAZ and all human TEAD paralogs through binding to the TEAD lipid pocket. GNE-7883 effectively reduces chromatin accessibility specifically at TEAD motifs, suppresses cell proliferation in a variety of cell line models and achieves strong antitumor efficacy in vivo. Furthermore, we uncovered that GNE-7883 effectively overcomes both intrinsic and acquired resistance to KRAS (Kirsten rat sarcoma viral oncogene homolog) G12C inhibitors in diverse preclinical models through the inhibition of YAP/TAZ activation. Taken together, this work demonstrates the activities of TEAD SMIs in YAP/TAZ-dependent cancers and highlights their potential broad applications in precision oncology and therapy resistance.

Perinatal Lead Exposure Promotes Sex-Specific Epigenetic Programming of Disease-Relevant Pathways in Mouse Heart

Environmental contaminants such as the metal lead (Pb) are associated with cardiovascular disease, but the underlying molecular mechanisms are poorly understood. In particular, little is known about how exposure to Pb during early development impacts the cardiac epigenome at any point across the life course and potential differences between sexes. In a mouse model of human-relevant perinatal exposures, we utilized RNA-seq and Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) to investigate the effects of Pb exposure during gestation and lactation on gene expression and DNA methylation, respectively, in the hearts of male and female mice at weaning. For ERRBS, we identified differentially methylated CpGs (DMCs) or differentially methylated 1000 bp regions (DMRs) based on a minimum absolute change in methylation of 10% and an FDR < 0.05. For gene expression data, an FDR < 0.05 was considered significant. No individual genes met the FDR cutoff for gene expression; however, we found that Pb exposure leads to significant changes in the expression of gene pathways relevant to cardiovascular development and disease. We further found that Pb promotes sex-specific changes in DNA methylation at hundreds of gene loci (280 DMCs and 99 DMRs in males, 189 DMCs and 121 DMRs in females), and pathway analysis revealed that these CpGs and regions collectively function in embryonic development. In males, differential methylation also occurred at genes related to immune function and metabolism. We then investigated whether genes exhibiting differential methylation at weaning were also differentially methylated in hearts from a cohort of Pb-exposed mice at adulthood. We found that a single gene, Galnt2, showed differential methylation in both sexes and time points. In a human cohort investigating the influence of prenatal Pb exposure on the epigenome, we also observed an inverse association between first trimester Pb concentrations and adolescent blood leukocyte DNA methylation at a locus in GALNT2, suggesting that this gene may represent a biomarker of Pb exposure across species. Together, these data, across two time points in mice and in a human birth cohort study, collectively demonstrate that Pb exposure promotes sex-specific programming of the cardiac epigenome, and provide potential mechanistic insight into how Pb causes cardiovascular disease.

A brief survey of tools for genomic regions enrichment analysis

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

BACKGROUND

Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1860 human genome-wide distal enhancer-to-target gene definitions (EnTDefs). To evaluate performance, we used gene set enrichment (GSE) testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology annotations, and other benchmarks.

RESULTS

The top ranked 741 (40%) EnTDefs significantly outperform the common, naïve approach of linking distal regions to the nearest genes, and the top 10 EnTDefs perform well when applied to ChIP-seq data of other cell types. The GSE-based ranking of EnTDefs is highly concordant with ranking based on overlap with curated benchmarks of enhancer-gene interactions. Both our top general EnTDef and cell-type-specific EnTDefs significantly outperform seven independent computational and experiment-based enhancer-gene pair datasets. We show that using our top EnTDefs for GSE with either genome-wide DNA methylation or ATAC-seq data is able to better recapitulate the biological processes changed in gene expression data performed in parallel for the same experiment than our lower-ranked EnTDefs.

CONCLUSIONS

Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

Sex-Specific Alterations in Cardiac DNA Methylation in Adult Mice by Perinatal Lead Exposure

Environmental factors play an important role in the etiology of cardiovascular diseases. Cardiovascular diseases exhibit marked sexual dimorphism; however, the sex-specific effects of environmental exposures on cardiac health are incompletely understood. Perinatal and adult exposures to the metal lead (Pb) are linked to several adverse cardiovascular outcomes, but the sex-specific effects of this toxicant on the heart have received little attention. Perinatal environmental exposures can lead to disease through disruption of the normal epigenetic programming that occurs during early development. Using a mouse model of human-relevant perinatal environmental exposure, we investigated the effects of exposure to Pb during gestation and lactation on DNA methylation in the hearts of adult offspring mice (n = 6 per sex). Two weeks prior to mating, dams were assigned to control or Pb acetate (32 ppm) water, and exposure continued until offspring were weaned at three weeks of age. Enhanced reduced-representation bisulfite sequencing was used to measure DNA methylation in the hearts of offspring at five months of age. Although Pb exposure stopped at three weeks of age, we discovered hundreds of differentially methylated cytosines (DMCs) and regions (DMRs) in males and females at five months of age. DMCs/DMRs and their associated genes were sex-specific, with a small, but statistically significant subset overlapping between sexes. Pathway analysis revealed altered methylation of genes important for cardiac and other tissue development in males, and histone demethylation in females. Together, these data demonstrate that perinatal exposure to Pb induces sex-specific changes in cardiac DNA methylation that are present long after cessation of exposure, and highlight the importance of considering sex in environmental epigenetics and mechanistic toxicology studies.

Tissue and sex-specific programming of DNA methylation by perinatal lead exposure: implications for environmental epigenetics studies

Early developmental environment can influence long-term health through reprogramming of the epigenome. Human environmental epigenetics studies rely on surrogate tissues, such as blood, to assess the effects of environment on disease-relevant but inaccessible target tissues. However, the extent to which environment-induced epigenetic changes are conserved between these tissues is unclear. A better understanding of this conservation is imperative for effective design and interpretation of human environmental epigenetics studies. The Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET II) consortium was established by the National Institute of Environmental Health Sciences to address the utility of surrogate tissues as proxies for toxicant-induced epigenetic changes in target tissues. We and others have recently reported that perinatal exposure to lead (Pb) is associated with adverse metabolic outcomes. Here, we investigated the sex-specific effects of perinatal exposure to a human environmentally relevant level of Pb on DNA methylation in paired liver and blood samples from adult mice using enhanced reduced-representation bisulphite sequencing. Although Pb exposure ceased at 3 weeks of age, we observed thousands of sex-specific differentially methylated cytosines in the blood and liver of Pb-exposed animals at 5 months of age, including 44 genomically imprinted loci. We observed significant tissue overlap in the genes mapping to differentially methylated cytosines. A small but significant subset of Pb-altered genes exhibit basal sex differences in gene expression in the mouse liver. Collectively, these data identify potential molecular targets for Pb-induced metabolic diseases, and inform the design of more robust human environmental epigenomics studies.

Sex-Specific Programming of Cardiac DNA Methylation by Developmental Phthalate Exposure

Phthalate plasticizers are ubiquitous chemicals linked to several cardiovascular diseases in animal models and humans. Despite this, the mechanisms by which phthalate exposures cause adverse cardiac health outcomes are unclear. In particular, whether phthalate exposures during pregnancy interfere with normal developmental programming of the cardiovascular system, and the resulting implications this may have for long-term disease risk, are unknown. Recent studies suggest that the effects of phthalates on metabolic and neurobehavioral outcomes are sex-specific. However, the influence of sex on cardiac susceptibility to phthalate exposures has not been investigated. One mechanism by which developmental exposures may influence long-term health is through altered programming of DNA methylation. In this work, we utilized an established mouse model of human-relevant perinatal exposure and enhanced reduced representation bisulfite sequencing to investigate the long-term effects of diethylhexyl phthalate (DEHP) exposure on DNA methylation in the hearts of adult male and female offspring at 5 months of age (n = 5-7 mice per sex and exposure). Perinatal DEHP exposure led to hundreds of sex-specific, differentially methylated cytosines (DMCs) and differentially methylated regions (DMRs) in the heart. Pathway analysis of DMCs revealed enrichment for several pathways in females, including insulin signaling, regulation of histone methylation, and tyrosine phosphatase activity. In males, DMCs were enriched for glucose transport, energy generation, and developmental programs. Notably, many sex-specific genes differentially methylated with DEHP exposure in our mouse model were also differentially methylated in published data of heart tissues collected from human heart failure patients. Together, these data highlight the potential role for DNA methylation in DEHP-induced cardiac effects and emphasize the importance of sex as a biological variable in environmental health studies.

Testing Proximity of Genomic Regions to Transcription Start Sites and Enhancers Complements Gene Set Enrichment Testing

Large sets of genomic regions are generated by the initial analysis of various genome-wide sequencing data, such as ChIP-seq and ATAC-seq experiments. Gene set enrichment (GSE) methods are commonly employed to determine the pathways associated with them. Given the pathways and other gene sets (e.g., GO terms) of significance, it is of great interest to know the extent to which each is driven by binding near transcription start sites (TSS) or near enhancers. Currently, no tool performs such an analysis. Here, we present a method that addresses this question to complement GSE methods for genomic regions. Specifically, the new method tests whether the genomic regions in a gene set are significantly closer to a TSS (or to an enhancer) than expected by chance given the total list of genomic regions, using a non-parametric test. Combining the results from a GSE test with our novel method provides additional information regarding the mode of regulation of each pathway, and additional evidence that the pathway is truly enriched. We illustrate our new method with a large set of ENCODE ChIP-seq data, using the chipenrich Bioconductor package. The results show that our method is a powerful complementary approach to help researchers interpret large sets of genomic regions.