The standard expression quantitative trait loci (eQTL) detects polymorphisms associated with gene expression without revealing causality. We introduce a coupled Bayesian regression approach--eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combination of regulatory single-nucleotide polymorphisms (SNPs) that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance but also predicts gene expression more accurately than other methods. Based on realistic simulated data, we demonstrate that eQTeL accurately detects causal regulatory SNPs, including those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal.

Heart failure is a complex clinical syndrome and has become the most common reason for adult hospitalization in developed countries. Two subtypes of heart failure, ischemic heart disease (ISCH) and dilated cardiomyopathy (DCM), have been studied using microarray platforms. However, microarray has limited resolution. Here we applied RNA sequencing (RNA-Seq) to identify gene signatures for heart failure from six individuals, including three controls, one ISCH and two DCM patients. Using genes identified from this small RNA-Seq dataset, we were able to accurately classify heart failure status in a much larger set of 313 individuals. The identified genes significantly overlapped with genes identified via genome-wide association studies for cardiometabolic traits and the promoters of those genes were enriched for binding sites for transcriptions factors. Our results indicate that it is possible to use RNA-Seq to classify disease status for complex diseases such as heart failure using an extremely small training dataset.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

For most complex traits, the majority of SNPs identified through genome-wide association studies (GWAS) reside within noncoding regions that have no known function. However, these regions are enriched for the regulatory enhancers specific to the cells relevant to the specific trait. Indeed, many of the GWAS loci that have been functionally characterized lie within enhancers that regulate expression levels of key genes. In order to identify polymorphisms with potential allele-specific regulatory effects, we developed a bioinformatics pipeline that harnesses epigenetic signatures as well as transcription factor (TF) binding motifs to identify putative enhancers containing a SNP with potential allele-specific TF binding in linkage disequilibrium (LD) with a GWAS-identified SNP. We applied the approach to GWAS findings for blood lipids, revealing 7 putative enhancers harboring associated SNPs, 3 of which lie within the introns of LCAT and ABCA1, genes that play crucial roles in cholesterol biogenesis and lipoprotein metabolism. All 3 enhancers demonstrated allele-specific in vitro regulatory activity in liver-derived cell lines. We demonstrated that these putative enhancers are in close physical proximity to the promoters of their respective genes, in situ, likely through chromatin looping. In addition, the associated alleles altered the likelihood of transcription activator STAT3 binding. Our results demonstrate that through our approach, the LD blocks that contain GWAS signals, often hundreds of kilobases in size with multiple SNPs serving as statistical proxies to the true functional site, can provide an experimentally testable hypothesis for the underlying regulatory mechanism linking genetic variants to complex traits.

Mammalian gene expression is often regulated by distal enhancers. However, little is known about higher order functional organization of enhancers. Using ∼100 K P300-bound regions as candidate enhancers, we investigated their correlated activity across 72 cell types based on DNAse hypersensitivity. We found widespread correlated activity between enhancers, which decreases with increasing inter-enhancer genomic distance. We found that correlated enhancers tend to share common transcription factor (TF) binding motifs, and several chromatin modification enzymes preferentially interact with these TFs. Presence of shared motifs in enhancer pairs can predict correlated activity with 73% accuracy. Also, genes near correlated enhancers exhibit correlated expression and share common function. Correlated enhancers tend to be spatially proximal. Interestingly, weak enhancers tend to correlate with significantly greater numbers of other enhancers relative to strong enhancers. Furthermore, strong/weak enhancers preferentially correlate with strong/weak enhancers, respectively. We constructed enhancer networks based on shared motif and correlated activity and show significant functional enrichment in their putative target gene clusters. Overall, our analyses show extensive correlated activity among enhancers and reveal clusters of enhancers whose activities are coordinately regulated by multiple potential mechanisms involving shared TF binding, chromatin modifying enzymes and 3D chromatin structure, which ultimately co-regulate functionally linked genes.

The soil-dwelling nematode C. elegans is a powerful system for comparative molecular analyses of environmental stress response mechanisms. Infection of worms with bacterial and fungal pathogens causes the activation of well-characterized innate immune transcriptional programs in pathogen-exposed hypodermal and intestinal tissues. However, the pathophysiological events that drive such transcriptional responses are not understood. Here, we show that infection-activated transcriptional responses are, in large part, recapitulated by either physiological or genetic activation of the osmotic stress response. Microarray profiling of wild type worms exposed to non-lethal hypertonicity identified a suite of genes that were also regulated by infection. Expression profiles of five different osmotic stress resistant (osr) mutants under isotonic conditions reiterated the wild type transcriptional response to osmotic stress and also showed substantial similarity to infection-induced gene expression under isotonic conditions. Computational, transgenic, and functional approaches revealed that two GATA transcription factors previously implicated in infection-induced transcriptional responses, elt-2 and elt-3, are also essential for coordinated tissue-specific activation of osmosensitive gene expression and promote survival under osmotically stressful conditions. Together, our data suggest infection and osmotic adaptation share previously unappreciated transcriptional similarities which might be controlled via regulation of tissue-specific GATA transcription factors.

Adipose inflammation plays a central role in obesity-related metabolic and cardiovascular complications. However, few human adipose-secreted proteins are known to mediate these processes. We hypothesized that microarray mRNA profiling of human adipose during evoked inflammation could identify novel adipocytokines.

Most current tumor immunotherapy strategies leverage cytotoxic CD8+ T cells. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity limits our ability to harness their activity. Here, we use single-cell mRNA sequencing to analyze the response of tumor-specific CD4+ TILs and draining lymph node (dLN) T cells. Computational approaches to characterize subpopulations identify TIL transcriptomic patterns strikingly distinct from acute and chronic anti-viral responses and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes T follicular helper (Tfh) cells but lacks Th1 cells. We identify a type I interferon-driven signature in Th1-like TILs and show that it is found in human cancers, in which it is negatively associated with response to checkpoint therapy. Our study provides a proof-of-concept methodology to characterize tumor-specific CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies.

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.

The forkhead transcription factor gene E1 (FOXE1) plays an important role in regulation of thyroid development, palate formation and hair morphogenesis in mammals. However, avian FOXE1 genes have not been characterized and as such, codon evolution of FOXE1 orthologs in a broader evolutionary context of mammals and birds is not known.

Genomes of higher eukaryotes have surprisingly long first introns and in some cases, the first introns have been shown to have higher conservation relative to other introns. However, the functional relevance of conserved regions in the first introns is poorly understood. Leveraging the recent ENCODE data, here we assess potential regulatory roles of conserved regions in the first intron of human genes.

After the initial enthusiasm of the human genome project, it became clear that without additional data pertaining to the epigenome, i.e., how the genome is marked at specific developmental periods, in different tissues, as well as across individuals and species-the promise of the genome sequencing project in understanding biology cannot be fulfilled. This realization prompted several large-scale efforts to map the epigenome, most notably the Encyclopedia of DNA Elements (ENCODE) project. While there is essentially a single genome in an individual, there are hundreds of epigenomes, corresponding to various types of epigenomic marks at different developmental times and in multiple tissue types. Unprecedented advances in next-generation sequencing (NGS) technologies, by virtue of low cost and high speeds that continue to improve at a rate beyond what is anticipated by Moore's law for computer hardware technologies, have revolutionized molecular biology and genetics research, and have in turn prompted innovative ways to reduce the problem of measuring cellular events involving DNA or RNA into a sequencing problem. In this article, we provide a brief overview of the epigenome, the various types of epigenomic data afforded by NGS, and some of the novel discoveries yielded by the epigenomics projects. We also provide ample references for the reader to get in-depth information on these topics.

Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.

Guilt-by-association codifies the empirical observation that a gene's function is informed by its neighborhood in a biological network. This would imply that when a gene's network context is altered, for instance in disease condition, so could be the gene's function. Although context-specific changes in biological networks have been explored, the potential changes they may induce on the functional roles of genes are yet to be characterized. Here we analyze, for the first time, the network-induced potential functional changes in breast cancer. Using transcriptomic samples for 1047 breast tumors and 110 healthy breast tissues from TCGA, we derive sample-specific protein interaction networks and assign sample-specific functions to genes via a diffusion strategy. Testing for significant changes in the inferred functions between normal and cancer samples, we find several functions to have significantly gained or lost genes in cancer, not due to differential expression of genes known to perform the function, but rather due to changes in the network topology. Our predicted functional changes are supported by mutational and copy number profiles in breast cancers. Our diffusion-based functional assignment provides a novel characterization of a tumor that is complementary to the standard approach based on functional annotation alone. Importantly, this characterization is effective in predicting patient survival, as well as in predicting several known histopathological subtypes of breast cancer.

MicroRNAs (miRNA) are regulatory genes that target and repress other RNA molecules via sequence-specific binding. Several biological processes are regulated across many organisms by evolutionarily conserved miRNAs. Plants and invertebrates employ their miRNA in defense against viruses by targeting and degrading viral products. Viruses also encode miRNAs and there is evidence to suggest that virus-encoded miRNAs target specific host genes and pathways that may be beneficial for their infectivity and/or proliferation. However, it is not clear whether there are general patterns underlying cellular targets of viral miRNAs.

The Cancer/Testis Antigens (CTAs) are a heterogeneous group of proteins whose expression is typically restricted to the testis. However, they are aberrantly expressed in most cancers that have been examined to date. Broadly speaking, the CTAs can be divided into two groups: the CTX antigens that are encoded by the X-linked genes and the non-X CT antigens that are encoded by the autosomes. Unlike the non-X CTAs, the CTX antigens form clusters of closely related gene families and their expression is frequently associated with advanced disease with poorer prognosis. Regardless however, the mechanism(s) underlying their selective derepression and stage-specific expression in cancer remain poorly understood, although promoter DNA demethylation is believed to be the major driver.

The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional sequences of genes and related genomic elements known to play important roles in neurotransmitter release would also be conserved.

Gene duplication is a major driver of evolutionary innovation as it allows for an organism to elaborate its existing biological functions via specialization or diversification of initially redundant gene paralogs. Gene function can diversify in several ways. Transcription factor gene paralogs in particular, can diversify either by changes in their tissue-specific expression pattern or by changes in the DNA binding site motif recognized by their protein product, which in turn alters their gene targets. The relationship between these two modes of functional diversification of transcription factor paralogs has not been previously investigated, and is essential for understanding adaptive evolution of transcription factor gene families.

Malaria-causing Plasmodium species exhibit marked differences including host choice and preference for invading particular cell types. The genetic bases of phenotypic differences between parasites can be understood, in part, by investigating constraints on gene expression and genic sequences, both coding and regulatory.