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.

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.

Blood cell development in Drosophila shares significant similarities with vertebrate. The conservation ranges from biphasic mode of hematopoiesis to signaling molecules crucial for progenitor cell formation, maintenance, and differentiation. Primitive hematopoiesis in Drosophila ensues in embryonic head mesoderm, whereas definitive hematopoiesis happens in larval hematopoietic organ, the lymph gland. This organ, with the onset of pupation, ruptures to release hemocytes into circulation. It is believed that the adult lacks a hematopoietic organ and survives on the contribution of both embryonic and larval hematopoiesis. However, our studies revealed a surge of blood cell development in the dorsal abdominal hemocyte clusters of adult fly. These active hematopoietic hubs are capable of blood cell specification and can respond to bacterial challenges. The presence of progenitors and differentiated hemocytes embedded in a functional network of Laminin A and Pericardin within this hematopoietic hub projects it as a simple version of the vertebrate bone marrow.

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.

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 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.

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.

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 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.

Intron diversity facilitates regulated gene expression and alternative splicing. Spliceosomes excise introns after recognizing their splicing signals: the 5'-splice site (5'ss), branchpoint (BP) and 3'-splice site (3'ss). The latter two signals are recognized by U2 small nuclear ribonucleoprotein (snRNP) and its accessory factors (U2AFs), but longer spacings between them result in weaker splicing. Here, we show that excision of introns with a BP-distant 3'ss (e.g. rap1 intron 2) requires the ubiquitin-fold-activated splicing regulator Sde2 in Schizosaccharomyces pombe. By monitoring splicing-specific ura4 reporters in a collection of S. pombe mutants, Cay1 and Tls1 were identified as additional regulators of this process. The role of Sde2, Cay1 and Tls1 was further confirmed by increasing BP-3'ss spacings in a canonical tho5 intron. We also examined BP-distant exons spliced independently of these factors and observed that RNA secondary structures possibly bridged the gap between the two signals. These proteins may guide the 3'ss towards the spliceosome's catalytic centre by folding the RNA between the BP and 3'ss. Orthologues of Sde2, Cay1 and Tls1, although missing in the intron-poor Saccharomyces cerevisiae, are present in intron-rich eukaryotes, including humans. This type of intron-specific pre-mRNA splicing appears to have evolved for regulated gene expression and alternative splicing of key heterochromatin factors.

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.

Fatty acid amide hydrolase (FAAH) degrades the endocannabinoid anandamide. A polymorphism in FAAH (FAAH C385A) reduces FAAH expression, increases anandamide levels, and increases the risk of obesity. Nevertheless, some studies have found no association between FAAH C385A and obesity. We investigated whether the environmental context governs the impact of FAAH C385A on metabolic outcomes. Using a C385A knock-in mouse model, we found that FAAH A/A mice are more susceptible to glucocorticoid-induced hyperphagia, weight gain, and activation of hypothalamic AMP-activated protein kinase (AMPK). AMPK inhibition occluded the amplified hyperphagic response to glucocorticoids in FAAH A/A mice. FAAH knockdown exclusively in agouti-related protein (AgRP) neurons mimicked the exaggerated feeding response of FAAH A/A mice to glucocorticoids. FAAH A/A mice likewise presented exaggerated orexigenic responses to ghrelin, while FAAH knockdown in AgRP neurons blunted leptin anorectic responses. Together, the FAAH A/A genotype amplifies orexigenic responses and decreases anorexigenic responses, providing a putative mechanism explaining the diverging human findings.

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 hippocampus (HPC), traditionally known for its role in learning and memory, has emerged as a controller of food intake. While prior studies primarily associated the HPC with food intake inhibition, recent research suggests a critical role in appetitive processes. We hypothesized that orexigenic HPC neurons differentially respond to fats and/or sugars, potent natural reinforcers that contribute to obesity development. Results uncover previously-unrecognized, spatially-distinct neuronal ensembles within the dorsal HPC (dHPC) that are responsive to separate nutrient signals originating from the gut. Using activity-dependent genetic capture of nutrient-responsive HPC neurons, we demonstrate a causal role of both populations in promoting nutrient-specific preference through different mechanisms. Sugar-responsive neurons encode an appetitive spatial memory engram for meal location, whereas fat-responsive neurons selectively enhance the preference and motivation for fat intake. Collectively, these findings uncover a neural basis for the exquisite specificity in processing macronutrient signals from a meal that shape dietary choices.

We investigated the lateralization of gut-innervating vagal sensory neurons and their roles in feeding behavior. Using genetic, anatomical, and behavioral analyses, we discovered a subset of highly lateralized vagal sensory neurons with distinct sensory responses to intestinal stimuli. Our results demonstrated that left vagal sensory neurons (LNG) are crucial for distension-induced satiety, while right vagal sensory neurons (RNG) mediate preference for nutritive foods. Furthermore, these lateralized neurons engage different central circuits, with LNG neurons recruiting brain regions associated with energy balance and RNG neurons activating areas related to salience, memory, and reward. Altogether, our findings unveil the diverse roles of asymmetrical gut-vagal-brain circuits in feeding behavior, offering new insights for potential therapeutic interventions targeting vagal nerve stimulation in metabolic and neuropsychiatric diseases.

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.

Diets deficient in protein often increase food consumption, body weight and fat mass; however, the underlying mechanisms remain poorly understood. We compared the effects of diets varying in protein concentrations on energy balance in obesity-prone rats. We demonstrate that protein-free (0% protein calories) diets decreased energy intake and increased energy expenditure, very low protein (5% protein) diets increased energy intake and expenditure, whereas moderately low protein (10% protein) diets increased energy intake without altering expenditure, relative to control diet (15% protein). These diet-induced alterations in energy expenditure are in part mediated through enhanced serotonergic and β-adrenergic signaling coupled with upregulation of key thermogenic markers in brown fat and skeletal muscle. The protein-free and very low protein diets decreased plasma concentrations of multiple essential amino acids, anorexigenic and metabolic hormones, but these diets increased the tissue expression and plasma concentrations of fibroblast growth factor-21. Protein-free and very low protein diets induced fatty liver, reduced energy digestibility, and decreased lean mass and body weight that persisted beyond the restriction period. In contrast, moderately low protein diets promoted gain in body weight and adiposity following the period of protein restriction. Together, our findings demonstrate that low protein diets produce divergent effects on energy balance.