CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Aberrant miR-29 is a predictive feature of severe phenotypes in pediatric Crohn's disease.

Crohn's disease (CD) is a chronic inflammatory gut disorder. Molecular mechanisms underlying the clinical heterogeneity of CD remain poorly understood. MicroRNAs (miRNAs) are important regulators of gut physiology, and several have been implicated in the pathogenesis of adult CD. However, there is a dearth of large-scale miRNA studies for pediatric CD. We hypothesized that specific miRNAs uniquely mark pediatric CD. We performed small RNA-Seq of patient-matched colon and ileum biopsies from treatment-naive pediatric patients with CD (n = 169) and a control cohort (n = 108). Comprehensive miRNA analysis revealed 58 miRNAs altered in pediatric CD. Notably, multinomial logistic regression analysis revealed that index levels of ileal miR-29 are strongly predictive of severe inflammation and stricturing. Transcriptomic analyses of transgenic mice overexpressing miR-29 show a significant reduction of the tight junction protein gene Pmp22 and classic Paneth cell markers. The dramatic loss of Paneth cells was confirmed by histologic assays. Moreover, we found that pediatric patients with CD with elevated miR-29 exhibit significantly lower Paneth cell counts, increased inflammation scores, and reduced levels of PMP22. These findings strongly indicate that miR-29 upregulation is a distinguishing feature of pediatric CD, highly predictive of severe phenotypes, and associated with inflammation and Paneth cell loss.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning.

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

ROCCO: a robust method for detection of open chromatin via convex optimization.

MOTIVATION: Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively. RESULTS: We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a stand-alone binary utility using pip/PyPI.

Toxicological and pharmacokinetic properties of sucralose-6-acetate and its parent sucralose: in vitro screening assays.

The purpose of this study was to determine the toxicological and pharmacokinetic properties of sucralose-6-acetate, a structural analog of the artificial sweetener sucralose. Sucralose-6-acetate is an intermediate and impurity in the manufacture of sucralose, and recent commercial sucralose samples were found to contain up to 0.67% sucralose-6-acetate. Studies in a rodent model found that sucralose-6-acetate is also present in fecal samples with levels up to 10% relative to sucralose which suggest that sucralose is also acetylated in the intestines. A MultiFlow® assay, a high-throughput genotoxicity screening tool, and a micronucleus (MN) test that detects cytogenetic damage both indicated that sucralose-6-acetate is genotoxic. The mechanism of action was classified as clastogenic (produces DNA strand breaks) using the MultiFlow® assay. The amount of sucralose-6-acetate in a single daily sucralose-sweetened drink might far exceed the threshold of toxicological concern for genotoxicity (TTCgenotox) of 0.15 µg/person/day. The RepliGut® System was employed to expose human intestinal epithelium to sucralose-6-acetate and sucralose, and an RNA-seq analysis was performed to determine gene expression induced by these exposures. Sucralose-6-acetate significantly increased the expression of genes associated with inflammation, oxidative stress, and cancer with greatest expression for the metallothionein 1 G gene (MT1G). Measurements of transepithelial electrical resistance (TEER) and permeability in human transverse colon epithelium indicated that sucralose-6-acetate and sucralose both impaired intestinal barrier integrity. Sucralose-6-acetate also inhibited two members of the cytochrome P450 family (CYP1A2 and CYP2C19). Overall, the toxicological and pharmacokinetic findings for sucralose-6-acetate raise significant health concerns regarding the safety and regulatory status of sucralose itself.

Prdm6 drives ductus arteriosus closure by promoting ductus arteriosus smooth muscle cell identity and contractility.

Based upon our demonstration that the smooth muscle cell-selective (SMC-selective) putative methyltransferase, Prdm6, interacts with myocardin-related transcription factor-A, we examined Prdm6's role in SMCs in vivo using cell type-specific knockout mouse models. Although SMC-specific depletion of Prdm6 in adult mice was well tolerated, Prdm6 depletion in Wnt1-expressing cells during development resulted in perinatal lethality and a completely penetrant patent ductus arteriosus (DA) phenotype. Lineage tracing experiments in Wnt1Cre2 Prdm6fl/fl ROSA26LacZ mice revealed normal neural crest-derived SMC investment of the outflow tract. In contrast, myography measurements on DA segments isolated from E18.5 embryos indicated that Prdm6 depletion significantly reduced DA tone and contractility. RNA-Seq analyses on DA and ascending aorta samples at E18.5 identified a DA-enriched gene program that included many SMC-selective contractile associated proteins that was downregulated by Prdm6 depletion. Chromatin immunoprecipitation-sequencing experiments in outflow tract SMCs demonstrated that 50% of the genes Prdm6 depletion altered contained Prdm6 binding sites. Finally, using several genome-wide data sets, we identified an SMC-selective enhancer within the Prdm6 third intron that exhibited allele-specific activity, providing evidence that rs17149944 may be the causal SNP for a cardiovascular disease GWAS locus identified within the human PRDM6 gene.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an inference framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. We leverage ML-based network inference to find networks that capture the strength of regulatory interactions. Our model first pinpoints a subset of genes, termed variational, whose expression variabilities typify the differences in network connectivity between the control and perturbed data. Variational genes, by being differentially expressed themselves or possessing differentially expressed neighbor genes, capture gene expression variability. CoVar then creates subnetworks comprising variational genes and their strongly connected neighbor genes and identifies core genes central to these subnetworks that influence the bulk of the variational activity. Through the analysis of yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar identifies key genes not found through independent differential expression analysis.

BET Protein Inhibition Regulates Macrophage Chromatin Accessibility and Microbiota-Dependent Colitis.

Introduction: In colitis, macrophage functionality is altered compared to normal homeostatic conditions. Loss of IL-10 signaling results in an inappropriate chronic inflammatory response to bacterial stimulation. It remains unknown if inhibition of bromodomain and extra-terminal domain (BET) proteins alters usage of DNA regulatory elements responsible for driving inflammatory gene expression. We determined if the BET inhibitor, (+)-JQ1, could suppress inflammatory activation of macrophages in Il10-/- mice. Methods: We performed ATAC-seq and RNA-seq on Il10-/- bone marrow-derived macrophages (BMDMs) cultured in the presence and absence of lipopolysaccharide (LPS) with and without treatment with (+)-JQ1 and evaluated changes in chromatin accessibility and gene expression. Germ-free Il10-/- mice were treated with (+)-JQ1, colonized with fecal slurries and underwent histological and molecular evaluation 14-days post colonization. Results: Treatment with (+)-JQ1 suppressed LPS-induced changes in chromatin at distal regulatory elements associated with inflammatory genes, particularly in regions that contain motifs for AP-1 and IRF transcription factors. This resulted in attenuation of inflammatory gene expression. Treatment with (+)-JQ1 in vivo resulted in a mild reduction in colitis severity as compared with vehicle-treated mice. Conclusion: We identified the mechanism of action associated with a new class of compounds that may mitigate aberrant macrophage responses to bacteria in colitis.

Single-Cell Analysis Reveals Unexpected Cellular Changes and Transposon Expression Signatures in the Colonic Epithelium of Treatment-Naïve Adult Crohn's Disease Patients.

BACKGROUND & AIMS: The intestinal barrier comprises a monolayer of specialized intestinal epithelial cells (IECs) that are critical in maintaining mucosal homeostasis. Dysfunction within various IEC fractions can alter intestinal permeability in a genetically susceptible host, resulting in a chronic and debilitating condition known as Crohn's disease (CD). Defining the molecular changes in each IEC type in CD will contribute to an improved understanding of the pathogenic processes and the identification of cell type-specific therapeutic targets. We performed, at single-cell resolution, a direct comparison of the colonic epithelial cellular and molecular landscape between treatment-naïve adult CD and non-inflammatory bowel disease control patients. METHODS: Colonic epithelial-enriched, single-cell sequencing from treatment-naïve adult CD and non-inflammatory bowel disease patients was investigated to identify disease-induced differences in IEC types. RESULTS: Our analysis showed that in CD patients there is a significant skew in the colonic epithelial cellular distribution away from canonical LGR5+ stem cells, located at the crypt bottom, and toward one specific subtype of mature colonocytes, located at the crypt top. Further analysis showed unique changes to gene expression programs in every major cell type, including a previously undescribed suppression in CD of most enteroendocrine driver genes as well as L-cell markers including GCG. We also dissect an incompletely understood SPIB+ cell cluster, revealing at least 4 subclusters that likely represent different stages of a maturational trajectory. One of these SPIB+ subclusters expresses crypt-top colonocyte markers and is up-regulated significantly in CD, whereas another subcluster strongly expresses and stains positive for lysozyme (albeit no other canonical Paneth cell marker), which surprisingly is greatly reduced in expression in CD. In addition, we also discovered transposable element markers of colonic epithelial cell types as well as transposable element families that are altered significantly in CD in a cell type-specific manner. Finally, through integration with data from genome-wide association studies, we show that genes implicated in CD risk show heretofore unknown cell type-specific patterns of aberrant expression in CD, providing unprecedented insight into the potential biological functions of these genes. CONCLUSIONS: Single-cell analysis shows a number of unexpected cellular and molecular features, including transposable element expression signatures, in the colonic epithelium of treatment-naïve adult CD.

Subcutaneous adipose tissue splice quantitative trait loci reveal differences in isoform usage associated with cardiometabolic traits.

Alternate splicing events can create isoforms that alter gene function, and genetic variants associated with alternate gene isoforms may reveal molecular mechanisms of disease. We used subcutaneous adipose tissue of 426 Finnish men from the METSIM study and identified splice junction quantitative trait loci (sQTLs) for 6,077 splice junctions (FDR < 1%). In the same individuals, we detected expression QTLs (eQTLs) for 59,443 exons and 15,397 genes (FDR < 1%). We identified 595 genes with an sQTL and exon eQTL but no gene eQTL, which could indicate potential isoform differences. Of the significant sQTL signals, 2,114 (39.8%) included at least one proxy variant (linkage disequilibrium r2 > 0.8) located within an intron spanned by the splice junction. We identified 203 sQTLs that colocalized with 141 genome-wide association study (GWAS) signals for cardiometabolic traits, including 25 signals for lipid traits, 24 signals for body mass index (BMI), and 12 signals for waist-hip ratio adjusted for BMI. Among all 141 GWAS signals colocalized with an sQTL, we detected 26 that also colocalized with an exon eQTL for an exon skipped by the sQTL splice junction. At a GWAS signal for high-density lipoprotein cholesterol colocalized with an NR1H3 sQTL splice junction, we show that the alternative splice product encodes an NR1H3 transcription factor that lacks a DNA binding domain and fails to activate transcription. Together, these results detect splicing events and candidate mechanisms that may contribute to gene function at GWAS loci.

Integrative analysis reveals mouse strain-dependent responses to acute ozone exposure associated with airway macrophage transcriptional activity.

Acute ozone (O3) exposure is associated with multiple adverse cardiorespiratory outcomes, the severity of which varies across individuals in human populations and inbred mouse strains. However, molecular determinants of response, including susceptibility biomarkers that distinguish who will develop severe injury and inflammation, are not well characterized. We and others have demonstrated that airway macrophages (AMs) are an important resident immune cell type that are functionally and transcriptionally responsive to O3 inhalation. Here, we sought to explore influences of strain, exposure, and strain-by-O3 exposure interactions on AM gene expression and identify transcriptional correlates of O3-induced inflammation and injury across six mouse strains, including five Collaborative Cross (CC) strains. We exposed adult mice of both sexes to filtered air (FA) or 2 ppm O3 for 3 h and measured inflammatory and injury parameters 21 h later. Mice exposed to O3 developed airway neutrophilia and lung injury with strain-dependent severity. In AMs, we identified a common core O3 transcriptional response signature across all strains, as well as a set of genes exhibiting strain-by-O3 exposure interactions. In particular, a prominent gene expression contrast emerged between a low- (CC017/Unc) and high-responding (CC003/Unc) strain, as reflected by cellular inflammation and injury. Further inspection indicated that differences in their baseline gene expression and chromatin accessibility profiles likely contribute to their divergent post-O3 exposure transcriptional responses. Together, these results suggest that aspects of O3-induced respiratory responses are mediated through altered AM transcriptional signatures and further confirm the importance of gene-environment interactions in mediating differential responsiveness to environmental agents.

A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.

Inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract. IBD patient conditions and treatments, such as with immunosuppressants, may result in a higher risk of viral and bacterial infection and more severe outcomes of infections. The effect of the clinical and demographic factors on the prognosis of COVID-19 among IBD patients is still a significant area of investigation. The lack of available data on a large set of COVID-19 infected IBD patients has hindered progress. To circumvent this lack of large patient data, we present a random sampling approach to generate clinical COVID-19 outcomes (outpatient management, hospitalized and recovered, and hospitalized and deceased) on 20,000 IBD patients modeled on reported summary statistics obtained from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD), an international database to monitor and report on outcomes of COVID-19 occurring in IBD patients. We apply machine learning approaches to perform a comprehensive analysis of the primary and secondary covariates to predict COVID-19 outcome in IBD patients. Our analysis reveals that age, medication usage and the number of comorbidities are the primary covariates, while IBD severity, smoking history, gender and IBD subtype (CD or UC) are key secondary features. In particular, elderly male patients with ulcerative colitis, several preexisting conditions, and who smoke comprise a highly vulnerable IBD population. Moreover, treatment with 5-ASAs (sulfasalazine/mesalamine) shows a high association with COVID-19/IBD mortality. Supervised machine learning that considers age, number of comorbidities and medication usage can predict COVID-19/IBD outcomes with approximately 70% accuracy. We explore the challenge of drawing demographic inferences from existing COVID-19/IBD data. Overall, there are fewer IBD case reports from US states with poor health ranking hindering these analyses. Generation of patient characteristics based on known summary statistics allows for increased power to detect IBD factors leading to variable COVID-19 outcomes. There is under-reporting of COVID-19 in IBD patients from US states with poor health ranking, underpinning the perils of using the repository to derive demographic information.

Increased colonic expression of ACE2 associates with poor prognosis in Crohn's disease.

The host receptor for SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2), is highly expressed in small intestine. Our aim was to study colonic ACE2 expression in Crohn's disease (CD) and non-inflammatory bowel disease (non-IBD) controls. We hypothesized that the colonic expression levels of ACE2 impacts CD course. We examined the expression of colonic ACE2 in 67 adult CD and 14 NIBD control patients using RNA-seq and quantitative (q) RT-PCR. We validated ACE2 protein expression and localization in formalin-fixed, paraffin-embedded matched colon and ileal tissues using immunohistochemistry. The impact of increased ACE2 expression in CD for the risk of surgery was evaluated by a multivariate regression analysis and a Kaplan-Meier estimator. To provide critical support for the generality of our findings, we analyzed previously published RNA-seq data from two large independent cohorts of CD patients. Colonic ACE2 expression was significantly higher in a subset of adult CD patients which was defined as the ACE2-high CD subset. IHC in a sampling of ACE2-high CD patients confirmed high ACE2 protein expression in the colon and ileum compared to ACE2-low CD and NIBD patients. Notably, we found that ACE2-high CD patients are significantly more likely to undergo surgery within 5 years of CD diagnosis, and a Cox regression analysis found that high ACE2 levels is an independent risk factor for surgery (OR 2.17; 95% CI, 1.10-4.26; p = 0.025). Increased intestinal expression of ACE2 is associated with deteriorated clinical outcomes in CD patients. These data point to the need for molecular stratification that can impact CD disease-related outcomes.

Increased Colonic Expression of ACE2 Associates with Poor Prognosis in Crohn's disease.

BACKGROUND AND AIMS: The host receptor for SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2), is highly expressed in small intestine. Our aim was to study colonic ACE2 expression in Crohn's disease (CD) and non-inflammatory bowel disease (non-IBD) controls. We hypothesized that the colonic expression levels of ACE2 impacts CD course. METHODS: We examined the expression of colon ACE2 using RNA-seq and quantitative (q) RT-PCR from 69 adult CD and 14 NIBD control patients. In a subset of this cohort we validated ACE2 protein expression and localization in formalin-fixed, paraffin-embedded matched colon and ileal tissues using immunohistochemistry. The impact of increased ACE2 expression in CD for the risk of surgery was evaluated by a multivariate regression analysis and a Kaplan-Meier estimator. To provide critical support for the generality of our findings, we analyzed previously published RNA-seq data from two large independent cohorts of CD patients. RESULTS: Colonic ACE2 expression was significantly higher in a subset of adult CD patients (ACE2-high CD). IHC in a sampling of ACE2-high CD patients confirmed high ACE2 protein expression in the colon and ileum compared to ACE2-low CD and NIBD patients. Notably, we found that ACE2-high CD patients are significantly more likely to undergo surgery within 5 years of diagnosis, with a Cox regression analysis finding that high ACE2 levels is an independent risk factor (OR 2.18; 95%CI, 1.05-4.55; p=0.037). CONCLUSION: Increased intestinal expression of ACE2 is associated with deteriorated clinical outcomes in CD patients. These data point to the need for molecular stratification that may impact CD disease-related outcomes.

Decreased Colonic Activin Receptor-Like Kinase 1 Disrupts Epithelial Barrier Integrity in Patients With Crohn's Disease.

BACKGROUND & AIMS: Intestinal epithelial cell (IEC) barrier dysfunction is critical to the development of Crohn's disease (CD). However, the mechanism is understudied. We recently reported increased microRNA-31-5p (miR-31-5p) expression in colonic IECs of CD patients, but downstream targets and functional consequences are unknown. METHODS: microRNA-31-5p target genes were identified by integrative analysis of RNA- and small RNA-sequencing data from colonic mucosa and confirmed by quantitative polymerase chain reaction in colonic IECs. Functional characterization of activin receptor-like kinase 1 (ACVRL1 or ALK1) in IECs was performed ex vivo using 2-dimensional cultured human primary colonic IECs. The impact of altered colonic ALK1 signaling in CD for the risk of surgery and endoscopic relapse was evaluated by a multivariate regression analysis and a Kaplan-Meier estimator. RESULTS: ALK1 was identified as a target of miR-31-5p in colonic IECs of CD patients and confirmed using a 3'-untranslated region reporter assay. Activation of ALK1 restricted the proliferation of colonic IECs in a 5-ethynyl-2-deoxyuridine proliferation assay and down-regulated the expression of stemness-related genes. Activated ALK1 signaling increased colonic IEC differentiation toward colonocytes. Down-regulated ALK1 signaling was associated with increased stemness and decreased colonocyte-specific marker expression in colonic IECs of CD patients compared with healthy controls. Activation of ALK1 enhanced epithelial barrier integrity in a transepithelial electrical resistance permeability assay. Lower colonic ALK1 expression was identified as an independent risk factor for surgery and was associated with a higher risk of endoscopic relapse in CD patients. CONCLUSIONS: Decreased colonic ALK1 disrupted colonic IEC barrier integrity and was associated with poor clinical outcomes in CD patients.

Crohn's Disease Differentially Affects Region-Specific Composition and Aerotolerance Profiles of Mucosally Adherent Bacteria.

BACKGROUND: The intestinal microbiota play a key role in the onset, progression, and recurrence of Crohn disease (CD). Most microbiome studies assay fecal material, which does not provide region-specific information on mucosally adherent bacteria that directly interact with host systems. Changes in luminal oxygen have been proposed as a contributor to CD dybiosis. METHODS: The authors generated 16S rRNA data using colonic and ileal mucosal bacteria from patients with CD and without inflammatory bowel disease. We developed profiles reflecting bacterial abundance within defined aerotolerance categories. Bacterial diversity, composition, and aerotolerance profiles were compared across intestinal regions and disease phenotypes. RESULTS: Bacterial diversity decreased in CD in both the ileum and the colon. Aerotolerance profiles significantly differed between intestinal segments in patients without inflammatory bowel disease, although both were dominated by obligate anaerobes, as expected. In CD, high relative levels of obligate anaerobes were maintained in the colon and increased in the ileum. Relative abundances of similar and distinct taxa were altered in colon and ileum. Notably, several obligate anaerobes, such as Bacteroides fragilis, dramatically increased in CD in one or both intestinal segments, although specific increasing taxa varied across patients. Increased abundance of taxa from the Proteobacteria phylum was found only in the ileum. Bacterial diversity was significantly reduced in resected tissues of patients who developed postoperative disease recurrence across 2 independent cohorts, with common lower abundance of bacteria from the Bacteroides, Streptococcus, and Blautia genera. CONCLUSIONS: Mucosally adherent bacteria in the colon and ileum show distinct alterations in CD that provide additional insights not revealed in fecal material.

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation.

Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Redefining the IBDs using genome-scale molecular phenotyping.

The IBDs, Crohn's disease and ulcerative colitis, are chronic inflammatory conditions of the gastrointestinal tract resulting from an aberrant immune response to enteric microbiota in genetically susceptible individuals. Disease presentation and progression within and across IBDs, especially Crohn's disease, are highly heterogeneous in location, severity of inflammation and other phenotypes. Current clinical classifications fail to accurately predict disease course and response to therapies. Genome-wide association studies have identified >240 loci that confer risk of IBD, but the clinical utility of these findings remains unclear, and mechanisms by which the genetic variants contribute to disease are largely unknown. In the past 5 years, the profiling of genome-wide gene expression, epigenomic features and gut microbiota composition in intestinal tissue and faecal samples has uncovered distinct molecular signatures that define IBD subtypes, including within Crohn's disease and ulcerative colitis. In this Review, we summarize studies in both adult and paediatric patients that have identified different IBD subtypes, which in some cases have been associated with distinct clinical phenotypes. We posit that genome-scale molecular phenotyping in large cohorts holds great promise not only to further our understanding of the diverse molecular causes of IBD but also for improving clinical trial design to develop more personalized disease management and treatment.