An RNA-informed dosage sensitivity map reflects the intrinsic functional nature of genes.

Understanding dosage sensitivity or why Mendelian diseases have dominant vs. recessive modes of inheritance is crucial for uncovering the etiology of human disease. Previous knowledge of dosage sensitivity is mainly based on observations of rare loss-of-function mutations or copy number changes, which are underpowered due to ultra rareness of such variants. Thus, the functional underpinnings of dosage constraint remain elusive. In this study, we aim to systematically quantify dosage perturbations from cis-regulatory variants in the general population to yield a tissue-specific dosage constraint map of genes and further explore their underlying functional logic. We reveal an inherent divergence of dosage constraints in genes by functional categories with signaling genes (transcription factors, protein kinases, ion channels, and cellular machinery) being dosage sensitive, while effector genes (transporters, metabolic enzymes, cytokines, and receptors) are generally dosage resilient. Instead of being a metric of functional dispensability, we show that dosage constraint reflects underlying homeostatic constraints arising from negative feedback. Finally, we employ machine learning to integrate DNA and RNA metrics to generate a comprehensive, tissue-specific map of dosage sensitivity (MoDs) for autosomal genes.

ebGSEA: an improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies.

MOTIVATION: The biological interpretation of differentially methylated sites derived from Epigenome-Wide-Association Studies (EWAS) remains a significant challenge. Gene Set Enrichment Analysis (GSEA) is a general tool to aid biological interpretation, yet its correct and unbiased implementation in the EWAS context is difficult due to the differential probe representation of Illumina Infinium DNA methylation beadchips. RESULTS: We present a novel GSEA method, called ebGSEA, which ranks genes, not CpGs, according to the overall level of differential methylation, as assessed using all the probes mapping to the given gene. Applied on simulated and real EWAS data, we show how ebGSEA may exhibit higher sensitivity and specificity than the current state-of-the-art, whilst also avoiding differential probe representation bias. Thus, ebGSEA will be a useful additional tool to aid the interpretation of EWAS data. AVAILABILITY AND IMPLEMENTATION: ebGSEA is available from https://github.com/aet21/ebGSEA, and has been incorporated into the ChAMP Bioconductor package (https://www.bioconductor.org). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EpiDISH web server: Epigenetic Dissection of Intra-Sample-Heterogeneity with online GUI.

SUMMARY: It is well recognized that cell-type heterogeneity hampers the interpretation of Epigenome-Wide Association Studies (EWAS). Many tools have emerged to address this issue, including several R/Bioconductor packages that infer cell-type composition. Here we present a web application for cell-type deconvolution, which offers the functionality of our EpiDISH Bioconductor/R package in a user-friendly GUI environment. Users can upload their data to infer cell-type composition and differentially methylated cytosines in individual cell-types (DMCTs) for a range of different tissues. AVAILABILITY AND IMPLEMENTATION: EpiDISH web server is implemented with Shiny in R, and is freely available at https://www.biosino.org/EpiDISH/.

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes.

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

The role of Sp140 revealed in IgE and mast cell responses in Collaborative Cross mice.

Mouse IgE and mast cell (MC) functions have been studied primarily using inbred strains. Here, we (a) identified effects of genetic background on mouse IgE and MC phenotypes, (b) defined the suitability of various strains for studying IgE and MC functions, and (c) began to study potentially novel genes involved in such functions. We screened 47 Collaborative Cross (CC) strains, as well as C57BL/6J and BALB/cJ mice, for strength of passive cutaneous anaphylaxis (PCA) and responses to the intestinal parasite Strongyloides venezuelensis (S.v.). CC mice exhibited a diversity in PCA strength and S.v. responses. Among strains tested, C57BL/6J and CC027 mice showed, respectively, moderate and uniquely potent MC activity. Quantitative trait locus analysis and RNA sequencing of BM-derived cultured MCs (BMCMCs) from CC027 mice suggested Sp140 as a candidate gene for MC activation. siRNA-mediated knock-down of Sp140 in BMCMCs decreased IgE-dependent histamine release and cytokine production. Our results demonstrated marked variations in IgE and MC activity in vivo, and in responses to S.v., across CC strains. C57BL/6J and CC027 represent useful models for studying MC functions. Additionally, we identified Sp140 as a gene that contributes to IgE-dependent MC activation.

A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix.

AIM: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. MATERIALS & METHODS: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. RESULTS: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. CONCLUSION: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues.