Adipogenic and SWAT cells separate from a common progenitor in human brown and white adipose depots.

Adipocyte function is a major determinant of metabolic disease, warranting investigations of regulating mechanisms. We show at single-cell resolution that progenitor cells from four human brown and white adipose depots separate into two main cell fates, an adipogenic and a structural branch, developing from a common progenitor. The adipogenic gene signature contains mitochondrial activity genes, and associates with genome-wide association study traits for fat distribution. Based on an extracellular matrix and developmental gene signature, we name the structural branch of cells structural Wnt-regulated adipose tissue-resident (SWAT) cells. When stripped from adipogenic cells, SWAT cells display a multipotent phenotype by reverting towards progenitor state or differentiating into new adipogenic cells, dependent on media. Label transfer algorithms recapitulate the cell types in human adipose tissue datasets. In conclusion, we provide a differentiation map of human adipocytes and define the multipotent SWAT cell, providing a new perspective on adipose tissue regulation.

Single-cell transcriptome and cell type-specific molecular pathways of human non-alcoholic steatohepatitis.

The aim of this study is to characterize cell type-specific transcriptional signatures in non-alcoholic steatohepatitis (NASH) to improve our understanding of the disease. We performed single-cell RNA sequencing on liver biopsies from 10 patients with NASH. We applied weighted gene co-expression network analysis and validated our findings using a publicly available RNA sequencing data set derived from 160 patients with non-alcoholic fatty liver disease (NAFLD) and 24 controls with normal liver histology. Our study provides a comprehensive single-cell analysis of NASH pathology in humans, describing 19,627 single-cell transcriptomes from biopsy-proven NASH patients. Our data suggest that the previous notion of "NASH-associated macrophages" can be explained by an up-regulation of normally existing subpopulations of liver macrophages. Similarly, we describe two distinct populations of activated hepatic stellate cells, associated with the level of fibrosis. Finally, we find that the expression of several circulating markers of NAFLD are co-regulated in hepatocytes together with predicted effector genes from NAFLD genome-wide association studies (GWAS), coupled to abnormalities in the complement system. In sum, our single-cell transcriptomic data set provides insights into novel cell type-specific and general biological processes associated with inflammation and fibrosis, emphasizing the importance of studying cell type-specific biological processes in human NASH.

Characterization of the COPD alveolar niche using single-cell RNA sequencing.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide, however our understanding of cell specific mechanisms underlying COPD pathobiology remains incomplete. Here, we analyze single-cell RNA sequencing profiles of explanted lung tissue from subjects with advanced COPD or control lungs, and we validate findings using single-cell RNA sequencing of lungs from mice exposed to 10 months of cigarette smoke, RNA sequencing of isolated human alveolar epithelial cells, functional in vitro models, and in situ hybridization and immunostaining of human lung tissue samples. We identify a subpopulation of alveolar epithelial type II cells with transcriptional evidence for aberrant cellular metabolism and reduced cellular stress tolerance in COPD. Using transcriptomic network analyses, we predict capillary endothelial cells are inflamed in COPD, particularly through increased CXCL-motif chemokine signaling. Finally, we detect a high-metallothionein expressing macrophage subpopulation enriched in advanced COPD. Collectively, these findings highlight cell-specific mechanisms involved in the pathobiology of advanced COPD.

Genetic insights into biological mechanisms governing human ovarian ageing.

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

Genetic mapping of etiologic brain cell types for obesity.

The underlying cell types mediating predisposition to obesity remain largely obscure. Here, we integrated recently published single-cell RNA-sequencing (scRNA-seq) data from 727 peripheral and nervous system cell types spanning 17 mouse organs with body mass index (BMI) genome-wide association study (GWAS) data from >457,000 individuals. Developing a novel strategy for integrating scRNA-seq data with GWAS data, we identified 26, exclusively neuronal, cell types from the hypothalamus, subthalamus, midbrain, hippocampus, thalamus, cortex, pons, medulla, pallidum that were significantly enriched for BMI heritability (p<1.6×10-4). Using genes harboring coding mutations associated with obesity, we replicated midbrain cell types from the anterior pretectal nucleus and periaqueductal gray (p<1.2×10-4). Together, our results suggest that brain nuclei regulating integration of sensory stimuli, learning and memory are likely to play a key role in obesity and provide testable hypotheses for mechanistic follow-up studies.

scVAE: variational auto-encoders for single-cell gene expression data.

MOTIVATION: Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations. RESULTS: We propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq datasets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types. AVAILABILITY AND IMPLEMENTATION: Our method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://github.com/scvae/scvae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Publisher Correction: Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes.

In the originally published version of this Article, the affiliation details for Santi González, Jian'an Luan and Claudia Langenberg were inadvertently omitted. Santi González should have been affiliated with 'Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology, 08034 Barcelona, Spain', and Jian'an Luan and Claudia Langenberg should have been affiliated with 'MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK'. Furthermore, the abstract contained an error in the SNP ID for the rare variant in chromosome Xq23, which was incorrectly given as rs146662057 and should have been rs146662075. These errors have now been corrected in both the PDF and HTML versions of the Article.

Profiling of G protein-coupled receptors in vagal afferents reveals novel gut-to-brain sensing mechanisms.

OBJECTIVES: G protein-coupled receptors (GPCRs) act as transmembrane molecular sensors of neurotransmitters, hormones, nutrients, and metabolites. Because unmyelinated vagal afferents richly innervate the gastrointestinal mucosa, gut-derived molecules may directly modulate the activity of vagal afferents through GPCRs. However, the types of GPCRs expressed in vagal afferents are largely unknown. Here, we determined the expression profile of all GPCRs expressed in vagal afferents of the mouse, with a special emphasis on those innervating the gastrointestinal tract. METHODS: Using a combination of high-throughput quantitative PCR, RNA sequencing, and in situ hybridization, we systematically quantified GPCRs expressed in vagal unmyelinated Nav1.8-expressing afferents. RESULTS: GPCRs for gut hormones that were the most enriched in Nav1.8-expressing vagal unmyelinated afferents included NTSR1, NPY2R, CCK1R, and to a lesser extent, GLP1R, but not GHSR and GIPR. Interestingly, both GLP1R and NPY2R were coexpressed with CCK1R. In contrast, NTSR1 was coexpressed with GPR65, a marker preferentially enriched in intestinal mucosal afferents. Only few microbiome-derived metabolite sensors such as GPR35 and, to a lesser extent, GPR119 and CaSR were identified in the Nav1.8-expressing vagal afferents. GPCRs involved in lipid sensing and inflammation (e.g. CB1R, CYSLTR2, PTGER4), and neurotransmitters signaling (CHRM4, DRD2, CRHR2) were also highly enriched in Nav1.8-expressing neurons. Finally, we identified 21 orphan GPCRs with unknown functions in vagal afferents. CONCLUSION: Overall, this study provides a comprehensive description of GPCR-dependent sensing mechanisms in vagal afferents, including novel coexpression patterns, and conceivably coaction of key receptors for gut-derived molecules involved in gut-brain communication.

Analysis of a gene panel for targeted sequencing of colorectal cancer samples.

Colorectal cancer (CRC) is a leading cause of death worldwide. Surgical intervention is a successful treatment for stage I patients, whereas other more advanced cases may require adjuvant chemotherapy. The selection of effective adjuvant treatments remains, however, challenging. Accurate patient stratification is necessary for the identification of the subset of patients likely responding to treatment, while sparing others from pernicious treatment. Targeted sequencing approaches may help in this regard, enabling rapid genetic investigation, and at the same time easily applicable in routine diagnosis. We propose a set of guidelines for the identification, including variant calling and filtering, of somatic mutations driving tumorigenesis in the absence of matched healthy tissue. We also discuss the inclusion criteria for the generation of our gene panel. Furthermore, we evaluate the prognostic impact of individual genes, using Cox regression models in the context of overall survival and disease-free survival. These analyses confirmed the role of commonly used biomarkers, and shed light on controversial genes such as CYP2C8. Applying those guidelines, we created a novel gene panel to investigate the onset and progression of CRC in 273 patients. Our comprehensive biomarker set includes 266 genes that may play a role in the progression through the different stages of the disease. Tracing the developmental state of the tumour, and its resistances, is instrumental in patient stratification and reliable decision making in precision clinical practice.

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches.

Gq and Gs signaling acting in synergy to control GLP-1 secretion.

GPR40 is generally known to signal through Gq. However, in transfected cells, certain synthetic agonists can make the receptor signal also through Gs and cAMP (Hauge et al., 2015). Here we find that, in colonic crypt cultures, the GLP-1 secretion induced by such Gq + Gs GPR40 agonists is indeed inhibited by blockers of both Gq and Gs and is eliminated by combining these. This in contrast to Gq-only GPR40 agonists which only are affected by the Gq inhibitor. Importantly, Gq-only GPR40 agonists in combination with low doses of selective synthetic agonists for Gs coupled receptors, e.g. GPR119 and TGR5 provide more than additive GLP-1 secretion both ex vivo and in vivo in mice. It is concluded that under physiological circumstances triglyceride metabolites, i.e. long chain fatty acids and 2-monoacyl glycerol plus bile acids, act synergistically through their respective receptors, GPR40, GPR119 and TGR5 to stimulate GLP-1 secretion robustly by combining Gq and Gs signaling pathways.

GPR119, a Major Enteroendocrine Sensor of Dietary Triglyceride Metabolites Coacting in Synergy With FFA1 (GPR40).

Triglycerides (TGs) are among the most efficacious stimulators of incretin secretion; however, the relative importance of FFA1 (G Protein-coupled Receptor [GPR] 40), FFA4 (GPR120), and GPR119, which all recognize TG metabolites, ie, long-chain fatty acid and 2-monoacylglycerol, respectively, is still unclear. Here, we find all 3 receptors to be highly expressed and highly enriched in fluorescence-activated cell sorting-purified GLP-1 and GIP cells isolated from transgenic reporter mice. In vivo, the TG-induced increase in plasma GIP was significantly reduced in FFA1-deficient mice (to 34%, mean of 4 experiments each with 8-10 animals), in GPR119-deficient mice (to 24%) and in FFA1/FFA4 double deficient mice (to 15%) but not in FFA4-deficient mice. The TG-induced increase in plasma GLP-1 was only significantly reduced in the GPR119-deficient and the FFA1/FFA4 double deficient mice, but not in the FFA1, and FFA4-deficient mice. In mouse colonic crypt cultures the synthetic FFA1 agonists, TAK-875 stimulated GLP-1 secretion to a similar extent as the prototype GLP-1 secretagogue neuromedin C; this, however, only corresponded to approximately half the maximal efficiency of the GPR119 agonist AR231453, whereas the GPR120 agonist Metabolex-209 had no effect. Importantly, when the FFA1 agonist was administered on top of appropriately low doses of the GPR119 agonist, a clear synergistic, ie, more than additive, effect was observed. It is concluded that the 2-monoacylglycerol receptor GPR119 is at least as important as the long-chain fatty acid receptor FFA1 in mediating the TG-induced secretion of incretins and that the 2 receptors act in synergy, whereas FFA4 plays a minor if any role.

Genome-wide association study identifies 74 loci associated with educational attainment.

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes.

Over 100 associated genetic loci have been robustly associated with schizophrenia. Gene prioritization and pathway analysis have focused on a priori hypotheses and thus may have been unduly influenced by prior assumptions and missed important causal genes and pathways. Using a data-driven approach, we show that genes in associated loci: (1) are highly expressed in cortical brain areas; (2) are enriched for ion channel pathways (false discovery rates <0.05); and (3) contain 62 genes that are functionally related to each other and hence represent promising candidates for experimental follow up. We validate the relevance of the prioritized genes by showing that they are enriched for rare disruptive variants and de novo variants from schizophrenia sequencing studies (odds ratio 1.67, P = 0.039), and are enriched for genes encoding members of mouse and human postsynaptic density proteomes (odds ratio 4.56, P = 5.00 × 10(-4); odds ratio 2.60, P = 0.049).The authors wish it to be known that, in their opinion, the first 2 authors should be regarded as joint First Author.

SNPsnap: a Web-based tool for identification and annotation of matched SNPs.

SUMMARY: An important computational step following genome-wide association studies (GWAS) is to assess whether disease or trait-associated single-nucleotide polymorphisms (SNPs) enrich for particular biological annotations. SNP-based enrichment analysis needs to account for biases such as co-localization of GWAS signals to gene-dense and high linkage disequilibrium (LD) regions, and correlations of gene size, location and function. The SNPsnap Web server enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on allele frequency, number of SNPs in LD, distance to nearest gene and gene density. AVAILABILITY AND IMPLEMENTATION: SNPsnap server is available at http://www.broadinstitute.org/mpg/snpsnap/. CONTACT: joelh@broadinstitute.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.