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.

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.

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.

Adipose tissue type and distribution are major determinants of metabolic disease, warranting investigations of depot-defining mechanisms. Here we show that progenitor cells from four human brown and white adipose depots separate into two main cell fates, a metabolic and a structural branch, during early differentiation at single cell resolution. The metabolic cell type expresses Adiponectin and is driven by an adipogenic transcriptional network including PPARG. Halfway through maturation, these cells have a brown adipocyte signature regardless of adipose depot origin. The structural cell type arises from the same progenitors as the metabolic cell type but expresses extracellular matrix factors and is driven by a competing osteoblast transcription factor network. The metabolic adipocyte gene signature associates with traits for fat distribution. In conclusion, we provide a seamless differentiation map of human adipocytes, providing an insight in the WAT browning process and a long-term perspective of cell-type specific targeting of metabolic disease.

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.

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.

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.

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 ( $$\left| {\hat r_{\mathrm{g}}} \right|$$ ~ 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. A genetic study identifies hundreds of loci associated with risk tolerance and risky behaviors, finds evidence of substantial shared genetic influences across these phenotypes, and implicates genes involved in neurotransmission.

We conduct a large-scale genetic association analysis of educational attainment in a sample of ~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 ~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. 200 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia 201 Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia 202 Centre for Clinical and Cognitive Neuroscience, Institute Brain Behaviour and Mental Health, Salford Royal Hospital, Manchester, M6 8HD, UK 203 Manchester Institute Collaborative Research in Ageing, University of Manchester, Manchester, M13 9PL, UK 204 Faculty of Medicine, University of Split, Croatia, Split 21000, Croatia 205 Department of Clinical Genetics, VU Medical Centre, Amsterdam, 1081 HV, The Netherlands 206 Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, The Capital Region, Frederiksberg, 2000, Denmark 207 Montpellier Business School, Montpellier, 34080, France 208 Panteia, Zoetermeer, 2715 CA, The Netherlands 209 Department of Psychiatry, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands 210 Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands 211 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands DATA AVAILABILITY AND ACCESSION CODES Summary statistics can be downloaded from www.thessgac.org/data. We provide association results for all SNPs that passed qualitycontrol filters in a GWAS meta-analysis of EduYears that excludes the research participants from 23andMe. SNP-level summary statistics from analyses based entirely or in part on 23andMe data can only be reported for up to 10,000 SNPs. We provide summary statistics for all lead SNPs identified in our GWAS analyses of Cognitive Performance, Math Ability, and Highest Math and the MTAG analyses of our four phenotypes. For the complete EduYears GWAS, which includes 23andMe, clumped results for the 3,575 SNPs with P < 10−5 are provided; this P-value threshold was chosen such that the total number of SNPs across the analyses that include data from 23andMe does not exceed 10,000. Contact information for each of the cohorts included in this paper can be found in the Supplementary Note. CODE AVAILABILITY: All software used to perform these analyses are available online. URLs: Social Science Genetic Association Consortium (SSGAC) website: http://www.thessgac.org/#!data/kuzq8. Minimac2: https://genome.sph.umich.edu/wiki/Minimac2 BEAGLE v2.1.2: http://faculty.washington.edu/browning/beagle/b3.html IMPUTE2 v2.3.1: http://mathgen.stats.ox.ac.uk/impute/impute_v2.html PBWT: https://github.com/richarddurbin/pbwt IMPUTE4: https://jmarchini.org/impute-4/ ShapeIT v2.r790: http://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html BOLT-LMM: https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ SNPTEST v2.4.1: https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html REGSCAN v0.2.0: https://www.geenivaramu.ee/en/tools/regscan METAL, release 2011–03-25: http://csg.sph.umich.edu/abecasis/metal/ EasyQC v9.0: http://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/ ldsc v1.0.0: https://github.com/bulik/ldsc Plink, 1.90b3p: http://zzz.bwh.harvard.edu/plink/plink2.shtml LDpred v0.9.09: https://bitbucket.org/bjarni_vilhjalmsson/ldpred Stata v14.2: https://www.stata.com/install-guide/windows/download/ DEPICT (downloaded Feb 2015): https://data.broadinstitute.org/mpg/depict/ MAGMA v1.06b: https://ctg.cncr.nl/software/magma PANTHER release 20170403: http://www.geneontology.org CAVIARBF v0.2.1: https://bitbucket.org/Wenan/caviarbf MTAG software v1.0.1: https://github.com/omeed-maghzian/mtag Lee et al. Page 5 Nat Genet. Author manuscript; available in PMC 2019 February 28. A uhor M anscript

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

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. (C) 2018 The Authors. Published by Elsevier GmbH.

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.

Recently, lasting remission of hyperglycemia was achieved in rodent models of type 2 diabetes (T2D) by a single intracerebroventricular (icv) injection of FGF-1. While the mechanism underlying this effect is unknown, the lack of association with changes of body fat mass or risk of hypoglycaemia raises the possibility that icv FGF-1 normalizes the defended level of glycemia, rather than simply lowering blood glucose levels. Recent work from our lab has identified gluco-regulatory neurocircuits in the mediobasal hypothalamus (MBH) as targets for this FGF-1 effect. To investigate the mechanism underlying FGF-1 action in this brain area, we used large-scale single cell RNA-sequencing to identify and characterize FGF-1-responsive cells in mouse MBH. Based on >70,000 single cell MBH transcriptomes from diabetic ob/ob mice harvested 5d after a single icv injection of either FGF-1 or vehicle, we identified >20 cell clusters and >900 cell type-specific differentially expressed genes (p<0.01). Among these potential targets of the MBH response to FGF-1 in diabetic mice, we found that FGF-1 treatment robustly increased the proportion of newly formed oligodendrocytes, a response that can be predicted to yield new mature oligodendrocytes and associated myelination of axons. These preliminary results demonstrate that transcriptional identification and characterisation of icv FGF-1-responsive cell clusters may both advance our understanding of brain control of glucose homeostasis and identify novel targets for diabetes prevention and treatment. Disclosure M.A. Bentsen: None. D. Rausch: None. J. Scarlett: None. K.M. Alonge: None. P.N. Timshel: None. Z. Mirzadeh: None. A. Secher: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Employee; Spouse/Partner; Gubra. R. Jorgensen: Employee; Self; Novo Nordisk Inc.. T. Pers: None. M.W. Schwartz: Consultant; Self; Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S.

Humans vary substantially in their willingness to take risks. In a combined sample of over one 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. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated (|rˆ g | ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

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.

The reanalysis of publicly available GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci , and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene ( AGTR2 ), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

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. (C) 2016 Elsevier Ireland Ltd. All rights reserved.