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Mechanisms of abdominal obesity GWAS variants have remained largely unknown. To elucidate these mechanisms, we leveraged subcutaneous adipose tissue (SAT) single nucleus RNA-sequencing and genomics data. After discovering that heritability of abdominal obesity is enriched in adipocytes, we focused on a SAT unique adipocyte marker gene, the transcription factor TBX15, and its abdominal obesity-associated deleterious missense variant, rs10494217. The allele frequency of rs10494217 revealed a north-to-south decreasing gradient, with consistent significant FST values observed for 25 different populations when compared to Finns, a population with a history of genetic isolation. Given the role of Tbx15 in mouse thermogenesis, the frequency may have increased as an adaptation to cold in Finns. Our selection analysis provided significant evidence of selection for the abdominal obesity risk allele T of rs10494217 in Finns, with a north-to-south decreasing trend in other populations, and demonstrated that latitude significantly predicts the allele frequency. We also discovered that the risk allele status significantly affects SAT adipocyte expression of multiple adipocyte marker genes in trans in two cohorts. Two of these trans genes have been connected to thermogenesis, supporting the thermogenic effect of the TBX15 missense variant as a possible cause of its selection. Adipose expression of one trans gene, a lncRNA, AC002066.1, was strongly associated with adipocyte size, implicating it in metabolically unhealthy adipocyte hypertrophy. In summary, the abdominal obesity variant rs10494217 was selected in Finns, and individuals with the risk allele have trans effects on adipocyte expression of genes relating to thermogenesis and adipocyte hypertrophy.

BACKGROUND: Obesity is a worldwide epidemic characterized by adipose tissue (AT) inflammation. AT is also a source of extracellular vesicles (EVs) that have recently been implicated in disorders related to metabolic syndrome. However, our understanding of mechanistic aspect of obesitys impact on EV secretion from human AT remains limited. METHODS: We investigated EVs from human Simpson Golabi Behmel Syndrome (SGBS) adipocytes, and from AT as well as plasma of subjects undergoing bariatric surgery. SGBS cells were treated with TNFα, palmitic acid, and eicosapentaenoic acid. Various analyses, including nanoparticle tracking analysis, electron microscopy, high-resolution confocal microscopy, and gas chromatography-mass spectrometry, were utilized to study EVs. Plasma EVs were analyzed with imaging flow cytometry. RESULTS: EVs from mature SGBS cells differed significantly in size and quantity compared to preadipocytes, disagreeing with previous findings in mouse adipocytes and indicating that adipogenesis promotes EV secretion in human adipocytes. Inflammatory stimuli also induced EV secretion, and altered EV fatty acid (FA) profiles more than those of cells, suggesting the role of EVs as rapid responders to metabolic shifts. Visceral AT (VAT) exhibited higher EV secretion compared to subcutaneous AT (SAT), with VAT EV counts positively correlating with plasma triacylglycerol (TAG) levels. Notably, the plasma EVs of subjects with obesity contained a higher number of adiponectin-positive EVs than those of lean subjects, further demonstrating higher AT EV secretion in obesity. Moreover, plasma EV counts of people with obesity positively correlated with body mass index and TNF expression in SAT, connecting increased EV secretion with AT expansion and inflammation. Finally, EVs from SGBS adipocytes and AT contained TAGs, and EV secretion increased despite signs of less active lipolytic pathways, indicating that AT EVs could be involved in the mobilization of excess lipids into circulation. CONCLUSIONS: We are the first to provide detailed FA profiles of human AT EVs. We report that AT EV secretion increases in human obesity, implicating their role in TAG transport and association with adverse metabolic parameters, thereby emphasizing their role in metabolic disorders. These findings promote our understanding of the roles that EVs play in human AT biology and metabolic disorders.

Recent studies demonstrate that liver secretory proteins, also known as hepatokines, regulate normal development, obesity, and simple steatosis to non-alcoholic steatohepatitis (NASH) progression. Using a panel of ∼100 diverse inbred strains of mice and a cohort of bariatric surgery patients, we found that one such hepatokine, inter-trypsin inhibitor heavy chain 3 (ITIH3), was progressively lower in severe non-alcoholic fatty liver disease (NAFLD) disease states highlighting an inverse relationship between Itih3/ITIH3 expression and NAFLD severity. Follow-up animal and cell culture models demonstrated that hepatic ITIH3 overexpression lowered liver triglyceride and lipid droplet accumulation, respectively. Conversely, ITIH3 knockdown in mice increased the liver triglyceride in two independent NAFLD models. Mechanistically, ITIH3 reduced mitochondrial respiration and this, in turn, reduced liver triglycerides, via downregulated de novo lipogenesis. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD.

BACKGROUND: Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS: We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS: We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS: Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.

Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or fill-in missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.

BACKGROUND: Adipocytes are crucial regulators of cardiovascular health. However, not much is known about gene expression profiles of adipocytes residing in nonfat cardiovascular tissues, their genetic regulation, and contribution to coronary artery disease. Here, we investigated whether and how the gene expression profiles of adipocytes in the subcutaneous adipose tissue differ from adipocytes residing in the heart. METHODS: We used single-nucleus RNA-sequencing data sets of subcutaneous adipose tissue and heart and performed in-depth analysis of tissue-resident adipocytes and their cell-cell interactions. RESULTS: We first discovered tissue-specific features of tissue-resident adipocytes, identified functional pathways involved in their tissue specificity, and found genes with cell type-specific expression enrichment in tissue-resident adipocytes. By following up these results, we discovered the propanoate metabolism pathway as a novel distinct characteristic of the heart-resident adipocytes and found a significant enrichment of coronary artery disease genome-wide association study risk variants among the right atrium-specific adipocyte marker genes. Our cell-cell communication analysis identified 22 specific heart adipocyte-associated ligand-receptor pairs and signaling pathways, including THBS (thrombospondin) and EPHA (ephrin type-A), further supporting the distinct tissue-resident role of heart adipocytes. Our results also suggest chamber-level coordination of heart adipocyte expression profiles as we observed a consistently larger number of adipocyte-associated ligand-receptor interactions and functional pathways in the atriums than ventricles. CONCLUSIONS: Overall, we introduce a new function and genetic link to coronary artery disease for the previously unexplored heart-resident adipocytes.

Abstract Background Obesity is a worldwide epidemic characterized by adipose tissue (AT) inflammation. AT is also a source of extracellular vesicles (EVs) that have recently been implicated in disorders related to metabolic syndrome. However, our understanding of mechanistic aspect of obesity’s impact on EV secretion from human AT remains limited. Methods We investigated EVs from human Simpson Golabi Behmel Syndrome (SGBS) adipocytes, and from AT as well as plasma of subjects undergoing bariatric surgery. SGBS cells were treated with TNFα, palmitic acid, and eicosapentaenoic acid. Various analyses, including nanoparticle tracking analysis, electron microscopy, high-resolution confocal microscopy, and gas chromatography–mass spectrometry, were utilized to study EVs. Plasma EVs were analyzed with imaging flow cytometry. Results EVs from mature SGBS cells differed significantly in size and quantity compared to preadipocytes, disagreeing with previous findings in mouse adipocytes and indicating that adipogenesis promotes EV secretion in human adipocytes. Inflammatory stimuli also induced EV secretion, and altered EV fatty acid (FA) profiles more than those of cells, suggesting the role of EVs as rapid responders to metabolic shifts. Visceral AT (VAT) exhibited higher EV secretion compared to subcutaneous AT (SAT), with VAT EV counts positively correlating with plasma triacylglycerol (TAG) levels. Notably, the plasma EVs of subjects with obesity contained a higher number of adiponectin-positive EVs than those of lean subjects, further demonstrating higher AT EV secretion in obesity. Moreover, plasma EV counts of people with obesity positively correlated with body mass index and TNF expression in SAT, connecting increased EV secretion with AT expansion and inflammation. Finally, EVs from SGBS adipocytes and AT contained TAGs, and EV secretion increased despite signs of less active lipolytic pathways, indicating that AT EVs could be involved in the mobilization of excess lipids into circulation. Conclusions We are the first to provide detailed FA profiles of human AT EVs. We report that AT EV secretion increases in human obesity, implicating their role in TAG transport and association with adverse metabolic parameters, thereby emphasizing their role in metabolic disorders. These findings promote our understanding of the roles that EVs play in human AT biology and metabolic disorders.

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of lifestyle diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be mismatched and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit genotype by environment (GxE) interactions, with different health effects in ancestral versus modern environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the matched to mismatched spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Background: Long non-coding RNAs (lncRNAs) can perform tasks of key relevance in fat cells, contributing, when defective, to the burden of obesity and its sequelae. Here, scrutiny of adipose tissue transcriptomes before and after bariatric surgery (GSE53378) granted identification of 496 lncRNAs linked to the obese phenotype. Only expression of linc-GALNTL6-4 displayed an average recovery over 2-fold and FDR-adjusted p-value

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a fast-growing, underdiagnosed, epidemic. We hypothesise that obesity-related inflammation compromises adipose tissue functions, preventing efficient fat storage, and thus driving ectopic fat accumulation into the liver.MethodsTo identify adipose-based mechanisms and potential serum biomarker candidates (SBCs) for NAFLD, we utilise dual-tissue RNA-sequencing (RNA-seq) data in adipose tissue and liver, paired with histology-based NAFLD diagnosis, from the same individuals in a cohort of obese individuals. We first scan for genes that are differentially expressed (DE) for NAFLD in obese individuals' subcutaneous adipose tissue but not in their liver; encode proteins secreted to serum; and show preferential adipose expression. Then the identified genes are filtered to key adipose-origin NAFLD genes by best subset analysis, knockdown experiments during human preadipocyte differentiation, recombinant protein treatment experiments in human liver HepG2 cells, and genetic analysis.FindingsWe discover a set of genes, including 10 SBCs, that may modulate NAFLD pathogenesis by impacting adipose tissue function. Based on best subset analysis, we further follow-up on two SBCs CCDC80 and SOD3 by knockdown in human preadipocytes and subsequent differentiation experiments, which show that they modulate crucial adipogenesis genes, LPL, SREBPF1, and LEP. We also show that treatment of the liver HepG2 cells with the CCDC80 and SOD3 recombinant proteins impacts genes related to steatosis and lipid processing, including PPARA, NFE2L2, and RNF128. Finally, utilizing the adipose NAFLD DE gene cis-regulatory variants associated with serum triglycerides (TGs) in extensive genome-wide association studies (GWASs), we demonstrate a unidirectional effect of serum TGs on NAFLD with Mendelian Randomization (MR) analysis. We also demonstrate that a single SNP regulating one of the SBC genes, rs2845885, produces a significant MR result by itself. This supports the conclusion that genetically regulated adipose expression of the NAFLD DE genes may contribute to NAFLD through changes in serum TG levels.InterpretationOur results from the dual-tissue transcriptomics screening improve the understanding of obesity-related NAFLD by providing a targeted set of 10 adipose tissue-active genes as new serum biomarker candidates for the currently grossly underdiagnosed fatty liver disease.FundingThe work was supported by NIH grants R01HG010505 and R01DK132775. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The KOBS study (J. P.) was supported by the Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2019), and the Academy of Finland grant (Contract no. 138006). This study was funded by the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant No. 802825 to M. U. K.). K. H. P. was funded by the Academy of Finland (grant numbers 272376, 266286, 314383, and 335443), the Finnish Medical Foundation, Gyllenberg Foundation, Novo Nordisk Foundation (grant numbers NNF10OC1013354, NNF17OC0027232, and NNF20OC0060547), Finnish Diabetes Research Foundation, Finnish Foundation for Cardiovascular Research, University of Helsinki, and Helsinki University Hospital and Government Research Funds. I. S. was funded by the Instrumentarium Science Foundation. Personal grants to U. T. A. were received from the Matti and Vappu Maukonen Foundation, Ella och Georg Ehrnrooths Stiftelse and the Finnish Foundation for Cardiovascular Research.

Background & aimsNonalcoholic steatohepatitis (NASH) is a complex disease involving both genetic and environmental factors in its onset and progression. We analyzed NASH phenotypes in a genetically diverse cohort of mice, the Hybrid Mouse Diversity Panel, to identify genes contributing to disease susceptibility.MethodsA "systems genetics" approach, involving integration of genetic, transcriptomic, and phenotypic data, was used to identify candidate genes and pathways in a mouse model of NASH. The causal role of Matrix Gla Protein (MGP) was validated using heterozygous MGP knockout (Mgp+/-) mice. The mechanistic role of MGP in transforming growth factor-beta (TGF-β) signaling was examined in the LX-2 stellate cell line by using a loss of function approach.ResultsLocal cis-acting regulation of MGP was correlated with fibrosis, suggesting a causal role in NASH, and this was validated using loss of function experiments in 2 models of diet-induced NASH. Using single-cell RNA sequencing, Mgp was found to be primarily expressed in hepatic stellate cells and dendritic cells in mice. Knockdown of MGP expression in stellate LX-2 cells led to a blunted response to TGF-β stimulation. This was associated with reduced regulatory SMAD phosphorylation and TGF-β receptor ALK1 expression as well as increased expression of inhibitory SMAD6. Hepatic MGP expression was found to be significantly correlated with the severity of fibrosis in livers of patients with NASH, suggesting relevance to human disease.ConclusionsMGP regulates liver fibrosis and TGF-β signaling in hepatic stellate cells and contributes to NASH pathogenesis.

Summary: Background: Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants. Findings: We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue. Interpretation: Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis. Funding: NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.

BackgroundHepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival.MethodsWe combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients' tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University.ResultsWe discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC.ConclusionsBy integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.

Abstract Background Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. Methods We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). Results We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. Conclusions Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.

Cellular senescence is an essentially irreversible growth arrest that occurs in response to various cellular stressors and may contribute to development of type 2 diabetes mellitus and nonalcoholic fatty liver disease (NAFLD). In this article, we investigated whether chronically elevated insulin levels are associated with cellular senescence in the human liver. In 107 individuals undergoing bariatric surgery, hepatic senescence markers were assessed by immunohistochemistry as well as transcriptomics. A subset of 180 participants from the ongoing Finnish Kuopio OBesity Surgery (KOBS) study was used as validation cohort. We found plasma insulin to be highly associated with various markers of cellular senescence in liver tissue. The liver transcriptome of individuals with high insulin revealed significant upregulation of several genes associated with senescence: p21, TGFβ, PI3K, HLA-G, IL8, p38, Ras, and E2F. Insulin associated with hepatic senescence independently of NAFLD and plasma glucose. By using transcriptomic data from the KOBS study, we could validate the association of insulin with p21 in the liver. Our results support a potential role for hyperinsulinemia in induction of cellular senescence in the liver. These findings suggest possible benefits of lowering insulin levels in obese individuals with insulin resistance.

Summary: Recent studies demonstrate that liver secretory proteins, also known as hepatokines, regulate normal development, obesity, and simple steatosis to non-alcoholic steatohepatitis (NASH) progression. Using a panel of ∼100 diverse inbred strains of mice and a cohort of bariatric surgery patients, we found that one such hepatokine, inter-trypsin inhibitor heavy chain 3 (ITIH3), was progressively lower in severe non-alcoholic fatty liver disease (NAFLD) disease states highlighting an inverse relationship between Itih3/ITIH3 expression and NAFLD severity. Follow-up animal and cell culture models demonstrated that hepatic ITIH3 overexpression lowered liver triglyceride and lipid droplet accumulation, respectively. Conversely, ITIH3 knockdown in mice increased the liver triglyceride in two independent NAFLD models. Mechanistically, ITIH3 reduced mitochondrial respiration and this, in turn, reduced liver triglycerides, via downregulated de novo lipogenesis. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD.

The prevalence of non-alcoholic fatty liver disease (NAFLD), now also known as metabolic dysfunction-associated fatty liver disease (MAFLD), is rapidly increasing worldwide due to the ongoing obesity epidemic. However, currently the NALFD diagnosis requires non-readily available imaging technologies or liver biopsy, which has drastically limited the sample sizes of NAFLD studies and hampered the discovery of its genetic component. Here we utilized the large UK Biobank (UKB) to accurately estimate the NAFLD status in UKB based on common serum traits and anthropometric measures. Scoring all individuals in UKB for NAFLD risk resulted in 28,396 NAFLD cases and 108,652 healthy individuals at a >90% confidence level. Using this imputed NAFLD status to perform the largest NAFLD genome-wide association study (GWAS) to date, we identified 94 independent (R2 < 0.2) NAFLD GWAS loci, of which 90 have not been identified before; built a polygenic risk score (PRS) model to predict the genetic risk of NAFLD; and used the GWAS variants of imputed NAFLD for a tissue-aware Mendelian randomization analysis that discovered a significant causal effect of NAFLD on coronary artery disease (CAD). In summary, we accurately estimated the NAFLD status in UKB using common serum traits and anthropometric measures, which empowered us to identify 90 GWAS NAFLD loci, build NAFLD PRS, and discover a significant causal effect of NAFLD on CAD.

The prevalence of nonalcoholic steatohepatitis (NASH) and liver cancer is increasing. De novo lipogenesis and fibrosis contribute to disease progression and cancerous transformation. Here, we found that β2-spectrin (SPTBN1) promotes sterol regulatory element (SRE)–binding protein (SREBP)–stimulated lipogenesis and development of liver cancer in mice fed a high-fat diet (HFD) or a western diet (WD). Either hepatocyte-specific knockout of SPTBN1 or siRNA-mediated therapy protected mice from HFD/WD-induced obesity and fibrosis, lipid accumulation, and tissue damage in the liver. Biochemical analysis suggested that HFD/WD induces SPTBN1 and SREBP1 cleavage by CASPASE-3 and that the cleaved products interact to promote expression of genes with sterol response elements. Analysis of human NASH tissue revealed increased SPTBN1 and CASPASE-3 expression. Thus, our data indicate that SPTBN1 represents a potential target for therapeutic intervention in NASH and liver cancer.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.

Abstract Obesity-induced adipose tissue dysfunction can cause low-grade inflammation and downstream obesity comorbidities. Although preadipocytes may contribute to this pro-inflammatory environment, the underlying mechanisms are unclear. We used human primary preadipocytes from body mass index (BMI) -discordant monozygotic (MZ) twin pairs to generate epigenetic (ATAC-sequence) and transcriptomic (RNA-sequence) data for testing whether increased BMI alters the subnuclear compartmentalization of open chromatin in the twins’ preadipocytes, causing downstream inflammation. Here we show that the co-accessibility of open chromatin, i.e. compartmentalization of chromatin activity, is altered in the higher vs lower BMI MZ siblings for a large subset ( ~ 88.5 Mb) of the active subnuclear compartments. Using the UK Biobank we show that variants within these regions contribute to systemic inflammation through interactions with BMI on C-reactive protein. In summary, open chromatin co-accessibility in human preadipocytes is disrupted among the higher BMI siblings, suggesting a mechanism how obesity may lead to inflammation via gene-environment interactions.

BackgroundObesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.MethodsWe searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function.ResultsHere, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network.ConclusionsOur study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity.

Abstract Background High serum triglyceride (TG) levels is an established risk factor for coronary heart disease (CHD). Fat is stored in the form of TGs in human adipose tissue. We hypothesized that gene co-expression networks in human adipose tissue may be correlated with serum TG levels and help reveal novel genes involved in TG regulation. Methods Gene co-expression networks were constructed from two Finnish and one Mexican study sample using the blockwiseModules R function in Weighted Gene Co-expression Network Analysis (WGCNA). Overlap between TG-associated networks from each of the three study samples were calculated using a Fisher’s Exact test. Gene ontology was used to determine known pathways enriched in each TG-associated network. Results We measured gene expression in adipose samples from two Finnish and one Mexican study sample. In each study sample, we observed a gene co-expression network that was significantly associated with serum TG levels. The TG modules observed in Finns and Mexicans significantly overlapped and shared 34 genes. Seven of the 34 genes (ARHGAP30, CCR1, CXCL16, FERMT3, HCST, RNASET2, SELPG) were identified as the key hub genes of all three TG modules. Furthermore, two of the 34 genes (ARHGAP9, LST1) reside in previous TG GWAS regions, suggesting them as the regional candidates underlying the GWAS signals. Conclusions This study presents a novel adipose gene co-expression network with 34 genes significantly correlated with serum TG across populations.

Abstract Background Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2). Results Notably, MicroMerge v2 includes a new one-to-one alignment option that creates merged pedigree and locus files that can be handled by most genetic analysis software. Other features in MicroMerge v2 enhance the following aspects of control: 1) optimizing the algorithm for different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 2) merging small data sets when a reliable set of allele frequencies are available, and 3) improving the quantity and 4) quality of merged data. We present results from simulated and real microsatellite genotype data sets, and conclude with an association analysis of three familial dyslipidemia (FD) study samples genotyped at different laboratories. Independent analysis of each FD data set did not yield consistent results, but analysis of the merged data sets identified strong association at locus D11S2002. Conclusion The MicroMerge v2 features will enable merging for a variety of genotype data sets, which in turn will facilitate meta-analyses for powering association analysis.

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.

Obesity is a major public health burden worldwide and is characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. Here we show that genetic polymorphisms near the human RIPK1 locus associate with increased RIPK1 gene expression and obesity. We show that one of these single nucleotide polymorphisms is within a binding site for E4BP4 and increases RIPK1 promoter activity and RIPK1 gene expression in adipose tissue. Therapeutic silencing of RIPK1 in vivo in a mouse model of diet-induced obesity dramatically reduces fat mass, total body weight and improves insulin sensitivity, while simultaneously reducing macrophage and promoting invariant natural killer T cell accumulation in adipose tissue. These findings demonstrate that RIPK1 is genetically associated with obesity, and reducing RIPK1 expression is a potential therapeutic approach to target obesity and related diseases.

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

Background & Aims: Nonalcoholic steatohepatitis (NASH) is a complex disease involving both genetic and environmental factors in its onset and progression. We analyzed NASH phenotypes in a genetically diverse cohort of mice, the Hybrid Mouse Diversity Panel, to identify genes contributing to disease susceptibility. Methods: A “systems genetics” approach, involving integration of genetic, transcriptomic, and phenotypic data, was used to identify candidate genes and pathways in a mouse model of NASH. The causal role of Matrix Gla Protein (MGP) was validated using heterozygous MGP knockout (Mgp+/-) mice. The mechanistic role of MGP in transforming growth factor-beta (TGF-β) signaling was examined in the LX-2 stellate cell line by using a loss of function approach. Results: Local cis-acting regulation of MGP was correlated with fibrosis, suggesting a causal role in NASH, and this was validated using loss of function experiments in 2 models of diet-induced NASH. Using single-cell RNA sequencing, Mgp was found to be primarily expressed in hepatic stellate cells and dendritic cells in mice. Knockdown of MGP expression in stellate LX-2 cells led to a blunted response to TGF-β stimulation. This was associated with reduced regulatory SMAD phosphorylation and TGF-β receptor ALK1 expression as well as increased expression of inhibitory SMAD6. Hepatic MGP expression was found to be significantly correlated with the severity of fibrosis in livers of patients with NASH, suggesting relevance to human disease. Conclusions: MGP regulates liver fibrosis and TGF-β signaling in hepatic stellate cells and contributes to NASH pathogenesis.

Obesity perturbs central functions of human adipose tissue, centred on differentiation of preadipocytes to adipocytes, i.e., adipogenesis. The large environmental component of obesity makes it important to elucidate epigenetic regulatory factors impacting adipogenesis. Promoter Capture Hi-C (pCHi-C) has been used to identify chromosomal interactions between promoters and associated regulatory elements. However, long range interactions (LRIs) greater than 1 Mb are often filtered out of pCHi-C datasets, due to technical challenges and their low prevalence. To elucidate the unknown role of LRIs in adipogenesis, we investigated preadipocyte differentiation to adipocytes using pCHi-C and bulk and single nucleus RNA-seq data. We first show that LRIs are reproducible between biological replicates, and they increase >2-fold in frequency across adipogenesis. We further demonstrate that genomic loci containing LRIs are more epigenetically repressed than regions without LRIs, corresponding to lower gene expression in the LRI regions. Accordingly, as preadipocytes differentiate into adipocytes, LRI regions are more likely to contain repressed preadipocyte marker genes; whereas these same LRI regions are depleted of actively expressed adipocyte marker genes. Finally, we show that LRIs can be used to restrict multiple testing of the long-range cis-eQTL analysis to identify variants that regulate genes via LRIs. We exemplify this by identifying a putative long range cis regulatory mechanism at the LYPLAL1/TGFB2 obesity locus. In summary, we identify LRIs that mark repressed regions of the genome, and these interactions increase across adipogenesis, pinpointing developmental regions that need to be repressed in a cell-type specific way for adipogenesis to proceed.

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.

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;

Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the genotype and dietary saturated fat intake on body mass index (BMI) in the UK Biobank. We discover 14 novel GxE variants in 12 lipid-responsive promoters, including well-known lipid genes (LIPE, CARM1, and PLIN2) and novel genes, such as LDB3, for which we provide further functional and integrative genomics evidence. We further identify 24 GxE variants in enhancers, totaling 38 new GxE variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can discover new functional GxE mechanisms in humans.

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.

Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.Editorial noteThis article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Summary: Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing, underdiagnosed, epidemic. We hypothesise that obesity-related inflammation compromises adipose tissue functions, preventing efficient fat storage, and thus driving ectopic fat accumulation into the liver. Methods: To identify adipose-based mechanisms and potential serum biomarker candidates (SBCs) for NAFLD, we utilise dual-tissue RNA-sequencing (RNA-seq) data in adipose tissue and liver, paired with histology-based NAFLD diagnosis, from the same individuals in a cohort of obese individuals. We first scan for genes that are differentially expressed (DE) for NAFLD in obese individuals’ subcutaneous adipose tissue but not in their liver; encode proteins secreted to serum; and show preferential adipose expression. Then the identified genes are filtered to key adipose-origin NAFLD genes by best subset analysis, knockdown experiments during human preadipocyte differentiation, recombinant protein treatment experiments in human liver HepG2 cells, and genetic analysis. Findings: We discover a set of genes, including 10 SBCs, that may modulate NAFLD pathogenesis by impacting adipose tissue function. Based on best subset analysis, we further follow-up on two SBCs CCDC80 and SOD3 by knockdown in human preadipocytes and subsequent differentiation experiments, which show that they modulate crucial adipogenesis genes, LPL, SREBPF1, and LEP. We also show that treatment of the liver HepG2 cells with the CCDC80 and SOD3 recombinant proteins impacts genes related to steatosis and lipid processing, including PPARA, NFE2L2, and RNF128. Finally, utilizing the adipose NAFLD DE gene cis-regulatory variants associated with serum triglycerides (TGs) in extensive genome-wide association studies (GWASs), we demonstrate a unidirectional effect of serum TGs on NAFLD with Mendelian Randomization (MR) analysis. We also demonstrate that a single SNP regulating one of the SBC genes, rs2845885, produces a significant MR result by itself. This supports the conclusion that genetically regulated adipose expression of the NAFLD DE genes may contribute to NAFLD through changes in serum TG levels. Interpretation: Our results from the dual-tissue transcriptomics screening improve the understanding of obesity-related NAFLD by providing a targeted set of 10 adipose tissue-active genes as new serum biomarker candidates for the currently grossly underdiagnosed fatty liver disease. Funding: The work was supported by NIH grants R01HG010505 and R01DK132775. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The KOBS study (J. P.) was supported by the Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005–2019), and the Academy of Finland grant (Contract no. 138006). This study was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation program (Grant No. 802825 to M. U. K.). K. H. P. was funded by the Academy of Finland (grant numbers 272376, 266286, 314383, and 335443), the Finnish Medical Foundation, Gyllenberg Foundation, Novo Nordisk Foundation (grant numbers NNF10OC1013354, NNF17OC0027232, and NNF20OC0060547), Finnish Diabetes Research Foundation, Finnish Foundation for Cardiovascular Research, University of Helsinki, and Helsinki University Hospital and Government Research Funds. I. S. was funded by the Instrumentarium Science Foundation. Personal grants to U. T. A. were received from the Matti and Vappu Maukonen Foundation, Ella och Georg Ehrnrooths Stiftelse and the Finnish Foundation for Cardiovascular Research.

Abstract Background Hepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival. Methods We combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients’ tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University. Results We discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC. Conclusions By integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.

Single-nucleus RNA sequencing (snRNA-seq) provides a powerful tool for studying cell type composition in heterogenous tissues. The liver is a vital organ composed of a diverse set of cell types; thus, single-cell technologies could greatly facilitate the deconvolution of liver tissue composition and various downstream omics analyses at the cell-type level. Applying single-cell technologies to fresh liver biopsies can, however, be very challenging, and snRNA-seq of snap-frozen liver biopsies requires some optimization given the high nucleic acid content of the solid liver tissue. Therefore, an optimized protocol for snRNA-seq specifically targeted for the use of frozen liver samples is needed to improve our understanding of human liver gene expression at the cell-type resolution. We present a protocol for performing nuclei isolation from snap-frozen liver tissues, as well as guidance on the application of snRNA-seq. We also provide guidance on optimizing the protocol to different tissue and sample types.

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

In the original version of this Article, Supplementary Table 10 contained incorrect primer sequences for the mobility shift assay for SNP rs4776984. These errors have now been fixed and the corrected version of the Supplementary Information PDF is available to download from the HTML version of the Article.

Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi-C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.