Long-range chromosomal interactions increase and mark repressed gene expression during adipogenesis

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.Peer reviewe

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005–2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a “systems genetics” approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

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.Peer reviewe

Risk Alleles of USF1 Gene Predict Cardiovascular Disease of Women in Two Prospective Studies

Upstream transcription factor 1 (USF1) is a ubiquitously expressed transcription factor controlling several critical genes in lipid and glucose metabolism. Of some 40 genes regulated by USF1, several are involved in the molecular pathogenesis of cardiovascular disease (CVD). Although the USF1 gene has been shown to have a critical role in the etiology of familial combined hyperlipidemia, which predisposes to early CVD, the gene's potential role as a risk factor for CVD events at the population level has not been established. Here we report the results from a prospective genetic–epidemiological study of the association between the USF1 variants, CVD, and mortality in two large Finnish cohorts. Haplotype-tagging single nucleotide polymorphisms exposing all common allelic variants of USF1 were genotyped in a prospective case-cohort design with two distinct cohorts followed up during 1992–2001 and 1997–2003. The total number of follow-up years was 112,435 in 14,140 individuals, of which 2,225 were selected for genotyping based on the case-cohort study strategy. After adjustment for conventional risk factors, we observed an association of USF1 with CVD and mortality among females. In combined analysis of the two cohorts, female carriers of a USF1 risk haplotype had a 2-fold risk of a CVD event (hazard ratio [HR] 2.02; 95% confidence interval [CI] 1.16–3.53; p = 0.01) and an increased risk of all-cause mortality (HR 2.52; 95% CI 1.46–4.35; p = 0.0009). A putative protective haplotype of USF1 was also identified. Our study shows how a gene identified in exceptional families proves to be important also at the population level, implying that allelic variants of USF1 significantly influence the prospective risk of CVD and even all-cause mortality in females

Identification of 90 NAFLD GWAS loci and establishment of NAFLD PRS and causal role of NAFLD in coronary artery disease

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 causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2x10(-3)and p = 3.1x10(-24)). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p Author summary Obesity is a global health epidemic predisposing to type 2 diabetes (T2D) and other cardiometabolic disorders. Previous studies have shown that obesity has a causal effect on T2D; however, it remains unknown whether obesity causes prediabetes and insulin resistance already in non-diabetic individuals. By utilizing almost half a million individuals from the UK Biobank and the Finnish METSIM cohort, we identified a significant causal effect of obesity on prediabetes and insulin resistance among the non-diabetic individuals. Next, we investigated the role of subcutaneous adipose tissue in these obesogenic effects. We discovered that the adipose mitochondrial gene expression and body fat percent are independently associated with insulin resistance after adjusting for the tissue heterogeneity. For the latter, we estimated the adipose cell type proportions by utilizing single-nucleus RNA sequencing of frozen adipose tissue biopsies. Moreover, we established a prediction model to estimate insulin resistance using body fat percent and adipose RNA-sequencing data, which enlightens the importance of adipose tissue in insulin resistance and provides a helpful tool to impute the insulin resistance for existing adipose RNA-sequencing cohorts. Overall, we discover the potential causal effect of obesity on prediabetes and insulin resistance and the key role of adipose tissue in insulin resistance.Peer reviewe

Indole-3-propionic acid, a gut-derived tryptophan metabolite, associates with hepatic fibrosis

Background and Aims: Gut microbiota-derived metabolites play a vital role in maintenance of human health and progression of disorders, including obesity and type 2 diabetes (T2D). Indole-3-propionic acid (IPA), a gut-derived tryptophan metabolite, has been recently shown to be lower in individuals with obesity and T2D. IPA’s beneficial effect on liver health has been also explored in rodent and cell models. In this study, we investigated the association of IPA with human liver histology and transcriptomics, and the potential of IPA to reduce hepatic stellate cell activation in vitro. Methods: A total of 233 subjects (72% women; age 48.3 ± 9.3 years; BMI 43.1 ± 5.4 kg/m2) undergoing bariatric surgery with detailed liver histology were included. Circulating IPA levels were measured using LC-MS and liver transcriptomics with total RNA-sequencing. LX-2 cells were used to study hepatoprotective effect of IPA in cells activated by TGF-β1. Results: Circulating IPA levels were found to be lower in individuals with liver fibrosis compared to those without fibrosis (p = 0.039 for all participants; p = 0.013 for 153 individuals without T2D). Accordingly, levels of circulating IPA associated with expression of 278 liver transcripts (p p Conclusion: The association of circulating IPA with liver fibrosis and the ability of IPA to reduce activation of LX-2 cells suggests that IPA may have a therapeutic potential. Further molecular studies are needed to investigate the mechanisms how IPA can ameliorate hepatic fibrosis.</p