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Aims/hypothesis PPARGC1A encodes peroxisome proliferator-activated receptor γ coactivator 1-α (PGC-1α), a central regulator of energy metabolism and mitochondrial function. A common polymorphism in PPARGC1A (rs8192678, C/T, Gly482Ser) has been associated with obesity and related metabolic disorders, but no published functional studies have investigated direct allele-specific effects in adipocyte biology. We examined whether rs8192678 is a causal variant and reveal its biological function in human white adipose cells. Methods We used CRISPR-Cas9 genome editing to perform an allelic switch (C-to-T or T-to-C) at rs8192678 in an isogenic human pre-adipocyte white adipose tissue (hWAs) cell line. Allele-edited single-cell clones were expanded and screened to obtain homozygous T/T (Ser482Ser), C/C (Gly482Gly) and heterozygous C/T (Gly482Ser) isogenic cell populations, followed by functional studies of the allele-dependent effects on white adipocyte differentiation and mitochondrial function. Results After differentiation, the C/C adipocytes were visibly less BODIPY-positive than T/T and C/T adipocytes, and had significantly lower triacylglycerol content. The C allele presented a dose-dependent lowering effect on lipogenesis, as well as lower expression of genes critical for adipogenesis, lipid catabolism, lipogenesis and lipolysis. Moreover, C/C adipocytes had decreased oxygen consumption rate (OCR) at basal and maximal respiration, and lower ATP-linked OCR. We determined that these effects were a consequence of a C-allele-driven dysregulation of PGC-1α protein content, turnover rate and transcriptional coactivator activity. Conclusions/interpretation Our data show allele-specific causal effects of the rs8192678 variant on adipogenic differentiation. The C allele confers lower levels of PPARGC1A mRNA and PGC-1α protein, as well as disrupted dynamics of PGC-1α turnover and activity, with downstream effects on cellular differentiation and mitochondrial function. Our study provides the first experimentally deduced insights on the effects of rs8192678 on adipocyte phenotype. Graphical Abstract

Elucidation of mechanisms that govern lipid storage, oxidative stress and insulin resistance may lead to improved therapeutic options for type 2 diabetes and other obesity-related diseases. Here, we find that adipose expression of the small neutral amino acid transporter SLC7A10, also known as alanine-serine-cysteine transporter 1 (ASC-1), shows strong inverse correlates with visceral adiposity, insulin resistance and adipocyte hypertrophy across multiple cohorts. Concordantly, loss of Slc7a10 function in zebrafish in vivo accelerates diet-induced body weight gain and adipocyte enlargement. Mechanistically, SLC7A10 inhibition in human and murine adipocytes decreases adipocyte serine uptake and total glutathione levels and promotes reactive oxygen species (ROS) generation. Conversely, SLC7A10 overexpression decreases ROS generation and increases mitochondrial respiratory capacity. RNA-sequencing revealed consistent changes in gene expression between human adipocytes and zebrafish visceral adipose tissue following loss of SLC7A10, e.g., upregulation of SCD (lipid storage) and downregulation of CPT1A (lipid oxidation). Interestingly, ROS scavenger reduced lipid accumulation and attenuated the lipid-storing effect of SLC7A10 inhibition. These data uncover adipocyte SLC7A10 as a novel important regulator of adipocyte resilience to nutrient and oxidative stress, in part by enhancing glutathione levels and mitochondrial respiration, conducive to decreased ROS generation, lipid accumulation, adipocyte hypertrophy, insulin resistance and type 2 diabetes.

Context The neutral amino acid transporter SLC7A10/ASC-1 is an adipocyte-expressed gene with reduced expression in insulin resistance and obesity. Inhibition of SLC7A10 in adipocytes was shown to increase lipid accumulation despite decreasing insulin-stimulated uptake of glucose, a key substrate for de novo lipogenesis. These data imply that alternative lipogenic substrates to glucose fuel continued lipid accumulation during insulin resistance in obesity. Objective We examined whether increased lipid accumulation during insulin resistance in adipocytes may involve alter flux of lipogenic amino acids dependent on SLC7A10 expression and activity, and whether this is reflected by extracellular and circulating concentrations of marker metabolites. Methods In adipocyte cultures with impaired SLC7A10, we performed RNA sequencing and relevant functional assays. By targeted metabolite analyses (GC-MS/MS), flux of all amino acids and selected metabolites were measured in human and mouse adipose cultures. Additionally, SLC7A10 mRNA levels in human subcutaneous adipose tissue (SAT) were correlated to candidate metabolites and adiposity phenotypes in 2 independent cohorts. Results SLC7A10 impairment altered expression of genes related to metabolic processes, including branched-chain amino acid (BCAA) catabolism, lipogenesis, and glyceroneogenesis. In 3T3-L1 adipocytes, SLC7A10 inhibition increased fatty acid uptake and cellular content of glycerol and cholesterol. SLC7A10 impairment in SAT cultures altered uptake of aspartate and glutamate, and increased net uptake of BCAAs, while increasing the net release of the valine catabolite 3- hydroxyisobutyrate (3-HIB). In human cohorts, SLC7A10 mRNA correlated inversely with total fat mass, circulating triacylglycerols, BCAAs, and 3-HIB. Conclusion Reduced SLC7A10 activity strongly affects flux of BCAAs in adipocytes, which may fuel continued lipogenesis during insulin resistance, and be reflected in increased circulating levels of the valine-derived catabolite 3-HIB.

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

SummaryCellular exposure to free fatty acids (FFA) is implicated in the pathogenesis of obesity-associated diseases. However, studies to date have assumed that a few select FFAs are representative of broad structural categories, and there are no scalable approaches to comprehensively assess the biological processes induced by exposure to diverse FFAs circulating in human plasma. Furthermore, assessing how these FFA- mediated processes interact with genetic risk for disease remains elusive. Here we report the design and implementation of FALCON (Fatty Acid Library for Comprehensive ONtologies) as an unbiased, scalable and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids (MUFAs) with a distinct lipidomic profile associated with decreased membrane fluidity. Furthermore, we developed a new approach to prioritize genes that reflect the combined effects of exposure to harmful FFAs and genetic risk for type 2 diabetes (T2D). Importantly, we found that c-MAF inducing protein (CMIP) protects cells from exposure to FFAs by modulating Akt signaling and we validated the role of CMIP in human pancreatic beta cells. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.HighlightsFALCON (Fatty Acid Library for Comprehensive ONtologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effectsFALCON is applicable to many and diverse cell typesA subset of monounsaturated FAs (MUFAs) equally or more toxic than canonical lipotoxic saturated FAs (SFAs) leads to decreased membrane fluidityNew approach prioritizes genes that represent the combined effects of environmental (FFA) exposure and genetic risk for diseaseC-Maf inducing protein (CMIP) is identified as a suppressor of FFA-induced lipotoxicity via Akt-mediated signaling

BackgroundGenetic variation at theMTIF3(Mitochondrial Translational Initiation Factor 3) locus has been robustly associated with obesity in humans, but the functional basis behind this association is not known.MethodsHere, we applied luciferase reporter assay to map potential functional variants in the haplotype block tagged by rs1885988 and used CRISPR-Cas9 to edit the potential functional variants to confirm the regulatory effects onMTIF3expression. We further conducted functional studies on MTIF3-deficient differentiated human white adipocyte cell line (hWAs-iCas9), generated through inducible expression of CRISPR-Cas9 combined with delivery of syntheticMTIF3-targeting guide RNA.ResultsWe demonstrate that rs67785913-centered DNA fragment (in LD with rs1885988, r2>0.8) enhances transcription in a luciferase reporter assay, and CRISPR/Cas9 edited rs67785913 CTCT cells show significantly higherMTIF3expression than rs67785913 CT cells. PerturbedMTIF3expression changed the expression of mitochondrial DNA-encoded genes, and reduced mitochondrial respiration, as well as altered endogenous fatty acid oxidation. Furthermore, after glucose restriction, theMTIF3knockout cells retained more triglycerides than control cells.ConclusionsThis study demonstrates an adipocyte function-specific role ofMTIF3, which originates in the maintenance of mitochondrial function, providing potential explanations for whyMTIF3genetic variation at rs67785913 is associated with body corpulence and response to weight loss interventions.

Scalable sequence–function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

Adipose tissue is an organ with great plasticity and its hypertrophic expansion is associated with adipocyte dysfunction. How changes in adipocyte morphology are linked to gene expression and which cellular functions are affected remains elusive. We show that adipocyte hypertrophy is associated with transcriptomic changes using RNA-Seq data obtained from adipose tissue and size-separated adipocytes. Genes involved in oxidative phosphorylation, protein biosynthesis and fatty acid metabolism were down-regulated in large adipocytes while genes involved in inflammation were upregulated. For mitochondrial function, a reduction in the expression of thermogenesis related genes and estimated brown/beige adipocyte content was observed in individuals with large adipocytes. As a novel finding the total adipose tissue fatty acid composition was dependent on cell size and depot. MR spectroscopy methods for clinical scanning were developed to characterize adipocyte size and fatty acid composition in a fast and non-invasive manner. Together, the present data provides mechanistic insights on how adipocyte hypertrophy contributes to the manifestation of metabolic disease at the molecular and cellular level. MR spectroscopy was identified as a promising technique for an in-parallel assessment of adipose morphology and fatty acid composition allowing to translate our findings into an improved, non-invasive phenotyping of adipose tissue.

White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense1. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes (T2D)2. Here, we provide detailed cellular atlases of human and murine subcutaneous and visceral white fat at single cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells (ASPCs), vascular, and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease, and we provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits, and cell types in the function of WAT across species, depots, and nutritional conditions.

For any given level of overall adiposity – as commonly quantified by body mass index (BMI) within clinical practice – individuals vary considerably in fat distribution. We and others have noted that increased visceral fat (VAT) is associated with increased cardiometabolic risk, while gluteofemoral fat (GFAT) may be protective. Familial partial lipodystrophy (FPLD) – often caused by rare variants in the LMNA gene – represents an extreme example of this paradigm, leading to a severe shift to visceral fat with subsequent insulin resistance and adverse metabolic profile. By contrast, the inherited basis of body fat distribution in the broader population is not fully understood. Here, we studied up to 38,965 UK Biobank participants with VAT, abdominal subcutaneous (ASAT), and GFAT volumes precisely quantified using abdominal MRI. Because genetic associations with these raw depot volumes were largely driven by variants known to affect BMI, we next studied six phenotypes of local adiposity: VAT adjusted for BMI (VATadjBMI), ASATadjBMI, GFATadjBMI, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 178 unique loci associated with at least one adiposity trait, including 29 newly-identified loci. Rare variant association studies extend prior evidence of association for PDE3B as an important modulator of fat distribution. Sex-specific analyses of local adiposity traits noted overall higher estimated heritability in females, increased effect sizes for identified loci, and 25 female-specific associations. Individuals in the extreme tails of fat distribution phenotypes were highly enriched for predisposing common variants, as quantified using polygenic scores. Taking GFATadjBMI as an example, individuals with extreme values were 3.8-fold (95%CI 2.8 to 5.2) more likely to have a polygenic score within the top 5% of the distribution. These results – using more precise and BMI-independent measures of local adiposity – confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared to healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies are paving the way for a principled approach to systematic Variants – to – Function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.

SummaryA primary obstacle in translating genetics and genomics data into therapeutic strategies is elucidating the cellular programs affected by genetic variants and genes associated with human diseases. Broadly applicable high-throughput, unbiased assays offer a path to rapidly characterize gene and variant function and thus illuminate disease mechanisms. Here, we report LipocyteProfiler, an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types. We apply LipocyteProfiler to adipocytes and hepatocytes and demonstrate its ability to survey diverse cellular mechanisms by generating rich context-, and process-specific morphological and cellular profiles. We then use LipocyteProfiler to identify known and novel cellular programs altered by polygenic risk of metabolic disease, including insulin resistance, waist-to-hip ratio and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse phenotypic profiling in lipid-storing cells, and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

The obesity-linked FTO regulatory circuitry shows cross-species molecular, cellular, metabolic, and organismal conservation., Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot–dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet–induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant’s action.

Obesity genes working together The biological causes of obesity are not well understood. Sobreira et al. examined the chromatin interactions between key genes in a locus known for its associations with obesity in human patients. In addition to directly interrogating the connections between these genes and examining the mechanisms that regulate their activity, the authors used mouse models to study the target genes' effects on both adipose tissue and brain cells that play a role in regulating dietary preferences. Science , abf1008, this issue p. 1085

Genome-wide epigenomic maps have revealed millions of putative enhancers and promoters, but experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited. Here, we present HiDRA (High-resolution Dissection of Regulatory Activity), a combined experimental and computational method for high-resolution genome-wide testing and dissection of putative regulatory regions. We test ~7 million accessible DNA fragments in a single experiment, by coupling accessible chromatin extraction with self-transcribing episomal reporters (ATAC-STARR-seq). By design, fragments are highly overlapping in densely-sampled accessible regions, enabling us to pinpoint driver regulatory nucleotides by exploiting differences in activity between partially-overlapping fragments using a machine learning model (SHARPR-RE). In GM12878 lymphoblastoid cells, we find ~65,000 regions showing enhancer function, and pinpoint ~13,000 high-resolution driver elements. These are enriched for regulatory motifs, evolutionarily-conserved nucleotides, and disease-associated genetic variants from genome-wide association studies. Overall, HiDRA provides a high-throughput, high-resolution approach for dissecting regulatory regions and driver nucleotides., Millions of enhancers are predicted, but their validation remains challenging. Here, the authors report genome-wide enhancer function quantification and high-resolution dissection for millions of accessible DNA fragments, revealing driver nucleotides and helping interpret non-coding disease variants.

Cold-induced non-shivering thermogenesis (CIT) of the human body is currently discussed as an important contributor to energy metabolism, and miRNAs have recently been reported as key regulators of metabolism. Identifying the link of CIT and circulating microRNAs (miRNAs) in a large cohort of human individuals remains elusive to date. Here, we analyzed a set of 158 miRNAs in the serum of 97 female and 72 male healthy individuals before and after cold exposure (CE). Validating the results of a miRNA array, a significant down-regulation of miR-375 was measured in individuals after CIT (P

Activation of brown adipose tissue may increase energy expenditure by non-shivering thermogenesis. Cold exposure is one of the options to activate brown adipocytes. To link changes in energy metabolism with microRNA expression (miRNAs), we analyzed 158 miRNAs in serum of 169 healthy individuals before and after cold exposure. Validating the results of a miRNA array, a significant down-regulation of miR-375 after cold exposure (P 0.0001) was detected. These changes went along with a significant negative correlation between miR-375 and visceral adipose tissue (VAT) mass (P 0.0001), implicating a specific function of miR-375 in this depot. Significantly higher expression levels of miR-375 were found in VAT in comparison to subcutaneous fat (SAT). Using in silico prediction, we identified putative miR-375 target genes involved in the thermogenesis pathway. Cold-stimulation of subcutaneous and visceral pre-adipocytes (PACs) led to significantly higher expression levels of FABP4, FGF21, PPARGC1A and PRDM16 in VC-PACs. Analyzing miR-375 knock down and cold stimulated VC-PACs revealed a significant up-regulation of thermogenesis associated genes PPARGC1A, ELOVL3 and PRDM16. In summary, our findings identified miR-375 as a potential adipogenic and thermogenesis-associated miRNA exclusively acting in visceral adipose tissue.

Purpose To develop a methodology for probing lipid droplet sizes with a clinical system based on a diffusion-weighted stimulated echo-prepared turbo spin-echo sequence and to validate the methodology in water-fat emulsions and show its applicability in ex vivo adipose-tissue samples. Methods A diffusion-weighted stimulated echo-prepared preparation was combined with a single-shot turbo spin-echo readout for measurements at different b-values and diffusion times. The droplet size was estimated with an analytical expression, and three fitting approaches were compared: magnitude-based spatial averaging with voxel-wise residual minimization, complex-based spatial averaging with voxel-wise residual minimization, and complex-based spatial averaging with neighborhood-regularized residual minimization. Simulations were performed to characterize the fitting residual landscape and the approaches' noise performance. The applicability was assessed in oil-in-water emulsions in comparison with laser deflection and in ten human white adipose tissue samples in comparison with histology. Results The fitting residual landscape showed a minimum valley with increasing extent as the droplet size increased. In phantoms, a very good agreement of the mean droplet size was observed between the diffusion-weighted MRI-based and the laser deflection measurements, showing the best performance with complex-based spatial averaging with neighborhood-regularized residual minimization processing (R2 /P: 0.971/0.014). In the human adipose-tissue samples, complex-based spatial averaging with neighborhood-regularized residual minimization processing showed a significant correlation (R2 /P: 0.531/0.017) compared with histology. Conclusion The proposed acquisition and parameter-estimation methodology was able to probe restricted diffusion effects in lipid droplets. The methodology was validated using phantoms, and its feasibility in measuring an apparent lipid droplet size was demonstrated ex vivo in white adipose tissue.

Summary Skeletal and glycemic traits have shared etiology, but the underlying genetic factors remain largely unknown. To identify genetic loci that may have pleiotropic effects, we studied Genome-wide association studies (GWASs) for bone mineral density and glycemic traits and identified a bivariate risk locus at 3q21. Using sequence and epigenetic modeling, we prioritized an adenylate cyclase 5 (ADCY5) intronic causal variant, rs56371916. This SNP changes the binding affinity of SREBP1 and leads to differential ADCY5 gene expression, altering the chromatin landscape from poised to repressed. These alterations result in bone- and type 2 diabetes-relevant cell-autonomous changes in lipid metabolism in osteoblasts and adipocytes. We validated our findings by directly manipulating the regulator SREBP1, the target gene ADCY5, and the variant rs56371916, which together imply a novel link between fatty acid oxidation and osteoblast differentiation. Our work, by systematic functional dissection of pleiotropic GWAS loci, represents a framework to uncover biological mechanisms affecting pleiotropic traits., Graphical abstract, Highlights • 3q21 variants are associated with type 2 diabetes and increased bone mineral density • 3q21 variants affect chromatin accessibility in mesenchymal cells • rs56371916 is a 3q21 causal variant and ADCY5 its target in adipocytes and osteoblasts • ADCY5 and rs56371916 affect lipid oxidation processes in adipocytes and osteoblasts, Nasa Sinnott-Armstrong and colleagues identify a pleiotropic risk locus at 3q21 that is associated with type 2 diabetes (T2D) and greater bone mineral density (BMD) and its associated cell-autonomous mechanisms in adipocytes and osteoblasts. Together, these findings provide a possible explanation for the perplexing finding that individuals with T2D have higher BMD but greater susceptibility to bone fracture.

While coding variants often have pleiotropic effects across multiple tissues, non-coding variants are thought to mediate their phenotypic effects by specific tissue and temporal regulation of gene expression. Here, we dissected the genetic and functional architecture of a genomic region within the FTO gene that is strongly associated with obesity risk. We show that multiple variants on a common haplotype modify the regulatory properties of several enhancers targeting IRX3 and IRX5 from megabase distances. We demonstrate that these enhancers impact gene expression in multiple tissues, including adipose and brain, and impart regulatory effects during a restricted temporal window. Our data indicate that the genetic architecture of disease-associated loci may involve extensive pleiotropy, allelic heterogeneity, shared allelic effects across tissues, and temporally-restricted effects.

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10−16, R2subcutaneous = 0.91, P < 2.2 × 10−16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10−69, βsubq = 0.45; Pvisc = 2.5 × 10−55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10−7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations., Author summary Fundamental aspects of biology such as how the size or number of adipocytes relates to obesity and cardiometabolic health are still unanswered. To answer such questions, fast, accurate and automated measurements need to be acquired, free from human biases. Glastonbury et al., 2020 describe a novel machine learning method to perform rapid acquisition of adipocyte area estimates from histological imaging data. Using these imaging derived phenotypes, Glastonbury et al., 2020 assess the relationship between adipocyte size and a range of cardio-metabolic comorbidities, demonstrating that adipocyte size can vary depending on where adipose is stored throughout the body. By tying genetics with imaging data, Glastonbury et al., 2020 were able to demonstrate that previous findings associating adipocyte size with Type 2 Diabetes variants, are likely to be false positives. This study provides a means of being able to scale up GWAS type analyses to imaging derived phenotypes.

Elucidation of mechanisms that govern lipid storage, oxidative stress, and insulin resistance may lead to improved therapeutic options for type 2 diabetes and other obesity-related diseases. Here, we find that adipose expression of the small neutral amino acid transporter SLC7A10, also known as alanine-serine-cysteine transporter-1 (ASC-1), shows strong inverse correlates with visceral adiposity, insulin resistance, and adipocyte hypertrophy across multiple cohorts. Concordantly, loss of Slc7a10 function in zebrafish in vivo accelerates diet-induced body weight gain and adipocyte enlargement. Mechanistically, SLC7A10 inhibition in human and murine adipocytes decreases adipocyte serine uptake and total glutathione levels and promotes reactive oxygen species (ROS) generation. Conversely, SLC7A10 overexpression decreases ROS generation and increases mitochondrial respiratory capacity. RNA sequencing revealed consistent changes in gene expression between human adipocytes and zebrafish visceral adipose tissue following loss of SLC7A10, e.g., upregulation of SCD (lipid storage) and downregulation of CPT1A (lipid oxidation). Interestingly, ROS scavenger reduced lipid accumulation and attenuated the lipid-storing effect of SLC7A10 inhibition. These data uncover adipocyte SLC7A10 as a novel important regulator of adipocyte resilience to nutrient and oxidative stress, in part by enhancing glutathione levels and mitochondrial respiration, conducive to decreased ROS generation, lipid accumulation, adipocyte hypertrophy, insulin resistance, and type 2 diabetes.

Context Cold exposure (CE) has been shown to enhance energy expenditure by activating brown adipose tissue thermogenesis and metabolism in humans. However, it remains to be elucidated if there are gender-specific differences in cold-induced thermogenesis and metabolism. Objective To study the impact of mild CE on resting energy expenditure (REE) and metabolism in males compared with females. Setting A cross-sectional study. Participants 117 healthy young Caucasians participated in this study (58 males). Mean age was 25.1 ± 3.6 years and mean body mass index 22.3 ± 1.7 kg/m2. Intervention Participants underwent a short-term CE using water perfused mattresses to activate nonshivering thermogenesis. Main Outcome Measures REE was assessed before and 2 hours after CE followed by blood sampling. Selected metabolites and hormones were measured. Skin temperatures were monitored at various sites throughout the experiment. Results Participants showed a significant increase in REE after CE (6.5%, P < .001). This increase did not differ between genders (P = .908). However, there were differences between males and females in changes of plasma glucose (–5.1% versus –7.4%, P = .024), leptin (–14.3% versus –30.1%, P < .001) and adiponectin (5.4% versus 12.8%, P = .018) after CE. We observed a significant decrease of the supraclavicular skin temperature in men (–0.3%, P = .034), but not in women (0.3%, P = .326)(P = .019 between genders). Conclusions We did not observe a difference in the thermogenic response, measured as change of REE, to CE in women compared with men. However, we found that some metabolic and hormonal changes were more pronounced in women than in men suggesting a gender-specific response to cold.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral= 0.94, P < 2.2 × 10−16, R2subcutaneous= 0.91, P < 2.2 × 10−16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10−69, βsubq = 0.45; Pvisc= 2.5 × 10−55, βvisc= 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta= 9.8 × 10−7). Lastly, we performed the largest GWAS and subsequent meta-analysis of adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

Genome-wide association studies (GWASs) have revealed numerous loci for areal bone mineral density (aBMD). We completed the first GWAS meta-analysis (n = 15,275) of lumbar spine volumetric BMD (vBMD) measured by quantitative computed tomography (QCT), allowing for examination of the trabecular bone compartment. SNPs that were significantly associated with vBMD were also examined in two GWAS meta-analyses to determine associations with morphometric vertebral fracture (n = 21,701) and clinical vertebral fracture (n = 5893). Expression quantitative trait locus (eQTL) analyses of iliac crest biopsies were performed in 84 postmenopausal women, and murine osteoblast expression of genes implicated by eQTL or by proximity to vBMD-associated SNPs was examined. We identified significant vBMD associations with five loci, including: 1p36.12, containing WNT4 and ZBTB40; 8q24, containing TNFRSF11B; and 13q14, containing AKAP11 and TNFSF11. Two loci (5p13 and 1p36.12) also contained associations with radiographic and clinical vertebral fracture, respectively. In 5p13, rs2468531 (minor allele frequency [MAF] = 3%) was associated with higher vBMD (β = 0.22, p = 1.9 × 10-8 ) and decreased risk of radiographic vertebral fracture (odds ratio [OR] = 0.75; false discovery rate [FDR] p = 0.01). In 1p36.12, rs12742784 (MAF = 21%) was associated with higher vBMD (β = 0.09, p = 1.2 × 10-10 ) and decreased risk of clinical vertebral fracture (OR = 0.82; FDR p = 7.4 × 10-4 ). Both SNPs are noncoding and were associated with increased mRNA expression levels in human bone biopsies: rs2468531 with SLC1A3 (β = 0.28, FDR p = 0.01, involved in glutamate signaling and osteogenic response to mechanical loading) and rs12742784 with EPHB2 (β = 0.12, FDR p = 1.7 × 10-3 , functions in bone-related ephrin signaling). Both genes are expressed in murine osteoblasts. This is the first study to link SLC1A3 and EPHB2 to clinically relevant vertebral osteoporosis phenotypes. These results may help elucidate vertebral bone biology and novel approaches to reducing vertebral fracture incidence. © 2016 American Society for Bone and Mineral Research.

We have prioritised a single nucleotide polymorphism (SNP) rs2645294 as one candidate functional SNP in the TBX15-WARS2 waist-hip-ratio locus using posterior probability analysis. This SNP is located in the 3′ untranslated region of the WARS2 (tryptophanyl tRNA synthetase 2, mitochondrial) gene with which it has an expression quantitative trait in subcutaneous white adipose tissue. We show that transcripts of the WARS2 gene in a human white adipose cell line, heterozygous for the rs2645294 SNP, showed allelic imbalance. We tested whether the rs2645294 SNP altered WARS2 RNA stability using three different methods: actinomycin-D inhibition and RNA decay, mature and nascent RNA analysis and luciferase reporter assays. We found no evidence of a difference in RNA stability between the rs2645294 alleles indicating that the allelic expression imbalance was likely due to transcriptional regulation., Highlights • SNP rs2645294 was identified by PPA as one potential functional SNP for WHRadjBMI associated with the TBX15/WARS2 locus • SNP rs2645294 has an expression QTL for the WARS2 gene • Transcripts containing SNP rs2645294 showed allelic expression imbalance in a heterozygous human white adipose cell line • SNP rs2645294 was a potential WARS2 3’UTR gene regulatory SNP • No difference in allelic RNA stability was found, imbalance likely originating from transcriptional regulatory mechanisms

Genomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

Genome-wide epigenomic maps revealed millions of regions showing signatures of enhancers, promoters, and other gene-regulatory elements1. However, high-throughput experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited in their scale and length of regions tested. Here, we present a new method, HiDRA (High-Definition Reporter Assay), that overcomes these limitations by combining components of Sharpr-MPRA2 and STARR-Seq3 with genome-wide selection of accessible regions from ATAC-Seq4. We used HiDRA to test ~7 million DNA fragments preferentially selected from accessible chromatin in the GM12878 lymphoblastoid cell line. By design, accessibility-selected fragments were highly overlapping (up to 370 per region), enabling us to pinpoint driver regulatory nucleotides by exploiting subtle differences in reporter activity between partially-overlapping fragments, using a new machine learning model SHARPR2. Our resulting maps include ~65,000 regions showing significant enhancer function and enriched for endogenous active histone marks (including H3K9ac, H3K27ac), regulatory sequence motifs, and regions bound by immune regulators. Within them, we discover ~13,000 high-resolution driver elements enriched for regulatory motifs and evolutionarily-conservednucleotides, and help predict causal genetic variants underlying disease from genome-wide association studies. Overall, HiDRA provides a general, scalable, high-throughput, and high-resolution approach for experimental dissection of regulatory regions and driver nucleotides in the context of human biology and disease.

Genome-wide association studies identified numerous disease risk loci. Delineating molecular mechanisms influenced by cis-regulatory variants is essential to understand gene regulation and ultimately disease pathophysiology. Combining bioinformatics and public domain chromatin information with quantitative proteomics supports prediction of cis-regulatory variants and enabled identification of allele-dependent binding of both, transcription factors and coregulators at the type 2 diabetes associated PPARG locus. We found rs7647481A nonrisk allele binding of Yin Yang 1 (YY1), confirmed by allele-specific chromatin immunoprecipitation in primary adipocytes. Quantitative proteomics also found the coregulator RING1 and YY1 binding protein (RYBP) whose mRNA levels correlate with improved insulin sensitivity in primary adipose cells carrying the rs7647481A nonrisk allele. Our findings support a concept with diverse cis-regulatory variants contributing to disease pathophysiology at one locus. Proteome-wide identification of both, transcription factors and coregulators, can profoundly improve understanding of mechanisms underlying genetic associations.

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p, We acknowledge the essential role of the Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) Consortium in development and support of this manuscript. CHARGE members include the Netherland’s Rotterdam Study (RS), Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), the NHLBI’s Atherosclerosis Risk in Communities (ARIC) Study, and Iceland’s Age, Gene/Environment Susceptibility (AGES) Reykjavik Study. Age, Gene/Environment Susceptibility Reykjavik Study (AGES-Reykjavik): has been funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, (VSN: 00-063) and the Data Protection Authority. The researchers are indebted to the participants for their willingness to participate in the study. Old Order Amish (OOA): this work was supported by NIH research grants U01 HL72515, U01 GM074518, R01 HL088119, R01 AR046838, and U01 HL084756. Partial funding was also provided by the Mid-Atlantic Nutrition and Obesity Research Center of Maryland (P30 DK072488).). L.M.Y.-A. was supported by F32AR059469 from NIH/NIAMS. M.F. was supported by American Heart Association grant 10SDG2690004. Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts N01-HC- 85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086; N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, N01-HC-85239, and by HHSN268201200036C and NHLBI grants HL080295, HL087652, HL105756, HL103612, HL120393, and HL130114 with additional contribution from NINDS. Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the NIA. See also http://www.chs-nhlbi.org/pi.htm. DNA handling and genotyping at Cedars-Sinai Medical Center was supported in part by the National Center for Research Resources, grant UL1RR033176, and is now at the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124; in addition to the National Institute of Diabetes and Digestive and Kidney Disease grant DK063491 to the Southern California Diabetes Endocrinology Research Center. CoLaus: The CoLaus study received financial contributions from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (grants 33CSCO-122661, 33CS30-139468, and 33CS30-148401). We thank Vincent Mooser and Gérard Waeber, Co-PIs of the CoLaus study. Special thanks to Yolande Barreau, Mathieu Firmann, Vladimir Mayor, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey, and Sylvie Mermoud for data collection. Data analysis was supervised by Sven Bergmann and Jacques S. Beckmann. The computations for this paper were performed in part at the Vital-IT Center for high-performance computing of the Swiss Institute of Bioinformatics. deCODE Study: The study was funded by deCODE Genetics, ehf. We thank all the participants of this study, the staff of deCODE Genetics core facilities and recruitment center and the densitometry clinic at the University Hospital for their important contributions to this work. The EPIC Study: The EPIC Obesity study is funded by Cancer Research United Kingdom and the Medical Research Council. I.B. acknowledges support from EU FP6 funding (contract no. LSHM-CT-2003-503041) and by the Wellcome Trust (WT098051). Erasmus Rucphen Family (ERF) Study: The study was supported by grants from The Netherlands Organisation for Scientific Research (NWO), Erasmus MC, the Centre for Medical Systems Biology (CMSB), and the European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. We are grateful to all general practitioners for their contributions, to Petra Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work and Peter Snijders for his help in data collection. Fenland: The Fenland Study is funded by the Wellcome Trust and the Medical Research Council, as well as by the Support for Science Funding programme and CamStrad. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for help with recruitment. We thank the Fenland Study co-ordination team and the Field Epidemiology team of the MRC Epidemiology Unit for recruitment and clinical testing. Tuomas O. Kilpeläinen was supported by the Danish Council for Independent Research (DFF—1333-00124 and Sapere Aude program grant DFF—1331-00730B). Framingham Osteoporosis Study (FOS)/Framingham Heart Study (FHS): The study was funded by grants from the US National Institute for Arthritis, Musculoskeletal and Skin Diseases and National Institute on Aging (R01 AR 41398 and U24AG051129; D.P.K. and R01AR057118; D.K. D.K. was also supported by FP7-PEOPLE-2012-Marie Curie Career Integration Grants (CIG)). The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health and Boston University School of Medicine were supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study (N01-HC-25195) and its contract with Affymetrix, Inc. for genotyping services (N02-HL-6-4278). Analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. A portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. eQTL HOb Study: The study was supported by Genome Quebec, Genome Canada and the Canadian Institutes of Health Research (CIHR). Gothenburg Osteoporosis and Obesity Determinants Study (GOOD): The study was funded by the Swedish Research Council, the Swedish Foundation for Strategic Research, The ALF/LUA research grant in Gothenburg, the Lundberg Foundation, the Emil and Vera Cornell Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlunds Foundation, the Västra Götaland Foundation, and the Göteborg Medical Society. We would like to thank Dr Tobias A. Knoch, Luc V. de Zeeuw, Anis Abuseiris, and Rob de Graaf as well as their institutions the Erasmus Computing Grid, Rotterdam, The Netherlands, and especially the national German MediGRID and Services@MediGRID part of the German D-Grid, both funded by the German Bundesministerium fuer Forschung und Technology under grants #01 AK 803 A-H and # 01 IG 07015G for access to their grid resources. We also thank Karol Estrada, Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands for advice regarding the grid resources. Health Aging and Body Composition Study (Health ABC): This study was funded by the National Institutes of Aging. This research was supported by NIA contracts N01AG62101, N01AG62103, and N01AG62106. The genome-wide association study was funded by NIA grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. Indiana: We thank the individuals who participated in this study, as well as the study coordinators, without whom this work would not have been possible. This work was supported by National Institutes of Health grants R01 AG 041517 and M01 RR-00750. Genotyping services were provided by CIDR. CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, National Library of Medicine. Kora (KORA F3 and KORA F4): The KORA research platform was initiated and financed by the Helmholtz Center Munich, German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823). Our research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. The London Life Sciences Population (LOLIPOP): The study was funded by the British Heart Foundation, Wellcome Trust, the Medical Research Council, and Kidney Research UK. The study also receives support from a National Institute for Health Research (NIHR) programme grant. Rotterdam Study (RSI, RSII & RSIII): The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project no. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera, Marjolein Peters, MSc, and Carolina Medina-Gomez, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and Carolina Medina-Gomez, PhD, for the creation and analysis of imputed data. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. We are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. We thank Dr Karol Estrada, Dr Fernando Rivadeneira, Dr Tobias A. Knoch, Anis Abuseiris, and Rob de Graaf (Erasmus MC Rotterdam, The Netherlands) for their help in creating GRIMP, and we thank BigGRID, MediGRID, and Services@MediGRID/D-Grid (funded by the German Bundesministerium fuer Forschung und Technology; grants 01 AK 803 A-H, 01 IG 07015G) for access to their grid computing resources. Rush Memory and Aging Project (MAP): The Memory and Aging Project was supported by National Institute on Aging grants R01AG17917, R01AG15819, and R01AG24480, the Illinois Department of Public Health, the Rush Clinical Translational Science Consortium, and a gift from Ms Marsha Dowd. TwinsUK (TUK): The study was funded by the Wellcome Trust, Arthritis Research UK, and the Chronic Disease Research Foundation. The study also received support from a National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London. We thank the staff and volunteers of the TwinsUK study. The study was also supported by Israel Science Foundation, grant number 994/10. Age, Gene/Environment Susceptibility Reykjavik Study (AGES-Reykjavik) has been funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee (VSN: 00-063) and the Data Protection Authority. The researchers are indebted to the participants for their willingness to participate in the study. Berlin Aging Study II (BASE-II) was supported by the German Federal Ministry of Education and Research (BMBF (grants #16SV5536K, #16SV5537, #16SV5538, and #16SV5837; previously #01UW0808)). Additional contributions (e.g., financial, equipment, logistics, personnel) are made from each of the other participating sites, i.e., the Max Planck Institute for Human Development (MPIB), Max Planck Institute for Molecular Genetics (MPIMG), Charite University Medicine, German Institute for Economic Research (DIW), all located in Berlin, Germany, and University of Lübeck in Lübeck, Germany. B-vitamins in the prevention of osteoporotic fractures (B-PROOF): B-PROOF is supported and funded by The Netherlands Organization for Health Research and Development (ZonMw, grant 6130.0031), the Hague; unrestricted grant from NZO (Dutch Dairy Association), Zoetermeer; Orthica, Almere; NCHA (Netherlands Consortium Healthy Ageing) Leiden/Rotterdam; Ministry of Economic Affairs, Agriculture and Innovation (project KB-15-004-003), the Hague; Wageningen University, Wageningen; VU University Medical Center, Amsterdam; Erasmus Medical Center, Rotterdam. All organizations are based in the Netherlands. We thank Dr Tobias A. Knoch, Anis Abuseiris, Karol Estrada, and Rob de Graaf as well as their institutions the Erasmus Grid Office, Erasmus MC Rotterdam, The Netherlands, and especially the national German MediGRID and Services@MediGRID part of the German D-Grid, both funded by the German Bundesministerium fuer Forschung und Technology (grants #01 AK 803 A-H and #01 IG 07015G) for access to their gird resources. Further, we gratefully thank all participants. Calcium Intake Fracture Outcome Study (CAIFOS): This study was funded by Healthway Health Promotion Foundation of Western Australia, Australasian Menopause Society and the Australian National Health and Medical Research Council Project Grants (254627, 303169, and 572604). We are grateful to the participants of the CAIFOS Study. The salary of Dr Lewis is supported by a National Health and Medical Research Council of Australia Career Development Fellowship. Danish Osteoporosis Study (DOPS): The study was supported by Karen Elise Jensen foundation. Family Heart Study (FamHS): The study was supported by NIH grants R01-HL-117078, R01-HL-087700, and R01-HL-088215 from NHLBI; and R01-DK-089256 and R01-DK-075681 from NIDDK. GenMets (Health 2000): S.R. was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (213506 and 129680), Academy of Finland (251217), the Finnish foundation for Cardiovascular Research and the Sigrid Juselius Foundation. S.M. was supported by grants #136895 and #141005, V.S. by grants #139635 and 129494, and M.P. by grant #269517 from the Academy of Finland and a grant from the Finnish Foundation for Cardiovascular Research. M.P. was supported by the Yrjö Jahnsson Foundation. Helsinki Birth Cohort Study (HBCS): We thank all study participants as well as everybody involved in the HBCS. HBCS has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Samfundet Folkhälsann, Novo Nordisk Foundation, Liv och Hälsa, Finska Läkaresällskapet, Signe and Ane Gyllenberg Foundation, University of Helsinki, European Science Foundation (EUROSTRESS), Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation, Juho Vainio Foundation, and Wellcome Trust (grant number WT089062). Johnston County Study: The Johnston County Osteoarthritis Project is supported in part by cooperative agreements S043, S1734, and S3486 from the Centers for Disease Control and Prevention/Association of Schools of Public Health; the NIAMS Multipurpose Arthritis and Musculoskeletal Disease Center grant 5-P60-AR30701; and the NIAMS Multidisciplinary Clinical Research Center grant 5 P60 AR49465-03. Genotyping services were provided by Algynomics company. Korean Genome Epidemiology Study (KoGES): Korean Genome Epidemiology Study (KoGES): This work was supported by the Research Program funded by the Korea Centers for Disease Control and Prevention (found 2001-347-6111-221, 2002-347-6111-221, 2009-E71007-00, 2010-E71004-00). Kora F3 and Kora F4: The KORA research platform was initiated and financed by the Helmholtz Center Munich, German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823). Our research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. LOLIP-REP-IA610: The study was supported by the Wellcome Trust. We thank the participants and research teams involved in LOLIPOP. LOLIP-REP-IA_I: The study was supported by the British Heart Foundation Grant SP/04/002. LOLIP-REP-IA_P: The study was supported by the British Heart Foundation Grant SP/04/002. METSIM: The study was supported by the Academy of Finland, the Finnish Diabetes Research Foundation, the Finnish Cardiovascular Research Foundation, the Strategic Research Funding from the University of Eastern Finland, Kuopio, and the EVO grant 5263 from the Kuopio University Hospital. MrOS Sweden: Financial support was received from the Swedish Research Council (2006-3832), the Swedish Foundation for Strategic Research, the ALF/LUA research grant in Gothenburg, the Lundberg Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlunds Foundation, the Västra Götaland Foundation, the Göteborg Medical Society, and the Novo Nordisk foundation. Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. MrOS US: The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provided funding for the MrOS ancillary study “GWAS in MrOS and SOF” under the grant number RC2ARO58973. Osteoporosis Prospective Risk Assessment study (OPRA): This work was supported by grants from the Swedish Research Council (K2009-53X-14691-07-3, K2010-77PK-21362-01-2), FAS (grant 2007-2125), Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. We are thankful to all the women who kindly participated in the study and to the staff at the Clinical and Molecular Osteoporosis Research Unit for helping in recruitment of study individuals. Orkney Complex Disease Study (ORCADES): ORCADES was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710), the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK (17539) and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. We acknowledge the invaluable contributions of Lorraine Anderson and the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. PEAK 25: This work was supported by grants from the Swedish Research Council (K2009-53X-14691-07-3, K2010-77PK-21362-01-2), FAS (grant 2007-2125), Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. We are thankful to all the women who kindly participated in the study and to the staff at the Clinical and Molecular Osteoporosis Research Unit for helping in recruitment of study individuals. Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS): The study was supported by grants from the Swedish research council (projects 2008-2202 and 2005-8214) and ALF/LUA research grants from Uppsala university hospital, Uppsala, Sweden. Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC): The RISC study is supported by European Union Grant QLG1-CT-2001-01252 and AstraZeneca. We thank Merck Research Labs for conducting DNA genotyping on RISC samples.Rotterdam III: Rotterdam Study (RS): See discovery. SHIP and SHIP TREND: This work was supported by SHIP, which is part of the Community Medicine Research Network of the University of Greifswald, Germany, by the Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania and the network “Greifswald Approach to Individualized Medicine (GANI_MED)” funded by the Federal Ministry of Education and Research (03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the “Center of Knowledge Interchange” program of the Siemens. A.G. and the Cache´ Campus program of the InterSystems GmbH. The SHIP authors are grateful to the contribution of Florian Ernst, Anja Wiechert, and Astrid Petersmann in generating the SNP data and to Mario Stanke for the opportunity to use his Server Cluster for SNP Imputation. Data analyses were further supported by the German Research Foundation (DFG Vo 955/10-1) and the Federal Ministry of Nutrition, Agriculture and Consumer’s Safety. SOF: The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provided funding for the SOF ancillary study “GWAS in MrOS and SOF” under the grant number RC2ARO58973. Uppsala Longitudinal Study of Adult Men (ULSAM): The study was funded by grants from the Swedish research council (projects 2008-2202 and 2005-8214), the Wallenberg foundation, and ALF/LUA research grants from Uppsala university hospital, Uppsala, Sweden. Andrew P. Morris is a Wellcome Trust Senior Fellow in Basic Biomedical Science, grant number WT098017. CROATIA-VIS (VIS): The CROATIA-Vis study was funded by grants from the Medical Research Council (UK) and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-1080315-0302). We acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health. The SNP genotyping for the CROATIA-Vis cohort was performed in the core genotyping laboratory of the Wellcome Trust Clinical Research Facility at the Western General Hospital, Edinburgh, Scotland. Women’s Health Initiative (WHI): The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts N01WH22110, 24152, 32100–2, 32105–6, 32108–9, 32111–13, 32115, 32118–32119, 32122, 42107–26, 42129–32, and 44221. We thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A listing of WHI investigators can be found at https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf. FUSION: This research was supported in part by US National Institutes of Health grants 1-ZIA-HG000024 (to F.S.C.), U01DK062370 (to M.B.), R00DK099240 (to S.C.J.P.), the American Diabetes Association Pathway to Stop Diabetes Grant 1-14-INI-07 (to S.C.J.P.), and Academy of Finland Grants 271961 and 272741 (to M.L.) and 258753 (to H.A.K.). We thank all the subjects for participation and the study personnel for excellent technical assistance. The Pima Indian Study: This study was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases, NIH, USA. Studies of a Targeted Risk Reduction Intervention with Defined Exercise (STRRIDE): This study was supported by the National Heart Lung and Blood Institute of the National Institutes of Health, HL57453 (WEK). Gene expression in old and young muscle biopsies: S.M. and T.G. were supported in part by NIH U24AG051129.

COL6A3 may modulate adipose tissue function in obesity and insulin resistance. A role for human adipocytes linking COL6A3 with insulin resistance warrants exploration.COL6A3 mRNA in abdominal subcutaneous adipose samples was compared between (1) BMI-matched obese subjects resistant or sensitive to insulin (surgical whole tissue biopsies, n = 30/group), (2) lean/overweight and obese subjects (isolated adipocytes from collagenase-treated surgical biopsies, n = 11/group), (3) developing primary human adipocytes with/without knockdown of the insulin-sensitizing adipogenic gene PPARG (collagenase-treated lipoaspirate, n = 5), and (4) small and large adipocytes from lean/overweight subjects (collagenase-treated surgical biopsies or lipoaspirate, n = 10). Insulin resistance and sensitivity were assessed by euglycemic-hyperinsulinemic clamp (glucose infusion rate 70 ?mol kg(-1) min(-1) , respectively) (1), or by HOMA-IR and TG/HDL ratio (2).Whole tissue COL6A3 mRNA was 2.6-fold higher in insulin resistant compared to sensitive subjects (P< 0.001). In isolated adipocytes, COL6A3 mRNA correlated positively with BMI (P = 0.007), HOMA-IR (P = 0.039), and TG/HDL (P = 0.004). PPARG knockdown in developing adipocytes increased COL6A3 mRNA 1.5-fold (P = 0.043). The inverse relationship with adipocyte development was further supported by 2.8-fold higher COL6A3 mRNA in small compared to large adipocytes (P = 0.004).Increased adipocyte COL6A3 expression associates with insulin resistance in humans, which may involve impaired PPAR?-mediated adipocyte development.

The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E−37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.