To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Identifying cis-regulatory elements is important to understanding how human pancreatic islets modulate gene expression in physiologic or pathophysiologic (e.g., diabetic) conditions. We conducted genome-wide analysis of DNase I hypersensitive sites, histone H3 lysine methylation modifications (K4me1, K4me3, K79me2), and CCCTC factor (CTCF) binding in human islets. This identified ∼18,000 putative promoters (several hundred unannotated and islet-active). Surprisingly, active promoter modifications were absent at genes encoding islet-specific hormones, suggesting a distinct regulatory mechanism. Of 34,039 distal (nonpromoter) regulatory elements, 47% are islet unique and 22% are CTCF bound. In the 18 type 2 diabetes (T2D)-associated loci, we identified 118 putative regulatory elements and confirmed enhancer activity for 12 of 33 tested. Among six regulatory elements harboring T2D-associated variants, two exhibit significant allele-specific differences in activity. These findings present a global snapshot of the human islet epigenome and should provide functional context for noncoding variants emerging from genetic studies of T2D and other islet disorders.

OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.

Stretch enhancers (SEs) are large chromatin-defined regulatory elements that are at least 3,000 base pairs (bps) long, in contrast to the median enhancer length of 800 bps. SEs tend to be cell-type specific, regulate cell-type specific gene expression, and are enriched in disease-associated genetic variants in disease-relevant cell types. Transcription factors (TFs) can bind to enhancers to modulate enhancer activity, and their sequence specificity can be represented by motifs. We hypothesize motifs can provide a biological context for how genetic variants contribute to disease.

Type 2 diabetes (T2D) results from the combined effects of genetic and environmental factors on multiple tissues over time. Of the >100 variants associated with T2D and related traits in genome-wide association studies (GWAS), >90% occur in non-coding regions, suggesting a strong regulatory component to T2D risk. Here to understand how T2D status, metabolic traits and genetic variation influence gene expression, we analyse skeletal muscle biopsies from 271 well-phenotyped Finnish participants with glucose tolerance ranging from normal to newly diagnosed T2D. We perform high-depth strand-specific mRNA-sequencing and dense genotyping. Computational integration of these data with epigenome data, including ATAC-seq on skeletal muscle, and transcriptome data across diverse tissues reveals that the tissue-specific genetic regulatory architecture of skeletal muscle is highly enriched in muscle stretch/super enhancers, including some that overlap T2D GWAS variants. In one such example, T2D risk alleles residing in a muscle stretch/super enhancer are linked to increased expression and alternative splicing of muscle-specific isoforms of ANK1.

The high-throughput detection of organelle composition and proteomic mapping of protein environment directly from primary tissue as well as the identification of interactors of insoluble proteins that form higher-order structures have remained challenges in biological research. We report a proximity-based labeling approach that uses an antibody to a target antigen to guide biotin deposition onto adjacent proteins in fixed cells and primary tissues, which allows proteins in close proximity to the target antigen to be captured and identified by mass spectrometry. We demonstrated the specificity and sensitivity of our method by examining the well-studied mitochondrial matrix. We then used the method to profile the dynamic interactome of lamin A/C in multiple cell and tissue types under various treatment conditions. The ability to detect proximal proteins and putative interactors in intact tissues, and to quantify changes caused by different conditions or in the presence of disease mutations, can provide a window into cell biology and disease pathogenesis.

Somatic mutations are critical for cancer development and may play a role in age-related functional decline. Here, we used deep sequencing to analyze the prevalence of somatic mutations during in vitro cell aging. Primary dermal fibroblasts from healthy subjects of young and advanced age, from Hutchinson-Gilford progeria syndrome and from xeroderma pigmentosum complementation groups A and C, were first restricted in number and then expanded in vitro. DNA was obtained from cells pre- and post-expansion and sequenced at high depth (1656× mean coverage), over a cumulative 290 kb target region, including the exons of 44 aging-related genes. Allele frequencies of 58 somatic mutations differed between the pre- and post-cell culture expansion passages. Mathematical modeling revealed that the frequency change of three of the 58 mutations was unlikely to be explained by genetic drift alone, indicative of positive selection. Two of these three mutations, CDKN2A c.53C>T (T18M) and ERCC8 c.*772T>A, were identified in cells from a patient with XPA. The allele frequency of the CDKN2A mutation increased from 0% to 55.3% with increasing cell culture passage. The third mutation, BRCA2 c.6222C>T (H2074H), was identified in a sample from a healthy individual of advanced age. However, further validation of the three mutations suggests that other unmeasured variants probably provide the selective advantage in these cells. Our results reinforce the notions that somatic mutations occur during aging and that some are under positive selection, supporting the model of increased tissue heterogeneity with increased age.

COVID-19 severity has varied widely, with demographic and cardio-metabolic factors increasing risk of severe reactions to SARS-CoV-2 infection, but the underlying mechanisms for this remain uncertain. We investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 ( ACE2 ), which has been shown to act as a receptor for SARS-CoV-2 cellular entry. In a meta-analysis of three independent studies including up to 1,471 participants, lower adipose tissue ACE2 expression was associated with adverse cardio-metabolic health indices including type 2 diabetes (T2D) and obesity status, higher serum fasting insulin and BMI, and lower serum HDL levels (P<5.32x10 -4 ). ACE2 expression levels were also associated with estimated proportions of cell types in adipose tissue; lower ACE2 expression was associated with a lower proportion of microvascular endothelial cells (P=4.25x10 -4 ) and higher macrophage proportion (P=2.74x10 -5 ), suggesting a link to inflammation. Despite an estimated heritability of 32%, we did not identify any proximal or distal genetic variants (eQTLs) associated with adipose tissue ACE2 expression. Our results demonstrate that at-risk individuals have lower background ACE2 levels in this highly relevant tissue. Further studies will be required to establish how this may contribute to increased COVID-19 severity.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare accelerated aging disorder most notably characterized by cardiovascular disease and premature death from myocardial infarction or stroke. The majority of cases are caused by a de novo single nucleotide mutation in the LMNA gene that activates a cryptic splice donor site, resulting in production of a toxic form of lamin A with a 50 amino acid internal deletion, termed progerin. We previously reported the generation of a transgenic murine model of progeria carrying a human BAC harboring the common mutation, G608G, which in the single-copy state develops features of HGPS that are limited to the vascular system. Here, we report the phenotype of mice bred to carry two copies of the BAC, which more completely recapitulate the phenotypic features of HGPS in skin, adipose, skeletal, and vascular tissues. We further show that genetic reduction of the mechanistic target of rapamycin (mTOR) significantly extends lifespan in these mice, providing a rationale for pharmacologic inhibition of the mTOR pathway in the treatment of HGPS.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Recent advances in sequencing technology have helped unveil the unexpected complexity and diversity of small RNAs. A critical step in small RNA library preparation for sequencing is the ligation of adapter sequences to both the 5' and 3' ends of small RNAs. Studies have shown that adapter ligation introduces a significant but widely unappreciated bias in the results of high-throughput small RNA sequencing. We show that due to this bias the two widely used Illumina library preparation protocols produce strikingly different microRNA (miRNA) expression profiles in the same batch of cells. There are 102 highly expressed miRNAs that are >5-fold differentially detected and some miRNAs, such as miR-24-3p, are over 30-fold differentially detected. While some level of bias in library preparation is not surprising, the apparent massive differential bias between these two widely used adapter sets is not well appreciated. In an attempt to mitigate this bias, the new Bioo Scientific NEXTflex V2 protocol utilizes a pool of adapters with random nucleotides at the ligation boundary. We show that this protocol is able to detect robustly several miRNAs that evade capture by the Illumina-based methods. While these analyses do not indicate a definitive gold standard for small RNA library preparation, the results of the NEXTflex protocol do correlate best with RT-qPCR. As increasingly more laboratories seek to study small RNAs, researchers should be aware of the extent to which the results may differ with different protocols, and should make an informed decision about the protocol that best fits their study.

Enhancers regulate spatiotemporal gene expression and impart cell-specific transcriptional outputs that drive cell identity. Super-enhancers (SEs), also known as stretch-enhancers, are a subset of enhancers especially important for genes associated with cell identity and genetic risk of disease. CD4(+) T cells are critical for host defence and autoimmunity. Here we analysed maps of mouse T-cell SEs as a non-biased means of identifying key regulatory nodes involved in cell specification. We found that cytokines and cytokine receptors were the dominant class of genes exhibiting SE architecture in T cells. Nonetheless, the locus encoding Bach2, a key negative regulator of effector differentiation, emerged as the most prominent T-cell SE, revealing a network in which SE-associated genes critical for T-cell biology are repressed by BACH2. Disease-associated single-nucleotide polymorphisms for immune-mediated disorders, including rheumatoid arthritis, were highly enriched for T-cell SEs versus typical enhancers or SEs in other cell lineages. Intriguingly, treatment of T cells with the Janus kinase (JAK) inhibitor tofacitinib disproportionately altered the expression of rheumatoid arthritis risk genes with SE structures. Together, these results indicate that genes with SE architecture in T cells encompass a variety of cytokines and cytokine receptors but are controlled by a 'guardian' transcription factor, itself endowed with an SE. Thus, enumeration of SEs allows the unbiased determination of key regulatory nodes in T cells, which are preferentially modulated by pharmacological intervention.

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r(2) = 0.84-0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164-FJ010174.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Recent clinical data have suggested a correlation between coronavirus disease 2019 (COVID-19) and diabetes. Here, we describe the detection of SARS-CoV-2 viral antigen in pancreatic beta cells in autopsy samples from individuals with COVID-19. Single-cell RNA sequencing and immunostaining from ex vivo infections confirmed that multiple types of pancreatic islet cells were susceptible to SARS-CoV-2, eliciting a cellular stress response and the induction of chemokines. Upon SARS-CoV-2 infection, beta cells showed a lower expression of insulin and a higher expression of alpha and acinar cell markers, including glucagon and trypsin1, respectively, suggesting cellular transdifferentiation. Trajectory analysis indicated that SARS-CoV-2 induced eIF2-pathway-mediated beta cell transdifferentiation, a phenotype that could be reversed with trans-integrated stress response inhibitor (trans-ISRIB). Altogether, this study demonstrates an example of SARS-CoV-2 infection causing cell fate change, which provides further insight into the pathomechanisms of COVID-19.

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on β cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on β cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with β cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.

We developed an efficient CRISPR prime editing protocol and generated isogenic-induced pluripotent stem cell (iPSC) lines carrying heterozygous or homozygous alleles for putatively causal single nucleotide variants at six type 2 diabetes loci (ABCC8, MTNR1B, TCF7L2, HNF4A, CAMK1D, and GCK). Our two-step sequence-based approach to first identify transfected cell pools with the highest fraction of edited cells significantly reduced the downstream efforts to isolate single clones of edited cells. We found that prime editing can make targeted genetic changes in iPSC and optimization of system components and guide RNA designs that were critical to achieve acceptable efficiency. Systems utilizing PEmax, epegRNA modifications, and MLH1dn provided significant benefit, producing editing efficiencies of 36-73%. Editing success and pegRNA design optimization required for each variant differed depending on the sequence at the target site. With attention to design, prime editing is a promising approach to generate isogenic iPSC lines, enabling the study of specific genetic changes in a common genetic background.