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.
Display omitted
•SARS-CoV-2 viral antigen is detected in beta cells of autopsies of COVID-19 subjects•SARS-CoV-2 infection causes beta cell transdifferentiation•SARS-CoV-2-induced beta cell transdifferentiation is mediated by eIF2 pathway•Trans-ISRIB reverses SARS-CoV-2 infection-induced beta cell transdifferentiation
Here, Tang et al. reported the detection of SARS-CoV-2 viral antigen in autopsy samples from COVID-19 subjects. In addition, SARS-CoV-2 infection induces eIF2-pathway-mediated beta cell transdifferentiation, a phenotype that can be reversed by trans-ISRIB.
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.
We used an unbiased genome-wide approach to identify exonic variants segregating with diabetes in a multigenerational Finnish family. At least eight members of this family presented with diabetes ...with age of diagnosis ranging from 18 to 51 years and a pattern suggesting autosomal dominant inheritance. We sequenced the exomes of four affected members of this family and performed follow-up genotyping of additional affected and unaffected family members. We uncovered a novel nonsynonymous variant (p.Trp314Arg) in the Wolfram syndrome 1 (WFS1) gene that segregates completely with the diabetic phenotype. Multipoint parametric linkage analysis with 13 members of this family identified a single linkage signal with maximum logarithm of odds score 3.01 at 4p16.2-p16.1, corresponding to a region harboring the WFS1 locus. Functional studies demonstrate a role for this variant in endoplasmic reticulum stress, which is consistent with the β-cell failure phenotype seen in mutation carriers. This represents the first compelling report of a mutation in WFS1 associated with dominantly inherited nonsyndromic adult-onset diabetes.
Massively parallel DNA sequencing technologies have greatly increased our ability to generate large amounts of sequencing data at a rapid pace. Several methods have been developed to enrich for ...genomic regions of interest for targeted sequencing. We have compared three of these methods: Molecular Inversion Probes (MIP), Solution Hybrid Selection (SHS), and Microarray-based Genomic Selection (MGS). Using HapMap DNA samples, we compared each of these methods with respect to their ability to capture an identical set of exons and evolutionarily conserved regions associated with 528 genes (2.61 Mb). For sequence analysis, we developed and used a novel Bayesian genotype-assigning algorithm, Most Probable Genotype (MPG). All three capture methods were effective, but sensitivities (percentage of targeted bases associated with high-quality genotypes) varied for an equivalent amount of pass-filtered sequence: for example, 70% (MIP), 84% (SHS), and 91% (MGS) for 400 Mb. In contrast, all methods yielded similar accuracies of >99.84% when compared to Infinium 1M SNP BeadChip-derived genotypes and >99.998% when compared to 30-fold coverage whole-genome shotgun sequencing data. We also observed a low false-positive rate with all three methods; of the heterozygous positions identified by each of the capture methods, >99.57% agreed with 1M SNP BeadChip, and >98.840% agreed with the whole-genome shotgun data. In addition, we successfully piloted the genomic enrichment of a set of 12 pooled samples via the MGS method using molecular bar codes. We find that these three genomic enrichment methods are highly accurate and practical, with sensitivities comparable to that of 30-fold coverage whole-genome shotgun data.
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.
Type 2 diabetes (T2D) mellitus is a complex polygenic disease. Multiple genome-wide association studies (GWAS) have identified hundreds of T2D-related genetic variants, the vast majority of which are ...non-coding. Pinpointing the actual causal variants and their target gene transcripts has been challenging. Building on recent advances in CRISPR technology and protocols that allow differentiation into functional pancreatic beta cells, we created isogenic hPSCs encompassing knockout (KO) mutations in 20 GWAS-identified T2D risk genes, and systematically examined their roles in beta cell differentiation, insulin production, glucose response, and apoptosis. As a result, loss of TCF7L2, HNF4A, TLE4, TGFB1, GIPR and COBLL1 severely impaired the differentiation capacities of hPSCs into functional beta cells and over half of the 20 T2D genes shared implications in beta cell dysfunctions including decreased insulin content, impaired insulin secretion and high susceptibility to lipotoxicity. Next, we sorted beta cells from each KO and performed RNA-seq and ATAC-seq assays. An integrative analysis revealed KO of HNF4A, HNF1A, ABCC8, KCNJ11, TLE4 and WDR13 resulted in extensive transcriptomic and epigenetic changes in the beta cells. We identify that KO of HNF4A results in dramatic expressional changes of dozens of known T2D effector genes and additional ones critical to beta cell functions and biology. An analysis of T2D GWAS credible set of SNPs using ATAC-seq and RNA-seq coupled with functional assays, helped us to nominate a single likely T2D causal SNP (rs7132908) at FAIM2 locus. Finally, a correlation analysis of gene expression levels and insulin content across KO lines identified 1,867 differential genes including PCSK1N that likely serves as a hub gene by mediating insulin production in beta cells. This study thus offers a powerful strategy to interrogate shared molecular pathways among multiple risk genes for diabetes.
Disclosure
D.Xue: None. J.Vandana: None. A.Chong: None. F.S.Collins: None. S.Chen: Consultant; Vesalius Therapeutics, Stock/Shareholder; Oncobeat Theraputics. N.Narisu: None. T.Yan: None. M.Zhang: None. C.Grenko: None. X.Tang: None. J.Zhu: None. L.L.Bonnycastle: None. M.Erdos: None.
Funding
American Diabetes Association (9-22-PDFPM-06 to D.X.)
We investigated the association of glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes with amino acid levels in the population-based Metabolic Syndrome in Men (METSIM) Study, ...including 9,369 nondiabetic or newly diagnosed type 2 diabetic Finnish men. Plasma levels of eight amino acids were measured with proton nuclear magnetic resonance spectroscopy. Increasing fasting and 2-h plasma glucose levels were associated with increasing levels of several amino acids and decreasing levels of histidine and glutamine. Alanine, leucine, isoleucine, tyrosine, and glutamine predicted incident type 2 diabetes in a 4.7-year follow-up of the METSIM Study, and their effects were largely mediated by insulin resistance (except for glutamine). We also found significant correlations between insulin sensitivity (Matsuda insulin sensitivity index) and mRNA expression of genes regulating amino acid degradation in 200 subcutaneous adipose tissue samples. Only 1 of 43 risk single nucleotide polymorphisms for type 2 diabetes or hyperglycemia, the glucose-increasing major C allele of rs780094 of GCKR, was significantly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. In conclusion, the levels of branched-chain, aromatic amino acids and alanine increased and the levels of glutamine and histidine decreased with increasing glycemia, reflecting, at least in part, insulin resistance. Only one single nucleotide polymorphism regulating hyperglycemia was significantly associated with amino acid levels.
Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable ...component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 NFBC) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics FUSION study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal ...variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.
PDF
