A collective diabetes cross in combination with a computational framework to dissect the genetics of human obesity and Type 2 diabetes

Novel loci for hyperglycemia identified by QTL mapping of longitudinal phenotypes and congenic analysis

We previously reported that four hyperglycemia loci are located on three chromosomes in the Nagoya-Shibata-Yasuda (NSY) mouse model, commonly used to study type 2 diabetes. However, we did not search for hyperglycemia loci across all chromosomes. In this study, we performed quantitative trait loci (QTLs) mapping of longitudinal phenotypes from crosses between NSY (hyperglycemic) and C3H (normoglycemic) mice. We identified four new QTLs for hyperglycemia, namely Nidd5nsy, Nidd6nsy, Nidd1c3h, and Nidd2c3h, on Chromosome 1, 4, 10, and 13, respectively. These QTLs were associated with hyperglycemia in young mice and had attenuated effects in older mice. Nidd5nsy and Nidd6nsy were hyperglycemic with NSY alleles, and Nidd1c3h and Nidd2c3h were hyperglycemic with C3H alleles. We further bred Nidd5nsy congenic mice and demonstrated that Nidd5nsy has a strong effect on hyperglycemia when young, accompanied by insulin resistance and visceral fat accumulation. These results showed that the effects of individual QTLs strengthened or weakened with age, and that the sum of the effects of QTLs captured the age-related deterioration of glucose tolerance in individuals. Our results support the importance of longitudinal phenotypes in the genetic analysis of polygenic traits and have implications for the genetic basis and pathogenesis of type 2 diabetes in humans.

E96V Mutation in the Kdelr3 Gene Is Associated with Type 2 Diabetes Susceptibility in Obese NZO Mice

Type 2 diabetes (T2D) represents a multifactorial metabolic disease with a strong genetic predisposition. Despite elaborate efforts in identifying the genetic variants determining individual susceptibility towards T2D, the majority of genetic factors driving disease development remain poorly understood. With the aim to identify novel T2D risk genes we previously generated an N2 outcross population using the two inbred mouse strains New Zealand obese (NZO) and C3HeB/FeJ (C3H). A linkage study performed in this population led to the identification of the novel T2D-associated quantitative trait locus (QTL) Nbg15 (NZO blood glucose on chromosome 15, Logarithm of odds (LOD) 6.6). In this study we used a combined approach of positional cloning, gene expression analyses and in silico predictions of DNA polymorphism on gene/protein function to dissect the genetic variants linking Nbg15 to the development of T2D. Moreover, we have generated congenic strains that associated the distal sublocus of Nbg15 to mechanisms altering pancreatic beta cell function. In this sublocus, Cbx6, Fam135b and Kdelr3 were nominated as potential causative genes associated with the Nbg15 driven effects. Moreover, a putative mutation in the Kdelr3 gene from NZO was identified, negatively influencing adaptive responses associated with pancreatic beta cell death and induction of endoplasmic reticulum stress. Importantly, knockdown of Kdelr3 in cultured Min6 beta cells altered insulin granules maturation and pro-insulin levels, pointing towards a crucial role of this gene in islets function and T2D susceptibility.

Heterogeneous Development of β-Cell Populations in Diabetes-Resistant and -Susceptible Mice

Progressive dysfunction and failure of insulin-releasing β-cells are a hallmark of type 2 diabetes (T2D). To study mechanisms of β-cell loss in T2D, we performed islet single-cell RNA sequencing of two obese mouse strains differing in their diabetes susceptibility. With mice on a control diet, we identified six β-cell clusters with similar abundance in both strains. However, after feeding of a diabetogenic diet for 2 days, β-cell cluster composition markedly differed between strains. Islets of diabetes-resistant mice developed into a protective β-cell cluster (Beta4), whereas those of diabetes-prone mice progressed toward stress-related clusters with a strikingly different expression pattern. Interestingly, the protective cluster showed indications of reduced β-cell identity, such as downregulation of GLUT2, GLP1R, and MafA, and in vitro knockdown of GLUT2 in β-cells-mimicking its phenotype-decreased stress response and apoptosis. This might explain enhanced β-cell survival of diabetes-resistant islets. In contrast, β-cells of diabetes-prone mice responded with expression changes indicating metabolic pressure and endoplasmic reticulum stress, presumably leading to later β-cell loss. In conclusion, failure of diabetes-prone mice to adapt gene expression toward a more dedifferentiated state in response to rising blood glucose levels leads to β-cell failure and diabetes development.

Comparative genomic analyses of multiple backcross mouse populations suggest SGCG as a novel potential obesity-modifier gene

To nominate novel disease genes for obesity and type 2 diabetes (T2D), we recently generated two mouse backcross populations of the T2D-susceptible New Zealand Obese (NZO/HI) mouse strain and two genetically different, lean and T2D-resistant strains, 129P2/OlaHsd and C3HeB/FeJ. Comparative linkage analysis of our two female backcross populations identified seven novel body fat-associated quantitative trait loci (QTL). Only the locus Nbw14 (NZO body weight on chromosome 14) showed linkage to obesity-related traits in both backcross populations, indicating that the causal gene variant is likely specific for the NZO strain as NZO allele carriers in both crosses displayed elevated body weight and fat mass. To identify candidate genes for Nbw14, we used a combined approach of gene expression and haplotype analysis to filter for NZO-specific gene variants in gonadal white adipose tissue (gWAT), defined as the main QTL-target tissue. Only two genes, Arl11 and Sgcg, fulfilled our candidate criteria. In addition, expression QTL analysis revealed cis-signals for both genes within the Nbw14 locus. Moreover, retroviral overexpression of Sgcg in 3 T3-L1 adipocytes resulted in increased insulin-stimulated glucose uptake. In humans, mRNA levels of SGCG correlated with BMI and body fat mass exclusively in diabetic subjects, suggesting that SGCG may present a novel marker for metabolically unhealthy obesity. In conclusion, our comparative-cross analysis could substantially improve the mapping resolution of the obesity locus Nbw14. Future studies will shine light on the mechanism by which Sgcg may protect from the development of obesity.

Identification of Novel Genes Involved in Hyperglycemia in Mice

Current attempts to prevent and manage type 2 diabetes have been moderately effective, and a better understanding of the molecular roots of this complex disease is important to develop more successful and precise treatment options. Recently, we initiated the collective diabetes cross, where four mouse inbred strains differing in their diabetes susceptibility were crossed with the obese and diabetes-prone NZO strain and identified the quantitative trait loci (QTL) Nidd13/NZO, a genomic region on chromosome 13 that correlates with hyperglycemia in NZO allele carriers compared to B6 controls. Subsequent analysis of the critical region, harboring 644 genes, included expression studies in pancreatic islets of congenic Nidd13/NZO mice, integration of single-cell data from parental NZO and B6 islets as well as haplotype analysis. Finally, of the five genes (Acot12, S100z, Ankrd55, Rnf180, and Iqgap2) within the polymorphic haplotype block that are differently expressed in islets of B6 compared to NZO mice, we identified the calcium-binding protein S100z gene to affect islet cell proliferation as well as apoptosis when overexpressed in MIN6 cells. In summary, we define S100z as the most striking gene to be causal for the diabetes QTL Nidd13/NZO by affecting β-cell proliferation and apoptosis. Thus, S100z is an entirely novel diabetes gene regulating islet cell function.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

MiR-205 is up-regulated in islets of diabetes-susceptible mice and targets the diabetes gene Tcf7l2

AIM

MicroRNAs play an important role in the maintenance of cellular functions by fine-tuning gene expression levels. The aim of the current study was to identify genetically caused changes in microRNA expression which associate with islet dysfunction in diabetic mice.

METHODS

To identify novel microRNAs involved in islet dysfunction, transcriptome and miRNome analyses were performed in islets of obese, diabetes-susceptible NZO and diabetes-resistant B6-ob/ob mice and results combined with quantitative trait loci (QTL) and functional in vitro analysis.

RESULTS

In islets of NZO and B6-ob/ob mice, 94 differentially expressed microRNAs were detected, of which 11 are located in diabetes QTL. Focusing on conserved microRNAs exhibiting the strongest expression difference and which have not been linked to islet function, miR-205-5p was selected for further analysis. According to transcriptome data and target prediction analyses, miR-205-5p affects genes involved in Wnt and calcium signalling as well as insulin secretion. Over-expression of miR-205-5p in the insulinoma cell line INS-1 increased insulin expression, left-shifted the glucose-dependence of insulin secretion and supressed the expression of the diabetes gene TCF7L2. The interaction between miR-205-5p and TCF7L2 was confirmed by luciferase reporter assay.

CONCLUSION

MiR-205-5p was identified as relevant microRNA involved in islet dysfunction by interacting with TCF7L2.

Alternative exon splicing and differential expression in pancreatic islets reveals candidate genes and pathways implicated in early diabetes development

Type 2 diabetes (T2D) has a strong genetic component. Most of the gene variants driving the pathogenesis of T2D seem to target pancreatic β-cell function. To identify novel gene variants acting at early stage of the disease, we analyzed whole transcriptome data to identify differential expression (DE) and alternative exon splicing (AS) transcripts in pancreatic islets collected from two metabolically diverse mouse strains at 6 weeks of age after three weeks of high-fat-diet intervention. Our analysis revealed 1218 DE and 436 AS genes in islets from NZO/Hl vs C3HeB/FeJ. Whereas some of the revealed genes present well-established markers for β-cell failure, such as Cd36 or Aldh1a3, we identified numerous DE/AS genes that have not been described in context with β-cell function before. The gene Lgals2, previously associated with human T2D development, was DE as well as AS and localizes in a quantitative trait locus (QTL) for blood glucose on Chr.15 that we reported recently in our N₂(NZOxC3H) population. In addition, pathway enrichment analysis of DE and AS genes showed an overlap of only half of the revealed pathways, indicating that DE and AS in large parts influence different pathways in T2D development. PPARG and adipogenesis pathways, two well-established metabolic pathways, were overrepresented for both DE and AS genes, probably as an adaptive mechanism to cope for increased cellular stress. Our results provide guidance for the identification of novel T2D candidate genes and demonstrate the presence of numerous AS transcripts possibly involved in islet function and maintenance of glucose homeostasis.

Immunity-related GTPase induces lipophagy to prevent excess hepatic lipid accumulation

BACKGROUND & AIMS

Currently, only a few genetic variants explain the heritability of fatty liver disease. Quantitative trait loci (QTL) analysis of mouse strains has identified the susceptibility locus Ltg/NZO (liver triglycerides from New Zealand obese [NZO] alleles) on chromosome 18 as associating with increased hepatic triglycerides. Herein, we aimed to identify genomic variants responsible for this association.

METHODS

Recombinant congenic mice carrying 5.3 Mbp of Ltg/NZO were fed a high-fat diet and characterized for liver fat. Bioinformatic analysis, mRNA profiles and electrophoretic mobility shift assays were performed to identify genes responsible for the Ltg/NZO phenotype. Candidate genes were manipulated in vivo by injecting specific microRNAs into C57BL/6 mice. Pulldown coupled with mass spectrometry-based proteomics and immunoprecipitation were performed to identify interaction partners of IFGGA2.

RESULTS

Through positional cloning, we identified 2 immunity-related GTPases (Ifgga2, Ifgga4) that prevent hepatic lipid storage. Expression of both murine genes and the human orthologue IRGM was significantly lower in fatty livers. Accordingly, liver-specific suppression of either Ifgga2 or Ifgga4 led to a 3-4-fold greater increase in hepatic fat content. In the liver of low-fat diet-fed mice, IFGGA2 localized to endosomes/lysosomes, while on a high-fat diet it associated with lipid droplets. Pulldown experiments and proteomics identified the lipase ATGL as a binding partner of IFGGA2 which was confirmed by co-immunoprecipitation. Both proteins partially co-localized with the autophagic marker LC3B. Ifgga2 suppression in hepatocytes reduced the amount of LC3B-II, whereas overexpression of Ifgga2 increased the association of LC3B with lipid droplets and decreased triglyceride storage.

CONCLUSION

IFGGA2 interacts with ATGL and protects against hepatic steatosis, most likely by enhancing the binding of LC3B to lipid droplets.

LAY SUMMARY

The genetic basis of non-alcoholic fatty liver disease remains incompletely defined. Herein, we identified members of the immunity-related GTPase family in mice and humans that act as regulators of hepatic fat accumulation, with links to autophagy. Overexpression of the gene Ifgga2 was shown to reduce hepatic lipid storage and could be a therapeutic target for the treatment of fatty liver disease.

Identification of Novel Potential Type 2 Diabetes Genes Mediating β-Cell Loss and Hyperglycemia Using Positional Cloning

Type 2 diabetes (T2D) is a complex metabolic disease regulated by an interaction of genetic predisposition and environmental factors. To understand the genetic contribution in the development of diabetes, mice varying in their disease susceptibility were crossed with the obese and diabetes-prone New Zealand obese (NZO) mouse. Subsequent whole-genome sequence scans revealed one major quantitative trait loci (QTL), Nidd/DBA on chromosome 4, linked to elevated blood glucose and reduced plasma insulin and low levels of pancreatic insulin. Phenotypical characterization of congenic mice carrying 13.6 Mbp of the critical fragment of DBA mice displayed severe hyperglycemia and impaired glucose clearance at week 10, decreased glucose response in week 13, and loss of β-cells and pancreatic insulin in week 16. To identify the responsible gene variant(s), further congenic mice were generated and phenotyped, which resulted in a fragment of 3.3 Mbp that was sufficient to induce hyperglycemia. By combining transcriptome analysis and haplotype mapping, the number of putative responsible variant(s) was narrowed from initial 284 to 18 genes, including gene models and non-coding RNAs. Consideration of haplotype blocks reduced the number of candidate genes to four (Kti12, Osbpl9, Ttc39a, and Calr4) as potential T2D candidates as they display a differential expression in pancreatic islets and/or sequence variation. In conclusion, the integration of comparative analysis of multiple inbred populations such as haplotype mapping, transcriptomics, and sequence data substantially improved the mapping resolution of the diabetes QTL Nidd/DBA. Future studies are necessary to understand the exact role of the different candidates in β-cell function and their contribution in maintaining glycemic control.

Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically characterised cohort☆

BACKGROUND & AIMS

Genetic factors associated with non-alcoholic fatty liver disease (NAFLD) remain incompletely understood. To date, most genome-wide association studies (GWASs) have adopted radiologically assessed hepatic triglyceride content as the reference phenotype and so cannot address steatohepatitis or fibrosis. We describe a GWAS encompassing the full spectrum of histologically characterised NAFLD.

METHODS

The GWAS involved 1,483 European NAFLD cases and 17,781 genetically matched controls. A replication cohort of 559 NAFLD cases and 945 controls was genotyped to confirm signals showing genome-wide or close to genome-wide significance.

RESULTS

Case-control analysis identified signals showing p values ≤5 × 10^-8 at 4 locations (chromosome [chr] 2 GCKR/C2ORF16; chr4 HSD17B13; chr19 TM6SF2; chr22 PNPLA3) together with 2 other signals with p <1 × 10^-7 (chr1 near LEPR and chr8 near IDO2/TC1). Case-only analysis of quantitative traits showed that the PNPLA3 signal (rs738409) had genome-wide significance for steatosis, fibrosis and NAFLD activity score and a new signal (PYGO1 rs62021874) had close to genome-wide significance for steatosis (p = 8.2 × 10^-8). Subgroup case-control analysis for NASH confirmed the PNPLA3 signal. The chr1 LEPR single nucleotide polymorphism also showed genome-wide significance for this phenotype. Considering the subgroup with advanced fibrosis (≥F3), the signals on chr2, chr19 and chr22 maintained their genome-wide significance. Except for GCKR/C2ORF16, the genome-wide significance signals were replicated.

CONCLUSIONS

This study confirms PNPLA3 as a risk factor for the full histological spectrum of NAFLD at genome-wide significance levels, with important contributions from TM6SF2 and HSD17B13. PYGO1 is a novel steatosis modifier, suggesting that Wnt signalling pathways may be relevant in NAFLD pathogenesis.

LAY SUMMARY

Non-alcoholic fatty liver disease is a common disease where excessive fat accumulates in the liver and may result in cirrhosis. To understand who is at risk of developing this disease and suffering liver damage, we undertook a genetic study to compare the genetic profiles of people suffering from fatty liver disease with genetic profiles seen in the general population. We found that particular sequences in 4 different areas of the human genome were seen at different frequencies in the fatty liver disease cases. These sequences may help predict an individual's risk of developing advanced disease. Some genes where these sequences are located may also be good targets for future drug treatments.

A decade of experience with genetically tailored pig models for diabetes and metabolic research

The global prevalence of diabetes mellitus and other metabolic diseases is rapidly increasing. Animal models play pivotal roles in unravelling disease mechanisms and developing and testing therapeutic strategies. Rodents are the most widely used animal models but may have limitations in their resemblance to human disease mechanisms and phenotypes. Findings in rodent models are consequently often difficult to extrapolate to human clinical trials. To overcome this ‘translational gap’, we and other groups are developing porcine disease models. Pigs share many anatomical and physiological traits with humans and thus hold great promise as translational animal models. Importantly, the toolbox for genetic engineering of pigs is rapidly expanding. Human disease mechanisms and targets can therefore be reproduced in pigs on a molecular level, resulting in precise and predictive porcine (PPP) models. In this short review, we summarize our work on the development of genetically (pre)diabetic pig models and how they have been used to study disease mechanisms and test therapeutic strategies. This includes the generation of reporter pigs for studying beta-cell maturation and physiology. Furthermore, genetically engineered pigs are promising donors of pancreatic islets for xenotransplantation. In summary, genetically tailored pig models have become an important link in the chain of translational diabetes and metabolic research.

Polymorphisms in miRNA binding sites involved in metabolic diseases in mice and humans

Type 2 diabetes and obesity are well-studied metabolic diseases, which are based on genetic and epigenetic alterations in combination with an obesogenic lifestyle. The aim of this study was to test whether SNPs in miRNA-mRNA binding sites that potentially disrupt binding, elevate the expression of miRNA targets, which participate in the development of metabolic diseases. A computational approach was developed that integrates transcriptomics, linkage analysis, miRNA-target prediction data, and sequence information of a mouse model of obesity and diabetes. A statistical analysis demonstrated a significant enrichment of 566 genes for a location in obesity- and diabetes-related QTL. They are expressed at higher levels in metabolically relevant tissues presumably due to altered miRNA-mRNA binding sites. Of these, 51 genes harbor conserved and impaired miRNA-mRNA-interactions in human. Among these, 38 genes have been associated to metabolic diseases according to the phenotypes of corresponding knockout mice or other results described in the literature. The remaining 13 genes (e.g. Jrk, Megf9, Slfn8 and Tmem132e) could be interesting candidates and will be investigated in the future.

Two Novel Candidate Genes for Insulin Secretion Identified by Comparative Genomics of Multiple Backcross Mouse Populations

To identify novel disease genes for type 2 diabetes (T2D) we generated two backcross populations of obese and diabetes-susceptible New Zealand Obese (NZO/HI) mice with the two lean mouse strains 129P2/OlaHsd and C3HeB/FeJ. Subsequent whole-genome linkage scans revealed 30 novel quantitative trait loci (QTL) for T2D-associated traits. The strongest association with blood glucose [12 cM, logarithm of the odds (LOD) 13.3] and plasma insulin (17 cM, LOD 4.8) was detected on proximal chromosome 7 (designated Nbg7p, NZO blood glucose on proximal chromosome 7) exclusively in the NZOxC3H crossbreeding, suggesting that the causal gene is contributed by the C3H genome. Introgression of the critical C3H fragment into the genetic NZO background by generating recombinant congenic strains and metabolic phenotyping validated the phenotype. For the detection of candidate genes in the critical region (30-46 Mb), we used a combined approach of haplotype and gene expression analysis to search for C3H-specific gene variants in the pancreatic islets, which appeared to be the most likely target tissue for the QTL. Two genes, Atp4a and Pop4, fulfilled the criteria from our candidate gene approaches. The knockdown of both genes in MIN6 cells led to decreased glucose-stimulated insulin secretion, indicating a regulatory role of both genes in insulin secretion, thereby possibly contributing to the phenotype linked to Nbg7p In conclusion, our combined- and comparative-cross analysis approach has successfully led to the identification of two novel diabetes susceptibility candidate genes, and thus has been proven to be a valuable tool for the discovery of novel disease genes.