Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice

Tachol1 QTL on mouse chromosome 1 is responsible for hypercholesterolemia and diet-induced obesity

Hypercholesterolemia raises the risk for cardiovascular complications and overall health. Hypercholesterolemia is common, affecting 10% of the general population of the US, and heritable. Most individuals with hypercholesterolemia have a polygenic predisposition to the condition. Previously we identified a quantitative trait locus, Tachol1, linked to hypercholesterolemia on mouse chromosome 1 (Chr1) in a cross between C57BL/6J (B6) and TALLYHO/JngJ (TH) mice, a polygenic model for human obesity, type 2 diabetes and hyperlipidemia. Subsequently, using congenic mice that carry a TH-derived genomic segment of Chr1 on a B6 background, we demonstrated that the distal segment of Chr1, where Tachol1 maps, is necessary to cause hypercholesterolemia, as well as diet-induced obesity. In this study, we generated overlapping subcongenic lines to the distal segment of congenic region and characterized subcongenic mice carrying the smallest TH region of Tachol1, ~ 16.2 Mb in size (B6.TH-Chr1-16.2 Mb). Both male and female B6.TH-Chr1-16.2 Mb mice showed a significantly increased plasma total cholesterol levels compared to B6 on both chow and high fat (HF) diet. B6.TH-Chr1-16.2 Mb mice also had greater fat mass than B6 on HF diet, without increasing food intake. The gene and protein expression levels of absent in melanoma 2 (Aim2) gene were significantly upregulated in B6.TH-Chr1-16.2 Mb mice compared to B6. In summary, we confirmed the effect of Tachol1 on hypercholesterolemia and diet-induced obesity using subcongenic analysis.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

Diet, sex, and genetic predisposition to obesity and type 2 diabetes modulate motor and anxiety-related behaviors in mice, and alter cerebellar gene expression

Obesity and type 2 diabetes (T2D) are serious health problems linked to neurobehavioral alterations. We compared motor function, anxiety-related behavior, and cerebellar gene expression in TALLYHO/Jng (TH), a polygenic model prone to insulin resistance, obesity, and T2D, and normal C57BL/6 J (B6) mice. Male and female mice were weaned onto chow or high fat (HF) diet at 4 weeks of age (wk), and experiments conducted at young (5 wk) and old (14 - 20 wk) ages. In the open field, distance traveled was significantly lower in TH (vs. B6). For old mice, anxiety-like behavior (time in edge zone) was significantly increased for TH (vs B6), females (vs males), and for both ages HF diet (vs chow). In Rota-Rod testing, latency to fall was significantly shorter in TH (vs B6). For young mice, longer latencies to fall were observed for females (vs males) and HF (vs chow). Grip strength in young mice was greater in TH (vs B6), and there was a diet-strain interaction, with TH on HF showing increased strength, whereas B6 on HF showed decreased strength. For older mice, there was a strain-sex interaction, with B6 males (but not TH males) showing increased strength compared to the same strain females. There were significant sex differences in cerebellar mRNA levels, with Tnfα higher, and Glut4 and Irs2 lower in females (vs males). There were significant strain effects for Gfap and Igf1 mRNA levels with lower in TH (vs B6). Altered cerebellar gene expression may contribute to strain differences in coordination and locomotion.

Current landscape of preclinical models of diabetic cardiomyopathy

Patients with diabetes have an increased risk of developing heart failure, preceded by (often asymptomatic) cardiac abnormalities, collectively called diabetic cardiomyopathy (DC). Diabetic heart failure lacks effective treatment, remaining an urgent, unmet clinical need. Although structural and functional characteristics of the diabetic human heart are well defined, clinical studies lack the ability to pinpoint the specific mechanisms responsible for DC. Preclinical animal models represent a vital component for understanding disease aetiology, which is essential for the discovery of new targeted treatments for diabetes-induced heart failure. In this review, we describe the current landscape of preclinical DC models (genetic, pharmacologically induced, and diet-induced models), highlighting their strengths and weaknesses and alignment to features of the human disease. Finally, we provide tools, resources, and recommendations to assist future preclinical translation addressing this knowledge gap.

An automated multi-modal graph-based pipeline for mouse genetic discovery

MOTIVATION

Our ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS.

RESULTS

To accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained from a protein-protein interaction network and protein sequence features into the analysis. The GNN model produces markedly improved results relative to that of a simple linear neural network. We demonstrate that GNNHap can identify novel causative genetic factors for murine models of diabetes/obesity and for cataract formation, which were validated by the phenotypes appearing in previously analyzed gene knockout mice. The diabetes/obesity results indicate how characterization of the underlying genetic architecture enables new therapies to be discovered and tested by applying 'precision medicine' principles to murine models.

AVAILABILITY AND IMPLEMENTATION

The GNNHap source code is freely available at https://github.com/zqfang/gnnhap, and the new version of the HBCGM program is available at https://github.com/zqfang/haplomap.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DEPP Deficiency Contributes to Browning of White Adipose Tissue

Decidual protein induced by progesterone (DEPP) was originally identified as a modulator in the process of decidualization in the endometrium. Here, we define that DEPP is involved in adipose tissue thermogenesis, which contributes to metabolic regulation. Knockdown of DEPP suppressed adipocyte differentiation and lipid accumulation in 3T3-L1 cells, induced expression of brown adipose tissue (BAT) markers in primary brown adipocyte and induced mouse embryonic fibroblasts (MEFs) differentiation to brown adipocytes. Moreover, DEPP deficiency in mice induced white adipocyte browning and enhanced BAT activity. Cold exposure stimulated more browning of white adipose tissue (WAT) and maintained higher body temperature in DEPP knockout mice compared to that in wild-type control mice. DEPP deficiency also protected mice against high-fat-diet-induced insulin resistance. Mechanistic studies demonstrated that DEPP competitively binds SIRT1, inhibiting the interaction between peroxisome proliferator-activated receptor gamma (PPARγ) and Sirtuin 1 (SIRT1). Collectively, these findings suggest that DEPP plays a crucial role in orchestrating thermogenesis through regulating adipocyte programs and thus might be a potential target for the treatment of metabolic disorders.

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.

MD2 deficiency prevents high-fat diet-induced AMPK suppression and lipid accumulation through regulating TBK1 in non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is the most predominant form of liver diseases worldwide. Recent evidence shows that myeloid differentiation factor 2 (MD2), a protein in innate immunity and inflammation, regulates liver injury in models of NAFLD. Here, we investigated a new mechanism by which MD2 participates in the pathogenesis of experimental NAFLD.

METHODS

Wild-type, Md2-/- and bone marrow reconstitution mice fed with high-fat diet (HFD) were used to identify the role of hepatocyte MD2 in NAFLD. Transcriptomic RNA-seq and pathway enrich analysis were performed to explore the potential mechanisms of MD2. In vitro, primary hepatocytes and macrophages were cultured for mechanistic studies.

RESULTS

Transcriptome analysis and bone marrow reconstitution studies showed that hepatocyte MD2 may participate in regulating lipid metabolism in models with NAFLD. We then discovered that Md2 deficiency in mice prevents HFD-mediated suppression of AMP-activated protein kinase (AMPK). This preservation of AMPK in Md2-deficient mice was associated with normalized sterol regulatory element binding protein 1 (SREBP1) transcriptional program and a lack of lipid accumulation in both hepatocytes and liver. We then showed that hepatocyte MD2 links HFD to AMPK/SREBP1 through TANK binding kinase 1 (TBK1). In addition, MD2-increased inflammatory factor from macrophages induces hepatic TBK1 activation and AMPK suppression.

CONCLUSION

Hepatocyte MD2 plays a pathogenic role in NAFLD through TBK1-AMPK/SREBP1 and lipid metabolism pathway. These studies provide new insight into a non-inflammatory function of MD2 and evidence for the important role of MD2 in NALFD.

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.

The genomics of rapid climatic adaptation and parallel evolution in North American house mice

Parallel changes in genotype and phenotype in response to similar selection pressures in different populations provide compelling evidence of adaptation. House mice (Mus musculus domesticus) have recently colonized North America and are found in a wide range of environments. Here we measure phenotypic and genotypic differentiation among house mice from five populations sampled across 21° of latitude in western North America, and we compare our results to a parallel latitudinal cline in eastern North America. First, we show that mice are genetically differentiated between transects, indicating that they have independently colonized similar environments in eastern and western North America. Next, we find genetically-based differences in body weight and nest building behavior between mice from the ends of the western transect which mirror differences seen in the eastern transect, demonstrating parallel phenotypic change. We then conduct genome-wide scans for selection and a genome-wide association study to identify targets of selection and candidate genes for body weight. We find some genomic signatures that are unique to each transect, indicating population-specific responses to selection. However, there is significant overlap between genes under selection in eastern and western house mouse transects, providing evidence of parallel genetic evolution in response to similar selection pressures across North America.

Comparative and Functional Analyses of Two Sequenced Paenibacillus polymyxa Genomes Provides Insights Into Their Potential Genes Related to Plant Growth-Promoting Features and Biocontrol Mechanisms

Many bacteria belonging to Paenibacillus polymyxa are plant growth-promoting rhizobacteria (PGPR) with the potential to promote plant growth and suppress phytopathogens and have been used as biological control agents (BCAs). However, the growth promotion and biocontrol mechanisms of P. polymyxa have not been thoroughly elucidated thus far. In this investigation, the genome sequences of two P. polymyxa strains, ZF129 and ZF197, with broad anti-pathogen activities and potential for growth promotion were comparatively studied. Comparative and functional analyses of the two sequenced P. polymyxa genomes showed that the ZF129 genome consists of one 5,703,931 bp circular chromosome and two 79,020 bp and 37,602 bp plasmids, designated pAP1 and pAP2, respectively. The complete genome sequence of ZF197 consists of one 5,507,169 bp circular chromosome and one 32,065 bp plasmid, designated pAP197. Phylogenetic analysis revealed that ZF129 is highly similar to two P. polymyxa strains, HY96-2 and SQR-21, while ZF197 is highly similar to P. polymyxa strain J. The genes responsible for secondary metabolite synthesis, plant growth-promoting traits, and systemic resistance inducer production were compared between strains ZF129 and ZF197 as well as other P. polymyxa strains. The results indicated that the variation of the corresponding genes or gene clusters between strains ZF129 and ZF197 may lead to different antagonistic activities of their volatiles or cell-free supernatants against Fusarium oxysporum. This work indicates that plant growth promotion by P. polymyxa is largely mediated by phytohormone production, increased nutrient availability and biocontrol mechanisms. This study provides an in-depth understanding of the genome architecture of P. polymyxa, revealing great potential for the application of this bacterium in the fields of agriculture and horticulture as a PGPR.

ABCA12 regulates insulin secretion from β-cells

Dysregulation of lipid homeostasis is intimately associated with defects in insulin secretion, a key feature of type 2 diabetes. Here, we explore the role of the putative lipid transporter ABCA12 in regulating insulin secretion from β-cells. Mice with β-cell-specific deletion of Abca12 display impaired glucose-stimulated insulin secretion and eventual islet inflammation and β-cell death. ABCA12's action in the pancreas is independent of changes in the abundance of two other cholesterol transporters, ABCA1 and ABCG1, or of changes in cellular cholesterol or ceramide content. Instead, loss of ABCA12 results in defects in the genesis and fusion of insulin secretory granules and increases in the abundance of lipid rafts at the cell membrane. These changes are associated with dysregulation of the small GTPase CDC42 and with decreased actin polymerisation. Our findings establish a new, pleiotropic role for ABCA12 in regulating pancreatic lipid homeostasis and insulin secretion.

An auto-inducible expression and high cell density fermentation of Beefy Meaty Peptide with Bacillus subtilis

Currently, some cases about the expression of flavor peptides with microorganisms were reported owing to the obvious advantages of biological expression over traditional methods. However, beefy meaty peptide (BMP), the focus of umami peptides, has neither been concerned in its safe expression nor its overproduction in fermenter. In this study, multi-copy BMP (8BMP) was successfully auto-inducibly expressed and efficiently produced in Bacillus subtilis 168. First, 8BMP was successfully auto-inducibly expressed with srfA promoter in B. subtilis 168. Further, the efficient production of 8BMP was researched in a 5-L fermenter: the fermentation optimized by Pontryagin's maximum principle obtained the highest 8BMP yield (3.16 g/L), which was 1.2 times and 1.8 times than that of two-stage feeding cultivation (2.67 g/L) and constant-rate feeding cultivation (1.75 g/L), respectively. Overall, the auto-inducible expression of 8BMP in B. subtilis and fermentation with Pontryagin's maximum principle are conductive for overproduction of BMP and other peptides.

Mitochondrial oxidative phosphorylation is impaired in TALLYHO mice, a new obesity and type 2 diabetes animal model

Type 2 diabetes has become an epidemic disease largely explained by the dramatic increase in obesity in recent years. Mitochondrial dysfunction is suggested as an underlying factor in obesity and type 2 diabetes. In this study, we evaluated changes in oxidative phosphorylation and mitochondrial biogenesis in a new human obesity and type 2 diabetes model, TALLYHO/Jng mice. We hypothesized that the sequence variants identified in the whole genome analysis of TALLYHO/Jng mice would affect oxidative phosphorylation and contribute to obesity and insulin resistant phenotypes. To test this hypothesis, we investigated differences in the expression and activity of oxidative phosphorylation complexes, including the transcription and translation of nuclear- and mitochondrial-encoded subunits and enzymatic activities, in the liver and kidney of TALLYHO/Jng and C57BL/6 J mice. A significant decrease was observed in the expression of nuclear- and mitochondrial-encoded subunits of complex I and IV, respectively, in TALLYHO/Jng mice, which coincided with significant reductions in their enzymatic activities. Furthermore, sequence variants were identified in oxidative phosphorylation complex subunits, a mitochondrial tRNA synthetase, and mitochondrial ribosomal proteins. Our data suggested that the lower expression and activity of oxidative phosphorylation complexes results in the diminished energy metabolism observed in TALLYHO/Jng mice. Sequence variants identified in mitochondrial proteins accentuated a defect in mitochondrial protein synthesis which also contributes to impaired biogenesis and oxidative phosphorylation in TALLYHO/Jng mice. These results demonstrated that the identification of factors contributing to mitochondrial dysfunction will allow us to improve the disease prognosis and treatment of obesity and type 2 diabetes in humans.

Genome-wide association studies revealed candidate genes for tail fat deposition and body size in the Hulun Buir sheep

Fat-tailed sheep have a unique characteristic of depositing fat in their tails. In the present study, we conducted genome-wide association studies (GWAS) on traits related to tail fat deposition and body size in the Hulun Buir sheep. A total number of 300 individuals belonging to two fat-tailed lines of the Hulun Buir sheep breed genotyped with the Ovine Infinium HD SNP BeadChip were included in the current study. Two mixed models, one for continuous and one for binary phenotypic traits, were employed to analyse ten traits, that is, body length (BL), body height (BH), chest girth (CG), tail length (TL), tail width (TW), tail circumference (TC), carcass weight (CW), tail fat weight (TF), ratio of CW to TF (RCT) and tail type (TT). We identified 7, 6, 7, 2, 10 and 1 SNPs significantly associated with traits TF, CW, RCT, TW, TT and CG, respectively. Their associated genomic regions harboured 42 positional candidate genes. Out of them, 13 candidate genes including SMURF2, FBF1, DTNBP1, SETD7 and RBM11 have been associated with fat metabolism in sheep. The RBM11 gene has already been identified in a previous study on signatures of selection in this specific sheep population. Two more genes, that is, SMARCA5 and GAB1 were associated with body size in sheep. The present study has identified candidate genes that might be implicated in tail fat deposition and body size in sheep.

Congenic mice demonstrate the presence of QTLs conferring obesity and hypercholesterolemia on chromosome 1 in the TALLYHO mouse

The TALLYHO (TH) mouse presents a metabolic syndrome of obesity, type 2 diabetes, and hyperlipidemia. Highly significant quantitative trait loci (QTLs) linked to adiposity and hypercholesterolemia were previously identified on chromosome (Chr) 1 in a genome-wide scan of F2 mice from C57BL/6J (B6) x TH. In this study, we generated congenic mouse strains that carry the Chr 1 QTLs derived from TH on a B6 background; B6.TH-Chr1-128Mb (128Mb in size) and B6.TH-Chr1-92Mb (92Mb in size, proximally overlapping). We characterized these congenic mice on chow and high fat (HF) diets. On chow, B6.TH-Chr1-128Mb congenic mice exhibited a slightly larger body fat mass compared with B6.TH-Chr1-92Mb congenic and B6 mice, while body fat mass between B6.TH-Chr1-92Mb congenic and B6 mice was comparable. Plasma total cholesterol levels were significantly higher in B6.TH-Chr1-128Mb congenics compared to B6.TH-Chr1-92Mb congenic and B6 mice. Again, there was no difference in plasma total cholesterol levels between B6.TH-Chr1-92Mb congenic and B6 mice. All animals gained more body fat and exhibited higher plasma total cholesterol levels when fed HF diets than fed chow, but these increases were greater in B6.TH-Chr1-128Mb congenics than in B6.TH-Chr1-92Mb congenic and B6 mice. These results confirmed the effect of the 128Mb TH segment from Chr 1 on body fat and plasma cholesterol values and showed that the distal segment of Chr 1 from TH is necessary to cause both phenotypes. Through bioinformatic approaches, we generated a list of potential candidate genes within the distal region of Chr 1 and tested Ifi202b and Apoa2. We conclude that Chr 1 QTLs largely confer obesity and hypercholesterolemia in TH mice and can be promising targets for identifying susceptibility genes. Congenic mouse strains will be a valuable resource for gene identification.

Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle

Background

Single nucleotide polymorphism (SNP) arrays for domestic cattle have catalyzed the identification of genetic markers associated with complex traits for inclusion in modern breeding and selection programs. Using actual and imputed Illumina 778K genotypes for 3887 U.S. beef cattle from 3 populations (Angus, Hereford, SimAngus), we performed genome-wide association analyses for feed efficiency and growth traits including average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), and residual feed intake (RFI), with marker-based heritability estimates produced for all traits and populations.

Results

Moderate and/or large-effect QTL were detected for all traits in all populations, as jointly defined by the estimated proportion of variance explained (PVE) by marker effects (PVE ≥ 1.0%) and a nominal P-value threshold (P ≤ 5e-05). Lead SNPs with PVE ≥ 2.0% were considered putative evidence of large-effect QTL (n = 52), whereas those with PVE ≥ 1.0% but < 2.0% were considered putative evidence for moderate-effect QTL (n = 35). Identical or proximal lead SNPs associated with ADG, DMI, MMWT, and RFI collectively supported the potential for either pleiotropic QTL, or independent but proximal causal mutations for multiple traits within and between the analyzed populations. Marker-based heritability estimates for all investigated traits ranged from 0.18 to 0.60 using 778K genotypes, or from 0.17 to 0.57 using 50K genotypes (reduced from Illumina 778K HD to Illumina Bovine SNP50). An investigation to determine if QTL detected by 778K analysis could also be detected using 50K genotypes produced variable results, suggesting that 50K analyses were generally insufficient for QTL detection in these populations, and that relevant breeding or selection programs should be based on higher density analyses (imputed or directly ascertained).

Conclusions

Fourteen moderate to large-effect QTL regions which ranged from being physically proximal (lead SNPs ≤ 3Mb) to fully overlapping for RFI, DMI, ADG, and MMWT were detected within and between populations, and included evidence for pleiotropy, proximal but independent causal mutations, and multi-breed QTL. Bovine positional candidate genes for these traits were functionally conserved across vertebrate species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3754-y) contains supplementary material, which is available to authorized users.

Genotype-dependent Metabolic Responses to Semi-Purified High-Sucrose High-Fat Diets in the TALLYHO/Jng vs. C57BL/6 Mouse during the Development of Obesity and Type 2 Diabetes

Background: The co-epidemic of obesity and type 2 diabetes is associated with increased morbidity and mortality. Genetic factors are highly involved in the development of these diseases, in the form of interactions of multiple genes within obesogenic and diabetogenic environments, such as a high fat diet. The TALLYHO/Jng (TH) mouse is an inbred polygenic model for human obesity and type 2 diabetes. In order to further develop the TH mouse as a clinically relevant model, we investigated diet dependence of obesity and type 2 diabetes in TH mice vs. C57BL/6 (B6) mice. Results: TH and B6 mice were weaned onto a standard rodent chow, semi-purified high-sucrose low-fat (HSLF), or semi-purified high-sucrose high-fat (HSHF) diet and maintained on these diets throughout the study. Despite similar fat contents in HSLF diets and chow, both B6 and TH mice responded to HSLF diets, with increases in adiposity. TH mice, but not B6 mice, exhibited significantly higher adiposity with severely aggravated glucose intolerance and hyperglycemia on HSHF diets compared to the other diets. HSLF diets also advanced diabetes in TH mice compared to chow, but it did not surpass the effects of HSHF diets. The severe glucose intolerance and hyperglycemia in TH mice on both HSLF and HSHF diets were accompanied by significantly reduced Glut4 mRNA levels compared to B6 mice. Conclusions: The present data demonstrate that diets are important modulators of genetic susceptibility to type 2 diabetes and obesity in TH mice. The interplay between heredity and dietary environment in TH mice appears to amplify insulin resistance, contributing to severe glucose intolerance and diabetes.

A20 reduces lipid storage and inflammation in hypertrophic adipocytes via p38 and Akt signaling

Adipose tissue plays a vital role in the development of obesity and related disorders. Our previous study showed that A20, an ubiquitin-editing enzyme with anti-inflammation function, attenuated free fatty acids (FFAs)-induced lipid accumulation in nonalcoholic steatohepatitis. Here, we investigated A20 expression in adipose tissue of obese individuals and its effects on 3T3-L1 lipogenesis as well as the likely mechanisms underlying this process. By re-annotation of raw microarray data downloaded from Gene Expression Omnibus, we found that obese individuals showed significantly higher A20 mRNA levels in adipocytes. In vitro, A20 inhibited MCP-1 and IL-6 secretion in adipocytes. Forced expression of A20 resulted in decreased expression of key markers of lipogenesis and adipogenesis, such as sterol regulatory element binding protein 1c (SREBP-1c) and adipogenesis (aP2), leading to less lipids accumulation in differentiated 3T3-L1 cells. This process was concomitant with attenuated activation of p38 and Akt signaling. Our results suggest that A20 may have therapeutic potential for obesity and related diseases. The mechanisms involved the suppression of lipid storage and inflammation in adipocytes.

Novel genes on rat chromosome 10 are linked to body fat mass, preadipocyte number and adipocyte size

BACKGROUND

The genetic architecture of obesity is multifactorial. We have previously identified a quantitative trait locus (QTL) on rat chromosome 10 in a F2 cross of Wistar Ottawa Karlsburg (WOKW) and Dark Agouti (DA) rats responsible for obesity-related traits. The QTL was confirmed in congenic DA.WOKW10 rats. To pinpoint the region carrying causal genes, we established two new subcongenic lines, L1 and L2, with smaller refined segments of chromosome 10 to identify novel candidate genes.

METHODS

All lines were extensively characterized under different diet conditions. We employed transcriptome analysis in visceral adipose tissue (VAT) by RNA-Seq technology to identify potential underlying genes in the segregating regions. Three candidate genes were measured in human paired samples of VAT and subcutaneous (SC) AT (SAT) (N=304) individuals with a wide range of body weight and glucose homeostasis parameters.

RESULTS

DA.WOKW and L1 subcongenic lines were protected against body fat gain under high-fat diet (HFD), whereas L2 and DA had significantly more body fat after high-fat feeding. Interestingly, adipocyte size distribution in SAT and epigonadal AT of L1 subcongenic rats did not undergo typical ballooning under HFD and the number of preadipocytes in AT was significantly elevated in L2 compared with L1 and parental rats. Transcriptome analysis identified three candidate genes in VAT on rat chromosome 10. In humans, these candidate genes were differentially expressed between SAT and VAT. Moreover, HID1 mRNA significantly correlates with parameters of obesity and glucose metabolism.

CONCLUSIONS

Our data suggest novel candidate genes for obesity that map on rat chromosome 10 in an interval 102.2-104.7 Mb and are strongly associated with body fat mass regulation, preadipocyte number and adipocyte size in rats. Among those genes, AT head involution defective (HID1) mRNA expression may be relevant for human fat distribution and glucose homeostasis.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Background

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.

Methods

Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.

Results

Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.

Conclusions

To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0229-0) contains supplementary material, which is available to authorized users.

The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models

Obesity-associated diabetes (“diabesity”) in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells. This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse. In lean strains such as C57BLKS/J, BTBR T+tf/J or DBA/2 J carrying diabetes susceptibility genes (“diabetes susceptible” background), it can be induced by introgression of the obesity-causing mutations Lep<ob> (ob) or Lepr<db> (db). Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1,Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation). Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism. Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease.

Effects of insulin resistance on skeletal muscle growth and exercise capacity in type 2 diabetic mouse models

Type 2 diabetes mellitus is associated with an accelerated muscle loss during aging, decreased muscle function, and increased disability. To better understand the mechanisms causing this muscle deterioration in type 2 diabetes, we assessed muscle weight, exercise capacity, and biochemistry in db/db and TallyHo mice at prediabetic and overtly diabetic ages. Maximum running speeds and muscle weights were already reduced in prediabetic db/db mice when compared with lean controls and more severely reduced in the overtly diabetic db/db mice. In contrast to db/db mice, TallyHo muscle size dramatically increased and maximum running speed was maintained during the progression from prediabetes to overt diabetes. Analysis of mechanisms that may contribute to decreased muscle weight in db/db mice demonstrated that insulin-dependent phosphorylation of enzymes that promote protein synthesis was severely blunted in db/db muscle. In addition, prediabetic (6-wk-old) and diabetic (12-wk-old) db/db muscle exhibited an increase in a marker of proteasomal protein degradation, the level of polyubiquitinated proteins. Chronic treadmill training of db/db mice improved glucose tolerance and exercise capacity, reduced markers of protein degradation, but only mildly increased muscle weight. The differences in muscle phenotype between these models of type 2 diabetes suggest that insulin resistance and chronic hyperglycemia alone are insufficient to rapidly decrease muscle size and function and that the effects of diabetes on muscle growth and function are animal model-dependent.

Islet adaptation to obesity and insulin resistance in WNIN/GR-Ob rats

WNIN/GR-Ob mutant rat is a novel animal model to study metabolic syndrome (obesity, insulin resistance, hyperinsulinemia, impaired glucose tolerance and cardiovascular diseases). We have investigated the islet characteristics of obese mutants at different age groups (1, 6 and 12 months) to assess the islet changes in response to early and chronic metabolic stress. Our data demonstrates altered islet cell morphology and function (hypertrophy, fibrotic lesions, vacuolation, decreased stimulation index, increased TNFα, ROS and TBARS levels) in mutants as compared to controls. Furthermore, network analysis (gene-gene interaction) studied in pancreas demonstrated increased inflammation as a key factor underlying obesity/metabolic syndrome in mutants. These observations pave way to explore this model to understand islet adaptation in response to metabolic syndrome.

AdR1-TG/TALLYHO mice have improved lipid accumulation and insulin sensitivity

BACKGROUND

Overexpression of adiponectin receptor 1 in macrophages can physiologically modulate metabolic activities in vivo by enhancing adiponectin actions in distal metabolically active tissues. To investigate the effects of enhanced adiponectin actions in TALLYHO (TH) diabetic mouse model, we crossed the adiponectin receptor 1 macrophage-specific transgenic mice (AdR1-TG) with the TALLYHO diabetic mice (TH) to examine the changes of lipid accumulation and insulin sensitivity in these mice.

METHODS

AdR1-TG/TH and the control WT/TH mice were fed either normal diet or high fat diet for 28weeks. Whole body weights of these mice were measured and mouse sera were analyzed for the levels of cholesterol, triglyceride, and free fatty acids. Glucose tolerance testing (GTT) and insulin tolerance testing (ITT) in these mice were performed to investigate systemic insulin sensitivity in vivo. Molecular markers for insulin signaling pathway in mouse skeletal muscle tissues, IRS-1 and AKT, were examined. Mouse serum insulin levels were measured and Sirt1 gene expression in mouse pancreatic tissues was also quantified related to the insulin secretion. The Caspase 3 protein levels were analyzed by Western blot methods.

RESULTS

Compared to the control WT/TH mice, AdR1-TG/TH mice showed significantly lower body weights under either normal diet or high fat diet and the mouse serum levels of cholesterol, triglyceride and free fatty acids were significantly decreased in the transgenic crossed mice when compared to those from the control mice. Improved GTT and ITT tests indicating increased systemic insulin sensitivity in the transgenic crossed mice demonstrated the enhanced adiponectin actions on the systemic metabolism in vivo. The increases of insulin secretion and its related gene expression were also detected in the transgenic crossed mice. In contrast, the control mice showed hypertrophy pancreases companying with high apoptosis gene expression. These results suggest that enhanced adiponectin actions by overexpressing adiponectin receptor 1 in macrophages can provide unique interactions with the metabolic tissues/cells, improving lipid accumulation and insulin sensitivity in TALLYHO diabetic mice.

Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight

Complex traits such as obesity are manifestations of intricate interactions of multiple genetic factors. However, such relationships are difficult to identify. Thanks to the recent advance in high-throughput technology, a large amount of data has been collected for various complex traits, including obesity. These data often measure different biological aspects of the traits of interest, including genotypic variations at the DNA level and gene expression alterations at the RNA level. Integration of such heterogeneous data provides promising opportunities to understand the genetic components and possibly genetic architecture of complex traits. In this paper, we propose a machine learning based method, module-guided Random Forests (mgRF), to integrate genotypic and gene expression data to investigate genetic factors and molecular mechanism underlying complex traits. mgRF is an augmented Random Forests method enhanced by a network analysis for identifying multiple correlated variables of different types. We applied mgRF to genetic markers and gene expression data from a cohort of F2 female mouse intercross. mgRF outperformed several existing methods in our extensive comparison. Our new approach has an improved performance when combining both genotypic and gene expression data compared to using either one of the two types of data alone. The resulting predictive variables identified by mgRF provide information of perturbed pathways that are related to body weight. More importantly, the results uncovered intricate interactions among genetic markers and genes that have been overlooked if only one type of data was examined. Our results shed light on genetic mechanisms of obesity and our approach provides a promising complementary framework to the “genetics of gene expression” analysis for integrating genotypic and gene expression information for analyzing complex traits.

Obesity has become a perilous global epidemic that can lead to complex diseases, such as diabetes and cardiovascular diseases. Much effort has been devoted to the studies of the genetic mechanisms that pillow the manifestation of obesity. Although a large quantity of experimental data has been accumulated lately using high-throughput techniques, our understanding of genetic mechanisms of obesity is still limited. The proposed method is motivated to address three critical issues that have impeded the existing methods. The first is the curse of dimensionality in selecting a subset of genetic elements related to the traits of interest from a large number of candidates. The second is genetic multiplicity underlying non-Mendelian traits, in which multiple genes are in interplay. The third issue is the integration of data from multiple sources in light of genetic multiplicity and curse of dimensionality. Here, we propose a new method, which augments the Random Forests method with a network-based analysis, to integrate genotypic and gene expression information and identify correlated multiple genetic elements underlying mouse weight. Our results shed light on complex genetic interactions underlying obesity, which can form viable hypotheses worthy of further investigation.

Experimental periodontitis induced by Porphyromonas gingivalis does not alter the onset or severity of diabetes in mice

BACKGROUND AND OBJECTIVE

Diabetes mellitus is believed to increase the risk and severity of periodontitis. However, less evidence is available on the converse effects of periodontitis on diabetes. The objective of the study was to investigate to what degree experimental periodontitis induced by Porphyromonas gingivalis might influence the onset and severity of diabetes in different mouse models.

MATERIAL AND METHODS

Twenty-eight male Tallyho/JngJ mice (type 2 diabetes), 20 male streptozotocin-induced diabetes C57BL/6J mice (type 1 diabetes) and 20 male C57BL/6J mice at 4 wks of age were evenly divided into two groups: periodontal infection and sham infection. Periodontitis was induced by Porphyromonas gingivalis W50 (P. gingivalis) oral inoculation before the development of diabetes. Sham-infected mice received vehicle as control. P. gingivalis in the oral cavity were identified by quantitative polymerase chain reaction. Fasting glucose, body weight and food intake levels were monitored and glucose tolerance tests were performed to assess glucose homeostasis for the onset and progression of diabetes. The level of alveolar bone loss and tumor necrosis factor-alpha were determined in week 20 when mice were killed.

RESULTS

Mice in the infection groups developed more alveolar bone loss than those in sham-infection groups (Tallyho p = 0.021; C57-STZ p = 0.014; C57 p = 0.035). Hyperglycemic mice exhibited significantly more bone loss compared to those normal glucose mice (Tallyho vs. C57 p = 0.029; C57-STZ vs. C57 p = 0.024). The level of tumor necrosis factor-alpha was consistent with that of periodontal bone loss and hyperglycemia. There was no significant effect of mouse species on the amount of bone loss at the same level of blood glucose. No statistically significant difference or trend in glucose metabolism was found between the infection and sham-infection group.

CONCLUSION

Diabetes enhanced the risk for periodontal disease induced by P. gingivalis. However, no converse impact was found between this periodontal infection and onset and severity of diabetes in both type 1 and 2 diabetes mice.

Identifying candidate genes for Type 2 Diabetes Mellitus and obesity through gene expression profiling in multiple tissues or cells

Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity.

Pathophysiology and genetics of obesity and diabetes in the New Zealand obese mouse: a model of the human metabolic syndrome

The New Zealand Obese (NZO) mouse is one of the most thoroughly investigated polygenic models for the human metabolic syndrome and type 2 diabetes. It presents the main characteristics of the disease complex, including early-onset obesity, insulin resistance, dyslipidemia, and hypertension. As a consequence of this syndrome, a combination of lipotoxicity and glucotoxicity produces beta-cell failure and apoptosis resulting in hypoinsulinemia and diabetic hyperglycemia. With NZO as a breeding partner, several adipogenic and diabetogenic gene variants have been identified by hypothesis-free positional cloning (Tbc1d1, Zfp69) or by combining genetic screens and candidate gene approaches (Pctp, Abcg1, Nmur2, Lepr). This chapter summarizes the present knowledge of the NZO strain and describes its pathophysiology as well as the known underlying genetic defects.

The TALLYHO mouse as a model of human type 2 diabetes

The TALLYHO/Jng (TH) mouse is an inbred polygenic model for type 2 diabetes (T2D) with moderate obesity. Both male and female TH mice are characterized by increased body and fat pad weights, hyperleptinemia, hyperinsulinemia, and hyperlipidemia. Glucose intolerance and hyperglycemia are exhibited only in males. Reduced 2-deoxy-glucose uptake occurs in adipose tissue and skeletal muscle of male TH mice. While both sexes of TH mice exhibit enlarged pancreatic islets, only males have degranulation and abnormal architecture in islets. Endothelial dysfunction and considerably decreased bone density are also observed in male TH mice. The blood pressure of male TH mice is normal. Genetic outcross experiments with non-diabetic strains revealed multiple susceptibility loci (quantitative trait loci) for obesity, hypertriglyceridemia, hypercholesterolemia, and hyperglycemia. In conclusion, TH mice encompass many aspects of polygenic human diabetes and are a very useful model for T2D.

Genetic effects at pleiotropic loci are context-dependent with consequences for the maintenance of genetic variation in populations

Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F₁₆ advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.

We look at gene-by-diet and gene-by-sex interactions underlying natural variation in multiple metabolic traits mapping to the same regions of the genome in a mouse model. We find that the underlying genetic architecture of these traits is different in different sex and diet contexts. We further use expression data and whole-genome polymorphism data to identify compelling candidates for experimental follow-up. We use these results to examine theoretical evolutionary predictions about how variation in populations can be maintained. There has been much discussion of late on how to use evolutionary theory to inform medical genomics. Mouse models may be especially appropriate for bridging the divide between evolutionary and biomedical research, because they allow the study of the effects of natural alleles on normal variation and because human-mouse homology is well defined. Our study is unique in examining quantitative trait loci from both evolutionary and biomedical perspectives, and we highlight the complex connections of the traits comprising the metabolic syndrome and the evolutionary implications of their underlying genetic architecture. This is important for understanding disease etiology and is relevant to personalized medicine.