Background: Humans with metabolic and inflammatory diseases frequently harbor lower levels of butyrate-producing bacteria in their gut. However, it is not known whether variation in the levels of these organisms is causally linked with disease development and whether diet modifies the impact of these bacteria on health. Results: We use germ-free apolipoprotein E-deficient mice colonized with synthetic microbial communities that differ in their capacity to generate butyrate to demonstrate that Roseburia intestinalis interacts with dietary components to (i) impact gene expression in the intestine, directing metabolism away from glycolysis and toward fatty acid utilization, (ii) improve intestinal barrier function, (iii) lower systemic inflammation and (iv) ameliorate atherosclerosis. Furthermore, intestinal administration of butyrate improves gut barrier function and reduces atherosclerosis development. Conclusions: Altogether, our results illustrate how modifiable diet-by-microbiota interactions impact cardiovascular disease, and suggest that interventions aimed at increasing the representation of butyrate-producing bacteria may provide protection against atherosclerosis. Overall design: Intestinal mRNA profiles of gnotobiotic ApoE KO mice colonized with "core" community or "core plus Roseburia intestinalis" were generated by deep sequencing using Illumina HiSeq.
Interactions between Roseburia intestinalis and diet modulate atherogenesis in a murine model.
Age, Specimen part, Subject
View SamplesWe report a multi-omic study of sex differences and gene-by-sex interactions across a panel of 100 inbred strains of mice (the Hybrid Mouse Diversity Panel, HMDP), with a focus on metabolic and cardiovascular traits. For all traits examined, including obesity, insulin resistance, fatty liver, atherosclerosis, and gut microbiota composition, sex differences were influenced by genetic background. Loci identified by genome-wide association studies (GWAS) of the traits were frequently influenced by sex. Lyplal1, a gene implicated in human obesity, was shown to underlie a sex-specific locus for diet induced obesity. Many of the sex-dependent traits showed interdependencies as judged by correlation and shared gene expression patterns, indicating higher order regulation. Global gene expression analyses of tissues across the HMDP indicated that sex differences in mitochondrial functions in adipose contributed to many of the traits. Consistent with this, we observed that females tended to be more resistant to the adverse effects of a high fat diet, with smaller adipocytes and increased “browning” of white adipose tissue as compared to males. Sex-specific differences in mitochondrial activity were confirmed by examining respiration of isolated mitochondria. Gonadectomy experiments revealed thousands of genes influenced by sex hormones. In liver, a tissue exhibiting particularly strong differences in gene expression between tissues, sex hormones appeared to be the primary driver of the differences, whereas in adipose organizational effects of sex appeared to be more important. Overall design: Sixteen male and sixteen female C57BL/6J were purchased from The Jackson Laboratory (Bar Harbor). Mice were either maintained on a chow diet (Ralston Purina Company) or placed on an HF/HS diet (Research Diets D12266B) at 8 weeks of age until 16 weeks of age. At 6 weeks of age the mice were gonadectomized under isoflurane anesthesia. Scrotal regions of male mice were bilaterally incised, testes removed, and the incisions closed with wound clips. Ovaries of female mice were removed through an incision just below the rib cage. There were four mice per group. The muscle layer was sutured, and the incision closed with wound clips. In sham-operated control mice, incisions were made and closed as described above. The gonads were briefly manipulated, but remained intact. Gonadal fat and liver samples were taken for RNASeq expression profiling.
Gene-by-Sex Interactions in Mitochondrial Functions and Cardio-Metabolic Traits.
Sex, Age, Cell line, Treatment, Subject
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Genetic architecture of insulin resistance in the mouse.
Sex, Age, Specimen part
View SamplesIdentify genes in the gonadal adipose tissue whose expression is under genetic regulation in the Hybrid Mouse Diversity Panel (HMDP). The HMDP comprises classical inbred and recombinant inbred wild type mice. The RMA values of genes were used for genome wide association as described in Parks et al Cell Metabolism 2015. These data are used to identify candidate genes at loci associated with obesity and dietary responsiveness.
Genetic architecture of insulin resistance in the mouse.
Sex, Age, Specimen part
View SamplesIdentify genes in the liver whose expression is under genetic regulation in the Hybrid Mouse Diversity Panel (HMDP). The HMDP comprises classical inbred and recombinant inbred wild type mice. The RMA values of genes were used for genome wide association as described in Parks et al Cell Metabolism 2015. These data are used to identify candidate genes at loci associated with obesity and dietary responsiveness.
Genetic architecture of insulin resistance in the mouse.
Sex, Age, Specimen part
View SamplesIdentify genes in the epididymal adipose tissue whose expression is under genetic regulation in the hybrid mouse diversity panel. The hybrid mouse diversity panel is comprised of classical inbred and recombinant inbred wild type mice. The RMA values of genes were used for genome wide association as described in Bennett et al Genome Research 2010. These data are used to identify candidate genes at loci associated with obesity and dietary responsiveness.
Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.
Sex, Age, Specimen part
View SamplesA major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
No sample metadata fields
View SamplesA major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
No sample metadata fields
View SamplesA major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
No sample metadata fields
View SamplesA major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
No sample metadata fields
View Samples