Project description:A 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. This SuperSeries is composed of the following subset Series: GSE11991: Liver gene expression profiling of lipoprotein lipase heterozygous knockout mice GSE11992: Liver gene expression profiling of cytosolic malic enzyme knockout mice GSE11993: Liver gene expression profiling of zinc finger binding protein 90 (Zfp90) transgenic mice GSE11994: Liver gene expression profiling of transforming growth factor beta receptor 2 heterozygous knockout (Tgfbr2+/-) mice GSE11995: Liver gene expression profiling of complement component 3a receptor 1 knockout (C3ar1-/-) mice GSE11996: Gas7 male transgenic liver expression vs FVB male wildtype control GSE11997: Gpx3 male transgenic liver expression vs B6/DBA male wildtype control GSE11998: Gyk female heterozygous liver expression vs C57Bl/6J female wildtype control GSE11999: Lactb male transgenic liver expression vs FVB male wildtype control Refer to individual Series
Project description:A 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. Keywords: Obesity study in transgenic animals A total of 8 mice were used for this study (4 Gpx3 transgenic and 4 B6/DBA wildtype). The gene expression data is from the livers of 23 week old male wildtype and transgenic mice. These mice were fed a 4% fat chow diet until 11 weeks of age and then were fed a 6% fat chow diet until sacrifice at 23 weeks of age.
Project description:The escalating prevalence of metabolic syndrome (MetS) poses significant risks to type 2 diabetes mellitus, cardiovascular diseases, and non-alcoholic fatty liver disease. High fructose intake has emerged as an environmental risk for MetS and the associated metabolic diseases. To examine inter-individual variability in MetS susceptibility in response to fructose consumption, here we fed three inbred mouse strains, namely C57BL/6J (B6), DBA (DBA) and FVB/NJ (FVB) with 8% fructose in drinking water for 12 weeks. We found that fructose-fed DBA mice had significantly higher amount of body weight, adiposity, and glucose intolerance starting from the 4th week of fructose feeding compared to the control group, while B6 and FVB showed no differences in these phenotypes over the course of fructose feeding. In addition, elevated insulin levels were found in fructose-fed DBA and FVB mice, and cholesterol levels were uniquely elevated in B6 mice. To explore the molecular underpinnings of the observed distinct phenotypic responses among strains, we applied RNA sequencing to investigate the effect of fructose on the transcriptional profiles of liver and hypothalamus tissues, revealing strain- and tissue-specific patterns of transcriptional and pathway perturbations. Strain-specific liver pathways altered by fructose include fatty acid and cholesterol metabolic pathways for B6 and PPAR signaling for DBA. In hypothalamus tissue, only B6 showed significantly enriched pathways such as protein folding, pancreatic secretion, and fatty acid beta-oxidation. Using network modeling, we predicted potential strain-specific key regulators of fructose response such as Fgf21 (DBA) and Lss (B6) in liver, and Fmod (B6) in hypothalamus. We validated strain-biased responses of Fgf21 and Lss to fructose in primary hepatocytes. Our findings support that fructose perturbs different tissue networks and pathways in genetically diverse mice and associates with distinct features of metabolic dysfunctions. These results highlight individualized molecular and metabolic responses to fructose consumption and may help guide the development of personalized strategies against fructose-induced MetS.
Project description:The escalating prevalence of metabolic syndrome (MetS) poses significant risks to type 2 diabetes mellitus, cardiovascular diseases, and non-alcoholic fatty liver disease. High fructose intake has emerged as an environmental risk for MetS and the associated metabolic diseases. To examine inter-individual variability in MetS susceptibility in response to fructose consumption, here we fed three inbred mouse strains, namely C57BL/6J (B6), DBA (DBA) and FVB/NJ (FVB) with 8% fructose in drinking water for 12 weeks. We found that fructose-fed DBA mice had significantly higher amount of body weight, adiposity, and glucose intolerance starting from the 4th week of fructose feeding compared to the control group, while B6 and FVB showed no differences in these phenotypes over the course of fructose feeding. In addition, elevated insulin levels were found in fructose-fed DBA and FVB mice, and cholesterol levels were uniquely elevated in B6 mice. To explore the molecular underpinnings of the observed distinct phenotypic responses among strains, we applied RNA sequencing to investigate the effect of fructose on the transcriptional profiles of liver and hypothalamus tissues, revealing strain- and tissue-specific patterns of transcriptional and pathway perturbations. Strain-specific liver pathways altered by fructose include fatty acid and cholesterol metabolic pathways for B6 and PPAR signaling for DBA. In hypothalamus tissue, only B6 showed significantly enriched pathways such as protein folding, pancreatic secretion, and fatty acid beta-oxidation. Using network modeling, we predicted potential strain-specific key regulators of fructose response such as Fgf21 (DBA) and Lss (B6) in liver, and Fmod (B6) in hypothalamus. We validated strain-biased responses of Fgf21 and Lss to fructose in primary hepatocytes. Our findings support that fructose perturbs different tissue networks and pathways in genetically diverse mice and associates with distinct features of metabolic dysfunctions. These results highlight individualized molecular and metabolic responses to fructose consumption and may help guide the development of personalized strategies against fructose-induced MetS.
Project description:The escalating prevalence of metabolic syndrome (MetS) poses significant risks to type 2 diabetes mellitus, cardiovascular diseases, and non-alcoholic fatty liver disease. High fructose intake has emerged as an environmental risk for MetS and the associated metabolic diseases. To examine inter-individual variability in MetS susceptibility in response to fructose consumption, here we fed three inbred mouse strains, namely C57BL/6J (B6), DBA (DBA) and FVB/NJ (FVB) with 8% fructose in drinking water for 12 weeks. We found that fructose-fed DBA mice had significantly higher amount of body weight, adiposity, and glucose intolerance starting from the 4th week of fructose feeding compared to the control group, while B6 and FVB showed no differences in these phenotypes over the course of fructose feeding. In addition, elevated insulin levels were found in fructose-fed DBA and FVB mice, and cholesterol levels were uniquely elevated in B6 mice. To explore the molecular underpinnings of the observed distinct phenotypic responses among strains, we applied RNA sequencing to investigate the effect of fructose on the transcriptional profiles of liver and hypothalamus tissues, revealing strain- and tissue-specific patterns of transcriptional and pathway perturbations. Strain-specific liver pathways altered by fructose include fatty acid and cholesterol metabolic pathways for B6 and PPAR signaling for DBA. In hypothalamus tissue, only B6 showed significantly enriched pathways such as protein folding, pancreatic secretion, and fatty acid beta-oxidation. Using network modeling, we predicted potential strain-specific key regulators of fructose response such as Fgf21 (DBA) and Lss (B6) in liver, and Fmod (B6) in hypothalamus. We validated strain-biased responses of Fgf21 and Lss to fructose in primary hepatocytes. Our findings support that fructose perturbs different tissue networks and pathways in genetically diverse mice and associates with distinct features of metabolic dysfunctions. These results highlight individualized molecular and metabolic responses to fructose consumption and may help guide the development of personalized strategies against fructose-induced MetS.
Project description:Untargeted lipidomics of liver samples from female and male DBA/2J or C57BL/6J mice fed a control diet, Western diet, or high- or low-isoleucine Western diet. Both positive and negative mode are included.