Liver gene expression profiling of cytosolic malic enzyme knockout mice
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Xia Yang
PROVIDER: E-GEOD-11992 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Yang Xia X Deignan Joshua L JL Qi Hongxiu H Zhu Jun J Qian Su S Zhong Judy J Torosyan Gevork G Majid Sana S Falkard Brie B Kleinhanz Robert R RR Karlsson Jenny J Castellani Lawrence W LW Mumick Sheena S Wang Kai K Xie Tao T Coon Michael M Zhang Chunsheng C Estrada-Smith Daria D Farber Charles R CR Wang Susanna S SS van Nas Atila A Ghazalpour Anatole A Zhang Bin B Macneil Douglas J DJ Lamb John R JR Dipple Katrina M KM Reitman Marc L ML Mehrabian Margarete M Lum Pek Y PY Schadt Eric E EE Lusis Aldons J AJ Drake Thomas A TA
Nature genetics 20090308 4
A principal 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 have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and ...[more]
Similar Datasets
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
2009-03-07 | E-GEOD-12000 | biostudies-arrayexpress
Project description:To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1,000 patients undergoing Roux-en-y gastric bypass and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than one hundred thousand gene expression traits representing four metabolically relevant tissues; liver, omental adipose, subcutaneous adipose and stomach. We successfully identified 24,531 eSNPs corresponding to ~10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high quality disease map for each tissue in morbidly obese patients to not only inform genetic associations indentified in this cohort, but in previously published genome wide association studies as well. eSNPs and gene co-expression modules identification in morbidly obese patients represent a great resource that will aid in elucidating the key networks associated with morbid obesity, response to RYGB and disease as a whole. Keywords: Tissue profiling in a human cohort. Liver tissue was collected from patients at the time of RYGB surgery at Massachusetts General Hospital between 2000 and 2007. Samples were collected in RNAlater (Ambion/Applied Biosystems), stored at -80° and shipped to Rosetta Inpharmatics Gene Expression Laboratory Seattle, WA for extraction, amplification, labeling, and microarray processing. Samples processed ranged in size from 100-200mg. Total RNA extracted from liver was converted to fluorescently labeled cRNA that was hybridized to custom 44K DNA oligonucleotide microarrays manufactured by Agilent Technologies as described previously (Hughes et al. 2001; Schadt et al. 2008). Successful gene expression profiling results were collected from 651 liver samples.
2011-04-01 | E-GEOD-24293 | biostudies-arrayexpress
Project description:To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1,000 patients undergoing Roux-en-y gastric bypass and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than one hundred thousand gene expression traits representing four metabolically relevant tissues; liver, subcutaneous adipose, omental adipose and stomach. We successfully identified 24,531 eSNPs corresponding to ~10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high quality disease map for each tissue in morbidly obese patients to not only inform genetic associations indentified in this cohort, but in previously published genome wide association studies as well. eSNPs and gene co-expression modules identification in morbidly obese patients represent a great resource that will aid in elucidating the key networks associated with morbid obesity, response to RYGB and disease as a whole. Keywords: Tissue profiling in a human cohort. Subcutaneous adipose tissue was collected from patients at the time of RYGB surgery at Massachusetts General Hospital between 2000 and 2007. Samples were collected in RNAlater (Ambion/Applied Biosystems), stored at -80° and shipped to Rosetta Inpharmatics Gene Expression Laboratory Seattle, WA for extraction, amplification, labeling, and microarray processing. Samples processed ranged in size from 100-200mg. Total RNA extracted from subcutaneous adipose was converted to fluorescently labeled cRNA that was hybridized to custom 44K DNA oligonucleotide microarrays manufactured by Agilent Technologies as described previously (Hughes et al. 2001; Schadt et al. 2008). Successful gene expression profiling results were collected from 701 subcutaneous adipose samples.
2011-04-01 | E-GEOD-24297 | biostudies-arrayexpress
Project description:To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1,000 patients undergoing Roux-en-y gastric bypass and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than one hundred thousand gene expression traits representing four metabolically relevant tissues; liver, omental adipose, subcutaneous adipose and stomach. We successfully identified 24,531 eSNPs corresponding to ~10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high quality disease map for each tissue in morbidly obese patients to not only inform genetic associations indentified in this cohort, but in previously published genome wide association studies as well. eSNPs and gene co-expression modules identification in morbidly obese patients represent a great resource that will aid in elucidating the key networks associated with morbid obesity, response to RYGB and disease as a whole. Keywords: Tissue profiling in a human cohort. Omental adipose tissue was collected from patients at the time of RYGB surgery at Massachusetts General Hospital between 2000 and 2007. Samples were collected in RNAlater (Ambion/Applied Biosystems), stored at -80° and shipped to Rosetta Inpharmatics Gene Expression Laboratory Seattle, WA for extraction, amplification, labeling, and microarray processing. Samples processed ranged in size from 100-200mg. Total RNA extracted from omental adipose was converted to fluorescently labeled cRNA that was hybridized to custom 44K DNA oligonucleotide microarrays manufactured by Agilent Technologies as described previously (Hughes et al. 2001; Schadt et al. 2008). Successful gene expression profiling results were collected from 848 omental adipose samples.
2011-04-01 | E-GEOD-24294 | biostudies-arrayexpress
Project description:The genetics of complex disease produce alterations in molecular interactions of cellular pathways which collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer´s disease (LOAD), we constructed gene regulatory networks in hundreds of autopsied brain tissues from LOAD patients and non-demented subjects. We demonstrate that LOAD reconfigures specific portions of the molecular interaction structure, and via an integrative network-based approach we rank ordered these sub-networks (modules) for relevance to LOAD pathology, highlighting the immune/microglia module as the top ranking. Through a Bayesian inference approach we identified multiple key causal regulators for LOAD brains. Autopsied tissues from dorsolateral prefrontal cortex (PFC), visual cortex (VC) and cerebellum (CR) in brains of LOAD patients, and non-demented healthy controls, collected through the Harvard Brain Tissue Resource Center (HBTRC), were profiled on a custom-made Agilent 44K array (GPL4372_1). All subjects were diagnosed at intake and each brain underwent extensive LOAD-related pathology examination. Gene expression analyses were adjusted for age and sex, postmortem interval (PMI) in hours, sample pH and RNA integrity number (RIN). In the overall cohort of LOAD and non-demented brains the mean ± SD for sample PMI, pH and RIN were 17.8±8.3, 6.4±0.3 and 6.8±0.8, respectively. 230 samples with all PFC, VC, and CR tissue profiled were included for further multi-tissue analysis.
2013-04-25 | E-GEOD-44768 | biostudies-arrayexpress
Project description:The genetics of complex disease produce alterations in molecular interactions of cellular pathways which collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer´s disease (LOAD), we constructed gene regulatory networks in hundreds of autopsied brain tissues from LOAD patients and non-demented subjects. We demonstrate that LOAD reconfigures specific portions of the molecular interaction structure, and via an integrative network-based approach we rank ordered these sub-networks (modules) for relevance to LOAD pathology, highlighting the immune/microglia module as the top ranking. Through a Bayesian inference approach we identified multiple key causal regulators for LOAD brains. Autopsied tissues from dorsolateral prefrontal cortex (PFC), visual cortex (VC) and cerebellum (CR) in brains of LOAD patients, and non-demented healthy controls, collected through the Harvard Brain Tissue Resource Center (HBTRC), were profiled on a custom-made Agilent 44K array (GPL4372_1). All subjects were diagnosed at intake and each brain underwent extensive LOAD-related pathology examination. Gene expression analyses were adjusted for age and sex, postmortem interval (PMI) in hours, sample pH and RNA integrity number (RIN). In the overall cohort of LOAD and non-demented brains the mean ± SD for sample PMI, pH and RIN were 17.8±8.3, 6.4±0.3 and 6.8±0.8, respectively. 230 samples with all PFC, VC, and CR tissue profiled were included for further multi-tissue analysis.
2013-04-25 | E-GEOD-44771 | biostudies-arrayexpress
Project description:The genetics of complex disease produce alterations in molecular interactions of cellular pathways which collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer´s disease (LOAD), we constructed gene regulatory networks in hundreds of autopsied brain tissues from LOAD patients and non-demented subjects. We demonstrate that LOAD reconfigures specific portions of the molecular interaction structure, and via an integrative network-based approach we rank ordered these sub-networks (modules) for relevance to LOAD pathology, highlighting the immune/microglia module as the top ranking. Through a Bayesian inference approach we identified multiple key causal regulators for LOAD brains. Autopsied tissues from dorsolateral prefrontal cortex (PFC), visual cortex (VC) and cerebellum (CR) in brains of LOAD patients, and non-demented healthy controls, collected through the Harvard Brain Tissue Resource Center (HBTRC), were profiled on a custom-made Agilent 44K array (GPL4372_1). All subjects were diagnosed at intake and each brain underwent extensive LOAD-related pathology examination. Gene expression analyses were adjusted for age and sex, postmortem interval (PMI) in hours, sample pH and RNA integrity number (RIN). In the overall cohort of LOAD and non-demented brains the mean ± SD for sample PMI, pH and RIN were 17.8±8.3, 6.4±0.3 and 6.8±0.8, respectively. 230 samples with all PFC, VC, and CR tissue profiled were included for further multi-tissue analysis.
2013-04-25 | E-GEOD-44770 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-04-25 | E-GEOD-44772 | biostudies-arrayexpress
Project description:Dissecting the shared etiology of different diseases could benefit from a systematic search for associated molecules and their interactions. We investigated genome-wide disruptions in the co-regulation of genes in two neurodegenerative diseases, Alzheimer's or Huntington's disease (AD or HD), using expression profiles from postmortem prefrontal cortex samples of 624 demented patients and non-demented control individuals with matched genotype and clinical data. A meta-analysis based screen for changes in coordinate expression patterns revealed differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former being dominant for both AD and HD. Integration of disruptions common to AD and HD with large-scale data on protein-protein and protein-DNA interactions yielded a 242-gene sub-network that was enriched for proteins involved in neuronal differentiation and genetic associations to brain structural changes and dementia in subjects aged over 70 years. Replication of the AD DC network in independent human and mouse cohorts lends confidence to the comprehensive view we offer on dysregulated brain molecular pathways in AD and HD. DLPFC (BA9) brain tissues of AD patients, HD patients and non-demented controls samples were obtained from Harvard Brain tissue resource center (HBTRC). The HBTRC samples were primarily of Caucasian ancestry, as only eight non-Caucasian outliers were identified, and therefore excluded for further analysis. Post-mortem interval (PMI) was 17.8+8.3 hours (mean ± standard deviation), sample pH was 6.4±0.3 and RNA integrity number (RIN) was 6.8±0.8 for the average sample in the overall cohort. Tissues were profiled on a custom-made Agilent 44K array (GPL4372). 624 individual DLPFC samples were profiled against a common DLPFC pool constructed from the same set of samples.
2014-07-31 | E-GEOD-33000 | biostudies-arrayexpress
Project description:We profiled gene expression in hypothalamus tissue from F2 progeny from a cross between the outbred M16 (selectively bred for rapid weight gain) and ICR (control) mouse strains. We developed a framework for reconstructing tissue-to-tissue coexpression networks between genes in hypothalamus, liver or hypothalamus tissues that are independent of networks constructed from single tissue analyses. The subnetworks we identify as specific to tissue-to-tissue interactions associate with multiple obesity-relevant biological functions like circadian rhythm, energy balance, stress response, or immune response. Keywords: Tissue profiling in a mouse F2 cross. We analyzed 308 hypothalamus samples.
2010-06-22 | E-GEOD-13745 | biostudies-arrayexpress