Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers
Abstract Common inflammatome gene signatures as well as disease‐specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug t...
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| Format: | Article |
| Language: | English |
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Springer Nature
2012-07-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/msb.2012.24 |
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| author | I‐Ming Wang Bin Zhang Xia Yang Jun Zhu Serguei Stepaniants Chunsheng Zhang Qingying Meng Mette Peters Yudong He Chester Ni Deborah Slipetz Michael A Crackower Hani Houshyar Christopher M Tan Ernest Asante‐Appiah Gary O'Neill Mingjuan Jane Luo Rolf Thieringer Jeffrey Yuan Chi‐Sung Chiu Pek Yee Lum John Lamb Yves Boie Hilary A Wilkinson Eric E Schadt Hongyue Dai Christopher Roberts |
| author_facet | I‐Ming Wang Bin Zhang Xia Yang Jun Zhu Serguei Stepaniants Chunsheng Zhang Qingying Meng Mette Peters Yudong He Chester Ni Deborah Slipetz Michael A Crackower Hani Houshyar Christopher M Tan Ernest Asante‐Appiah Gary O'Neill Mingjuan Jane Luo Rolf Thieringer Jeffrey Yuan Chi‐Sung Chiu Pek Yee Lum John Lamb Yves Boie Hilary A Wilkinson Eric E Schadt Hongyue Dai Christopher Roberts |
| author_sort | I‐Ming Wang |
| collection | DOAJ |
| description | Abstract Common inflammatome gene signatures as well as disease‐specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co‐expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue‐specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response‐related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non‐drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases. |
| format | Article |
| id | doaj-art-7f8736e3694946c78134d40fac8a24ec |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2012-07-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-7f8736e3694946c78134d40fac8a24ec2025-08-20T03:41:57ZengSpringer NatureMolecular Systems Biology1744-42922012-07-018111610.1038/msb.2012.24Systems analysis of eleven rodent disease models reveals an inflammatome signature and key driversI‐Ming Wang0Bin Zhang1Xia Yang2Jun Zhu3Serguei Stepaniants4Chunsheng Zhang5Qingying Meng6Mette Peters7Yudong He8Chester Ni9Deborah Slipetz10Michael A Crackower11Hani Houshyar12Christopher M Tan13Ernest Asante‐Appiah14Gary O'Neill15Mingjuan Jane Luo16Rolf Thieringer17Jeffrey Yuan18Chi‐Sung Chiu19Pek Yee Lum20John Lamb21Yves Boie22Hilary A Wilkinson23Eric E Schadt24Hongyue Dai25Christopher Roberts26Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc.Department of Genetics and Genomic Sciences, Mount Sinai School of MedicineSage BionetworksDepartment of Genetics and Genomic Sciences, Mount Sinai School of MedicineCovance Genomics LaboratoryInformatics and Analysis, Merck Research Laboratories, Merck & Co., Inc.Department of Integrative Biology and Physiology, University of CaliforniaSage BionetworksInvestigative Toxicology Department, AmgenSystems Immunology, Benaroya Research InstituteDepartment of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc.Department of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc.In Vivo Pharmacology, Merck Research Laboratories, Merck & Co., Inc.In Vivo Pharmacology, Merck Research Laboratories, Merck & Co., Inc.Department of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc.Department of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc.Lilly Research Laboratories, Lilly Corporate CenterExternal Scientific Affairs, Merck Research Laboratories, Merck & Co., Inc.World Wide Regulatory Affairs, Merck Research Laboratories, Merck & Co., Inc.In Vivo Pharmacology, Merck Research Laboratories, Merck & Co., Inc.Product, Ayasdi Inc.Oncology Research Unit, PfizerGenomics Platform, Broad InstituteDepartment of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc.Department of Genetics and Genomic Sciences, Mount Sinai School of MedicineInformatics and Analysis, Merck Research Laboratories, Merck & Co., Inc.Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc.Abstract Common inflammatome gene signatures as well as disease‐specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co‐expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue‐specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response‐related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non‐drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.https://doi.org/10.1038/msb.2012.24Bayesian networkco‐expression networkinflammatomeinflammatory diseaseskey regulators |
| spellingShingle | I‐Ming Wang Bin Zhang Xia Yang Jun Zhu Serguei Stepaniants Chunsheng Zhang Qingying Meng Mette Peters Yudong He Chester Ni Deborah Slipetz Michael A Crackower Hani Houshyar Christopher M Tan Ernest Asante‐Appiah Gary O'Neill Mingjuan Jane Luo Rolf Thieringer Jeffrey Yuan Chi‐Sung Chiu Pek Yee Lum John Lamb Yves Boie Hilary A Wilkinson Eric E Schadt Hongyue Dai Christopher Roberts Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers Molecular Systems Biology Bayesian network co‐expression network inflammatome inflammatory diseases key regulators |
| title | Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| title_full | Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| title_fullStr | Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| title_full_unstemmed | Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| title_short | Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| title_sort | systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers |
| topic | Bayesian network co‐expression network inflammatome inflammatory diseases key regulators |
| url | https://doi.org/10.1038/msb.2012.24 |
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