Yang Xiang, Ulrike Kogel, Stephan Gebel, Michael J Peck, Manuel C Peitsch, Viatcheslav R Akmaev, Julia Hoeng
{"title":"利用逆向工程和正向模拟(REFS™)从A/J小鼠吸入研究中发现肺气肿相关分子网络。","authors":"Yang Xiang, Ulrike Kogel, Stephan Gebel, Michael J Peck, Manuel C Peitsch, Viatcheslav R Akmaev, Julia Hoeng","doi":"10.4137/GRSB.S13140","DOIUrl":null,"url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is a respiratory disorder caused by extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD develops are not fully understood, though it is believed that the disease process includes a genetic component, as not all smokers develop COPD. To investigate the mechanisms that lead to the development of COPD/emphysema, we measured whole genome gene expression and several COPD-relevant biological endpoints in mouse lung tissue after exposure to two CS doses for various lengths of time. A novel and powerful method, Reverse Engineering and Forward Simulation (REFS™), was employed to identify key molecular drivers by integrating the gene expression data and four measured COPD-relevant endpoints (matrix metalloproteinase (MMP) activity, MMP-9 levels, tissue inhibitor of metalloproteinase-1 levels and lung weight). An ensemble of molecular networks was generated using REFS™, and simulations showed that it could successfully recover the measured experimental data for gene expression and COPD-relevant endpoints. The ensemble of networks was then employed to simulate thousands of in silico gene knockdown experiments. Thirty-three molecular key drivers for the above four COPD-relevant endpoints were therefore identified, with the majority shown to be enriched in inflammation and COPD. </p>","PeriodicalId":73138,"journal":{"name":"Gene regulation and systems biology","volume":" ","pages":"45-61"},"PeriodicalIF":0.0000,"publicationDate":"2014-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/GRSB.S13140","citationCount":"5","resultStr":"{\"title\":\"Discovery of Emphysema Relevant Molecular Networks from an A/J Mouse Inhalation Study Using Reverse Engineering and Forward Simulation (REFS™).\",\"authors\":\"Yang Xiang, Ulrike Kogel, Stephan Gebel, Michael J Peck, Manuel C Peitsch, Viatcheslav R Akmaev, Julia Hoeng\",\"doi\":\"10.4137/GRSB.S13140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Chronic obstructive pulmonary disease (COPD) is a respiratory disorder caused by extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD develops are not fully understood, though it is believed that the disease process includes a genetic component, as not all smokers develop COPD. To investigate the mechanisms that lead to the development of COPD/emphysema, we measured whole genome gene expression and several COPD-relevant biological endpoints in mouse lung tissue after exposure to two CS doses for various lengths of time. A novel and powerful method, Reverse Engineering and Forward Simulation (REFS™), was employed to identify key molecular drivers by integrating the gene expression data and four measured COPD-relevant endpoints (matrix metalloproteinase (MMP) activity, MMP-9 levels, tissue inhibitor of metalloproteinase-1 levels and lung weight). An ensemble of molecular networks was generated using REFS™, and simulations showed that it could successfully recover the measured experimental data for gene expression and COPD-relevant endpoints. The ensemble of networks was then employed to simulate thousands of in silico gene knockdown experiments. Thirty-three molecular key drivers for the above four COPD-relevant endpoints were therefore identified, with the majority shown to be enriched in inflammation and COPD. </p>\",\"PeriodicalId\":73138,\"journal\":{\"name\":\"Gene regulation and systems biology\",\"volume\":\" \",\"pages\":\"45-61\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-02-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4137/GRSB.S13140\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gene regulation and systems biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4137/GRSB.S13140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2014/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gene regulation and systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/GRSB.S13140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2014/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Discovery of Emphysema Relevant Molecular Networks from an A/J Mouse Inhalation Study Using Reverse Engineering and Forward Simulation (REFS™).
Chronic obstructive pulmonary disease (COPD) is a respiratory disorder caused by extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD develops are not fully understood, though it is believed that the disease process includes a genetic component, as not all smokers develop COPD. To investigate the mechanisms that lead to the development of COPD/emphysema, we measured whole genome gene expression and several COPD-relevant biological endpoints in mouse lung tissue after exposure to two CS doses for various lengths of time. A novel and powerful method, Reverse Engineering and Forward Simulation (REFS™), was employed to identify key molecular drivers by integrating the gene expression data and four measured COPD-relevant endpoints (matrix metalloproteinase (MMP) activity, MMP-9 levels, tissue inhibitor of metalloproteinase-1 levels and lung weight). An ensemble of molecular networks was generated using REFS™, and simulations showed that it could successfully recover the measured experimental data for gene expression and COPD-relevant endpoints. The ensemble of networks was then employed to simulate thousands of in silico gene knockdown experiments. Thirty-three molecular key drivers for the above four COPD-relevant endpoints were therefore identified, with the majority shown to be enriched in inflammation and COPD.