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。

Gene regulation and systems biology Pub Date : 2014-02-19 eCollection Date: 2014-01-01 DOI:10.4137/GRSB.S13140
Yang Xiang, Ulrike Kogel, Stephan Gebel, Michael J Peck, Manuel C Peitsch, Viatcheslav R Akmaev, Julia Hoeng
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引用次数: 5

摘要

慢性阻塞性肺疾病(COPD)是由于呼吸道长期暴露于有害刺激(主要是香烟烟雾)而引起的一种呼吸系统疾病。COPD发展的机制尚不完全清楚,尽管人们认为该疾病过程包括遗传成分,因为并非所有吸烟者都会患上COPD。为了研究导致COPD/肺气肿发展的机制,我们测量了暴露于两种CS剂量不同时间后小鼠肺组织的全基因组基因表达和几种COPD相关生物学终点。通过整合基因表达数据和四个copd相关终点(基质金属蛋白酶(MMP)活性、MMP-9水平、金属蛋白酶-1组织抑制剂水平和肺重量),采用一种新颖而强大的方法——逆向工程和正向模拟(REFS™)来识别关键的分子驱动因素。使用REFS™生成了一个分子网络集合,模拟结果表明,它可以成功地恢复基因表达和copd相关终点的测量实验数据。然后利用网络集合来模拟数千个硅基因敲除实验。因此,确定了上述四个COPD相关终点的33个分子关键驱动因素,其中大多数显示在炎症和COPD中富集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discovery of Emphysema Relevant Molecular Networks from an A/J Mouse Inhalation Study Using Reverse Engineering and Forward Simulation (REFS™).

Discovery of Emphysema Relevant Molecular Networks from an A/J Mouse Inhalation Study Using Reverse Engineering and Forward Simulation (REFS™).

Discovery of Emphysema Relevant Molecular Networks from an A/J Mouse Inhalation Study Using Reverse Engineering and Forward Simulation (REFS™).

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.

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