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Meta-analysis of Host Response Networks Identifies a Common Core in Tuberculosis

NPJ systems biology and applications(2017)SCI 2区SCI 1区

Molecular Biophysics Unit

Cited 47|Views32
Abstract
Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host's whole blood transcriptomic profiles that were integrated into a genome-scale protein-protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data.
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Biochemical networks,Regulatory networks,Life Sciences,general,Systems Biology,Computer Appl. in Life Sciences,Computational Biology/Bioinformatics,Bioinformatics
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要点】:本研究通过元分析整合了宿主全血转录组数据,发现了活动性肺结核中高度活跃的共有核心响应网络,并在治疗过程中观察到部分逆转,创新性地提出了一种基于网络的整合分析方法。

方法】:研究采用整合全血转录组数据至基因组规模蛋白-蛋白相互作用网络,构建了活动性肺结核的响应网络,并监控了治疗过程中的变化。

实验】:实验利用多个高通量转录组数据集,通过集成分析发现了包含380个基因的共有核心响应网络,其中STAT1、PLSCR1、C1QB、OAS1、GBP2和PSMB9是显著的枢纽基因。该核心网络在独立数据集(印度队列)上得到了验证,表明该方法能够有效识别感染分子响应的共有调控因子。