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首页> 外文期刊>EBioMedicine >Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks
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Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

机译：通过建模和挖掘分子相互作用网络对基于血液的肺结核生物标记物进行公正鉴定

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摘要

Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.
机译：结核病（TB）的有效诊断面临许多挑战，要求将重点从以病原体为中心的诊断转向识别基于宿主的多标志物。转录组学提供了一系列差异表达的基因，但仅靠自身无法确定对该疾病表型影响最大的因素。在这里，我们描述了采用无偏方法来识别生物标记签名的计算流水线。来自结核病患者全血样本的RNA测序数据与一个精选的全基因组分子相互作用网络整合在一起，从中我们可以全面了解宿主因结核病发生的变异。然后，我们实施一种敏感的网络挖掘方法，以筛选对疾病改变最为重要的基因候选者。然后，我们应用一系列过滤器，其中包括对多个公共可用数据集的适用性以及对独立患者样本的附加验证，并确定包含10个基因的签名-FCGR1A，HK3，RAB13，RBBP8，IFI44L，TIMM10，BCL6，SMARCD3，CYP4F3和SLPI，在大多数情况下可以区分结核病和健康对照，并区分结核病与潜伏性结核病和HIV。该签名有可能充当结核病的诊断标记。

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