Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

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• 作者：Awanti Sambarey^a ; Abhinandan Devaprasad^a ; ¹ ; Abhilash Mohan^a ; ¹ ; Asma Ahmed^b ; Soumya Nayak^b ; Soumya Swaminathan^c ; George D'Souza^d ; Anto Jesuraj^d ; Chirag Dhar^d ; Subash Babu^e ; Annapurna Vyakarnam^b ; ^f ; Nagasuma Chandra^a ; ^{nchandra@biochem.iisc.ernet.in}
• 关键词：Tuberculosis ; Biomarkers ; Network biology ; Computational medicine ; Diagnostics
• 刊名：EBioMedicine
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：15
• 期：Complete
• 页码：112-126
• 全文大小：2271 K
• 卷排序：15

文摘

An integrated systems biology approach has been adopted to study the host response to tuberculosis. A multi-gene host biomarker signature is identified for detecting pulmonary tuberculosis from patient blood samples The signature discriminates TB from HIV and latent-TB and can serve as an adjuvant tool in confirming TB diagnosisHost factors that are altered significantly due to tuberculosis are investigated, with an aim to identify a biomarker panel. A network approach provides a genome-wide view of the molecular interactions, analogous to a road network of a city. By comparing networks between healthy and TB samples, we identify the set of variations in a systematic fashion, analogous to identifying all major variations in the traffic flow in a city between two time points. We then apply a series of filters to identify the most discriminating genes among them. The 10-gene signature is seen to be characteristic of TB.