Unbiased identification of blood-based biomarkers for pulmonary tuberculosis by modeling and mining molecular interaction networks.

Sambarey, A .; Devaprasad, A .; Mohan, A .; Ahmed, A .; Nayak, S .; Swaminathan, S .; D'Souza, G .; Jesuraj, A .; Dhar, C .; Babu, S .; Vyakarnam, A .; Chandra, N .

EBiomedicine; 2017; 15; 112-126.

Abstract: 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.

Keywords: Tuberculosis; Biomarkers; Network biology; Computational medicine; Diagnostics

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