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

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.
Highlights • 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 diagnosis Host 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.