Meta-analysis of host response networks identifies a common core in tuberculosis

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.
Tuberculosis: an underlying common-core host response network Patients suffering from tuberculosis (TB) show a high extent of variations in their gene expression patterns. Such heterogeneity poses major road blocks to our understanding of how hosts respond to the disease. A number of studies have profiled transcriptomes of human blood samples from TB patients, but a meta-analysis indicates that very few changes are consistently seen. The problem, to a large extent, lies with the way large data is analysed. We have used a genome-wide network approach to characterise the host response and have identified a common-core in the TB response networks of different patients, indicating the presence of unified host response mechanisms. This core network provides a comprehensive view into the most significant regulators of the infection-mediated biological processes across patients from different populations, and it shows partial reversals upon treatment.