Immune Subtyping in Latent Tuberculosis

Abstract

Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

“…The pathogenesis of TB is closely related to the host's immune response. Therefore, to promote the development of sensitive methods for the detection and treatment of LTBI, it is particularly urgent to understand the host responses involved in the establishment and maintenance of LTBI (Banerjee et al, 2021).…”

Inhibition of leukotriene‐B4 signalling‐mediated host response to tuberculosis is a potential mode of adjunctive host‐directed therapy

“…The most feasible flows of information in the active TB condition in comparison to healthy controls were then mined using the in-house algorithm ResponseNet [16][17][18][19]. The 0.02 percentile of the top paths were selected from both the TB response LTnet and TB response HPPINv2 to construct the top active TB response networks for each dataset.…”

A 9-gene biomarker panel identifies bacterial coinfections in culture-negative COVID-19 cases

“…This method has been described in detail in Ravichandran et al, 2021 and Banerjee et al, 2021. 9,18 Using a similar approach, Top Perturbed Response networks were generated for the 'Probable Bacterial Coinfection' condition with respect to 'Only COVID-19', which captures the most important differences in the hostresponse between bacterial coinfection and only COVID-19.…”