Immune Subtyping in Latent Tuberculosis.

Ushashi Banerjee,Priyanka Baloni,Nagasuma Chandra,Amit Singh

doi:10.3389/fimmu.2021.595746

Ushashi Banerjee, Priyanka Baloni + Show 2 more

Open Access

https://doi.org/10.3389/fimmu.2021.595746

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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.

Highlights

Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens known to humans
To obtain a systems perspective of the host immune responses associated with LTB infection (LTBI), we first analyzed 3 publicly available transcriptome datasets, GSE19439, GSE19444 and GSE28623, that contained information on samples from LTBI, uninfected healthy controls (HC) as well as active TB cases and subsequently validated our findings in 2 other independent LTBI transcriptome datasets (GSE107993 and GSE107994)
We found very few differentially expressed genes (DEGs) between LTBI and HC, with a fold change ≥ ± 1.5, FDR adjusted p value ≤0.05 (2 in GSE19439, 1 in GSE19444 and 0 in GSE28623) (Figure 1A), a significant number of genes

Summary

Introduction

Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens known to humans. Despite the availability of an array of anti-mycobacterial drugs, tuberculosis (TB) is still the leading cause of death among infectious agents. It is the active form of TB that causes morbidity, contagiousness and mortality, a majority of Mtb infected individuals remain latently infected [1]. These individuals, accounting for approximately 1.7 billion people worldwide, harbor the dormant pathogen while remaining clinically asymptomatic for decades and carry a 10% lifetime risk of TB reactivation and act as reservoirs of the TB bacilli [1, 2]. Establishment and maintenance of latency result from an equilibrium in the host-pathogen interactions where the host immune response can successfully contain the spread of the bacteria by forming granulomatous lesions but fails to completely eradicate it [3]

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