Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

Abstract

BackgroundTuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers.MethodsThe blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels.ResultsPatients with PTB had higher levels of four immune subpopulations like CD8+ T cells (P = 1.9 × 10−8), natural killer (NK) cells resting (P = 6.3 × 10−5), monocytes (P = 6.4 × 10−6), and neutrophils (P = 1.6 × 10−7). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8+ T cell-associated genes (ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E, and IL7R) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r2 ≥ |0.7|) approaches.ConclusionThe changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

“…Other hub-central gene of the brown module, including CSF1 ( Chatterjee et al, 2021 ), PLAUR ( McLoughlin et al, 2021b ), ITGB1 ( Yang et al, 2017 ), CCND1 ( Koo et al, 2012 ; Looney et al, 2021 ), CSF2 ( Marsay et al, 2013 ; Shukla et al, 2017 ; Abdelaal et al, 2022 ), CTLA4 ( Zhang et al, 2021 ), FLNA ( Xu et al, 2015 ), CCND2 ( Lavalett et al, 2017 ), WEE1 ( Jayaswal et al, 2010 ), MMP9 ( Blanco et al, 2012 ; McLoughlin et al, 2014 ), CDC25A ( Shapira et al, 2020 ), CSF2RB ( Benmerzoug et al, 2018 ), TIMP1 ( Sun et al, 2020 ), CD69 ( Li et al, 2011 ; Chen et al, 2020 ), PTPN22 ( Boechat et al, 2013 ), CD38 ( Silveira-Mattos et al, 2019 ), IL7R ( Jenum et al, 2016 ; Alsulaimany et al, 2022 ), IL1RN ( Alcaraz-López et al, 2020 ), and IDO1 ( Weiner et al, 2012 ; Gautam et al, 2018 ) were also involved in host-pathogen interactions as well as suppression of host immune response. For example, the CTLA4 hub-central gene encodes an inhibitor of T cell-mediated response ( Schneider et al, 2006 ), and the upregulation of this gene in response to M. bovis infection may reflect a mechanism of immunomodulation used by M. bovis to subvert a host T-cell response ( Killick et al, 2011 ).…”

“…Therefore, enhancing of ITK signaling has been introduced as an alternative strategy to target infection with highly virulent strains of M. tuberculosis ( Huang et al, 2020 ). Remarkably, several hub-central genes of the green module, including ITK , CD2 , CD247 , ZAP70 , CD3D , and CD3E , were identified as potential therapeutic targets for pulmonary TB by a computational drug-ability effort ( Alsulaimany et al, 2022 ).…”