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

Faten Ahmad Alsulaimany; Nidal M Omer Zabermawi; Haifa Almukadi; Snijesh V Parambath; Preetha Jayasheela Shetty; Venkatesh Vaidyanathan; Ramu Elango; Babajan Babanaganapalli; Noor Ahmad Shaik

doi:10.3389/fmed.2021.812857

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

Front Med (Lausanne). 2022 Feb 7:8:812857. doi: 10.3389/fmed.2021.812857. eCollection 2021.

Authors

Affiliations

¹ Department of Biology, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
² Department of Pharmacology and Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
³ Division of Molecular Medicine, St. John's Research Institute, Bangalore, India.
⁴ Department of Biomedical Sciences, College of Medicine, Gulf Medical University, Ajman, United Arab Emirates.
⁵ Auckland Cancer Society Research Centre (ACSRC), Faculty of Medical and Health Sciences (FM&HS), The University of Auckland, Auckland, New Zealand.
⁶ Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia.
⁷ Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Background: Tuberculosis (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.

Methods: The 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.

Results: Patients 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.

Conclusion: The changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

Keywords: CD8+T cells; Mycobacterium tuberculosis; drug target; gene express profile; immune pathways.