In silico transcriptomic analysis for biomarker discovery in TB and HIV co-infection

Area of Science:

• Infectious Diseases
• Genomics
• Computational Biology

Background:

• Tuberculosis (TB) and human immunodeficiency virus (HIV) are major global health concerns, frequently co-occurring and worsening patient outcomes.
• Tuberculous meningitis (TBM) represents the most severe form of TB, increasing the disease burden.
• Identifying reliable biomarkers is critical for early diagnosis, monitoring disease progression, and developing targeted treatments.

Purpose of the Study:

• To employ an integrated in-silico approach for analyzing gene expression in TB, HIV, and TBM.
• To identify novel and established biomarkers for early detection and monitoring of these infections.
• To gain insights into the molecular mechanisms underlying TB, HIV, and TBM.

Main Methods:

• Utilized gene expression datasets from the NCBI GEO database (GSE165708, GSE111459).
• Performed differential gene expression analysis (DEGs) using R and Bioconductor.
• Conducted functional enrichment analysis (GO, KEGG) and constructed gene co-expression networks to identify hub genes.

Main Results:

• Identified novel biomarker candidates (e.g., DDX58, IFIH1, IFIT3, ISG15, MX1, RSAD2, IFI44, CD8A, IL2RB, LCK) across MTB, HIV, HIV-MTB, and TBM.
• Validated established immune biomarkers (e.g., TNF, STAT1, IRF1, IRF7, IL1B, CD4, TLR2, CD28).
• Highlighted the roles of these biomarkers in infection-associated immune modulation.

Conclusions:

• In-silico methods effectively identify novel and known biomarkers for TB and HIV.
• Findings offer insights into molecular mechanisms of these complex infections.
• Provides a framework for experimental validation and precision medicine through molecular stratification.