Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine...

Area of Science:

• Infectious Diseases
• Molecular Biology
• Bioinformatics

Background:

• Rising cases of COVID-19 (caused by SARS-CoV-2) present challenges, especially when co-occurring with tuberculosis (TB).
• Coinfection with COVID-19 and TB can lead to complex health complications and treatment difficulties.
• The precise molecular mechanisms governing the interplay between TB and COVID-19 remain largely unknown.

Purpose of the Study:

• To elucidate the molecular mechanisms underlying the interaction between TB and COVID-19.
• To identify shared molecular pathways and biomarkers in patients coinfected with TB and COVID-19.
• To explore potential therapeutic targets for managing TB-COVID-19 coinfection.

Main Methods:

• Transcriptome analysis using RNA-sequencing datasets (GSE114192, GSE163151) to identify common differentially expressed genes (DEGs) between TB and COVID-19.
• Application of bioinformatics tools for pathway, enrichment, and network analyses.
• Protein-protein interaction analysis and machine learning to identify key hub genes, with verification using additional datasets (GSE171110, GSE54992, GSE79362).

Main Results:

• Identified 124 common DEGs between TB and COVID-19, revealing shared pathogenic pathways.
• Discovered key hub genes, including GAS6, OAS3, and PDCD1LG2, through network and machine learning analyses.
• Established potential molecular targets and pathways relevant to the coinfection.

Conclusions:

• Transcriptome analysis successfully identified shared molecular mechanisms and biomarkers in TB-COVID-19 coinfection.
• The identified hub genes and pathways offer insights into the complex relationship between these two diseases.
• Understanding protein-drug interactions related to these mechanisms may provide a basis for novel therapeutic strategies against TB-COVID-19 coinfection.