Exploring the shared pathogenic mechanisms of tuberculosis and COVID-19: emphasizing the role of VNN1 in severe COVID-19

Front Cell Infect Microbiol. 2024 Nov 21:14:1453466. doi: 10.3389/fcimb.2024.1453466. eCollection 2024.

Abstract

Background: In recent years, COVID-19 and tuberculosis have emerged as major infectious diseases, significantly contributing to global mortality as respiratory illnesses. There is increasing evidence of a reciprocal influence between these diseases, exacerbating their incidence, severity, and mortality rates.

Methods: This study involved retrieving COVID-19 and tuberculosis data from the GEO database and identifying common differentially expressed genes. Machine learning techniques, specifically random forest analysis, were applied to pinpoint key genes for diagnosing COVID-19. The Cibersort algorithm was employed to estimate immune cell infiltration in individuals with COVID-19. Additionally, single-cell sequencing was used to study the distribution of VNN1 within immune cells, and molecular docking provided insights into potential drugs targeting these critical prognosis genes.

Results: GMNN, SCD, and FUT7 were identified as robust diagnostic markers for COVID-19 across training and validation datasets. Importantly, VNN1 was associated with the progression of severe COVID-19, showing a strong correlation with clinical indicators and immune cell infiltration. Single-cell sequencing demonstrated a predominant distribution of VNN1 in neutrophils, and molecular docking highlighted potential pharmacological targets for VNN1.

Conclusions: This study enhances our understanding of the shared pathogenic mechanisms underlying tuberculosis and COVID-19, providing essential insights that could improve the diagnosis and treatment of severe COVID-19 cases.

Keywords: COVID-19; VNN1; immune infiltration; machine learning; mechanical ventilation; molecular docking; single-cell sequencing; tuberculosis.

MeSH terms

  • COVID-19* / immunology
  • Humans
  • Machine Learning
  • Molecular Docking Simulation*
  • Neutrophils / immunology
  • SARS-CoV-2* / genetics
  • SARS-CoV-2* / immunology
  • Single-Cell Analysis
  • Tuberculosis* / immunology

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. A sub-project of the Anhui Medical University National first-class undergraduate specialty construction program (2020SJJXSFK1364), Young Jianghuai famous medical training project, Excellent physician Training program of Anhui Medical University, Construction projects of key disciplines in Hefei (Occupational medicine), Health Research Program of Anhui (AHWJ2023A30009), and the Applied Medical Research Project of Hefei Health Commission (Hwk2021zd008, Hwk2022zd013).