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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.
Sec. Molecular Viral Pathogenesis
Volume 14 - 2024 | doi: 10.3389/fcimb.2024.1453466
This article is part of the Research Topic Pathogenesis, Diagnosis, and Treatments of SARS-CoV-2 Co-infection with Influenza Viruses or Other Respiratory Pathogens View all 8 articles

Exploring the Shared Pathogenic Mechanisms of Tuberculosis and COVID-19: Emphasizing the Role of VNN1 in Severe COVID-19

Provisionally accepted
Peng Sun Peng Sun 1yue Wang yue Wang 2Sijing Zhou Sijing Zhou 3Hui Liang Hui Liang 1Binbin Zhang Binbin Zhang 1Pulin Li Pulin Li 1Rui Han Rui Han 1guanghe fei guanghe fei 1chao cao chao cao 4Ran Wang Ran Wang 1*
  • 1 First Affiliated Hospital of Anhui Medical University, Hefei, China
  • 2 The Second People's Hospital of Hefei, Hefei, Anhui Province, China
  • 3 Hefei third clinical college, Anhui Medical University, Hefei, Anhui Province, China
  • 4 Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    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, Tuberculosis, machine learning, single-cell sequencing, VNN1, molecular docking, Immune infiltration, mechanical ventilation

    Received: 23 Jun 2024; Accepted: 28 Oct 2024.

    Copyright: © 2024 Sun, Wang, Zhou, Liang, Zhang, Li, Han, fei, cao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ran Wang, First Affiliated Hospital of Anhui Medical University, Hefei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.