AUTHOR=Lv Yongbiao , Zhang Tian , Cai Junxiang , Huang Chushuan , Zhan Shaofeng , Liu Jianbo 

TITLE=Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.952987

DOI=10.3389/fimmu.2022.952987

ISSN=1664-3224

ABSTRACT=Background: The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global crisis. Although many people recover from COVID-19 infection, they are likely to develop persistent symptoms similar to myalgia encephalomyelitis/chronic fatigue syndrome(ME/CFS) after discharge. Those constellations of symptoms persist for months after infection called Long COVID, which may lead to considerable financial burden and healthcare challenges. However, the mechanisms underlying Long COVID and ME/CFS remain unclear.
Methods: We collected the genes associated with Long COVID and ME/CFS in databases by restricted screening conditions and clinical sample data sets with limited filters. The common genes for Long COVID and ME/CFS were finally obtained by taking the intersection. We performed several advanced bioinformatics analyses based on common genes, including gene ontology and pathway enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, transcription factor-miRNA co-regulatory network analysis, and candidate drug analysis prediction.
Results: We found 9 common genes between Long COVID and ME/CFS and gain a piece of detailed information on their biological functions and signaling pathways through enrichment analysis. Five hub proteins(IL6, IL1B, CD8A, TP53, CXCL8) were collected by the PPI network. The TF-gene and TF-miRNA coregulatory networks were demonstrated by NetworkAnalyst. In the end, 10 potential chemical compounds were predicted.
Conclusion: This study revealed common gene interaction networks of Long COVID and ME/CFS and predicted potential therapeutic drugs for clinical practice. Our findings help to identify the potential biological mechanism between Long COVID and ME/CFS. However, more evidence from laboratory and multicenter is required to explore greater mechanistic insight before clinical application in the future.