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

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 those of myalgic 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 datasets 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 analyses, protein-protein interaction (PPI) analysis, transcription factor (TF)-gene interaction network analysis, transcription factor-miRNA co-regulatory network analysis, and candidate drug analysis prediction.

Results: We found nine common genes between Long COVID and ME/CFS and gained a piece of detailed information on their biological functions and signaling pathways through enrichment analysis. Five hub proteins (IL-6, IL-1B, CD8A, TP53, and 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 laboratory and multicenter evidence is required to explore greater mechanistic insight before clinical application in the future.

Keywords: Long COVID; ME/CFS; bioinformatics analyses; myalgic encephalomyelitis/chronic fatigue syndrome; protein–protein interaction network; systems biology.

Figure 1

Workflow of our research.

Figure 2

Common genes representation through a Venn diagram. 9 genes were found as common genes from 49 related genes of long COVID and 1023 related genes of ME/CFS.

Figure 3

GO terms of common genes between long COVID and ME/CFS. (A) Biological Processes, (B) Molecular Function, (C) Cellular Component.

Figure 4

Pathway enrichment analysis of common genes between long COVID and ME/CFS. (A) Wikipathway, (B) BioCarta Pathway, (C) Reactome Pathway, (D) KEGG Human Pathway.

Figure 5

PPI network of common genes among long COVID and ME/CFS. In the figure, nodes in red color represent common genes and edges represent the interactions between nodes. The analyzed network holds 50 nodes and 375 edges.

Figure 6

Detection of hub genes from the PPIs network of common genes. The highlighted 5 hub genes, based on their degree, are IL6, IL1B, CD8A, TP53, CXCL8. The network has 49 nodes and 373 edges.

Figure 7

Network for TF-gene interaction with common differentially expressed genes. The highlighted red color node represents the common genes and other nodes represent TF-genes. The network consists of 136 nodes and 156 edges.

Figure 8

The network presents the TF-miRNA coregulatory network. The network consists of 240 nodes and 327 edges including 130 TF-genes, 102 miRNA and 9 common genes. The nodes in red color are the common genes, yellow nodes represent miRNA and green nodes indicate TF-genes.