Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and ME/CFS, 2022, Lv et al

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

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.

https://www.frontiersin.org/articles/10.3389/fimmu.2022.952987/full

 

Tried to read the article, but very limited cognitive functioning today, so I have no idea how large the sample sizes were and how they selected which genes to look at.

 

I may be misunderstanding but as far as I can tell there is no defined sampling, they've simply taken gene associations from three databases and looked for crossovers between ME/CFS (no clarity on diagnostic criteria) and Long COVID. It doesn't look very meaningful to me to look for associations between an illness diagnosed by symptoms only and which shows a high degree of suspected heterogeneity with an ill defined disease that involves multiple heterogenous post COVID 19 sequalae.

 

Thank you @CRG

 

I was curious what the data on genes in ME/CFS was like. One of the databases they used was disgenet.org.

disgenet.org. reports an association with the FURIN gene. Unfortunately the database contains garbage. Just look at this https://www.disgenet.org/browser/0/1/1/C0015674/

Amusingly, the FURIN gene is also known as PACE gene and the disgenet.org software wasn't sophisticated enough to understand that all this talk about the PACE trial and CFS isn't about genetics.

This doesn't mean that the study is bad. They probably curated the gene list to avoid data that is obviously garbage. I hope so at least.