Bioinformatic analyses hinted at augmented T helper 17 cell differentiation and cytokine response as the central mechanism of COVID‐19–associated Guillain‐Barré syndrome

Autor: Zheng Li, Matthew T. V. Chan, Xingye Li, Shugang Li, Sunny H. Wong, Ziheng Huang, Cheng Huang, Lin Zhang, Jianxiong Shen, William K.K. Wu
Rok vydání: 2021
Předmět:
Zdroj: Cell Proliferation
ISSN: 1365-2184
0960-7722
Popis: Objectives Guillain‐Barré syndrome (GBS) results from autoimmune attack on the peripheral nerves, causing sensory, motor and autonomic abnormalities. Emerging evidence suggests that there might be an association between COVID‐19 and GBS. Nevertheless, the underlying pathophysiological mechanism remains unclear. Materials and Methods We performed bioinformatic analyses to delineate the potential genetic crosstalk between COVID‐19 and GBS. Results COVID‐19 and GBS were associated with a similar subset of immune/inflammation regulatory genes, including TNF, CSF2, IL2RA, IL1B, IL4, IL6 and IL10. Protein‐protein interaction network analysis revealed that the combined gene set showed an increased connectivity as compared to COVID‐19 or GBS alone, particularly the potentiated interactions with CD86, IL23A, IL27, ISG20, PTGS2, HLA‐DRB1, HLA‐DQB1 and ITGAM, and these genes are related to Th17 cell differentiation. Transcriptome analysis of peripheral blood mononuclear cells from patients with COVID‐19 and GBS further demonstrated the activation of interleukin‐17 signalling in both conditions. Conclusions Augmented Th17 cell differentiation and cytokine response was identified in both COVID‐19 and GBS. PBMC transcriptome analysis also suggested the pivotal involvement of Th17 signalling pathway. In conclusion, our data suggested aberrant Th17 cell differentiation as a possible mechanism by which COVID‐19 can increase the risk of GBS.
Functional protein‐protein association networks of (A) COVID‐19– and (B) GBS‐related genes were analyzed by STRING. Genes associated with the two disease entities (COVID‐19, disease id: C000657245, version 4, N.PMIDs ≥ 4; GBS, disease id: C0018378, Gene‐Disease Association Score ≥ 0.02) were retrieved from DisGeNET, a knowledge platform for disease genomics. Only highly confident interactions derived from ‘Experiments’, ‘Databases’, ‘Co‐expression’ and ‘Co‐occurrence’ with the minimum required interaction score of 0.900 were shown. The Markov cluster (MLC) algorithm with an inflation parameter of 3 was used for network clustering.
Databáze: OpenAIRE
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