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We have read with great interest the article by Cui et al. that previously reported the correlation between high inflammasome expression and pyroptosis with severe COVID-19 infection in cancer patients.
Recently, several studies have reported that patients with SLE might have a higher risk of severe COVID-19, and the risk of SLE was substantially higher in COVID-19 individuals.
Characteristics associated with poor COVID-19 outcomes in individuals with systemic lupus erythematosus: data from the COVID-19 Global Rheumatology Alliance.
Severe COVID-19 and SLE seem to have a strong connection; however, the potential molecular mechanisms are unclear.
In this study, we used blood transcriptome analysis to explore the potential mechanisms and key genes hiding in the crosstalk between severe COVID-19 and SLE. Two severe COVID-19 datasets (GSE164805 and GSE171110) and one SLE dataset (GSE45291) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) and then analyzed. We selected 4920 differentially expressed genes (DEGs), including 2310 upregulated genes and 2610 downregulated genes, from 54 patients suffering from severe COVID-19 (Fig. 1A). Furthermore, a total of 1448 DEGs, including 377 upregulated genes and 694 downregulated genes, were identified from 292 SLE patients (Fig. 1B). We then took the intersection of the selected DEGs, and a total of 272 genes were obtained (Fig. 1C). To reveal the potential functions of those DEGs, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. For enrichment of GO terms, the shared DEGs were mainly involved in the immune system process, multi-organism pathway, immune response, and transcription factor binding and related to cytosol and mitochondrion (Fig. 1D). For KEGG pathway enrichment analysis, the top five significant pathways were Cytokine-cytokine receptor interaction, influenza A, Th17 cell differentiation, Epstein-Barr virus infection, and ribosome (Fig. 1E).
Fig. 1Identification and visualization of the DEGs of severe COVID-19 and SLE. (A) The volcano plot of the severe COVID-19 datasets. (B) The volcano plot of the SLE dataset. (C) The Veen diagram showed an overlap of DEGs. (D) GO enrichment analysis of the shared DEGs. (E) KEGG enrichment analysis of the shared DEGs.
To further identify key genes affecting the interactions between severe COVID-19 and SLE, we used STRING (https://string-db.org/) and Cytoscape to screen the top 20 hub genes using six topological algorithms of the plugin CytoHubba and obtained their intersection (Fig. 2A). The final 17 hub genes included IFIT3, RSAD2, IFIT1, IFI44L, IFI44, OAS3, OAS1, OAS2, IFIT2, IFI35, OASL, IFI27, IFIT5, XAF1, USP18, HERC5, and EIF2AK2. GO enrichment analysis showed that these genes functioned in the defense response, innate immune response, biotic stimulus response, and infection response, and were mainly related to the cytosol and RNA binding (Fig. 2B). KEGG pathway enrichment analysis revealed that these genes were mainly involved in immune- and infection-related pathways such as Hepatitis C and NOD-like receptor signaling pathways (Fig. 2C). In addition, we verified the expression of these 17 DEGs in the severe COVID-19 and SLE datasets. Interestingly, all these key genes were significantly highly expressed in both the severe COVID-19 cohorts and the SLE cohort compared to those in healthy individuals (Fig. 2D and E). Furthermore, we constructed a transcription factor (TF)-miRNA-hub gene network to present potential regulatory mechanisms by NetworkAnalyst.
The network consists of 72 nodes and 78 edges, including 41 TFs, 21 miRNAs, and 10 key genes (Fig. 2F).
Fig. 2Identification of key genes in COVID-19 and SLE datasets and construction of the TF-miRNA-hub gene network. (A) The Veen diagram displayed 17 overlapping hub genes filtered by six algorithms. (B) GO enrichment analysis of the key genes. (C) KEGG enrichment analysis of the key genes. (D) Box plots demonstrated that 17 key genes are significantly upregulated in patients with severe COVID-19. (E) Box plots demonstrated that 17 key genes are significantly upregulated in SLE patients. (F) The TF-miRNA-hub gene network presented the interactions between TFs, miRNAs, and 10 hub genes. The red nodes represent hub genes, the yellow nodes represent TFs, and the green nodes represent miRNAs (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001).
Our work identified some key genes and presented molecular mechanisms of the correlation between severe COVID-19 and SLE. Notably, most selected genes belong to interferon-induced proteins with tetratricopeptide repeats (IFITs) or oligoadenylate synthetase (OAS) gene families. IFITs and OAS gene families are both induced by Interferons (IFNs), participate in regulating innate immune response, and play antiviral functions.
Previous research has proven that IFNs and the innate immune response are involved in the progression of COVID-19 and SLE. The dysregulation of IFNs and perturbations in adaptive immune systems, which is prevalent in SLE, may lead to severe COVID-19.
In short, our results revealed potential mechanisms and key biomarkers that contribute to the crosstalk between severe COVID-19 and SLE, which provides new insight into COVID-19 and SLE and contributes to precise diagnosis and treatment.
Funding
This work was funded by the National College Students’ Innovation and Entrepreneurship Training Program (No. 202210366002) and the College Students’ Innovation and Entrepreneurship Training Program of Anhui Province (No. S202210366021 and S202210366017).
CRediT authorship contribution statement
Ruogang Meng: Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing – original draft. Ning Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. Feixiang Yang: Data curation, Formal analysis, Investigation, Validation. Zhihao Xu: Formal analysis, Investigation, Validation. Zhengyang Wu: Formal analysis, Investigation, Validation. Yinan Du: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.
Conflict of interest
We declared no conflicts of interest or competing interests.
Acknowledgments
We thank the Mathematical Medicine Integration Innovation Training Program for Undergraduate Students (MITUS) for providing valuable guidance, support, research opportunities, and resources.
References
Cui H.
Liu J.
Zhang L.
The high expression of key components of inflammasome and pyroptosis might lead to severe COVID-19 infection in cancer patients.
Characteristics associated with poor COVID-19 outcomes in individuals with systemic lupus erythematosus: data from the COVID-19 Global Rheumatology Alliance.
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