Abstract
Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene–phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype–gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.
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N.A.C. was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Grant 88887.518451/2020-00. T.W.K. was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Grant 150181/2023-0. F.S.L.V. is recipient of a CNPq scholarship, Grant 312960/2021-2.
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Conceptualization: NAC, TWK, MRM, and FSLV; Methodology: NAC, TWK, and MRM; Formal analysis and investigation: NAC, VOL, and TWK; Writing—original draft preparation: NAC and TWK; Writing—review and editing: NAC, VOL, TWK, MRM, and FSLV; Funding acquisition: FSLV; Resources: TWK, MRM, and FSLV; Supervision: TWK, MRM, and FSLV.
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Supplementary file1 (XLSX 2 KB) Meta-analysis results of cellular models data. Meta-analysis results of transcriptome data from cellular models of SARS-CoV-2 infections
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Supplementary file2 (XLSX 2 KB) DisGeNet enrichment analysis of lung autopsy data. Gene-disease association of perturbed genes from lung autopsy meta-analysis
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Supplementary file3 (PDF 2 KB) Gene ontology enrichment analysis of cellular models. Biological processes identified through gene ontology analysis using perturbed genes from cellular models meta-analysis
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Supplementary file4 (XLSX 2 KB) Reactome enrichment analysis of cellular models. Reactome pathways identified using perturbed genes from cellular models meta-analysis
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Supplementary file5 (XLSX 2 KB) KEGG pathways enrichment analysis of infected cellular models. KEGG enrichment analysis using metaFC values from the meta-analysis of cellular models of infection
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Supplementary file6 (XLSX 2 KB) Meta-analysis results of lung autopsy data. Meta-analysis results of transcriptome data from lung autopsy of COVID-19 patients
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Supplementary file7 (XLSX 2 KB) Gene ontology enrichment analysis of lung autopsy. Biological processes enriched in gene ontology analysis using perturbed genes from lung autopsy meta-analysis
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Supplementary file9 (XLSX 2 KB) Collected datasets from NCBI. Information of selected datasets and samples included in the meta-analysis
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Supplementary file10 (XLSX 2 KB) Reactome enrichment analysis of lung autopsy. Reactome pathways identified using perturbed genes from lung autopsy meta-analysis
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Supplementary file11 (XLSX 2 KB) KEGG pathways enrichment analysis of COVID-19 lung autopsy. KEGG enrichment analysis using metaFC values from the meta-analysis of lung autopsy
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Cadore, N.A., Lord, V.O., Recamonde-Mendoza, M. et al. Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection. Biochem Genet 62, 892–914 (2024). https://doi.org/10.1007/s10528-023-10453-2
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DOI: https://doi.org/10.1007/s10528-023-10453-2