Gene crosstalk between COVID-19 and preeclampsia revealed by blood transcriptome analysis.

Background: The extensive spread of coronavirus disease 2019 (COVID-19) has led to a rapid increase in global mortality. Preeclampsia is a commonly observed pregnancy ailment characterized by high maternal morbidity and mortality rates, in addition to the restriction of fetal growth within the uterine environment. Pregnant individuals afflicted with vascular disorders, including preeclampsia, exhibit an increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection via mechanisms that have not been fully delineated. Additionally, the intricate molecular mechanisms underlying preeclampsia and COVID-19 have not been fully elucidated. This study aimed to discern commonalities in gene expression, regulators, and pathways shared between COVID-19 and preeclampsia. The objective was to uncover potential insights that could contribute to novel treatment strategies for both COVID-19 and preeclampsia. Method: Transcriptomic datasets for COVID-19 peripheral blood (GSE152418) and preeclampsia blood (GSE48424) were initially sourced from the Gene Expression Omnibus (GEO) database. Subsequent to that, we conducted a subanalysis by selecting females from the GSE152418 dataset and employed the "Deseq2" package to identify genes that exhibited differential expression. Simultaneously, the "limma" package was applied to identify differentially expressed genes (DEGs) in the preeclampsia dataset (GSE48424). Following that, an intersection analysis was conducted to identify the common DEGs obtained from both the COVID-19 and preeclampsia datasets. The identified shared DEGs were subsequently utilized for functional enrichment analysis, transcription factor (TF) and microRNAs (miRNA) prediction, pathway analysis, and identification of potential candidate drugs. Finally, to validate the bioinformatics findings, we collected peripheral blood mononuclear cell (PBMC) samples from healthy individuals, COVID-19 patients, and Preeclampsia patients. The abundance of the top 10 Hub genes in both diseases was assessed using real-time quantitative polymerase chain reaction (RT-qPCR). Result: A total of 355 overlapping DEGs were identified in both preeclampsia and COVID-19 datasets. Subsequent ontological analysis, encompassing Gene Ontology (GO) functional assessment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, revealed a significant association between the two conditions. Protein-protein interactions (PPIs) were constructed using the STRING database. Additionally, the top 10 hub genes (MRPL11, MRPS12, UQCRH, ATP5I, UQCRQ, ATP5D, COX6B1, ATP5O, ATP5H, NDUFA6) were selected based on their ranking scores using the degree algorithm, which considered the shared DEGs. Moreover, transcription factor-gene interactions, protein-drug interactions, co-regulatory networks of DEGs and miRNAs, and protein-drug interactions involving the shared DEGs were also identified in the datasets. Finally, RT-PCR results confirmed that 10 hub genes do exhibit distinct expression profiles in the two diseases. Conclusion: This study successfully identified overlapping DEGs, functional pathways, and regulatory elements between COVID-19 and preeclampsia. The findings provide valuable insights into the shared molecular mechanisms and potential therapeutic targets for both diseases. The validation through RT-qPCR further supports the distinct expression profiles of the identified hub genes in COVID-19 and preeclampsia, emphasizing their potential roles as biomarkers or therapeutic targets in these conditions.