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bioRxiv · 10.1101/2022.03.30.486356

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: insights from machine learning and bioinformatics approaches

Hoque, M. N.·Khan, M. A.·Hossain, M. A.·Hasan, M. I.·Rahman, M. H.·Soliman, M. E.·Araf, Y.·Zheng, C.·Islam, T.
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Abstract

SARS-CoV-2 continues to spread and evolve worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. Moreover, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. Therefore, we used the machine learning methods to analyze 22 RNA-seq datasets from COVID-19 patients (n=8), recovered individuals (n=7), and healthy individuals (n=7) to find disease-related differentially expressed genes (DEGs). In comparison to healthy controls, we found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals, respectively. We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. In COVID-19 patients, the DEG- miRNA and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the most highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, we detected DEGs impacted by SARS-CoV-2 infection and mediated by TFs and miRNA expression, indicating that SARS-CoV-2 infection may contribute to various comorbidities. These pathogenetic findings can provide some crucial insights into the complex interplay between COVID-19 and the recovery stage and support its importance in the therapeutic development strategy to combat against COVID-19 pandemic. IMPORTANCEDespite it has now been over two years since the beginning of the COVID-19 pandemic, many crucial questions about SARS-CoV-2 infection and the different COVID-19 symptoms it causes remain unresolved. An intriguing question about COVID-19 is how SARS-CoV-2 interplays with the host during infection and how SARS-CoV-2 infection can cause so many disease symptoms. Our analysis of three different datasets (COVID-19, recovered, and healthy) revealed significantly higher DEGs in COVID-19 patients than recovered humans and healthy controls. Some of these DEGs were found to be co-expressed in both COVID-19 patients. They recovered humans supporting the notion that DEGs level is directly correlated with the viral load, disease progression, and different comorbidities. The protein-protein interaction consisting of 24 nodes and 72 edges recognized eight hub-nodes as potential hub-proteins (i.e., RPL4, RPS4X, RPL19, RPS12, RPL19, EIF3E, MT-CYB, and MT-ATP6). Protein-chemical interaction analysis identified three chemical agents (e.g., Valproic Acid, Alfatoxin B1, and Cyclosporine) enriched in COVID-19 patients and recovered individuals. Mental retardation, mental deficiency, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs.

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Hoque, M. N., Khan, M. A., Hossain, M. A., Hasan, M. I., Rahman, M. H., Soliman, M. E., Araf, Y., Zheng, C., Islam, T.. 2022-03-30. Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: insights from machine learning and bioinformatics approaches. https://doi.org/10.1101/2022.03.30.486356

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