Abstract
Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.
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Data Availability
The data that support the findings of this study are available in NCBI Sequence Read Archive at https://www.ncbi.nlm.nih.gov/sra, reference number PRJNA736437, PRJNA737991. These data were derived from the following resources available in the public domain: PRJNA555016: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA736437/ and PRJNA394051: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA737991/
Change history
05 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10528-023-10511-9
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The authors would like to acknowledge the Department of Bioinformatics for providing high-performance computational facilities. RD would like to thank NSFDC, Ministry of Social Justice and Empowerment, Govt. of India, and UGC for providing a Junior Research Fellowship.
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The research idea was conceived, and the work was designed by RD and AV. The experiments and analyses were performed by RD. VSP and DP supported the sequence data analysis and image preparation. The experiments were supervised and approved by AV. All of the authors have equal contribution in drafting the manuscript. Finally, the manuscript was approved by all the authors.
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Das, R., Sinnarasan, V.S.P., Paul, D. et al. A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection. Biochem Genet 62, 987–1006 (2024). https://doi.org/10.1007/s10528-023-10458-x
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DOI: https://doi.org/10.1007/s10528-023-10458-x