Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

Area of Science:

• Virology
• Genomics
• Bioinformatics

Background:

• MicroRNAs (miRNAs) are key gene regulators.
• RNA viruses, including SARS-CoV-2, may produce miRNA-like sequences with regulatory functions.
• Understanding these interactions is crucial for deciphering viral pathogenesis and host response.

Purpose of the Study:

• To identify potential miRNA precursors and mature miRNA-like sequences in the SARS-CoV-2 genome using machine learning.
• To investigate potential interactions between viral miRNAs and human genes.
• To explore interactions between human miRNAs and SARS-CoV-2 genes.

Main Methods:

• Machine learning-based analysis of the SARS-CoV-2 genome to predict miRNA-like hairpins.
• Targeting analysis to identify interactions between predicted viral miRNAs and human genes.
• Functional enrichment analysis (PANTHER) of targeted human genes.
• Analysis of human miRNA targeting of viral genes.

Main Results:

• Identified 950 hairpin structures in the SARS-CoV-2 genome, with 29 predicted as precursor miRNAs.
• Discovered 30 viral mature miRNA-like sequences targeting 1,367 human genes involved in transcription, metabolism, and signaling pathways.
• Found that human miRNAs can target key SARS-CoV-2 genes (S, M, N, E, ORF1ab, etc.).

Conclusions:

• SARS-CoV-2 genome contains sequences with potential miRNA activity, capable of targeting numerous human genes, particularly those involved in transcription.
• Human miRNAs can target essential viral genes, suggesting a role in controlling viral replication.
• These findings open avenues for developing novel miRNA-based therapeutics for SARS-CoV-2 infections.