Abstract
Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host–pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.
Highlights
MicroRNAs are noncoding RNAs involved in post-transcriptional gene regulation
Since miRNAs seem to be involved in host–pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) messenger RNAs (mRNAs) as an antiviral defence mechanism
According to the competitive endogenous RNA hypothesis, RNA transcripts such as circRNAs, messenger RNAs, and long non-coding RNAs, include miRNA response elements and these are in competition among themselves for miRNA binding to be able to regulate the expression of each other [5]
Summary
MicroRNAs (miRNAs) are noncoding RNAs involved in post-transcriptional gene regulation. The precursor miRNAs (pre-miRNAs) fold into characteristic hairpin structures that are used as the primary feature source in many bioinformatics approaches [1]. Another class of noncoding and endogenous RNAs is circular RNAs (circRNAs) that are generated by a unique splicing reaction known as back-splicing [2]. Previous studies showed that miRNA and circRNA expressions were changed during infections of both DNA and RNA viruses [6]. We used available differentially expressed miRNA information of SARS-CoV-2 infected cells to build a machine learning based model for prediction. D. Saçar Demirci: Circular RNA–MicroRNA–MRNA interaction targeting network analysis is performed to identify biologically significant processes in SARS-CoV-2 infection. These findings could increase the perceptions of infection through RNA-mediated host–virus interactions and lead to development of new strategies for antiviral agents
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