key: cord-0942262-81wmqicg
authors: Demirci, Yılmaz Mehmet; Saçar Demirci, Müşerref Duygu
title: Circular RNA–MicroRNA–MRNA interaction predictions in SARS-CoV-2 infection
date: 2021-03-17
journal: J Integr Bioinform
DOI: 10.1515/jib-2020-0047
sha: 7d941b1f26886db21b6fc3e324dd639d91f271e9
doc_id: 942262
cord_uid: 81wmqicg

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.

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] . CircRNAs seem to be expressed in a widespread manner and they have important functions in regulation especially as sponges providing binding sites for miRNAs and RNA binding proteins [3] and a player in the regulation of alternative splicing [4] .

According to the competitive endogenous RNA (ceRNA) hypothesis, RNA transcripts such as circRNAs, messenger RNAs (mRNAs), 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] . Previous studies showed that not only miRNA but also circRNA expressions were changed during infections of both DNA and RNA viruses [6] . Although there is not much information about circRNAs' roles during infection of emerging Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), another member of coronaviruses, Middle East respiratory syndrome coronavirus (MERS-CoV) infection resulted in expression changes of host circRNAs [3] .

In this study, we used available differentially expressed miRNA information of SARS-CoV-2 infected cells to build a machine learning based model for prediction. In addition, a comprehensive circRNA-miRNA-mRNA targeting network analysis is performed to identify biologically significant processes in SARS-CoV-2 infection. Our results show that various cellular processes including apoptosis might be affected by the competition of cellular and viral RNAs. These findings could increase the perceptions of infection through RNA-mediated host-virus interactions and lead to development of new strategies for antiviral agents.

Various studies attempted to identify human miRNAs that could target viruses [7] [8] [9] [10] . Although there are not many experimentally validated examples of miRNAs encoded by RNA viruses, computational predictions show that SARS-CoV-2 genome could produce miRNAs that could target human mRNAs [11] .

Currently there is not much information about the differences in expression levels of miRNAs during SARS-CoV-2 infection. It has been shown that, highly pathogenic MERS-CoV infection causes substantial changes in the expression of many host cell circRNAs, miRNAs, and mRNAs [3] .

All data analysis, machine learning and prediction workflows were generated by using the Konstanz information miner (KNIME) platform [12] . MiRNA -target predictions were performed by using psRNATarget tool [13] .

An RNA sequence could include four bases (A, G, C, and U) that can form base pairs such as A-U, G-C, and G-U. RNAfold software from the Vienna package was used with default setting to create secondary structures [14] . For better representation, the nucleotides involved in base pairs are shown as A, G, C, and U in Figure 1 , while non-base paired ones are shown as A ′ , G ′ , C ′ , and U ′ , respectively. The workflow generated in KNIME uses RNA sequence and dot-bracket representations of secondary structure to modify bases of the sequence as uppercase and lowercase characters [15] . Zhang We used the same base grouping scheme and defined three maps 1, 2 and 3 (Figure 1 ), where n is the length of the hairpin sequence and i is the index of base in the sequence.

In order to represent miRNA hairpin secondary structure as vectors, based on the definitions from Figure 1 , 36-dimensional vector was calculated as shown in Figure 2 . 

Human miRNA sequences were obtained from MiRBase (Release 22.1) [17] , human circRNA data set was downloaded from circAtlas 2.0 [18] , SARS-CoV-2 CDS were based on RefSeq_NC_045512.2 from NCBI. Differentially expressed miRNA list was based on the results of Chow and Salmena [19] with some changes, since their list is composed of mature miRNAs, we used the hairpin sequences of those available (Table 1 ). The list of miRNAs used for training of differential expression prediction.

Upregulated hsa-mir-4485, hsa-mir-483, hsa-mir-6891, hsa-mir-4284, hsa-mir-4463, hsa-mir-155, hsa-mir-107, hsa-mir-29b-2, hsa-mir-139, hsa-mir-299, hsa-mir-501, hsa-mir-4745, hsa-mir-12136 Downregulated hsa-let-7a-1, hsa-let-7a-2, hsa-let-7a-3, hsa-mir-374a, hsa-mir-194-1,hsa-mir-194-2, hsa-mir-4454, hsa-mir-135b, hsa-mir-16-2, hsa-mir-23b, hsa-mir-21, hsa-let-7f-1, hsa-mir-429, hsa-mir-5701-1, hsa-mir-5701-2, hsa-mir-5701-3, hsa-mir-450b, hsa-mir-7-1, hsa-mir-26b, hsa-mir-23c, hsa-mir-374c, hsa-mir-374b, hsa-mir-26a-1, hsa-mir-365a, hsa-mir-365b, hsa-mir-940, hsa-mir-362, hsa-mir-1275, hsa-mir-1296, hsa-mir-126, hsa-mir-548d-2 

The differential expression prediction workflow was created by using 70% learning and 30% testing ratios and three different classifiers; random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP) were trained with 100-fold MCCV [20] (Figure 3 ). Among 2654 mature human miRNAs available in miRBase, 2498 were involved in 272,822 total targeting events with 18,950 human genes; 2498 were involved in 393,877 total targeting events with 208,642 circRNAs and 484 miRNAs targeted 11 SARS-CoV-2 genes. Some of the miRNAs reported as differentially expressed in Calu3 cells infected with SARS-CoV-2 or mock from GSE148729 did not have any predicted targets ( Table 2) .

Upregulated human miRNA hsa-miR-6891-5p might target not only human genes and circRNAs but also ORF3a gene of SARS-CoV-2 (Table 2) . PANTHER Gene Ontology analysis [21] of human gene targets showed that various biological processes could potentially be affected by the actions of this miRNA (Figure 4 ).

Inter-kingdom communication mechanisms mediated by RNAs have been investigated for several organisms including a variety of viruses, Toxoplasma gondii (protozoan eukaryotic parasite) [22] , Histoplasma capsulatum (infectious fungus) [23] . Viruses are parasites that depend on their host for many of their processes. Usually viral infections result in alterations of cellular pathways to modulate viral gene expression and/or accommodate virus in a favourable environment. In some cases, e.g. SARS-CoV-2 infection, host post-transcriptional gene regulation elements like miRNAs might also show differential expression levels during infection [19] . In this study, we analysed such human miRNAs (Table 1) to build a machine learning based workflow that might be used for prediction of expression changes of miRNAs during SARS-CoV-2 infection. Among the 300 models generated, the highest accuracy value was observed with RF classifier (Figure 3 ). While applying machine learning approaches to miRNA datasets, there are various elements that would affect the overall performance [24] . Among them, feature sets [25, 26] and the quality of data [27] might be the most important parts. When there are more datasets available, the workflow can be easily updated to include them and it is also possible to use this workflow for any kind of differentially expressed miRNAs.

There is not much known about the individual functions of circRNAs but they are acknowledged as sponges providing binding sites for miRNAs and some RNA-binding proteins [28] . The activities of host circRNAs have been investigated in Hepatitis C virus-infected cells [6] and MERS-CoV infection [3] . We performed a comprehensive target prediction analysis for human miRNAs to measure their capacity to bind human mRNAs, human circRNAs and SARS-CoV-2 genes. Based on the results represented in Table 2 , SARS-CoV-2 ORF3a is the only viral target for upregulated human miRNAs. Since ORF3a protein is associated with apoptosis which is an essential mechanism for host antiviral defence to control viral infection [29] , upregulation of hsa-miR-6891-5p might be crucial to decrease ORF3a expression during certain stages of infection.

Out of 2498 miRNAs that have predicted targets, 2448 had more targets in circRNAs, 27 had more in mRNAs and 23 miRNAs had equal number of targets in both groups. If the mRNA and circRNA targets of specific miRNAs are coexpressed there might be a competition for miRNA binding and considering the wide range of biological processes of a single miRNA's targets (Figure 4 ) circRNA-miRNA-mRNA network could play important roles in overall gene expression especially when there is a new set of genes as target candidates during viral infections. 

On the performance of pre-microRNA detection algorithms

The biogenesis and emerging roles of circular RNAs

Competing endogenous RNA network profiling reveals novel host dependency factors required for MERS-CoV propagation

Circular RNAs: diversity of form and function

A ceRNA hypothesis: the rosetta stone of a hidden RNA language?

Host-derived circular RNAs display proviral activities in Hepatitis C virus-infected cells

Viruses and miRNAs: more friends than foes

On the importance of host MicroRNAs during viral infection

A machine learning approach for MicroRNA precursor prediction in retro-transcribing virus genomes

Role of microRNAs in antiviral responses to dengue infection

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

KNIME: the Konstanz information miner

PsRNATarget: a plant small RNA target analysis server

Vienna RNA secondary structure server

MicroRNA prediction based on 3D graphical representation of RNA secondary structures

A dynamic 3D graphical representation for RNA structure analysis and its application in non-coding RNA classification

miRBase: microRNA sequences and annotation

CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes

Prediction and analysis of SARS-CoV-2-targeting MicroRNA in human lung epithelium

Monte Carlo cross validation

PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees

The expressed MicroRNA-mRNA interactions of Toxoplasma gondii

Computational prediction of microRNAs in Histoplasma capsulatum

Delineating the impact of machine learning elements in pre-microRNA detection

Comparison of four ab initio MicroRNA prediction tools

The impact of feature selection on one and two-class classification performance for plant microRNAs

Improving the quality of positive datasets for the establishment of machine learning models for pre-microRNA detection

Circular RNAs act as miRNA sponges

The ORF3a protein of SARS-CoV-2 induces apoptosis in cells