key: cord-0942720-016odyhf authors: Verma, Sonia; Dwivedy, Abhisek; Kumar, Neeraj; Biswal, Bichitra K. title: Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets date: 2020-11-03 journal: bioRxiv DOI: 10.1101/2020.11.02.365049 sha: 448dc8c5c814f313b155e050c9e5a05b17fefb7a doc_id: 942720 cord_uid: 016odyhf Over the past two decades, there has been a continued research on the role of small non-coding RNAs including microRNAs (miRNAs) in various diseases. Studies have shown that viruses modulate the host cellular machinery and hijack its metabolic and immune signalling pathways by miRNA mediated gene silencing. Given the immensity of coronavirus disease 19 (COVID-19) pandemic and the strong association of viral encoded miRNAs with their pathogenesis, it is important to study Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) miRNAs. To address this unexplored area, we identified 8 putative novel miRNAs from SARS-CoV-2 genome and explored their possible human gene targets. A significant proportion of these targets populated key immune and metabolic pathways such as MAPK signalling pathway, maturity-onset diabetes, Insulin signalling pathway, endocytosis, RNA transport, TGF-β signalling pathway, to name a few. The data from this work is backed up by recently reported high-throughput transcriptomics datasets obtains from SARS-CoV-2 infected samples. Analysis of these datasets reveal that a significant proportion of the target human genes were down-regulated upon SARS-CoV-2 infection. The current study brings to light probable host metabolic and immune pathways susceptible to viral miRNA mediated silencing in a SARS-CoV-2 infection, and discusses its effects on the host pathophysiology. infected over 40 million people worldwide and caused over 1.1 million deaths as of October 53 2020, resulting in a high fatality rate (4). The common symptoms of COVID-19 are fever, sore 54 throat, dry cough, breathlessness, malaise, and fatigue. In rare cases, patients develop acute 55 respiratory distress syndrome and sepsis followed by respiratory and heart failure (5). One of the 56 challenges to control this virus is unavailability of effective therapeutics. The problem is further 57 compounded by the lack of our understanding regarding molecular mechanisms involved in the 58 pathogenesis of SARS-CoV-2 including the host-pathogen interactions. 59 MiRNAs are 18-22 nucleotides long noncoding RNAs that are found in animals, plants, and 60 some viruses. They regulate genetic network pathways by targeting messenger RNAs (mRNAs) 61 to translational repression or degradation. The biogenesis of miRNAs begins by RNA 62 polymerase II mediated transcription of genome into long primary-miRNAs (pri-miRNAs). The 63 4 nuclear Ribonuclease type III enzyme, Drosha cleaves pri-miRNA into small hairpin precursor-64 miRNAs (pre-miRNAs) which are then exported to the cytoplasm by nuclear membrane protein, 65 Exportin5. In the cytoplasm, another Ribonuclease III enzyme-Dicer processes the pre-miRNA 66 to generate a miRNA duplex intermediate (miRNA: miRNA*). One strand of this intermediate, 67 mature miRNA binds to protein Argonaute-1 generating the miRNA inducing silencing complex 68 (RISC) which guides the binding of mature miRNA to target mRNAs (6). Besides eukaryotes, 69 viruses also encode miRNAs to target host genes involved in important biological processes like 70 cell growth, cell differentiation, and cellular defense mechanisms as well as to regulate viral gene 71 expressions to modulate the viral replication cycle (7). Therefore, viral miRNAs aid virus 72 particles in their continuous proliferation and evasion of the host immune system (8, 9) . 73 Various studies have demonstrated a strong association between viral miRNAs and infection 74 (8, 9) . However, there is no evidence to suggest the presence of miRNA sequences in SARS- 75 CoV-2 genome. To address this problem, we applied computational approaches to analyze the 76 SARS-CoV-2 genome sequence for possible miRNAs. This was of high interest and therefore 77 the secondary goal of the study was to investigate the potential targets of these miRNAs along 78 with their associated Gene Ontologies (GO) and signalling pathways. Our data provides a list of 79 potential viral miRNAs from SARS-CoV-2 and predicts their roles in COVID-19 80 pathophysiology, thus facilitating further works on the potential biological roles of these 81 miRNAs. The putative miRNAs in SARS-CoV-2 genome 86 As pre-miRNAs form hair-pin loop structures during their biogenesis, we first searched for such 87 arrangements in the SARS-CoV-2 genomic sequence using the VMir software (10). The analysis 88 demonstrated that the direct and reverse genomic strands consist of twenty-four and twenty-two 89 hair-pin loop sequences, respectively. The length of these sequences ranged between 70 and 148 90 nucleotides, and they had VMir score ≥115, hairpin size ≥ 70, and window count ≥ 35 ( Figure 91 1A, Supplementary Material -Sheet 1). 92 Next, using MFold web server the Minimal Folding Free Energy (MFE) of each sequence was 93 calculated because MFE of the pre-miRNA folding is inversely proportional to the 94 thermodynamic stability of the molecule (11). The secondary structure of these sequences was 95 also predicted using MFold. In total, forty-two potential pre-miRNAs (direct strand-23; reverse 96 strand-18), with MFE ≤ -18 kcal/mol, were recognized to be thermodynamically stable 97 (Supplementary Material -Sheet 2). 98 The Minimal Folding Free Energy Index (MFEI) distinguishes miRNAs from other coding and 99 non-coding RNAs as pre-miRNAs display higher MFEI value than mRNAs (0.66), tRNAs (0.64) 100 and rRNAs (0.59) (12). Therefore, the MFEI for the forty-one potential pre-miRNAs was (Table 106 6 1). As both the strands can serve as mature miRNA depending on the assembly of RISC 107 complex, mature miRNAs on both the arms of stem-loop were used for further analysis. This work is the first to report that SARS-CoV-2 genome may encode miRNAs and therefore 109 careful validation of these findings was carried out against previously published data. A The SARS-CoV-2 genome sequence was downloaded from the NCBI with accession number The potential pre-miRNA secondary structures were assessed using the following criteria: The MatureBayes tool was used at default parameters to predict mature miRNAs (13). MatureBayes incorporates the Naïve Bayes classifier for identifying the putative mature miRNA 297 molecules based on the sequence and structure of the miRNA precursors. Potential Target genes prediction 299 miRDB (15), a web server was used for predicting the target genes in the human genome for the 300 mature miRNAs. The server uses the MirTarget algorithm, which is based on a 7-mer seeding 301 approach to predict miRNAs targets in the human gene's 3' UTRs. The algorithm scans the 3' 302 UTR of human genes for hybridization with miRNAs sequence. Target genes with miRDB score 303 ≥ 80 were selected as a predicted target with a score ≥ 80 is most likely to exist in a 304 physiological scenario and not require any other supporting evidence. Gene ontology is a bioinformatics tool used for investigating functional relationships between 307 gene products and predicting three key aspects: biological process, cellular component, and majorly to the MAPK signalling pathway generated by MCODE analysis (see Figure 6 ). 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