key: cord-339431-kyr5lv15 authors: Saçar Demirci, Müşerref Duygu; Adan, Aysun title: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection date: 2020-03-17 journal: bioRxiv DOI: 10.1101/2020.03.15.992438 sha: doc_id: 339431 cord_uid: kyr5lv15 MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that have been found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs that would target their own genes or alter the host gene expression, there are examples of miRNAs functioning as an antiviral defense mechanism. In the case of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA – based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Our PANTHER gene function analysis results indicate that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription. For instance, a transcriptional regulator, STAT1 and transcription machinery might be targeted by virus-derived miRNAs. In addition, many known human miRNAs appear to be able to target viral genes. Considering the fact that miRNA-based therapies have been successful before, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics. Coronaviruses are positive-single stranded RNA (+ssRNA) viruses with exceptionally large genomes of ~ 30 kb with 5′cap structure and 3′polyA tail. The coronavirus subfamily is divided into four genera: α, β, γ, and δ based on serotype and genome features 3. The genome of a typical CoV codes for at least 6 different open reading frames (ORFs), which has variations based on the CoV type 4. Some ORFs encode non-structural proteins while others code for structural proteins required for viral replication and pathogenesis. Structural proteins include spike (S) glycoprotein, matrix (M) protein, small envelope (E) protein, and nucleocapsid (N) protein with various roles for virus enterance and spread. SARS-CoV-2 belongs to β CoV with 45-90% genetic similarity to SARS-CoV based on sequence analysis and might share similar viral genomic and transcriptomic complexity 3,5. Currently, it has been also revealed that SARS-COV-2 has a very high homology with bat CoVs, which indicated how it is transmitted to human without knowing intermediate carriers 6. S protein of SARS-CoV-2 has a strong interaction with human angiotensin-converting enzyme 2 (ACE2) expressed on alveolar epithelial cells which shows the way of virus infection in human 7. MicroRNAs (miRNAs) are small, noncoding RNAs that play role in regulation of the gene expression in various organisms ranging from viruses to higher eukaryotes. It has been estimated that miRNAs might influence around 60% of mammalian genes and their main effect is on regulatory pathways including cancer, apoptosis, metabolism and development 8. Although the current release of miRNAs, the standard miRNA depository, lists miRNAs of 271 organisms, only 34 of them are viruses 9. While, the first virus-encoded miRNAs was discovered for the human Epstein-Barr virus (EBV) 10, more than 320 viral miRNA precursors were reported so far. Although it has been shown that various DNA viruses express miRNAs, it is still debatable if RNA viruses could also encode. The major concerns regarding miRNAs of RNA viruses are based on 11: -the fact that RNA viruses that replicate in cytoplasm, do not have access to nuclear miRNA machinery -since RNA is the genetic material, miRNA production would interfere with viral replication On the other hand, many features of miRNA -based gene regulation seems to be especially beneficial for viruses. For instance, through targeting specific human genes by viral miRNAs, it is possible to form an environment suitable for survival and replication of the virus. Furthermore, for viral miRNAs it is likely to escape host defense system since host itself generates miRNAs in the same manner. Currently, options for the prevention and treatment of CoVs are very limited due to the complexity. Therefore, detailed analysis of CoV-host interactions is quite important to understand viral pathogenesis and to determine the outcomes of infection. Although there are studies regarding to the viral replication and their interaction with host innate immune system, the role of miRNA-mediated RNA-silencing in SARS-CoV-2 infection has not been enlightened yet. In this study, SARS-CoV-2 genome was searched for miRNA-like sequences and potential host-virus interactions based on miRNA actions were analyzed. Data analysis, pre-miRNA prediction, mature miRNA detection workflows were generated by using the Konstanz Information Miner (KNIME) platform 12 Data Genome data of the virus was obtained from NCBI: Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome GenBank: MN908947.3 MiRNA prediction workflow izMiR 13 Pre-miRNA prediction Genome sequences of SARS-CoV-2 were transcribed (T->U) and divided into 500 nt long fragments with 250 overlaps. Then these fragments were folded into their secondary structures by using RNAfold 15 with default settings and hairpin structures were extracted, producing 950 hairpins in total. A modified version of izMiR (SVM classifier is changed to Random Forest and latest miRBase version was used for learning) was applied to these hairpins with ranging lengths (from 7 to 176) ( Figure 1 ). Based on the mean value of averages of 3 classifiers' prediction scores (Decision Tree, Naive Bayes and Random Forest), 29 hairpins passed 0.900 threshold and used for further analysis. Selected hairpins were further processed into smaller sequences; maximum 23 nt length with 6 nt overlaps. Then, these fragments were filtered based on minimum length of 15 and their location on the hairpins (sequences not involving any loop nucleotides were included). Target search of these remaining 30 candidate mature miRNAs were performed against human and SARS-CoV-2 genes by using psRNATarget tool with default settings 16. Moreover, human mature miRNAs' from miRBase were applied for searching their targets in SARS-CoV-2 genes. The targets of viral miRNAs in human genes were further analyzed for their Gene Ontology (GO). To achieve this, PANTHER Classification System (http://www.pantherdb.org) was used 17 . Searching SARS-CoV-2 genome for sequences forming hairpin structures resulted in 950 hairpins with varying lengths (Supplementary Files). In order to use machine learning based miRNA prediction approach of izMiR workflows, hundreds of features were calculated for all of the pre-miRNA candidate sequences. Among those, minimum free energy (mfe) values required for the folding of secondary structures of hairpin sequences and hairpin sequence lengths of known human miRNAs from miRBase and predicted hairpins of SARS-CoV-2 were compared ( Figure 1 ). Based on the box-plots shown in Figure 1 , most of the extracted viral hairpins seem to be smaller than human miRNA precursors. Since viruses would need to use at least some members of host miRNA biogenesis pathway elements, viral miRNAs should be similar to host miRNAs to a certain degree. Therefore, a classification scheme trained with known human miRNAs was applied on SARS-CoV-2 hairpins. Only 29 hairpins out of 950 passed the 0.900 prediction score threshold and used for further analysis. From these hairpins, 30 mature miRNA candidates were extracted and their possible targets for human and SARS-CoV-2 genes were investigated. SARS-CoV-2 miRNA candidates were further analyzed to test if they were similar to any of the known mature miRNAs from 271 organism listed in miRBase. To achieve this, a basic similarity search was performed based on the Levenshtein distance calculations in KNIME. However, there was no significant similarity between hairpin or mature sequences. While predicted mature miRNAs of SARS-CoV-2 were used to find their targets in SARS-CoV-2 and human genes, mature miRNAs of human were also applied on SARS-CoV-2 genes (Table 1 , Supplementary Files). Although miRNA based self-regulation of viral gene expression is a hypothetical case, SARS-CoV-2 ORF1ab polyprotein gene might be the only one that could be a target of viral miRNAs. In total 1367 human genes seem to be targeted by viral miRNAs. Table 1 lists some of the predicted targets of SARS-CoV-2 miRNAs in human genes that have roles in transcription. The full list of miRNAtarget predictions are available in Supplementary Files. ORF1ab appeared to be the target of 369 different human mature miRNAs ( Table 2) . As expected, number of targeting events appear to be correlated with the gene length. Blocking nuclear import of STAT1 by binding to nuclear imports 28 Lastly, in order to understand the main mechanisms that would be affected by the influence of viral miRNAs on human genes, PANTHER Classification System was applied to targeted human genes. Based on the results presented in Graph is limited to the pathways that have at least 10 genes. Pathways of genes were obtained from Panther. Y-axis shows the number of genes with respected pathways. Legend is sorted from maximum to minimum (top to bottom). The potential roles of miRNA-mediated RNA interference in infection biology has been defined as an essential regulatory molecular pathway. 44. Although encoding miRNAs seems quite problematic for RNA viruses due to the nature of miRNA biogenesis pathway, it is possible to circumvent these problems through different ways as seen in HIV-1 36. Therefore, we analyzed possible human genes targeted by predicted miRNA like small RNAs ( Table 1) Table 2 , it can be concluded that increases in the level of host miRNAs targeting virulent genes such as S, M, N, E and ORF1ab would block viral entry and replication. Moreover, decreasing the levels of host miRNAs would make SARS-CoV-2 more replicative and visible for the host immune system. However, alterations in host miRNA levels would interfere with specific cellular processes which are crucial for the host biology. In our study, we have also identified possible miRNA like small RNAs from SARS-CoV-2 genome which target important human genes. Therefore, antagomirs targeting viral miRNAs could be also designed even though there are only a few studies for DNA viruses 45. However, all these therapeutic possibilities need further mechanistical evaluations to understand how they regulate virus-host interaction. Therefore, further in vitro, ex vivo and in vivo studies will be required to validate candidate miRNAs for SARS-CoV-2 infection. 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