PMC:7354481 / 15195-22570 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7354481","sourcedb":"PMC","sourceid":"7354481","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7354481","text":"3. Results\n\n3.1. Analysis of SARS-CoV-2 Viral Genome for miR Sequences with High Human Similarity and Functional Characterisation\nThe miRBase online database holds 2565 miR sequences and from these we identified regions of the SARS-CoV-2 viral genome, which showed high similarity to human miRs. Similarly, we analysed SARS-CoV-2 genomes obtained from different geographical areas for comparison. Despite the relatively large SARS-COV-2 genome, only a few miRs were found to show similarities with human miRs (Table 1).\nWe have found five highly significant miRs from four different countries; Turkey, Italy, Spain, and the UK; one RefSeq sequence from Wuhan and one SARS-CoV-2 genome from the VeroE6 cell line: miR-8066 (e-values; 1.6 for Wuhan, 2.8 for Valencia, 1.6 for both Italy and England, 2.8 for VeroE6 cells SARS-CoV-2 genomes); miR-5197-3p (e-values; 1.6 for Wuhan, 2.1 for VeroE6 cells, 2.8 for Valencia and 1.9 for both Italy and England SARS-CoV-2 genomes), and miR-3611 (e-values; 2.8 for Wuhan, 3.3 for Valencia and 2.8 for both Italy and England, 2.8 for VeroE6 cells SARS-CoV-2 genomes); miR-3934-3p (e-values; 3.4–5.0), and miR-1307-3p (e-values; 4.3–6.3). We could, however, detect a similar alignment with miR-1307-3p in SARS-CoV-2-infected Vero E6 cells. Additionally, we found that the same miR sequences exist within four genomes of SARS-CoV-2, within the lower e-values of 5.0–10.0 were miR-3691-3p and miR-1468-5p. Again, miR-3691-3p was not a positive hit in the SARS-CoV-2-infected Vero E6 cell line. All of these miR similarities to human miRs were conserved in all studied genomes. We then used DianaTools to identify the potential pathways to which these miRs contribute (Figure 1). The functional characterizations of these highly conserved miRs were analyzed with KEGG molecular pathways. The selected intersected pathways were analyzed as significant targets as p value 0.05 with threshold value 0.8 and Fisher’s Exact Test (hypergeometric distribution) calculations by miRPath version 3.0 in the microT-CDS database. As shown in Figure 1. miR-8066 and miR-5197-3p are critical on TGF-β and mucin type O-Glycan biosynthesis pathways. miR-8066 is also related to cytokine-cytokine receptor interaction. miR-5197 is significantly related to morphine addiction and metabolism of xenobiotics by cytochrome P450 mechanisms. These two miRs were highly conserved, and their coexistence was significant within the four-genome search. miR-3611 was the other leading miR, which possesses co-occurrence potential with miR-8066 and miR-5197 in all genomes that were effective on GABAergic synapse, morphine addiction and metabolism of xenobiotics by cytochrome P450 mechanisms. Although co-occurrences of miR-1468-5p and miR-1307-3p were similar to miR-3611, it was less effective on all evaluated metabolic pathways. miR-3934-3p was effective on glycosaminoglycan biosynthesis—heparan sulfate/heparin, other types of O-glycan biosynthesis, and vitamin digestion-absorption mechanisms, respectively.\nWhen we evaluated significant union gene-based pathway alterations for selected miRNAs, we found that mucin type O-glycan biosynthesis, morphine addiction, TGF-β signaling pathway, axon guidance and GABAergic synapse mechanisms were significantly affected according to KEGG pathway analysis on the miRPath (Table 2A).\nIn a similar setting, GO enrichment analysis results were again obtained from miRPath analysis. We determined that the clusters, based on the selected miRs’ target genes, were organelle, cellular nitrogen compound metabolic process, ion binding, biosynthetic process, nucleic acid binding transcriptional factor activity, and cellular protein modification process (Table 2B).\nIn the current analysis, we detected mutations in miR-1307 and miR-8066 only. Additionally, a mutation on miR-129-2-3p was found on only the Icelandic SARS-CoV-2 genome (Table 3). Moreover, miR-129-2-3p is one of the selected miR (Table 1) found only in the Wuhan and Italy genomes, although in less than 5% of the genomes sequenced. These three miRs are potentially involved in mucin type O-glycan biosynthesis, TGF-β signalling pathway, amphetamine addiction, cytokine-cytokine receptor interaction and nicotinate-nicotinamide metabolism. All of these pathways are associated with host responses against SARS-CoV-2, and virus pathogenesis in host cells. The mutations on selected miRs may affect their presence in different strains and may alter their potential host-mediated responses. The remainder of the miRs, presented in Table 3, was found to be conserved. Therefore, our data suggest that either these sequences are crucial for SARS-CoV-2, or their locations are important for the virus to survive. Using the GISAID database, we analysed a sample of viral genomic sequences from several geographical areas for mutations in the potential miR sequences (n = 28–133). The majority of the miRs studied showed very few base changes in these sequences, with \u003c1% overall. MiRs miR-1468-5p and, particularly, miR-1307-3p showed an increased percentage of mutations. All the mutations analysed reduced the microRNA base similarity and decreased the score value to below significance (\u003c70).\nDue to the increased mutation ratios in SARS-CoV-2 strains isolated from different geographical regions, we checked the conservation of miR mimic sequences in different sequence results. The comparison of depicted miR sequences from different SARS-Cov-2 strains (Table S1), with the Wuhan SARS-CoV-2 (HCOV-19/WUHAN/WH01/2019|EPI_ISL_406798|2019-12-26), was analyzed by BLAST nucleotide search with default values.\n\n3.2. Analysis of Gene Alterations in NHEB Bronchial Epithelial and A549 Cells Due to SARS-CoV-2 Infection\nIn the final part of the study, we analyzed the gene expression alterations due to SARS-CoV-2-mediated infection in NHEB bronchial epithelial cells and A549 lung cancer cell lines. For this purpose, we used the Bioproject PRJNA615032 publicly available data with the Rosalind bioinformatics data analysis server.\nThe meta-analysis results were evaluated for the differential gene expression at a 1.5 fold change cut-off level. In total, 124 genes were selected according to a statistical p value threshold of \u003c0.05, and analyzed for the related signaling axis to understand the disease pathophysiology, as shown in Figure 2A,B. While 104 genes were upregulated in SARS-CoV-2-infected cells, 20 genes were downregulated.\nAll of these genes were also analysed using cluster analysis tools provided by the Rosalind bioinformatic data analysis server for different pathways. As shown in Table 4, the Wiki pathways, Bioplanet, KEGG, REACTOME, Panther, Pathway Interaction DB, and the number of virus-host response pathways, were significantly altered. These significantly altered pathways showed correspondingly similar patterns with SARS-CoV-2-mediated known clinical pathologies. These pathways were based on major differences of the target genes for inflammation responses.\nThe predicted pathways in cell lines showed similar significance with selected miRs that mainly target inflammation and virus pathogenesis (Figure 1 and Table 2). Thus, we concluded that the selected miRs, that showed high similarity with human miRs, are also the critical targets, which project the major clinical pathophysiological conditions related to pathway 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