Evaluation of altered miRNA expression pattern to predict COVID-19 severity.

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Swati Srivastava, Iti Garg, Yamini Singh, Ramesh Meena, Anju A Hembrom, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1180630/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Outbreak of COVID-19 pandemic in December 2019 affected millions of people globally. After substantial research, there is no specific and reliable biomarker available till date. Present study was designed to identify specific biomarkers to predict COVID-19 severity and tool for formulating treatment. A small cohort of subjects (n=43) were enrolled and categorized in four study groups; Dead (n=16), Severe (n=10) and Moderate (n=7) patients and healthy controls (n=10). Small RNA sequencing was done on Illumina platform after isolation of microRNA from peripheral blood. Differential expression (DE) of miRNA (patients groups compared to control) revealed 118 down-regulated and 103 up-regulated known miRNAs with fold change (FC) expression ≥2 folds and p≤0.05. DE miRNAs were then subjected to functional enrichment and network analysis. Bioinformatic analysis resulted in 31 miRNAs (24 Down-regulated; 7 up-regulated) significantly associated with COVID-19 having AUC>0.8 obtained from ROC curve. Seventeen out of 31 DE miRNAs have been linked to COVID-19 in previous studies. Three miRNAs, hsa-miR-147b-5p and hsa-miR-107 (down-regulated) and hsa-miR-1299 (up-regulated) showed significant unique DE in Dead patients. Another set of 4 miRNAs, hsa-miR-224-5p (down-regulated) and hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p were differentially up-regulated uniquely in Severe patients. Members of three miRNA families, hsa-miR-20, hsa-miR-32 and hsa-miR-548 were significantly down-regulated in all patients group in comparison to healthy controls. Thus a distinct miRNA expression profile was observed in Dead, Severe and Moderate COVID-19 patients. Present study suggests a panel of miRNAs which identified in COVID-19 patients and could be utilized as potential diagnostic biomarkers for predicting COVID-19 severity. COVID-19 miRNA Biomarker Sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved and spread as pandemic. This highly transmissible virus, primarily affecting lungs and causing severe pneumonia, belongs to genus Betacoronavirus and was first identified in China in late 2019 [ 1 ]. Most common symptoms of this novel coronavirus include fever, cough, cold, shortness of breath, nausea, fatigue, muscle soreness, sputum production, dyspnea, diarrhea and severe pneumonia [ 2 , 3 ] and start appearing after an incubation period of 1 to 14 days. SARS-CoV-2 genome has a positive single stranded RNA with size of around 30kbs in length having major structural proteins like spike glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N). S protein has two major subunits, S1 and S2, which binds to host angiotensin-converting enzyme 2 (ACE-2) and facilitates entry of virus into target proteins [ 4 ]. The symptoms of COVID-19 may range from mild, moderate to severe depending upon age, immune response and co-morbidity in a person. Major determinants of infection severity include high levels of circulating cytokines and chemokines (importantly interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α), lymphopenia and immune cell infiltration in infected organ, which are responsible for varied immune response [ 5 , 6 ]. Thus progression and severity of COVID-19 disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection largely depends on host cell response. Till now, COVID-19 treatment modalities include anti-biotic and anti-viral drugs, glucocorticoids/steroids treatment, invasive and non-invasive ventilation and renal replacement therapy [ 2 , 7 , 8 ]. However, none of these strategies are very specific, completely safe and definite for COVID-19 treatment and have varying response on individuals [ 9 ]. MicroRNAs (miRNAs) are small, non-coding post transcriptional repressors of gene expression which inhibit gene activity either by binding to a specific region of gene in 3′-untranslated region (3′-UTR) thereby degrading mRNA or by blocking the translation process [ 10 ]. Extensive research in the last decade established that small RNAs play a central role in pathogenesis and progression of many diseases. Previous studies have demonstrated that the binding of miRNAs on viral RNA has adverse effects on the virus genome [ 10 ]. Host miRNA expression can be important anti-viral tool [ 11 ] as they stimulate immune response [ 12 ] by mediating T cells and antiviral effector functions [ 13 ]. Thus miRNA biomarkers could be an alternate treatment strategy for COVID-19 [ 14 ]. In addition, pathogeneis of several human diseases are associated with dysregulation of miRNAs and biological processes controlled by these miRNAs. In this regard, previous reports have shown that severity of COVID-19 infection is associated with changes in miRNA levels especially in patients with co-morbidities such as respiratory diseases, diabetes, heart failure and kidney problems [ 15 ]. In case of COVID-19 infection, miRNAs can inhibit the viral translation by binding to 3′-UTR of the viral genome or target the receptors, structural or non-structural proteins of virus without affecting the expression of human genes [ 16 ]. Mechanism of epigenetic interplay of SARS-CoV-2 and host miRNAs is yet to be elucidated. However, understanding miRNA expression pattern in COVID-19 patients could help in explaining the basis of pathogenesis of novel SARS-CoV-2. Present study was undertaken to examine the miRNA expression pattern in peripheral blood of COVID-19 infected individuals with different levels of infection severity in comparison to healthy controls in order to identify potential miRNA biomarkers that can predict the severity of COVID-19 infection. Methodology Study Design The collection of samples and data was according to the guidelines laid by Indian Council of Medical Research (ICMR) and was duly approved by ethical committee. High throughput miRNA sequencing and bioinformatics analysis was employed to study differential expression pattern of miRNAs in three study groups (i) Dead COVID-19 patients, (ii) Severe COVID-19 patients and (iii) Moderate COVID-19 patients in comparison to healthy controls. Sample collection Peripheral blood samples were collected in PAXgene blood RNA tubes (Qiagen, Germany) from a total of 33 COVID-19 infected patients who reported to Rajiv Gandhi Super Specialty Hospital (RGSSH), Delhi, India. The diagnosis of COVID-19 infection was confirmed by clinicians using RT-PCR and the patients were grouped under dead (n=16), severe(n=7) and moderate(n=10) category based on the severity of infection and symptoms, according to the guidelines laid by Ministry of health and family welfare, Government of India ( https://www.mohfw.gov.in/pdf/ ). The medical reports so obtained, were analyzed at Defence Institute of Physiology and Allied Sciences (DIPAS), DRDO, Delhi. Patients were categorized into severe, moderate and mild cases based on infection severity according to the guidelines laid by Ministry of health and family welfare, Government of India ( https://www.mhfw.gov.in/pdf/ ). Patient categorization along with their basic details (age, gender), medication details and co-morbidity status has been elaborated in Table 1 . Blood was also collected in PAXgene blood RNA tubes (Qiagen, Germany) from ten healthy individuals (controls) who did not succumb to COVID-19 infection. Table 1 Basic characteristics and clinical profile of COVID-19 patients included in the study Variables All Patients N= 33 Patients with co-morbidity Patients without co-morbidity Patients with no information about co-morbidity Age Group (years) = 60 11 10 1 - Gender MALE 22 16 4 2 FEMALE 11 9 1 1 Severity of Infection Moderate 7 6 - 1 Severe 10 7 1 2 Dead 16 12 4 - RNA extraction and quantitation Isolation of total RNA including miRNA from human whole blood was done using PAXgene Blood miRNA kit (Cat No. 763134, Qiagen) following manufacturer’s protocol. Briefly, tubes were incubated at room temperature for two hours before starting the isolation. The tubes were centrifuged using swing out rotor at 3000-5000xg for 10 min at 15°C. The supernatant was decanted, the pellet dissolved in RNase free water and centrifuged. Again the supernatant was discarded and the pellet was washed with BM1. The samples were transferred to new microcentrifuge tubes, 300µl of buffer BM2 and 40µl of Proteinase K was added, vortex mixed briefly and incubated for 10 min at 55°C shaker incubator at 400-1400 rpm. The samples are loaded into PAXgene shredder spin column placed in 2ml processing tube and centrifuged at maximum speed for 3 min. The supernatant obtained was carefully transferred to new microcentrifuge tube and 700µl of isopropanol was added and mixed. The sample was transferred into PAXgene RNA spin column, placed in 2ml processing tube, and centrifuged for 1 min at 8000-20,000 xg. The column wash was performed according to manufacturer’s protocol. On column Dnase treatment was done using 80µl of DNase. The concentration and purity of the RNA extracted was evaluated using Qubitfluorometer (Thermo Fisher Scientific, MA, USA). The samples were run on Bioanalyzer (Agilent) and % miRNA was obtained. Library Preparation and sequencing Small RNA sequencing (smRNA) libraries were prepared with QIAseq® miRNA Library Kit (Cat: 331502) protocol (Qiagen, Maryland, U.S.A.) at Genotypic Technology Pvt. Ltd., Bangalore, India. Briefly, 10ng-63ng of Qubit quantified total RNA was used as starting material. 3’ adapters were ligated to the specific 3’OH group of micro RNAs followed by ligation of 5’ adapter. Adapter ligated fragment was reverse transcribed with Unique Molecular Index (UMI) assignment by priming with reverse transcription primers. cDNA thus formed was enriched and barcoded by PCR amplification (17 -19 cycles). The 3’ and 5’ adapters used in the prep are: 5' GTTCAGAGTTCTACAGTCCGACGATC; 5' AACTGTAGGCACCATCAAT. The Illumina-compatible sequencing libraries were quantified by Qubit fluorometer. The fragment size distribution of the libraries was analyzed on Agilent 2200 TapeStation. The sequencing was carried out for 50 cycles on Illumina NovaSeq 6000 sequencing platform following manufacturer’s instructions at Genotypic Technology Pvt. Ltd., Bangalore, India (Figure 1 ). Data Analysis The raw reads were processed for filtering of specific length of 16 –40 bases and were mapped to genome. The raw data was processed by srna-workbenchV3.0_ALPHA 1 which was used to trim 3' adapter and performed length filtering. The criteria for removing contaminated reads were elimination of low quality reads (<q30), elimination of 3' adapters and reads 40bp as well as elimination of reads matching to reads matching to other ncRNAs (r, t, sn, and snoRNAs). These reads were further mapped to non-coding RNA database. The final clean reads were made unique and hence read count profile was generated. This information was used for understanding the expression pattern of miRNAs. Differential Expression (DE) analysis was carried out using DESeq 6 tool. The unmapped reads were further used for classification of known and novel miRNAs. Conserved miRNAs were identified by a homology approach against matured human miRNAs from miRBase. Homology search was performed using miRNA sequences retrieved from miRbase-22 using ncbi-blast-2.2.30 with e-value of e- 4 and non-gapped alignment. Novel miRNAs were identified based on the secondary structure prediction and target prediction. Sequences which have no homology with known miRNAs were extracted and considered for prediction of potential novel miRNAs. Firstly, the sequences were aligned to the reference genome using bowtie 2. The aligned sequences were used for novel miRNA prediction using Mireap_0.22b 5. The prediction is based on the identification of stem loop structure and miRNA with such secondary structure are reported as potential novel miRNAs (Figure 1 ). Differential expression (DE) analysis and selection of candidate miRNAs DE of miRNAs was compared across three study groups, Dead, Severe and Moderate to identify unique and common miRNAs expressed in each group. Selection criteria for miRNAs in each group were on the basis of fold change and p-value. Receiver operating characteristic (ROC) curve for each DE miRNA was plotted using webtool, EPITOOLS ( https://epitools.ausvet.com.au/roccurves ). Target prediction for all shortlisted miRNAs was done using online tool, miRDB [ 17 , 18 ]. Target genes of DE miRNAs were subjected to pathway and network analysis. Candidate miRNAs were selected on the basis of their significant expression and target genes associated with biological pathways closely involved in COVID-19 pathogenesis. Results RNA quality assessment The qualitative and quantitative assessment of samples was done to obtain optimal yield for Illumina library preparation. The ratio of absorbance at 260 nm and 280 nm was used to assess the purity RNA, which lied in between 2.0-2.1. Library sequencing and data de-multiplexing The Illumina libraries showed average fragment size range of 220 bp with sufficient concentration to get the desired amount of sequencing data. The data obtained from the sequencing run was demultiplexed using Bcl2fastq software v2.20 and FastQ files were generated based on the unique dual barcode sequences. The sequencing quality was assessed using FastQC v0.11.8 software. The adapter sequences were trimmed and bases above Q30 were considered and low quality bases were filtered off during read pre-processing and used for downstream analysis. Raw data Processing An average of 19,182,338.5 total reads high quality and non-redundant reads were retained for analysis. After trimming of the raw reads an average of 13,113,032.2 reads. Homo sapiens (GRCh38) genome downloaded from the Ensembl database and mo sapiens cDNA sequences from Ensembl was used as reference genome. An average of 1048 known miRNAs and 244 novel miRNAs were identified in all samples. Average total number of miRNA (known and novel) identified across each group along with average total number of miRNA detected with read count >=50 and read count >=10 is depicted in Table 2 . Table 2 Sequencing data report Subjects Dead Severe Moderate Control Avg. Total Reads 19797381 19453872.5 19054232 18423869.7 Avg. Trimmed Reads 16928692 17361656.8 16618799 1542981.6 % of reads align to Genome 87.43 87.92 89.14 88.68 % of reads align to ncRNA 7.33 6.58 7.81 8.81 Known miRNA Avg. total miRNA detected 988 1060 1054 1022 Avg. Total miRNA detected with read count ≥500 307 312 308 277 Avg. Total miRNA detected with read count >10 530 536 525 486 Novel miRNA Avg. total miRNA detected 238 251 249 159 Avg. Total miRNA detected with read count ≥500 17 17 18 14 Avg. Total miRNA detected with read count >10 99 109 107 68 Differential expression (DE) analysis Known miRNAs Variation in the reads were normalized by library normalization method opted from DESeq library. DESeq calculated size factor, each read count is normalized by dividing with size factor. Mean normalized read counts of the samples in a given condition were used for DGE calculation. To understand the regulation of expression between the samples, log2fold of 2 fold change (FC) and p value ≤0.05 was used as cutoff. MiRNAs ≥2 FC were considered as “UP” regulated, miRNAs ≤-2 FC were considered as “DOWN” and those between 2 and 2 were flagged as “NEUTRAL”. Fold changes were calculated based of “Expression of treated sample/ Expression of control sample. A total of 103 known miRNAs were up-regulated, 44 in dead, 46 in severe and 13 in moderate patients group. Similarly, 118 known miRNAs were down-regulated, 39 in dead patients, 34 in severe patients and 45 in moderate patients group. Venn diagram (plotted using webtool ) identified the common and unique DE miRNAs which were further categorized into those above > 2, >3 and >4 FC (Figure 2). The details of both common and unique miRNAs in the study groups as obtained from venn diagram has been detailed in Table 3 . Volcano plots were also generated to identify DE miRNAs in the three study groups (as depicted in Figure 2), using webtool VolcaNoseR (doi: 10.1101/2020.05.07.082263 ). Table 3 List of differentially expressed (DE) miRNAs in all study groups (Severe, Moderate and Mild COVID-19 Patients) Study Groups Expression Number MiRNAs identified Common miRNAs amongst Study Groups Dead, Moderate & Severe Up-Regulated 4 hsa-miR-320a-5p, hsa-miR-377-5p ,hsa-miR-4800-3p, hsa-miR-4484 Down-Regulated 15 hsa-miR-20b-5p, hsa-miR-559, hsa-miR-4999-5p, hsa-miR-624-5p, hsa-miR-496, hsa-miR-1255b-2-3p, hsa-miR-15a-5p, hsa-miR-624-3p, hsa-miR-3613-5p, hsa-miR-20a-5p, hsa-miR-106a-5p, hsa-miR-126-3p, hsa-miR-590-3p, hsa-miR-31-5p, hsa-miR-32-5p. Dead & Severe Up-Regulated 18 hsa-miR-6502-5p ,hsa-miR-34c-5p, hsa-miR-6868-3p, hsa-miR-5585-3p, hsa-miR-212-5p, hsa-miR-3614-3p, hsa-miR-1262, hsa-miR-2115-5p, hsa-miR-3162-3p, hsa-miR-4772-5p, hsa-miR-96-5p, hsa-miR-4668-3p, hsa-miR-618, hsa-miR-2355-3p, hsa-miR-143-3p, hsa-miR-203a-3p, hsa-miR-1179, hsa-miR-671-5p, Down-Regulated 8 hsa-miR-6859-3p, hsa-miR-1248, hsa-miR-374b-3p, hsa-miR-449a, hsa-miR-3184-5p, hsa-miR-1911-5p, hsa-miR-411-5p, hsa-miR-153-3p. Dead & Moderate Up-Regulated 1 hsa-miR-203b-5p Down-Regulated 4 hsa-miR-548am-5p, hsa-miR-1277-5p, hsa-miR-548l ,hsa-miR-95-3p. Moderate & Severe Up-Regulated 2 hsa-miR-6793-5p, hsa-miR-885-5p. Down-Regulated 5 hsa-miR-548b-5p, hsa-miR-5698, hsa-miR-486-3p, hsa-miR-16-5p, hsa-miR-374a-5p. Unique to study groups Dead Up-Regulated 21 hsa-miR-582-3p, hsa-miR-1908-5p, hsa-miR-125b-1-3p, hsa-miR-490-3p, hsa-miR-490-5p, hsa-miR-518c-3p, hsa-miR-6876-3p, hsa-miR-193b-5p, hsa-miR-1299, hsa-miR-3074-5p, hsa-miR-3158-3p, hsa-miR-320b, hsa-miR-3614-5p, hsa-miR-27a-5p, hsa-miR-125a-3p, hsa-miR-5189-5p, hsa-miR-760, hsa-miR-504-5p, hsa-miR-6787-3p, hsa-miR-3177-3p, hsa-miR-1256. Down-Regulated 12 hsa-miR-551b-3p, hsa-miR-6751-5p, hsa-miR-147b-5p, hsa-miR-3683, hsa-miR-449b-5p, hsa-miR-548p, hsa-miR-218-5p, hsa-miR-369-3p, hsa-miR-3136-5p, hsa-miR-20a-3p, hsa-miR-107, hsa-miR-548aq-5p. Severe Up-Regulated 22 hsa-miR-193a-3p, hsa-miR-4755-3p, hsa-miR-4707-3p, hsa-miR-4743-3p, hsa-miR-145-3p, hsa-miR-495-3p, hsa-miR-202-5p, hsa-miR-221-3p, hsa-miR-4642, hsa-miR-181a-5p, hsa-miR-3679-3p, hsa-miR-582-5p, hsa-miR-335-3p, hsa-miR-4659b-3p, hsa-miR-605-5p, hsa-miR-337-3p, hsa-miR-7107-3p, hsa-miR-377-3p, hsa-miR-1178-3p, hsa-miR-130a-5p, hsa-miR-433-3p, hsa-miR-6834-3p. Down-Regulated 6 hsa-miR-224-5p, hsa-miR-101-2-5p, hsa-miR-6825-5p, hsa-miR-6736-5p, hsa-miR-1278, hsa-miR-3912-3p. Moderate Up-Regulated 6 hsa-miR-6507-5p, hsa-miR-3675-3p, hsa-miR-4700-5p, hsa-miR-205-5p, hsa-miR-3944-3p, hsa-miR-3611. Down-Regulated 21 hsa-miR-196b-5p, hsa-miR-199a-3p,hsa-miR-32-3p, hsa-miR-548am-3p, hsa-let-7f-5p, hsa-miR-651-5p, hsa-miR-144-3p, hsa-miR-17-5p, hsa-miR-141-3p, hsa-miR-98-5p, hsa-miR-374c-3p, hsa-miR-493-5p, hsa-miR-26b-5p, hsa-let-7g-5p, hsa-miR-5583-3p, hsa-let-7a-5p, hsa-miR-548k, hsa-miR-21-5p, hsa-miR-545-3p, hsa-miR-454-3p, hsa-miR-1289. Also, 25 miRNAs were commonly up-regulated and 32 were commonly down-regulated in all three study groups in comparison to compared to healthy controls. Heat map in figure 3 depicts these miRNAs. The differential colour pattern shows the change in expression amongst the patient groups. Group wise principal component analysis (PCA) of common up-regulated and down-regulated miRNAs shows that the three study groups lying in different quadrants showing distinct pattern of expression. Novel miRNAs Besides known miRNAs, DE was also observed in novel miRNA sequences. A total of 19 novel miRNAs were observed (log2 FC±2, p value≤0.05), out of which 8 were up-regulated miRNAs (2 in Dead, 5 in severe and 1 in moderate group) and 11 were down-regulated miRNAs (2 in Dead, 2 in Severe and 1 in Moderate group and 6 in All patients group). A novel miRNA sequence was commonly up-regulated in Dead and Severe group and another was commonly down-regulated in Moderate and All patients group (highlighted in Table 3 ). MiRNA target prediction and downstream bioinformatic analysis for these novel miRNAs was not carried out. Table 4 Novel miRNA sequences (log2 FC±2, p value≤0.05). Sequences shaded in grey colour are common amongst the two or more study groups miRNA log2FoldChange p-Value Dead vs Control ATTCTACAGTCCTACGAGCA -4.807 0.037 AGACTGACCCAGAGTCTCAAGC 6.016 0.044 TCAGCGTTGGTTCCCGTCTTGGCC -2.948 0.044 CCTCTCCGCCACCTCCACCGCGGC 4.940 0.049 Severe vs Control AGACTGACCCAGAGTCTCAAGC 5.619 0.009 TATCCCACCACTGCCACCATTATT -3.280 0.014 ACTGCCTTTTGATGACCGGGACGA 4.259 0.014 CACTGACCCTCTTCTCTGCACAGC 4.134 0.028 TACCTTCACACCTCTGACTCTGA 3.745 0.047 CTTGAGACTCTGGGTCAGTCTATT 3.104 0.049 TGCCCTGAGACTTTTGCTCTAAT -4.188 0.050 Moderate vs Control GCGGCCGGGGAGAACTGCGCCTGC 3.623 0.020 TGAGGTAGTAGGTTGTATAGTTT -4.205 0.035 All patients vs control CGTCCAGTCCTGGCCCCCTAGGAG -4.625 0.007 TGAGGTAGTAGGTTGTATAGTTT -3.381 0.014 ACTGACCCTCTTCTCTGCACAGCT -2.972 0.030 CTCGTACCGTGAGTAATAATGCG -3.053 0.030 CAACGGAATCCCAAAAGCAGCTG -4.528 0.035 TTTAGTGGCTCCCTCTGCCTGC -5.099 0.042 Receiver-operating characteristic (ROC) analysis of DE miRNA Sample wise expression data for each DE miRNA was subjected to receiver-operating characteristic (ROC) analysis using online webtool EPITOOLS ( https://epitools.ausvet.com.au/roccurves ) (Figure 4 ). This probability curve that plots true positive rate against false positive rate essentially separated the low quality data and further refined the list of DE miRNAs. All DE miRNAs were subjected to ROC analysis. Cut-off value of area under the ROC curve (AUC) was taken as 0.8 to select miRNAs for further analysis. MiRNAs having AUC values lower than 0.8 were not included for further analysis. 46 down-regulated and 37 up-regulated miRNAs showed AUC values >0.8. These miRNAs were then further subjected to target prediction followed by pathway and network analysis. Figure 4 and figure 5 shows ROC curves and sample wise box plots respectively, of 31 miRNAs which were finally selected as candidate miRNAs for COVID-19 infection severity and disease prognosis. Target prediction of miRNAs Target gene prediction was done online using miRDB database [ 17 , 18 ]. Only those target genes were selected for further analysis that showed target score match of 80 percent. 3 miRNAs did not show any target genes above 80 percent match and hence were not included in downstream analysis. Pathway and network analysis Pathway analysis was done using PANTHER database [ 19 ]. Each miRNA target gene list was sought for genes involved in eighteen biological pathways putatively associated in COVID-19 pathogenesis. These included inflammation, B-cell and T-cell activation, interleukin signaling, blood coagulation, oxidative stress response etc. Those miRNAs showing over 45 target genes involved in these pathways were selected as candidate miRNAs significantly linked to COVID-19 prognosis (Figure 6 ). Network analysis for the candidate miRNAs (up-regulated and down-regulated) was performed using online database, MiRnet to create MiRNA-mRNA target networks (Figure 6 ). Panel of candidate miRNAs potentially involved in COVID-19 infection severity Present analysis generated a panel of 31 miRNAs. These were 24 down-regulated and 7 up-regulated miRNAs, including both unique and common elements in the study groups. Hsa-miR-147b-5p and hsa-miR-107 was uniquely down-regulated whereas hsa-miR-1299 was uniquely up-regulated in Dead patients. In the Severe patients group, hsa-miR-224-5p was uniquely down-regulated and three miRNAs, hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p were uniquely up-regulated. Eleven miRNAs were uniquely down-regulated in Moderate patients group, including, hsa-miR-32-3p, hsa-miR-144-3p, hsa-miR-5583-3p, hsa-miR-26b-5p, hsa-miR-199a-3p, hsa-miR-98-5p, hsa-miR-548k, hsa-miR-493-5p, hsa-miR-454-3p, hsa-miR-141-3p and hsa-miR17-5p. Only one miRNA, hsa-miR-4700-5p was uniquely up-regulated in moderate patients. Ten miRNAs common to all three study groups were significantly down-regulated. These were hsa-miR, 31-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-590-3p, hsa-miR-15a-5p, hsa-miR-374b-3p, hsa-miR-16-5p, hsa-miR-1277-5p, hsa-miR-32-5p, hsa-miR-548I. Two miRNAs were commonly up-regulated, hsa-miR-4668-3p and hsa-miR-320a-5p. Table 5 Disease association of 31 DE miRNAs identified in the present study S. No. MiRNA Association with COVID-19 Association with other Diseases 1 miR-147b-5p Unknown Tuberculosis [ 30 ], Cancer [ 31 ] 2 miR-107 Unknown Cancer [ 32 ], Alzheimer [ 33 ] 3 miR-224-5p Unknown Cancer [ 34 ], Cystic Fibrosis [ 35 ] 4 miR-32-3p Down-regulated in COVID-19 [ 36 ] Myocardial Injury [ 37 ], Ischemia [ 38 ] 5 miR-144-3p Reduced by more than 1.3-fold [ 39 ] Myocardial Infarction [ 40 ], Cancer [ 41 ] 6 miR-5583-3p Unknown Cancer [ 42 ], Arthritis [ 43 ] 7 miR-26b-5p Regulator of ACE2 network [ 44 ] Cardiac hypertrophy [ 39 ]; Cancer [ 45 ] 8 miR-199a-3p Anti-viral miRNA, down-regulated [ 46 ] Cancer [ 47 , 48 ] 9 miR-98-5p Regulator of TMPRSS2 transcription [ 49 ] Asthma [ 50 ] 10 miR-548k Unknown Cancer [ 51 ], Myasthenia gravis [ 52 ] 11 miR-493-5p Unknown Stroke [ 53 ], Cancer [ 42 ] 12 miR-454-3p Down-regulated in COVID-19 [ 25 ] Cancer [ 54 ] Alzheimer's Disease [ 55 ] 13 miR-141-3p Indirectly affect ACE2 / TMPRSS2 expression [ 56 ] Stroke [ 57 ], Atherosclerosis [ 58 ] 14 miR-17-5p Down-regulated in COVID-19 [ 25 ] Myocardial infarction [ 59 ], Chronic kidney disease [ 60 ] 15 miR-31-5p Up-regulated in COVID-19 [ 61 ] Diabetes [ 37 ], Osteoarthritis [ 62 ] 16 miR-20b-5p Unknown Coronary Heart disease [ 63 ], Cancer [ 64 ] 17 miR-20a-5p Anti-viral miRNA, down regulated [ 46 ] Diabetic cardimyopthy [ 65 ], Nonalcoholic fatty liver disease [ 66 ] 18 miR-590-3p SARS-CoV-2 Spike transfected cells release a significant amount of exosomes loaded with miRNAs [ 67 ] Chronic Heart Failure [ 68 ], Cardiomyocyte injury [ 69 ] 19 miR-15a-5p Up-regulated in COVID-19 [ 70 ] Acute myocardial infarction [ 71 ], Endometriosis [ 72 ] 20 miR-374b-3p Host immune response [ 73 ] Systemic lupus erythematosus [ 53 ], Osteoporosis [ 74 ] 21 miR-16-5p Regulator of ACE2 network [ 44 ] Cardiomyocyte injury [ 75 ], Atherosclerosis [ 76 ] 22 miR-1277-5p Unknown Osteoarthritis [ 77 ], Parkinsons [ 78 ] 23 miR-32-5p Silencing of Tmprss2 with maximum gene suppression [ 36 ] Coronary artery disease [ 79 ], Acute myocardial infarction [ 80 ] 24 miR-548l Unknown Cancer [ 81 , 82 ] Up-regulated 25 miR-1299 Unknown Cancer [ 83 ], Diabetes [ 84 ] 26 miR-4659b-3p Unknown Unknown 27 miR-495-3p Unknown Rheumatoid Arthritis [ 85 ], Chronic Kidney disease [ 86 ] 28 miR-335-3p Unknown Hypertension [ 87 ], Cancer [ 88 ] 29 miR-4700-5p Unknown Cancer [ 89 ] 30 miR-4688-3p Unknown Aneurysm [ 90 ], Cancer [ 91 ]) 31 miR-320a-5p Down-Regulated in COVID-19 [ 23 ] Thrombosis [ 92 ], Chronic heart failure [ 93 ] Discussion Present study generated a panel of 31 candidate miRNAs (24 Down-Regulated and 7 up-Regulated) which could be potentially involved in COVID-19 disease severity. These circulating candidate miRNAs were identified in Dead, Severe and Moderate COVID-19 patients with a distinct miRNA signature pattern in each study group. The criteria for selecting candidate DE miRNA after sequencing were extremely stringent in the current study. The selection was based on miRNA expression fold change ±≥2, p≤0.05, AUC≥0.8 and number of target genes associated with selected pathways ≥45. Panel of biomarkers generated in the present study could be more Robust than depending upon a single biomarker. Pubmed search revealed that all the DE miRNAs identified in the present study, have established role in cancer, coronary artery disease, chronic heart failure, diabetic retinopathy, stroke, hypertension, thrombosis etc. except for up-regulated hsa-miR-4659b-3p (Table 5 ). Interestingly, 15 out of 24 down-regulated miRNAs and 2 out of 7 up-regulated have been associated with COVID-19 in the previous reports (Table 5 ). On the other hand, 9 down-regulated and 5 up-regulated miRNAs have been empanelled for COVID-19 association for the first time in this study. Several recent studies have listed various miRNAs significantly linked to COVID-19 pathology and severity. In a recently published study consisting of 10 COVID-19 patients and 10 healthy subjects, researchers observed alteration of 55 miRNAs in COVID-19 patients. They stated that miR-31-5p, associated with inflammation, is most up-regulated among other 55miRNAs. Along with this, they revealed by supervised machine learning that miR-423-5p, miR-23a-3p and miR-195-5p are independently associated with COVID-19 patients (Farr et al. 2021). On the contrary, we report significant down-regulation of hsa-miR-31-5p in all COVID-19 patients in the present study. In another recent study, transcriptome analysis was done in COVID-19 patients and observed that miR-2392, a circulatory miRNA, has strong association COVID-19 infection. They designed and tested miRNA -based antiviral therapeutics that targeted miR-2392 by using in vitro human, in vivo hamster models and potential findings were noted [ 20 ]. It is also well known that miRNA are potential regulator of endothelial homeostasis which are in turn associated with vascular diseases. Predominance of endothelial dysfunction and thrombus formation has been reported in various clinical studies in COVID-19 patients. Centa and co-workers, observed the levels of miRNAs, miR-26a-5p, miR-29b-3p and miR-34a-5p, which are associated with regulation of endothelial and inflammatory signaling pathways as well as viral diseases in lung biopsies of COVID-19 patients and healthy subjects [ 21 ]. Present study also identified hsa-miR-26b-5p significantly down-regulated in Moderate patients group. Another interesting finding was that hsa-miR-320a-5p, which has been previously linked to thrombosis [ 22 ] was up-regulated in all patient groups indicating that COVID-19 pathology leads to thrombus formation. However, a contradicting finding by Deuker and co-workers suggest that hsa-miR-320 family is significantly down-Regulated in COVID-19 [ 23 ]. A small cohort study was done in serum of 40 patients diagnosed with COVID-19 along with healthy subjects and expression pattern of 20 miRNAs was evaluated using Reverse transcription quantitative real-time PCR (RT-qPCR). Researchers observed that out of twenty miRNAs, only nine miRNAs were deferentially expressed in study groups. Expression levels of hsa-miR-190a and hsa-miR-203 were significantly elevated in COVID-19 patients with respect to healthy subjects. On the other side seven miRNAs (hsa-let-7d, hsa-miR-17, hsa-miR-34b, hsa-miR-93, hsa-miR-200b, hsa-miR-200c and hsa-miR-223) showed reduced level of expression in COVID-19 patients in comparison to healthy subjects [ 24 ]. However, hsa-miR-17 is significantly down-regulated in Moderate patients group in the current study, which is in concurrence with the findings of Li and co-workers [ 25 ]. In their study, on 10 COVID-19 patients and four healthy subjects they found up-regulation of 35miRNAs as well as down Regulation of 38 miRNAs in patient group [ 25 ]. One observational and multicentric study was conducted in Spain from March to June 2020 consisting of 84 COVID-19 patients. They observed that differential expression of two miRNAs (hsa-miR-192-5p and hsa-miR-323a-3p) in ICU non survivors from survivors. They also identified three miRNAs (hsa-miR-148a-3p, hsa-miR-451a, hsa-miR-486-5p) that distinguishes between ICU and ward patients [ 26 ]. Present study also identified two down-regulated (hsa-miR-147b-5p and hsa-miR-107) and one up-regulated (hsa-miR-1299) miRNAs uniquely expressed in Dead patients group (non-survivors). These miRNAs have not been linked to COVID-19 so far! Also, four uniquely expressed miRNAs (1 down-regulated: hsa-miR-224-5p and 3 up-regulated: hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p) were expressed in Severe patients group. Interestingly, none of these four miRNAs have been directly linked to COVID-19 so far, however hsa-miR-335-5p has been associated with COVID-19 in a previous report in which bioinformatic analysis predicted that hsa-miR-335-5p is modulated by Spike and ACE together with histone deacetylate (HDAC) pathway [ 27 ]. There are few other clinical studies on miRNAs and COVID-19. In one of them, elevated levels of expression of miR-499, miR-21, miR-155 and miR-208-a were determined in serum of COVID-19 infected patients in comparison to healthy subject [ 28 ]. Another clinical study was done in 150 COVID-19 patients and 50 controls along with evaluation of plasma miR-155 expression level using RT-qPCR. They demonstrated that there is increased miR-155 expression level in COVID-19 patients, severe and non-survivors in comparison to controls, moderate, survivors COVID-19 patients respectively [ 29 ]. Of note, we identified three families of miRNA significantly down-regulated in COVID-19 patients. Hsa-miR-20a-5p and hsa-miR-20b-5p were commonly down-regulated in all patient groups. Also, hsa-miR-32-5p and hsa-miR-548I, both down-regulated in all patient groups and their family members, hsa-miR-32-3p and hsa-miR-548K both were down-regulated in moderate patients group. There is a complex interplay of host induced miRNAs and virus encoded miRNAs that determine the severity of infection and host defence response. Recent reports indicate that miRNAs could be promising tools for early diagnosis and treatment of COVID-19 infection. This alternative novel therapy with miRNA regulation could prevent disease progression and severe condition [ 14 ]. The miRNAs identified in the present study could be used as potential biomarkers for identification of individuals who could be more prone to develop severe symptoms of COVID-19 as well as could be used as putative targets for developing treatment strategies for COVID-19 in future. Conclusion The miRNA signatures identified in the present study could be used as novel tool for early prediction of vital status of COVID-19 patients during disease prognosis. Specific miRNA expression data may be useful in prediction of severity of COVID-19 infection in an individual. Validation of these miRNA targets with larger cohort could also be helpful in using them as therapeutic targets. Declarations Acknowledgement The authors are extremely thankful to the technical staff of RGSSH and DIPAS for providing their untiring efforts and support in collection of CVOID-19 patient samples. 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Medical science monitor: international medical journal of experimental and clinical research , 24 , 2031–2037. https://doi.org/10.12659/msm.906596 Li, F., Li, S. S., Chen, H., Zhao, J. Z., Hao, J., Liu, J. M., Zu, X. G., & Cui, W. (2021). miR-320 accelerates chronic heart failure with cardiac fibrosis through activation of the IL6/STAT3 axis. Aging , 13 (18), 22516–22527. https://doi.org/10.18632/aging.203562 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1180630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":70848282,"identity":"90edd862-2a11-4f9a-91b3-10f9c9c0a3ec","order_by":0,"name":"Swati 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analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/24b3c59c3e23c22fad2e047b.png"},{"id":16567995,"identity":"1209996b-974f-4a06-a1e3-e9c19c40b3ea","added_by":"auto","created_at":"2021-12-17 22:39:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":425428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiagrammatic representation of miRNA sequencing data analysis (A) Study groups (B) Library preparation and sequencing (C) Venn diagram shows common and unique differentially expressed miRNA in each study group i.e. Dead, Sever, Moderate with respect to Healthy subjects. Histograms shows distribution of differentially expressed miRNAs in each study group on the basis of Fold Change \u0026gt;±2. (D)Volcano plot of DE miRNAs (Fold Change \u0026gt;±2) with p value ≤0.05 in Dead, Severe and Moderate COVID-19 patients.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/07b3ba3831dc2036f34a12f6.png"},{"id":16568102,"identity":"68a09e75-245b-455f-9dc5-48c9918e94b1","added_by":"auto","created_at":"2021-12-17 22:42:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHeat map and PCA analysis: (\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eA) Heat map of differentially expressed (DE) miRNAs common in the three study groups. Each column represents a group of COVID-19 patient (Dead, Severe, Moderate), and each row represents a single miRNA. 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Area under the ROC curve (AUC); SEN, sensitivity; SPE, specificity.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/785924a4fdb7cf225dfde22f.png"},{"id":16567998,"identity":"fe2f8324-21a4-4ea3-85fc-f65d9752b6b4","added_by":"auto","created_at":"2021-12-17 22:39:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":406643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBox plot representation of 31 candidate microRNAs. Each plot represents Patient miRNA expression profile (Dead, Severe or Moderate) in comparison to healthy controls\u0026nbsp;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/4d284d1dac7703b9fb879183.png"},{"id":16567999,"identity":"830cd068-b361-4ceb-993e-990427ef5b92","added_by":"auto","created_at":"2021-12-17 22:39:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":664312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eA) Pathway analysis of differentially expressed Down-regulated (n=24) and Up-regulated (n=7) miRNAs predicted by PANTHER online tool. (B). Network analysis of differentially expressed miRNAs; Down-regulated (n=24) and Up-regulated (n=7) miRNAs of Dead, Severe and Moderate COVID-19 patients.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/03dad39b2ef14253a6b2e7a8.png"},{"id":16568127,"identity":"ced7fab0-794d-47a5-bf36-ce2b8ca32a48","added_by":"auto","created_at":"2021-12-17 22:45:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1180630/v1/35fcac8a-5bef-4c5c-8144-ce5f3594edb8.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEvaluation of altered miRNA expression pattern to predict COVID-19 severity.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved and spread as pandemic. This highly transmissible virus, primarily affecting lungs and causing severe pneumonia, belongs to genus Betacoronavirus and was first identified in China in late 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most common symptoms of this novel coronavirus include fever, cough, cold, shortness of breath, nausea, fatigue, muscle soreness, sputum production, dyspnea, diarrhea and severe pneumonia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and start appearing after an incubation period of 1 to 14 days. SARS-CoV-2 genome has a positive single stranded RNA with size of around 30kbs in length having major structural proteins like spike glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N). S protein has two major subunits, S1 and S2, which binds to host angiotensin-converting enzyme 2 (ACE-2) and facilitates entry of virus into target proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The symptoms of COVID-19 may range from mild, moderate to severe depending upon age, immune response and co-morbidity in a person. Major determinants of infection severity include high levels of circulating cytokines and chemokines (importantly interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α), lymphopenia and immune cell infiltration in infected organ, which are responsible for varied immune response [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus progression and severity of COVID-19 disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection largely depends on host cell response. Till now, COVID-19 treatment modalities include anti-biotic and anti-viral drugs, glucocorticoids/steroids treatment, invasive and non-invasive ventilation and renal replacement therapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, none of these strategies are very specific, completely safe and definite for COVID-19 treatment and have varying response on individuals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are small, non-coding post transcriptional repressors of gene expression which inhibit gene activity either by binding to a specific region of gene in 3\u0026prime;-untranslated region (3\u0026prime;-UTR) thereby degrading mRNA or by blocking the translation process [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Extensive research in the last decade established that small RNAs play a central role in pathogenesis and progression of many diseases. Previous studies have demonstrated that the binding of miRNAs on viral RNA has adverse effects on the virus genome [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Host miRNA expression can be important anti-viral tool [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] as they stimulate immune response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] by mediating T cells and antiviral effector functions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus miRNA biomarkers could be an alternate treatment strategy for COVID-19 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, pathogeneis of several human diseases are associated with dysregulation of miRNAs and biological processes controlled by these miRNAs. In this regard, previous reports have shown that severity of COVID-19 infection is associated with changes in miRNA levels especially in patients with co-morbidities such as respiratory diseases, diabetes, heart failure and kidney problems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In case of COVID-19 infection, miRNAs can inhibit the viral translation by binding to 3\u0026prime;-UTR of the viral genome or target the receptors, structural or non-structural proteins of virus without affecting the expression of human genes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMechanism of epigenetic interplay of SARS-CoV-2 and host miRNAs is yet to be elucidated. However, understanding miRNA expression pattern in COVID-19 patients could help in explaining the basis of pathogenesis of novel SARS-CoV-2. Present study was undertaken to examine the miRNA expression pattern in peripheral blood of COVID-19 infected individuals with different levels of infection severity in comparison to healthy controls in order to identify potential miRNA biomarkers that can predict the severity of COVID-19 infection.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThe collection of samples and data was according to the guidelines laid by Indian Council of Medical Research (ICMR) and was duly approved by ethical committee. High throughput miRNA sequencing and bioinformatics analysis was employed to study differential expression pattern of miRNAs in three study groups (i) Dead COVID-19 patients, (ii) Severe COVID-19 patients and (iii) Moderate COVID-19 patients in comparison to healthy controls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eSample collection\u003c/h2\u003e\n \u003cp\u003ePeripheral blood samples were collected in PAXgene blood RNA tubes (Qiagen, Germany) from a total of 33 COVID-19 infected patients who reported to Rajiv Gandhi Super Specialty Hospital (RGSSH), Delhi, India. The diagnosis of COVID-19 infection was confirmed by clinicians using RT-PCR and the patients were grouped under dead (n=16), severe(n=7) and moderate(n=10) category based on the severity of infection and symptoms, according to the guidelines laid by Ministry of health and family welfare, Government of India (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mohfw.gov.in/pdf/\u003c/span\u003e\u003c/span\u003e). The medical reports so obtained, were analyzed at Defence Institute of Physiology and Allied Sciences (DIPAS), DRDO, Delhi. Patients were categorized into severe, moderate and mild cases based on infection severity according to the guidelines laid by Ministry of health and family welfare, Government of India (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhfw.gov.in/pdf/\u003c/span\u003e\u003c/span\u003e). Patient categorization along with their basic details (age, gender), medication details and co-morbidity status has been elaborated in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Blood was also collected in PAXgene blood RNA tubes (Qiagen, Germany) from ten healthy individuals (controls) who did not succumb to COVID-19 infection.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eBasic characteristics and clinical profile of COVID-19 patients included in the study\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Patients\u003c/p\u003e\n \u003cp\u003eN= 33\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients with\u003c/p\u003e\n \u003cp\u003eco-morbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients without\u003c/p\u003e\n \u003cp\u003eco-morbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients with no information about co-morbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity of Infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eRNA extraction and quantitation\u003c/h2\u003e\n \u003cp\u003eIsolation of total RNA including miRNA from human whole blood was done using PAXgene Blood miRNA kit (Cat No. 763134, Qiagen) following manufacturer\u0026rsquo;s protocol. Briefly, tubes were incubated at room temperature for two hours before starting the isolation. The tubes were centrifuged using swing out rotor at 3000-5000xg for 10 min at 15\u0026deg;C. The supernatant was decanted, the pellet dissolved in RNase free water and centrifuged. Again the supernatant was discarded and the pellet was washed with BM1. The samples were transferred to new microcentrifuge tubes, 300\u0026micro;l of buffer BM2 and 40\u0026micro;l of Proteinase K was added, vortex mixed briefly and incubated for 10 min at 55\u0026deg;C shaker incubator at 400-1400 rpm. The samples are loaded into PAXgene shredder spin column placed in 2ml processing tube and centrifuged at maximum speed for 3 min. The supernatant obtained was carefully transferred to new microcentrifuge tube and 700\u0026micro;l of isopropanol was added and mixed. The sample was transferred into PAXgene RNA spin column, placed in 2ml processing tube, and centrifuged for 1 min at 8000-20,000 xg. The column wash was performed according to manufacturer\u0026rsquo;s protocol. On column Dnase treatment was done using 80\u0026micro;l of DNase. The concentration and purity of the RNA extracted was evaluated using Qubitfluorometer (Thermo Fisher Scientific, MA, USA). The samples were run on Bioanalyzer (Agilent) and % miRNA was obtained.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eLibrary Preparation and sequencing\u003c/h2\u003e\n \u003cp\u003eSmall RNA sequencing (smRNA) libraries were prepared with QIAseq\u0026reg; miRNA Library Kit (Cat: 331502) protocol (Qiagen, Maryland, U.S.A.) at Genotypic Technology Pvt. Ltd., Bangalore, India. Briefly, 10ng-63ng of Qubit quantified total RNA was used as starting material. 3\u0026rsquo; adapters were ligated to the specific 3\u0026rsquo;OH group of micro RNAs followed by ligation of 5\u0026rsquo; adapter. Adapter ligated fragment was reverse transcribed with Unique Molecular Index (UMI) assignment by priming with reverse transcription primers. cDNA thus formed was enriched and barcoded by PCR amplification (17 -19 cycles). The 3\u0026rsquo; and 5\u0026rsquo; adapters used in the prep are: 5\u0026apos; GTTCAGAGTTCTACAGTCCGACGATC; 5\u0026apos; AACTGTAGGCACCATCAAT. The Illumina-compatible sequencing libraries were quantified by Qubit fluorometer. The fragment size distribution of the libraries was analyzed on Agilent 2200 TapeStation. The sequencing was carried out for 50 cycles on Illumina NovaSeq 6000 sequencing platform following manufacturer\u0026rsquo;s instructions at Genotypic Technology Pvt. Ltd., Bangalore, India (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eThe raw reads were processed for filtering of specific length of 16 \u0026ndash;40 bases and were mapped to genome. The raw data was processed by srna-workbenchV3.0_ALPHA 1 which was used to trim 3\u0026apos; adapter and performed length filtering. The criteria for removing contaminated reads were elimination of low quality reads (\u0026lt;q30), elimination of 3\u0026apos; adapters and reads \u0026lt;16bp and \u0026gt;40bp as well as elimination of reads matching to reads matching to other ncRNAs (r, t, sn, and snoRNAs). These reads were further mapped to non-coding RNA database. The final clean reads were made unique and hence read count profile was generated. This information was used for understanding the expression pattern of miRNAs. Differential Expression (DE) analysis was carried out using DESeq 6 tool. The unmapped reads were further used for classification of known and novel miRNAs. Conserved miRNAs were identified by a homology approach against matured human miRNAs from miRBase. Homology search was performed using miRNA sequences retrieved from miRbase-22 using ncbi-blast-2.2.30 with e-value of e- 4 and non-gapped alignment. Novel miRNAs were identified based on the secondary structure prediction and target prediction. Sequences which have no homology with known miRNAs were extracted and considered for prediction of potential novel miRNAs. Firstly, the sequences were aligned to the reference genome using bowtie 2. The aligned sequences were used for novel miRNA prediction using Mireap_0.22b 5. The prediction is based on the identification of stem loop structure and miRNA with such secondary structure are reported as potential novel miRNAs (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eDifferential expression (DE) analysis and selection of candidate miRNAs\u003c/h2\u003e\n \u003cp\u003eDE of miRNAs was compared across three study groups, Dead, Severe and Moderate to identify unique and common miRNAs expressed in each group. Selection criteria for miRNAs in each group were on the basis of fold change and p-value. Receiver operating characteristic (ROC) curve for each DE miRNA was plotted using webtool, EPITOOLS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://epitools.ausvet.com.au/roccurves\u003c/span\u003e\u003c/span\u003e). Target prediction for all shortlisted miRNAs was done using online tool, miRDB [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Target genes of DE miRNAs were subjected to pathway and network analysis. Candidate miRNAs were selected on the basis of their significant expression and target genes associated with biological pathways closely involved in COVID-19 pathogenesis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eRNA quality assessment\u003c/h2\u003e\n \u003cp\u003eThe qualitative and quantitative assessment of samples was done to obtain optimal yield for Illumina library preparation. The ratio of absorbance at 260 nm and 280 nm was used to assess the purity RNA, which lied in between 2.0-2.1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eLibrary sequencing and data de-multiplexing\u003c/h2\u003e\n \u003cp\u003eThe Illumina libraries showed average fragment size range of 220 bp with sufficient concentration to get the desired amount of sequencing data. The data obtained from the sequencing run was demultiplexed using Bcl2fastq software v2.20 and FastQ files were generated based on the unique dual barcode sequences. The sequencing quality was assessed using FastQC v0.11.8 software. The adapter sequences were trimmed and bases above Q30 were considered and low quality bases were filtered off during read pre-processing and used for downstream analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003eRaw data Processing\u003c/h2\u003e\n \u003cp\u003eAn average of 19,182,338.5 total reads high quality and non-redundant reads were retained for analysis. After trimming of the raw reads an average of 13,113,032.2 reads. Homo sapiens (GRCh38) genome downloaded from the Ensembl database and mo sapiens cDNA sequences from Ensembl was used as reference genome. An average of 1048 known miRNAs and 244 novel miRNAs were identified in all samples. Average total number of miRNA (known and novel) identified across each group along with average total number of miRNA detected with read count \u0026gt;=50 and read count \u0026gt;=10 is depicted in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eSequencing data report\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Total Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19797381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19453872.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19054232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18423869.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Trimmed Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16928692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17361656.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16618799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1542981.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of reads align to Genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of reads align to ncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnown miRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. total miRNA detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Total miRNA detected with read count \u0026ge;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Total miRNA detected with read count \u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovel miRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. total miRNA detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Total miRNA detected with read count \u0026ge;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. Total miRNA detected with read count \u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003eDifferential expression (DE) analysis\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec14\"\u003e\n \u003ch2\u003eKnown miRNAs\u003c/h2\u003e\n \u003cp\u003eVariation in the reads were normalized by library normalization method opted from DESeq library. DESeq calculated size factor, each read count is normalized by dividing with size factor. Mean normalized read counts of the samples in a given condition were used for DGE calculation. To understand the regulation of expression between the samples, log2fold of 2 fold change (FC) and p value \u0026le;0.05 was used as cutoff. MiRNAs \u0026ge;2 FC were considered as \u0026ldquo;UP\u0026rdquo; regulated, miRNAs \u0026le;-2 FC were considered as \u0026ldquo;DOWN\u0026rdquo; and those between 2 and 2 were flagged as \u0026ldquo;NEUTRAL\u0026rdquo;. Fold changes were calculated based of \u0026ldquo;Expression of treated sample/ Expression of control sample. A total of 103 known miRNAs were up-regulated, 44 in dead, 46 in severe and 13 in moderate patients group. Similarly, 118 known miRNAs were down-regulated, 39 in dead patients, 34 in severe patients and 45 in moderate patients group. Venn diagram (plotted using webtool \u0026lt; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/webtools/Venn/\u0026gt;\u003c/span\u003e\u003c/span\u003e) identified the common and unique DE miRNAs which were further categorized into those above \u0026gt; 2, \u0026gt;3 and \u0026gt;4 FC (Figure 2). The details of both common and unique miRNAs in the study groups as obtained from venn diagram has been detailed in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eVolcano plots were also generated to identify DE miRNAs in the three study groups (as depicted in Figure 2), using webtool VolcaNoseR (doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.05.07.082263\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eList of differentially expressed (DE) miRNAs in all study groups (Severe, Moderate and Mild COVID-19 Patients)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiRNAs identified\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon miRNAs amongst Study Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead, Moderate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-320a-5p, hsa-miR-377-5p ,hsa-miR-4800-3p, hsa-miR-4484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-20b-5p, hsa-miR-559, hsa-miR-4999-5p, hsa-miR-624-5p, hsa-miR-496, hsa-miR-1255b-2-3p, hsa-miR-15a-5p, hsa-miR-624-3p, hsa-miR-3613-5p, hsa-miR-20a-5p, hsa-miR-106a-5p, hsa-miR-126-3p, hsa-miR-590-3p, hsa-miR-31-5p, hsa-miR-32-5p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-6502-5p ,hsa-miR-34c-5p, hsa-miR-6868-3p, hsa-miR-5585-3p, hsa-miR-212-5p, hsa-miR-3614-3p, hsa-miR-1262, hsa-miR-2115-5p, hsa-miR-3162-3p, hsa-miR-4772-5p, hsa-miR-96-5p, hsa-miR-4668-3p, hsa-miR-618, hsa-miR-2355-3p, hsa-miR-143-3p, hsa-miR-203a-3p, hsa-miR-1179, hsa-miR-671-5p,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-6859-3p, hsa-miR-1248, hsa-miR-374b-3p, hsa-miR-449a, hsa-miR-3184-5p, hsa-miR-1911-5p, hsa-miR-411-5p, hsa-miR-153-3p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-203b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-548am-5p, hsa-miR-1277-5p, hsa-miR-548l ,hsa-miR-95-3p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-6793-5p, hsa-miR-885-5p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-548b-5p, hsa-miR-5698, hsa-miR-486-3p, hsa-miR-16-5p, hsa-miR-374a-5p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique to study groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-582-3p, hsa-miR-1908-5p, hsa-miR-125b-1-3p, hsa-miR-490-3p, hsa-miR-490-5p, hsa-miR-518c-3p, hsa-miR-6876-3p, hsa-miR-193b-5p, hsa-miR-1299, hsa-miR-3074-5p, hsa-miR-3158-3p, hsa-miR-320b, hsa-miR-3614-5p, hsa-miR-27a-5p, hsa-miR-125a-3p, hsa-miR-5189-5p, hsa-miR-760, hsa-miR-504-5p, hsa-miR-6787-3p, hsa-miR-3177-3p, hsa-miR-1256.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-551b-3p, hsa-miR-6751-5p, hsa-miR-147b-5p, hsa-miR-3683, hsa-miR-449b-5p, hsa-miR-548p, hsa-miR-218-5p, hsa-miR-369-3p, hsa-miR-3136-5p, hsa-miR-20a-3p, hsa-miR-107, hsa-miR-548aq-5p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-193a-3p, hsa-miR-4755-3p, hsa-miR-4707-3p, hsa-miR-4743-3p, hsa-miR-145-3p, hsa-miR-495-3p, hsa-miR-202-5p, hsa-miR-221-3p, hsa-miR-4642, hsa-miR-181a-5p, hsa-miR-3679-3p, hsa-miR-582-5p, hsa-miR-335-3p, hsa-miR-4659b-3p, hsa-miR-605-5p, hsa-miR-337-3p, hsa-miR-7107-3p, hsa-miR-377-3p, hsa-miR-1178-3p, hsa-miR-130a-5p, hsa-miR-433-3p, hsa-miR-6834-3p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-224-5p, hsa-miR-101-2-5p, hsa-miR-6825-5p, hsa-miR-6736-5p, hsa-miR-1278, hsa-miR-3912-3p.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-6507-5p, hsa-miR-3675-3p, hsa-miR-4700-5p, hsa-miR-205-5p, hsa-miR-3944-3p, hsa-miR-3611.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-196b-5p, hsa-miR-199a-3p,hsa-miR-32-3p, hsa-miR-548am-3p, hsa-let-7f-5p, hsa-miR-651-5p, hsa-miR-144-3p, hsa-miR-17-5p, hsa-miR-141-3p, hsa-miR-98-5p, hsa-miR-374c-3p, hsa-miR-493-5p, hsa-miR-26b-5p, hsa-let-7g-5p, hsa-miR-5583-3p, hsa-let-7a-5p, hsa-miR-548k, hsa-miR-21-5p, hsa-miR-545-3p, hsa-miR-454-3p, hsa-miR-1289.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAlso, 25 miRNAs were commonly up-regulated and 32 were commonly down-regulated in all three study groups in comparison to compared to healthy controls. Heat map in figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e depicts these miRNAs. The differential colour pattern shows the change in expression amongst the patient groups. Group wise principal component analysis (PCA) of common up-regulated and down-regulated miRNAs shows that the three study groups lying in different quadrants showing distinct pattern of expression.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003eNovel miRNAs\u003c/h2\u003e\n \u003cp\u003eBesides known miRNAs, DE was also observed in novel miRNA sequences. A total of 19 novel miRNAs were observed (log2 FC\u0026plusmn;2, p value\u0026le;0.05), out of which 8 were up-regulated miRNAs (2 in Dead, 5 in severe and 1 in moderate group) and 11 were down-regulated miRNAs (2 in Dead, 2 in Severe and 1 in Moderate group and 6 in All patients group). A novel miRNA sequence was commonly up-regulated in Dead and Severe group and another was commonly down-regulated in Moderate and All patients group (highlighted in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). MiRNA target prediction and downstream bioinformatic analysis for these novel miRNAs was not carried out.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eNovel miRNA sequences (log2 FC\u0026plusmn;2, p value\u0026le;0.05). Sequences shaded in grey colour are common amongst the two or more study groups\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2FoldChange\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead vs Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATTCTACAGTCCTACGAGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGACTGACCCAGAGTCTCAAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCAGCGTTGGTTCCCGTCTTGGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCTCTCCGCCACCTCCACCGCGGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere vs Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGACTGACCCAGAGTCTCAAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTATCCCACCACTGCCACCATTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACTGCCTTTTGATGACCGGGACGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCACTGACCCTCTTCTCTGCACAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTACCTTCACACCTCTGACTCTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTTGAGACTCTGGGTCAGTCTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGCCCTGAGACTTTTGCTCTAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate vs Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCGGCCGGGGAGAACTGCGCCTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGAGGTAGTAGGTTGTATAGTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients vs control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGTCCAGTCCTGGCCCCCTAGGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGAGGTAGTAGGTTGTATAGTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACTGACCCTCTTCTCTGCACAGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTCGTACCGTGAGTAATAATGCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAACGGAATCCCAAAAGCAGCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTTAGTGGCTCCCTCTGCCTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eReceiver-operating characteristic (ROC) analysis of DE miRNA\u003c/h2\u003e\n \u003cp\u003eSample wise expression data for each DE miRNA was subjected to receiver-operating characteristic (ROC) analysis using online webtool EPITOOLS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://epitools.ausvet.com.au/roccurves\u003c/span\u003e\u003c/span\u003e) (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This probability curve that plots true positive rate against false positive rate essentially separated the low quality data and further refined the list of DE miRNAs. All DE miRNAs were subjected to ROC analysis. Cut-off value of area under the ROC curve (AUC) was taken as 0.8 to select miRNAs for further analysis. MiRNAs having AUC values lower than 0.8 were not included for further analysis. 46 down-regulated and 37 up-regulated miRNAs showed AUC values \u0026gt;0.8. These miRNAs were then further subjected to target prediction followed by pathway and network analysis. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows ROC curves and sample wise box plots respectively, of 31 miRNAs which were finally selected as candidate miRNAs for COVID-19 infection severity and disease prognosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003eTarget prediction of miRNAs\u003c/h2\u003e\n \u003cp\u003eTarget gene prediction was done online using miRDB database [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Only those target genes were selected for further analysis that showed target score match of 80 percent. 3 miRNAs did not show any target genes above 80 percent match and hence were not included in downstream analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003ePathway and network analysis\u003c/h2\u003e\n \u003cp\u003ePathway analysis was done using PANTHER database [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Each miRNA target gene list was sought for genes involved in eighteen biological pathways putatively associated in COVID-19 pathogenesis. These included inflammation, B-cell and T-cell activation, interleukin signaling, blood coagulation, oxidative stress response etc. Those miRNAs showing over 45 target genes involved in these pathways were selected as candidate miRNAs significantly linked to COVID-19 prognosis (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Network analysis for the candidate miRNAs (up-regulated and down-regulated) was performed using online database, MiRnet to create MiRNA-mRNA target networks (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003ePanel of candidate miRNAs potentially involved in COVID-19 infection severity\u003c/h2\u003e\n \u003cp\u003ePresent analysis generated a panel of 31 miRNAs. These were 24 down-regulated and 7 up-regulated miRNAs, including both unique and common elements in the study groups.\u003c/p\u003e\n \u003cp\u003eHsa-miR-147b-5p and hsa-miR-107 was uniquely down-regulated whereas hsa-miR-1299 was uniquely up-regulated in Dead patients.\u003c/p\u003e\n \u003cp\u003eIn the Severe patients group, hsa-miR-224-5p was uniquely down-regulated and three miRNAs, hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p were uniquely up-regulated.\u003c/p\u003e\n \u003cp\u003eEleven miRNAs were uniquely down-regulated in Moderate patients group, including, hsa-miR-32-3p, hsa-miR-144-3p, hsa-miR-5583-3p, hsa-miR-26b-5p, hsa-miR-199a-3p, hsa-miR-98-5p, hsa-miR-548k, hsa-miR-493-5p, hsa-miR-454-3p, hsa-miR-141-3p and hsa-miR17-5p. Only one miRNA, hsa-miR-4700-5p was uniquely up-regulated in moderate patients.\u003c/p\u003e\n \u003cp\u003eTen miRNAs common to all three study groups were significantly down-regulated. These were hsa-miR, 31-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-590-3p, hsa-miR-15a-5p, hsa-miR-374b-3p, hsa-miR-16-5p, hsa-miR-1277-5p, hsa-miR-32-5p, hsa-miR-548I. Two miRNAs were commonly up-regulated, hsa-miR-4668-3p and hsa-miR-320a-5p.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eDisease association of 31 DE miRNAs identified in the present study\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssociation with COVID-19\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssociation with other Diseases\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-147b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTuberculosis [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], Alzheimer [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-224-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], Cystic Fibrosis [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-32-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDown-regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyocardial Injury [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], Ischemia [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-144-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduced by more than 1.3-fold [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyocardial Infarction [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-5583-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], Arthritis [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-26b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulator of ACE2 network [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac hypertrophy [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]; Cancer [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-199a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-viral miRNA, down-regulated [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-98-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulator of TMPRSS2 transcription [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-548k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], Myasthenia gravis [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-493-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-454-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDown-regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e] Alzheimer\u0026apos;s Disease [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-141-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirectly affect ACE2 / TMPRSS2 expression [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e], Atherosclerosis [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-17-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDown-regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyocardial infarction [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], Chronic kidney disease [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-31-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp-regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], Osteoarthritis [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-20b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary Heart disease [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-20a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-viral miRNA, down regulated [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetic cardimyopthy [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e], Nonalcoholic fatty liver disease [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-590-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARS-CoV-2 Spike transfected cells release a significant amount of exosomes loaded with miRNAs [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic Heart Failure [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e], Cardiomyocyte injury [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-15a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp-regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute myocardial infarction [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e], Endometriosis [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-374b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHost immune response [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystemic lupus erythematosus [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], Osteoporosis [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-16-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulator of ACE2 network [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiomyocyte injury [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], Atherosclerosis [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-1277-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsteoarthritis [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e], Parkinsons [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-32-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilencing of Tmprss2 with maximum gene suppression [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary artery disease [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e], Acute myocardial infarction [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-548l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp-regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-1299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e], Diabetes [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-4659b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-495-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatoid Arthritis [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e], Chronic Kidney disease [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-335-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-4700-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-4688-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAneurysm [\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e], Cancer [\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e])\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-320a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDown-Regulated in COVID-19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThrombosis [\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e], Chronic heart failure [\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePresent study generated a panel of 31 candidate miRNAs (24 Down-Regulated and 7 up-Regulated) which could be potentially involved in COVID-19 disease severity. These circulating candidate miRNAs were identified in Dead, Severe and Moderate COVID-19 patients with a distinct miRNA signature pattern in each study group. The criteria for selecting candidate DE miRNA after sequencing were extremely stringent in the current study. The selection was based on miRNA expression fold change \u0026plusmn;\u0026ge;2, p\u0026le;0.05, AUC\u0026ge;0.8 and number of target genes associated with selected pathways \u0026ge;45. Panel of biomarkers generated in the present study could be more Robust than depending upon a single biomarker.\u003c/p\u003e \u003cp\u003ePubmed search revealed that all the DE miRNAs identified in the present study, have established role in cancer, coronary artery disease, chronic heart failure, diabetic retinopathy, stroke, hypertension, thrombosis etc. except for up-regulated hsa-miR-4659b-3p (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Interestingly, 15 out of 24 down-regulated miRNAs and 2 out of 7 up-regulated have been associated with COVID-19 in the previous reports (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On the other hand, 9 down-regulated and 5 up-regulated miRNAs have been empanelled for COVID-19 association for the first time in this study.\u003c/p\u003e \u003cp\u003eSeveral recent studies have listed various miRNAs significantly linked to COVID-19 pathology and severity. In a recently published study consisting of 10 COVID-19 patients and 10 healthy subjects, researchers observed alteration of 55 miRNAs in COVID-19 patients. They stated that miR-31-5p, associated with inflammation, is most up-regulated among other 55miRNAs. Along with this, they revealed by supervised machine learning that miR-423-5p, miR-23a-3p and miR-195-5p are independently associated with COVID-19 patients (Farr et al. 2021). On the contrary, we report significant down-regulation of hsa-miR-31-5p in all COVID-19 patients in the present study. In another recent study, transcriptome analysis was done in COVID-19 patients and observed that miR-2392, a circulatory miRNA, has strong association COVID-19 infection. They designed and tested miRNA -based antiviral therapeutics that targeted miR-2392 by using \u003cem\u003ein vitro\u003c/em\u003e human, \u003cem\u003ein vivo\u003c/em\u003e hamster models and potential findings were noted [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is also well known that miRNA are potential regulator of endothelial homeostasis which are in turn associated with vascular diseases. Predominance of endothelial dysfunction and thrombus formation has been reported in various clinical studies in COVID-19 patients. Centa and co-workers, observed the levels of miRNAs, miR-26a-5p, miR-29b-3p and miR-34a-5p, which are associated with regulation of endothelial and inflammatory signaling pathways as well as viral diseases in lung biopsies of COVID-19 patients and healthy subjects [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Present study also identified hsa-miR-26b-5p significantly down-regulated in Moderate patients group. Another interesting finding was that hsa-miR-320a-5p, which has been previously linked to thrombosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was up-regulated in all patient groups indicating that COVID-19 pathology leads to thrombus formation. However, a contradicting finding by Deuker and co-workers suggest that hsa-miR-320 family is significantly down-Regulated in COVID-19 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA small cohort study was done in serum of 40 patients diagnosed with COVID-19 along with healthy subjects and expression pattern of 20 miRNAs was evaluated using Reverse transcription quantitative real-time PCR (RT-qPCR). Researchers observed that out of twenty miRNAs, only nine miRNAs were deferentially expressed in study groups. Expression levels of hsa-miR-190a and hsa-miR-203 were significantly elevated in COVID-19 patients with respect to healthy subjects. On the other side seven miRNAs (hsa-let-7d, hsa-miR-17, hsa-miR-34b, hsa-miR-93, hsa-miR-200b, hsa-miR-200c and hsa-miR-223) showed reduced level of expression in COVID-19 patients in comparison to healthy subjects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, hsa-miR-17 is significantly down-regulated in Moderate patients group in the current study, which is in concurrence with the findings of Li and co-workers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In their study, on 10 COVID-19 patients and four healthy subjects they found up-regulation of 35miRNAs as well as down Regulation of 38 miRNAs in patient group [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne observational and multicentric study was conducted in Spain from March to June 2020 consisting of 84 COVID-19 patients. They observed that differential expression of two miRNAs (hsa-miR-192-5p and hsa-miR-323a-3p) in ICU non survivors from survivors. They also identified three miRNAs (hsa-miR-148a-3p, hsa-miR-451a, hsa-miR-486-5p) that distinguishes between ICU and ward patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Present study also identified two down-regulated (hsa-miR-147b-5p and hsa-miR-107) and one up-regulated (hsa-miR-1299) miRNAs uniquely expressed in Dead patients group (non-survivors). These miRNAs have not been linked to COVID-19 so far! Also, four uniquely expressed miRNAs (1 down-regulated: hsa-miR-224-5p and 3 up-regulated: hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p) were expressed in Severe patients group. Interestingly, none of these four miRNAs have been directly linked to COVID-19 so far, however hsa-miR-335-5p has been associated with COVID-19 in a previous report in which bioinformatic analysis predicted that hsa-miR-335-5p is modulated by Spike and ACE together with histone deacetylate (HDAC) pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are few other clinical studies on miRNAs and COVID-19. In one of them, elevated levels of expression of miR-499, miR-21, miR-155 and miR-208-a were determined in serum of COVID-19 infected patients in comparison to healthy subject [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another clinical study was done in 150 COVID-19 patients and 50 controls along with evaluation of plasma miR-155 expression level using RT-qPCR. They demonstrated that there is increased miR-155 expression level in COVID-19 patients, severe and non-survivors in comparison to controls, moderate, survivors COVID-19 patients respectively [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf note, we identified three families of miRNA significantly down-regulated in COVID-19 patients. Hsa-miR-20a-5p and hsa-miR-20b-5p were commonly down-regulated in all patient groups. Also, hsa-miR-32-5p and hsa-miR-548I, both down-regulated in all patient groups and their family members, hsa-miR-32-3p and hsa-miR-548K both were down-regulated in moderate patients group.\u003c/p\u003e \u003cp\u003eThere is a complex interplay of host induced miRNAs and virus encoded miRNAs that determine the severity of infection and host defence response. Recent reports indicate that miRNAs could be promising tools for early diagnosis and treatment of COVID-19 infection. This alternative novel therapy with miRNA regulation could prevent disease progression and severe condition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The miRNAs identified in the present study could be used as potential biomarkers for identification of individuals who could be more prone to develop severe symptoms of COVID-19 as well as could be used as putative targets for developing treatment strategies for COVID-19 in future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe miRNA signatures identified in the present study could be used as novel tool for early prediction of vital status of COVID-19 patients during disease prognosis. Specific miRNA expression data may be useful in prediction of severity of COVID-19 infection in an individual. Validation of these miRNA targets with larger cohort could also be helpful in using them as therapeutic targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are extremely thankful to the technical staff of RGSSH and DIPAS for providing their untiring efforts and support in collection of CVOID-19 patient samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have received funding for this work from DIPAS, DRDO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest or financial disclosure related to this publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCorman, V. 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(2021). miR-320 accelerates chronic heart failure with cardiac fibrosis through activation of the IL6/STAT3 axis. \u003cem\u003eAging\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(18), 22516\u0026ndash;22527.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.203562\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"DIPAS, DRDO, INDIA","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, miRNA, Biomarker, Sequencing","lastPublishedDoi":"10.21203/rs.3.rs-1180630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1180630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOutbreak of COVID-19 pandemic in December 2019 affected millions of people globally. After substantial research, there is no specific and reliable biomarker available till date. Present study was designed to identify specific biomarkers to predict COVID-19 severity and tool for formulating treatment. A small cohort of subjects (n=43) were enrolled and categorized in four study groups; Dead (n=16), Severe (n=10) and Moderate (n=7) patients and healthy controls (n=10). Small RNA sequencing was done on Illumina platform after isolation of microRNA from peripheral blood. Differential expression (DE) of miRNA (patients groups compared to control) revealed 118 down-regulated and 103 up-regulated known miRNAs with fold change (FC) expression \u0026ge;2 folds and p\u0026le;0.05. DE miRNAs were then subjected to functional enrichment and network analysis. Bioinformatic analysis resulted in 31 miRNAs (24 Down-regulated; 7 up-regulated) significantly associated with COVID-19 having AUC\u0026gt;0.8 obtained from ROC curve. Seventeen out of 31 DE miRNAs have been linked to COVID-19 in previous studies. Three miRNAs, hsa-miR-147b-5p and hsa-miR-107 (down-regulated) and hsa-miR-1299 (up-regulated) showed significant unique DE in Dead patients. Another set of 4 miRNAs, hsa-miR-224-5p (down-regulated) and hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p were differentially up-regulated uniquely in Severe patients. Members of three miRNA families, hsa-miR-20, hsa-miR-32 and hsa-miR-548 were significantly down-regulated in all patients group in comparison to healthy controls. Thus a distinct miRNA expression profile was observed in Dead, Severe and Moderate COVID-19 patients. Present study suggests a panel of miRNAs which identified in COVID-19 patients and could be utilized as potential diagnostic biomarkers for predicting COVID-19 severity.\u003c/p\u003e","manuscriptTitle":"Evaluation of altered miRNA expression pattern to predict COVID-19 severity.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-17 22:39:32","doi":"10.21203/rs.3.rs-1180630/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4991a0b-8518-4744-8afd-094d398b0f51","owner":[],"postedDate":"December 17th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2021-12-17T22:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-17 22:39:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1180630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1180630","identity":"rs-1180630","version":["v1"]},"buildId":"PRX_uewTKcrkEM4liVh3c","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}