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Aging and disease    2020, Vol. 11 Issue (3) : 509-522     DOI: 10.14336/AD.2020.0428
Orginal Article |
COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile
Fulzele Sadanand1,2,*, Sahay Bikash3, Yusufu Ibrahim1, Lee Tae Jin4, Sharma Ashok4, Kolhe Ravindra5, Isales Carlos M1,2
1Department of Medicine, Augusta University, Augusta, GA, USA.
2Center for Healthy Aging, Augusta University, Augusta, GA, USA.
3Department of Infectious Diseases and Immunology, University of Florida, Gainesville, FL, USA.
4Center for Biotechnology and Genomic Medicine, Augusta University, Augusta, GA 30912, USA.
5Departments of Pathology, Augusta University, Augusta, GA 30912, USA
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The World health organization (WHO) declared Coronavirus disease 2019 (COVID-19) a global pandemic and a severe public health crisis. Drastic measures to combat COVID-19 are warranted due to its contagiousness and higher mortality rates, specifically in the aged patient population. At the current stage, due to the lack of effective treatment strategies for COVID-19 innovative approaches need to be considered. It is well known that host cellular miRNAs can directly target both viral 3'UTR and coding region of the viral genome to induce the antiviral effect. In this study, we did in silico analysis of human miRNAs targeting SARS (4 isolates) and COVID-19 (29 recent isolates from different regions) genome and correlated our findings with aging and underlying conditions. We found 848 common miRNAs targeting the SARS genome and 873 common microRNAs targeting the COVID-19 genome. Out of a total of 848 miRNAs from SARS, only 558 commonly present in all COVID-19 isolates. Interestingly, 315 miRNAs are unique for COVID-19 isolates and 290 miRNAs unique to SARS. We also noted that out of 29 COVID-19 isolates, 19 isolates have identical miRNA targets. The COVID-19 isolates, Netherland (EPI_ISL_422601), Australia (EPI_ISL_413214), and Wuhan (EPI_ISL_403931) showed six, four, and four unique miRNAs targets, respectively. Furthermore, GO, and KEGG pathway analysis showed that COVID-19 targeting human miRNAs involved in various age-related signaling and diseases. Recent studies also suggested that some of the human miRNAs targeting COVID-19 decreased with aging and underlying conditions. GO and KEGG identified impaired signaling pathway may be due to low abundance miRNA which might be one of the contributing factors for the increasing severity and mortality in aged individuals and with other underlying conditions. Further, in vitro and in vivo studies are needed to validate some of these targets and identify potential therapeutic targets.

Keywords Coronavirus      microRNAs      aging     
Corresponding Authors: Fulzele Sadanand   
About author:

These authors shared first-authorship.

Just Accepted Date: 29 April 2020   Issue Date: 13 May 2020
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Fulzele Sadanand
Sahay Bikash
Yusufu Ibrahim
Lee Tae Jin
Sharma Ashok
Kolhe Ravindra
Isales Carlos M
Cite this article:   
Fulzele Sadanand,Sahay Bikash,Yusufu Ibrahim, et al. COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile[J]. Aging and disease, 2020, 11(3): 509-522.
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Virus typeGenBank IDLocationMonth and year of isolates/sequencedSequence Length
Number of miR Targets
SARSAY338175.1TaiwanJuly 200329573855
SARSAY348314.1TaiwanJuly 200329573855
SARSAY291451.1TaiwanJuly 200329729858
SARSNC_004718.3CanadaApril 200329751857
COVID -19EPI_ISL_406798Wuhan/ChinaDecember 201929866893
COVID -19EPI_ISL_403929Wuhan/ChinaDecember 201929890900
COVID -19EPI_ISL_402121Wuhan/ChinaDecember 201929891898
COVID -19EPI_ISL_402123Wuhan/ChinaDecember 201929899900
COVID -19EPI_ISL_403931Wuhan/ChinaDecember 201929889903
COVID -19EPI_ISL_403930Wuhan/ChinaDecember 201929899899
COVID -19NC_045512.2Wuhan (China)January 202029903900
COVID -19MT007544.1AustraliaJanuary 202029893902
COVID -19EPI_ISL_406862GermanyJanuary 202029782896
COVID -19EPI_ISL_403962ThailandJanuary 202029848897
COVID -19EPI_ISL_412974ItalyJanuary 202029903900
COVID -19EPI_ISL_407893AustraliaJanuary 202029782898
COVID -19EPI_ISL_406223Arizona/USAJanuary 202029882900
COVID -19EPI_ISL_406597FranceJanuary 202029809901
COVID -19EPI_ISL_420799S. KoreaFebruary 202029882901
COVID -19EPI_ISL_413214AustraliaFebruary 202029782899
COVID -19EPI_ISL_419211IsrealFebruary 202029851897
COVID -19MT050493.1IndiaFenruary 202029851895
COVID -19MT066176.1TaiwanFebruary 202029870900
COVID -19EPI_ISL_418001PortugalMarch 202029763895
COVID -19EPI_ISL_417507USAMarch 202029782898
COVID -19MT159718.1USA (Cruise A)March 202029882900
COVID -19MT126808.1BrazilMarch 202029876900
COVID -19EPI_ISL_428847SingaporeApril 202029888900
COVID -19EPI_ISL_426565Arizona/USAApril 202029882897
COVID -19EPI_ISL_420144GeorgiaApril 202029833900
COVID -19EPI_ISL_427391TurkeyApril 202029895899
COVID -19EPI_ISL_429223SwitzerlandApril 202029894895
COVID -19EPI_ISL_422601NetherlandApril 202029775902
Table 1  Details of SARS and COVID-19 isolates from different geographic locations, sequence length, and the number of human miRNA targets.
Table 2  Sequence homology between the SARS and COVID-19 isolates from different geographic locations.
Figure 1.  Phylogenetic analysis of Coronavirus isolates from different geographic locations. The phylogenetic analysis shows sequence relatedness among COVID-19 isolates (blue) and SARS isolates (black).
miRNAsTarget ScoreNumber of Sites and Seed locations of miRNAs and COVID-19 genome binding sites
9916 SITES (3163, 5384, 8458, 8614, 13090, 14562, 14781, 19857, 24094, 24634, 25683, 26723, 28921, 28935, 28938, 29023)
(Note: miR-15b-5p, and miR-15a-5p have same target site)
miR-548c-5p9715 SITES (2733, 4025, 4531, 6783, 7774, 9508, 10962, 11641, 11672, 12950, 13644, 20196, 21886, 23026, 25807)
miR-548d-3p9413 SITES (6960, 7245, 7272, 8927, 11540, 13459, 15517, 15814, 18367, 21100, 22217, 22583, 26653)
miR-409-3p9612 SITES (4990, 8386, 11785, 12403, 12525, 17285, 19760, 19803, 20759, 20829, 28767, 29694)
miR-30b-5p9514 SITES (3451, 4974, 7939, 9354, 10426, 11657, 16863, 19567, 19710, 20069, 20360, 26729, 27955, 28140)
miR-505-3p9511 SITES (152, 8488, 10609, 10792, 14208, 15648, 17580, 18123, 18156, 18612, 18906)
Table 3  List of human miRNAs with higher target score (above 94), the number of binding sites, and miRNAs seed binding site on COVID-19 isolates.
Serial. NoImportant findings on human miRNAs targeting Coronavirus
1848 miRNAs are common in SARS
2873 miRNAs are common inCOVID-19
3558 miRNAs are common between SARS and COVID-19
4315 miRNAs are unique to COVID-19
5290 miRNAs are unique to SARS
610 COVID-19 isolates have some unique miR targets
7MT050493.1 (India): 1 unique miRNA (hsa-miR-449c-3p)
8MT007544.1 (Australia): 2 unique miRNAs (hsa-miR-4538, hsa-miR-4453)
9EPI_ISL_402121 (Wuhan/China): 1 unique miRNA (hsa-miR-5590-5p)
10EPI_ISL_402123 (Wuhan/China): 1 unique miRNA (hsa-miR-106a-3p)
11EPI_ISL_420799 (South Korea): 1 unique miRNA (hsa-miR-4641)
12EPI_ISL_427391 (Turkey): 1 unique miRNA (hsa-miR-496)
13EPI_ISL_429223 (Switzerland): 1 unique miRNA (hsa-miR-146b-3p)
14EPI_ISL_403931 (Wuhan): 4 unique miRNAs (hsa-miR-4474-3p, hsa-miR-6762-3p, hsa-miR-10401-5p, hsa-miR-4304)
15EPI_ISL_413214 (Australia): 4 unique miRNAs (hsa-miR-5088-5p, hsa-miR-9900, hsa-miR-3677-5p, hsa-miR-892c-5p)
16EPI_ISL_422601 (Netherland): 6 unique miRNAs (hsa-miR-4666a-3p, hsa-miR-98-3p, hsa-let-7b-3p, hsa-let-7a-3p, hsa-miR-381-3p, hsa-miR-300)
Table 4  Summary of important findings on human miRNAs targeting SARS and COVID-19 genome.
KEGG pathwayp-value#genes#miRNAs
Proteoglycans in cancer5.75E-0814576
Hippo signaling pathway1.04E-0711374
Arrhythmogenic right ventricular cardiomyopathy (ARVC)6.54E-075772
Adherens junction6.54E-076275
Renal cell carcinoma2.40E-065674
Wnt signaling pathway2.99E-0610776
Fatty acid biosynthesis1.25E-05950
ECM-receptor interaction1.25E-055670
Axon guidance1.58E-059475
FoxO signaling pathway4.68E-0510076
Ubiquitin mediated proteolysis5.75E-0510276
Pathways in cancer6.76E-0527576
ErbB signaling pathway8.02E-056675
Pancreatic cancer0.0001655373
TGF-beta signaling pathway0.0002345773
Focal adhesion0.00023414774
Rap1 signaling pathway0.00023414876
Gap junction0.0007536476
Long-term depression0.0009624573
N-Glycan biosynthesis0.0011193369
Prion diseases0.0011662066
Fatty acid metabolism0.0015473169
Endometrial cancer0.0015674172
Signaling pathways regulating pluripotency of stem cells0.0015679976
Prostate cancer0.0017696675
Colorectal cancer0.0024584972
Cell cycle0.0027038973
PI3K-Akt signaling pathway0.00270322576
Circadian rhythm0.005912670
Prolactin signaling pathway0.0063645075
Adrenergic signaling in cardiomyocytes0.0067169777
Glycosaminoglycan biosynthesis - heparan sulfate / heparin0.0069641762
Dorso-ventral axis formation0.0116822373
AMPK signaling pathway0.0121718775
Tight junction0.0126169876
Thyroid hormone signaling pathway0.014957972
Morphine addiction0.014956373
Oocyte meiosis0.014957975
Ras signaling pathway0.0149514576
Lysine degradation0.0165073366
Amphetamine addiction0.0166874572
Sphingolipid signaling pathway0.0166877976
Glutamatergic synapse0.0166877776
mRNA surveillance pathway0.017136474
RNA transport0.0183311275
MAPK signaling pathway0.01874516677
Chronic myeloid leukemia0.019255174
Estrogen signaling pathway0.0220666576
GABAergic synapse0.0235225973
p53 signaling pathway0.0263524873
Biosynthesis of unsaturated fatty acids0.0273421549
mTOR signaling pathway0.0317974570
Regulation of actin cytoskeleton0.03729813975
Protein processing in endoplasmic reticulum0.03808411274
cAMP signaling pathway0.03808413076
Oxytocin signaling pathway0.03808410477
Glycosaminoglycan biosynthesis - keratan sulfate0.0394241223
Central carbon metabolism in cancer0.046644670
Table 5  Human miRNAs targeting the COVID-19 genome regulating KEGG pathway.
Figure 2.  Common and different human miRNAs targeting SARS and COVID-19 isolates from different geographic locations.
GO Categoryp-value#genes#miRNAs
ion binding5.53E-2861164
cellular nitrogen compound metabolic process1.82E-2347463
biosynthetic process1.36E-1338842
neurotrophin TRK receptor signaling pathway7.06E-134444
protein binding transcription factor activity1.83E-127529
Fc-epsilon receptor signaling pathway5.76E-123224
protein complex6.82E-1138564
gene expression4.82E-107035
cellular protein modification process7.10E-1023241
extracellular matrix disassembly1.72E-092614
viral process1.82E-096049
symbiosis, encompassing mutualism through parasitism1.82E-096649
small molecule metabolic process4.04E-0922957
catabolic process1.70E-0819758
collagen catabolic process3.52E-082212
cellular component assembly5.85E-0814136
macromolecular complex assembly1.77E-0710136
blood coagulation8.36E-075526
nucleic acid binding transcription factor activity1.97E-0610738
protein complex assembly4.08E-068848
epidermal growth factor receptor signaling pathway1.64E-053123
enzyme binding1.92E-0513051
extracellular matrix organization2.18E-055121
cellular protein metabolic process3.33E-054929
xenobiotic metabolic process4.07E-052321
immune system process4.82E-0516036
nucleobase-containing compound catabolic process6.69E-059253
endoplasmic reticulum lumen0.0001543052916
response to stress0.00015993421039
innate immune response0.0002244628028
microtubule organizing center0.0004537485643
Fc-gamma receptor signaling pathway involved in phagocytosis0.000932321217
toll-like receptor TLR1:TLR2 signaling pathway0.0016155041115
toll-like receptor TLR6:TLR2 signaling pathway0.0016155041115
fibroblast growth factor receptor signaling pathway0.0016155042623
mitotic cell cycle0.0016850883945
glutathione derivative biosynthetic process0.001906053712
DNA metabolic process0.001918187934
phosphatidylinositol-mediated signaling0.0027370272022
toll-like receptor 2 signaling pathway0.0059682041217
cytoskeleton-dependent intracellular transport0.0072631341715
toll-like receptor 4 signaling pathway0.0072631341417
membrane organization0.0073466685645
cellular response to jasmonic acid stimulus0.00756371131
cell motility0.0080919766031
G2/M transition of mitotic cell cycle0.0100599052036
cell-cell signaling0.0109592156531
platelet degranulation0.0119411991115
protein N-linked glycosylation via asparagine0.0128234731414
homeostatic process0.0132230788127
post-translational protein modification0.0139168861820
toll-like receptor 10 signaling pathway0.015857167914
cell death0.0158571678525
substrate-dependent cell migration, cell extension0.01871762859
nervous system development0.0198127895126
toll-like receptor 9 signaling pathway0.0217908351016
RNA binding0.02179083516842
platelet activation0.0232234542220
extracellular matrix structural constituent0.034117778164
transcription coactivator activity0.0341177784123
cytoskeletal protein binding0.0363708467134
toll-like receptor 5 signaling pathway0.039177086914
axon guidance0.0403714364921
cAMP metabolic process0.04377834939
TRIF-dependent toll-like receptor signaling pathway0.045227284914
Table 6  Human miRNAs targeting the COVID-19 genome regulating GO pathway.
miRNADecrease Expression in age related diseases (Human)Reference
miR-15b-5pCoronary Artery DiseaseZhu et al 2017 [37]
miR-15a-5pKidney diseaseShang et al 2019 [38]
miR-548c-5pColorectal CancerPeng et al 2018 [49]
miR-548d-3pOsteosarcomaChen et al 2019 [50]
miR-409-3pOsteosarcomaWu et al 2019 [51]
miR-30b-5pPlasma (Aging)Hatse et al 2014 [52]
miR-505-3pProstate cancerTang et al 2019 [53]
miR-520c-3pObesity/diabetesOrtega et al 2013 [39]
miR-30e-3pMyocardial InjuryWang et al 2017 [40]
miR-23cHepatocellular carcinomaZhang et al 2018 [41]
miR-30d-5pNon-small cell lung cancerGao et al, 2018 [42]
miR-4684-3pColorectal cancerWu et al, 2015 [43]
miR-518a-5pGastrointestinal tumorsShi et al, 2016 [44]
Table 7  List of selected human miRNAs targeting the COVID-19 genome down-regulated with age and underlying conditions.
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