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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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Abstract  

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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http://www.aginganddisease.org/EN/10.14336/AD.2020.0428     OR
Virus type GenBank ID Location Month and year of isolates/sequenced Sequence Length
(Nucleotides)		 Number of miR Targets
SARS AY338175.1 Taiwan July 2003 29573 855
SARS AY348314.1 Taiwan July 2003 29573 855
SARS AY291451.1 Taiwan July 2003 29729 858
SARS NC_004718.3 Canada April 2003 29751 857
COVID -19 EPI_ISL_406798 Wuhan/China December 2019 29866 893
COVID -19 EPI_ISL_403929 Wuhan/China December 2019 29890 900
COVID -19 EPI_ISL_402121 Wuhan/China December 2019 29891 898
COVID -19 EPI_ISL_402123 Wuhan/China December 2019 29899 900
COVID -19 EPI_ISL_403931 Wuhan/China December 2019 29889 903
COVID -19 EPI_ISL_403930 Wuhan/China December 2019 29899 899
COVID -19 NC_045512.2 Wuhan (China) January 2020 29903 900
COVID -19 MT007544.1 Australia January 2020 29893 902
COVID -19 EPI_ISL_406862 Germany January 2020 29782 896
COVID -19 EPI_ISL_403962 Thailand January 2020 29848 897
COVID -19 EPI_ISL_412974 Italy January 2020 29903 900
COVID -19 EPI_ISL_407893 Australia January 2020 29782 898
COVID -19 EPI_ISL_406223 Arizona/USA January 2020 29882 900
COVID -19 EPI_ISL_406597 France January 2020 29809 901
COVID -19 EPI_ISL_420799 S. Korea February 2020 29882 901
COVID -19 EPI_ISL_413214 Australia February 2020 29782 899
COVID -19 EPI_ISL_419211 Isreal February 2020 29851 897
COVID -19 MT050493.1 India Fenruary 2020 29851 895
COVID -19 MT066176.1 Taiwan February 2020 29870 900
COVID -19 EPI_ISL_418001 Portugal March 2020 29763 895
COVID -19 EPI_ISL_417507 USA March 2020 29782 898
COVID -19 MT159718.1 USA (Cruise A) March 2020 29882 900
COVID -19 MT126808.1 Brazil March 2020 29876 900
COVID -19 EPI_ISL_428847 Singapore April 2020 29888 900
COVID -19 EPI_ISL_426565 Arizona/USA April 2020 29882 897
COVID -19 EPI_ISL_420144 Georgia April 2020 29833 900
COVID -19 EPI_ISL_427391 Turkey April 2020 29895 899
COVID -19 EPI_ISL_429223 Switzerland April 2020 29894 895
COVID -19 EPI_ISL_422601 Netherland April 2020 29775 902
Table 1  Details of SARS and COVID-19 isolates from different geographic locations, sequence length, and the number of human miRNA targets.
AY291451.1 NC_004718.3 AY338175.1 AY348314.1 MT007544.1 EPI_ISL_429223 EPI_ISL_418001 EPI_ISL_420144 EPI_ISL_428847 EPI_ISL_427391 EPI_ISL_426565 EPI_ISL_403931 EPI_ISL_422601 MT050493.1 EPI_ISL_413214 EPI_ISL_419211 EPI_ISL_417507 EPI_ISL_406862 EPI_ISL_420799 EPI_ISL_402123 EPI_ISL_406223 EPI_ISL_407893 EPI_ISL_406597 EPI_ISL_406798 MT066176.1 MT126808.1 MT159718.1 EPI_ISL_402121 EPI_ISL_412974 EPI_ISL_403930 EPI_ISL_403962 EPI_ISL_403929 NC_045512.2
AY291451.1 100 100 100 78.8 78.8 78.7 78.7 78.8 78.7 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8
NC_004718.3 100 100 100 78.8 78.8 78.7 78.7 78.8 78.7 78.8 78.8 78.7 78.8 78.8 78.8 78.7 78.8 78.8 78.8 78.8 78.8 78.7 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8 78.8
AY338175.1 100 100 100 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7
AY348314.1 100 100 100 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7 78.7
MT007544.1 78.8 78.8 78.7 78.7 99.9 99.9 99.9 99.9 99.8 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 100 99.9 99.9 99.9 99.9 99.9 100 100 100 100 100
EPI_ISL_429223 78.8 78.8 78.7 78.7 99.9 100 100 99.9 99.9 100 99.9 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_418001 78.7 78.7 78.7 78.7 99.9 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_420144 78.7 78.7 78.7 78.7 99.9 100 100 99.9 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_428847 78.8 78.8 78.7 78.7 99.9 99.9 99.9 99.9 99.9 99.9 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_427391 78.7 78.7 78.7 78.7 99.8 99.9 100 99.9 99.9 99.9 99.9 100 99.9 99.9 99.9 99.9 100 99.9 99.9 99.9 100 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9 99.9
EPI_ISL_426565 78.8 78.8 78.7 78.7 99.9 100 100 100 99.9 99.9 99.9 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_403931 78.8 78.8 78.7 78.7 99.9 99.9 100 100 100 99.9 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_422601 78.8 78.7 78.7 78.7 99.9 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
MT050493.1 78.8 78.8 78.7 78.7 99.9 99.9 100 100 100 99.9 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_413214 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_419211 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_417507 78.8 78.7 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_406862 78.8 78.8 78.7 78.7 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_420799 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_402123 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_406223 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_407893 78.8 78.8 78.7 78.7 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_406597 78.8 78.7 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_406798 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
MT066176.1 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
MT126808.1 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
MT159718.1 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_402121 78.8 78.8 78.7 78.7 99.9 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_412974 78.8 78.8 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_403930 78.8 78.8 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_403962 78.8 78.8 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
EPI_ISL_403929 78.8 78.8 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
NC_045512.2 78.8 78.8 78.7 78.7 100 100 100 100 100 99.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
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).
miRNAs Target Score Number of Sites and Seed locations of miRNAs and COVID-19 genome binding sites
miR-15b-5p
miR-15a-5p		 99 16 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-5p 97 15 SITES (2733, 4025, 4531, 6783, 7774, 9508, 10962, 11641, 11672, 12950, 13644, 20196, 21886, 23026, 25807)
miR-548d-3p 94 13 SITES (6960, 7245, 7272, 8927, 11540, 13459, 15517, 15814, 18367, 21100, 22217, 22583, 26653)
miR-409-3p 96 12 SITES (4990, 8386, 11785, 12403, 12525, 17285, 19760, 19803, 20759, 20829, 28767, 29694)
miR-30b-5p 95 14 SITES (3451, 4974, 7939, 9354, 10426, 11657, 16863, 19567, 19710, 20069, 20360, 26729, 27955, 28140)
miR-505-3p 95 11 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. No Important findings on human miRNAs targeting Coronavirus
1 848 miRNAs are common in SARS
2 873 miRNAs are common inCOVID-19
3 558 miRNAs are common between SARS and COVID-19
4 315 miRNAs are unique to COVID-19
5 290 miRNAs are unique to SARS
6 10 COVID-19 isolates have some unique miR targets
7 MT050493.1 (India): 1 unique miRNA (hsa-miR-449c-3p)
8 MT007544.1 (Australia): 2 unique miRNAs (hsa-miR-4538, hsa-miR-4453)
9 EPI_ISL_402121 (Wuhan/China): 1 unique miRNA (hsa-miR-5590-5p)
10 EPI_ISL_402123 (Wuhan/China): 1 unique miRNA (hsa-miR-106a-3p)
11 EPI_ISL_420799 (South Korea): 1 unique miRNA (hsa-miR-4641)
12 EPI_ISL_427391 (Turkey): 1 unique miRNA (hsa-miR-496)
13 EPI_ISL_429223 (Switzerland): 1 unique miRNA (hsa-miR-146b-3p)
14 EPI_ISL_403931 (Wuhan): 4 unique miRNAs (hsa-miR-4474-3p, hsa-miR-6762-3p, hsa-miR-10401-5p, hsa-miR-4304)
15 EPI_ISL_413214 (Australia): 4 unique miRNAs (hsa-miR-5088-5p, hsa-miR-9900, hsa-miR-3677-5p, hsa-miR-892c-5p)
16 EPI_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 pathway p-value #genes #miRNAs
Proteoglycans in cancer 5.75E-08 145 76
Hippo signaling pathway 1.04E-07 113 74
Arrhythmogenic right ventricular cardiomyopathy (ARVC) 6.54E-07 57 72
Adherens junction 6.54E-07 62 75
Renal cell carcinoma 2.40E-06 56 74
Wnt signaling pathway 2.99E-06 107 76
Fatty acid biosynthesis 1.25E-05 9 50
ECM-receptor interaction 1.25E-05 56 70
Axon guidance 1.58E-05 94 75
FoxO signaling pathway 4.68E-05 100 76
Ubiquitin mediated proteolysis 5.75E-05 102 76
Pathways in cancer 6.76E-05 275 76
ErbB signaling pathway 8.02E-05 66 75
Pancreatic cancer 0.000165 53 73
TGF-beta signaling pathway 0.000234 57 73
Focal adhesion 0.000234 147 74
Rap1 signaling pathway 0.000234 148 76
Gap junction 0.000753 64 76
Long-term depression 0.000962 45 73
N-Glycan biosynthesis 0.001119 33 69
Prion diseases 0.001166 20 66
Endocytosis 0.001469 140 75
Fatty acid metabolism 0.001547 31 69
Endometrial cancer 0.001567 41 72
Signaling pathways regulating pluripotency of stem cells 0.001567 99 76
Prostate cancer 0.001769 66 75
Colorectal cancer 0.002458 49 72
Cell cycle 0.002703 89 73
PI3K-Akt signaling pathway 0.002703 225 76
Melanoma 0.00405 54 73
Circadian rhythm 0.00591 26 70
Prolactin signaling pathway 0.006364 50 75
Adrenergic signaling in cardiomyocytes 0.006716 97 77
Glycosaminoglycan biosynthesis - heparan sulfate / heparin 0.006964 17 62
Dorso-ventral axis formation 0.011682 23 73
AMPK signaling pathway 0.012171 87 75
Glioma 0.012308 45 72
Tight junction 0.012616 98 76
Thyroid hormone signaling pathway 0.01495 79 72
Morphine addiction 0.01495 63 73
Oocyte meiosis 0.01495 79 75
Ras signaling pathway 0.01495 145 76
Lysine degradation 0.016507 33 66
Amphetamine addiction 0.016687 45 72
Sphingolipid signaling pathway 0.016687 79 76
Glutamatergic synapse 0.016687 77 76
mRNA surveillance pathway 0.01713 64 74
RNA transport 0.01833 112 75
MAPK signaling pathway 0.018745 166 77
Chronic myeloid leukemia 0.01925 51 74
Estrogen signaling pathway 0.022066 65 76
GABAergic synapse 0.023522 59 73
p53 signaling pathway 0.026352 48 73
Biosynthesis of unsaturated fatty acids 0.027342 15 49
mTOR signaling pathway 0.031797 45 70
Regulation of actin cytoskeleton 0.037298 139 75
Protein processing in endoplasmic reticulum 0.038084 112 74
cAMP signaling pathway 0.038084 130 76
Oxytocin signaling pathway 0.038084 104 77
Glycosaminoglycan biosynthesis - keratan sulfate 0.039424 12 23
Central carbon metabolism in cancer 0.04664 46 70
Melanogenesis 0.04852 68 76
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 Category p-value #genes #miRNAs
organelle 1.26E-49 980 64
ion binding 5.53E-28 611 64
cellular nitrogen compound metabolic process 1.82E-23 474 63
biosynthetic process 1.36E-13 388 42
neurotrophin TRK receptor signaling pathway 7.06E-13 44 44
protein binding transcription factor activity 1.83E-12 75 29
Fc-epsilon receptor signaling pathway 5.76E-12 32 24
protein complex 6.82E-11 385 64
gene expression 4.82E-10 70 35
cellular protein modification process 7.10E-10 232 41
molecular_function 7.10E-10 1552 66
extracellular matrix disassembly 1.72E-09 26 14
viral process 1.82E-09 60 49
symbiosis, encompassing mutualism through parasitism 1.82E-09 66 49
small molecule metabolic process 4.04E-09 229 57
catabolic process 1.70E-08 197 58
collagen catabolic process 3.52E-08 22 12
cellular component assembly 5.85E-08 141 36
cellular_component 7.90E-08 1559 66
macromolecular complex assembly 1.77E-07 101 36
blood coagulation 8.36E-07 55 26
nucleic acid binding transcription factor activity 1.97E-06 107 38
cytosol 3.58E-06 267 57
protein complex assembly 4.08E-06 88 48
epidermal growth factor receptor signaling pathway 1.64E-05 31 23
enzyme binding 1.92E-05 130 51
extracellular matrix organization 2.18E-05 51 21
nucleoplasm 2.49E-05 122 56
cellular protein metabolic process 3.33E-05 49 29
xenobiotic metabolic process 4.07E-05 23 21
immune system process 4.82E-05 160 36
nucleobase-containing compound catabolic process 6.69E-05 92 53
endoplasmic reticulum lumen 0.000154305 29 16
response to stress 0.000159934 210 39
innate immune response 0.000224462 80 28
microtubule organizing center 0.000453748 56 43
Fc-gamma receptor signaling pathway involved in phagocytosis 0.00093232 12 17
toll-like receptor TLR1:TLR2 signaling pathway 0.001615504 11 15
toll-like receptor TLR6:TLR2 signaling pathway 0.001615504 11 15
fibroblast growth factor receptor signaling pathway 0.001615504 26 23
mitotic cell cycle 0.001685088 39 45
glutathione derivative biosynthetic process 0.001906053 7 12
DNA metabolic process 0.00191818 79 34
biological_process 0.00191818 1484 66
phosphatidylinositol-mediated signaling 0.002737027 20 22
toll-like receptor 2 signaling pathway 0.005968204 12 17
cytoskeleton-dependent intracellular transport 0.007263134 17 15
toll-like receptor 4 signaling pathway 0.007263134 14 17
membrane organization 0.007346668 56 45
cellular response to jasmonic acid stimulus 0.007563711 3 1
cell motility 0.008091976 60 31
G2/M transition of mitotic cell cycle 0.010059905 20 36
cell-cell signaling 0.010959215 65 31
platelet degranulation 0.011941199 11 15
protein N-linked glycosylation via asparagine 0.012823473 14 14
homeostatic process 0.013223078 81 27
post-translational protein modification 0.013916886 18 20
toll-like receptor 10 signaling pathway 0.015857167 9 14
cell death 0.015857167 85 25
substrate-dependent cell migration, cell extension 0.018717628 5 9
nervous system development 0.019812789 51 26
toll-like receptor 9 signaling pathway 0.021790835 10 16
RNA binding 0.021790835 168 42
platelet activation 0.023223454 22 20
extracellular matrix structural constituent 0.034117778 16 4
transcription coactivator activity 0.034117778 41 23
cytoskeletal protein binding 0.036370846 71 34
toll-like receptor 5 signaling pathway 0.039177086 9 14
axon guidance 0.040371436 49 21
cAMP metabolic process 0.043778349 3 9
TRIF-dependent toll-like receptor signaling pathway 0.045227284 9 14
Table 6  Human miRNAs targeting the COVID-19 genome regulating GO pathway.
miRNA Decrease Expression in age related diseases (Human) Reference
miR-15b-5p Coronary Artery Disease Zhu et al 2017 [37]
miR-15a-5p Kidney disease Shang et al 2019 [38]
miR-548c-5p Colorectal Cancer Peng et al 2018 [49]
miR-548d-3p Osteosarcoma Chen et al 2019 [50]
miR-409-3p Osteosarcoma Wu et al 2019 [51]
miR-30b-5p Plasma (Aging) Hatse et al 2014 [52]
miR-505-3p Prostate cancer Tang et al 2019 [53]
miR-520c-3p Obesity/diabetes Ortega et al 2013 [39]
miR-30e-3p Myocardial Injury Wang et al 2017 [40]
miR-23c Hepatocellular carcinoma Zhang et al 2018 [41]
miR-30d-5p Non-small cell lung cancer Gao et al, 2018 [42]
miR-4684-3p Colorectal cancer Wu et al, 2015 [43]
miR-518a-5p Gastrointestinal tumors Shi 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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