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. 2020 Apr 28;11(3):509–522. doi: 10.14336/AD.2020.0428

COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile

Sadanand Fulzele 1,2,*, Bikash Sahay 3, Ibrahim Yusufu 1, Tae Jin Lee 4, Ashok Sharma 4, Ravindra Kolhe 5, Carlos M Isales 1,2
PMCID: PMC7220294  PMID: 32489698

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

Novel Coronavirus was identified near the end of 2019, presenting with a spectrum of symptoms including febrile illness, cough, severe pneumonia, and in some patients, death. The pathogen is now widely called Coronavirus disease 2019 (COVID-19) and has rapidly turned into a global pandemic since originally being identified in Wuhan, China. Coronaviridae is the family of single-stranded (+ssRNA), pleomorphic, enveloped RNA viruses that consists of the Coronavirus genus [1]. COVID-19 is also referred to as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to the genomic and symptomatic similarities with the SARS-CoV that caused an epidemic in 2002-2004 [2]. SARS-CoV-2 is predominantly spread by person-to-person contact via virally loaded respiratory droplets [3]. However, the transmission may also occur by touching contaminated surfaces and subsequently touching one's eyes, nose, or mouth [4, 5]. The incubation period, the period before the first symptoms present itself, is on average of 4-5 days, with 95% presenting within 12.5 days [6,7].

The World Health Organization (WHO) reported 2,920,905 confirmed infected, and 203,269 COVID-19 related deaths globally as of April 25th, 2020 [8]. Drastic measures to combat SARS-CoV-2 are warranted due to its contagiousness, viability, and higher death toll compared to its predecessor SARS-CoV. Although SARS-CoV had a significantly higher case-fatality (around 10%) [9], the virus was not viable enough to remain in the human population and never spread in the United States and other countries like SARS-CoV-2 [10]. SARS-CoV-2, on the other hand, appears to rapidly spread, which is contributing to its global spread and a significantly higher number of cases.

Severe and critical cases of COVID-19 disproportionately affect middle-aged and older/aged populations, with increased mortality in aged adults [11]. Illness severity ranges from asymptomatic, mild, severe, and critical. Mild illness is characterized as no to mild pneumonia [11]. The severe disease presents with dyspnea, hypoxia, or >50% lung involvement, and critical illness occurs when there is respiratory failure, shock, or multiorgan failure [11]. The Centers for Disease Control and Prevention (CDC) and the WHO estimate the mortality rates of SARS-CoV-2 to be around 3.4% and case fatality rates (CFR) may increase up to 10-27 % in individuals 80 years old or older [8, 12]. SARS-CoV-2 is a novel virus, and very little is known about it. In such a scenario, it is crucial to understand the COVID-19 pathobiology to identify the innovative treatment strategy. Our laboratory and others have reported that miRNAs play a critical role in age-related complications [13-16].

MiRNAs play a vital role in the pathogenesis of various diseases, including viral infections, disease progression, and inhibition [17-23]. MicroRNAs are small noncoding RNAs, bind to 3'UTR of mRNA, and inhibit translation or induce degradation of mRNAs [19, 21, 23]. Recent studies suggested that host cellular miRNAs can directly target both viral 3’UTR and coding region of the viral genome to induce antiviral effect [19, 21, 23]. For example, the number of groups previously reported that host miRNAs (miR-323, miR-491, miR-485, miR-654, and miR-3145) bind to influenza PB1 gene coding region, degrade RNA and inhibit viral translation and reduce the accumulation of viral particles [19, 24, 25]. Furthermore, the host cellular miRNA-29a inhibit Human immunodeficiency virus type 1 (HIV-1) nef protein expression and thus, inhibit viral replication [26]. On the contrary, some groups also suggested the positive effect of host miRNAs on viral replication. For example, miR-122 binding to 3’ and 5’ UTR of hepatotropic virus RNA and increase viral RNA stability leads to viral propagation [18, 20, 22]. Based on the above reports, we did in silico analysis of miRNAs targeting SARS and COVID-19 (recent isolates from different regions) to understand the pathophysiology and identify novel therapeutic targets.

MATERIALS AND METHODS

Viral genome sequence retrieval, homology, and phylogenetic analyses

The complete genome sequences of the SARS and COVID-19 isolates were retrieved from the GenBank database. We retrieved four SARS and 29 COVID-19 sequences from NCBI and GISAID for for homology and phylogenetic analysis. Details of sequence identification are summarizing in Table 1. The sequences were aligned using the Multiple Sequence Alignment tool at Clustal Omega 1.2.3 on Geneious Prime 2020.1.1. The phylogenetic alignment tree generated using the neighbor-joining method.

Table 1.

Details of SARS and COVID-19 isolates from different geographic locations, sequence length, and the number of human miRNA targets.

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
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COVID-19 genome and human MiRNA target analysis

As previously mentioned above, miRNAs are known to target 3'UTR and coding sequences and prevent mRNA translation or degrade RNA. Keeping this in consideration, we used the whole viral genome sequence for miRNA target analysis. We used miRDB (http://www.mirdb.org/) software to identify novel human miRNAs targeting the COVID-19 viral genome [27, 28]. Furthermore, we used this data to correlate with the existing literature.

GO, and KEGG pathway analysis

Gene Ontology (GO) and KEGG signaling pathway analyses were performed on human microRNAs targeting the COVID-19 genome using DIANA-miRPath v 3.0 (http://diana.imis.athena-innovation.gr/DianaTools/index.php) [29]. We used miRNAs with a target score above 90 because these miRNAs have high probability of being real targets [27, 28].

RESULTS

Viral genome sequence homology and phylogenetic analyses

The sequences of the COVID-19 isolates have been stored, compiled, and analyzed by various sources; one of the major resources for these sequences has been the Global Initiative on Sharing All Influenza Data (GISAID), which is a public-private initiative initially made for sharing sequences of influenza data. The organization provides a complete daily analysis of the viruses uploaded by the various researchers across the Globe. The COVID-19 belongs to a family of RNA viruses, which are very stable, unlikely other RNA viruses common to us, such as Human Immunodeficiency Virus (HIV), and Foot and Mouth Diseases Virus (FMDV).

However, GISAID showed a constant drift in the COVID-19 population-based upon three specific mutations in its genome. To assimilate different sequences, we chose 29 genome sequences of COVID-19 from five continents covering 17 countries (Table 1). To gather the most diverse sequences, we collected these sequences based upon their date of isolation. Among 29 viral sequences, six isolated in 2019, and the remaining 23 were isolated at different months of 2020 from January to April (Table 1). These 29 COVID-19 sequences were compared with the four SARS genome sequences to evaluate the differences between the COVID-19 and SARS and among different isolates of COVID-19. The sequences were analyzed using Geneious Prime 2020.1.1. The data suggested the SARS genomes are approximately 78.7% similar to the COVID-19 sequences (Table 2). All 29 sequences of COVID-19 are very similar (99.9% - 100% sequence similarity). Phylogenetic analysis showed that COVID-19 is closely related to SARS isolates but still genetically different (Fig. 1). All 29 COVID-19 isolates are close to each other with little change in sequence. Since variability among different COVID-19 isolates was minimal, we calculated the number of different individual nucleotides in each COVID-19 isolates that revealed a total nucleotide difference among these COVID-19 is from 1-55 nucleotides in the entire genome of approximately 29kb (Supplementary Table 1). The most diverse sequence that has a difference of 55 nucleotides were between the isolates from Australia (MT007544.1) and Turkey (EPI_ISL_427391) isolated in 2019 and April 2020, respectively (Fig. 1 & Table 2, Supplementary Table 1).

Table 2.

Sequence homology between the SARS and COVID-19 isolates from different geographic locations.

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
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Figure 1.

Figure 1.

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Phylogenetic analysis of Coronavirus isolates from different geographic locations. The phylogenetic analysis shows sequence relatedness among COVID-19 isolates (blue) and SARS isolates (black).

MicroRNAs targeting SARS and COVID-19 genome

As we mentioned above, we performed miRNA analysis on the whole genome of SARS and COVID-19 due to the efficiency of miRNAs targeting both 3'UTR and coding region. Our analysis found some interesting results. We found 848 common miRNAs targeting the SARS genome (four isolates, NC_004718.3, AY291451, AY338175, AY348314) and 873 common microRNAs targeting the 29 isolates of COVID-19 genome (Supplementary Table 2). Out of a total of 848 miRNAs from SARS, only 558 commonly present in all COVID-19 isolates (Fig. 2, Supplementary Table 2). Interestingly, 315 miRNAs are unique for COVID-19 isolates and 290 miRNAs unique to SRAS (Fig. 2). Furthermore, the COVID-19 targeting miRNAs with a higher target score (above 94) showed more than ten target sites and maximum complementary miRNA-RNA seed paring (~6-8 nucleotide seed base pairing) (Table 3). We also noted that out of 29 COVID-19 isolates, 19 have identical miRNA targets (Table 4). Ten isolates have unique miRNAs targets. Among ten isolates, Netherland (EPI_ISL_422601), Australia (EPI_ISL_413214), and Wuhan (EPI_ISL_403931) showed six, four, and four unique miRNAs targets respectively. The detail is given in the table (Table 4).

Figure 2.

Figure 2.

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Common and different human miRNAs targeting SARS and COVID-19 isolates from different geographic locations.

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.

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)
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Table 4.

Summary of important findings on human miRNAs targeting SARS and COVID-19 genome.

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)
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GO and KEGG pathway analysis of miRNAs targeting COVID-19

To identify the functional relevance of human miRNAs targeting COVID-19, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation and GO analysis. The KEGG annotation analysis data reveal that these miRNAs plays important in various Cancer signaling (e.g Renal, Pancreatic, Prostate, Colorectal, Melanoma ), Hippo signaling pathway, cardiomyopathy, Wnt signaling, Circadian rhythm, stem cell signaling, Adrenergic signaling in cardiomyocytes and number of other signaling pathways (Table 5). GO analysis data showed more than 72 biological processes were associated with the miRNAs targeting COVID-19 (Table 6). The biological processes such as organelle, ion binding, cellular, biosynthesis process, protein complex, immune response, and viral process are regulated by human miRNAs targeting the COVID-19 genome. Details of KEGG annotation and GO analysis are shown in Table 5, and Table 6, respectively.

Table 5.

Human miRNAs targeting the COVID-19 genome regulating KEGG pathway.

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
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Table 6.

Human miRNAs targeting the COVID-19 genome regulating GO pathway.

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
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DISCUSSION

Host cellular miRNAs are known to play an antiviral role in the number of published studies [18-23]. In this study, we performed in silico analysis of human cellular miRNAs targeting SARS and COVID-19 (isolates) genome and identified some novel miRNAs. We identified number (558) of common human cellular miRNAs targeting both SARS and COVID-19 genome. Top 10 common miRNAs have a target score of ≥95 (target score between 99-95), and each miRNA contains more than at least ten sites in the targeting viral genome (Table 3), indicating possible antiviral property for coronavirus infection (for both SARS and COVID-19). It will be interesting to verify these miRNA in-vitro and in-vivo animal models to use as a therapeutic target in the future. Cocktail of multiple miRNAs mimics through the intranasal route will be useful in coronavirus infection. These human miRNAs targeting coronavirus can be useful to combat any future outbreaks. In a previous report, host cellular miRNAs-181 binds to the ORF-4 region at the viral genome of porcine reproductive and respiratory syndrome virus (PRRSV) to inhibit its replication [17]. One step further, Guo et al. (2013) delivered intranasal inhalation of miR-181 mimics to slow down the progression of PRRSV in an experimental porcine model [17]. Another study used intranasal inoculation of miR-130 mimic to protect piglets from lethal challenge of PRRSV [30]. Similarly, the intranasal administration of chemically modified five miRNA mimics protected mice from H1N1 viral infection [31].

In humans, coronavirus infection is pervasive and usually causes common cold-like symptoms, but some of them can lead to serious illness. Previous coronavirus outbreak in 2002, known as SARS-CoV spread worldwide but was contained quickly [9]. On the other hand, the current coronavirus (COVID-19) is highly contagious and spread worldwide rapidly and became a global pandemic [8,11]. It will be interesting to know what separates these two viruses based on the genomic sequence. Ours (Table 2 and Fig. 1) and other data [32-34] of SARS-CoV and COVID-19 genome sequence analysis showed sequence homology of around ~78.7%, which was obvious, but our miRNA target scan data showed striking results. We found considerable changes in the number of human miRNAs targeting SARS-CoV (848 miRNAs) and COVID-19 (~873 miRNAs). Moreover, we also found unique miRNAs for SARS-CoV (290 miRNAs) and COVID-19 (315 miRNAs) isolates. At nucleotide (Table 2) and phylogenetic levels (Fig. 1), both viruses look closely related, but host cellular miRNAs target comparison differences are much more significant. This might be one of the reasons for COVID-19 to increase infectivity and easy viral propagation compared to SARS-CoV. The COVID-19 recent isolates (twenty-nine different isolates) are closely related at nucleotide, phylogenetic (Fig. 1 and Table 2), and host cellular miRNAs target level (Fig. 2 and Supplementary Table 2). The Netherland (EPI_ISL_422601), Australia (EPI_ISL_413214), and Wuhan (EPI_ISL_403931) isolates have more mutation and few different host cellular miRNAs targeting sites compared to other 26 COVID-19 isolates (Table 4). In our study, we used 29 isolates from different geographical reagion, which informs that COVID-19 nucleotide sequences and thus the human miRNAs target sites are not changing among the isolates/viruses (Table 4). In most of the COVID-19 cases, the virus entered a country from different geographical locations. At the time of writing this paper, only the USA and China had sequenced multiple viruses isolates from a different region. There is a need to sequence more of the COVID-19 viral genome from different parts of each country before coming to any conclusion.

The most important and striking feature of COVID-19 is the increased case fatality rate in aged individuals. The CDC reports 45% of cases requiring hospitalization are patients ≥65 years old [12]. Furthermore, 80% of COVID-19 related deaths occur in these patients (≥65 years old) [12]. Moreover, individuals with underlying conditions such as cardiovascular, chronic lung disease, diabetes, kidney, liver diseases, and cancer are at higher risk for severe illness and mortality. On the contrary, <1% mortality is observed in patients between 20-54 years of age or younger [12]. Based on miRNA target analysis and published literature, we hypothesized that the COVID-19 genome replicates and propagates viral particles easily in the aged individuals compared to younger due to the overall low abundance of miRNA expression with age. Recent human studies reported that the overall expression of miRNAs levels goes down with age [16, 35, 36]. For example, Huan et al. (2018) reported that 81% (103 miRNAs) microRNAs were negatively, and 19% (24 miRNAs) positively correlated with age in human peripheral blood [36]. We speculate that in younger individuals with COVID-19 infection, host cellular miRNAs bind to the complementary site of the viral RNA genome and prevent its replication; however, in an aged individual, this mechanism is not that efficient leads to accumulation of viral particle and severe illness. This might be true for underlying conditions as well. Some of the miRNAs targeting the COVID-19 genome showed low abundance (down-regulated) with some underlying conditions (Table 7). For example, miR-15b-5p (Coronary artery disease) [37], miR-15a-5p (Kidney disease) [38], miR-520c-3p (obesity/diabetes) [39], miR-30e-3p (Myocardial Injury) [40], miR-23c (hepatocellular carcinoma) [41], miR-30d-5p (non-small cell lung cancer) [42], miR-4684-3p (colorectal cancer) 43], and miR-518a-5p (Gastrointestinal stromal tumors) [44] down-regulated in pathophysiological condition.

Table 7.

List of selected human miRNAs targeting the COVID-19 genome down-regulated with age and underlying conditions.

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]
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To identify the biological relevance of COVID-19 targeting human cellular miRNAs, KEGG pathway annotation, and GO analysis was performed on target gene pools. KEGG annotation data showed that COVID-19 targeting human miRNAs plays an important role in various cancer signaling, Hippo signaling pathways, cardiac (e.g cardiomyopathy, Adrenergic signaling in cardiomyocytes), cell cycle, FoxO and number of other signaling pathways (Table.5). Furthermore, GO pathway annotation analysis showed that the COVID-19 targeting human miRNAs involved in important signaling dysregulated during age-related complications, such as biosynthesis, metabolic, cellular protein modification, and cellular component assembly. Most importantly, we also found that signaling related to immune response, Fc-epsilon receptor signaling pathway, viral process, and symbiosis, encompassing mutualism through parasitism signaling affected by the COVID-19 targeting human cellular miRNAs (Table.6). The signaling mentioned above is important to fight against viral infections [45-48]. Both KEGG and GO pathway analysis revealed that COVID-19 targeting human cellular miRNAs are involved in the number of age-related complications. Impaired GO and KEGG pathways may be due to low abundance miRNA and might be one of the contributing factors for the increasing severity and mortality of the COVID-19 infection in aged individuals and with other underlying conditions.

The dual role of host cellular miRNAs cannot be denied in the viral replication or inhibition. Host miRNAs can have both functions, antiviral, which is beneficial to host, or miRNA-viral genome interaction can increase stability and beneficiary for viral propagation [18, 20, 22]. In a viral infection, we hypothesized that host cellular miRNAs predominantly have antiviral activity. Though possible, positive regulation of viral genome translation by miRNAs is less likely. Our data and hypothesis are based on in silico analysis and realize that not all miRNAs targets will be real. Considering the many miRNAs targets identified in our in-silico analysis, even if 5% of total miRNAs targets are real (based on target score), it is possible that these could be used for therapeutic purposes. Further, in vitro and in vivo studies will be needed to validate some of these targets. However, in view of the current lack of effective treatment strategies, innovative approaches need to be considered.

Supplementary Materials

The Supplemenantry data can be found online at: www.aginganddisease.org/EN/10.14336/AD.2020.0428.

AD-11-3-509-s.pdf (959.8KB, pdf)

Acknowledgments

We would like to thank The Department of Medicine for their support. This publication is based upon work supported in part by the National Institutes of Health AG036675 (National Institute on Aging-AG036675 S.F, and C.I). The above-mentioned funding did not lead to any conflict of interest regarding the publication of this manuscript.

Footnotes

Competing interests

The author declares no competing interests.

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Supplementary Materials

The Supplemenantry data can be found online at: www.aginganddisease.org/EN/10.14336/AD.2020.0428.

AD-11-3-509-s.pdf (959.8KB, pdf)

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