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Point mutation of COVID-19 proteins: A study on noval corona virus (nCov) correlation with MERS and H1N1 viruses and in silico investigation of nCoV proteins for future applications

Abstract

Coronavirus disease (COVID 19) which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) was first reported in Wuhan, China in December 2019. The disease transformed to a pandemic and affected people’s lives all over the world. It caused death to millions of people all over the world. In this project we focused on finding out the correlation of SARS-CoV2 with other respiratory diseases causing viruses like MERS and H1N1 influenza viruses. We further investigated to understand the mutations that occur in the sequences of the SARS-CoV2 during the spread of the disease and correlated it with the functional domains of proteins. The resulted phylogenetic tree indicated that SARS-CoV2 is closely related to the MERS and H1N1 viruses are distantly related. The mutation analysis of 10 different proteins of the SARS-CoV2 shows that there were more than 50 point-mutations among 34 countries sequences for six proteins. Interestingly, four proteins did not any mutation during the analysis. Therefore, these four proteins may be taken into consideration during the development of the diagnostics or therapeutics against this disease.

1Introduction

The COVID-19 is the biggest pandemic ever heard due to any kind of disaster. The disease was born around the end of December 2019, in the city of Wuhan in China. The name of the disease was due to the virus type, Coronavirus, in the year 2019 (COVID-19), and the causative virus was identified as Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) [1]. Coronaviruses are a group of viruses that cause upper respiratory infection in mammals, birds and develop lethal condition in humans [2]. In the nine month period COVID-19 reached to more than 200 countries and infected more than 36 million and caused 1 million death worldwide [3]. However, the family has historical mark on the globe as another common cold like pandemic with massive death in last two decades.

The symptoms of the novel coronavirus (nCoV) infection has some similarity with SARS, MERS (Middle East respiratory syndrome) and H1N1 (influenza virus) related infections as all were associated with respiratory tract infections [4–6]. The nCoV infections were found to be associated with angiotensin-converting enzyme 2 (ACE2) receptor, mostly present in lungs [7]. However, it was recently reported that infection is not limited to lung nevertheless it reached to the abdominal region [8]. Additionally, in severe cases microcirculatory disorders and systemic endothelial dysfunction were reported recently [9–11]. The present report is based on an in silico study of nCoV and associated virus genome sequences analysis to understand the relationship with these respiratory tract infecting viruses. However, the genome sequences of nCoV, SARS-CoV, MERS-CoV was compared with bat-CoV (RaTG13) by Zhou et al where they found similarity 96% with nCoV and 76% with SARS-CoV [6, 12]. Additionally, we have compared the all the protein sequences submitted for nCoV on NCBI from all over the world to find the regions where mutation not happened during the spread which is common in viruses. Later we compared with the sequences submitted from India to understand the domains of the viral protein using bioinformatics tools.

2Methods

2.1Sequence collection

We collected 34 complete genome sequences of nCoV freshly submitted in NCBI database till 18 March 2020. Additionally, we have collected 4 MERS-CoV sequences of China and U.S from the Viral Genome Database and 9 H1N1 influenza virus sequences (submitted from India and China) from Influenza Virus Database using Open Flu Database (Table 1a & b). Next, we collected all types of proteins sequences for the same nCoV from NCBI which was used for comparison with MERS and HIN1 genome.

Table 1a

Gene codes and countries for the sources of genome sequences for nCoV

SARS-CoV-2 complete genome
GenBank IDLocality
MT007544Australia: Victoria
MT126808Brazil
MT135041China: Beijing_1
MT121215China: Shanghai_2
MN996527China: Wuhan_3
MT256924Colombia: Antioquia
MT020781Finland
MT012098India: Kerala State_1
MT050493India: Kerala State_2
MT281530Iran
MT276597Israel_1
MT276598Israel_2
MT066156Italy
LC528232Japan_1
LC528233Japan_2
LC529905Japan_3
MT072688Nepal
MT240479Pakistan: Gilgit_1
MT262993Pakistan: KPK_2
MT263074Peru
MT039890South Korea
MT198652Spain: Valencia_1
MT233519Spain: Valencia_2
MT233520Spain: Valencia_3
MT093571Sweden
MT066175Taiwan_1
MT066176Taiwan_2
MT192759Taiwan_3
MN994467USA: CA_1
MT276329USA: FL_2
MT106054USA: TX_3
MN985325USA: WA_4
MT192772Viet Nam: Ho Chi Minh city_1
MT192773Viet Nam: Ho Chi Minh city_2
Table 1b

Gene codes and countries for the sources of genome sequences for MERS and H1N1 (influenza virus)

MERS-CoV
GenBank IDLocality
KT006149China
KJ813439USA
KP223131USA
KJ829365USA
Influenza viruses (H1N1)
OFL181342China: Beijing
OFL180257China: Beijing
OFL180259China: Beijing
OFL287088India: Bangalore
OFL287089India: Bangalore
OFL287090India: Bangalore
OFL287092India: Bangalore
OFL287093India: Bangalore
OFL287094India: Bangalore

*OFL - OpenFlu database by Swiss Institute of Bioinformatics.

2.2Multiple sequence alignment using clustal omega

The collected genome sequences were used for multiple sequence alignment (MSA) using online tool, Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) which helped us to collect Jalview format of the aligned sequences [13, 14].

2.3Jalview based analysis of genome and protein sequences

Jalview (https://www.jalview.org/) is a free bioinformatics tool for the analysis of DNA, RNA, and proteins. After performing MSA in clustal omega, we download Jalview format of MSA result and then we use Jalview offline software to visualize the result and export the data in FASTA format [15].

2.4MEGA (Molecular evolutionary genetics analysis) for phylogenetic analysis

The exported FASTA file of the MSA was opened using an offline tool, MEGA-X [https://www.megasoftware.net/] for phylogenetic analysis and construction of a phylogenetic tree was generated using maximum likelihood method [16].

2.5InterPro scan database for domain analysis

The InterPro Scan database has the information to understand the protein families and its functional domain. To understand the functional domain of each protein we used an Indian submitted sequence for nCoV (MT012098). We obtained the domain information from the database of individual proteins of the virus and correlated the mutation result [17].

3Results

3.1Phylogenetic analysis of nCoV with MERS-CoV and H1N1

To understand the relationship among the nCoV and other respiratory diseases we collected 34 complete genome sequences of coronavirus of various countries which were submitted till 18 March 2020 in the NCBI database. The evolutionary relationships for these nCoV sequences were analysed with 4 MERS sequences and 9 H1N1 influenza virus sequences. The obtained phylogenetic tree revealed that the nCoV is distantly related to H1N1 (influenza virus) and MERS is closely related (Fig. 1). Our data supports the recent study shown its relation with various SARS viruses including MERS [6, 16].

Fig. 1

Phylogenetic analysis of nCoV, MERS and H1N1 (influenza virus): The 34 complete genome sequences of nCoV were compared with 4 MERS sequences and 9 H1N1 sequences in order to build a phylogenetic tree in the MEGA-X software using the Maximum Likelihood method and Tamura-Nei model [18, 19]. In the analysis the codon positions included were 1st, 2nd, 3rd and the non-coding regions. There was a total of 29945 positions in the final dataset. The default settings were used for the analysis.

Phylogenetic analysis of nCoV, MERS and H1N1 (influenza virus): The 34 complete genome sequences of nCoV were compared with 4 MERS sequences and 9 H1N1 sequences in order to build a phylogenetic tree in the MEGA-X software using the Maximum Likelihood method and Tamura-Nei model [18, 19]. In the analysis the codon positions included were 1st, 2nd, 3rd and the non-coding regions. There was a total of 29945 positions in the final dataset. The default settings were used for the analysis.

3.2Mutation analysis using jalview in nCoV proteins

Viruses are known for changing their coat proteins during their life-cycle since it utilises host expression system. Considering the possibility of changes in nCoV associated proteins during the pandemic we compared all the 34 entries for the mutation occurred in the viral proteins. The individual proteins were studied using MSA in CLUSTAL omega followed by Jalview analysis for mutation search. We compared 10 different proteins present in nCoV: orf1ab polyprotein, surface glycoprotein (spike protein), orf3a protein, envelop protein, membrane glycoprotein, orf6 protein, orf7a protein, orf8 protein, nucleocapsid phospho-protein and orf10 protein. Interestingly, we found mutation in the 6 proteins among various countries’ submitted sequences. However, no mutations were observed among the 34 countries’ sequences for the four viral proteins (membrane glycoprotein, orf6 protein, orf7a protein and orf10 protein) during our analysis (Table 2).

Table 2

Mutations in different sequences of different countries

ProteinGenBankCountryMutationLocation
ofr 1abMT240479PAKISTAN1/1–7096Arginine to cysteine207
MT281530IRAN/1–7096Valine to isoleucine378
MT240479PAKISTAN1/1–7096Valine to isoleucine378
MN994467USA1/1–7096Serine to asparagine428
MT050493INDIA 2/1–7096Isoleucine to valine476
MT012098INDIA1/1–7096Isoleucine to theronine671
MT093571SWEDEN/1–7096Glycine to serine818
MT039890SOUTH/1–7096Methionine to isoleucine902
MT135041CHINA1/1–7096Leucine to phenylalanine1599
MT121215CHINA2/1–7096Proline to serine1921
MT050493INDIA2/1–7096Proline to leucine2079
MT012098INDIA1/1–7096Proline to serine2144
MT263074PERU/1–7096Asparagine to asparatic acid2894
MT240479PAKISTAN1/1–7096Proline to leucine2985
MT233520SPAIN3/1–7096Phenylalanine to tyrosine3071
MT198652SPAIN1/1–7096Phenylalanine to tyrosine3071
MT233519SPAIN2/1–7096Phenylalanine to tyrosine3071
MT192772VIETNAM1/1–7096Arginine to cysteine3323
MT192773VIETNAM2/1–7096Arginine to cysteine3323
MT126808BRAZIL/1–7096Leucine to phenylalanine3606
MT276597ISRAEL 1/1–7096Leucine to phenylalanine3606
LC528232JAPAN 1/1–7096Leucine to phenylalanine3606
LC528233JAPAN 2/1–7096Leucine to phenylalanine3606
MT240479PAKISTAN1/1–7096Leucine to phenylalanine3606
MT281530IRAN1/1–7096Leucine to phenylalanine3606
MT093571SWEDEN/1–7096Phenylalanine to leucine4321
MT263074PERU/1–7096Proline to leucine4715
MT276597ISRAEL1/1–7096Proline to leucine4715
MT276329USA2/1–7096Proline to leucine4715
MT012098INDIA1/1–7096Alanine to valine4798
MT050493INDIA2/1–7096Threonine to isoleucine5540
MT281530IRAN/1–7098Threonine to isoleucine6040
MT106054USA3/1–7096Aspartic acid to alanine6306
MT039890SOUTH/1–7096Threonine to methionine6893
MN996527CHINA3/1–7096Aspartic acid to asparagine7020
Surface glycoproteinMT039890SOUTH/1–75Leucine to histidine37
orf3a proteinMT281530IRAN/1–275Tryptophan to leucine128
LC529905JAPAN3/1–275Leucine to valine140
MT198652SPAIN1/1–275Glycine to valine196
MT233519SPAIN2/1–275Glycine to valine196
MT233520SPAIN3/1–275Glycine to valine196
MT039890SOUTH/1–275Glycine to valine251
MT007544AUSTRALIA/1–275Glycine to valine251
MT066156ITALY/1–275Glycine to valine251
MT093571SWEDEN/1–275Glycine to valine251
MT126808BRAZIL/1–275Glycine to valine251
Envelop proteinMT039890SOUTH/1–75Leucine to histidine37
Membrane glycoproteinAllNO MUTATION
orf 6 proteinAllNO MUTATION
orf7a proteinAllNO MUTATION
orf 8 proteinMT106054USA 3/1–121Threonine to isoleucine11
MN994467USA 1/1–121Valine to leucine62
MN994467USA 1/1–121Leucine to serine84
MT106054USA 3/1–121Leucine to serine84
MT135041CHINA1/1–121Leucine to serine84
MT256924COLOMBIA/1–121Leucine to serine84
MT050493INDIA 2/1–121Leucine to serine84
MT198652SPAIN 1/1–121Leucine to serine84
MT233519SPAIN 2/1–121Leucine to serine84
MT233520SPAIN 3/1–121Leucine to serine84
MT066175TIAWAN 1/1–121Leucine to serine84
MN985325USA 4/1–121Leucine to serine84
Nucleocapsid phosphor-proteinMT198652SPAIN /1–419Serine to leucine197
MT198652SPAIN 1/1–419Serine to leucine197
MT233519SPAIN 2/1–419Serine to leucine197
MT276598ISRAEL 2/1–419Arginine to lysine203
MT263074PERU/1–419Arginine to lysine203
MT276329USA2/1–419Arginine to lysine203
MT276598ISRAEL 2/1–419Glysine to arginine204
MT263074PERU/1–419Glysine to arginine204
MT276329USA2/1–419Glysine to arginine204
MT256924COLOMIA/1–419Glysine to cysteine238
LC529905JAPAN3/1–419Proline to serine344
orf 10 proteinAllNO MUTATION

The highest point mutations were observed in orf1ab (35 different positions), orf 8 protein (12 positions) and nucleocapsid phosphor-protein (11 positions) among the sequences used for analysis [20]. Next, we needed to know the protein domains affected by the mutation which directed us to do domain analysis.

3.3Domain analysis of individual proteins of an Indian sequence of SARS-CoV-2

To understand the domains of individual proteins in nCoV we used the coronavirus sequence submitted from India (with the Acc. no.- MT012098). In order to perform the domain analysis of individual protein of the virus, first we collected the amino acid sequence of individual proteins then sequence of the individual protein was uploaded on the InterPro Scan online tool separately and the results were obtained [17, 21]. The obtained results for all 10 proteins were represented in Table 3 along with the gene ontology (GO).

Table 3

Different proteins and domains in the SARS-CoV2 sequences

Gene codeProtein nameDomain name and IPR codeAmino acid rangeFunctions &Gene Ontology (GO)
QHS34545.1ORF 1abNSP 1 (IPR02590)13–127Viral genome replication (GO:0019079)
SARS-CoV_Nsp3_N (IPR0024358)920–986Transcription, DNA-templated (GO:0006351)
Macro_dom (IPR002589)1025–1194Viral protein processing (GO:0019082)
Nsp3_PLR2pro (IPR022733)1498–1561Viral RNA genome replication (GO:0039694)
Nsp3_coronavir (IPR024375)1351–1493Proteolysis (GO:0006508)
Viral_protease (IPR014827)1564–1882Transferase activity (GO:0016740)
Peptidase_C30/C16 (IPR013016)1634–1898
NAR_dom (IPR032592)1922–2019Cysteine–type peptidase activity (GO:0008234)
Corona_NSP4_C (IPR032505)3166–3261nucleic acid binding (GO:0003676)
Peptidase_C30 (IPR008740)3264–3582Zinc ion binding (GO:0008270)
NPS7 (IPR014828)3860–3942RNA-directed 5’-3’ RNA polymerase activity (GO:0003968)
NSP8 (IPR014829)3943–4140ATP binding (GO:0005524)
NSP9 (IPR014822)4141–4253Cysteine-type endopeptidase activity (GO:0004197)
RNA_synth_NSP10_coronavirus (IPR018995)4262–4384
RNA_pol_N_coronovir (IPPR009469)4407–4758RNA binding (GO:0003723)
RNA-dir_pol_Psvirus (IPR007094)5004–5166
CV_ZBD (IPR027352)5325–5408
(+)RNA_virus_helicase_core_dom (IPR027351)5581–5932Methyltransferase activity (GO:0008168)
NSP11 (IPR009466)5929–6520Exoribonuclease activity, producing 5’-phosphomonoesters (GO:0016896)
Coronavirus_NSP16 (IPR009461)6800–7095Omega peptidase activity (GO:0008242)
QHS34546.1S-proteinSpike_rcpt_bd (IPR018548)285–538Membrane fusion (GO:0061025)
Corona_S2 (IPR002552)641–1225Receptor-mediated virion attachment to host cell (GO:0046813
QHS34547.1ORF 3aSARS_Coronavirus_Orf3/3a (IPR024407)1–230
QHS34548.1E-proteinNO DOMAIN
QHS34549.1M-proteinCorona_M (IPR002574)1–177Viral life cycle (GO:0019058)
QHS34550.1ORF 6NO DOMAIN
QHS34551.1ORF 7SARS_X4 (IPR014888)1–054
QHS34552.1ORF 8Corona_NS8 (IPR022722)1–074
QHS34553.1ORF 9Corona_nucleocap (IPR001218)1–374Viral nucleocapsid (GO:0019013)

The proteins of SARS-CoV-2 of an Indian sequence in which the domains are present are as follows:

orf1ab polyprotein was the largest protein with 20 domains, surface glycoprotein has two domains but other proteins (orf3a, M-protein, orf7a, orf8 and nucleocapsid phosphor-protein) has one domain each. Interestingly, we did not observe any domains in the analysis of envelope (E) protein, orf6 protein and orf10 protein.

The domain analysis of one submission (MT012098) for SARS-CoV-2 revealed the information about the domains of nCoV proteins. Later the MSA based mutation analysis results were mapped with domain analysis results considering all 34 entries must have similar domain distributions. The mapped results were represented in Table 4.

Table 4

Mappinng of mutation analysis data with the domain analysis data

ProteinGenBankMutationLocationPredicted domain
ORF 1abMT240479Arginine to cysteine207No domain predicted
MT281530Valine to isoleucine378No domain predicted
MT240479Valine to isoleucine378No domain predicted
MN994467Serine to asparagine428No domain predicted
MT050493Isoleucine to valine476No domain predicted
MT012098Isoleucine to theronine671No domain predicted
MT093571Glycine to serine818No domain predicted
MT039890Methionine to isoleucine902No domain predicted
MT135041Leucine to phenylalanine1599Viral protease
MT121215Proline to serine1921No domain predicted
MT050493Proline to leucine2079No domain predicted
MT012098Proline to serine2144No domain predicted
MT263074Asparagine to asparatic acid2894No domain predicted
MT240479Proline to leucine2985No domain predicted
MT233520Phenylalanine to tyrosine3071No domain predicted
MT198652Phenylalanine to tyrosine3071No domain predicted
MT233519Phenylalanine to tyrosine3071No domain predicted
MT192772Arginine to cysteine3323Peptidase_C30/C16
MT192773Arginine to cysteine3323Peptidase_C30/C16
MT126808Leucine to phenylalanine3606No domain predicted
MT276597Leucine to phenylalanine3606No domain predicted
LC528232Leucine to phenylalanine3606No domain predicted
LC528233Leucine to phenylalanine3606No domain predicted
MT240479Leucine to phenylalanine3606No domain predicted
MT281530Leucine to phenylalanine3606No domain predicted
MT093571Phenylalanine to leucine4321RNA-syn-NSP10-coronavirus
MT263074Proline to leucine4715RNA_pol_N_coronovir
MT276597Proline to leucine4715RNA_pol_N_coronovir
MT276329Proline to leucine4715RNA_pol_N_coronovir
MT012098Alanine to valine4798No domain predicted
MT050493Threonine to isoleucine5540No domain predicted
MT281530Threonine to isoleucine6040NSP11
MT106054Aspartic acid to alanine6306No domain predicted
MT039890Threonine to methionine6893NSP16
MN996527Aspartic acid to asparagine7020NSP16
Surface glycoproteinMT039890Leucine to histidine37No domain predicted
Orf 3aMT281530Tryptophan to leucine128No domain predicted
LC529905Leucine to valine140No domain predicted
MT198652Glycine to valine196No domain predicted
MT233519Glycine to valine196No domain predicted
MT233520Glycine to valine196No domain predicted
MT039890Glycine to valine251No domain predicted
MT007544Glycine to valine251No domain predicted
MT066156Glycine to valine251No domain predicted
MT093571Glycine to valine251No domain predicted
MT126808Glycine to valine251No domain predicted
Envelop proteinMT039890Leucine to histidine37No domain predicted
Membrane glycoproteinNO MUTATION
orf 6 proteinNO MUTATION
orf7a proteinNO MUTATION
orf 8MT106054Threonine to isoleucine11Corona_NS8
MN994467Valine to leucine62Corona_NS8
MN994467Leucine to serine84No domain predicted
MT106054Leucine to serine84No domain predicted
MT135041Leucine to serine84No domain predicted
MT256924Leucine to serine84No domain predicted
MT050493Leucine to serine84No domain predicted
MT198652Leucine to serine84No domain predicted
MT233519Leucine to serine84No domain predicted
MT233520Leucine to serine84No domain predicted
MT066175Leucine to serine84No domain predicted
MN985325Leucine to serine84No domain predicted
Nucleocapsid phosphor- proteinMT198652Serine to leucine197Corona_nucleoca
MT198652Serine to leucine197Corona_nucleoca
MT233519Serine to leucine197Corona_nucleoca
MT276598Arginine to lysine203Corona_nucleoca
MT263074Arginine to lysine203Corona_nucleoca
MT276329Arginine to lysine203Corona_nucleoca
MT276598Glysine to arginine204Corona_nucleoca
MT263074Glysine to arginine204Corona_nucleoca
MT276329Glysine to arginine204Corona_nucleoca
MT256924Glysine to cysteine238No domain predicted
LC529905Proline to serine344No domain predicted
orf 10 proteinNO MUTATION

4Discussion and conclusion

In our study, we compared the genome sequences of upper respiratory tract infecting viruses to check the relationship with nCoV. In the present study we also compared various proteins of nCoV to find out the mutation during spread of the disease. The overall finding suggest that the nCoV belong to the same family which caused SARS and MERS like pandemic earlier in small part of the world [2]. The mutation analysis suggested that the highest number (10) of mutation was found in orf8 protein where leucine was mutated to serine in counties like -USA, India, Spain and China but all these are at the region which does not belong to any functional domain of the protein. Next was glycine to valin in orf3 protein (8) among Spain, South Korea, Australia, Italy, Sweden and Brazil submitted nCoV sequences at unpredictable domains. The similar analysis we did for various point mutations in the given table below (Table 5). Finding the significance of these mutations can be correlated with the severity of cases in certain countries. However, for identification of new targetable proteins those proteins can be used which did not show any mutation.

Table 5

Comparative analysis of point mutations

Type of mutationNo. of mutationProteinCountry
Leucine to serine10orf 8USA
China
Colombia
India
Spain
Taiwan
Glycine to valine8orf 3aSpain
South Korea
Australia
Italy
Sweden
Brazil
Leucine to phenylalanine7Orf 1abChina
Brazil
Israel
Japan
Pakistan
Iran
Proline to leucine5Orf 1abIndia
Pakistan
Peru
USA
Proline to serine3Orf 1ab Nucleocapsid phosphor-proteinChina
India
Japan
Phenyalanine to tyrosine3Orf 1abSpain
Threonine to isoleucine3Orf 1abIndia
Iran
Orf 8
USA
Serine to leucine3Nucleocapsid phosphor-proteinSpain
Arginine to lysine3Nucleocapsid phosphor-proteinIsrael
Peru
USA
Glycine to arginine3Nucleocapsid phosphor-proteinIsrael
Peru
USA
Arginine to cysteine3Orf 1abPakistan
Veitnam
Valine to isoleucine2Orf 1abIran
Pakistan
Leucine to histidine2Surface glycoproteinSouth Korea
Envelop proteinSouth Korea
Serine to asparagine1Orf 1abUSA
Isoleucine to valine1Orf 1abIndia
Isoleucine to threonine1Orf 1abIndia
Glycine to serine1Orf 1abSweden
Methionine to isoleucine1Orf 1abSouth
Asparagine to aspartic acid1Orf 1abPeru
Alanine to valine1Orf 1abIndia
Aspartic acid to alanine1Orf 1abUSA
Threonine to methionine1Orf 1abSouth
Aspartic acid to asparagine1Orf 1abChina
Tryptophan to leucine1Orf 3aIran
Leucine to valine1Orf 3aJapan
Valine to leucine1Orf 8USA
Glycine to cysteine1Nucleocapsid phosphor-proteinColombia

Conflicts of interest

The authors have no conflict of interest to report.

References

[1] 

COVID-19. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/covid-19-pandemic. Accessed 30 Apr 2020.

[2] 

Schoeman D , Fielding BC . Coronavirus envelope protein: current knowledge. Virol J. 2019;16:69.

[3] 

Coronavirus Update (Live): 36,792,906 Cases and 1,067,469 Deaths from COVID-19 Virus Pandemic Worldometer.[https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?%22. Accessed 9 Oct 2020.]

[4] 

Petersen E , Koopmans M , Go U , Hamer DH , Petrosillo N , Castelli F , et al. Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. Lancet Infect Dis. 2020;20:e238–44.

[5] 

He D , Zhao S , Li Y , Cao P , Gao D , Lou Y , et al. Comparing COVID-19 and the 1918–19 influenza pandemics in the United Kingdom. Int J Infect Dis. 2020;98:67–70.

[6] 

Zhou P , Yang X-L , Wang X-G , Hu B , Zhang L , Zhang W , et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270–3.

[7] 

Ni W , Yang X , Yang D , Bao J , Li R , Xiao Y , et al. Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19. Crit Care. 2020;24:422.

[8] 

Jung EM , Stroszczynski C , Jung F . Contrast enhanced ultrasound (CEUS) to assess pleural pulmonal changes in severe COVID-19 infection: First results. Clin Hemorheol Microcirc. 2020;75:19–26.

[9] 

Jung F , Krüger-Genge A , Franke RP , Hufert F , Küpper J-H . COVID-19 and the endothelium. Clin Hemorheol Microcirc. 2020;75:7–11.

[10] 

Martini R . The compelling arguments for the need of microvascular investigation in COVID-19 critical patients. Clin Hemorheol Microcirc. 2020;75:27–34.

[11] 

Jung EM , Stroszczynski C , Jung F . Contrast enhanced ultrasonography (CEUS) to detect abdominal microcirculatory disorders in severe cases of COVID-19 infection: First experience. Clin Hemorheol Microcirc. 2020;74:353–61.

[12] 

Singh AK , Chaterjee A , Sirohi S , Sharma N , Kathuria A . Convalescent plasma therapy: A promising solution for SARS-CoV-2 outbreak. J Cell Biotechnol. 2021;7:11–7.

[13] 

Sievers F , Wilm A , Dineen D , Gibson TJ , Karplus K , Li W , et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539.

[14] 

Sievers F , Higgins DG . Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 2018;27:135–45.

[15] 

Waterhouse AM , Procter JB , Martin DMA , Clamp M , Barton GJ . Jalview Version 2–a multiple sequence alignment editor and analysis workbench. Bioinforma Oxf Engl. 2009;25:1189–91.

[16] 

Ceraolo C , Giorgi FM . Genomic variance of the 2019-nCoV coronavirus. J Med Virol. 2020;92:522–8.

[17] 

Mitchell AL , Attwood TK , Babbitt PC , Blum M , Bork P , Bridge A , et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 2019;47:D351–60.

[18] 

Tamura K , Nei M . Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol. 1993;10:512–26.

[19] 

Kumar S , Stecher G , Li M , Knyaz C , Tamura K . MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol. 2018;35:1547–9.

[20] 

Yu Y , Santat LA , Choi S . 6 - Bioinformatics Packages for Sequence Analysis. In: AroraDK, BerkaRM, SinghGB, editors. Applied Mycology and Biotechnology. Elsevier; 2006; p. 143–60.

[21] 

Finn RD , Attwood TK , Babbitt PC , Bateman A , Bork P , Bridge AJ , et al. InterPro in 2017—beyond protein family and domain annotations. Nucleic Acids Res. 2017;45:D190–9.