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.
Introduction
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.
Methods
Sequence 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.
Gene codes and countries for the sources of genome sequences for nCoV
Gene codes and countries for the sources of genome sequences for nCoV
Gene codes and countries for the sources of genome sequences for MERS and H1N1 (influenza virus)
*OFL - OpenFlu database by Swiss Institute of Bioinformatics.
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].
Jalview 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].
MEGA (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].
InterPro 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].
Results
Phylogenetic 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].
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.
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).
Mutations in different sequences of different countries
Mutations in different sequences of different countries
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.
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).
Different proteins and domains in the SARS-CoV2 sequences
Different proteins and domains in the SARS-CoV2 sequences
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.
Mappinng of mutation analysis data with the domain analysis data
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.
Comparative analysis of point mutations
Conflicts of interest
The authors have no conflict of interest to report.