It is widely known fact about the global pandemic caused by Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2) to humans, which imposed immediate lockdown of effected territories in the prevailing provinces. However, few provinces were able to control infection severity with lower death rates. Interestingly three types of genomic features were noticed through comparative genomics in the available genome sequences SARS-CoV-2, due to the insertion/deletions of orf3a, orf6, orf7a and orf7b. Whole genome phylogeny (n=75 genomes) revealed a large diversity within the SARS-CoV-2, and distributed in 6 clusters namely China, Diamond princess, Asian, European, USA and Beijing. This study asserts diversity in the genome with high mutation rate and migration of carriers over the world. Here, we describe the polymorphic loci of Spike glycoprotein and its putative mechanism for pathogenicity, which unveiled the presence of GPI anchor amidation, PPI hotspot, O-linked glycosylation, catalytic site, Iron binding site, signal cleavage, disulphide linkage, sulfation, transmembrane region, and C-terminal signal sites. Mutational changes at spike glycoprotein of South Korea, India, Greece, Spain, Australia, Sweden and Yunnan samples possibly suggest the prevalence of mutated strains with either low or high virulence. The regions at the spike glycoprotein also have high binding capacity to angiotensin converting enzyme 2 (ACE2) suggesting a key link for explaining damage to multiple organs including lungs, kidney and heart. Factors influencing the mutations at the spike glycoprotein region will need to be investigated to understand and neutralize the upsurge of the alarming Pandemic and to control the global spread of the disease.
Research Article
Comparative genomics and diversity of SARS-CoV-2 suggest potential regional virulence
https://doi.org/10.21203/rs.3.rs-29557/v1
This work is licensed under a CC BY 4.0 License
Version 1
posted 20 May, 2020
You are reading this latest preprint version
In the history of global infections, COVID–19 (Corona Virus disease) has left its dangerous, uncontrollable outbreak footprint. Towards the end of the year 2019, people of Wuhan, the capital city of Hubei province in China, developed a strange pneumonia-like infection due to an unknown aetiology. It was later recognized to be a part of coronavirus family1. This pandemic was spread over 210 countries with over 3,248,685 confirmed cases and 229,399 deaths world-wide till date (01 May, 2020) (https://www.worldmeters.info/coronavirus). As of the date of manuscript preparation, the US now has almost 1/3 of all COVID–19 cases worldwide. With a high mortality rate of about 3–6% across the world, the havoc created by COVID–19 has been massive. The transmission was vast and understanding the genome variation has been in priority. Till date, no specific drugs or vaccines are available to control the infection, and symptomatic treatments to block the viral replication is in early trials. Considering COVID–19 as a major public health emergency, globally several countries have suspended their trade and called off social events to prevent community transmission. Furthermore, to battle the virus, countries world-wide have resorted to self-quarantine and social-isolation as containment strategy for the benefit of the mankind. Medical supplies, protective agents and hand hygiene are the only resort to prevent the transmission dynamics of this deadly disease. Ian M. Jones had suggested that the SARS-CoV–2 mutates rapidly in the respiratory tract2. The data sharing among collaborators or investigators had made the analysis more accurate and easy. Moreover, certain strains prevailing at few provinces had shown low mortality rates. Whereas, countries like Italy, Spain, USA and a few more had high mortality rates indicating the presence of evolved virulent strain when compared to the original strain from Wuhan. Understanding the genetically distinct variants in a phenotype is very important in analysing the pathogenic mechanism of infected hosts.
As RNA viruses tend to evolve rapidly among large populations with short generation times 3, monitoring evolutionary patterns in “real time” is important. This leads to the emergence of many new novel strains of COVID–19, it has become an important aspect to differentiate between the virulent strains of SARS-CoVs in the current scenario. The virus was found more related to the betacoronavirus of bats (RaTG13) and pangolins (Manis javanica) with 96.2% and 91.02% homology respectively. The spike gene of SARS-CoV–2 has shown slight variation with polybasic cleavage site (PCS)4,5. The PCS of Spike protein gets cleaved by furin leaving its infection to different organs of the host 4–6. The whole-genome sequence (WGS) data would probably show its evolution and reasons for its mutation rates1,7. Laboratories from many countries have deposited over 2400 genome sequences of SARS-CoV–2 at the NCBI, GISAID and Nextstrain databases (https://nextstrain.org/ncov), which allowed us to analyze this novel virus. Acknowledging the importance of the spread and the evolution of the virulent pathogens, the NEXTstrain database provided the necessary information related to the phylodynamics, genomes and the surveillance data 8.
It has been hypothesized that the pathogenesis of disease is possibly due to alveolar damage followed by spleen atrophy, enlarged liver, injury to kidney and neuronal dysfunction in patients9,10. The ability of SARS-CoV–2 to interact with the kidneys of host and shed of viral particles through faecal and urine of patients suggests the multiple organ damage with increased severity11. Though, the target organ reported was lungs due to the specific binding of SARS-CoV–2 to ACE2 receptors, the presence of other sites responsible for effective binding of the spike protein in other organs remains an enigma. Hence, the present study focused on the variations in the genomes of COVID–19, which were distributed worldwide. Further investigation on the mutated strains pathogenesis in biopsy samples of different human organs will be investigated.
Genome diversity and comparative genomics among SARS-CoV–2
In the present investigation, SARS-COV–2 genome sequences were retrieved from NCBI and GISAID database (till April 25th 2020). Among the 520 complete genome data of SARS-CoV–2, the genomes which showed variations in their size and their geographic region were targeted. A total of 75 complete sequences of SARS-CoV–2 that were prevalent in countries like China, USA, France, Australia, Spain, Italy, India, Nepal, Taiwan, South Korea, South Africa, Greece, Sweden, Pakistan, Peru, Brazil, Iraq, Turkey and Israel were collected (Table S1). The knowledge of patient’s ethnicity and racial background were not readily available for all the samples. The genome analysis revealed that the SARS-CoV–2 is a 30 Kbp genome with over 10 to 12 genes (Table 1). The largest genome size was noticed in a Shanghai patient (SH01) of China (Accession number MT121215) with 29945 bp (reported on 2nd Feb 2020). While the smaller genome size of 29852 bp was detected in a USA patient (CA6) (Accession number MT044258) (isolated on 27th Jan 2020). These two samples were compared to Wuhan-Hu–1 (MN908947 or NC045512), which is of 29,903 bp genome size and serves as a reference sample (Fig. 1a). Comparative genomics of these three isolates revealed that the SH01 sample had a deletion of ORF3a, ORF6, ORF7a, and 7b, while CA6 isolate had a deletion of ORF7b. However, the genome size of SH01 was noticed to be larger when compared to the other two samples. Though the function of these genes was not known, their absence had revealed diversity at strain level (Table 1, Fig. 1b).
Most of the coronaviruses (CoVs) of the Coronaviridae family possess two overlapping ORF1a and ORF1b polypeptides and other structural proteins like Spike (S), Envelope (E), membrane (M) and nucleocapsid (N)12. Among the samples that were analysed, ORF7b was present only in 8 samples of ncov-FIN, Yunnan–01, WH09/CHN, WIV02, WIV04, WIV05, WIV06 and WIV07. Subsequently, ORF3a, ORF7a, ORF7b and ORF8 were found to be deleted in HU/DP/Kng/19–20, SH01/CHN, WHU01 and WHU02 samples. However, the severity of the infection in these variants associated with the corresponding patient is not yet known, as the case history details are not available. These mutations could make an impact on the immunogenic changes that would either suppress or become more virulent than the wild type strain. The prevalence of more virulent strains may increase the severity of outbreak. However, extensive research has to be conducted to correlate the nature of mutations with the outbreak severity.
Table 1: General features of genes involved in SARS-CoV-2 of three isolates, i.e., Wuhan-Hu-1, CA6 and SH01 (in this study Wuhan-Hu-1 served as a reference sample)
|
S.NO |
Gene name |
Description |
Gene length (bp) |
||
|
Wuhan-Hu-1 |
CA6 |
SH01 |
|||
|
1 |
orf1ab |
Polyprotein |
21291 |
21267 |
21271 |
|
2 |
orf S |
Surface glycoprotein |
3822 |
3822 |
3822 |
|
3 |
orf 3a |
Hypothetical protein |
828 |
828 |
- |
|
4 |
orf E |
Envelope protein |
228 |
228 |
228 |
|
5 |
orf M |
Membrane protein |
669 |
669 |
669 |
|
6 |
orf 6 |
Hypothetical protein |
186 |
186 |
- |
|
7 |
orf 7a |
Hypothetical protein |
366 |
366 |
- |
|
8 |
orf 7b |
Hypothetical protein |
132 |
- |
- |
|
9 |
orf 8 |
Hypothetical protein |
366 |
366 |
- |
|
10 |
orf N |
Nucleocapsid phosphoprotein |
1260 |
1260 |
1260 |
|
11 |
orf 10 |
Hypothetical protein |
117 |
117 |
117 |
- absence of genes
A recent work published by Tang and his co-workers2 suggested the prevalence of two types of COVID–19, named as L and S type, based on their SNPs at ORF1ab and ORF8. L type was prevalent and accounted for about 70% of infected China population during Jan-Feb 2020. In the same study, they have noticed many nonsynonymous mutations in the 103 samples analyzed. However, the factors behind the emergence of L and S type are still ambiguous. In the current study, around 75 samples from different parts of the world were considered to study their evolutionary patterns. The genome size is diverse and shows many deletions and insertions. In any case, the genetic information indicates that SARS-CoV–2 is not derived from available virus data in a laboratory, as it shares homology towards SARS, betacoronavirus of bats, and pangolins14.
Phylogenetic evolution among COVID–19 positive samples
Due to the variations in the genome sequences of SARS-CoV–2, a genome phylogeny was constructed to understand the evolution and transmission pattern. The dendrogram suggested six groups (Fig. 2), of which group 1 had, isolates from Wuhan (IPBCAMS-WH–1/2019, WH–2/2019, and WH–3/2019), Shanghai (SH01/CHN), USA (USA-CA2), Australia (AUS/VIC01), South Korea (SNU01) and Sweden (Human/2020/SWE). These eight samples had a close resemblance with Wuhan-Hu–1 (reference isolate) and hence this group is described as Wuhan group. Group 2 is almost a clone where one international conveyance, i.e., Diamond Princess Cruise from Japan, and had over 700 coronavirus cases, with patients from the different parts of the world such as USA, Hongkong, Japan and China, the spread of the infection was vast in other countries. This group 2 is named as Diamond Princess cruise group, and the patients were quarantined in the ship for two weeks. The third European group had patients from Italy, Finland, Brazil, and a few cases from China, Japan, Taiwan and USA were grouped together, suggesting transmission from the Wuhan epicentre. Next is the Asian group (group IV), isolated in subjects from China, Japan, Taiwan, Nepal, India, and Hongkong. Group V is the USA group with the samples clubbed together with the patients from California (CA), Texas (TX), Washington (WA), and Illinois (IL). However, WA had a close cluster indicating the community transfer at USA. Also, a few patients of the USA had a travel history to China and other COVID–19 affected areas. Group VI is the Beijing group of China, which emerged from a patient from Yunnan. Phylogenomics suggests the high rate of either co-infection or recombination between different strains of SARS-CoV–2, showing its diversity. However, multiple samples have to be studied to ascertain sustainable facts, given their uncertain nature of genome variations. Tang et al.,2 could differentiate the SARS-CoV–2 by phylogenetics into L and S types based on their aggressiveness. It was perceived that L type was more virulent than S type and mainly possessed isolates from Wuhan, France, Australia, Singapore, USA, Hongkong, Taiwan, Japan and other countries. It will be always interesting to study the transmission of these mutations and its pathogenesis in any prevalent area.
Notable changes at Spike glycoprotein
Regardless of critical advances in cutting edge sequencing innovations, which have encouraged the disclosure of thousands of novel zoonotic viruses, methods for downstream evaluation of these novel sequences are deficient. Hence, an approach to determine the functional viromics in a more applicable way to understand the host-protein interactions is obligatory. The Spike (S) protein plays a role in the entry of virus into host cells, by binding to angiotensin converting enzyme 2 (ACE–2). The motif finder programme of S protein in Wuhan-Hu–1 showed 9 Pfam motifs (Fig. S1a) (https://www.genome.jp/tools/motif/). The S protein of CoVs isolated from bats and infected humans had >98% homology with few mutations. Basically, the S protein had an identical ribosome binding domain (RBD) and an O-linked glycan residue domain with polybasic cleavage site (RRARS) which was analysed through multiple alignment by Geneious Prime programme15. In the current investigation, we find that the RBD of all 75 samples is highly conserved with 9 amino acid variations when compared to Bat-RaTG13 (Fig. S1b). Similarly, the O-linked glycan residue domain had an insertion of four nucleotides PRRA (Fig. S1b). Later on, the samples which showed mutations at Spike glycoprotein were retrieved from Nextstrain database. Among the 358 samples analysed (data not shown), over 33 samples showed variations and suggested strain variation (Table 2). In samples from Peru (1), Israel (1), Greece (3), Spain (2), France (1), India (10) showed a common mutational site at D614G. However, these samples had variations in other ORFs of their genome, suggesting strain diversity. It was found that most of the strains possessed a unique pattern showing its strain-specificity. However, the immunological aspects of various strains and analysis is still lacking and need to be investigated. In the entire study, the structural genes of SARS-CoV–2 were mutated more rapidly than the non-structural genes.
It has been reported that Human angiotensin converting enzyme II (ACE2) receptor is the binding site for most SARS-CoV11. This was supported by another study which asserted that the novel SARS-CoV–2 utilizes the ACE2 to bind and find its entry in to the host cell4. ACE2 expression in organs like kidney and heart has been reported, providing a mechanism for the multi-organ dysfunction that can be seen with SARS-CoV–2 infection16,17. Interspecies diversity within different bat species harbouring the coronavirus was found18. In the same study, a surveillance of bat-CoV’s revealed the presence of SARS-like coronavirus, unclassified betacoronavirus and new betacoronavirus species. The co-infection of these CoVs in mineshaft bat species showed potential infection in the host. Further, the RBD of pangolin CoVs are indistinguishable from that of SARS-CoV–2 at 6 of 6 key amino acids examined previously18,19. This observation shows that entry of CoV in a host with human-like ACE2 could choose for a RBD with high-affinity15. Whether the ACE2 expression in these organs affects the SARS-CoV–2 infectivity remains unclear. Majority of the scientific reports state that acute kidney injury (AKI), abdominal discomfort and cardiac damage are the most commonly reported symptoms of COVID–1920,21 suggesting that SARS-CoV–2 may have a tropism for these organs. Such recombination’s and transmission could likewise choose for the insertion of the polybasic cleavage site (PCS), which is absent in pangolin and bats coronaviruses12. These PCS are highly conserved in a particular strain and shows their high pathogenicity, leading to a possible pandemic outbreak with high mortality or morbidity rate22, as observed in H5N1 virus. A putative recognition motif i.e., PRRARSV is present in all the sample analysed and is the active site for furin recognition14,23. The natural selection of virus with cleavage site would probably have taken when such virus similar to the existing SARS-CoV had undergone several passages under in vitro cell-line models. It is improbable that the O-linked glycan site would have triggered without immune pressure, which was not present in the cell-lines. The insertion of PRRA amino acids, make the SARS-CoV–2 novel and more pathogenic than SARS and MERS.
Table 2: Mutational changes observed different ORF’s of SARS-CoV-2 (mutations at spike glycoprotein (S) was represented in separate column)
Prevalence of mutant strains in certain provinces as biological markers
The samples from Brazil (3), France (1), Greece (4), Spain (3), Turkey (1), Peru (1), Israel (1), Sweden (7), India (12), China (3), USA (1), South Korea (1), South Africa (1) and Australia (2) possessed mutational changes at the Spike protein of SARS-CoV–2 and followed by their countries mortality rate was assessed (Table 3). Deletion of an amino acid tyrosine (Y), lysine (K) and Guanine (G) at 144, 528 and 107 positions was noticed in subjects of Indian (MT012098), Spain (MT233521) and France (MT320538), respectively, who had a travel history from Wuhan, China (https://www.covid19india.org/, www.nextstrain.org). Though the spike protein had no variation at the ribosome binding site, the mutations noticed in these 42 samples would either increase or decrease the severity of the outbreak. However, further analysis is required to prove the severity of these samples. The prevalence of these strains in different geographical regions has to be assessed, as these might serve as biomarkers in understanding the antigenic and immunogenic changes.
Table 3: Mutational changes in the spike protein of SARS-CoV-2 with their countries mortality rates
|
Accession Number |
Sample ID |
Country |
Mutations at S protein |
No. of confirmed cases |
No. of Deaths |
Mortality rate (%) |
|
|
MT350282 |
BRA/SP02cc/2020 |
Brazil |
N74K |
79695 |
5513 |
6.91 |
|
|
MT263074 |
PER/Peru-10/2020 |
Peru |
D614G |
33931 |
943 |
2.77 |
|
|
MT233521 |
Valencia6/2020 |
Spain |
K528del |
239639 |
24543 |
10.24 |
|
|
MT292569 |
Valencia13/2020 |
Spain |
D614G |
239639 |
24543 |
10.24 |
|
|
MT292575 |
Valencia16/2020 |
Spain |
D614G |
239639 |
24543 |
10.24 |
|
|
MT276598 |
ISR/ISR-IT0320 |
Israel |
D614G |
15870 |
219 |
1.37 |
|
|
MT328032 |
GRC/10/2020 |
Greece |
D614G |
2576 |
136 |
5.39 |
|
|
MT328035 |
GRC/13/2020 |
Greece |
D614G |
2576 |
136 |
5.39 |
|
|
MT328034 |
GRC/16/2020 |
Greece |
I197Y |
2576 |
136 |
5.39 |
|
|
MT328033 |
GRC/12/2020 |
Greece |
D614G |
2576 |
136 |
5.39 |
|
|
MT093571 |
210/human/2020/SWE |
Sweden |
F797C |
21092 |
2586 |
12.26 |
|
|
MT049951 |
Yunnan-01/human/2020 |
China |
Y28N |
82862 |
4633 |
5.59 |
|
|
MT039890 |
SNU01/South Korea |
South Korea |
S221W |
10765 |
247 |
2.29 |
|
|
MT007544 |
Australia/VIC01/2020 |
Australia |
S247R |
6753 |
91 |
1.34 |
|
|
MT327745 |
TUR/ERAGEM-001/2020 |
Turkey |
V772I |
117589 |
3081 |
2.62 |
|
|
MT320538 |
FRA/KRA-ROB/2020 |
France |
G107del, D614G |
166420 |
24087 |
14.47 |
|
|
MT300186 |
USA-CA |
United States of America |
D614G |
1067382 |
61849 |
5.79 |
|
|
MT324062 |
ZAF/R03006/2020 |
South Africa |
D614G |
5350 |
103 |
2.42 |
|
|
MT012098 |
29/human/2020/IND |
India |
Y144del, R408I |
33610 |
1079 |
3.21 |
|
|
MT050493 |
166/human/2020/IND |
India |
A930V |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_426414 |
India/GBRC1/2020 |
India |
Q271R, D614G |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_426415 |
India/GBRC1s/2020 |
India |
Q271R, D614G |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_430468 |
India/S2/2020/West Bengal |
India |
D614G, G1124V |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_430464 |
India/S3/2020/West Bengal |
India |
D614G, G1124V |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_424365 |
India/3239/2020 |
India |
D614G |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_428482 |
India/nimh-0182/2020 |
India |
D614G, C1250F |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_426424 |
USA/IN_92003/2020 |
India |
D614G, L1203F |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_426423 |
USA/IN_82003/2020 |
India |
D614G, L1203F |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_426422 |
USA/IN_72003/2020 |
India |
D614G, L1203F |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_420551 |
India/777/2020 |
India |
D614G |
33610 |
1079 |
3.21 |
|
|
EPI_ISL_429691 |
BRA/CV35/2020 |
Brazil |
Y695S, G832D, H1088N |
79695 |
5513 |
6.91 |
|
|
EPI_ISL_429677 |
BRA/CV17/2020 |
Brazil |
K776T |
79695 |
5513 |
6.91 |
|
|
EPI_ISL_426882 |
Australia/VIC913/2020 |
Australia |
G446V, G1124V |
6753 |
91 |
1.34 |
|
|
EPI_ISL_429129 |
Sweden/20-08681 |
Sweden |
D80Y |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_430859 |
Sweden/20-08717 |
Sweden |
K1073N |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_429152 |
Sweden/20-50179 |
Sweden |
V62F |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_429157 |
Sweden/20-50234 |
Sweden |
M1237I |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_429116 |
Sweden/20-08143 |
Sweden |
Y917H |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_411951 |
Sweden/01/2020 |
Sweden |
F797C |
21092 |
2586 |
12.26 |
|
|
EPI_ISL_415709 |
Hanghou/ZJU-01 |
China |
R682Q |
82862 |
4633 |
5.56 |
|
|
EPI_ISL_421259 |
Pingxiang/JX151 |
China |
S254F |
82862 |
4633 |
5.56 |
While analysing the cases in these provinces, it was noticed that the death rate was low in South Korea, Greece, Brazil, Israel, Peru, Turkey, South Africa and Australia, thus COVID–19 cases curve has declined. However, these states also followed many measures in controlling the outbreak such as early lockdowns, self-isolation, social distancing, hygienic practicesas instructed by their governments. However, in Sweden, and India the COVID–19 cases are being analysed and the graph is up surging due to a hike in the confirmed cases (https://www.worldometers.info/coronavirus/). It can be seen that the death toll is comparatively low in these areas when compared to the other areas such as Wuhan, Italy, Spain, France, United States of America and Germany. This might indicate that the prevalence of mutated strains which might have emerged during coinfection within the provinces, would have either reduced or increased its severity. Furthermore, the pathogenicity probabilistically was assessed in the putative neutral variants. The MutPred Indel software could assess the sites responsible for its virulence (Table 4). The sample Human/2020/SWE from Sweden had not shown any pathogenic sites when compared to Wuhan-Hu–1 (reference strain). However, most of the subjects analysed had putative variants of S protein showing several post-translational mechanisms such as, catalytic site, proteolytic cleavage, Iron binding site, glycosyl-phosphatidylinositol (GPI) anchor amidation, PPI hotspot, sulfation, transmembrane region, copper binding, signal cleavage, cytoplasmic loop, C-terminal signal, and O-linked glycosylation sites, suggesting probability of more virulence in these samples (P>0.5). Most of the isolates had catalytic site, PPI hotspot and Iron binding as their common mechanism for pathogenesis. However, including these mechanisms many other isolates possessed extra mechanisms for their mode of action. For example, the mechanism of palmitoylation was noted only in a sample from an Indian subject (166/Human/2020/IND). The subjects from Turkey and Brazil had disulfite linkage and sulfation as their mechanism. Considering the prevailing situation in India, the presence of pathogenic variant of spike protein it can be postulated that the rate of COVID–19 cases would increase eventually during the next few days. Hence, every citizen has to be abide to the preventive measures.
Table 4: Pathogenicity prediction with MutPred-Indel model in the putative Spike protein variants of SARS-CoV-2 samples
|
Sample ID |
Country |
Site |
P-score |
Mechanism for pathogenicity |
|
Wuhan-Hu-1 (MN908947) (reference sample) |
China |
S221 |
0.515 |
Catalytic site, Iron binding |
|
Human/2020/SWE |
Sweden |
- |
- |
- |
|
29/Human/2020/IND |
India |
H145 |
0.385 |
PPI hotspot, Catalytic site, Iron binding, O-linked glycosylation, C-terminal signal |
|
166/Human/2020/IND |
India |
S247 |
0.36415 |
GPI anchor amidation, PPI hotspot,, Catalytic site, signal cleavage, Iron binding, palmitoylation |
|
Yunnan-01 |
Yunnan |
N28 |
0.449 |
Iron binding |
|
SNU-01 |
South Korea |
S221 |
0.354 |
Catalytic site, Iron binding |
|
Aus/VIC01 |
Australia |
R247 |
0.4547 |
PPI hotspot, Proteolytic cleavage, Copper binding, Catalytic site, Iron binding |
|
TUR/ERAGEM-001/2020
|
Turkey |
V3F |
0.35058 |
Catalytic site, PPI hotspot, Iron binding, Disulfide linkage, Sulfation |
|
BRA/SP02cc/2020 |
Brazil |
K83 |
0.346 |
Catalytic site, PPI hotspot, Iron binding, Disulfide linkage, Sulfation |
|
ZAF/R03006/2020 |
South Africa |
G623 |
0.3458 |
Catalytic site, Iron binding, PPI hotspot, Disulfide linkage, GPI anchor amidation |
|
GRC/13/2020 |
Greece |
I206 |
0.35209 |
PPI hotspot, Catalytic site, Iron binding, Disulfide linkage, GPI anchor amidation |
|
India/GBRC1s/2020 |
India |
R271 |
0.36268 |
PPI hotspot, Catalytic site, Iron binding, Disulfide linkage, GPI anchor amidation |
|
FRA/KRA-ROB/2020 |
France |
L1203 |
0.541 |
PPI hotshot, Catalytic site, Iron binding |
|
USA/IN_82003/2020 |
USA |
F1203 |
0.453 |
Signal cleavage, Iron binding, Transmembrane region, signal helix, PPI hotspot |
|
Valencia6_ESP |
Spain |
S529 |
0.370 |
Iron binding, GPI anchor amidation, Catalytic site, Signal cleavage, C-terminal signal |
|
SWE/20-08681 |
Sweden |
Y80 |
0.589 |
PPI hotspot, Iron binding |
|
SWE/20-08717 |
Sweden |
Y80 |
0.389 |
Cytoplasmic loop, PPI hotspot, O-linked glycosylation, catalytic site, signal helix |
|
SWE/20-50179 |
Sweden |
V62 |
0.517 |
Iron binding, PPI hotspot, catalytic site, cytoplasmic loop, O-linked glycosylation |
|
SWE/20-50234 |
Sweden |
Y80 |
0.389 |
PPI hotspot, cytoplasmic loop, C-terminal signal, Signal cleavage, Iron binding |
|
SWE/20-08143 |
Sweden |
Y80 |
0.390 |
PPI hotspot, O-linked glycosylation, catalytic site, Iron binding, C-terminal signal |
|
SWE/20-01/2020 |
Sweden |
Y80 |
0.391 |
PPI hotspot, Catalytic site, Iron binding, C-terminal signal, signal helix |
- no mechanism of pathogenicity was detected
Petit et al. 24suggested that palmitoylation aids in providing anchoring ability during cell fusion and receptor binding in SARS-CoV, this mechanism noted in COVID–19 sample suggest conformational changes during palmitoylation process leading to signal transduction mechanism at both intra- and- extracellular domains. Sulfation is a process for protein-protein interaction and found to play a role in extracellular extension for high affinity towards binding, leading to the activation of receptors and stability of proteins by correct protein folding mechanism 25. Hence, mutational changes in the spike glycoprotein may instigate its conformational changes, which is most likely to prompt the evolving antigenicity26. Studies pertaining to the localization of amino acids associated with this protein among different variants of SARS-CoV–2, are readily not available. A recent study on protein-protein interactions (PPI) by Gordon et al.27 had suggested that the spike protein has the ability to interact with GOLGA7-ZDHHC5 acyl transferase complex and can be a therapeutic target. GPI anchor sites are also found to target host innate defense system, which allows functions in trafficking, cell adhesion and metabolism. It was reported that, Bone marrow stroma antigen 2 (BST2), also called as CD317 or tetherin has a capacity to inhibit the enveloped virus release into the host, hence such sites can be targeted for therapeutics28. It will be important to explore these mutational changes. Along these lines, reinforcing SARS-CoV–2 surveillance among different geographical regions can provide scientific evidence for its more pathogenicity and allows in taking preventive and controlling measures in the transmission of disease.
ACE2 expression in human organs targeted in kidney
SARS-CoV–2 infection starts by binding of the viral surface spike protein to the human angiotensin-converting enzyme 2 (ACE2) receptor following modification of the spike protein by transmembrane protease serine 2 (TMPRSS2)29. Initially, ACE2 is expressed in the lung (principally Type II alveolar cells7) and seems to be the predominant portal of entry. Considering SARS entry into target human cells, it can be observed that the expression of ACE2 protein is significantly found in the epithelial cells of the lung alveoli and small intestine and endothelial cells of organs including spleen, kidney, liver, lymph nodes, brain30,31. Burgeoning data confirm association of COVID–19 infection with increased morbidity and mortality from kidney disease. It is important to investigate whether SARS-CoV–2 replication occurs in these organs contributing to the virus disseminating throughout the body.
High expression of ACE2 was noticed in proximal tubular cells and to a lesser extent in podocytes, however, kidney glomerular endothelial and mesangial cells were not affected17. It was perceived that only 6% of patients infected with SARS-CoV experienced Acute Kidney Injury (AKI) during SARS outbreak during 2003 32. Furthermore, AKI was identified as a serious complication of SARS, with mortality of 92% in patients 32. During post-mortem from SARS patients, SARS-CoV viral particles were noticed in renal specimens, suggesting AKI was caused by active replication of SARS-CoV in tubular cells 32. They suggested that renal impairment was likely associated with multi-organ failure as SARS-CoV was not demonstrable in any of the examined patients. Further, AKI (including cytokine release syndrome and SARS patients) might be a specific pathogenic condition, and might not be due to the active replication of virus at kidneys 32, 33. An increased viral infection in alveolar cells leads to the production of large amount of cytokines, causing multiple-organ failure. Previously a study had reported that release of interferon-gamma-related cytokine increased the severity of organ damage in SARS patients34. Recently, a study described that the human kidney is a specific target for SARS-CoV–2 infection35. The difference between the higher renal tropism of SARS-CoV–2 versus SARS-CoV can be assessed by the increased affinity of SARSCoV–2 for ACE2, contributing towards pronounced infection of the kidney, leading to viral reservoir 36.
In addition, a small survey on COVID–19 patients has revealed that, proteinuria and haematuria are common features that were noticed in 40% of patients post hospitalization 37. A reduced density of inflammation and edema was observed in CT scan reports of kidneys samples infected with SARS-CoV–238. Furthermore, SARS-CoV–2 seems to be affected more by AKI frequently than subjects infected with SARS-CoV37. A very recent study by Yao et al.39 confirms that SARS-CoV–2 infection damages vessels, kidney and other organs, in addition to the lungs,. Hyaline thrombi are found in small vessels in different organs. It is of utmost importance to investigate pathological changes in autopsy material. Before organ donation is considered in future, it will be important to investigate whether the SARS-CoV–2 has infected the kidneys; the risk of such organ grafts has not been reported as yet. In any case, it has been indicated that SARSCoV–2 has a high tropism for the kidney, where it has been shown to reproduce in practically 30% of COVID–19 patients40. Consequently, screening for COVID–19 in kidney donors is probably more important during screening time and need to be quarantined for 14–28 days who possess either symptoms or had a travel history to high-risk regions 31. A research study demonstrated that more than 66% of patients had died with COVID–19 infection who had diabetes or cardiovascular disease41. As a first-line treatment, angiotensin-receptor blockers (ARBs) were given to COVID–19 patients. Certain reports revealed that ARBs were found to express ACE2 by nearly 2 to 5 fold in kidney and heart samples 42–45. Since SARSCoV–2 has a high tropism for the kidney35,40, investigating how ARBs affect the infection rate and renal and cardiac injury in COVID–19 infection is of great importance.
Bat coronavirus remains a considerable worldwide risk to general wellbeing of humans. The genomic highlights depicted in the present study might clarify the transmissibility of SARS-CoV–2 in human race, yet its inception is a question. Despite the fact that the nCoV–2019 had close genetic relatedness towards RaTG13 of bat coronavirus, which was isolated in 2013 (however, the genome data of this strain was made open just after the outbreak of COVID–19). The current investigation provided three types of variants in the SARS-CoV–2 genome. The phylogeny showed six clusters which includes Wuhan, Diamond princess, European, Asian, USA and Beijing group. The polybasic cleavage site in the Spike protein of COVID–19 isolates is very conserved and different from bat and pangolins CoV’s, suggesting its novel pathogenic nature. Mutations in the spike protein could either reduce or increase the severity of the outbreak. The mechanism of pathogenicity among putative variants of spike surface glycoprotein suggested more virulence in few samples of India, Australia, Greece, South Korea and Yunnan. The available clinical data have confirmed that AKI is one of the main risk factors in the prognosis of COVID–19. Patients with diabetic nephropathy, end-stage renal disease, and, renal transplantation may be at high risk of the SAR-CoV–2. However, spread of SARS-like infections from various intermediate animals may assist in explaining its emergence or outbreaks. The recognizable proof of a potential intermediate host of SARS-CoV–2, along with their genome sequence data of the virus at early stage, would also be profoundly useful.
Information related to daily cases of COVID–19 and SARS-CoV–2 genome data
The genome data of SARS-CoV–2 was retrieved from the public repositories like NCBI data and the global information on COVID–19 cases was obtained through worldometers (https://www.worldometers.info/coronavirus/) and NEXTstrain (https://nextstrain.org/ncov) websites. Totally 75 genomes were considered based on the variation in their genome size, country and divergence. The samples used in the current study are enlisted in Table S1. However, the genome ethnicity and racial inheritance of all the samples are not available.
Comparative genome analysis
Three genomes MT121215 and MT044528 were considered in the study which possessed highest and lowest genome size, and were compared to the reference sample (MN908947). The comparative genome analysis was performed by using Geneious Prime Software Version 2019.2.113.
Phylogenetic evolution
To further analyze the evolution of isolates, a phylogenetic tree (n = 75) was constructed using the complete genome data of SARS-CoV–2 by using MEGA-X (Molecular Evolutionary Genetic Analysis) software46. The evolutionary history was deduced by using the Neighbor-Joining method with 500 bootstrap replicates47. Further, the evolutionary distances were computed using the Maximum Composite Likelihood method. The analysis involved 75 nucleotide sequences. There was a total of 39547 positions in the final dataset.
Bioinformatics tools used in the analysis of spike protein
Multiple sequence analysis of the spike protein among the 75 isolates was performed by using Clustal W programme48. The VIPR database is used to analyse the single nucleotide polymorphism (SNP) at spike glycoprotein as described by Pickett et al.49. Further Genome Detective Virus Tools was also used to look at the mutational analysis (https://www.genomedetective.com/app/typingtool/virus/). The pfam motifs were analysed by using genome motifs database (https://www.genome.jp/tools/motif/). Further, the ribosome binding region and the polybasic cleavage site was determined as described by Andersen et al.15.
Pathogenicity prediction in the phenotypes
To further assess the pathogenicity of the variants (putatively neutral) a machine learning-based method software package was employed for the spike protein phenotypes. The MutPred-Indel software assess the probabilistically the pathogenicity of the neutral variants and suggests the features affecting the phenotypes50.
Authors Contribution
SMD performed the bioinformatics analysis on the collected data, planned the work and wrote the manuscript, AP had collected the data and worked on kidney biopsy samples of COVID-19, BK edited the manuscript, and KS monitored the analysis and edited the manuscript.
Acknowledgment
The authors thank all family members and friends for all the support during the crisis period of COVID-19. The work was not supported by any fund, the article was written to analyse the genome data of COVID-19 considering the present awareness.
Conflict of Interest
The author claims no conflict of interest.
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