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. 2020 Jun 2;98(7):495–504. doi: 10.2471/BLT.20.253591

Variant analysis of SARS-CoV-2 genomes

Analyse des variantes du génome de SARS-CoV-2

Análisis de variantes de los genomas del SARS-CoV-2

تحليل الأشكال المختلفة لجينومات مرض سارس كوف 2

严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 基因组的变异体分析

Анализ вариантов геномов SARS-CoV-2

Takahiko Koyama a,✉, Daniel Platt a, Laxmi Parida a
PMCID: PMC7375210  PMID: 32742035

Abstract

Objective

To analyse genome variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

Methods

Between 1 February and 1 May 2020, we downloaded 10 022 SARS CoV-2 genomes from four databases. The genomes were from infected patients in 68 countries. We identified variants by extracting pairwise alignment to the reference genome NC_045512, using the EMBOSS needle. Nucleotide variants in the coding regions were converted to corresponding encoded amino acid residues. For clade analysis, we used the open source software Bayesian evolutionary analysis by sampling trees, version 2.5.

Findings

We identified 5775 distinct genome variants, including 2969 missense mutations, 1965 synonymous mutations, 484 mutations in the non-coding regions, 142 non-coding deletions, 100 in-frame deletions, 66 non-coding insertions, 36 stop-gained variants, 11 frameshift deletions and two in-frame insertions. The most common variants were the synonymous 3037C > T (6334 samples), P4715L in the open reading frame 1ab (6319 samples) and D614G in the spike protein (6294 samples). We identified six major clades, (that is, basal, D614G, L84S, L3606F, D448del and G392D) and 14 subclades. Regarding the base changes, the C > T mutation was the most common with 1670 distinct variants.

Conclusion

We found that several variants of the SARS-CoV-2 genome exist and that the D614G clade has become the most common variant since December 2019. The evolutionary analysis indicated structured transmission, with the possibility of multiple introductions into the population.

Introduction

In late 2019, several people in Wuhan, China, were presenting with severe pneumonia at the hospitals. As the number of patients rapidly increased, the Chinese government decided on 23 January 2020 to lock down the city to contain the virus. Unfortunately, the virus had already spread across China and throughout the world. The World Health Organization (WHO) officially declared the outbreak a pandemic on March 11, 2020. As of 23 May 2020, over 5 million cases worldwide had been reported to WHO and the death toll has exceeded 330 000.1

Researchers isolated the virus causing the pneumonia in December 2019 and found it to be a strain of β-coronavirus (CoV). The virus showed a high nucleotide sequence homology with two severe acute respiratory syndrome (SARS)-like bat coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21 (88% homology) and with SARS-CoV (79.5% homology), while only 50% homology with the Middle East respiratory syndrome coronavirus (MERS) CoV.2,3 The virus, now named SARS-CoV-2, contains a single positive stranded RNA (ribonucleic acid) of 30 kilobases, which encodes for 10 genes.4 Researchers have shown that the virus can enter cells by binding the angiotensin-converting enzyme 2 (ACE2), through its receptor binding domain in the spike protein.5

The virus causes the coronavirus disease 2019 (COVID-19), with common symptoms such as fever, cough, shortness of breath and fatigue.6,7 Early data indicated that about 20% of patients develop severe COVID-19 requiring hospitalization, including 5% who are admitted to the intensive care unit.8 Initial estimates of the case fatality rates were from 3.4% to 6.6% which is lower than that of SARS or MERS, 9.6% and 34.3% respectively.9–11 The mortality from COVID-19 is higher in people older than 65 years and in people with underlying comorbidities, such as chronic lung disease, serious heart conditions, high blood pressure, obesity and diabetes.12–14

Community transmission of the virus, as well as anti-viral treatments, can engender novel mutations in the virus, potentially resulting in more virulent strains with higher mortality rates or emergence of strains resistant to treatment.15 Therefore, systematic tracking of demographic and clinical patient information, as well as strain information is indispensable to effectively combat COVID-19.

Here we analysed the SARS-CoV-2 genome from 10 022 samples to understand the variability in the viral genome landscape and to identify emerging clades.

Methods

In total, we downloaded 15 755 genome sequences from the following databases: the Chinese National Microbiology Data Center on 1 February 2020; the Chinese National Genomics Data Center Genome Warehouse on 4 February 2020; GISAID16 on 1 May 2020 and GenBank on 1 May 2020. We removed redundant sequences with the China National Center for Bioinformation annotations. To reduce the number of false positive variants, we removed sequences with more than 50 ambiguous bases.

For this study, we used the sequence of established SARS-CoV-2 reference genome, NC_045512.17 This genome was sequenced in December 2019. Each sample was first aligned to the reference genome in a pairwise manner using EMBOSS needle (Hinxton, Cambridge, England), with a default gap penalty of 10 and extension penalty of 0.5.18 Then, we developed a custom script in Python (Python Software Foundation, Wilmington, United States of America) to extract the differences between the genome variants and the reference genome. Nucleotide variants in the coding regions were converted to corresponding encoded amino acid residues. For the open reading frame 1 (ORF1), we used the protein coordinates from YP_009724389.119 for translation. Finally, we carefully investigated stop-gained and frameshift variants causing deletions and insertions to detect potential artefacts caused by undetermined or ambiguous bases. The results are provided in a list of variants (available in the data repository).20

Using the identified recurrent variants, we performed hierarchical clustering in SciPy library, Python, to identify clades. First, a binary matrix of samples and distinct variants was created. Then, we did hierarchical clustering using the Ward’s method21 in SciPy library.22

We investigated the mutation patterns of SARS-CoV-2 to find potential causes of mutations, by looking at the changes in bases. Since coronavirus genomes are positive sense, single stranded RNA, we did not combine C > T with G > A mutations.

The spike protein is a key protein for SARS-CoV-2 viral entry and a target for vaccine development. We, therefore, wanted to find amino acid conservation between other coronavirus sequences in the spike protein. We used the basic local alignment search tool BLAST (National Center for Biotechnology Information [NCBI], Bethesda, United States)23 followed by the constraint-based multiple alignment tool COBALT (NCBI, Bethesda, United States).24 We carefully investigated mutations within the receptor binding domain and predicted B-cell epitopes.25,26 The mutations were further analysed to identify cross species conservation and to understand the nature of amino acid changes. We visualized the aligned sequence using the open source software alv.27

For the phylogenetic analysis, we used the open source software Bayesian evolutionary analysis by sampling trees (BEAST), version 2.5.28 BEAST uses a Bayesian Monte-Carlo algorithm generating a distribution of likely phylogenies given a set of priors, based on the probabilities of those tree configurations determined from the viral genomes. This analysis presents a different view than the variant analysis described above and is an independent test of the structure that individual haplogroup markers identify. First, we aligned sequences to NC_045512, using the multiple sequence alignment software, MAFFT.29 Subsequently, we adjusted for length and sequencing errors, by truncating the bases in the 5’-UTR and 3’-UTR, without losing key sites. We excluded sequences showing a variability higher than 30 bases. For an optimal output of the phylogenetic tree, we randomly selected a subset of 2000 samples by using a random number generator in Python. We ran BEAST using sample collection dates with the Hasegawa-Kishino-Yano mutation model,30 with the strict clock mode. Finally, we estimated the mutation rate and median tree height from the resulting BEAST trees.

Results

In total, we analysed 10 022 SARS CoV-2 genomes (sequences are available from the data repository)20 from 68 countries. Most genomes came from the United States of America (3543 samples), followed by the United Kingdom of Great Britain and Northern Ireland (1987 samples) and Australia (760 samples; Box 1). We detected in total 65776 variants with 5775 distinct variants. The 5775 distinct variants consist of 2969 missense mutations, 1965 synonymous mutations, 484 mutations in the non-coding regions, 142 non-coding deletions, 100 in-frame deletions, 66 non-coding insertions, 36 stop-gained variants, 11 frameshift deletions and two in-frame insertions (Table 1).

Box 1. Number of samples of severe acute respiratory syndrome coronavirus 2 from each country or territory included in sequence analysis, 2019–2020.

United States 3543 samples; United Kingdom 1987 samples; Australia 760 samples; Iceland 461 samples; Netherlands 402 samples; China 342 samples; Belgium 335 samples; Denmark 260 samples; France 218 samples; Spain 148 samples; Russian Federation 141 samples; Canada 117 samples; Luxembourg 112 samples; Sweden 107 samples; Portugal 96 samples; Japan 95 samples; Taiwan, China 85 samples; Singapore 71 samples; Germany 61 samples; Switzerland 55 samples; India 51 samples; Italy 44 samples; Brazil 43 samples; China, Hong Kong Special Administrative Region 43 samples; Greece 41 samples; Republic of Korea 36 samples; Czechia 34 samples; Turkey 25 samples; Argentina 24 samples; Finland 24 samples; Thailand 22 samples; Jordan 20 samples; Norway 18 samples; Austria 15 samples; Senegal 15 samples; Democratic Republic of the Congo 14 samples; Georgia 12 samples; Malaysia 12 samples; Mexico 11 samples; Ireland 10 samples; Latvia 10 samples; Viet Nam 10 samples; Poland 9 samples; Sri Lanka 8 samples; Chile 7 samples; Kuwait 7 samples; New Zealand 6 samples; Costa Rica 5 samples; South Africa 5 samples; Estonia 4 samples; Slovakia 4 samples; Slovenia 4 samples; Algeria 3 samples; Gambia 3 samples; Hungary 3 samples; Israel 3 samples; Pakistan 3 samples; Saudi Arabia 3 samples; Belarus 2 samples; Nepal 2 samples; Peru 2 samples; Philippines 2 samples; Qatar 2 samples; Cambodia 1 sample; Colombia 1 sample; Egypt 1 sample; Iran (Islamic Republic of) 1 sample; and Lithuania 1 sample.

Table 1. Number of gene variants in SARS-CoV-2 genomes,2019–2020.

Genome segmenta Missense mutation Synonymous mutation Non-coding region
 In-frame
 Frameshift deletion Stop-gained Total
Mutation Deletion Insertion Deletion Insertion
ORF1ab 1905 1344 0 0 0 57 2 7 13 3328
S 394 260 0 0 0 27 0 0 6 687
ORF3a 169 71 0 0 0 5 0 1 1 247
E 27 15 0 0 0 1 0 0 0 43
M 53 71 0 0 0 0 0 0 0 124
ORF6 28 11 0 0 0 2 0 0 2 43
ORF7 59 29 0 0 0 1 0 2 6 97
ORF8 68 26 0 0 0 1 0 0 7 102
ORF10 20 12 0 0 0 0 0 1 1 34
N 246 126 0 0 0 6 0 0 0 378
Intergenic 0 0 0 7 2 0 0 0 0 9
5’-UTR 0 0 260 50 37 0 0 0 0 347
3’-UTR 0 0 224 85 27 0 0 0 0 336
Total 2969 1965 484 142 66 100 2 11 36 5775
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E: envelope protein; M: membrane glycoprotein; N: nucleocapsid phosphoprotein; ORF: open reading frame; S: spike glycoprotein; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; UTR: untranslated region.

a Genes are in italics.

Note: We compared 10 022 genomes to the NC_045512 genome sequence.17

Of the 2969 missense variants, 1905 variants are found in ORF1ab, which is the longest ORF occupying two thirds of the entire genome. ORF1ab is transcribed into a multiprotein and subsequently cleaved into 16 nonstructural proteins (NSPs). Of these proteins, NSP3 has the largest number of missense variants among ORF1ab proteins. Of the NSP3 missense variants, A58T was the most common (159 samples) followed by P153L (101 samples; Table 2). We also detected mutations in the nonstructural protein RNA-dependent RNA polymerase (RdRp), such as P323L (6319 samples). Deletions are also common in 3′-5′exonuclease (11 deletions) including those resulting in frameshifts. A comprehensive list of variants is available in data repository.20

Table 2. Number of variants in the open reading frame 1ab of SARS-CoV-2 genomes, by final cleaved protein, 2019–2020.

Final proteina Missense mutation Synonymous mutation Non-coding region
 In-frame
 Frameshift deletion Stop-gained Total
Mutation Deletion Insertion Deletion Insertion
NSP1 64 45 0 0 0 13 0 1 0 123
NSP2 237 130 0 0 0 5 0 0 0 372
NSP3 547 349 0 0 0 16 0 2 3 917
NSP4 116 113 0 0 0 1 0 0 1 232
3CLPro 67 54 0 0 0 0 0 0 0 121
NSP6 82 67 0 0 0 4 1 2 0 156
NSP7 27 21 0 0 0 0 0 0 0 48
NSP8 60 25 0 0 0 1 0 0 1 87
NSP9 29 22 0 0 0 0 0 0 1 52
NSP10 25 25 0 0 0 0 0 0 2 52
RdRp 194 157 0 0 0 2 0 1 3 357
Helicase 148 101 0 0 0 0 0 0 0 249
ExoN 141 118 0 0 0 11 0 1 2 273
endoRNase 92 67 0 0 0 3 0 0 0 162
OMT 76 50 0 0 0 1 1 0 0 128
Total 1905 1344 0 0 0 57 2 7 13 3329
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3CLPro: 3C like protease; ExoN: 3-’5′ exonuclease; NSP: non-structural protein; OMT: O-methyltransferase; RdRp: RNA-dependent RNA polymerase; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

a The open reading frame 1ab gene codes for a polyprotein, which a viral protease cleaves in to several protein after translation.

Note: We compared 10 022 genomes to the NC_045512 genome sequence.17

Variants with recurrence over 100 samples are shown in Table 3. The most common variants were the synonymous variant 3037C > T (6334 samples), ORF1ab P4715L (RdRp P323L; 6319 samples) and SD614G (6294 samples). They occur simultaneously in over 3000 samples, mainly from Europe and the United States. Other variants including ORF3a Q57H (2893 samples), ORF1ab T265I (NSP3 T85I; 2442 samples), ORF8 L84S (1669 samples), N203_204delinsKR (1573 samples), ORF1ab L3606F (NSP6 L37F; 1070 samples) were the key variants for identifying clades.

Table 3. Variants of SARS-CoV-2 genomes observed in more than 100 samples, 2019–2020.

Genomic change Type of mutation Gene/protein Amino acid change No. of samples
3037C > T Synonymous ORF1ab/NSP3 F924F/F106F 6334
14408C > T Missense ORF1ab/RdRp P4715L/P323L 6319
23403A > G Missense S D614G 6294
241C > T Non-coding 5’-UTR NA 5928
25563G > T Missense ORF3a Q57H 2893
1059C > T Missense ORF1ab/NSP2 T265I/T85I 2442
28144T > C Missense ORF8 L84S 1669
8782C > T Synonymous ORF1ab/NSP4 S2839S/S76S 1598
28881_28883delinsAAC Missense N 203_204delinsKR 1573
18060C > T Synonymous ORF1ab/ExoN L5932L/L7L 1178
17858A > G Missense ORF1ab/helicase Y5865C/Y541C 1166
17747C > T Missense ORF1ab/helicase P5828L/P504L 1147
11083G > T Missense ORF1ab/NSP6 L3606F/L37F 1070
14805C > T Synonymous ORF1ab/RdRp Y4847Y/Y455Y 844
26144G > T Missense ORF3a G251V 769
20268A > G Synonymous ORF1ab/endoRNase L6668L/L216L 452
17247T > C Synonymous ORF1ab/helicase R5661R/R337R 325
2558C > T Missense ORF1ab/NSP2 P765S/P585S 274
15324C > T Synonymous ORF1ab/RdRp N5020N/N628N 267
1605_1607del In-frame deletion ORF1ab/NSP2 D448del/D268del 250
18877C > T Synonymous ORF1ab/ExoN L6205L/L280L 234
2480A > G Missense ORF1ab/NSP2 I739V/I559V 232
27046C > T Missense M T175M 221
11916C > T Missense ORF1ab/NSP7 S3884L/S25L 185
2416C > T Synonymous ORF1ab/NSP2 Y717Y/Y537Y 170
1440G > A Missense ORF1ab/NSP2 G392D/G212D 164
27964C > T Missense ORF8 S24L 164
36C > T Non-coding 5’-UTR NA 163
2891G > A Missense ORF1ab/NSP3 A876T/A58T 159
28854C > T Missense N S194L 155
1397G > A Missense ORF1ab/NSP2 V378I/V198I 139
28657C > T Synonymous N D128D 139
28688T > C Synonymous N L139L 138
18998C > T Missense ORF1ab/ExoN A6245V/A320V 137
28311C > T Missense N P13L 136
28863C > T Missense N S197L 136
9477T > A Missense ORF1ab/NSP4 F3071Y/F308Y 136
25979G > T Missense ORF3a G196V 132
29742G > T Non-coding 3’-UTR NA 131
25429G > T Missense ORF3a V13L 128
24034C > T Synonymous S N824N 118
29870C > A Non-coding 3’-UTR NA 115
28077G > C Missense ORF8 V62L 113
26729T > C Synonymous M A69A 106
27_37del Non-coding deletion 5’-UTR NA 106
19_24del Non-coding deletion 5’-UTR NA 105
514T > C Synonymous ORF1ab/NSP1 H83H/H83H 105
23731C > T Synonymous S T723T 102
3177C > T Missense ORF1ab/NSP3 P971L/T1198K 101
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del: deletion; delins: deletion–insertion; ExoN: 3’-5′ exonuclease; NSP: non-structural protein; M: membrane glycoprotein; N: nucleocapsid phosphoprotein; NA: not applicable; ORF: open reading frame; RdRp: RNA-dependent RNA polymerase; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; S: spike glycoprotein; UTR: untranslated region.

Note: We compared 10 022 genomes to the NC_045512 genome sequence.17

We identified six major clades with 14 subclades (Fig. 1 and Table 4). The largest clade is D614G clade with five subclades. Most samples in the D614G clade also display the non-coding variant 241C > T, the synonymous variant 3037C > T and ORF1ab P4715L. Within D614G clade, D614G/Q57H/T265I subclade forms the largest subclade with 2391 samples. The second largest major clade is L84S clade, which was observed among travellers from Wuhan in the early days of the outbreak, and the clade consists of 1662 samples with 2 subclades. The L84S/P5828L/ subclade is predominantly observed in the United States. Among the L3606F subclades, L3606F/G251V/ forms the largest group with 419 samples. G251V frequently appears in samples from the United Kingdom (329 samples), Australia (95 samples), the United States (80 samples) and Iceland (76 samples). However, the basal clade now accounts only for a small fraction of genomes (670 samples mainly from China). The remaining two clades D448del and G392D are small and they are without any significant subclades at this point.

Fig. 1.

A graphical representation of variants found in SARS-CoV-2 genomes, 2019–2020

3CLPro: 3C like protease; del: deletion; delins: deletion–insertion; E: envelope protein; ExoN: 3’-5’ exonuclease; M: membrane glycoprotein; N: nucleocapsid phosphoprotein; NA: not applicable; NSP: non-structural protein; OMT: O-methyltransferase; ORF: open reading frame; RdRp: RNA-dependent RNA polymerase; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; S: spike glycoprotein; UTR: untranslated region.

Notes: Variants are coloured depending on the type of mutations (missense, synonymous, non-coding, stop-gained, and frameshift). Major variants are annotated, and clades are indicated by horizontal colour stripes. Continents and countries from where samples originated are shown in the bars on the left. The gene structure is displayed at the bottom. Countries with samples in the African continent: Algeria, Democratic Republic of the Congo, Egypt, Gambia, Senegal and South Africa; Asian continent: Cambodia, China, Georgia, India, Iran (Islamic Republic of), Israel, Japan, Jordan, Kuwait, Malaysia, Nepal, Pakistan, Philippines, Qatar, Republic of Korea, Saudi Arabia, Singapore, Sri Lanka, Thailand and Viet Nam; European continent: Austria, Belarus, Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, Russian Federation, Turkey and United Kingdom; North America: Canada, Mexico and United States; Oceania; Australia and New Zealand; South America: Argentina, Brazil, Chile, Colombia, Costa Rica and Peru.

Fig. 1

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Table 4. Major clades of SARS-CoV-2 genomes, 2019–2020.

Clade/sublevel 1/sublevel 2 First observation of strain
 No. of samples
Date Accession no. Country
Basala Dec 2019 MN90894 China 670
D614G// 24 Jan 2020 EPI_ISL_422425 China 1889
D614G/Q57H/ 26 Feb 2020 EPI_ISL_418219 France 469
    D614G/Q57H/T265I 21 Feb 2020 EPI_ISL_418218 France 2391
D614G/203_204delinsKR/ 25 Feb 2020 EPI_ISL_412912 Germany 1330
    D614G/203_204delinsKR/T175M 1 Mar 2020 EPI_ISL_413647 and EPI_ISL_417688 Portugal and Iceland 215
L84S// 30 Dec 2019 MT291826 China 525
L84S/P5828L 20 Feb 2020 EPI_ISL_413456 United States 1137
L3606F// 18 Jan 2020 EPI_ISL_408481 China 182
L3606F/V378I/ 18 Jan 2020 EPI_ISL_412981 China 127
L3606F/G251V/ 29 Jan 2020 EPI_ISL_412974 Italy 419
    L3606F/G251V/P765S 20 Feb 2020 EPI_ISL_415128 Brazil 260
D448del// 8 Feb 2020 EPI_ISL_410486, France 248
G392D// 25 Feb 2020 EPI_ISL_414497 Germany 160
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Del: deletion; delins: deletion–insertion; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

a The reference genome (NC_045512)17 used in this study belongs to the basal clade.

All non-coding deletions are either located within 3’-UTR, 5’-UTR or intergenic regions. Of the in-frame deletions, ORF1 D448del stands out with 250 samples. In contrast, we only detected two distinct in-frame insertions in our data set. We also detected 11 frameshift deletions and 36 stop-gained variants. The recurrent stop-gained variant Y4379* (NSP10 Y126*) is found in 51 samples in the D614G clade. NSP10 Y126* is located only 13 residues upstream of the stop codon; therefore, a truncation may not significantly affect function of the protein. Most of frameshift variants in ORF1ab do not recur except for S135fs (three samples) and L3606fs (two samples). Although frameshift variants are considered deleterious, for instance, S135fs (more precisely S135Rfs*9) caused by 670_671del, ORF1ab is truncated at residue 143 before NSP2 and translation might resume from the methionine at residue 174 near the end of NSP1. Other notable recurrent frameshift variants include ORF3a V256fs and ORF7 I103fs.

The most common base change is C > T (Fig. 2). As expected,31 we observed a strong bias in transition versus transversion ratio (7:3). C > T transitions might be intervened by cytosine deaminases. Surprisingly, G > T transversions, likely introduced by oxo-guanine from reactive oxygen species,32 were also frequently observed.

Fig. 2.

Base pair changes observed in SARS-CoV-2 genomes, 2019–2020

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

Notes: The data come from 10 022 analysed genomes. The arrows indicate how bases are changed. Numbers next to the arrows indicate the number of distinct variants with those types of changes.

Fig. 2

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Assessing variants in the spike protein revealed 427 distinct non-synonymous variants with many variants located within the receptor binding domain and B-cell epitopes (Fig. 3). Among the variants in the receptor binding domain, V483A (26 samples), G476S (9 samples) and V367F (12 samples) are highly recurrent.

Fig. 3.

Annotation of SARS-CO-2 variants in the alignment of the amino acid sequence of the spike protein from several coronaviruses, 2019–2020

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

Notes: We aligned amino acids sequences of the Spike protein from SARS-CoV-2 (YP_009724390.1), Bat CoV RaTG13 (QHR63300.2), Bat SARS-like CoVs(AVP78042.1, AVP78031.1, ATO98205.1 and ATO98157.1) and SARS-like CoV WIV16 (ALK02457.1). Receptor binding domain and predicted B-cell epitopes are highlighted and the variants we identified in those segments are marked. The colour coding for the amino acids is by amino acid characteristic.

Fig. 3

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Fig. 4 shows the consensus tree from the phylogenetic analysis. The tree has a coalescence centre with exponential expansion identified by haplotype markers. The colour mapped phylogenies largely support the 14 identified subclades. We note that substantial numbers of samples from the United States show affinity with European lineages rather than those directly derived from East Asia. Except for the earliest cases, European clades dominate even in samples from western states in the United States. Further, European samples tend to associate with lineages that expanded through Australia.

Fig. 4.

Phylogenetic tree for the SARS-CoV-2 genomes, 2019–2020

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

Notes: Each sample is coloured with corresponding subclade. We used the Bayesian evolutionary analysis by sampling trees software.28

Fig. 4

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Estimation of mutation rate showed a median of 1.12 × 10−3 mutations per site-year (95% confidence interval, CI: 9.86 × 10−4 to 1.85 × 10−4). The median tree height was 5.1 months (95% CI: 4.8 to 5.52).

Discussion

Here we show the evolution of the SARS-Co-2 genome as it has spread across the world. Although, our methods do not allow us to investigate whether the mutations observed led to a loss or gain of function, we can speculate on the implications of viral function of these mutations.

The most common clade identified was the D614G variant, which is located in a B-cell epitope with a highly immunodominant region and may therefore affect vaccine effectiveness.33 Although amino acids are quite conserved in this epitope, we identified 14 other variants besides D614G. Almost all strains with D614G mutation also have a mutation in the protein responsible for replication (ORF1ab P4715L; RdRp P323L), which might affect replication speed of the virus. This protein is the target of the anti-viral drugs, remdesivir and favipiravir, and the susceptibility for mutations suggests that treatment resistive strains may emerge quickly. Mutations in the receptor binding domain of the spike protein suggest that these variants are unlikely to reduce binding affinity with ACE2, since that would decrease the fitness of the virus. V483A and G476S are primarily observed in samples from the United States, whereas V367F is found in samples from China, Hong Kong Special Administrative Region, France and the Netherlands. The V367F and D364Y variants have been reported to enhance the structural stability of the spike protein facilitating more efficient binding to the ACE2 receptor.34 In summary, structural and functional changes concomitant with spike protein mutations should be meticulously studied during therapy design and development.

We detected several non-recurring frameshift variants, which can be sequencing artefacts. The frameshift at Y3 in ORF10, although only detected in one sample, might not be essential for survival of the new coronavirus, since ORF10, a short 38-residue peptide, is not homologous with other proteins in the NCBI repository.

The phylogenetic analysis suggest population structuring in the evolution of SARS-CoV-2. The analysis provides an independent test of the major clades we identified, as well as the geographic expansions of the variants. While the earliest samples from the United Stated appear to be derived from China, belonging either to basal or L84S clades, the European clades, such as D614G/Q57H, tend to associate with most of the subsequent increase in infected people in the United States. D614G was first observed in late January in China and became the largest clade in three months. The mutation rate of 1.12 × 10−3 mutations per site-year is similar to 0.80 × 10−3 to 2.38 × 10−3 mutations per site-year reported for SARS-CoV-1.35

The rapid increase of infected people will provide more genome samples that could offer further insights to the viral dissemination, particularly the possibility of at least two zoonotic transmissions of SARS-CoV-2 into the human population. An understanding of the biological reservoirs carrying coronaviruses and the modalities of contact with human population through trade, travel or recreation will be important to understand future risks for novel infections. Further, populations may be infected or even re-infected via multiple travel routes.

The number of people with confirmed COVID-19 has rapidly increased over the last five months with no sign of decline in the near future. The fight against COVID-19 will be long, until vaccines and other effective therapies are developed. To facilitate rapid therapeutic development, clinicopathological, genomic and other societal information must be shared with researchers, physicians and public health officials. Given the evolving nature of the SARS-CoV-2 genome, drug and vaccine developers should continue to be vigilant for emergence of new variants or sub-strains of the virus.

Acknowledgements

We gratefully acknowledge the authors, originating and submitting laboratories of the sequences from GISAID’s EpiFlu Database, GenBank, and NGDC Genome Warehouse, and the National Microbiology Data Center on which this research is based. The list of genomes is available from the data repository.20 We also thank Jane Snowdon and Dilhan Weeraratne.

Competing interests:

None declared.

References

  • 1.Coronavirus disease (COVID-19). Situation Report – 124. Geneva: World Health Organization; 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200523-covid-19-sitrep-124.pdf?sfvrsn=9626d639_2 [cited 2020 28 May].
  • 2.Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020. February 22;395(10224):565–74. 10.1016/S0140-6736(20)30251-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020. March;579(7798):265–9. 10.1038/s41586-020-2008-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Wuhan seafood market pneumonia virus isolate Wuhan-Hu-1, complete genome. NCBI Reference Sequence: NC_045512.1. Bethesda: National Center for Biotechnology Information; 2020. Available from: https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.1 [cited 2020 May 29].
  • 5.Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020. April 16;181(2):271–280.e8. 10.1016/j.cell.2020.02.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan. China: JAMA; 2020. 10.1001/jama.2020.1585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. ; China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020. April 30;382(18):1708–20. 10.1056/NEJMoa2002032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020. February 24;323(13):1239–42. 10.1001/jama.2020.2648 [DOI] [PubMed] [Google Scholar]
  • 9.Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020. February 15;395(10223):470–3. 10.1016/S0140-6736(20)30185-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Cumulative Number of Reported Probable Cases of SARS [internet]. Geneva: World Health Organization; 2020. https://www.who.int/csr/sars/country/2003_07_11/en/ [cited 2020 May 29].
  • 11.Middle East respiratory syndrome coronavirus (MERS-CoV) [internet]. Geneva: World Health Organization; 2020. https://www.who.int/emergencies/mers-cov/en/ [cited 2020 May 29].
  • 12.Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. ; and the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city area. JAMA. 2020. April 22. Epub ahead of print. 10.1001/jama.2020.6775 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020. February 15;395(10223):507–13. 10.1016/S0140-6736(20)30211-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020. May;8(5):475–81. 10.1016/S2213-2600(20)30079-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sanjuán R, Domingo-Calap P. Mechanisms of viral mutation. Cell Mol Life Sci. 2016. December;73(23):4433–48. 10.1007/s00018-016-2299-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Shu Y, McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality. Euro Surveill. 2017. March 30;22(13):30494. 10.2807/1560-7917.ES.2017.22.13.30494 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome. NCBI Reference Sequence: NC_045512.2. Bethesda: National Center for Biotechnology Information; 2020. Available from: https://www.ncbi.nlm.nih.gov/nuccore/1798174254 [cited 2020 May 19].
  • 18.Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970. March;48(3):443–53. 10.1016/0022-2836(70)90057-4 [DOI] [PubMed] [Google Scholar]
  • 19.orf1ab polyprotein [Severe acute respiratory syndrome coronavirus 2]. NCBI Reference Sequence: YP_009724389.1. Bethesda: National Center for Biotechnology Information; 2020. Available from: https://www.ncbi.nlm.nih.gov/protein/1796318597 [cited 2020 May 29].
  • 20.Koyama T, Platt D, Parida L. Variant analysis of SARS-CoV-2 genomes [data repository]. Meyrin: European Organization for Nuclear Research; 2020. 10.5281/zenodo.3840465 10.5281/zenodo.3840465 [DOI] [PMC free article] [PubMed]
  • 21.Ward JH Jr. Hierarchical Grouping to Optimize an Objective Function. J Am Stat Assoc. 1963;58(301):236–44. 10.1080/01621459.1963.10500845 [DOI] [Google Scholar]
  • 22.Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. ; SciPy 1.0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020. March;17(3):261–72. 10.1038/s41592-019-0686-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990. October 5;215(3):403–10. 10.1016/S0022-2836(05)80360-2 [DOI] [PubMed] [Google Scholar]
  • 24.Papadopoulos JS, Agarwala R. COBALT: constraint-based alignment tool for multiple protein sequences. Bioinformatics. 2007. May 1;23(9):1073–9. 10.1093/bioinformatics/btm076 [DOI] [PubMed] [Google Scholar]
  • 25.Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2. Cell Host Microbe. 2020. April 8;27(4):671–680.e2. 10.1016/j.chom.2020.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Liu Z, Xiao X, Wei X, Li J, Yang J, Tan H, et al. Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS-CoV-2. J Med Virol. 2020. February 26;92(6):595–601. 10.1002/jmv.25726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Arvestad L. alv: a console-based viewer for molecular sequence alignments. J Open Source Softw. 2018;3(31):955 10.21105/joss.00955 [DOI] [Google Scholar]
  • 28.Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput Biol. 2019. April 8;15(4):e1006650. 10.1371/journal.pcbi.1006650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002. July 15;30(14):3059–66. 10.1093/nar/gkf436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Hasegawa M, Kishino H, Yano T. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol. 1985;22(2):160–74. 10.1007/BF02101694 [DOI] [PubMed] [Google Scholar]
  • 31.Lyons DM, Lauring AS. Evidence for the selective basis of transition-to-transversion substitution bias in two RNA viruses. Mol Biol Evol. 2017. December 1;34(12):3205–15. 10.1093/molbev/msx251 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Li Z, Wu J, Deleo CJ. RNA damage and surveillance under oxidative stress. IUBMB Life. 2006. October;58(10):581–8. 10.1080/15216540600946456 [DOI] [PubMed] [Google Scholar]
  • 33.Koyama T, Weeraratne D, Snowdon JL, Parida L. Emergence of drift variants that may affect COVID-19 vaccine development and antibody treatment. Pathogens. 2020. April 26;9(5):324. 10.3390/pathogens9050324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ou J, Zhou Z, Dai R, Zhang J, Lan W, Zhao S, et al. Emergence of RBD mutations in circulating SARS-CoV-2 strains enhancing the structural stability and human ACE2 receptor affinity of the spike protein. [preprint]. Cold Spring Habor: medRxiv; 2020. 10.1101/2020.03.15.991844 10.1101/2020.03.15.991844 [DOI]
  • 35.Zhao Z, Li H, Wu X, Zhong Y, Zhang K, Zhang YP, et al. Moderate mutation rate in the SARS coronavirus genome and its implications. BMC Evol Biol. 2004. June 28;4(1):21. 10.1186/1471-2148-4-21 [DOI] [PMC free article] [PubMed] [Google Scholar]

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