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. 2022 Mar 31;315:198765. doi: 10.1016/j.virusres.2022.198765

Mutational cascade of SARS-CoV-2 leading to evolution and emergence of omicron variant

Kanika Bansal a,, Sanjeet Kumar b,
PMCID: PMC8968180  PMID: 35367284

Abstract

Background

Emergence of new variant of SARS-CoV-2, namely omicron, has posed a global concern because of its high rate of transmissibility and mutations in its genome. Researchers worldwide are trying to understand the evolution and emergence of such variants to understand the mutational cascade events.

Methods

We have considered all omicron genomes (n = 302 genomes) available till 2nd December 2021 in the public repository of GISAID along with representatives of variants of concern (VOC), i.e., alpha, beta, gamma, delta, and omicron; variant of interest (VOI) mu and lambda; and variant under monitoring (VUM). Whole genome-based phylogeny and mutational analysis were performed to understand the evolution of SARS CoV-2 leading to emergence of omicron variant.

Results

Whole genome-based phylogeny depicted two phylogroups (PG-I and PG-II) forming variant specific clades except for gamma and VUM GH. Mutational analysis detected 18,261 mutations in the omicron variant, majority of which were non-synonymous mutations in spike (A67, T547K, D614G, H655Y, N679K, P681H, D796Y, N856K, Q954H), followed by RNA dependent RNA polymerase (rdrp) (A1892T, I189V, P314L, K38R, T492I, V57V), ORF6 (M19M) and nucleocapsid protein (RG203KR).

Conclusion

Delta and omicron have evolutionary diverged into distinct phylogroups and do not share a common ancestry. While, omicron shares common ancestry with VOI lambda and its evolution is mainly derived by the non-synonymous mutations.

Keywords: SARS-CoV-2, COVID-19, genome-wide, evolution, variants, VOC, VOI, VUM, SNP, mutation, non-synonymous, silent mutation, spike, RNA dependent RNA polymerase, NSP, UTR: Abbreviations: VOC, variant of concern; VOI, variant of interest; VUM, variant under monitoring; NSP, non-structural protein; UTR, untranslated region; rdrp, RNA dependent RNA polymerase

Abbreviations: VOC, Variant of concern; VOI, Variant of interest; VUM, Variant under monitoring; NSP, Non-structural protein; UTR, Untranslated region; rdrp, RNA dependent RNA polymerase

1. Introduction

Throughout the globe resurgence of COVID-19 cases has been linked to the emergence of new variants of concern (https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/first-and-second-waves-of-coronavirus) (Thakur et al., 2021). Currently, the world is witnessing a new variant namely, omicron which was first reported in South Africa on 24th November 2021 from the specimen collected on 9th November 2021(https://www.who.int/publications/m/item/enhancing-readiness-for-omicron-(b.1.1.529)-technical-brief-and-priority-actions-for-member-states). On 26th November 2021, World Health Organisation (WHO) assigned omicron to the ‘variant of concern’ (VOC) category due to its ability to poses a higher risk of reinfection as compared to previously reported variants (https://www.who.int/news/item/26–11–2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern; https://www.who.int/news/item/28–11–2021-update-on-omicron). According to the 1st December 2021 update, omicron is reported in at least 23 countries from five out of six WHO regions, with most cases in Africa and Europe (https://www.cnbc.com/2021/12/01/who-says-omicron-has-been-found-in-23-countries-across-the-world.html).

There is a lot of uncertainty surrounding the omicron variant. For its risk assessment, scientists and researchers are investigating the intensity of its spread, extent of its infection, effectiveness of detection methods, therapeutics, and vaccine efficacy (Knoll & Wonodi, 2021; Lipsitch & Dean, 2020; Pegu et al., 2021). The onset of omicron is reported with mild diseases suggests its low or mild severity than its previous counterparts like delta (Ewen Callaway, 2021; E. Callaway & Ledford, 2021). It is known to have a very high mutation rate with more than 30 mutational changes in its spike protein (Ewen Callaway, 2021) (https://www.who.int/publications/m/item/enhancing-readiness-for-omicron-(b.1.1.529)-technical-brief-and-priority-actions-for-member-states)

Globally, high risk of reinfection with omicron variant and its ability to evade vaccine-induced immunity resulting in the emergence of new variants of SARS-CoV-2 (Pulliam et al., 2021). Since COVID-19 inception, researchers have been trying to investigate its origin and evolution (Bansal, Kumar, & Patil, 2021; Singh & Soojin, 2021; Tang et al., 2020). We are currently witnessing a global molecular arms race between SARS-CoV-2 and its preventive therapeutics based on diverse regimes such as DNA, RNA, protein or inactivated whole-virion, etc. (Andreadakis et al., 2020; Corey, Mascola, Fauci, & Collins, 2020; Sharma, Sultan, Ding, & Triggle, 2020). This global crisis can be addressed by a very rapid immunization program worldwide. Moreover, the real-time monitoring of evolutionary cascade of SARS-CoV-2 leading to novel variants is utmost. Earlier investigation of several VOC and VOI suggests some of the crucial mutations for viral survival and high infectivity in humans (Boehm et al., 2021; Kumar & Bansal, 2021; Schmidt et al., 2021). However, mutations giving rise to omicron and intra-omicron genomic diversity are not yet analyzed at a population level.

In the present study, we aim to look for the mutational profile of under-monitoring variants reported till now to understand the emergence of a heavily mutated variant named omicron. Interestingly, whole genome-based phylogeny suggests two major phylogroups PG-I and PG-II. Further, mutational analysis depicted the key role of non-synonymous mutations in the evolution of novel variant. Such genome-wide mutational landscape is required for surveillance and vaccine development.

2. Results

2.1. Phylogenomics suggests common ancestry of omicron and lambda variants

Whole genome-based phylogeny (n = 478 genomes) representing VOC (alpha, beta, gamma, delta, and omicron), VOI (mu and lambda) and VUM depicts two major phylogroups PG-I and PG-II (Fig. 1 and Table 1 ). Here, the reference strain of SARS-CoV-2 (Wuhan-Hu-1, NC_045512.2) is taken as an outgroup. PG-I has VOC: gamma, beta, and delta; VOI: mu and VUM: GH. Whereas, PG-II includes VOC: alpha, omicron and VOI: lambda. Interestingly, two VOCs, delta and omicron, belong to different phylogroups. Phylogeny depicted that omicron shares a common ancestry with VOI lambda represented by a black asterisk in Fig. 1. Interestingly, three isolates from Italy (EPI_ISL_6854346, EPI_ISL_6854347, and EPI_ISL_6854348) form a diversified sub-lineage among the omicron population. Additionally, EPI_ISL_6886594 from Germany is a diversified omicron strain.

Fig. 1.

Fig. 1:

Maximum likelihood whole genome-based phylogeny of SARS-CoV-2 VOCs, VOIs and VUMs. Here, phylogroups (PG-I and PG-II) and clades (alpha, beta, gamma, delta, omicron, mu etc.) are marked with respective colors as indicated. Bootstrap values are represented by the radius of circle at the nodes. Common ancestry of omicron and lambda is marked by black star.

Table 1.

Metadata of the VOCs, VOIs and VUMs strains used in the present study.

graphic file with name fx1_lrg.gif
graphic file with name fx2_lrg.gif
graphic file with name fx3_lrg.gif

2.2. Very high non-synonymous mutations give rise to omicron

Mutation is driving the evolution and emergence of new variants of COVID-19 worldwide (Islam et al., 2021; Kumar & Bansal, 2021; Thakur et al., 2021). Availability of genomic resources have enabled the research community in tracking mutational events and linking them to new variants (Mercatelli & Giorgi, 2020; Rambaut et al., 2020). Analysis and routine surveillance from South Africa suggested omicron ability to evade immunity from prior infection as compared to other VOCs (Pulliam et al., 2021). In the present study, we intend to understand the evolution and emergence of omicron by its mutational landscape at population level.

We have performed a mutational analysis with respect to the reference genome of SARS-CoV-2 (NC_045512.2) (Fig. 2 ). Total mutations detected in the dataset were 24,189, and omicron genomes constituted 18,261 mutations (supplementary table 1). For all the strains under study, we have calculated the total number of mutations detected (supplementary table 2). Average mutations per genome for the omicron variant were detected to be 60.5. For the limited genomes of VOCs, VOIs and VUMs, average mutations for GH, delta, mu, gamma, alpha, lambda and beta were 48, 39, 38.5, 37.8, 30.7, 27.4 and 24.2 respectively. This clearly depicts high number of mutations in the omicron variant as compared to other variants of SARS-CoV-2. Except for omicron, average mutations for other variants were calculated on the basis of limited genomes, which might not represent the true mutational events for them. Since, omicron is the recently emerged variant, aim of present study was to understand its mutational landscape at population level.

Fig. 2.

Fig. 2:

Mutational analysis of omicron. Six panel image displays the most mutated samples, overall mutations per samples, most frequent events per class of mutation category, changes of nucleotide per type, nucleotide wise most frequent events and protein level most frequent events for the genomes used in the study.

Interestingly, >97% (n = 17,703 mutations) of the mutations in omicron were in the coding region, and remaining 558 were detected in the extragenic region of the genome. Amongst the coding gene mutations, 2965 were indels while 14,738 were SNPs constituting non-synonymous (n = 11,995 mutations) and synonymous mutations (n = 2743 mutations). Single nucleotide transitions are shown to be major mutational types amongst the SARS-CoV-2 genomes (Kumar & Bansal, 2021; Mercatelli & Giorgi, 2020).

Interestingly, mutational events are highly skewed towards the spike protein, which constitutes ∼60% (n = 10,658) of the total mutations in the coding genomic region (n = 17,703) (Fig. 3 ). The majority of spike protein mutations encompass A67, T547K, D614G, H655Y, N679K, P681H, D796Y, N856K, Q954H, which are reported in all the omicron genomes analysed (Table 3). Count of mutations in the spike was followed by RNA dependent RNA polymerase (rdrp) (n = 4142) constituting A1892T, I189V, P314L, K38R, T492I, V57V in all omicron genomes analyzed (Fig. 3 and Table 3). Remaining 2903 mutations were detected in rest of the coding genomic region (Table 2 , 3 , and supplementary table 1), where M19M in ORF6, and RG203KR in nucleocapsid protein are amongst the most prevalent mutations in omicron (Fig. 3).

Fig. 3.

Fig. 3:

Mutational analysis of omicron (A) Number of mutations in the coding region is in the centre of the pie-chart representing indels (black), synonymous (yellow) and non-synonymous (red) SNPs. Type and number of mutations in the extergenic region is represented by pie charts blue, light blue and white as represented in the color legends. (B) Bar graph representing number of mutations in the genomic region of SARS-CoV-2. (C) Some of the top mutations (pl. refer Table 3 for all top mutations in omicron) among the omicron variant are represented by stars of black: indels, yellow: synonymous and red: non-synonymous mutations.

Table 3.

Top mutations (>185 in count) in omicron variant as compared to the reference sequence NC_045512.2.

annotation protein variant varclass Count Refpos refvar qvar qpos qlength
Spike S A67 deletion_frameshift 575 21,762 C . 21,483 29,387
Predicted phosphoesterase, papain-like proteinase NSP3 A1892T SNP 302 8393 G A 8124 29,387
Transmembrane protein NSP6 I189V SNP 302 11,537 A G 11,259 29,387
RNA-dependent RNA polymerase, post-ribosomal frameshift NSP12b P314L SNP 302 14,408 C T 14,130 29,387
Spike S T547K SNP 302 23,202 C A 22,915 29,387
Spike S D614G SNP 302 23,403 A G 23,116 29,387
Spike S H655Y SNP 302 23,525 C T 23,238 29,387
ORF6 protein ORF6 M19M SNP_silent 302 27,259 A C 26,972 29,387
Predicted phosphoesterase, papain-like proteinase NSP3 K38R SNP 301 2832 A G 2566 29,387
Spike S N679K SNP 301 23,599 T G 23,312 29,387
Transmembrane protein NSP4 T492I SNP 301 10,029 C T 9760 29,378
Nucleocapsid protein N RG203K* SNP 301 28,881 GGG AAT 28,806 29,693
Growth-factor-like protein NSP10 V57V SNP_silent 300 13,195 T C 12,917 29,387
Spike S P681H SNP 300 23,604 C A 23,317 29,387
Spike S D796Y SNP 300 23,948 G T 23,661 29,387
Spike S N856K SNP 300 24,130 C A 23,843 29,387
Spike S Q954H SNP 300 24,424 A T 24,137 29,387
Nucleocapsid protein N RG203KR SNP 300 28,881 GGG AAC 28,594 29,387
RNA-dependent RNA polymerase, post-ribosomal frameshift NSP12b N591N SNP_silent 298 15,240 C T 14,962 29,387
Spike S T95I SNP 298 21,846 C T 21,562 29,387
Predicted phosphoesterase, papain-like proteinase NSP3 F106F SNP_silent 297 3037 C T 2771 29,387
Spike S G339D SNP 297 22,578 G A 22,291 29,387
ORF3a protein ORF3a T64T SNP_silent 297 25,584 C T 25,297 29,387
NA 5′UTR 241 extragenic 297 241 C T 187 29,693
3C-like proteinase NSP5 P132H SNP 296 10,449 C A 10,180 29,387
3′-to-5′ exonuclease NSP14 I42V SNP 296 18,163 A G 17,885 29,387
Envelope E T9I SNP 296 26,270 C T 25,983 29,387
ORF7b protein ORF7b L17L SNP_silent 296 27,807 C T 27,520 29,387
Spike S N969K SNP 294 24,469 T A 24,182 29,387
Predicted phosphoesterase, papain-like proteinase NSP3 A889A SNP_silent 293 5386 T G 5120 29,387
Spike S L981F SNP 292 24,503 C T 24,216 29,387
Spike S D1146D SNP_silent 292 25,000 C T 24,713 29,387
Membrane M A63T SNP 289 26,709 G A 26,422 29,387
Predicted phosphoesterase, papain-like proteinase NSP3 S1265 deletion 288 6513 GTT . 6246 29,387
Transmembrane protein NSP6 L105 deletion 287 11,286 TGTCTGGTT . 11,016 29,387
Spike S I68 deletion_frameshift 287 21,767 CATG . 21,486 29,387
Spike S E484A SNP 284 23,013 A C 22,726 29,387
Spike S S477N SNP 283 22,992 G A 22,705 29,387
Spike S T478K SNP 283 22,995 C A 22,708 29,387
Spike S Q493R SNP 282 23,040 A G 22,753 29,387
Spike S Q498R SNP 281 23,055 A G 22,768 29,387
Spike S N501Y SNP 281 23,063 A T 22,776 29,387
Spike S G496S SNP 280 23,048 G A 22,761 29,387
Spike S Y505H SNP 277 23,075 T C 22,788 29,387
Membrane M D3G SNP 275 26,530 A G 26,243 29,387
Membrane M Q19E SNP 272 26,577 C G 26,290 29,387
Spike S S371L SNP 270 22,673 TC CT 22,386 29,387
Spike S S373P SNP 270 22,679 T C 22,392 29,387
Spike S G142 deletion 260 21,987 GTGTTTATT . 21,702 29,387
Spike S S375F SNP 260 22,686 C T 22,399 29,387
ORF7b protein ORF7b E3* SNP_stop 253 27,762 G T 27,687 29,752
Spike S I210 insertion_frameshift 243 22,193 . T 21,901 29,387
Spike S R214 insertion_frameshift 243 22,203 . A 21,916 29,387
Spike S R214R SNP_silent 243 22,204 T A 21,917 29,387
Nucleocapsid protein N E31 deletion 243 28,362 GAGAACGCA . 28,074 29,378
Spike S L212* SNP_stop 243 22,197 T G 22,118 29,749
Spike S N211K SNP 242 22,195 T G 21,903 29,387
Spike S L212C SNP 242 22,197 TA GC 21,905 29,387
Spike S S214 insertion 242 22,201 . AGC 21,910 29,387
Spike S V213 insertion_frameshift 242 22,202 . A 21,914 29,387
NA 3′UTR 28,271 extragenic 242 28,271 A T 27,984 29,378
Nucleocapsid protein N P13L SNP 241 28,311 C T 28,024 29,378
Spike S N764K SNP 234 23,854 C A 23,567 29,387
Spike S G446S SNP 203 22,898 G A 22,611 29,387
Spike S N440K SNP 199 22,882 T G 22,595 29,387
Spike S K417N SNP 183 22,813 G T 22,526 29,387

Table 2.

Genomic region wise mutational count of the omicron isolates by taking NC_045512.2 as a reference.

Genomic region Mutational count Annotation
5′UTR 309 5′ Untranslated region
NSP1 5 RNA dependent RNA polymerase
NSP2 31
NSP3 1572
NSP4 325
NSP5 317
NSP6 595
NSP7 0
NSP8 2
NSP9 9
NSP10 301
NSP11 0
NSP12a 0
NSP12b 632
NSP13 14
NSP14 319
NSP15 6
NSP16 14
S 10,658 Spike
ORF3a 313 ORF3a protein
E 296 Envelope
M 850 Membrane
ORF6 303 ORF6 protein
ORF7a 2 ORF7a protein
ORF7b 311 ORF7b protein
ORF8 4 ORF8 protein
N 823 Nucleocapsid protein
ORF10 1 ORF10 protein
3′UTR 249 3′ Untranslated region

2.3. Low intra-sequence diversity amongst omicron variant

Intra-strain diversity among the omicron variant strains reported worldwide will be crucial in understanding the genome dynamics and rapid evolution of SARS-CoV-2. We performed the mutational analysis on the current dataset using omicron (OL677199) isolated from Canada on 23rd November 2021 as the reference genome (supplementary table 3). Most of the strains (n = 298), irrespective of their geographic origin, had less than ten mutations depicting low intra-strain diversity among omicron strains. We found omicron variants had >55 mutations when compared with other VOCs and VOIs. However, four of the isolates two from Europe (Italy) (EPI_ISL_6854347 (n = 23 mutations) and EPI_ISL_6854346 (n = 14 mutations) and two from South Africa (EPI_ISL_6699742 (n = 12 mutations) and EPI_ISL_6774091 (n = 11 mutations) were most diversified among the omicron genomes.

3. Methods

3.1. Identification and procurement of SARS-CoV-2 genome from the public repository

We have considered all the available genomes of omicron variant available in public domain until 6 pm Indian Standard Time (IST) on 2nd December 2021 from GISAID (n = 302 genomes). A total of 25 strains from each variant of concern, namely alpha (B.1.1.7), beta (B.1.351), gamma (P.1) and delta (B.1.617.2) and variant of interest, namely lambda (C.37) and mu (B.1.621). We have also considered 25 strains from variant under monitoring, namely GH (B.1.640). These all strains are from their respective earlier reports in the public domain. Pangolin COVID-19 lineage assigner webserver (https://pangolin.cog-uk.io/) was used to truly demarcate the strains of across variants. The investigation suggested that 9 out of 25 strains does not belong to gamma (P.1) and 1 out of 25 strains doesn't belong to VUM GH (B.1.640) and were wrongly classified earlier. A detailed list of all the strains used in the study is provided in Table 1.

3.2. Phylogenetic analysis

A total of 477 high-quality genomes, including the major variants spread across the globe were taken into consideration. Multiple sequence alignment was performed for all the genomes using MAFFT v7.467 (Nakamura, Yamada, Tomii, & Katoh, 2018) followed by phylogenetic tree construction using fasttree v2.1.8 with double precision (Price, Dehal, & Arkin, 2010) with gamma time reversal method. Visualization of the obtained phylogenetic tree was performed using iTol v6 (Letunic & Bork, 2019). Different variants were marked in accordance with different colors as mentioned in the legends.

3.3. Mutational analysis

Mutational analysis of all the strains (n=477) in the study was performed with two different reference genomes. First with NC_045512.2 (Wuhan-Hu-1) strain (reference SARS CoV-2 strain) and another with first reported strain of omicron variant (OL677199.1) (https://www.ncbi.nlm.nih.gov/nuccore/OL677199) using nucmer v3.1 (Delcher, Phillippy, Carlton, & Salzberg, 2002). We have used a well-documented R script described earlier (Mercatelli & Giorgi, 2020). Here, we have used gff3 annotation and reference genome file to extract genomic coordinate of SARS-CoV-2 proteins. R library package seqinr (https://cran.r-project.org/web/packages/seqinr/index.html) and biostring package (https://bioconductor.org/packages/release/bioc/html/Biostrings.html) of bioconductor was implemented to obtain the list of all the mutational events. Mutational events were calculated with respect to two different references (Reference SARS CoV-2 strain: NC_045512.2) (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2) and omicron (OL677199.1) (https://www.ncbi.nlm.nih.gov/nuccore/OL677199) separately. Further, the average mutations for a variant were calculated by adding up the mutations in each variant and dividing them by the total number of genomes of the variant used in the present study.

Funding Information

Nil

Author contribution statement

Both the authors’ KB and SK have contributed equally to the data curation, analysis, and writing of the manuscript.

CRediT authorship contribution statement

Kanika Bansal: Data curation, Formal analysis, Writing – original draft. Sanjeet Kumar: Data curation, Formal analysis, Writing – original draft.

Declaration of Competing Interest

The author declares no competing interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

Authors acknowledge the support and motivation from Dr. Prabhu B.Patil – CSIR-Institute of Microbial Technology, Chandigarh. We are also thankful to Dr. Santosh Kumar Sethi for his kind support during the process of study. We also acknowledge GISAID initiative for extensive curation and availability of genomic resource in public domain.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.virusres.2022.198765.

Appendix. Supplementary materials

mmc1.xlsx (7.9MB, xlsx)

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