key: cord-0933131-u1m6blu8
authors: Bhattacharya, Manojit; Sharma, Ashish Ranjan; Dhama, Kuldeep; Agoramoorthy, Govindasamy; Chakraborty, Chiranjib
title: Omicron variant (B.1.1.529) of SARS-CoV-2: understanding mutations in the genome, S-glycoprotein, and antibody-binding regions
date: 2022-03-08
journal: GeroScience
DOI: 10.1007/s11357-022-00532-4
sha: 9ab72778e6d73d32963db84cdfc59b286f751654
doc_id: 933131
cord_uid: u1m6blu8

The Omicron variant has been detected in nearly 150 countries. We analyzed the mutational landscape of Omicron throughout the genome, focusing the S-glycoprotein. We also evaluated mutations in the antibody-binding regions and observed some important mutations overlapping those of previous variants including N501Y, D614G, H655Y, N679K, and P681H. Various new receptor-binding domain mutations were detected, including Q493K, G496S, Q498R, S477N, G466S, N440K, and Y505H. New mutations were found in the NTD (Δ143-145, A67V, T95I, L212I, and Δ211) including one new mutation in fusion peptide (D796Y). There are several mutations in the antibody-binding region including K417N, E484A, Q493K, Q498R, N501Y, and Y505H and several near the antibody-binding region (S477N, T478K, G496S, G446S, and N440K). The impact of mutations in regions important for the affinity between spike proteins and neutralizing antibodies was evaluated. Furthermore, we examined the effect of significant antibody-binding mutations (K417N, T478K, E484A, and N501Y) on antibody affinity, stability to ACE2 interaction, and possibility of amino acid substitution. All the four mutations destabilize the antibody-binding affinity. This study reveals future directions for developing neutralizing antibodies against the Omicron variant. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11357-022-00532-4.

A new variant of SARS-CoV-2 was detected in South Africa in late November 2021 and as Omicron by the WHO. Omicron was categorized as a VOC on November 26, 2021 . After the first Omicron case was identified in Botswana, in Southern Africa, the virus sequence was determined [1, 2] . The variant has created a huge concern among scientist and policymakers worldwide because of its large number of mutations [3] . After detection in South Africa, the new variant spreaded rapidly, and an Omicron-infected case was observed in Hong Kong within few days [1] . The first confirmed case of Omicron was identified in the USA on December 1, 2021. The US-CDC COVID-19 response team reported at least one Omicron variant case in each of the 22 states across the country [4] . Simultaneously, Brandal et al. revealed that 117 individuals were infected with Omicron in Norway and that no patients with this variant were hospitalized until December 13, 2021 [5] .

Similarly, Espenhain et al. reported 785 Omicron-positive cases in Denmark, among which 76% patients were vaccinated and 7.1% patients were booster-vaccinated. Additionally, 4.3% of the patients had previously been infected with SARS-CoV-2 [6] . Later, Thakur and Kanta Ratho observed that the variant had been transmitted to over 77 countries within a month with the largest number of cases observed in the UK, USA, and South Africa. The spread of Omicron variant has created high health concerns and global panic [7] . Approximately 128 countries have submitted genome sequences to the GISAID database. A study of this data revealed that Omicron rapidly replaced Delta as the dominant variant [8, 9] , Through sequence analysis, Wang and Cheng observed that the Omicron variant evolved rapidly and confirmed that it had replaced the Delta variant as the dominant strain [10] .

Notably, the appearance of new variants of SARS-CoV-2, particularly VOCs such as Delta, Beta, and Alpha, have been associated with new disease waves in many countries leading to increased infections and, some cases, worldwide dissemination [11] . For example, increased infection with the Delta variant (B.1.617.2) in India resulted in a serious second wave that killed large numbers of people [12] . Similarly, the emergence of the Alpha variant initiated a second wave in the UK [13] . The emergence of the variant has created new concerns related to the pandemic. Many countries are facing new waves because of the sudden appearance of the Omicron variant and are preparing countermeasures. Preliminary reports revealed the high transmissibility of Omicron. However, Mahase reported that hospital admission is less (50-70% lower) for Omicron infected cases compared to Delta infected cases [14] , which relieves some concerns.

The Omicron variant is highly mutated [3, 15] , containing more mutations than to all previous variants of SARS-CoV-2 [16] . Omicron variant contains approximately 50 mutations throughout its genome among which approximately, 32 mutations are localized to the coding sequence for the S-glycoprotein [3, 15, 17, 18] . Some mutations including T478K, N501Y, N655Y, N679K, and P681H overlap with those in other VOCs or VOIs such as Delta, Gamma, Alpha, and Beta [1, [19] [20] [21] . However, Omicron has been described as an immensely mutated variant containing an "unusual constellation of mutations" [22] . Therefore, understanding the mutation landscape of the Omicron variant is crucial, particularly the effect of mutations on antibody escape which has commonly been reported for SARS-CoV-2 variants [23] . A recent study revealed mutations in the RBD and NTD. V445E and K444 Q/R/N in the RBD and K150 T/Q/R/E, and N148S in NTD may be responsible for antibody escape [24] .

Another study observed that the Omicron variant decreased neutralization ability using post-immunization serum from individuals administered two doses of Oxford-AstraZeneca vaccine or two doses of the Pfizer-BioNTech vaccine [25] , indicating a nAb escape event. Studies are needed to understand the immune escape and antibody escape by the Omicron variant based on the large number of mutations in its spike protein.

This study was performed to understand the mutational landscape of the Omicron variant throughout the genome, particularly in S-glycoprotein, and identify different mutations in the antibody-binding regions that may affect nAb binding.

Literature were collected on Omicron variant from several databases and search engines such as Pub-Med [26, 27] , Google Scholar [28] , and Web of Science [29] . Data on the Omicron variant were obtained from the eCDC [30] ; WHO [31], and US CDC [32] . In addition, a keyword search was performed for the Omicron variant using different significant or relative keywords and genomic information was obtained from the GISAID database [33, 34] . PDB files were retrieved from the Fig. 1 Flowchart describes the adopted methodology RCSBPDB database for S-glycoprotein/S-glycoprotein-antibody interactions [35] .

We collected the data from the GISAID database [33, 34] to illustrate the mutational landscape both for the S-glycoprotein and throughout the genome. Mutation data were also collected or fetched from different websites such as eCDC [30] ; WHO [31], and CDC [32] . To understand the mutations and their properties, we collected data from PubMed [26, 27] , Google Scholar [28] , and Web of Science [29] . We used PDB files for 3D model development, and the PDB files were retrieved from the RCSBPDB database for S-glycoprotein [35] . Decreased protein stability and an increased risk of infectivity [50] 16. A67V Ala 67 Val -- 

The mutational event diversity of SARS-CoV-2 and S-glycoprotein of Omicron and another lineage were analyzed to reveal the number of mutations in specific positions using Nextstrain server [36, 37] . The Nextstrain server, a database used to analyzed viral genomes, fetched the sequence of SARS-CoV-2 from the GISAID database to plot the mutational event diversity.

We comprehensively evaluated antibody-binding region and its interaction positions using four classes of antibodies (class-1 antibody to class-IV antibody). We developed a 3D model to show the interactions of antibodies with the RBD. Furthermore, we mapped mutations in the RBD of Omicron, which were compared with mutations in the antibody binding positions, and developed 3D model of RBD mutations in or near the antibodybinding region.

The risk level of each virus variant is associated with antibody-binding and neutralization of the variant. Sun et al. developed a tool based on a machine learning model to analyzed the risk [38] . We determined various parameters using the machine learning model, such as the stability of binding with ACE2, binding affinity of antibody, and substitution risk of amino acids in a specific position of S-glycoprotein. We used VarEPS to analyze critical mutations related to antibody-binding sites and their significant features, such as the affinity between spike protein and neutralizing antibodies and risk analysis of important mutations [38] . We used the same machine learning model risk analysis for four significant antibody-binding mutations (K417, T478, E484, and N501).

MATLAB was used to draw the pie chart, graphs, and plots [39] . A flowchart of the method is presented in Fig. 1 . 

Overall understanding the mutational landscape and 3D model development of S-glycoprotein

The two schematic diagrams represent the mutational landscape of the Omicron variant. We have drawn a diagram to show different mutations in S-glycoprotein (Fig. 2a) . At the same time, analysis of significant mutations in S-glycoprotein revealed the largest number of mutations in the RBD (Fig. S1) . Similarly, we have drawn a schematic diagram for all significant mutations in non-structural and structural proteins other than S-glycoprotein (Fig. 2b) . Analysis of nonstructural proteins showed that NSP3 contains the largest number of significant mutations (Fig. S2) .

Similarly, the analysis of structural proteins showed that S-glycoprotein has the largest number of significant mutations (Fig. S3) . The different mutations in S-protein and their important properties such as antibody resistance, higher binding affinity, transmutability, and higher infectivity from different published scientific articles are listed in Table 1 .

We developed a 3D model of S-glycoprotein from Omicron covering all mutations in the protein (Fig. 3a) . We identified several overlapping mutations in Omicron, and VOCs and VOIs such as Delta, Gamma, Alpha, and Beta. We detected several overlapping significant mutations with characteristic features that are similar to those in VOCs or VOIs (Table 2 ). Mutations previously reported as important in different variants were N501Y, D614G, H655Y, N679K, and P681H. However, we observed several novel mutations in different parts of S-glycoprotein (Fig. 3b) . Significant mutations in the RBD include Q493K, G496S, Q498R, S477N, G466S, N440K, and Y505H, whereas those in the NTD included Δ143-145, A67V, T95I, L212I, and Δ211. Similarly, D796Y was identified as a new mutation in fusion protein (FP).

We analyzed mutational event diversity in the genome at both the nucleotide and amino acid levels. First, we depicted the mutational event diversity in the genome at the nucleotide level for Omicron and all SARS-CoV-2 lineages (Fig. 4a) , revealing several mutations in the Omicron genome. Our detected three mutations at nucleotide 5924 (codon position 1887 at the protein level) in ORF1a and four mutations nucleotide position 22,686 (codon position 375 at the protein level) in S-glycoprotein in Omicron. Similar results were observed throughout the genome.

Together, analysis of the mutational event diversity in all SARS-CoV-2 lineages showed the largest number of mutations in some parts of the genome at the nucleotide level. We observed 80 mutations at nucleotide position 11,083 (codon position 3606 at the protein level) in ORF1a and 328 mutations in S-glycoprotein at nucleotide position 21,987 (codon position 142 at the protein level).

We analyzed the mutational event diversity at the amino acid level in Omicron and all SARS-CoV-2 ( Fig. 4b) and found mutational events in several positions in the genome of Omicron. We found three mutations at codon position 2084 at the protein level in ORF1a and four mutations at codon position 375 in protein, the S-glycoprotein in Omicron.

The mutational event diversity in all SARS-CoV-2 lineages showed the largest number of mutations in few parts at the amino acid level. We observed 87 mutations in codon 3606 in ORF1a and 340 mutations at codon 142 in the S-glycoprotein.

Similar to previous analysis, we observed mutational event diversity in S-glycoprotein at both the nucleotide and amino acid levels. We analyzed this diversity at the nucleotide level in Omicron and all SARS-CoV-2 lineages (Fig. 5a) . Diverse mutations were found at the nucleotide level in the S-glycoprotein of Omicron, including four mutations in S-glycoprotein at nucleotide position 22,686 (codon position 375 at the protein level), three mutations at nucleotide position 23,664 (codon position 701 at the protein Similarly, we analyzed the mutational event diversity of S-glycoprotein at the amino acid level in Omicron and all SARS-CoV-2 lineages (Fig. 5b) . We detected mutational events in several positions throughout S-glycoprotein in Omicron with two mutations at codon 145 four mutations at codon 375 Analysis of the mutational event diversity in all SARS-CoV-2 lineages revealed a large number of mutations in some regions at the amino acid level in the S-glycoprotein. We identified 340 mutations at codon position 142 and 69 mutations at codon position 950 in S-glycoprotein.

We found mutations in the antibody-binding region of the Omicron variant. We used four classes of antibodies (Class-1 antibody to Class-IV antibody) to examine the amino acid residue positions involved in S-glycoprotein binding to the antibodies. Here, we show the results for two Class-1 antibody-binding regions from two PDB entries (PDB ID: 7CZQ and 78DO). In the first interaction, P2B-1A10 nAb (Class-1 antibody) interacted with the RBD of S-glycoprotein (Fig. 6a) through interacting residues K417, Y449, L455, F456, A475, N487, N489, Q493, G496, T500, N501, G502, and Y505. In the second interaction, we found that C548 nAb (Class-1 antibody) interacted with the S-glycoprotein RBD region (Fig. 6b) at L455, F486, Q498, F490, Q493, and E484. All interacting residues were compared with mutated residues in S-glycoprotein in Omicron. We found several RBD mutations in or near the antibody-binding region (Fig. 6c) . K417N, E484A, Q493K, Q498R, N501Y, and Y505H were in the antibody-binding region, and S477N, T478K, G496S, G446S, and N440K were near the antibody-binding region.

Omicron variant: its risk mutations regarding the affinity between spike proteins and neutralizing antibodies

We analyzed the risk of mutation in S-glycoprotein in the Omicron variant and categorized the mutations affecting the affinity between spike protein and neutralizing antibodies. We identified three categories of mutations in S-glycoprotein, highly destabilizing, destabilizing, and stabilizing. We detected 19 mutations in the RBD and NTD regions of S-glycoprotein of B.1.1.529 that can interact with neutralizing antibodies. These mutations were categorized as highly destabilizing, destabilizing and stabilizing (Fig. 7a) . Among the 19 mutations (RBD and NTD), 13 were highly destabilizing, and six mutations were destabilizing (Fig. 7b) .

Risk analysis of significant four antibody-binding mutations (K417, T478, E484, and N501)

The risk level of mutation analysis in four positions of the RBD regions (K417, T478, E484, and N501) showed four residues that were significant for the interaction with the antibody.

Risk analysis of amino acid mutation at the significant K417 position revealed the risk level of 19 types of mutations in different variants (Fig. 8a) . The K417N mutation destabilized the S-glycoprotein. However, some variants contained a mutation at residue K417 (Fig. 8b) . At the same time, the strain count for K417N mutation was 43,172; this mutation was first reported in Qatar.

Similarly, amino acid mutation risk analysis for the T478 position was performed 19 types of mutation risks were found in 19 types of variants (Fig. 8c) . The T478K mutation destabilized the interaction between the antibody and S-glycoprotein. However, some variants with the T478 residue were observed (Fig. 8d) . The strain count for the T478K mutation was observed as 1,585,374, and this variant (K417N) was first reported in the USA.

We analyzed the risk of amino acid mutation at position E484, and the risk level was determined for different 19 types of mutations in different 19 variants (Fig. 8e) . The E484A mutation destabilized binding between the antibody and S-glycoprotein RBD. Moreover, some variants showed mutation at residue E484 (Fig. 8f) . The strain count for the E484A was 319, and this variant was first reported in Spain.

Risk analysis of mutation at amino acid position N501 revealed the risk level of 19 types of mutations in 19 variants (Fig. 8g) . The N501 mutation also destabilized the binding between the antibody and S-glycoprotein' RBD. Moreover, some variants with the mutation at residue N501 were detected (Fig. 8h) . The strain count for the N501Y mutation was 1,353,726, and this variant was first recorded in Spain.

The Omicron variant is highly concerned globally because of its large number of mutations [40, 41] . Several overlapping mutations have been observed in previous VOCs and VOIs. The characteristic Fig. 7 The mutational risk of the S-glycoprotein of B.1.1.529 that can interact with neutralizing antibodies and categorizations of the mutations. a Mutational risk of the S-glycoprotein of B.1.1.529 that can interact with neutralizing antibodies, and ranking of the mutations. With reference to interaction, the mutational risk has been illustrated in three categories in respect of antibody affinity/interaction, which is highly destabilizing, destabilizing, and stabilizing. b Number of highly destabilizing and destabilizing mutations in the S-glycoprotein of B.1.1.529. We have 19 mutations related to Ab binding (both in NTD and RBD region), 13 are highly destabilizing, and six mutations are destabilizing. We also pointed out the locations of the highly destabilizing, and destabilizing mutation in the schematic diagram of the S-glycoprotein. We have used the VarEPS server for the analysis Table 2 ). The variant acquired some new mutations and the characteristic features of these new mutations are unknown. Lupala et al. found that the RBD region of Omicron can bind to the hACE2 receptor more strongly through increased hydrogen bonding interface compare to other variants [42] .

In addition to point mutations, deletions and insertion are clustered in the NTD region of the S1 subunit. Deletions were also found randomly in the NSP3, NSP6, and N genes. The biological consequences of deletions and insertions in the Omicron variant are unclear. However, ΔH69/V70 has been observed in different lineages, including in the B.1.1.7 lineage (Alpha variant) and is associated with cleaved spike integration, which increases the infectivity of the Alpha variant. The rapid syncytium formation of B.1.1.7 variant S-glycoprotein requires the deletion of H69/V70; however, this deletion does not affect antibodies escape [43] . Additionally, insertions associated with the NTD and these insertions are linked with the immune or antibody escape of different variants. For example, insertion of an 11-residue fragment in between Y248 and L249 in the N5 loop of the NTD is associated with the prevention of neutralization [44] . However, in Omicron deletions and insertions in the NTD may result in conformational changes that potentially affect the RBD. Therefore, additional studies are needed to evaluate deletions and insertions in different genome positions, including the S gene of the Omicron variant.

We examined the total mutational landscape throughout the genome, particularly in S-glycoprotein of the Omicron variant, and the critical mutations in antibody-binding regions can hinder nAb binding and enhance the potentiality of nAb/mAb escape or resistance.

To understand the mutational landscape, we analyzed the mutation event diversity in the genome of Omicron and all SARS-CoV-2 lineages. We also analyzed the mutational event diversity to understand the mutation pattern throughout the S-glycoprotein of Omicron and all SARS-CoV-2 lineages. This analysis improves the understanding of different mutations in the antibody-binding regions. Several mutations in antibody-binding regions were detected, and bioinformatics analysis showed that most of the mutations were highly destabilizing or destabilizing. The destabilized interface may facilitate in nAb/mAb escape or resistance.

Mutations in the antibody-binding region of the Omicron variant and its antibody escape ability have been evaluated previously. For example, Kannan et al. observed several significant mutations in the antibody-binding regions [45] . Coa et al. illustrated an nAb escape event by the Omicron variant, with the K417N, G446S, E484A, Q493K/R, and N440K mutations, involved in antibody escape [46] . Some studies showed that a booster vaccine dose may increase the potency of Omicron neutralization. A survey by Lippi et al. suggested that the booster vaccine dose effectively neutralize Omicron, providing continuous augmentation of neutralizing potency against the variant [47] . Our study also revealed all the mutations in the antibody-binding region of the Omicron variant and antibody escape event by the variant.

We analyzed the entire mutational landscape of the genome and S-glycoprotein of the Omicron variant. We also identified the significant mutations in the antibody-binding region and antibody escape event by the newly emerging Omicron variant. Our study will help future researchers to develop methods for restoration of neutralizing antibodies against the variant and design antibody-based therapeutics against Omicron variant, as per new mutations. At the same time, these results provide a foundation for further studies of neutralizing antibody activity against all current circulating variants.

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