The structural role of SARS-CoV-2 genetic background in the emergence and success of spike mutations: the case of the spike A222V mutation

Ginex, Tiziana; Marco-Marı́n, Clara; Wieczór, Miłosz; Mata, Carlos P.; Krieger, James; Maria, Lopez-Redondo,; Francés‐Gómez, Clara; Ruiz-Rodríguez, Paula; Melero, Roberto; Sorzano, Carlos Óscar S.; Martínez, Marta; Gougeard, Nadine; Forcada-Nadal, Alicia; Zamora-Caballero, Sara; Gozalbo-Rovira, Roberto; Sanz-Frasquet, Carla; Bravo, Jerónimo; Rubio, Vicente; Marina, Alberto; Geller, Ron; Comas, Iñaki; Gil, Carmen; Coscollá, Mireia; Orozco, Modesto; Llácer, J.L.; Carazo, José-Marı́a

doi:10.1101/2021.12.05.471263

Cited by 9 publications

(7 citation statements)

References 70 publications

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“…The role of each specific mutation has not been completely elucidated, but they have all been implicated in immune escape [36][37][38]. In addition, the mutation S:A222V, present in AY.26, slightly increases human cell receptor (ACE2) binding [39], while the mutations N:R203M, common to the three sublineages, and N:G215C, present in AY.20 and AY.100, have been shown to improve viral replication and assembly [40,41]. Even though Deltadefining mutations do not include changes in ORF1a, many Delta sublineages harbor specific mutations in this ORF.…”

Section: Discussionmentioning

confidence: 99%

Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico

Taboada

Zárate

García-López

et al. 2022

Viruses

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In this study, we analyzed the sequences of SARS-CoV-2 isolates of the Delta variant in Mexico, which has completely replaced other previously circulating variants in the country due to its transmission advantage. Among all the Delta sublineages that were detected, 81.5 % were classified as AY.20, AY.26, and AY.100. According to publicly available data, these only reached a world prevalence of less than 1%, suggesting a possible Mexican origin. The signature mutations of these sublineages are described herein, and phylogenetic analyses and haplotype networks are used to track their spread across the country. Other frequently detected sublineages include AY.3, AY.62, AY.103, and AY.113. Over time, the main sublineages showed different geographical distributions, with AY.20 predominant in Central Mexico, AY.26 in the North, and AY.100 in the Northwest and South/Southeast. This work describes the circulation, from May to November 2021, of the primary sublineages of the Delta variant associated with the third wave of the COVID-19 pandemic in Mexico and highlights the importance of SARS-CoV-2 genomic surveillance for the timely identification of emerging variants that may impact public health.

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Section: Discussionmentioning

confidence: 99%

Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico

Taboada

Zárate

García-López

et al. 2022

Viruses

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“…EVEscape is in general more predictive of frequent VOCs than EVE alone (Figure S14B). The few VOCs mutations (i.e., A222V and T547K) with significant EVE-but not EVEscape-scores are known both for functional improvements such as monomer packing and RBD opening and for not impacting escape 46,47 . On the other hand, mutations with the highest EVEscape but low EVE scores include R190S and R408S, which are in hydrophobic pockets that likely facilitate significant immune escape 48 .…”

Section: Evescape Anticipates Mutations In Pandemic and Future Strainsmentioning

confidence: 99%

Learning from pre-pandemic data to forecast viral escape

Thadani

Gurev

Notin

et al. 2022

Preprint

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From early detection of variants of concern to vaccine and therapeutic design, pandemic preparedness depends on identifying viral mutations that escape the response of the host immune system. While experimental scans are useful for quantifying escape potential, they remain laborious and impractical for exploring the combinatorial space of mutations. Here we introduce a biologically grounded model to quantify the viral escape potential of mutations at scale. Our method - EVEscape - brings together fitness predictions from evolutionary models, structure-based features that assess antibody binding potential, and distances between mutated and wild-type residues. Unlike other models that predict variants of concern based on newly observed variants, EVEscape has no reliance on recent community prevalence, and is applicable before surveillance sequencing or experimental scans are broadly available. We validate EVEscape predictions against experimental data on H1N1, HIV and SARS-CoV-2, including data on immune escape. For SARS-CoV-2, we show that EVEscape anticipates mutation frequency, strain prevalence, and escape mutations. Drawing from GISAID, we provide continually updated escape predictions for all current strains of SARS-CoV-2.

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“…Therefore, AY.28 appears to have a fitness advantage over the parental delta variant (B.1.617.2) and AY.104, possibly due to the presence of the A222V mutation, which has been previously suggested to associate with higher transmissibility 7 . It was shown that the presence of the A222V mutation promotes an increased opening of the receptor binding domain (RBD) and slightly increases binding to ACE2 compared to the D614G SARS-CoV-2 variant 16 . However, AY.104 is also possibly more transmissible than the parental delta variant (B.1.617.2) as this only accounted for 3.39% of the sequenced variants by the first week of December.…”

Section: Discussionmentioning

confidence: 99%

Molecular epidemiology of AY.28 and AY.104 delta sub-lineages in Sri Lanka

Ranasinghe

Jayathilaka

Jeewandara

et al. 2022

Preprint

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Background: The worst SARS-CoV-2 outbreak in Sri Lanka was due to the two Sri Lankan delta sub-lineages AY.28 and AY.104. We proceeded to further characterize the mutations and clinical disease severity of these two sub-lineages. Methods: 705 delta SARS-CoV-2 genomes sequenced by our laboratory from mid-May to November 2021 using Illumina and Oxford Nanopore were included in the analysis. The clinical disease severity of 440/705 individuals were further analyzed to determine if infection with either AY.28 or AY.104 was associated with more severe disease. Sub-genomic RNA (sg-RNA) expression was analyzed using periscope. Results: AY.28 was the dominant variant throughout the outbreak, accounting for 67.7% of infections during the peak of the outbreak. AY.28 had three lineage defining mutations in the spike protein: A222V (92.80%), A701S (88.06%), and A1078S (92.04%) and seven in the ORF1a: R24C, K634N, P1640L, A2994V, A3209V, V3718A, and T3750I. AY.104 was characterized by the high prevalence of T95I (90.81%) and T572L (65.01%) mutations in the spike protein and by the absence of P1640L (94.28%) in ORF1a with the presence of A1918V (98.58%) mutation. The mean sgRNA expression levels of ORF6 in AY.28 were significantly higher compared to AY.104 (p < 0.0001) and B.1.617.2 (p < 0.01). Also, ORF3a showed significantly higher sgRNA expression in AY.28 compared to AY.104 (p < 0.0001). There was no difference in the clinical disease severity or duration of hospitalization in individuals infected with these sub lineages. Conclusions: Therefore, AY.28 appears to have a fitness advantage over the parental delta variant (B.1.617.2) and AY.104 possibly due to the A222V mutation. AY.28 also had a higher expression of sg-RNA compared to other sub-lineages. The clinical implications of these should be further investigated.

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The structural role of SARS-CoV-2 genetic background in the emergence and success of spike mutations: the case of the spike A222V mutation

Cited by 9 publications

References 70 publications

Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico

Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico

Learning from pre-pandemic data to forecast viral escape

Molecular epidemiology of AY.28 and AY.104 delta sub-lineages in Sri Lanka

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