Evolution of SARS-CoV-2 during the first year of the COVID-19 pandemic in Northwestern Argentina

Montaño, Romina Zambrana; Culasso, Andrés Carlos Alberto; Fernández, Franco Daniel; Marquez, Nathalie; Debat, Humberto Julio; Salmeron, Mariana; Zamora, Ana María; Huidobro, Gustavo Ruiz de; Costas, Dardo; Alabarse, Graciela; Charre, Miguel; Fridman, Ariel; Mamani, Claudia; Vaca, Fabiana; Diaz, Claudia Maza; Raskovsky, Viviana; Lavaque, Esteban; Lesser, Veronica; Cajal, Pamela; Aguero, Fernanda; Calvente, Cintia; Torres, Carolina; Viegas, Mariana

doi:10.1016/j.virusres.2022.198936

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…We identified cluster-defining mutations that were later selected for Pango lineage definitions, such as the G22599A and G5924A mutations for BA.1.1 (clusters 1 and 5 in the DAPC) and BA.1.17 (cluster 4 in the DAPC), respectively ( Figure 2 B). The subdivision observed in our study, as well as some of the cluster-defining mutations (G5924A, G22599A, C2470T, and A29301G), also agrees with recent phylogenetic reconstructions ( Figure 2 B) [ 20 , 21 ].…”

Section: Discussionsupporting

confidence: 92%

“…Although the population genomics framework adopted in our study identified this diagnostic mutation, which defines cluster 1 of the BA.1 lineage, the test for deviations from neutrality returned a significant negative value only for this cluster (Tajima’s D= − 2.425328), indicating the selective advantage, as well as signals of population expansion, of this cluster across the globe ( Figure 2 B) [ 23 - 25 ]. In addition to G22599A (S:R346K), we also identified the mutation C23664T (S:A701V), which, in conjunction with S:N501Y, provides a mild advantage to the virus by increasing the rate of infection [ 20 ]. However, the C23664T mutation is not unique to the Omicron lineage and is also observed in other SARS-CoV-2 variants of concern [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to G22599A (S:R346K), we also identified the mutation C23664T (S:A701V), which, in conjunction with S:N501Y, provides a mild advantage to the virus by increasing the rate of infection [ 20 ]. However, the C23664T mutation is not unique to the Omicron lineage and is also observed in other SARS-CoV-2 variants of concern [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

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Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach

Garg¹,

Lamba²,

Chattopadhyay³

2023

JMIR Bioinform Biotech

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Background A thorough understanding of the patterns of genetic subdivision in a pathogen can provide crucial information that is necessary to prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging, and traditional methods for understanding genetic subdivision often fail. Objective The aim of our study was to use population genomics methods to identify the subtle subdivisions and demographic history of the Omicron variant, in addition to those captured by the Pango lineage. Methods We used a combination of an evolutionary network approach and multivariate statistical protocols to understand the subdivision and spread of the Omicron variant. We identified subdivisions within the BA.1 and BA.2 lineages and further identified the mutations associated with each cluster. We further characterized the overall genomic diversity of the Omicron variant and assessed the selection pressure for each of the genetic clusters identified. Results We observed concordant results, using two different methods to understand genetic subdivision. The overall pattern of subdivision in the Omicron variant was in broad agreement with the Pango lineage definition. Further, 1 cluster of the BA.1 lineage and 3 clusters of the BA.2 lineage revealed statistically significant signatures of selection or demographic expansion (Tajima’s D<−2), suggesting the role of microevolutionary processes in the spread of the virus. Conclusions We provide an easy framework for assessing the genetic structure and demographic history of SARS-CoV-2, which can be particularly useful for understanding the local history of the virus. We identified important mutations that are advantageous to some lineages of Omicron and aid in the transmission of the virus. This is crucial information for policy makers, as preventive measures can be designed to mitigate further spread based on a holistic understanding of the variability of the virus and the evolutionary processes aiding its spread.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach

Garg¹,

Lamba²,

Chattopadhyay³

2023

JMIR Bioinform Biotech

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“…The evolutionary pattern of Omicron lineages detected in the third and fourth waves in Argentina reflects the distribution described in other countries from South America, the United States and Western Europe, as was observed in the pre-Omicron period [ 12 ]. However, this pattern differed from that of the first and second waves, when there was a high level of clustering and geographical structure within Argentina [ 12 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

Omicron Waves in Argentina: Dynamics of SARS-CoV-2 Lineages BA.1, BA.2 and the Emerging BA.2.12.1 and BA.4/BA.5

Torres

Jodar

Acuña

et al. 2023

Viruses

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The COVID-19 pandemic has lately been driven by Omicron. This work aimed to study the dynamics of SARS-CoV-2 Omicron lineages during the third and fourth waves of COVID-19 in Argentina. Molecular surveillance was performed on 3431 samples from Argentina, between EW44/2021 and EW31/2022. Sequencing, phylogenetic and phylodynamic analyses were performed. A differential dynamic between the Omicron waves was found. The third wave was associated with lineage BA.1, characterized by a high number of cases, very fast displacement of Delta, doubling times of 3.3 days and a low level of lineage diversity and clustering. In contrast, the fourth wave was longer but associated with a lower number of cases, initially caused by BA.2, and later by BA.4/BA.5, with doubling times of about 10 days. Several BA.2 and BA.4/BA.5 sublineages and introductions were detected, although very few clusters with a constrained geographical distribution were observed, suggesting limited transmission chains. The differential dynamic could be due to waning immunity and an increase in population gatherings in the BA.1 wave, and a boosted population (for vaccination or recent prior immunity for BA.1 infection) in the wave caused by BA2/BA.4/BA.5, which may have limited the establishment of the new lineages.

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“…In Peru, B.1.1 was detected at a frequency of 1%, while in Chile, it was reported as one of five lineages that dominated early in the pandemic [ 30 , 31 ]. B.1.1.348 was detected in Argentina during the first wave of SARS-CoV-2 infections at a low frequency of 3.7% and was present in Peru at 3% and at a higher frequency of 9.7% in Colombia [ 30 , 32 , 33 ]. Chile also reported B.1.1.348 as one of five dominant lineages early in the pandemic [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Molecular Epidemiology of SARS-CoV-2 during Five COVID-19 Waves and the Significance of Low-Frequency Lineages

Subramoney

Mtileni

Giandhari

et al. 2023

Viruses

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SARS-CoV-2 lineages and variants of concern (VOC) have gained more efficient transmission and immune evasion properties with time. We describe the circulation of VOCs in South Africa and the potential role of low-frequency lineages on the emergence of future lineages. Whole genome sequencing was performed on SARS-CoV-2 samples from South Africa. Sequences were analysed with Nextstrain pangolin tools and Stanford University Coronavirus Antiviral & Resistance Database. In 2020, 24 lineages were detected, with B.1 (3%; 8/278), B.1.1 (16%; 45/278), B.1.1.348 (3%; 8/278), B.1.1.52 (5%; 13/278), C.1 (13%; 37/278) and C.2 (2%; 6/278) circulating during the first wave. Beta emerged late in 2020, dominating the second wave of infection. B.1 and B.1.1 continued to circulate at low frequencies in 2021 and B.1.1 re-emerged in 2022. Beta was outcompeted by Delta in 2021, which was thereafter outcompeted by Omicron sub-lineages during the 4th and 5th waves in 2022. Several significant mutations identified in VOCs were also detected in low-frequency lineages, including S68F (E protein); I82T (M protein); P13L, R203K and G204R/K (N protein); R126S (ORF3a); P323L (RdRp); and N501Y, E484K, D614G, H655Y and N679K (S protein). Low-frequency variants, together with VOCs circulating, may lead to convergence and the emergence of future lineages that may increase transmissibility, infectivity and escape vaccine-induced or natural host immunity.

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Evolution of SARS-CoV-2 during the first year of the COVID-19 pandemic in Northwestern Argentina

Cited by 6 publications

References 33 publications

Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach

Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach

Omicron Waves in Argentina: Dynamics of SARS-CoV-2 Lineages BA.1, BA.2 and the Emerging BA.2.12.1 and BA.4/BA.5

Molecular Epidemiology of SARS-CoV-2 during Five COVID-19 Waves and the Significance of Low-Frequency Lineages

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