Multiscale Feedback Loops in SARS-CoV-2 Viral Evolution

Barrett, Christopher L.; Bura, Andrei C.; He, Qijun; Huang, Fenix W. D.; Li, Thomas J. X.; Waterman, Michael S.; Reidys, Christian M.

doi:10.1089/cmb.2020.0343

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Additionally, it appears to be associated with a higher viral load in samples of the human upper respiratory tract and with greater infectivity in pseudotyping assays 46 . The interaction between A23403G and C14408T mutations may be related to greater viral fitness 47 . Both were found together in 100% of the analyzed samples, similar to that found by Liu et al (2020) (74%) 48 , that suggested there is a biological connection between these changes.…”

Section: Resultsmentioning

confidence: 99%

SARS-CoV-2 genomic surveillance in Rondônia, Brazilian Western Amazon

Souza

Nogueira-Lima

Roca

et al. 2021

Sci Rep

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SARS-CoV-2 has spread rapidly around the world, with Brazil currently considered an epicenter of the pandemic. The Northern region has the second highest incidence coefficient, as well as the third highest mortality rate in the country. This study aimed to investigate information about the evolutionary history of epidemic spread and genetic aspects of strains isolated on the Western Amazon, in the State of Rondônia, Brazil. It was possible to detect a total of 22 mutations. Some of these alterations may possibly be related to effects on transmissibility, the fidelity of RNA replication, the ability of cancer patients to respond to infection, beyond a mutation that emerged after the introduction of SARS-CoV-2 in Rondônia. At least two events of introduction were detected, corresponding to the B.1 and B.1.1 European lineages. An introduction was observed possibly through Argentina, where strains originated that circulated in the Minas Gerais and Ceará Brazilian states, prior to Rondônia (B.1.), as well as through the Minas Gerais state and the Federal District, which gave rise to strains that spread to Rondônia, from the capital to more rural parts of the state (B.1.1.). The findings show the need to monitor the genetic epidemiology of COVID-19, in order to surveil the virus’s evolution, dispersion and diversity.

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Section: Resultsmentioning

confidence: 99%

SARS-CoV-2 genomic surveillance in Rondônia, Brazilian Western Amazon

Souza

Nogueira-Lima

Roca

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Mutations on viral genomes do not always appear independently. For example, the S:D614G change, caused by the nucleotide mutation A23404G on the SARS-CoV-2 genome, was almost always accompanied by three other nucleotide mutations: C241T, C3037T and C14408T [36]: these four positions exhibited a co-evolutionary pattern [50, 2]. In fact, it is by construction that positions in a molecule that share a common constraint do not evolve independently, and therefore leave a signature in patterns of homologous sequences [12, 38].…”

Section: Introductionmentioning

confidence: 99%

Motifs in Sars-Cov-2 Evolution

Barrett

Bura

et al. 2023

Preprint

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We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of co-evolving sites in the alignment (motifs), identified via co-evolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the co-evolutionary coupling of pairs and manifest as co-evolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness and robustness. Lastly, we study how well each lineage is classified by such a system.

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“…For example, the S:D614G change, caused by the nucleotide mutation A23404G on the SARS-CoV-2 genome, was almost always accompanied by three other nucleotide mutations: C241T, C3037T, and C14408T (Plante et al 2021). These four positions exhibited a coevolutionary pattern (Barrett et al 2020;Yin 2020). In fact, it is by construction that positions in a molecule that share a common constraint do not evolve independently and therefore leave a signature in patterns of homologous sequences (Dutheil 2012;Priya and Shanker 2021).…”

Section: Introductionmentioning

confidence: 99%

Motifs in SARS-CoV-2 evolution

Barrett,

Bura,

et al. 2023

RNA

Self Cite

View full text Add to dashboard Cite

We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of co-evolving sites in the alignment (motifs), identified via co-evolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the co-evolutionary coupling of pairs and manifest as co-evolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across $21$ lineages and $27$ countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness and robustness. Lastly, we study how well each lineage is classified by such a system.

show abstract

Multiscale Feedback Loops in SARS-CoV-2 Viral Evolution

Cited by 5 publications

References 32 publications

SARS-CoV-2 genomic surveillance in Rondônia, Brazilian Western Amazon

SARS-CoV-2 genomic surveillance in Rondônia, Brazilian Western Amazon

Motifs in Sars-Cov-2 Evolution

Motifs in SARS-CoV-2 evolution

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