Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2

Yu, Dalang; Yang, Xiao; Tang, Bixia; Pan, Yi-Hsuan; Yang, Jianing; Zhu, Junwei; Duan, Guangya; Hao, Ziqian; Mu, Haibo; Dai, Lina; Hu, Wangjie; Zhang, Mochen; Cui, Ying; Jin, Tao; Li, Cuiping; Ma, Lina; team, Language translation; Su, Xiao; Zhang, Guoqing; Zhao, Wenming; Li, Haipeng

doi:10.1101/2020.12.23.20248612

Cited by 4 publications

(24 citation statements)

References 50 publications

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“…The annotated evolutionary tree and evolutionary network data were obtained from the Coronavirus GenBrowser ( 18 ) and VENAS ( 19 ). All sequence data of SARS-CoV-2 were obtained from the 2019nCoVR database ( 20, 21 ), which is an integrated resource based on Global Initiative on Sharing All Influenza Data (GISAID) ( 22, 23 ), National Center for Biotechnology Information (NCBI) GenBank ( 24 ), China National GeneBank DataBase (CNGBdb) ( 25 ), the Genome Warehouse (GWH) ( 26 ), and the National Microbiology Data Center (NMDC, https://nmdc.cn/).…”

Section: Methodsmentioning

confidence: 99%

“…The binomial probability was used to test the null hypothesis, and the test was one-tailed. The analysis was based on the data version “data.2021-03-06” ( n = 400,051) of Coronavirus GenBrowser (CGB) ( 18 ), where n is the number of viral strains.…”

Section: Methodsmentioning

confidence: 99%

“…(A) The different transmissibility between the B.1.1.7-like and the B.1.1.7 strains via employing the CGB ( 18 ). Strains were filtered for different countries or continents.…”

Section: Methodsmentioning

confidence: 99%

“…To examine the reappearance of mutations, recurrent mutations and mutations due to recombination were considered. Considering the degeneracy of the genetic code, we searched amino acid mutations by using the form of amino acid change, instead of that of nucleotide change in the CGB ( 18 ). To search the non-coding deletion (g.a28271-) in the evolutionary tree, we used the string “A28271-”.…”

Section: Methodsmentioning

confidence: 99%

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A non-coding A-to-T Kozak site change related to the transmissibility of Alpha, Delta and Omicron VOCs

Yang

Zhang

et al. 2021

Preprint

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The SARS-CoV-2 variant of concern B.1.1.7 has quickly spread. To identify its crucial mutations, we explored the B.1.1.7 associated mutations on an evolutionary tree by the Coronavirus GenBrowser and VENAS. We found that a non-coding deletion g.a28271-, at upstream of the nucleocapsid (N) gene, has triggered the high transmissibility of B.1.1.7. The deletion changes the core Kozak site of the N gene and may reduce the expression of N protein and increase that of ORF9b. The expression of ORF9b is also regulated by another mutation (g.gat28280cta) that mutates the core Kozak sites of the ORF9b gene. If both mutations back-mutate, B.1.1.7 variant loses its high transmissibility. Moreover, the deletion may interact with ORF1a:p.SGF3675-, S:p.P681H, and S:p.T716I to increase the viral transmissibility. Overall, these results demonstrate the importance of the non-coding deletion and provide evolutionary insight into the crucial mutations of B.1.1.7.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…(A) The different transmissibility between the B.1.1.7-like and the B.1.1.7 strains via employing the CGB ( 18 ). Strains were filtered for different countries or continents.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A non-coding A-to-T Kozak site change related to the transmissibility of Alpha, Delta and Omicron VOCs

Yang

Zhang

et al. 2021

Preprint

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“…In the SARS-CoV-2 meta-population is easy to anticipate that an enormous number of new mutants, randomly and recurringly, are spontaneously arising (considering that the SARS-CoV-2 mutation rate is higher than 10 −4 , an infected host can produce at least 10 10 new viruses, in the world there are many millions of infected people). Certainly, among these new mutants some can arise that are more infective than the current ones, even mutants resistant to the already developed vaccines.…”

Section: Discussionmentioning

confidence: 99%

Sword of Damocles or choosing well. Population genetics sheds light into the future of the COVID-19 pandemic and SARS-CoV-2 new mutant strains

Alcaniz

Lopez-Rodas

Costaş

2021

Preprint

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An immense scientific effort has been made worldwide due to Covid-19’s pandemic magnitude. It has made possible to identify almost 300,000 SARS-CoV-2 different genetic variants, connecting them with clinical and epidemiological findings. Among this immense data collection, that constitutes the biggest evolutionary experiment in history, is buried the answer to what will happen in the future. Will new strains, more contagious than the current ones or resistant to the vaccines, arise by mutation? Although theoretic population genetics is, by far, the most powerful tool we have to do an accurate prediction, it has been barely used for the study of SARS-CoV-2 due to its conceptual difficulty. Having in mind that the size of the SARS-CoV-2 population is astronomical we can apply a discrete treatment, based on the branching process method, Fokker-Plank equations and Kolmogoroff’s forward equations, to calculate the survival likelihood through time, to elucidate the likelihood to become dominant genotypes and how long will this take, for new SARS-CoV-2 mutants depending on their selective advantage. Results show that most of the new mutants that will arise in the SARS-CoV-2 meta-population will stay at very low frequencies. However, some few new mutants, significantly more infectious than current ones, will still emerge and become dominant in the population favoured by a great selective advantage. Far from showing a “mutational meltdown”, SARS-CoV-2 meta-population will increase its fitness becoming more infective. There is a probability, small but finite, that new mutants arise resistant to some vaccines. High infected numbers and slow vaccination programs will significantly increase this likelihood.

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Epistasis-Driven Evolution of the SARS-CoV-2 Secondary Structure

2022

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Epistasis is an evolutionary phenomenon whereby the fitness effect of a mutation depends on the genetic background in which it arises. A key source of epistasis in an RNA molecule is its secondary structure, which contains functionally important topological motifs held together by hydrogen bonds between Watson–Crick (WC) base pairs. Here we study epistasis in the secondary structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by examining properties of derived alleles arising from substitution mutations at ancestral WC base-paired and unpaired (UP) sites in 15 conserved topological motifs across the genome. We uncover fewer derived alleles and lower derived allele frequencies at WC than at UP sites, supporting the hypothesis that modifications to the secondary structure are often deleterious. At WC sites, we also find lower derived allele frequencies for mutations that abolish base pairing than for those that yield G·U “wobbles,” illustrating that weak base pairing can partially preserve the integrity of the secondary structure. Last, we show that WC sites under the strongest epistatic constraint reside in a three-stemmed pseudoknot motif that plays an essential role in programmed ribosomal frameshifting, whereas those under the weakest epistatic constraint are located in 3’ UTR motifs that regulate viral replication and pathogenicity. Our findings demonstrate the importance of epistasis in the evolution of the SARS-CoV-2 secondary structure, as well as highlight putative structural and functional targets of different forms of natural selection.

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Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2

Cited by 4 publications

References 50 publications

A non-coding A-to-T Kozak site change related to the transmissibility of Alpha, Delta and Omicron VOCs

A non-coding A-to-T Kozak site change related to the transmissibility of Alpha, Delta and Omicron VOCs

Sword of Damocles or choosing well. Population genetics sheds light into the future of the COVID-19 pandemic and SARS-CoV-2 new mutant strains

Epistasis-Driven Evolution of the SARS-CoV-2 Secondary Structure

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