Genomic surveillance of SARS-CoV-2 reveals community transmission of a major lineage during the early pandemic phase in Brazil

Resende, Paola Cristina; Delatorre, Edson; Gräf, Tiago; Mir, Daiana; Motta, Fernando Couto; Appolinario, Luciana; Paixão, Ariane Gomes; Ogrzewalska, Maria; Caetano, Braula; Santos, Mirleide Cordeiro dos; Ferreira, Jessylene Almeida; Souza, Edivaldo C; Silva, Sandro Patroca da; Fernandes, Sandra Bianchini; Vianna, Lucas A.; Costa, Larissa Kelly Cunha; Ferro, Jean F G; Nardy, Vanessa Brandão; Croda, Júlio; Oliveira, Wanderson Kleber de; Abreu, André Luis de; Bello, Gonzalo; Siqueira, Marilda M.

doi:10.1101/2020.06.17.158006

Cited by 26 publications

(36 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SARS-CoV-2 genome sequence submission to the Pangolin and CoV-GLUE algorithms resulted in the same lineage classification in all cases, defining all but one virus belonging to clade B1.1, while the remaining sequence was classified as B.1. A phylogenetic analysis of the viruses together with sequences previously defined as Brazilian circulating strains B1.1-BR and B1.1-EU/BR showed that most B1.1 genomes generated in this study clustered with B1.1-BR sequences ( Fig 1A) [16]. A phylogenetic tree including all local SARS-CoV-2 sequences isolated from patients residing in the state of Rio de Janeiro available at the GISAID database (accessed on July 27th, 2020, S1 Table) was performed to investigate potential epidemiological linkage between samples ( Fig 1B).…”

Section: Phylogenetic and Epidemiological Profile Of Sars-cov-2 Sequementioning

confidence: 54%

“…For SARS-CoV-2 lineage classification, consensus genomes were submitted to Pangolin software (https://github.com/cov-lineages/pangolin, downloaded on June 10 th , 2020) and to CoV-GLUE lineage system (http://cov-glue.cvr.gla.ac.uk/#/home, accessed on June 10 th , 2020) [25], both based on the nomenclature proposed by Rambaut et al [26]. An alignment including the consensus sequences generated and genomes from Brazilian sequences available on the GISAID Database classified as B1, B1.1 and the Brazilian clusters B1.1-BR/ B1.1-EU/BR (S1 Table) were submitted to a maximum likelihood phylogenic reconstruction using PhyML v.3.0 and the best model of nucleotide substitution was defined with Model Generator (GTR) to investigate the sublineage classification of the study sequences [16,27,28]. Furthermore, a phylogenetic analysis that included the generated consensus sequences generated along with all SARS-CoV-2 sequences from Rio de Janeiro state (Brazil) presently available at GISAID (https://www.epicov.org/epi3/frontend, accessed on July 27 th , 2020, S1 Table) was performed in order to investigate epidemiological relatedness of sequences.…”

Section: Sars-cov-2 Classification and Phylogenetic Analysismentioning

confidence: 99%

See 1 more Smart Citation

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

Siqueira

Goes

Alves

et al. 2020

Preprint

View full text Add to dashboard Cite

Numerous factors have been identified to influence susceptibility to SARS-CoV-2 infection and disease severity. Cancer patients are more prone to clinically evolve to more severe COVID-19 conditions, but the determinants of such a more severe outcome remain largely unknown. We have determined the full-length SARS-CoV-2 genomic sequences of cancer patients and healthcare workers (HCW; non-cancer controls) by deep sequencing and investigated the within-host viral quasispecies of each infection, quantifying intrahost genetic diversity. Naso- and oropharyngeal SARS-CoV-2+ swabs from 57 cancer patients and 14 healthcare workers (HCW) from the Brazilian Cancer Institute were collected in April–May 2020. Complete genome amplification using ARTIC network V3 multiplex primers was performed followed by next-generation sequencing. Assemblies were conducted in Geneious R11, where consensus sequences were extracted and intrahost single nucleotide variants (iSNVs) were identified. Maximum likelihood phylogenetic analysis was performed using PhyMLv.3.0 and lineages were classified using Pangolin and CoV-GLUE. Phylogenetic analysis showed that all but one strain belonged to clade B1.1. Four genetically linked mutations known as the globally dominant SARS-CoV-2 haplotype (C241T, C3037T, C14408T and A23403G) were found in the majority of consensus sequences. SNV signatures of previously characterized Brazilian genomes were also observed in most samples. Another 85 SNVs were found at a lower frequency (1.4-19.7%). Cancer patients displayed a significantly higher intrahost viral genetic diversity compared to HCW (p = 0.009). Intrahost genetic diversity in cancer patients was independent of SARS-CoV-2 Ct values, and was not associated with disease severity, use of corticosteroids, or use of antivirals, characteristics that could influence viral diversity. Such a feature may explain, at least in part, the more adverse outcomes to which cancer/COVID-19 patients experience.Author SummaryCancer patients are more prone to clinically evolve to more severe COVID-19 conditions, but the determinants of such a more severe outcome remain largely unknown. In this study, phylogenetic and variation analysis of SARS-CoV-2 genomes from cancer patients and non-cancer healthcare workers at the Brazilian National Cancer Institute were characterized by deep sequencing. Viral genomes showed signatures characteristic of Brazilian viruses, consistent with the hypothesis of local, community transmission rather than virus importation from abroad. Despite most genomes in patients and healthcare workers belonging to the same lineage, intrahost variability was higher in cancer patients when compared to non-cancer counterparts. The intrahost genomic diversity analysis presented in our study highlights the relaxed evolution of SARS-CoV-2 in a vulnerable population of cancer patients. The high number of minor variations can result in the selection of immune escape variants, resistance to potential drugs, and/or increased pathogenicity. The impact of this higher intrahost variability over time warrants further investigation.

show abstract

Section: Phylogenetic and Epidemiological Profile Of Sars-cov-2 Sequementioning

confidence: 54%

Section: Sars-cov-2 Classification and Phylogenetic Analysismentioning

confidence: 99%

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

Siqueira

Goes

Alves

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For Spain, NYC and Brazil, the TN10 projections point the origin of local viral transmissions 10-15 days earlier than the estimated T MRCA of major transmission lineages detected (Table). (11,13,22,23) For UK and Belgium, by its turn, the TN10 and TN100 projections recovered dates that were 15-20 days earlier than the estimated T MRCA of local lineages detected (Table). (14,17) The T MRCA of local SARS-CoV-2 transmission lineages in France, Germany and Netherlands were not described so far.…”

mentioning

confidence: 95%

Tracking the onset date of the community spread of SARS-CoV-2 in western countries

Delatorre

Mir

Gräf

et al. 2020

Mem. Inst. Oswaldo Cruz

Self Cite

View full text Add to dashboard Cite

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly spread around the world during 2020, but the precise time in which the virus began to spread locally is difficult to trace for most countries. Here, we estimate the probable onset date of the community spread of SARS-CoV-2 for heavily affected countries from Western Europe and the Americas on the basis of the cumulative number of deaths reported during the early stage of the epidemic. Our results support that SARS-CoV-2 probably started to spread locally in all western countries analysed between mid-January and mid-February 2020, thus long before community transmission was officially recognised and control measures were implemented.

show abstract

“…1B) can be inferred using published estimates of the evolutionary rate of the SARS-2 population in the human population, assuming that the pre-and post-emergence rates are the same (Rodríguez-Román and Gibbs, 2020). Various estimates of the SARS-2 evolutionary rate have been published recently; 1.126 x 10 -3 (95 % BCI: 1.03-1.23 x 10 -3 ) substitutions per site per year (s/s/y) (Candido et al, 2020), 1.1×10 −3 s/s/y (95% CI 7.03×10 −4 and 1.5×10 −3 s/s/y) (Duchêne et al, 2020), 9.41×10 -4 s/s/y +/-4.99×10 -5 (Pybus et al, 2020) and 8 x 10 -4 s/s/y (Resende et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Comparisons of the genome of SARS-CoV-2 and those of other betacoronaviruses

Rodríguez‐Román

Gibbs

2020

Preprint

View full text Add to dashboard Cite

The genome of SARS-CoV-2 virus causing the worldwide pandemic of COVID-19 is most closely related to viral metagenomes isolated from bats and, more distantly, pangolins. All are of sarbecoviruses of the genus Betacoronavirus. We have unravelled their recombinational and mutational histories. All showed clear evidence of recombination, most events involving the 3’ half of the genomes. The 5’ region of their genomes was mostly recombinant free, and a phylogeny calculated from this region confirmed that SARS-CoV-2 is closer to RmYN02 than RaTG13, and showed that SARS-CoV-2 diverged from RmYN02 at least 26 years ago, and both diverged from RaTG13 at least 37 years ago; recombinant regions specific to these three viruses provided no additional information as they matched no other Genbank sequences closely. Simple pairwise comparisons of genomes show that there are three regions where most non-synonymous changes probably occurred; the DUF3655 region of the nsp3, the S gene and ORF 8 gene. Differences in the last two of those regions have probably resulted from recombinational changes, however differences in the DUF3655 region may have resulted from selection. A hexamer of the proteins encoded by the nsp3 region may form the molecular pore spanning the double membrane of the coronavirus replication organelle (Wolff et al., 2020), and perhaps the acidic polypeptide encoded by DUF3655 lines it, and presents a novel target for pharmaceutical intervention.

show abstract

Genomic surveillance of SARS-CoV-2 reveals community transmission of a major lineage during the early pandemic phase in Brazil

Abstract: 42

Cited by 26 publications

References 38 publications

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

Tracking the onset date of the community spread of SARS-CoV-2 in western countries

Comparisons of the genome of SARS-CoV-2 and those of other betacoronaviruses

Contact Info

Product

Resources

About