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Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.
Torres Ortiz, Arturo; Kendall, Michelle; Storey, Nathaniel; Hatcher, James; Dunn, Helen; Roy, Sunando; Williams, Rachel; Williams, Charlotte; Goldstein, Richard A; Didelot, Xavier; Harris, Kathryn; Breuer, Judith; Grandjean, Louis.
Afiliação
  • Torres Ortiz A; Department of Infectious Diseases, Imperial College London, London, United Kingdom.
  • Kendall M; Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom.
  • Storey N; Department of Statistics, University of Warwick, Coventry, United Kingdom.
  • Hatcher J; Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
  • Dunn H; Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
  • Roy S; Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
  • Williams R; Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom.
  • Williams C; UCL Genomics, University College London, London, United Kingdom.
  • Goldstein RA; UCL Genomics, University College London, London, United Kingdom.
  • Didelot X; UCL Genomics, University College London, London, United Kingdom.
  • Harris K; Department of Statistics, University of Warwick, Coventry, United Kingdom.
  • Breuer J; Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
  • Grandjean L; Department of Virology, East & South East London Pathology Partnership, Royal London Hospital, Barts Health NHS Trust, London, United Kingdom.
Elife ; 122023 09 21.
Article em En | MEDLINE | ID: mdl-37732733
During an infectious disease outbreak, tracing who infected whom allows public health scientists to see how a pathogen is spreading and to establish effective control measures. Traditionally, this involves identifying the individuals an infected person comes into contact with and monitoring whether they also become unwell. However, this information is not always available and can be inaccurate. One alternative is to track the genetic data of pathogens as they spread. Over time, pathogens accumulate mutations in their genes that can be used to distinguish them from one another. Genetically similar pathogens are more likely to have spread during the same outbreak, while genetically dissimilar pathogens may have come from different outbreaks. However, there are limitations to this approach. For example, some pathogens accumulate genetic mutations very slowly and may not change enough during an outbreak to be distinguishable from one another. Additionally, some pathogens can spread rapidly, leaving less time for mutations to occur between transmission events. To overcome these challenges, Torres Ortiz et al. developed a more sensitive approach to pathogen genetic testing that took advantage of the multiple pathogen populations that often coexist in an infected patient. Rather than tracking only the most dominant genetic version of the pathogen, this method also looked at the less dominant ones. Torres Ortiz et al. performed genome sequencing of SARS-CoV-2 (the virus that causes COVID-19) samples from 451 healthcare workers, patients, and patient contacts at participating London hospitals. Analysis showed that it was possible to detect multiple genetic populations of the virus within individual patients. These subpopulations were often more similar in patients that had been in contact with one another than in those that had not. Tracking the genetic data of all viral populations enabled Torres Ortiz et al. to trace transmission more accurately than if only the dominant population was used. More accurate genetic tracing could help public health scientists better track pathogen transmission and control outbreaks. This may be especially beneficial in hospital settings where outbreaks can be smaller, and it is important to understand if transmission is occurring within the hospital or if the pathogen is imported from the community. Further research will help scientists understand how pathogen population genetics evolve during outbreaks and may improve the detection of subpopulations present at very low frequencies.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 / 4_TD Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / COVID-19 Limite: Humans Idioma: En Revista: Elife Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 / 4_TD Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / COVID-19 Limite: Humans Idioma: En Revista: Elife Ano de publicação: 2023 Tipo de documento: Article