Details

Autor(en) / Beteiligte

• Bollen, Nena
• Artesi, Maria
• Durkin, Keith
• Hong, Samuel L.
• Potter, Barney
• Boujemla, Bouchra
• Vanmechelen, Bert
• Martí-Carreras, Joan
• Wawina-Bokalanga, Tony
• Meex, Cécile
• Bontems, Sébastien
• Hayette, Marie-Pierre
• André, Emmanuel
• Maes, Piet
• Bours, Vincent
• Baele, Guy
• Dellicour, Simon

Titel

Exploiting genomic surveillance to map the spatio-temporal dispersal of SARS-CoV-2 spike mutations in Belgium across 2020

Ist Teil von

• Scientific reports, 2021-09, Vol.11 (1), p.18580-18580, Article 18580

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Abstract At the end of 2020, several new variants of SARS-CoV-2—designated variants of concern—were detected and quickly suspected to be associated with a higher transmissibility and possible escape of vaccine-induced immunity. In Belgium, this discovery has motivated the initiation of a more ambitious genomic surveillance program, which is drastically increasing the number of SARS-CoV-2 genomes to analyse for monitoring the circulation of viral lineages and variants of concern. In order to efficiently analyse the massive collection of genomic data that are the result of such increased sequencing efforts, streamlined analytical strategies are crucial. In this study, we illustrate how to efficiently map the spatio-temporal dispersal of target mutations at a regional level. As a proof of concept, we focus on the Belgian province of Liège that has been consistently sampled throughout 2020, but was also one of the main epicenters of the second European epidemic wave. Specifically, we employ a recently developed phylogeographic workflow to infer the regional dispersal history of viral lineages associated with three specific mutations on the spike protein (S98F, A222V and S477N) and to quantify their relative importance through time. Our analytical pipeline enables analysing large data sets and has the potential to be quickly applied and updated to track target mutations in space and time throughout the course of an epidemic.