Inferring Viral Transmission Pathways from Within-Host Variation.

Specht, Ivan O A; Petros, Brittany A; Moreno, Gage K; Brock-Fisher, Taylor; Krasilnikova, Lydia A; Schifferli, Mark; Yang, Katherine; Cronan, Paul; Glennon, Olivia; Schaffner, Stephen F; Park, Daniel J; MacInnis, Bronwyn L; Ozonoff, Al; Fry, Ben; Mitzenmacher, Michael D; Varilly, Patrick; Sabeti, Pardis C

Specht, Ivan O A; Petros, Brittany A; Moreno, Gage K; Brock-Fisher, Taylor; Krasilnikova, Lydia A; Schifferli, Mark; Yang, Katherine; Cronan, Paul; Glennon, Olivia; Schaffner, Stephen F; Park, Daniel J; MacInnis, Bronwyn L; Ozonoff, Al; Fry, Ben; Mitzenmacher, Michael D; Varilly, Patrick; Sabeti, Pardis C.

Afiliação

Specht IOA; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Petros BA; Harvard College, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
Moreno GK; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Brock-Fisher T; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.
Krasilnikova LA; Harvard/MIT MD-PhD Program, Boston, MA 02115, USA.
Schifferli M; Systems, Synthetic, and Quantitative Biology PhD Program, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
Yang K; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cronan P; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Glennon O; Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
Schaffner SF; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Park DJ; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
MacInnis BL; Fathom Information Design, Boston, MA 02114, USA.
Ozonoff A; Fathom Information Design, Boston, MA 02114, USA.
Fry B; Fathom Information Design, Boston, MA 02114, USA.
Mitzenmacher MD; Fathom Information Design, Boston, MA 02114, USA.
Varilly P; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Sabeti PC; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

medRxiv ; 2023 Oct 15.

Article em En | MEDLINE | ID: mdl-37873325

ABSTRACT

ABSTRACT

Genome sequencing can offer critical insight into pathogen spread in viral outbreaks, but existing transmission inference methods use simplistic evolutionary models and only incorporate a portion of available genetic data. Here, we develop a robust evolutionary model for transmission reconstruction that tracks the genetic composition of within-host viral populations over time and the lineages transmitted between hosts. We confirm that our model reliably describes within-host variant frequencies in a dataset of 134,682 SARS-CoV-2 deep-sequenced genomes from Massachusetts, USA. We then demonstrate that our reconstruction approach infers transmissions more accurately than two leading methods on synthetic data, as well as in a controlled outbreak of bovine respiratory syncytial virus and an epidemiologically-investigated SARS-CoV-2 outbreak in South Africa. Finally, we apply our transmission reconstruction tool to 5,692 outbreaks among the 134,682 Massachusetts genomes. Our methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, and provide an adaptable mathematical framework for tracking within-host evolution.

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Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: MedRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos