Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data

Madikay Senghore (), Hannah Read, Priyali Oza, Sarah Johnson, Hemanoel Passarelli-Araujo, Bradford P. Taylor, Stephen Ashley, Alex Grey, Alanna Callendrello, Robyn Lee, Matthew R. Goddard, Thomas Lumley, William P. Hanage and Siouxsie Wiles ()
Additional contact information
Madikay Senghore: Harvard TH Chan School of Public Health
Hannah Read: University of Auckland
Priyali Oza: University of Auckland
Sarah Johnson: University of Auckland
Hemanoel Passarelli-Araujo: Harvard TH Chan School of Public Health
Bradford P. Taylor: Harvard TH Chan School of Public Health
Stephen Ashley: University of Auckland
Alex Grey: University of Auckland
Alanna Callendrello: Harvard TH Chan School of Public Health
Robyn Lee: Harvard TH Chan School of Public Health
Matthew R. Goddard: University of Auckland
Thomas Lumley: University of Auckland
William P. Hanage: Harvard TH Chan School of Public Health
Siouxsie Wiles: University of Auckland

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs – two hosts in which infection was transmitted from one to the other – is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42211-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42211-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42211-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().