Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

Abstract Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gianluigi Rossi, Joseph Crispell, Daniel Balaz, Samantha J. Lycett, Clare H. Benton, Richard J. Delahay, Rowland R. Kao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2d89509e3c38429ba11b3ccf9cd3ae90
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2d89509e3c38429ba11b3ccf9cd3ae90
record_format	dspace
spelling	oai:doaj.org-article:2d89509e3c38429ba11b3ccf9cd3ae902021-12-02T12:42:27ZIdentifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations10.1038/s41598-020-78900-32045-2322https://doaj.org/article/2d89509e3c38429ba11b3ccf9cd3ae902020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78900-3https://doaj.org/toc/2045-2322Abstract Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.Gianluigi RossiJoseph CrispellDaniel BalazSamantha J. LycettClare H. BentonRichard J. DelahayRowland R. KaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gianluigi Rossi Joseph Crispell Daniel Balaz Samantha J. Lycett Clare H. Benton Richard J. Delahay Rowland R. Kao Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
description	Abstract Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.
format	article
author	Gianluigi Rossi Joseph Crispell Daniel Balaz Samantha J. Lycett Clare H. Benton Richard J. Delahay Rowland R. Kao
author_facet	Gianluigi Rossi Joseph Crispell Daniel Balaz Samantha J. Lycett Clare H. Benton Richard J. Delahay Rowland R. Kao
author_sort	Gianluigi Rossi
title	Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
title_short	Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
title_full	Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
title_fullStr	Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
title_full_unstemmed	Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations
title_sort	identifying likely transmissions in mycobacterium bovis infected populations of cattle and badgers using the kolmogorov forward equations
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/2d89509e3c38429ba11b3ccf9cd3ae90
work_keys_str_mv	AT gianluigirossi identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT josephcrispell identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT danielbalaz identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT samanthajlycett identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT clarehbenton identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT richardjdelahay identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations AT rowlandrkao identifyinglikelytransmissionsinmycobacteriumbovisinfectedpopulationsofcattleandbadgersusingthekolmogorovforwardequations
_version_	1718393674326343680

Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

Ejemplares similares