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Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk

Salmonella enterica is an animal and zoonotic pathogen of worldwide importance. Salmonella serovars that differ in their host and tissue tropisms exist. Cattle are an important reservoir of human nontyphoidal salmonellosis, and contaminated bovine peripheral lymph nodes enter the food chain via grou...

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Autores principales: Vohra, Prerna, Bugarel, Marie, Turner, Frances, Loneragan, Guy H., Hope, Jayne C., Hopkins, John, Stevens, Mark P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795071/
https://www.ncbi.nlm.nih.gov/pubmed/29180370
http://dx.doi.org/10.1128/AEM.02262-17
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author Vohra, Prerna
Bugarel, Marie
Turner, Frances
Loneragan, Guy H.
Hope, Jayne C.
Hopkins, John
Stevens, Mark P.
author_facet Vohra, Prerna
Bugarel, Marie
Turner, Frances
Loneragan, Guy H.
Hope, Jayne C.
Hopkins, John
Stevens, Mark P.
author_sort Vohra, Prerna
collection PubMed
description Salmonella enterica is an animal and zoonotic pathogen of worldwide importance. Salmonella serovars that differ in their host and tissue tropisms exist. Cattle are an important reservoir of human nontyphoidal salmonellosis, and contaminated bovine peripheral lymph nodes enter the food chain via ground beef. The relative abilities of different serovars to survive within the bovine lymphatic system are poorly understood and constrain the development of control strategies. This problem was addressed by developing a massively parallel whole-genome sequencing method to study mixed-serovar infections in vivo. Salmonella serovars differ genetically by naturally occurring single nucleotide polymorphisms (SNPs) in certain genes. It was hypothesized that these SNPs could be used as markers to simultaneously identify serovars in mixed populations and quantify the abundance of each member in a population. The performance of the method was validated in vitro using simulated pools containing up to 11 serovars in various proportions. It was then applied to study serovar survival in vivo in cattle challenged orally with the same 11 serovars. All the serovars successfully colonized the bovine lymphatic system, including the peripheral lymph nodes, and thus pose similar risks of zoonosis. This method enables the fates of multiple genetically unmodified strains to be evaluated simultaneously in a single animal. It could be useful in reducing the number of animals required to study mixed-strain infections and in testing the cross-protective efficacy of vaccines and treatments. It also has the potential to be applied to diverse bacterial species which possess shared but polymorphic alleles. IMPORTANCE While some Salmonella serovars are more frequently isolated from lymph nodes rather than the feces and environment of cattle, the relative abilities of serovars to survive within the lymphatic system of cattle remain ill defined. A sequencing-based method which used available information from sequenced Salmonella genomes to study the dynamics of mixed-serovar infections in vivo was developed. The main advantages of the method include the simultaneous identification and quantification of multiple strains without any genetic modification and minimal animal use. This approach could be used in vaccination trials or in epidemiological surveys where an understanding of the dynamics of closely related strains of a pathogen in mixed populations could inform the prediction of zoonotic risk and the development of intervention strategies.
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spelling pubmed-57950712018-02-12 Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk Vohra, Prerna Bugarel, Marie Turner, Frances Loneragan, Guy H. Hope, Jayne C. Hopkins, John Stevens, Mark P. Appl Environ Microbiol Methods Salmonella enterica is an animal and zoonotic pathogen of worldwide importance. Salmonella serovars that differ in their host and tissue tropisms exist. Cattle are an important reservoir of human nontyphoidal salmonellosis, and contaminated bovine peripheral lymph nodes enter the food chain via ground beef. The relative abilities of different serovars to survive within the bovine lymphatic system are poorly understood and constrain the development of control strategies. This problem was addressed by developing a massively parallel whole-genome sequencing method to study mixed-serovar infections in vivo. Salmonella serovars differ genetically by naturally occurring single nucleotide polymorphisms (SNPs) in certain genes. It was hypothesized that these SNPs could be used as markers to simultaneously identify serovars in mixed populations and quantify the abundance of each member in a population. The performance of the method was validated in vitro using simulated pools containing up to 11 serovars in various proportions. It was then applied to study serovar survival in vivo in cattle challenged orally with the same 11 serovars. All the serovars successfully colonized the bovine lymphatic system, including the peripheral lymph nodes, and thus pose similar risks of zoonosis. This method enables the fates of multiple genetically unmodified strains to be evaluated simultaneously in a single animal. It could be useful in reducing the number of animals required to study mixed-strain infections and in testing the cross-protective efficacy of vaccines and treatments. It also has the potential to be applied to diverse bacterial species which possess shared but polymorphic alleles. IMPORTANCE While some Salmonella serovars are more frequently isolated from lymph nodes rather than the feces and environment of cattle, the relative abilities of serovars to survive within the lymphatic system of cattle remain ill defined. A sequencing-based method which used available information from sequenced Salmonella genomes to study the dynamics of mixed-serovar infections in vivo was developed. The main advantages of the method include the simultaneous identification and quantification of multiple strains without any genetic modification and minimal animal use. This approach could be used in vaccination trials or in epidemiological surveys where an understanding of the dynamics of closely related strains of a pathogen in mixed populations could inform the prediction of zoonotic risk and the development of intervention strategies. American Society for Microbiology 2018-01-31 /pmc/articles/PMC5795071/ /pubmed/29180370 http://dx.doi.org/10.1128/AEM.02262-17 Text en Copyright © 2018 Vohra et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Methods
Vohra, Prerna
Bugarel, Marie
Turner, Frances
Loneragan, Guy H.
Hope, Jayne C.
Hopkins, John
Stevens, Mark P.
Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title_full Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title_fullStr Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title_full_unstemmed Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title_short Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk
title_sort quantifying the survival of multiple salmonella enterica serovars in vivo via massively parallel whole-genome sequencing to predict zoonotic risk
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795071/
https://www.ncbi.nlm.nih.gov/pubmed/29180370
http://dx.doi.org/10.1128/AEM.02262-17
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