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Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds

Streptococcus uberis is considered one of the most important pathogens associated with bovine mastitis. While traditionally acknowledged as an environmental pathogen, S. uberis has been shown to adopt a contagious epidemiological pattern in several dairy herds. Since different control strategies are...

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Autores principales: Albuquerque, Pedro, Ribeiro, Niza, Almeida, Alexandre, Panschin, Irena, Porfirio, Afonso, Vales, Marta, Diniz, Francisca, Madeira, Helena, Tavares, Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5258699/
https://www.ncbi.nlm.nih.gov/pubmed/28174566
http://dx.doi.org/10.3389/fmicb.2017.00054
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author Albuquerque, Pedro
Ribeiro, Niza
Almeida, Alexandre
Panschin, Irena
Porfirio, Afonso
Vales, Marta
Diniz, Francisca
Madeira, Helena
Tavares, Fernando
author_facet Albuquerque, Pedro
Ribeiro, Niza
Almeida, Alexandre
Panschin, Irena
Porfirio, Afonso
Vales, Marta
Diniz, Francisca
Madeira, Helena
Tavares, Fernando
author_sort Albuquerque, Pedro
collection PubMed
description Streptococcus uberis is considered one of the most important pathogens associated with bovine mastitis. While traditionally acknowledged as an environmental pathogen, S. uberis has been shown to adopt a contagious epidemiological pattern in several dairy herds. Since different control strategies are employed depending on the mode of transmission, in-depth studies of S. uberis populations are essential to determine the best practices to control this pathogen. In this work, we optimized and validated a dot blot platform, combined with automatic image analysis, to rapidly assess the population structure of infective S. uberis, and evaluated its efficiency when compared to multilocus sequence analysis (MLSA) genotyping. Two dairy herds with prevalent S. uberis infections were followed in a 6 month period, in order to collect and characterize isolates from cows with persistent infections. These herds, located in Portugal (Barcelos and Maia regions), had similar management practices, with the herd from Barcelos being smaller and having a better milking parlor management, since infected cow segregation was immediate. A total of 54 S. uberis isolates were obtained from 24 different cows from the two herds. To overcome operator-dependent analysis of the dot blots and increase the technique's consistency and reliability, the hybridization signals were converted into probability values, with average probabilities higher than 0.5 being considered positive results. These data allowed to confirm the isolates' identity as S. uberis using taxa-specific markers and to determine the presence of virulence- and antibiotic resistance-related genes. In addition, MLSA allowed to disclose the most prevalent S. uberis clonal lineages in both herds. Seven different clusters were identified, with Barcelos showing a high clonal diversity and Maia a dominant lineage infecting most cows, suggesting distinct epidemiological patterns, with S. uberis displaying an environmental or contagious transmission pattern depending on the herd. Overall, this work showed the utility of dot blot and MLSA to characterize population structure and epidemiological patterns of mastitis-causing S. uberis. This approach allowed to disclose prevalent virulence patterns and clonal lineages of S. uberis in two distinct herds, and gain insights on the impact of herd management practices on pathogen population structure.
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spelling pubmed-52586992017-02-07 Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds Albuquerque, Pedro Ribeiro, Niza Almeida, Alexandre Panschin, Irena Porfirio, Afonso Vales, Marta Diniz, Francisca Madeira, Helena Tavares, Fernando Front Microbiol Microbiology Streptococcus uberis is considered one of the most important pathogens associated with bovine mastitis. While traditionally acknowledged as an environmental pathogen, S. uberis has been shown to adopt a contagious epidemiological pattern in several dairy herds. Since different control strategies are employed depending on the mode of transmission, in-depth studies of S. uberis populations are essential to determine the best practices to control this pathogen. In this work, we optimized and validated a dot blot platform, combined with automatic image analysis, to rapidly assess the population structure of infective S. uberis, and evaluated its efficiency when compared to multilocus sequence analysis (MLSA) genotyping. Two dairy herds with prevalent S. uberis infections were followed in a 6 month period, in order to collect and characterize isolates from cows with persistent infections. These herds, located in Portugal (Barcelos and Maia regions), had similar management practices, with the herd from Barcelos being smaller and having a better milking parlor management, since infected cow segregation was immediate. A total of 54 S. uberis isolates were obtained from 24 different cows from the two herds. To overcome operator-dependent analysis of the dot blots and increase the technique's consistency and reliability, the hybridization signals were converted into probability values, with average probabilities higher than 0.5 being considered positive results. These data allowed to confirm the isolates' identity as S. uberis using taxa-specific markers and to determine the presence of virulence- and antibiotic resistance-related genes. In addition, MLSA allowed to disclose the most prevalent S. uberis clonal lineages in both herds. Seven different clusters were identified, with Barcelos showing a high clonal diversity and Maia a dominant lineage infecting most cows, suggesting distinct epidemiological patterns, with S. uberis displaying an environmental or contagious transmission pattern depending on the herd. Overall, this work showed the utility of dot blot and MLSA to characterize population structure and epidemiological patterns of mastitis-causing S. uberis. This approach allowed to disclose prevalent virulence patterns and clonal lineages of S. uberis in two distinct herds, and gain insights on the impact of herd management practices on pathogen population structure. Frontiers Media S.A. 2017-01-24 /pmc/articles/PMC5258699/ /pubmed/28174566 http://dx.doi.org/10.3389/fmicb.2017.00054 Text en Copyright © 2017 Albuquerque, Ribeiro, Almeida, Panschin, Porfirio, Vales, Diniz, Madeira and Tavares. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Albuquerque, Pedro
Ribeiro, Niza
Almeida, Alexandre
Panschin, Irena
Porfirio, Afonso
Vales, Marta
Diniz, Francisca
Madeira, Helena
Tavares, Fernando
Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title_full Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title_fullStr Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title_full_unstemmed Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title_short Application of a Dot Blot Hybridization Platform to Assess Streptococcus uberis Population Structure in Dairy Herds
title_sort application of a dot blot hybridization platform to assess streptococcus uberis population structure in dairy herds
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5258699/
https://www.ncbi.nlm.nih.gov/pubmed/28174566
http://dx.doi.org/10.3389/fmicb.2017.00054
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