Literature DB >> 28003195

New Multilocus Variable-Number Tandem-Repeat Analysis (MLVA) Scheme for Fine-Scale Monitoring and Microevolution-Related Study of Ralstonia pseudosolanacearum Phylotype I Populations.

Jérémy Guinard1,2, Anne Latreille2, Fabien Guérin1, Stéphane Poussier1, Emmanuel Wicker3.   

Abstract

Bacterial wilt caused by the Ralstonia solanacearum species complex (RSSC) is considered one of the most harmful plant diseases in the world. Special attention should be paid to R. pseudosolanacearum phylotype I due to its large host range, its worldwide distribution, and its high evolutionary potential. So far, the molecular epidemiology and population genetics of this bacterium are poorly understood. Until now, the genetic structure of the RSSC has been analyzed on the worldwide and regional scales. Emerging questions regarding evolutionary forces in RSSC adaptation to hosts now require genetic markers that are able to monitor RSSC field populations. In this study, we aimed to evaluate the multilocus variable-number tandem-repeat analysis (MLVA) approach for its ability to discriminate genetically close phylotype I strains and for population genetics studies. We developed a new MLVA scheme (MLVA-7) allowing us to genotype 580 R. pseudosolanacearum phylotype I strains extracted from susceptible and resistant hosts and from different habitats (stem, soil, and rhizosphere). Based on specificity, polymorphism, and the amplification success rate, we selected seven fast-evolving variable-number tandem-repeat (VNTR) markers. The newly developed MLVA-7 scheme showed higher discriminatory power than the previously published MLVA-13 scheme when applied to collections sampled from the same location on different dates and to collections from different locations on very small scales. Our study provides a valuable tool for fine-scale monitoring and microevolution-related study of R. pseudosolanacearum phylotype I populations.IMPORTANCE Understanding the evolutionary dynamics of adaptation of plant pathogens to new hosts or ecological niches has become a key point for the development of innovative disease management strategies, including durable resistance. Whereas the molecular mechanisms underlying virulence or pathogenicity changes have been studied thoroughly, the population genetics of plant pathogen adaptation remains an open, unexplored field, especially for plant-pathogenic bacteria. MLVA has become increasingly popular for epidemiosurveillance and molecular epidemiology studies of plant pathogens. However, this method has been used mostly for genotyping and identification on a regional or global scale. In this study, we developed a new MLVA scheme, targeting phylotype I of the soilborne Ralstonia solanacearum species complex (RSSC), specifically to address the bacterial population genetics on the field scale. Such a MLVA scheme, based on fast-evolving loci, may be a tool of choice for field experimental evolution and spatial genetics studies.
Copyright © 2017 American Society for Microbiology.

Entities:  

Keywords:  VNTR; bacterial wilt; epidemiosurveillance; microevolution; molecular epidemiology; population biology

Mesh:

Substances:

Year:  2017        PMID: 28003195      PMCID: PMC5311393          DOI: 10.1128/AEM.03095-16

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  64 in total

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Journal:  PLoS One       Date:  2013-11-22       Impact factor: 3.240

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Journal:  PLoS One       Date:  2019-04-11       Impact factor: 3.240

4.  Contrasting genetic diversity and structure among Malagasy Ralstonia pseudosolanacearum phylotype I populations inferred from an optimized Multilocus Variable Number of Tandem Repeat Analysis scheme.

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5.  Meta-analysis of the Ralstonia solanacearum species complex (RSSC) based on comparative evolutionary genomics and reverse ecology.

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6.  Molecular Epidemiology of Bacterial Wilt in the Madagascar Highlands Caused by Andean (Phylotype IIB-1) and African (Phylotype III) Brown Rot Strains of the Ralstonia solanacearum Species Complex.

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