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Genetic analysis of Vibrio parahaemolyticus O3:K6 strains that have been isolated in Mexico since 1998

CARLOS ABRAHAM GUERRERO RUIZ (2017, [Artículo])

Vibrio parahaemolyticus is an important human pathogen that has been isolated worldwide from clinical cases, most of which have been associated with seafood consumption. Environmental and clinical toxigenic strains of V. parahaemolyticus that were isolated in Mexico from 1998 to 2012, including those from the only outbreak that has been reported in this country, were characterized genetically to assess the presence of the O3:K6 pandemic clone, and their genetic relationship to strains that are related to the pandemic clonal complex (CC3). Pathogenic tdh+ and tdh+/trh+ strains were analyzed by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). Also, the entire genome of a Mexican O3:K6 strain was sequenced. Most of the strains were tdh/ORF8-positive and corresponded to the O3:K6 serotype. By PFGE and MLST, there was very close genetic relationship between ORF8/O3:K6 strains, and very high genetic diversities from non-pandemic strains. The genetic relationship is very close among O3:K6 strains that were isolated in Mexico and sequences that were available for strains in the CC3, based on the PubMLST database. The whole-genome sequence of CICESE-170 strain had high similarity with that of the reference RIMD 2210633 strain, and harbored 7 pathogenicity islands, including the 4 that denote O3:K6 pandemic strains. These results indicate that pandemic strains that have been isolated in Mexico show very close genetic relationship among them and with those isolated worldwide. © 2017 Guerrero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Article, bacterial strain, biofouling, controlled study, Crassostrea, food intake, gene sequence, genetic analysis, genetic variability, Japan, Mexican, Mexico, molecular phylogeny, nonhuman, pandemic, pathogenicity island, sea food, serotyping, toxi BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA GENÉTICA GENÉTICA

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding