Dataset Information

Universal Stress Proteins Contribute Edwardsiella ictaluri Virulence in Catfish.

ABSTRACT: Edwardsiella ictaluri is an intracellular Gram-negative facultative pathogen causing enteric septicemia of catfish (ESC), a common disease resulting in substantial economic losses in the U.S. catfish industry. Previously, we demonstrated that several universal stress proteins (USPs) are highly expressed under in vitro and in vivo stress conditions, indicating their importance for E. ictaluri survival. However, the roles of these USPs in E. ictaluri virulence is not known yet. In this work, 10 usp genes of E. ictaluri were in-frame deleted and characterized in vitro and in vivo. Results show that all USP mutants were sensitive to acidic condition (pH 5.5), and Ei?usp05 and Ei?usp08 were very sensitive to oxidative stress (0.1% H2O2). Virulence studies indicated that Ei?usp05, Ei?usp07, Ei?usp08, Ei?usp09, Ei?usp10, and Ei?usp13 were attenuated significantly compared to E. ictaluri wild-type (EiWT; 20, 45, 20, 20, 55, and 10% vs. 74.1% mortality, respectively). Efficacy experiments showed that vaccination of catfish fingerlings with Ei?usp05, Ei?usp07, Ei?usp08, Ei?usp09, Ei?usp10, and Ei?usp13 provided complete protection against EiWT compared to sham-vaccinated fish (0% vs. 58.33% mortality). Our results support that USPs contribute E. ictaluri virulence in catfish.

SUBMITTER: Akgul A

PROVIDER: S-EPMC6279896 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Universal Stress Proteins Contribute Edwardsiella ictaluri Virulence in Catfish.

Akgul Ali A Nho Seong Won SW Kalindamar Safak S Tekedar Hasan C HC Abdalhamed Hossam H Lawrence Mark L ML Karsi Attila A

Frontiers in microbiology 20181128

Edwardsiella ictaluri is an intracellular Gram-negative facultative pathogen causing enteric septicemia of catfish (ESC), a common disease resulting in substantial economic losses in the U.S. catfish industry. Previously, we demonstrated that several universal stress proteins (USPs) are highly expressed under in vitro and in vivo stress conditions, indicating their importance for E. ictaluri survival. However, the roles of these USPs in E. ictaluri virulence is ...[more]

PMID: 30546354

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Project description:Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1-15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3-19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3-5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318-400 SNPs for survival status and 1,362-1,589 SNPs for survival time) were somewhat lower (0.3-15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.

Dataset Information

Universal Stress Proteins Contribute Edwardsiella ictaluri Virulence in Catfish.

Publications

Universal Stress Proteins Contribute <i>Edwardsiella ictaluri</i> Virulence in Catfish.

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