Dataset Information

Fur-regulated iron uptake system of Edwardsiella ictaluri and its influence on pathogenesis and immunogenicity in the catfish host.

ABSTRACT: The ability of bacterial pathogens to take up iron from the host during infection is necessary for their multiplication within the host. However, host high-affinity iron binding proteins limit levels of free iron in fluids and tissues. To overcome this deficiency of iron during infection, bacterial pathogens have developed iron uptake systems that are upregulated in the absence of iron, typically tightly controlled by the ferric uptake regulator (Fur) protein. The iron uptake system of Edwardsiella ictaluri, a host-restricted pathogen of channel catfish (Ictalurus punctatus) and the main pathogen of this fish in aquaculture, is unknown. Here we describe the E. ictaluri Fur protein, the iron uptake machinery controlled by Fur, and the effects of fur gene deletion on virulence and immunogenicity in the fish host. Analysis of the E. ictaluri Fur protein shows that it lacks the N-terminal region found in the majority of pathogen-encoded Fur proteins. However, it is fully functional in regulated genes encoding iron uptake proteins. E. ictaluri grown under iron-limited conditions upregulates an outer membrane protein (HemR) that shows heme-hemoglobin transport activity and is tightly regulated by Fur. In vivo studies showed that an E. ictaluri ?fur mutant is attenuated and immune protective in zebrafish (Danio rerio) and catfish (Ictalurus punctatus), triggering systemic immunity. We conclude that an E. ictaluri ?fur mutant could be an effective component of an immersion-oral vaccine for the catfish industry.

SUBMITTER: Santander J

PROVIDER: S-EPMC3434582 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Fur-regulated iron uptake system of Edwardsiella ictaluri and its influence on pathogenesis and immunogenicity in the catfish host.

Santander Javier J Golden Greg G Wanda Soo-Young SY Curtiss Roy R

Infection and immunity 20120521 8

The ability of bacterial pathogens to take up iron from the host during infection is necessary for their multiplication within the host. However, host high-affinity iron binding proteins limit levels of free iron in fluids and tissues. To overcome this deficiency of iron during infection, bacterial pathogens have developed iron uptake systems that are upregulated in the absence of iron, typically tightly controlled by the ferric uptake regulator (Fur) protein. The iron uptake system of Edwardsie ...[more]

PMID: 22615248

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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

Fur-regulated iron uptake system of Edwardsiella ictaluri and its influence on pathogenesis and immunogenicity in the catfish host.

Publications

Fur-regulated iron uptake system of Edwardsiella ictaluri and its influence on pathogenesis and immunogenicity in the catfish host.

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