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Diverse Bacteriophages Infecting the Bacterial Striped Catfish Pathogen Edwardsiella ictaluri.

ABSTRACT: Bacteriophages infecting Edwardsiella ictaluri have been less investigated, although the host bacterium is one of the most important fish pathogens causing enteric septicemia of catfish (ESC). We present here two distinctly novel bacteriophages vB_EiM_PVN06 and vB_EiA_PVN09 infecting Edwardsiella ictaluri E1, with their geographical origins from the Mekong Delta, Vietnam. Bacteriophage vB_EiM_PVN06 native to a mud sample reveals complete differences of biological properties with the phage vB_EiA_PVN09 originated from a viscus of a healthy catfish (Pangasianodon hypophthalmus) cultured in the same area. Morphological analyses combined with genomic data indicate that phage vB_EiM_PVN06 is classified to Myoviridae family and shares high similarity with E. ictaluri phage PEi21 genome, while vB_EiA_PVN09 is a member of Teseptimavirus genus, Autographiviridae family, and mostly closes to phage vB_EcoP_IME390. The vB_EiA_PVN09 is a T7-like bacteriophage, which has been firstly found infecting to E. ictaluri, and host range analysis also evidences for the cross-infection of this phage to Escherichia coli K12 and Escherichia coli DH5α. Together, our research highlights the diversity of bacteriophages infecting the pathogen E. ictaluri and suggests further explorations of lytic phages in environmental niches, to be exploited in feasible strategies of phage therapy in ESC disease control.

SUBMITTER: Nguyen TT

PROVIDER: S-EPMC8465730 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Diverse Bacteriophages Infecting the Bacterial Striped Catfish Pathogen Edwardsiella ictaluri.

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