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Improving virus production through quasispecies genomic selection and molecular breeding.

ABSTRACT: Virus production still is a challenging issue in antigen manufacture, particularly with slow-growing viruses. Deep-sequencing of genomic regions indicative of efficient replication may be used to identify high-fitness minority individuals suppressed by the ensemble of mutants in a virus quasispecies. Molecular breeding of quasispecies containing colonizer individuals, under regimes allowing more than one replicative cycle, is a strategy to select the fittest competitors among the colonizers. A slow-growing cell culture-adapted hepatitis A virus strain was employed as a model for this strategy. Using genomic selection in two regions predictive of efficient translation, the internal ribosome entry site and the VP1-coding region, high-fitness minority colonizer individuals were identified in a population adapted to conditions of artificially-induced cellular transcription shut-off. Molecular breeding of this population with a second one, also adapted to transcription shut-off and showing an overall colonizer phenotype, allowed the selection of a fast-growing population of great biotechnological potential.

SUBMITTER: Perez-Rodriguez FJ

PROVIDER: S-EPMC5093897 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Improving virus production through quasispecies genomic selection and molecular breeding.

Pérez-Rodríguez Francisco J FJ D'Andrea Lucía L de Castellarnau Montserrat M Costafreda Maria Isabel MI Guix Susana S Ribes Enric E Quer Josep J Gregori Josep J Bosch Albert A Pintó Rosa M RM

Scientific reports 20161103

Virus production still is a challenging issue in antigen manufacture, particularly with slow-growing viruses. Deep-sequencing of genomic regions indicative of efficient replication may be used to identify high-fitness minority individuals suppressed by the ensemble of mutants in a virus quasispecies. Molecular breeding of quasispecies containing colonizer individuals, under regimes allowing more than one replicative cycle, is a strategy to select the fittest competitors among the colonizers. A s ...[more]

PMID: 27808108

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Project description:Key messageWe evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to [Formula: see text] = 0.19 for this derived trait.

Dataset Information

Improving virus production through quasispecies genomic selection and molecular breeding.

Publications

Improving virus production through quasispecies genomic selection and molecular breeding.

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