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Engineering Potato Starch with a Higher Phosphate Content.

ABSTRACT: Phosphate esters are responsible for valuable and unique functionalities of starch for industrial applications. Also in the cell phosphate esters play a role in starch metabolism, which so far has not been well characterized in storage starch. Laforin, a human enzyme composed of a carbohydrate-binding module and a dual-specificity phosphatase domain, is involved in the dephosphorylation of glycogen. To modify phosphate content and better understand starch (de)phosphorylation in storage starch, laforin was engineered and introduced into potato (cultivar Kardal). Interestingly, expression of an (engineered) laforin in potato resulted in significantly higher phosphate content of starch, and this result was confirmed in amylose-free potato genetic background (amf). Modified starches exhibited altered granule morphology and size compared to the control. About 20-30% of the transgenic lines of each series showed red-staining granules upon incubation with iodine, and contained higher phosphate content than the blue-stained starch granules. Moreover, low amylose content and altered gelatinization properties were observed in these red-stained starches. Principle component and correlation analysis disclosed a complex correlation between starch composition and starch physico-chemical properties. Ultimately, the expression level of endogenous genes involved in starch metabolism was analysed, revealing a compensatory response to the decrease of phosphate content in potato starch. This study provides a new perspective for engineering starch phosphate content in planta by making use of the compensatory mechanism in the plant itself.

SUBMITTER: Xu X

PROVIDER: S-EPMC5215930 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Key messageGenomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato. Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30-0.31 and 0.42-0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

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Engineering Potato Starch with a Higher Phosphate Content.

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