Amylopectin chain length distribution and starch physicochemical properties in a glutinous rice mutant panel: A PLSR
Laiquan Shi1, Renying Wu1, Xuan Zhang1
1Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Yangzhou University, Yangzhou, 225009, China; Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province/Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, 225009, China.
Abstract:
A glutinous rice cultivar and its 11 derived amino acid substitution mutants of starch synthase IIIa (SSIIIa) and branching enzyme IIb (BEIIb) were investigated for the contributions of amylopectin chain length distributions (CLDs) to starch physicochemical properties. The CLDs quantified using four commonly adopted classification methods exhibited substantial and fraction-specific variations among the mutants. The partial least squares (PLS) regression models for relationships between CLDs and starch physicochemical properties exhibited satisfactory predictive ability, as indicated by Q2(cum) over 0.5 for the majority (19/24) of models. Regression coefficients and loading plots revealed that short branch-chain fractions were positively correlated with relative crystallinity and lamellar peak intensity, while negatively correlated with lamellar distance, gelatinization temperature, and gelatinization enthalpy. In contrast, average chain length (ACL) and long branch-chain fractions exhibited opposite trends. Very short branch-chain fractions showed positive correlations with high-temperature swelling power and setback viscosity, whereas medium-long branch-chain fractions were negatively correlated with high-temperature swelling power, setback viscosity, and breakdown viscosity, but positively correlated with hot viscosity. Furthermore, longer branch-chain fractions and increased ACL tended to elevate resistant starch while reducing slowly digestible starch. Notably, PLS discriminant analysis based on CLDs effectively distinguished ssIIIa mutants from beIIb mutants within the mutation population.
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