Parenchyma cells from tubers of Solanum tuberosum L. convert several externally supplied sugars to starch but the rates vary largely. Conversion of glucose 1-phosphate to starch is exceptionally efficient. In this communication, tuber slices were incubated with either of four solutions containing equimolar [U-14C]glucose 1-phosphate, [U-14C]sucrose, [U-14C]glucose 1-phosphate plus unlabelled equimolar sucrose or [U-14C]sucrose plus unlabelled equimolar glucose 1-phosphate. 14C-incorporation into starch was monitored. In slices from freshly harvested tubers each unlabelled compound strongly enhanced 14C incorporation into starch indicating closely interacting paths of starch biosynthesis. However, enhancement disappeared when the tubers were stored. The two paths (and, consequently, the mutual enhancement effect) differ in temperature dependence. At lower temperatures, the glucose 1-phosphate-dependent path is functional, reaching maximal activity at approximately 20 °C but the flux of the sucrose-dependent route strongly increases above 20 °C. Results are confirmed by in vitro experiments using [U-14C]glucose 1-phosphate or adenosine-[U-14C]glucose and by quantitative zymograms of starch synthase or phosphorylase activity. In mutants almost completely lacking the plastidial phosphorylase isozyme(s), the glucose 1-phosphate-dependent path is largely impeded. Irrespective of the size of the granules, glucose 1-phosphate-dependent incorporation per granule surface area is essentially equal. Furthermore, within the granules no preference of distinct glucosyl acceptor sites was detectable. Thus, the path is integrated into the entire granule biosynthesis. In vitro 14C-incorporation into starch granules mediated by the recombinant plastidial phosphorylase isozyme clearly differed from the in situ results. Taken together, the data clearly demonstrate that two closely but flexibly interacting general paths of starch biosynthesis are functional in potato tuber cells.
Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.
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