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Folding and Lipid Composition Determine Membrane Interaction of the Disordered Protein COR15A
(2018)
Plants from temperate climates, such as the model plant Arabidopsis thaliana, are challenged with seasonal low temperatures that lead to increased freezing tolerance in fall in a process termed cold acclimation. Among other adaptations, this involves the accumulation of cold-regulated (COR) proteins, such as the intrinsically disordered chloroplast-localized protein COR15A. Together with its close homolog COR15B, it stabilizes chloroplast membranes during freezing. COR15A folds into amphipathic alpha-helices in the presence of high concentrations of low-molecular-mass crowders or upon dehydration. Under these conditions, the (partially) folded protein binds peripherally to membranes. In our study, we have used coarse-grained molecular dynamics simulations to elucidate the details of COR15A-membrane binding and its effects on membrane structure and dynamics. Simulation results indicate that at least partial folding of COR15A and the presence of highly unsaturated galactolipids in the membranes are necessary for efficient membrane binding. The bound protein is stabilized on the membrane by interactions of charged and polar amino acids with galactolipid headgroups and by interactions of hydrophobic amino acids with the upper part of the fatty acyl chains. Experimentally, the presence of liposomes made from a mixture of lipids mimicking chloroplast membranes induces additional folding in COR15A under conditions of partial dehydration, in agreement with the simulation results.
Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker‐assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT‐PCR and GC‐MS profiling, respectively. Transcript marker candidates were selected from a published RNA‐Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.