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Geranylgeranyl diphosphate (GGPP) produced by GGPP synthase (GGPPS) serves as a precursor for many plastidial isoprenoids, including carotenoids. Phytoene synthase (PSY) converts GGPP into phytoene, the first committed intermediate of the carotenoid pathway.Here we used biochemical, molecular, and genetic tools to characterise the plastidial members of the GGPPS family in tomato (Solanum lycopersicum) and their interaction with PSY isoforms.The three tomato GGPPS isoforms found to localise in plastids (SlG1, 2 and 3) exhibit similar kinetic parameters. Gene expression analyses showed a preferential association of individual GGPPS and PSY isoforms when carotenoid biosynthesis was induced during root mycorrhization, seedling de-etiolation and fruit ripening. SlG2, but not SlG3, physically interacts with PSY proteins. By contrast, CRISPR-Cas9 mutants defective in SlG3 showed a stronger impact on carotenoid levels and derived metabolic, physiological and developmental phenotypes compared with those impaired in SlG2. Double mutants defective in both genes could not be rescued.Our work demonstrates that the bulk of GGPP production in tomato chloroplasts and chromoplasts relies on two cooperating GGPPS paralogues, unlike other plant species such as Arabidopsis thaliana, rice or pepper, which produce their essential plastidial isoprenoids using a single GGPPS isoform.

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Abstract:The influence of material variability upon the multiaxial LCF assessment of engineering components is missing for a comprehensive description. In this paper, a probabilistic framework is established for multiaxial LCF assessment of notched components by using the Chaboche plasticity model and Fatemi-Socie criterion. Simulations from experimental results of two steels reveal that the scatter in fatigue lives can be well described by quantifying the variability of four material parameters . A procedure for choosing the safety factor for fatigue design has been derived by using first order approximation.

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A wide range of studies and experimental evidence have shown that the lower bound of fatigue properties can be correctly predicted by considering the maximum occurring defect size. The estimate of this dimension can be done by analysing the defect sizes using the statistics of extremes. The scope of this paper is to discuss and investigate the two key points in a successful application of this technique: the first is the choice of statistical method for the analysis of data; the second is the knowledge of the minimum number of defects needed to obtain a good estimate of extreme defects. The results obtained in this study allow one to formulate a procedure for estimating the extreme defects with a precision suitable for fatigue strength prediction.