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A loss of dehydration tolerance in wheat seedlings on the fifth day following imbibition is associated with a disturbance in cellular redox homeostasis, as documented by a shift of the reduced/oxidized glutathione ratio to a more oxidized state and a significant increase in the ratio of protein thiols to the total thiol group content. Therefore, the identification and characterization of redox-sensitive proteins are important steps toward understanding the molecular mechanisms of the loss of dehydration tolerance. In the present study, proteins that were differentially expressed between fully turgid (control), dehydrated tolerant (four-day-old) and dehydrated sensitive (six-day-old) wheat seedlings were analysed. Protein spots having at least a significant (p < 0.05) two-fold change in protein abundance were selected by Delta2D as differentially expressed, identified by MALDI-TOF and LC-MS/MS, and classified according to their function. The observed changes in the proteomic patterns of the differentially S-nitrosylated and S-glutathionylated proteins were highly specific in dehydration-tolerant and-sensitive wheat seedlings. The metabolic function of these proteins indicates that dehydration tolerance is mainly related to nucleic acids, protein metabolism, and energy metabolism. It has been proven that leaf-specific thionins BTH6 and DB4, chloroplastic 50S ribosomal protein L16, phospholipase A1-II delta, and chloroplastic thioredoxin M2 are both S-nitrosylated and S-glutathionylated upon water deficiency. Our results revealed the existence of interplay between S-nitrosylation and S-glutathionylation, two redox-regulated protein posttranslational modifications that could enhance plant defence mechanisms and/or facilitate the acclimation of plants to unfavourable environmental conditions. (C) 2016 Elsevier Masson SAS. All rights reserved.
Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.