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The thylakoid‐associated kinases STN7 and STN8 are involved in short‐ and long‐term acclimation of photosynthetic electron transport to changing light conditions. Here we report the identification of STN7/STN8 in vivo targets that connect photosynthetic electron transport with metabolism and gene expression. Comparative phosphoproteomics with the stn7 and stn8 single and double mutants identified two proteases, one RNA‐binding protein, a ribosomal protein, the large subunit of Rubisco and a ferredoxin‐NADP reductase as targets for the thylakoid‐associated kinases. Phosphorylation of three of the above proteins can be partially complemented by STN8 in the stn7 single mutant, albeit at lower efficiency, while phosphorylation of the remaining three proteins strictly depends on STN7. The properties of the STN7‐dependent phosphorylation site are similar to those of phosphorylated light‐harvesting complex proteins entailing glycine or another small hydrophobic amino acid in the −1 position. Our analysis uncovers the STN7/STN8 kinases as mediators between photosynthetic electron transport, its immediate downstream sinks and long‐term adaptation processes affecting metabolite accumulation and gene expression.
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We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.