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Journal of Experimental Botany, Vol. 67, No. 13 pp. 3873–3882, 2016 doi:10.1093/jxb/erw098 Advance Access publication 11 March 2016 REVIEW PAPER Protein phosphorylation in chloroplasts – a survey of phosphorylation targets Sacha Baginsky1,* 1 Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, Weinbergweg 22, 06120 Halle (Saale), Germany * Correspondence: [email protected] Received 20 November 2015; Accepted 16 February 2016 Editor: Markus Teige, University of Vienna Abstract The development of new software tools, improved mass spectrometry equipment, a suite of optimized scan types, and better-quality phosphopeptide affinity capture have paved the way for an explosion of mass spectrometry data on phosphopeptides. Because phosphoproteomics achieves good sensitivity, most studies use complete cell extracts for phosphopeptide enrichment and identification without prior enrichment of proteins or subcellular compartments. As a consequence, the phosphoproteome of cell organelles often comes as a by-product from large-scale studies and is commonly assembled from these in meta-analyses. This review aims at providing some guidance on the limitations of meta-analyses that combine data from analyses with different scopes, reports on the current status of knowledge on chloroplast phosphorylation targets, provides initial insights into phosphorylation site conservation in different plant species, and highlights emerging information on the integration of gene expression with metabolism and photosynthesis by means of protein phosphorylation. Key words: Chloroplast, mass spectrometry, phosphoproteomics, phosphorylation, protein kinases, photosynthesis. Assembling organellar phosphoproteomes – the limitations of meta-analyses and pitfalls in data interpretation Recent years have seen a remarkable increase in the number of identified phosphopeptides from different cell organelles and plant species (J. Li et al., 2015). The data have been collected and deposited into phosphoproteome databases such as PhosphAT (http://phosphat. uni-hohenheim.de/) for Arabidopsis thaliana (Arsova and Schulze, 2012), RIPP-DB (http://metadb.riken.jp) for rice (Oryza sativa) and Arabidopsis (Nakagami et al., 2010), Medicago PhosphoProtein Database (http://www.phospho.medicago.wisc.edu) for Medicago truncatula (Rose et al., 2012), and P3DB (http://www.p3db.org/) for various plant species (Yao et al., 2014). The effort to collect and disseminate phosphoproteomics data is an important community service, and the idea of a database as a one-stop source of information on phosphoproteins is appealing. However, the assembly of interpreted MS/ MS spectra from many different analyses in one database may cumulate the false identifications of every individual study. Unless cumulated datasets are re-analysed with well-defined database matching parameters, it is not possible to determine their false discovery rate (FDR). This problem is small when the cumulated data originate from a few up-to-date studies that usually operate at FDRs between 0.1 and 1% at the spectrum or peptide level, but it becomes relevant as the number of analyses in an assembled dataset increases. We have recently shown that the assembly of large datasets may cause problems for specific subsets of proteins, as © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: [email protected] 3874 | Baginsky illustrated by the re-analysis of the original spectra pointing at putative tyrosine phosphorylated chloroplast proteins (Lu et al., 2015b). In a large-scale meta-analysis based on data available in PhosphAT, 27 published papers as well as unpublished in-house data from the Schulze laboratory were integrated into one dataset. With this combination, a surprisingly high rate of tyrosine phosphorylation in the entire dataset and in chloroplast proteins was reported, even though the dataset was thoroughly filtered for repeated identifications (van Wijk et al., 2014). The high rate of phosphotyrosine detection was explained by an optimized MS setup, in particular the inclusion of the phosphotyrosine immonium ion at m/z 216.043 during the MS and MS/MS acquisition, which gave rise to up to 15% tyrosine phosphorylation in individual studies (van Wijk et al., 2014). It is certainly possible that detection of tyrosine phosphorylation can be increased by specialized scan types, but the remarkably high rates in some of these datasets contradict the observations made in many previous analyses (see discussion in de la Fuente van Bentem and Hirt, 2009). In a re-assessment of peptide spectrum matches to tyrosine phosphorylated peptides in chloroplast proteins, only three out of 54 tyrosine phosphorylated peptides from the above dataset were supported by additional software and only at relaxed matching parameters. The difference in the interpretation of spectra between the software tools resulted from poor spectrum quality and searches in large search spaces that allowed many degrees of freedom for the match. In addition to questionable peptide spectrum matches, incorrect phosphorylation site assignments were reported in some cases where the phosphopeptide was correctly identified (Lu et al., 2015b). This is possible in spectra in which the diagnostic fragment ions that distinguish between the phosphorylation of two closely spaced hydroxylated amino acids are missing. Standard software tools often fail to make a clear distinction between certain or ambiguous phosphorylation site assignments. Additional software is required to specifically search and score diagnostic fragment ions to support the phosphorylation of one or the other amino acid within the peptide sequence. Different software tools are available that are tailored for phosphorylation site identification and some of these are now routinely integrated into peptide identification pipelines (e.g. PhosCalc, PhosphoRS) (Cox and Mann, 2008; MacLean et al., 2008; Martin et al., 2010; Tyanova et al., 2015). Identifying the phosphorylation site is important because it is needed to design a targeted experiment in which the phosphorylated amino acid is exchanged by alanine or aspartate/ glutamate to ablate or mimic phosphorylation in functional studies. Furthermore, phosphorylation sites are embedded in an amino acid context that constitutes motifs for specific kinases. Identification of the exact site of phosphorylation is often hampered in spectra that were generated by collisioninduced dissociation, because they comprise a dominant peak originating from the neutral loss of phosphoric acid from the parent peptide while other fragment ions are of low intensity (see example in Fig. 1). Most up-to-date phosphoproteome analyses circumvent the unfavourable dynamic range of fragment ion detection by using the neutral loss peak for further fragmentation at slightly elevated collision energies in a procedure that is referred to as multistage activation (MSA). This eradicates the neutral loss peak and generates higher intensity product ions (b- or y-ions, see Fig. 1 for an explanation) for spectra interpretation and phosphorylation site assignment (see example in Fig. 1) (Thingholm et al., 2009; Wu et al., 2013). However, this advantage of MSA is counteracted by a loss of information on the stability of the phosphoester bond, hampering the rapid validation of spectra by visual inspection [e.g. as performed by Reiland et al. (2009, 2011)]. A more recent approach to increase the sequence coverage in MS/MS spectra employs electron capture dissociation (ECT) or electron transfer dissociation (ETD) of the parent ion. This fragmentation technique operates with radical anions that induce peptide cleavage along the peptide backbone while side chains and modifications such as phosphorylations are left intact (Syka et al., 2004). Surprisingly, despite its great promise, ETC/ETD fragmentation techniques have not reached the phosphoproteomics mainstream and their application is mostly restricted to analyses with mammalian cells or proof-of-concept studies. Given the above, researchers are left with the dilemma that a wealth of valuable data on the phosphoproteome of cells and their organelles are at their hands in user-friendly databases, but the reliability of the individual peptide spectrum matches is sometimes uncertain. It is therefore advisable to scrutinize biologically relevant phosphopeptide identifications using additional criteria. Some assessment of data quality is possible with the following sets of questions: what mass accuracy was used for peptide identification, and what was the FDR of the entire reported dataset? What software was used for peptide identification and does it employ tools to identify the exact site of phosphorylation? What were the degrees of freedom for database matches, that is, did the authors allow for a very large search space with many combinatorial possibilities for peptide matching – for example, by allowing numerous posttranslational modifications for the peptide match? In the optimal case, the MS/MS spectrum should be retrieved and the quality of the match assessed by some of the heuristics of peptide identification (e.g. as detailed in Lu et al., 2015b and references therein). In the following, we assessed the current state-of-the art of chloroplast phosphoproteomics by searching for chloroplast phosphoproteins from rice, maize (Zea mays), and Arabidopsis. Current status of the plastid phosphoproteome in model plant species Because very few, mostly small-scale, phosphoproteome analyses were performed with isolated organelles, identifying phosphorylated plastid proteins from data on full cell extracts requires a high-quality proteome reference table. The Arabidopsis chloroplast proteome map contains around 1800 proteins that were identified as plastid proteins by a combination of proteomics data, localization of GFP-tagged proteins, and software-based prediction (van Wijk and Baginsky, 2011; Tanz et al., 2013). The maize plastid reference proteome is Chloroplast phosphoproteins | 3875 Fig. 1. MS/MS spectra of the phosphopeptide APVpSDGGISPATNLK acquired by collision-induced dissociation (CID, upper spectrum) or multi-stage activation (MSA, lower spectrum). The neutral loss peak is indicated in the upper spectrum by a star, representing the doubly charged parent ion (M, carrying 2H+) that lost phosphoric acid (H3PO4) during the CID process. In very simple terms, the b-ions represent fragment ions that contain the original N-terminus and carry the positive charge at their different C-termini in the form of an acylium ion. The individual ions differ by the residue masses of the amino acids in the peptide sequence following peptide bond cleavage. Similarly, y-ions comprise the original C-terminus, differ by the residue masses of the amino acids in the peptide backbone, and carry the charge at their different N-termini in the form of a protonated primary amine. Losses of phosphoric acid from the original peptide are designated as ‘−98’, losses of H2O or NH3 are indicated as such. Phosphorylation site assignment is based on the availability of fragment ions that support one or another amino acid phosphorylation within the amino acid sequence of the peptide spectrum match. mostly based on homology to proteins in the Arabidopsis chloroplast proteome, and proteomics analyses with isolated plastids comprising 1565 protein matches to chloroplasts and other plastid types (Huang et al., 2013). For rice, around 500 proteins were identified from isolated etioplasts and etio-chloroplasts by MS (von Zychlinski et al., 2005; Kleffmann et al., 2007). This set of proteins was supplemented with homologs to Arabidopsis plastid proteins in cases where the protein contained a predictable plastid transit peptide, resulting in 1806 protein assignments to rice plastids (Lu et al., 2015a). Based on the above described reference maps, we identified plastid phosphoproteins from full cell phosphoproteomics experiments by matching the identified phosphopeptides to proteins in the respective proteome reference table. In the PhosphAT database (http://phosphat.uni-hohenheim. de/), around 800 Arabidopsis chloroplast proteins can be identified as phosphoproteins (Arsova and Schulze, 2012). Because assembled datasets may contain many false identifications and/or incorrectly assigned phosphorylation sites (see above), we decided to restrict the dataset assessed here to three individual large-scale studies that applied stringent selection criteria for peptide identification at FDRs < 1%. Early studies by Reiland and colleagues identified 225 chloroplast proteins as phosphorylated (Reiland et al., 2009, 2011), and more recent analyses by Roitinger and colleagues identified 353 chloroplast phosphoproteins (Roitinger et al., 2015). Both datasets are represented in PhosphAT and represent a sub-fraction of the 800 phosphoproteins mentioned above. Given that the FDRs of these datasets are known, we did not apply any further filtering for the assembly of the dataset and accepted all phosphopeptide identifications as reported in the original publications. Of the identified chloroplast phosphoproteins, 151 were present in all three datasets, 74 were exclusive to the Reiland dataset, 202 were exclusive to the Roitinger dataset, and 373 were identified by any one of the numerous other studies represented in PhosphAT. The three analyses used to assemble the plastid phosphoproteome reported here contributed 427 chloroplast phosphoproteins with a rate of less than 1% tyrosine phosphorylation at the peptide level (Supplementary Table S1A). 3876 | Baginsky For rice and maize, only a few phosphoproteome analyses have been performed and the problem of cumulated FDRs is smaller, because the individual studies applied stringent matching criteria (see discussion below). We identified 227 phosphopeptides from 100 chloroplast proteins in a recent comprehensive maize phosphoproteome study (Facette et al., 2013) (Supplementary Table 1B). Using commercial software integrated in Spectrum Mill (Agilent) to identify the exact site of phosphorylation, 128 phosphopeptides allowed unambiguous identification of the phosphorylation site while 98 peptides remained ambiguous (see supplemental table 1 in Facette et al., 2013). Of the localized sites, 99 (72%) were phosphorylated at serine, 32 (23%) at threonine, and 6 (4%) at tyrosine (note that some of the peptides carry several phosphate groups) (Supplementary Table S1B). In two rice datasets that used high resolution Orbitraps we identified 302 phosphopeptides from 127 chloroplast proteins (Nakagami et al., 2010; Lu et al., 2015a). Nakagami and colleagues (Nakagami et al., 2010) applied PhosCalc 1.2 (McLean et al., 2008) for phosphorylation site assignment, resulting in 85% phosphoserine, 12% phosphothreonine, and 3% phosphotyrosine detection. A more recent study employed a Q-Exactive Plus mass spectrometer to identify phosphopeptides from rice leaves before and after infection with Xanthomonas oryzae pv. oryzae, using PhosphoRS for phosphorylation site assignment (Hou et al., 2015). In that study, 267 phosphopeptides were identified in 148 chloroplast proteins. With a PhosphoRS score ≥0.9, the rate of phosphorylated amino acids was 79% serine (207 sites), 20% threonine (53 sites), and 1% tyrosine (3 sites, note that some peptides carry several phosphorylation sites). The different studies together identified 216 unique chloroplast phosphoproteins, 53 of which were identified by all analyses (Supplementar Table S1C). As a general trend there is a relatively small overlap in phosphoprotein identification, even in cases where photosynthetically active leaf material was analysed. Some of these differences might have a biologically relevant background, that is, they reflect the dynamic adaptation to different plant growth parameters such as light intensity and quality, soil-water content, humidity, and age. More importantly, however, there is large variability with regards the applied MS methods and data interpretation software. For example, Facette and colleagues used the commercial tool Spectrum Mill (Agilent) for phosphopeptide identification and phosphorylation site assignment. Low resolution data were searched at mass tolerances of ±2.5 Da for precursor ions and ±0.7 Da for fragment ions, and matches with an FDR <1% were reported (Facette et al., 2013). The three rice studies used high resolution Orbitrap and Q-Exactive Plus data and the spectra were searched at stringent matching parameters of precursor and fragment ion mass tolerances of 3 ppm/0.8 Da with two missed cleavages allowed (Nakagami et al., 2010), 10 ppm/0.6 Da with one missed cleavage allowed (Lu et al., 2015a), and 20 ppm/0.05 Da with one missed cleavage allowed (Hou et al., 2015). The differences in the search parameters in combination with software tools that score fragment ions differently can have significant impacts on the results of the database search. Variances in spectrum matching are of the kind that one search strategy could find a significant peptide match to a spectrum while the same spectrum does not produce a significant match at different settings or with other software. By no means should two search strategies result in different significant matches for the same peptide. However, with the up-to-date software that is now commonly used in data analysis, this is almost never the case (Lu et al., 2015b). Given the above, a comparison of data from different laboratories must be interpreted with great caution because every study suffers from the lack of comprehensiveness. Thus, it is not possible to make conclusions about conservation of phosphorylation sites from identified phosphorylation events only, because of the highly dynamic nature inherent to posttranslational regulation. Nonetheless, sequence comparisons are meaningful to assess the conservation of ‘phosphorylatability’ at certain sites, even though these sites may not have been identified as phosphorylated. In this context it is furthermore relevant to assess the conservation of the phosphorylation motif, because single amino acid exchanges in the context of the phosphorylation site can alter the specificity for a certain kinase. For a preliminary assessment of the functional relevance of phosphorylation, it is also relevant if the hydroxylated amino acid in a phosphorylation site is replaced by a negatively charged amino acid such as glutamate or aspartate in another species. In these cases, there is an apparent requirement for a negative charge at a certain position in the protein, indicating potential functional relevance (Beltrao et al., 2013). At present, only a few comparative phosphoproteome studies have been reported for plants. At a global scale, Nakagami and colleagues found a relatively good conservation of phosphorylation sites between Arabidopsis and rice. Around 50% of the sites identified in rice or Arabidopsis were conserved in the other organism (Nakagami et al., 2010). In a focused analysis, Lu and colleagues detected relatively weak conservation of CKII phosphorylation sites between Arabidopsis and rice chloroplasts (Lu et al., 2015a). Clearly, the degree of phosphorylation site conservation is kinase- and target protein-specific and generic statements on phosphorylation site conservation are not informative. In the following we compared a subset of the phosphorylation data for the three plant species analysed here, using either direct comparison of identified phosphopeptides or multiple sequence alignments based on ClustalOmega (W. Li et al., 2015). We accepted the phosphopeptide identifications as reported in the individual studies detailed above and did not apply additional filtering criteria. For the discussion of the phosphorylated amino acids, we relied on the phosphorylation site assignment of the software PhosphoRS and/or PhosCalc, the commercial tool integrated in Spectrum Mill (Agilent), or a calculated delta ion score from SEQUEST searches (Eng et al., 1994) between rank 1 and rank 2 hits (provided that they differed only by the localization of the phosphorylation site) greater than 0.4, as suggested by Beausoleil and colleagues (2006). The different analytical depths achieved with the different species made a global comparison meaningless, so we focused our subsequent Chloroplast phosphoproteins | 3877 comparison on the major chloroplast functions in photosynthesis and gene expression. Phosphorylation of thylakoid membrane proteins The regulation of short-term acclimation responses of photosynthetic light reactions by phosphorylation is a classic example for posttranslational regulation (Bennett, 1977). The regulatory system is driven by the thylakoid-associated kinases STN7 and STN8 that phosphorylate light-harvesting complex and photosystem core proteins (Rochaix, 2014). In phosphoproteomics experiment with photosynthetic leaf tissue, phosphorylated thylakoid membrane proteins usually constitute the largest group of phosphoproteins, which is also the case in the datasets assembled here (Supplementary Table S1A–C). The maize dataset is the smallest dataset and, with one exception (LHCI-2.1), LHCII proteins were exclusively identified as the thylakoid phosphoproteins, including both major and minor antenna proteins such as LHCII-1.5, LHCII-6 (CP242), LHCII-5 (CP26), and LHCII-4.1 (CP29). As reported earlier for Arabidopsis, several phosphorylation events in outer antenna proteins occurred at serine as in the peptides AASGpSPWYG in LHCII-1.5 and LGWGpSGpSPEK in LHCI-2.1 (Supplementary Table S1A). The common approach of characterizing thylakoid protein phosphorylation using phosphothreonine antibody blots therefore excludes many phosphorylation events from functional characterization. In one study on thylakoid protein phosphorylation in maize bundle sheath and mesophyll chloroplasts under highand low-light conditions, several serine residues were found to be phosphorylated in a light-regulated manner, including one site in CP26, suggesting a function in short-term acclimation to high light (Fristedt et al., 2012). Tyrosine phosphorylation was detected in one study in LHCII-1.5 in the peptide pYLGPFpSGEPP in maize thylakoids (Facette et al., 2013). This peptide was detected neither in an analysis with enriched thylakoid membrane proteins, nor in the homologous protein in three different Arabidopsis studies as detailed above. With the highest sampling depth, Arabidopsis phosphoproteomics data comprise 10 LHCII, 13 PSII core, 4 LHCI, and 10 PSI core proteins. None of them was phosphorylated at a tyrosine residues (Supplementary Table S1A). The role of phosphorylation in short-term acclimation to light quality and quantity is conserved in the three plant species, but the details of the regulation differ between monocotyledonous and dicotyledonous plants and even more so between plants and algae. For the minor antenna protein Lhcb4 (CP29), at least six phosphorylation sites have been identified in the algae Chlamydomonas reinhardtii that differ from the phosphorylation sites identified in higher plants (Chen et al., 2013). Although most of the sites that are potentially phosphorylated are conserved among rice, maize, and Arabidopsis, the signals triggering CP29 phosphorylation differ, suggesting that different kinases act on CP29 in monocots and dicots (Chen et al., 2013; Betterle et al., 2015). In dicots, CP29 phosphorylation is weakly detectable and dependent on the STN7 kinase, which is inhibited by a reduced ferredoxin/thioredoxin system under high-light conditions (Lemeille et al., 2009). In monocots, high-light conditions trigger the STN7-independent phosphorylation of CP29 at Thr83 (Betterle et al., 2015). Given that the CP29 kinase also requires a reduced plastoquinone pool for activity, its characteristics align with those of STN8 (Vainonen et al., 2005). A thorough discussion of phosphorylation site conservation of CP29 in different plant species is available in a review of Chen and colleagues (Chen et al., 2013). This example illustrates that the regulation of photosynthetic light reactions by phosphorylation employs different protein kinases even at conserved phosphorylation sites, adding a new layer of dynamic regulation onto a well-established regulatory system. Clearly, more research is required to understand the signal integration by the phosphoproteome network, and phosphoproteomics with different plant species, mutant lines, and under different conditions emerges as the method of choice for data acquisition. Phosphorylation of Calvin cycle enzymes The regulation of photosynthetic light reactions by phosphorylation is inherently coupled with the regulation of the Calvin cycle as the major sink for photosynthetic electrons. In addition to the established redox regulation of Calvin cycle enzymes, phosphorylation emerges as a new type of regulation that can target individual enzymes with higher specificity. Rubisco activase (RCA) initiates carbon fixation by Rubisco in an ATP-dependent manner by removing a bound sugar phosphate in its active centre, thus preparing Rubisco for catalytic activity. Therefore, signals affecting RCA activity affect the entire Calvin cycle. RCA is one of the most abundant phosphoproteins in photosynthetically active plant material, where it is phosphorylated at Thr78 and Ser172 (Boex-Fontvieille et al., 2014). Both sites are in conserved functional domains but only the phosphorylation site at Thr78 is responsive to light conditions, that is, it has a higher phosphorylation status in the dark. Its localization in the N-terminal domain, which is important for the interaction with Rubisco, suggests that Thr78 phosphorylation has an inhibitory effect on Rubisco activation (van de Loo and Salvucci, 1996; Stotz et al., 2011; Boex-Fontvieille et al., 2014). Although Thr78 is placed in the highly conserved N-terminal domain, the threonine itself is not conserved and is replaced by isoleucine in rice and maize (Fig. 2). Consistent with this exchange, there was no RCA phosphorylation in photosynthetically active maize chloroplasts while RCA was phosphorylated at the serine residue in GLAYDISDDQQDI in rice chloroplasts (Supplementary Table 1B, C). A modification of catalytic properties and an accumulation of metabolite precursor was recently reported for transketolase (TKL). Of the two paralogues TKL1 and TKL2, TKL1 represents the main isoform expressed in leaf tissue and is phosphorylated at Ser428 by a soluble chloroplast kinase in a Ca2+-dependent manner. In vitro characterization of TKL1 activity with the wild type and the phosphomimetic mutant S428D suggested an effect of phosphorylation on TKL activity (Rocha et al., 2014). While the TKL kinase is currently unknown, the phosphorylation motif suggests 3878 | Baginsky Fig. 2. Multiple sequence alignment of an excerpt of the RCA sequence by ClustalOmega (http://www.ebi.ac.uk/Tools/msa/clustalo). The phosphorylation sites identified in Arabidopsis and rice are highlighted in red. To date, phosphorylation has not been detected in maize chloroplasts. a proline-directed kinase as a possible candidate. The phosphorylation site at Ser428 is conserved in all higher plants but not in mosses or algae, where it is replaced by aspartate. Despite its conservation, Ser428 was only identified in Arabidopsis phosphoproteomics data, whereas it was Ser458 that was phosphorylated in rice (Supplementary Table S1A). In maize, no phosphorylation was observed, suggesting that TKL is not a major phosphoprotein and probably phosphorylated only under conditions that alter stroma Ca2+ concentrations. The assumed regulatory connection between Ca2+, a Ca2+-dependent protein kinase, and the activity of TKL can be tested experimentally under conditions that alter stroma Ca2+ concentrations, such as light-to-dark shifting of plants and exposure to pathogenassociated molecular patterns (Sai and Johnson, 2002; Nomura et al., 2012). Three Calvin cycle enzymes are phosphorylated in all three organisms analysed here: phosphoglycerate kinase (PGK), glyceraldehyde 3-phosphate dehydrogenase (GAP-DH), and Rubisco (Supplementary Table S1A). However, the function of phosphorylation is completely unknown. For PGK, the phosphorylation site in VGAVSpSPK is identical in rice and maize, and probably used by a proline-directed kinase. It is appealing to assume that it is the same kinase as for the phosphorylation of TKL1, but the kinase responsible for the phosphorylation of Calvin cycle enzymes at this motif is unknown. The assumed SP-motif is not conserved in Arabidopsis and instead replaced by the amino acid composition NP. Consistent with a necessity for this motif for the activity of a proline-directed kinase in Arabidopsis chloroplast, Arabidopsis PGK is not phosphorylated anywhere close to this site and is instead phosphorylated at a serine residue close to the mature N-terminus in the peptide SVGDLTSADLK. For GAP-DH and Rubisco, we have the unusual case that several phosphorylation sites were identified, but there is almost no overlap between sites in the different organisms (Supplementary Table S1A–C). This could indicate that the phosphorylations observed here are functionally not relevant, but further experiments are necessary to understand the dynamic regulation of Calvin cycle enzymes by phosphorylation and to pinpoint the protein kinases involved (Friso and van Wijk, 2015). TAC phosphorylation – regulation of longterm acclimation Photosynthetic acclimation not only comprises short-term acclimation responses at the light harvesting and photosystem core proteins, but also long-term acclimation that affects plastid and nuclear gene expression. We searched for phosphoproteins in the plastid gene expression apparatus with a special focus on the transcription system. We define the transcription system here in terms of its organizational unit as the transcriptionally active chromosome (TAC) (Pfalz et al., 2006; Pfalz and Pfannschmidt, 2013). For reasons of sampling depth, we identified phosphorylated TAC subunits almost exclusively in the Arabidopsis thaliana dataset, with the exception of a pfkB-type carbohydrate kinase and TAC16, which are phosphorylated in all three species. The phosphorylation sites in TAC16 are mostly located in the C-terminal region that is rich in acidic amino acids (Supplementary Fig. S1). Of the different identified phosphorylation sites, only one threonine in the peptide sequence I/VApTVR is phosphorylated in all three plant species (Table 1). This site has been identified previously as the STN7 site by comparative phosphoproteomics using wild type and the stn7 mutant (Ingelsson and Vener, 2012). TAC16 is a highly abundant protein that exceeds the abundance of all other TAC subunits by one order of magnitude, thus it is not a stoichiometric component of the TAC. Only a sub-fraction of TAC16 associates with the transcription system while the remaining protein is associated with the thylakoid membrane. It is not required for TAC assembly, but rather functions in recruiting the TAC to the thylakoid membrane. Ingelsson and colleagues reported that phosphorylated TAC16 is associated with the thylakoid membrane but is excluded from TAC association, suggesting that phosphorylation regulates its distribution between thylakoid membrane and the TAC complex (Ingelsson and Vener, 2012). As an alternative explanation, cause and effect could Chloroplast phosphoproteins | 3879 be reversed, that is, TAC-associated TAC16 could be inaccessible to the TAC16 kinase. For reasons detailed above, TAC16 is most likely not involved in transcriptional regulation. There are a number of other TAC phosphorylation sites for which the responsible protein kinases are unknown, and others that are phosphorylated at canonical CKII phosphorylation motifs (Table 1). It has recently been shown that TAC10 is phosphorylated by plastid casein kinase II (Schonberg et al., 2014). The CKII phosphorylation site was found to be phosphorylated only in Arabidopsis because the site is not conserved in rice or maize (Table 1) (Lu et al., 2015a). Other phosphorylation targets are TAC5, TAC10, TAC15, and the two subunits FLN1 and FLN2. The latter two enzymes resemble fructokinases but an amino acid exchange in the active site eliminates fructokinase activity. Instead, both enzymes are intrinsic components of the plastid TAC and both interact with thioredoxin Z (Trx-Z), which is essential for TAC assembly and associated with the transcriptional core of the complex (Arsova et al., 2010). At least one of the FLN subunits is phosphorylated in all three plant species. FLN1 is phosphorylated at an unknown site in Arabidopsis (Table 1), whereas FLN2 is phosphorylated at one uncharacterized site and one site that resembles a CKII motif (Table 1). Rice chloroplasts have a TAC composition similar to that in Arabidopsis (Lu et al., 2015a) and a phosphorylated CKII motif was identified in an FLN homolog in the peptide NTQEpSDpSEGEEEPPK. Similarly, an FLN2 homolog is phosphorylated at a CKII site in the peptide GLpSDEpSDGETSTK in maize, suggesting that there is conserved phosphorylation of FLN homologs in all three species with an apparent regulatory requirement for CKII phosphorylation (Table 1). New avenues for the regulation of photosynthesis and its integration with metabolism and gene expression by protein phosphorylation Both short- and long-term acclimation processes are regulated by phosphorylation and chloroplast protein kinases probably function as signalling mediators between these two Table 1. Phosphorylation sites in proteins associated with the transcriptional core of the transcriptionally active chromosome (TAC) (Pfalz and Pfannschmidt, 2013). The star (*) indicates experimental evidence for phosphorylation of the site by a known chloroplast kinase as detailed in the main text. TAC subunit/identifier Description Phosphopeptide Remarks FLN1 FLN2 FLN2 FLN2 FLN1 FLN1 FLN2 ASINGpSGITNGAAA AAAApSpSDVEEVK RVpTACpSpTMKISK DGLpSDEpSDGET RKpSPSPSPPK NTQEpSDpSEGEEEPPK RKVKpTVEELS unknown probable CKII site unknown probable CKII site unknown probable CKII site unknown TAC5 LFMDEDVETDKDEASTMKK probable CKII site TAC10 LSELpSDDEDFDEQK CKII site* FLN1/FLN2 AT3G54090 AT1G69200 AT1G69200 GRMZM2G103843 LOC_Os01g63220 LOC_Os01g63220 LOC_Os03g40550 TAC5 AT4G13670 TAC10 AT3G48500 TAC15 AT5G54180 TAC16 AT3G46780 TAC15 ELAFAMGAVTR unknown TAC16 GRMZM2G449496 TAC16 LOC_Os05g22614 TAC16 ADAVGVpTVDGLFNK DISpSGLSWNK EAEAApSLAEDAQQK KQpTAFQLGK LGpSQFATAIQNASEpTPK pSQPLTISDLIEK QRPpSSPFASK TKGDDDSEGK VQVApTVR AQAEEEpTLASER AQIApTVR TpTPSEEAAATP AQIApTVR SSTTSSpTETGK QASLENLLpSR LAGVApTQDSDE QAEEDpTTpTVK unknown unknown unknown unknown unknown unknown unknown unknown STN7* unknown probable STN7 site unknown probable STN7 site unknown unknown unknown unknown 3880 | Baginsky processes. However, we are only now beginning to understand the connection between the two acclimation processes. Initial data on crosstalk between the soluble kinase pCKII and regulation of thylakoid-associated processes has become available through phosphopeptide chip and large-scale phosphoproteome analyses (Schonberg et al., 2014). Alb3 is phosphorylated by pCKII at Ser422, which is located in the C-terminal stroma exposed region. Because Alb3 is essential for the integration of light-harvesting proteins into the thylakoid membrane, an influence of Alb3 phosphorylation on the assembly of thylakoid membrane protein complexes is conceivable (Sundberg et al., 1997). CKII furthermore phosphorylates RCA at Thr78, suggesting a regulatory impact of pCKII on carbon assimilation that affects photosynthetic electron transport through its function as an electron sink (Schönberg et al., 2014, see above). The pleiotropic nature of pCKII makes it difficult to dissect its functions in mutant analyses. CKII knock-down plants show delayed flowering under longday conditions and an entrained circadian rhythm under constant light conditions. Furthermore, the chloroplast CKII alpha subunit seems to function redundantly to nucleo-/cytoplasmic CKII in regulating abscisic acid responses and lateral root formation (Wang et al., 2014; Mulekar and Huq, 2015). How these phenotypes emerge from the set of know CKII substrates is unknown. The regulation of processes in the chloroplast stroma by the thylakoid-associated kinases STN7 and/or STN8 is an elegant way to connect the redox status of the plastoquinone pool with the regulation of carbohydrate metabolism or transcription. In Chlamydomonas, a loose consensus motif was reported for the STN7 orthologue Stt77 (Lemeille and Rochaix, 2010; Lemeille et al., 2010). This consensus was defined as a threonine residue surrounded by basic amino acids such as (K/R/H)(K/R/H)TX(K/R/H)(K/R/H). With this motif, RpoD, a sigma factor for the plastid-encoded plastid RNA polymerase, was identified as a potential target of Stt7, suggesting a possible link between short- and long-term adaptation processes. However, any evidence that this phosphorylation site is used in vivo is missing and direct crosstalk between the thylakoid kinases and the gene expression system has not been established in any organism. Compared with the established STN7 phosphorylation site VQVApTVR in TAC16, ASINGpSGIT in FLN1, GAVpTR in TAC5, ELAFAMGAVpTR in TAC15, and RVpTAC in FLN2 loosely resemble basic properties of the STN7-site in Arabidopsis, with the presence of alanine and one nonpolar aliphatic branched-chain amino acid in its very close surrounding (Table 1). This suggests that direct regulation of chloroplast transcription by STN7 may be possible. The chloroplast sensor kinase CSK has been proposed to regulate transcription via a redox-mediated process (Puthiyaveetil et al., 2008, 2012). This effect was indirectly inferred from mutant analyses; phosphorylation targets of CSK were not identified and a motif preference for this kinase is unavailable. Instead of receiving signalling input from the thylakoid kinases STN7 or STN8, CSK forms a protein complex with SIG1 and pCKII, suggesting the existence of a regulon at the transcriptional level (Puthiyaveetil et al., 2012). Further research is required here to establish the molecular details of its regulatory functions. STN7 mutants show a significant reorganization of their metabolome in comparison to wild type, suggesting a function of STN7 in controlling metabolic processes (Brautigam et al., 2009; Pfannschmidt, 2010). This could occur indirectly by fine-tuning metabolism via unknown routes in response to altered light-harvesting properties compared to wild type, or directly by the phosphorylation of soluble stroma enzymes. For example, some of the phosphorylation events in Calvin cycle enzymes loosely resemble properties of the STN7 sites, such as two phosphorylation sites in phosphoglycerate kinase (in SVGDLTpSADLK and in KLApSLADLY) and one site in Rubisco (in LSGGDHIHAGpTVVGK) (Baginsky and Gruissem, 2009). For STN8, a large-scale survey reported an ATPase F0 subunit as a potential target at the peptide ALDpSQIAALpSED, but the exact site of phosphorylation could not be determined so a consensus phosphorylation motif for STN8 is currently missing (Reiland et al., 2011). A new player in signal integration is the ABC1 kinases, which belong to a group of kinases comprising at least 16 members in algae and higher plants, most of which localize to plastids or mitochondria (Lundquist et al., 2012). The ABC1 kinases regulate photosynthesis through their influence on tocopherol and prenylquinone accumulation, as demonstrated in ABC1 kinase mutants (Lundquist et al., 2013; Martinis et al., 2014). While a biochemical characterization of their catalytic properties is missing, as is their preferred phosphorylation motif, two analyses characterized the phenotypes of ABC1 kinase mutant plants. The phenotypes of abc1k1 and abc1k3 mutants support the role of these kinases in regulating photosynthesis and integrating it with carbohydrate metabolism. A defect in abc1k1 results in a deficiency of plastoquinone and luteins, and a defective tocopherol metabolism. Consistently, Abc1K1 phosphorylates VTE1, a key enzyme of tocopherol biosynthesis in plastoglobules in in vitro phosphorylation assays (Martinis et al., 2013). In addition, the abc1k1 kinase mutant has a significantly altered sugar metabolism in high-light conditions. Thus the ABC1 kinases may function as signalling hubs to control plastid metabolism and coordinate it with photosynthetic performance. Conclusions and outlook Recent years have seen a significant increase in the number of established chloroplast phosphorylation targets. New approaches in their characterization have led to a modified understanding of the pathways of metabolic control processes. For future research directions we see three main areas of immediate importance. First, there is an urgent need to understand the connections between short- and long-term adaptation processes. It is possible that this type of control is exerted by the thylakoid-associated protein kinases, but direct proof is currently missing. Short- and long-term adaptation operate at different time scales, so a central question concerns the reversibility of the signals at the gene expression level. Additional components must be involved and it is currently Chloroplast phosphoproteins | 3881 unclear whether phosphatases or proteases are the predominant player in the reversion of phosphorylation-triggered signals. Second, the role of pCKII must be further elucidated because it may act to integrate gene expression regulation with the control of photosynthesis and metabolism. This could work via an interaction with other kinases or other phosphoproteins, such as demonstrated for a transcriptional regulon that exists among pCKII, CSK, and SIG1. Since this complex assembles only a fraction of the chloroplast pCKII that engages in different protein complexes, an important question concerns the regulation of the assembly of this regulon and its activity. And third, there is the family of ABC1 kinases that are the established control point in the assembly of components of the photosynthetic electron transport and its integration with chloroplast metabolism. Their characterization should include a biochemical characterization and a search for the signals controlling their activity. Supplementary data Supplementary data are available at JXB online. Fig. S1. Multiple sequence alignment of TAC16 by ClustalOmega (http://www.ebi.ac.uk/Tools/msa/clustalo). The phosphorylation sites identified in Arabidopsis, rice, and maize are highlighted in red. Table S1. Identified phosphoproteins and phosphopeptides from Arabidopsis (A), maize (B), and rice (C). Acknowledgement I would like to thank Stefan Helm and Dr Anja Rödiger for help with the assembly of this manuscript and Johann Galonska and Wolfgang Höhenwarter for the CID/MSA spectra. This work is supported by DFG grant BA 1902/2-2 and by the EU Initial training network AccliPhot (PITN-GA-2012–316427). References Arsova B, Hoja U, Wimmelbacher M, Greiner E, Ustun S, Melzer M, Petersen K, Lein W, Bornke F. 2010. Plastidial thioredoxin z interacts with two fructokinase-like proteins in a thiol-dependent manner: evidence for an essential role in chloroplast development in Arabidopsis and Nicotiana benthamiana. Plant Cell 22, 1498–1515. Arsova B, Schulze WX. 2012. 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