Download Protein phosphorylation in chloroplasts – a survey of

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
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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).
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