Update on Plastid Proteomics in Higher Plants
Plastid Proteomics in Higher Plants: Current State
and Future Goals1
Klaas J. van Wijk* and Sacha Baginsky
Department of Plant Biology, Cornell University, Ithaca, New York 14853 (K.J.v.W.); and
Martin-Luther-Universität Halle-Wittenberg, Institut für Biochemie, 06120 Halle, Germany (S.B.)
SIGNIFICANCE OF PLASTIDS IN PLANT BIOLOGY
Plastids are plant cell organelles with many essential
functions in plant metabolism. Among these are photosynthesis, amino acid and fatty acid biosynthesis, as
well as the synthesis of several secondary metabolites.
All plastids originate from undifferentiated proplastids,
which are restricted to meristematic tissues and undifferentiated cells. Depending on the tissue, proplastids
can develop into different plastid types (e.g. amyloplasts in storage tissue, chloroplasts in photosynthetic
tissues, and chromoplasts in fruits and flowers). Other
specialized plastid types include gerontoplasts, the
plastids of senescent leaves that are important for
resource allocation, oleoplasts, which are oil storage
plastids in olive (Olea europaea), and etioplasts, the final
stage of proplastid development in photosynthetic tissues in the dark (Wise, 2006). Finally, plastid types can
possibly specialize to different degrees depending on
cell type, developmental state, and (a)biotic conditions.
An extreme case are the highly specialized C4 chloroplasts in bundle-sheath and mesophyll cells in the
maize (Zea mays) leaf, with strong differences in proteome composition (Friso et al., 2010). With the ability to
develop and differentiate, plastids add versatile biosynthetic capacity to the plant cell and are responsible
for unique biosynthetic pathways that make plants
unrivaled biochemical factories that are essential for life
on earth. Thus, significant research efforts are underway that aim at understanding plastid biology in depth.
A decade ago, the first plastid proteomics study was
published and the potential of plastid proteomics was
outlined (van Wijk, 2000). Since then, proteomics of
plastids and plant (sub)proteomes has delivered on its
promise. Here, we provide an update on the current
status of plastid proteome research a decade after the
first reports.
1
This work was supported by the National Science Foundation (grant nos. MCB–1021963, IOS–0701736, and IOS–0922560), by
the Swiss National Science Foundation (grant no. 31003A_127202),
and by the Martin-Luther-University Halle-Wittenberg.
* Corresponding author; e-mail [email protected].
www.plantphysiol.org/cgi/doi/10.1104/pp.111.172932
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ADVANCES IN PLASTID PROTEOMICS AND
PROTEOMICS TECHNOLOGY
Plant and plastid proteomics are now well established scientific disciplines with many laboratories
contributing to their progress. Not surprisingly, a significant fraction of the plastid proteome is characterized today, and available information includes protein
quantities, protein interactions, and posttranslational
modifications (PTMs), as will be briefly highlighted in
this update. However several of the challenges for
plastid proteomics outlined 10 years ago still exist
today, including the detection of low-abundance proteins (e.g. more than 10,000-fold lower than Rubisco)
and capturing the dynamics of plastid proteomes.
Much of the progress has been driven by technology
development and improved genomics resources. The
main difference between proteomic technologies today and 10 years ago is the much improved sensitivity
(routinely at 1–50 fmol), the accelerated duty cycle
(now tandem mass spectrometry [MS/MS] scans
within a few hundred ms), the improved mass accuracy
(down to a few ppm for peptides), and the increased
resolution (up to 100,000) of the latest generation mass
spectrometers. Furthermore coupling of nano-liquid
chromatography (LC) with MS/MS is now routine,
and split-free nano-LC systems now deliver low flow
rates for nanospray ionization with excellent reproducibility. Also important are improved software tools
for the reliable identification of peptides based on MS/
MS spectra along with statistically sound estimates of
false discovery rates in large data sets. With the maturation of proteomics work flows, quantitative information for plastid proteins became available. These
new technologies, in combination with the availability
of multiple sequenced plant genomes, now allow for
answering more comprehensive and sophisticated questions as compared with a decade ago (Baginsky, 2009;
Gstaiger and Aebersold, 2009; Schulze and Usadel,
2010; Walther and Mann, 2010).
In this update, we review progress on plastid proteomics and lay out a series of challenges that can be
addressed within the next few years. Table I provides
an overview with Web-based plant proteomics resources that are relevant for plastid proteomics. We
will center this update on the concept of a plastid
protein atlas. Box 1 provides examples of the wide
range of queries that a high-quality plastid protein atlas
Plant PhysiologyÒ, April 2011, Vol. 155, pp. 1578–1588, www.plantphysiol.org Ó 2011 American Society of Plant Biologists
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Plastid Proteomics
Table I. Plant and plastid proteomics databases that provide information and tools for finding plastid proteins in Arabidopsis and other
plant species, as well as functional annotation, posttranslational modifications, peptide information, and spectral data
Abbreviations for species are as follows: At, Arabidopsis; Ca, bell pepper; Cr, the green algae C. reinhardtii; Mt, Medicago truncatula; Nt, tobacco;
Os, rice; St, potato; Zm, maize.
Database
Species
AtProteome
(http://gator.
mascproteomics.org/)
At
PPDB
(http://ppdb.tc.
cornell.edu)
At, Zm,
Os
AT_CHLORO
(http://www.
grenoble.prabi.fr/
at_chloro/)
At
plprot
(http://fgczatproteome.
unizh.ch)
SUBA
(http://suba.
plantenergy.
uw.a.edu.au/)
At, Os,
Nt, Ca
At
Main
Purpose/Objective
Experimental
information
about identified
proteotypic
peptides, protein
abundance in
different organs,
and spectral/peptide
evidence for gene
models
Curated information
for all proteins and
protein models in At
and Zm, including
protein information
and functional
annotation;
experimental
information about
leaf and subcellular
fractions with
MS-based
identification details,
including spectral
counts and PTMs
In-house analysis of
At chloroplast
proteome and its
substructures
(envelope, stroma,
thylakoid) with
detailed proteomic
information (peptides,
molecular mass, retention
times, identification
statistics)
Proteome analyses
of Os etioplasts, At
chloroplast, Ca
chromoplasts, and
the undifferentiated
proplastid-like
organelles of Nt BY2
cells, plastid typespecific functions
Facilitate subcellular
protein localization
analysis based on
different public
prediction tools,
proteomics papers,
and GFP/yellow
fluorescent protein
localization studies;
allow combinatorial
queries on the
contained data
In-House
Experimental
Plant Material
Type of
In-House
Experimental
Information
Functional
Annotation
(Name
and
Function)
Subcellular
Localization
Predictors
TAIR
None, but
detailed
organ
information
None
Zm and At
In-house
leaves and
MS-based
chloroplast
identification:
fractions (e.g.
peptides, ion
stroma, thylakoids,
scores, ppm,
lumen,
Mowse
plastoglobules,
scores, meta
etc.); for Zm,
information
also BSC-and
MC-specific
chloroplasts to
study C4 effects;
for At, also
different mutant
background in
ecotype Columbia
Chloroplast
In-house
fractions
MS-based
(envelope, stroma,
identification:
thylakoid)
peptides, ion
scores, ppm,
Mowse
scores, meta
information
Manual
curation
of name,
functional
annotation
by MapMan
Chloroplast
stroma,
thylakoid,
envelope
None
Manual
curation
of name,
functional
annotation
by MapMan
Chloroplast
stroma,
thylakoid,
envelope
None
Different
plastid
types from
various
species
None
Identifications
from isolated
plastids
None
Links to
TAIR,
AmiGO,
and
UniPROT
Users can
employ
various
queries to
generate
answers
Multiple
localization
predictors
Different
organs of At
None; literature
only
In-house
MS-based
identification:
peptides, ion
scores, ppm,
Mowse scores,
meta
information
Peptide
dentifications,
homologs
identified in
other plastid
types, interactive
two-dimensional
PAGE from
differently
illuminated
etioplasts
Not applicable
(Table continues on following page.)
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van Wijk and Baginsky
Table I. (Continued from previous page.)
Database
Species
PhosPhAt
(http://phosphat.
mpimp-golm.
mpg.de/)
At
RIPP-DB
(http://database.
riken.jp/sw/links/
en/ria102i/)
At, Os
ProMex
(http://promex.
pph.univie.
ac.at/promex)
At, Cr,
Mt, St
Type of
In-House
Experimental
Information
In-House
Experimental
Plant Material
Main
Purpose/Objective
Phosphoproteome
information
from published
and unpublished
sources, identified
peptides, or
ions with annotated
phosphorylation
sites (where available);
provides a P-site
prediction tool
At
Plant
phosphoproteome
database with
information
on phosphopeptides
by LC-MS/MS-based
shotgun
phosphoproteomics
Mass spectral
reference
database of tryptic
peptides from
plant proteomes
Os and At cell
cultures
phosphoproteome
at different
conditions
Different sources
should ultimately be able to answer. For more exhaustive overviews on proteomics of plastids, we refer to
two recent review articles and the references therein
(Baginsky, 2009; Agrawal et al., 2010); space restrictions
do not allow us to cite recent literature more extensively.
DEVELOPMENT OF A PLASTID PROTEIN ATLAS
The concept of a protein atlas was established several years ago in particular for the human proteome
(http://www.proteinatlas.org/). This concept involves the generation of protein inventories for each
organ and subcellular localization tagged with additional protein information, such as splice variants,
PTMs, protein-protein interactions, etc. Similar efforts
are under way to collect all available information for
plants to generate a plant proteome atlas that includes
proteome information for the different plant organs
and their organelles. Here, we concentrate on the
plastid proteome atlas. In order to disseminate biologically useful information, such a plastid atlas should
include the following: (1) protein accessions for each
plastid type, including cellular specialization and
subplastid localization; (2) information on peptide
coverage of each identified protein and possibly different gene models; (3) steady-state protein abundance
under a set of well-defined (a)biotic conditions as well
as developmental states; (4) protein-protein interactions, protein-nucleotide assemblies, and oligomeric
Specific
information
about peptide
properties,
annotated
biological
function, as
well as
the analytical
context;
provides the
phosphopeptide
spectrum
Described in
associated
papers
Display of
MS/MS
spectra with
annotation
Functional
Annotation
(Name
and
Function)
Subcellular
Localization
Predictors
TAIR
None
Plant specific
P-predictor
(pSer, pThr,
pTyr)
Hyperlinks
to other
databases
None
None
None
None
None
state; (5) reversible and irreversible PTMs; and (6)
bioinformatics information such as subcellular localization predictions and network information.
Plastids are among the best characterized cell organelles at the proteome level, and a quality chloroplast protein atlas is now emerging. However, the
plastid protein atlas is far from complete, and strategies to improve proteome coverage and in-depth
characterization must be developed and implemented.
In the following paragraphs, we will review the status
of each of the six components of the plastid protein
atlas and outline strategies for improvement.
Improved Protein Inventories for Each Plastid Type,
Including Cellular Specialization and
Subplastid Localization
The predicted size of the combined proteome of all
plastid types ranges from 2,000 to 3,500 proteins in
Arabidopsis (Arabidopsis thaliana), representing about
7% to 12% of all predicted protein-encoding genes.
However, only about 1,200 proteins are currently
recognized as being plastid localized (see the Plant
Proteome Database [PPDB] at http://ppdb.tc.cornell.
edu). Comparing this experimental plastid proteome
data set with the predicted plastid proteome showed
that, in particular, plastid proteins involved in signaling and plastid gene expression and RNA metabolism
are strongly underrepresented. There are several reasons why a significant percentage of plastid proteins
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Plastid Proteomics
Box 1. Examples of the wide range of queries that a high-quality plastid protein atlas should ultimately be able to answer. Current
answers for Arabidopsis proteins are provided with reference to the databases and resources listed in Table I. Information for
maize and rice is available in a subset of the databases (Table I). This box also serves to better identify the lack of information and
challenges for plastid proteome research and resource development for the immediate future.
has not yet been recognized: (1) low abundance in
chloroplasts (i.e. their detection is obscured by highly
abundant photosynthetic proteins); (2) specific expression in a certain plastid type other than chloroplast; (3)
only expressed under very specific conditions (developmental state, abiotic condition, or biotic challenge);
or (4) too few ionizable tryptic peptides (e.g. transmembrane proteins with very short loops and tails or
very small or basic proteins). Plastid proteome coverage can be improved by using better MS instrumentation with higher sensitivity, accuracy, and faster duty
cycle, the use of alternative enzymes for protein digestion, more specific (e.g. affinity-based) fractionation of plastid proteomes, or increased efforts to
analyze a more diverse set of plastid types, including
heterotrophic plastids. However, as analytical sensi-
tivity increases with these additional efforts, the challenge to distinguish between true positive and false
positive plastid proteins increases as well.
Based on the last decade of plastid proteome research, it is clear that objective filtering strategies for
false positive identification and/or assignment to
plastids are essential. The most practical solution
involves repeated analysis of independent plastid
preparations and the use of quantitative protein information for improved filtering of the identified proteins, based on two steps: (1) repeat observations in
independent plastid preparations, because proteins
that are observed at high frequency across these preparations are more likely to be bona fide plastid proteins; and (2) combined proteome information from
unfractionated tissue and different purified organelles
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van Wijk and Baginsky
to recognize false positives that more highly accumulate in other subcellular locations, which requires
quantitative information about relative protein abundance. Such relative quantification for these different
sample types ideally should be done with the same
experimental work flow, and a good example is available from the Localization of Organelle Proteins by
Isotope Tagging (LOPIT) technique, which uses relative protein quantification along density gradients to
assign proteins to organelles by association (Dunkley
et al., 2006). The “frequency” filter is based on the
assumption that nonplastid contaminants or false
positive identifications are random events; therefore,
this first filter does not remove systematic false positives, such as high-abundance cytosolic proteins,
which can contaminate isolated chloroplasts. A small
percentage of plastid proteins are also located elsewhere in the cell, and approximately 50 dual-targeted
proteins have been discovered for Arabidopsis so far
(Carrie et al., 2009). Most of these have shared locations with mitochondria and are involved in plastid or
mitochondrial gene expression (e.g. t-RNA synthetases); however, shared localizations with the nucleus,
peroxisome, or cytosol have also been described. Detection of such dual locations requires independent
information, typically from image analysis using fluorescent fusion proteins and ideally also from phenotypical analysis of mutants.
Collection of all available protein localization data
from individual functional studies, as well as proteomics studies, is an important tool in the conclusive
assignment of proteins to the plastid. For instance, it
helps to recognize abundant proteins often identified
in dozens of proteomics papers as potential contaminants. The SUBA database (http://suba.plantenergy.
uwa.edu.au/) collects information for Arabidopsis
that is available about the localization of a certain
protein (e.g. MS/MS data, GFP localization, and prediction tools) and allows assembling lists of organellar
proteins with self-defined reliability criteria. The
PPDB accumulates similar information for Arabidopsis as well as maize and combines it with in-house
MS/MS-based quantitative information on total leaf
extracts and isolated plastid fraction (stored in PPDB)
to manually evaluate this information and make a
manual assignment for subcellular localization. This
manual curation step using a conservative threshold
(i.e. no call is made unless there is deemed sufficient
evidence) has been proven to result in high-confidence
localization calls as judged by comparisons with subsequent independent experimental localization studies by GFP fusions and image analysis.
Another way to help complete the plastid proteome
inventory is to analyze plastid types specialized for
specific tasks in their resident tissue (organ or cell
type) because they differ considerably in their protein
composition. However, this is challenging for Arabidopsis, since its seeds and flowers are small and it
does not develop storage organs. Thus, organelle
isolation is often impractical, and proteome analyses
are better performed at the level of the entire organ, as
illustrated by the analysis of plastids in seeds (Chen
et al., 2009). Several groups tried to circumvent this
problem by using different plant species (e.g. tobacco
[Nicotiana tabacum], bell pepper [Capsicum annuum],
spinach [Spinacia oleracea], pea [Pisum sativum], wheat
[Triticum aestivum], potato [Solanum tuberosum], tomato
[Solanum lycopersicum], or Brassica rapa) for the analysis
of amyloplasts, chromoplast, proplastids, and leucoplasts (Agrawal et al., 2010). However, so far, this has
not significantly increased the number of identified
plastid proteins, in part due to the lack of complete
genome sequence information. Exceptions are rice
(Oryza sativa) and maize, because good-quality genome
annotation is available for these two organisms and the
coverage of the plastid proteome of maize is now quite
comparable to Arabidopsis, in part because cell typespecific chloroplasts, specialized for specific functions,
were included (Friso et al., 2010). Importantly, this
allowed the identification of C4-specific metabolic chloroplast envelope transporters and also helped identify
many new subunits of the elusive thylakoid NADPH
dehydrogenase complex involved in cyclic electron
flow (Braütigam et al., 2008; Majeran et al., 2008).
The spatial distribution of proteins within chloroplasts has been the target of several proteome analyses, originally starting with the thylakoid lumen and
peripheral soluble thylakoid proteins (Peltier et al.,
2000), followed by systematic analyses of the thylakoid
and envelope membrane proteomes, the soluble
stroma proteome, specialized thylakoid-associated
lipoprotein particles, assigned plastoglobules, and
proteins associated with the plastid chromosome
(Baginsky, 2009; Agrawal et al., 2010). A recent study
separated the Arabidopsis chloroplast proteome into
soluble proteins and thylakoid and envelope membrane proteins (Ferro et al., 2010). Protein localization
to each subcompartment was based on the abundance
distribution of identified proteins in different purified
fractions. Information about the protein composition
of the chloroplast subcompartments is available in
PPDB and AT_CHLORO (http://www.grenoble.
prabi.fr/at_chloro/). Because of space constraints in
this update, we refer the reader to the most recent and
comprehensive review with extensive literature citations (Agrawal et al., 2010) instead of discussing the
original literature in this report.
Discovery and Significance of Gene Models
Many genes have more than one annotated gene
model; in some cases the different models only affect
untranslated 5# and 3# ends, whereas in others this
affects the actual translated region. This is achieved by
different transcription start sites or by alternative
splicing (AS). AS has received considerable attention
at the transcript level, in particular since new generation sequencing techniques now allow for large-scale
detection of AS. At least 20% of plant genes have one
or more alternative transcript isoform. The majority of
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Plastid Proteomics
these AS events have not been functionally characterized, but evidence suggests that AS participates in
important plant functions, including stress response,
and may impact domestication and trait selection.
Alternative transcription start sites or AS can result in
proteins with different N or C termini or internal
protein regions, potentially affecting subcellular localization and functions. Indeed, one of the mechanisms
for dual targeting is that two different proteins that
differ in their N termini are generated from a single
gene (Peeters and Small, 2001). Matching MS data to
these different gene models can help to identify the most
relevant predicted protein forms. In the PPDB (for
Arabidopsis and maize) and AtProteome (http://
www.pep2pro.ethz.ch), peptide identification data are
projected on each gene model, allowing evaluation of the
most relevant models. However, a systematic analysis of
the consequences of AS at the plant proteome level has
not been carried out; this is not surprising, given the
challenges associated with obtaining nearly complete
sequence coverage (i.e. the percentage of primary amino
acid sequence for which peptides are detected) that is
required to distinguish different gene models.
In the case of MS-based quantitative proteomics,
decisions have to be made on how to handle protein
models. For instance, one model may have more
matched peptides than another model. One solution
is to select only the information for the highest scoring
model or, alternatively, to collect and sum all matched
peptides for all protein models of a gene. In practice,
this may not affect most quantifications, but it is
important to systematically implement a chosen procedure. The van Wijk laboratory consistently selected
the higher scoring protein model (calculated across all
samples for the specific analysis), and if there was no
difference in protein score between models, the model
with the lowest digit was selected (Friso et al., 2010).
Other laboratories sum up all spectral counts for a
gene and remove the model information (Baerenfaller
et al., 2008). Either method has its merits, and it is
important that the applied procedure be transparent.
Protein Abundance within the Plastid
The range of protein accumulation levels in plant
organs and within the plastid likely spans up to
approximately 10 orders of magnitude. Using onedimensional gel separation, followed by in-gel digestion and the latest generation of tandem mass
spectrometers for untargeted (“shotgun”) analysis with
data-dependent acquisition, proteins are typically
identified within an abundance range of 5 to maximally 6 orders of magnitude. Mapping plastid protein
abundance is important to understand the composition of protein complexes, functionalities of plastid
membranes and plastid particles such as plastoglobules or nucleoids, as well as understanding plastid
metabolism and consideration of metabolic flux. In
addition, as discussed in the previous section, relative
protein abundance measurements are also an impor-
tant tool to evaluate if proteins are indeed plastid
localized. When discussing protein quantification, we
must distinguish between (1) measuring protein mass
or protein concentration within a sample and (2)
comparing relative protein concentrations (or mass)
of the same protein between different samples. The
latter case is often referred to as measuring differential
protein expression or “functional proteomics” [e.g.
when studying the effect of (a)biotic stress, developmental processes, or mutants]. Most (plant) protein
quantification studies relate to differential expression
(functional proteomics). In this section, we will discuss
the first case, whereas the second case is briefly
discussed below (see Employing the Plastid Proteome
Atlas for Functional Analysis).
The two strategies that have so far been employed to
map protein abundance within the plastid are (1)
image analysis of stained two-dimensional gels and
(2) MS-based quantification using spectral counting.
Quantification using two-dimensional gel electrophoresis with isoelectric focusing (IEF) as the first dimension was used in most gel-based studies (e.g. for the
thylakoid lumen [Schubert et al., 2002] or soluble
proteins in rice etioplasts [Kleffmann et al., 2007]);
however, in most other studies, this was applied to
“functional proteomics.” Two-dimensional gel electrophoresis with native gel electrophoresis as the first
dimension was used to determine a quantitative map
of soluble chloroplast proteins and their oligomeric
states in the stroma of Arabidopsis (Peltier et al., 2006).
In a subsequent study, Arabidopsis stromal proteins
were quantified using MS-based spectral counting
(Zybailov et al., 2008). Both complementary procedures were also carried out for chloroplast membranes
and stromal fractions of isolated bundle sheath and
mesophyll cells of maize leaves (Majeran et al., 2008).
The advantage of IEF-based two-dimensional gels lies
mostly in the higher resolution of IEF compared with
native gels; however, IEF gels systematically lead to
(often strong) underestimation of higher molecular
mass proteins and hydrophobic proteins, whereas
proteins with extreme pI (less than 4 or greater than
10) are harder to resolve. For the mapping of absolute
protein abundances, including membrane proteins,
colorless native or blue native gels are thus the better
alternatives.
Directly comparing image- and MS-based methodologies showed that image-based quantification is
very limited in the number of proteins that can be
accurately quantified, because protein spots need to be
fully separated from other spots to avoid quantifying
protein mixtures. Furthermore, the quantification is
significantly affected by the amino acid composition,
because current dyes bind in particular to basic residues, leading to overestimation or underestimation of
proteins, depending on the amino acid composition.
MS-based quantification allows for the quantification
of a much larger number of proteins, typically resulting in a higher dynamic range. However, highly
abundant proteins (e.g. the approximately 10–20
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most abundant proteins in a sample) are often underestimated because of the necessary use of datadependent acquisition (for numbers, see Zybailov et al.,
2008), whereas proteins quantified with low numbers
of MS/MS spectra can show quite large sample-tosample variation. In general, proteins are most accurately quantified if multiple unique peptides are
detected, each in high numbers. Conversion of protein
mass quantification (either by the image analysis- or
MS-based quantification) to protein concentration
requires normalization by either the number of predicted tryptic peptides within the relevant mass window (in the case of MS-based quantification) or by
protein length or mass (for both image- and MS-based
quantification). Despite the advantages described
above, two-dimensional PAGE still has a place in
quantitative proteomics, in particular for the analysis
of protein complexes and because it provides an
immediate visible overview of the proteome.
The “gold standard” for protein abundance measurements is to spike the sample with isotope-labeled
proteins or proteotypic peptides, assigned as “isotope
dilution” (Brun et al., 2009). These peptides can be
generated by in vitro synthesis or by expression as a
concatamer of proteotypic peptides after construction
of a synthetic gene, QconCAT. Both methods require
significant investments and typically are applied to
smaller numbers of proteins; therefore, these techniques are currently not practical for the quantification
of hundreds of proteins and have so far been applied
only to targeted analysis of selected plastid pathways
(Wienkoop et al., 2010). However, efforts are under
way to establish QconCAT to determine the stoichiometry of the Clp protease complex and for the
quantification of specific plastid (plant) metabolic
pathways or plastid processes.
Protein-Protein Interactions, Protein-Nucleotide
Assemblies, and Oligomeric State
To carry out their metabolic, structural, or signaling
functions, many plastid proteins form transient or
stable interactions with other proteins. Few undirected
systematic protein interaction studies have been carried out for soluble stromal complexes, either by
native gel electrophoresis (below 800 kD) or by chromatography (above 800 kD; Olinares et al., 2010); these
two complementary studies provide an overview of
the oligomeric state of more than 1,000 proteins. In
particular, protein assemblies larger than 800 kD are
dominated by functions in plastid gene expression,
including nucleoids, mRNA metabolism, and ribosomes. The interaction of plastid proteins with DNA
or RNA constitutes a regulatory network of gene
expression. The largest structures of several megadaltons are nucleoids also known as transcriptionally
active chromosome, which contains several copies of
plastid DNA and dozens of DNA- and RNA-binding
proteins, including proteins likely regulating nucleoid
activities through reduction/oxidation or phosphory-
lation (Pfalz et al., 2006). Envelope membrane-protein
complexes are dominated by the translocon complexes
at the inner and outer envelope membranes. These
import complexes are functionally relatively well
characterized by a variety of techniques, including
blue-native gels (Kikuchi et al., 2009, and refs. therein).
The abundant photosynthetic protein complexes in the
thylakoid membrane have been a target for biochemical research for several decades and are now well
characterized through a number of methodologies.
Most proteins in these complexes have been identified and characterized by MS, and for some of them,
PTMs have been determined by intact protein MS
(Whitelegge, 2004).
More detailed protein-protein interaction studies,
using either coimmunoprecipitation or affinity purification using transgenic plants that express tagged
transgenes, are needed to better characterize the plastid proteome interactome. This will help to better
understand in particular the regulation of metabolism
and plastid gene expression and to build reliable
protein interaction networks to complement the plastid proteome atlas.
Reversible and Irreversible PTMs
Most proteins undergo reversible and sometimes
irreversible modifications. Large-scale analysis of
PTMs, using a high-resolution, high-accuracy LTQOrbitrap mass spectrometer, was carried out for chloroplast membranes and stroma as well as total leaf
extracts, and the frequencies of many PTMs were
calculated (Zybailov et al., 2009). This analysis provides a framework for search parameters and the use
of retention times for improved assignment of PTMs in
large-scale proteomics and helps in distinguishing
artificial modifications from those with a biological
relevance. For nucleus-encoded plastid proteins, the
most typical irreversible in vivo modification is proteolytic cleavage of the N-terminal transit peptide, the
cTP. In the case of most plastid-encoded proteins,
typically the N-terminal Met is removed by methione
amino peptidases, which have been identified in plastids. Another frequent N-terminal modification that
occurs after the removal of N-terminal targeting information is N-terminal acetylation. Because N-terminal
acetylation requires in situ enzyme activity, it provides
a reliable determination of the N terminus and thus
valuable information about the processing site for
transit peptides of imported chloroplast proteins.
Thus, N-terminal acetylation allows mapping the in
vivo N termini of plastid and cytosolic proteins.
Kleffmann et al. (2007) established for a small set of
proteins from rice etioplasts the in vivo N terminus
and found that there is good agreement between the
detected N-terminal peptide and the predicted processing peptidase cleavage site. Similarly, Zybailov
et al. (2008) identified a larger set of N-terminal
acetylated proteins in Arabidopsis chloroplasts and
provided additional context information for the pro-
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Plastid Proteomics
cessing protease cleavage site, also indicating that the
predictive cleavage site is one residue off from the
actual cleavage site. Improvements for cleavage site
prediction should be possible based on the now available larger training set.
PTMs often determine enzymatic activities and rapidly adjust enzyme activity to the requirements of
cellular metabolism; protein abundance likely corresponds to maximal (theoretical) activity but is not
always a good indicator for in vivo enzyme activity
and its net contribution to cell metabolism. It is well
established that reversible phosphorylation and reduction/oxidation (e.g. through the action of different
types of plastid thioredoxins) are key regulators of
plastid metabolism as well as plastid gene expression
(Dietz and Pfannschmidt, 2010). Several proteomics studies identified thioredoxin targets by affinity
chromatography, whereas other redox proteomics approaches used diagonal electrophoresis under reducing and oxidizing conditions to identify proteins
under redox control in vivo (Dietz and Pfannschmidt,
2010). These analyses demonstrated that many chloroplast functions are regulated by thioredoxinmediated disulfide/dithiol exchange or by currently
unknown redox modulators. Among these functions
are isoprenoid and tetrapyrrole biosynthesis, starch
biosynthesis and degradation, gene expression, protein folding and degradation, and vitamin biosynthesis. Redox targets in the thylakoid lumen were
identified, and inhibition of the activity of the xanthophyll cycle enzyme violaxanthin deepoxidase by reduction (i.e. dithiol generation) was established (Dietz
and Pfannschmidt, 2010).
Over the last few years, two thylakoid-associated
kinases (STN7 and STN8) as well as a thylakoidassociated phosphatase (TAP38/PPH1) have been
identified, and their functions were investigated by
functional analysis of Arabidopsis mutants (Lemeille
and Rochaix, 2010). The reversible phosphorylation
system at the thylakoid membrane regulates photosynthetic state transitions to optimize light absorption
as well as long-term light adaptation. A total of 175
phosphorylated chloroplast proteins were identified,
with 80% Ser and 20% Thr phosphorylation but no Tyr
phosphorylation. One of the thylakoid kinases, STN7,
was found to be an abundant phosphoprotein in vivo,
suggesting the existence of kinase cascades in the
chloroplast. Information about the exact site of phosphorylation was used to extract kinase motifs that are
useful footprints for kinase activity in vivo (Reiland
et al., 2009). Cumulative evidence for plant proteome
phosphorylations are collected in various databases,
such as the PhosPhAt database for Arabidopsis
(http://phosphat.mpimp-golm.mpg.de/).
Subcellular Localization Predictions and
Network Information
The distribution of cellular functions to distinct cell
organelles is an important organization principle that
needs to be understood to model metabolic and protein interaction networks and to make predictions at
the systems scale. Thus, analyses of the protein composition of cell organelles were reported for virtually
all plant cell organelles or membranes (Baginsky, 2009;
Agrawal et al., 2010). At present, plant modeling and
systems analysis approaches with subcellular organelles suffer from incomplete proteome identification
and annotation. More complete organelle inventories
will strengthen modeling efforts, and higher network
consistencies should be obtained. In order to make a
contribution to model quality, however, protein localization data should have low false positive rates (e.g.
below 1%). Therefore, conservative assignment of
protein subcellular localization in papers and public
databases is better than overassignment of proteins,
particularly since it is not really possible to associate a
P value for subcellular localization assignment based
on experimental data. Thus, the community’s goal
should be a plastid proteome atlas with high sensitivity and a very low false positive rate.
In addition to the experimental organelle proteome
analysis, subcellular localization prediction is a possible source of information for “missing” plastid proteins, even if suboptimal. The generation of software
routines to predict subcellular protein localization for
plants, other eukaryotes, as well as prokaryotes has
been in progress for well over a decade, in particular
inspired by the increasing amount of protein inventories for different subcellular localizations. These inventories provide essential training and test sets.
Whereas the prediction of N-terminal signal peptides
for signal recognition particle-dependent targeting to
the endoplasmic reticulum is rather accurate and
sensitive, prediction of plastid localization is much
less satisfactory and still attracts considerable attention. A consensus prediction combining several predictors using a naive Bayes method was suggested to
improve both sensitivity and specificity for plastid and
mitochondrial proteins (Schwacke et al., 2007). In the
last 2 to 3 years, several new localization predictors (e.
g. AtSubP, Subchlo, RSLpred, MultiP, Plant-mPLoc)
were published for plants, mostly focusing on Arabidopsis. While each predictor may have advantages
over the others, it is not clear that their prediction has a
better true positive discovery rate for plastid proteins
(i.e. a higher sensitivity) at a lower false positive
discovery rate (i.e. a better specificity) than the most
popular predictor, TargetP (http://www.cbs.dtu.dk/
services/TargetP/).
TargetP is still the most commonly used predictor
for plastid as well as plant mitochondrial localization
that not only predicts localization but also the cTP and
mTP cleavage sites. There is still some controversy
regarding the true positive prediction rate of TargetP,
which was found to differ between experimental data
sets. While plastid proteome studies from the van Wijk
laboratory and others reported true positive prediction
rates in the range of 85%, consistent with the benchmark tests obtained during TargetP training, other
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van Wijk and Baginsky
groups found much lower prediction rates on their
plastid protein set (Armbruster et al., 2011). Higher
TargetP true positive rates (sensitivity) are usually
observed when proteins were eliminated, not repeatedly detected in plastid preparations, while also
applying conservative thresholds for protein identification (see below). Importantly, sets of detected lowabundance Arabidopsis proteins (several orders of
magnitude lower than Rubisco large subunit; e.g.
those involved in RNA metabolism) have similar true
positive prediction rates as high-abundance proteins
(Olinares et al., 2010). However, proteins located in the
outer plastid envelope membrane or those reversibly
associated with the outer envelope should be excluded
from such prediction analysis because they do not
possess a cleavable N-terminal plastid-targeting sequence. The main shortcoming of TargetP is the high
false positive rate (low accuracy), likely around 35%,
leading to an overprediction for plastid proteins.
The current sensitivity and accuracy of TargetP are
clearly not perfect, and the much larger sets of established subcellular proteomes for Arabidopsis (and to a
lesser degree also maize and rice) should be useful to
improve the performance of plastid localization predictors. In addition, it is quite likely that a subset of
nucleus-encoded plastid proteins have atypical targeting information. For instance, it has been shown for a
few plastid proteins that they are targeted to the
plastid via the endoplasmic reticulum and that the N
terminus of these precursor proteins contains a secretory signal peptide, followed by a cTP (Villarejo et al.,
2005). However, scanning for signal peptides of approximately 1,000 established plastid proteins in Arabidopsis suggested that probably very few proteins take
this route (Zybailov et al., 2008). It is possible that there
is yet another pathway (or recognition system) for
protein translocation across the envelope that accounts
for the imperfect true positive rate; the recent finding
of an envelope-localized SEC system may be relevant
here (Skalitzky et al., 2011). Finally, it may be optimal
to develop and test localization software for specific
species, plant families, or even clades. For instance,
monocotyledons such as rice, sorghum (Sorghum bicolor), and maize may have systematically different
protein targeting information as compared with dicotyledons such as Arabidopsis, tobacco, pea, and spinach. Indeed, systematic analyses of established rice
plastid proteins as well as rice orthologs for Arabidopsis chloroplast proteins showed that Ala instead of
Ser or Thr is overrepresented in the cTP (Kleffmann
et al., 2007; Zybailov et al., 2008).
With detailed information about the enzymatic inventory of organelles, their specific contribution to
metabolism and signaling is also accessible to largescale modeling approaches. Genome-scale metabolic
networks for the C3 and C4 plants Arabidopsis and
maize, respectively, as well as the green algae Chlamydomonas reinhardtii were constructed that take into
account compartmentalization and allow assessment
of the specific contribution of cell organelles to me-
tabolism (Dal’Molin et al., 2010). Large-scale proteinprotein interaction networks also benefit significantly
from knowledge about the colocalization of proteins in
the same organelle. This information decreases false
discovery rates in large-scale interaction data sets for
Arabidopsis, thereby increasing the reliability of predicted interaction networks. Progress has been made
for the assembly of plant organellar phosphorylation
networks and for chloroplasts, in particular the (de)
phosphorylation-driven movement of light-harvesting
complexes in the thylakoid membrane (assigned state
transitions; Lemeille and Rochaix, 2010). Studies in
nonplant species have shown that, using phosphoproteomics information, it is possible to infer in vivo
kinase activities from phosphorylation motifs to provide information about kinase/substrate relationships
and, together with localization information, construct
in vivo phosphorylation networks. Thus, protein inventories of cell organelles are important constraints in
constructing signal transduction networks. Last but
not least, publicly available and reliable protein subcellular localization will be helpful and cost effective
in the functional analysis of genes and proteins as the
need to determine the localization for each protein is
fulfilled.
EMPLOYING THE PLASTID PROTEOME ATLAS FOR
FUNCTIONAL ANALYSIS AND SYSTEMS BIOLOGY
Even if the plastid protein atlas is not complete, it
does provide a rich source of information and a great
tool for detailed functional studies. Table I lists the
available proteomics resources with relevance to plastid biology, and Box 1 provides a number of example
questions that can be addressed with the available
tools. Now that the subcellular localization of many
proteins is known, it is possible to analyze the qualitative and quantitative effects of mutations of specific
organelles without actually purifying these organelles.
For instance, quantitative comparative proteome analysis of chloroplasts from wild-type and different
chloroplast Clp protease mutants was done using
MS-based quantification of total Arabidopsis leaf extracts without actually isolating chloroplasts (Kim
et al., 2009). The advantages of characterizing quantitative effects on the chloroplast proteome through
analysis of total leaf extracts, rather than through
analysis of isolated chloroplasts, are that (1) mutants
with strong growth defects can be analyzed, because
isolation of chloroplast from such mutants can be very
hard or even practically impossible; and (2) more
accurate results are obtained for chloroplast mutants
with heterogeneity in their leaf phenotype (often with
strongest phenotypes in the youngest leaves), because
isolation of chloroplasts from such leaves could result
in selection of a subset of chloroplast phenotypes not
representing the overall chloroplast population. Furthermore, such subcellular proteome information for
maize allowed others to help resolve the kinetics of
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Plastid Proteomics
organelle biogenesis, the formation of cellular structures, and metabolism during maize leaf development
and C4 cellular differentiation (Majeran et al., 2010).
The current generation of mass spectrometers have
sufficient sensitivity and throughput to detect and
quantify a high number of chloroplast proteins even in
complex mixtures. Furthermore, such a “total leaf”
approach can be helpful for analyses of dynamic PTMs
that prevent lengthy organelle isolation procedures
(Reiland et al., 2009), in particular if no inhibitors can
be applied to prevent change in such PTMs. With a
plastid protein atlas for Arabidopsis and maize at
hand, it can be expected that large-scale comparisons
of chloroplast proteomes, their PTMs, and interaction
networks under different conditions and in different
genetic backgrounds or developmental states will
provide novel insights into plastid biology.
DEPOSITION OF PROTEOMICS AND MS
INFORMATION IN PUBIC REPOSITORIES
Most published plastid proteomics studies of Arabidopsis provide tables containing lists of the identified
proteins using standardized, nonredundant accession
numbers provided through The Arabidopsis Information Resource (TAIR). For other plant species, this is
more varied either because there is no sequenced
genome or significant EST available or because databases are searched, such as the National Center for
Biotechnology Information, that contain redundant
sets of accessions (e.g. older and newer versions of
genes); this can complicate the incorporation of such
data sets by other laboratories. However, submission
of the underlying mass spectra with associated metadata to public repositories such as the Proteomics
Identifications Database (PRIDE; http://www.ebi.ac.
uk/pride) will allow other laboratories to make use of
these studies. And even for Arabidopsis and other
new model (crop) species such as maize and rice, it is
important that the mass spectral data be deposited, for
instance to help improve search engines, improve
genome annotation, or allow for comparative analysis
by other laboratories. Indeed, several journals (e.g.
Molecular and Cellular Proteomics and Nature Biotechnology) now require the submission of mass spectral
data to such public repositories, as is customary for
microarray data or RNAseq data sets. Further more
detailed descriptions of experimental conditions and
acquisition parameters are outlined in the Minimum
Information About a Proteomics Experiment descriptions and enforced by several journals. We strongly
support following these standards and the deposition
of mass spectral data (e.g. converted MGF files) into
PRIDE or other repositories.
CONCLUSION
Proteomics of chloroplasts and other plastid types
has provided extensive protein inventories as well as
information about PTMs, protein abundances, and
protein interactions. Proteomics and MS technologies
feeding into plastid proteome information now allow
system-level analysis of chloroplast biology, including
chloroplast development, signaling, and interaction
networks. For the reasons detailed above, we consider
a high-quality plastid proteome atlas a milestone in
the quest for biologically meaningful systems biology
approaches. Together with parallel efforts for other
organelles (e.g. mitochondria and peroxisomes), this
will help to drive a better understanding of plant
growth and development and help to realize the
potential of plant systems biology.
ACKNOWLEDGMENTS
We thank the members of our laboratories for discussions and feedback on
the manuscript. Furthermore, we sincerely apologize to all colleagues whose
work could not be cited because of space constraints.
Received January 20, 2011; accepted February 21, 2011; published February
24, 2011.
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