Skip to main content
NCBI home page
Plant Physiology logoLink to Plant Physiology
. 2012 Oct 1;160(4):2189–2201. doi: 10.1104/pp.112.204032

Common and Specific Protein Accumulation Patterns in Different Albino/Pale-Green Mutants Reveals Regulon Organization at the Proteome Level1,[W]

Reiko Motohashi 1, Anja Rödiger 1, Birgit Agne 1, Katja Baerenfaller 1, Sacha Baginsky 1,*
PMCID: PMC3510140  PMID: 23027667

Abstract

Research interest in proteomics is increasingly shifting toward the reverse genetic characterization of gene function at the proteome level. In plants, several distinct gene defects perturb photosynthetic capacity, resulting in the loss of chlorophyll and an albino or pale-green phenotype. Because photosynthesis is interconnected with the entire plant metabolism and its regulation, all albino plants share common characteristics that are determined by the switch from autotrophic to heterotrophic growth. Reverse genetic characterizations of such plants often cannot distinguish between specific consequences of a gene defect from generic effects in response to perturbations in photosynthetic capacity. Here, we set out to define common and specific features of protein accumulation in three different albino/pale-green plant lines. Using quantitative proteomics, we report a common molecular phenotype that connects the loss of photosynthetic capacity with other chloroplast and cellular functions, such as protein folding and stability, plastid protein import, and the expression of stress-related genes. Surprisingly, we do not find significant differences in the expression of key transcriptional regulators, suggesting that substantial regulation occurs at the posttranscriptional level. We examine the influence of different normalization schemes on the quantitative proteomics data and report all identified proteins along with their fold changes and P values in albino plants in comparison with the wild type. Our analysis provides initial guidance for the distinction between general and specific adaptations of the proteome in photosynthesis-impaired plants.

Biological systems are designed in a modular fashion, and it is one goal of systems biology to unravel the design of such modules and understand their robustness and their interconnection. The modular design includes processes at the metabolic and regulatory levels, with so-called “regulons” constituting one design principle in this hierarchy. Regulons are defined as regulatory entities that constitute sets of genes that are coregulated under a variety of different conditions, per definition by a common transcriptional regulator (Shen-Orr et al., 2002). Knowledge about the members of such regulons is very useful because it allows predicting the gene expression response for many genes to a number of different stimuli. The identification of regulons relies on their common transcriptional regulation, but it is unclear how far regulons are reflected at the proteome level, because protein accumulation cannot be inferred directly from transcript levels. Large-scale quantitative proteomics is now sufficiently advanced to allow insights into common regulatory processes that constitute quantitative changes in the proteome. Although technical constraints still result in incomplete data sets and a limited number of replicates and different test conditions, solutions for these constraints were developed, and proteomics is now providing a new perspective for the analysis of regulatory units (Vogel and Marcotte, 2012).

In plant cells, many cellular processes are directly or indirectly connected to photosynthesis. Plants have developed distinct mechanisms to respond to perturbations in photosynthetic performance both for situations with too much and too little light (Rochaix, 2011). Similarly, mutations in several different proteins result in an albino/pale-green (APG) phenotype, in which photosynthetic functions are perturbed (Myouga et al., 2010). In such cases, it is difficult to distinguish direct consequences of the gene defect from indirect consequences caused by an impairment of photosynthesis, complicating the interpretation of data from a molecular characterization of such mutants. In a study using 101 different plant growth conditions and distinct genetic perturbations, 13 different regulons were defined for nucleus-encoded chloroplast proteins, and a master switch was postulated that controls the expression of sets of nucleus-encoded plastid proteins (Richly et al., 2003; Biehl et al., 2005). These data were acquired at the transcriptional level, and it is currently unclear whether the regulatory pattern is also reflected at the protein level. Knowledge about covarying functions at the proteome level is very useful because it allows associating the regulation of individual genes at different levels (transcriptional, posttranscriptional, and posttranslational) with that of a group of genes. This way, the interpretation of protein expression data for the characterization of mutant plants increases in accuracy, which is especially relevant for the interpretation of molecular data from mutants with an albino or pale-green phenotype.

At present, a differentiated view on the general adaptations of the proteome to the loss of photosynthetic capacity is missing. The expression of genes for photosynthetic proteins is reduced in photosynthesis-impaired plants, which is a result of the coordinated regulation of gene expression in the nuclear and the plastid genomes (Lopez-Juez and Pyke, 2005). Gene expression in the two genetic systems is coordinated by a number of different regulatory circuits that communicate the developmental and biochemical status of the plastid to the nucleus (Pesaresi et al., 2007; Woodson and Chory, 2008). Several mutants were identified that lost the connection between the two genetic systems and therefore were termed genome uncoupled (gun). The characterization of the gun mutants supports regulatory switches that determine the accumulation of sets of proteins by plastid-to-nucleus (retrograde) and nucleus-to-plastid (anterograde) signaling (Mochizuki et al., 2008; Woodson and Chory, 2008).

While the transcriptional regulation of plastid protein expression and the nature of retrograde signaling components are well documented, less is known about the regulation of protein accumulation. The importance of this type of information can be exemplified by the characterization of the plastid protein import2 (ppi2) mutant, which has a defect in Toc159, one important receptor of the plastid protein import machinery (Bauer et al., 2000). The ppi2 plants develop an APG phenotype that results in their inability to sustain an autotrophic metabolism (Agne and Kessler, 2009; Schleiff and Becker, 2011). Because Toc159 is a protein import receptor, a naive interpretation of the phenotype would predict that it is caused by an impairment of photosynthesis due to a defect in the import of proteins for photosynthetic functions. However, the transcript profile of the ppi2 mutant alone largely explains the APG phenotype, because the transcript levels for many nucleus-encoded plastid proteins including those involved in photosynthesis are down-regulated (Bischof et al., 2011). This transcriptional response to impaired plastid functions is partially reverted in the gun1/ppi2 double mutant, arguing for a retrograde signaling pathway that coordinates the expression of groups of plastid genes with the functional status of the chloroplast that operates via Gun1 (Kakizaki et al., 2009).

The perturbation of photosynthesis in APG mutants interferes with primary energy metabolism and probably causes common proteome adaptations that are independent of the molecular process affected by the mutation (Richly et al., 2003; Ruckle et al., 2012). To provide information about coregulation at the proteome level, we set out to distinguish common from specific protein accumulation in three selected apg mutants from a collection of activator (Ac)/dissociation (Ds) transposon-tagged lines that was generated at RIKEN (Myouga et al., 2010). We have chosen the apg1, apg2, and apg3 lines because they are well characterized and the functional basis for their defect resides in proteins that operate in different chloroplast functions.

RESULTS AND DISCUSSION

Quantitative Comparison of Different APG Mutant Plant Proteomes

For our comparative proteome analysis, we selected three different plant lines from the collection of APG mutants at RIKEN (Myouga et al., 2010; Table I). The apg1 plant has a defect in an 2-methyl-6-phytyl-1,4-benzoquinone/2-methyl-6-solanyl-1,4-benzoquinone (MPBQ/MSBQ) methyltransferase that has S-adenosyl-Met-dependent methylation activity and is involved in plastoquinone and tocopherol biosynthesis (Motohashi et al., 2003). The apg2 line has a defect in the Twin Arginine Translocon component C (TatC) that is essential for the import of proteins into the thylakoid lumen via the Tat pathway (Motohashi et al., 2001; Albiniak et al., 2012). The apg3 mutant has a defect in the peptide chain release factor that is required for the termination of translation at UAG/UAA stop codons (Motohashi et al., 2007). Thus, the defective genes are involved in different chloroplast functions (Table I). As expected for APG mutants, there is a defect in photosynthesis resulting in the loss of chlorophyll and other pigments and a substantially reduced maximum quantum yield at PSII. While apg1 and apg3 mutant plants show residual photosynthetic yield, apg2 is the most severely affected plant, with no PSII fluorescence detectable (Table I; Fig. 1), which is also visible as the complete absence of any thylakoid-like structures in apg2. With these plants, we performed high-throughput proteomics analyses in three biological replicates using normalized spectral counting for protein quantification. Altogether, 2,229, 2,566, 2,562, and 1,660 proteins were identified from apg1, apg2, apg3, and the wild type, respectively (Fig. 1; Supplemental Table S1).

Table I. Characteristics of the apg mutants employed for this study.

Mutant Line Identifier Protein Annotation Function Yield (PSII) Reference
apg1 At3g63410 MPBQ/MSBQ methyltransferase Methylation, plastoquinone, and vitamin E biosynthesis 0.06 ± 0.02 Motohashi et al. (2003)
apg2 At2g01110 Translocon TatC Translocase in the thylakoid membrane, transport of lumenal proteins 0 Motohashi et al. (2001)
apg3 At3g62910 Peptide chain release factor1 Protein biosynthesis, translation 0.09 ± 0.01 Motohashi et al. (2007)
Open in a new tab

Figure 1.

Figure 1.

Open in a new tab

Characterization of apg mutant plants in comparison with wild-type (WT) plants of the same age. A, Photograph of the three apg mutant plant lines used for the proteome analysis in comparison with the wild type. B, Numbers of identified proteins from the apg mutants and from the wild type. Protein identification was based on liquid chromatography-tandem mass spectrometry analyses from three independent biological replicates for all four plant lines.

The overall number of identified spectra and the number of identified peptides are similar between the samples (Supplemental Table S1). Comparing the number of proteins, peptides, and spectra identifying chloroplast proteins from the different data sets (Reiland et al., 2009), we find a smaller number of identified proteins that were identified with a higher number of spectra in the wild-type plant material (Fig. 2A; Supplemental Table S1). This demonstrates that the higher detection rate of proteins in the albino mutant plants is owed to the favorable dynamic range of the albino plant proteome that is not dominated by highly abundant photosynthetic proteins. We calculated protein abundance values for proteins that were detected with at least 10 peptide spectrum matches (PSMs) using normalized spectral count quantification (Baerenfaller et al., 2008). Comparison of the quantitative protein accumulation revealed that the major differences between the wild-type and albino proteomes are determined by chloroplast proteins (Fig. 2B). The correlation coefficient between the abundances of chloroplast proteins reaches as low as 0.4, while the lowest correlation between “other” proteins is around 0.62. The greatest difference (i.e. the lowest correlation between the chloroplast proteins from the wild type and albino mutants) is observed with the apg2 data set, which corresponds to the finding that the apg2 mutant is the albino plant that is most significantly impaired in photosynthesis (Table I). This supports the starting hypothesis of this work, that the ability to perform photosynthesis largely determines the accumulation of plastid proteins.

Figure 2.

Figure 2.

Open in a new tab

Protein identification and quantitative protein accumulation in different cell organelles. A, The relative distribution of spectra identifying proteins from different organelles (as indicated) is presented as percentage values. B, Correlation matrix showing the Spearman rank correlation coefficients between the different proteome data sets obtained with the wild type (WT) and the three apg mutants. The plastid proteome and the proteome of “other” cell organelles were analyzed separately.

The basic assumption in normalization procedures is that the abundance of the majority of proteins is not different between two samples. However, it is conceivable that a proteome analysis using complete cellular proteins may result in the underrepresentation of spectra from chloroplast proteins in albino plants compared with the wild type, because photosynthetic proteins make up more mass of the entire proteome in the wild type than they do in albinos (Friso et al., 2011). We find a higher number of spectra identifying chloroplast proteins in the wild type, but the differences are rather small (Fig. 2A). In order to test whether this difference creates a bias for quantification, we applied a different normalization scheme for protein quantification using only spectra identifying chloroplast proteins. We tested its effect on the statistical evaluation of the data by comparing the P values from a two-sided Student’s t test and the fold changes obtained with the two alternative normalization schemes. We found that the differences are minor and that there is only a slight increase in the number of P values below a 0.05 threshold when only chloroplast proteins are used for normalization (Supplemental Table S2). This is most probably a result of the dampening effect exerted by the greater number of unchanged proteins that is taken into account if the normalization is based on all proteins. Thus, our data are largely unaffected by the normalization scheme. However, including more unchanging proteins in the normalization makes the procedure more robust, because this is consistent with the underlying basic assumption (see above). Furthermore, restricting the normalization to a selected subset of proteins creates a dependency between the normalized abundance values and the protein selection, which hinders comparisons of the results between studies using different selections. Therefore, we conclude that a restricted normalization to chloroplast proteins is not useful for the data set we acquired here.

The Common Molecular Phenotype: Stress-Related Gene Expression, Down-Regulation of Photosynthetic Proteins, and Up-Regulation of Proteins Involved in Plastid Protein Import

Comparing the quantitative protein accumulation of proteins identified with at least 10 spectra in the three mutant plant lines with the wild type, we find a significant number of proteins that show the same trend in all three different mutants despite the different plastid functions that are affected by the gene defects. Together, 315 proteins identified with at least 10 PSMs were identified in all three apg mutants but not in the wild type (Supplemental Table S3). Quantitative differences were detected for 76 proteins with more than 10-fold and 175 proteins with more than 5-fold higher normalized spectral counting (nSpC) values in all three apg plants compared with the wild type. A list of all identified proteins with the same trend of accumulation is presented in Supplemental Table S3. A visual presentation of the common molecular phenotype concerning metabolism is presented in Figure 3 (based on MapMan [Thimm et al., 2004]). Proteins involved in photosynthesis, chlorophyll synthesis, primary ammonium assimilation, and a few other enzymes accumulate to higher levels in the wild type, while most other functions, especially proteins of energy metabolism, accumulate to higher levels in the apg mutants (Fig. 3; Supplemental Table S3). There is a remarkably increased accumulation of proteins involved in protein degradation in the apg mutants, which is probably caused by a general stress response. Protein folding stress can also be inferred from the increased accumulation of numerous proteins that are responsive to heat stress in the mutants (Supplemental Fig. S1). The functional categorization of proteins that accumulate to higher levels in apg mutants supports the bias for stress-induced proteins (Fig. 4).

Figure 3.

Figure 3.

Open in a new tab

MapMan overview of the set of proteins with a function in metabolism that show identical accumulation characteristics in all three apg mutants compared with the wild type. For this mapping, proteins were considered that (1) accumulate to at least five times higher levels in the mutants compared with the wild type, (2) were only identified in all three mutants, (3) accumulate to at least two times lower levels in the mutants compared with the wild type, or (4) were only identified in the wild type. We calculated a mean fold-change value from the data and calculated log2 values from these mean regulation factors. This set of proteins comprises 638 proteins and was used for the mapping in the category “metabolism.” Red indicates higher in the mutants, and green indicates higher in the wild type. CHO, Carbohydrate; OPP, oxidative pentose phosphate; TCA, tricarboxylic acid.

Figure 4.

Figure 4.

Open in a new tab

Functional categorization of the set of proteins with identical accumulation characteristics in all three apg mutants compared with the wild type. Proteins for “other” cell organelles (A) and plastid proteins (B) were analyzed separately with TopGo using the elim method (Alexa et al., 2006). Provided are the functional categories (x axis) that are significantly overrepresented along with a measure for the P value for overrepresentation [log (1/P value)]. Higher y axis values indicate higher confidence. 10× and 5× Up indicate 10-fold and 5-fold higher abundance in all three mutants compared with the wild type; 2× Down indicates 2-fold lower abundance in all three apg mutants compared with the wild type.

As far as chloroplast proteins are concerned, we find 123 plastid proteins that accumulate to higher levels in albino mutants, among them 18 by a factor of more than 10-fold and 49 by a factor of more than 5-fold, while 56 plastid proteins were identified in all three apg mutants but not in the wild type. These proteins are enriched in the Gene Ontology (GO) terms “amino acid metabolic process,” “biosynthetic process,” and “protein targeting to chloroplasts” (Fig. 4). Among the latter set of proteins are Toc159, Toc33, Toc75, Toc64, Tic110, and Tic55 (Table II; Fig. 5A). We verified the increased accumulation of Toc75, Tic110, and Tic40 in the apg mutants by immunoblotting in comparison with the largely unchanged OEC33 and FNR (Fig. 5B). Tic21 and Toc34 were also detected, but their accumulation was only higher in apg2 and apg3 compared with the wild type but not in apg1. This could suggest that the plant cell responds to different perturbations of plastid metabolism with an up-regulation of plastid protein import capacity (Fig. 5A). Alternatively, the higher nSpC values for Toc and Tic components may indicate that the plastid envelope membrane constitutes a higher fraction of the proteome in apg plants compared with the wild type. To distinguish between these possibilities, we analyzed the general accumulation of envelope membrane proteins using the AT_Chloro envelope data set as a benchmark (Ferro et al., 2010). Fifty-three envelope proteins were identified with at least 10 spectra and considered for quantitative comparison. The mean nSpC values of all but four envelope proteins are higher in apg mutants, but their increase in abundance is lower compared with the increase of the import machinery (Supplemental Tables S1 and S4). On the one hand, this supports the hypothesis that the envelope membrane proteome has a higher relative abundance in apg mutants compared with the wild type, but on the other hand, it shows that the increased accumulation of the import machinery is, at least in part, a specific response to the loss of photosynthetic capacity (Fig. 5C).

Table II. Set of proteins that are specific for the individual apg lines.

Provided are the total number of identified spectra as well as the nSpC values as a measure for protein abundance. Each presented nSpC value is the mean value from the results of three independent biological replicates. Proteins marked with asterisks are also present in the transcriptional network of the respective mutants (top 100 coexpressed [ATTED-II]).

Identifier Annotation σ Spectra apg1 apg2 apg3 Wild Type
APG1 specific
 AT3G16140 PSAH-1 24 0.85 0 0 0.22
 AT1G31190 Inositol monophosphatase 19 0.06 0.13 0.14 0.13
 AT2G15290* TIC21 21 0.10 0.30 0.37 0.22
 AT5G16150 Glc transporter1 11 0.00 0.08 0.17 0.04
 AT3G63410 APG1 178 0.08 1.72 1.68 0.77
 AT5G05000 TOC34 25 0.04 0.20 0.30 0.08
 AT5G35790 Glc-6-P dehydrogenase1 13 0.00 0.03 0.18 0.01
 AT5G24400 EMB2024 14 0.00 0.15 0.17 0.05
 AT5G12860* Dicarboxylate transporter1 56 0.06 0.54 0.61 0.15
 AT5G56500 Chaperonin60 21 0.01 0.13 0.14 0.03
 AT1G52230 PSAH-2 10 1.29 0.00 0.00 0.00
 AT1G16720 High-chlorophyll fluorescence173 39 0.45 0.14 0.08 0.15
 AT1G50250 FTSH1 40 0.58 0.11 0.03 0.17
 ATCG00790 Ribosomal protein L16 36 2.26 1.07 0.50 0.22
 AT5G44650* Unnamed protein product 33 0.90 0.31 0.20 0.34
 AT2G30950 Yellow variegated2 122 1.55 0.32 0.37 0.63
 AT1G44575 Non-photochemical quenching4 261 7.50 0.30 3.75 3.84
 ATCG00120 ATPase α-subunit 1,618 21.89 7.15 3.53 19.53
APG2 specific
 ATCG00500 Acetyl-CoA carboxylase β 13 0.02 0.19 0.02 0.04
 AT3G02660 EMB2768 11 0.00 0.13 0.02 0.02
 AT4G02530 Thylakoid lumen protein 23 0.35 0.05 0.26 0.14
 AT5G47110 Lil3 protein 20 0.25 0.08 0.24 0.16
 AT4G27440 NADPH: protochlorophyllide oxidoreductase B 37 0.23 0.07 0.33 0.20
 AT4G21280 PSBQ 115 0.46 0 0.67 1.19
 ATCG00020 PSBA 745 3.04 0 0.47 26.54
 AT2G20260 PSAE-2 27 0.11 0 0.46 0.90
 AT3G47470 LHCA4 233 1.60 0 0.42 4.47
 ATCG00340 PSAA 394 0.73 0 0.32 6.97
 AT4G28750 PSAE-1 30 0.12 0 0.24 0.67
 AT1G61520 LHCA3 90 0.89 0 0.18 1.51
 ATCG00270 PSBD 1,432 3.90 0 0.16 45.45
 AT5G23060 Ca2+-sensor protein 226 0.46 0 0.15 2.99
 AT3G16000 MAR-binding filament-like protein1 48 0.20 0 0.11 0.17
 AT5G17170 Enhancer of SOS3-1 13 0.06 0 0.08 0.16
 AT4G12800 PSAL 99 0.65 0 0.06 3.43
 AT1G03600 PSII protein 23 0.09 0 0.05 0.58
 AT4G02770 PSAD-1 19 0.23 0 0.05 0.33
 AT1G15820 LHCB6 98 0.60 0 0.05 2.28
 AT5G37360* Unnamed protein 12 0.03 0 0.04 0.29
 AT3G08940 LHCB4.2 42 0.10 0 0.02 0.70
 AT1G56500* Haloacid dehalogenase 28 0.01 0 0.01 0.18
 AT3G16290 EMB2083 15 0.00 0.20 0.10 0.00
 AT3G06730 Thioredoxin 13 0.08 0.89 0.44 0.00
 AT1G80560 3-Isopropylmalate dehydrogenase 25 0.23 0.60 0.27 0.00
 AT5G10920 Argininosuccinate lyase 10 0.00 0.27 0.11 0.00
 AT4G16390 RNA-binding protein P67 53 0.28 0.77 0.31 0.00
 AT3G48500 TAC10 12 0.07 0.19 0.05 0.00
 ATCG01280 Hypothetical protein 38 0.01 0.26 0.06 0.00
 ATCG00860 Unknown protein (regulated by AtSIG6) 38 0.01 0.26 0.06 0.00
 ATCG00170 RNA polymerase β′-subunit2 69 0.22 0.59 0.11 0.00
 AT5G08650* GTP-binding protein LepA 12 0.10 0.21 0.03 0.00
 AT5G64580 AAA-type ATPase 13 0.02 0.22 0.02 0.00
 ATCG00180 RNA polymerase β′-subunit1 12 0.06 0.17 0.03 0.01
 AT5G19620 Outer envelope protein80 25 0.11 0.26 0.10 0.02
 AT4G38460 Geranylgeranyl reductase 14 0.05 0.55 0.10 0.03
 AT1G21440 Mutase 18 0.10 0.74 0.26 0.03
 AT4G13670 PTAC5 17 0.17 0.47 0.16 0.03
 AT5G51070 Early response to dehydration1 17 0.05 0.15 0.07 0.03
 AT3G08640 Unknown protein 10 0.11 0.31 0.14 0.04
 AT5G23890 Unknown protein 63 0.24 0.60 0.29 0.09
 AT5G28500 Unknown protein 53 0.30 0.94 0.49 0.24
 AT1G77490 l-Ascorbate peroxidase 20 0.04 0.18 0.04 0.36
 AT3G63160 Unknown protein 13 0.20 1.18 0.49 0.69
APG3 specific
 AT5G49030* Ovule abortion2 23 0.02 0.01 0.10 0.03
 AT5G13490 ADP/ATP carrier2 11 0.02 0.02 0.17 0.04
 AT1G67090 RBCS1A 638 6.56 6.29 2.13 5.34
 ATCG00720 Cytochrome b6 subunit 224 1.98 0.30 0 7.38
 AT1G68590 30S ribosomal protein 22 0.27 0.21 0 0.38
 AT3G58010 Plastoglobulin 34kD 33 0.14 0.19 0 0.44
 AT2G30790 PSBP-2 11 0.12 0.15 0 0.42
 AT3G15520 Cyclophilin-like peptidyl-prolyl cis-trans isomerase 12 0.02 0.10 0 0.12
 AT5G07020 Pro-rich protein 26 0.20 0.09 0 0.67
 ATCG00540 Cytochrome f apoprotein 353 1.36 0.03 0 5.56
 ATCG00490 RBCL 18,804 216.89 146.83 18.68 222.56
 AT3G17970 ATTOC64-III 12 0.06 0.03 0.32 0.00
 AT1G36390 Cochaperone grpE 11 0.00 0.17 0.44 0.00
 AT4G14070 Acyl-activating enzyme15 11 0.07 0.03 0.15 0.02
 AT1G58290 Glutamyl-tRNA reductase 20 0.09 0.13 0.40 0.02
 AT1G77590 Long chain acyl-CoA synthetase9 12 0.06 0.04 0.29 0.02
 AT5G35790 Glc-6-P dehydrogenase1 13 0.00 0.07 0.33 0.02
 AT4G04770 Nucleosome assembly protein1 20 0.08 0.13 0.41 0.02
 AT4G30920 Aminopeptidase 30 0.14 0.19 0.49 0.04
 AT3G04870 Carotene 7.8-desaturase 22 0.06 0.19 0.45 0.06
 AT2G41680* NADPH-dependent thioredoxin reductase 44 0.20 0.26 0.95 0.26
 AT5G45170 Haloacid dehalogenase-like hydrolase 25 0.11 0.05 0.46 0.32
 AT3G10670 Non-intrinsic ATP-binding cassette protein7 40 0.31 0.35 0.87 0.33
 AT2G20260 PSAE-2 27 0.22 0.00 0.83 1.61
Open in a new tab

Figure 5.

Figure 5.

Open in a new tab

Identification of proteins from the envelope membrane system. A, Hierarchical clustering of the mean nSpC values for known components of the plastid protein import machinery (Agne and Kessler, 2009). B, Immunoblot analysis of protein accumulation using antibodies for the indicated proteins in the apg mutants and in the wild type (WT). C, Box plot with the mean nSpC values obtained for all identified proteins from the chloroplast envelope membrane system as described in AT_Chloro (Ferro et al., 2010; designated as E) and all proteins from the import machinery (designated as I). Altogether, 53 proteins were identified with clear envelope localization and considered for the data presented as E (Ferro et al., 2010). The proteins of the import machinery in A were not considered for E.

Plastid gene expression is an important housekeeping function; therefore, we analyzed the accumulation of proteins from the plastid gene expression system. Plastid-encoded proteins cluster in two different clusters, with proteins that accumulate to higher levels in the apg mutants and with proteins that accumulate to higher levels in the wild type. The latter category is restricted to proteins involved in photosynthesis, while the former group of proteins contains mostly proteins from the plastid gene expression system (Fig. 6). Among these are ribosomal proteins and the subunits of the plastid-encoded RNA polymerase (PEP). Their higher accumulation in nonphotosynthetic plastids is somewhat surprising, because PEP is the polymerase mainly responsible for photosynthetic gene expression (Liere et al., 2011). The molecular phenotype observed here is consistent with a situation in which PEP transcription is impaired (e.g. similar to the molecular pattern observed in mutants depleted in subunit 3 of the transcriptionally active chromosome [TAC3]; Yagi et al., 2012). However, most of the subunits of the TAC, including TAC3, accumulate to higher levels in apg mutants (Reiss and Link, 1985; Pfalz et al., 2006; Schröter et al., 2010; Fig. 7).

Figure 6.

Figure 6.

Open in a new tab

Abundance of chloroplast-encoded proteins. Hierarchical clustering of the mean nSpC values for plastid-encoded proteins reveals two main clusters. Cluster I contains proteins from the genetic system that accumulate to higher levels in the apg mutants, and cluster II contains mostly photosynthetic proteins that accumulate to higher levels in the wild type (WT).

Figure 7.

Figure 7.

Open in a new tab

Accumulation of the subunits of the PEP and the TAC. Hierarchical clustering of the mean nSpC values is shown for proteins of the plastid transcription system. TAC subunits were identified from Pfalz et al. (2006). WT, Wild type.

The paradox of a high abundance of PEP and TAC subunits (Fig. 7; Supplemental Table S4) and a decreased accumulation of plastid-encoded photosynthetic proteins can be explained by posttranscriptional regulation, a high protein turnover of photosynthetic proteins, or the regulation of PEP by nucleus-encoded σ factors (see below). We have not identified σ factors in our proteome analysis, but we have identified several nucleases involved in plastid RNA metabolism (Stoppel and Meurer, 2012). The abundances of the endonucleases CSP41 A and B and the polyribonucleotide phosphorylase are identical between mutants and the wild type, while an RNaseJ homolog accumulates to higher levels in apg mutants (At5g63420). Furthermore, the abundance of two Clp protease subunits (At4g17040 and At1g02560) is increased by a factor of more than 10 in the apg mutants compared with the wild type, suggesting increased protein turnover (Fig. 7). TAC16 shows a specialized accumulation pattern that is distinct from that of the other TAC subunits in the hierarchical clustering (Fig. 7). It was previously suggested that TAC16 anchors plastid DNA in the thylakoid membrane (Majeran et al., 2012), which may explain the decreased accumulation of TAC16 in the apg mutants.

The 40 plastid proteins that accumulate to lower levels in the apg mutants and the five proteins exclusively identified in the wild type are significantly enriched in photosynthetic proteins, especially those that are involved in the light reactions of photosynthesis (Fig. 2; Supplemental Table S1). Eleven of these proteins are plastid encoded, suggesting that the systematic down-regulation of plastid genes for photosynthetic functions is an important determinant of the common molecular phenotype (Fig. 6). Consistent with the regulon hypothesis, the nucleus-encoded photosynthetic proteins show a high degree of connectivity in the ATTED-II coexpression network (Obayashi et al., 2009), and 25 of these form a cluster with a correlation higher than 0.8 (Supplemental Fig. S2). Furthermore, two important enzymes are among those proteins with higher abundance in wild-type chloroplasts: 9-cis-epoxycarotenoid-dioxygenase 4 (AT4G19170) and magnesium-protoporphyrin-IX-methyltransferase (AT4G25080). Most Calvin cycle enzymes are not affected by the APG phenotype; in contrast, some of them even accumulate to higher levels in the apg mutants. However, their accumulation pattern shows some differentiation between the mutant plants. For example, the large subunit of Rubisco is almost depleted from apg3, while it accumulates to near wild-type concentrations in apg1 and apg2 (Supplemental Table S1).

The proteome characterization revealed covariance in genes for plastid protein import, stress response, and photosynthesis, suggesting that their expression is coordinated by some master regulator in response to impaired photosynthesis (i.e. that they constitute regulons). The expression of the plastid genome occurred in two major expression groups (see above); therefore, we asked whether plastid σ factors may act as transcriptional master regulators of plastid genome expression, giving rise to the molecular phenotype reported here. Surprisingly, reverse transcription (RT)-PCR did not reveal significant differences in σ factor expression between wild-type and albino plant material (Supplemental Fig. S3). This was unexpected in light of the differential accumulation of σ factor target genes. For example, the plastid translation system accumulates to higher levels in the apg mutants, while the expression of SIG2 (responsible for the expression of tRNA genes) is largely unchanged. Similarly, despite the decreased accumulation of photosynthetic proteins, SIG1, SIG3, SIG5, and SIG6 are expressed at similar levels in the wild type and in the apg mutants. Thus, our data suggest that the accumulation of proteins in the three albino mutants analyzed here is not a direct consequence of σ factor expression at the transcriptional level and that regulatory processes at the posttranscriptional and posttranslational levels determine the accumulation of plastid-encoded proteins (Lerbs-Mache, 2011; Türkeri et al., 2012).

Specific Effects of the Individual Mutations

The protein accumulation data for Calvin cycle enzymes show that despite the existence of a common molecular APG phenotype, there is clearly a differentiation in the expression pattern of some proteins between the different mutants. Thus, the comparative analysis of apg mutant proteomes is not only important for the definition of a common molecular phenotype but may also be a way toward the identification of more specific processes that are affected by the mutation. We selected proteins that are differentially regulated between the wild type and the mutant by selecting a 3-fold change cutoff for higher accumulation and a 2-fold change cutoff for lower accumulation of proteins in the mutants compared with the wild type and removed all proteins from the data set that have the same accumulation characteristic in at least one other apg mutant. We furthermore included proteins that were absent from only one apg mutant line but not from the others and the wild type, and we also included those whose accumulation in one apg line exceeded that in all other apg lines by a factor of at least 2. This left us with a restricted set of proteins for every mutant, which is presented in Table II. Two proteins, PSA-E2 (AT2G20260) and Glc-6-P dehydrogenase (AT5G35790), show “specific” accumulation in two different apg lines. This is possible because they are specifically low in one and particularly high in another apg mutant (Table II). Although the proteins in Table II sensu stricto are not specific for the individual apg lines, their accumulation pattern in one apg mutant deviates from that of all other mutants. Therefore, we refer to these proteins as specific although we are aware that this is not entirely correct.

In the apg1-specific protein set, we find several proteins that are involved in transport processes. APG1 is an envelope protein, and it is conceivable that its absence from the envelope membrane affects several other proteins in the envelope subproteome. Among these proteins are two components of the plastid protein import machinery, Toc34 and Tic21, the dicarboxylate transporter Dit1, and a plastidic Glc transporter. All of these have lower concentrations in apg1 compared with the other albino mutants. Especially the differential accumulation of Dit1 and the Glc transporter is unexpected, because we anticipated that the heterotrophic metabolism is largely similar in nonphotosynthetic plants. However, the specific absence of two subsequent enzymes of the oxidative pentose phosphate pathway, Glc-6-P dehydrogenase (AT5G35790) and 6-phosphogluconolactonase3 (AT5G24400), suggests differences in the heterotrophic metabolism in apg1 compared with the other apg mutants. Interestingly, Dit1 is transcriptionally coregulated with Tic21 (ATTED-II), suggesting a functional connection between these proteins that is currently not understood (Obayashi et al., 2009; Viana et al., 2010). Further surprising is the accumulation of PSAH in apg1 that reaches beyond wild-type levels, despite the otherwise decreased accumulation of thylakoid membrane proteins in apg1. The same holds true for NPQ4 and the proteases FtsH1 and Var2 (Table II). Their increased accumulation may be due to elevated light stress in apg1, which seems to be more sensitive to light than the wild type and the other mutants (Motohashi et al., 2003).

The apg2 mutant is most severely affected in photosynthetic performance; therefore, the apg2-specific proteins are enriched in photosynthetic proteins. The Tat pathway delivers important photosynthetic proteins into the thylakoid lumen, and a defect in this pathway prevents the assembly of a functional thylakoid membrane system (Motohashi et al., 2001). The thylakoid proteins At4g02530 and PSBQ are abundant lumen proteins that specifically fail to accumulate in the apg2 mutant (Table II). Probably as a result of the lack of thylakoid membrane assembly, protochlorophyllide oxidoreductase B (PORB) and Lil3 also do not accumulate in apg2. The unknown protein At5g37360 is coregulated with APG2 at the transcriptional level (ATTED-II); thus; its failure to accumulate may indicate that they are functionally connected (Obayashi et al., 2009). Several proteins accumulate to higher levels in apg2 compared with the other apg lines, among them several unknown proteins (At5g23890, At5g28500, At3g08640) and proteins of the gene expression system (e.g. PEP β′, TAC5, TAC10, RNA-binding protein p67), which may be a compensatory reaction to the failure to accumulate functional thylakoid membranes.

In the set of apg3-specific proteins are two proteins with a function in translation, a 30S ribosomal protein, and OVA2, which functions in tRNA amino acylation (Berg et al., 2005). The levels of abundant plastid-encoded proteins with a UAG/UAA stop codon are decreased in apg3, probably because plastid translation is less efficient in the absence of the peptide chain-release factor (Motohashi et al., 2007). This is the case for cytochrome f apoprotein, cytochrome b6, and the large subunit of Rubisco, whose levels are significantly decreased in apg3 compared with the other apg mutants. A defect in the peptide chain-release factor predominantly affects photosynthetic proteins that accumulate to high levels in wild-type chloroplasts. The expression of proteins for other plastid-encoded functions is not affected, and proteins of the plastid gene expression system accumulate in apg3 above wild-type levels (Fig. 6). The exact function of the peptide chain-release factor and its specific contribution to the expression of plastid genes need further investigation.

We compared the top 100 coregulated genes in the transcriptional networks of the three genes that are defective in the apg lines with their specific set of proteins (using ATTED-II; Obayashi et al., 2009). For APG1, this comparison revealed an unexpected regulatory connection between plastoquinone/vitamin E biosynthesis and protein synthesis and degradation (Table III). The APG1 transcriptional network is significantly enriched for proteins involved in “translation” and “ribosome biogenesis” (Table III), while the specific set of proteins comprises two FtsH proteases. Three proteins are found in both sets: Tic21, Dit1, and the unknown protein At5g44650 (Table II, asterisks). The latter interacts with YCF3 and functions in PSI assembly (Albus et al., 2010). A regulatory link between APG1 and PSI is further suggested by two PSAH subunits that accumulate to high levels in the apg1 background (Table II). APG1 mutant plants lack plastoquinone, and it is conceivable that redox imbalances at the thylakoid membrane are the reason for the increased expression of some PSI subunits and assembly factors. The APG2 network is enriched for “thylakoid membrane organization” and “regulation of photosynthesis,” which is also reflected at the proteome level (Table III). This link was expected because, as a TatC subunit, APG2 is crucial for thylakoid lumen functionality and, thus, for the light reactions of photosynthesis. The gene network is also enriched for “positive regulation of catalytic activity,” which is due to thioredoxins. Thus, the tight redox balance between the thylakoid lumen and the stroma is reflected in the transcriptional network. The APG3 network is enriched for “translation” and partially overlapping with the APG1 network (Table III). Notably, NADPH-dependent thioredoxin reductase C (NTRC; At2g41680) is present in the coexpression networks of APG1 and APG3 and in the set of APG3-specific proteins (Tables II and III). NTRC accumulates to high levels in APG3, which is probably a consequence of the regulatory link between translation and redox homeostasis in and around the thylakoid membrane system, as suggested for APG1.

Table III. Functional categorization of coexpressed genes as retrieved from ATTED-II (Obayashi et al., 2009) and mutant-specific proteins retrieved from Table II.

Coexpressed genes were retrieved from ATTED-II, ranked by the “mutual rank” coexpression, and the first 100 genes were downloaded and used for the analysis. We performed functional classification using the elim method as described in the legend to Figure 4 (Alexa et al., 2006).

Mutant GO Identifier Term elim (P Value)
apg1
 Genes
GO:0006412 Translation 1.80E-12
GO:0042254 Ribosome biogenesis 3.90E-09
GO:0044267 Cellular protein metabolic process 0.0012
 Proteins
GO:0010304 PSII-associated light-harvesting complex 0.012
GO:0044265 Cellular macromolecule catabolic process 0.012
GO:0044257 Cellular protein catabolic process 0.012
apg2
 Genes
GO:0043085 Positive regulation of catalytic activity 0.0013
GO:0010027 Thylakoid membrane organization 0.0062
GO:0010109 Regulation of photosynthesis 0.0062
 Proteins
GO:0009765 Photosynthesis, light harvesting 0.003
apg3
 Genes
GO:0006418 tRNA amino acylation for protein translation 5.90E-05
GO:0042254 Ribosome biogenesis 0.0002
GO:0006412 Translation 0.00055
GO:0009793 Embryo development ending in seed dormancy 0.00497
GO:0048481 Ovule development 0.0081
 Proteins
GO:0051186 Cofactor metabolic process 0.031
Open in a new tab

CONCLUSION

The comparative quantitative proteome analysis of three different APG plants in comparison with the wild type revealed a common molecular phenotype for photosynthesis-impaired mutants. The quantitative accumulation of photosynthetic proteins shows a gradual differentiation that correlates with the residual PSII fluorescence and chlorophyll accumulation. Fine-grained regulon identification at the proteome level requires many more quantitative proteomics data sets acquired with plant lines deficient in different aspects of chloroplast functions. Nonetheless, from our small-scale data set, “stress-related proteins,” “proteolytic activities,” “plastid protein import,” and certainly “photosynthesis” emerge as regulons at the proteome level. The high number of covarying proteins is remarkable because we used apg mutants that are affected in their photosynthetic performance to different degrees (Table I). A clear-cut distinction between pale green on the one hand and albino on the other will identify more proteins that constitute a common molecular phenotype of photosynthesis-impaired plants. At the same time, this may increase the stringency in the identification of mutant-specific proteins, which is one goal in our quest for the characterization of systemic regulation. It shall be our goal to provide more information on proteins that are coregulated under conditions of impaired photosynthesis in the future and this way increase the specificity in the functional characterization of photosynthesis-impaired mutants.

MATERIALS AND METHODS

Plant Material and Growth Conditions

Three apg mutants were isolated from transposon-tagged lines as described previously (Myouga et al., 2010). The wild type, apg1, apg2, and apg3 were grown on germination medium containing Murashige and Skoog salts and 1% Suc for 3 weeks in a growth chamber maintained at 22°C with 16-h-light/8-h-dark cycles. In order to eliminate a potential growth bias in the chamber, the mutant and wild-type plants were sown at the same time and rotated every 3 d. Pooled samples were gathered from at least five dishes.

Protein Extraction and In-Gel Tryptic Digestion

Plant material was frozen in liquid nitrogen and homogenized with mortar and pestle. Proteins were extracted in extraction buffer (40 mm Tris-HCl, pH 6.8, 10% [v/v] glycerol, 4% SDS, and 0.01% bromphenol blue), and insoluble material was removed by centrifugation (20 min at 16,000g). Approximately 200 µg of protein per lane was loaded onto a 10% Tris-Gly SDS gel (18 cm × 16 cm × 1 mm), and after electrophoretic separation of the proteins, the gel was stained with Coomassie blue. The gel lanes were cut into 15 pieces each. In-gel tryptic digestion was performed as described previously (Shevchenko et al., 1996), and peptides were SepPak purified and cleared by centrifugation. After evaporation, dried peptide pellets were stored until further analysis.

Mass Spectrometric Analysis

Dried peptide pellets were dissolved in 20 µL of 2% acetonitrile and 0.1% formic acid, and 2 to 4 µL was injected into the liquid chromatography system. The liquid chromatography gradient was developed from 0 to 5 min in 8% solvent B; 5 to 90 min in 8% to 40% solvent B; 90 to 95 min in 40% to 85% solvent B; 95 to 105 min in 85% solvent B; 105 to 110 min in 85% to 8% solvent B; and 110 to 120 min in 8% solvent B at a flow rate of 0.24 µL min−1. Mass spectra were acquired on an LTQ ion trap (Thermo Fisher). During the acquisition, cycles of survey scans were followed by four data-dependent scans of the four most abundant peptide peaks. Mass spectrometry data were acquired over the complete liquid chromatography run (cycle time, 30 ms; scan range, 500–2,000 D; normalized collision energy, 35.0 V).

Mass Spectrometry Data Interpretation and Protein Quantification by nSpC

The mass spectrometry data were matched to peptides with TurboSequest and PeptideProphet (Keller et al., 2002) searching The Arabidopsis Information Resource 10 protein database (Lamesch et al., 2012) with concatenated decoy database supplemented with contaminants (71,032 entries). The search parameters were as follows: requirement for tryptic ends, one missed cleavage site allowed, mass tolerance of ±3 D, variable modification of oxidized Met, and static modification of carbamylated Cys. Peptide spectrum assignments were filtered for peptide unambiguity in the pep2pro database (Baerenfaller et al., 2011). Accepting only unambiguous peptides with P > 0.9 resulted in a spectrum false discovery rate of 0.7%. The data are available from the pep2pro Web site at www.pep2pro.ethz.ch and were uploaded to PRIDE (www.ebi.ac.uk/pride), experiment numbers 27035 to 27038. Protein quantification was based on nSpC according to Baerenfaller et al. (2008), in which the expected number of peptides based on the theoretical tryptic peptides per protein is balanced with the detected number of peptides for any given protein and sample-to-sample variations are eliminated by normalizing to the total number of peptide spectrum matches in the sample:

graphic file with name PP_204032E01_LW.jpg

where nSpCk is the nSpC for protein K, Spectrak is the measured spectra for protein K (peptide spectrum matches), TTPk is the theoretical tryptic peptides of protein K, MS is the total number of measured spectra in the data set, and MP is the total number of theoretical tryptic peptides of the identified proteins in the data set.

In some instances, an alternative normalization scheme was used, using only chloroplast proteins for the calculation of total PSM in the sample. Chloroplast proteins were extracted from the data set using a chloroplast proteome reference table described previously (Reiland et al., 2009).

Statistical Data Handling and Hierarchical Clustering

With the above-described normalization procedure, a numerical value that resembles protein abundance can be assigned to every identified protein. We used the data from the three replicates to calculate fold changes of the mean abundance values for all identified proteins and to calculate P values with Student’s t test (Welch test). Proteins that are not detected in one or the other sample receive a zero as abundance value. Thus, undetected proteins are treated as low-abundance proteins because they are represented by a small nSpC value. To distinguish between changed and unchanged protein abundance, we take both criteria, fold change and P value, into account for data interpretation. Hierarchical clustering was performed with the Multi Experiment Viewer. Prior to clustering, data were log2 transformed. Clusters were calculated by average linkage clustering using Pearson correlation as the distance metric.

Western Analysis

In total, 25 µg of protein for every mutant was separated by SDS-PAGE on 10% polyacrylamide gels and transferred onto polyvinylidene difluoride membranes by semidry blotting. Detection of reacting proteins was done using enhanced chemiluminescence, and images were obtained by the Fusion Fx7 image-acquisition system (Peqlab). Antiserum against Tic40 was purchased from Agrisera, antisera against Toc75 and Toc110 were provided by Prof. F. Kessler (University of Neuchatel), and antisera against FNR and OEC33 were provided by Prof. R.B. Klösgen (University of Halle-Wittenberg). All primary antibodies were detected with horseradish peroxidase-conjugated goat anti-rabbit antibodies (Agrisera).

RT-PCR

Total RNA from 3-week-old seedlings was prepared using the RNeasy Mini Kit (Qiagen). RT-PCR was performed according to the protocol of the Prime Script High Fidelity RT-PCR kit (Takara). First-strand cDNA was synthesized from 1 μg of total RNA. PCR was performed with the primers indicated below. First-strand cDNA synthesis reactions comprised 5 min at 65°C and 10 min at 4°C, and second-strand reactions comprised 30 min at 42°C and 5 min at 95°C. The PCRs used 30 cycles of 10 s at 98°C, 15 s at 55°C, and 40 s at 72°C. Primers were as follows: At1g08540 (SIG1) forward, 5′-CATCAGGTATTCCGTCTGTGAA-3′, and reverse, 5′-TTCATCCCAGCTCCTTGATAAT-3′; At1g64860.2 (SIG2) forward, 5′-TGCGCTTGTCTAAGAAAATCAA-3′, and reverse, 5′-CGCTGAGCAATAGACATAACCA-3′; At3g53920.1 (SIG3) forward, 5′-TGGTCGTTTCTATCCTCAGTCA-3′, and reverse, 5′-AACGTTTCCTCTAGTGCCAAAA-3′; At5g13730.1 (SIG4) forward, 5′-TTTAGAGGAGGTTCAGCTTTGC-3′, and reverse, 5′-CCTGTTGCAATAGAAACAACCA-3′; At5g24120.1 (SIG5) forward, 5′-TTGCTTTGAAGAGAAACGTTCA-3′, and reverse, 5′-TACTTCACCCGCAGTCATATTG-3′; At2g36990.1 (SIG6) forward, 5′-GATGCTTCATGATCAAACAACAA-3′, and reverse, 5′-TGAAACCAATTCATCAAACTGC-3′; ACT2 forward, 5′-CTAAGCTCTCAAGATCAAAGG-3′, and reverse, 5′-ACATTGCAAAGAGTTTCAAGGT-3′.

Supplemental Data

The following materials are available in the online version of this article.

Acknowledgments

We thank the Functional Genomics Center Zurich and Daniel Stekhoven from the Seminar of Statistics at Eidgenössische Technische Hochschule in Zurich for statistical support.

Glossary

APG

albino/pale-green

nSpC

normalized spectral counting

GO

Gene Ontology

PEP

plastid-encoded RNA polymerase

RT

reverse transcription

PSM

peptide spectrum matches

References

  1. Agne B, Kessler F. (2009) Protein transport in organelles: the Toc complex way of preprotein import. FEBS J 276: 1156–1165 [DOI] [PubMed] [Google Scholar]
  2. Albiniak AM, Baglieri J, Robinson C. (2012) Targeting of lumenal proteins across the thylakoid membrane. J Exp Bot 63: 1689–1698 [DOI] [PubMed] [Google Scholar]
  3. Albus CA, Ruf S, Schöttler MA, Lein W, Kehr J, Bock R. (2010) Y3IP1, a nucleus-encoded thylakoid protein, cooperates with the plastid-encoded Ycf3 protein in photosystem I assembly of tobacco and Arabidopsis. Plant Cell 22: 2838–2855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alexa A, Rahnenführer J, Lengauer T. (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22: 1600–1607 [DOI] [PubMed] [Google Scholar]
  5. Baerenfaller K, Grossmann J, Grobei MA, Hull R, Hirsch-Hoffmann M, Yalovsky S, Zimmermann P, Grossniklaus U, Gruissem W, Baginsky S. (2008) Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320: 938–941 [DOI] [PubMed] [Google Scholar]
  6. Baerenfaller K, Hirsch-Hoffmann M, Svozil J, Hull R, Russenberger D, Bischof S, Lu Q, Gruissem W, Baginsky S. (2011) pep2pro: a new tool for comprehensive proteome data analysis to reveal information about organ-specific proteomes in Arabidopsis thaliana. Integr Biol (Camb) 3: 225–237 [DOI] [PubMed] [Google Scholar]
  7. Bauer J, Chen K, Hiltbunner A, Wehrli E, Eugster M, Schnell D, Kessler F. (2000) The major protein import receptor of plastids is essential for chloroplast biogenesis. Nature 403: 203–207 [DOI] [PubMed] [Google Scholar]
  8. Berg M, Rogers R, Muralla R, Meinke D. (2005) Requirement of aminoacyl-tRNA synthetases for gametogenesis and embryo development in Arabidopsis. Plant J 44: 866–878 [DOI] [PubMed] [Google Scholar]
  9. Biehl A, Richly E, Noutsos C, Salamini F, Leister D. (2005) Analysis of 101 nuclear transcriptomes reveals 23 distinct regulons and their relationship to metabolism, chromosomal gene distribution and co-ordination of nuclear and plastid gene expression. Gene 344: 33–41 [DOI] [PubMed] [Google Scholar]
  10. Bischof S, Baerenfaller K, Wildhaber T, Troesch R, Vidi PA, Roschitzki B, Hirsch-Hoffmann M, Hennig L, Kessler F, Gruissem W, et al. (2011) Plastid proteome assembly without Toc159: photosynthetic protein import and accumulation of N-acetylated plastid precursor proteins. Plant Cell 23: 3911–3928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ferro M, Brugière S, Salvi D, Seigneurin-Berny D, Court M, Moyet L, Ramus C, Miras S, Mellal M, Le Gall S, et al. (2010) AT_CHLORO, a comprehensive chloroplast proteome database with subplastidial localization and curated information on envelope proteins. Mol Cell Proteomics 9: 1063–1084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Friso G, Olinares PD, van Wijk KJ. (2011) The workflow for quantitative proteome analysis of chloroplast development and differentiation, chloroplast mutants, and protein interactions by spectral counting. Methods Mol Biol 775: 265–282 [DOI] [PubMed] [Google Scholar]
  13. Kakizaki T, Matsumura H, Nakayama K, Che FS, Terauchi R, Inaba T. (2009) Coordination of plastid protein import and nuclear gene expression by plastid-to-nucleus retrograde signaling. Plant Physiol 151: 1339–1353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Keller A, Nesvizhskii AI, Kolker E, Aebersold R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74: 5383–5392 [DOI] [PubMed] [Google Scholar]
  15. Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, et al. (2012) The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res 40: D1202–D1210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Lerbs-Mache S. (2011) Function of plastid sigma factors in higher plants: regulation of gene expression or just preservation of constitutive transcription? Plant Mol Biol 76: 235–249 [DOI] [PubMed] [Google Scholar]
  17. Liere K, Weihe A, Börner T. (2011) The transcription machineries of plant mitochondria and chloroplasts: composition, function, and regulation. J Plant Physiol 168: 1345–1360 [DOI] [PubMed] [Google Scholar]
  18. Lopez-Juez E, Pyke KA. (2005) Plastids unleashed: their development and their integration in plant development. Int J Dev Biol 49: 557–577 [DOI] [PubMed] [Google Scholar]
  19. Majeran W, Friso G, Asakura Y, Qu X, Huang M, Ponnala L, Watkins KP, Barkan A, van Wijk KJ. (2012) Nucleoid-enriched proteomes in developing plastids and chloroplasts from maize leaves: a new conceptual framework for nucleoid functions. Plant Physiol 158: 156–189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Mochizuki N, Tanaka R, Tanaka A, Masuda T, Nagatani A. (2008) The steady-state level of Mg-protoporphyrin IX is not a determinant of plastid-to-nucleus signaling in Arabidopsis. Proc Natl Acad Sci USA 105: 15184–15189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Motohashi R, Ito T, Kobayashi M, Taji T, Nagata N, Asami T, Yoshida S, Yamaguchi-Shinozaki K, Shinozaki K. (2003) Functional analysis of the 37 kDa inner envelope membrane polypeptide in chloroplast biogenesis using a Ds-tagged Arabidopsis pale-green mutant. Plant J 34: 719–731 [DOI] [PubMed] [Google Scholar]
  22. Motohashi R, Nagata N, Ito T, Takahashi S, Hobo T, Yoshida S, Shinozaki K. (2001) An essential role of a TatC homologue of a Delta pH-dependent protein transporter in thylakoid membrane formation during chloroplast development in Arabidopsis thaliana. Proc Natl Acad Sci USA 98: 10499–10504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Motohashi R, Yamazaki T, Myouga F, Ito T, Ito K, Satou M, Kobayashi M, Nagata N, Yoshida S, Nagashima A, et al. (2007) Chloroplast ribosome release factor 1 (AtcpRF1) is essential for chloroplast development. Plant Mol Biol 64: 481–497 [DOI] [PubMed] [Google Scholar]
  24. Myouga F, Akiyama K, Motohashi R, Kuromori T, Ito T, Iizumi H, Ryusui R, Sakurai T, Shinozaki K. (2010) The Chloroplast Function Database: a large-scale collection of Arabidopsis Ds/Spm- or T-DNA-tagged homozygous lines for nuclear-encoded chloroplast proteins, and their systematic phenotype analysis. Plant J 61: 529–542 [DOI] [PubMed] [Google Scholar]
  25. Obayashi T, Hayashi S, Saeki M, Ohta H, Kinoshita K. (2009) ATTED-II provides coexpressed gene networks for Arabidopsis. Nucleic Acids Res 37: D987–D991 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Pesaresi P, Schneider A, Kleine T, Leister D. (2007) Interorganellar communication. Curr Opin Plant Biol 10: 600–606 [DOI] [PubMed] [Google Scholar]
  27. Pfalz J, Liere K, Kandlbinder A, Dietz KJ, Oelmüller R. (2006) pTAC2, -6, and -12 are components of the transcriptionally active plastid chromosome that are required for plastid gene expression. Plant Cell 18: 176–197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Reiland S, Messerli G, Baerenfaller K, Gerrits B, Endler A, Grossmann J, Gruissem W, Baginsky S. (2009) Large-scale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol 150: 889–903 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Reiss T, Link G. (1985) Characterization of transcriptionally active DNA-protein complexes from chloroplasts and etioplasts of mustard (Sinapis alba L.). Eur J Biochem 148: 207–212 [DOI] [PubMed] [Google Scholar]
  30. Richly E, Dietzmann A, Biehl A, Kurth J, Laloi C, Apel K, Salamini F, Leister D. (2003) Covariations in the nuclear chloroplast transcriptome reveal a regulatory master-switch. EMBO Rep 4: 491–498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Rochaix JD. (2011) Regulation of photosynthetic electron transport. Biochim Biophys Acta 1807: 375–383 [DOI] [PubMed] [Google Scholar]
  32. Ruckle ME, Burgoon LD, Lawrence LA, Sinkler CA, Larkin RM. (2012) Plastids are major regulators of light signaling in Arabidopsis. Plant Physiol 159: 366–390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Schleiff E, Becker T. (2011) Common ground for protein translocation: access control for mitochondria and chloroplasts. Nat Rev Mol Cell Biol 12: 48–59 [DOI] [PubMed] [Google Scholar]
  34. Schröter Y, Steiner S, Matthäi K, Pfannschmidt T. (2010) Analysis of oligomeric protein complexes in the chloroplast sub-proteome of nucleic acid-binding proteins from mustard reveals potential redox regulators of plastid gene expression. Proteomics 10: 2191–2204 [DOI] [PubMed] [Google Scholar]
  35. Shen-Orr SS, Milo R, Mangan S, Alon U. (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31: 64–68 [DOI] [PubMed] [Google Scholar]
  36. Shevchenko A, Wilm M, Vorm O, Mann M. (1996) Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal Chem 68: 850–858 [DOI] [PubMed] [Google Scholar]
  37. Stoppel R, Meurer J. (2012) The cutting crew: ribonucleases are key players in the control of plastid gene expression. J Exp Bot 63: 1663–1673 [DOI] [PubMed] [Google Scholar]
  38. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M. (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37: 914–939 [DOI] [PubMed] [Google Scholar]
  39. Türkeri H, Schweer J, Link G. (2012) Phylogenetic and functional features of the plastid transcription kinase cpCK2 from Arabidopsis signify a role of cysteinyl SH-groups in regulatory phosphorylation of plastid sigma factors. FEBS J 279: 395–409 [DOI] [PubMed] [Google Scholar]
  40. Viana AA, Li M, Schnell DJ. (2010) Determinants for stop-transfer and post-import pathways for protein targeting to the chloroplast inner envelope membrane. J Biol Chem 285: 12948–12960 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Vogel C, Marcotte EM. (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13: 227–232 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Woodson JD, Chory J. (2008) Coordination of gene expression between organellar and nuclear genomes. Nat Rev Genet 9: 383–395 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Yagi Y, Ishizaki Y, Nakahira Y, Tozawa Y, Shiina T. (2012) Eukaryotic-type plastid nucleoid protein pTAC3 is essential for transcription by the bacterial-type plastid RNA polymerase. Proc Natl Acad Sci USA 109: 7541–7546 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Plant Physiology are provided here courtesy of Oxford University Press

RESOURCES