Experimental Analysis of the Rice Mitochondrial Proteome, Its Biogenesis, and Heterogeneity (original) (raw)

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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Australian Research Council Centre of Excellence in Plant Energy Biology, M316, University of Western Australia, Crawley, 6009 Western Australia, Australia

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This work was supported by the Australian Research Council (ARC) through the Discovery Programme (grant no. DP0664692 to A.H.M. and J.W.). N.L.T. and H.E. are supported as ARC Australian Postdoctoral Fellows (grant nos. DP0772155 and DP0773152), and A.H.M. is an ARC Australian Professorial Fellow (grant no. DP0771156).

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: A. Harvey Millar (hmillar@cyllene.uwa.edu.au).

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15 October 2008

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12 November 2008

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14 November 2008

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Shaobai Huang, Nicolas L. Taylor, Reena Narsai, Holger Eubel, James Whelan, A. Harvey Millar, Experimental Analysis of the Rice Mitochondrial Proteome, Its Biogenesis, and Heterogeneity , Plant Physiology, Volume 149, Issue 2, February 2009, Pages 719–734, https://doi.org/10.1104/pp.108.131300
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Abstract

Mitochondria in rice (Oryza sativa) are vital in expanding our understanding of the cellular response to reoxygenation of tissues after anaerobiosis, the crossroads of carbon and nitrogen metabolism, and the role of respiratory energy generation in cytoplasmic male sterility. We have combined density gradient and surface charge purification techniques with proteomics to provide an in-depth proteome of rice shoot mitochondria covering both soluble and integral membrane proteins. Quantitative comparisons of mitochondria purified by density gradients and after further surface charge purification have been used to ensure that the proteins identified copurify with mitochondria and to remove contaminants from the analysis. This rigorous approach to defining a subcellular proteome has yielded 322 nonredundant rice proteins and highlighted contaminants in previously reported rice mitochondrial proteomes. Comparative analysis with the Arabidopsis (Arabidopsis thaliana) mitochondrial proteome reveals conservation of a broad range of known and unknown function proteins in plant mitochondria, with only approximately 20% not having a clear homolog in the Arabidopsis mitochondrial proteome. As in Arabidopsis, only approximately 60% of the rice mitochondrial proteome is predictable using current organelle-targeting prediction tools. Use of the rice protein data set to explore rice transcript data provided insights into rice mitochondrial biogenesis during seed germination, leaf development, and heterogeneity in the expression of nucleus-encoded mitochondrial components in different rice tissues. Highlights include the identification of components involved in thiamine synthesis, evidence for coexpressed and unregulated expression of specific components of protein complexes, a selective anther-enhanced subclass of the decarboxylating segment of the tricarboxylic acid cycle, the differential expression of DNA and RNA replication components, and enhanced expression of specific metabolic components in photosynthetic tissues.

As rice (Oryza sativa) is the one of the major food supplies for the expanding world population, especially in developing countries, exploiting a molecular understanding of rice biology has the potential to aid humanity in a profound way, as has been seen in the development and use of vitamin A-enhanced cv Golden Rice (Paine et al., 2005). Mitochondria are essential for all plant species as the energy production factory for ATP production via respiratory oxidation of organic acids and the transfer of electrons to O2. But the role and nature of mitochondria in rice take on special significance due to their early growth habitat in hypoxic or even anaerobic environments (Perata and Voesenek, 2007) and the need for mitochondrial biogenesis during the reoxygenation phase (Millar et al., 2004a; Howell et al., 2007). Rice seed embryos contain highly reduced protomitochondrial structures that mature to fully functional mitochondria through a complex biogenesis process involving induction of the general import pathway (Howell et al., 2006) and oxygen signaling of transcription (Howell et al., 2007). Furthermore, the farming practice of using hybrid rice production to boost crop yields relies on cytoplasmic male-sterile lines that have dysfunctional mitochondria in their pollen and restorer lines that recover mitochondrial function and thus fertility to the hybrid (Eckardt, 2006; Wang et al., 2006). Mitochondria in dicots are known to play critical roles in the synthesis of vitamins and cofactors important for human nutrition, including vitamin C (Bartoli et al., 2000; Millar et al., 2003), folate (Ravanel et al., 2001), biotin (Picciocchi et al., 2003), and lipoic acid (Yasuno and Wada, 2002), but there is little research in rice to confirm these roles or investigate these processes at the molecular level. Photorespiration in C3 plants like rice depends on the prevailing CO2 concentrations and involves a critical role of mitochondria in carbon recycling. But despite attempts to engineer C4 metabolism in rice and thus eliminate photorespiration (Ku et al., 1999), there has been little analysis of the photorespiratory machinery and related metabolism as integral components in rice mitochondrial function.

The coordination of biochemical processes to perform the functions of mitochondria requires many hundreds of different proteins working together in protein complexes, in two membrane systems, and in several aqueous spaces. The majority of mitochondrial proteins are encoded in the nucleus and transported into mitochondria as cytosolic precursor proteins by the mitochondrial protein import machinery. Prediction tools based on N-terminal portions of protein sequences are unable to predict localization to a high fidelity (Heazlewood et al., 2005), so the best option is direct experimental analysis of the rice mitochondrial proteome. We have previously reported rice mitochondrial isolation and analysis, using Percoll gradient purification, two-dimensional isoelectric focusing (IEF)/SDS-PAGE, blue native (BN)-PAGE, and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the identification of 122 nonredundant rice mitochondrial proteins (Heazlewood et al., 2003). Subsequently, a separate set of 112 nonredundant rice mitochondrial proteins was identified and listed in the rice proteome database (Komatsu, 2005) using mitochondria isolated by Suc gradient centrifugation and gel-based spot analysis. However, there is less than 20% overlap between the protein lists reported in these two studies.

The removal of contaminants is essential for accurate curation of subcellular organelle proteomes. While dual targeting of some proteins to multiple compartments occurs in plants (Peeters and Small, 2001), the question of contamination between compartments needs to be resolved in a quantitative fashion before such a claim can be considered. Isolation of mitochondria using the traditional differential and gradient centrifugation methods based on size and density has been applied to mitochondrial proteomic analysis in a variety of plant species (Kruft et al., 2001; Millar et al., 2001; Bardel et al., 2002; Heazlewood et al., 2003, 2004). However, a range of contaminants have been found when data obtained by these methods are compared with proteins identified in other cellular organelles by mass spectrometry and/or independent experiments (Heazlewood et al., 2005). Free-flow electrophoresis in zone electrophoresis mode (ZE-FFE) has been used to purify yeast mitochondria to an increased homogeneity based on the surface charge of the organelles (Zischka et al., 2006). Recent separation of plant organelles using ZE-FFE has allowed a deeper and more comprehensive analysis of Arabidopsis (Arabidopsis thaliana) organellar proteomes and highlighted that some proteins reported as dual targeted can be explained as contaminants through quantitative analysis (Eubel et al., 2007).

In this study, traditional differential and gradient centrifugation were combined with FFE separation to isolate rice mitochondria. Through the direct analysis of trypsin-digested mitochondrial peptides by LC-MS/MS and gel-based analysis of rice mitochondrial proteins and the removal of contaminants by quantitative comparison of mitochondria prior to FFE separation, a refined rice mitochondrial data set of 322 proteins is presented. The expanded rice mitochondrial data set is comparable in size and complexity to the previously published Arabidopsis data set (Heazlewood et al., 2004). Analysis revealed that rice and Arabidopsis mitochondria share conserved energy production and metabolism proteins. Interestingly, a significant proportion of the set of proteins with unknown function identified have clear homologs in Arabidopsis mitochondria. This indicates that a range of conserved functions exist that are carried out by unknown function proteins in plant mitochondria that deserve future investigation. The use of this protein data set to explore rice transcript data has given several insights into rice mitochondrial biogenesis during seed germination and heterogeneity between rice tissues. Highlights include evidence for coexpressed and unregulated expression of specific components of protein complexes, a selective anther-enhanced version of the decarboxylating segment of the tricarboxylic acid (TCA) cycle, the differential expression of DNA and RNA replication components, and enhanced expression of specific mitochondrial metabolism in photosynthetic tissues.

RESULTS

Purification of Isolated Rice Mitochondria Using FFE

The integrity of the mitochondrial proteome is largely dependent on the purification of the isolated organelles away from other cellular contaminants. A two-Percoll gradient density separation technique to isolate mitochondria from dark-grown Arabidopsis cells (Millar et al., 2001; Heazlewood et al., 2004) works efficiently in dark-grown rice shoots and yield extracts largely free of contamination by cytosol, peroxisomes, plastids, and other membranes (Heazlewood et al., 2003). To further purify these organelles, washed and Percoll-free organelles were then injected into the separation chamber of the FFE instrument. The visible turbidity pattern in the separation chamber was similar to that observed in the separation of Arabidopsis organelles with two main streams and two minor streams (Eubel et al., 2007), and these streams were reflected in the pattern of the 280-nm absorbance of collected fractions on 96-well plates (Supplemental Fig. S1). Based on comparison with the separation pattern in Arabidopsis (Eubel et al., 2007), the major peak 2 (fractions 25–36) would likely contain enriched mitochondria, and the peaks 1 (fractions 16–22) and 3 (fractions 38–44) would contain plastids and peroxisomes, respectively.

To confirm the distribution of organelles in different FFE fractions, an aliquot of every third fraction was separated by one-dimensional SDS-PAGE (Fig. 1A

Figure 1.

A, Coomassie Brilliant Blue-stained one-dimensional SDS-PAGE and immunoblots of every third of the 45 fractions collected after FFE. The gels were loaded on a volume basis to monitor the distribution of three marker proteins for mitochondria (mtHSP70), plastids (RbcS), and peroxisomes (KAT2). The numbers at right represent molecular mass in kilodaltons. B, Coomassie Brilliant Blue-stained one-dimensional SDS-PAGE of pooled fractions 16 to 21 and 27 to 30 collected after FFE. The bands denoted with numbers were analyzed by MS/MS for protein identification, and identified proteins are presented in Table I. The numbers at right represent molecular mass in kilodaltons.

). Western blotting was applied for analysis using antibodies raised against protein markers for mitochondria (mtHSP70), plastids (small subunit of Rubisco; RbcS), and peroxisomes (3-ketoacyl-CoA thiolase; KAT2; Fig. 1A). The mtHSP70 was present in fractions 27 to 36, which also contained the bulk of the protein content visible in colloidal Coomassie Brilliant Blue-stained gel lanes and in 280-nm absorbance measurements (Fig. 1A; Supplemental Fig. S1), indicating that those fractions constitute the mitochondrial portion of sample. The plastidic RbcS was mainly present in the fractions around 18, indicating that plastids were enriched in those fractions after FFE. The minor RbcS peak that copurified with mitochondrial fractions is most likely due to RbcS from ruptured plastids that adhere to the mitochondrial membrane, but it could be due to a very small proportion of chloroplast having the same surface charge as mitochondria. A similar bimodal distribution of RbcS is seen in Arabidopsis mitochondria purified by FFE (Eubel et al., 2007). The peroxisomal KAT2 marker was enriched in the fractions 33 to 45, deflecting to the right (cathodic) side of the mitochondrial fractions. Mitochondria-enriched fractions (27–30) and the suspected plastidic fractions (16–21) were collected after FFE for one-dimensional SDS-PAGE separation and showed different protein patterns after Coomassie Brilliant Blue staining (Fig. 1B). In the mitochondria-enriched fraction, classical mitochondrial proteins were found, such as HSP70 (Os02g53420), ATP/ADP carrier protein (Os02g48720), and MnSOD (Os05g25850; Fig. 1B; Table I

Table I.

Selected protein bands identified from enriched fractions containing mitochondria and chloroplasts (Fig. 1), and selected protein spots identified from DIGE two-dimensional gels that decreased in abundance after FFE purification of mitochondria (Fig. 2)

Proteins were identified by MS/MS. The predicted molecular mass in kilodaltons (MM) and pI of the match are shown along with the MOWSE score (P < 0.05 when score > 37), number of peptides matched to tandem mass spectra, and the percentage coverage of the matched sequence. The localization of the identified proteins is listed based on their description. The average DIGE ratio represents the decrease of spot abundance after FFE purification (Fig. 2).

Table I.

Selected protein bands identified from enriched fractions containing mitochondria and chloroplasts (Fig. 1), and selected protein spots identified from DIGE two-dimensional gels that decreased in abundance after FFE purification of mitochondria (Fig. 2)

Proteins were identified by MS/MS. The predicted molecular mass in kilodaltons (MM) and pI of the match are shown along with the MOWSE score (P < 0.05 when score > 37), number of peptides matched to tandem mass spectra, and the percentage coverage of the matched sequence. The localization of the identified proteins is listed based on their description. The average DIGE ratio represents the decrease of spot abundance after FFE purification (Fig. 2).

). In the plastid-enriched fraction, typical plastidic proteins were identified, such as TIC110 (Os10g35010), chaperone clpB1 (Os10g35010), and transaldolases (Os01g70170 and Os08g05830; Fig. 1B; Table I). Based on the results obtained above, fractions 27 to 31 were selected as FFE-purified mitochondrial organelles for further analysis.

The FFE-purified mitochondrial samples were compared with Percoll gradient-prepared mitochondria samples before FFE purification using differential two-dimensional IEF/SDS-PAGE by labeling proteins with fluorescent Cydyes (Fig. 2

Figure 2.

DIGE on two-dimensional IEF/SDS-PAGE gels. Samples before FFE treatment (−FFE; labeled with Cy3, shown in red) and after FFE treatment (+FFE; labeled with Cy5, shown in green) were compared. The top panels are gel images of each fluorescence signal, and the bottom panel is a combined fluorescence image electronically overlaid using ImageQuant TL software (GE Healthcare). Yellow spots represent proteins of equal abundance before and after FFE purification. Spots that are more abundant in samples before FFE purification are red, and those more abundant in samples after FFE purification are green. The numbered arrows indicate proteins with statistically significantly decreased abundance after FFE purification (n = 3, P < 0.05), which were selected for MS/MS-based identification.

). The overall spot patterns were consistent with the rice mitochondrial profiles published previously (Heazlewood et al., 2003). Spots with the decreased abundance after FFE (indicated by arrows in Fig. 2) were selected for protein identification using LC-MS/MS. The proteins identified as putative contaminants are listed in Table I. Most of them are plastidic, peroxisomal, or cytosolic proteins but also include bovine serum albumin that was added in the mitochondrial extraction buffer.

Gel-Based Analysis of FFE-Purified Rice Mitochondrial Proteins

To identify the proteins in FFE-purified rice mitochondria, preparative IEF/SDS-PAGE gels were run and analyzed. A set of 291 abundant spots from these gels (Supplemental Fig. S2) were excised and subjected to in-gel digestion followed by MS/MS-based analysis of the resultant peptides. Comparison of spot locations with the quantitative data in Figure 2 ensured that these spots were not decreased during the FFE process, indicating that they were retained or enriched by the mitochondrial purification. This analysis led to identification of a set of 146 nonredundant proteins from rice. We also reanalyzed a set of 89 spots excised from BN/SDS-PAGE gels to separate rice mitochondrial protein complexes as described by Heazlewood et al. (2003); this allowed identification of these proteins against the latest set of predicted rice protein sequences (Osa5). There were a set of 88 nonredundant protein sequences identified from BN-PAGE-separated proteins; 57 were unique protein sequences not identified by the IEF/SDS-PAGE because most were membrane protein subunits of the respiratory chain complexes, which do not enter IEF gels. Taken together, a set of 203 nonredundant proteins were identified based on the gel separations.

Non-Gel-Based Analysis of FFE-Purified Rice Mitochondrial Proteins Using Complex Mixture LC-MS/MS

Non-gel-based LC-MS/MS of rice mitochondrial peptides allowed us to identify the highly hydrophobic, basic, and small or large molecular mass proteins excluded from polyacrylamide gel-based analysis. The whole mitochondrial samples before and after FFE purification were analyzed with three independent biological samples using LC-MS/MS. This analysis allowed us to quantitatively compare the ratio of peptide numbers found for each protein before and after FFE purification, which provided additional criteria to remove contaminants. There were a total of 357 nonredundant proteins found by LC-MS/MS from the samples before and after FFE purification (Fig. 3

Figure 3.

Distribution of the ratios of the number of peptides from a given protein identified before FFE purification to those identified after FFE purification by LC-MS/MS. Three biological samples, each consisting of pre-FFE and post-FFE samples, were analyzed. The area of each bar highlighted in gray represents the contaminants based on their functional classification and our manual analysis (listed in Supplemental Table S2A), while the white areas of each bar indicate mitochondrial proteins in each ratio class.

). A set of 56 proteins were only found in the samples before FFE purification, and most of these proteins were contaminants from the plastids, cytosol, and peroxisomes (Supplemental Table S2A). For example, 17, 11, and 10 peptides were found for peroxisomal hydroxyacid oxidase 1 (Os07g05820), cytosolic Ala aminotransferase 2 (Os07g01760), and plastidic ATP synthase CF1 _β_-chain (Osp1g00410), respectively (Supplemental Table S2A). There were 262 proteins for which peptides were found in samples both before and after FFE purification. Nearly 71% of proteins were enriched (ratio of peptide number identified, ≤1.0) after FFE purification (Fig. 3). A large proportion of proteins with peptide ratios greater than 1.6 could be confidently classified as contaminants (Fig. 3; Supplemental Table S2A). These data were consistent with the results from differential in-gel electrophoresis (DIGE) experiments (Fig. 2; Table I) but provide a much deeper assessment of low-level contaminants and low-abundance mitochondrial proteins. The possible contaminants were removed based on peptide ratios before and after FFE purification and DIGE data as described above. For the 52 proteins only found in FFE-purified samples, MS spectral quality, peptide number, and protein coverage were also used as additional criteria to distinguish contaminants from mitochondrial proteins (Supplemental Table S2B).

Broad Analysis of Mitochondrial Functions Identified in Rice

Combining the gel-based and non-gel-based approaches and after removal of contaminants, a nonredundant set of 322 proteins can be conservatively defined as rice mitochondrial proteins (Supplemental Table S3). In this set of rice mitochondrial proteins, 168 proteins were found using the gel-based method and 307 proteins were found using LC-based methods (Supplemental Table S3). Seventy-eight of the 122 nonredundant rice mitochondrial proteins reported previously using Percoll gradient centrifugation purification methods (Heazlewood et al., 2003) were confirmed in this study (Supplemental Table S3). Half of the unconfirmed proteins (21 of 43) from Heazlewood et al. (2003) were proteins predicted from retrotransposon sequences and unknown function proteins. Surprisingly, only six proteins were confirmed from a set of 112 nonredundant proteins listed as mitochondrial in the rice proteome database (Komatsu, 2005; http://gene64.dna.affrc.go.jp/RPD/), apparently due to the heavy contamination of the rice mitochondrial samples used to generate these identifications.

Each rice mitochondrial protein was assigned to one of 17 functional categories (Fig. 4

Figure 4.

Functional distribution of the 322 rice mitochondrial proteins (white bars) alongside 416 Arabidopsis mitochondrial proteins (gray bars) from Heazlewood et al. (2004). Rice mitochondrial protein data were extracted from Supplemental Table S3.

; Supplemental Table S3). In this data set, known function proteins were highly represented by those involved in energy production (complexes I–V, 22%) and metabolism (TCA and general metabolism, 28%; Fig. 4), while the proteins with unknown function represented 17% of the mitochondrial protein set (Fig. 4). The numbers of proteins involved in energy production and metabolism are very similar in the rice and Arabidopsis mitochondrial data sets (Fig. 4). There are fewer proteins identified to be involved in electron transport chain assembly and signaling, stress defense, carriers and transporters, protein import/fate, and unknown proteins in the rice compared with the Arabidopsis mitochondrial data sets (Fig. 4).

Prediction of Rice Mitochondrial Proteins

This experimentally determined rice mitochondrial protein set provides an opportunity to test the sensitivity of different organelle-targeting prediction programs in rice. When our set of 313 nucleus-encoded rice mitochondrial proteins were analyzed, the accuracy of mitochondrial prediction by four leading targeting prediction programs ranged from 61% to 66% (Table II

Table II.

Evaluation of mitochondrial prediction programs using experimental sets of rice and Arabidopsis nucleus-encoded mitochondrial proteins

Two mitochondrial sets were used in the analysis: a set of 313 nucleus-encoded rice mitochondrial proteins (Supplemental Table S3) and a set of 405 nucleus-encoded Arabidopsis mitochondrial proteins from Heazlewood et al. (2004).

Table II.

Evaluation of mitochondrial prediction programs using experimental sets of rice and Arabidopsis nucleus-encoded mitochondrial proteins

Two mitochondrial sets were used in the analysis: a set of 313 nucleus-encoded rice mitochondrial proteins (Supplemental Table S3) and a set of 405 nucleus-encoded Arabidopsis mitochondrial proteins from Heazlewood et al. (2004).

), which was higher than the 40% to 50% prediction rate observed for the Arabidopsis mitochondrial protein set (Heazlewood et al., 2004). The high accuracy of prediction by the four programs in this study may be due to the higher purity of the FFE-purified mitochondrial sample and the removal of proteins during FFE that only bind to the surface of the mitochondrial outer membrane but are not imported. Proteins most often predicted by these programs belonged to electron transport chain assembly, DNA and RNA replication, TCA cycle, and complex III (Fig. 5

Figure 5.

Proportion of mitochondrial proteins in different functional categories that were predicted to be localized in mitochondria using the four major prediction programs listed in Table II. In each bar, the white region was not predicted by any program, the black region was predicted by all four programs, while increasingly gray bars indicate mitochondrial prediction by one, two, or three programs. The functional classifications of a total of 322 proteins were taken from Supplemental Table S3.

; Supplemental Table S3). On the other hand, the proteins from the functional groups of carriers, protein import and fate, complex V, and unknown proteins had a low proportion of proteins predicted to be mitochondrially localized (Fig. 5; Supplemental Table S3). Some of these mitochondrial proteins have known internal targeting sequences rather than classical N-terminal sequences, and these are not detected by these prediction programs. However, for many proteins, the mechanism of sorting to mitochondria is still unknown.

Global Analysis of Expression Pattern of the Identified Rice Mitochondrial Proteins

The plant mitochondrial proteome changes during development of plant organs as well as differing in various cell and tissue types. We have already shown in Arabidopsis that 40% of proteins change more than 2-fold in abundance when comparing mitochondrial proteomes from photosynthetic and nonphotosynthetic tissues (Lee et al., 2008). The combination of proteome and gene expression data can provide a more global understanding of gene functions in particular organs and developmental stages and in response to stresses. To analyze the gene expression pattern, we extracted the available rice microarray data from the National Center for Biotechnology Information gene expression omnibus (http://www.ncbi.nlm.nih.gov/geo) of six independent studies with relevance for mitochondrial function (Walia et al., 2005, 2007; Jain et al., 2007; Lasanthi-Kudahettige et al., 2007; Li et al., 2007; Ribot et al., 2008) and our own rice microarray data during the first 24 h of germination. From 322 mitochondrial proteins identified, 306 had representative probe set identifiers on the microarrays. Analysis of the combined microarray expression data is described in “Materials and Methods.” The genes were divided into 12 hierarchical clusters (Fig. 6A

Figure 6.

Hierarchical clustering of transcripts for the nucleus-encoded components of the rice mitochondrial proteome (A), and functional categorization of the transcripts grouped into each cluster (B). Hierarchical clustering was carried out using average linkage based on Euclidean distance of the 306 mitochondrial genes across all of the Affymetrix rice genome arrays carried out on various tissue types and conditions. Twelve distinct clusters were colored and numbered (shown in Supplemental Table S5). The proportion of components from each functional categorization present in each of the 12 clusters is defined in A, and B shows the number of components in each functional categorization above each column.

) based on differential expression patterns in different organs or treatments, with seven major clusters containing more than 16 genes in each cluster and five minor clusters containing fewer than 10 genes in each cluster. Cluster 1 was defined by high expression of photorespiratory components in leaf tissue that were largely absent in most other samples except young seedlings. Cluster 4 was defined by expression in anoxically grown 4-d coleoptiles, and cluster 5 was defined by a set of genes that peak in expression in mature anthers. Cluster 6 displayed high expression in suspension cells and 12- and 24-h imbibed seeds and to a lesser extent in developing seeds. Clusters 8 and 11 were defined by expression in most of the arrays except 0- to 3-h imbibed seed, while cluster 10 members were evenly expressed in all rice tissues. The proportion of each cluster in different functional classes is summarized in Figure 6B. Detailed information is given in Supplemental Figure S3 and Table S5, and highlights are described in the following sections based on the functional classification of proteins and their expression patterns.

The Respiratory Apparatus and Its Expression

The mitochondrion is an energy factory for ATP production coupled to the respiratory oxidation of organic acids and the transfer of electrons to O2. Over 71 proteins of the five electron transport chain complexes and 28 protein subunits of TCA cycle enzymes have been identified (Fig. 4), representing 31% of the rice mitochondrial protein set. Most of these proteins have orthologs in Arabidopsis, and most have also been experimentally shown to be mitochondrial proteins in Arabidopsis (Supplemental Table S3). This highlights that rice and Arabidopsis have a very conserved composition of the respiration chain complexes and the TCA cycle. Six proteins involved in alternative pathway respiration were also found, namely cytosol-facing NADH dehydrogenases that donate electrons to ubiquinone and bypass complex I and components of the electron-transfer flavoprotein pathway that reduces ubiquinone and is linked to the matrix branched-chain amino acid degradation pathway (Supplemental Table S3; Taylor et al., 2004). No alternative oxidases that oxidize ubiquinol and consume O2 were found in this rice study, which is consistent with the very low expression of alternative oxidases in rice shoots under normal conditions (Saika et al., 2002) and the very low cyanide-insensitive respiratory rate of isolated rice mitochondria (Heazlewood et al., 2003; Millar et al., 2004a). The genes encoding the subunits of the oxidative phosphorylation complexes (complexes I–V) were highly expressed across all tissues (mainly cluster 11 in Fig. 6), confirming the essential function of the respiration chain for energy production. Comparison of the expression profiles of the genes for the subunits in each respiratory complex separately revealed in each case a core of coexpressed subunits and a series of apparently aberrantly expressed subunits (Supplemental Fig. S3).

A series of genes encoding TCA cycle subunits were highly expressed in the anther; these were nearly all components of the pyruvate dehydrogenase complex (PDH) and the initial steps of the TCA cycle (citrate synthase, aconitase, and isocitrate dehydrogenase [ICDH]). In most cases, another isoform was also in our list of TCA cycle components and had a much more ubiquitous gene expression pattern (most notably, PDH E1_α_ Os02g50620 versus Os12g08260, PDH E1_β_ Os09g33500 versus Os08g42410, PDH E2 Os06g01630 versus Os02g01500, aconitase Os03g04410 versus Os08g09200, and ICDH Os04g40310 versus Os02g38200). In contrast to this, later steps of the TCA cycle, such as malate dehydrogenase (Os01g46070 and Os05g49880), were more evenly expressed across tissue types (Supplemental Fig. S3). Mature anthers might thus possess a very high metabolic activity for energy production during pollen formation, and potentially this is mediated by a specific highly expressed form of the TCA cycle in pollen.

General Metabolism in Rice Mitochondria

Plant mitochondria are also involved in synthesis of vitamins, cofactors, and lipids, metabolism of amino acids, photorespiratory Gly oxidation, and export of organic acid intermediates for other cellular biosynthesis. Rice mitochondrial proteins involved in these processes were also evident in our protein lists. In total, 64 proteins were identified as being involved in a range of metabolic pathways (Supplemental Table S3). Subdivision of this functional classification showed 16 proteins involved in amino acid metabolism, seven proteins involved in aldehyde/alcohol metabolism, six proteins involved in lipid synthesis, and six proteins involved in metabolism of nucleotides. Photorespiratory genes (Gly decarboxylase complex subunits and Ser hydroxymethyltransferase) were selectively expressed in leaf tissues (cluster 1, Fig. 6A), while components linked to C1 metabolism (glyoxylate, formate, and tetrahydrofolate metabolism) did not show leaf enhanced expression profiles (Supplemental Fig. S3). Two isoforms of the H protein of the Gly decarboxylase complex (Os06g45670 and Os02g07410) did not show leaf enhanced expression patterns but were broadly expressed with the C1 metabolism genes (Supplemental Fig. S3). The role of Gly decarboxylase in plant mitochondrial C1 metabolism outside of its photorespiratory role is still largely unexplored in plants. A 4-methyl-5-thiazole monophosphate biosynthesis protein (Os01g11880) predicted to be involved in thiamine biosynthesis was enriched after FFE purification (Supplemental Table S3), representing, to our knowledge, the first component of thiamine synthesis found in rice mitochondria.

DNA Replication, Transcription, and Translation

There were 19 proteins in the DNA replication and transcription category (Supplemental Table S3). Among them, five were DAG-like proteins and eight were pentatricopeptide repeat (PPR) proteins. Genetic evidence shows that DAG proteins influence DNA synthesis and alter chloroplast differentiation (Bisanz et al., 2003), but while DAG proteins have also been reported in the Arabidopsis mitochondrial proteome, the specific function of this class of proteins in mitochondria is still unknown. There are 477 PPRs in the rice genome and 450 PPRs in Arabidopsis, and most are predicted to be targeted to mitochondria or plastids (Small and Peeters, 2000; O'Toole et al., 2008). PPR proteins are associated with both the transcriptional (Pfalz et al., 2006) and translational (Pusnik et al., 2007) machinery and are involved in many stages of mRNA splicing (de Longevialle et al., 2007). In rice, two mitochondrial PPR proteins were reported to be restorers of cytoplasmic male sterility (Wang et al., 2006). Here, we have found eight PPRs that complement two other PPRs found in rice mitochondria by Heazlewood et al. (2003). Five of the PPRs identified in our study are orthologs of PPRs previously identified in Arabidopsis mitochondria. For example, OsPPR_02g58300 is an ortholog of the P-class PPR336 protein At1g61870. PPR336-like proteins are known to be extrinsically attached to the inner mitochondrial membrane and to be associated with polysomal RNA (Uyttewaal et al., 2008). OsPPR_02g02020 is an ortholog of At2g37230, which was found in the thylakoid membrane of Arabidopsis (Peltier et al., 2004), even though these proteins were predicted to be mitochondrial in both rice and Arabidopsis. To our knowledge, the possibility of orthologous PPRs swapping location between mitochondria and chloroplasts in different plant species or being dual targeted has not been reported. OsPPR_04g09530, to our knowledge, is the first DYW-type PPR found by mass spectrometry. This class of PPRs often has an RNA-editing role and is typically expressed at very low levels. Further studies are required to investigate the function of these eight PPRs in rice mitochondria.

Fourteen proteins are listed in the group of proteins involved in translation. Six subunits of the putative mitochondrial ribosome were identified: L1, L27, and L30 of the 50S complex and S12, S18, and S19 of the 30S complex. Even with this small number of predicted subunits of the mitochondrial ribosome, clear differences between Arabidopsis and rice are apparent. The nucleus-encoded S12 protein in rice (Os12g33930) is most similar to the mitochondria-encoded S12 in Arabidopsis (AtMg00980), while the mitochondria-encoded S19 in rice (OsM1g00450) is orthologous to a nucleus-encoded ribosomal subunit in Arabidopsis (At5g47320). Extensive studies have been performed on ribosomal proteins shifting or swapping their location during recent evolution in plants (Adams et al., 2000, 2002). The S19 gene has been transferred to the nucleus in many cereals, with the notable exception of rice, where an intact gene and transcript have been reported in mitochondria (Fallahi et al., 2005). Here, we show clear evidence for this S19 protein accumulating in rice mitochondria. The S12 is mitochondrially encoded and cotranscribed with nad3 in many dicots and monocots (Perrotta et al., 1996), but our match here is to a nuclear gene. So it seems that in rice it has been transferred to the nucleus, which has also been reported for the dicot Oenothera (Grohmann et al., 1992). Elongation factors and tRNA synthetases make up the majority of the other translation components, as they do in the proteome analysis of Arabidopsis mitochondria. The genes encoding DNA replication, transcription, and translation factors were most highly expressed in suspension cells and in the imbibed germinating seeds at 12 and 24 h, as shown in cluster 6 (Fig. 6; Supplemental Fig. S3). This pattern of expression might be related to the high rate of mitochondrial division and recombination in dividing cells and the early steps of mitochondrial biogenesis during germination.

Components of Protein Import and Fate

There were 19 proteins identified involved in protein import and fate (Supplemental Table S3). These included proteins involved in protein import and sorting, presequence cleavage, and proteolysis, and all of them had clear orthologs in the Arabidopsis mitochondrial proteome. The translocase of the outer membrane (TOM) was represented by TOM40, TOM20, and TOM22 subunits, while the only translocase of the inner membrane (TIM) components found were the intermembrane space members of the carrier import pathway, Tim8, Tim9, and Tim13 homologs. Lon, ClpX, and FtsH homologs were found, representing the three main classes of mitochondrial proteases in plants. The expression of genes encoding these proteins was mainly grouped into clusters 6 and 10, with notable expression in suspension cells, seeds, and embryos during the early stages of germination (Fig. 6; Supplemental Fig. S3). These results were consistent with our reports of the substantial mRNA pool in dry rice seeds for the genes encoding import components (Howell et al., 2006).

Heat Shock and Stress Response Proteins

There are 15 heat shock proteins and putative or well known molecular chaperones listed in our current data set (Supplemental Table S3). These included the classical 60/10-, 70-, and 80-kD chaperone classes. While these chaperone and heat shock protein classes are sometimes components induced by stresses with roles in stress tolerance (Schöffl et al., 1998), we found little evidence for this in the transcriptional data from stress responses (such as drought, salt, and cold). Instead, the expression of genes encoding these proteins was grouped into clusters 6 and 10, due mainly to high expression in suspension cells and seeds and during early germination (Fig. 6; Supplemental Fig. S3). This suggests primary transcriptional regulation of these proteins in response to cell division, expansion, and growth rather than stress tolerance.

There were nine proteins with putative roles in stress response or oxidative stress in our data set (Supplemental Table S3). Mitochondria are often exposed to self-generated reactive oxygen species, primarily through the ubisemiquinone intermediate, formed by the NADH:ubiquinone oxidoreductase (complex I) or ubiquinone:cytochrome c oxidoreductase (complex III) of the electron transport chain, which can reduce O2 to O2 − (Moller, 2001a). Antioxidant systems consisting of MnSOD, components of the dual-targeted ascorbate/glutathione cycle, and peroxiredoxin and glutaredoxin family proteins were experimentally identified here (Supplemental Table S3). Most of genes encoding stress-responsive proteins and antioxidants were highly expressed in the suspension cells (cluster 6, Fig. 6). Surprisingly, except for a few selective proteins that were induced (several of the ascorbate/glutathione cycle components), these components were not a group positively induced in response to the different environmental stresses tested, including drought, salt, and cold (Fig. 6; Supplemental Fig. S3).

Proteins of Unknown Function in Rice Mitochondria

A total of 55 proteins were identified as proteins with unknown functions. Thirty-five of these rice unknown proteins were predicted as mitochondrially localized by at least one targeting prediction program, but only five of these unknown proteins have been identified in our previous study of the rice mitochondrial proteome (Heazlewood et al., 2004; Supplemental Table S3). Forty-nine of these 55 rice proteins had clear Arabidopsis orthologs (Supplemental Table S3), and 22 of them have been identified as Arabidopsis mitochondrial proteins in the SUBA database (Heazlewood et al., 2005). These results indicate a substantial conservation of unknown function proteins between mitochondria from different plant species, even though there is great divergence between unknown function mitochondria proteins from different eukaryotic lineages (Heazlewood et al., 2004). The identification of these conserved unknown function mitochondrial proteins provides a focus for the use of reverse genetics to identify novel mitochondrial functions in plants.

The overall expression pattern of genes encoding proteins with unknown function was dispersed among other mitochondrial components, as shown in Figure 6, but some gene expression data could be grouped into different clusters or specific tissues. For examples, seven unknowns (Os05g08920, Os07g26700, Os04g41950, Os03g20860, Os05g01300, Os02g01450, and Os02g07910) are most highly expressed in the mature anthers (Fig. 6; Supplemental Fig. S3), which might be related to high mitochondrial metabolism in the same way as specific isoforms of TCA cycle enzymes show this distribution, as indicated above. Eight unknown function genes (Os10g40410, Os03g38520, Os03g48110, Os09g31260, Os05g46450, Os01g68030, Os06g33920, and Os02g35610) are coexpressed with genes encoding subunits of mitochondrial respiratory complexes (Fig. 6A; Supplemental Table S5), indicating that those genes might be involved in energy production or are associated with or are assembly factors for these complexes. Another five unknown function genes (Os06g22070, Os04g54410, Os08g34130, Os01g50310, and Os11g14990) are coexpressed with genes encoding proteins for DNA transcription and replication (Fig. 6A; Supplemental Table S5), indicating that those genes might be involved in similar functions, while Os01g05010 is tightly coexpressed with several mitochondrial ribosomal components. In cluster 7, comprising three genes (Fig. 6A; Supplemental Table S5), one unknown function gene (Os05g39390) was coexpressed with two genes encoding proteins involved in ubiquinone reduction, suggesting that this gene might have a related function.

DISCUSSION

In this study, the combination of FFE-based plant mitochondria separation (Eubel et al., 2007) and traditional differential and gradient centrifugations was applied to determine the rice mitochondrial proteome. This was achieved by organellar fraction selection using marker antibodies as well as differences in spot abundance in DIGE and peptide number ratios before and after FFE purification. The final rice mitochondrial data set provides evidence to confirm most of the fundamental biological processes in mitochondria previously uncovered in Arabidopsis (Fig. 4; Heazlewood et al., 2004). For example, the majority of mitochondrial proteins involved in energy production have highly conserved amino acid sequences between rice and Arabidopsis (Supplemental Table S3). A notable exception is the subunits of succinate dehydrogenase (SDH) or complex II. Differences between complex II in higher plants and mammals have been known for some time, most notably in the similarity of sequences for SDH1 and SDH2 but great divergence in sequences for the hydrophobic membrane anchor proteins SDH3 and SDH4 (Burger et al., 1996). In dicots, plant mitochondrial complex II appears to have four additional subunits in BN-PAGE-purified preparations, termed SDH5, -6, -7, and -8 (Millar et al., 2004b). In rice, we identified classical SDH1 and SDH2 subunits, but the SDH3 isoform we identified, Os02g02940, is very poorly related to SDH3 (At5g09600) in Arabidopsis. We did not identify the small SDH4 protein in rice, and the annotated SDH4 gene from rice (Os01g70980) bares little resemblance at all to the Arabidopsis SDH4. Furthermore, we found conserved homologs for SDH5, -6, and -7 (Supplemental Table S3) but no evidence for an SDH8 in rice analogous to the Arabidopsis SDH8. As BN-PAGE separation of mitochondrial membrane complexes from monocot mitochondria does not resolve complex II (Eubel et al., 2003; Heazlewood et al., 2003; Millar et al., 2004b), the potential differences in mitochondrial complex II between monocots and dicots requires further research.

Evidence for the dual targeting of proteins to both mitochondria and chloroplasts has increased with the greater ease in GFP-tagging experiments and the increased interest in processes common to both organelles. In Arabidopsis, almost all organellar aminoacyl-tRNA synthetases are dual targeted, as shown by in vivo GFP and in vitro organelle import (Duchene et al., 2005). Two aminoacyl-tRNA synthases (Os01g31610, orthologous to At3g02660, and Os07g07060, orthologous to At5g52520; Supplemental Table S3) found in this study were also present in the proteome set of etioplasts (Kleffmann et al., 2007), indicating that both tRNA synthases could be dual targeted. Antioxidant enzymes classically found in the chloroplast stroma, such as ascorbate peroxidase (At4g08390) and monohydroascorbate reductase (At1g63940), have been shown to be dual targeted to mitochondria in Arabidopsis (Chew et al., 2003). Similarly, ascorbate peroxidase (Os12g07820, orthologous to At4g08390) and monohydroascorbate reductase (Os08g05570, orthologous to At1g63940) are also presented in both our mitochondrial samples and the rice plastid proteome (Kleffmann et al., 2007; Supplemental Table S3).

Glycolytic enzymes are traditionally regarded as cytosol-abundant proteins. Interestingly, 5% to 10% of the cytosolic isoforms of each glycolytic enzyme, at least in Arabidopsis, is associated with the outer membrane surface of the mitochondrion (Giegé et al., 2003). It appears that glycolytic enzymes are associated dynamically with mitochondria to support respiration and that substrate channeling restricts the use of intermediates by competing metabolic pathways (Graham et al., 2007). In plants, hexokinase is associated with the outer mitochondrial membrane (Dry et al., 1983; da-Silva et al., 2004; Damari-Weissler et al., 2006; Kim et al., 2006). In this study, three hexokinases were identified as mitochondrial proteins (Os01g53930, Os01g71320, and Os05g44760; Supplemental Table S3). In animal cells, mitochondrially associated hexokinase play a pivotal role in the regulation of apoptosis (Downward, 2003; Birnbaum, 2004; Majewski et al., 2004). In plants such as Nicotiana benthamiana, a similar function of mitochondrially associated hexokinases in the control of programmed cell death has been reported (Kim et al., 2006), suggesting a link between Glc metabolism and apoptosis in plant cells. Apart from hexokinases, however, other glycolytic enzymes, such as Fru-bisP aldolase (Os01g02880), pyruvate kinase (Os03g20880), d-3-phosphoglycerate dehydrogenase (Os04g55720), and triosephosphate isomerase (Os09g36450), were only found in rice mitochondrial samples before, but not after, FFE purification (Supplemental Table S2A). The separation of glycolytic enzymes from Arabidopsis mitochondria by FFE has also been observed (Eubel et al., 2007), suggesting that the FFE process allows a removal of peripherally associated cytosolic proteins from the organelle surface.

The relatively low levels of transcript abundance observed for the majority of genes in mature leaf tissue (last four columns in Fig. 6A), with the notable exception of genes encoding subunits of Gly decarboxylase, are in agreement with previous studies on the expression of genes encoding mitochondrial proteins in monocots. It has been previously shown that subunits of Gly decarboxylase were only detectable at the protein level in older regions of wheat (Triticum aestivum) leaf tissue (Rogers et al., 1991), while transcripts for mitochondria-encoded genes were abundant in the first 1 to 2 cm from the basal meristem but declined sharply afterward (Topping and Leaver, 1990). The relatively low abundance of transcripts from genes encoding mitochondrial proteins from 17-d-old rice leaf tissue in the Ribot et al. (2008) data suggests that a similar pattern occurs for nucleus-located genes in rice and wheat. In contrast, transcript abundance from genes encoding subunits of Gly decarboxylase peaked in older leaf tissue of both rice and wheat. Additionally, four other genes for rice mitochondrial components displayed high levels of expression in mature leaf tissue (Os11g24450, general dicarboxylate/tricarboxylate carrier; Os05g50840, probable CoA transporter; Os06g39344, branched-chain amino acid catabolism enzyme enoyl-CoA hydratase; Os05g49880, TCA cycle enzyme malate dehydrogenase), which suggests a role related to photosynthetic metabolism or mitochondria-chloroplast interaction. We have already noted increased abundance of malate dehydrogenase and branched-chain amino acid metabolic machinery in dicot leaf mitochondria (Lee et al., 2008), and plant mitochondrial dicarboxylate-tricarboxylate carriers have been proposed to play important roles in nitrogen assimilation and export or import of reducing equivalents to mitochondria in leaves (Picault et al., 2002).

A range of proteins identified that are involved in aldehyde/alcoholic metabolism might be related to alcoholic metabolism in rice mitochondria. Ethanolic fermentation is not only observed in anaerobic plant tissues but also in aerobic tissues such as anthers (Tadege et al., 1999). The mechanism and regulation of aerobic ethanolic fermentation in the anther is still unclear, but it likely involves a new steady state in which pyruvate is distributed between PDH and an aerobic TCA cycle in mitochondria and a cytosolic fermentation pathway involving ADH and PDC. Our data suggest that metabolism of pyruvate might be changed in anther mitochondria by differential expression of PDH complexes and early steps of the TCA cycle that might coexist with aerobic alcoholic fermentation. The PDH complex is the key entry point of carbon into the TCA cycle and is considered to be a key point in regulation, as phosphorylation/dephosphorylation controls its activity. It would be particularly interesting to investigate whether there are any differences in the kinetics of PDH between pollen and other tissues that allow low-affinity PDC (K m in approximately millimolar range) and higher affinity PDHs (K m in approximately micromolar range) to simultaneously utilize a common pyruvate pool. Aldehyde dehydrogenases (ALDHs) have been reported as major mitochondrial proteins in pea (Pisum sativum) leaves and roots (Bardel et al., 2002), and two ALDHs were also observed in the Arabidopsis mitochondrial proteome (Millar et al., 2001; Heazlewood et al., 2004). In this study, three abundant rice mitochondrial ALDHs (Os06g15990, Os02g49720, and Os05g45960) were found. Os06g15990 and Os02g49720 are orthologs of rf2a and rf2b, respectively, encoding ALDHs in maize (Zea mays; Liu and Schnable, 2002). In maize, rf2a is involved in restoring male fertility to Texas cytoplasmic male-sterile plants (Cui et al., 1996). The mechanism of restoration of male fertility in maize by rf2 encoding ALDH is still unclear. One possible role of rf2-encoded ALDH is the removal of the products of lipid peroxidation that would be expected to accumulate preferentially in T-cytoplasm cells if these cells produce more reactive oxygen species than normal cytoplasm cells (Liu et al., 2001; Moller, 2001b). The mitochondrial ALDH (Os02g49720) can also be induced in rice seedlings by submergence, having a very similar pattern of expression to classic anaerobic proteins such as ADH1 and PDC1 (Nakazono et al., 2000). These authors suggested that induction of mitochondrial ALDH in rice under submergence might protect mitochondrial damage by diffused acetaldehyde produced by PDC during aerobic ethanolic fermentation (Nakazono et al., 2000). Logically, the restoration of maize male fertility by mitochondrial ALDH might be partially due to protection of pollen mitochondria from acetaldehyde during aerobic ethanolic fermentation. There are also four sex-determination TASSELSEED2-like proteins (Os07g40250, Os07g46840, Os07g46920, and Os07g46930) found in our proteome set, which encode short-chain alcohol dehydrogenases based on annotation. Their maize homolog, TASSELSEED2, is required for stage-specific floral organ abortion (DeLong et al., 1993) and can reduce a broad range of substrates tested, including steroids and dicarbonyl and quinone compounds (Wu et al., 2007). The substrate range of these rice mitochondrial short-chain alcohol dehydrogenase-like proteins and their potential involvement in alcoholic metabolism in specific rice organs deserve further investigation. Further exploring the expression and kinetics of the components of rice mitochondrial pyruvate, aldehyde, and alcohol metabolism identified here may uncover a key mechanistic basis of cytoplasmic male sterility.

Thiamine (vitamin B1) synthesis in plants was thought at one time to be plastid specific (Belanger et al., 1995), while in yeast it is a mitochondrial process (Eijssen et al., 2008). It is synthesized through the convergence of two independent biosynthetic pathways, one that synthesizes 2-methyl-4-amino-5-hydroxymethylpyrimidine pyrophosphate to form the pyrimidine moiety and another that synthesizes 4-methyl-5-(hydroxyethyl) thiazole phosphate to form the thiazole moiety, but the specific number and identity of the enzymes involved in plants are still unclear. In Arabidopsis, the product of the thiamine biosynthesis gene (THI1), which catalyzes Gly, NAD+, and a sulfur donor into hydroxyethylthiazole, is dual targeted to chloroplasts and mitochondria (Chabregas et al., 2001, 2003). A 4-methyl-5-thiazole monophosphate biosynthesis protein (Os01g11880) that is unrelated to THI1 but is a probable THIJ (catalyzing phosphorylation of hydroxymethylpyrimidine to hydroxymethylpyrimidine monophosphate in thiamine biosynthesis) was enriched after FFE purification (ratio = 0.33) and is predicted as a mitochondrial protein by three different targeting programs (Supplemental Table S2). This finding provides further experimental evidence that plant mitochondria are involved in thiamine synthesis in plants.

The plant mitochondrial proteome is a changing entity over time and in different cells and tissues. This is evident by looking at mitochondria from photosynthetic and nonphotosynthetic plant tissues (Bardel et al., 2002; Lee et al., 2008) and at the mitochondrial components in the Arabidopsis proteome map generated from different organs, developmental stages, and undifferentiated culture cells (Baerenfaller et al., 2008). The combination of proteome and gene expression on this scale in rice will greatly benefit our global understanding of the functions of genes for mitochondrial proteins in particular organ and developmental stages. Future combinations of data sets focusing on mitochondrial function will allow the common patterns of expression, and thus putative regulators of mitochondrial biogenesis, stress response, and other aspects of the nucleus-mitochondria interaction in rice, to be uncovered.

MATERIALS AND METHODS

Growth of Rice Seedlings

Batches of 200 g of rice (Oryza sativa ‘Amaroo’) seeds were washed in 1% (v/v) bleach for 10 min, rinsed in distilled water, grown in the dark in vermiculite trays (30 × 40 cm) at a constant 30°C, and watered daily, and the shoot tissues were harvested at 10 d for mitochondrial isolation.

Rice Mitochondrial Isolation

Rice mitochondrial isolation was performed by differential centrifugation followed by Percoll gradients as described by Heazlewood et al. (2003). After 0% to 4.4% (v/v) Percoll gradient centrifugation, the enriched mitochondrial fractions were washed three times with FFE separation medium (10 mm acetic acid, 10 mm triethanolamine, 1 mm EDTA, and 280 mm Suc, pH 7.4).

FFE

FFE was performed using the BD FFE system (Becton Dickinson) with a separation chamber height of 0.5 mm. The separation and counterflow medium (10 mm acetic acid, 10 mm triethanolamine, 1 mm EDTA, and 280 mm Suc; medium inlets 2–6 and counterflow inlets 1–3) as well as electrode stabilization medium (100 mm acetic acid, 100 mm triethanolamine, 10 mm EDTA, and 200 mm Suc; medium inlets 1 and 7) were injected into the separation chamber at a speed of 200 mL h−1. Media for anode and cathode circuits consisted of 100 mm triethanolamine and 10 mm EDTA, respectively. A voltage of 600 V was applied. Before the FFE run, the sample (approximately 10 _μ_g protein _μ_L−1) was subjected to one stroke in a Potter-Elvehjem homogenizer. Sample injection speed was 3,000 to 3,500 _μ_L h−1 depending on the sample and the level of contamination. Fractions were collected on 2-mL 96-well plates. The separation chamber was cooled to 5°C, and the sample and 96-well plates were cooled in an ice bath.

One-Dimensional SDS-PAGE and Immunoblotting

Precast gels with 12% (w/v) acrylamide and 1 mm Tris-HCl (Bio-Rad) were used for analytical purposes and western blotting. Protein assays (Bradford, 1976) were performed on pooled FFE fractions. Proteins were transferred onto nitrocellulose membranes and probed with a 1:5,000 dilution of the primary antibodies mtHSP70:PM003 from Dr. Tom Elthon, Kat2 (Germain et al., 2001), and RbcS, raised against tobacco (Nicotiana benthamiana) Rubisco in rabbits, supplied by Dr. Spencer Whitney (Australian National University). Horseradish peroxidase-conjugated secondary antibodies at a dilution of 1:10,000 were used for the chemiluminescent detection of the immune signal.

Two-Dimensional Gel Electrophoresis

Mitochondrial protein samples (700 μ_g) were extracted by addition of cold acetone (−20°C) to a final concentration of 80% (v/v). Samples were stored at −80°C for 4 h and then centrifuged at 20,000_g at 4°C for 15 min. The pellets were resuspended in IEF sample buffer (7 m urea, 2 m thiourea, 4% [w/v] CHAPS, and 40 mm Tris, pH 8.5). Aliquots of 450 _μ_L were used to reswell immobilized pH gradient strips pH3-10 NL (24 cm; GE Healthcare) according to the manufacturer's instructions. The strips were run for 24 h in Ettan IPGphor3 (GE Healthcare) according to the manufacturer's instruction. The strips were then transferred to an equilibration buffer (50 mm Tris-HCl [pH 6.8], 4 m urea, 2% [w/v] SDS, 0.001% [w/v] bromphenol blue, and 100 mm _β_-mecaptoethanol) and incubated for 20 min at room temperature with rocking. After a brief wash in 1× gel buffer, the strips were transferred to 12% acrylamide Gly gels and covered with 1.2% (v/w) agarose in gel buffer. Second-dimensional gels were run at 50 mA per gel for 6 h. Proteins were visualized by colloidal Coomassie Brilliant Blue (G250) staining.

DIGE Two-Dimensional IEF/SDS-PAGE

Samples (50 _μ_g) of pre- and post-FFE, as well as 50 _μ_g of a 1:1 mixture of both samples, were acetone precipitated, resolubilized in lysis buffer (7 m urea, 2 m thiourea, 4% [w/v] CHAPS, and 40 mm Tris base, pH 8.5), and individually labeled with 400 mm of weight- and pI-matched fluorescent dyes Cy2, Cy3, and Cy5 (GE Healthcare). Samples were then combined and separated on IEF strips pH3-10NL (24 cm; GE Healthcare) according to the manufacturer's instructions. After first dimension running, the strips were then transferred to an equilibration buffer consisting of 50 mm Tris-HCl (pH 6.8), 4 m urea, 2% (w/v) SDS, 0.001% (w/v) bromphenol blue, and 100 mm _β_-mecaptoethanol and incubated for 20 min at room temperature with rocking. After a brief wash in 1× gel buffer, the strips were transferred to 12% (w/v) acrylamide Gly gels and covered with 1.2% agarose in gel buffer. Second dimensional gels were run at 50 mA per gel for 6 h. Proteins were visualized on a Typhoon laser scanner (GE Healthcare), and image comparison was performed using the DECYDER software package (version 6.5; GE Healthcare). Three independent experiments were performed, and each of the resulting three gel sets was first analyzed using differential in-gel analysis mode DECYDER prior to a comprehensive biological variance analysis including all three gel sets. Gel spots were filtered according to their presence and average abundance ratio. Gel images were electronically overlaid using ImageQuant TL software (GE Healthcare).

Analysis of Peptides from In-Gel-Digested Protein Samples

Protein samples to be analyzed were cut from the gels and were in-gel digested according to the method described by Taylor et al. (2005). Samples were then dried in a vacuum centrifuge, resuspended in 5% (v/v) acetonitrile and 0.1% (v/v) formic acid, and analyzed on an Agilent XCT Ultra Ion Trap mass spectrometer (Agilent Technologies) according to Meyer et al. (2007). Resulting MS/MS-derived spectra were analyzed against an in-house rice database (release 5; Rice_osa5) of The Institute for Genomic Research Rice Pseudomolecules and Genome Annotation and mitochondrial and plastid protein sets (combined database contained 66,874 sequences and 29,957,714 residues). The database was searched using the Mascot search engine version 2.2.03 and utilizing error tolerances of ±1.2 D for MS and ±0.6 D for MS/MS, Max Missed Cleavages set to 1, with variable modification of oxidation (M) and carbamidomethyl (C), instrument set to ESI-TRAP, and peptide charge set at 2+ and 3+. RICE-ALL.pep is a nonredundant database with systematically named protein sequences based on rice genome sequencing and annotation. Results were filtered using standard scoring, maximum number of hits set to AUTO, and ion score cutoff at 37. The significance threshold P ≤ 0.05 and Require Bold Red were also set. In order to estimate the false-positive rate of this protein strategy, a single concatenated file was generated by MASCAT (Agilent Technologies) that comprised all of the MS/MS output data. The concatenated file was then used to search against Rice_osa5 (target), reversed (decoy), and randomized (decoy) databases using the above search strategy. The false-positive rate in target-decoy searches was found to be 4.7% for peptides with ion scores > 37, which was calculated using the equation described previously (Elias et al., 2005).

Analysis of Peptides from Whole Organelle Digests

Whole organelle protein extracts were digested overnight at 37°C in the presence of trypsin, and insoluble components were removed by centrifugation at 20,000_g_ for 5 min. Samples were analyzed on an Agilent 6510 Q-TOF mass spectrometer with an HPLC Chip Cube source (Agilent Technologies). The chip consisted of a 40-nL enrichment column (Zorbax 300SB-C18; 5-_μ_m pore size) and a 150-mm separation column (Zorbax 300SB-C18; 5-_μ_m pore size) driven by the Agilent Technologies 1100 series nano/capillary liquid chromatography system. Both systems were controlled by MassHunter Workstation Data Acquisition for Q-TOF (version B.01.02, Build 65.4, Patches 1,2,3,4; Agilent Technologies). Peptides were loaded onto the trapping column at 4 _μ_L min−1 in 5% (v/v) acetonitrile and 0.1% (v/v) formic acid with the chip switched to enrichment and using the capillary pump. The chip was then switched to separation, and peptides eluted during a 1-h gradient (5% [v/v] acetonitrile to 40% [v/v] acetonitrile) directly into the mass spectrometer. The mass spectrometer was run in positive ion mode, and MS scans were run over a mass-to-charge ratio range of 275 to 1,500 and at four spectra per second. Precursor ions were selected for auto MS/MS at an absolute threshold of 500 and a relative threshold of 0.01, with maximum three precursors per cycle, and active exclusion set at two spectra and released after 1 min. Precursor charge-state selection and preference was set to 2+ and then 3+, and precursors were selected by charge and then abundance. Resulting MS/MS spectra were opened in MassHunter Workstation Qualitative Analysis (version B.01.02, Build 1.2.122.1, Patches 3; Agilent Technologies), and MS/MS compounds were detected by Find Auto MS/MS using default settings. The resulting compounds were then exported as mzdata files, which when appropriate were combined using mzdata Combinator version 1.0.4 (West Australian Centre of Excellence in Computational Systems Biology; http://www.plantenergy.uwa.edu.au/wacecsb/software.shtml). Searches were conducted using Mascot Search Engine version 2.2.03 (Matrix Science) with mass error tolerances of ±100 ppm for MS and ±0.5 D for MS/MS, maximum missed cleavages set to 1, with variable modification of oxidation (M) and carbamidomethyl (C), instrument set to ESI-QUAD-TOF, and peptide charge set at 2+ and 3+. Results were filtered using mudpit scoring, maximum number of hits set to 400, and ion score cutoff at 20. The significance threshold P ≤ 0.05 and Require Bold Red were also set. The false-positive peptide identification rate under the matching criteria described above was estimated at 1.5%. The total number of times each protein was identified after FFE purification using non-gel-based methods in four independent runs is listed in the Supplemental Table S4, with the removed contaminants listed in Supplemental Table S2B.

Rice Germination Microarray Analysis and Comparison with Public Rice Microarray Data

Dehulled, sterilized rice seeds were grown under aerobic conditions in the dark at 30°C as described previously (Howell et al., 2006). RNA isolation, cDNA synthesis, and quantitative reverse transcription-PCR were conducted according to methods described previously (Howell et al., 2006). Transcriptomic analysis was performed using Affymetrix GeneChip Rice Genome Arrays (Affymetrix), and three biological replicates were analyzed for each time point. Preparation of labeled copy RNA from 2 to 3 _μ_g of total RNA, target hybridization, as well as washing, staining, and scanning of the arrays were carried out exactly as described in the Affymetrix GeneChip Expression Analysis Technical Manual, using the Affymetrix One-Cycle Target Labeling and Control Reagents, an Affymetrix GeneChip Hybridization Oven 640, an Affymetrix Fluidics Station 450, and an Affymetrix GeneChip Scanner 3000 7G at the appropriate steps. Data quality was assessed using GCOS 1.4 (Affymetrix) before CEL files were imported into Avadis 4.3 (Strand Genomics) for further analysis. Raw intensity data were initially normalized using the MAS5 algorithm allowing probe identifiers called present to be determined. Only those probe sets that were called present in at least two of three replicates in at least one time point were included for further analysis. Ambiguous probe sets and bacterial controls were also removed, resulting in a final data set of 24,150 probe sets. All microarray data have been deposited in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/) under accession code E-MEXP-1766.

In order to examine transcript abundance changes across different tissues and under different conditions and to compare these with the obtained germination transcript abundance profiles, rice array data were retrieved from the Gene Expression Omnibus within the National Center for Biotechnology Information database (GSE6901, GSE7951 GSE6908, GSE4438, GDS1383, and GSE7256). All data were MAS5.0 normalized and normalized against average ubiquitin expression for that array. These normalized array data were then compiled together, and for each probe set the maximum expression was set to 1.0, with all other data relative to this. This normalization allowed cross-comparison of arrays from all of the different studies at once. The arrays analyzed included all arrays from this study together with publicly available rice genome arrays carried out from different tissues/conditions. Hierarchical clustering across all of the arrays was carried out with average linkage clustering based on Euclidian distance using Partek Genomics suite software, version 6.3 (Supplemental Table S5).

Supplemental Data

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

• Supplemental Figure S1. Separation of Percoll-purified mitochondria from rice shoots by FFE into 96 fractions.
• Supplemental Figure S2. Coomassie Brilliant Blue-stained IEF/SDS-PAGE separation of rice mitochondrial proteins (pI = 3–10) and number of protein spots for analysis in Supplemental Table S1.
• Supplemental Figure S3. Hierarchical clustering of the expression on nuclear genes encoding rice mitochondrial proteins based on their functional classification.
• Supplemental Table S1. Identification of proteins from FFE-purified rice mitochondria using MS/MS after separation of proteins by IEF/SDS-PAGE.
• Supplemental Table S2. Proteins removed as contaminants in rice mitochondrial preparations.
• Supplemental Table S3. Final list of proteins classified as rice mitochondrial proteins.
• Supplemental Table S4. Total protein identifications found by four independent non-gel-based LC runs after FFE purification of rice mitochondria
• Supplemental Table S5. Hierarchical clustering of all arrays using average linkage clustering based on Euclidian distance, which was used to generate Figure 6.

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Author notes

1

This work was supported by the Australian Research Council (ARC) through the Discovery Programme (grant no. DP0664692 to A.H.M. and J.W.). N.L.T. and H.E. are supported as ARC Australian Postdoctoral Fellows (grant nos. DP0772155 and DP0773152), and A.H.M. is an ARC Australian Professorial Fellow (grant no. DP0771156).

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: A. Harvey Millar (hmillar@cyllene.uwa.edu.au).

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© 2009 American Society of Plant Biologists

© The Author(s) 2009. Published by Oxford University Press on behalf of American Society of Plant Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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