Experimental analysis of the rice mitochondrial proteome, its biogenesis, and heterogeneity

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.

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.

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.

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.

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.

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.

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.