A systematic characterization of mitochondrial proteome from human T leukemia cells

Abstract

Global understanding of tissue-specific differences in mitochondrial signal transduction requires comprehensive mitochondrial protein identification from multiple cell and tissue types. Here, we explore the feasibility and efficiency of protein identification using the one-dimensional gel electrophoresis in combination with the nano liquid-chromatography tandem mass spectrometry (GeLC-MS/MS). The use of only 40 mug of purified mitochondrial proteins and data analysis using stringent scoring criteria and the molecular mass validation of the gel slices enables the identification of 227 known mitochondrial proteins (membrane and soluble) and 453 additional proteins likely to be associated with mitochondria. Replicate analyses of 60 mug of mitochondrial proteins on the faster scanning LTQ mass spectrometer validate all the previously identified proteins and most of the single hit proteins except the 81 single hit proteins. Among the identified proteins, 466 proteins are known to functionally participate in various processes such as respiration, tricarboxylic acid cycle (TCA cycle), amino acid and nucleotide metabolism, glycolysis, protection against oxidative stress, mitochondrial assembly, molecular transport, protein biosynthesis, cell cycle control, and many known cellular processes. The distribution of identified proteins in terms of size, pI, and hydrophobicity reveal that the present analytical strategy is largely unbiased and very efficient. Thus, we conclude that this approach is suitable for characterizing subcellular proteomes form multiple cells and tissues.

Fig. 1. Purification and characterization of Jurkat T cell mitochondria

A, equal amounts of proteins (15 μg) were loaded onto a 10% SDS-PAGE and analyzed by Western blotting with indicated antibodies against marker proteins from mitochondria, cytosol, or nucleus. Antibodies against LDH and PCNA were used as markers for cytosolic and nuclear/cytosolic fractions, respectively. Antibodies directed against F₀F₁ ATP synthase subunit α (F1α) and cytochrome c (Cyto. C) were used as markers for mitochondrial fraction. B, electron micrograph of purified mitochondrial fraction is shown indicating the longitudinal and cross-sections of mitochondria. Bar, 1 μm.

Fig. 2. Proteomic characterization of purified Jurkat mitochondria

A, total mitochondrial lysate (40 μg) was separated by the use of 10% gel and visualized by Coomassie blue staining. Twenty-two gel slices were excised from this gel, subjected to in-gel trypsin digestion followed by the nano-capillary LC-MS/MS analysis. Molecular mass markers are shown on the left. Regions of the gel slices are shown on the right. Identified protein names and their theoretically calculated molecular masses from gel slice number 16 is listed on the right of the gel. B, schematic flow chart indicating LC-MS steps and proteomics data analysis steps using a number of specialized bioinformatics software tools. Total number of MS/MS attempts from validation experiments, experiment 2 and 3, are also shown. C, specificity of protein identification from corresponding gel slices. Most of the identified proteins were restricted to one or two adjacent gel slices. A comprehensive list of identified peptides and their respective gel slices are included in supplemental Table IV.

Fig. 3. Distribution of identified peptide number per mitochondrial protein from Jurkat T leukemia cells

Distribution of the 680 identified mitochondrial proteins from Jurkat T cells in relation to the number of the matching peptides used for identity assignment is shown. The bars indicate the percentages of the proteins identified with the corresponding number of matching peptides.

Fig. 4. Physiochemical characteristics of mitochondrial proteins form Jurkat T leukemia cells

Distribution of the 680 identified mitochondrial proteins from Jurkat T cells in relation to their pI (A) and molecular mass (B) are shown. The 680 identified proteins were sorted based on their theoretical pI and molecular mass ranges as indicated.

Fig. 5. Functional classification of mitochondrial proteins from Jurkat T leukemia cells

Distribution of the 466 functionally classified proteins. Protein functions were assigned according to the MitoProteome database (www.mitoproteome.org) and information provided in the National Center for Biotechnology Information’s Locuslink website (www.ncbi.nlm.nih.gov/Locuslink). Among the 227 proteins with known mitochondrial assignments, all of them except the 12 proteins have known function.

Fig. 6. Subcellular distribution of functionally unknown class of proteins by the use of bioinformatics prediction tool

Subcellular distribution of 214 functionally unknown proteins is shown. Proteins were assigned to subcellular distribution by the PSORT II prediction software tool (psort.ims.u-tokyo.ac.jp/cgi-bin/runpsort.pI).

Fig. 7. Identification of Bax protein in mitochondria from human Jurkat leukemia cells

A, amino acid sequence of Bax protein and the identified peptides are shown. Each of the independently identified peptides with high Pcomp values are underlined and displayed in color. B, MS/MS spectra from two of the peptides are shown. The theoretical predicted and experimentally identified b and y ion series are shown.