Pago efectivo

The two parallel localization experiments for each of the adipose tissue types i.e. BAT and WAT (Figure 9.1) provides us with two levels of confidence for a protein to be localized in either mitochondrion versus its post mitochondrial fraction or nuclei. Subsequently, as described in section 9.3.3 and illustrated in figure 9.2, for each experiment we categorize each protein as either being MITO, BORDER or Non-MITO by using conservative probability cutoffs (section 9.2.5). Furthermore, we used this dual localization evidences for each tissue type to define the core mitochondrial proteome of that tissue type. The classification contingency matrix along with the numbers of proteins identified as mitochondrial or non-mitochondrial are illustrated in Figure 9.4. In BAT we putatively assign 650 proteins to mitochondria (out of 1,753) and in WAT we assign 1,512 proteins to mitochondria (out of 3,345). Though in terms of the numbers the BAT mitochondrial proteome is approximately one-third of the WAT mitochondrial proteome, this disparity diminishes when we look at the percentage coverage of BAT and WAT mitochondrial proteome w.r.t the quantified proteome in each tissue type. BAT mitochondrial proteins cover roughly 37% of the total tissue specific organelle proteome while WAT covers 45%. Lastly, we use the union of these two tissue type mitochondrial proteomes to arrive at 1,517 putative mitochondrial proteins in adipose tissue of mouse.

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Figure 9.4 contingency matrixes for combining evidence from two parallel experiments each in BAT and WAT to define core-mitochondrial proteome of adipose tissue (WAT). The class assigned (Mito, Border, Nuc/PMF) in each experiment ( Nuc vs. Mito and PMF vs. Mito; Figure 9.1) were combined to assign a final class to the proteins as shown in the matrix. Blue boxes contain the mitochondrial protein numbers in BAT (upper matrix) and WAT (lower matrix). In total we obtained 1,517 core mitochondrial proteins.

9.4 Discussion

Protein localization studies in sub-cellular organelles have become a mainstay in current systems biology enterprises, and are definitely the first steps towards realizing their promises and goals147,349. In recent years many proteomics methods based on MS technology have been reported for establishing the protein localization and organelle localizomes332-334. The most

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effective of these methods combine elaborate cellular fractionation, mass-spectrometry and quantitative proteomics. Protein Correlation Profiling (PCP) and Localization of Organelle Proteins by Isotope Tagging (LOPIT) are the representative approaches in this category104,350. While they provide definite results they also require simultaneous analysis of many fractions and are limited by the efficiency of fractionation, most importantly they require prior knowledge of organelle marker proteins. We devised a simpler and generic approach whereby we build upon the strengths of quantitative proteomics and SILAC and do away with multiple fractionations. Furthermore we use a probabilistic localization method with simplest computational assumptions (or model) and do not require prior knowledge of organelle marker proteins. As a proof of concept we use this framework to assign putative localization to proteins identified from BAT and WAT mitochondria.

Mitochondria have been one of the most widely studied sub-cellular organelles and they play pivotal roles in myriad biological processes including growth, division, energy metabolism and apoptosis351-353. Due to their expansive role in cellular processes they are also implicated in myriad diseases including obesity, diabetes, cancer, neurodegenerative and cardio-vascular disorders354,355. The advent of high throughput “omics” disciplines has helped tremendously in generating parts list of mitochondria at various levels of organization including genome, proteome, metabolome and interactome; thereby providing valuable insights into its biology and cellular function356. The study of mammalian mitochondrial proteomes is a challenge owing to its dynamic constitution, and the complexity of the cellular milieu in these systems. In the recent past proteomics and functional genomics approaches have been employed for establishing the mitochondrial proteome of mouse and humans57,59,342,357. According to the latest estimates there are ~1500 mitochondrial proteins in humans358. While recent systemic analysis approaches are trying to enumerate the complete mitochondrial proteome in model organisms359, there is still clear consensus that many more mitochondrial proteins remain to be discovered and characterized360. Moreover, study of the tissue specific organellar proteome is of immense value as it correctly portrays the physiology which is often not captured by studying cell lines of similar origin342. Therefore we sought to explore the mitochondrial proteome from brown and white adipose tissue of mouse as a model system for humans361.

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In two sets of parallel experiments we were able to quantify and elucidate 650 (BAT) and 1,512 (WAT) putative mitochondrial proteins according to very high probability scores obtained by our bioinformatics approach. These proteins show very high concordance with the known Gene Ontology mitochondrial localizations. Furthermore, by combining the localization evidences from these two separate tissue specific experiments we were able to derive a set of 1,517 putative mitochondrial proteins in mouse adipose tissue. This candidate mitochondrial catalogue was further used in a setup for systems level analysis of in vivo quantitative profiling of mitochondrial proteome of BAT and WAT. Figure 9.5 shows the schematic of this experiment; briefly tissue mitochondria were isolated and quantified against mitochondria isolated from SILAC labeled cognate cell types (3T3-L1 and brown adipocytes). The mitochondrial proteome identified and quantified in this experiment was filtered against the candidate mitochondrial catalogue established in earlier steps to arrive at high confidence in vivo quantitative BAT vs. WAT mitochondrial proteome of 978 proteins. The relative ratios of proteins in two tissue types provided a quantitative landscape of the differences in BAT versus WAT mitochondrial proteins, which were further shown to be involved in many biological processes and pathways. One of the key findings therein was that there is considerable specialization and divergence of key mitochondrial pathways in the two tissue types as shown in Figure 9.6. For instance, strongly up- regulated pathways in BAT mitochondria were ubiquinone biosynthesis (p=2.8E-12), oxidative phosphorylation (p=7.6E-28), and citrate cycle (p=8.6E-10). In WAT mitochondria up-regulation of pathways was less pronounced, and significantly up-regulated pathways were androgen/estrogen metabolism (p=3.8E-3), fatty acid synthesis (p=2.4E-3), pyruvate metabolism (p=1.6E-4), and metabolism of xenobiotics (p=8.4E-2).

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Figure 9.5 Strategy of quantitative proteomic analysis of mitochondria in BAT vs. WAT. Mitochondria isolated from brown and white adipocytes were mixed with SILAC labeled mitochondria isolated from bat and 3T3-L1 cells that served as internal standards. In vivo relative protein levels of BAT versus WAT were obtained from the “ratio of ratios” of peptide levels measured by mass spectrometry. The final dataset was the WB-mitochondrial core proteome. Proteins were qualified into one of five protein categories obtained by subdividing the BAT/WAT distribution in percentiles (10%, 25%, 75%, and 90%). The categories were named vH-BAT, H-BAT, 1to1, H-WAT, and vH-WAT to express their relative abundance in BAT versus WAT.

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Figure 9.6 Map of metabolic pathways in primary adipocyte mitochondria from the systematic analysis of quantitative proteomic data. Relative activity of pathways in BAT and WAT mitochondria were inferred from the protein ratios obtained from quantitative proteomics(Figure 9.5). Proteins were subdivided into five categories (see Figure 2) and color-coded based on their BAT/WAT protein ratios: vH-BAT (red), H-BAT (orange), 1to1 (grey), H-WAT (green), vH-WAT (dark green). Non-filled boxes represent proteins that were not identified in our proteomic survey or that were non-mitochondrial based on our localization assignment. A tentative localization of MCC-32 with its putative interaction partners as obtained from our preliminary experiments is also shown.

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In summary our framework adds to the growing toolkit of organellar proteomics and is very generic in terms of applicability. The simplicity and flexibility of our approach makes it amenable for global-study of other organelle proteomes and opens newer vistas towards creation of organelle cellular maps334. The framework and the mitochondrial database generated will be of great value to the ongoing systems biology and „omics‟ endeavors.

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