Organellar Proteomics: Tissue Specificity, Absolute Quantity, Post-translational Modifications and Protein-Protein Interactions
Received Date: Mar 07, 2017 / Accepted Date: Mar 08, 2017 / Published Date: Mar 24, 2017
Organellar proteomics combines subcellular fractionation and mass spectrometry-based protein identifications. In the past decades, organellar proteomic analysis has been carried out for virtually every subcellular compartment in mammalian cells and tissues [1-4]. While conventional biochemical and biophysical approaches study the structures and functions of individual proteins, mass spectrometrybased proteomic studies allow us to understand the entire proteome or sub proteome systematically by: (i) Identifying proteins present in each subcellular organelle; (ii) Quantifying their expression levels; and (iii) Characterizing their posttranslational modifications and proteinprotein interactions. All of the above determines the protein’s function and activity.
Mass spectrometry analyses of different cell lines and multiple tissues have been carried out to address how tissues differ in gene and protein expression and how this contributes to tissue-specific biological functions [5-7]. Particularly important to subcellular organelles, existing proteomics studies indicated that mitochondria are functionally specialized in different tissues, which is crucial to its involvement in normal physiology and diseases [8-10]. Our results also indicated that the endoplasmic reticulum, another important organelle, expresses unique proteins to accommodate its tissue-specific functions [11,12]. In order to comprehensively unveil the tissuespecific and species-specific organellar markers, we will need to perform more quantitative organellar proteomics analyses. It is of equal importance that we will need the bioinformatics tools to mine the existing proteomics data sets to discover such markers.
Nowadays, quantitative mass spectrometry-based technologies have been widely used to compare relative protein abundances in different samples. Such examples include several of our studies on organellar protein changes in different cellular or diseased states [13-15]. More recently, Selected Reaction Monitoring (SRM)-based targeted proteomics approaches have been used to obtain absolute quantities and copy numbers of cellular proteins [16-18]. Due to the high cost of the stable isotope labeled synthetic peptides, it is not yet feasible to obtain such absolute quantification for the entire cellular proteome or a sub proteome of a given organelle. Therefore, computational methods are needed and several have already been developed to overcome these limitations [18-20]. By quantitatively determining protein copy numbers, proteomics studies can lead to a deeper understanding of protein stoichiometry and cellular functions of a specific organelle.
In addition to tissue specificity and quantitative measurements, proteomics can also help to identify the role of multiprotein complexes in exerting specific organellar functions. The biological function of proteins often relies on the association and dissociation of protein complexes. These complexes are dynamic in nature, often controlled by posttranslational modifications such as acetylation, phosphorylation, and ubiquitination . Modern proteomics technologies have made it feasible to detect and quantify changes of protein modifications, protein conformations and protein interactions. Other biochemical approaches such as native gel electrophoresis and chemical crosslinking are often utilized in combination with mass spectrometry [4,21]. Such proteomic studies are expected to advance our understanding of the function and protein composition of each subcellular organelle in normal and disease states.
Our deeper understanding of biological functions of subcellular organelles will rely on a comprehensive characterization of their tissuespecific protein compositions, determination of protein copy numbers and dynamic protein-protein interactions. Proteomic technology developments in the past years have made such tasks feasible. Major advances in this area are expected in near future. Advances in bioinformatics and machine-learning technologies will also facilitate us to obtain a systematic understanding of how organellar sub proteome functions in a systematic manner.
This work is supported by National Institute of Diabetes and Digestive and Kidney Diseases grants 1R56DK102039 and 1R01DK110314 to X. C.
- Yates JR, Gilchrist A, Howell KE, Begeron (2005) Proteomics of organelles and large cellular structures. Nat Rev Mol Cell Biol 6: 702-714.
- Walther TC, Mann M (2010) Mass spectrometry-based proteomics in cell biology. J Cell Biol 190: 491-500.
- Au CE, Bell AW, Gilchrist A, Hiding J, Nilsson T, et al. (2007) Organellar proteomics to create the cell map. Curr Opin Cell Biol 19: 376-385.
- Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537: 347-355.
- Azimifar SB, Nagaraj N, Cox J, Mann M (2014) Cell-type-resolved quantitative proteomics of murine liver. Cell Metabol 20: 1076-1087.
- Richards AL, Merrill AE, Coon JJ (2015) Proteome sequencing goes deep. Curr Opin Chem Biol 24: 11-17.
- Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N (2015) Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci 18: 1819-1831.
- Forner F, Kumar C, Luber CA, Froome T, Klingenspor M (2009) Proteome differences between brown and white fat mitochondria reveal specialized metabolic functions. Cell Metabol 10: 324-335.
- Mootha VK, Bunkenborg J, Olsen JV, Hjerrild M, Wisniewski JR, et al. (2003) Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 115: 629-640.
- Kislinger T, Cox B, Kannan A, Chung C, Hu P, et al. (2006) Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125: 173-186.
- Chen X, Karnovsky A, Sans MD, Andrews PC, Williams JA (2010) Molecular characterization of the endoplasmic reticulum: insights from proteomic studies. Proteomics 10: 4040-4052.
- Lee JS, Wu Y, Schnepp P, Fang J, Zhang X (2015) Proteomics analysis of rough endoplasmic reticulum in pancreatic beta cells. Proteomics 15: 1508-1511.
- Chen X, Sans MD, Strahler JR, Karnovsky A, Ernst SA (2010) Quantitative organellar proteomics analysis of rough endoplasmic reticulum from normal and acute pancreatitis rat pancreas. J Proteome Res 9: 885-896.
- Chen X, Ulintz PJ, Simon ES, Williams JA, Andrews PC (2008) Global topology analysis of pancreatic zymogen granule membrane proteins. Mol Cell Proteomics 7: 2323-2336.
- Chen X, Simon ES, Xiang Y, Kachman M, Andrews PC (2010) Quantitative proteomics analysis of cell cycle-regulated Golgi disassembly and reassembly. J Biol Chem 285: 7197-7207.
- Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R (2009) Full dynamic range proteome analysis of Saccharomyces cerevisiae by targeted proteomics. Cell 138: 795-806.
- Anderson L, Hunter CL (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 5: 573-588.
- Malmstrom J, Beck M, Schmidt A, Lange V, Deutsch EW, et al. (2009) Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460: 762-765.
- Lu P, Vogel C, Wang R, Yao X, Marcotte EM (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25: 117-124.
- Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, et al. (2011) Global quantification of mammalian gene expression control. Nature 473: 337-342.
- Heck AJ (2008) Native mass spectrometry: a bridge between interactomics and structural biology. Nat Method 5: 927-933.
Citation: Moussa AE, Chen X (2017) Organellar Proteomics: Tissue Specificity, Absolute Quantity, Post-translational Modifications and Protein-Protein Interactions. J Data Mining Genomics Proteomics 8: e129. Doi: 10.4172/2153-0602.1000e129
Copyright: © 2017 Moussa AE, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Select your language of interest to view the total content in your interested language
Share This Article
5th International Conference on Glycobiology, Lipids & Proteomics
August 27-28, 2018 Toronto, Canada
International Conference on Computational Biology and Bioinformatics
Sep 05-06 2018 Tokyo, Japan
12th International Conference on Advancements in Bioinformatics and Drug Discovery
November 26-27, 2018 Dublin, Ireland
- Total views: 451
- [From(publication date): 0-2017 - Jun 24, 2018]
- Breakdown by view type
- HTML page views: 405
- PDF downloads: 46