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      Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans

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          Abstract

          Mass spectrometry based methods for relative proteome quantification have broadly impacted life science research. However, important research directions, particularly those involving mathematical modeling and simulation of biological processes, also critically depend on absolutely quantitative data, i.e. knowledge of the concentration of the expressed proteins as a function of cellular state. Until now, absolute protein concentration measurements of a significant fraction of the proteome (73%) have only been derived from genetically altered S. cerevisiae cells 1, a technique that is not directly portable from yeast to other species. In this study we developed and applied a mass spectrometry based strategy to determine the absolute quantity i.e. the average number of protein copies per cell in a cell population, for a significant fraction of the proteome in genetically unperturbed cells. Applying the technology to the human pathogen Leptospira interrogans, a spirochete responsible for Leptospirosis 4, we generated an absolute protein abundance scale for 83% of the mass spectrometry detectable proteome, from cells at different states. Taking advantage of the unique cellular dimensions of L. interrogans, we used cryo electron tomography (cryoET) morphological measurements to verify at the single cell level the average absolute abundance values of selected proteins determined by mass spectrometry on a population of cells. As the strategy is relatively fast and applicable to any cell type we expect that it will become a cornerstone of quantitative biology and systems biology.

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          Most cited references23

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

            We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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              Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation.

              We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about approximately 5,600 ( approximately 540 in E. coli) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                16 July 2009
                15 July 2009
                6 August 2009
                7 February 2010
                : 460
                : 7256
                : 762-765
                Affiliations
                [1 ] Institute of Molecular Systems Biology, ETH Zurich (Swiss Federal Institute of Technology), Wolfgang Pauli-Str. 16, CH-8093 Zurich, Switzerland
                [2 ] Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103-8904, USA
                [3 ] Faculty of Science, University of Zurich, Switzerland
                [4 ] Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland
                Author notes
                [5 ] correspondences should be addressed to aebersold@ 123456imsb.biol.ethz.ch
                [*]

                contributed equally.

                Article
                nihpa120631
                10.1038/nature08184
                2723184
                19606093
                13c4b4b7-20b5-4f0e-a355-fd6f453cdb7f
                History
                Funding
                Funded by: Division of Heart and Vascular Diseases : NHLBI
                Award ID: N01 HV028179 ||HV
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