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      Evaluation of iTRAQ and SWATH-MS for the Quantification of Proteins Associated with Insulin Resistance in Human Duodenal Biopsy Samples

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          Abstract

          Insulin resistance (IR) is associated with increased production of triglyceride-rich lipoproteins of intestinal origin. In order to assess whether insulin resistance affects the proteins involved in lipid metabolism, we used two mass spectrometry based quantitative proteomics techniques to compare the intestinal proteome of 14 IR patients to that of 15 insulin sensitive (IS) control patients matched for age and waist circumference. A total of 3886 proteins were identified by the iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) mass spectrometry approach and 2290 by the SWATH-MS strategy (Serial Window Acquisition of Theoretical Spectra). Using these two methods, 208 common proteins were identified with a confidence corresponding to FDR < 1%, and quantified with p-value < 0.05. The quantification of those 208 proteins has a Pearson correlation coefficient (r 2) of 0.728 across the two techniques. Gene Ontology analyses of the differentially expressed proteins revealed that annotations related to lipid metabolic process and oxidation reduction process are overly represented in the set of under-expressed proteins in IR subjects. Furthermore, both methods quantified proteins of relevance to IR. These data also showed that SWATH-MS is a promising and compelling alternative to iTRAQ for protein quantitation of complex mixtures.

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          Most cited references22

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          The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.

          The Paragon Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user interface that removes the user expertise requirement, presenting control settings in the language of the laboratory that are translated to optimal algorithmic settings. To validate this new algorithm, a comparison with Mascot is presented for a series of analogous searches to explore the relative impact of increasing search space probed with Mascot by relaxing the tryptic digestion conformance requirements from trypsin to semitrypsin to no enzyme and with the Paragon Algorithm using its Rapid mode and Thorough mode with and without tryptic specificity. Although they performed similarly for small search space, dramatic differences were observed in large search space. With the Paragon Algorithm, hundreds of biological and artifact modifications, all possible substitutions, and all levels of conformance to the expected digestion pattern can be searched in a single search step, yet the typical cost in search time is only 2-5 times that of conventional small search space. Despite this large increase in effective search space, there is no drastic loss of discrimination that typically accompanies the exploration of large search space.
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            iTRAQ underestimation in simple and complex mixtures: "the good, the bad and the ugly".

            The increasing popularity of iTRAQ for quantitative proteomics applications makes it necessary to evaluate its relevance, accuracy, and precision for biological interpretation. Here, we have assessed (a) the accuracy and precision of iTRAQ quantification in a controlled experimental setup, using low- and high-complexity protein mixtures; and (b) the potential pitfalls that hamper the applicability and attainable dynamic range of iTRAQ: isotopic contamination, background interference, and signal-to-noise ratio. Our data suggest greater dynamic crosstalk between interfering factors affecting underestimations, and that these interferences were largely scenario-specific, dependent on sample complexity. The good is the potential for iTRAQ to provide accurate quantification spanning 2 orders of magnitude. This potential is however limited by two factors. (1) The bad: the existence of isotopic impurities that can be corrected for; provided accurate isotopic factors are at one's disposal. (2) The ugly: we demonstrate here the interference of mixed MS/MS contribution occurring during precursor selection, an issue that is currently very difficult to minimize. In light of our results, we propose a list of advice for iTRAQ data analysis that could routinely ameliorate quantitative interpretation of proteomic data sets.
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              Nonlinear fitting method for determining local false discovery rates from decoy database searches.

              False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 May 2015
                2015
                : 10
                : 5
                : e0125934
                Affiliations
                [1 ]Proteomics Center, CHU de Québec Research Center and Department of Molecular Medicine, Laval University, Quebec, Canada
                [2 ]Lipid Research Center, Centre Hospitalier de l’Université Laval Research Center, Laval University, Quebec, Canada
                USDA-ARS, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SB BN IK AT VL BL PC AD. Performed the experiments: SB BN IK AT VL. Analyzed the data: SB FF BN IK. Contributed reagents/materials/analysis tools: SB FF BN IK BL PC AD. Wrote the paper: SB FF BN IK BL PC AD.

                Article
                PONE-D-14-51718
                10.1371/journal.pone.0125934
                4423961
                25950531
                b2c4f62b-2efb-4900-8949-15b26da3037f
                Copyright @ 2015

                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

                History
                : 17 November 2014
                : 19 March 2015
                Page count
                Figures: 6, Tables: 0, Pages: 16
                Funding
                AD holds a Réseau de médecine génétique appliquée (RMGA) salary award. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All 126 files have been submitted to the PRIDE database (ProteomeXchange) with identifier PXD001506.

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