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      A global Staphylococcus aureus proteome resource applied to the in vivo characterization of host-pathogen interactions

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          Abstract

          Data-independent acquisition mass spectrometry promises higher performance in terms of quantification and reproducibility compared to data-dependent acquisition mass spectrometry methods. To enable high-accuracy quantification of Staphylococcus aureus proteins, we have developed a global ion library for data-independent acquisition approaches employing high-resolution time of flight or Orbitrap instruments for this human pathogen. We applied this ion library resource to investigate the time-resolved adaptation of S. aureus to the intracellular niche in human bronchial epithelial cells and in a murine pneumonia model. In epithelial cells, abundance changes for more than 400  S. aureus proteins were quantified, revealing, e.g., the precise temporal regulation of the SigB-dependent stress response and differential regulation of translation, fermentation, and amino acid biosynthesis. Using an in vivo murine pneumonia model, our data-independent acquisition quantification analysis revealed for the first time the in vivo proteome adaptation of S. aureus. From approximately 2.15 × 10 5S. aureus cells, 578 proteins were identified. Increased abundance of proteins required for oxidative stress response, amino acid biosynthesis, and fermentation together with decreased abundance of ribosomal proteins and nucleotide reductase NrdEF was observed in post-infection samples compared to the pre-infection state.

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          Most cited references45

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          Waves of resistance: Staphylococcus aureus in the antibiotic era.

          Staphylococcus aureus is notorious for its ability to become resistant to antibiotics. Infections that are caused by antibiotic-resistant strains often occur in epidemic waves that are initiated by one or a few successful clones. Methicillin-resistant S. aureus (MRSA) features prominently in these epidemics. Historically associated with hospitals and other health care settings, MRSA has now emerged as a widespread cause of community infections. Community or community-associated MRSA (CA-MRSA) can spread rapidly among healthy individuals. Outbreaks of CA-MRSA infections have been reported worldwide, and CA-MRSA strains are now epidemic in the United States. Here, we review the molecular epidemiology of the epidemic waves of penicillin- and methicillin-resistant strains of S. aureus that have occurred since 1940, with a focus on the clinical and molecular epidemiology of CA-MRSA.
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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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              Using iRT, a normalized retention time for more targeted measurement of peptides.

              Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.
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                Author and article information

                Contributors
                frank.schmidt@uni-greifswald.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 September 2017
                8 September 2017
                2017
                : 7
                : 9718
                Affiliations
                [1 ]GRID grid.5603.0, Interfaculty Institute for Genetics and Functional Genomics, , Department of Functional Genomics, University Medicine Greifswald, ; Greifswald, Germany
                [2 ]GRID grid.5603.0, Institute for Microbiology, , Ernst-Moritz-Arndt-University, ; Greifswald, Germany
                [3 ]GRID grid.5603.0, Interfaculty Institute for Genetics and Functional Genomics, , Department Genetics of Microorganisms, Ernst-Moritz-Arndt-University, ; Greifswald, Germany
                [4 ]ISNI 0000 0004 0463 2320, GRID grid.64212.33, Institute for Systems Biology, ; Seattle, WA USA
                [5 ]GRID grid.5603.0, ZIK-FunGene, Interfaculty Institute for Genetics and Functional Genomics, , Department of Functional Genomics, University Medicine Greifswald, ; Greifswald, Germany
                Author information
                http://orcid.org/0000-0002-3553-4315
                http://orcid.org/0000-0002-6382-6681
                Article
                10059
                10.1038/s41598-017-10059-w
                5591248
                28887440
                83561693-cdfb-4142-8bb5-f1dd15f88c13
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 February 2017
                : 24 July 2017
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