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      Proteins involved in embryo-maternal interaction around the signalling of maternal recognition of pregnancy in the horse

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          Abstract

          During maternal recognition of pregnancy (MRP), a conceptus-derived signal leads to the persistence of the corpus luteum and the maintenance of gestation. In the horse, the nature of this signal remains to be elucidated. Several studies have focused on the changes in gene expression during MRP, but little information exists at the protein level. The aim of this study was to identify the proteins at the embryo-maternal interface around signalling of MRP in the horse (day 13) by means of mass spectrometry. A distinct influence of pregnancy was established, with 119 proteins differentially expressed in the uterine fluid of pregnant mares compared to cyclic mares and with upregulation of several inhibitors of the prostaglandin synthesis during pregnancy. By creating an overview of the proteins at the embryo-maternal interface in the horse, this study provides a solid foundation for further targeted studies of proteins potentially involved in embryo-maternal interactions, MRP and pregnancy loss in the horse.

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          Glutathione transferases.

          John Hayes, Jack U. Flanagan, Ian R Jowsey (2005)
          This review describes the three mammalian glutathione transferase (GST) families, namely cytosolic, mitochondrial, and microsomal GST, the latter now designated MAPEG. Besides detoxifying electrophilic xenobiotics, such as chemical carcinogens, environmental pollutants, and antitumor agents, these transferases inactivate endogenous alpha,beta-unsaturated aldehydes, quinones, epoxides, and hydroperoxides formed as secondary metabolites during oxidative stress. These enzymes are also intimately involved in the biosynthesis of leukotrienes, prostaglandins, testosterone, and progesterone, as well as the degradation of tyrosine. Among their substrates, GSTs conjugate the signaling molecules 15-deoxy-delta(12,14)-prostaglandin J2 (15d-PGJ2) and 4-hydroxynonenal with glutathione, and consequently they antagonize expression of genes trans-activated by the peroxisome proliferator-activated receptor gamma (PPARgamma) and nuclear factor-erythroid 2 p45-related factor 2 (Nrf2). Through metabolism of 15d-PGJ2, GST may enhance gene expression driven by nuclear factor-kappaB (NF-kappaB). Cytosolic human GST exhibit genetic polymorphisms and this variation can increase susceptibility to carcinogenesis and inflammatory disease. Polymorphisms in human MAPEG are associated with alterations in lung function and increased risk of myocardial infarction and stroke. Targeted disruption of murine genes has demonstrated that cytosolic GST isoenzymes are broadly cytoprotective, whereas MAPEG proteins have proinflammatory activities. Furthermore, knockout of mouse GSTA4 and GSTZ1 leads to overexpression of transferases in the Alpha, Mu, and Pi classes, an observation suggesting they are part of an adaptive mechanism that responds to endogenous chemical cues such as 4-hydroxynonenal and tyrosine degradation products. Consistent with this hypothesis, the promoters of cytosolic GST and MAPEG genes contain antioxidant response elements through which they are transcriptionally activated during exposure to Michael reaction acceptors and oxidative stress.
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            Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures.

            Scott Geromanos, J. Silva, Dan Golick … (2009)
            A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.
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              Bioinformatic analysis of proteomics data

              Andreas C Schmidt, Ignasi Forne, Axel Imhof (2014)
              Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages.
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                Author and article information

                Contributors
                Katrien Smits: Katrien.Smits@UGent.be
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 27 March 2018
                Publication date PMC-release: 27 March 2018
                Publication date Collection: 2018
                Volume: 8
                Electronic Location Identifier: 5249
                Affiliations
                [1 ]ISNI 0000 0001 2069 7798, GRID grid.5342.0, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, , Ghent University, ; Merelbeke, Belgium
                [2 ]ISNI 0000 0001 2069 7798, GRID grid.5342.0, Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, , Ghent University, ; Gent, Belgium
                [3 ]ISNI 0000 0001 2069 7798, GRID grid.5342.0, Laboratory of Animal Genetics, Faculty of Veterinary Medicine, , Ghent University, ; Merelbeke, Belgium
                Author information
                http://orcid.org/0000-0002-7124-610X
                http://orcid.org/0000-0002-6994-7687
                http://orcid.org/0000-0001-8815-5485
                http://orcid.org/0000-0002-0635-661X
                Article
                Publisher ID: 23537
                DOI: 10.1038/s41598-018-23537-6
                PMC ID: 5869742
                PubMed ID: 29588480
                SO-VID: 47f7e7d7-1f57-4cd7-bc07-f225bc5215ee
                Copyright © © The Author(s) 2018
                License:

                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
                Date received : 10 July 2017
                Date accepted : 14 March 2018
                Categories
                Subject: Article
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                issue-copyright-statement © The Author(s) 2018

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