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      Exosomes Cause Preterm Birth in Mice: Evidence for Paracrine Signaling in Pregnancy

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          Abstract

          Endocrine factors and signals of fetal organ maturation are reported determinants of birth timing. To test the hypothesis that paracrine signaling by exosomes are key regulators of parturition, maternal plasma exosomes from CD-1 mice were isolated and characterized throughout gestation and the biological pathways associated with differentially-expressed cargo proteins were determined. Results indicate that the shape and size of exosomes remained constant throughout gestation; however, a progressive increase in the quantity of exosomes carrying inflammatory mediators was observed from gestation day (E)5 to E19. In addition, the effects of late-gestation (E18) plasma exosomes derived from feto-maternal uterine tissues on parturition was determined. Intraperitoneal injection of E18 exosomes into E15 mice localized in maternal reproductive tract tissues and in intrauterine fetal compartments. Compared to controls that delivered at term, preterm birth occurred in exosome-treated mice on E18 and was preceded by increased inflammatory mediators on E17 in the cervix, uterus, and fetal membranes but not in the placenta. This effect was not observed in mice injected with early-gestation (E9) exosomes. This study provides evidence that exosomes function as paracrine mediators of labor and delivery.

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          Most cited references 93

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences.

            G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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              A statistical model for identifying proteins by tandem mass spectrometry.

              A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.
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                Author and article information

                Contributors
                ram.menon@utmb.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 January 2019
                24 January 2019
                2019
                : 9
                Affiliations
                [1 ]ISNI 0000 0001 1547 9964, GRID grid.176731.5, Division of Maternal-Fetal Medicine and Perinatal Research, Department of Obstetrics and Gynecology, , The University of Texas Medical Branch, ; Galveston, Texas USA
                [2 ]ISNI 0000 0001 1547 9964, GRID grid.176731.5, Department of Biochemistry and Molecular Biology, , The University of Texas Medical Branch, ; Galveston, Texas USA
                [3 ]ISNI 0000 0000 9852 649X, GRID grid.43582.38, Longo Center for Perinatal Biology, Departments of Basic Sciences and Pediatrics, , Loma Linda University School of Medicine, ; Loma Linda, CA USA
                Article
                37002
                10.1038/s41598-018-37002-x
                6345869
                30679631
                © The Author(s) 2019

                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/.

                Funding
                Funded by: AMAG Pharmaceuticals
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