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      Quantitative proteomic profiling of Cervicovaginal fluid from pregnant women with term and preterm birth

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          Abstract

          Background

          Preterm birth (PTB) is one of major causes of perinatal mortality and neonatal morbidity, but knowledge of its complex etiology is still limited. Here we present cervicovaginal fluid (CVF) protein profiles of pregnant women who subsequently delivered at spontaneous preterm or term, aiming to identify differentially expressed CVF proteins in PTB and term birth.

          Methods

          The CVF proteome of women who sequentially delivered at preterm and term was analyzed using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with two-dimensional nanoflow liquid chromatography-tandem mass spectrometry (2D-nLC-MS/MS). We compared the CVF proteome of PTB ( n = 5) and control subjects (term birth, n = 7) using pooled control CVF (term birth, n = 20) as spike-in standard.

          Results

          We identified 1294 CVF proteins, of which 605 were newly identified proteins. Of 990 proteins quantified in both PTB and term birth, 52 proteins were significantly up/down-regulated in PTB compared to term birth. The differentially expressed proteins were functionally associated to immune response, endopeptidase inhibitors and structural constituent of cytoskeleton. Finally, we confirm the down-regulation of SERPINB7 (a serine-type protease inhibitor) in PTB compared to control by Western blot.

          Conclusions

          Taken together, our study provide quantitative CVF proteome profiles of pregnant women who ultimately delivered at preterm and term. These promising results could help to improve the understanding of PTB etiology and to discover biomarkers for asymptomatic PTB.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12953-021-00171-1.

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          Most cited references54

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              The Perseus computational platform for comprehensive analysis of (prote)omics data.

              A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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                Author and article information

                Contributors
                sklee@kyuh.ac.kr
                djkang@kriss.re.kr
                Journal
                Proteome Sci
                Proteome Sci
                Proteome Science
                BioMed Central (London )
                1477-5956
                15 February 2021
                15 February 2021
                2021
                : 19
                : 3
                Affiliations
                [1 ]GRID grid.410883.6, ISNI 0000 0001 2301 0664, Center for Bioanalysis, Division of Chemical and Medical Metrology, , Korea Research Institute of Standards and Science, ; 267 Gajeong-Ro, Yuseong-Gu, Daejeon, 34113 South Korea
                [2 ]GRID grid.263736.5, ISNI 0000 0001 0286 5954, Department of Chemistry, , Sogang University, ; Seoul, 04107 South Korea
                [3 ]GRID grid.411127.0, ISNI 0000 0004 0618 6707, Department of Obstetrics and Gynecology, , Konyang University Hospital, ; 158 Gasuwondong-Ro, Seo-Gu, Daejeon, 3535 South Korea
                Author information
                http://orcid.org/0000-0002-5924-9674
                Article
                171
                10.1186/s12953-021-00171-1
                7885372
                33588889
                b3497156-9a31-4436-99ba-50b993e045e3
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 March 2020
                : 4 February 2021
                Funding
                Funded by: Korean Health Technology R&D Project
                Award ID: HI14C0368
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Molecular biology
                cervicovaginal fluid,preterm birth,quantitative proteomics
                Molecular biology
                cervicovaginal fluid, preterm birth, quantitative proteomics

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