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      Effects of arginine vasopressin on the urine proteome in rats

      1 , 1 , 1 , , 1 , 2


      PeerJ Inc.

      Urine proteome, Arginine vasopressin, Biomarkers

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          Biomarkers are the measurable changes associated with a physiological or pathophysiological process. The content of urine frequently changes because it is not controlled by homeostatic mechanisms, and these alterations can be a source of biomarkers. However, urine is affected by many factors. In this study, vasoconstrictor and antidiuretic arginine vasopressin (AVP) were infused into rats using an osmotic pump. The rats’ urinary proteome after one week of infusion was analyzed by label-free LC-MS/MS. A total of 408 proteins were identified; among these proteins, eight and 10 proteins had significantly altered expression in the low and high dose groups, respectively, compared with the control group using the one-way ANOVA analysis followed by post hoc analysis with the least significant difference (LSD) test or Dunnett’s T3 test. Three differential proteins were described in prior studies as related to AVP physiological processes, and nine differential proteins are known disease biomarkers. Sixteen of the 17 differential proteins have human orthologs. These results suggest that we should consider the effects of AVP on urinary proteins in future urinary disease biomarker researches. The study data provide clues regarding underlying mechanisms associated with AVP for future physiological researches on AVP. This study provide a sensitive changes associated with AVP. However, the limitation of this result is that the candidate biomarkers should be further verified and filtered. Large clinical samples must be examined to verify the differential proteins identified in this study before these proteins are used as biomarkers for pathological AVP increased diseases, such as syndrome of inappropriate antidiuretic hormone secretion (SIADH).

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          Most cited references 62

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              Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.

                Author and article information

                PeerJ Inc. (San Francisco, USA )
                23 May 2017
                : 5
                [1 ]Department of Pathophysiology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College , Beijing, China
                [2 ]Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering and Biotechnology Beijing Key Laboratory , Beijing, China
                ©2017 An et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                Funded by: National Key Research and Development Program of China
                Award ID: 2016 YFC 1306300
                Funded by: National Basic Research Program of China (973 Program)
                Award ID: 2013CB530805
                Funded by: Beijing Natural Science Foundation
                Award ID: 7173264
                Award ID: 7172076
                Funded by: Fundamental Research Funds for the Central Universities
                Award ID: 10300-31042110
                Funded by: Beijing Normal University
                Award ID: 11100704
                This work was supported by the National Key Research and Development Program of China (No. 2016 YFC 1306300), National Basic Research Program of China (973 Program) (No. 2013CB530805), Beijing Natural Science Foundation (Nos. 7173264, 7172076), the Fundamental Research Funds for the Central Universities (No.10300-31042110) and the Beijing Normal University (No. 11100704). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Molecular Biology

                biomarkers, arginine vasopressin, urine proteome


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