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      Differential urinary glycoproteome analysis of type 2 diabetic nephropathy using 2D-LC–MS/MS and iTRAQ quantification

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          Abstract

          Background

          Diabetic nephropathy (DN) is the leading cause of chronic kidney failure and end-stage kidney disease. More accurate and non-invasive test for the diagnosis and monitoring the progression of DN is urgently needed for the better care of such patients.

          Methods

          In this study we utilized urinary glycoproteome to discover the differential proteins during the course of type 2 DN. The urinary glycoproteins from normal controls, normalbuminuira, microalbuminura, and macroalbuminuria patients were enriched by concanavalin A (ConA) and analyzed by 2DLC/MS/MS and isobaric tags for relative and absolute quantitation quantification.

          Results

          A total of 478 proteins were identified and 408 were annotated as N-linked glycoproteins. A total of 72, 107 and 123 differential proteins were identified in normalbuminuria, microalbuminuria and macroalbuminuria, respectively. By bioinformatics analysis, in normalbuminruia state, cell proliferation and cell movement were activated, which might reflect the compensatory phase during the disease development. In micro- and macro-albuminuria, cell death and apoptosis was activated, which might reflect the de-compensatory phase. Pathway analysis showed acute phase proteins, the member of high density lipoprotein and low density lipoprotein proteins were changed, indicating the role of the inflammatory response and lipid metabolism abnormality in the pathogenesis of DN. Six selected differential proteins were validated by Western Blot. Alpha-1-antitrypsin (SERPINA1) and Ceruloplasmin are the two markers with excellent area under curve values (0.929 and 1.000 respectively) to distinguish the microalbuminuria and normalbuminuria. For the first time, we found pro-epidermal growth factor and prolactin-inducible protein were decreased in macroalbuminuria stage, which might reflect the inhibition of cell viability and the activation of cell death in kidney.

          Conclusions

          Above data indicated that urinary glycoproteome could be useful to distinguish the differences in protein profiles in different stages in DN, which will help better individualized care of patients in DN.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12967-015-0712-9) contains supplementary material, which is available to authorized users.

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          Most cited references50

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          The PANTHER database of protein families, subfamilies, functions and pathways

          PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The latest version, 5.0, contains 6683 protein families, divided into 31 705 subfamilies, covering ∼90% of mammalian protein-coding genes. PANTHER 5.0 includes a number of significant improvements over previous versions, most notably (i) representation of pathways (primarily signaling pathways) and association with subfamilies and individual protein sequences; (ii) an improved methodology for defining the PANTHER families and subfamilies, and for building the HMMs; (iii) resources for scoring sequences against PANTHER HMMs both over the web and locally; and (iv) a number of new web resources to facilitate analysis of large gene lists, including data generated from high-throughput expression experiments. Efforts are underway to add PANTHER to the InterPro suite of databases, and to make PANTHER consistent with the PIRSF database. PANTHER is now publicly available without restriction at http://panther.appliedbiosystems.com.
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            Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population.

            For the general population, the clinical relevance of an increased urinary albumin excretion rate is still debated. Therefore, we examined the relationship between urinary albumin excretion and all-cause mortality and mortality caused by cardiovascular (CV) disease and non-CV disease in the general population. In the period 1997 to 1998, all inhabitants of the city of Groningen, the Netherlands, aged between 28 and 75 years (n=85 421) were sent a postal questionnaire collecting information about risk factors for CV disease and CV morbidity and a vial to collect an early morning urine sample for measurement of urinary albumin concentration (UAC). The vital status of the cohort was subsequently obtained from the municipal register, and the cause of death was obtained from the Central Bureau of Statistics. Of these 85 421 subjects, 40 856 (47.8%) responded, and 40 548 could be included in the analysis. During a median follow-up period of 961 days (maximum 1139 days), 516 deaths with known cause were recorded. We found a positive dose-response relationship between increasing UAC and mortality. A higher UAC increased the risk of both CV and non-CV death after adjustment for other well-recognized CV risk factors, with the increase being significantly higher for CV mortality than for non-CV mortality (P=0.014). A 2-fold increase in UAC was associated with a relative risk of 1.29 for CV mortality (95% CI 1.18 to 1.40) and 1.12 (95% CI 1.04 to 1.21) for non-CV mortality. Urinary albumin excretion is a predictor of all-cause mortality in the general population. The excess risk was more attributable to death from CV causes, independent of the effects of other CV risk factors, and the relationship was already apparent at levels of albuminuria currently considered to be normal.
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              Nephropathy in diabetes.

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                Author and article information

                Contributors
                gzg0625@sina.com
                fdh830826@sina.com
                menglinli86@gmail.com
                scshaochen@126.com
                taojianling@outlook.com
                sunwei1018@hotmail.com
                mingxili@hotmail.com
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                25 November 2015
                25 November 2015
                2015
                : 13
                : 371
                Affiliations
                [ ]Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing, 100005 China
                [ ]Department of Nephrology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyan, Wangfujing Street, Beijing, China
                [ ]National Key Laboratory of Medical Molecular Biology, Department of Physiology and Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing, 100005 China
                [ ]The Center for Biomedical Information, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing, 100005 China
                Article
                712
                10.1186/s12967-015-0712-9
                4660682
                26608305
                c23fb675-0035-4240-88cb-c0eff72d02bd
                © Guo et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.

                History
                : 4 August 2015
                : 23 October 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Medicine
                diabetic nephropathy,glycoproteomics,biomarker
                Medicine
                diabetic nephropathy, glycoproteomics, biomarker

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