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Early second-trimester plasma protein profiling using multiplexed isobaric tandem mass tag (TMT) labeling predicts gestational diabetes mellitus

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Abstract

Aims

Gestational diabetes mellitus (GDM) is associated with an increased risk of serious complications for mother and child during pregnancy. The main option for diagnosis of GDM is 75 g oral glucose tolerance test (OGTT) at 24–28 gestation weeks, when harms to both mother and child have already potentially occurred. The aim of this study was to investigate new biomarkers for earlier detection and assessment of GDM at early second trimester (16–18 gestation weeks).

Methods

We systematically used multiplexed isobaric tandem mass tag labeling combined with liquid chromatography mass spectrometry (LC-MS/MS) to screen differentially expressed proteins in plasma collected at 16–18 gestational weeks between pregnant women with and without GDM outcome.

Results

A total of 828 proteins were identified, of which 36 proteins implicated in immune response, inflammation, transport, platelet aggregation, catalyze and defense response were identified as differentially regulated proteins in GDM. To assess the validity of the results, four selected proteins including C-reactive protein, sex hormone-binding globulin, Ficolin 3 and pregnancy-specific beta-1-glycoprotein 4 were selected for subsequent Western blot analysis.

Conclusions

This is the first comprehensive study that integrates multiple state-of-the-art proteomic technologies to discover the earlier potential plasma biomarkers for GDM.

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References

  1. Coustan DR (2013) Gestational diabetes mellitus. Clin Chem 59(9):1310–1321

    Article  CAS  PubMed  Google Scholar 

  2. Ruchat SM, Mottola MF (2013) The important role of physical activity in the prevention and management of gestational diabetes mellitus. Diabetes Metab Res Rev 29(5):334–346

    Article  PubMed  Google Scholar 

  3. Wahabi HA, Alzeidan RA, Esmaeil SA (2012) Pre-pregnancy care for women with pre-gestational diabetes mellitus: a systematic review and meta-analysis. BMC Public Health 12:792

    Article  PubMed Central  PubMed  Google Scholar 

  4. Buchanan TA, Xiang AH, Page KA (2012) Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol 8(11):639–649

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PM, Damm P, Dyer AR, Hod M, Kitzmiller JL, Lowe LP, McIntyre HD, Oats JJ, Omori Y (2012) The diagnosis of gestational diabetes mellitus: new paradigms or status quo? J Matern Fetal Neonatal Med 25(12):2564–2569

    Article  PubMed  Google Scholar 

  6. Oostdam N, van Poppel MN, Wouters MG, van Mechelen W (2011) Interventions for preventing gestational diabetes mellitus: a systematic review and meta-analysis. J Womens Health (Larchmt) 20(10):1551–1563

    Article  Google Scholar 

  7. Mirabelli P, Incoronato M (2013) Usefulness of traditional serum biomarkers for management of breast cancer patients. Biomed Res Int 2013:685641

    Article  PubMed Central  PubMed  Google Scholar 

  8. Elvidge T, Matthews IP, Gregory C, Hoogendoorn B (2013) Feasibility of using biomarkers in blood serum as markers of effect following exposure of the lungs to particulate matter air pollution. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 31(1):1–44

    Article  CAS  PubMed  Google Scholar 

  9. Liu B, Xu Y, Voss C, Qiu FH, Zhao MZ, Liu YD, Nie J, Wang ZL (2012) Altered protein expression in gestational diabetes mellitus placentas provides insight into insulin resistance and coagulation/fibrinolysis pathways. PLoS ONE 7(9):e44701

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Oliva K, Barker G, Rice GE, Bailey MJ, Lappas M (2013) 2d-DIGE to identify proteins associated with gestational diabetes in omental adipose tissue. J Endocrinol 218(2):165–178

    Article  CAS  PubMed  Google Scholar 

  11. Adkins JN, Varnum SM, Auberry KJ, Moore RJ, Angell NH, Smith RD, Springer DL, Pounds JG (2002) Toward a human blood serum proteome analysis by multidimensional separation coupled with mass spectrometry. Mol Cell Proteomics 1(12):947–955

    Article  CAS  PubMed  Google Scholar 

  12. Antoniewicz MR (2013) Tandem mass spectrometry for measuring stable-isotope labeling. Curr Opin Biotechnol 24(1):48–53

    Article  CAS  PubMed  Google Scholar 

  13. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, silac, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376–386

    Article  CAS  PubMed  Google Scholar 

  14. Wiese S, Reidegeld KA, Meyer HE, Warscheid B (2007) Protein labeling by itraq: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7(3):340–350

    Article  CAS  PubMed  Google Scholar 

  15. Sui P, Watanabe H, Ossipov MH, Porreca F, Bakalkin G, Bergquist J, Artemenko K (2013) Dimethyl-labeling-based protein quantification and pathway search: a novel method of spinal cord analysis applicable for neurological studies. J Proteome Res 12(5):2245–2252

    Article  CAS  PubMed  Google Scholar 

  16. Rayavarapu S, Coley W, Cakir E, Jahnke V, Takeda S, Aoki Y, Grodish-Dressman H, Jaiswal JK, Hoffman EP, Brown KJ, Hathout Y, Nagaraju K (2013) Identification of disease specific pathways using in vivo silac proteomics in dystrophin deficient mdx mouse. Mol Cell Proteomics 12(5):1061–1073

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Dayon L, Sanchez JC (2012) Relative protein quantification by ms/ms using the tandem mass tag technology. Methods Mol Biol 893:115–127

    Article  CAS  PubMed  Google Scholar 

  18. Tsuchida S, Satoh M, Kawashima Y, Sogawa K, Kado S, Sawai S, Nishimura M, Ogita M, Takeuchi Y, Kobyashi H, Aoki A, Kodera Y, Matsushita K, Izumi Y, Nomura F (2013) Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease. Proteomics 13(15):2339–2350

    Article  CAS  PubMed  Google Scholar 

  19. Maes E, Valkenborg D, Mertens I, Broeckx V, Baggerman G, Sagaert X, Landuyt B, Prenen H, Schoofs L (2013) Proteomic analysis of formalin-fixed paraffin-embedded colorectal cancer tissue using tandem mass tag protein labeling. Mol BioSyst 9(11):2686–2695

    Article  CAS  PubMed  Google Scholar 

  20. Ruckhaberle E, Karn T, Hanker L, Schwarz J, Schulz-Knappe P, Kuhn K, Bohm G, Selzer S, Erhard N, Engels K, Holtrich U, Kaufmann M, Rody A (2010) Breast cancer proteomics—differences in protein expression between estrogen receptor-positive and -negative tumors identified by tandem mass tag technology. Breast Care (Basel) 5(1):7–10

    Article  Google Scholar 

  21. Georgiou HM, Lappas M, Georgiou GM, Marita A, Bryant VJ, Hiscock R, Permezel M, Khalil Z, Rice GE (2008) Screening for biomarkers predictive of gestational diabetes mellitus. Acta Diabetol 45(3):157–165

    Article  CAS  PubMed  Google Scholar 

  22. Farrah T, Deutsch EW, Omenn GS, Campbell DS, Sun Z, Bletz JA, Mallick P, Katz JE, Malmstrom J, Ossola R, Watts JD, Lin B, Zhang H, Moritz RL, Aebersold R (2011) A high-confidence human plasma proteome reference set with estimated concentrations in peptideatlas. Mol Cell Proteomics 10(9):M110–M006353

    Article  PubMed Central  PubMed  Google Scholar 

  23. Maged AM, Moety GA, Mostafa WA, Hamed DA (2013) Comparative study between different biomarkers for early prediction of gestational diabetes mellitus. J Matern Fetal Neonatal Med 27(11):1108–1112

    Article  PubMed  Google Scholar 

  24. D’Anna R, Baviera G, De Vivo A, Facciola G, Di Benedetto A, Corrado F (2006) C-reactive protein as an early predictor of gestational diabetes mellitus. J Reprod Med 51(1):55–58

    PubMed  Google Scholar 

  25. Caglar GS, Ozdemir ED, Cengiz SD, Demirtas S (2012) Sex-hormone-binding globulin early in pregnancy for the prediction of severe gestational diabetes mellitus and related complications. J Obstet Gynaecol Res 38(11):1286–1293

    Article  PubMed  Google Scholar 

  26. Li RX, Chen HB, Tu K, Zhao SL, Zhou H, Li SJ, Dai J, Li QR, Nie S, Li YX, Jia WP, Zeng R, Wu JR (2008) Localized-statistical quantification of human serum proteome associated with type 2 diabetes. PLoS ONE 3(9):e3224

    Article  PubMed Central  PubMed  Google Scholar 

  27. Fuchtenbusch M, Bonifacio E, Lampasona V, Knopff A, Ziegler AG (2004) Immune responses to glutamic acid decarboxylase and insulin in patients with gestational diabetes. Clin Exp Immunol 135(2):318–321

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Han S, Middleton P, Crowther CA (2012) Exercise for pregnant women for preventing gestational diabetes mellitus. Cochrane Database Syst Rev 7:CD009021

    PubMed  Google Scholar 

  29. Kim SY, England JL, Sharma JA, Njoroge T (2011) Gestational diabetes mellitus and risk of childhood overweight and obesity in offspring: a systematic review. Exp Diabetes Res 2011:541308

    Article  PubMed Central  PubMed  Google Scholar 

  30. Aguiar FJ, Ferreira-Junior M, Sales MM, Cruz-Neto LM, Fonseca LA, Sumita NM, Duarte NJ, Lichtenstein A, Duarte AJ (2013) C-reactive protein: clinical applications and proposals for a rational use. Rev Assoc Med Bras 59(1):85–92

    Article  PubMed  Google Scholar 

  31. Gabay C, Kushner I (1999) Acute-phase proteins and other systemic responses to inflammation. N Engl J Med 340(6):448–454

    Article  CAS  PubMed  Google Scholar 

  32. Szalai AJ, Agrawal A, Greenhough TJ, Volanakis JE (1999) C-reactive protein: structural biology and host defense function. Clin Chem Lab Med 37(3):265–270

    Article  CAS  PubMed  Google Scholar 

  33. Le TN, Nestler JE, Strauss JF 3rd, Wickham EP 3rd (2012) Sex hormone-binding globulin and type 2 diabetes mellitus. Trends Endocrinol Metab 23(1):32–40

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Chen C, Smothers J, Lange A, Nestler JE, Strauss Iii JF, Wickham Iii EP (2010) Sex hormone-binding globulin genetic variation: associations with type 2 diabetes mellitus and polycystic ovary syndrome. Minerva Endocrinol 35(4):271–280

    PubMed Central  CAS  PubMed  Google Scholar 

  35. Kopp HP, Festa A, Krugluger W, Schernthaner G (2001) Low levels of sex-hormone-binding globulin predict insulin requirement in patients with gestation diabetes mellitus. Exp Clin Endocrinol Diabetes 109(7):365–369

    Article  CAS  PubMed  Google Scholar 

  36. Martinez FF, Cervi L, Knubel CP, Panzetta-Dutari GM, Motran CC (2013) The role of pregnancy-specific glycoprotein 1a (psg1a) in regulating the innate and adaptive immune response. Am J Reprod Immunol 69(4):383–394

    Article  CAS  PubMed  Google Scholar 

  37. Grudzinskas JG, Gordon YB, Menabawey M, Lee JN, Wadsworth J, Chard T (1983) Identification of high-risk pregnancy by the routine measurement of pregnancy-specific beta 1-glycoprotein. Am J Obstet Gynecol 147(1):10–12

    CAS  PubMed  Google Scholar 

  38. Zhang XL, Ali MA (2008) Ficolins: structure, function and associated diseases. Adv Exp Med Biol 632:105–115

    CAS  PubMed  Google Scholar 

  39. Szala A, Sawicki S, Swierzko AS, Szemraj J, Sniadecki M, Michalski M, Kaluzynski A, Lukasiewicz J, Maciejewska A, Wydra D, Kilpatrick DC, Matsushita M, Cedzynski M (2013) Ficolin-2 and ficolin-3 in women with malignant and benign ovarian tumours. Cancer Immunol Immunother 62(8):1411–1419

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Halmos A, Rigo J Jr, Szijarto J, Fust G, Prohaszka Z, Molvarec A (2012) Circulating ficolin-2 and ficolin-3 in normal pregnancy and pre-eclampsia. Clin Exp Immunol 169(1):49–56

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Chen H, Lu J, Chen X, Yu H, Zhang L, Bao Y, Lu F, Tang J, Gu C, Jia W (2012) Low serum levels of the innate immune component ficolin-3 is associated with insulin resistance and predicts the development of type 2 diabetes. J Mol Cell Biol 4(4):256–257

    Article  CAS  PubMed  Google Scholar 

  42. Rho JH, Roehrl MH, Wang JY (2009) Tissue proteomics reveals differential and compartment-specific expression of the homologs transgelin and transgelin-2 in lung adenocarcinoma and its stroma. J Proteome Res 8(12):5610–5618

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Zhang Y, Ye Y, Shen D, Jiang K, Zhang H, Sun W, Zhang J, Xu F, Cui Z, Wang S (2010) Identification of transgelin-2 as a biomarker of colorectal cancer by laser capture microdissection and quantitative proteome analysis. Cancer Sci 101(2):523–529

    Article  CAS  PubMed  Google Scholar 

  44. De Seymour JV, Conlon CA, Sulek K, Villas Bôas SG, McCowan LM, Kenny LC, Baker PN (2014) Early pregnancy metabolite profiling discovers a potential biomarker for the subsequent development of gestational diabetes mellitus. Acta Diabetol 51(5):887–890

    Article  PubMed  Google Scholar 

  45. He X, de Seymour JV, Sulek K, Qi H, Zhang H, Han TL, Villas-Bôas SG, Baker PN (2015) Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus. Acta Diabetol [Epub ahead of print]. (http://link.springer.com/article/10.1007/s00592-015-0737-9)

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (81000258, 81100436), the Natural Science Foundation of Jiangsu Province (BK2010586), the Bureau of Nanjing City Science and Technology Development Fund (201104014), the Open topic of State Key Laboratory of Reproductive Medicine (SKLRM-KF-201109, SKLRM-B12) and the Nanjing Medical Technology Development Project [Grant Numbers YKK14126, QRX11210, QRX11211].

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Correspondence to Xiaoyan Huang or Zhonghua Shi.

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Conflict of interest

All authors have no conflicts of interest to declare.

Ethical standard

This study was performed in accordance with the Ethics Committee of Nanjing Medical University with an Institutional Review Board Number of 2012-NFLZ-32, the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Human and animal rights

The study was approved by the Ethics Committee of Nanjing Medical University with an Institutional Review Board (IRB) Number of 2012-NFLZ-32. The blood sample-collection was performed in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the revised Helsinki Declaration in 2008.

Informed consent

Informed consent was obtained from all patients for being included in the study.

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Zhao, C., Wang, F., Wang, P. et al. Early second-trimester plasma protein profiling using multiplexed isobaric tandem mass tag (TMT) labeling predicts gestational diabetes mellitus. Acta Diabetol 52, 1103–1112 (2015). https://doi.org/10.1007/s00592-015-0796-y

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  • DOI: https://doi.org/10.1007/s00592-015-0796-y

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