Advertisement

Acta Diabetologica

, Volume 52, Issue 6, pp 1103–1112 | Cite as

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

  • Chun Zhao
  • Fuqiang Wang
  • Ping Wang
  • Hongjuan Ding
  • Xiaoyan HuangEmail author
  • Zhonghua ShiEmail author
Original Article

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.

Keywords

Gestational diabetes mellitus Plasma TMT LC-MS/MS 

Notes

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].

Compliance with ethical standards

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.

Supplementary material

592_2015_796_MOESM1_ESM.xls (616 kb)
Supplementary material 1 (XLS 616 kb)

References

  1. 1.
    Coustan DR (2013) Gestational diabetes mellitus. Clin Chem 59(9):1310–1321CrossRefPubMedGoogle Scholar
  2. 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):334–346CrossRefPubMedGoogle Scholar
  3. 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 12:792PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Buchanan TA, Xiang AH, Page KA (2012) Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol 8(11):639–649PubMedCentralCrossRefPubMedGoogle Scholar
  5. 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):2564–2569CrossRefPubMedGoogle Scholar
  6. 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):1551–1563CrossRefGoogle Scholar
  7. 7.
    Mirabelli P, Incoronato M (2013) Usefulness of traditional serum biomarkers for management of breast cancer patients. Biomed Res Int 2013:685641PubMedCentralCrossRefPubMedGoogle Scholar
  8. 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):1–44CrossRefPubMedGoogle Scholar
  9. 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):e44701PubMedCentralCrossRefPubMedGoogle Scholar
  10. 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):165–178CrossRefPubMedGoogle Scholar
  11. 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):947–955CrossRefPubMedGoogle Scholar
  12. 12.
    Antoniewicz MR (2013) Tandem mass spectrometry for measuring stable-isotope labeling. Curr Opin Biotechnol 24(1):48–53CrossRefPubMedGoogle Scholar
  13. 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):376–386CrossRefPubMedGoogle Scholar
  14. 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):340–350CrossRefPubMedGoogle Scholar
  15. 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):2245–2252CrossRefPubMedGoogle Scholar
  16. 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):1061–1073PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Dayon L, Sanchez JC (2012) Relative protein quantification by ms/ms using the tandem mass tag technology. Methods Mol Biol 893:115–127CrossRefPubMedGoogle Scholar
  18. 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):2339–2350CrossRefPubMedGoogle Scholar
  19. 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):2686–2695CrossRefPubMedGoogle Scholar
  20. 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):7–10CrossRefGoogle Scholar
  21. 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):157–165CrossRefPubMedGoogle Scholar
  22. 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–M006353PubMedCentralCrossRefPubMedGoogle Scholar
  23. 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):1108–1112CrossRefPubMedGoogle Scholar
  24. 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):55–58PubMedGoogle Scholar
  25. 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):1286–1293CrossRefPubMedGoogle Scholar
  26. 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):e3224PubMedCentralCrossRefPubMedGoogle Scholar
  27. 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):318–321PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Han S, Middleton P, Crowther CA (2012) Exercise for pregnant women for preventing gestational diabetes mellitus. Cochrane Database Syst Rev 7:CD009021PubMedGoogle Scholar
  29. 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 2011:541308PubMedCentralCrossRefPubMedGoogle Scholar
  30. 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):85–92CrossRefPubMedGoogle Scholar
  31. 31.
    Gabay C, Kushner I (1999) Acute-phase proteins and other systemic responses to inflammation. N Engl J Med 340(6):448–454CrossRefPubMedGoogle Scholar
  32. 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):265–270CrossRefPubMedGoogle Scholar
  33. 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):32–40PubMedCentralCrossRefPubMedGoogle Scholar
  34. 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):271–280PubMedCentralPubMedGoogle Scholar
  35. 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):365–369CrossRefPubMedGoogle Scholar
  36. 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):383–394CrossRefPubMedGoogle Scholar
  37. 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):10–12PubMedGoogle Scholar
  38. 38.
    Zhang XL, Ali MA (2008) Ficolins: structure, function and associated diseases. Adv Exp Med Biol 632:105–115PubMedGoogle Scholar
  39. 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):1411–1419PubMedCentralCrossRefPubMedGoogle Scholar
  40. 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):49–56PubMedCentralCrossRefPubMedGoogle Scholar
  41. 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):256–257CrossRefPubMedGoogle Scholar
  42. 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):5610–5618PubMedCentralCrossRefPubMedGoogle Scholar
  43. 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):523–529CrossRefPubMedGoogle Scholar
  44. 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):887–890CrossRefPubMedGoogle Scholar
  45. 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)

Copyright information

© Springer-Verlag Italia 2015

Authors and Affiliations

  1. 1.State Key Laboratory of Reproductive MedicineNanjing Maternity and Child Health Care Hospital Affiliated to Nanjing Medical UniversityNanjingChina
  2. 2.State Key Laboratory of Reproductive MedicineNanjing Medical UniversityNanjingChina
  3. 3.Liver Transplantation Center of the First Affiliated Hospital to Nanjing Medical UniversityNanjingChina

Personalised recommendations