Early second-trimester plasma protein profiling using multiplexed isobaric tandem mass tag (TMT) labeling predicts gestational diabetes mellitus
- 568 Downloads
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).
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.
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.
This is the first comprehensive study that integrates multiple state-of-the-art proteomic technologies to discover the earlier potential plasma biomarkers for GDM.
KeywordsGestational diabetes mellitus Plasma TMT LC-MS/MS
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.
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 was obtained from all patients for being included in the study.
- 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
- 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
- 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
- 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
- 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
- 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)