Integrated Proteomic and Metabolomic prediction of Term Preeclampsia.
Ray Bahado-SinghLiona C PoonAli YilmazArgyro SyngelakiOnur TurkogluPraveen KumarJoseph KirmaMatthew AllosVeronica AccurtiJiansheng LiPeng ZhaoStewart Francis GrahamDavid R CoolKypros NicolaidesPublished in: Scientific reports (2017)
Term preeclampsia (tPE), ≥37 weeks, is the most common form of PE and the most difficult to predict. Little is known about its pathogenesis. This study aims to elucidate the pathogenesis and assess early prediction of tPE using serial integrated metabolomic and proteomic systems biology approaches. Serial first- (11-14 weeks) and third-trimester (30-34 weeks) serum samples were analyzed using targeted metabolomic (1H NMR and DI-LC-MS/MS) and proteomic (MALDI-TOF/TOF-MS) platforms. We analyzed 35 tPE cases and 63 controls. Serial first- (sphingomyelin C18:1 and urea) and third-trimester (hexose and citrate) metabolite screening predicted tPE with an area under the receiver operating characteristic curve (AUC) (95% CI) = 0.817 (0.732-0.902) and a sensitivity of 81.6% and specificity of 71.0%. Serial first [TATA box binding protein-associated factor (TBP)] and third-trimester [Testis-expressed sequence 15 protein (TEX15)] protein biomarkers highly accurately predicted tPE with an AUC (95% CI) of 0.987 (0.961-1.000), sensitivity 100% and specificity 98.4%. Integrated pathway over-representation analysis combining metabolomic and proteomic data revealed significant alterations in signal transduction, G protein coupled receptors, serotonin and glycosaminoglycan metabolisms among others. This is the first report of serial integrated and combined metabolomic and proteomic analysis of tPE. High predictive accuracy and potentially important pathogenic information were achieved.
Keyphrases
- gestational age
- binding protein
- preterm birth
- label free
- pregnancy outcomes
- mass spectrometry
- early onset
- preterm infants
- magnetic resonance
- high resolution
- amino acid
- healthcare
- ms ms
- electronic health record
- biofilm formation
- structural basis
- pseudomonas aeruginosa
- social media
- artificial intelligence
- data analysis
- neural network