Integrated Proteomic and Metabolomic prediction of Term Preeclampsia

Abstract

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.

Systems Biology of Cancer Metastasis

“…The objective of these technologies is essentially to systematically explore the mechanism of gene regulation resulting in a given transcriptomic output. Several attempts are now made to correlatively analyze the transcriptomic, proteomic, and even metabolomics scales systematically in a physiological system (Bahado-Singh et al, 2017;Garcia et al, 2012;Rabinowitz et al, 2017;Zhao et al, 2018). Such integrative approaches will be crucial in mechanistically understanding cancer metastasis as a multiscale disease ( Figure 3).…”

Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction

“…Metabolomics is one of these technologies used for the metabolites identification, small molecules that represent the final line of gene expression and a phenotypic signature in high resolution of the disease desired to be studied [79, 80]. There have been efforts to seek a metabolic profile that may be associated with preeclampsia [78, 81–91], but no predictive models have been suggested so far that may have real clinical applicability or that have been validated in large populations. The elucidation of the metabolic profile of preeclampsia will be able to act not only for prediction but also to provide a better understanding of the aggravation with regard to cellular and molecular mechanisms.…”

Application of metabolomics to preeclampsia diagnosis

“…The articles selected were reviewed and the following information is summarized in Table 1: name of the first author, year of publication, country where study was done, number of participants, biological specimen, parity, statistical analysis, type of metabolomics method, a list of metabolites, direction of metabolites compared with control, and model characteristics (sensitivity, specificity and accuracy). Within this group, 16 studies investigated blood (serum/plasma) (Kenny et al 2005(Kenny et al , 2008(Kenny et al , 2010Odibo et al 2011;Bahado-Singh et al 2012, 2013, 2017a, 2017bSenyavina et al 2013;Austdal et al 2014Austdal et al , 2015aKuc et al 2014;Mukherjee et al 2014;Koster et al 2015;Chen et al 2017) and two of which also evaluated urine (Austdal et al 2014(Austdal et al , 2015a. One study used urine (Diaz et al 2013) and finally four studies used placenta (Jain et al 2004;Dunn et al 2012;Austdal et al 2015b;Zhou et al 2017), one of which assessed placental mitochondria (Zhou et al 2017).…”