Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers

Abstract

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p < 0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

Keywords: LC-MS/MS; biomarkers; plasma; preeclampsia; proteomics.

Graphical abstract

Fig. 1

A Study schema for plasma proteome profiling. A, liquid chromatography-mass spectrometry (LC-MS/MS) process, (B) LC-MS/MS analysis sets: Set 1 (PE 1–3), Set 2 (Control 1, PE 4–6), Set 3 (Control 2, PE 7–9), Set 4 (Control 3, PE 10–11). C, analysis scheme summary. D, Principle Component Analysis (PCA) of plasma profiling data. E, heat map of differentially expressed proteins. F, Gene Ontology (GO) analysis of differentially expressed proteins (DEPs) in preeclampsia (PE) (GRCh37/hg19 assembly).

Fig. 2

Interaction networks of differentially expressed proteins in preeclampsia. Each node represents a protein, and each edge represents an interaction. Red and green represent upregulated and downregulated proteins in the PE group when compared to the control group, respectively. Preeclampsia (PE), Control (Con).

Fig. 3

Analysis of differentially expressed proteins in preeclampsia (PE) by multiple reaction monitoring (MRM).A, upregulated proteins in PE. B, downregulated proteins in PE. After the protein name, the unique peptide sequence, used in MRM is presented.

Fig. 4

Significantly increased three proteins in PE, LCN2, APOM, and QSOX1. A, result of ELISA analysis of three markers. B, receiver operator characterization (ROC) curve and AUC of the levels of three markers. C, AUC of the levels of combination of three markers.

Fig. 5

Receiver Operator Characterization (ROC) curves and AUC values for all differentially expressed proteins.A, ROC curves and AUC values for upregulated proteins in PE, B, downregulated proteins in PE.