RT Journal Article
SR Electronic
T1 Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 425306
DO 10.1101/425306
A1 Shiying Hao
A1 Jin You
A1 Lin Chen
A1 Hui Zhao
A1 Yujuan Huang
A1 Le Zheng
A1 Lu Tian
A1 Ivana Maric
A1 Xin Liu
A1 Tian Li
A1 Ylayaly K. Bianco
A1 Virginia D. Winn
A1 Nima Aghaeepour
A1 Brice Gaudilliere
A1 Martin S. Angst
A1 Xin Zhou
A1 Yu-Ming Li
A1 Lihong Mo
A1 Ronald J. Wong
A1 Gary M. Shaw
A1 David K. Stevenson
A1 Harvey J. Cohen
A1 Doff B. Mcelhinney
A1 Karl G. Sylvester
A1 Xuefeng B. Ling
YR 2018
UL http://biorxiv.org/content/early/2018/09/26/425306.abstract
AB Background Placental protein expression plays a crucial biological role during normal and complicated pregnancies. We hypothesized that: (1) circulating pregnancy-associated, placenta-related protein levels throughout gestation reflect the uncomplicated, full-term temporal progression of human gestation, and effectively estimates gestational ages (GAs); (2) pregnancies with underlying placental pathology, such as preeclampsia (PE), are associated with disruptions in this GA estimation in early gestation; (3) malfunctions of this GA estimation can be employed to identify impending PE. In addition, to explore the underlying biology and PE etiology, we set to compare protein gestational patterns of human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms.Methods Serum levels of circulating placenta-related proteins – leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)– were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 PE subjects with 61 samples). Linear multivariate analysis of the targeted serological protein levels was performed to estimate the normal GA. Logarithmic transformed mean-squared errors of GA estimations were used to identify impending PE. Then the human gestational protein patterns were compared to those in the pregnant HO-1 mice.Results An elastic net (EN)-based gestational dating model was developed (R2 = 0.76) and validated (R2 = 0.61) using the serum levels of the 6 proteins at various GAs from women with normal uncomplicated pregnancies (n = 10 for training and n = 6 for validation). In pregnancies complicated by PE (n = 14), the EN model was not (R2 = −0.17) associated with GA at sampling in PE. Statistically significant deviations from the normal GA EN model estimations were observed in PE-associated pregnancies between GAs of 16–30 weeks (P = 0.01). The EN model developed with 5 proteins (ELA excluded due to the lack of robustness of the mouse ELA essay) performed similarly on normal human (R2 = 0.68) and WT mouse (R2 = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (human: R2 = 0.27) and mouse HO-1 Het (mouse: R2 = 0.30) pregnancies. LEP out performed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse gestations.Conclusions As revealed in both human and mouse GA EN analyses, temporal serological placenta-related protein patterns are tightly regulated throughout normal human pregnancies and can be significantly disrupted in pathologic PE states. LEP changes earlier during gestation than the well-established late GA PE biomarkers (sFlt-1 and PlGF). Our HO-1 Het mouse analysis provides direct evidence of the causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model. Therefore, longitudinal analyses of pregnancy-related protein patterns in sera, may not only help in the exploration of underlying PE pathophysiology but also provide better clinical utility in PE assessment.PEPreeclampsiaCSHL1Chorionic somatomammotropin hormone like 1LEPLeptinGAGestational agesFlt-1Soluble fms-like tyrosine kinasePlGFPlacental growth factorHO-1Heme oxygenase-1ELAElabelaWTWild-typeENElastic netSMEMean squared errorAUCArea under the curveROCReceiver operating characteristicPPVPositive predictive valueNPVNegative predictive valueBMIBody mass index