An untargeted approach for the analysis of the urine peptidome of women with preeclampsia.

Preeclampsia (PE) is a pregnancy complication characterized by high blood pressure and proteinuria. The disorder usually occurs after the 20th week of pregnancy and gets worse over time. PE increases the risk of poor outcomes for both the mother and the baby. In the study we applied LC-MS/MS method for the analysis of the urine peptidome of women with PE. Samples were prepared using size-exclusion chromatography method which gives more than twice peptides identities if compared with solid phase extraction. Thirty urine samples from women with mild and severe preeclampsia and the control group were analyzed. In total 1786 peptides were identified using complementary search engines (Mascot, MaxQuant and PEAKS). A high level of agreement in peptide identification was observed with previously published data. Label-free data comparison resulted in 35 peptides which reliably distinguished a particular PE group (severe or mild) from controls. Our results revealed unique identifications (correlate to alpha-1-antitrypsin, collagen alpha-1(I) chain, collagen alpha-1 (III) chain, and uromodulin, for instance) that can potentially serve as early indicators of PE.

PP086. The modern search for possible predictors of preeclampsia in the first trimester of pregnancy (preliminary study).

INTRODUCTION: Preeclampsia is a major complication affecting at least 3-4% of all pregnancies and is globally responsible for approximately 50,000 maternal deaths annually. Currently the main cause of preeclampsia is a shallow placentation, with abnormal invasion of cytotrophoblasts and incomplete remodeling of placenta-supplying maternal uterine spiral arteries. However, up to 20weeks, these processes are asymptomatic, although they are accompanied by the release of various macromolecules in the bloodstream of the mother, which are potential biomarkers of disease, needed to detect. Much progress has been made in recent years towards first-trimester models using combined factors that will be of realistic use in clinical practice.The ability to predict the most severe forms of preeclampsia would allow closer surveillance and earlier intervention to improve pregnancy outcomes. OBJECTIVES: To determine the significance of measuring the concentration of PlGF and sFlt-1 and their ratio in 11-13 weeks of pregnancy as early markers of preeclampsia. METHODS: The study included 85 pregnant women in 11-13 weeks with subsequent uncomplicated pregnancy and 11 patients which developed various hypertensive disorders, four of them have developed severe (n=2) and mild (n=2) preeclampsia later. Concentrations of markers were determined on automated analyzers Cobas E411 (Hoffmann La Roche) using test kits Elecsys PlGF and Elecsys sFlt-1. RESULTS: In the first trimester the concentration of sFlt-1 in healthy pregnant women was 1618,0±18,2pg/ml, PlGF - 47,5±3,5pg/ml, the sFlt-1/PLGF ratio - 35,9±3,2. In the second trimester parameters were as follows: sFlt-1 - 1898,0±29,8pg/ml, PlGF - 208,0±11,0pg/ml, the sFlt-1/PLGF ratio-11,2±2,6. Two patients with severe preeclampsia had sFlt-1 concentration at 11-12 weeks of 2185 and 14923pg/ml, PlGF - 12,8 and 61.3pg/ml, the ratio sFlt-1/PLGF - 170,7 and 243.4. In two patients with mild preeclampsia, these figures were in 1700 and 2312pg/ml, 38.2 and 37,6pg/ml, 44,4 and 61,4, respectively. CONCLUSION: The optimal prediction biomarker does probably not exist. Our preliminary results confirm the feasibility of determining concentrations of sFlt-1 and PlGF, and their ratio in the 1st trimester of pregnancy to assess the risk of preeclampsia in order to reduce the frequency of obstetric complications and perinatal loss. Effective prediction of PE can be achieved at 11-13weeks' gestation. Further studies on the development and implementation a modified integrated screening program for early diagnosis of PE at 11-14weeks of gestation should be based on the assessment of maternal factors, biophysical, biochemical, and molecular genetic markers are needed. Better individualised patient prediction will allow us to target existing prevention, as it is, and to also develop new treatments in future.