Abstract
Background: Amniotic fluid is a complex mixture and reflects the physiological status of the developing fetus. Many proteins presented in amniotic fluid are of exceptional interest because their expression reflects physiological and pathological conditions of the fetus and/or pregnancy.
Objective: In the present study, we performed proteomics-based identification of biomarkers of amniotic fluid from normal and pathological pregnancy.
Methods: Proteins isolated from amniotic fluid of three different status of pregnancy - normal, preeclampsia and polyhydramnios, were fractionated by 2DE, visualized by Coomassie blue staining and all visualized proteins subjected for identification with mass spectrometry. Around 40 proteins were identified by mass spectrometry analysis. Changes in identified protein levels defined in three different pregnancy status were evaluated by using computational methods.
Results: In our study we characterized proteins isolated from amniotic fluid obtained from normal pregnancy, developed preeclampsia and polyhydramnios. Some proteins’ are variable in their expression level in comparison to pathological cases with normal pregnancy. These proteins are involved in various cellular processes and are responsible for cell signaling and regulation (apolipoprotein, peroxiredoxin, protein AMBP, putative elongation factor, clusterin, etc.), metabolic processes (lumican, vitamin D binding protein, etc.), macromolecular transport (ceruloplasmin, corticosteroidbinding protein, etc.), proteins specific for pregnancy and embryo development (angiotensinogen, fibrinogen beta chain, etc.) and others.
Conclusion: In the study new proteins typical for amniotic fluid of preeclampsia and polyhydramnios pregnancies were identified. These proteins are associated with development, signal transduction, and metabolic processes. Results could be valuable for developing biomarkers for fetal development and pregnancy-related disorders.
Keywords: Amniotic fluid, preeclampsia, polyhydramnios, pregnancy, proteomics.
Graphical Abstract