Current model systems for the study of preeclampsia | JoVE Visualize

Area of Science:

Obstetrics and Gynecology
Maternal-Fetal Medicine
Computational Biology

Background:

Preeclampsia (PE) is a complex pregnancy disorder affecting 8% of pregnancies globally, characterized by hypertension and proteinuria after 20 weeks gestation.
Its etiology and pathophysiology remain incompletely understood, though deficient trophoblast invasion and maternal factors are implicated in endothelial dysfunction and inflammatory responses.
Severe PE can lead to eclampsia, systemic endothelial dysfunction, and maternal or fetal death.

Purpose of the Study:

To review and describe the principal in vitro, in vivo, and in silico models used for studying preeclampsia (PE).
To highlight the potential of these models in understanding PE pathogenesis, identifying biomarkers, and evaluating novel therapeutic strategies.
To emphasize the underrepresentation of in silico models in previous reviews and their unique contributions to PE research.

Main Methods:

Review of existing literature on various models applied to preeclampsia research.
Focus on models related to placental development, trophoblast invasion, uteroplacental ischemia, angiogenesis, oxygen deregulation, and immune responses.
Discussion of computational and mathematical modeling approaches, including metabolic networks, gene prioritization, protein-protein interactions, and genetic analyses for PE prediction and classification.

Main Results:

Various models (in vitro, in vivo, in silico) are crucial for investigating PE pathophysiology and developing predictive biomarkers.
Mathematical and computational models offer advanced insights into PE genetics, networks, and prediction.
The review consolidates information on diverse modeling approaches, including less-explored in silico methods.

Conclusions:

The described models provide a foundation for understanding PE and testing interventions before clinical trials.
Further development of novel modeling approaches is needed to advance knowledge of PE pathophysiology.
In silico models, in particular, offer unique advantages for comprehensive PE research and biomarker discovery.