Progesterone Changes the Pregnancy-Induced Adaptation of Cardiomyocyte Kv2.1 Channels via MicroRNA-29b.
Shuang LiangYu-Shuang SunLu LiYao LongMeng WangHou-Zhi YangChun-Di LiYan WangShan-Shan LiXu ChenXin JinPublished in: Cardiovascular therapeutics (2022)
The cardiovascular system adaptation occurs during pregnancy to ensure adequate maternal circulation. Progesterone (P4) is widely used in hormone therapy to support pregnancy, but little is known about its effects on maternal cardiac function. In this study, we investigated the cardiac repolarization and ion channel expression in pregnant subjects and mice models and studied the effects of P4 administrations on these pregnancy-mediated adaptations. P4 administrations shortened the prolongation of QTC intervals and action potential duration (APD) that occurred during pregnancy, which was mainly attributable to the reduction in the voltage-gated potassium (Kv) current under basal conditions. In vitro studies indicated that P4 regulated the Kv2.1 channel in a bidirectional manner. At a low dose (1 μ M), P4 induced upregulation of Kv2.1 through P4 receptor, while at a higher dose (5 μ M), P4 downregulated Kv2.1 by targeting microRNA-29b (miR-29b). Our data showed that P4 modulated maternal cardiac repolarization by regulating Kv2.1 channel activity during pregnancy. Kv2.1, as well as miR-29b, might be used as potential therapeutic targets for adaptations of the maternal cardiovascular system or evaluation of progesterone medication during pregnancy.
Keyphrases
- pregnancy outcomes
- image quality
- dual energy
- low dose
- birth weight
- high glucose
- pregnant women
- poor prognosis
- preterm birth
- diabetic rats
- computed tomography
- emergency department
- stem cells
- heart failure
- signaling pathway
- high dose
- estrogen receptor
- magnetic resonance imaging
- angiotensin ii
- high intensity
- gestational age
- body mass index
- physical activity
- skeletal muscle
- bone marrow
- big data
- transcription factor
- electronic health record
- adipose tissue
- metabolic syndrome
- mass spectrometry
- deep learning
- single molecule
- insulin resistance
- high speed