Introduction: Studies indicate that ∼7% of pregnant individuals in North America consume cannabis in pregnancy. Pre-clinical studies have established that maternal exposure to Δ 9 -tetrahydrocannabinol (THC; major psychoactive component in cannabis) leads to fetal growth restriction and impaired cardiac function in offspring. However, the effects of maternal exposure to cannabidiol (CBD; major non-euphoric constituent) on cardiac outcomes in offspring remain unknown. Therefore, our objective is to investigate the functional and underlying molecular impacts in the hearts of offspring exposed to CBD in pregnancy. Methods: Pregnant Wistar rats were exposed to either 3 or 30 mg/kg CBD or vehicle control i.p. daily from gestational day 6 to term. Echocardiography was used to assess cardiac function in male and female offspring at postnatal day (PND) 21. Furthermore, quantitative polymerase chain reaction (qPCR), immunoblotting, and bulk RNA-sequencing (RNA-seq) were performed on PND21 offspring hearts. Results: Despite no differences in the heart-to-body weight ratio, both doses of CBD led to reduced cardiac function exclusively in male offspring at 3 weeks of age. Underlying this, significant alterations in the expression of the endocannabinoid system (ECS; e.g., decreased cannabinoid receptor 2) were observed. In addition, bulk RNA-seq data demonstrated transcriptional pathways significantly enriched in mitochondrial function/metabolism as well as development. Conclusion: Collectively, we demonstrated for the first time that gestational exposure to CBD, a constituent perceived as safe, leads to early sex-specific postnatal cardiac deficits and alterations in the cardiac ECS in offspring.
Keyphrases
- rna seq
- high fat diet
- single cell
- preterm infants
- left ventricular
- pregnancy outcomes
- pregnant women
- body weight
- birth weight
- adipose tissue
- gestational age
- gene expression
- physical activity
- preterm birth
- weight gain
- heart failure
- computed tomography
- emergency department
- traumatic brain injury
- deep learning
- poor prognosis
- machine learning
- single molecule
- atrial fibrillation
- pulmonary hypertension
- social support
- weight loss
- electronic health record
- artificial intelligence
- skeletal muscle
- adverse drug
- data analysis