Despite advances in prenatal screening and a notable decrease in mortality rates, congenital heart disease (CHD) remains the most prevalent congenital disorder in newborns globally. Current therapeutic surgical approaches face challenges due to the significant rise in complications and disabilities. Emerging cardiac regenerative therapies offer promising adjuncts for CHD treatment. One novel avenue involves investigating methods to stimulate cardiomyocyte proliferation. However, the mechanism of altered cardiomyocyte proliferation in CHD is not fully understood, and there are few feasible approaches to stimulate cardiomyocyte cell cycling for optimal healing in CHD patients. In this review, we explore recent progress in understanding genetic and epigenetic mechanisms underlying defective cardiomyocyte proliferation in CHD from development through birth. Targeting cell cycle pathways shows promise for enhancing cardiomyocyte cytokinesis, division, and regeneration to repair heart defects. Advancements in human disease modeling techniques, CRISPR-based genome and epigenome editing, and next-generation sequencing technologies will expedite the exploration of abnormal machinery governing cardiomyocyte differentiation, proliferation, and maturation across diverse genetic backgrounds of CHD. Ongoing studies on screening drugs that regulate cell cycling are poised to translate this nascent technology of enhancing cardiomyocyte proliferation into a new therapeutic paradigm for CHD surgical interventions.
Keyphrases
- congenital heart disease
- angiotensin ii
- signaling pathway
- cell cycle
- stem cells
- high glucose
- genome wide
- dna methylation
- cell therapy
- crispr cas
- endothelial cells
- pregnant women
- single cell
- gene expression
- ejection fraction
- physical activity
- risk factors
- heart failure
- cell proliferation
- type diabetes
- copy number
- cardiovascular disease
- prognostic factors
- mesenchymal stem cells
- bone marrow
- cardiovascular events
- genome editing
- gestational age
- deep learning
- machine learning
- preterm birth