New CRISPR-Cas9 approach confers protection from ischemia/reperfusion injury

A new CRISPR-Cas9 approach that targets a detrimental signaling pathway in the heart confers protection from ischemia/reperfusion injury, according to a study in mice. The findings suggest that gene editing could offer a permanent and advanced strategy for treating heart disease – the leading cause of worldwide morbidity and mortality – and even serve as an intervention to repair cardiac damage immediately after a heart attack. CRISPR-Cas9 gene editing has shown promise as a therapeutic approach to treating rare hereditary diseases.

However, most gene editing strategies focus on correcting specific genetic mutations that only occur in a small subset of patients and often before disease onset. Broader applications of the approach remain limited. Here, Simon Lebek, Eric Olson, and colleagues present a CRISPR-Cas9 gene editing therapy that could be used to treat a range of patients with heart disease. Ischemia/reperfusion (IR) injury is a type of tissue damage that occurs after a variety of cardiovascular insults, including stroke and heart attack.

Chronic overactivation of Ca2+/calmodulin-dependent protein kinase IIδ (CaMKIIδ) is known to cause several cardiac diseases in humans and mice, including IR injury. Oxidation of methionine residues promotes CaMKIIδ hyperactivation. Lebek et al. found that using CRISPR-Cas9 adenine base editing to eliminate the oxidative activation sites of the CaMKIIδ gene in cardiomyocytes protected them from IR injury in mouse models. What's more, Lebek et al. found that injecting gene editing reagents into mice soon after IR injury allowed the animals to recover cardiac function after severe damage.

Journal reference:

Lebek, S., et al. (2023) Ablation of CaMKIIδ oxidation by CRISPR-Cas9 base editing as a therapy for cardiac disease. Science.


The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
You might also like...
Advancing gene regulatory network inference with causal discovery and graph neural networks