Surface-Engineered Monocyte Inhibits Atherosclerotic Plaque Destabilization via Graphene Quantum Dot-Mediated MicroRNA Delivery.
Feila LiuNing DingDa HuoGuanyuan YangKeyu WeiGe GuanYanzhao LiJingyuan YangTianran WangYeqin WangJu TanWen ZengChuhong ZhuPublished in: Advanced healthcare materials (2019)
Rupture-prone atherosclerotic plaque is the cause of the high mortality and morbidity rates that accompany atherosclerosis-associated diseases. MicroRNAs can regulate the expression of a variety of atherosclerotic inflammation-related genes in macrophages. There are currently no definitive methods for delivering microRNAs into the interior of plaque. Monocytes typically possess a pathological feature that allows them to be recruited to atherosclerotic plaque resulting in rupture-prone; however, whether monocytes can be modified to be gene carriers remains unclear. In this study, a novel monocyte surface-engineered gene-delivery system based on graphene quantum dots (GQDs) is developed. Briefly, GQDs-microRNA223 linked by disulfide bonds are grafted onto the monocyte membrane via a carefully designed C18-peptide (C18P) containing a hydrophobic end to afford the designed monocyte-C18P-GQDs-miR223 architecture. The system can reach and enter the interior of the plaque and release the GQDs-miRNA via C18P digestion. The released GQDs-miRNA are taken up by the macrophages in atherosclerotic plaques, and the disulfide linkages between the GQDs and the miRNA are cleaved through γ-interferon-inducible lysosomal thiol reductase (GILT) in the lysosome. Under the protection of GQDs, miRNA cargos are transfected into the cytosol and subsequently undergo nuclear translocation, allowing a significantly reduced plaque burden by regulating inflammatory response in vivo.
Keyphrases
- dendritic cells
- coronary artery disease
- peripheral blood
- inflammatory response
- endothelial cells
- quantum dots
- cell proliferation
- oxidative stress
- copy number
- genome wide
- poor prognosis
- machine learning
- cardiovascular events
- risk factors
- deep learning
- type diabetes
- squamous cell carcinoma
- sensitive detection
- long noncoding rna
- toll like receptor
- anaerobic digestion
- rectal cancer