Electrostatic Attractive Self-Delivery of siRNA and Light-Induced Self-Escape for Synergistic Gene Therapy.
Yuxin YangHaijun NingTianping XiaJianjun DuWen SunJiangli FanXiaojun PengPublished in: Advanced materials (Deerfield Beach, Fla.) (2023)
Small interfering RNA (siRNA) holds immense promise for suppressing gene expression and treating various life-threatening diseases, including cancer. However, efficient delivery and lysosomal escape remain critical challenges that hinder the therapeutic effectiveness of siRNA. Herein, cationic photosensitizer (NB-Br) was grafted onto polo-like kinase 1 (PLK1) siRNA to form an amphiphilic siRNA-photosensitizer conjugate (siPLK1-NB), which could self-assemble into nanoparticles (siPLK1-NB NPs) via electrostatic attraction. Notably, siPLK1-NB NPs exhibited rapid and efficient cell endocytosis, as well as outstanding tumor-targeting property in multiple tumor-bearing mice models. When siPLK1-NB NPs were located inside tumor cell lysosomes, the generated reactive oxygen species (ROS) after photoactivation could disrupt the lysosome membrane structure and facilitate siRNA escape from lysosomes. Under light irradiation, siPLK1-NB NPs could downregulate PLK1 expression and induce photodynamic killing, effectively inhibiting tumor cell growth both in vitro and in vivo. Consequently, this study provides a novel design strategy for carrier-free siRNA delivery systems. To the best of our knowledge, this is the first report of a carrier-free siRNA delivery system based on electrostatic attraction. This article is protected by copyright. All rights reserved.
Keyphrases
- cancer therapy
- drug delivery
- gene expression
- reactive oxygen species
- photodynamic therapy
- gene therapy
- healthcare
- poor prognosis
- single cell
- cell therapy
- stem cells
- signaling pathway
- systematic review
- dna damage
- randomized controlled trial
- molecular dynamics simulations
- tyrosine kinase
- mesenchymal stem cells
- skeletal muscle
- papillary thyroid
- type diabetes
- bone marrow
- metabolic syndrome
- machine learning
- oxide nanoparticles
- insulin resistance