作为最通用和最精确的基因编辑技术,主要编辑(PE)可以为大多数人类遗传疾病建立持久的治疗方法。已经基于编辑器机器或pegRNA开发了几代PE,以实现任何类型的遗传校正。然而,由于发展的早期阶段,PE复杂元素需要进行优化以实现更高效的编辑。许多研究人员已经考虑了编辑蛋白和pegRNA的智能优化,但通用PE机器目前的缺点仍有待解决。PE元素的改性,微调宿主基因,操纵表观遗传学,和阻断免疫反应可以用来达到更有效的初免编辑。此外,参与PE过程的宿主因素,修复和先天免疫系统等基因尚未确定,和PE细胞上下文依赖性仍然知之甚少。关于大尺寸的PE元件,交付是一项重大挑战,通用病毒或非病毒平台的开发仍远未完成。具有缩短的RT变体的PE版本仍然太大,无法适应常见的病毒载体。新版本中应考虑针对PE元件和递送载体的免疫反应。主要编辑过程的预测仍有待改进,以用于筛选和验证目的。在这次审查中,PE的基础,包括几代人,潜力,优化,delivery,体内屏障,并将讨论该技术的未来前景。
As the most versatile and precise gene editing technology, prime editing (PE) can establish a durable cure for most human genetic disorders. Several generations of PE have been developed based on an editor machine or prime editing guide RNA (pegRNA) to achieve any kind of genetic correction. However, due to the early stage of development, PE complex elements need to be optimized for more efficient editing. Smart optimization of editor proteins as well as pegRNA has been contemplated by many researchers, but the universal PE machine\'s current shortcomings remain to be solved. The modification of PE elements, fine-tuning of the host genes, manipulation of epigenetics, and blockage of immune responses could be used to reach more efficient PE. Moreover, the host factors involved in the PE process, such as repair and innate immune system genes, have not been determined, and PE cell context dependency is still poorly understood. Regarding the large size of the PE elements, delivery is a significant challenge and the development of a universal viral or nonviral platform is still far from complete. PE versions with shortened variants of reverse transcriptase are still too large to fit in common viral vectors. Overall, PE faces challenges in optimization for efficiency, high context dependency during the cell cycling, and delivery due to the large size of elements. In addition, immune responses, unpredictability of outcomes, and off-target effects further limit its application, making it essential to address these issues for broader use in nonpersonalized gene editing. Besides, due to the limited number of suitable animal models and computational modeling, the prediction of the PE process remains challenging. In this review, the fundamentals of PE, including generations, potential, optimization, delivery, in vivo barriers, and the future landscape of the technology are discussed.