Back

A rapid and efficient red-light-activated Cre recombinase system for genome engineering in mammalian cells and transgenic mice

Zhou, Y.; Wei, Y.; Yin, J.; Kong, D.; Li, W.; Wang, X.; Yao, Y.; Huang, Q.; Li, L.; Liu, M.; Qiao, L.; Li, H.; Zhao, J.; Zhong, T. P.; Li, D.; Duan, L.; Guan, N.; Ye, H.

2025-03-16 synthetic biology
10.1101/2025.03.16.643490 bioRxiv
Show abstract

The Cre-loxP recombination system enables precise genome engineering; however, existing photoactivatable Cre tools suffer from several limitations, including low DNA recombination efficiency, background activation, slow activation kinetics, and poor tissue penetration. Here, we present REDMAPCre, a red-light-controlled split-Cre system based on the {Delta}PhyA/FHY1 interaction. REDMAPCre enables rapid activation (1-second illumination) and achieves an 85-fold increase in recombination efficiency. We demonstrate its efficient regulation of DNA recombination in mammalian cells and mice, as well as its compatibility with other inducible recombinase systems for Boolean logic-gated DNA recombination. Using a single-vector adeno-associated virus (AAV) delivery system, we successfully induced REDMAPCre-mediated DNA recombination in mice. Furthermore, we generated a REDMAPCre transgenic mouse line and validated its efficient, light-dependent recombination across multiple organs. To explore its functional applications, REDMAPCre transgenic mice were crossed with the relative Cre-dependent reporter mice, enabling optogenetic induction of insulin resistance and hepatic lipid accumulation via Cre-dependent overexpression of ubiquitin-like with PHD and ring finger domains 1 (UHRF1), as well as targeted cell ablation through diphtheria toxin fragment A (DTA) expression. Collectively, REDMAPCre provides a powerful tool for achieving remote control of recombination and facilitating functional genetic studies in living systems.

Published in Nucleic Acids Research (predicted rank #7) · training set

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.