Nature Structural & Molecular BiologyBioengineeringPeer-reviewed journal article
Structure-guided discovery and engineering of miniature CRISPR-Cas12m for epigenome editing
Tao YuiD, Meng JiiD, Donglin YuiD, Zhao Guan, Rongyi ZhuiD, Yupeng Jiang, Zhiyi Yang, Lizhen Qiu, Ziyi Zhang, Jiawei Mu, Fengbiao MaoiD, Kuanhui XiangiD, Lin BaiiD, Kailong LiiD
Abstract
CRISPR-based epigenome editing represents a programmable strategy to precisely modulate gene expression, holding immense promise for therapeutic applications. However, the large size of the dCas proteins substantially impedes the delivery via adeno-associated virus (AAV) vectors. Here, through iterative bioinformatics analysis, structure-guided predictions, and functional assays, we identified and characterized PmCas12m, a novel miniature subtype V-M CRISPR-Cas12m. PmCas12m exhibited flexible 5-YTN-3 PAM-dependent recognition and robust double-stranded DNA binding properties, while lacking DNA cleavage activity, thus positioning it as an ideal tool for epigenome editing. Cryogenic electron microscopy (cryo-EM) structures of PmCas12m unveiled its unique molecular mechanism of DNA binding facilitating interference. Guided by these structural insights, we employed deep mutational scanning (DMS) and protein engineering to develop xCas12m, a hypercompact variant with highly potent and specific epigenome editing capabilities in human cells. We further constructed the xCas12m-CRISPRoff platform in a single AAV vector, which achieved durable epigenetic silencing and effective inhibition of hepatitis B virus (HBV) infection in a mouse model. Collectively, these findings establish xCas12m as a versatile epigenome editing platform with transformative potential for treating diseases, paving the way for clinical translation of epigenetic therapies.
- organism: human
- organism: mouse
- assay: CRISPR screen
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
- approach: biophysical modeling
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 38%
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