Jing Liu
5 Papers
8 Citations
Jing Liu is an academic researcher. The author has contributed to research in topics: RNA & Gene. The author has an hindex of 3, co-authored 5 publications.
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Papers
A rationally engineered cytosine base editor retains high on-target activity while reducing both DNA and RNA off-target effects.
Erwei Zuo,Yidi Sun,Yidi Sun,Tanglong Yuan,Bingbing He,Changyang Zhou,Wenqin Ying,Jing Liu,Wu Wei,Wu Wei,Rong Zeng,Rong Zeng,Yixue Li,Hui Yang +13 more
TL;DR: Structural and biochemical insights help engineer a cytosine base editor variant that possesses improved on-target activity with minimal DNA and RNA off-target editing.
150
Modulation of metabolic functions through Cas13d-mediated gene knockdown in liver
Bingbing He,Wenbo Peng,Wenbo Peng,Jia Huang,Hang Zhang,Yingsi Zhou,Xiali Yang,Jing Liu,Zhijie Li,Zhijie Li,Chunlong Xu,Mingxing Xue,Hui Yang,Pengyu Huang,Pengyu Huang +14 more
TL;DR: The present work supplies a successful proof-of-concept trial that suggests efficient and regulatory knockdown of target metabolic genes for a designed metabolism modulation in the liver in mouse hepatocytes.
High-fidelity base editor with no detectable genome-wide off-target effects
Erwei Zuo,Yidi Sun,Yidi Sun,Tanglong Yuan,Bingbing He,Changyang Zhou,Wenqin Ying,Jing Liu,Wu Wei,Wu Wei,Rong Zeng,Rong Zeng,Yixue Li,Hui Yang +13 more
TL;DR: It is found DNA off-target SNVs induced by BE3 could be completely eliminated in BE3R126E but the off- target RNA SNVs was only slightly reduced, and the feasibility of engineering base editors for high fidelity deaminases is suggested.
9
Modulation of metabolic functions through Cas13d-mediated gene knockdown in liver
Bingbing He,Wenbo Peng,Wenbo Peng,Jia Huang,Hang Zhang,Yingsi Zhou,Xiali Yang,Jing Liu,Zhijie Li,Zhijie Li,Chunlong Xu,Mingxing Xue,Hui Yang,Pengyu Huang,Pengyu Huang +14 more
TL;DR: The CasRx system was demonstrated to efficiently and functionally knock down genes related to metabolism functions, including Pten, Pcsk9 and lncLstr, in mouse hepatocytes, providing an effective strategy to reversibly modulate metabolic genes.
2
Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.
Tanglong Yuan,Nana Yan,Tianyi Fei,Jitan Zheng,Juan Meng,Nana Li,Jing Liu,Haihang Zhang,Long Xie,Wenqin Ying,Di Li,Lei Shi,Yongsen Sun,Yongyao Li,Yixue Li,Yidi Sun,Erwei Zuo +16 more
TL;DR: In this paper, a deep learning model was developed to predict the OPTI-CGBE editing outcome for targeted sites with specific sequence context, which is useful for efficient and precise base editing.