Strategies to Minimizing off-Target Effects in CRISPR-CAS9 Genome Editing: A Systematic Literature Review
Kata Kunci:
CRISPR-Cas9, off-target, genome editingAbstrak
CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats – Associated Protein 9) is a leading innovation in the field of genome editing due to its high efficiency, flexibility, and accuracy. However, the application of this technology faces challenges regarding off-target effects. Various strategies have been developed to enhance the specificity of CRISPR-Cas9 technology. This study analysis strategies developed over the past decade to minimize off-target effects, based on scientific evidence. A systematic literature review was conducted by examining scientific articles published in international journals indexed in Google Scholar and PubMed. The results indicate that off-target effects can be minimized through a combination of methods, including Cas9 engineering, sgRNA design optimization, the use of base editing, and the application of computational systems for accurate off-target predictions. This study provides an overview of recent developments in improving the safety and precision of genome editing and serves as a scientific reference for future research.
Referensi
Aljabali, A. A. A., El-Tanani, M., & Tambuwala, M. M. (2024). Principles of CRISPR-Cas9 technology: Advancements in genome editing and emerging trends in drug delivery. Journal of Drug Delivery Science and Technology, 92, 105338. https://doi.org/10.1016/j.jddst.2024.105338
Anthon, C., Corsi, G. I., & Gorodkin, J. (2022). CRISPRon/off: CRISPR/Cas9 on- and off-target gRNA design. Bioinformatics, 38(24), 5437–5439. https://doi.org/10.1093/bioinformatics/btac697
Cao, J., Wu, L., Zhang, S.-M., Lu, M., Cheung, W. K. C., Cai, W., Gale, M., Xu, Q., & Yan, Q. (2016). An easy and efficient inducible CRISPR/Cas9 platform with improved specificity for multiple gene targeting. Nucleic Acids Research, gkw660. https://doi.org/10.1093/nar/gkw660
Charlier, J., Sherkatghanad, Z., & Makarenkov, V. (2025). Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction. PLOS Computational Biology, 21(10), e1013606. https://doi.org/10.1371/journal.pcbi.1013606
Chaudhari, H. G., Penterman, J., Whitton, H. J., Spencer, S. J., Flanagan, N., Lei Zhang, M. C., Huang, E., Khedkar, A. S., Toomey, J. M., Shearer, C. A., Needham, A. W., Ho, T. W., Kulman, J. D., Cradick, T. J., & Kernytsky, A. (2020). Evaluation of Homology-Independent CRISPR-Cas9 Off-Target Assessment Methods. The CRISPR Journal, 3(6), 440–453. https://doi.org/10.1089/crispr.2020.0053
Coelho, M. A., de Braekeleer, E., Firth, M., Bista, M., Lukasiak, S., Cuomo, M. E., & Taylor, B. J. M. (2020). CRISPR GUARD protects off-target sites from Cas9 nuclease activity using short guide RNAs. Nature Communications, 11(1), 4132. https://doi.org/10.1038/s41467-020-17952-5
Du, W., Zhang, T., Guo, L., Zheng, Y., Zhang, H., Zhu, X., Tang, D., Hu, H., Chen, L., & Liu, C. (2026). Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives. Journal of Translational Medicine, 24(1), 769. https://doi.org/10.1186/s12967-026-08175-1
Fu, B. X. H., Smith, J. D., Fuchs, R. T., Mabuchi, M., Curcuru, J., Robb, G. B., & Fire, A. Z. (2019). Target-dependent nickase activities of the CRISPR–Cas nucleases Cpf1 and Cas9. Nature Microbiology, 4(5), 888–897. https://doi.org/10.1038/s41564-019-0382-0
Garrood, W. T., Kranjc, N., Petri, K., Kim, D. Y., Guo, J. A., Hammond, A. M., Morianou, I., Pattanayak, V., Joung, J. K., Crisanti, A., & Simoni, A. (2021). Analysis of off-target effects in CRISPR-based gene drives in the human malaria mosquito. Proceedings of the National Academy of Sciences, 118(22). https://doi.org/10.1073/pnas.2004838117
Guo, C., Ma, X., Gao, F., & Guo, Y. (2023). Off-target effects in CRISPR/Cas9 gene editing. Frontiers in Bioengineering and Biotechnology, 11. https://doi.org/10.3389/fbioe.2023.1143157
Han, Y., Yuan, Z., Zhong, S. L., Xu, H., & Jiang, S. (2023). Minimizing the off-target frequency of the CRISPR/Cas9 system via zwitterionic polymer conjugation and peptide fusion. Chemical Science, 14(23), 6375–6382. https://doi.org/10.1039/D2SC07067G
Lin, J., & Wong, K.-C. (2018). Off-target predictions in CRISPR-Cas9 gene editing using deep learning. Bioinformatics, 34(17), i656–i663. https://doi.org/10.1093/bioinformatics/bty554
Liu, Q., Cheng, X., Liu, G., Li, B., & Liu, X. (2020). Deep learning improves the ability of sgRNA off-target propensity prediction. BMC Bioinformatics, 21(1), 51. https://doi.org/10.1186/s12859-020-3395-z
Liu, Q., He, D., & Xie, L. (2019). Prediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature. PLOS Computational Biology, 15(10), e1007480. https://doi.org/10.1371/journal.pcbi.1007480
Luo, Y., Chen, Y., Xie, H., Zhu, W., & Zhang, G. (2024). Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT. Computers in Biology and Medicine, 169, 107932. https://doi.org/10.1016/j.compbiomed.2024.107932
Martin, F., Sánchez-Hernández, S., Gutiérrez-Guerrero, A., Pinedo-Gomez, J., & Benabdellah, K. (2016). Biased and Unbiased Methods for the Detection of Off-Target Cleavage by CRISPR/Cas9: An Overview. International Journal of Molecular Sciences, 17(9), 1507. https://doi.org/10.3390/ijms17091507
Naeem, M., & Alkhnbashi, O. S. (2023). Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects. International Journal of Molecular Sciences, 24(7), 6261. https://doi.org/10.3390/ijms24076261
Pan, X., Qu, K., Yuan, H., Xiang, X., Anthon, C., Pashkova, L., Liang, X., Han, P., Corsi, G. I., Xu, F., Liu, P., Zhong, J., Zhou, Y., Ma, T., Jiang, H., Liu, J., Wang, J., Jessen, N., Bolund, L., … Luo, Y. (2022). Massively targeted evaluation of therapeutic CRISPR off-targets in cells. Nature Communications, 13(1), 4049. https://doi.org/10.1038/s41467-022-31543-6
Riaz, S., Bukhari, B., Essa, R., Perveen, I., Haq, M. A. ul, Ahmad, M., Ashfaq, R., Ahmad, A., Abbas, N., Mazhar, S., Saleem, Y., Nadeem, A. A., Syed, Q., & Abidi, S. I. H. (2025). Harnessing CRISPR/Cas9 for Poultry Improvement: Applications in Egg Albumin and Industrial Innovation. Journal of Pharma and Biomedics, 3(2), 245–251. https://doi.org/10.56810/jpbm.003.02.0089
Shen, C.-C., Hsu, M.-N., Chang, C.-W., Lin, M.-W., Hwu, J.-R., Tu, Y., & Hu, Y.-C. (2019). Synthetic switch to minimize CRISPR off-target effects by self-restricting Cas9 transcription and translation. Nucleic Acids Research, 47(3), e13–e13. https://doi.org/10.1093/nar/gky1165
Tran, N. T., Danner, E., Li, X., Graf, R., Lebedin, M., de la Rosa, K., Kühn, R., Rajewsky, K., & Chu, V. T. (2022). Precise CRISPR-Cas–mediated gene repair with minimal off-target and unintended on-target mutations in human hematopoietic stem cells. Science Advances, 8(22). https://doi.org/10.1126/sciadv.abm9106
Tycko, J., Wainberg, M., Marinov, G. K., Ursu, O., Hess, G. T., Ego, B. K., Aradhana, Li, A., Truong, A., Trevino, A. E., Spees, K., Yao, D., Kaplow, I. M., Greenside, P. G., Morgens, D. W., Phanstiel, D. H., Snyder, M. P., Bintu, L., Greenleaf, W. J., … Bassik, M. C. (2019). Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements. Nature Communications, 10(1), 4063. https://doi.org/10.1038/s41467-019-11955-7
Wang, M., Zhang, Y., Bi, C., & Li, M. (2025). CRISPR-Cas9-induced double-strand breaks disrupt maintenance of epigenetic information. Genome Biology, 26(1), 411. https://doi.org/10.1186/s13059-025-03851-9
Wang, S., Ren, S., Bai, R., Xiao, P., Zhou, Q., Zhou, Y., Zhou, Z., Niu, Y., Ji, W., & Chen, Y. (2018). No off-target mutations in functional genome regions of a CRISPR/Cas9-generated monkey model of muscular dystrophy. Journal of Biological Chemistry, 293(30), 11654–11658. https://doi.org/10.1074/jbc.AC118.004404
Yu, W., Chen, J., Guo, J., Yu, F., Wang, G., Lin, J., Dai, X., Tan, X., Ma, P., Wu, L., Zhang, Y., Huang, S., Lan, P., Bian, Q., Huang, X., Wei, J., Cheng, T., Zheng, X., & Qiao, Y. (2026). Guide RNA reprogramming facilitates minimized tracrRNA-dependent off-target and versatile CRISPR/Cas9 engineering. Nature Communications, 17(1), 7678. https://doi.org/10.1038/s41467-026-74520-z
Zhang, G., Luo, Y., Dai, X., & Dai, Z. (2023). Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities. Briefings in Bioinformatics, 24(6). https://doi.org/10.1093/bib/bbad333
Zhang, Z.-R., & Jiang, Z.-R. (2022). Effective use of sequence information to predict CRISPR-Cas9 off-target. Computational and Structural Biotechnology Journal, 20, 650–661. https://doi.org/10.1016/j.csbj.2022.01.006
Zhao, N., Zhou, J., Tao, T., Wang, Q., Tang, J., Li, D., Gou, S., Guan, Z., Olajide, J. S., Lin, J., Wang, S., Li, X., Zhou, J., Gao, Z., & Wang, G. (2024). Evolved cytidine and adenine base editors with high precision and minimized off-target activity by a continuous directed evolution system in mammalian cells. Nature Communications, 15(1), 8140. https://doi.org/10.1038/s41467-024-52483-3
Zhou, J., Liu, Y., Wei, Y., Zheng, S., Gou, S., Chen, T., Yang, Y., Lan, T., Chen, M., Liao, Y., Zhang, Q., Tang, C., Liu, Y., Wu, Y., Peng, X., Gao, M., Wang, J., Zhang, K., Lai, L., & Zou, Q. (2022a). Eliminating predictable DNA off-target effects of cytosine base editor by using dual guiders including sgRNA and TALE. Molecular Therapy, 30(7), 2443–2451. https://doi.org/10.1016/j.ymthe.2022.04.010
Zhou, J., Liu, Y., Wei, Y., Zheng, S., Gou, S., Chen, T., Yang, Y., Lan, T., Chen, M., Liao, Y., Zhang, Q., Tang, C., Liu, Y., Wu, Y., Peng, X., Gao, M., Wang, J., Zhang, K., Lai, L., & Zou, Q. (2022b). Eliminating predictable DNA off-target effects of cytosine base editor by using dual guiders including sgRNA and TALE. Molecular Therapy, 30(7), 2443–2451. https://doi.org/10.1016/j.ymthe.2022.04.010
Zhu, L. J., Lawrence, M., Gupta, A., Pagès, H., Kucukural, A., Garber, M., & Wolfe, S. A. (2017). GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases. BMC Genomics, 18(1), 379. https://doi.org/10.1186/s12864-017-3746-y
Zischewski, J., Fischer, R., & Bortesi, L. (2017). Detection of on-target and off-target mutations generated by CRISPR/Cas9 and other sequence-specific nucleases. Biotechnology Advances, 35(1), 95–104. https://doi.org/10.1016/j.biotechadv.2016.12.003
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Ferencia Esananda Rattu, Natasha Novida Sipahutar
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
