The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Repurposing the CRISPR-Cas9 system for targeted DNA methylation
Open accessAbstract
Epigenetic studies relied so far on correlations between epigenetic marks and gene expression pattern. Technologies developed for epigenome editing now enable direct study of functional relevance of precise epigenetic modifications and gene regulation. The reversible nature of epigenetic modifications, including DNA methylation, has been already exploited in cancer therapy for remodeling the aberrant epigenetic landscape. However, this was achieved non-selectively using epigenetic inhibitors. Epigenetic editing at specific loci represents a novel approach that might selectively and heritably alter gene expression. Here, we developed a CRISPR-Cas9-based tool for specific DNA methylation consisting of deactivated Cas9 (dCas9) nuclease and catalytic domain of the DNA methyltransferase DNMT3A targeted by co-expression of a guide RNA to any 20 bp DNA sequence followed by the NGG trinucleotide. We demonstrated targeted CpG methylation in a ∼35 bp wide region by the fusion protein. We also showed that multiple guide RNAs could target the dCas9-DNMT3A construct to multiple adjacent sites, which enabled methylation of a larger part of the promoter. DNA methylation activity was specific for the targeted region and heritable across mitotic divisions. Finally, we demonstrated that directed DNA methylation of a wider promoter region of the target loci IL6ST and BACH2 decreased their expression.
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Citations
Cites
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
Genome engineering using the CRISPR-Cas9 system
Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013) · Nature Protocols · doi:10.1038/nprot.2013.143 · Cited by 11,133
Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression
Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013) · Cell · doi:10.1016/j.cell.2013.02.022 · Cited by 5,016 · cited 2×
“Even when lower transcript levels were confidently detected after using the inactive construct, they could be explained by CRISPR interference (16), where inactive Cas9 competes with transcription factors for binding to the promoter region.”
BackgroundCrystal structure of Cas9 in complex with guide RNA and target DNA
Nishimasu, H., Ran, F. A., Hsu, P. D., Konermann, S., Shehata, S. I., Dohmae, N., Ishitani, R., Zhang, F., & Nureki, O. (2014) · Cell · doi:10.1016/j.cell.2014.02.001 · Cited by 2,306
Acute Exercise Remodels Promoter Methylation in Human Skeletal Muscle
Barrès, R., Yan, J., Egan, B., Treebak, J. T., Rasmussen, M., Fritz, T., Caidahl, K., Krook, A., O'Gorman, D. J., & Zierath, J. R. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2012.01.001 · Cited by 965 · cited 1×
“The reversible nature of epigenetic modifications including DNA methylation (17,18) has been already exploited by the use of epigenetic inhibitors (such as inhibitors of DNA methyltransferases or histone deacetylases) in cancer therapy in order to remodel the aberrant epigenetic landscape (19,20).”
BackgroundOptical control of mammalian endogenous transcription and epigenetic states
Konermann, S., Brigham, M. D., Trevino, A. E., Hsu, P. D., Heidenreich, M., Cong, L., Platt, R. J., Scott, D. A., Church, G. M., & Zhang, F. (2013) · Nature · doi:10.1038/nature12466 · Cited by 803
Tsaprouni, L. G., Yang, T.-P., Bell, J., Dick, K. J., Kanoni, S., Nisbet, J., Viñuela, A., Grundberg, E., Nelson, C. P., Meduri, E., et al. (2014) · Epigenetics · doi:10.4161/15592294.2014.969637 · Cited by 319
Mechanisms of disease: The human N-glycome
Lauc, G., Pezer, M., Rudan, I., & Campbell, H. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2015.10.016 · Cited by 184
Epigenetic cancer therapy: rationales, targets and drugs
Rius, M., & Lyko, F. (2012) · Oncogene · doi:10.1038/onc.2011.601 · Cited by 157 · cited 1×
“The reversible nature of epigenetic modifications including DNA methylation (17,18) has been already exploited by the use of epigenetic inhibitors (such as inhibitors of DNA methyltransferases or histone deacetylases) in cancer therapy in order to remodel the aberrant epigenetic landscape (19,20).”
Background
Cited by
The CRISPR tool kit for genome editing and beyond
Adli, M. (2018) · Nature Communications · doi:10.1038/s41467-018-04252-2 · Cited by 1,545
Editing DNA Methylation in the Mammalian Genome
Liu, X. S., Wu, H., Ji, X., Stelzer, Y., Wu, X., Czauderna, S., Shu, J., Dadon, D., Young, R. A., & Jaenisch, R. (2016) · Cell · doi:10.1016/j.cell.2016.08.056 · Cited by 1,224
“also reported that dCas9 fused with Dnmt3a can be used to methylate two human gene promoters (Vojta et al., 2016), and Xu et al.”
Horlbeck, M. A., Gilbert, L. A., Villalta, J. E., Adamson, B., Pak, R. A., Chen, Y., Fields, A. P., Park, C. Y., Corn, J. E., Kampmann, M., et al. (2016) · eLife · doi:10.7554/elife.19760 · Cited by 894
CRISPR-Based Technologies for the Manipulation of Eukaryotic Genomes
Komor, A. C., Badran, A. H., & Liu, D. R. (2017) · Cell · doi:10.1016/j.cell.2016.10.044 · Cited by 1,013
CRISPR/Cas9 therapeutics: progress and prospects
Li, T., Yang, Y., Qi, H., Cui, W., Zhang, L., Fu, X., He, X., Liu, M., Li, P.-F., & Yu, T. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01309-7 · Cited by 719
Metadata
- Authors
- Vojta, A., Dobrinić, P., Tadić, V., Bočkor, L., Korać, P., Julg, B., Klasić, M., & Zoldoš, V.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-03-11
- Volume
- 44
- Issue
- 12
- Pages
- 5615-5628
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2294327742
- Cited by
- 777
- References
- 10
- FWCI
- 48.30
- Type
- journal article
- Language
- en
- Open access
- gold
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- Vojta, A., Dobrinić, P., Tadić, V., Bočkor, L., Korać, P., Julg, B., Klasić, M., & Zoldoš, V. (2016). Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Research. https://doi.org/10.1093/nar/gkw159
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