The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Improved drought stress tolerance in Arabidopsis by CRISPR/dCas9 fusion with a Histone AcetylTransferase
Open accessAbstract
Drought episodes decrease plant growth and productivity, which in turn cause high economic losses. Plants naturally sense and respond to water stress by activating specific signalling pathways leading to physiological and developmental adaptations. Genetically engineering genes that belong to these pathways might improve the drought tolerance of plants. The abscisic acid (ABA)-responsive element binding protein 1/ABRE binding factor (AREB1/ABF2) is a key positive regulator of the drought stress response. We investigated whether the CRISPR activation (CRISPRa) system that targets AREB1 might contribute to improve drought stress tolerance in Arabidopsis. Arabidopsis histone acetyltransferase 1 (AtHAT1) promotes gene expression activation by switching chromatin to a relaxed state. Stable transgenic plants expressing chimeric dCas9 HAT were first generated. Then, we showed that the CRISPRa dCas9 HAT mechanism increased the promoter activity controlling the β-glucuronidase (GUS) reporter gene. To activate the endogenous promoter of AREB1, the CRISPRa dCas9 HAT system was set up, and resultant plants showed a dwarf phenotype. Our qRT-PCR experiments indicated that both AREB1 and RD29A, a gene positively regulated by AREB1, exhibited higher gene expression than the control plants. The plants generated here showed higher chlorophyll content and faster stomatal aperture under water deficit, in addition to a better survival rate after drought stress. Altogether, we report that CRISPRa dCas9 HAT is a valuable biotechnological tool to improve drought stress tolerance through the positive regulation of AREB1.
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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
A new mathematical model for relative quantification in real-time RT-PCR
Pfaffl, M. W. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.9.e45 · Cited by 34,610
COPPER ENZYMES IN ISOLATED CHLOROPLASTS. POLYPHENOLOXIDASE IN BETA VULGARIS
Arnon, D. I. (1949) · Plant Physiology · doi:10.1104/pp.24.1.1 · Cited by 22,451
Floral dip: a simplified method forAgrobacterium‐mediated transformation ofArabidopsis thaliana
Clough, S. J., & Bent, A. F. (1998) · The Plant Journal · doi:10.1046/j.1365-313x.1998.00343.x · Cited by 21,934
Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo
Baker, N. R. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092759 · Cited by 4,639
Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation
Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.029 · Cited by 2,728
Crop production under drought and heat stress: plant responses and management options.
Fahad, S., Bajwa, A. A., Nazir, U., Anjum, S. A., Farooq, A., Zohaib, A., Sadia, S., Nasim, W., Adkins, S., Saud, S., et al. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01147 · Cited by 2,629
MatInspector and beyond: promoter analysis based on transcription factor binding sites
Cartharius, K., Frech, K., Grote, K., Klocke, B., Haltmeier, M., Klingenhoff, A., Frisch, M., Bayerlein, M., & Werner, T. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti473 · Cited by 1,979
Hilton, I. B., D'Ippolito, A. M., Vockley, C. M., Thakore, P. I., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3199 · Cited by 1,885
Highly efficient Cas9-mediated transcriptional programming
Chavez, A., Scheiman, J., Vora, S., Pruitt, B. W., Tuttle, M., P R Iyer, E., Lin, S., Kiani, S., Guzman, C. D., Wiegand, D. J., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3312 · Cited by 1,622
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CRISPR/Cas: A powerful tool for gene function study and crop improvement
Zhang, D., Zhang, Z., Unver, T., & Zhang, B. (2021) · Journal of Advanced Research · doi:10.1016/j.jare.2020.10.003 · Cited by 373
Genome editing and improvement of abiotic stress tolerance in crop plants
Yadav, R. K., Tripathi, M. K., Tiwari, S., Tripathi, N., Asati, R., Chauhan, S., Tiwari, P. N., & Payasi, D. K. (2023) · Life · doi:10.3390/life13071456 · Cited by 75
Gene editing for plant resistance to abiotic factors: a systematic review
Nascimento, F. D. S., Rocha, A. D. J., Soares, J. M. D. S., Mascarenhas, M. S., Ferreira, M. D. S., Morais Lino, L. S., Ramos, A. P. D. S., Diniz, L. E. C., Mendes, T. A. D. O., Ferreira, C. F., et al. (2023) · Plants · doi:10.3390/plants12020305 · Cited by 71
Tariq, A., Mushtaq, M., Yaqoob, H., Bhat, B. A., Zargar, S. M., Raza, A., Ali, S., Charagh, S., Mubarik, M. S., Zaman, Q. U., et al. (2023) · GM Crops & Food · doi:10.1080/21645698.2023.2219111 · Cited by 45
Metadata
- Authors
- Roca Paixão, J. F., Gillet, F.-X., Ribeiro, T. P., Bournaud, C., Lourenço-Tessutti, I. T., Noriega, D. D., Melo, B. P. D., de Almeida-Engler, J., & Grossi-de-Sa, M. F.
- Venue
- Scientific Reports
- Published
- 2019-05-30
- Volume
- 9
- Issue
- 1
- Pages
- 8080-8080
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2947921650
- Cited by
- 308
- References
- 51
- FWCI
- 19.50
- Type
- journal article
- Language
- en
- Open access
- gold
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- Roca Paixão, J. F., Gillet, F.-X., Ribeiro, T. P., Bournaud, C., Lourenço-Tessutti, I. T., Noriega, D. D., Melo, B. P. D., de Almeida-Engler, J., & Grossi-de-Sa, M. F. (2019). Improved drought stress tolerance in Arabidopsis by CRISPR/dCas9 fusion with a Histone AcetylTransferase. Scientific Reports. https://doi.org/10.1038/s41598-019-44571-y
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