The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Mutual potentiation of plant immunity by cell-surface and intracellular receptors
Open accessAbstract
The plant immune system involves cell-surface receptors that detect intercellular pathogen-derived molecules, and intracellular receptors that activate immunity upon detection of pathogen-secreted effector proteins that act inside the plant cell. Immunity mediated by surface receptors has been extensively studied1, but that mediated by intracellular receptors has rarely been investigated in the absence of surface-receptor-mediated immunity. Furthermore, interactions between these two immune pathways are poorly understood. Here, by activating intracellular receptors without inducing surface-receptor-mediated immunity, we analyse interactions between these two distinct immune systems in Arabidopsis. Pathogen recognition by surface receptors activates multiple protein kinases and NADPH oxidases, and we find that intracellular receptors primarily potentiate the activation of these proteins by increasing their abundance through several mechanisms. Likewise, the hypersensitive response that depends on intracellular receptors is strongly enhanced by the activation of surface receptors. Activation of either immune system alone is insufficient to provide effective resistance against the bacterial pathogen Pseudomonas syringae. Thus, immune pathways activated by cell-surface and intracellular receptors in plants mutually potentiate to activate strong defences against pathogens. These findings reshape our understanding of plant immunity and have broad implications for crop improvement. In Arabidopsis, two distinct types of immunity—that mediated by cell-surface receptors and that mediated by intracellular receptors—interact with and mutually enhance each other to provide effective defence against pathogens.
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Cites
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Jones, J. D. G., & Dangl, J. L. (2006) · Nature · doi:10.1038/nature05286 · Cited by 13,177
Self-citationNear-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Mutation of the mouse klotho gene leads to a syndrome resembling ageing
Kuro-o, M., Matsumura, Y., Aizawa, H., Kawaguchi, H., Suga, T., Utsugi, T., Ohyama, Y., Kurabayashi, M., Kaname, T., Kume, E., et al. (1997) · Nature · doi:10.1038/36285 · Cited by 3,837
The NLRP3 Inflammasome: an overview of mechanisms of activation and regulation
Kelley, N., Jeltema, D., Duan, Y., & He, Y. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20133328 · Cited by 3,539
Peptidoglycan recognition by the innate immune system
Wolf, A. J., & Underhill, D. M. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.136 · Cited by 469
Cited by
Pattern-recognition receptors are required for NLR-mediated plant immunity
Yuan, M., Jiang, Z., Bi, G., Nomura, K., Liu, M., Wang, Y., Cai, B., Zhou, J.-M., He, S. Y., & Xin, X.-F. (2021) · Nature · doi:10.1038/s41586-021-03316-6 · Cited by 1,257
PTI-ETI crosstalk: an integrative view of plant immunity
Yuan, M., Ngou, B. P. M., Ding, P., & Xin, X.-F. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102030 · Cited by 915
Self-citationThe plant immune system: From discovery to deployment
Jones, J. D. G., Staskawicz, B. J., & Dangl, J. L. (2024) · Cell · doi:10.1016/j.cell.2024.03.045 · Cited by 544
Self-citationEvolutionary trajectory of pattern recognition receptors in plants
(2024) · doi:10.1038/s41467-023-44408-3 · Cited by 119
Li, L., & Weigel, D. (2021) · Annual Review of Phytopathology · doi:10.1146/annurev-phyto-020620-114826 · Cited by 48
Metadata
- Authors
- Ngou, B. P. M., Ahn, H.-K., Ding, P., & Jones, J. D. G.
- Venue
- Nature
- Published
- 2021-03-10
- Volume
- 592
- Issue
- 7852
- Pages
- 110-115
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3135815505
- Cited by
- 1,100
- References
- 6
- FWCI
- 100.68
- Type
- journal article
- Language
- en
- Open access
- green
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