The trans-zeatin-type side-chain modification of cytokinins controls rice growth
Kiba, T.; Mizutani, K.; Nakahara, A.; Takebayashi, Y.; Kojima, M.; Hobo, T.; Osakabe, Y.; Osakabe, K.; Sakakibara, H.
Show abstract
Cytokinins (CKs), a class of phytohormones with vital roles in growth and development, occur naturally with various side-chain structures, including N6-({Delta}2-isopentenyl)adenine-, cis-zeatin- and trans-zeatin (tZ)-types. Recent studies in a model dicot plant Arabidopsis demonstrated that tZ-type CKs are biosynthesized via cytochrome P450 monooxygenase (P450) CYP735A, and have a specific function in shoot growth promotion. Although the function of some of these CKs has been demonstrated in a few dicotyledonous plant species, the significance of these variations and their biosynthetic mechanism and function in monocots and in plants with distinctive side-chain profiles than Arabidopsis, such as Oryza sativa (rice), remain elusive. In this study, we characterized CYP735A3 and CYP735A4 to investigate the role of tZ-type CKs in rice. Complementation test of the Arabidopsis CYP735A-deficient mutant and CK profiling of loss-of-function rice mutant, cyp735a3 cyp735a4, demonstrated that CYP735A3 and CYP735A4 encode P450s required for tZ-type side-chain modification in rice. CYP735As are expressed in both roots and shoots. The cyp735a3 cyp735a4 mutants exhibited growth retardation concomitant with reduction in CK activity in both roots and shoots, indicating that tZ-type CKs function in growth promotion of both organs. Expression analysis revealed that tZ-type CK biosynthesis is negatively regulated by auxin, abscisic acid, and cytokinin and positively by dual nitrogen nutrient signals, namely glutamine-related and nitrate-specific signals. These results suggest that the physiological role of tZ-type CKs in rice is different from that in Arabidopsis and they control growth of both roots and shoots in response to internal and environmental cues in rice.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Rice NIN-LIKE PROTEIN 1 Rapidly Responds to Nitrogen Deficiency and Improves Yield and Nitrogen Use Efficiency 97%
- IRONMAN interacts with OsHRZ1 and OsHRZ2 to maintain Fe homeostasis 97%
- Overexpression of the chloroplastic 2-oxoglutarate/malate transporter in rice disturbs carbon and nitrogen homeostasis 97%
Similar papers in this journal
- Multifaceted roles of rice ABA/stress-induced intrinsically disordered proteins in augmenting drought resistance 97%
- Auxin-responsive (phospho)proteome analysis reveals regulation of cell cycle and ethylene signaling during rice crown root development 96%
- Complete loss of RelA and SpoT homologs in Arabidopsis reveals the importance of the plastidial stringent response in the interplay between chloroplast metabolism and plant defense response 96%
Similar papers in this journal
- Regulation of CYP94B1 by WRKY33 controls apoplastic barrier formation in the roots leading to salt tolerance 97%
- ZAXINONE SYNTHASE 2 regulates growth and arbuscular mycorrhizal symbiosis in rice 97%
- The exogenous application of the apocarotenoid retinaldehyde negatively regulates auxin-mediated root growth 97%
Similar papers in this journal
- Rice embryogenic trigger BABY BOOM1 promotes somatic embryogenesis by upregulation of auxin biosynthesis genes 97%
- Glucosinolate and phenylpropanoid biosynthesis are linked by proteasome-dependent degradation of PAL 97%
- Conifers exhibit a characteristic inactivation of auxin to maintain tissue homeostasis 97%
Similar papers in this journal
- OsbZIP47 an integrator for meristem regulators during rice plant growth and development 96%
- Red light controls adventitious root regeneration by modulating hormone homeostasis in Picea abies seedlings 96%
- MAP Kinase OsMEK2 and OsMPK1 Signaling for Ferroptotic Cell Death in Rice-Magnaporthe oryzae Interactions 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.