Post-transcriptional regulation plays a crucial role in plant adaptation to environmental disturbances;. h However, the molecular mechanisms linking mRNA metabolism to physiological heat stress tolerance in rice remain poorly understood. CCR4-associated factor 1 (CAF1) functions as a core deadenylase within the CCR4-NOT complex, but the roles of individual rice CAF1 paralogs in heat responses remain unclear. In this study, we investigated the role of OsCAF1A in rice, and heat responses with a particular focus on the contribution of its conserved catalytic-site residues. We show that OsCAF1A expression is markedly induced by moderate and severe heat condition in both roots and shoots. Under normal growth conditions, both wild-type OsCAF1A and a catalytic-site mutant exhibit diffuse cytoplasmic and nuclear localization. Upon heat, wild-type OsCAF1A undergoes rapid redistribution into cytoplasmic foci that overlap with the processing body (PB) marker OsDCP2. In contrast, mutation of the conserved catalytic-site residues Asp45 and Glu47 is associated with reduced redistribution into OsDCP2-labeled PBs. Functional analyses further indicate that a mutation in the conserved catalytic-site residues of OsCAF1A is associated with altered heat phenotypes in rice. Overexpression of wild-type OsCAF1A was associated with increased seedling survival and membrane stability under heat, whereas expression of the catalytic-site mutant was associated with increased heat sensitivity. Together, these findings support an association between conserved catalytic-site residues in OsCAF1A, heat-induced processing body association, and phenotypes in rice.
Low phosphate conditions severely limits rice growth and yield, threatening agricultural sustainability and food security. Balancing growth and stress tolerance is therefore a key agricultural challenge. This study investigated the physiological and molecular basis of AgNP-enhanced phosphate deficiency tolerance in rice plants. Rice plants were pre-treated with AgNPs (0–100 mg L−1) for two weeks, followed by cultivation under phosphate-deficient conditions for six weeks. Growth traits (root and shoot length), relative chlorophyll concentration, phosphate concentration, antioxidant enzyme activity, and expression of low-phosphate response genes were evaluated across six AgNP concentrations. Among the tested concentrations, 25 mg L−1 AgNP produced the most significant growth-promoting effects. Root length was significantly enhanced, being twice as higher compared with the control at 25 mg L−1, indicating stimulation of root development. In contrast, shoot length declined ( 1.15-fold relative to the control) when AgNP concentrations exceeded 25 mg L−1. Higher concentrations of AgNPs stimulated antioxidant enzyme activity. Notably, a synergistic interaction between AgNP pre-treatment and phosphate deficiency was observed: 25 mg L−1 AgNP mitigated phosphate starvation effects by increasing root length, SPAD values, and phosphate accumulation, while also upregulating genes involved in low-phosphate response pathways. Pre-treatment with 25 mg L−1 AgNP was associated with improved rice resilience to phosphate deficiency by promoting root development, enhancing chlorophyll and phosphate concentration, and activating low-Pi response genes. Excessive AgNP concentrations, however, may inhibit shoot growth and induce oxidative imbalances.
Plants adjust traits such as stomatal conductance (gs) and leaf area (LA) to cope with resource fluctuations, and while species differ in their responses to water scarcity, fertilization can enhance resilience. This study assessed how seasonal variation, fertilization, and their interaction influence functional adjustments in tree legumes: three non-N-fixers (Cenostigma tocantinum, Senna reticulata and Dipteryx odorata), one N fixer (Clitoria fairchildiana), and two facultative N fixers (Inga edulis and Acacia mangium). The experiment included six species with nine replicates per treatment, monitored over a 25-month period. Gas exchange, nutrient and carbohydrate concentrations, and leaf area were measured on fertilized and unfertilized plants under both drought and wet conditions, enabling analysis of both physiological and biochemical responses across different environmental and nutritional scenarios. All species reduced their gs, photosynthetic performance, and transpiration rates during drought, and fertilization imposed little effect on these seasonal changes. However, fertilization increased LA in D. odorata and C. fairchildiana, but not in A. mangium. Seasonal changes and fertilization also influenced macro (P, K, Ca) and micronutrients (Fe, Zn), with responses differing significantly among species. Most species, particularly those that were fertilized, exhibited lower concentration of carbohydrates (starch and soluble sugars) during drought but restored these reserves upon recovery. These functional acclimations allow species to adjust more effectively and optimize resource use in degraded Amazonian environments. These results suggest that selecting tree legumes with greater functional plasticity may enhance reforestation success and ecosystem resilience in degraded Amazonian landscapes, subsidizing restoration strategies and policy decisions for tropical forest management.
In this paper, we propose an Artificial Neural Network (ANN)-based model that can be used to predict the natural rubber concentration in rubber dandelion (Taraxacum kok-saghyz) roots. Morphological characteristics of the plants serve as the input for the neural network, whereas the predicted variable is the rubber yield. Analysis of plant accessions HOTEL and OH1004 indicated that eight of the investigated physiological traits correlated closely with the targeted rubber concentration. The architecture of the model was tailored to this input data and subsequently trained on measured values. Post-training network analysis was also performed, which helped identify the morphological characteristics that influenced rubber concentration the most. To broaden the application areas of the model, a generalized architecture is also proposed, which leverages the flexibility of neural networks and simultaneously uses quantitative and qualitative data as input. The measured vs. calculated rubber concentration correlation plots have shown that the generalized model is capable of providing reliable results for multiple plant accessions.
Producing and storing non-structural carbohydrates (NSC) during the short rainy season in a tropical dry forest (TDF) may determine the survival of woody species. As rainy seasons become increasingly irregular under global climate change, advancing our understanding of these dynamics is crucial for discussing the performance of woody species. In this study, we investigated two woody species widely found in the Brazilian tropical dry forest (Caatinga), Pityrocarpa moniliformis and Cenostigma microphyllum, focusing on the seasonal dynamics of NSC in leaves, stems, and roots, as well as the water status of adult plants under semi-arid climate conditions. We measured soluble sugars, starch, xylem water potential, and stomatal conductance across climatic seasons and their transitions. Both species decreased stomatal conductance during the transition from the rainy to the dry season. We observed that P. moniliformis reduced starch reserves and increased soluble sugar concentrations during this transition, while maintaining a higher water status compared to C. microphyllum. In contrast, C. microphyllum maintained relatively stable NSC concentrations across the evaluated periods. Our results suggest that the response of woody species is species-specific and does not always involve the preservation of carbohydrate reserves, as these species may tolerate low xylem water potential during the dry season. Furthermore, although both species are highly abundant in the Caatinga, their strategies of NSC dynamics among plant organs are divergent. The ecophysiological significance of these contrasting carbon allocation patterns is discussed.
