Our study shows that AtDJB3 modulates seed amyloid-like protein assemblies to regulate ABA sensitivity, thereby coordinating germination and early seedling development, and shaping adaptive responses during early plant growth. Molecular chaperones are highly abundant in seeds, yet their roles during the transition from seed to seedling remain poorly defined. In this study, we identify AtDJB3, an aggregate-remodeling J-domain protein (JDP), as a regulator of abscisic acid (ABA)-mediated responses during early developmental transitions in Arabidopsis thaliana. AtDJB3 expression is induced by ABA and depends on the transcription factors ABI3 and ABI5 during germination. A T-DNA insertion mutant of AtDJB3 (atdjb3-1) exhibited enhanced germination but impaired post-germinative growth in the presence of ABA compared to wild-type plants, indicating a role for AtDJB3 in fine-tuning ABA responses across distinct developmental phases. Consistently, atdjb3-1 seeds showed improved radicle emergence and cotyledon opening under osmotic stress, often associated with ABA-mediated growth inhibition. Notably, dry atdjb3-1 seeds accumulated elevated levels of amyloid-like protein assemblies, linking altered aggregate remodeling to the observed changes in ABA-regulated seed-to-seedling progression. Together, these findings implicate AtDJB3 as an important component of the ABA signaling network that coordinates developmental timing and stress adaptation in A. thaliana
Our findings reveal a complex genetic architecture underlying rice husk silica traits, identifying key QTLs, candidate genes, and epistatic interactions with potential applications in rice breeding and silica valorization. Rice contains large amounts of amorphous silica in its tissues. Rice husk (RH) contains 17–30
Continuous blue light enhances drought tolerance in Arabidopsis by modulating ABA and GA pathways, whilst controlled GA catabolism, especially via GA2ox7/8, is important for stomatal regulation and drought adaptation. In response to drought, plants undergo physiological and developmental adjustments to reduce water loss and survive the stress, including reduced shoot growth, altered root architecture, and stomatal closure. These processes are regulated by phytohormones, including gibberellins (GAs), which mediate growth adjustments, and abscisic acid (ABA), which promotes stomatal closure. In addition, light signalling plays a crucial role in plant adaptation to drought. Here, we investigated how progressive water restriction affects hormone levels, gene expression, and leaf temperature in Arabidopsis thaliana shoots and roots under two contrasting light regimes: white light under short-day and continuous low-intensity blue light, with a focus on GA and ABA signalling pathways. The hormonal contribution to the drought response was investigated by analysis of ABA and GA mutants. Plants grown under continuous blue light experienced less-drought stress compared with white light as reflected in growth performance and differential gene expression. Drought increased ABA levels in leaves and roots, with stronger accumulation in roots under both light conditions. By contrast, GA production was decreased in leaves and increased in roots under drought in white light, but with little change in blue light. The change in GA status in leaves was associated with down-regulation of GA-biosynthetic 20-OXIDASE genes and up-regulation of GA-catabolic 2-OXIDASE genes (AtGA2ox6, AtGA2ox9). ABA- and GA-deficient mutants were unaffected by water restriction, whilst GA-overproducers lacking GA2ox genes or overexpressing AtGA20ox1 were hypersensitive to drought, with the C20-GA2ox genes GA2ox7 and GA2ox8 proving particularly critical with compromised stomatal regulation.
CRISPR-dCas9 epigenome editing represents a paradigm shift from static DNA sequence editing to dynamic reprogramming of gene expression, offering a powerful and precise toolbox with the potential to develop stable and climate-resilient crops without altering DNA sequences. The escalating challenges of global food security, driven by climate change and population growth, necessitate innovative crop improvement strategies that extend beyond conventional breeding. Although CRISPR-Cas9 has revolutionized plant biotechnology through direct genome editing, CRISPR-based epigenome editing has emerged as a new frontier. By repurposing nuclease-dead Cas9 as a programmable targeting platform fused to epigenetic effector domains, researchers can precisely modulate gene expression without altering the DNA sequence. This approach has already yielded compelling successes, such as the engineering of durable resistance to cassava bacterial blight through targeted epigenetic silencing of a susceptibility gene. This review focuses primarily on the current state of CRISPR-based epigenome editing systems, including acetyltransferases, demethylases, DNA methyltransferases, and deacetylases, and their applications in crop improvement research. We critically evaluate their mechanisms in rewriting epigenetic regulatory information, their demonstrated efficacy in enhancing yield components, disease resistance, and abiotic stress tolerance, and their potential to create stable meiotically heritable epialleles, although most demonstrations remain at the proof-of-concept stage in model plants, with field validation in major crops still limited. This review provides a balanced analysis of key challenges, including editing specificity, context-dependency, delivery limitations, and regulatory considerations. Finally, we outline future directions focusing on technological refinement, multi-omics integration, and field validation. Epigenome editing shows promise as a conceptually distinct and transformative approach in crop biotechnology, through targeted modulation of a plant’s epigenetic landscape. However, its full potential hinges on overcoming challenges related to stability, predictability, and field-scale validation.
This review concludes that integrating AI with smart sensing has the potential to substantially enhance hydroponic production through precise monitoring, adaptive control, and improved resource-use efficiency. Hydroponics, a key approach in controlled-environment agriculture (CEA), enables precise regulation of plant growth conditions, improving resource efficiency and year-round crop production. Recent advances in artificial intelligence (AI) and sensor technologies offer opportunities to enhance nutrient management, monitor plant health, and predict physiological responses in real time. This review critically evaluates AI-enabled hydroponic systems, focusing on sensor-driven monitoring of solution chemistry (EC, pH, DO), environmental parameters, and plant-health indicators through imaging. We highlight the application of time-series forecasting models, computer vision, and reinforcement learning for adaptive control, emphasizing their impact on plant growth, nutrient uptake, and stress mitigation. Key challenges, including sensor drift, model generalization across crops, and adoption barriers, are discussed. Finally, we outline strategies for integrating AI with plant-centered hydroponic design, including hybrid control frameworks, prototype concepts for explainable AI dashboards, and open datasets. The dashboard concept is presented as a future design direction to improve transparency and operator trust rather than as a validated user interface evaluated in this review. The accompanying conceptual implementation example illustrates how the reviewed AI methodologies could be integrated within an AIoT-enabled hydroponic system, while quantitative validation across multiple crops, cultivation cycles, and production environments remains necessary before generalizing their effectiveness.
