Polyploidy and DNA methylation are important features of plant genome evolution, and the relationships with gene expression vary with genetic backgrounds. We profiled genome-wide DNA methylation in a natural hexaploid and a natural tetraploid A. deliciosa, a natural diploid A. chinensis, and cultivated diploid A. chinensis 'Hongyang', and explored fruit transcript abundance between the hexaploid A. deliciosa and A. chinensis 'Hongyang'. Methylation patterns differed among the four materials, but did not show a monotonic trend across the sampled ploidy levels. Differentially methylated regions (DMRs) distinguished the hexaploid material from the other three materials, and DMR-associated genes were represented in stress- and metabolism-related pathways. In the exploratory fruit comparison, 60.3% of gene pairs had lower TPM values and 5.8% had higher TPM values in A. deliciosa than in A. chinensis. Genome-wide methylation-expression correlations in hexaploid A. deliciosa were generally weak. An additional leaf-matched analysis identified negative CHG and CHH associations in gene bodies and their repeat-associated portions, with different promoter associations. These observations identify candidate methylation and expression differences for studies of Actinidia genome evolution.
The concept of plant intelligence (PI), once controversial, is gaining empirical traction as a novel paradigm for understanding sophisticated plant behaviors. This review synthesizes the biological foundations of this intelligence across genetics, biochemistry, molecular biology, and cellular biology, arguing it emerges from decentralized, integrated systems. Specifically, we dissect its foundations in genetics (e.g., hereditary information and evolutionary constraints), biochemistry (e.g., metabolic networks and signaling molecules), molecular biology (e.g., signal transduction and gene regulatory networks), and cellular biology (e.g., cellular sentience and tissue-level communication). We critically examine the methodological and conceptual challenges in studying non-neural cognition and highlight how contemporary tools such as live imaging, single-cell omics, optogenetics, and systems biology are overcoming these hurdles. Looking forward, we explore how nascent frontiers such as synthetic biology, plant-computer biohybrids, and artificial intelligence-driven in silico models promise to revolutionize the field. Overall, the present study contributing on opening a new research direction of plant biointelligence (PbI) is of fundamental implications on stimulating innovations in fundamental biology, providing blueprints of synthesizing PI with frontier biotechnologies, and offering profound insights into the nature of intelligence and, even, life.
Grapes are highly nutritious, and their cultivation offers substantial economic benefits. Shortening the grape development period allows for earlier market availability and mitigates the risks associated with environmental stressors. The early-maturing grape cultivar, Summer Black (SB), and its bud mutation cultivar, Early Summer Seedless (ESS), were used to study early-stage berry development. Veraison is a critical phase in early fruit maturity. Transcriptomic analysis identified 35 genes that were closely associated with early fruit maturity. MADS-box genes participate in flower and fruit development. Within this family, the VvAGL (AGAMOS-like) family, comprising 34 members, plays a vital role in grape growth. Intersectional analysis of VvAGL with key genes related to early fruit maturity revealed that VvAGL15 is a candidate transcription factor involved in grape development. Using transgenic tomato plants, we found that VvAGL15-overexpressing lines exhibited faster plant growth, flowering, color transformation, and maturation than the MicroTom line, suggesting that VvAGL15 has the potential to promote these processes. A protein related to anthocyanin metabolism, VvCHI1, was identified from a yeast two-hybrid screening library. One-to-one validation, bimolecular fluorescence complementation, and pull-down assays showed that VvAGL15 interacts with VvCHI1. Together, these findings identify VvAGL15 as a strong candidate regulator of grape berry ripening, potentially acting through its association with VvCHI1 to influence anthocyanin metabolism. However, direct functional validation in grape remains necessary to confirm its native causal role. These results provide valuable genetic resources and candidate targets for the development of early-maturing cultivars.
For decades, plant biology has relied on a narrow set of classical model organisms—such as Arabidopsis thaliana, Oryza sativa, and Zea mays—to uncover core physiological, developmental, and molecular pathways. These systems enabled foundational discoveries, yet their glycophytic, annual, and domesticated life histories limit their relevance for studying many adaptive traits found across global plant diversity. Key innovations—including CAM and C4 photosynthesis, extreme salinity and desiccation tolerance, perennial wood formation, specialized secondary metabolism, and parasitic interactions—remain largely inaccessible within classical frameworks. Recent advances in third‑generation long‑read sequencing (PacBio HiFi, ONT Duplex), telomere‑to‑telomere (T2T) phased assemblies, pangenomics, single‑cell and spatial transcriptomics, and multi‑omics integration are dismantling these historical barriers. Together, these technologies enable high‑resolution characterization of polyploid, structurally complex, and ecologically specialized genomes, allowing previously intractable species to be developed as functional models. This review outlines the shift from traditional model‑centric approaches toward a trait‑centric, multi‑model framework. We evaluate the limitations of homology‑based functional inference across divergent regulatory and metabolic networks, highlight structural genomic constraints that undermine automated annotation transfer, and examine genomic enablers that resolve polyploidy, structural variation, and pangenomic diversity. We further synthesize emerging model systems—including halophytes, extremophytes, resurrection plants, alkaloid‑producing taxa, perennial woody species, parasitic lineages, and early‑diverging clades—that provide direct access to traits absent from classical references. Finally, we discuss persistent bottlenecks in transformation recalcitrance and functional gene validation, emphasizing how morphogenic regulators, viral vectors, CRISPR platforms, and AI‑driven biological foundation models are enabling direct in planta functional genomics across diverse species. Collectively, these developments establish a modernized, trait‑centric, multi‑model landscape that expands biological discovery and strengthens applications in agriculture, biotechnology, medicine, and biodiversity conservation.
Soil salinity severely constrains growth and phytochemical quality in medicinal plants. Chitosan-based biostimulants offer a promising strategy for stress mitigation, yet comparative assessments of conventional versus nano-chitosan under salinity, particularly when coupled with interpretable modeling, remain scarce. This study investigated the effects of four salinity levels (0, 50, 100, and 150 mM NaCl) and foliar applications of chitosan and nano-chitosan (0, 0.25, and 0.5 g L⁻¹) on morphological, physiological, and biochemical traits of Calendula officinalis under controlled greenhouse conditions. A hybrid interpretable modeling framework (IGANN–GPR) was developed that integrates generalized additive neural networks with Gaussian process regression, enabling both high-accuracy prediction and principled uncertainty quantification across fifteen plant response variables. The model achieved excellent performance (R = 0.84–0.99), with exceptional accuracy for photosynthetic pigments (R ≥ 0.98) and robust estimation of oxidative stress markers and secondary metabolites. Residual analysis confirmed unbiased predictions and stable generalization. Feature importance analysis revealed that nano-chitosan (35.9%) slightly outperformed salinity (34.0%) and conventional chitosan (30.1%) in influencing plant responses. Interpretable dose–response functions identified a distinct salinity threshold (~75 mM NaCl), beyond which growth and chlorophyll content declined sharply, while proline and phenolic compounds accumulated progressively. Nano-chitosan exhibited rapid sigmoidal activation at lower concentrations, suggesting enhanced bioavailability and more effective modulation of antioxidant enzymes and membrane integrity compared with conventional chitosan. These findings demonstrate that nano-chitosan alleviates salinity-induced oxidative damage by strengthening redox homeostasis and osmotic adjustment. By integrating predictive accuracy with mechanistic interpretability and uncertainty quantification, the proposed hybrid modeling framework offers a robust, transparent, and systems-level tool for understanding stress physiology and supporting precision management of medicinal crops under saline conditions.
