Rootstocks are commonly used in melon cultivation to mitigate biotic and abiotic stresses and improve fruit quality. However, the extent to which rootstocks influence nutrient allocation, growth dynamics, and fruit quality formation in the scion remains insufficiently understood. This study compared the effects of two pumpkin rootstocks (Tianzhen No. 1 and Sizhuang No. 12) on melon fruit quality by examining nutrient partitioning, sugar accumulation, hormonal balance, and expression of sugar metabolism-related genes. Compared with non-grafted plants, rootstock grafting enhanced plant growth and increased total N, P, and K uptake (mg plant-1) as well as tissue nutrient concentrations (mg g-1 DW) during vine growth and fruit maturation stages. Tianzhen No. 1 showed higher K distribution to the scion (97.6%) and greater fruit K accumulation (45.88 mg/g) than Sizhuang No. 12. Fruits grafted onto Tianzhen No. 1 also exhibited higher total soluble solids (15.96 °Brix) and soluble sugar contents, including glucose (18.28 mg g-1), fructose (20.20 mg g-1), and sucrose (144.25 mg g-1), whereas fruits grafted onto Sizhuang No. 12 showed higher firmness (4.18 kg cm-2) compared with Tianzhen No. 1-grafted fruits (3.07 kg cm-2). At fruit maturation, Tianzhen No. 1-grafted melons had higher ABA content (14.49 ng/g) and upregulated expression of sugar transporter genes (HT2, HT7, SWEET4, SWEET7) and genes encoding key metabolic enzymes (SPS1, NIN3) compared to Sizhuang No. 12. These observed associations suggest that rootstock-mediated differences in nutrient uptake may contribute to altered scion fruit quality metabolism. Overall, the findings provide insights into the physiological and molecular basis of rootstock-dependent variation in melon fruit quality and may contribute to the development of targeted rootstock selection strategies in melon breeding programs.

[Objective]Large language models (LLMs) have demonstrated strong capabilities in natural language understanding, knowledge integration, and complex reasoning, offering new opportunities for intelligent decision-making in agriculture. However, their direct application in agricultural production and facility environment control remains challenging due to strong physical constraints and high operational risks. The lack of real-world interaction and executable decision grounding limits the practical effectiveness of conventional LLMs in such scenarios. To address these challenges, a tool-augmented LLM-based agricultural intelligent agent system, termed AgriAgent, was proposed, and a digital-twin-based evaluation platform for agricultural decision-making was developed. By integrating a high-fidelity digital twin environment with an end-to-end agent architecture, the decision-making performance of agricultural intelligent agents with different parameter scales was systematically evaluated across multiple crops and climate scenarios.[Methods]A high-fidelity agricultural digital twin evaluation platform was constructed using the decision support system for agrotechnology transfer (DSSAT) v4.8 crop growth model as the core simulation engine to model crop growth under diverse environmental conditions and management strategies. Meteorological driving data were obtained from the Seoul Historical Weather Data dataset. Through data cleaning, missing-value imputation, unit normalization, and time-series reconstruction, the raw meteorological data were transformed into standardized inputs compatible with DSSAT. Three climate scenarios representing different environmental complexities were designed, including a regular scenario, a perturbed scenario, and an extreme scenario. The regular scenario employed historical observations, the perturbed scenario introduced stochastic disturbances to simulate short-term climate variability, and the extreme scenario incorporated multi-factor coupled stresses such as high temperatures and excessive precipitation during sensitive growth stages. In total, 90 annual climate driving sequences were generated. Fixed soil profile parameters calibrated by domain experts were applied across all simulations to minimize confounding effects. Within this digital twin environment, a tool-augmented agricultural intelligent agent, AgriAgent, was implemented using a modular architecture consisting of a sensor module, memory module, retriever, large language model, and tool executor, forming a closed-loop decision-making framework. In each decision cycle, the agent perceived environmental and crop state information, including soil moisture and nutrient status, meteorological conditions, crop growth stages, and stress indicators. State summaries and historical decisions were stored in memory, while agronomic knowledge was retrieved through a retrieval-augmented generation mechanism. Based on integrated information, the LLM generated structured environmental control commands in JSON format, which were validated and constrained by the tool executor before updating the DSSAT environment. The system supported irrigation, supplementary lighting, ventilation, heating, fertilization, and CO2 enrichment. Five representative crops: maize, millet, sugar beet, tomato, and cabbage were simulated under the three climate scenarios over complete growing seasons, resulting in 450 crop-scenario combinations. An unmanaged DSSAT simulation served as the baseline. AgriAgent models with three parameter scales (1.5B, 3B, and 7B), built on the Qwen2.5 series, were evaluated. Crop economic yield expressed as dry matter at physiological maturity was adopted as the evaluation metric.[Results and Discussions]The results showed that AgriAgent consistently outperformed the baseline across all crops and climate scenarios, with model scale exerting a significant influence on decision-making performance. AgriAgent-7B achieved the best overall performance under regular, perturbed, and extreme scenarios, demonstrating strong generalization ability and environmental adaptability. By dynamically adjusting water, nutrient, light, and thermal management strategies, the agent effectively mitigated environmental stresses even under multi-factor coupled extreme climate conditions. Under extreme scenarios, AgriAgent-7B increased yields by 463.60% for maize, 351.20% for millet, 125.40% for sugar beet, 1 537.46% for tomato, and 1 185.14% for cabbage compared with the baseline. Particularly large gains were observed for high-value crops such as tomato and cabbage, highlighting the advantages of the proposed framework for precision-controlled facility agriculture. In contrast, AgriAgent-1.5B exhibited performance comparable to the baseline, while AgriAgent-3B achieved moderate improvements but remained inferior to the 7B model. These findings indicate a clear scaling effect, suggesting that larger models possess stronger capabilities in multi-source information integration, long-term temporal reasoning, and adaptation to complex environments.[Conclusions]This study developed a digital-twin-based agricultural decision evaluation platform and proposed a tool-augmented, end-to-end agricultural intelligent agent named AgriAgent. Experiments across multiple crops and climate scenarios verified the effectiveness and robustness of the proposed framework for dynamic agricultural decision-making. The results demonstrate that integrating knowledge retrieval, reasoning, and tool execution within a closed-loop LLM-based agent enables stable, reliable, and adaptive environmental control, providing a feasible technical pathway and standardized evaluation paradigm for intelligent agriculture.

In tomato production, grafting enhances stress resistance, increases yield, and improves fruit quality. However, the selection of rootstock types limits its broader adoption. This study systematically evaluated the effects of grafting with 16 different rootstocks on tomato survival rate and yield. Fruits from four rootstocks, Gangshi 319 self-grafted (CK), Gangshi 319 seedlings (A), Torubam (T), and Fanzhen No. 1 (F), were further selected for fruit quality analysis and broad target metabolomics. The results showed that, except for Qiezhen No. 3 (QZ3), the graft survival rates of all rootstocks exceeded 95%. Grafting with rootstock F significantly increased yield per plant and soluble solids content, whereas rootstock T significantly reduced both traits. Broad target metabolomics analysis identified 18 major metabolite categories, including lipids, ketoaldehydes and esters, and terpenoids. KEGG pathway enrichment analysis revealed that differentially accumulated metabolites between the F and T treatments were primarily enriched in pathways such as the citric acid cycle, phenylpropanoid biosynthesis, glyoxylate and dicarboxylate metabolism, flavonoid biosynthesis, cysteine and methionine metabolism, and glycerophospholipid metabolism. These findings indicate that rootstock F effectively enhances tomato fruit yield and soluble solids accumulation by coordinating primary and secondary metabolism. This study provides important metabolic level insights for the selection and application of high quality and high yield tomato rootstocks in grafting.

Plant leaf starch content is a critical indicator of metabolic status, yet traditional enzymatic methods are destructive, labor-intensive, and costly. This study proposes a novel non-destructive detection method using watermelon-pumpkin grafted seedlings. To optimize hardware design, 12 characteristic wavelengths were identified via competitive adaptive reweighted sampling (CARS). A portable multispectral imaging system was developed, featuring narrowband LEDs and integrated human-computer interaction software for real-time visualization. We constructed a multimodal deep learning architecture that integrates a convolutional neural network (CNN) for spatial feature extraction from RGB images, a fully connected neural network (FCNN) for spectral data, and a Transformer network for high-level feature fusion. Experimental results showed that the ShuffleNet v2-Transformer model achieved an R2 of 0.956 (RMSE = 0.036) for watermelon leaves, while the EfficientNet b1-Transformer model reached an R2 of 0.967 (RMSE = 0.052) for pumpkin leaves. This multimodal approach significantly outperformed conventional PLSR and single-modal CNN models, demonstrating superior ability in processing long-range dependencies within spectral-spatial data. The system enables accurate detection with a throughput of 120 samples per hour at a hardware cost approximately 90% lower than commercial multispectral cameras. This provides an efficient, low-cost solution for large-scale monitoring of plant physiological indicators in precision breeding.

As petroleum-based plastics contribute significantly to environmental pollution, the development of novel biodegradable and even edible packaging materials has become a research hotspot. In this study, lotus seed protein hydrolysate (LSPH) was extracted and used to fabricate a propylene glycol alginate (PGA)/LSPH composite film. The antioxidant activity of LSPH was predicted through structural analysis and bioinformatics tools, complemented by peptide sequencing and amino acid composition analysis. The composite film with 0.2% LSPH/PGA exhibited the highest tensile strength (35 MPa) along with improved elongation at break. Moreover, this film demonstrated the lowest water vapor permeability (WVP), enhanced surface hydrophobicity, and increased thermal stability. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) analyses indicated that the addition of LSPH led to the formation of non-covalent bonds-including hydrogen bonds, hydrophobic interactions, and electrostatic interactions-with other components in the composite film, contributing to a homogeneous and smooth microstructure.

Plant grafting, a series of tissue reunion processes, exhibits varying levels of compatibility across different species. Despite extensive research, the response of the scion to various compatible rootstocks remains poorly understood. In this study, we utilized transcriptomic analyses and gene functional validation experiments to investigate the role of xyloglucan endotransglucosylase/hydrolase (XTH) genes and their products in graft healing, specifically examining their effects on callus proliferation and graft survival in response to rootstocks with differing compatibilities across multiple species. Our results indicated that the less compatible bottle gourd rootstocks stimulate increased callus proliferation at the graft junctions with melon (Cucumis melo) scions. Virus-induced gene silencing of a highly expressed XTH gene, CmXTH9, in melon led to lower survival rates and reduced callus proliferation at the graft boundary. Furthermore, grafting accompanied upregulation of 25% to 55% of XTH family genes in the grafts of Arabidopsis thaliana, Nicotiana benthamiana, and Oryza sativa, which are distributed across different phylogenetic branches. Successful heterografts typically induced more family genes with greater upregulation than unsuccessful grafts. Consistently, an Arabidopsis thaliana Atxth4;Atxth7 mutant decreased grafting success rates and diminished callus proliferation at the wound site. These results underscore the conserved function of XTHs in graft union development and highlight their role in graft healing.

BackgroundMelon (Cucumis melo L.) is a widely cultivated fruit globally, valued for its high nutritional content and diverse culinary uses. However, the molecular mechanisms underlying flavor enhancement mediated by grafting remain poorly understood.ObjectiveThis study aimed to elucidate the molecular regulatory mechanisms of flavor formation in melons grafted onto different rootstocks: Cucurbita moschata (QY1) and Cucumis metuliferus (ZM4). MethodsTranscriptomic and metabolomic analyses were integrated to systematically dissect dynamic changes in gene expression and metabolite accumulation in grafted systems (ZM4/SG, QY1/SG). ResultsThe two rootstocks induced distinct genetic and metabolic adaptation strategies. The ZM4 rootstock activated genes related to carbohydrate metabolism, such as sucrose-cleaving enzymes (INV, bglX/bglB) and nucleotide-sugar synthases (UGDH, GAE), promoting the accumulation of glucose and fructose and expanding UDP-sugar precursors for glycosylation. These genetic changes enhanced anabolic flux, leading to the accumulation of cell wall polysaccharides (e.g., mannan) and high-value glycosides (e.g., vanilloyl glucose). Additionally, ZM4/SG exhibited enhanced stress resilience via activation of AKR1A1 and MIOX, resulting in increased accumulation of xylitol and trehalose-6-phosphate. In contrast, the QY1 rootstock activated energy metabolism pathways, upregulating E3.2.1.21 and MIOX to promote glycoside hydrolysis and NADPH regeneration, thereby strengthening energy metabolism.DiscussionThis study clarifies the mechanism of rootstock-mediated metabolic flux reprogramming: ZM4/SG coordinates hydrolytic, biosynthetic, and stress-responsive pathways to redirect carbon flux toward structural polysaccharides and high-value glycosides, providing molecular targets for improving melon aroma and flavor quality. The results align with the hypothesis that rootstocks regulate fruit quality traits and establish a working model for understanding synergistic regulatory networks between rootstocks and scions. This lays a theoretical foundation for developing precise quality modulation strategies in melons.

Drought stress severely constrains the growth, yield, and accumulation of bioactive compounds in Dendrobium officinale (D. officinale), a valuable medicinal orchid, and this challenge is exacerbated under simulated wild cultivation where plants are inevitably exposed to recurring water deficits. Basic helix-loop-helix (bHLH) transcription factors are well-established regulators of plant abiotic stress responses. However, the molecular mechanisms by which bHLH transcription factors respond to drought stress in this species remain largely unknown. In this study, a bHLH transcription factor gene, DobHLH25, was cloned from D. officinale. Phylogenetic analysis revealed that DobHLH25 shares the highest sequence identity with its ortholog in Dendrobium nobile. Additionally, subcellular localization analysis indicated that DobHLH25 is targeted to the nucleus and possesses a functional transcriptional activation domain. Expression pattern analysis showed that DobHLH25 is most abundantly expressed in old leaves, and its expression in roots, stems, and leaves is induced by polyethylene glycol treatments. Heterologous expression of DobHLH25 in Arabidopsis thaliana resulted in higher seed germination rates and longer root lengths under mannitol-induced osmotic stress compared to wild-type plants. Under drought stress, DobHLH25 heterologous expression lines exhibited higher survival rates, reduced leaf water loss, lower malondialdehyde accumulation, and increased proline content. Moreover, the activities of antioxidant enzymes such as superoxide dismutase and peroxidase were significantly enhanced, and the expression levels of multiple drought-responsive genes were markedly upregulated. Collectively, these findings suggest a correlation between DobHLH25 expression and plant drought tolerance, as evidenced by reduced oxidative damage, increased osmolyte accumulation, enhanced antioxidant enzyme activities, and upregulation of drought-responsive genes. Together, these results suggest that DobHLH25 plays a positive role in drought tolerance, and provides a basis for future dissection of its regulatory network in D. officinale.

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

Grafting is a traditional horticultural practice that enhances plant resilience against biotic and abiotic stresses. However, the influence of specific tissues, such as rootstock cotyledons, on graft union formation is not well understood. This study investigates the impact of rootstock cotyledon removal on graft healing in watermelon and its underlying mechanisms. Our results indicate that grafting with rootstock cotyledons (+C) consistently resulted in higher survival rates and better growth outcomes compared to grafting without rootstock cotyledons (-C). This effect was more pronounced in cultivated watermelon rootstocks, which have lower hypocotyl sugar content than wild watermelon rootstocks. Transcriptomic analysis revealed that cotyledon removal disrupted sugar metabolism and affected gene expression related to cell division and tissue development. A fructokinase, ClFRK1, was identified among the candidate genes positively correlated with graft survival rate and healing degree. Silencing ClFRK1 reduced callus proliferation, delayed graft healing and reduced survival rate. Conversely, fructose treatment increased ClFRK1 expression levels at the graft junction, which promoted callus proliferation and vascular reconnection. We propose a novel regulatory model for how ClFRK1 regulates graft union formation. These findings underscore new insights into the interactions and synergistic processes between the graft interface and non-grafted organs during graft union formation and also enrich our understanding of fructokinase.

Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles, in smart greenhouse ecosystems. However, collecting the imaging dataset is a challenge facing the deep learning detection of plant phenotype given the dynamic changes among leaves and the temporospatial limits of camara sampling. To address this issue, digital cousin is an improvement on digital twins that can be used to create virtual entities of plants through the creation of dynamic 3D structures and plant attributes using RGB image datasets in a simulation environment, using the principles of the variations and interactions of plants in the physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian splatting is selected to reconstruct and store the 3D model of the plant with 7000 and 30,000 training rounds, enabling the capture of RGB images and the detection of the phenotypes of the seedlings, overcoming temporal and spatial limitations. In the second phase, an improved YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding the LADH, SPPELAN, and Focaler-ECIoU modules. Compared with the original YOLOv8, the precision of our model is 91%, and the loss metric is lower by approximately 0.24. Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M).

IntroductionApplying 3D reconstruction techniques to individual plants has enhanced high-throughput phenotyping and provided accurate data support for developing "digital twins" in the agricultural domain. High costs, slow processing times, intricate workflows, and limited automation often constrain the application of existing 3D reconstruction platforms.MethodsWe develop a 3D reconstruction platform for complex plants to overcome these issues. Initially, a video acquisition system is built based on "camera to plant" mode. Then, we extract the keyframes in the videos. After that, Zhang Zhengyou's calibration method and Structure from Motion(SfM)are utilized to estimate the camera parameters. Next, Camera poses estimated from SfM were automatically calibrated using camera imaging trajectories as prior knowledge. Finally, Object-Based NeRF we proposed is utilized for the fine-scale reconstruction of plants. The OB-NeRF algorithm introduced a new ray sampling strategy that improved the efficiency and quality of target plant reconstruction without segmenting the background of images. Furthermore, the precision of the reconstruction was enhanced by optimizing camera poses. An exposure adjustment phase was integrated to improve the algorithm's robustness in uneven lighting conditions. The training process was significantly accelerated through the use of shallow MLP and multi-resolution hash encoding. Lastly, the camera imaging trajectories contributed to the automatic localization of target plants within the scene, enabling the automated extraction of Mesh. Results and discussionOur pipeline reconstructed high-quality neural radiance fields of the target plant from captured videos in just 250 seconds, enabling the synthesis of novel viewpoint images and the extraction of Mesh. OB-NeRF surpasses NeRF in PSNR evaluation and reduces the reconstruction time from over 10 hours to just 30 Seconds. Compared to Instant-NGP, NeRFacto, and NeuS, OB-NeRF achieves higher reconstruction quality in a shorter reconstruction time. Moreover, Our reconstructed 3D model demonstrated superior texture and geometric fidelity compared to those generated by COLMAP and Kinect-based reconstruction methods. The $R^2$ was 0.9933,0.9881 and 0.9883 for plant height, leaf length, and leaf width, respectively. The MAE was 2.0947, 0.1898, and 0.1199 cm. The 3D reconstruction platform introduced in this study provides a robust foundation for high-throughput phenotyping and the creation of agricultural “digital twins”.

The root-knot nematode (Meloidogyne incognita) poses a major threat to global agriculture by impairing root function, reducing nutrient uptake, and ultimately limiting seed development and crop productivity. This study investigated the molecular and metabolic defense responses of Cucumis metuliferus (prickly pear) to M. incognita infection. Gene expression and metabolic pathway reprogramming in M. incognita-infected roots were examined using integrated transcriptomics and metabolomics approaches. The identified genes were involved in stress responses and defense activation. Furthermore, metabolite profiling revealed significant shifts in secondary metabolite production, with an upregulation of defense-related compounds like jasmonic acid, salicylic acid, and prostaglandins. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis highlighted critical pathways such as biotin metabolism and nucleotide metabolism, underscoring the adaptive metabolic responses of C. metuliferus plants. GO (Gene Ontology) analysis from the integrated transcriptomics and metabolomics data highlighted significant upregulation of enzymatic pathways, transporter activities, and reorganization of cellular structures. Furthermore, KEGG pathway analysis revealed activation of secondary metabolite biosynthesis, immune-related signaling pathways, and metabolic reprogramming including increased carbon metabolism and nucleotide biosynthesis. This study provides a valuable molecular framework for breeding of M. incognita-resistant cultivars, ultimately supporting more stable seed distribution and agricultural productivity in M. incognita-prone regions.

BACKGROUND:Cyclocodon lancifolius, a traditional Chinese medicinal herb with considerable pharmacological and commercial value, is increasingly threatened by root rot disease during large-scale cultivation. Despite its rising economic significance, the disease's etiology, associated microbial dynamics, and effective management strategies remain largely unclear. This study addresses these gaps by systematically characterizing the microbial communities of healthy and diseased plants, identifying the primary pathogenic agents, and assessing the in vitro antifungal efficacy of selected fungicides. RESULTS:The findings revealed a marked reduction in microbial diversity within both diseased root tissues and their surrounding rhizosphere soils. This decline was accompanied by pronounced shifts in microbial community composition, characterized by an increased abundance of pathogenic fungi, with Fusarium solani being particularly dominant. Pathogenicity tests confirmed that F. solani is the principal pathogen, because isolates DHG1 and DHG2 successfully induced typical root rot symptoms. Optimal growth conditions for the pathogen included alkaline environments (pH 6-9) and moderate temperatures (25-28 °C), with lactose and proline identified as the preferred carbon and nitrogen sources, respectively. Among the tested fungicides, fludioxonil was the most potent in vitro, and hymexazol elicited the most uniform inhibitory response. CONCLUSION:This research constitutes the first in-depth investigation into root rot affecting C. lancifolius, by incorporating microbial community dynamics, pathogen characterization, and fungicide efficacy assessment. F. solani was identified as the primary pathogen. Its environmental preferences and chemical sensitivities were evaluated, offering valuable insights for early diagnosis, targeted intervention, and the development of sustainable cultivation practices for this underexplored medicinal plant. © 2025 Society of Chemical Industry.

BACKGROUND: Cyclocodon lancifolius is an increasingly valued dual-purpose medicinal-edible plant resource in contemporary society. Owing to the coincidence of its rapid seedling growth phase with the season characterized by frequent droughts, water-deficit stress severely compromises seedling development and biomass accumulation under field cultivation. Nevertheless, the Physiological and molecular mechanisms underlying drought stress perception, signal transduction and adaptive responses in C. lancifolius seedlings remain virtually uncharacterized. Consequently, an integrated dual-omics approach combing transcriptomics and metabolomics is imperative to initially dissect the drought-responsive regulatory networks, thereby providing a foundational framework for elucidating the complete signal transduction cascades that govern drought adaptation in this species. RESULTS: Seven-month-old C. lancifolius seedlings were subjected to three treatments: control (C), moderately drought (MD), and severely drought (SD). Drought stress inhibited the growth and development, and photosynthesis of C. lancifolius seedlings, significantly reducing Tr, PN, Ci, Gs, LCP, and CE in photosynthetic characteristics. Drought stress also modulated other physiological and biochemical characteristics, including reduced relative water content (RWC), leaf biomass, and chlorophyll content, which affect membrane lipid peroxidation and osmotic adjustment by increasing electrolyte permeability, malondialdehyde (MDA), and proline content. Concurrently, antioxidant enzymes such as peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) were activated to scavenge excess reactive oxygen species (ROS). Transcriptome analysis showed that drought stress induced more downregulated genes than upregulated genes in C. lancifolius seedlings. Metabolomics analysis revealed that there were more upregulated metabolites than downregulated metabolites. Combined transcriptomic and metabolomic analyses highlighted the crucial roles of starch and sucrose metabolism, glutathione metabolism, phenylpropanoid biosynthesis and flavonoid biosynthesis in drought tolerance of C. lancifolius seedlings, and explore the synthetic pathway of luteolin and caffeoylquinic acid. It was discovered that certain drought conditions promoted the caffeoylquinic acid accumulation and reduced the content of luteolin. CONCLUSIONS: The study provides the first expressed genome resource for C. lancifolius, a novel medicinal and edible plant. Integrated transcriptomic and metabolomic data collectively reveal the critical molecular regulatory network underlying drought stress response in C. lancifolius. Thus, these findings lay the foundation for molecular breeding of drought-resistant varieties for C. lancifolius and other medicinal plants.

>Melon（Cucumis melo L.） is a globally important fruit crop appreciated for its sweet taste, unique aroma, and nutritional value（Kaleem et al., 2024）. Aroma, shaped by volatile organic compounds（VOCs）, is a key trait influencing consumer preference. These VOCs are mainly derived from amino acids, fatty acids, and terpenoid pathways（Chen et al., 2023）. Esters contribute to fruity and sweet notes, whereas terpenes and C 9 aldehydes/alcohols impart floral and melon-like aromas,respectively（Mayobre et al., 2024）.

Grafting in watermelon using traditional methods often causes rootstock regrowth, increasing labor demand and production costs. Although cotyledon-less splice grafting eliminates regrowth by excising meristem tissue, its success rate has consistently been lower. Here, we developed a novel cotyledon-less splice grafting methodology that achieved high survival rates by modulating pre-grafting light intensities from 100 to 300 μmol·m-2·s-1 for scion and rootstock, generating four experimental groups: high-light intensity scion/high-light intensity rootstock (HS/HR), high-light intensity scion/low-light intensity rootstock (HS/LR), low-light intensity scion/high-light intensity rootstock (LS/HR), and low-light intensity scion/low-light intensity rootstock (LS/LR). The results demonstrated that HS/HR and LS/HR exhibited the highest survival rates, nearly 98%, and displayed high seedling quality, markedly enhanced graft-union adhesion, and accelerated vascular reconnection. Pretreatment of high light intensity increased starch accumulation in rootstock hypocotyls, enhancing tolerance to carbon starvation after grafting especially in the cotyledon-less grafts. Metabolomic analysis identified elevated levels of key metabolites, including auxins, cytokinins, D-galactose, galactinol, starch, cinnamic acid, M-coumaric acid, and vanilloloside. Transcriptomic profiling revealed significant enrichment of plant hormone signal, starch and sucrose metabolism, and phenylpropanoid biosynthesis pathways in scion and rootstock tissues underpinning hormonal regulation, carbohydrate metabolism, and lignin biosynthesis under high-light conditions. WGCNA identified key co-expression modules associated with graft healing traits and key metabolites. Furthermore, graft healing related genes (PXY, NAC086, CALS7, and TMO6) were upregulated. In conclusion, our findings underscore the critical role of light intensity in orchestrating transcriptional and metabolic networks to optimize graft healing, providing a physiological and molecular foundation for improving cotyledon-less grafting efficiency.

IntroductionLow-light-stress is a common meteorological disaster that can result in slender seedlings. The photoreceptors play a crucial role in perceiving and regulating plants' tolerance to low-light-stress. However, the low-light-stress tolerance of cucumber has not been effectively evaluated, and the functions of these photoreceptor genes in cucumber, particularly under low-light-stress conditions, are not clear.MethodsHerein, we evaluated the growth characteristics of cucumber seedlings under various LED light treatment. The low-light-stress tolerant cucumber CR and intolerant cucumber CR were used as plant materials for gene expression analysis, and then the function of CsCRY1 was analyzed.ResultsThe results revealed that light treatment below 40 μmol m-2 s-1 can quickly and effectively induce low-light-stress response. Then, cucumber CR exhibited remarkable tolerance to low-light-stress was screened. Moreover, a total of 11 photoreceptor genes were identified and evaluated. Among them, the cryptochrome 1 (CRY1) had the highest expression level and was only induced in the low-light sensitive cucumber CS. The transcript CsaV3_3G047490.1 is predicted to encode a previously unknown CsCRY1 protein, which lacks 70 amino acids at its C-terminus due to alternative 5′ splice sites within the final intron of the CsCRY1 gene.DiscussionCRY1 is a crucial photoreceptor that plays pivotal roles in regulating plants' tolerance to low-light stress. In this study, we discovered that alternative splicing of CsCRY1 generates multiple transcripts encoding distinct CsCRY1 protein variants, providing valuable insights for future exploration and utilization of CsCRY1 in cucumber.

Grafting is a propagation method extensively utilized in cucurbits. However, the mechanisms underlying graft healing remain poorly understood. This study employed self-grafted watermelon plants to investigate how rootstock cotyledon affects healing. The complete removal of rootstock cotyledons significantly hindered scion growth, as evidenced by reductions in scion fresh weight and the area of true leaves. Physiological assessments revealed reduced callus formation, weaker adhesion forces, a more pronounced necrotic layer, and decreased rates of xylem and phloem reconnection at the graft junction when rootstock cotyledons were completely removed. Additionally, auxin levels at the rootstock graft junction notably decreased following cotyledon removal. In contrast, the exogenous application of indole-3-acetic acid (IAA) notably enhanced graft healing. Moreover, gene expression analysis of the PIN auxin efflux carriers in the rootstock cotyledons indicated significant activation of ClPIN1a postgrafting. Furthermore, we developed an improved Virus-Induced Gene Silencing (VIGS) system for cucurbits using seeds soaking method. This method achieved an infection success rate of 83% with 60%-75% gene silencing efficiency, compared to the 37% success rate with 40%-60% efficiency seen with traditional cotyledon infection. Combining our novel VIGS approach with cotyledon grafting techniques, we demonstrated that rootstock cotyledons regulate callus formation through ClPIN1a-mediated endogenous auxin release, thus facilitating graft union development. These findings suggest potential strategies for enhancing watermelon graft healing by manipulating rootstock cotyledons.