First detected in environmental matrices in the early 2010s, UV-328 has aroused widespread concern due to its toxic risks to terrestrial vascular plants. However, its impacts on photosynthetic performance of bryophytes remain unclear. In this study, we exposed the moss Niphotrichum japonicum to a range of UV-328 concentrations and examined its morphological changes, oxidative stress, photosynthetic pigments, chlorophyll fluorescence kinetics, and energy allocation. Our results show that UV-328 caused dose-dependent leaf yellowing, surface shrinkage, and papillae collapse. It degraded photosynthetic pigments, disrupted chlorophyll a/b balance, and induced excess reactive oxygen accumulation. Chlorophyll fluorescence data indicated that UV-328 noticeably lowered Fv/Fm, Y(II), and qP. OJIP kinetics confirmed multi-target damage to the photosynthetic chain, including disruption of the oxygen evolving complex, decline of PSII energetic grouping, blockage of electron transfer from QA- to QB, inhibition of plastoquinone pool turnover, and PSI terminal electron transport. Energy flux parameters pointed to fewer active PSII reaction centers and a higher light-harvesting load on each remaining center, which uncoupled light capture from electron transfer. The concentration that caused clear photosynthetic damage fell between 100 and 150 μM. In conclusion, UV-328 impacts moss photosynthesis through oxidative stress, structural damage, blocked electron transport, and energy imbalance.
The comparative UHPLC-MS/MS analysis of the underground and aerial parts of Primula veris subsp. columnae (Ten.) Lȕdi revealed a diverse range of secondary metabolites, including flavonoids and phenolic compounds and their glycosides, bisbibenzyl compounds and triterpene saponins. Phytochemical study of the aerial parts led to the isolation of 13 surface flavonoids and 6 flavonoid glycosides, of which 8,2'-dimethoxy-5-hydroxyflavone and 8,3'-dimethoxy-5-hydroxyflavone are new natural compounds. From the underground parts, a new triterpene saponin, primulasaponin VI, was isolated together with the known primulasaponins I, III and V; priverosaponin B 22-acetate; primulaverin; primverin; gaultherin; and 3-methoxy-4-primeverosylacetophenone. The structures of the new compounds were elucidated by NMR and HR-ESI-MS. The acetone extract obtained from the aerial parts and methanol extracts obtained from both aerial and underground parts were evaluated for their antibiofilm activity against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The methanol extract obtained from underground parts possessed the strongest overall antibiofilm activity, particularly against E. coli and S. aureus. While the acetone extract inhibited the biofilm formation by E. coli and S. aureus, it promoted biofilm formation by P. aeruginosa. All tested extracts exhibited low cytotoxicity toward human keratinocytes and melanoma cells.
Developing effective rhizobial inoculants for high-latitude soybean production requires strains that are both well-adapted to local soil conditions and capable of sustaining symbiotic nitrogen fixation under abiotic stress. In this study, 66 indigenous rhizobial strains isolated from four ecological regions of Heilongjiang Province, China, i.e., the Songnen Plain, Sanjiang Plain, Northwest Arid Sandy Region, and Daxing'anling-Xiaoxing'anling mountainous area, were characterized for phenotypic diversity, abiotic stress tolerance, symbiotic nitrogen fixation capacity, and plant growth-promoting (PGPR) traits. Carbon and nitrogen source utilization, physiological and biochemical properties, and tolerance to salinity, pH extremes, temperature, and drought were assessed and analyzed by hierarchical cluster analysis. The strains showed habitat-associated phenotypic differentiation, with stress-tolerance traits accounting for the greatest proportion of variation. Isolates from the Northwest Arid Sandy Region displayed the broadest stress tolerance, while Sanjiang Plain isolates showed more conservative phenotypic profiles. Eight representative strains were subsequently evaluated for symbiotic performance and PGPR activity. Nodule dry weight, nitrogenase activity, and total plant nitrogen content were strongly intercorrelated and appeared more reliable indicators of nitrogen-fixation efficiency than nodule number alone. Songnen Plain strains SN1 and SN8 showed the highest symbiotic performance, while Northwest Arid Sandy Region strain FS1 exhibited the greatest ACC deaminase activity. A two-dimensional ranking framework integrating symbiotic and non-symbiotic traits ordered the strains as SN1 > SN8 > SJ1 > SJ11 > DX5 > DX1 > FS7 > FS1, identifying SN1 and SN8 as the most promising candidates requiring field validation for inoculant applications. These findings suggest that multi-trait evaluation frameworks may offer a practical and reproducible approach to screening indigenous rhizobia for region-specific biofertilizer applications in cold-region soybean agriculture.
Herbaceous and subshrub species of the genus Ajuga are recognized as valuable sources of bioactive metabolites with diverse pharmacological properties. This study comparatively evaluated the phytochemical composition and the in vitro biological activities of Ajuga chamaepitys, A. genevensis, A. laxmannii, and A. reptans collected from different localities in Serbia. UHPLC-MS/MS profiling identified p-hydroxybenzoic acid and ferulic acid as the predominant phenolic compounds. A. reptans exhibited the highest total phenolic content (120.52 mg GAE/g dw), along with the strongest antioxidant activity in DPPH, ABTS, β-carotene bleaching, and FRAP assays, as well as the highest antibacterial activity. A. laxmannii contained the highest flavonoid content (48.95 mg QuE/g dw) and showed the strongest antifungal activity, whereas A. genevensis demonstrated the most potent tyrosinase inhibitory activity. Notably, A. genevensis extracts exhibited pronounced anti-pyocyanin activity, approximately twofold stronger than ampicillin. These findings demonstrate significant species- and locality-dependent variation in the phytochemical composition and biological activity and identify Serbian Ajuga species as promising natural sources of antioxidant, antimicrobial, and antivirulence compounds with potential pharmaceutical and food applications.
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.
Tomato (Solanum lycopersicum L.) exhibits extensive cultivar diversity in fruit morphology, color, and bioactive compounds. These are closely associated with genetic background, ripening stage, and carotenoid metabolic regulation. Carotenoids, especially lycopene and β-carotene, are major determinants of tomato coloration and are also nutritionally and industrially important high-value-added antioxidants. Carotenoid profiles are regulated by changes in related biosynthetic pathways, including precursor supply, desaturation, isomerization, and cyclization. Key genes such as PSY1, CRTISO, LCY-B and LCY-E play central roles in carotenoid synthesis and accumulation, thereby determining color phenotypes. Mutations and genome-editing (CRISPR/Cas9) approaches can modify carotenoid metabolic flux and develop cultivars with improved nutritional traits. Furthermore, advanced and sustainable recovery strategies for carotenoids (ultrasound-, microwave-, enzyme-, high-pressure-, deep eutectic solvent-, and supercritical fluid-assisted extraction methods) have been developed for efficient pigment extraction. In this review, we compare the physiological and molecular characteristics of diverse cultivars and traits of advanced bioprocesses in terms of carotenoid extraction efficiency and solvent safety. Finally, we discuss various encapsulation techniques that improve product stability and storage performance in industrial applications. Taken together, we review practical strategies combining cultivar-based carotenoid production, sustainable recovery, and product stabilization.
Water and nitrogen application rates directly affect crop yield formation and protein accumulation. Nitrogen metabolism, as a key physiological process linking water and nitrogen supply with crop growth, plays a critical role in regulating nitrogen uptake, transformation, and accumulation. However, the functional relationships among different nitrogen metabolism indicators in mediating the formation of high-quality and high-yield alfalfa under water-nitrogen regulation remain unclear. In this study, alfalfa (Medicago sativa L.) was subjected to four nitrogen application levels [N0 (0 kg·hm-2), N1 (80 kg·hm-2), N2 (160 kg·hm-2), N3 (240 kg·hm-2)] and four irrigation gradients [severe deficit (W0, 45-60% θf), moderate deficit (W1, 55-70% θf), mild deficit (W2, 65-80% θf), and full irrigation (W3, 75-90% θf), where θf represents field capacity]. The relationships between water-nitrogen regulation and alfalfa nitrogen metabolism and productive performance were analyzed. The results showed that (1) both irrigation amount and nitrogen application rate significantly affected the activities of leaf nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and soluble protein (SP) content (p < 0.05). Aboveground nitrogen content (TN) initially increased and then decreased with increasing irrigation and nitrogen application, reaching its maximum under W2N2, with leaves being the primary site of aboveground nitrogen accumulation. (2) Under W2N2, alfalfa yield and crude protein accumulation (CP-a) both reached their maximum values, averaging 10.22 t·ha-1 and 1187.10 kg·ha-1, respectively. (3) Structural equation modeling (SEM) indicated that water-nitrogen regulation primarily influenced yield and CP-a formation through its effects on GS activity and TN. Based on these findings, a comprehensive evaluation model (GS-TN-Yield-CP-a) was constructed, and the preliminary suitable water-nitrogen regulation ranges for high-quality and high-yield alfalfa were identified as an irrigation amount of 69.4-85.9% θf and a nitrogen application rate of 102.6-222.3 kg·hm-2. These results can provide a theoretical basis for high-quality and high-yield alfalfa management in arid and semi-arid regions, but further verification through field experiments is still required.
The wild dragon fruit (Selenicereus spp.) with orange peel and fuchsia pulp, known as "Naranja de Churuja", represents a promising plant genetic resource from the Amazonas region of Peru. The objective of this study was to develop an in vitro micropropagation protocol for this local accession by evaluating shoot induction and proliferation, in vitro rooting, and ex vitro acclimatization. During the multiplication phase, nine combinations of 6-benzylaminopurine (BAP) and indole-3-butyric acid (IBA) were evaluated using cladode segments. Shoot induction was high in most treatments (95.56-100.00%), and the combination of 2.0 mg L-1 BAP + 0.1 mg L-1 IBA produced the highest number of shoots per explant (9.67), whereas 1.0 mg L-1 BAP without IBA promoted the greatest shoot elongation (2.34 cm). During the rooting phase, 1.50 mg L-1 IBA produced the highest number of roots per explant (3.41), whereas 1.00 mg L-1 IBA generated the greatest shoot length (4.12 cm). During ex vitro acclimatization, all plants survived (100% survival), but growth depended significantly on the substrate, with the moss treatment promoting the greatest development. In conclusion, an efficient micropropagation protocol was established for the wild "Naranja de Churuja" accession, with potential for mass propagation, germplasm conservation, and future biotechnological applications, including genetic transformation and genome editing.
High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an R2 of 0.83 and an RMSE of 25.6 Mg·ha-1; on the Hubei dataset, it achieved an R2 of 0.86 and an RMSE of 26.8 Mg·ha-1. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring.
As global warming raises ambient temperatures, plant growth and productivity are being profoundly affected. Although the impacts of extreme heat stress have received extensive attention, the developmental consequences of moderately elevated temperatures remain less understood. As sessile organisms, plants rely on developmental plasticity to adjust their growth and development in response to warm environments. Understanding how plants sense temperature and convert this signal into developmental outputs is crucial for determining their adaptive strategies to climate change and for applying this knowledge to breed climate-resilient crops. Here, we review current advances in understanding how moderately elevated temperatures regulate developmental plasticity throughout the plant life cycle, including the perception of moderate warmth, the resulting developmental plasticity during vegetative and reproductive growth, and the coordination and trade-offs between these two phases. We also highlight major unanswered questions in this field and propose strategies for manipulating developmental plasticity to breed climate-resilient crops.
Splicing factors, as core determinants of splice-site selection and dynamic spliceosome assembly, play pivotal roles in stress responses. This review systematically categorizes splicing factors involved in plant abiotic stress responses according to their functions as major spliceosomal components, dividing them into small nuclear ribonucleoproteins (snRNPs) and associated components, spliceosome assembly and disassembly factors, splicing regulatory factors, and proteins related to non-canonical RNA splicing. On this basis, we summarize their regulatory mechanisms of these factors under salt, drought, abscisic acid (ABA) signaling, temperature, and oxidative stresses. Through analyses across multiple species—including Arabidopsis thaliana, rice, maize, soybean, and wheat—we reveal both the evolutionary conservation and species-specific divergence of splicing-factor-mediated regulation. Currently, a large amount of research is still mainly at the transcriptome analysis or single phenotype validation stages, lacking in-depth analysis of direct targets, splicing isomer functions, and molecular mechanisms. Furthermore, current research is heavily concentrated on Arabidopsis, with relatively insufficient functional validation and breeding applications in crops such as maize and wheat. Despite substantial progress, several bottlenecks remain for translational applications in breeding, such as functional redundancy among splicing factor family members, growth penalties associated with overexpression, and tissue-specific and developmental-stage-dependent effects. To address these challenges, we discuss promising strategies, including CRISPR/Cas9-mediated splice-site editing, the use of inducible or tissue-specific promoters, and targeted modulation of upstream kinases, although extensive field trials and rigorous evaluations remain necessary. Collectively, this review provides a theoretical framework for understanding the roles of splicing factors in RNA-level regulation of plant stress adaptation and highlights their potential for breeding improvement.
Sorghum seeds accumulate substantial amounts of condensed tannins (CTs), which are also referred to as proanthocyanidins (PAs), contributing to their characteristic astringent taste. Flavan-3-ol polymers, known as PAs, are sequestered within plant vacuoles and become catalytically activated via laccase enzymes. However, the biological roles and regulatory pathways of laccases in sorghum are still largely unclear. Here, integrated transcriptomic and metabolomic profiling of developing sorghum seeds identified 7942 differentially expressed genes between low- and high-CT lines, with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealing flavonoid biosynthesis as a key pathway; weighted gene co-expression network analysis (WGCNA) further pinpointed SbLAC14 as a hub gene within the module most strongly correlated with CT content. We then examined its regulation by microRNA397 (SbmiR397-5p). Dual-luciferase assays confirmed the binding of SbmiR397-5p to SbLAC14 in co-transformed tobacco leaves. Overexpressing SbLAC14 in transgenic Arabidopsis significantly increased CT accumulation while decreasing catechin and epicatechin levels. Furthermore, transgenic plants overexpressing miR397 (OEmiR397-5p) exhibited reduced CT content, accompanied by a lightening of seed color. Conversely, transgenic lines overexpressing a miR397-insensitive laccase transcript exhibited a reversed phenotypic outcome. Our findings indicate that SbmiR397-5p negatively regulates the expression of SbLAC14 in relation to CT biosynthesis, identifying it as a potential target for manipulating CT metabolism in sorghum. Those results provide a genetic entry point for metabolic engineering and breeding efforts aimed at modulating grain phenolic profiles.
Melon (Cucumis melo L.) germplasm resources are vital for seed-industry innovation and cultivar breeding. To systematically characterize their phenotypic variation, classification relationships, and breeding potential, 403 melon accessions were evaluated using nine quantitative characteristics specified in the guidelines for tests of distinctness, uniformity, and stability. These characteristics included fruit longitudinal diameter, fruit transverse diameter, fruit peduncle length, internode length, flesh thickness, petiole length, rind thickness, soluble solids content, and seed cavity diameter. The evaluated population exhibited extensive phenotypic variation, with coefficients of variation ranging from 14.0% to 57.4%. Rind thickness showed the greatest variation, while fruit transverse diameter, flesh thickness, fruit peduncle length, and fruit longitudinal diameter also displayed considerable differentiation. The Shannon-Weaver diversity index ranged from 1.74 to 1.96, indicating substantial diversity in fruit morphology, vegetative growth, internal fruit structure, and quality-related characteristics. Spearman rank correlation analysis revealed close developmental relationships among fruit-related characteristics, with rind thickness showing the strongest positive correlation with flesh thickness. Principal component analysis extracted three components with eigenvalues greater than one, which together explained 65.22% of the total phenotypic variation and primarily represented fruit morphological structure, vegetative growth, and fruit quality. Hierarchical cluster analysis classified the 403 accessions into four quantitative phenotypic groups, with highly significant differences among the groups for all nine characteristics. Furthermore, a weighted comprehensive selection index identified several accessions with superior combined agronomic performance, among which accession 20225110454A ranked first. These findings provide a quantitative phenotypic basis for melon germplasm conservation, core collection development, parental selection, and breeding-oriented resource utilization.
NCED proteins play a critical role in drought, salt, and cold stress responses through ABA biosynthesis in plants. However, little information is currently available regarding the NCED gene family in Brassica species. Twenty-six putative BjNCED genes were identified in the genome of Brassica juncea, and found to be distributed on 15 chromosomes. Phylogenetic analysis suggested that these members could be classified into six subfamilies. The putative cis-elements were identified in the promoter regions of these BjNCED genes, and thought to be related to phytohormones, light, and abiotic stress responses. qRT-PCR analysis of five genes (BjNCED1, BjNCED4, BjNCED7, BjNCED16 and BjNCED18) revealed that, under salt stress, BjNCED16 and BjNCED18 showed transient induction, whereas exogenous ABA produced gene-specific and time-dependent expression patterns, including pronounced induction of BjNCED16 at 24 h. Low-temperature treatment strongly induced four of the five examined genes at 6 h. Analysis of the BjNCED genes in this study provides a valuable foundation for future investigations into the functional roles of the BjNCED family in response to growth, development and stress.
Current interest in the use of medicinal plants for the prevention and management of various diseases has increased significantly due to the need to identify effective, safe, and sustainable natural agents. Glechoma hederacea L., a perennial species belonging to the Lamiaceae family, has traditionally been used in European folk medicine for the treatment of respiratory, inflammatory, and digestive disorders. Modern research has highlighted a complex phytochemical profile dominated by phenolic acids, flavonoids, and volatile compounds that have been associated with a variety of biological activities. Experimental studies have reported antioxidant, antimicrobial, anti-inflammatory, and cytoprotective effects. The present review summarizes current data regarding the phytochemical composition and biological properties of Glechoma hederacea, with particular emphasis on its potential applications in oral hygiene products and complementary dental therapy.
The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.
Microplastics (MPs) are increasingly accumulating in agricultural soils, while their effects on crop-associated rhizosphere microbial communities and ecological functions remain insufficiently understood, particularly for different polymer types. In this study, polyethylene (PE) and polystyrene (PS) microplastics with the same particle size (2 μm) were applied at different concentrations (0, 0.1%, 0.5%, 1%, and 5%, w/w) to investigate their effects on oat rhizosphere bacterial communities and predicted functional potentials. The results showed that microplastic addition significantly altered bacterial community diversity, composition, and predicted functional profiles. Compared to the control (Ctrl), the 1% PE treatment significantly reduced bacterial richness-related indices (p < 0.05), whereas PS mainly affected bacterial diversity. Microplastic treatments also reshaped dominant bacterial taxa and altered the predicted functional potentials associated with carbon and nitrogen cycling. In particular, the 1% PE treatment significantly reduced the predicted relative abundance of the carbon fixation-related gene cbbL (p < 0.05). In addition, high-concentration (5%) PE enhanced several predicted functional potentials related to nitrogen cycling, including nifH, ureA, and amoC. Overall, PE and PS microplastics induced polymer- and concentration-dependent changes in oat rhizosphere bacterial communities. These findings suggest that microplastic accumulation in agricultural soils may influence ecosystem processes by modifying microbial diversity and potential biogeochemical functions. This study provides new insights into the ecological consequences of different microplastic polymers in crop rhizosphere ecosystems and highlights the importance of considering polymer-specific effects when evaluating soil microplastic pollution.
Soil salinization and alkalization caused by global climate change have become major constraints on crop production worldwide. Quinoa (Chenopodium quinoa Willd.), owing to its exceptional adaptability to adverse environmental conditions, is considered a promising crop for saline-alkali agriculture. Successful screening and evaluation of saline-alkali-tolerant germplasm at the germination stage is essential for breeding quinoa varieties with enhanced tolerance to compound saline-alkali stress. In this study, 23 quinoa accessions originating from different regions were evaluated for saline-alkali tolerance by exposing seeds to compound saline-alkali solutions at different concentrations in a Petri dish germination assay. During the germination stage, relative vigor index, germination rate, germination energy, shoot length, root length, and fresh weight were determined, and saline-alkali tolerance was assessed using multivariate statistical analyses. The germination ability and seedling growth potential of quinoa germplasm gradually declined with increasing saline-alkali stress. Based on the comprehensive saline-alkali tolerance index at the germination stage, eight accessions were identified as highly tolerant and four accessions as highly sensitive to saline-alkali stress. Among them, LL1 and Z27 exhibited outstanding tolerance and were identified as elite saline-alkali-tolerant germplasm resources. The results indicated that vigor index and fresh weight at the germination stage can serve as key parameters for evaluating saline-alkali tolerance in quinoa germplasm. Overall, this study provides valuable germplasm resources and a practical basis for breeding new quinoa varieties with enhanced saline-alkali tolerance, as well as for the identification and utilization of stress-resistance-related genes in quinoa.
This study addresses the need for effective natural inhibitors of enzymatic browning in fresh-cut produce by exploring plant-derived compounds with activity against apple polyphenol oxidase (PPO). Extracts from 26 wild plant species were screened using thin-layer chromatography (TLC)-coupled bioautographic assays specifically adapted to apple PPO. Among these, D. fullonum showed clear inhibitory activity and was selected for further investigation. Extracts from different plant organs revealed that PPO inhibition was localized in the leaves, while stems and flower receptacles were inactive. Bioautography-guided fractionation of leaf extracts enabled the isolation of active fractions, and structural characterization by HRMS and NMR indicated that the activity is associated with the iridoid glycosides sylvestrosides III and IV. Antioxidant assays (DPPH, ABTS, CUPRAC) and TLC profiling demonstrated that PPO inhibition was not directly correlated with radical scavenging activity. In situ application of the leaf extract (0.5% w/v) on fresh-cut apple slices significantly delayed browning during refrigerated storage, showing comparable or improved performance relative to ascorbic acid at later stages. These results highlight D. fullonum as a promising source of natural antibrowning agents and demonstrate the utility of food-specific bioautographic assays for the targeted discovery of functional plant metabolites.
Low-temperature stress severely restricts the growth, development, and ornamental value of Osmanthus fragrans Lour. However, the molecular mechanisms by which brassinolide (BR) and melatonin (MT) alleviate low-temperature-induced damage remain unclear. Here, O. fragrans branches were exposed to low-temperature stress (5, 0, −5, −10, −15, and −20 °C for 12 h) and treated with exogenous MT (50, 100, and 200 μM) or BR (0.5, 1, and 2 μM). An integrated approach combining phenotypic observation, physiological measurements, transcriptomics, and metabolomics was employed to elucidate the regulatory mechanisms underlying BR- and MT-mediated cold tolerance. The results showed that low-temperature stress significantly increased electrolyte leakage (EL), malondialdehyde (MDA), and hydrogen peroxide (H2O2) accumulation, while reducing superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities. Compared with the control, BR and MT treatments alleviated leaf chlorosis and wilting, reduced oxidative damage, and enhanced antioxidant enzyme activities. Integrated transcriptome–metabolome analyses demonstrated that BR and MT commonly activated phenylpropanoid and flavonoid biosynthesis, thereby promoting antioxidant metabolite accumulation, while suppressing α-linolenic acid and linoleic acid metabolism associated with stress-induced lipid remodeling. Network-based transcriptomic analyses identified transcription factors, including ARF, EIL, bHLH, and GRAS, as potential regulators of cold-responsive pathways. Furthermore, BR primarily regulated hormone-responsive networks, whereas MT mainly maintained redox homeostasis and metabolic reprogramming. These findings reveal the coordinated regulatory mechanisms underlying BR- and MT-mediated cold tolerance, providing potential targets for improving cold resilience in O. fragrans.
