Frontiers in Plant Science最新文献

Safflower productivity is hindered by soil salinity, making the identification of genetic markers essential for breeding resilient cultivars. Despite the substantial yield losses caused by salt stress, research on parental genotypes and candidate genes associated with salt tolerance remains limited. A pot experiment with 94 safflower genotypes exposed to four sodium chloride (NaCl) concentrations at the seedling stage explored salt tolerance genetics. Results showed significant variability among genotypes, NaCl treatments, and their interactions for most traits, except biological yield (BY) and fresh shoot weight (FSW). Traits showed reductions from 8% (number of leaves) to 76% (dry root weight) under NaCl stress. Broad sense heritability ranged from 17% to 97%. Correlation analysis revealed positive associations among traits, except FSW and BY. PCA grouped genotypes into three distinct clusters. Using stress tolerance indices (> 0.65) and superior performance above the population mean, three top-performing safflower genotypes were identified. A genome-wide association study (GWAS) revealed 322 marker-trait associations (MTAs), distributed as follows: 34 for BY, 25 for dry root weight (DRW), 44 for dry shoot weight (DSW), 48 for fresh root weight (FRW), 46 for FSW, 60 for number of leaves (NL), 47 for plant height (PH), and 18 for root length (RL). Gene annotation revealed key candidates influencing salinity tolerance, including PLA1, APK4, GINT1, TPLATE, UL13M, SPP2, FRF3, AT1G33770, AT5G01610, DTX50, and RAF1. These genes regulate sulfation of secondary metabolites, chloroplast development, site-specific cell wall modifications, sucrose biosynthesis, and calcium signaling, as well as the functions of hypothetical proteins or proteins with unknown roles. Validating these candidate genes, in silico transcriptomics showed significant upregulation of PLA1, SPS2, and DTX50, alongside downregulation of APK4, GINT1, TPLATE, UL13M, FRF3, AT1G33770, AT5G01610, and RAF1 under salinity. These findings highlight the top-performing genotypes for salt-tolerant cultivar development and warrant further functional studies on the identified candidate genes to gain a deeper understanding of their mechanisms under salt stress.

Diabetes mellitus is a chronic metabolic disorder that affects millions of people globally. Among three types of diabetes, Type 2 diabetes mellitus (T2DM) is a rapidly growing global health challenge. Despite available modern antidiabetic drugs, patients still struggle with side effects and treatment failure, as an alternative to this, there is a crucial requirement to develop a potential and traditional plant-based medicine which could be a safer sources and multi-target therapies to treat chronic disease like diabetes. Solanum lasiocarpum (S. lasiocarpum) is a sour fruit-vegetable being widely used in Southeast Asia as both food and traditional medicine, including for the management of diabetes. However, its active components and antidiabetic mechanisms have not been systematically explored. In this study, we combined metabolomics, proteomics, and transcriptomics to investigate the bioactive pathways and potential molecular targets of S. lasiocarpum. Untargeted UHPLC-QTOF-MS profiling identified 45 candidate bioactive compounds with good predicted gastrointestinal absorption, and the network pharmacology analysis linked these compounds to 43 diabetes-related human targets. Protein-protein interaction analysis highlighted several core nodes, including TNF, PPARG, IL6, AKT1, and STAT3, and functional enrichment suggested roles in hormone regulation, inflammation, glucose and lipid metabolism, and vascular function. De novo transcriptome assembly and data-independent acquisition-based proteomics of mature S. lasiocarpum fruit showed that central carbon metabolism is highly active and that the shikimate, phenylpropanoid, and flavonoid pathways are strongly expressed at both gene and protein levels. Key enzymes such as EPSPS, PAL, C4H, 4CL, CHS, CHI, F3H, and FLS formed a coherent biosynthetic network supporting sustained production of phenolic and flavonoid metabolites. Integrating these omics layers with target prediction suggests that S. lasiocarpum may exert antidiabetic effects by modulating a TNF-PPARG axis, reducing pro-inflammatory signaling while supporting insulin-sensitizing pathways. Overall, these results support the traditional use of S. lasiocarpum and provide a multi-omics resource to prioritise candidate metabolites, enzymes and targets for follow-up studies. As the pathway links were inferred computationally, the proposed TNF-PPARG-centred mechanism should be regarded as hypothesis-generating and will require validation in experimental models and, ultimately, well-designed human intervention trials.

Rice early tillering characteristics are key indicators for high-yield breeding, with tiller number and tillering rate as core parameters. High-throughput, temporal, and precise monitoring of tiller numbers via drone digital imagery provides quantitative support for tillering trait screening in breeding, serving as an important auxiliary tool for smart breeding. However, during the early tillering stage, complex backgrounds (e.g., water bodies, soil) and small, dense breeding plots pose challenges to high-throughput rice plant extraction and accurate tiller number estimation. To address this, this study proposes a rice tiller number estimation method based on an improved Swin-UNet model and multi-feature fusion. A PSO-optimized XGBoost model was constructed for tiller number estimation by integrating selected features. Experimental results show that the improved Swin-UNet model achieved a segmentation accuracy of 92.5% (7.2% higher than U-Net), and the PSO-XGBoost model, using 12 features (10 morphological and 2 color), yielded R²=0.85 and RMSE = 0.35. Application verification on 576 untrained breeding plots generated tiller number thematic maps, providing data support for germplasm tillering trait identification and advancing smart breeding.

Rice (Oryza sativa L.) is one of the most fundamental staple cereal crops feeding more than half of the global population. General Combining Ability (GCA) is the average performance of a genotype across multiple hybrid combinations, mainly due to additive genetic effects while Specific Combining Ability (SCA) is the deviation from expected performance in specific crosses, attributed to non-additive genetic effects such as dominance and epistasis. The aim of the current study was to determine combining ability and gene action of 4 key rice traits such as flowering, plant height, grain size and yield. The study was carried out at Lifuwu Agricultural Research Station - Experimental Fields in Salima District (in Malawi) during the 2024/2025 rainy season in a Randomized Complete Block Design (RCBD) with 3 replications using a total of 15 rice genotypes. Since genetic variance components are not directly observable, crossing methods such as North Carolina Design II (NCD II) was therefore used in the current study to reveal those parameters. Gen stat 19th edition was used for the analysis of majority of the dataset in the current study and Analysis of variance showed significant differences among genotypes, indicating substantial genetic variability across traits. Kudya rice genotype exhibited the highest positive GCA effects (1.015) indicating its strong potential as a grain yield contributor in hybrid combinations. The highest positive SCA effect was exhibited in a cross between Kudya and Kayanjamalo rice germplasm, indicating strong non-additive genetic contribution to yield performance. The highest mean grain yield per plant was recorded in the cross of Kudya × Kayanjamalo (19.0 g), while the lowest was observed in Uwemi × Kilombero (10.3 g). The implication of this study in rice breeding is that superior parents and hybrid combinations for grain yield, earliness to maturity and grain quality were identified for future breeding programmes.

Reactive oxygen species (ROS) are central players in plant abiotic stress responses, functioning as both toxic byproducts and vital signaling molecules. Under normal physiological conditions, ROS participate in the regulation of plant growth and development. However, under stress conditions, ROS metabolism exhibits remarkable stress-specificity, leading to either adaptive signaling or oxidative damage. A comparative understanding of these distinct patterns is critical for advancing stress tolerance engineering. This review systematically elaborates on the mechanisms of ROS production under various abiotic stresses, their dual roles in signaling and oxidative damage, and the corresponding multilayer antioxidant adaptations in plants. We place particular emphasis on comparing the characteristic ROS signatures and regulatory networks triggered by drought, salinity, extreme temperatures, heavy metals, ultraviolet radiation and ozone. Furthermore, we summarize cutting-edge technologies for in vivo ROS detection that are revolutionizing the spatiotemporal understanding of ROS dynamics, these advanced tools enable real-time, subcellular resolution of ROS production, scavenging, and signaling processes, thereby propelling the mechanistic dissection of plant redox homeostasis under stress. Ultimately, we highlight how plants achieve acclimation by precisely orchestrating the "double-edged sword" nature of ROS through an integrated regulatory network. This synthesis not only consolidates the mechanistic understanding but also offers a strategic perspective for designing crops with tailored ROS regulatory capacities to enhance resilience in a changing climate.

Introduction: Biochar (BC) and nano-particles have emerged as promising strategies to mitigate heavy metal toxicity and remediate polluted soils. Vanadium (V) is a toxic metal posing hazardous impacts to plants and humans. The role of BC and nano-particles, particularly their combination to alleviate V toxicity, is poorly understood. Thus, this study explored the role of BC and silicon nano-particles (Si-NPs) partnership in mitigating the V toxicity in rice.

Methods: The study has five treatments: control, V stress (30 mg kg^-1 soil), V stress (30 mg kg^-1 soil) + biochar (3%), V stress (30 mg kg^-1 soil) + Si-NPs (150 mg kg^-1 soil), and V stress (30 mg kg^-1 soil) + biochar (3%) + SiO-NPs (150 mg kg^-1 soil).

Results: The study results revealed that V toxicity decreased rice growth by declining root growth, chlorophyll pigments (78.72-111.50%), nitrogen assimilation, and increasing oxidative stress, membrane damage, and V accumulation in rice plants. Biochar + Si-NPs enhanced rice biomass (20.33%) and grain yield (67.64%) by increasing antioxidant activities (54.12-99.38%), nutrient uptake (58.80-81%), osmolytes synthesis, and decreasing V accretion in rice roots (64.05%) and shoots (91.65%). This increase in rice growth was also linked with an increase in activity of nitrogen assimilation enzymes (nitrate reductase: NR, 65%, glutamine synthetase: GS, 71.82%, glutamate synthase: GOGAT, 106% and glutamate dehydrogenase: GH, 25%) and iron plaque formation.

Conclusion: These findings suggest that the partnership between BC and Si-NPs enhanced root growth, chlorophyll synthesis, antioxidant activity, nitrogen assimilation, and iron plaque formation, while decreasing oxidative damage and V accumulation, thereby increasing plant growth. Thus, a combination of BC and Si-NPs can be an important strategy to mitigate the V toxicity and enhance rice production in V-polluted soils.

Cucumber sex expression is a key agronomic trait determining yield, but whether its formations is related to rhizosphere soil microbes remains poorly understood. This study compared the soil microbial community structures in rhizosphere between gynoecious and monoecious cucumbers to identify potential associations. The results showed that bacterial genera including Sphingomonas, and other unclassified taxa, were significantly enriched in the rhizosphere of the gynoecious plants. In contrast, members of Rokubacteriales and other taxa were significantly enriched in rhizosphere of monoecious cucumbers. For fungi, genera such as Aspergillus, Plectosphaerella, and Chaetomella were enriched in rhizosphere of gynoecious plants. Conversely, Trichoderma, Emericellopsis, Collariella, and Cordana were significantly enriched in monoecious cucumbers. Correlation network analysis revealed that the rhizosphere microbial network (especially the bacterial community) was more stable and displayed greater interspecific cooperation in monoecious cucumbers. Functional prediction revealed that multiple nitrogen-cycling processes of bacterial communities, including nitrification, aerobic nitrite oxidation, nitrite and nitrate ammonification, aerobic ammonia oxidation, and arsenate respiration were detected in rhizosphere of the gynoecious cucumbers. By contrast, hydrocarbon degradation functions, particularly those for aromatic and aliphatic non-methane hydrocarbons were significantly enriched in rhizosphere of monoecious cucumbers. Moreover, the rhizosphere of gynoecious plants harbored a higher abundance of saprotrophic and symbiotrophic fungi but a lower abundance of pathotrophic fungi compared with monoecious cucumbers. These findings demonstrate that the composition and potential functions of the rhizosphere microbiota differ between gynoecious and monoecious plants, indicating that soil microbes in rhizosphere play a role in the sex expression of cucumber varieties.

Non-typhoidal Salmonella enterica serovars are enteric pathogens in humans that can be acquired from poultry products. Salmonella colonisation in poultry is an important cause of economic losses. Due to challenges in controlling Salmonella in poultry and the emergence of well-adapted, antibiotic-resistant serovars, there is a need for innovative control strategies, such as vaccines. In this work, plants were used to produce conserved antigenic epitopes of Salmonella FepA, an outer membrane protein involved in iron uptake, genetically fused in tandem to self-assembling lumazine synthase from Brucella spp. (BLS). The recombinant proteins were purified, characterized, and their immunogenicity was assessed in chickens. Results indicated that the recombinant proteins assemble into decameric particles. These proteins elicit antigen-specific antibodies in chickens that bind to the Salmonella's cell surface. These results demonstrate that the candidate vaccine has the potential to control Salmonella colonization in poultry, helping prevent food chain contamination.

Introduction: This study investigates the activity concentrations of ²³²Th, ²²⁶Ra, ⁴⁰K, and ¹³⁷Cs in soil and moss samples collected from locations in Iğdır Province, Türkiye to evaluate spatial patterns and radionuclide accumulation behavior.

Methods: High-purity germanium (HPGe) gamma spectrometry was used to quantify radionuclide activities.

Results: Statistical analyses included Shapiro-Wilk normality testing, descriptive comparisons between soil and moss, and correlation assessments. Concentration ratios (CR = A_moss/A_soil) were calculated to evaluate radionuclide accumulation patterns across species and sites. Spatial variability and multivariate structure were examined using PCA and k-means clustering to identify site- and nuclide-driven grouping patterns. Key radiological parameters calculated for the health risk analysis included absorbed gamma dose rate, internal and external hazard indices, radium equivalent activity, and annual effective dose equivalent. In moss samples, the mean activity concentrations of ²²⁶Ra, ²³²Th, ⁴⁰K and ¹³⁷Cs were measured as 13.74 ± 0.83 Bq kg^-1, 13.79 ± 1.1 Bq kg^-1, 244.72 ± 7.6 Bq kg^-1, 129.47 ± 1.74 Bq kg^-1, respectively, and in soil samples, 23.74 ± 0.82 Bq kg^-1, 22.53 ± 1.11 Bq kg^-1, 427.01 ± 8.95 Bq kg^-1, 215.74 ± 1.83 Bq kg^-1, respectively.

Discussion: All calculated radiological hazard indices, derived from natural radionuclide concentrations, were within permissible recommended limits. Slightly elevated annual effective dose values and absorbed gamma dose rates are observed for the total activity concentrations of both anthropogenic and natural radionuclides, exceeding world population-weighted outdoor averages.