Frontiers in Plant Science最新文献

Plant growth-promoting endophytes (PGPE) in halophytes have the potential to enhance plant stress resistance and promote growth, demonstrating broad application prospects in agriculture. The culturable microorganisms inhabiting in halophytes and their potential roles in enhancing salt-stress resistance of crops remain limited. This study isolated culturable endophytic bacteria from the roots of two dominant desert halophytes, Haloxylon ammodendron and Halostachys caspica, determined their growth-promoting abilities, and evaluated their capability in improving wheat performance under salt stress. Five saline-alkali tolerant bacterial strains-identified as Priestia endophyticus (S1, Y5), Priestia licheniformis (S2), Streptomyces griseorubens (S7), and Nocardiopsis aegyptia (Y6)-were characterized. These bacterial strains exhibited robust survival in 1.4 mol/L NaCl and high-pH environments (pH > 11.0), while demonstrating multiple growth-promoting traits, including indole-3-acetic acid (IAA) production and inorganic phosphate solubilization. All of the five strains (except for S2) and mixed culture improved the germination potential at 100 mmol/L NaCl. The strains S7, Y5, and mixed culture significantly increased plant height, root length, above ground fresh and dry weight compared to 200 mmol/L NaCl stressed seedlings (200CK)(p < 0.05). Salt stress significantly decreased chlorophyll content by 25.82% and 34.06% under 100 and 200 mmol/L NaCl in comparison to CK. Conversely, PGPE inoculation significantly promoted chlorophyll synthesis of seedlings under salt stress. PGPE inoculation reduced enzyme activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) relative to the salt stressed seedlings. All inoculation treatments significantly decreased SOD activity by 20.2%-34.62%, and POD activity by 30.79%-53.38%, relative to 200CK. These findings demonstrate that these strains isolated from halophytic plants exhibit positive effects in ameliorating salt stress and improving the growth of wheat seedlings, highlighting their potential for enhancing agricultural productivity in saline-alkali soils.

Cover crops are increasingly important in sustainable vineyard management, yet species selection remains site-specific and challenging. We evaluated candidates in two commercial organic vineyards in the semi-arid Okanagan Valley, British Columbia-CFF (13-year Merlot) and KOW (10-year Zweigelt)-using a hybrid multicriteria decision analysis (MCDA) [Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS)]. Nine in-row and 15 inter-row treatments (annuals and perennials) were evaluated in the 2019 growing season. Field measures included biomass, ground cover, interference with the fruiting zone, invasiveness, pest/disease effects, drought and winter tolerance, and traffic tolerance. In both vineyards, risk of invasiveness (~43-60%) and interference with fruiting zones (~30%) were the most influential criteria. The top under-vine annuals were Lens culinaris (spring lentil), Brassica napus cv. Winfred, and Brassica rapa (purple-top turnip) with high relative closeness (RC_i = 0.87-0.96). Among perennials, Ladino Trifolium repens cv. Crescendo ranked the highest (RC_i ~ 0.84), supporting its use as a durable under-vine cover. For inter-rows, Pisum sativum + Secale cereale (pea-rye) led at CFF (RC_i = 0.89) and placed second at KOW, Trifolium incarnatum ranked second at CFF (RC_i = 0.83), and Trifolium alexandrinum led at KOW (RC_i = 0.94). Other annual clovers were intermediate (RC_i = 0.79-0.88), performing best on finer-textured, cooler, moister sites. The leading perennial inter-row mix was Lolium perenne + Raphanus sativus var. longipinnatus (tillage radish; RC_i = 0.94), followed by a Festuca spp. mix (RC_i = 0.65-0.69). This MCDA delivers clear, literature-aligned rankings and a data-driven framework to guide regionally adapted cover-crop choices and future web-based decision tools.

This study explores the use of hyperspectral imaging (HSI) combined with machine learning to detect physiological alterations in cassava leaves caused by Xanthomonas phaseoli pv. manihotis (Xpm), a bacterial plant disease that causes significant yield losses worldwide. Therefore, the use of hyperspectral images associated with machine learning can provide information rapidly and accurately, aiming to support decision-making. HSI captures spectral data that reflects biochemical changes in infected plant tissues. An image set of cassava healthy and symptomatic leaves (402 and 450, respectively) were imaged using a hyperspectral camera across wavelengths from 400 to 1000 nm, with image calibration and spectral normalization to improve data quality. Spectral parameters, such as mean reflectance and spectral differences (healthy vs. infected), were analyzed. Six machine learning models were tested for classification: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). SVM performed best, achieving the highest accuracy (91.41%), followed by MLP (87.89%), XGBoost (79.69%), and RF (77.34%). DT and KNN had the lowest accuracy (71.88% and 70.31%, respectively). The results suggest that HSI, particularly when combined with SVM, offers a rapid and accurate method for diagnosing cassava bacterial blight, with potential for large-scale field applications.

The oilseeds are abundant in oils and proteins, and the production of high-quality oilseeds represents a major objective in modern agriculture. However, oilseed production is constrained by biotic and abiotic stresses, resulting in the decreasing in yield and quality. The seed microbiome has been recognized as a critical determinant of plant health. However, its composition and functional roles in various oilseed crops remains poorly explored. In this study, we utilized 16S rRNA gene amplicon sequencing to compare the bacterial component of seed microbiome and predict their metabolic potential in four oil crops (rapeseed, sunflower, sesame, soybean). Our results revealed that the oilseed harbored high diverse of microbes, and the assembly of microbial community was not random but driven by species and cultivar. From the perspective of microbial functions, the lipid metabolism and other secondary metabolites of seed microbes were associated with corresponding metabolic processes in seeds, such as glucosinolate and linoleic acid, reflecting the functional connection between seed metabolites and seed microbes. Furthermore, the core microbiome was obtained among four oilseed groups, consisting of 18 bacterial amplicon sequence variants (ASVs), including putative plant-beneficial resources, such as Sphingomonas. Notably, strain SE-S32 (Sphingomonas endophytica) isolated from rapeseed seed, one of the core microbes, could improve the resistance of various crops, indicating that seed core microbes could serve as a microbial inoculant among multiple crops. These results provide new insights into the correlation between seed microbiome and seed metabolites, establishing a theoretical foundation for developing microbial strategies to improve oilseed quality and plant health.

Introduction: Accurate detection of vegetation cover type changes in forest sub-compartments (FSCs) is essential for supporting informed forest management decisions. Although various forest change detection algorithms have been developed, fine-scaledetection at the FSC level has received limited attention.

Methods: This study addresses this gap by developing an FSC-scale vegetation cover type change detection method that couples spectral and texture information from Sentinel-2 multispectral imagery with forest management planning and design investigation (FMPI) data. Original spectral bands, vegetation indices, and texture features were extracted and used to construct a classification model based on a particle swarm optimization-back propagation neural network (PSO-BPNN). To evaluate performance, the proposed PSO-BPNN method was compared with random forest (RF), support vector machine (SVM), and conventional back-propagation neural network (BPNN) models.

Results: Results indicate that PSO-BPNN consistently outperformed the other algorithms in change detection at the FSC scale. Specifically, the method achieved an overall accuracy of 91% for change identification, with a Kappa coefficient of 0.86. In the validation dataset, it successfully detected approximately 80% of the changed FSCs.

Discussion: The proposed approach offers a robust and accurate solution for fine-scale forest change monitoring, it enhances the scientific basis for sustainable forest resource management.

Introduction: Climate change poses significant challenges to viticulture, increasing the need for sustainable adaptation strategies such as the identification of resilient Vitis vinifera L. varieties.

Methods: This study characterized the anthocyanin content, profile, and color parameters of 27 red grape varieties cultivated under the same terroir in the Douro Demarcated Region over two consecutive years. Berry biochemical analyses, including chromatographic and colorimetric techniques, alongside gene expression of the anthocyanin biosynthesis genes MybA1, UFGT, and OMT, were conducted to assess varietal and annual variability.

Results: Total anthocyanin content varied significantly among varieties, ranging from 0.14 mg malvidin-3-O-glucoside equivalents per g of dry weight (mg M3G·g^-1 DW) in 'Bastardo' to 8.63 mg M3G·g^-1 DW in 'Vinhão'. While most varieties demonstrated increased anthocyanin content in the warmer and drier 2022 season, such as 'Tinto Cão' and 'Touriga Franca'; a few displayed notable declines, notably 'Vinhão', highlighting differential responses to abiotic stress. Anthocyanin profiles were dominated by malvidin derivatives, which correlated with enhanced color stability. Nonetheless, cyanidin-3-O-glucoside increased in 2022 in some varieties, while delphinidin and petunidin-3-O-glucosides decreased. CIELAB parameters indicated darker and higher color saturation in berries in 2022, being associated with increases in total anthocyanin content and malvidin derived compounds. Gene expression analysis of MybA1, UFGT, and OMT in six varieties revealed different behaviors.

Discussion: Among all varieties under study, stable anthocyanin profiles across years were observed which could suggest increased resilience potential. These findings highlight the interplay between genetic and environmental factors in shaping anthocyanin dynamics, supporting the use of varietal selection as an adaptation strategy to optimize quality, resilience, and sustainability in wine regions under climate change.

Litchi (Litchi chinensis Sonn.), an important fruit originating in China, has received increasing attention for improving breeding efficiency in recent years due to the growing adoption of hybridization. It is a monoecious plant and predominantly relies on cross-pollination for seed production. Thus, enhancing pollen quality is essential for fruit set, which is closely associated with the developmental stages of male flowers. In this study, anthers of the litchi cv. 'D13' at five late developmental stages (half-exposed stage, fully-exposed stage, filament-elongated stage, anthesis, and browning stage) were investigated using paraffin sectioning, staining, in vitro germination, and controlled pollination techniques. Cytological characteristics of pollen were evaluated using paraffin sectioning, while pollen viability was assessed by I₂-KI staining, and the germination rate was determined through in vitro culture on sucrose media. Furthermore, pollen collected from these five stages was used for controlled pollination of the male-sterile line 'MS1'. The results indicated that pollen collected at the filament-elongated stage and anthesis exhibited higher viability and germination potential, which resulted in improved fruit set in the male-sterile line 'MS1'. The study identified the filament-elongated stage and anthesis as optimal stages for pollen collection in litchi.

Introduction: Root- and leaf-associated microbiomes are crucial for plant health and influence the yield and quality of the products. The composition of microbes and their association with the host depend on different factors that must be continuously investigated.

Results: We examined the composition and structure of bacterial and fungal communities in four compartments (root, rhizoplane, rhizosphere, and leaf endosphere) of two grapevine varieties ('Alachua' and 'Noble') targeting the 16S rRNA V5-V7 and ITS regions.

Results and discussion: A comparison of the effects of the varieties and compartments showed that they were the major factors contributing to variations in the microbial structures. Bacterial alpha diversity significantly decreased from the rhizosphere to leaf endosphere, while the fungal alpha diversity did not show linear variations. According to normalized stochastic ratio analysis, deterministic processes dominated the bacterial and fungal assemblies of the leaf endosphere while stochastic processes in the rhizosphere and rhizoplane dominated the microbial assemblies. Assembly processes in bacterial and fungal roots differed (stochastic processes in bacteria and deterministic processes in fungi). Twenty shared core operational taxonomic units (OTUs) (bacteria, 13; fungi, 7) were identified across all compartments. Various stilbene compounds in leaf were significantly correlated with these shared core microbes, and weighted gene co-expression network analysis revealed that some hub genes were correlated with these metabolites. Thus, their role as regulators of grapevine microbiome interactions needs to be further evaluated. This study provided new profiles of the microbiota in different grapevines compartments, which suggested their association with grape metabolites and plant genes, representing a major development for further studies focused in understanding the role of these microorganisms for grapevine production.

Introduction: As a traditional Chinese medicine abundant in sesquiterpenoids, Atractylodes chinensis has various pharmacological activities. Nevertheless, the synthesis and metabolism mechanisms of its sesquiterpenoids under drought stress are still not fully elucidated.

Methods: Therefore, this study investigated changes in sesquiterpenoid component contents, gene expression profiles, and metabolite accumulation of A. chinensis under drought stress.

Results: Results showed that the moderate drought stress (MDS) significantly increased the contents of atractylodin, β-eudesmol, and atractylenolide I. Compared with the control group (CK), 10,528, 9,755, and 10,562 differentially expressed genes (DEGs) were identified in the light drought stress (LDS), MDS, and severe drought stress (SDS) groups, respectively. These DEGs are involved in plant-pathogen interaction, plant hormone signal transduction, plant MAPK signal transduction, and starch and sucrose metabolism. Metabolic analysis detected 2,101, 2,112, and 2,144 differentially accumulated metabolites (DAMs) in the LDS, MDS, and SDS groups, including atractylodin, β-eudesmol, and atractylenolide I. These DAMs are primarily enriched in three pathways: "ABC transporters", "D-amino acid metabolism", and "aminoacyl-tRNA biosynthesis". Furthermore, we screened and characterized the expression patterns of DEGs and accumulation levels of DAMs involved in the sesquiterpenoid biosynthesis pathway. Notably, the genes TRINITY_DN12874_c1_g1, TRINITY_DN114406_c0_g1, TRINITY_DN2331_c0_g2, TRINITY_DN7401_c0_g1, TRINITY_DN11676_c0_g1, along with the compound Germacra-1(10),4,11(13)-trien-12-ol, are speculated to be key genes and critical metabolite responding to drought stress, respectively.

Discussion: These findings enhance our understanding of the mechanisms by which drought stress modulates the sesquiterpenoid biosynthesis pathways in A. chinensis.