Plant Methods最新文献

Background: Oak wilt disease, caused by Bretziella fagacearum is a significant threat to oak (Quercus spp.) tree health in the United States and Eastern Canada. The disease may cause dramatic damage to natural and urban ecosystems without management. Early and accurate diagnosis followed by timely treatment increases the level of disease control success.

Results: A rapid assay based on loop mediated isothermal amplification (LAMP) was first developed with fluorescence detection of B. fagacearum after 30-minute reaction time. Six different primers were designed to specifically bind and amplify the pathogen's DNA. To simplify the use of this assay in the field, gold nanoparticles (AuNPs) were designed to bind to the DNA amplicon obtained from the LAMP reaction. Upon inducing precipitation, the AuNP-amplicons settle as a red pellet visible to the naked eye, indicative of pathogen presence. Both infected and healthy red oak samples were tested using this visualization method. The assay was found to have high diagnostic sensitivity and specificity for the B. fagacearum isolate studied. Moreover, the developed assay was able to detect the pathogen in crude DNA extracts of diseased oak wood samples, which further reduced the time required to process samples.

Conclusions: In summary, the LAMP assay coupled with oligonucleotide-conjugated gold nanoparticle visualization is a promising method for accurate and rapid molecular-based diagnosis of B. fagacearum in field settings. The new method can be adapted to other forest and plant diseases by simply designing new primers.

Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions.

Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( $R^2=0.82$ ) and root surface area ( $R^2=0.8$ ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits.

Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

Background: Elucidating the intricate structural organization and spatial gradients of biomolecular composition within the rhizosphere is critical to understanding important biogeochemical processes, which include the mechanisms of root-microbe interactions for maintaining sustainable plant ecosystem services. While various analytical methods have been developed to assess the spatial heterogeneity within the rhizosphere, a comprehensive view of the fine distribution of metabolites within the root-soil interface has remained a significant challenge. This is primarily due to the difficulty of maintaining the original spatial organization during sample preparation without compromising its molecular content.

Results: In this study, we present a novel approach, RhizoMAP, in which the rhizosphere molecules are imprinted on selected polymer membranes and then spatially profiled using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). We enhanced the performance of RhizoMAP by combining the use of two thin (< 20 μm) membranes (polyester and polycarbonate) with distinct MALDI sample preparations. This optimization allowed us to gain insight into the distribution of over 500 different molecules within the rhizosphere of poplar (Populus trichocarpa) grown in rhizoboxes filled with mycorrhizae soil. These two membranes, coupled with three different sample preparation conditions, enabled us to capture the distribution of a wide variety of molecules that included phytohormones, amino acids, sugars, sugar glycosides, polycarboxylic acids components of the Krebs cycle, fatty acids, short aldehydes and ketones, terpenes, volatile organic compounds, fertilizers from the soil, and others. Their spatial distribution varies greatly, with some following root traces, others showing diffusion from roots, some associated with soil particles, and many having distinct hot spots along the plant root or surrounding soil. Moreover, we showed how RhizoMAP can be used to localize the origin of the molecules and molecular transformation during root growth. Finally, we demonstrated the power of RhizoMAP to capture molecular distributions of key metabolites throughout a 20 cm deep rhizosphere.

Conclusions: RhizoMAP is a method that provides nondestructive, untargeted, broad, and sensitive metabolite imaging of root-associated molecules, exudates, and soil organic matter throughout the rhizosphere, as demonstrated in a lab-controlled native soil environment.

Plants must cope with ever-changing temperature conditions in their environment. In many plant species, suboptimal high and low temperatures can induce adaptive mechanisms that allow optimal performance. Thermomorphogenesis is the acclimation to high ambient temperature, whereas cold acclimation refers to the acquisition of cold tolerance following a period of low temperatures. The molecular mechanisms underlying thermomorphogenesis and cold acclimation are increasingly well understood but neither signalling components that have an apparent role in acclimation to both cold and warmth, nor factors determining dose-responsiveness, are currently well defined. This can be explained in part by practical limitations, as applying temperature gradients requires the use of multiple growth conditions simultaneously, usually unavailable in research laboratories. Here we demonstrate that commercially available thermal gradient tables can be used to grow and assess plants over a defined and adjustable steep temperature gradient within one experiment. We describe technical and thermodynamic aspects and provide considerations for plant growth and treatment. We show that plants display the expected morphological, physiological, developmental and molecular responses that are typically associated with high temperature and cold acclimation. This includes temperature dose-response effects on seed germination, hypocotyl elongation, leaf development, hyponasty, rosette growth, temperature marker gene expression, stomatal conductance, chlorophyll content, ion leakage and hydrogen peroxide levels. In conclusion, thermal gradient table systems enable standardized and predictable environments to study plant responses to varying temperature regimes and can be swiftly implemented in research on temperature signalling and response.

Background: Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.

Results: The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.

Conclusions: The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Clematis graveolens Lindl., an indigenous climbing plant found in the Himalayan areas, is used by local communities for the treatment of neck tumors. The objective of this work is to examine the comprehensive metabolomic profile, antioxidant capability, in vitro and in silico anti-glioma effects on U-87 human glioma cell lines of the crude extract and fractions from C. graveolens. Liquid chromatography coupled with mass spectroscopy (LC-MS/MS) was used to establish detailed metabolite profiling of C. graveolens. The assessment of cell cytotoxicity was conducted using MTT cell viability assay on U-87 and BHK-21. Through molecular docking studies, the mode of inhibition and binding interaction between identified compounds and target proteins were also determined to evaluate the in vitro results. The use of LC-MS/MS-based global natural products social (GNPS) molecular networking analysis resulted in the identification of 27 compounds. The crude extract, ethyl acetate fraction, and chloroform fraction exhibited significant inhibitory activity against the U-87 cell lines, with IC₅₀ values of 112.0, 138.1, and 142.7 µg/mL, respectively. The ethyl acetate fraction exhibited significant inhibitory concentration for 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) activity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity and the metal chelation activity with IC₅₀ value of 39.50 µg/mL, 32.27 µg/mL, and 53.46 µg/mL, respectively. The crude extract showed maximum total phenolic, and total flavonoid concentration measuring 338.7 µg GAE/mg, and 177.04 µg QE/mg, respectively. The findings of this study indicate that C. graveolens consists of a diverse range of active phytoconstituents that possess antioxidant and anti-glioma properties.

Background: Since traditional germination test methods have drawbacks such as slow efficiency, proneness to error, and damage to seeds, a non-destructive testing method is proposed for full-process germination of radish seeds, which improves the monitoring efficiency of seed quality.

Results: Based on YOLOv8n, a lightweight test model YOLOv8-R is proposed, where the number of parameters, the amount of calculation, and size of weights are significantly reduced by replacing the backbone network with PP-LCNet, the neck part with CCFM, the C2f of the neck part with OREPA, the SPPF with FocalModulation, and the Detect of the head part with LADH. The ablation test and comparative test prove the performance of the model. With adoption of germination rate, germination index, and germination potential as the three vitality indicators, the seed germination phenotype collection system and YOLOv8-R model are used to analyze the full time-series sequence effects of different ZnO NPs concentrations on germination of radish seeds under varying degrees of salt stress.

Conclusions: The results show that salt stress inhibits the germination of radish seeds and that the inhibition effect is more obvious with the increased concentration of NaCl solution; in cultivation with deionized water, the germination rate of radish seeds does not change significantly with increased concentration of ZnO NPs, but the germination index and germination potential increase initially and then decline; in cultivation with NaCl solution, the germination rate, germination potential and germination index of radish seeds first increase and then decline with increased concentration of ZnO NPs.