Plant Methods最新文献

Backgrounds: Existing methods for fluoride (F^-) determination in plant material require expensive equipment and specialized reagents. This study aimed to develop a simple and cost-effective method for fluoride analysis in plant samples.

Results: Using an orthogonal assay design with certified reference material, this study optimized a sodium hydroxide extraction method (5 mol·L^-1) with heating at 120 °C for 0.5 h, followed by the addition of potassium acetate, ionic strength adjustment, and measurement via an ion-selective electrode. The method achieved a limit of detection (LOD) and limit of quantification (LOQ) of 1.41 and 4.71 mg·kg⁻¹, respectively. Recovery rates ranged from 84.74 to 89.34% in Arabidopsis thaliana (intraday relative standard deviation [RSD] ≤ 2.31%, inter-day RSD ≤ 4.17%) and from 83.53 to 91.55% in Camellia sinensis (intraday RSD ≤ 3.11%, inter-day RSD ≤ 4.98%). In A. thaliana cultivated in NaF-dosed (500 µM) nutrient solution, the fluoride concentration in the shoot was 16.00 mg·kg^-1; In C. sinensis grown under 250 µM NaF treatment, the shoot fluoride concentration was 292.71 mg·kg^-1. Moreover, the fluoride concentration in Tea products purchased from local supermarkets ranged from 16.28 to 61.78 mg kg^-1.

Conclusion: This study presents a simple, reliable, and cost-effective method for fluoride analysis in plant materials, which can be further validated through inter-laboratory testing to establish a standardized approach.

Background: Modern plant breeding strategies rely on the intensive use of advanced genomic tools to expedite the development of improved crop varieties. Genomic DNA extraction from crop seeds eliminates the need to grow plants in contrast to fresh leaf tissue; however, it can still be a bottleneck due to the presence of stored compounds and the complexity of the matrix. The interaction of environmentally benign choline-based ionic liquids (ILs) with DNA offers an innovative approach to enhance the quality of extracted DNA from seeds. While prior IL-based plant DNA extraction workflows have primarily supported polymerase chain reaction (PCR) and quantitative PCR-based applications, their suitability for high-throughput sequencing (HTS) remained largely unexplored. This study explores the efficacy of IL-assisted method for genomic DNA extraction from soybean (Glycine max) seeds, addressing the limited application of ILs in HTS.

Results: The optimized DNA extraction method, utilizing 25% (w/v) choline formate, enabled the recovery of high-purity DNA with abundant fragment sizes > 20 kb, suitable for downstream applications including PCR, whole genome amplification (WGA), simple sequence repeat (SSR) amplification, and high-throughput Illumina sequencing. The IL-method was benchmarked against a silica-binding method using cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) as lysis agents using a commercial plant DNA extraction kit in terms of DNA yield, purity, abundant DNA fragment size distribution, and integrity. In addition, DNA isolated from this method demonstrated successful PCR amplification of markers from both the nuclear and plastid genomes and yielded > 99% whole genome coverage with Illumina (PE150) sequencing reads.

Conclusions: This is the first known instance of a whole genome sequence generated from DNA extracted with ILs. These findings mark a significant milestone in establishing ILs as promising alternatives to conventional methods for seed DNA extraction, with potential utility in third generation (long-read) sequencing experiments.

Background: Spray-induced gene silencing (SIGS) is a promising strategy for controlling plant diseases caused by pests, fungi, and viruses. The method involves spraying on plant surfaces double-stranded RNAs (dsRNAs) that target pathogen genes and inhibit pathogen growth via activation of the RNA interference machinery. Despite its potential, significant challenges remain in the application of SIGS, including producing large quantities of dsRNAs for field applications. While industrial-scale dsRNA production is feasible, most research laboratories still rely on costly and labor-intensive in vitro transcription kits that are difficult to scale up for field trials. Therefore, there is a critical need for highly efficient and scalable methods for producing diverse dsRNAs in research laboratories.

Results: This study introduces pSIG plasmids, MoClo-compatible vectors designed for efficient dsRNA production in the Escherichia coli RNase III-deficient strain HT115 (DE3). The pSIG vectors enable the assembly of multiple DNA fragments in a single reaction using highly efficient Golden Gate cloning, thereby allowing the production of chimeric dsRNAs to simultaneously silence multiple genes in target pests and pathogens. To demonstrate the efficacy of this system, we generated 12 dsRNAs targeting essential genes in Botrytis cinerea. The results revealed that silencing the Bcerg1, Bcerg2, and Bcerg27 genes involved in the ergosterol biosynthesis pathway, significantly reduced fungal infection in plant leaves. Furthermore, we synthesized a chimeric dsRNA, Bcergi, that incorporates target fragments from Bcerg1, Bcerg2, and Bcerg27. Nevertheless, the Bcerg1 dsRNA alone achieved greater disease suppression than the chimeric Bcergi dsRNA.

Conclusions: Here, we developed a highly efficient and scalable method for producing chimeric dsRNAs in E. coli HT115 (DE3) in research laboratories using our homemade pSIG plasmid vectors. This approach addresses key challenges in SIGS research, including the need to produce large quantities of dsRNA and identify effective dsRNAs, thus enhancing the feasibility of SIGS as a sustainable strategy for controlling plant diseases and pests in crops.

Background: Soil compaction is defined as the reduction of air-filled pore space affecting soil density, water conductivity and nutrient availability. These conditions negatively influence root morphology, root development and plant growth leading to yield loss. To date, the ability of roots to penetrate compacted soil has been investigated using high density agar or wax-petrolatum layers as a proxy for compaction. Nevertheless, these methods are not realistic and fail to account for the root-soil interaction that influences root growth ability.

Results: Artificially compacted soil lumps were prepared using natural field soil mixed with sand and vermiculite in a 1:1:0.2 ratio and adjusted to a final water content of 31%. A Genome Wide Association Study (GWAS) was performed to validate this new methodology, combining a panel of 139 barley cultivars with a Single Nucleotide Polymorphism (SNP) dataset of 5,317 polymorphic markers. The panel was evaluated at seedling stage for four traits: total root length, average of diameter width, seminal root number, shoot: root weight ratio and two novel Quantitative Trait Loci (QTLs) associated with total root length were identified on Chr 4 H and 5 H. Four genes (a Nitrate Transporter1 (NRT1)/Peptide Transporter (PTR) family protein 2.2, a Hedgehog-interacting-like protein, an expansin and a cyclic nucleotide-gated channel) were hypothesized as plausible candidates for further investigation, given their implication in root development. In addition, the new phenotyping method revealed an altered plagiogravitropism phenomenon in barley during root emergence in compact substrates. In uncompacted soil, only the primary root exhibits vertical gravitropic set-point angle while a variable number of embryonic seminal roots develop with a shallower growth angle. In contrast, in compacted substrate all roots developed vertically to restore the growth angle after reaching a length of 4-5 millimetres.

Conclusions: A methodology based on root-soil interaction is presented as a new method for root growth evaluation and genomic studies in seedlings growing in compacted soil.

Stomatal morphology plays a critical role in regulating plant gas exchange influencing water use efficiency and ecological adaptability. While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. In this study, we evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping. The approach enables non-ML experts to efficiently segment stomatal structures across diverse datasets, including images from different plant species, magnifications, and imprint methods. We trained a single model based on images from five datasets and tested its performance on unseen data, achieving high accuracy for stomatal density (R² = 0.98) and size (R² = 0.90). Thresholding approaches applied to the U-Net segmentations further improved accuracy, particularly for density measurements. Despite significant variability between datasets, our findings demonstrate the feasibility of training a single segmentation model to analyze diverse stomatal data sets. Validation approaches showed that a semi-automatic approach involving correcting segmentations was five times faster than manual annotation while maintaining comparable accuracy. Our results also illustrate that ML metrics, such as the F1 score, correlate with accuracy in the statistical analysis of trait measurements with improvements diminishing after 2:30 h model training. The final model achieved high precision, allowing the detection of highly significant biological differences in stomatal morphology within plant, between genotypes and across growing environments. This study highlights interactive ML with corrective annotation as a robust and accessible tool for accelerating phenotyping in plant sciences, reducing technical barriers and promoting high-throughput analysis.

Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T₁ chaffiness, (2) T₂ chalky rice kernel percentage (CRK%), and (3) T₃ head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity.

Results: The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R² = 0.9987, RMSE = 1.302; CRK%: R² = 0.9397, RMSE = 8.91; HRR%: R² = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains.

Conclusions: Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.

Background: Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detection of crop infection by fungus is the very first step to deploying a timely and effective treatment. Early and reliable detection is thus key to improving yields, sustainability, and achieving food security. Conventional diagnostic methods are however often destructive, slow, or requiring visible symptoms which appear late in the infection process. To overcome these challenges, we propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time.

Results: We demonstrate the use of low-cost OCT to monitoring wheat (cultivar AxC 169) when infected by Septoria tritici. We show that OCT analysis can effectively detect signs of infection before any external symptoms appear. Although OCT cannot directly visualize fungal hyphae, OCT reveals apparent morphological changes of the mesophyll where the fungal filaments are expected to develop. This study thus focuses on monitoring and correlating changes within the mesophyll structural organisation with the state of infection. It results in distinct statistical difference between intact and infected wheat plants two days only after infection. We then demonstrate the use of machine learning (ML) for high throughput segmentation of OCT scans, providing a foundation for future automated fungus-detection analysis.

Conclusions: This work highlights the potential of OCT, combined with ML tools, to enable rapid, non-invasive, and early diagnosis of crop fungal infections, opening new avenues for precision agriculture and sustainable disease management.

Background: Global warming is currently occurring at a rapid rate and is having a particularly severe impact on plants, which, as sessile organisms, have a limited ability to escape high temperatures. This requires a better understanding of the thermal limits for different plant species and a better understanding of the processes involved in the development of heat injury in plant leaves. Heat injury results from multiple processes and occurs at the molecular level, involving increased membrane fluidity, lipid peroxidation, and protein aggregation and denaturation.

Results: We have tested whether the DSC method allows the detection of heat-induced denaturation and aggregation of molecules in intact leaves. During controlled heating a consistent and repeatable pattern was observed in the DSC plot, from which critical heat thresholds could be derived. These critical temperatures were in good agreement with the temperatures determined using classical methods and also clearly mark the thermal limits of molecular structures. The advantage of the DCS method is the precise, rapid and easy detection of heat thresholds. Finally, taken all thresholds together, we can draw a better image of the sequence of events associated with heat injury in plant leaves: heat injury begins with membrane leakage and continues with protein denaturation and aggregation at high (sublethal, lethal) temperatures.

Conclusion: Since heat injury results from multiple processes, a holistic understanding requires the acquisition of parameters indicative of different processes. The presented DSC method, which allows the detection of denaturation and aggregation of cellular compounds, therefore complements well the classical methods that reflect photosynthetic impairment and whole leaf tissue damage. The new simple and rapid method requires only a minimal amount of leaf material and allows rapid collection of data on damaging temperatures for different plants, which is particularly important in the face of rapidly progressing climatic changes.