Plant Methods最新文献

Background: Gene expression is a fundamental process for plants to express their phenotype, and its analysis is the basis of molecular studies. However, the instability of RNA often poses an obstacle to analyzing plants grown in fields or remote locations where the availability of liquid nitrogen or dry ice is limited. To deepen our understanding of plant phenotypes and tolerance to field-specific stresses, it is crucial to develop methodologies to maintain plant RNA intact and safely transfer it for downstream analyses such as qPCR and RNA-seq.

Results: In this study, the author developed a novel tissue preservation method that involved the infiltration of RNA preservation solution into the leaf apoplast using a syringe and subsequent storage at 4 °C. RNA-seq using samples stored for 5 d and principal component analyses showed that rice leaves treated with the infiltration method maintained the original transcriptome pattern better than those treated with the traditional method when the leaves were simply immersed in the solution. Additionally, it was also found that extracted RNA can be transported with minimum risk of degradation when it is bound to the membrane of RNA extraction kits. The developed infiltration method was applied to rice plants grown in a local farmer's field in northern Madagascar to analyze the expression of nutrient-responsive genes, suggesting nutrient imbalances in some of the fields examined.

Conclusions: This study showed that the developed infiltration method was effective in preserving the transcriptome status of rice and sorghum leaves when liquid nitrogen or a deep freezer is not available. The developed method was useful for diagnosing plants in the field based on the expression of nutrient-responsive marker genes. Moreover, the method used to protect RNA samples from degradation during transportation offers the possibility to use them for RNA-seq. This novel technique could pave the way for revealing the molecular basis of plant phenotypes by accelerating gene expression analyses using plant samples that are unique in the field.

Genetic transformation is a pivotal approach in plant genetic engineering. Peanut (Arachis hypogaea L.) is an important oil and cash crop, but the stable genetic transformation of peanut is still difficult and inefficient. Recently, the pollen tube injection pathway has been shown to be effective for the genetic transformation of peanut. However, the poor reproducibility of this pathway is still controversial. In this study, the appropriate time and location of injection, along with transgenic screening, were systematically investigated in the pollen tube mediated peanut genetic transformation. Our findings revealed that Agrobacterium injections could be conducted within a time window of two to three hours preceding and succeeding the blooming process. Among the various selective markers evaluated, the Basta screening emerged as the most expedient, followed closely by the DsRed visual screening. According to resistance screening and molecular identification, the average transformation efficiency was 2.6% in the heritable transgenic progenies, which was more likely affected by individual operation by style cavity injection. Furthermore, the use of synergistic FT artificially regulated the blooming of peanuts under indoor conditions, facilitating operations involving keel petal injection and ultimately enhancing the genetic transformation efficiency. Thus, our study systematically validated the feasibility of peanut genetic transformation through an optimized pollen-tube injection technique without tissue culture, potentially guiding future advancements in peanut engineering and molecular breeding programs.

Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var. pyramidalis, a species widely employed for constructing farmland shelterbelts in water-limited regions of northern China but notably susceptible to drought. These samples were dehydrated to varying degrees to generate concurrent LWC measurements and hyperspectral images, enabling the development of narrow-band and multivariate spectral prediction models for LWC estimation. Two visible-spectrum narrow-band indices identified, the single-band index (R₆₂₇) and the band subtraction index (R₄₃₇ - R₄₄₄), demonstrated a strong correlation with LWC. Despite certain influences of variable preprocessing and selection on multivariate model performance, most models exhibited robust predictive accuracy for LWC. The FDRL-UVE-PLSR combination emerged as the optimal multivariate model, with R² values reaching 0.9925 and 0.9853 and RMSE values below 0.0124 and 0.0264 for the calibration and validation datasets, respectively. Using this optimal model, along with localized spectral smoothing, moisture distribution across leaf surfaces was visualized, revealing lower water retention at the leaf margins compared to central regions. These methodologies provide critical insights into subtle water-associated physiological processes at the leaf scale and facilitate high-frequency, large-scale assessments and monitoring of drought stress levels and the risk of drought-induced tree mortality and forest degradation in drylands.

Background: Nitrate (NO₃^-) is one of the two major forms of inorganic nitrogen absorbed by plant roots, and the tissue nitrate concentration in roots is considered important for optimizing developmental programs. Technologies to quantify the expression levels of nitrate transporters and assimilating enzymes at the cellular level have improved drastically in the past decade. However, a technological gap remains for detecting nitrate at a high spatial resolution. Using extraction-based methods, it is challenging to reliably estimate nitrate concentration from a small volume of cells (i.e., with high spatial resolution), since targeting a small or specific group of cells is physically difficult. Alternatively, nitrate detection with microelectrodes offers subcellular resolution with high cell specificity, but this method has some limitations on cell accessibility and detection speed. Finally, optical nitrate biosensors have very good (in-vivo) sensitivity (below 1 mM) and cellular-level spatial resolution, but require plant transformation, limiting their applicability. In this work, we apply Raman microspectroscopy for high-dynamic range in-vivo mapping of nitrate in different developmental zones of Arabidopsis thaliana roots in-situ.

Results: As a proof of concept, we have used Raman microspectroscopy for in-vivo mapping of nitrate content in roots of Arabidopsis seedlings grown on agar media with different nitrate concentrations. Our results revealed that the root nitrate concentration increases gradually from the meristematic zone (~ 250 µm from the root cap) to the maturation zone (~ 3 mm from the root cap) in roots grown under typical growth conditions used for Arabidopsis, a trend that has not been previously reported. This trend was observed for plants grown in agar media with different nitrate concentrations (0.5-10 mM). These results were validated through destructive measurement of nitrate concentration.

Conclusions: We present a methodology based on Raman microspectroscopy for in-vivo label-free mapping of nitrate within small root tissue volumes in Arabidopsis. Measurements are done in-situ without additional sample preparation. Our measurements revealed nitrate concentration changes from lower to higher concentration from tip to mature root tissue. Accumulation of nitrate in the maturation zone tissue shows a saturation behavior. The presented Raman-based approach allows for in-situ non-destructive measurements of Raman-active compounds.

Background: Root growth is most commonly determined with the destructive soil core method, which is very labor-intensive and destroys the plants at the sampling spots. The alternative minirhizotron technique allows for root growth observation throughout the growing season at the same spot but necessitates a high-throughput image analysis for being labor- and cost-efficient. In this study, wheat root development in agronomically varied situations was monitored with minirhizotrons over the growing period in two years, paralleled by destructive samplings at two dates. The aims of this study were to (i) adapt an existing CNN-based segmentation method for wheat minirhizotron images, (ii) verify the results of minirhizotron measurements with root growth data obtained by the destructive soil core method, and (iii) investigate the effect of the presence of the minirhizotron tubes on root growth.

Results: The previously existing CNN could successfully be adapted for wheat root images. The minirhizotron technique seems to be more suitable for root growth observation in the subsoil, where a good agreement with destructively gathered data was found, while root length results in the topsoil were dissatisfactory in comparison to the soil core method in both years. The tube presence was found to affect root growth only if not installed with a good soil-tube contact which can be achieved by slurrying, i.e. filling gaps with a soil/water suspension.

Conclusions: Overall, the minirhizotron technique in combination with high-throughput image analysis seems to be an alternative and valuable technique for suitable research questions in root research targeting the subsoil.

In recent years, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have emerged as critical regulators in plant biology, governing complex gene regulatory networks. In the context of disease resistance in Hevea brasiliensis, the rubber tree, significant progress has been made in understanding its response to anthracnose disease, a serious threat posed by fungal pathogens impacting global rubber tree cultivation and latex quality. While advances have been achieved in unraveling the genetic and molecular foundations underlying anthracnose resistance, gaps persist in comprehending the regulatory roles of lncRNAs and miRNAs under such stress conditions. The specific contributions of these non-coding RNAs in orchestrating molecular responses against anthracnose in H. brasiliensis remain unclear, necessitating further exploration to uncover strategies that increase disease resistance. Here, we integrate lncRNA sequencing, miRNA sequencing, and degradome sequencing to decipher the regulatory landscape of lncRNAs and miRNAs in H. brasiliensis under anthracnose stress. We investigated the genomic and regulatory profiles of differentially expressed lncRNAs (DE-lncRNAs) and constructed a competitive endogenous RNA (ceRNA) regulatory network in response to pathogenic infection. Additionally, we elucidated the functional roles of HblncRNA29219 and its antisense hbr-miR482a, as well as the miR390-TAS3-ARF pathway, in enhancing anthracnose resistance. These findings provide valuable insights into plant-microbe interactions and hold promising implications for advancing agricultural crop protection strategies. This comprehensive analysis sheds light on non-coding RNA-mediated regulatory mechanisms in H. brasiliensis under pathogen stress, establishing a foundation for innovative approaches aimed at enhancing crop resilience and sustainability in agriculture.

For the first time, a novel simple label-free electrochemical immunosensor was fabricated for sensitive detection of the coat protein of beet necrotic yellow vein virus (CP-BNYVV) as the causal agent of Rhizomania disease in sugar beet. To boost the amplification of the electrochemical signal, gold nanoparticles-reduced graphene oxide (AuNPs-rGO) nanocomposite was employed to modify the glassy carbon electrode. Anti-BNYVV polyclonal was immobilized onto a modified electrode by applying a thiol linker via a self-assembly monolayer (SAM) and activating the functionalized surface using (3-aminopropyl triethoxysilane) and glutaraldehyde. The determination step relied on the forming of an immunocomplex between the antigen and oriented antibody, resulting in a decrease in current in the [Fe (CN)₆]^3-/4- redox reaction. The response value exhibited direct proportionality to the concentrations of CP-BNYVV. Scanning electron microscopy, energy dispersive x-ray, cyclic voltammetry, and electrochemical impedance spectroscopy techniques collectively provided a comprehensive understanding of the structural, morphological, and electrochemical features during the modification steps. Under optimized experimental conditions, the fast Fourier transform square wave voltammetry responds to the logarithm of CP-BNYVV concentrations in a wide linear range from 0.5 to 50000 pg/mL and the limit of detection is calculated to be 150 fg/mL, implying the admirable sensitivity. Selectivity assay exhibited no cross-reactivity with other proteins from interfering virus samples. Satisfactory reproducibility and stability were achieved with a relative standard deviation of 3.1% and a stable value of 90% after 25 days, respectively. More importantly, the high performance of the immunosensor resulted in the direct detection of CP-BNYVV in spiked and infected plant samples, which affords a sensing platform with huge potential application for the early detection of BNYVV virus in field conditions.

Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point (π_tlp) is gaining attention as an indicator of drought tolerance in plants, though estimating π_tlp often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing π_tlp and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π_tlp, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π_tlp across and within 12 clones of grape vine (Vitis vinifera subsp. vinifera) and one wild relative (Vitis riparia), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4-5% of the variation in π_tlp while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π_tlp variation in grape vine. Also at the leaf level, π_tlp measured using a dewpoint hygrometer approximated π_tlp values (r² = 0.254) and conserved π_tlp rankings from P-V curves (Spearman's ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t-test p = 0.01), average difference in π_tlp for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of π_tlp measured by the two methods were also statistically correlated (r² = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π_tlp rankings across clones (Spearman's ρ = 0.692). The dewpoint hygrometer (taking ∼ 10-15 min on average per measurement) captures fine-scale intraspecific variation in π_tlp, with results that approximate those from P-V curves (taking 2-3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π_tlp, and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.

Background: Climate change and the growing demand for agricultural water threaten global food security. Understanding water use characteristics of major crops from leaf to field scale is critical, particularly for identifying crop varieties with enhanced water-use efficiency (WUE) and stress tolerance. Traditional methods to assess WUE are either by gas exchange measurements at the leaf level or labor-intensive manual pot weighing at the whole-plant level, both of which have limited throughput.

Results: Here, we developed a microcontroller-based low-cost system that integrates pot weighing, automated water supply, and real-time monitoring of plant water consumption via Wi-Fi. We validated the system using major crops (rice soybean, maize) under diverse stress conditions (salt, waterlogging, drought). Salt-tolerant rice maintained higher water consumption and growth under salinity than salt-sensitive rice. Waterlogged soybean exhibited reduced water use and growth. Long-term experiments revealed significant WUE differences between rice varieties and morphological adaptations represented by altered shoot-to-root ratios under constant drought conditions in maize.

Conclusions: We demonstrate that the system can be used for varietal differences between major crops in their response to drought, waterlogging, and salinity stress. This system enables high-throughput, long-term evaluation of water use characteristics, facilitating the selection and development of water-saving and stress-tolerant crop varieties.

Background: Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders.

Results: Advanced deep learning (DL) techniques such as Segment Anything Model (SAM) and YOLO foundation models (YOLOv7, YOLOv8, YOLOv9, and YOLO-NAS), were used to accurately categorize PPD severity from RGB images captured by cameras or cell phones. YOLOv8 achieved the highest overall mean Average Precision (mAP) of 80.4%, demonstrating superior performance in detecting and classifying different PPD levels across all three models. Although YOLO-NAS had some instability during training, it demonstrated stronger performance in detecting the PPD_0 class, with a mAP of 91.3%. YOLOv7 exhibited the lowest performance across all classes, with an overall mAP of 75.5%. Despite challenges with similar color intensities in the image data, the combination of SAM, image processing techniques such as RGB color filtering, and machine learning (ML) algorithms was effective in removing yellow and gray color sections, significantly reducing the Mean Absolute Error (MAE) in PPD estimation from 20.01 to 15.50. Moreover, Artificial Intelligence (AI)-based algorithms allow for efficient analysis of large datasets, enabling rapid screening of cassava roots for PPD symptoms. This approach is much faster and more streamlined compared to the labor-intensive and time-consuming manual visual scoring methods.

Conclusion: These results highlight the significant advancements in PPD detection and quantification in cassava samples using cutting-edge AI techniques. The integration of YOLO foundation models, alongside SAM and image processing methods, has demonstrated promising precision even in scenarios where experts struggle to differentiate closely related classes. This AI-powered model not only effectively streamlines the PPD assessment in the pre-breeding pipeline but also enhances the overall effectiveness of cassava breeding programs, facilitating the selection of PPD-resistant varieties through controlled screening. By improving the precision of PPD assessments, this research contributes to the broader goal of enhancing cassava productivity, quality, and resilience, ultimately supporting global food security efforts.