Model Plant Research Articles

Efficient Selection of the 2,4-Dichlorophenoxyacetic Acid Nanobody Gene from the Phage Library Constructed with Sorted Specific Cells and Expression in Plants to Confer Herbicide Resistance.

Immunomodulation in biotechnological processes requires an adequate level of specific, high-affinity recombinant antibodies expressed in the affected cells. Here, we report a new strategy to obtain a sensitive nanobody against the 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide. Unlike the previous methods that used total peripheral blood lymphocytes, specific peripheral lymphocytes stained with a fluorescein isothiocyanate-labeled 2,4-D coating antigen were isolated via fluorescence-activated cell sorting and used for phage display library construction. This strategy significantly reduced interference of anticarrier protein nanobody phages to small-molecule nanobody development and required only two cycles of panning to obtain the desired positive clones. Nb4-11, one of the most sensitive phage clones, showed good sensitivity and specificity against 2,4-D. Compared with previously reported 2,4-D nanobodies, the sequence of Nb4-11 exhibited completely different complementarity-determining regions (CDRs). The half-maximum inhibition concentration of the Nb4-11-based ic-ELISA was 29.3 ± 1.9 ng/mL. The Nb4-11 gene was subsequently transferred into Arabidopsis thaliana to confer herbicide resistance, and homozygous transgenic T3 lines were obtained. Stable expression of the 2,4-D nanobody in the model plant was confirmed via PCR, ic-ELISA, and Western blot analysis. In the dose-response bioassay, the transgenic T3 lines were resistant to 2 g of 2,4-D ai/ha. This work offers a new way to sort specific peripheral lymphocytes, efficiently develop nanobodies against small molecules, and create a novel mechanism for herbicide resistance based on the expression of nanobodies in plants.

A text-based, generative deep learning model for soil reflectance spectrum simulation in the solar range (400–2499 nm)

Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches are limited by high non-uniformity of available published datasets that severely limits the amount of data usable for model calibration. To address these limitations, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra from soil property inputs was developed based on the denoising diffusion probabilistic model (DDPM). The model was trained on an extensive dataset comprising nearly 180,000 soil spectra-property set pairs from 17 published datasets. The model generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format, which means the model can handle variable formats for property reporting. Because the model is generative, it can simulate reasonable output spectra based on an incomplete set of available input properties, which becomes more reliable as the input property set becomes more complete. Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the target solar range (400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. It can also be easily integrated with other soil–plant radiation models used for remote sensing research such as PROSAIL and Helios 3D plant modeling software. The testing results of the SOGM on new datasets not included in model training demonstrated that the model can generate reasonable soil reflectance spectra based on available property inputs. Results also show soil clay/sand/silt fraction, organic carbon content, nitrogen content, and iron content tended to be important properties for spectra simulation. Inclusion of some trace minerals like nickel as model inputs decreased model performance because of their low concentrations and large propensity for ground-truth measurement error.

Development of a Versatile Plant-Derived Mitochondrial Targeting Sequence Based on a Reporter Protein Sorting Analysis and Biological Information.

Methods for the delivery of exogenous substances to specific organelles are important because each organelle functions according to its own role. Specifically, mitochondria play an important role in energy production. Recently, plant mitochondrial transformation via delivery methods to mitochondria has been actively researched. Mitochondrial targeting sequences (MTSs) are essential for transporting bioactive molecules, such as nucleic acids, to mitochondria. However, the selectivity and efficacy of MTSs as carrier molecules in plants are not yet sufficient. In this study, we developed an effective MTS in plants via a quantitative comparison of the targeting functions of several MTSs. The presequence of HSP60 from Nicotiana tabacum, which is highly similar to that of several other model plants, showed high mitochondrial-targeting ability among the MTSs tested. This result suggests the applicability of the HSP60 presequence for MTSs in various plants. We further investigated this HSP60 presequence through stepwise shortening on the basis of secondary structure prediction, aiming to simplify synthesis and increase the solubility of the peptides. As shown by assessment of the mitochondrial targeting ability, the 15 residues from the N-terminus of the HSP60 presequence for the MTS, which is particularly conserved among various model plants, retained a targeting efficacy comparable to that of the full-length HSP60 presequence. This developed sequence from the HSP60 sequence is a promising MTS for transfection into plant mitochondria.

Updates to McCree's photosynthetically active radiation curve — 55 years later

Our interpretation of photosynthetically active radiation in plants has evolved since the 1970s with new data explaining the underlying mechanisms. To update McCree's founding work, this study explored the spectral response of photosynthesis in young tomato (Solanum lycopersicum cv. Beefsteak) and lettuce (Lactuca sativa cv. Breen) plants using a narrow-spectrum light unit and a portable photosynthesis system equipped with a whole plant chamber. Highly resolved spectral photosynthesis curves using 1-nm increments at 10 nm full width at half maximum (FWHM) were generated. Results show that the lowest quantum yields were observed at 450 nm and 660 nm, two wavelengths commonly used to improve photosynthesis in research. Different trends and amplified peaks were observed among the spectral quantum yield curves of tomato and lettuce plants and those of earlier studies with red and blue light. An opposing phenomenon was observed, where blue light is more efficient than red light. This is based on the narrower wavelength data acquired in both experimental plant species. Findings represent the most detailed and highly resolved spectral photosynthesis and quantum yield curves to date using experimental model plants (tomato and lettuce).

A Call for a Different Plant Modelling Paradigm and a New Generation of Software

This paper is an invitation to change the plant modeling paradigm to achieve much easier convergence and ensure model consistency between different incarnations of the plant model, while retaining accuracy on par with the rigorous models. Rigorous physical properties [property/mole] are replaced by [property/mass] approximation at local conditions, making bulk properties much less sensitive to changes in stream composition. This eliminates the need for stream mole fractions and stream compositions can be described by linear component mass flows. Detailed process flow diagram is a common topology for all models. Different incarnations of node models are employed at different levels of abstraction (planning, scheduling, optimization, control, design), ensuring inheritance, consistency and increasing accuracy of solutions as plant model instances move from mass to mass-energy, to mass-energy-and-approximate-stream-properties. After the plant model with approximate properties is solved, they are updated via rigorous thermodynamic methods and the plant model is resolved until convergence.

The effect of supplemental LED lighting in the range of UV, blue, and red wavelengths at different ratios on the accumulation of phenolic compounds in pak choi and swiss chard

Phenolic compounds are known for their health-promoting effects on humans. Pak choi (Brassica rapa ssp. chinensis) and Swiss chard (Beta vulgaris subsp. vulgaris) are used here as model plants, as they are eaten raw as baby leaf lettuce and differ in their phenolic compound profile while showing similar morphology. In a greenhouse an artificial light source with UV-B (215 mW m−2), blue (104 μmol/m−2 s−1) and red (245 μmol/m−2 s−1) LEDs was implemented to increase phenolic compounds during the last days before harvest. Pak choi shows an increase or trend towards an increase in the monoacylated triglycosides of kaempferol and quercetin after 4 days of irradiation for 4 h each. For example kaempferol-3-caffeoyl-sophoroside-7-glucoside was increased at low PAR values in the third run and red-dominated light treatment by up to 120 %. In addition, it was observed that the red variety ‘Amur’ has higher concentrations of quercetin glycosides which were increased often. In swiss chard, on the other hand, there was only a sporadic increase in vitexin glycosides. Despite very different concentrations in some samples, 2″-glucosyl-vitexin and 2′′-glucosyl-6′′-malonyl-vitexin showed significant increases of up to 350 % in the two chard varieties Lukullus and Rhubarb chard. The results suggest that the exposure time or intensity of UV-B radiation needs to be optimized for each species and has not yet consistently led to an increase but trends in phenolic compounds and in antioxidant activity in this study.

Spatial-spectral feature mining in hyperspectral corn leaf venation structure and its application in nitrogen content estimation

The growth of corn plants requires a significant amount of nitrogen nutrient supply to ensure the yield. Meanwhile, overfertilization could result in adverse environmental impacts, making it crucial to monitor the nitrogen supply condition in corn plants precisely. Hyperspectral imaging (HSI) could be the solution to this challenge, as HSI plant phenotyping technology has been widely utilized to nondestructively identify plant traits, such as mineral nutrient deficiencies in corn plants. However, limitations persist where the conventional HSI nitrogen prediction models mainly relied on the overall colors of the plant tissue extracted from the spectral domain but rarely included the spatially distributed information on the leaf level. This study aimed to examine if including new information from the spatial domain could potentially improve the performance of HSI models. Because the venation structure plays a crucial role in the distribution of various nutrients across the leaf, a series of physiological responses caused by nitrogen deficiency could result in non-uniform color patterns related to the veins. The recent advancement of HSI techniques such as LeafSpec, whose imaging quality and spatial-spectral resolution have been significantly improved, made it possible to extract the venation structure together with high-resolution spatial-spectral features. In this study, the hypothesis was that utilizing high-resolution spatial-spectral features extracted based on the venation structure could yield a better-performed nitrogen content prediction model compared to the averaged spectrum of the corn leaf. First, a venation structure segmentation algorithm was developed using the Local Binary Patterns (LBP) method for hyperspectral corn leaf images scanned with LeafSpec. Second, for every hyperspectral leaf image, 131 spectral index heatmaps were generated, and 30 leaf regions were picked based on the venation structure, resulting in 3930 spatial-spectral features. Third, the features were picked using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. As the results tested with a field assay involving two different corn genotypes and two nitrogen treatments, the Partial Least Square Regression (PLS-R) model built with selected spatial-spectral features outperformed the model built with the averaged leaf spectra in terms of the Root Mean Squared Error (RMSE) of predictions. The results provided encouraging proof and guidance for the potential benefit of using high-resolution spatial-spectral features derived from hyperspectral leaf images to improve the accuracy and robustness of the nitrogen content prediction models for corn plants.

From Plant Modeling to Narrative Science: Growing and Enjoying Scientific Fruits Naturally and Humanistically

From Plant Modeling to Narrative Science: Growing and Enjoying Scientific Fruits Naturally and Humanistically

Inverter Intensive Hybrid Power Plant Modeling With Small-Signal Stability Augmentation Through Flexible Operation Mode Transition

Inverter Intensive Hybrid Power Plant Modeling With Small-Signal Stability Augmentation Through Flexible Operation Mode Transition

NAC100 regulates silique growth during the initial phase of fruit development through the gibberellin biosynthetic pathway

The NAC transcription factor family is a large class of DNA-binding proteins found in several plant species. In the model plant Arabidopsis thaliana, NAC transcription factors are expressed in different organs, and they are known to modulate many diverse developmental processes, such as meristem formation, flower and fruit development, leaf and fruit senescence. From a previous time-lapse transcriptomic analysis of developing siliques performed by our group, we found a NAC transcription factor, NAC100, that is upregulated during silique development. In this work, we characterized the role of the NAC100 transcription factor and demonstrated that NAC100 contributes to regulating silique growth during the initial phase of their development. nac100 mutant siliques are smaller but such defects can be rescued through the application of exogenous bioactive gibberellin. Gene expression analysis, transactivation assay and endogenous gibberellin quantification indicate that NAC100 modulates gibberellin metabolism, by both directly and indirectly regulating GA-metabolic genes expression, ultimately affecting silique elongation.

Small break LOCA studies for different layouts of passive safety systems in the IRIS reactor

IRIS (International Reactor Innovative and Secure) is an integral, medium power, light water reactor with advanced safety features. In the first decade of the 21st century, 22 institutions under the leadership of Westinghouse Electric Corporation were involved in its development. The University of Zagreb, along with the Polytechnic of Milan, was in charge of performing safety analyses. A detailed plant model is developed using the RELAP5 code for the analyses of thermal–hydraulic processes in the reactor vessel, the GOTHIC code for the analysis of the processes in the containment and, in addition, the ASYST code for the calculation of a severe accident. Some of the previous small break loss-of-coolant accident analyzes at the existing pipelines are repeated to test the improved plant model. However, the focus of the paper is on the new set of analyzes of hypothetical breaks along the reactor vessel with the aim of determining whether the passive safety systems can ensure successful core cooling. For this purpose, two models are developed with different configurations of the emergency heat removal system and the safety systems inside the containment that inject water into the reactor vessel. The results show the complex and rather ambiguous dependence of the reactor coolant system thermal–hydraulic behaviour on the selected boundary conditions. The scenarios analyzed vary from design basis events to severe accidents. The capabilities of specific safety systems in mitigating the consequences of an accident are determined, depending on the position and size of the break on the reactor vessel wall.

Temporal forecasting of plant height and canopy diameter from RGB images using a CNN-based regression model for ornamental pepper plants (Capsicum spp.) growing under high-temperature stress

Temporal forecasting of plant height and canopy diameter from RGB images using a CNN-based regression model for ornamental pepper plants (Capsicum spp.) growing under high-temperature stress

Bibliometric and meta-analysis on the publication status, research trends and impact inducing factors of JA–SA interactions in plants

Interactions between jasmonic acid (JA) and salicylic acid (SA) pathways in plants are important for regulating metabolite production and resistance functions against environmental stresses. These interactions in plants have mostly been reported to be antagonistic, but also to be synergistic under specific external inducing conditions. At present, publications on plant JA–SA interactions lack a bibliometric analysis. External inducing factors that elicit synergism of JA–SA interactions need to be explored. Here, we use bibliometrics to analyze publications on plant JA–SA interactions over the past three decades, and analyze external inducing factors that influence the quality of JA–SA interactions in plants by meta-analysis. More contributions have been made by authors in China, Netherlands, the United States of America, and Germany than elsewhere. Considerable research has been performed on variation in plant defense mediated by two pathways, the transduction mechanisms of JA–SA signaling crosstalk, and plant hormone signaling networks. Meta-analysis showed that the excitation sequence of the two pathways, and the concentrations of pathway excitors are key factors that affect pathways interactions. The JA and SA pathways tend to be reciprocally antagonistic when elicited simultaneously, whereas JA–SA interactions tend to be synergistic when the two pathways are elicited at different times and the pre-treated inducer is at a lower concentration. The SA pathway is more susceptible to being synergized by the JA pathway. Key molecular nodes identified in the JA–SA signaling interaction in model plants, and prospects for future research are discussed.

Parameter estimation in solar power plant systems: a comparative study of recursive and iterative techniques

The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant’s performance and reliability. In this study, we utilized the prediction error method (PEM), a robust algorithm for system identification, to capture the plant’s operational characteristics with precision. Additionally, we employed both recursive and hierarchical algorithms to identify the system parameters effectively. The results from each identification method are rigorously quantified and analyzed, allowing for a detailed comparison of their performance. This comprehensive evaluation not only highlights the strengths and weaknesses of each approach, but also provides valuable insights into their practical applications in the context of solar power systems.

A K-means cluster division of regional photovoltaic power stations considering the consistency of photovoltaic output

The uncertainty and volatility of photovoltaics seriously impact the grid's power quality. Short-term photovoltaic(PV) forecasts have a positive effect on the stable operation of the power system. The accuracy of cluster division is a key factor in the output prediction of regional PV power stations. This paper proposes a cluster division method, including a novel feature selection technique and an optimized cluster algorithm based on K-means. The proposed method performs feature analysis and parameter optimization of the division of regional photovoltaic plant clusters, analyzes the clustering dimension of photovoltaic output consistency, and establishes a K-means clustering model of photovoltaic power plants that considers time, space, and inherent characteristics of power plants first. Then, a prediction model based on Long Short-Term Memory (LSTM) is established for each cluster to realize the prediction of regional cluster photovoltaic output. The simulation results demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed method is 18.27 % and Root Mean Square Error (RMSE) is 45.79 %, which verifies the superiority of the proposed method over comparison models. It shows that the proposed method can effectively solve the problem of low prediction accuracy caused by weak output consistency of power stations in regional photovoltaic clusters.

Allelic variation in the promoter of WRKY22 enhances humid adaptation of Arabidopsis thaliana

Submergence tolerance is a complex trait governed by multiple genetic loci. Arabidopsis thaliana, widely distributed from arid to humid regions, offers an opportunity to explore the underlying genetic components and their interactive mechanisms. In this study, we utilized map-based cloning techniques to identify the WRKY22 genetic locus, which activates RAP2.12, a locus identified previously, possibly through ethylene signal transduction. WRKY22 expression is inhibited by ARRs but activated by the WRKY70 transcription factors. The cooperative interaction between these regulators enhances A. thaliana’s ability to adapt more effectively to contrasting habitats. A two-nucleotide deletion in the WRKY22 promoter region prevents its inhibition by phosphorylated ARRs, maintaining its high expression in humid accessions. This WRKY22 allele without ARRs binding element underwent strong natural selection during the southeastern colonization of A. thaliana to the humid Yangtze River basin. However, the WRKY70-activated WT box in the WRKY22 promoter region shows no variation between humid and arid accessions. These findings together establish one previously unreported molecular module composed by multiple regulators, with WRKY22 playing a key role in the ecological shift of A. thaliana. Expanding upon the identified locus, we overexpressed BnaC2.WRKY22 in Brassica napus and successfully achieved a significant improvement in submergence tolerance, highlighting a functional conservatism of this gene across related species. This further demonstrates the potential for transferring knowledge from model plants to crops for the breeding of flood-tolerant crop varieties.

Systematic Development of Peptide-Based Biostimulants from Whey Protein Hydrolysates

Due to global warming, agricultural production systems are exposed to increasing abiotic stresses, which threaten an economically and environmentally sustainable food production. Innovative environmentally friendly solutions are needed to cope with climate-related risks and to reduce the use of high amounts of synthetic agrochemicals. A promising solution to foster sustainable agriculture involves the use of biostimulants. This strategy, however, is not adopted by the industry to a great degree due to the scarce number of reproducible effects observed in published studies as well as the lack of fundamental knowledge about their mode of action. Biostimulants are substances that positively impact plant physiology by enhancing growth, improving fruit quality, and increasing stress resilience. Among these, protein hydrolysates stand out as a particularly promising category. However, their precise mechanisms of action and the optimal conditions for their application are still not fully understood. This project aims to develop peptide-based biostimulants in a reproducible manner, ensuring their availability for use in plant model systems under strictly controlled conditions. To achieve this, various protein hydrolysates will be produced through the enzymatic hydrolysis of whey, a by-product of cheese production. These hydrolysates will be employed in subsequent studies within plant model systems.

LRR1 involved in the abscisic acid signaling pathway to regulate the early growth and development of Arabidopsis thaliana.

Living organisms possess the remarkable capacity to swiftly adapt to fluctuations in their environment. In the context of cell signal transduction, a significant challenge lies in ensuring the effective perception of external signals and the execution of appropriate responses. To investigate this phenomenon, a recent study utilized Arabidopsis thaliana as a model plant and induced stress by administering abscisic acid (ABA), a plant hormone, to elucidate the involvement of leucine-rich repeat receptor-like kinase1 (LRR1) in ABA signaling pathways. Homozygous T-DNA insertion alleles for LRR1 and KIN7 were isolated. Quantitative real-time PCR (qRT-PCR) was performed to confirm the expression of the LRR1 gene. Subcellular localization and beta-glucuronidase (GUS) tissue labeling techniques were utilized to determine the expression pattern of the LRR1 gene in cells and tissues. Yeast two-hybrid complementation, bimolecular fluorescence complementation assay, and GST pull-down assays were conducted to validate the interaction of LRR1 proteins. Phenotypic analyses revealed that lrr1 and kin7 mutants are less sensitive to the inhibitory effects of ABA on germination and cotyledon greening that is seen in WT. Mutants LRR1 and kinase 7 (KIN7) exhibited resistance to ABA and displayed normal growth patterns under control conditions. The double mutant lrr1kin7 showed reduced responsiveness to ABA. Conversely, overexpression lines LRR1ox2 and LRR1ox10 demonstrated heightened sensitivity to ABA, resulting in severe growth reduction. qRT-PCR assay indicated that exogenous application of ABA led to significant down-regulation of ABI3, ABI4, and ABI5 transcription factors in LRR1 material compared to wild-type WT material. An investigation was conducted to determine the expression pattern and transcriptional level of LRR1 in Arabidopsis. The results revealed ubiquitous expression of LRR1 across all developmental stages and tissue tested. Subcellular localization assays confirmed the presence of LRR1 on the plasma membrane of cells. Furthermore, BiFC assay, yeast two-hybrid complementation, and GST pull-down assays demonstrated an interaction between LRR1 and PYL6 in vitro. These findings provide substantial insights into the involvement of LRR1 in the ABA signaling pathway while regulating seed germination and cotyledon greening during early development in Arabidopsis. This study significantly advances our understanding regarding the correlation between LRR1 and ABA signaling pathways with potential applications for enhancing crop stress resistance.

Nanopore direct RNA sequencing reveals N6-methyladenosine and polyadenylation landscapes on long non-coding RNAs in Arabidopsis thaliana

BackgroundLong non-coding RNAs (lncRNAs) play important roles in various biological processes, including stage development in plants. N6-methyladenosine (m6A) modification and polyadenylation are noteworthy regulatory processes that impact transcript functions by modulating their abundance. However, the specific landscapes of m6A modification and polyadenylation on lncRNAs remain largely unexplored. The advent of nanopore direct RNA sequencing (DRS) provides unprecedented opportunities for directly detecting m6A modifications and estimating polyadenine (poly[A]) tail lengths on individual RNA molecules.ResultsHere we utilized nanopore DRS to identify lncRNAs and map the transcriptome-wide m6A modification and polyadenylation landscapes in the model plant Arabidopsis thaliana. Leveraging the Low-abundance Aware Full-length Isoform clusTEr (LAFITE) assembly pipeline, we identified 1149 novel lncRNAs in seventeen nanopore DRS datasets from the wild-type Columbia-0. Through the precise detection of 2381 m6A modification sites on lncRNAs at single-base resolution, we observed that lncRNAs exhibited lower methylation levels compared to protein-coding RNAs, and m6A modification facilitated lncRNA abundance. Additionally, we estimated the poly(A) tail lengths of individual lncRNAs and found that poly(A) tails contributed to lncRNA stability, while their effect was not length-dependent. Furthermore, by comparing lncRNA abundance between 2-week seedlings and 5-week floral buds, we revealed the dynamic expression patterns of lncRNAs during the transition from the vegetative stage to the reproductive stage. These observations provided insights into their potential roles in specific tissues or stages in Arabidopsis, including regulating stage development. Moreover, by integrating information on m6A modification, we unveiled a positive correlation between methylation variances and differential expressions of lncRNAs during stage development.ConclusionsThese findings highlighted the significance of epigenetic modification and post-transcriptional processing in shaping lncRNA expression and their functions during Arabidopsis stage development, contributing to the growing field of lncRNA research in plants.Clinical trial numberNot applicable.

Ethylene modulates wheat response to phosphate deficiency.

Ethylene involves in the response to P deficiency in some model plants, but its relevance to wheat remains limited. Following our recent study demonstrating the role of differentially expressed genes (DEGs) encoding ethylene response factors (ERFs) in response to P starvation in wheat, this study aims to investigate the remodelling of ethylene pathway and the physiological roles of ethylene in wheat under P deficiency using transcriptome analysis and the addition of exogenous ethylene analogue ethephon or ethylene inhibitors. ERFs with at least a two-fold change upon P deficiency were biasedly enriched on chromosome 4 B. A group of genes encoding ACC synthase and ACC oxidase were upregulated under P starvation, indicating an increase in ACC and ethylene content, which was verified by biochemical measurements and gas chromatography-mass spectrometry analysis. Under P deficiency, both root and shoot biomass decreased with the application of exogenous ethephon or ethylene inhibitors, while root fork numbers and root surface area decreased upon ethephon treatment. The phosphate (Pi) concentrations in roots and old leaves increased with ethephon treatment, and it's redistribution in roots and younger leaves was altered under Pi starvation. Our findings could serve as a guideline for breeding germplasm with high Pi efficiency.