Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening of Arabidopsis Germination and Rosette Growth

Environmental stresses have a significant effect on agricultural crop productivity worldwide. Exposure of seeds to abiotic stresses, such as salinity among others, results in lower seed viability, reduced germination, and poor seedling establishment. Alternative agronomic practices, e.g., the use of plant biostimulants, have attracted considerable interest from the scientific community and commercial enterprises. Biostimulants, i.e., products of biological origin (including bacteria, fungi, seaweeds, higher plants, or animals) have significant potential for (i) improving physiological processes in plants and (ii) stimulating germination, growth and stress tolerance. However, biostimulants are diverse, and can range from single compounds to complex matrices with different groups of bioactive components that have only been partly characterized. Due to the complex mixtures of biologically active compounds present in biostimulants, efficient methods for characterizing their potential mode of action are needed. In this study, we report the development of a novel complex approach to biological activity testing, based on multi-trait high-throughput screening (MTHTS) of Arabidopsis characteristics. These include the in vitro germination rate, early seedling establishment capacity, growth capacity under stress and stress response. The method is suitable for identifying new biostimulants and characterizing their mode of action. Representatives of compatible solutes such as amino acids and polyamines known to be present in many of the biostimulant irrespective of their origin, i.e., well-established biostimulants that enhance stress tolerance and crop productivity, were used for the assay optimization and validation. The selected compounds were applied through seed priming over a broad concentration range and the effect was investigated simultaneously under control, moderate stress and severe salt stress conditions. The new MTHTS approach represents a powerful tool in the field of biostimulant research and development and offers direct classification of the biostimulants mode of action into three categories: (1) plant growth promotors/inhibitors, (2) stress alleviators, and (3) combined action

Plant responses to fungal volatiles involve global posttranslational thiol redox proteome changes that affect photosynthesis

Microorganisms produce volatile compounds (VCs) that promote plant growth and photosynthesis through complex mechanisms involving cytokinin (CK) and abscisic acid (ABA). We hypothesized that plants' responses to microbial VCs involve posttranslational modifications of the thiol redox proteome through action of plastidial NADPH-dependent thioredoxin reductase C (NTRC), which regulates chloroplast redox status via its functional relationship with 2-Cys peroxiredoxins. To test this hypothesis, we analysed developmental, metabolic, hormonal, genetic, and redox proteomic responses of wild-type (WT) plants and a NTRC knockout mutant (ntrc) to VCs emitted by the phytopathogen Alternaria alternata. Fungal VC-promoted growth, changes in root architecture, shifts in expression of VC-responsive CK- and ABA-regulated genes, and increases in photosynthetic capacity were substantially weaker in ntrc plants than in WT plants. As in WT plants, fungal VCs strongly promoted growth, chlorophyll accumulation, and photosynthesis in ntrc–Δ2cp plants with reduced 2-Cys peroxiredoxin expression. OxiTRAQ-based quantitative and site-specific redox proteomic analyses revealed that VCs promote global reduction of the thiol redox proteome (especially of photosynthesis-related proteins) of WT leaves but its oxidation in ntrc leaves. Our findings show that NTRC is an important mediator of plant responses to microbial VCs through mechanisms involving global thiol redox proteome changes that affect photosynthesis.Comisión Interministerial de Ciencia y Tecnología BIO2013‐ 49125‐C2‐1‐P, BIO2017‐85195‐C2‐1‐P, BIO2016‐78747‐PEuropean Regional Development CZ.02.1.01/0.0/0.0/16_019/0000827Ministry of Education, Youth and Sport of the Czech Republic LO1204Japan Society for the Promotion of Sciences 15H02486Gobierno de Navarra P1004 PROMEBIO, P1044 AGROEST

Plastidic phosphoglucose isomerase is an important determinant of starch accumulation in mesophyll cells, growth, photosynthetic capacity, and biosynthesis of plastidic cytokinins in Arabidopsis

Póster presentado en el XXI Reunión de la Sociedad Española de Fisiología Vegetal y el XIV Congreso Hispano-Luso Fisiología Vegetal, celebrado en Toledo del 14 al 17 de junio de 2015.Peer Reviewe

Abscisic acid induced a negative geotropic response in dark-incubated Chlamydomonas reinhardtii

© 2019, The Author(s). The phytohormone abscisic acid (ABA) plays a role in stresses that alter plant water status and may also regulate root gravitropism and hydrotropism. ABA also exists in the aquatic algal progenitors of land plants, but other than its involvement in stress responses, its physiological role in these microorganisms remains elusive. We show that exogenous ABA significantly altered the HCO3− uptake of Chamydomonas reinhardtii in a light-intensity-dependent manner. In high light ABA enhanced HCO3− uptake, while under low light uptake was diminished. In the dark, ABA induced a negative geotropic movement of the algae to an extent dependent on the time of sampling during the light/dark cycle. The algae also showed a differential, light-dependent directional taxis response to a fixed ABA source, moving horizontally towards the source in the light and away in the dark. We conclude that light and ABA signal competitively in order for algae to position themselves in the water column to minimise photo-oxidative stress and optimise photosynthetic efficiency. We suggest that the development of this response mechanism in motile algae may have been an important step in the evolution of terrestrial plants and that its retention therein strongly implicates ABA in the regulation of their relevant tropisms

An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions

High-throughput plant phenotyping platforms provide new possibilities for automated, fast scoring of several plant growth and development traits, followed over time using non-invasive sensors. Using Arabidopsis as a model offers important advantages for high-throughput screening with the opportunity to extrapolate the results obtained to other crops of commercial interest. In this study we describe the development of a highly reproducible high-throughput Arabidopsis in vitro bioassay established using our OloPhen platform, suitable for analysis of rosette growth in multi-well plates. This method was successfully validated on example of multivariate analysis of Arabidopsis rosette growth in different salt concentrations and the interaction with varying nutritional composition of the growth medium. Several traits such as changes in the rosette area, relative growth rate, survival rate and homogeneity of the population are scored using fully automated RGB imaging and subsequent image analysis. The assay can be used for fast screening of the biological activity of chemical libraries, phenotypes of transgenic or recombinant inbred lines, or to search for potential quantitative trait loci. It is especially valuable for selecting genotypes or growth conditions that improve plant stress tolerance

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA.

Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest-the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use

Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates

Fig 3 -

Distortion correction A) A detail of a single block before the affine correction. Notice the skewed vertical boundaries of the plots. B) The same block after the affine correction. The individual plots have now rectangular boundaries.</p

Instance segmentation of individual fields.

The digital image of a field block highlighted in yellow in Fig 2B is summed along the shorter edge. A) Vertical normalized sums of the processed image (blue) with automatically identified left (green) and right (orange) edges of the individual field plots. B) First derivative of the sum in panel A, enabling the automatic detection of the plot edges.</p