High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays

Strategies to improve the efficiency and quality of mutant breeding using heavy-ion beam irradiation

Heavy-ion beam irradiation (HIBI) is useful for generating new germplasm in plants and microorganisms due to its ability to induce high mutagenesis rate, broad mutagenesis spectrum, and excellent stability of mutants. However, due to the random mutagenesis and associated mutant breeding modalities, it is imperative to improve HIBI-based mutant breeding efficiency and quality. This review discusses and summarizes the findings of existing theoretical and technical studies and presents a set of tandem strategies to enable efficient and high-quality HIBI-based mutant breeding practices. These strategies: adjust the mutation-inducing techniques, regulate cellular response states, formulate high-throughput screening schemes, and apply the generated superior genetic elements to genetic engineering approaches, thereby, improving the implications and expanding the scope of HIBI-based mutant breeding. These strategies aim to improve the mutagenesis rate, screening efficiency, and utilization of positive mutations. Here, we propose a model based on the integration of these strategies that would leverage the advantages of HIBI while compensating for its present shortcomings. Owing to the unique advantages of HIBI in creating high-quality genetic resources, we believe this review will contribute toward improving HIBI-based breeding.

Plant acclimation to ionising radiation requires activation of a detoxification pathway against carbonyl-containing lipid oxidation products

Ionising γ radiation produces reactive oxygen species by water radiolysis, providing an interesting model approach for studying oxidative stress in plants. Three-week old plants of Arabidopsis thaliana were exposed to a low dose rate (25 mGy h-1) of γ radiation for up to 21 days. This treatment had no effect on plant growth and morphology, but it induced chronic oxidation of lipids which was associated with an accumulation of reactive carbonyl species (RCS). However, contrary to lipid peroxidation, lipid RCS accumulation was transient only, being maximal after 1 day of irradiation and decreasing back to the initial level during the subsequent days of continuous irradiation. This indicates the induction of a carbonyl-metabolising process during chronic ionising radiation. Accordingly, the γ-radiation treatment induced the expression of xenobiotic detoxification-related genes (AER, SDR1, SDR3, ALDH4, and ANAC102). The transcriptomic response of some of those genes (AER, SDR1, and ANAC102) was deregulated in the tga256 mutant affected in three TGAII transcription factors, leading to enhanced and/or prolonged accumulation of RCS and to a marked inhibition of plant growth during irradiation compared to the wild type. These results show that Arabidopsis is able to acclimate to chronic oxidative stress and that this phenomenon requires activation of a carbonyl detoxification mechanism controlled by TGAII. This acclimation did not occur when plants were exposed to an acute γ radiation stress (100 Gy) which led to persistent accumulation of RCS and marked inhibition of plant growth. This study shows the role of secondary products of lipid peroxidation in the detrimental effects of reactive oxygen species.

SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images

BACKGROUND

Investigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici-a frequently occurring and damaging disease on wheat crops.

RESULTS

We developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions.

CONCLUSIONS

SeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.

Plant hormesis: Revising of the concepts of biostimulation, elicitation and their application in a sustainable agricultural production

Current research in basic and applied knowledge of plant science has aimed to unravel the role of the interaction between environmental factors and the genome in the physiology of plants to confer the ability to overcome challenges in a climate change scenario. Evidence shows that factors causing environmental stress (stressors), whether of biological, chemical, or physical origin, induce eustressing or distressing effects in plants depending on the dose. The latter suggests the induction of the "hormesis" phenomenon. Sustainable crop production requires a better understanding of hormesis, its basic concepts, and the input variables to make its management feasible. This implies that acknowledging hormesis in plant research could allow specifying beneficial effects to effectively manage environmental stressors according to cultivation goals. Several factors have been useful in this regard, which at low doses show beneficial eustressing effects (biostimulant/elicitor), while at higher doses, they show distressing toxic effects. These insights highlight biostimulants/elicitors as tools to be included in integrated crop management strategies for reaching sustainability in plant science and agricultural studies. In addition, compelling evidence on the inheritance of elicited traits in plants unfolds the possibility of implementing stressors as a tool in plant breeding.

Arabidopsis thaliana Accessions from the Chernobyl Exclusion Zone Show Decreased Sensitivity to Additional Acute Irradiation

Chronic ionising radiation exposure is a main consequence of radioactive pollution of the environment. The development of functional genomics approaches coupled with morphological and physiological studies allows new insights into plant adaptation to life under chronic irradiation. Using morphological, reproductive, physiological, and transcriptomic experiments, we evaluated the way in which Arabidopsis thaliana natural accessions from the Chernobyl exclusion zone recover from chronic low-dose and acute high-dose γ-irradiation of seeds. Plants from radioactively contaminated areas were characterized by lower germination efficiency, suppressed growth, decreased chlorophyll fluorescence, and phytohormonal changes. The transcriptomes of plants chronically exposed to low-dose radiation indicated the repression of mobile genetic elements and deregulation of genes related to abiotic stress tolerance. Furthermore, these chronically irradiated natural accessions showed higher tolerance to acute 150 Gy γ-irradiation of seeds, according to transcriptome and phytohormonal profiles. Overall, the lower sensitivity of the accessions from radioactively contaminated areas to acute high-dose irradiation may come at the cost of their growth performance under normal conditions.

Environmental hormesis of non-specific and specific adaptive mechanisms in plants

Adaptive responses of plants are important not only for local processes in populations and communities but also for global processes in the biosphere through the primary production of ecosystems. In recent years, the concept of environmental hormesis has been increasingly used to explain the adaptive responses of living organisms, including plants, to low doses of natural factors, both abiotic and biotic, as well as various anthropogenic impacts. However, the issues of whether plant hormesis is similar/different when it is induced by mild stressors having different specific effects and what is the contribution of hormetic stimulation of non-specific and specific adaptive mechanisms in plant resilience to strong stressors (i.e., preconditioning) remains unclear. This paper analyses hormetic stimulation of non-specific and specific adaptive mechanisms in plants and its significance for preconditioning, the phenomenon of the hormetic trade-off for these mechanisms, and the position of hormetic stimulation of non-specific and specific adaptive mechanisms in the system of plant adaptations to environmental challenges. The analysis has shown that both non-specific and specific adaptive mechanisms of plants can be stimulated hormetically by mild stressors and are important for plant preconditioning. Due to limited plant resources, non-specific and specific adaptive mechanisms have hormetic trades-offs 1 (hormesis accompanied by the deterioration of some plant traits) and 2 (hormesis of some plant traits with the invariability of others). At the same time, hormetic trade-off 2 is observed much more often than hormetic trade-off 1, at least, this was demonstrated here for non-specific adaptive responses of plants. The hormetic stimulation of non-specific and specific adaptive mechanisms is part of the inducible adaptation of plants caused by stress factors and is an adaptation to random (unpredictable) changes in the environment.

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15-21 DAS) and late (∼21-23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17-21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.