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Einspanier S, Tominello-Ramirez C, Hasler M, Barbacci A, Raffaele S, Stam R. High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0214. [PMID: 39105186 PMCID: PMC11298253 DOI: 10.34133/plantphenomics.0214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 06/19/2024] [Indexed: 08/07/2024]
Abstract
Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.
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Affiliation(s)
- Severin Einspanier
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Christopher Tominello-Ramirez
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Mario Hasler
- Lehrfach Variationsstatistik, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, Kiel, 24118 Kiel, Germany
| | - Adelin Barbacci
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Sylvain Raffaele
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Remco Stam
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
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2
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Spanò R, Petrozza A, Summerer S, Fortunato S, de Pinto MC, Cellini F, Mascia T. Overview of transcriptome changes and phenomic profile of sanitized artichoke vis-à-vis non-sanitized plants. PLANT BIOLOGY (STUTTGART, GERMANY) 2024; 26:715-726. [PMID: 38924230 DOI: 10.1111/plb.13675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/19/2024] [Indexed: 06/28/2024]
Abstract
Plant tissue in vitro culture is increasingly used in agriculture to improve crop production, nutritional quality, and commercial value. In plant virology, the technique is used as sanitation protocol to produce virus-free plants. Sanitized (S) artichokes show increased vigour compared to their non-sanitized (NS) counterparts, because viral infections lead to a decline of growth and development. To investigate mechanisms that control the complex traits related to morphology, growth, and yield in S artichokes compared to NS plants, RNAseq analysis and phenotyping by imaging were used. The role of peroxidases (POD) was also investigated to understand their involvement in sanitized plant development. Results showed that virus infection affected regulation of cell cycle, gene expression and signal transduction modulating cellular response to stimulus/stress. Moreover, primary metabolism and photosynthesis were also influenced, contributing to explain the main morphological differences observed between S and NS artichokes. Sanitized artichokes are also characterized by higher POD activity, probably associated with increased plant growth, rather than strengthening of cell walls. Overall, results show that the differences in development of S artichokes may be derived from the in vitro culture stressor, as well as through pathogen elimination, which, in turn, improve qualitative and quantitative artichoke production.
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Affiliation(s)
- R Spanò
- Department of Soil, Plant and Food Sciences, University of Bari "Aldo Moro", Bari, Italy
| | - A Petrozza
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - S Summerer
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - S Fortunato
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", Bari, Italy
| | - M C de Pinto
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", Bari, Italy
| | - F Cellini
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), Centro Ricerche Metapontum Agrobios, Metaponto di Bernalda, Italy
| | - T Mascia
- Department of Soil, Plant and Food Sciences, University of Bari "Aldo Moro", Bari, Italy
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3
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Tyagi A, Mir ZA, Ali S. Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2024; 24:3261. [PMID: 38894052 PMCID: PMC11174810 DOI: 10.3390/s24113261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants' exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.
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Affiliation(s)
- Anshika Tyagi
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Zahoor Ahmad Mir
- Department of Plant Science and Agriculture, University of Manitoba, Winnipeg, MB R2M0TB, Canada;
| | - Sajad Ali
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea
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4
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Holan KL, White CH, Whitham SA. Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem. PHYTOPATHOLOGY 2024; 114:990-999. [PMID: 38281155 DOI: 10.1094/phyto-09-23-0313-kc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
- Katerina L Holan
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014
| | - Charles H White
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523
| | - Steven A Whitham
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing. FRONTIERS IN PLANT SCIENCE 2023; 14:1204791. [PMID: 38053768 PMCID: PMC10694231 DOI: 10.3389/fpls.2023.1204791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/30/2023] [Indexed: 12/07/2023]
Abstract
Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benoit Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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7
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Grishina A, Sherstneva O, Zhavoronkova A, Ageyeva M, Zdobnova T, Lysov M, Brilkina A, Vodeneev V. Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants. PLANTS (BASEL, SWITZERLAND) 2023; 12:3831. [PMID: 38005728 PMCID: PMC10674761 DOI: 10.3390/plants12223831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.
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Affiliation(s)
- Alyona Grishina
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Oksana Sherstneva
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Anna Zhavoronkova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Maria Ageyeva
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Tatiana Zdobnova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Maxim Lysov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Anna Brilkina
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Vladimir Vodeneev
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
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8
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Singh BK, Delgado-Baquerizo M, Egidi E, Guirado E, Leach JE, Liu H, Trivedi P. Climate change impacts on plant pathogens, food security and paths forward. Nat Rev Microbiol 2023; 21:640-656. [PMID: 37131070 PMCID: PMC10153038 DOI: 10.1038/s41579-023-00900-7] [Citation(s) in RCA: 97] [Impact Index Per Article: 97.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2023] [Indexed: 05/04/2023]
Abstract
Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.
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Affiliation(s)
- Brajesh K Singh
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia.
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, Australia.
| | - Manuel Delgado-Baquerizo
- Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain
- Unidad Asociada CSIC-UPO (BioFun), Universidad Pablo de Olavide, Sevilla, Spain
| | - Eleonora Egidi
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Emilio Guirado
- Multidisciplinary Institute for Environment Studies 'Ramon Margalef', University of Alicante, Alicante, Spain
| | - Jan E Leach
- Microbiome Newtork and Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - Hongwei Liu
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Pankaj Trivedi
- Microbiome Newtork and Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
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9
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Maina AW, Oerke EC. Characterization of Rice- Magnaporthe oryzae Interactions by Hyperspectral Imaging. PLANT DISEASE 2023; 107:3139-3147. [PMID: 37871165 DOI: 10.1094/pdis-10-22-2294-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.
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Affiliation(s)
- Angeline W Maina
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany
| | - Erich-Christian Oerke
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany
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10
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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Venbrux M, Crauwels S, Rediers H. Current and emerging trends in techniques for plant pathogen detection. FRONTIERS IN PLANT SCIENCE 2023; 14:1120968. [PMID: 37223788 PMCID: PMC10200959 DOI: 10.3389/fpls.2023.1120968] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/21/2023] [Indexed: 05/25/2023]
Abstract
Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.
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Affiliation(s)
- Marc Venbrux
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
| | - Sam Crauwels
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
- Leuven Plant Institute (LPI), KU Leuven, Leuven, Belgium
| | - Hans Rediers
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
- Leuven Plant Institute (LPI), KU Leuven, Leuven, Belgium
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Kushalappa AC, Hegde NG, Yogendra KN. Metabolic pathway genes for editing to enhance multiple disease resistance in plants. JOURNAL OF PLANT RESEARCH 2022; 135:705-722. [PMID: 36036859 DOI: 10.1007/s10265-022-01409-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Diseases are one of the major constraints in commercial crop production. Genetic diversity in varieties is the best option to manage diseases. Molecular marker-assisted breeding has produced hundreds of varieties with good yields, but the resistance level is not satisfactory. With the advent of whole genome sequencing, genome editing is emerging as an excellent option to improve the inadequate traits in these varieties. Plants produce thousands of antimicrobial secondary metabolites, which as polymers and conjugates are deposited to reinforce the secondary cell walls to contain the pathogen to an initial infection area. The resistance metabolites or the structures produced from them by plants are either constitutive (CR) or induced (IR), following pathogen invasion. The production of each resistance metabolite is controlled by a network of biosynthetic R genes, which are regulated by a hierarchy of R genes. A commercial variety also has most of these R genes, as in resistant, but a few may be mutated (SNPs/InDels). A few mutated genes, in one or more metabolic pathways, depending on the host-pathogen interaction, can be edited, and stacked to increase resistance metabolites or structures produced by them, to achieve required levels of multiple pathogen resistance under field conditions.
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Affiliation(s)
- Ajjamada C Kushalappa
- Plant Science Department, McGill University, Ste.-Anne-de-Bellevue, QC, H9X 3V9, Canada.
| | - Niranjan G Hegde
- Plant Science Department, McGill University, Ste.-Anne-de-Bellevue, QC, H9X 3V9, Canada
| | - Kalenahalli N Yogendra
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, Telangana, India
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