Picturing pathogen infection in plants

Photosynthesis Responses to the Infection with Plant Pathogens

Photosynthesis, the remarkable process by which green plants synthesize nutrients using light energy, plays a crucial role in sustaining life on Earth. However, the effects of pathogens on photosynthesis are not widely understood. In general, a reduction of photosynthesis occurs upon the infection with pathogens. Two main scenarios are responsible for the reduction in photosynthetic capacity. In the first scenario, the pathogen attacks green aerial tissues, such as when caused by fungal and bacterial leaf spots and blights, which affect photosynthesis by destroying green leaf tissue or causing defoliation. This leads to a decrease in the photosynthetic area, ultimately reducing photosynthesis. Interestingly, even when the overall chlorophyll content of leaves is significantly reduced due to pathogen invasion, the remaining chlorophyll-containing leaf area may maintain or even enhance its photosynthetic efficiency. This compensatory mechanism helps mitigate the loss of photosynthetic area. However, the overall yield of the plant is still affected. The second scenario is a reduction in chlorophyll content due to chlorosis, which is characterized by yellowing of leaves. It is a common symptom of plant diseases. It refers to a reduction in the amount of chlorophyll per chloroplast rather than a decrease in chloroplast number. Diseases caused by viruses and phytoplasmas often exhibit chlorosis. While pathogens disrupt photosynthesis, plants exhibit significant adaptations to cope with these challenges. Understanding these interactions is essential for sustainable agriculture and ecosystem health. Thus, in this review, we discuss the effect of several pathogens on the photosynthesis processes and efficiency in detail. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

New Approaches to Plant Pathogen Detection and Disease Diagnosis

Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants

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.

Miniaturized Vis-NIR handheld spectrometer for non-invasive pigment quantification in agritech applications

Advanced precision agriculture requires the objective measurement of the structural and functional properties of plants. Biochemical profiles in leaves can differ depending on plant growing conditions. By quantitatively detecting these changes, farm production processes can be optimized to achieve high-yield, high-quality, and nutrient dense agricultural products. To enable the rapid and non-destructive detection on site, this study demonstrates the development of a new custom-designed portable handheld Vis-NIR spectrometer that collects leaf reflectance spectra, wirelessly transfers the spectral data through Bluetooth, and provides both raw spectral data and processed information. The spectrometer has two preprogramed methods: anthocyanin and chlorophyll quantification. Anthocyanin content of red and green lettuce estimated with the new spectrometer showed an excellent correlation coefficient of 0.84 with those determined by a destructive gold standard biochemical method. The differences in chlorophyll content were measured using leaf senescence as a case study. Chlorophyll Index calculated with the handheld spectrometer gradually decreased with leaf age as chlorophyll degrades during the process of senescence. The estimated chlorophyll values were highly correlated with those obtained from a commercial fluorescence-based chlorophyll meter with a correlation coefficient of 0.77. The developed portable handheld Vis-NIR spectrometer could be a simple, cost-effective, and easy to operate tool that can be used to non-invasively monitor plant pigment and nutrient content efficiently.

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

OsVTC1-1 Gene Silencing Promotes a Defense Response in Rice and Enhances Resistance to Magnaporthe oryzae

Rice blast disease is a serious disease in rice caused by Magnaporthe oryzae (M. oryzae). Ascorbic acid (AsA), or vitamin C, is a strong antioxidant that prevents oxidative damage to cellular components and plays an essential role in plant defense response. GDP-D-mannose pyrophosphorylase (GMP or VTC1) is an enzyme that generates GDP-D-mannose for AsA, cell wall, and glycoprotein synthesis. The OsVTC1 gene has three homologs in the rice genome: OsVTC1-1, OsVTC1-3, and OsVTC1-8. Using OsVTC1-1 RNAi lines, this study investigated the role of the OsVTC1-1 gene during rice blast fungus inoculation. The OsVTC1-1 RNAi inoculated with rice blast fungus induced changes to cell wall monosaccharides, photosynthetic efficiency, reactive oxygen species (ROS) accumulation, and malondialdehyde (MDA) content. Additionally, the OsVTC1-1 RNAi lines were shown to be more resistant to rice blast fungus than the wild type. Genes and proteins related to defense response, plant hormone synthesis, and signaling pathways, especially salicylic acid and jasmonic acid, were up-regulated in the OsVTC1-1 RNAi lines after rice blast inoculation. These results suggest that the OsVTC1-1 gene regulates rice blast resistance through several defense mechanisms, including hormone synthesis and signaling pathways.

Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris

A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI₁-DBI₃), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.

Integration of Metabolomics and Transcriptomics for Investigating the Tolerance of Foxtail Millet (Setaria italica) to Atrazine Stress

Foxtail millet (Setaria italica) is a monotypic species widely planted in China. However, residual atrazine, a commonly used maize herbicide, in soil, is a major abiotic stress to millet. Here, we investigated atrazine tolerance in millet based on the field experiments, then obtained an atrazine-resistant variety (Gongai2, GA2) and an atrazine-sensitive variety (Longgu31, LG31). To examine the effects of atrazine on genes and metabolites in millet plants, we compared the transcriptomic and metabolomic profiles between GA2 and LG31 seedling leaves. The results showed that 2,208 differentially expressed genes (DEGs; 501 upregulated, 1,707 downregulated) and 192 differentially expressed metabolites (DEMs; 82 upregulated, 110 downregulate) were identified in atrazine-treated GA2, while in atrazine-treated LG31, 1,773 DEGs (761 upregulated, 1,012 downregulated) and 215 DEMs (95 upregulated, 120 downregulated) were identified. The bioinformatics analysis of DEGs and DEMs showed that many biosynthetic metabolism pathways were significantly enriched in GA2 and LG31, such as glutathione metabolism (oxiglutatione, γ-glutamylcysteine; GSTU6, GSTU1, GSTF1), amino acid biosynthesis (L-cysteine, N-acetyl-L-glutamic acid; ArgB, GS, hisC, POX1), and phenylpropanoid biosynthesis [trans-5-o-(4-coumaroyl)shikimate; HST, C3'H]. Meanwhile, the co-expression analysis indicated that GA2 plants had enhanced atrazine tolerance owing to improved glutathione metabolism and proline biosynthesis, and the enrichment of scopoletin may help LG31 plants resist atrazine stress. Herein, we screened an atrazine-resistant millet variety and generated valuable information that may deepen our understanding of the complex molecular mechanism underlying the response to atrazine stress in millet.

Chlorophyll Fluorescence Imaging as a Tool for Evaluating Disease Resistance of Common Bean Lines in the Western Amazon Region of Colombia

The evaluation of disease resistance is considered an important aspect of phenotyping for crop improvement. Identification of advanced lines of the common bean with disease resistance contributes to improved grain yields. This study aimed to determine the response of the photosynthetic apparatus to natural pathogen infection by using chlorophyll (Chl_a) fluorescence parameters and their relationship to the agronomic performance of 59 common bean lines and comparing the photosynthetic responses of naturally infected vs. healthy leaves. The study was conducted over two seasons under acid soil and high temperature conditions in the western Amazon region of Colombia. A disease susceptibility index (DSI) was developed and validated using chlorophyll a (Chl_a) fluorescence as a tool to identify Mesoamerican and Andean lines of common bean (Phaseolus vulgaris L.) that are resistant to pathogens. A negative effect on the functional status of the photosynthetic apparatus was found with the presence of pathogen infection, a situation that allowed the identification of four typologies based on the DSI values ((i) moderately resistant; (ii) moderately susceptible; (iii) susceptible; and (iv) highly susceptible). Moderately resistant lines, five of them from the Mesoamerican gene pool (ALB 350, SMC 200, BFS 10, SER 16, SMN 27) and one from the Andean gene pool (DAB 295), allocated a higher proportion of energy to photochemical processes, which increased the rate of electron transfer resulting in a lower sensitivity to disease stress. This photosynthetic response was associated with lower values of DSI, which translated into an increase in the accumulation of dry matter accumulation in different plant organs (leaves, stem, pods and roots). Thus, DSI values based on chlorophyll fluorescence response to pathogen infection could serve as a phenotyping tool for evaluating advanced common bean lines. Six common bean lines (ALB 350, BFS 10, DAB 295, SER 16, SMC 200 and SMN 27) were identified as less sensitive to disease stress under field conditions in the western Amazon region of Colombia, and these could serve as useful parents for improving the common bean for multiple stress resistance.

Retrieving the in vivo Scopoletin Fluorescence Excitation Band Allows the Non-invasive Investigation of the Plant-Pathogen Early Events in Tobacco Leaves

In this study, we developed and applied a new spectroscopic fluorescence method for the in vivo detection of the early events in the interaction between tobacco (Nicotiana tabacum L.) plants and pathogenic bacteria. The leaf disks were infiltrated with a bacterial suspension in sterile physiological solution (SPS), or with SPS alone as control. The virulent Pseudomonas syringae pv. tabaci strain ATCC 11528, its non-pathogenic ΔhrpA mutant, and the avirulent P. syringae pv. tomato strain DC3000 were used. At different post-infiltration time-points, the in vivo fluorescence spectra on leaf disks were acquired by a fiber bundle-spectrofluorimeter. The excitation spectra of the leaf blue emission at 460 nm, which is mainly due to the accumulation of coumarins following a bacterial infiltration, were processed by using a two-bands Gaussian fitting that enabled us to isolate the scopoletin (SCT) contribution. The pH-dependent fluorescence of SCT and scopolin (SCL), as determined by in vitro data and their intracellular localization, as determined by confocal microscopy, suggested the use of the longer wavelength excitation band at 385 nm of 460 nm emission (F385_460) to follow the metabolic evolution of SCT during the plant-bacteria interaction. It was found to be directly correlated (R 2 = 0.84) to the leaf SCT content, but not to that of SCL, determined by HPLC analysis. The technique applied to the time-course monitoring of the bacteria-plant interaction clearly showed that the amount and the timing of SCT accumulation, estimated by F385_460, was correlated with the resistance to the pathogen. As expected, this host defense response was delayed after P. syringae pv. tabaci ATCC 11528 infiltration, in comparison to P. syringae pv. tomato DC3000. Furthermore, no significant increase of F385_460 (SCT) was observed when using the non-pathogenic ΔhrpA mutant of P. syringae pv. tabaci ATCC 11528, which lacks a functional Type Three Secretion System (TTSS). Our study showed the reliability of the developed fluorimetric method for a rapid and non-invasive monitoring of bacteria-induced first events related to the metabolite-based defense response in tobacco leaves. This technique could allow a fast selection of pathogen-resistant cultivars, as well as the on-site early diagnosis of tobacco plant diseases by using suitable fluorescence sensors.

Pre-Symptomatic Detection of Viral Infection in Tobacco Leaves Using PAM Fluorometry

Chlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana. Infection-induced changes in chlorophyll fluorescence parameters (quantum yield of photosystem II photochemistry (ΦPSII) and non-photochemical fluorescence quenching (NPQ)) in the non-inoculated leaf were recorded and compared with the spatial distribution of the virus detected by the fluorescence of GFP associated with the virus. We determined infection-related changes at different points of the light-induced chlorophyll fluorescence kinetics and at different days after inoculation. A slight change in the light-adapted steady-state values of ΦPSII and NPQ was observed in the infected area of the non-inoculated leaf. In contrast to the steady-state parameters, the dynamics of ΦPSII and NPQ caused by the dark-light transition in healthy and infected areas differed significantly starting from the second day after the detection of the virus in a non-inoculated leaf. The coefficients of correlation between chlorophyll fluorescence parameters and virus localization were 0.67 for ΦPSII and 0.76 for NPQ. In general, the results demonstrate the possibility of reliable pre-symptomatic detection of the spread of a viral infection using chlorophyll fluorescence imaging.

Divergent abiotic spectral pathways unravel pathogen stress signals across species

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.

Thermal Imaging for Plant Stress Detection and Phenotyping

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

Complexity of Brassica oleracea-Alternaria brassicicola Susceptible Interaction Reveals Downregulation of Photosynthesis at Ultrastructural, Transcriptional, and Physiological Levels

Black spot disease, caused by Alternaria brassicicola in Brassica species, is one of the most devastating diseases all over the world, especially since there is no known fully resistant Brassica cultivar. In this study, the visualization of black spot disease development on Brassica oleracea var. capitata f. alba (white cabbage) leaves and subsequent ultrastructural, molecular and physiological investigations were conducted. Inter- and intracellular hyphae growth within leaf tissues led to the loss of host cell integrity and various levels of organelle disintegration. Severe symptoms of chloroplast damage included the degeneration of chloroplast envelope and grana, and the loss of electron denseness by stroma at the advanced stage of infection. Transcriptional profiling of infected leaves revealed that photosynthesis was the most negatively regulated biological process. However, in infected leaves, chlorophyll and carotenoid content did not decrease until 48 hpi, and several chlorophyll a fluorescence parameters, such as photosystem II quantum yield (Fv/Fm), non-photochemical quenching (NPQ), or plant vitality parameter (Rdf) decreased significantly at 24 and 48 hpi compared to control leaves. Our results indicate that the initial stages of interaction between B. oleracea and A. brassicicola are not uniform within an inoculation site and show a complexity of host responses and fungal attempts to overcome host cell defense mechanisms. The downregulation of photosynthesis at the early stage of this susceptible interaction suggests that it may be a part of a host defense strategy, or, alternatively, that chloroplasts are targets for the unknown virulence factor(s) of A. brassicicola. However, the observed decrease of photosynthetic efficiency at the later stages of infection is a result of the fungus-induced necrotic lesion expansion.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.

Effects of Stripe Rust Infection on the Levels of Redox Balance and Photosynthetic Capacities in Wheat

Wheat stripe rust (Puccinia striiformis f. sp. tritici, Pst) is the most destructive wheat disease and a major problem for the productivity of wheat in the world. To obtain a better understanding about different effects of redox homeostasis and photosystem (PS) to Pst infection in wheat, we investigated the differences in photosynthesis and the antioxidant defense system in wheat cultivar Chuanmai42 (CM42) in response to two Chinese Pst races known as CYR32 and V26. The results showed that V26-infected wheat accumulated a higher reactive oxygen species (ROS), cell death, and energy dissipation than CYR32-infected wheat when compared with the control. Furthermore, we found that the activities of three antioxidant enzymes (APX, GR, and GPX) and four resistance-related enzymes in CYR32-infected wheat were significantly higher than that in V26-infected wheat. In addition, quantitative RT-PCR indicated that the expression levels of two genes associated with resistant stripe rust in CYR32-infected wheat were clearly higher than that in V26-infected wheat. Compared with CYR32-infected wheat, lower photochemical efficiencies were observed in V26-infected wheat at the adult stage. Meanwhile, only a marked decline in D1 protein was observed in V26-infected wheat. We therefore deduced that wheat with stripe rust resistance could maintain high resistance and photosynthetic capacity by regulating the antioxidant system, disease-resistant related enzymes and genes, and the levels of PSII reaction center proteins.

The fungal endophyte Fusarium solani provokes differential effects on the fitness of two Lotus species

The interactions established between plants and endophytic fungi span a continuum from beneficial to pathogenic associations. The aim of this work was to isolate potentially beneficial fungal endophytes in the legume Lotus tenuis and explore the mechanisms underlying their effects. One of the nine fungal strains isolated was identified as Fusarium solani and shows the highest phosphate-solubilisation activity, and also grows endophytically in roots of L. japonicus and L. tenuis. Interestingly, fungal invasion enhances plant growth in L. japonicus but provokes a contrasting effect in L. tenuis. These differences were also evidenced when the rate of photosynthesis as well as sugars and K contents were assessed. Our results indicate that the differential responses observed are due to distinct mechanisms deployed during the establishment of the interactions that involve the regulation of photosynthesis, potassium homeostasis, and carbohydrate metabolism. These responses are employed by these plant species to maintain fitness during the endophytic interaction.

Noninvasive Phenotyping of Plant-Pathogen Interaction: Consecutive In Situ Imaging of Fluorescing Pseudomonas syringae, Plant Phenolic Fluorescence, and Chlorophyll Fluorescence in Arabidopsis Leaves

Plant-pathogen interactions have been widely studied, but mostly from the site of the plant secondary defense. Less is known about the effects of pathogen infection on plant primary metabolism. The possibility to transform a fluorescing protein into prokaryotes is a promising phenotyping tool to follow a bacterial infection in plants in a noninvasive manner. In the present study, virulent and avirulent Pseudomonas syringae strains were transformed with green fluorescent protein (GFP) to follow the spread of bacteria in vivo by imaging Pulse-Amplitude-Modulation (PAM) fluorescence and conventional binocular microscopy. The combination of various wavelengths and filters allowed simultaneous detection of GFP-transformed bacteria, PAM chlorophyll fluorescence, and phenolic fluorescence from pathogen-infected plant leaves. The results show that fluorescence imaging allows spatiotemporal monitoring of pathogen spread as well as phenolic and chlorophyll fluorescence in situ, thus providing a novel means to study complex plant-pathogen interactions and relate the responses of primary and secondary metabolism to pathogen spread and multiplication. The study establishes a deeper understanding of imaging data and their implementation into disease screening.

Tomato Yellow Leaf Curl China Virus Impairs Photosynthesis in the Infected Nicotiana benthamiana with βC1 as an Aggravating Factor

Tomato yellow leaf curl China virus is a species of the widespread geminiviruses. The infection of Nicotiana benthamiana by Tomato yellow leaf curl China virus (TYLCCNV) causes a reduction in photosynthetic activity, which is part of the viral symptoms. βC1 is a viral factor encoded by the betasatellite DNA (DNAβ) accompanying TYLCCNV. It is a major viral pathogenicity factor of TYLCCNV. To elucidate the effect of βC1 on plants' photosynthesis, we measured the relative chlorophyll (Chl) content and Chl fluorescence in TYLCCNV-infected and βC1 transgenic N. benthamiana plants. The results showed that Chl content is reduced in TYLCCNV A-infected, TYLCCNV A plus DNAβ (TYLCCNV A + β)-infected and βC1 transgenic plants. Further, changes in Chl fluorescence parameters, such as electron transport rate, F v /F m , NPQ, and qP, revealed that photosynthetic efficiency is compromised in the aforementioned N. benthamiana plants. The presense of βC1 aggravated the decrease of Chl content and photosynthetic efficiency during viral infection. Additionally, the real-time quantitative PCR analysis of oxygen evolving complex genes in photosystem II, such as PsbO, PsbP, PsbQ, and PsbR, showed a significant reduction of the relative expression of these genes at the late stage of TYLCCNV A + β infection and at the vegetative stage of βC1 transgenic N. benthamiana plants. In summary, this study revealed the pathogenicity of TYLCCNV in photosynthesis and disclosed the effect of βC1 in exacerbating the damage in photosynthesis efficiency by TYLCCNV infection.

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Noninvasive determination of toxic stress biomarkers by high-throughput screening of photoautotrophic cell suspension cultures with multicolor fluorescence imaging

BACKGROUND

With increasing pollution, herbicide application and interest in plant phenotyping, sensors capturing early responses to toxic stress are demanded for screening susceptible or resistant plant varieties. Standard toxicity tests on plants are laborious, demanding in terms of space and material, and the measurement of growth-inhibition based endpoints takes relatively long time. The aim of this work was to explore the potential of photoautotrophic cell suspension cultures for high-throughput early toxicity screening based on imaging techniques. The investigation of the universal potential of fluorescence imaging methods involved testing of three toxicants with different modes of action (DCMU, glyphosate and chromium).

RESULTS

The increased pace of testing was achieved by using non-destructive imaging methods-multicolor fluorescence (MCF) and chlorophyll fluorescence (ChlF). These methods detected the negative effects of the toxicants earlier than it was reflected in plant growth inhibition (decrease in leaf area and final dry weight). Moreover, more subtle and transient effects not resulting in growth inhibition could be detected by fluorescence. The pace and sensitivity of stress detection was further enhanced by using photoautotrophic cell suspension cultures. These reacted sooner, more pronouncedly and to lower concentrations of the tested toxicants than the plants. Toxicant-specific stress signatures were observed as a combination of MCF and ChlF parameters and timing of the response. Principal component analysis was found to be useful for reduction of the collected multidimensional data sets to a few informative parameters allowing comparison of the toxicant signatures.

CONCLUSIONS

Photoautotrophic cell suspension cultures have proved to be useful for rapid high-throughput screening of toxic stress and display a potential for employment as an alternative to tests on whole plants. The MCF and ChlF methods are capable of distinguishing early stress signatures of at least three different modes of action.

Detection of White Root Rot in Avocado Trees by Remote Sensing

White root rot, caused by the soilborne fungus Rosellinia necatrix, is an important constraint to production for a wide range of woody crop plants such as avocado trees. The current methods of detection of white root rot are based on microbial and molecular techniques, and their application at orchard scale is limited. In this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods. Normalized difference vegetation index (NDVI) and normalized canopy temperature (canopy temperature - air temperature) were tested as predictors of disease by several algorithms. Among them, logistic regression analysis (LRA) trained on NDVI data showed the highest sensitivity and lowest rate of false negatives. This algorithm based on NDVI could be a quick and feasible method to detect trees potentially affected by white root rot in avocado orchards.

RNA-seq analysis and fluorescence imaging of melon powdery mildew disease reveal an orchestrated reprogramming of host physiology

The cucurbit powdery mildew elicited by Podosphaera xanthii is one of the most important limiting factors in cucurbit production. Our knowledge of the genetic and molecular bases underlying the physiological processes governing this disease is very limited. We used RNA-sequencing to identify differentially expressed genes in leaves of Cucumis melo upon inoculation with P. xanthii, using RNA samples obtained at different time points during the early stages of infection and their corresponding uninfected controls. In parallel, melon plants were phenotypically characterized using imaging techniques. We found a high number of differentially expressed genes (DEGs) in infected plants, which allowed for the identification of many plant processes that were dysregulated by the infection. Among those, genes involved in photosynthesis and related processes were found to be upregulated, whereas genes involved in secondary metabolism pathways, such as phenylpropanoid biosynthesis, were downregulated. These changes in gene expression could be functionally validated by chlorophyll fluorescence imaging and blue-green fluorescence imaging analyses, which corroborated the alterations in photosynthetic activity and the suppression of phenolic compound biosynthesis. The powdery mildew disease in melon is a consequence of a complex and multifaceted process that involves the dysregulation of many plant pathways such as primary and secondary metabolism.

Small, Smaller, Nano: New Applications for Potato Virus X in Nanotechnology

Nanotechnology is an expanding interdisciplinary field concerning the development and application of nanostructured materials derived from inorganic compounds or organic polymers and peptides. Among these latter materials, proteinaceous plant virus nanoparticles have emerged as a key platform for the introduction of tailored functionalities by genetic engineering and conjugation chemistry. Tobacco mosaic virus and Cowpea mosaic virus have already been developed for bioimaging, vaccination and electronics applications, but the flexible and filamentous Potato virus X (PVX) has received comparatively little attention. The filamentous structure of PVX particles allows them to carry large payloads, which are advantageous for applications such as biomedical imaging in which multi-functional scaffolds with a high aspect ratio are required. In this context, PVX achieves superior tumor homing and retention properties compared to spherical nanoparticles. Because PVX is a protein-based nanoparticle, its unique functional properties are combined with enhanced biocompatibility, making it much more suitable for biomedical applications than synthetic nanomaterials. Moreover, PVX nanoparticles have very low toxicity in vivo, and superior pharmacokinetic profiles. This review focuses on the production of PVX nanoparticles engineered using chemical and/or biological techniques, and describes current and future opportunities and challenges for the application of PVX nanoparticles in medicine, diagnostics, materials science, and biocatalysis.

Modulation of plant and bacterial polyamine metabolism during the compatible interaction between tomato and Pseudomonas syringae

The polyamines putrescine, spermidine and spermine participate in a variety of cellular processes in all organisms. Many studies have shown that these polycations are important for plant immunity, as well as for the virulence of diverse fungal phytopathogens. However, the polyamines' roles in the pathogenesis of phytopathogenic bacteria have not been thoroughly elucidated to date. To obtain more information on this topic, we assessed the changes in polyamine homeostasis during the infection of tomato plants by Pseudomonas syringae. Our results showed that polyamine biosynthesis and catabolism are activated in both tomato and bacteria during the pathogenic interaction. This activation results in the accumulation of putrescine in whole leaf tissues, as well as in the apoplastic fluids, which is explained by the induction of its synthesis in plant cells and also on the basis of its excretion by bacteria. We showed that the excretion of this polyamine by P. syringae is stimulated under virulence-inducing conditions, suggesting that it plays a role in plant colonization. However, no activation of bacterial virulence traits or induction of plant invasion was observed after the exogenous addition of putrescine. In addition, no connection was found between this polyamine and plant defence responses. Although further research is warranted to unravel the biological functions of these molecules during plant-bacterial interactions, this study contributes to a better understanding of the changes associated with the homeostasis of polyamines during plant pathogenesis.

Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security¹, ecosystem integrity and societies in general^2-5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment⁶. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa's geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage^7,8. X. fastidiosa can infect more than 350 plant species worldwide⁹, and early detection is critical for its eradication⁸. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.

Detection of Bacterial Infection in Melon Plants by Classification Methods Based on Imaging Data

The bacterium Dickeya dadantii is responsible of important economic losses in crop yield worldwide. In melon leaves, D. dadantii produced multiple necrotic spots surrounded by a chlorotic halo, followed by necrosis of the whole infiltrated area and chlorosis in the surrounding tissues. The extent of these symptoms, as well as the day of appearance, was dose-dependent. Several imaging techniques (variable chlorophyll fluorescence, multicolor fluorescence, and thermography) provided spatial and temporal information about alterations in the primary and secondary metabolism, as well as the stomatal activity in the infected leaves. Detection of diseased leaves was carried out by using machine learning on the numerical data provided by these imaging techniques. Mathematical algorithms based on data from infiltrated areas offered 96.5 to 99.1% accuracy when classifying them as mock vs. bacteria-infiltrated. These algorithms also showed a high performance of classification of whole leaves, providing accuracy values of up to 96%. Thus, the detection of disease on whole leaves by a model trained on infiltrated areas appears as a reliable method that could be scaled-up for use in plant breeding programs or precision agriculture.

Use of multicolour fluorescence imaging for diagnosis of bacterial and fungal infection on zucchini by implementing machine learning

Zucchini (Cucurbita pepo L.) is a cucurbitaceous plant ranking high in economic importance among vegetable crops worldwide. Pathogen infections cause alterations in plants primary and secondary metabolism that lead to a significant decrease in crop quality and yield. Such changes can be monitored by remote and proximal sensing, providing spatial and temporal information about the infection process. Remote sensing can also provide specific signatures of disease that could be used in phenotyping and to detect a pest, forecast its evolution and predict crop yield. In this work, metabolic changes triggered by soft rot (caused by Dickeya dadantii) and powdery mildew (caused by Podosphaera fusca) on zucchini leaves have been studied by multicolour fluorescence imaging and by thermography. The fluorescence parameter F520/F680 showed statistically significant differences between infected (with D. dadantii or P. fusca) and mock-control leaves during the whole period of study. Artificial neural networks, logistic regression analyses and support vector machines trained with a set of features characterising the histograms of F520/F680 images could be used as classifiers, discriminating between healthy and infected leaves. These results show the applicability of multicolour fluorescence imaging on plant phenotyping.

Gaining Insight into Exclusive and Common Transcriptomic Features Linked with Biotic Stress Responses in Malus

Identifying key information in transcriptomic data is very important, especially when the "omic" study deals with plant responses to stresses in field conditions where a high number of variables and disturbing factors may affect the analysis. In this meta-analysis we collected 12 transcriptomic works in Malus in order to identify which key genes, proteins, gene categories are involved in general plant pathological conditions and those features linked with exclusive biotic stress responses. Those genes that are only related with molecular responses to pathogen attacks and those linked with other plant physiological processes were identified. A pipeline composed by pathway and gene set enrichment analysis, protein-protein interaction networks and gene visualization tools was employed. A total of 13,230 genes of the 12 studies were analyzed with functional data mining tools: 5,215 were upregulated, 8,015 were downregulated. Gene set enrichment analysis pointed out that photosynthesis was inhibited by Erwinia amylovora and fungal pathogens. Different hormonal crosstalk was linked with responses to different pathogens. Gibberellin-related pathways, ABA-related were mostly repressed by fungal pathogens. Relating to transcription factors, genes encoding MYBs and WRKY2 were downregulated by fungal pathogens and 12 WRKYs were commonly regulated by different biotic stresses The protein-protein interaction analysis discovered the presence of several proteins affected by more than one biotic stress including a WRKY40 and some highly interactive proteins such as heat shock proteins. This study represents a first preliminary curated meta-analysis of apple transcriptomic responses to biotic stresses.

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as "healthy" or "infected." The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.