Temporal and Spatial Resolution of Activated Plant Defense Responses in Leaves of Nicotiana benthamiana Infected with Dickeya dadantii

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.

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.

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.

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.

Colonisation of Oncidium orchid roots by the endophyte Piriformospora indica restricts Erwinia chrysanthemi infection, stimulates accumulation of NBS-LRR resistance gene transcripts and represses their targeting micro-RNAs in leaves

BACKGROUND

Erwinia chrysanthemi (Ec) is a destructive pathogen which causes soft-rot diseases in diverse plant species including orchids. We investigated whether colonization of Oncidium roots by the endophytic fungus Piriformospora indica (Pi) restricts Ec-induced disease development in leaves, and whether this might be related to the regulation of nucleotide binding site-leucine rich repeat (NBS-LRR) Resistance (R) genes.

RESULTS

Root colonization of Oncidium stackings by Pi restricts progression of Ec-induced disease development in the leaves. Since Pi does not inhibit Ec growth on agar plates, we tested whether NBS-LRR R gene transcripts and the levels of their potential target miRNAs in Oncidium leaves might be regulated by Pi. Using bioinformatic tools, we first identified NBS-LRR R gene sequences from Oncidium, which are predicted to be targets of miRNAs. Among them, the expression of two R genes was repressed and the accumulation of several regulatory miRNA stimulated by Ec in the leaves of Oncidium plants. This correlated with the progression of disease development, jasmonic and salicylic acid accumulation, ethylene synthesis and H₂O₂ production after Ec infection of Oncidium leaves. Interestingly, root colonization by Pi restricted disease development in the leaves, and this was accompanied by higher expression levels of several defense-related R genes and lower expression level of their target miRNA.

CONCLUSION

Based on these data we propose that Pi controls the levels of NBS-LRR R mRNAs and their target miRNAs in leaves. This regulatory circuit correlates with the protection of Oncidium plants against Ec infection, and molecular and biochemical investigations will demonstrate in the future whether, and if so, to what extent these two observations are related to each other.

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.

The Tryptophan decarboxylase 1 Gene From Aegilops variabilis No.1 Regulate the Resistance Against Cereal Cyst Nematode by Altering the Downstream Secondary Metabolite Contents Rather Than Auxin Synthesis

Cereal cyst nematode (CCN, Heterodera avenae) is a most important pathogen of wheat and causes tremendous yield loss annually over the world. Since the lack of resistance materials among wheat cultivars, identification and characterization of the resistance-related genes from the relatives of wheat is a necessary and efficient way. As a close relative of wheat with high resistance against CCN, Aegilops variabilis No.1 is believed to be a valuable source for wheat breeding against this devastating disease. However so far, very few resistance-associated genes have been characterized from this species. In this study, we present that the tryptophan decarboxylase genes from Ae. variabilis No.1 (AeVTDC1 and AeVTDC2) were both induced by CCN juveniles at the early stage of resistance response (30 h post-inoculation), with AeVTDC1 more sensitive to CCN infection than AeVTDC2. Silencing of AeVTDC1 led to compromised immunity to CCN with more CCN intrusion into roots; while overexpression AeVTDC1 in Nicotiana tabacum dramatically enhanced the resistance of plants by reducing the knots formed on roots. Metabolism analysis showed that the contents of secondary metabolites with activity of resistance to varied pathogens correlated with the expression level of AeVTDC1 in both Ae. variabilis No.1 and the transgenic tobacco plants. In addition, the content of IAA was not affected by either silencing or overexpressing of AeVTDC1. Hence, our research provided AeVTDC1 a valuable target that mediates resistance to CCN and root knot nematode (RKN, Meloidogyne naasi) without influencing the auxin biosynthesis.

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.

Virulence determines beneficial trade-offs in the response of virus-infected plants to drought via induction of salicylic acid

It has been hypothesized that plants can get beneficial trade-offs from viral infections when grown under drought conditions. However, experimental support for a positive correlation between virus-induced drought tolerance and increased host fitness is scarce. We investigated whether increased virulence exhibited by the synergistic interaction involving Potato virus X (PVX) and Plum pox virus (PPV) improves tolerance to drought and host fitness in Nicotiana benthamiana and Arabidopsis thaliana. Infection by the pair PPV/PVX and by PPV expressing the virulence protein P25 of PVX conferred an enhanced drought-tolerant phenotype compared with single infections with either PPV or PVX. Decreased transpiration rates in virus-infected plants were correlated with drought tolerance in N. benthamiana but not in Arabidopsis. Metabolite and hormonal profiles of Arabidopsis plants infected with the different viruses showed a range of changes that positively correlated with a greater impact on drought tolerance. Virus infection enhanced drought tolerance in both species by increasing salicylic acid accumulation in an abscisic acid-independent manner. Viable offspring derived from Arabidopsis plants infected with PPV increased relative to non-infected plants, when exposed to drought. By contrast, the detrimental effect caused by the more virulent viruses overcame potential benefits associated with increased drought tolerance on host fitness.

Picturing pathogen infection in plants

Several imaging techniques have provided valuable tools to evaluate the impact of biotic stress on host plants. The use of these techniques enables the study of plant-pathogen interactions by analysing the spatial and temporal heterogeneity of foliar metabolism during pathogenesis. In this work we review the use of imaging techniques based on chlorophyll fluorescence, multicolour fluorescence and thermography for the study of virus, bacteria and fungi-infected plants. These studies have revealed the impact of pathogen challenge on photosynthetic performance, secondary metabolism, as well as leaf transpiration as a promising tool for field and greenhouse management of diseases. Images of standard chlorophyll fluorescence (Chl-F) parameters obtained during Chl-F induction kinetics related to photochemical processes and those involved in energy dissipation, could be good stress indicators to monitor pathogenesis. Changes on UV-induced blue (F440) and green fluorescence (F520) measured by multicolour fluorescence imaging in pathogen-challenged plants seem to be related with the up-regulation of the plant secondary metabolism and with an increase in phenolic compounds involved in plant defence, such as scopoletin, chlorogenic or ferulic acids. Thermal imaging visualizes the leaf transpiration map during pathogenesis and emphasizes the key role of stomata on innate plant immunity. Using several imaging techniques in parallel could allow obtaining disease signatures for a specific pathogen. These techniques have also turned out to be very useful for presymptomatic pathogen detection, and powerful non-destructive tools for precision agriculture. Their applicability at lab-scale, in the field by remote sensing, and in high-throughput plant phenotyping, makes them particularly useful. Thermal sensors are widely used in crop fields to detect early changes in leaf transpiration induced by both air-borne and soil-borne pathogens. The limitations of measuring photosynthesis by Chl-F at the canopy level are being solved, while the use of multispectral fluorescence imaging is very challenging due to the type of light excitation that is used.

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.