Picturing pathogen infection in plants

Barón, Matilde; Pineda, Mónica; Pérez-Bueno, María Luisa

doi:10.1515/znc-2016-0134

Cited by 49 publications

(48 citation statements)

References 101 publications

(159 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Impairment in photosynthesis has been documented in several reports on virus-infected plants (Barón et al, 2016; Bhattacharyya et al, 2015; Chaerle et al, 2004; Lohaus et al, 2000; Pérez-Bueno et al, 2006; Pineda et al, 2008, 2011; Spoustová et al, 2013; Zhao et al, 2016). PSII is a component of plants’ photosynthetic machinery that is susceptible to environmental stresses (Nath et al, 2013).…”

Section: Discussionmentioning

confidence: 98%

“…For Potato virus Y infection, NPQ was reported to be the most useful fluorescence parameter for assessing the viral effect, but no changes showed up in NPQ before the appearance of viral symptoms (Spoustová et al, 2013). However, in Pepper mild mottle virus (PMMoV)–infected N. benthamiana plants, the changes in NPQ were detected in both symptomatic and asymptomatic leaves (Barón et al, 2016), and Tobacco mosaic virus infection triggered a presymptomatic rise in Chl fluorescence intensity in tobacco (Chaerle et al, 2004). In our research, we found that ETR, NPQ , F v / F m , and qP were affected in both symptomatic and asymptomatic leaves, during infection by both TYLCCNV A and TYLCCNV A + β, with the coinfection of DNAβ bringing more severe damages to plants’ photosynthesis efficiency.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

“…The implementation of precision agriculture relies on the development of technologies that allow the identification and mapping of constraints in the crop fields, such as imaging techniques (Mulla, 2013). They can be used to evaluate the effects of stress on plant metabolism (Cerovic et al, 1999; Barón et al, 2012, 2016). Consequently, imaging techniques are powerful non-destructive tools that have become essential: they provide crucial information for the decision-making and for the right timing of the procedures to be applied (Usha and Singh, 2013; Li et al, 2014; Mahlein, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Leaf temperature inversely correlates with transpiration and stomatal conductance (Jones, 1999), which is tightly regulated by plants as a general mechanism of defense upon abiotic stress, but also against pathogens (Melotto et al, 2008). This technique has been used in the study of infections by virus, bacteria, and fungi, as reviewed by Barón et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Using several imaging techniques in parallel could improve the performance of detection and allowed pre-symptomatic identification of a stress. It could also be used to improve the discrimination abilities to identify disease signatures for a specific pathogen (Mahlein et al, 2012;Baron et al, 2016). The method presented in this study could be generically applied on a combination of multi-modal images.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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 Méline et al. Combination of Multiple CF Imagesan 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.

show abstract

Picturing pathogen infection in plants

Cited by 49 publications

References 101 publications

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 Impairs Photosynthesis in the Infected Nicotiana benthamiana with βC1 as an Aggravating Factor

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

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

Contact Info

Product

Resources

About