Hydathode immunity protects the Arabidopsis leaf vasculature against colonization by bacterial pathogens

Paauw, Misha; Hulten, Marieke van; Chatterjee, Sayantani; Berg, Jeroen A.; Taks, Nanne W.; Giesbers, Marcel; Richard, Manon; Burg, Harrold A. van den

doi:10.1016/j.cub.2023.01.013

Cited by 13 publications

(32 citation statements)

References 94 publications

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“…This is substantiated by the slope values of the linear regression lines tted through the data points, which are below one (slope = 0.27 and 0.35 at 10 dpi and 14 dpi, respectively). This quanti es previously reported observations (13,20) and indicates that bacterial colonization precedes the development of the typical chlorotic V-shaped lesions associated with black rot disease.…”

Section: Bacterial Spread Precedes the Development Of Disease Symptomssupporting

confidence: 89%

“…The Xcc strain used in this study is a derivative of Xcc strain 8004 (34), carrying a xopAC gene deletion (35) and a targeted genomic insertion of a Tn7:luxCDABE:mTq2 reporter cassette (20,25), and is listed in table S2. Xcc was cultured at 28°C on KADO agar plates (10 g•L − 1 sucrose, 8 g•L − 1 casamino acids, 4…”

Section: Bacterial Strains and Culture Conditionsmentioning

confidence: 99%

“…Xcc disease assays were performed as described (19,20). Brie y, four-week-old plants were used for the disease assays.…”

Section: Xcc Disease Assaysmentioning

confidence: 99%

“…This has revealed highly asymmetric colonization patterns for certain pathogenic bacteria such as the vascular pathogen Xcc (18, 20). Xcc enters plant leaves via hydathodes, which are organs at the leaf margin, and then continues its colonization via the leaf vasculature connected to these hydathodes (18,20). Only at a late stage, the leaf mesophyll becomes colonized.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we have established a procedure to detect bacterial luminescence in planta for 126 individual Arabidopsis leaves at once using 40×30 cm light-sensitive lms (19). We successfully applied this system to detect differences in disease susceptibility between Arabidopsis accessions and mutants (20). While this method is more suitable to quantify leaf colonization by the vasculature-infecting Xcc than methods that use counting of colony forming units per leaf area, this method still uses an ordinal qualitive scale based on a luminescence index score that typi es the bacterial spread of the luminescent Xcc in the Arabidopsis leaves in time (19).…”

Section: Introductionmentioning

confidence: 99%

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ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

Paauw,

Hardeman,

Pfeilmeier

et al. 2024

Preprint

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Background Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in an quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, it provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from diseased (yellow) leaf area. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar way as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

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Section: Bacterial Spread Precedes the Development Of Disease Symptomssupporting

confidence: 89%