Improving common bacterial blight phenotyping by using rub-inoculation and machine learning : cheaper, better, faster, stronger Article - 2022

Justine Foucher, Mylène Ruh, Martial Briand, Anne Préveaux, Florian Barbazange, Tristan Boureau, Marie-Agnès Jacques, Nicolas Chen

Justine Foucher, Mylène Ruh, Martial Briand, Anne Préveaux, Florian Barbazange, Tristan Boureau, Marie-Agnès Jacques, Nicolas Chen, « Improving common bacterial blight phenotyping by using rub-inoculation and machine learning : cheaper, better, faster, stronger  », Phytopathology, 2022, pp. 691-699. ISSN 0031-949X

Abstract

Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant-pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and Xanthomonas citri pv. fuscans (Xpp-Xcf) is a major threat to common bean. The pathogenicity of these bacteria is variable among strains, and depends mainly on a type III secretion system and associated type III effectors such as transcription activator-like effectors (TALEs). Because the impact of a single gene is often small and difficult to detect, a discriminating methodology is required to distinguish the slight phenotype changes induced during the progression of the disease. Here, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains. Interestingly, rub-inoculation of the first leaves combined with symptom assessment by machine learning-based imaging allowed significant distinction between wild-type and mutant strains. By contrast, dip-inoculation of first trifoliate leaves combined with chlorophyll fluorescence imaging did not differentiate the strains. Furthermore, the new method developed here led to the miniaturization of pathogenicity tests and significant time savings.

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