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Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease.
Tuesday, 2016/11/15 | 07:30:30

Mutka AM, Fentress SJ, Sher JW, Berry JC, Pretz C, Nusinow DA, Bart R.

Plant Physiol. 2016 Oct;172(2):650-660.

Abstract

Plant disease symptoms exhibit complex spatial and temporal patterns that are challenging to quantify. Image-based phenotyping approaches enable multidimensional characterization of host-microbe interactions and are well suited to capture spatial and temporal data that are key to understanding disease progression. We applied image-based methods to investigate cassava bacterial blight, which is caused by the pathogen Xanthomonas axonopodis pv. manihotis (Xam). We generated Xam strains in which individual predicted type III effector (T3E) genes were mutated and applied multiple imaging approaches to investigate the role of these proteins in bacterial virulence. Specifically, we quantified bacterial populations, water-soaking disease symptoms, and pathogen spread from the site of inoculation over time for strains with mutations in avrBs2, xopX, and xopK as compared to wild-type Xam ∆avrBs2 and ∆xopX both showed reduced growth in planta and delayed spread through the vasculature system of cassava. ∆avrBs2 exhibited reduced water-soaking symptoms at the site of inoculation. In contrast, ∆xopK exhibited enhanced induction of disease symptoms at the site of inoculation but reduced spread through the vasculature. Our results highlight the importance of adopting a multipronged approach to plant disease phenotyping to more fully understand the roles of T3Es in virulence. Finally, we demonstrate that the approaches used in this study can be extended to many host-microbe systems and increase the dimensions of phenotype that can be explored.

 

See: https://www.ncbi.nlm.nih.gov/pubmed/27443602

 

Figure 2: Quantification of disease symptoms caused by Xam on cassava leaves using imaging. A, Illustration of the imaging setup. Leaves were syringe infiltrated with bacterial solutions (OD600 = 0.001) and taped to a black surface for imaging of the abaxial side of the leaf. Raspberry Pi microcomputer with attached camera collected images once per hour. Image analysis steps in ImageJ are shown and described in “Materials and Methods.” The number of black pixels was quantified to determine the area of disease. B–D, Quantification of water-soaking symptoms caused by the Xam wild-type strain and three mutants over time. Dots represent individual measurements determined from image analysis, and local regression fitted curves are plotted for each bacterial strain. Shaded areas represent the 95% confidence interval for each curve. The experiment was repeated three additional times with similar results (see Supplemental Fig. S3). E, Results of generalized linear mixed model analysis, combining data from all replicate experiments. Combined estimated slopes and se are presented, as well as P values for each pairwise statistical contrast.

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