Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

Abstract

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

Plant phenotyping relevance

画像ベースの病斑追跡と時空間モデルを組み合わせ、病斑拡散パラメータを推定する手法が研究の中心であるため。

Code and data availability

The article cites two public Recherche Data Gouv deposits containing this paper's own phenotyping assets: the image sequences of growing lesions on pea stipules used for monitoring, and the segmentation outputs used for image-based phenotyping. Both are explicitly referenced with DOIs in the reference list.