IGEPP, INRAE, Institut Agro, University of Rennes, Rennes, France.
ICJ, CNRS, Jean Monnet University, Saint-Etienne, France.
PLoS Comput Biol. 2023 Nov 20;19(11):e1011627. doi: 10.1371/journal.pcbi.1011627. eCollection 2023 Nov.
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.
病原体在宿主内的传播是研究植物-病原体相互作用的一个重要过程。然而，除了病变面积等常见特征外，植物-病原体病变的发展实际上很难进行特征描述。在这里，我们通过将基于图像的表型分析与数学建模相结合来解决这个问题。我们考虑了在豌豆叶柄上 Peyronellaea pinodes 的传播情况，这些叶柄每天都用可见图像进行监测。我们假设病原体在宿主组织上的传播可以用 Fisher-KPP 模型来描述，其中病变的传播取决于逻辑增长和均匀扩散。通过对注册图像集进行变分数据同化方法来估计模型参数。这个建模框架用于比较两种具有不同部分抗性水平的豌豆品种上侵袭性分离株的传播情况。我们表明，在最具抗性的品种上预期的传播速度较慢实际上是由于扩散系数显著降低所致。这项研究表明，将成像与空间机械模型相结合可以提供一种方法来区分宿主-病原体相互作用中涉及的一些过程，进一步的发展可能允许更好地识别随后用于遗传和生态研究的定量特征。
