Quantifying the Predictability of Lesion Growth and Its Contribution to Quantitative Resistance Using Field...

Area of Science:

• Plant Pathology
• Agricultural Science
• Computational Biology

Background:

• Quantitative resistance (QR) mechanisms in plant-pathogen interactions are crucial for managing crop diseases.
• Lesion expansion is a predicted key factor in foliar disease epidemics, but field measurement has been challenging.

Purpose of the Study:

• To precisely measure individual lesion expansion in wheat caused by Zymoseptoria tritici under field conditions.
• To assess the role of lesion expansion in quantitative resistance (QR) and its heritability.
• To evaluate lesion expansion as a selection target for durable resistance in wheat breeding.

Main Methods:

• Utilized deep learning-based image analysis to track thousands of individual Zymoseptoria tritici lesions across 14 wheat cultivars over two field seasons.
• Enabled precise, objective field measurements of lesion growth, totaling 27,218 measurements.
• Analyzed associations between lesion appearance traits, host genotype, environment, and lesion growth.

Main Results:

• Deep learning enabled unprecedented scale and precision in measuring lesion expansion in the field.
• Lesion appearance traits showed consistent associations with lesion growth, influenced by specific host-pathogen genotype interactions.
• Lesion growth was significantly heritable (h2 ≥ 0.40) and strongly associated with overall QR to Septoria tritici blotch (STB) after accounting for environmental and host effects.

Conclusions:

• Lesion expansion is a significant component of quantitative resistance to Septoria tritici blotch in most wheat cultivars.
• The developed deep learning approach facilitates dissecting individual resistance components, supporting knowledge-based breeding for durable QR.
• Targeting lesion expansion offers a promising strategy for enhancing wheat's resistance to STB.