Low-cost phenotyping system unveils key insights into quantitative disease resistance in wild tomatoes

Quantitative disease resistance (QDR) is a complex but durable form of plant disease resistance that provides partial protection against a broad range of pathogens. Unlike qualitative resistance, driven by major resistance (R) genes, QDR is polygenic and manifests in various ways, such as delayed lesion development or reduced infection frequency.

Understanding QDR’s underlying genetic and regulatory mechanisms has long been a challenge, hindered by the need for advanced phenotyping technology and complex data analysis.

A study published in Plant Phenomics on 5 Aug 2024, offers a roadmap for breeding more disease-resilient tomato varieties.

The research team investigated quantitative disease resistance (QDR) in four wild tomato species—S. habrochaites, S. lycopersicoides, S. pennellii, and S. pimpinellifolium—against Sclerotinia sclerotiorum using the “Navautron” automated phenotyping system. This system continuously captured images of infected leaves, and a segmentation algorithm was used to quantify parameters like infection frequency (IF), lag-phase duration, lesion doubling time (LDT), and the area under the disease progress curve (AUDPC).

Statistical models, including generalized least squares and generalized linear models, were applied to account for variability. The study revealed significant phenotypic diversity in QDR across the species. S. pimpinellifolium had the shortest lag phase (36.2 hours), while S. habrochaites and S. pennellii exhibited longer lag phases (approximately 59 hours).

Lesion growth analysis showed that S. pimpinellifolium and S. pennellii had the fastest lesion expansion, with doubling times of 11 hours, whereas S. habrochaites and S. lycopersicoides had slower growth rates, up to 36 and 41 hours, respectively.

Infection frequency also varied, with S. habrochaites showing the lowest rate (80%) compared to higher rates in S. lycopersicoides and S. pennellii (93%–95%). Further, intraspecific variation was assessed, revealing that S. pennellii displayed a wide range of lag phases among its accessions, while S. lycopersicoides was more consistent.

An analysis of lesion growth in S. pennellii accessions highlighted genotype-dependent resistance, with LA1941 exhibiting the lowest infection rate and LA1809 the highest severity. Correlation analysis indicated that QDR parameters, such as lag phase and LDT, are largely independent.

These findings underscore the complex interplay between genetic background and QDR, with implications for breeding disease-resistant crops.

According to the study’s senior researcher, Dr. Remco Stam, “Unlocking the potential of QDR in crop breeding has been a long-standing challenge. Our study showcases a cost-effective phenotyping system that can provide high-resolution data crucial for understanding and utilizing QDR traits in wild crop relatives.”

The study underscores the power of low-cost, high-efficiency phenotyping systems in plant pathology research. By breaking down QDR into distinct mechanisms, scientists can more effectively breed crops that are not only resistant but also capable of enduring diverse environmental stresses. These advancements offer hope for sustainable agriculture, where plants can defend themselves against diseases without heavy reliance on fungicides or major R-genes.