Low-cost phenotyping system unveils key insights into quantitative disease resistance in wild tomatoes
image: Wild tomato species possess a broad diversity of resistance against S. sclerotiorum.
Credit: The authors
The research, using a simple yet effective approach, sheds light on the genetic diversity of QDR and its potential applications for breeding disease-resistant crops.
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 (DOI: 10.34133/plantphenomics.0214) 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 lead researcher, Dr. Remco Stam, “Unlocking the potential of QDR in crop breeding has been a long-standing challeng. 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.
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References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0214
Funding information
This work was partly funded by the DFG (STA1547/6 and SFB924) and the ANR (ANR-21-CE20-30) for the collaborative project ResiDEvo. Exchange visits were supported by the DAAD and the Partenariat Hubert Curien programme of Campus France.
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.