Abstract
Tomato (Solanum lycopersicum L.) has served as an important model system for studying the genetics and molecular basis of resistance mechanisms in plants. An unprecedented challenge is now to capitalize on the genetic and genomic achievements obtained in this species. In this study, we show that information on the tomato genome can be used predictively to link resistance function with specific sequences. An integrated genomic approach for identifying new resistance (R) gene candidates was developed. An R gene functional map was created by co-localization of candidate pathogen recognition genes and anchoring molecular markers associated with resistance phenotypes. In-depth characterization of the identified pathogen recognition genes was performed. Finally, in order to highlight expressed pathogen recognition genes and to provide a first step in validation, the tomato transcriptome was explored and basic molecular analyses were conducted. Such methodology can help to better direct positional cloning, reducing the amount of effort required to identify a functional gene. The resulting candidate loci selected are available for exploiting their specific function.
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Acknowledgments
We thank Dr Mark Walters for English language editing of the manuscript. This research was carried out within the GenoPOM-PRO Project funded by Italian Ministry of Education, University and Research.
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Supplemental Table S3. List of expressed predicted pathogen receptor genes co-localizing with R loci reported in Table 2. (DOCX 25 kb)
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Supplemental Table S4. List of primers used for validation of pathogen recognition genes predicted in silico. (DOCX 24 kb)
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Supplemental Table S5. Identical sites and pairwise identity scores of 10 selected amplicons with annotated SL2.40 gene sequences. (DOCX 62 kb)
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Supplemental Figure S1. Phylogenetic analysis: panel-A) NBS proteins tree; panel-B) eLRR-Ser/Thr proteins tree; panel-C) Kinase proteins tree. (TIFF 2,306 kb)
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Andolfo, G., Sanseverino, W., Aversano, R. et al. Genome-wide identification and analysis of candidate genes for disease resistance in tomato. Mol Breeding 33, 227–233 (2014). https://doi.org/10.1007/s11032-013-9928-7
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DOI: https://doi.org/10.1007/s11032-013-9928-7