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Root system architecture (RSA) manipulation may improve water and nutrient capture by plants under normal and extreme climate conditions. With the aim of initiating the genetic dissection of RSA in tomato, we established a defined ontology that allowed the curated annotation of the observed phenotypes on 12 traits at four consecutive growth stages. In addition, we established a quick approach for the molecular identification of the mutations associated with the trait-of-interest by using a whole-genome sequencing approach that does not require the building of an additional mapping population. As a proof-of-concept, we screened 4543 seedlings from 300 tomato M3 lines (Solanum lycopersicum L. cv. Micro-Tom) generated by chemical mutagenesis with ethyl methanesulfonate. We studied the growth and early development of both the root system (primary and lateral roots) and the aerial part of the seedlings as well as the wound-induced adventitious roots emerging from the hypocotyl. We identified 659 individuals (belonging to 203 M3 lines) whose early seedling and RSA phenotypes differed from those of their reference background. We confirmed the genetic segregation of the mutant phenotypes affecting primary root length, seedling viability and early RSA in 31 M4 families derived from 15 M3 lines selected in our screen. Finally, we identified a missense mutation in the SlCESA3 gene causing a seedling-lethal phenotype with short roots. Our results validated the experimental approach used for the identification of tomato mutants during early growth, which will allow the molecular identification of the genes involved. Copyright © 2020 Elsevier B.V. All rights reserved.

Citation

Aurora Alaguero-Cordovilla, Francisco Javier Gran-Gómez, Paula Jadczak, Mariem Mhimdi, Sergio Ibáñez, Cécile Bres, Daniel Just, Christophe Rothan, José Manuel Pérez-Pérez. A quick protocol for the identification and characterization of early growth mutants in tomato. Plant science : an international journal of experimental plant biology. 2020 Dec;301:110673

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PMID: 33218638

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