A suitable strategy to find IAA metabolism mutants
Jazyk angličtina Země Dánsko Médium print
Typ dokumentu časopisecké články
Grantová podpora
KAW 2016.0352
The Knut and Alice Wallenberg Foundation
CTS 12:289
Carl Tryggers foundation
CTS 13:275
Carl Tryggers foundation
2014-04514
Vetenskapsrådet
JCK-1111
Kempestiftelserna
JCK-1811
Kempestiftelserna
PID2023-147737NA-I00
Agencia Estatal de Investigación
RYC2021-030895-I
Agencia Estatal de Investigación
IGA_PrF_2024_013
Palacký University Olomouc
PubMed
40113441
PubMed Central
PMC11925725
DOI
10.1111/ppl.70166
Knihovny.cz E-zdroje
- MeSH
- Arabidopsis * genetika metabolismus MeSH
- fenotyp MeSH
- kyseliny indoloctové * metabolismus MeSH
- mutace * genetika MeSH
- proteiny huseníčku genetika metabolismus MeSH
- regulace genové exprese u rostlin MeSH
- regulátory růstu rostlin metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- indoleacetic acid MeSH Prohlížeč
- kyseliny indoloctové * MeSH
- proteiny huseníčku MeSH
- regulátory růstu rostlin MeSH
Indole-3-acetic acid (IAA), the most common form of auxin, is involved in a great range of plant physiological processes. IAA is synthesized from the amino acid tryptophan and can be transported and inactivated in a myriad of ways. Despite intense research efforts, there are still dark corners in our comprehension of IAA metabolism and its interplays with other pathways. Genetic screens are a powerful tool for unbiasedly looking for new players in a given biological process. However, pleiotropism of auxin-related phenotypes and indirect effects make it necessary to incorporate additional screening steps to specifically find mutants affected in IAA homeostasis. We previously developed and validated a high-throughput methodology to simultaneously quantify IAA, key precursors, and inactive forms from as little as 10 mg of fresh tissue. We have carried out a genetic screening to identify mutants involved in IAA metabolism. Auxin reporters DR5pro:VENUS and 35Spro:DII-VENUS were EMS-mutagenized and subjected to a parallel morphological and reporter-signal pre-screen. We then obtained the auxin metabolite profile of 325 M3 selected lines and used multivariate data analysis to identify potential IAA-metabolism mutants. To test the screening design, we identified the causal mutations in three of the candidate lines by mapping-by-sequencing: dii365.3, dii571.1 and dr693. These carry new alleles of CYP83A1, MIAO, and SUPERROOT2, respectively, all of which have been previously involved in auxin homeostasis. Our results support the suitability of this approach to find new genes involved in IAA metabolism.
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