Unprecedented Number of Recurrent Mutations Drive the Rapid Evolution of Pesticide Resistance in a Notorious Pest

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Publicado en:bioRxiv (Feb 7, 2025)
Autor principal: Li-Jun, Cao
Otros Autores: Jin-Cui, Chen, Thia, Joshua A, Schmidt, Thomas L, Ffrench-Constant, Richard, Lin-Xi, Zhang, Yang, Yu, Meng-Chu, Yuan, Jia-Yue, Zhang, Xiao-Yang, Zhang, Yang, Qiong, Ya-Jun Gong, Hu, Li, Xue-Xin, Chen, Hoffmann, Ary A, Shu-Jun, Wei
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Cold Spring Harbor Laboratory Press
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Acceso en línea:Citation/Abstract
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Resumen:AbstractThe genetic basis of pesticide resistance has been widely studied, but the exact nature of this evolutionary process in the field is often unclear, particularly when a limited number of populations is considered and when there is a lag between the evolutionary event and its investigation. We showed that an unprecedented number of recurrently evolved mutations drive the rapid evolution of the notorious pest, the two-spotted spider mite, to a recently commercialized acaricide, cyetpyrafen. We first observed high levels of resistance that appeared and became widespread within a few years. Genome scans revealed genetic heterogeneity of resistance among populations and identified 15 target mutations on eight amino acid residues of the pesticide target, with as many as five substitutions on one residue. No mutations were present in screened historical specimens, suggesting that mutations arose rapidly through de novo substitutions or from very rare segregating mutations. Identical mutations could recurrently appear in different genetic backgrounds, increasing the likelihood of resistance evolution. Additionally, we showed that single mutations conferred high levels of resistance, acting as large-effect alleles. This study demonstrates the high number of mutational options available for the evolution of target site resistance in this pest that challenges resistance management practices.Competing Interest StatementThe authors have declared no competing interest.
ISSN:2692-8205
DOI:10.1101/2025.02.06.636798
Fuente:Biological Science Database