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Recurrent Selection with Sub-Lethal Doses of Mesotrione Reduces Sensitivity in Amaranthus palmeri.

Jason K NorsworthyVijay K VaranasiMuthukumar V BagavathiannanChad Brabham
Published in: Plants (Basel, Switzerland) (2021)
Amaranthus palmeri, ranked as the most prolific and troublesome weed in North America, has evolved resistance to several herbicide sites of action. Repeated use of any one herbicide, especially at lower than recommended doses, can lead to evolution of weed resistance, and, therefore, a better understanding of the process of resistance evolution is essential for the management of A. palmeri and other difficult-to-control weed species. Amaranthus palmeri rapidly developed resistance to 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors such as mesotrione. The objective of this study was to test the potential for low-dose applications of mesotrione to select for reduced susceptibility over multiple generations in an A. palmeri population collected from an agricultural field in 2001. F0 plants from the population were initially treated with sub-lethal mesotrione rates and evaluated for survival three weeks after treatment. All F0 plants were controlled at the 1× rate (x = 105 g ai ha-1). However, 2.5% of the F0 plants survived the 0.5× treatment. The recurrent selection process using plants surviving various mesotrione rates was continued until the F4 generation was reached. Based on the GR50 values, the sensitivity index was determined to be 1.7 for the F4 generation. Compared to F0, HPPD gene expression level in the F3 population increased. Results indicate that after several rounds of recurrent selection, the successive generations of A. palmeri became less responsive to mesotrione, which may explain the reduced sensitivity of this weed to HPPD-inhibiting herbicides. The results have significance in light of the recently released soybean and soon to be released cotton varieties with resistance to HPPD inhibitors.
Keyphrases
  • gene expression
  • low dose
  • climate change
  • dna methylation
  • drug delivery
  • artificial intelligence
  • deep learning
  • combination therapy
  • human health
  • smoking cessation