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The identification of small molecule inhibitors of the plant inositol phosphorylceramide synthase which demonstrate herbicidal activity.

Elizabeth C Pinneh | John G Mina | Michael J R Stark | Stephen D Lindell | Peter Luemmen | Marc R Knight | Patrick G Steel | Paul W Denny
Scientific reports | 2019

Resistance to 157 different herbicides and 88% of known sites of action has been observed, with many weeds resistant to two or more modes. Coupled with tighter environmental regulation, this demonstrates the need to identify new modes of action and novel herbicides. The plant sphingolipid biosynthetic enzyme, inositol phosphorylceramide synthase (IPCS), has been identified as a novel, putative herbicide target. The non-mammalian nature of this enzyme offers the potential of discovering plant specific inhibitory compounds with minimal impact on animals and humans, perhaps leading to the development of new non-toxic herbicides. The best characterised and most highly expressed isoform of the enzyme in the model-dicot Arabidopsis, AtIPCS2, was formatted into a yeast-based assay which was then utilized to screen a proprietary library of over 11,000 compounds provided by Bayer AG. Hits from this screen were validated in a secondary in vitro enzyme assay. These studies led to the identification of a potent inhibitor that showed selectivity for AtIPCS2 over the yeast orthologue, and activity against Arabidopsis seedlings. This work highlighted the use of a yeast-based screening assay to discover herbicidal compounds and the status of the plant IPCS as a novel herbicidal target.

Pubmed ID: 31147620

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Associated grants

  • Agency: Medical Research Council, United Kingdom
    Id: MR/P027989/1
  • Agency: Biotechnology and Biological Sciences Research Council, United Kingdom
    Id: BB/M024156/1
  • Agency: Biotechnology and Biological Sciences Research Council, United Kingdom
    Id: BB/K012703/1

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GraphPad Prism (tool)

RRID:SCR_002798

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

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