Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts.

Abstract

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.