1. Pattern recognition analysis of endogenous cell metabolites for high throughput mode of action identification: removing the postscreening dilemma associated with whole-organism high throughput screening.
- Author
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Hole SJ, Howe PW, Stanley PD, and Hadfield ST
- Subjects
- Automation, Cell Extracts, Multivariate Analysis, Photosynthetic Reaction Center Complex Proteins antagonists & inhibitors, Photosynthetic Reaction Center Complex Proteins metabolism, Photosystem II Protein Complex, Plant Cells, Plants enzymology, Reproducibility of Results, Glycine max cytology, Glycine max drug effects, Glycine max enzymology, Glycine max metabolism, Time Factors, Zea mays cytology, Zea mays drug effects, Zea mays enzymology, Zea mays metabolism, Drug Evaluation, Preclinical methods, Enzyme Inhibitors pharmacology, Herbicides pharmacology, Magnetic Resonance Spectroscopy methods, Pattern Recognition, Automated, Plants drug effects, Plants metabolism
- Abstract
Although whole-organism HTS can give clear indications of in vivo activity, typically few clues are given as to the mechanism of action (MOA), and determining the MOA for large numbers of active compounds can be costly and complex-an alternative approach is required. This report demonstrates that it is possible to conduct relatively high throughput MOA characterization of HTS hits utilizing a single sample preparation and analytical method. By monitoring a wide range of endogenous cellular metabolites via (1)H nuclear magnetic resonance spectroscopy, the MOA of herbicides can be predicted using computational methods to compare the metabolite perturbation patterns. Herbicides that induce a characteristic pattern of metabolic perturbation in maize include inhibitors of acetolactate synthase, acetyl co-enzyme A carboxylase, protoporphyrinogen oxidase, 5-enolpyruvylshikimate-3-phosphate synthase, and phytoene desaturase. In soya, photosystem II inhibitors can also be detected, further demonstrating that this method is not limited to inhibitors of enzymes that directly act upon endogenous metabolites, or a single species. The methods, including data analysis, can be readily automated, enabling relatively high throughput MOA elucidation of whole-organism screen hits. Additionally, for compounds with a novel MOA, this approach may lead to MOA identification faster than traditional methods. It is envisaged that application of these data analysis methods to other data types-for example, transcription (mRNA) or translation (protein) profiles-is likely to permit higher throughput with smaller sample requirements, along with ability to discriminate MOAs that are not adequately discriminated based upon endogenous metabolite profiles.
- Published
- 2000
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