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Author(s): Dobbs, D.L. | Fu, A. | Honavar, V.G. | Lonosky, P.M. | Rodermel, S.R. | Zhang, X.S. |
Year: 2004
Citation: PLANT PHYSIOLOGY Volume: 134 Issue: 2 Pages: 560-574
Abstract: Proteomics studies to explore global patterns of protein expression in plant and green algal systems have proliferated within the past few years. Although most of these studies have involved mapping of the proteomes of various organs, tissues, cells, or organelles, comparative proteomics experiments have also led to the identification of proteins that change in abundance in various developmental or physiological contexts. Despite the growing use of proteomics in plant studies, questions of reproducibility have not generally been addressed, nor have quantitative methods been widely used, for example, to identify protein expression classes. In this report, we use the de-etiolation (greening) of maize (Zea mays) chloroplasts as a model system to explore these questions, and we outline a reproducible protocol to identify changes in the plastid proteome that occur during the greening process using techniques of two-dimensional gel electrophoresis and mass spectrometry. We also evaluate hierarchical and nonhierarchical statistical methods to analyze the patterns of expression of 526 high-quality, unique spots on the two-dimensional gels. We conclude that Adaptive Resonance Theory 2-a nonhierarchical, neural clustering technique that has not been previously applied to gene expression data-is a powerful technique for discriminating protein expression classes during greening. Our experiments provide a foundation for the use of proteomics in the design of experiments to address fundamental questions in plant physiology and molecular biology.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
ART 2-A,
