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BIOINFORMATICS

A Proteomic Analysis of Maize Chloroplast Biogenesis¹

Departments of Genetics, Development, and Cell Biology (P.M.L., D.L.D., A.F., S.R.R.) and Computer Science (X.Z., V.G.H.) and Bioinformatics and Computational Biology Major (P.M.L., X.Z., V.G.H., D.L.D., S.R.R.), Iowa State University, Ames, Iowa 50011

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.

¹ This work was supported by the National Science Foundation (Integrative Graduate Education and Research Traineeship [IGERT] training grant in Bioinformatics and Computational Biology to P.L.) and by the U.S. Department of Energy (Energy Biosciences; grant no. DE–FG02–94ER20147 to S.R.).

^* Corresponding author; e-mail rodermel{at}iastate.edu; fax 515–294–1337.

Received August 20, 2003; returned for revision October 7, 2003; accepted October 23, 2003.

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