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SYSTEMS BIOLOGY, MOLECULAR BIOLOGY, AND GENE REGULATION

Neural Network Analyses of Infrared Spectra for Classifying Cell Wall Architectures^1,[W],[OA]

Department of Biological Sciences (M.C.M., J.C.T., T.L.) and Department of Botany and Plant Pathology (B.R.U., A.O., N.C.C.), Purdue University, West Lafayette, Indiana 47907; Department of Food Material Sciences, Institute of Food Research, Colney, Norwich NR4 7UA, United Kingdom (M.D., R.H.W.); and Department of Cell and Developmental Biology, John Innes Centre, Colney, Norwich NR4 7UH, United Kingdom (B.W.)

About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1 3),(1 4)--D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 3),(1 4)--D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification can be broadened to phenotypes resulting from mutations in genes encoding proteins for which a function is yet to be described.

² These authors contributed equally to the paper.

³ Present address: Department of Plant Biology, Cornell University, Ithaca, NY 14853.

⁴ Present address: Department of Food Science, Cornell University, Ithaca, NY 14853.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Nicholas C. Carpita (carpita{at}purdue.edu).

^[W] The online version of this article contains Web-only data.

^[OA] Open Access articles can be viewed online without a subscription.

www.plantphysiol.org/cgi/doi/10.1104/pp.106.093054

^* Corresponding author; e-mail carpita{at}purdue.edu; fax 765–494–0363.

Received November 15, 2006; accepted December 11, 2006; published January 12, 2007.

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