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Published on January 12, 2007; 10.1104/pp.106.093054

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Received November 15, 2006
Accepted December 11, 2006

Neural Network Analyses of Infrared Spectra for Classifying Cell Wall Architectures

Maureen C. McCann , Marianne Defernez , Breeanna R. Urbanowicz , Jagdish C. Tewari , Tiffany Langewisch , Anna Olek , Brian Wells , Reginald H. Wilson , and Nicholas C. Carpita *

Department of Biological Sciences, Purdue University, West Lafayette, IN 47907-1392, U.S.A.; Department of Food Material Sciences, Institute of Food Research, Colney, Norwich NR4 7UA, U.K.; Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana 47907-2054, U.S.A.; Department of Cell and Developmental Biology, John Innes Centre, Colney, Norwich NR4 7UH, U.K.

* Corresponding author; email: carpita{at}purdue.edu.

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 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-day 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)-{beta}-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)-{beta}-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-day 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.




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