Abstract
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)-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-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.
Highlights
About 10% of plant genomes are devoted to cell wall biogenesis
Such approaches have been most successful in yeast (Saccharomyces cerevisiae), where well-characterized metabolic pathways can be studied by application of several technology platforms to mutants representative of most of the elements of the pathway (Ideker et al, 2001)
Germination and elongation of Wisconsin 22 (W22) coleoptiles is delayed about one half day with respect to hybrid coleoptiles, but cessation of growth and senescence are coincident with the hybrid
Summary
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. Changes in cell wall composition included a transient appearance of the (1 / 3),(1 / 4)-b-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. Somerville et al (2004) have suggested that a systems approach is useful to integrate information derived from transcript profiling methods and proteomics to define the relationship between genotypes and cell wall structure and function Such approaches have been most successful in yeast (Saccharomyces cerevisiae), where well-characterized metabolic pathways can be studied by application of several technology platforms to mutants representative of most of the elements of the pathway (Ideker et al, 2001). Classifying Cell Wall Architectures knowledge of pathways of synthesis, assembly, and disassembly of cell walls is quite limited because of the lack of a range of characterized mutants
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