Neural Network Analyses of Infrared Spectra for Classifying Cell Wall Architectures

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)-β-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)-β-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

The Arabidopsis MUM2 Gene Encodes a β-Galactosidase Required for the Production of Seed Coat Mucilage with Correct Hydration Properties[W]

Seed coat development in Arabidopsis thaliana involves a complex pathway where cells of the outer integument differentiate into a highly specialized cell type after fertilization. One aspect of this developmental process involves the secretion of a large amount of pectinaceous mucilage into the apoplast. When the mature seed coat is exposed to water, this mucilage expands to break the primary cell wall and encapsulate the seed. The mucilage-modified2 (mum2) mutant is characterized by a failure to extrude mucilage on hydration, although mucilage is produced as normal during development. The defect in mum2 appears to reside in the mucilage itself, as mucilage fails to expand even when the barrier of the primary cell wall is removed. We have cloned the MUM2 gene and expressed recombinant MUM2 protein, which has β-galactosidase activity. Biochemical analysis of the mum2 mucilage reveals alterations in pectins that are consistent with a defect in β-galactosidase activity, and we have demonstrated that MUM2 is localized to the cell wall. We propose that MUM2 is involved in modifying mucilage to allow it to expand upon hydration, establishing a link between the galactosyl side-chain structure of pectin and its physical properties

DOI 10.1007/s00425-005-1563-z PROGRESS REPORT

Artificial neural networks Plant cell walls are composed of independent but interacting networks of carbohydrates, proteins, and aromatic substances. Interacting with this complex matrix are several hundred enzymes and other proteins, som