Forward genetic screens have led to the isolation of several genes involved in secondary cell wall formation. A variety of evidence, however, suggests that the list of genes identified is not exhaustive. To address this problem, microarray data have been generated from tissue undergoing secondary cell wall formation and used to identify genes that exhibit a similar expression pattern to the secondary cell wall–specific cellulose synthase genes IRREGULAR XYLEM1 (IRX1) and IRX3. Cross-referencing this analysis with publicly available microarray data resulted in the selection of 16 genes for reverse genetic analysis. Lines containing an insertion in seven of these genes exhibited a clear irx phenotype characteristic of a secondary cell wall defect. Only one line, containing an insertion in a member of the COBRA gene family, exhibited a large decrease in cellulose content. Five of the genes identified as being essential for secondary cell wall biosynthesis have not been previously characterized. These genes are likely to define entirely novel processes in secondary cell wall formation and illustrate the success of combining expression data with reverse genetics to address gene function

The importance of metabolomic data in functional genomic investigations is increasingly becoming evident, as is its utility in novel biomarker discovery. We demonstrate a simple approach to the screening of metabolic information that we believe will be valuable in generating metabolomic data. Laser desorption ionisation mass spectrometry on porous silicon was effective in detecting 22 of 30 metabolites in a mixture in the negative-ion mode and 19 of 30 metabolites in the positive-ion mode, without the employment of any prior analyte separation steps. Overall, 26 of the 30 metabolites could be covered between the positive and negative-ion modes. Although the response for the metabolites at a given concentration differed, it was possible to generate direct quantitative information for a given analyte in the mixture. This technique was subsequently used to generate metabolic footprints from cell-free supernatants and, when combined with chemometric analysis, enabled us to discriminate haploid yeast single-gene deletants (mutants). In particular, the metabolic footprint of a deletion mutant in a gene encoding a transcriptional activator (Gln3p) showed increased levels of peaks, including one corresponding to glutamate, compared to the other mutants and the wildtype strain tested, enabling its discrimination based on metabolic information.

Laser desorption/ionization mass spectrometry (LDI-MS) on porous silicon is a promising analytical strategy for the rapid detection of metabolites in biological matrices. We show that both oxidized and unoxidized porous silicon surfaces are useful in detecting protonated/deprotonated molecules from compounds when analyzed in mixtures. We demonstrate the feasibility of using this technique for the simultaneous detection of multiple analytes using a synthetic cocktail of 30 compounds commonly associated with prokaryotic and eukaryotic primary metabolism. The predominantly detected species were the protonated molecules or their sodium/potassium adducts in the positiveion mode and the deprotonated molecules in the negative-ion mode, as opposed to fragments or other adducts. Surface oxidation appears to influence mass spectral responses; in particular, in the mixture we studied, the signal intensities of the hydrophobic amino acids were noticeably reduced. We show that whilst quantitative changes in individual analytes can be detected, ion suppression effects interfere when analyte levels are altered significantly. However, the response of most analytes was relatively unaffected by changes in the concentration of one of the analytes, so long as it was not allowed to dominate the mixture, which may limit the dynamic range of this approach. The differences in the response of the analytes when analyzed in mixtures could not be accounted for by considering their gas-phase and aqueous basicities alone. The implications of these findings in using the technique for metabolome analyses are discussed. Copyright # 2007 John Wiley & Sons, Ltd.There is an increasing realization that analyses characterizing the metabolome (intermediates of metabolism generally considered to be small molecular weight species) have a significant role in functional genomic investigations, and in turn in our understanding of biological systems. 1-5 These analyses typically involve large-scale high-throughput screening of many analytes simultaneously, and include determining changes (ideally quantitatively) in the metabolite profiles of the intracellular 6 and extracellular 7,8 matrices. To fulfill these requirements several approaches are being investigated, 5,9,10 including methods based on hyphenated techniques, such as chromatographic (GC, 11 LC 12 ) or electrophoretic (CE) 6,13 separations followed by mass spectrometry. Other techniques being studied for the purpose include two-dimensional (2D) thin-layer chromatography, 14 NMR spectroscopy 15 and vibrational spectroscopic techniques including Fourier transform infrared (FT-IR) and Raman spectroscopies. 16 An ideal requirement for profiling on a metabolome scale is to keep the sample processing steps minimal and develop simple and rational protocols, not only for ease of operation when dealing with many samples, but also because this will minimize sample intervention (e.g., no chemical derivatization needed) and keep the analysis times within manageable limits for high-throughput metabolite profi...

Orange juice is a hugely popular, widely consumed, and high price commodity typically traded in a concentrate form making it highly susceptible to adulteration.

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