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Genomic analysis of H. salinarum indicated that the de novo pathway for aromatic amino acid (AroAA) biosynthesis does not follow the classical pathway but begins from non-classical precursors, as is the case for M. jannaschii. The first two steps in the pathway were predicted to be carried out by genes OE1472F and OE1475F, while the 3rd step follows the canonical pathway involving gene OE1477R. The functions of these genes and their products were tested by biochemical and genetic methods. In this study, we provide evidence that supports the role of proteins OE1472F and OE1475F catalyzing consecutive enzymatic reactions leading to the production of 3-dehydroquinate (DHQ), after which AroAA production proceeds via the canonical pathway starting with the formation of DHS (dehydroshikimate), catalyzed by the product of ORF OE1477R. Nutritional requirements and AroAA uptake studies of the mutants gave results that were consistent with the proposed roles of these ORFs in AroAA biosynthesis. DNA microarray data indicated that the 13 genes of the canonical pathway appear to be utilised for AroAA biosynthesis in H. salinarum, as they are differentially expressed when cells are grown in medium lacking AroAA. [ABSTRACT FROM AUTHOR]

Background: A key stage for all microarray analyses is the extraction of feature-intensities from an image. If this step goes wrong, then subsequent preprocessing and processing stages will stand little chance of rectifying the matter. Illumina employ random construction of their BeadArrays, making feature-intensity extraction even more important for the Illumina platform than for other technologies. In this paper we show that using raw Illumina data it is possible to identify, control, and perhaps correct for a range of spatial-related phenomena that affect feature-intensity extraction. Results: We note that feature intensities can be unnaturally high when in the proximity of a number of phenomena relating either to the images themselves or to the layout of the beads on an array. Additionally we note that beads neighbour beads of the same type more often than one might expect, which may cause concern in some models of hybridization. We highlight issues in the identification of a bead's location, and in particular how this both affects and is affected by its intensity. Finally we show that beads can be wrongly identified in the image on either a local or array-wide scale, with obvious implications for data quality. Conclusions: The image processing issues identified will often pass unnoticed by an analysis of the standard data returned from an experiment. We detail some simple diagnostics that can be implemented to identify problems of this nature, and outline approaches to correcting for such problems. These approaches require access to the raw data from the arrays, not just the summarized data usually returned, making the acquisition of such raw data highly desirable. [ABSTRACT FROM AUTHOR]