The entire DNA sequence of chromosome III of the yeast Saccharomyces cerevisiae has been determined. This is the first complete sequence analysis of an entire chromosome from any organism. The 315-kilobase sequence reveals 182 open reading frames for proteins longer than 100 amino acids, of which 37 correspond to known genes and 29 more show some similarity to sequences in databases. Of 55 new open reading frames analysed by gene disruption, three are essential genes; of 42 non-essential genes that were tested, 14 show some discernible effect on phenotype and the remaining 28 have no overt function.

The complete DNA sequence of the yeast Saccharomyces cerevisiae chromosome XI has been determined. In addition to a compact arrangement of potential protein coding sequences, the 666,448-base-pair sequence has revealed general chromosome patterns; in particular, alternating regional variations in average base composition correlate with variations in local gene density along the chromosome. Significant discrepancies with the previously published genetic map demonstrate the need for using independent physical mapping criteria.

Filamentous fungi have a high capacity for producing large amounts of secreted proteins, a property that has been exploited for commercial production of recombinant proteins. However, the secretory pathway, which is key to the production of extracellular proteins, is rather poorly characterized in filamentous fungi compared to yeast. We report the effects of recombinant protein secretion on gene expression levels in Aspergillus nidulans by directly comparing a bovine chymosin-producing strain with its parental wild-type strain in continuous culture by using expressed sequence tag microarrays. This approach demonstrated more subtle and specific changes in gene expression than those observed when mimicking the effects of protein overproduction by using a secretion blocker. The impact of overexpressing a secreted recombinant protein more closely resembles the unfolded-protein response in vivo.

The science of taxonomy is constantly improving as new techniques are developed. Current practice is to construct phylogenetic trees based on the analysis of the DNA sequence of single genes, or parts of single genes. However, this approach has recently been brought into question as several tree topologies may be produced for the same clade when the sequences for various different genes are used. The availability of complete genome sequences for several organisms has seen the adoption of microarray technology to construct molecular phylogenies of bacteria, based on all of the genes. Similar techniques have been used to reveal the relationships between different strains of the yeast Saccharomyces cerevisiae. We have exploited microarray technology to construct a molecular phylogeny for the Saccharomyces sensu stricto complex of yeast species, which is based on all of the protein-encoding genes revealed by the complete genome sequence of the paradigmatic species, S. cerevisiae. We also analyze different strains of S. cerevisiae itself, as well as the putative species S. boulardii. We show that in addition to the phylogeny produced, we can identify and analyze individual ORF traits and interpret the results to give a detailed explanation of evolutionary events underlying the phylogeny.

The genome of Phanerochaete chrysosporium strain ME446 contains multiple, non-allelic, cellobiohydrolase I (CBHI)-like sequences, at least two of which are expressed in a cellulose-dependent manner. Each of the expressed genes contains two identically positioned introns within its coding region. The lengths and sequences of these introns are different and one is not excised from all transcripts, raising the possibility that subtly different protein products may be expressed from a common gene. Introns are also present upstream of both genes but these differ in number and position, as well as sequence and length. Endoglucanase-like sequences could not be identified and it is suggested that variant CBHI-like proteins may provide endoglucanase activity in this fungus.

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The European Functional Analysis Network (EUROFAN) is systematically analysing the function of novel Saccharomyces cerevisiae genes revealed by genome sequencing. As part of this effort our consortium has performed a detailed transcript analysis for 250 novel ORFs on chromosome XIV. All transcripts were quantified by Northern analysis under three quasi‐steady‐state conditions (exponential growth on rich fermentative, rich non‐fermentative, and minimal fermentative media) and eight transient conditions (glucose derepression, glucose upshift, stationary phase, nitrogen starvation, osmo‐stress, heat‐shock, and two control conditions). Transcripts were detected for 82% of the 250 ORFs, and only one ORF did not yield a transcript of the expected length (YNL285w). Transcripts ranged from low (62%), moderate (16%) to high abundance (2%) relative to the ACT1 mRNA. The levels of 73% of the 206 chromosome XIV transcripts detected fluctuated in response to the transient states tested. However, only a small number responded strongly to the transients: eight ORFs were induced upon glucose upshift; five were repressed by glucose; six were induced in response to nitrogen starvation; three were induced in stationary phase; five were induced by osmo‐stress; four were induced by heat‐shock. These data provide useful clues about the general function of these ORFs and add to our understanding of gene regulation on a genome‐wide basis. Copyright © 1999 John Wiley & Sons, Ltd.

upshift tend to employ favoured codons, whereas those overexpressed in starvation conditions do not. These results are interpreted in terms of a model in which competition between mRNA molecules for translational capacity selects for codons translated by abundant tRNAs. Keywords: gene expression/genome analysis/mRNA/ Saccharomyces cerevisiae/stress responses IntroductionThe availability of the complete genome sequence of the eukaryotic microorganism, Saccharomyces cerevisiae (Goffeau et al., 1996) has allowed researchers to monitor gene transcription on a global (or genome-wide) scale for the ®rst time. The resulting pro®les de®ne the complete set of mRNA molecules (the transcriptome; Velculescu et al., 1997) present in the yeast cell under a given set of physiological or developmental conditions (Oliver, 1997). Massively parallel analytical procedures are used in transcriptome analysis that involve the hybridization of labelled mRNA or cDNA molecules to arrays of`target' molecules representing all of the~6000 protein-encoding genes de®ned by the yeast genome (Mewes et al., 1997). These targets may be either oligonucleotides (Wodicka et al., 1997) or PCR products (Lashkari et al., 1997;Hauser et al., 1998) fabricated in either`micro' (on glass slides or chips; Lashkari et al., 1997;Wodicka et al., 1997) or`macro' (on nylon or polypropylene membranes; Hauser et al., 1998) formats. The mRNA or cDNA hybridization probes may be labelled either radioactively (usually with 33 P; Hauser et al., 1998) or¯uorescently (usually with Cy5 or Cy3; Winzeler et al., 1999). Whatever the experimental protocol employed, all transcriptome analyses using hybridization arrays have in common that they produce massive amounts of data that have to be`mined', using computational techniques, in order to extract meaningful biological information. A number of algorithms have been developed Brown et al., 2000;Kell and King, 2000) to permit the comparison of the transcription patterns of all 6000 protein-encoding genes in different physiological conditions or throughout a time course of development (Cho et al., 1998;Chu, 1998;Spellman et al., 1998) or physiological adaptation . While these algorithms are effective in clustering together genes that show similar patterns of regulation, it is clear that the composition of any particular cluster is enormously sensitive to the thresholds set either for transcript detection or for a signi®cant level of regulation, and thus to the way in which the data have been normalized or otherwise processed.Because of these concerns about data processing, it is important that we make use of existing biological knowledge in mining hybridization array data. This may be done in two ways, either empirically (e.g. by adjusting threshold levels until genes already known to be co-regulated are clustered together) or, more formally, by using supervised methods of machine learning (Brown et al., 2000;Kell and King, 2000). Whatever approach is used, there is the problem that the prior knowledge has been gained using a di...