Microarray karyotyping of maltose-fermenting Saccharomyces yeasts with differing maltotriose utilization profiles reveals copy number variation in genes involved in maltose and maltotriose utilization

AIMS

We performed an analysis of maltotriose utilization by 52 Saccharomyces yeast strains able to ferment maltose efficiently and correlated the observed phenotypes with differences in the copy number of genes possibly involved in maltotriose utilization by yeast cells.

METHODS AND RESULTS

The analysis of maltose and maltotriose utilization by laboratory and industrial strains of the species Saccharomyces cerevisiae and Saccharomyces pastorianus (a natural S. cerevisiae/Saccharomyces bayanus hybrid) was carried out using microscale liquid cultivation, as well as in aerobic batch cultures. All strains utilize maltose efficiently as a carbon source, but three different phenotypes were observed for maltotriose utilization: efficient growth, slow/delayed growth and no growth. Through microarray karyotyping and pulsed-field gel electrophoresis blots, we analysed the copy number and localization of several maltose-related genes in selected S. cerevisiae strains. While most strains lacked the MPH2 and MPH3 transporter genes, almost all strains analysed had the AGT1 gene and increased copy number of MALx1 permeases.

CONCLUSIONS

Our results showed that S. pastorianus yeast strains utilized maltotriose more efficiently than S. cerevisiae strains and highlighted the importance of the AGT1 gene for efficient maltotriose utilization by S. cerevisiae yeasts.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our results revealed new maltotriose utilization phenotypes, contributing to a better understanding of the metabolism of this carbon source for improved fermentation by Saccharomyces yeasts.

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

Analysis of large-scale gene expression data

DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.

Missing value estimation methods for DNA microarrays

MOTIVATION

Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.

RESULTS

We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.

Missing value estimation methods for DNA microarrays

MOTIVATION

Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.

RESULTS

We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.

Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data

Upon the completion of the SACCHAROMYCES: cerevisiae genomic sequence in 1996 [Goffeau,A. et al. (1997) NATURE:, 387, 5], several creative and ambitious projects have been initiated to explore the functions of gene products or gene expression on a genome-wide scale. To help researchers take advantage of these projects, the SACCHAROMYCES: Genome Database (SGD) has created two new tools, Function Junction and Expression Connection. Together, the tools form a central resource for querying multiple large-scale analysis projects for data about individual genes. Function Junction provides information from diverse projects that shed light on the role a gene product plays in the cell, while Expression Connection delivers information produced by the ever-increasing number of microarray projects. WWW access to SGD is available at genome-www.stanford. edu/Saccharomyces/.

The Stanford Microarray Database

The Stanford Microarray Database (SMD) stores raw and normalized data from microarray experiments, and provides web interfaces for researchers to retrieve, analyze and visualize their data. The two immediate goals for SMD are to serve as a storage site for microarray data from ongoing research at Stanford University, and to facilitate the public dissemination of that data once published, or released by the researcher. Of paramount importance is the connection of microarray data with the biological data that pertains to the DNA deposited on the microarray (genes, clones etc.). SMD makes use of many public resources to connect expression information to the relevant biology, including SGD [Ball,C.A., Dolinski,K., Dwight,S.S., Harris,M.A., Issel-Tarver,L., Kasarskis,A., Scafe,C.R., Sherlock,G., Binkley,G., Jin,H. et al. (2000) Nucleic Acids Res., 28, 77-80], YPD and WormPD [Costanzo,M.C., Hogan,J.D., Cusick,M.E., Davis,B.P., Fancher,A.M., Hodges,P.E., Kondu,P., Lengieza,C., Lew-Smith,J.E., Lingner,C. et al. (2000) Nucleic Acids Res., 28, 73-76], Unigene [Wheeler,D.L., Chappey,C., Lash,A.E., Leipe,D.D., Madden,T.L., Schuler,G.D., Tatusova,T.A. and Rapp,B.A. (2000) Nucleic Acids Res., 28, 10-14], dbEST [Boguski,M.S., Lowe,T.M. and Tolstoshev,C.M. (1993) Nature Genet., 4, 332-333] and SWISS-PROT [Bairoch,A. and Apweiler,R. (2000) Nucleic Acids Res., 28, 45-48] and can be accessed at http://genome-www.stanford.edu/microarray.

A whole-genome microarray reveals genetic diversity among Helicobacter pylori strains

Helicobacter pylori colonizes the stomach of half of the world's population, causing a wide spectrum of disease ranging from asymptomatic gastritis to ulcers to gastric cancer. Although the basis for these diverse clinical outcomes is not understood, more severe disease is associated with strains harboring a pathogenicity island. To characterize the genetic diversity of more and less virulent strains, we examined the genomic content of 15 H. pylori clinical isolates by using a whole genome H. pylori DNA microarray. We found that a full 22% of H. pylori genes are dispensable in one or more strains, thus defining a minimal functional core of 1281 H. pylori genes. While the core genes encode most metabolic and cellular processes, the strain-specific genes include genes unique to H. pylori, restriction modification genes, transposases, and genes encoding cell surface proteins, which may aid the bacteria under specific circumstances during their long-term infection of genetically diverse hosts. We observed distinct patterns of the strain-specific gene distribution along the chromosome, which may result from different mechanisms of gene acquisition and loss. Among the strain-specific genes, we have found a class of candidate virulence genes identified by their coinheritance with the pathogenicity island.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

Analysis of large-scale gene expression data

The advent of cDNA and oligonucleotide microarray technologies has led to a paradigm shift in biological investigation, such that the bottleneck in research is shifting from data generation to data analysis. Hierarchical clustering, divisive clustering, self-organizing maps and k-means clustering have all been recently used to make sense of this mass of data.

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma, is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease. We proposed that this variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Using DNA microarrays, we have conducted a systematic characterization of gene expression in B-cell malignancies. Here we show that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour. We identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. The molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer.

Integrating functional genomic information into the Saccharomyces genome database

The Saccharomyces Genome Database (SGD) stores and organizes information about the nearly 6200 genes in the yeast genome. The information is organized around the 'locus page' and directs users to the detailed information they seek. SGD is endeavoring to integrate the existing information about yeast genes with the large volume of data generated by functional analyses that are beginning to appear in the literature and on web sites. New features will include searches of systematic analyses and Gene Summary Paragraphs that succinctly review the literature for each gene. In addition to current information, such as gene product and phenotype descriptions, the new locus page will also describe a gene product's cellular process, function and localization using a controlled vocabulary developed in collaboration with two other model organism databases. We describe these developments in SGD through the newly reorganized locus page. The SGD is accessible via the WWW at http://genome-www.stanford.edu/Saccharomyces/

Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure

The Saccharomyces Genome Database (SGD) collects and organizes information about the molecular biology and genetics of the yeast Saccharomyces cerevisiae. The latest protein structure and comparison tools available at SGD are presented here. With the completion of the yeast sequence and the Caenorhabditis elegans sequence soon to follow, comparison of proteins from complete eukaryotic proteomes will be an extremely powerful way to learn more about a particular protein's structure, its function, and its relationships with other proteins. SGD can be accessed through the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/

Comparison of the complete protein sets of worm and yeast: orthology and divergence

Comparative analysis of predicted protein sequences encoded by the genomes of Caenorhabditis elegans and Saccharomyces cerevisiae suggests that most of the core biological functions are carried out by orthologous proteins (proteins of different species that can be traced back to a common ancestor) that occur in comparable numbers. The specialized processes of signal transduction and regulatory control that are unique to the multicellular worm appear to use novel proteins, many of which re-use conserved domains. Major expansion of the number of some of these domains seen in the worm may have contributed to the advent of multicellularity. The proteins conserved in yeast and worm are likely to have orthologs throughout eukaryotes; in contrast, the proteins unique to the worm may well define metazoans.

Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization

We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu

Molecular cloning and analysis of CDC28 and cyclin homologues from the human fungal pathogen Candida albicans

In the budding yeast Saccharomyces cerevisiae, progress of the cell cycle beyond the major control point in G1 phase, termed START, requires activation of the evolutionarily conserved Cdc28 protein kinase by direct association with G1 cyclins. We have used a conditional lethal mutation in CDC28 of S. cerevisiae to clone a functional homologue from the human fungal pathogen Candida albicans. The protein sequence, deduced from the nucleotide sequence, is 79% identical to that of S. cerevisiae Cdc28 and as such is the most closely related protein yet identified. We have also isolated from C. albicans two genes encoding putative G1 cyclins, by their ability to rescue a conditional G1 cyclin defect in S. cerevisiae; one of these genes encodes a protein of 697 amino acids and is identical to the product of the previously described CCN1 gene. The second gene codes for a protein of 465 residues, which has significant homology to S. cerevisiae Cln3. These data suggest that the events and regulatory mechanisms operating at START are highly conserved between these two organisms.

Starting to cycle: G1 controls regulating cell division in budding yeast

In Saccharomyces cerevisiae, START has been shown to comprise a series of tightly regulated reactions by which the cellular environment is assessed and under appropriate conditions, cells are commited to a further round of mitotic division. The key effector of START is the product of the CDC28 gene and the mechanisms by which the protein kinase activity of this gene product is regulated at START are well characterized. This is in contrast to the events which follow p34CDC28 activation and the way in which progress to S phase is achieved, which are less clear. We suggest two possible models to describe the regulation of these events. Firstly, it is conceivable that the only post-START targets of the p34CDC28/G1 cyclin kinase complex are components of the SBF and DSC1 transcription factors. This would require that either SBF or DSC1 regulates CDC4 function either directly by activating the transcription of CDC4 itself or else indirectly by activating the transcription of a mediator of CDC4 function in a manner analogous to the way in which the control of CDC7 function may be mediated by transcriptional regulation of DBF4 (Jackson et al., 1993). Potential regulatory effectors of CDC4 function include SCM4, which suppresses cdc4 mutations in an allele-specific manner (Smith et al., 1992) or its homologue HFS1 (J. Hartley & J. Rosamond, unpublished). This possibility is supported by the finding that CDC4 has no upstream SCB or MCB elements, whereas SCM4 and HFS1 have either an exact or close match to the SCB. This model would further require that genes needed for bud emergence and spindle pole body duplication are also subject to transcriptional regulation by DSC1 or SBF. An alternative model is that the p34CDC28/G1 cyclin complexes have several targets post-START, one being DSC1 and the others being as yet unidentified components of the pathways leading to CDC4 function, spindle pole body duplication and bud emergence. This model could account for the functional redundancy observed amongst the G1 cyclins with the various cyclins providing substrate specificity for the kinase complex. We suggest that a complex containing Cln3 protein is primarily responsible for, and acts most efficiently on, the targets containing Swi6 protein (SBF and DSC1), with complexes containing other G1 cyclins (Cln1 and/or Cln2 proteins) principally involved in activating the other pathways. However, there must be overlap in the function of these complexes with each cyclin able to substitute for some or all of the functions when necessary, albeit with differing efficiencies. This hypothesis is supported by several observations.(ABSTRACT TRUNCATED AT 400 WORDS)