Andrew Hayes

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are “silent,” that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing “metabolic snapshots,” can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY—an abbreviation for functional analysis by co-responses in yeast.

Growth control of the eukaryote cell: a systems biology study in yeast

BACKGROUND Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. RESULTS Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. CONCLUSION This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry

A method for the global analysis of yeast intracellular metabolites, based on electrospray mass spectrometry (ES-MS), has been developed. This has involved the optimization of methods for quenching metabolism in Saccharomyces cerevisiae and extracting the metabolites for analysis by positive-ion electrospray mass spectrometry. The influence of cultivation conditions, sampling, quenching and extraction conditions, concentration step, and storage have all been studied and adapted to allow direct infusion of samples into the mass spectrometer and the acquisition of metabolic profiles with simultaneous detection of more than 25 intracellular metabolites. The method, which can be applied to other micro-organisms and biological systems, may be used for comparative analysis and screening of metabolite profiles of yeast strains and mutants under controlled conditions in order to elucidate gene function via metabolomics. Examples of the application of this analytical strategy to specific yeast strains and single-ORF yeast deletion mutants generated through the EUROFAN programme are presented.

Suitability of replacement markers for functional analysis studies in Saccharomyces cerevisiae

The complete yeast sequence contains a large proportion of genes whose biological function is completely unknown. One approach to elucidating the function of these novel genes is by quantitative methods that exploit the concepts of metabolic control analysis. An important first step in such an analysis is to determine the effects of deleting individual genes on the growth rate (or fitness) of Saccharomyces cerevisiae. Since the specific growth‐rate effects of most genes are likely to be small, they are most readily determined by competition against a standard strain in chemostat cultures where the true steady state demanded by metabolic control analysis may be achieved. We have constructed two different standard strains in which the HO gene is replaced by either HIS3 or kanMX. We demonstrate that HO is a selectively neutral site for gene replacement. However, there is a significant marker effect associated with HIS3 which, moreover, is dependent on the physiological conditions used for the competition experiments. In contrast, the kanMX marker exhibited only a small effect on specific growth rate (≤±4%). These data suggest that nutritional markers should not be used to generate deletion mutants for the quantitative analysis of gene function in yeast but that kanMX replacements may be used, with confidence, for such studies.

Hybridization array technology coupled with chemostat culture: Tools to interrogate gene expression in Saccharomyces cerevisiae

Hybridization array technology is increasingly being used for the analysis of gene expression in the yeast Saccharomyces cerevisiae. It is a powerful technique in which the relative abundance of all the mRNA molecules transcribed under a particular condition may be simultaneously measured. However, most studies performed using this technique are carried out in batch culture where the growth rate and environment are continuously changing. Often, the experimental condition being studied also impacts on the growth rate of the cells. Changes in growth rate affect the pattern of gene expression. Consequently, the analysis and interpretation of experimental results obtained in this way are inherently problematic due to the difficulty in discriminating between effects due to the experimental condition per se and concomitant growth rate-related effects. Here, we present a method that addresses this problem by exploiting chemostat culture, in which the cells can be grown at a fixed growth rate, in combination with hybridization array technology. We use two experimental examples to illustrate the advantages of using this approach and then describe a specific application of this approach to investigate the effect of carbon and nitrogen limitation at the transcriptome level.

Conceptual modelling of genomic information.

MOTIVATION Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Identification and characterization of high-flux-control genes of yeast through competition analyses in continuous cultures

Using competition experiments in continuous cultures grown in different nutrient environments (glucose limited, ammonium limited, phosphate limited and white grape juice), we identified genes that show haploinsufficiency phenotypes (reduced growth rate when hemizygous) or haploproficiency phenotypes (increased growth rate when hemizygous). Haploproficient genes (815, 1,194, 733 and 654 in glucose-limited, ammonium-limited, phosphate-limited and white grape juice environments, respectively) frequently show that phenotype in a specific environmental context. For instance, genes encoding components of the ubiquitination pathway or the proteasome show haploproficiency in nitrogen-limited conditions where protein conservation may be beneficial. Haploinsufficiency is more likely to be observed in all environments, as is the case with genes determining polar growth of the cell. Haploproficient genes seem randomly distributed in the genome, whereas haploinsufficient genes (685, 765, 1,277 and 217 in glucose-limited, ammonium-limited, phosphate-limited and white grape juice environments, respectively) are over-represented on chromosome III. This chromosome determines a yeasts mating type, and the concentration of haploinsufficient genes there may be a mechanism to prevent its loss.

Quantitative analysis of yeast gene function using competition experiments in continuous culture

One possible route to the evaluation of gene function is a quantitative approach based on the concepts of metabolic control analysis (MCA). An important first step in such an analysis is to determine the effect of deleting individual genes on the growth rate (or fitness) of S. cerevisiae. Since the specific growth‐rate effects of most genes are likely to be small, we employed competition experiments in chemostat culture to measure the proportion of deletion mutants relative to that of a standard strain by using a quantitative PCR method. In this paper, we show that both densitometry and GeneScan™ analysis can be used with similar accuracy and reproducibility to determine the proportions of (at least) two strains simultaneously, in the range 10–90% of the total cell population. Furthermore, we report on a model competition experiment between two diploid nuclear petite mutants, homozygous for deletions in the cox5a or pet191 genes, and the standard strain (ho::kanMX4/ho::kanMX4) in chemostat cultures under six different physiological conditions. The results indicate that competition experiments in continuous culture are a suitable method to distinguish quantitatively between deletion mutants that qualitatively exhibit the same phenotype.

Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.

Peptide mass fingerprinting (PMF) is a powerful technique for identification of proteins derived from in‐gel digests by virtue of their matrix‐assisted laser desorption/ionization‐time of flight mass spectra. However, there are circumstances where the under‐representation of peptides in the mass spectrum and the complexity of the source proteome mean that PMF is inadequate as an identification tool. In this paper, we show that identification is substantially enhanced by inclusion of composition data for a single amino acid. Labelling in vivo with a stable isotope labelled amino acid (in this paper, decadeuterated leucine) identifies the number of such amino acids in each digest fragment, and show a considerable gain in the ability of PMF to identify the parent protein. The method is tolerant to the extent of labelling, and as such, may be applicable to a range of single cell systems.

Transcript analysis of 1003 novel yeast genes using high-throughput northern hybridizations

The expression of 1008 open reading frames (ORFs) from the yeast Saccharomyces cerevisiae has been examined under eight different physiological conditions, using classical northern analysis. These northern data have been compared with publicly available data from a microarray analysis of the diauxic transition in S.cerevisiae. The results demonstrate the importance of comparing biologically equivalent situations and of the standardization of data normalization procedures. We have also used our northern data to identify co‐regulated gene clusters and define the putative target sites of transcriptional activators responsible for their control. Clusters containing genes of known function identify target sites of known activators. In contrast, clusters comprised solely of genes of unknown function usually define novel putative target sites. Finally, we have examined possible global controls on gene expression. It was discovered that ORFs that are highly expressed following a nutritional 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.