David C. Hoyle

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.

Making sense of microarray data distributions

MOTIVATION Typical analysis of microarray data has focused on spot by spot comparisons within a single organism. Less analysis has been done on the comparison of the entire distribution of spot intensities between experiments and between organisms. RESULTS Here we show that mRNA transcription data from a wide range of organisms and measured with a range of experimental platforms show close agreement with Benfords law (Benford, PROC: Am. Phil. Soc., 78, 551-572, 1938) and Zipfs law (Zipf, The Psycho-biology of Language: an Introduction to Dynamic Philology, 1936 and Human Behaviour and the Principle of Least Effort, 1949). The distribution of the bulk of microarray spot intensities is well approximated by a log-normal with the tail of the distribution being closer to power law. The variance, sigma(2), of log spot intensity shows a positive correlation with genome size (in terms of number of genes) and is therefore relatively fixed within some range for a given organism. The measured value of sigma(2) can be significantly smaller than the expected value if the mRNA is extracted from a sample of mixed cell types. Our research demonstrates that useful biological findings may result from analyzing microarray data at the level of entire intensity distributions.

Genotypic and Physiological Characterization of Saccharomyces boulardii, the Probiotic Strain of Saccharomyces cerevisiae

ABSTRACT Saccharomyces boulardii, a yeast that was isolated from fruit in Indochina, has been used as a remedy for diarrhea since 1950 and is now a commercially available treatment throughout Europe, Africa, and South America. Though initially classified as a separate species of Saccharomyces, recent publications have shown that the genome of S. boulardii is so similar to Saccharomyces cerevisiae that the two should be classified as conspecific. This raises the question of the distinguishing molecular and phenotypic characteristics present in S. boulardii that make it perform more effectively as a probiotic organism compared to other strains of S. cerevisiae. This investigation reports some of these distinguishing characteristics including enhanced ability for pseudohyphal switching upon nitrogen limitation and increased resistance to acidic pH. However, these differences did not correlate with increased adherence to epithelial cells or transit through mouse gut. Pertinent characteristics of the S. boulardii genome such as trisomy of chromosome IX, altered copy number of a number of individual genes, and sporulation deficiency have been revealed by comparative genome hybridization using oligonucleotide-based microarrays coupled with a rigorous statistical analysis. The contributions of the different genomic and phenotypic features of S. boulardii to its probiotic nature are discussed.

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.

Analysis of the transcriptome of the protozoan Theileria parva using MPSS reveals that the majority of genes are transcriptionally active in the schizont stage

Massively parallel signature sequencing (MPSS) was used to analyze the transcriptome of the intracellular protozoan Theileria parva. In total 1 095 000, 20 bp sequences representing 4371 different signatures were generated from T.parva schizonts. Reproducible signatures were identified within 73% of potentially detectable predicted genes and 83% had signatures in at least one MPSS cycle. A predicted leader peptide was detected on 405 expressed genes. The quantitative range of signatures was 4–52 256 transcripts per million (t.p.m.). Rare transcripts (<50 t.p.m.) were detected from 36% of genes. Sequence signatures approximated a lognormal distribution, as in microarray. Transcripts were widely distributed throughout the genome, although only 47% of 138 telomere-associated open reading frames exhibited signatures. Antisense signatures comprised 13.8% of the total, comparable with Plasmodium. Eighty five predicted genes with antisense signatures lacked a sense signature. Antisense transcripts were independently amplified from schizont cDNA and verified by sequencing. The MPSS transcripts per million for seven genes encoding schizont antigens recognized by bovine CD8 T cells varied 1000-fold. There was concordance between transcription and protein expression for heat shock proteins that were very highly expressed according to MPSS and proteomics. The data suggests a low level of baseline transcription from the majority of protein-coding genes.

Application of the comprehensive set of heterozygous yeast deletion mutants to elucidate the molecular basis of cellular chromium toxicity.

BackgroundThe serious biological consequences of metal toxicity are well documented, but the key modes of action of most metals are unknown. To help unravel molecular mechanisms underlying the action of chromium, a metal of major toxicological importance, we grew over 6,000 heterozygous yeast mutants in competition in the presence of chromium. Microarray-based screens of these heterozygotes are truly genome-wide as they include both essential and non-essential genes.ResultsThe screening data indicated that proteasomal (protein degradation) activity is crucial for cellular chromium (Cr) resistance. Further investigations showed that Cr causes the accumulation of insoluble and toxic protein aggregates, which predominantly arise from proteins synthesised during Cr exposure. A protein-synthesis defect provoked by Cr was identified as mRNA mistranslation, which was oxygen-dependent. Moreover, Cr exhibited synergistic toxicity with a ribosome-targeting drug (paromomycin) that is known to act via mistranslation, while manipulation of translational accuracy modulated Cr toxicity.ConclusionThe datasets from the heterozygote screen represent an important public resource that may be exploited to discover the toxic mechanisms of chromium. That potential was validated here with the demonstration that mRNA mistranslation is a primary cause of cellular Cr toxicity.

Strong position-dependent effects of sequence mismatches on signal ratios measured using long oligonucleotide microarrays

BackgroundMicroarrays are an important and widely used tool. Applications include capturing genomic DNA for high-throughput sequencing in addition to the traditional monitoring of gene expression and identifying DNA copy number variations. Sequence mismatches between probe and target strands are known to affect the stability of the probe-target duplex, and hence the strength of the observed signals from microarrays.ResultsWe describe a large-scale investigation of microarray hybridisations to murine probes with known sequence mismatches, demonstrating that the effect of mismatches is strongly position-dependent and for small numbers of sequence mismatches is correlated with the maximum length of perfectly matched probe-target duplex. Length of perfect match explained 43% of the variance in log2 signal ratios between probes with one and two mismatches. The correlation with maximum length of perfect match does not conform to expectations based on considering the effect of mismatches purely in terms of reducing the binding energy. However, it can be explained qualitatively by considering the entropic contribution to duplex stability from configurations of differing perfect match length.ConclusionThe results of this study have implications in terms of array design and analysis. They highlight the significant effect that short sequence mismatches can have upon microarray hybridisation intensities even for long oligonucleotide probes.All microarray data presented in this study are available from the GEO database [1], under accession number [GEO: GSE9669]

Identification of secondary targets of N-containing bisphosphonates in mammalian cells via parallel competition analysis of the barcoded yeast deletion collection

BackgroundNitrogen-containing bisphosphonates are the elected drugs for the treatment of diseases in which excessive bone resorption occurs, for example, osteoporosis and cancer-induced bone diseases. The only known target of nitrogen-containing bisphosphonates is farnesyl pyrophosphate synthase, which ensures prenylation of prosurvival proteins, such as Ras. However, it is likely that the action of nitrogen-containing bisphosphonates involves additional unknown mechanisms. To identify novel targets of nitrogen-containing bisphosphonates, we used a genome-wide high-throughput screening in which 5,936 Saccharomyces cerevisiae heterozygote barcoded mutants were grown competitively in the presence of sub-lethal doses of three nitrogen-containing bisphosphonates (risedronate, alendronate and ibandronate). Strains carrying deletions in genes encoding potential drug targets show a variation of the intensity of their corresponding barcodes on the hybridization array over the time.ResultsWith this approach, we identified novel targets of nitrogen-containing bisphosphonates, such as tubulin cofactor B and ASK/DBF4 (Activator of S-phase kinase). The up-regulation of tubulin cofactor B may explain some previously unknown effects of nitrogen-containing bisphosphonates on microtubule dynamics and organization. As nitrogen-containing bisphosphonates induce extensive DNA damage, we also document the role of DBF4 as a key player in nitrogen-containing bisphosphonate-induced cytotoxicity, thus explaining the effects on the cell-cycle.ConclusionsThe dataset obtained from the yeast screen was validated in a mammalian system, allowing the discovery of new biological processes involved in the cellular response to nitrogen-containing bisphosphonates and opening up opportunities for development of new anticancer drugs.

Genome-wide analysis of the effects of heat shock on a Saccharomyces cerevisiae mutant with a constitutively activated cAMP-dependent pathway

We have used DNA microarray technology and 2-D gel electrophoresis combined with mass spectrometry to investigate the effects of a drastic heat shock from 30℃ to 50℃ on a genome-wide scale. This experimental condition is used to differentiate between wild-type cells and those with a constitutively active cAMP-dependent pathway in Saccharomyces cerevisiae. Whilst more than 50% of the former survive this shock, almost all of the latter lose viability. We compared the transcriptomes of the wildtype and a mutant strain deleted for the gene PDE2, encoding the high-affinity cAMP phosphodiesterase before and after heat shock treatment. We also compared the two heat-shocked samples with one another, allowing us to determine the changes that occur in the pde2Δ mutant which cause such a dramatic loss of viability after heat shock. Several genes involved in ergosterol biosynthesis and carbon source utilization had altered expression levels, suggesting that these processes might be potential factors in heat shock survival. These predictions and also the effect of the different phases of the cell cycle were confirmed by biochemical and phenotypic analyses. 146 genes of previously unknown function were identified amongst the genes with altered expression levels and deletion mutants in 13 of these genes were found to be highly sensitive to heat shock. Differences in response to heat shock were also observed at the level of the proteome, with a higher level of protein degradation in the mutant, as revealed by comparing 2-D gels of wild-type and mutant heat-shocked samples and mass spectrometry analysis of the differentially produced proteins.

Accuracy of Pseudo-Inverse Covariance Learning—A Random Matrix Theory Analysis

For many learning problems, estimates of the inverse population covariance are required and often obtained by inverting the sample covariance matrix. Increasingly for modern scientific data sets, the number of sample points is less than the number of features and so the sample covariance is not invertible. In such circumstances, the Moore-Penrose pseudo-inverse sample covariance matrix, constructed from the eigenvectors corresponding to nonzero sample covariance eigenvalues, is often used as an approximation to the inverse population covariance matrix. The reconstruction error of the pseudo-inverse sample covariance matrix in estimating the true inverse covariance can be quantified via the Frobenius norm of the difference between the two. The reconstruction error is dominated by the smallest nonzero sample covariance eigenvalues and diverges as the sample size becomes comparable to the number of features. For high-dimensional data, we use random matrix theory techniques and results to study the reconstruction error for a wide class of population covariance matrices. We also show how bagging and random subspace methods can result in a reduction in the reconstruction error and can be combined to improve the accuracy of classifiers that utilize the pseudo-inverse sample covariance matrix. We test our analysis on both simulated and benchmark data sets.