Nianshu Zhang

Bio: Nianshu Zhang is an academic researcher from University of Manchester. The author has contributed to research in topics: Saccharomyces cerevisiae & Gene. The author has an hindex of 10, co-authored 15 publications receiving 1652 citations. Previous affiliations of Nianshu Zhang include Victoria University of Manchester.

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

Abstract: 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

Abstract: 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-rateassociated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently

Doxycycline, the drug used to control the tet-regulatable promoter system, has no effect on global gene expression in Saccharomyces cerevisiae.

Abstract: The tet-regulatable promoter system is commonly used for genetic studies in many eukaryotic organisms. The promoter is regulated using doxycycline. There are no obvious phenotypic effects observed when doxycycline is added to the growth medium of yeast to control expression from the promoter. It is widely accepted that doxycycline is innocuous to yeast. Global genetic studies are now commonplace and the tetO-system is being used in transcriptome studies. Hence, we wanted to ensure that the absence of phenotypic effects, on addition of doxycycline to the growth medium, is mirrored in transcriptome data. We have demonstrated that doxycycline has no significant effect on global transcription levels and will continue to use the tetO-regulatable promoter system for genetic studies.

Glycosylation deficiency phenotypes resulting from depletion of GDP-mannose pyrophosphorylase in two yeast species.

Abstract: The genes encoding GDP-mannose pyrophosphorylase from Saccharomyces cerevisiae (SRB1/PSA1) and Candida albicans (CaSRB1) were expressed under the control of the tightly regulated promoters of MET3 and CaMET3 respectively. Northern analysis showed that the addition of methionine effectively blocks the transcription of pMET3-SRB1/PSA1 and pCaMET3CaSRB1 expression cassettes, which had been integrated into the genomes of appropriate mutants. Methionine-mediated repression of CaSRB1 caused loss of viability in C. albicans, demonstrating that, as in S. cerevisiae, the gene is essential for growth. Depletion of GDP-mannose pyrophosphorylase had a highly pleiotropic effect in the two yeasts. The major phenotypes observed were lysis, failure of cell separation and/or cytokinesis, impaired bud growth and bud's site selection, clumping and flocculation, as well as increased sensitivity to a wide range of antifungal drugs and cell wall inhibitors, and impaired hyphal switching ability. These phenotypes resulted from defects in glycosylation, as demonstrated by reduced affinity for Alcian blue and sensitivity to hygromycin B. Our results provide new information about the roles of protein glycosylation in yeast and, in particular, the steps that require GDP-mannose in the fungal pathogen C. albicans.

Metabolomics - the link between genotypes and phenotypes

Abstract: Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms ‘transcriptome’ and ‘proteome’, the set of metabolites synthesized by a biological system constitute its ‘metabolome’. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

The druggable genome

Abstract: An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.

Metabolite profiles and the risk of developing diabetes

Abstract: Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics) We investigated whether metabolite profiles could predict the development of diabetes Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS) Cases and controls were matched for age, body mass index and fasting glucose Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile) The results were replicated in an independent, prospective cohort These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment

Metabonomics: a platform for studying drug toxicity and gene function

Abstract: The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.