Biomedical subjects

Zhikang Yin

Publications and source records attributed to Zhikang Yin.

5 recordsLinked to original sources

PubMed2005

Proteomic changes associated with inactivation of the Candida glabrata ACE2 virulence-moderating gene.

Inactivation of the gene encoding the transcriptional activator Ace2 in the fungal pathogen Candida glabrata results in an almost 200-fold increase in virulence characterised by acute mortality and a massive over-stimulation of the pro-inflammatory arm of the innate immune system. In this study we have adopted a proteomics approach to identify cellular functions regulated by C. glabrata Ace2 that might contribute to this increase in virulence. A two-dimensional polyacrylamide gel electrophoresis map of the C. glabrata proteome was constructed. We identified a total of 123 proteins, 61 of which displayed reproducible and statistically significant alterations in their levels following inactivation of ACE2. Of these, the levels of 32 proteins were elevated, and 29 were reduced in ace2 cells. These data show that Ace2 influences metabolism, protein synthesis, folding and targeting, and aspects of cell growth and polarisation. Some of these functions are likely to contribute to the effects of Ace2 upon the virulence of C. glabrata.

Candida glabrata↗

PubMed2004

PEDRo: a database for storing, searching and disseminating experimental proteomics data.

BACKGROUND: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. RESULTS: This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. CONCLUSIONS: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

Animals↗

PubMed2004

Proteomic response to amino acid starvation in Candida albicans and Saccharomyces cerevisiae.

Saccharomyces cerevisiae activates general amino acid control (GCN) in response to amino acid starvation. Some aspects of this response are known to be conserved in other fungi including Candida albicans, the major systemic fungal pathogen of humans. Here, we describe a proteomic comparison of the GCN responses in S. cerevisiae and C. albicans. We have used high-resolution two-dimensional (2-D) gel electrophoresis and peptide mass fingerprinting to develop a 2-D protein map of C. albicans. A total of 391 protein spots, representing 316 open reading frames, were identified. Fifty-five C. albicans and 65 S. cerevisiae proteins were identified that responded reproducibly to 3-aminotriazole (3AT) in a Gcn4p-dependent fashion. The changes in the S. cerevisiae proteome correlated with the response in the S. cerevisiae transcript profile to 3AT treatment (rank correlation coefficient = 0.59; Natarajan et al., Molec. Cell. Biol. 2001, 21, 4347-4368). Significant aspects of the GCN response were conserved in C. albicans and S. cerevisiae. In both fungi, amino acid biosynthetic enzymes on multiple metabolic pathways were induced by 3AT in a Gcn4p-dependent fashion. Carbon metabolism functions were also induced. However, subtle differences were observed between these fungi. For example, purine biosynthetic enzymes were induced in S. cerevisiae, but were not significantly induced in C. albicans. These differences presumably reflect the contrasting niches of these relatively benign and pathogenic yeasts, respectively.

Amino Acids↗

PubMed2003

A systematic approach to modeling, capturing, and disseminating proteomics experimental data.

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

Database Management Systems↗

PubMed2003

Glucose triggers different global responses in yeast, depending on the strength of the signal, and transiently stabilizes ribosomal protein mRNAs.

Glucose exerts profound effects upon yeast physiology. In general, the effects of high glucose concentrations (>1%) upon Saccharomyces cerevisiae have been studied. In this paper, we have characterized the global responses of yeast cells to very low (0.01%), low (0.1%) and high glucose signals (1.0%) by transcript profiling. We show that yeast is more sensitive to very low glucose signals than was previously thought, and that yeast displays different responses to these different glucose signals. Genes involved in central metabolic pathways respond rapidly to very low glucose signals, whereas genes involved in the biogenesis of cytoplasmic ribosomes generally respond only to glucose concentrations of> 0.1%. We also show that cytoplasmic ribosomal protein mRNAs are transiently stabilized by glucose, indicating that both transcriptional and post-transcriptional mechanisms combine to accelerate the accumulation of ribosomal protein mRNAs. Presumably, this facilitates rapid ribosome biogenesis after exposure to glucose. However, our data indicate that yeast activates ribosome biogenesis only when sufficient glucose is available to make this metabolic investment worthwhile. In contrast, the regulation of metabolic functions in response to very low glucose signals presumably ensures that yeast can exploit even minute amounts of this preferred nutrient.

Carbon↗