Genome-wide analysis of the yeast transcriptome upon heat and cold shock.

DNA arrays were used to measure changes in transcript levels as yeast cells responded to temperature shocks. The number of genes upregulated by temperature shifts from 30 to 37 or 45 was correlated with the severity of the stress. Pre-adaptation of cells, by growth at 37 previous to the 45 shift, caused a decrease in the number of genes related to this response. Heat shock also caused downregulation of a set of genes related to metabolism, cell growth and division, transcription, ribosomal proteins, protein synthesis and destination. Probably all of these responses combine to slow down cell growth and division during heat shock, thus saving energy for cell rescue. The presence of putative binding sites for Xbp1p in the promoters of these genes suggests a hypothetical role for this transcriptional repressor, although other mechanisms may be considered. The response to cold shock (4) affected a small number of genes, but the vast majority of those genes induced by exposure to 4 were also induced during heat shock; these genes share in their promoters cis-regulatory elements previously related to other stress responses.

Genome-wide analysis of yeast transcription upon calcium shortage.

Several regulatory circuits related to important functions, like membrane excitation, immunoresponse, replication, control of the cell cycle and differentiation, among others, cause an increase in intracellular calcium level that finally has a consequence upon transcription of specific genes. The sequencing of the whole genome of eukaryotic cells enables genome-wide analysis of gene expression under many conditions not yet assessed by conventional methods. Using the array technology, the effect of calcium shortage in yeast cells was studied. Correspondence analysis of data showed that there is a response in transcription that is correlated to calcium shortage. The distribution of up-regulated-genes in functional categories suggests a regulatory connection between the cell-cycle progression and the energetic metabolic requirements for growth and division. In silico analysis of promoters reveals the frequent appearance of the Mlu I cell cycle box (MCB) cis element that binds the transcriptional regulatory factor Mcm1.

Correspondence analysis applied to microarray data.

Correspondence analysis is an explorative computational method for the study of associations between variables. Much like principal component analysis, it displays a low-dimensional projection of the data, e.g., into a plane. It does this, though, for two variables simultaneously, thus revealing associations between them. Here, we demonstrate the applicability of correspondence analysis to and high value for the analysis of microarray data, displaying associations between genes and experiments. To introduce the method, we show its application to the well-known Saccharomyces cerevisiae cell-cycle synchronization data by Spellman et al. [Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273-3297], allowing for comparison with their visualization of this data set. Furthermore, we apply correspondence analysis to a non-time-series data set of our own, thus supporting its general applicability to microarray data of different complexity, underlying structure, and experimental strategy (both two-channel fluorescence-tag and radioactive labeling).

Whole genome analysis of a wine yeast strain.

Saccharomyces cerevisiae strains frequently exhibit rather specific phenotypic features needed for adaptation to a special environment. Wine yeast strains are able to ferment musts, for example, while other industrial or laboratory strains fail to do so. The genetic differences that characterize wine yeast strains are poorly understood, however. As a first search of genetic differences between wine and laboratory strains, we performed DNA-array analyses on the typical wine yeast strain T73 and the standard laboratory background in S288c. Our analysis shows that even under normal conditions, logarithmic growth in YPD medium, the two strains have expression patterns that differ significantly in more than 40 genes. Subsequent studies indicated that these differences correlate with small changes in promoter regions or variations in gene copy number. Blotting copy numbers vs. transcript levels produced patterns, which were specific for the individual strains and could be used for a characterization of unknown samples.

Processing and quality control of DNA array hybridization data.

MOTIVATION: The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS: Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY: http://www.dkfz.de/tbi CONTACT: M.Vingron@dkfz-heidelberg.de

Transcriptional profiling on all open reading frames of Saccharomyces cerevisiae.

Open reading frames (6116) of the budding yeast Saccharomyces cerevisiae were PCR-amplified from genomic DNA using 12,232 primers specific to the ends of the coding sequences; the success rate of amplification was 97%. PCR-products were made accessible to hybridization by being arrayed at very high density on solid support media using various robotic devices. Probes made from total RNA preparations were hybridized for the analysis of the transcriptional activity of yeast under various growth conditions and of different strains. Experimental factors that proved critical to the performance, such as different RNA isolation procedures and the assessment of hybridization results, for example, were investigated in detail. Various software tools were developed that permit convenient handling and sound analysis of the large data quantities obtained from transcriptional profiling studies. Comprehensive arrays are being distributed within the European Yeast Functional Analysis Network (EUROFAN) and beyond.