Project description:This series represents Experiment 3 of the yeast desiccation / rehydration time course analysis. Samples include Control, 50% dry, Dry, 15 min. post rehydration, 45 min. post rehydration, 90 min. post rehydration, and 360 min. post rehydration. Keywords = BY4743 Keywords = desiccation Keywords = glucose-limited Keywords = rehydration Keywords = yeast Keywords: time-course
Project description:This series represents Experiment 2 of the yeast desiccation / rehydration time course analysis. Samples include Control, 50% dry, Dry, 15 min. post rehydration, 45 min. post rehydration, 90 min. post rehydration, and 360 min. post rehydration. Keywords = BY4743 Keywords = desiccation Keywords = glucose-limited Keywords = rehydration Keywords = yeast Keywords: time-course
Project description:This series represents Experiment 1 of the yeast desiccation / rehydration time course analysis. Samples include Control, 50% dry, Dry, 15 min. post rehydration, 45 min. post rehydration, 90 min. post rehydration, and 360 min. post rehydration. Keywords = BY4743 Keywords = desiccation Keywords = glucose-limited Keywords = rehydration Keywords = yeast Keywords: time-course
Project description:Background: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. Results: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r=0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. Conclusion: We conclude that much of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.
Project description:Saccharomyces cerevisiae is unique among yeasts for its ability to grow rapidly in the complete absence of oxygen. S. cerevisiae is therefore an ideal eukaryotic model to study physiological adaptation to anaerobiosis. Recent transcriptome analyses have identified hundreds of genes that are transcriptionally regulated by oxygen availability but the relevance of this cellular response has not been systematically investigated at the key control level of the proteome. Therefore, the proteomic response of the S. cerevisiae to anaerobiosis was investigated using metabolic stable isotope labeling in aerobic and anaerobic glucose-limited chemostat cultures, followed by proteome analysis to relatively quantify protein expression. Using independent replicate cultures and stringent statistical filtering, a robust dataset of 474 quantified proteins was generated, of which 249 showed differential expression levels. While some of these changes were consistent with previous transcriptome studies, many responses of S. cerevisiae to oxygen availability were hitherto unreported. Comparison of transcriptome and proteome from identical cultivations yielded strong evidence for post-transcriptional regulation of key cellular processes, including glycolysis, amino-acyl tRNA synthesis, purine-nucleotide synthesis and amino-acid biosynthesis. The use of chemostat cultures provided well-controlled and reproducible culture conditions, which are essential for generating robust datasets at different cellular information levels. Integration of transcriptome and proteome data led to new insights in the physiology of anaerobically growing yeast that would not have been apparent from differential analyses at either the messenger RNA or protein level alone, thus illustrating the power of multi-level studies in yeast systems biology. Protein levels versus transcript level: Systematic analysis of the control levels at which the yeast response to anaerobiosis takes place was performed using previously published transcript data obtained from yeast cultures grown under strictly identical conditions as described for the current proteome analysis. Affymetrix microarrays from five aerobic and four anaerobic independent culture replicates were used for this analysis. These comparison data are summarized in the table below. These array data are publicly available at the gene expression repository Gene Expression Omnibus under accession number GSE4804. Keywords: proteomic, nanoflow-LC-MS/MS
Project description:This time course microarray experiment was performed on Saccharomyces cerevisiae to determine the global gene expression alterations due to 3-trifluoromethyl-4-nitrophenol (TFM) exposure over time. In this experiment, yeast grown in standard, glucose-containing media were treated with 0.05mM TFM over a four hour period.
Project description:Time course comparison to tissue origin and with control cell line HT29 derived from colorectal adenocarcinoma. Status of expression pattern is different in adenocarcinoma of each patient. Human tumor cells extensively changes their gene- and protein expression patterns during their cultivation, clonal selection and expansion, thereby loosing many of the characteristics of their primary origin. In this study we analyzed if these expression changes could be circumvented by using short-term primary cell culture models derived from colorectal cancer patients. We compared several primary cells from tumor tissues using a standardized protocol which yielded similar cell populations. For monitoring the gene expression changes induced by cell preparation and cultivation we collected the tissues immediately after resection and isolated cells before seeding, and after 24 and 72 hours of cultivation from each patient. Keywords: time course