Project description:Iron-resistant Saccharomyces cerevisiae mutant was obtained by evolutionary engineering selection strategy. The mutant obtained “M8FE” is much more resistant to iron stress than the reference strain which was used to select this mutant. Mutant can resist up to 35mM Iron* stress whereas the reference strain cannot. Whole-genome microarray analysis might be promising to identify the iron resistance mechanisms and stress response upon high levels of iron in the yeast cells. Iron-resistant mutant is also cross resistant to Cobalt, Chromium and Nickel but sensitive to Zinc. * refers to [NH4]2[Fe][SO4]2 and FeCl2.
Project description:Iron-resistant Saccharomyces cerevisiae mutant was obtained by evolutionary engineering selection strategy. The mutant obtained M-bM-^@M-^\M8FEM-bM-^@M-^] is much more resistant to iron stress than the reference strain which was used to select this mutant. Mutant can resist up to 35mM Iron* stress whereas the reference strain cannot. Whole-genome microarray analysis might be promising to identify the iron resistance mechanisms and stress response upon high levels of iron in the yeast cells. Iron-resistant mutant is also cross resistant to Cobalt, Chromium and Nickel but sensitive to Zinc. * refers to [NH4]2[Fe][SO4]2 and FeCl2. The reference Saccharomyces cerevisiae strain and the iron-resistant mutant were grown in minimal medium to an Optical Density (OD600) of 1.00 which correspond to the logarithmic growth phase of the yeast cells. Cultures were harvested and whole RNA isolation was carried out. The experiment was repeated three times.
Project description:High throughput sequencing is a powerful tool to investigate complex cellular phenotypes in functional genomics studies. Sequencing of transcriptional molecules, RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared to traditional expression analysis based on microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in analysis of RNA-seq data and to cross-compare the results with those obtained through a microarray platform. We used the well-characterized Saccharomyces cervevisiae strain CEN.PK 113-7D grown under two different physiological conditions (batch and chemostat) as a case study. In our work, we addressed the influence of genetic variability on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and Tophat), the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and noiSeq) and we explored the consistency between the two main approaches for RNA-seq: reference mapping and de novo assembly. High reproducibility in data generated through RNA-seq among different biological replicates (correlation ≥ 0.99) and high consistency with the results identified with RNA-seq and microarray data analysis (correlation ≥ 0.91) were observed. The results from differential gene expression identification as well as the results of integrated analysis based on the different methods are in good agreement. Overall, our study provides a useful and comprehensive comparison of the workflow for transcriptome analysis using RNA-seq technique.
Project description:A propolis-resistant Saccharomyces cerevisiae mutant strain was obtained using an evolutionary engineering strategy based on successive batch cultivation under gradually increasing propolis levels. The mutant strain FD 11 was selected at a propolis concentration that the reference strain could not grow at all. Whole-genome transcriptomic analysis of FD11 was performed with respect to its reference strain to determine differences in gene expression levels between the two strains. Saccharomyces cerevisiae