Project description:Our study involves a transcriptomic approach to the analysis of industrial yeast metabolism. Historically, among the hundreds of yeast species, Saccharomyces cerevisiae has played an important role in scientific investigations and industrial applications, and it is universally acknowledged as one of the model systems for eukaryotic organisms. Yeast is also an important component of the wine fermentation process and determines various attributes of the final product. Our research takes a holistic approach to the improvement of industrial yeast strains by integrating large data sets from various yeast strains during fermentation. This means that analysis can be done in such a way as to co-evaluate several parameters simultaneously to identify points of interest and target genes for metabolic engineering. Eventually we hope to construct an accurate information matrix and a more complete cellular map for the fermenting yeast. This will enable accurate model-building for industrial yeast and facilitated the design of intelligent yeast improvement strategies which can be applied via traditional avenues of molecular biology. Experiment Overall Design: Five different Saccharomyces cerevisiae strains used in industrial winemaking processes were used in synthetic must (MS300) fermentations. All fermentations were carried out in triplicate, so each sample is represented by three completely independent biological repeats. Samples for microarray analysis were taken at three different time points during fermentation, representative of the exponential (day2), early stationary (day5) and late stationary (day14) growth stages.
Project description:The ascomycete Trichoderma reesei is an industrial producer of cellulolytic and hemicellulolytic enzymes and also serves as a model for investigations on these enzymes and their genes. The strain QM9978 has a cellulase negative phenotype and therefore presents a valuable tool for understanding the mechanisms underlying cellulase regulation. A transcriptomic analyses of the cellulase negative strain QM9978 and the original strain QM6a have been performed to identify the genetic differences between QM6a and QM9978 leading to the cellulase-negative phenotype
Project description:Our study involves a transcriptomic approach to the analysis of industrial yeast metabolism. Historically, among the hundreds of yeast species, Saccharomyces cerevisiae has played an important role in scientific investigations and industrial applications, and it is universally acknowledged as one of the model systems for eukaryotic organisms. Yeast is also an important component of the wine fermentation process and determines various attributes of the final product. Our research takes a holistic approach to the improvement of industrial yeast strains by integrating large data sets from various yeast strains during fermentation. This means that analysis can be done in such a way as to co-evaluate several parameters simultaneously to identify points of interest and target genes for metabolic engineering. Eventually we hope to construct an accurate information matrix and a more complete cellular map for the fermenting yeast. This will enable accurate model-building for industrial yeast and facilitated the design of intelligent yeast improvement strategies which can be applied via traditional avenues of molecular biology.
Project description:The transcription factor KlPdr1p, belonging to the Zn2Cys6 family, is a central regulator of efflux pump expression in Kluyveromyces lactis. To better understand how KlPDR1-mediated drug resistance is achieved in K. lactis, we used DNA microarrays to identify genes whose expression was affected by deletion or overexpression of the KlPDR1 gene. All microarray experiments were performed using the 30K Kluyveromyces lactis NRRL Y-1140 microarray (MYcroarray, 5692 Plymouth Road, Ann Arbor, MI 48105, USA). Exponentially growing (1 x 107 cells ml-1) K. lactis PM6-7A cells (wild-type, PM6-7A/pdr1∆ and the wild-type transformed with multicopy plasmid carrying the gain-of-function allele of KlPDR1* gene) (Balazfyova et al. 2013), were collected and total RNA was isolated using RNeasy midi kit (Qiagen GmbH, Germany). 1 mg of total RNA was linearly amplified and labelled using Amino allyl MessageAmpII aRNA Amplification kit (Ambion, USA) with two different fluorescent dyes; AlexaFluor647 and AlexaFluor555 (Life Technologies, Germany). 4 µg of labelled RNA was hybridized (18 h at 45°C) in 6x SSPE with addition of formamide (10%), tween-20 (0,01%) and microarray-specofoc control oligos (1%, MYcroarray, USA). After washing, microarray images and two-color GPR output files were obtained using the microarray scanner InnoScan 900 and Mapix software version 7.3.1 (Inopsys, France). The two-color GPR files were processed using the R version 3.0.2 (R Core Team (2014). R: A language and enviroment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org) and functions available in the limma package (Smyth 2005). Briefly, the two-color GPR files were omported using the read.maimages() function, background-substracted using the “minimum“ method and within-array-normalized using the “loess“ method. The between-array normalisation was achieved using the “Aquantile“ method. For further analysis, only genes with log2FC ˃2 were selected and confirmed using qPCR.