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      Multiway real-time PCR gene expression profiling in yeast Saccharomyces cerevisiae reveals altered transcriptional response of ADH-genes to glucose stimuli

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          Abstract

          Background

          The large sensitivity, high reproducibility and essentially unlimited dynamic range of real-time PCR to measure gene expression in complex samples provides the opportunity for powerful multivariate and multiway studies of biological phenomena. In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc. Here we perform a multiway study of the temporal response of four yeast Saccharomyces cerevisiae strains with different glucose uptake rates upon altered metabolic conditions.

          Results

          We measured the expression of 18 genes as function of time after addition of glucose to four strains of yeast grown in ethanol. The data are analyzed by matrix-augmented PCA, which is a generalization of PCA for 3-way data, and the results are confirmed by hierarchical clustering and clustering by Kohonen self-organizing map. Our approach identifies gene groups that respond similarly to the change of nutrient, and genes that behave differently in mutant strains. Of particular interest is our finding that ADH4 and ADH6 show a behavior typical of glucose-induced genes, while ADH3 and ADH5 are repressed after glucose addition.

          Conclusion

          Multiway real-time PCR gene expression profiling is a powerful technique which can be utilized to characterize functions of new genes by, for example, comparing their temporal response after perturbation in different genetic variants of the studied subject. The technique also identifies genes that show perturbed expression in specific strains.

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          Most cited references32

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          Principal Component Analysis

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            Exploring the metabolic and genetic control of gene expression on a genomic scale.

            DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns.
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              The real-time polymerase chain reaction.

              The scientific, medical, and diagnostic communities have been presented the most powerful tool for quantitative nucleic acids analysis: real-time PCR [Bustin, S.A., 2004. A-Z of Quantitative PCR. IUL Press, San Diego, CA]. This new technique is a refinement of the original Polymerase Chain Reaction (PCR) developed by Kary Mullis and coworkers in the mid 80:ies [Saiki, R.K., et al., 1985. Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia, Science 230, 1350], for which Kary Mullis was awarded the 1993 year's Nobel prize in Chemistry. By PCR essentially any nucleic acid sequence present in a complex sample can be amplified in a cyclic process to generate a large number of identical copies that can readily be analyzed. This made it possible, for example, to manipulate DNA for cloning purposes, genetic engineering, and sequencing. But as an analytical technique the original PCR method had some serious limitations. By first amplifying the DNA sequence and then analyzing the product, quantification was exceedingly difficult since the PCR gave rise to essentially the same amount of product independently of the initial amount of DNA template molecules that were present. This limitation was resolved in 1992 by the development of real-time PCR by Higuchi et al. [Higuchi, R., Dollinger, G., Walsh, P.S., Griffith, R., 1992. Simultaneous amplification and detection of specific DNA-sequences. Bio-Technology 10(4), 413-417]. In real-time PCR the amount of product formed is monitored during the course of the reaction by monitoring the fluorescence of dyes or probes introduced into the reaction that is proportional to the amount of product formed, and the number of amplification cycles required to obtain a particular amount of DNA molecules is registered. Assuming a certain amplification efficiency, which typically is close to a doubling of the number of molecules per amplification cycle, it is possible to calculate the number of DNA molecules of the amplified sequence that were initially present in the sample. With the highly efficient detection chemistries, sensitive instrumentation, and optimized assays that are available today the number of DNA molecules of a particular sequence in a complex sample can be determined with unprecedented accuracy and sensitivity sufficient to detect a single molecule. Typical uses of real-time PCR include pathogen detection, gene expression analysis, single nucleotide polymorphism (SNP) analysis, analysis of chromosome aberrations, and most recently also protein detection by real-time immuno PCR.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2008
                16 April 2008
                : 9
                : 170
                Affiliations
                [1 ]TATAA Biocenter, Odinsgatan 28, 411 03 Göteborg, Sweden
                [2 ]Stem Cell Center, Lund University, BMC B10, 221 84 Lund, Sweden
                [3 ]Deparment of Cell and Molecular Biology, Göteborg University, BOX 462, 405 30 Göteborg, Sweden
                [4 ]Department of Analytical Chemistry, University of A Coruna, A Zapateira s/n, E-15071 A Coruna, Spain
                [5 ]Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Box 808, L-365, Livermore, CA 94551, USA
                [6 ]MultiD Analyses AB, Odinsgatan 28, 411 03 Göteborg, Sweden
                [7 ]Institute of Biotechnology, Academy of Sciences of the Czech Republic, Videnska 1083, Prague 4, 142 20, Czech Republic
                [8 ]Department of Clinical Neuroscience and Rehabilitation, Institute of Neurosciences and Physiology, Sahlgrenska Academy at Göteborg University, Medicinaregatan 9A, 413 90 Göteborg, Sweden
                Article
                1471-2164-9-170
                10.1186/1471-2164-9-170
                2335116
                18412983
                b120bda1-c225-46e9-a5e8-d5467eea35a1
                Copyright © 2008 Ståhlberg et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 September 2007
                : 16 April 2008
                Categories
                Research Article

                Genetics
                Genetics

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