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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.

Ståhlberg A, Elbing K, Andrade-Garda JM, Sjögreen B, Forootan A, Kubista M - BMC Genomics (2008)

Bottom Line: In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc.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.The technique also identifies genes that show perturbed expression in specific strains.

View Article: PubMed Central - HTML - PubMed

Affiliation: TATAA Biocenter, Odinsgatan 28, 411 03 Göteborg, Sweden. anders.stahlberg@neuro.gu.se

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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Diagram showing lamination and catenation of data matrices.
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Figure 2: Diagram showing lamination and catenation of data matrices.

Mentions: For proper comparison samples should be normalized. Parameters such as mass, volume, cell number and total RNA have been used but none of these can compensate for variations in RNA quality and the presence of reaction inhibitors. Today, normalization is usually performed with internal reference genes, which always should be validated to have constant expression under the conditions of the study [34]. Identifying appropriate reference genes for data normalization and validating them on representative samples is a challenging problem in expression profiling, because the expression of all genes seem to be regulated under some conditions. Different algorithms have been developed to identify the most suitable reference genes. geNorm [18] and NormFinder [19] are among the most popular. The two methods are based on somewhat different assumptions. While geNorm identifies the pair of genes with most correlated expression relative to all the other genes by an elimination approach, Normfinder identifies the gene(-s) that shows least variation. Normfinder also distinguishes between intra- and inter-group variation, where the latter is the systematic difference in expression between the subgroups (here the four strains). The data, in the form of cycle of threshold (CT)-values, were arranged in one matrix per strain, with the genes as columns and the sampled time points as rows. The two PCR replicates measured for each gene were averaged. The four 8 × 18 matrices were then laminated into a 32 × 18 matrix (Figure 2) and an extra classification column was added to index the strains. The results of geNorm and Normfinder are shown in Figure 3. geNorm, which treats all data as a homogenous group, identified PDA1 and IPP1 as the most stable pair of genes and ACT1 as the 3rd best reference gene candidate. Normfinder selected the genes in the stability order PDA1 > IPP1 > ACT1, with all exhibiting insignificant intra and intergroup variability. Hence, we conclude that ACT1, IPP1 and PDA1 are suitable reference genes for our study of yeast metabolism. This conclusion was supported by principal component analysis, which showed that ACT1, IPP1 and PDA1 cluster in scatter plots evidencing that they have similar behavior (data not shown).


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

Ståhlberg A, Elbing K, Andrade-Garda JM, Sjögreen B, Forootan A, Kubista M - BMC Genomics (2008)

Diagram showing lamination and catenation of data matrices.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2335116&req=5

Figure 2: Diagram showing lamination and catenation of data matrices.
Mentions: For proper comparison samples should be normalized. Parameters such as mass, volume, cell number and total RNA have been used but none of these can compensate for variations in RNA quality and the presence of reaction inhibitors. Today, normalization is usually performed with internal reference genes, which always should be validated to have constant expression under the conditions of the study [34]. Identifying appropriate reference genes for data normalization and validating them on representative samples is a challenging problem in expression profiling, because the expression of all genes seem to be regulated under some conditions. Different algorithms have been developed to identify the most suitable reference genes. geNorm [18] and NormFinder [19] are among the most popular. The two methods are based on somewhat different assumptions. While geNorm identifies the pair of genes with most correlated expression relative to all the other genes by an elimination approach, Normfinder identifies the gene(-s) that shows least variation. Normfinder also distinguishes between intra- and inter-group variation, where the latter is the systematic difference in expression between the subgroups (here the four strains). The data, in the form of cycle of threshold (CT)-values, were arranged in one matrix per strain, with the genes as columns and the sampled time points as rows. The two PCR replicates measured for each gene were averaged. The four 8 × 18 matrices were then laminated into a 32 × 18 matrix (Figure 2) and an extra classification column was added to index the strains. The results of geNorm and Normfinder are shown in Figure 3. geNorm, which treats all data as a homogenous group, identified PDA1 and IPP1 as the most stable pair of genes and ACT1 as the 3rd best reference gene candidate. Normfinder selected the genes in the stability order PDA1 > IPP1 > ACT1, with all exhibiting insignificant intra and intergroup variability. Hence, we conclude that ACT1, IPP1 and PDA1 are suitable reference genes for our study of yeast metabolism. This conclusion was supported by principal component analysis, which showed that ACT1, IPP1 and PDA1 cluster in scatter plots evidencing that they have similar behavior (data not shown).

Bottom Line: In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc.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.The technique also identifies genes that show perturbed expression in specific strains.

View Article: PubMed Central - HTML - PubMed

Affiliation: TATAA Biocenter, Odinsgatan 28, 411 03 Göteborg, Sweden. anders.stahlberg@neuro.gu.se

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.

Show MeSH
Related in: MedlinePlus