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Selective inhibition of yeast regulons by daunorubicin: a transcriptome-wide analysis.

Rojas M, Casado M, Portugal J, Piña B - BMC Genomics (2008)

Bottom Line: Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin.The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks.This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut for Molecular Biology of Barcelona, IBMB-CSIC, Jordi Girona, 18, 08034 Barcelona, Spain. mrabmc@cid.csic.es <mrabmc@cid.csic.es>

ABSTRACT

Background: The antitumor drug daunorubicin exerts some of its cytotoxic effects by binding to DNA and inhibiting the transcription of different genes. We analysed this effect in vivo at the transcriptome level using the budding yeast Saccharomyces cerevisiae as a model and sublethal (IC40) concentrations of the drug to minimise general toxic effects.

Results: Daunorubicin affected a minor proportion (14%) of the yeast transcriptome, increasing the expression of 195 genes and reducing expression of 280 genes. Daunorubicin down-regulated genes included essentially all genes involved in the glycolytic pathway, the tricarboxylic acid cycle and alcohol metabolism, whereas transcription of ribosomal protein genes was not affected or even slightly increased. This pattern is consistent with a specific inhibition of glucose usage in treated cells, with only minor effects on proliferation or other basic cell functions. Analysis of promoters of down-regulated genes showed that they belong to a limited number of transcriptional regulatory units (regulons). Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin.

Conclusion: The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks. This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

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Related in: MedlinePlus

Transcription ratios between daunorubicin-treated cells and three strains deleted for different transcription factors. The X-axis corresponds to microarray data for cells treated with daunorubicin for four hours (treated vs. untreated, log2 values). The Y-axis corresponds to data from reference [17]. Only data for the 475 genes affected by daunorubicin were considered.
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Figure 5: Transcription ratios between daunorubicin-treated cells and three strains deleted for different transcription factors. The X-axis corresponds to microarray data for cells treated with daunorubicin for four hours (treated vs. untreated, log2 values). The Y-axis corresponds to data from reference [17]. Only data for the 475 genes affected by daunorubicin were considered.

Mentions: A direct prediction of the DNA-binding competition model for daunorubicin action is that its presence in the cell should produce a phenocopy of genetic deletion of these factors [12], or their partial depletion [7]. To test this prediction, we compared the effects of daunorubicin shown here with a large dataset of deletions of 42 transcription factors, many of them coincident with the set in Table 6[17]. Table 8 shows the correlation between microarray data from six deletion strains [17] and the corresponding figures from the 4 h daunorubicin-treatment dataset. For these calculations, ratios between deleted and wild type strains were compared to 4 h to 0 h ratios, only for those genes that showed significant variations in expression (positive or negative) due to daunorubicin treatment. The six strains shown in Table 8 are the only ones in the dataset [17] showing positive and significant correlation (p < 0.001, Bonferroni) with daunorubicin-treatment data. The best correlation values corresponded to three strains deleted for factors Adr1p, Cst6p and Sok2p; graphs in Figure 5 show expression ratios for these three strains plotted against the corresponding values from daunorubicin treatment. These plots strongly suggest that at least part of the changes in transcription ratios induced by daunorubicin may be due to competition of the drug with these and other transcription factors for binding to consensus DNA sequences.


Selective inhibition of yeast regulons by daunorubicin: a transcriptome-wide analysis.

Rojas M, Casado M, Portugal J, Piña B - BMC Genomics (2008)

Transcription ratios between daunorubicin-treated cells and three strains deleted for different transcription factors. The X-axis corresponds to microarray data for cells treated with daunorubicin for four hours (treated vs. untreated, log2 values). The Y-axis corresponds to data from reference [17]. Only data for the 475 genes affected by daunorubicin were considered.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2536678&req=5

Figure 5: Transcription ratios between daunorubicin-treated cells and three strains deleted for different transcription factors. The X-axis corresponds to microarray data for cells treated with daunorubicin for four hours (treated vs. untreated, log2 values). The Y-axis corresponds to data from reference [17]. Only data for the 475 genes affected by daunorubicin were considered.
Mentions: A direct prediction of the DNA-binding competition model for daunorubicin action is that its presence in the cell should produce a phenocopy of genetic deletion of these factors [12], or their partial depletion [7]. To test this prediction, we compared the effects of daunorubicin shown here with a large dataset of deletions of 42 transcription factors, many of them coincident with the set in Table 6[17]. Table 8 shows the correlation between microarray data from six deletion strains [17] and the corresponding figures from the 4 h daunorubicin-treatment dataset. For these calculations, ratios between deleted and wild type strains were compared to 4 h to 0 h ratios, only for those genes that showed significant variations in expression (positive or negative) due to daunorubicin treatment. The six strains shown in Table 8 are the only ones in the dataset [17] showing positive and significant correlation (p < 0.001, Bonferroni) with daunorubicin-treatment data. The best correlation values corresponded to three strains deleted for factors Adr1p, Cst6p and Sok2p; graphs in Figure 5 show expression ratios for these three strains plotted against the corresponding values from daunorubicin treatment. These plots strongly suggest that at least part of the changes in transcription ratios induced by daunorubicin may be due to competition of the drug with these and other transcription factors for binding to consensus DNA sequences.

Bottom Line: Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin.The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks.This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut for Molecular Biology of Barcelona, IBMB-CSIC, Jordi Girona, 18, 08034 Barcelona, Spain. mrabmc@cid.csic.es <mrabmc@cid.csic.es>

ABSTRACT

Background: The antitumor drug daunorubicin exerts some of its cytotoxic effects by binding to DNA and inhibiting the transcription of different genes. We analysed this effect in vivo at the transcriptome level using the budding yeast Saccharomyces cerevisiae as a model and sublethal (IC40) concentrations of the drug to minimise general toxic effects.

Results: Daunorubicin affected a minor proportion (14%) of the yeast transcriptome, increasing the expression of 195 genes and reducing expression of 280 genes. Daunorubicin down-regulated genes included essentially all genes involved in the glycolytic pathway, the tricarboxylic acid cycle and alcohol metabolism, whereas transcription of ribosomal protein genes was not affected or even slightly increased. This pattern is consistent with a specific inhibition of glucose usage in treated cells, with only minor effects on proliferation or other basic cell functions. Analysis of promoters of down-regulated genes showed that they belong to a limited number of transcriptional regulatory units (regulons). Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin.

Conclusion: The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks. This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

Show MeSH
Related in: MedlinePlus