Sky1 regulates the expression of sulfur metabolism genes in response to cisplatin

Abstract

Cisplatin is commonly used in cancer therapy and yeast cells are also sensitive to this compound. We present a transcriptome analysis discriminating between RNA changes induced by cisplatin treatment, which are dependent on or independent of SKY1 function – a gene whose deletion increases resistance to the drug. Gene expression changes produced by addition of cisplatin to W303 and W303-Δsky1 cells were recorded using DNA microarrays. The data, validated by quantitative PCR, revealed 122 differentially expressed genes: 69 upregulated and 53 downregulated. Among the upregulated genes, those related to sulfur metabolism were over-represented and partially dependent on Sky1. Deletions of MET4 or other genes encoding co-regulators of the expression of sulfur-metabolism-related genes, with the exception of MET28, did not modify the cisplatin sensitivity of yeast cells. One of the genes with the highest cisplatin-induced upregulation was SEO1, encoding a putative permease of sulfur compounds. We also measured the platinum, sulfur and glutathione content in W303, W303-Δsky1 and W303-Δseo1 cells after cisplatin treatment, and integration of the data suggested that these transcriptional changes might represent a cellular response that allowed chelation of cisplatin with sulfur-containing amino acids and also helped DNA repair by stimulating purine biosynthesis. The transcription pattern of stimulation of sulfur-containing amino acids and purine synthesis decreased, or even disappeared, in the W303-Δsky1 strain.

Ixr1 Regulates Ribosomal Gene Transcription and Yeast Response to Cisplatin

“…The effects of cisplatin treatment upon the S . cerevisiae transcriptome have been previously reported 61 . The major functional group over-represented among downregulated genes is related to ribosome biogenesis, including genes involved in the maturation of SSU-rRNA from tricistronic rRNA transcript, or those participating in ribosomal small subunit assembly and/or rRNA export from the nucleus 61 .…”

“…cerevisiae transcriptome have been previously reported 61 . The major functional group over-represented among downregulated genes is related to ribosome biogenesis, including genes involved in the maturation of SSU-rRNA from tricistronic rRNA transcript, or those participating in ribosomal small subunit assembly and/or rRNA export from the nucleus 61 . We have directly measured the cisplatin effect on the levels of 18S, 5.8S, and 25S rRNAs, and their pre-processed forms (35S, 27S, 20S) in the wild type strain W303, and results obtained (Figure S2 ) also confirmed the decrease observed in the previous transcriptome assay 61 .…”

Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

“…Sixteen genes in PT were identified as targets for all of the 7 TFs detected in the proteome analysis of the nucleus. These TFs and the genes that are regulated by all 7 TFs, retrieved from YEASTRACT based on experimental and expression evidence, were used to construct a matrix of TF protein abundance (obtained from the nuclear-enriched fraction of the NUCLEA dataset) against the gene transcript abundance (obtained from (41) under similar experimental conditions to those for the proteomics analysis carried out here). This information matrix was then employed to construct a mathematical abstraction of the functional fingerprints of these transcriptional regulatory proteins of the yeast protein transactions (Figure 4a).…”

“…Transcription factor abundance as a proxy for target gene mRNA expression, (a) Nuclear abundance patterns of the transcription factors from PT quantified in the proteomics study. The mRNA levels for their target genes were adopted from a study conducted under similar conditions (41). (b) and (c) The goodness of fit measures for the non-linear model predicting target gene mRNA expression levels utilising transcription factor protein abundance.…”

“…Symbolic regression (SR) was employed as described by (27,28). The gene expression data (Gene Expression Omnibus accession number GSE41094) was adopted from Rodriguez-Lombardero et al (29). Rank correlations were employed in evaluating the emPAI values that correspond to protein abundance (30).…”