Literature DB >> 9199298

Met31p and Met32p, two related zinc finger proteins, are involved in transcriptional regulation of yeast sulfur amino acid metabolism.

P L Blaiseau1, A D Isnard, Y Surdin-Kerjan, D Thomas.   

Abstract

Sulfur amino acid metabolism in Saccharomyces cerevisiae is regulated by the level of intracellular S-adenosylmethionine (AdoMet). Two cis-acting elements have been previously identified within the 5' upstream regions of the structural genes of the sulfur network. The first contains the CACGTG motif and is the target of the transcription activation complex Cbflp-Met4p-Met28p. We report here the identification of two new factors, Met31p and Met32p, that recognize the second cis-acting element. Met31p was isolated through the use of the one-hybrid method, while Met32p was identified during the analysis of the yeast methionine transport system. Met31p and Met32p are highly related zinc finger-containing proteins. Both LexA-Met31p and LexA-Met32p fusion proteins activate the transcription of a LexAop-containing promoter in a Met4p-dependent manner. Northern blot analyses of cells that do not express either Met31p and/or Met32p suggest that the function of the two proteins during the transcriptional regulation of the sulfur network varies from one gene to the other. While the expression of both the MET3 and MET14 genes was shown to strictly depend upon the presence of either Met31p or Met32p, the transcription of the MET25 gene is constitutive in cells lacking both Met31p and Met32p. These results therefore emphasise the diversity of the mechanisms allowing regulation of the expression of the methionine biosynthetic genes.

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Year:  1997        PMID: 9199298      PMCID: PMC232216          DOI: 10.1128/MCB.17.7.3640

Source DB:  PubMed          Journal:  Mol Cell Biol        ISSN: 0270-7306            Impact factor:   4.272


  31 in total

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  59 in total

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8.  Dissection of combinatorial control by the Met4 transcriptional complex.

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9.  Backup in gene regulatory networks explains differences between binding and knockout results.

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10.  Extracting biologically significant patterns from short time series gene expression data.

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