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Integrating phenotypic and expression profiles to map arsenic-response networks

BACKGROUND: Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known bio...

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Autores principales: Haugen, Astrid C, Kelley, Ryan, Collins, Jennifer B, Tucker, Charles J, Deng, Changchun, Afshari, Cynthia A, Brown, J Martin, Ideker, Trey, Van Houten, Bennett
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC545798/
https://www.ncbi.nlm.nih.gov/pubmed/15575969
http://dx.doi.org/10.1186/gb-2004-5-12-r95
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author Haugen, Astrid C
Kelley, Ryan
Collins, Jennifer B
Tucker, Charles J
Deng, Changchun
Afshari, Cynthia A
Brown, J Martin
Ideker, Trey
Van Houten, Bennett
author_facet Haugen, Astrid C
Kelley, Ryan
Collins, Jennifer B
Tucker, Charles J
Deng, Changchun
Afshari, Cynthia A
Brown, J Martin
Ideker, Trey
Van Houten, Bennett
author_sort Haugen, Astrid C
collection PubMed
description BACKGROUND: Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known biochemical reactions in yeast, as well as the yeast network of 20,985 protein-protein/protein-DNA interactions. RESULTS: While the expression data unveiled no significant nodes in the metabolic network, the regulatory network revealed several important nodes as centers of arsenic-induced activity. The highest-scoring proteins included Fhl1, Msn2, Msn4, Yap1, Cad1 (Yap2), Pre1, Hsf1 and Met31. Contrary to the gene-expression analyses, the phenotypic-profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate, and serine, threonine and glutamate biosynthesis. We also carried out transcriptional profiling of specific deletion strains, confirming that the transcription factors Yap1, Arr1 (Yap8), and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact. CONCLUSIONS: By integrating phenotypic and transcriptional profiling and mapping the data onto the metabolic and regulatory networks, we have shown that arsenic is likely to channel sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Furthermore, we show that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that transcriptional and phenotypic profiling implicate distinct, but related, pathways.
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spelling pubmed-5457982005-01-27 Integrating phenotypic and expression profiles to map arsenic-response networks Haugen, Astrid C Kelley, Ryan Collins, Jennifer B Tucker, Charles J Deng, Changchun Afshari, Cynthia A Brown, J Martin Ideker, Trey Van Houten, Bennett Genome Biol Research BACKGROUND: Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known biochemical reactions in yeast, as well as the yeast network of 20,985 protein-protein/protein-DNA interactions. RESULTS: While the expression data unveiled no significant nodes in the metabolic network, the regulatory network revealed several important nodes as centers of arsenic-induced activity. The highest-scoring proteins included Fhl1, Msn2, Msn4, Yap1, Cad1 (Yap2), Pre1, Hsf1 and Met31. Contrary to the gene-expression analyses, the phenotypic-profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate, and serine, threonine and glutamate biosynthesis. We also carried out transcriptional profiling of specific deletion strains, confirming that the transcription factors Yap1, Arr1 (Yap8), and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact. CONCLUSIONS: By integrating phenotypic and transcriptional profiling and mapping the data onto the metabolic and regulatory networks, we have shown that arsenic is likely to channel sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Furthermore, we show that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that transcriptional and phenotypic profiling implicate distinct, but related, pathways. BioMed Central 2004 2004-11-29 /pmc/articles/PMC545798/ /pubmed/15575969 http://dx.doi.org/10.1186/gb-2004-5-12-r95 Text en Copyright © 2004 Haugen et al.; licensee BioMed Central Ltd.
spellingShingle Research
Haugen, Astrid C
Kelley, Ryan
Collins, Jennifer B
Tucker, Charles J
Deng, Changchun
Afshari, Cynthia A
Brown, J Martin
Ideker, Trey
Van Houten, Bennett
Integrating phenotypic and expression profiles to map arsenic-response networks
title Integrating phenotypic and expression profiles to map arsenic-response networks
title_full Integrating phenotypic and expression profiles to map arsenic-response networks
title_fullStr Integrating phenotypic and expression profiles to map arsenic-response networks
title_full_unstemmed Integrating phenotypic and expression profiles to map arsenic-response networks
title_short Integrating phenotypic and expression profiles to map arsenic-response networks
title_sort integrating phenotypic and expression profiles to map arsenic-response networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC545798/
https://www.ncbi.nlm.nih.gov/pubmed/15575969
http://dx.doi.org/10.1186/gb-2004-5-12-r95
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