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Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae
The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry–based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The American Society for Cell Biology
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710237/ https://www.ncbi.nlm.nih.gov/pubmed/26399295 http://dx.doi.org/10.1091/mbc.E15-07-0499 |
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author | Paulo, Joao A. O’Connell, Jeremy D. Gaun, Aleksandr Gygi, Steven P. |
author_facet | Paulo, Joao A. O’Connell, Jeremy D. Gaun, Aleksandr Gygi, Steven P. |
author_sort | Paulo, Joao A. |
collection | PubMed |
description | The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry–based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid–related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a resource, these data can be mined further in efforts to understand better the roles of carbon source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production. |
format | Online Article Text |
id | pubmed-4710237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | The American Society for Cell Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-47102372016-01-20 Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae Paulo, Joao A. O’Connell, Jeremy D. Gaun, Aleksandr Gygi, Steven P. Mol Biol Cell Big Data The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry–based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid–related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a resource, these data can be mined further in efforts to understand better the roles of carbon source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production. The American Society for Cell Biology 2015-11-05 /pmc/articles/PMC4710237/ /pubmed/26399295 http://dx.doi.org/10.1091/mbc.E15-07-0499 Text en © 2015 Paulo et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0). “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society for Cell Biology. |
spellingShingle | Big Data Paulo, Joao A. O’Connell, Jeremy D. Gaun, Aleksandr Gygi, Steven P. Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title | Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title_full | Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title_fullStr | Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title_full_unstemmed | Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title_short | Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae |
title_sort | proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in saccharomyces cerevisiae |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710237/ https://www.ncbi.nlm.nih.gov/pubmed/26399295 http://dx.doi.org/10.1091/mbc.E15-07-0499 |
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