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Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates
Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783400/ https://www.ncbi.nlm.nih.gov/pubmed/24086506 http://dx.doi.org/10.1371/journal.pone.0075320 |
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author | Geiler-Samerotte, Kerry A. Hashimoto, Tatsunori Dion, Michael F. Budnik, Bogdan A. Airoldi, Edoardo M. Drummond, D. Allan |
author_facet | Geiler-Samerotte, Kerry A. Hashimoto, Tatsunori Dion, Michael F. Budnik, Bogdan A. Airoldi, Edoardo M. Drummond, D. Allan |
author_sort | Geiler-Samerotte, Kerry A. |
collection | PubMed |
description | Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection. |
format | Online Article Text |
id | pubmed-3783400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37834002013-10-01 Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates Geiler-Samerotte, Kerry A. Hashimoto, Tatsunori Dion, Michael F. Budnik, Bogdan A. Airoldi, Edoardo M. Drummond, D. Allan PLoS One Research Article Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection. Public Library of Science 2013-09-25 /pmc/articles/PMC3783400/ /pubmed/24086506 http://dx.doi.org/10.1371/journal.pone.0075320 Text en © 2013 Geiler-Samerotte et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Geiler-Samerotte, Kerry A. Hashimoto, Tatsunori Dion, Michael F. Budnik, Bogdan A. Airoldi, Edoardo M. Drummond, D. Allan Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title | Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title_full | Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title_fullStr | Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title_full_unstemmed | Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title_short | Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates |
title_sort | quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783400/ https://www.ncbi.nlm.nih.gov/pubmed/24086506 http://dx.doi.org/10.1371/journal.pone.0075320 |
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