Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Geiler-Samerotte, Kerry A., Hashimoto, Tatsunori, Dion, Michael F., Budnik, Bogdan A., Airoldi, Edoardo M., Drummond, D. Allan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
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
_version_ 1782285656280530944
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
work_keys_str_mv AT geilersamerottekerrya quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates
AT hashimototatsunori quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates
AT dionmichaelf quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates
AT budnikbogdana quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates
AT airoldiedoardom quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates
AT drummonddallan quantifyingconditiondependentintracellularproteinlevelsenableshighprecisionfitnessestimates