Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast

Persistent Link:
http://hdl.handle.net/10150/610103
Title:
Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast
Author:
Brettner, Leandra M.; Masel, Joanna
Affiliation:
Present address: Ecology & Evolutionary Biology, University of Arizona, 1041 E Lowell St, Tucson, AZ, 85721, USA; Present address: Bioengineering, University of Washington, 3720 15th Ave NE, Seattle, WA, 98195, USA
Issue Date:
2012
Publisher:
BioMed Central
Citation:
Brettner and Masel BMC Systems Biology 2012, 6:128 http://www.biomedcentral.com/1752-0509/6/128
Journal:
BMC Systems Biology
Rights:
© 2012 Brettner and Masel; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
BACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.
EISSN:
1752-0509
DOI:
10.1186/1752-0509-6-128
Keywords:
Protein-protein interaction networks; Stochastic gene expression; Evolutionary constraint; Correlomics; Cooperativity; Phenotypic plasticity
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1752-0509/6/128

Full metadata record

DC FieldValue Language
dc.contributor.authorBrettner, Leandra M.en
dc.contributor.authorMasel, Joannaen
dc.date.accessioned2016-05-20T08:58:39Z-
dc.date.available2016-05-20T08:58:39Z-
dc.date.issued2012en
dc.identifier.citationBrettner and Masel BMC Systems Biology 2012, 6:128 http://www.biomedcentral.com/1752-0509/6/128en
dc.identifier.doi10.1186/1752-0509-6-128en
dc.identifier.urihttp://hdl.handle.net/10150/610103-
dc.description.abstractBACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1752-0509/6/128en
dc.rights© 2012 Brettner and Masel; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.subjectProtein-protein interaction networksen
dc.subjectStochastic gene expressionen
dc.subjectEvolutionary constrainten
dc.subjectCorrelomicsen
dc.subjectCooperativityen
dc.subjectPhenotypic plasticityen
dc.titleProtein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeasten
dc.typeArticleen
dc.identifier.eissn1752-0509en
dc.contributor.departmentPresent address: Ecology & Evolutionary Biology, University of Arizona, 1041 E Lowell St, Tucson, AZ, 85721, USAen
dc.contributor.departmentPresent address: Bioengineering, University of Washington, 3720 15th Ave NE, Seattle, WA, 98195, USAen
dc.identifier.journalBMC Systems Biologyen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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