The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Network Hubs Buffer Environmental Variation in Saccharomyces cerevisiae
Open accessAbstract
Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation. A gene product that normally contributes to this robustness is termed a phenotypic capacitor. When a phenotypic capacitor fails, for example when challenged by a harsh environment or mutation, the system becomes less robust and thus produces greater phenotypic variation. A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate, whereas its failure provides a mechanism by which evolutionary change might be promoted. The primary example to date of a phenotypic capacitor is Hsp90, a molecular chaperone that targets a large set of signal transduction proteins. In both Drosophila and Arabidopsis, compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype. For some traits, Hsp90 also appears to buffer stochastic variation, yet the relationship between environmental and genetic buffering remains an important unresolved question. We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors. To test this prediction, we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae. We find more than 300 gene products that, when absent, increase morphological variation. Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity, RNA elongation, protein modification, cell cycle, and response to stimuli such as stress. Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate. Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome. Capacitors with a duplicate are highly connected in the protein-protein interaction network and show considerable divergence in expression from their paralogs. In contrast, capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness. These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture.
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Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
A Set of Measures of Centrality Based on Betweenness
Freeman, L. C. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,295
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Vogelstein, B., Lane, D., & Levine, A. J. (2000) · Nature · doi:10.1038/35042675 · Cited by 6,607 · cited 1×
“], or with genes with a large number of interactions, such as the transcription factor p53 [”
BackgroundPAML: a program package for phylogenetic analysis by maximum likelihood
Yang, Z. (1997) · Bioinformatics · doi:10.1093/bioinformatics/13.5.555 · Cited by 5,373
Lethality and centrality in protein networks
Jeong, H., Mason, S. P., Barabási, A.-L., & Oltvai, Z. N. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,287
The Evolutionary Fate and Consequences of Duplicate Genes
Lynch, M., & Conery, J. S. (2000) · Science · doi:10.1126/science.290.5494.1151 · Cited by 4,988
Functional profiling of the Saccharomyces cerevisiae genome
Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Véronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., André, B., et al. (2002) · Nature · doi:10.1038/nature00935 · Cited by 4,316
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960
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Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science · doi:10.1126/science.1180823 · Cited by 2,162
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., Wang, W., Usaj, M., Hanchard, J., Lee, S. D., et al. (2016) · Science · doi:10.1126/science.aaf1420 · Cited by 1,240
Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant
Levy, S. F., Ziv, N., & Siegal, M. L. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001325 · Cited by 371
Self-citationSkinner, M. K. (2015) · Genome Biology and Evolution · doi:10.1093/gbe/evv073 · Cited by 342
Robustness: mechanisms and consequences
Masel, J., & Siegal, M. L. (2009) · Trends in Genetics · doi:10.1016/j.tig.2009.07.005 · Cited by 348
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Metadata
- Authors
- Levy, S. F., & Siegal, M. L.
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-11-04
- Volume
- 6
- Issue
- 11
- Pages
- e264
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- OpenAlex
- W2151385152
- Cited by
- 286
- References
- 89
- FWCI
- 7.44
- Type
- journal article
- Language
- en
- Open access
- gold
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