Network evolution: rewiring and signatures of conservation in signaling

The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constru...

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Published inPLoS computational biology Vol. 8; no. 3; p. e1002411
Main Authors Sun, Mark G F, Sikora, Martin, Costanzo, Michael, Boone, Charles, Kim, Philip M
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.03.2012
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Abstract The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.
AbstractList The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe . We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules. Protein interaction networks control virtually all cellular processes. The rules governing their evolution have remained elusive, as comprehensive protein interaction data is available for only a small number of very distant species, making evolutionary network studies difficult. Here we attempt to overcome this limitation by computationally constructing protein interaction networks for 23 relatively tightly spaced yeast species. We focus on networks consisting of kinase and peptide binding domain interactions, which play central roles in signaling pathways. These networks enable us to investigate evolutionary network mechanisms. We are able, for the first time, to accurately quantify the contribution of different rewiring mechanisms. Interaction change appears to be mainly accomplished through binding site evolution rather than through gene gain or loss. This is in contrast to other evolutionary processes, where gene duplication or deletion is a major driving factor. Moreover, our analysis reveals that interaction changes are very fast – fast enough that the number of changes saturates, i.e., the actual rate of change has been strongly underestimated in previous studies. Our analysis also reveals different mechanisms by which certain interactions are conserved throughout evolution. Our results likely transfer to other species and networks, and will benefit future evolutionary studies of signaling pathways.
The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.
  The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.
Audience Academic
Author Kim, Philip M
Costanzo, Michael
Sun, Mark G F
Sikora, Martin
Boone, Charles
AuthorAffiliation University of Zurich and Swiss Institute of Bioinformatics, Switzerland
4 Institut de Biologia Evolutiva (UPF-CSIC), CEXS-UPF-PRBB, Barcelona, Spain
3 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
5 Department of Molecular Genetics, University of Toronto, Toronto, Canada
1 Department of Computer Science, University of Toronto, Toronto, Canada
2 Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
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ContentType Journal Article
Copyright COPYRIGHT 2012 Public Library of Science
2012 Sun et al. 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 credited: Sun MGF, Sikora M, Costanzo M, Boone C, Kim PM (2012) Network Evolution: Rewiring and Signatures of Conservation in Signaling. PLoS Comput Biol 8(3): e1002411. doi:10.1371/journal.pcbi.1002411
Sun et al. 2012
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Issue 3
Keywords Fungi
Signal Transduction
Computer Simulation
Conserved Sequence
Fungal Proteins
Models, Genetic
Evolution, Molecular
src Homology Domains
Language English
License 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.
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content type line 23
Current address: Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
Conceived and designed the experiments: MGFS MS PMK. Performed the experiments: MGFS MS. Analyzed the data: MGFS MS PMK. Contributed reagents/materials/analysis tools: MGFS MS. Wrote the paper: MGFS MC CB PMK.
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SSID ssj0035896
Score 2.2204514
Snippet The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate...
  The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we...
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doaj
pubmedcentral
proquest
gale
crossref
pubmed
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
StartPage e1002411
SubjectTerms Biology
Comparative analysis
Computational biology
Computer Simulation
Conserved Sequence - genetics
Evolution, Molecular
Fungal Proteins - genetics
Fungi - genetics
Genetics
Homology (Biology)
Kinases
Models, Genetic
Peptides
Phosphotransferases
Phylogenetics
Physiological aspects
Protein binding
Proteins
Signal transduction
Signal Transduction - genetics
src Homology Domains - genetics
Studies
Yeast
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Title Network evolution: rewiring and signatures of conservation in signaling
URI https://www.ncbi.nlm.nih.gov/pubmed/22438796
https://www.proquest.com/docview/1313185101
https://search.proquest.com/docview/940836377
https://pubmed.ncbi.nlm.nih.gov/PMC3305342
https://doaj.org/article/366119c6c05e4a4cb0cff7a10089fc4e
http://dx.doi.org/10.1371/journal.pcbi.1002411
Volume 8
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