ArticleNucleic acids research2010
Protein evolution in yeast transcription factor subnetworks.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes.PloS one · 2017Article
- Impacts of pretranscriptional DNA methylation, transcriptional transcription factor, and posttranscriptional microRNA regulations on protein evolutionary rate.Genome biology and evolution · 2014Article
- Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors.Frontiers in genetics · 2014Article
- Topology of molecular interaction networks.BMC systems biology · 2013Review
- The impact of trans-regulation on the evolutionary rates of metazoan proteins.Nucleic acids research · 2013Article
- SH3 interactome conserves general function over specific form.Molecular systems biology · 2013Article
- Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems.Biotechnology journal · 2012Review
- Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.PLoS computational biology · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When averaged over the full yeast protein-protein interaction and transcriptional regulatory networks, protein hubs with many interaction partners or regulators tend to evolve significantly more slowly due to increased negative selection. However, genome-wide analysis of protein evolution in the subnetworks of associations involving yeast transcription factors (TFs) reveals that TF hubs do not tend to evolve significantly more slowly than TF non-hubs. This result holds for all four major types of TF hubs: interaction hubs, regulatory in-degree and out-degree hubs, as well as co-regulatory hubs that jointly regulate target genes with many TFs. Furthermore, TF regulatory in-degree hubs tend to evolve significantly more quickly than TF non-hubs. Most importantly, the correlations between evolutionary rate (K(A)/K(S)) and degrees for TFs are significantly more positive than those for generic proteins within the same global protein-protein interaction and transcriptional regulatory networks. Compared to generic protein hubs, TF hubs operate at a higher level in the hierarchical structure of cellular networks, and hence experience additional evolutionary forces (relaxed negative selection or positive selection through network rewiring). The striking difference between the evolution of TF hubs and generic protein hubs demonstrates that components within the same global network can be governed by distinct organizational and evolutionary principles.
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Registered trials
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