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Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002734
Pubmed ID
Authors

Jasmin Coulombe-Huntington, Yu Xia

Abstract

The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
France 1 2%
Australia 1 2%
Korea, Republic of 1 2%
Russia 1 2%
Canada 1 2%
Unknown 56 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 29%
Student > Ph. D. Student 16 25%
Professor 8 12%
Student > Master 7 11%
Professor > Associate Professor 5 8%
Other 9 14%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 58%
Biochemistry, Genetics and Molecular Biology 11 17%
Computer Science 7 11%
Engineering 3 5%
Chemical Engineering 1 2%
Other 2 3%
Unknown 3 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 October 2012.
All research outputs
#16,048,009
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#6,968
of 8,960 outputs
Outputs of similar age
#117,830
of 193,741 outputs
Outputs of similar age from PLoS Computational Biology
#69
of 109 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 193,741 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.