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Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.

Coulombe-Huntington J, Xia Y - PLoS Comput. Biol. (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Program, Boston University, Boston, MA, USA. jasmin@bu.edu

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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Distinct evolutionary trends of TFs.Unlike average proteins, TF Ka/Ks correlates positively with regulatory in-degree and very poorly with CAI and the evolutionary rate of PPI network neighbors. Ka/Ks is displayed as a function of regulatory in-degree (A–B), CAI (C–D) and median Ka/Ks of interacting proteins (E–F) for all proteins (A,C,E) and TFs (B,D,F). Numbers above the bars represent the number of TFs/proteins in the bin.
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pcbi-1002734-g001: Distinct evolutionary trends of TFs.Unlike average proteins, TF Ka/Ks correlates positively with regulatory in-degree and very poorly with CAI and the evolutionary rate of PPI network neighbors. Ka/Ks is displayed as a function of regulatory in-degree (A–B), CAI (C–D) and median Ka/Ks of interacting proteins (E–F) for all proteins (A,C,E) and TFs (B,D,F). Numbers above the bars represent the number of TFs/proteins in the bin.

Mentions: Similarly to PPI degree, but with a much weaker correlation, generic proteins with more regulators (higher in-degree) tend to evolve slower. In contrast, the effect of regulatory in-degree on TFs has been shown to be opposite, with each additional regulator contributing on average towards faster evolution of the TF [10]. Using our new method and a regulatory network based on a collection of ChIP-chip studies [15], we confirmed the earlier finding that the slope relating TFs' regulatory in-degree and evolutionary rate is significantly more positive than expected by chance (p = 0.0093, CAI p = 0.042, CE p = 0.0041). The opposing trends relating in-degree and Ka/Ks for all proteins and TFs are shown in Figure 1A and Figure 1B, respectively. To understand why high in-degree TFs tend to evolve at a faster rate, we decided to look at the genes they regulate. Although the median evolutionary rate of target genes is not significantly associated to the in-degree of regulators, we found that TFs' in-degree significantly correlates with the fraction of target genes which are missing an ortholog in the comparison species (ρ = 0.20 p = 0.016; CE ρ = 0.21 p = 0.041), S. paradoxus. These results suggest that the regulatory in-degree of TFs is tied to the species specificity of the transcriptional modules they regulate. High in-degree TFs may be more likely to undergo reduced negative selection than low in-degree TFs because the impairment of their regulatory functions is less likely to disrupt core processes. At the same time, high in-degree TFs may be more likely to undergo enhanced positive selection because they tend to regulate more species-specific functions.


Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.

Coulombe-Huntington J, Xia Y - PLoS Comput. Biol. (2012)

Distinct evolutionary trends of TFs.Unlike average proteins, TF Ka/Ks correlates positively with regulatory in-degree and very poorly with CAI and the evolutionary rate of PPI network neighbors. Ka/Ks is displayed as a function of regulatory in-degree (A–B), CAI (C–D) and median Ka/Ks of interacting proteins (E–F) for all proteins (A,C,E) and TFs (B,D,F). Numbers above the bars represent the number of TFs/proteins in the bin.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475661&req=5

pcbi-1002734-g001: Distinct evolutionary trends of TFs.Unlike average proteins, TF Ka/Ks correlates positively with regulatory in-degree and very poorly with CAI and the evolutionary rate of PPI network neighbors. Ka/Ks is displayed as a function of regulatory in-degree (A–B), CAI (C–D) and median Ka/Ks of interacting proteins (E–F) for all proteins (A,C,E) and TFs (B,D,F). Numbers above the bars represent the number of TFs/proteins in the bin.
Mentions: Similarly to PPI degree, but with a much weaker correlation, generic proteins with more regulators (higher in-degree) tend to evolve slower. In contrast, the effect of regulatory in-degree on TFs has been shown to be opposite, with each additional regulator contributing on average towards faster evolution of the TF [10]. Using our new method and a regulatory network based on a collection of ChIP-chip studies [15], we confirmed the earlier finding that the slope relating TFs' regulatory in-degree and evolutionary rate is significantly more positive than expected by chance (p = 0.0093, CAI p = 0.042, CE p = 0.0041). The opposing trends relating in-degree and Ka/Ks for all proteins and TFs are shown in Figure 1A and Figure 1B, respectively. To understand why high in-degree TFs tend to evolve at a faster rate, we decided to look at the genes they regulate. Although the median evolutionary rate of target genes is not significantly associated to the in-degree of regulators, we found that TFs' in-degree significantly correlates with the fraction of target genes which are missing an ortholog in the comparison species (ρ = 0.20 p = 0.016; CE ρ = 0.21 p = 0.041), S. paradoxus. These results suggest that the regulatory in-degree of TFs is tied to the species specificity of the transcriptional modules they regulate. High in-degree TFs may be more likely to undergo reduced negative selection than low in-degree TFs because the impairment of their regulatory functions is less likely to disrupt core processes. At the same time, high in-degree TFs may be more likely to undergo enhanced positive selection because they tend to regulate more species-specific functions.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Program, Boston University, Boston, MA, USA. jasmin@bu.edu

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.

Show MeSH