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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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Comparison of different genomic and network features influencing TF and protein evolutionary rate.For each determinant, absolute Spearman's rank correlation coefficient (ρ) for TFs is displayed on the left and for all proteins, on the right, with the color of the box representing the direction of the trend. The * indicates the most dominant correlation for each protein set. While CAI is the dominant correlate with Ka/Ks for generic proteins, target gene Ka/Ks is the strongest correlate for TF Ka/Ks.
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pcbi-1002734-g003: Comparison of different genomic and network features influencing TF and protein evolutionary rate.For each determinant, absolute Spearman's rank correlation coefficient (ρ) for TFs is displayed on the left and for all proteins, on the right, with the color of the box representing the direction of the trend. The * indicates the most dominant correlation for each protein set. While CAI is the dominant correlate with Ka/Ks for generic proteins, target gene Ka/Ks is the strongest correlate for TF Ka/Ks.

Mentions: In addition to separately assessing the genomic and network correlates of TF evolutionary rate, it is important to compare their relative contributions to identify the most dominant determinants of TF evolutionary rate, and whether they differ from those of generic proteins. Figure 3 shows the Spearman's rank correlation coefficients (ρ) relating different genomic and network properties to Ka/Ks for TFs and for all proteins. This figure clearly shows how features like expression, CAI, which is tightly coupled to expression [17], and PPI degree dominate the evolutionary rate determinant landscape of average proteins. In contrast, median target Ka/Ks dominates the TF landscape, with other regulatory network properties playing an important role, such as in-degree, median regulator Ka/Ks and the fraction of target genes missing in S. paradoxus. This shows that the regulatory network structure is the most important factor determining TF evolutionary rate, suggesting that the function and evolution of TFs is primarily defined at the network level. The dominance of this so far overlooked relationship between TF and target evolution could also potentially explain the eccentricity of other TF evolutionary trends. The observation that TFs have significantly different evolutionary rate determinants was confirmed individually for each variable earlier in the Results section, using sampling of random proteins and rigorous statistical tests as described in the Methods section.


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

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

Comparison of different genomic and network features influencing TF and protein evolutionary rate.For each determinant, absolute Spearman's rank correlation coefficient (ρ) for TFs is displayed on the left and for all proteins, on the right, with the color of the box representing the direction of the trend. The * indicates the most dominant correlation for each protein set. While CAI is the dominant correlate with Ka/Ks for generic proteins, target gene Ka/Ks is the strongest correlate for TF Ka/Ks.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002734-g003: Comparison of different genomic and network features influencing TF and protein evolutionary rate.For each determinant, absolute Spearman's rank correlation coefficient (ρ) for TFs is displayed on the left and for all proteins, on the right, with the color of the box representing the direction of the trend. The * indicates the most dominant correlation for each protein set. While CAI is the dominant correlate with Ka/Ks for generic proteins, target gene Ka/Ks is the strongest correlate for TF Ka/Ks.
Mentions: In addition to separately assessing the genomic and network correlates of TF evolutionary rate, it is important to compare their relative contributions to identify the most dominant determinants of TF evolutionary rate, and whether they differ from those of generic proteins. Figure 3 shows the Spearman's rank correlation coefficients (ρ) relating different genomic and network properties to Ka/Ks for TFs and for all proteins. This figure clearly shows how features like expression, CAI, which is tightly coupled to expression [17], and PPI degree dominate the evolutionary rate determinant landscape of average proteins. In contrast, median target Ka/Ks dominates the TF landscape, with other regulatory network properties playing an important role, such as in-degree, median regulator Ka/Ks and the fraction of target genes missing in S. paradoxus. This shows that the regulatory network structure is the most important factor determining TF evolutionary rate, suggesting that the function and evolution of TFs is primarily defined at the network level. The dominance of this so far overlooked relationship between TF and target evolution could also potentially explain the eccentricity of other TF evolutionary trends. The observation that TFs have significantly different evolutionary rate determinants was confirmed individually for each variable earlier in the Results section, using sampling of random proteins and rigorous statistical tests as described in the Methods section.

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