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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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TFs co-evolve with activated targets, but not with repressed targets.Edge signs are inferred from TF knock-out expression data. Each data point is based on a TF with 5 or more targets regulated in the same direction. (A) Median Ka/Ks of activated target genes as a function of TF Ka/Ks. (B) Median Ka/Ks of repressed target genes as a function of TF Ka/Ks. (C) Fraction of activated targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. (D) Fraction of repressed targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. Numbers above the bars represent the number of TFs in the bin.
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pcbi-1002734-g005: TFs co-evolve with activated targets, but not with repressed targets.Edge signs are inferred from TF knock-out expression data. Each data point is based on a TF with 5 or more targets regulated in the same direction. (A) Median Ka/Ks of activated target genes as a function of TF Ka/Ks. (B) Median Ka/Ks of repressed target genes as a function of TF Ka/Ks. (C) Fraction of activated targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. (D) Fraction of repressed targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. Numbers above the bars represent the number of TFs in the bin.

Mentions: Regulatory networks are composed of two inherently distinct edge types, activating (or positive) edges and repressive (or negative) edges, which could potentially play divergent roles on the evolutionary modularity of the network. We used previously published TF knock-out microarray data [22] to infer the sign of ChIP-chip based regulatory network edges. Using the microarray fold-changes (see Methods for details), we were able to infer the mode of regulation for 4,010 of the ChIP-chip regulatory edges, 2,628 activating and 1,382 repressive. By overlaying these two datasets, we decomposed the network into positive and negative regulatory subnetworks and studied how the mode of regulation affects TF-target evolutionary relationships. For TFs with 5 or more targets of the same regulatory sign, we found that median Ka/Ks of activated targets significantly follows TF Ka/Ks (ρ = 0.26, p = 0.0036), while median Ka/Ks of repressed targets shows no significant correlation (ρ = 0.068, p = 0.46). We also found that TF Ka/Ks predicts the fraction of activated targets which are missing in the comparison species S. paradoxus (ρ = 0.29, p = 0.0038) but not for repressed targets (ρ = −0.079, p = 0.52). Table 2 shows the correlation coefficients and associated p-values for activating and repressive networks, where transcriptional edges are inferred either from ChIP-chip or from literature curation of small-scale experimental studies [15]. As shown in Table 2, both the significance of the activating edge relations and the lack of a significant trend for repressive edge relations were confirmed using the literature curated network. Figure 5 shows how activated and repressed target evolutionary properties have a different effect on TF Ka/Ks. These results demonstrate that TFs evolve in synchrony with the targets they activate but not the targets they repress.


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

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

TFs co-evolve with activated targets, but not with repressed targets.Edge signs are inferred from TF knock-out expression data. Each data point is based on a TF with 5 or more targets regulated in the same direction. (A) Median Ka/Ks of activated target genes as a function of TF Ka/Ks. (B) Median Ka/Ks of repressed target genes as a function of TF Ka/Ks. (C) Fraction of activated targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. (D) Fraction of repressed targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. Numbers above the bars represent the number of TFs in the bin.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002734-g005: TFs co-evolve with activated targets, but not with repressed targets.Edge signs are inferred from TF knock-out expression data. Each data point is based on a TF with 5 or more targets regulated in the same direction. (A) Median Ka/Ks of activated target genes as a function of TF Ka/Ks. (B) Median Ka/Ks of repressed target genes as a function of TF Ka/Ks. (C) Fraction of activated targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. (D) Fraction of repressed targets missing an ortholog in S. paradoxus as a function of TF Ka/Ks. Numbers above the bars represent the number of TFs in the bin.
Mentions: Regulatory networks are composed of two inherently distinct edge types, activating (or positive) edges and repressive (or negative) edges, which could potentially play divergent roles on the evolutionary modularity of the network. We used previously published TF knock-out microarray data [22] to infer the sign of ChIP-chip based regulatory network edges. Using the microarray fold-changes (see Methods for details), we were able to infer the mode of regulation for 4,010 of the ChIP-chip regulatory edges, 2,628 activating and 1,382 repressive. By overlaying these two datasets, we decomposed the network into positive and negative regulatory subnetworks and studied how the mode of regulation affects TF-target evolutionary relationships. For TFs with 5 or more targets of the same regulatory sign, we found that median Ka/Ks of activated targets significantly follows TF Ka/Ks (ρ = 0.26, p = 0.0036), while median Ka/Ks of repressed targets shows no significant correlation (ρ = 0.068, p = 0.46). We also found that TF Ka/Ks predicts the fraction of activated targets which are missing in the comparison species S. paradoxus (ρ = 0.29, p = 0.0038) but not for repressed targets (ρ = −0.079, p = 0.52). Table 2 shows the correlation coefficients and associated p-values for activating and repressive networks, where transcriptional edges are inferred either from ChIP-chip or from literature curation of small-scale experimental studies [15]. As shown in Table 2, both the significance of the activating edge relations and the lack of a significant trend for repressive edge relations were confirmed using the literature curated network. Figure 5 shows how activated and repressed target evolutionary properties have a different effect on TF Ka/Ks. These results demonstrate that TFs evolve in synchrony with the targets they activate but not the targets they repress.

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
Related in: MedlinePlus