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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 and their targets co-evolve as modules.Each data point is based on a TF with 3 or more targets. (A) TF Ka/Ks as a function of the median Ka/Ks of target genes. (B) TF Ka/Ks as a function of the fraction of target genes missing an ortholog in S. paradoxus (lost in S. paradoxus or gained in S. cerevisiae). Numbers above the bars represent the number of TFs in the bin.
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pcbi-1002734-g002: TFs and their targets co-evolve as modules.Each data point is based on a TF with 3 or more targets. (A) TF Ka/Ks as a function of the median Ka/Ks of target genes. (B) TF Ka/Ks as a function of the fraction of target genes missing an ortholog in S. paradoxus (lost in S. paradoxus or gained in S. cerevisiae). Numbers above the bars represent the number of TFs in the bin.

Mentions: To understand the evolutionary behavior of TFs, it is imperative that we study the evolution of their target genes. The function of TFs is inherently expressed through the regulation of their target genes and this network-centric role of TFs might be what distinguishes their evolution from that of other proteins. Using the ChIP-chip based regulatory network and Spearman's rank correlation coefficient (ρ), we asked whether median target evolutionary rate was predictive of TF evolutionary rate. As shown in Figure 2A, we discovered that the evolutionary rate of TFs significantly follows the median rate of its target genes (ρ = 0.25, p = 0.0033), suggesting that TFs and their target genes constitute co-evolving modules. Figure S2A shows that the correlation holds using Ka/Ks values obtained from comparing S. cerevisiae to its next closest sequenced cousin, S. mikatae (ρ = 0.23, p = 0.0059). We also confirmed the significance of this effect using the network of confirmed edges (ρ = 0.23, p = 0.020) and using an alternative regulatory network based entirely on literature curation of small-scale experimental studies [15] (ρ = 0.26, p = 0.0018), henceforth referred to as the literature curated network. As shown in Figure S3, Ka/Ks itself cannot be used to predict regulatory interactions in general, but it does provide some predictive power in the TF subnetwork (predicting TFs that regulate TFs). Furthermore, we show that targets of the same TF in the network of confirmed edges tend to have closer than expected evolutionary rates (p = 0.011) and mRNA expression levels (p = 1.13×10−4), using the Wilcoxon rank-sum test (see Methods for details) than targets of different TFs. Although the co-evolution of co-regulated genes is easily explained by their similar expression levels, the co-evolution of TFs and their target genes indicates that TF evolution is directly influenced by their position and role in the regulatory network.


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

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

TFs and their targets co-evolve as modules.Each data point is based on a TF with 3 or more targets. (A) TF Ka/Ks as a function of the median Ka/Ks of target genes. (B) TF Ka/Ks as a function of the fraction of target genes missing an ortholog in S. paradoxus (lost in S. paradoxus or gained in S. cerevisiae). 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-g002: TFs and their targets co-evolve as modules.Each data point is based on a TF with 3 or more targets. (A) TF Ka/Ks as a function of the median Ka/Ks of target genes. (B) TF Ka/Ks as a function of the fraction of target genes missing an ortholog in S. paradoxus (lost in S. paradoxus or gained in S. cerevisiae). Numbers above the bars represent the number of TFs in the bin.
Mentions: To understand the evolutionary behavior of TFs, it is imperative that we study the evolution of their target genes. The function of TFs is inherently expressed through the regulation of their target genes and this network-centric role of TFs might be what distinguishes their evolution from that of other proteins. Using the ChIP-chip based regulatory network and Spearman's rank correlation coefficient (ρ), we asked whether median target evolutionary rate was predictive of TF evolutionary rate. As shown in Figure 2A, we discovered that the evolutionary rate of TFs significantly follows the median rate of its target genes (ρ = 0.25, p = 0.0033), suggesting that TFs and their target genes constitute co-evolving modules. Figure S2A shows that the correlation holds using Ka/Ks values obtained from comparing S. cerevisiae to its next closest sequenced cousin, S. mikatae (ρ = 0.23, p = 0.0059). We also confirmed the significance of this effect using the network of confirmed edges (ρ = 0.23, p = 0.020) and using an alternative regulatory network based entirely on literature curation of small-scale experimental studies [15] (ρ = 0.26, p = 0.0018), henceforth referred to as the literature curated network. As shown in Figure S3, Ka/Ks itself cannot be used to predict regulatory interactions in general, but it does provide some predictive power in the TF subnetwork (predicting TFs that regulate TFs). Furthermore, we show that targets of the same TF in the network of confirmed edges tend to have closer than expected evolutionary rates (p = 0.011) and mRNA expression levels (p = 1.13×10−4), using the Wilcoxon rank-sum test (see Methods for details) than targets of different TFs. Although the co-evolution of co-regulated genes is easily explained by their similar expression levels, the co-evolution of TFs and their target genes indicates that TF evolution is directly influenced by their position and role in the regulatory network.

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