Dataset Information

Dynamics of Transcription Factor Binding Site Evolution.

ABSTRACT: Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ? 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of "pre-sites" or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics.

SUBMITTER: Tugrul M

PROVIDER: S-EPMC4636380 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Dynamics of Transcription Factor Binding Site Evolution.

Tuğrul Murat M Paixão Tiago T Barton Nicholas H NH Tkačik Gašper G

PLoS genetics 20151106 11

Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite popu ...[more]

PMID: 26545200

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Project description:Empirical studies have revealed that regulatory DNA sequences such as enhancers or promoters often harbor multiple binding sites for the same transcription factor. Such "homotypic site clustering" has been hypothesized as arising out of functional requirements of the sequences. Here, we propose an alternative explanation of this phenomenon that multisite enhancers are common because they are favored by evolutionary sampling of the genotype-phenotype landscape. To test this hypothesis, we developed a new computational framework specialized for population genetic simulations of enhancer evolution. It uses a thermodynamics-based model of enhancer function, integrating information from strong as well as weak binding sites, to determine the strength of selection. Using this framework, we found that even when simpler genotypes exist for a desired strength of regulation, relatively complex genotypes (enhancers with more sites) are more readily reached by the simulated evolutionary process. We show that there are more ways to "build" a fit genotype with many weak sites than with a few strong sites, and this is why evolution finds complex genotypes more often. Our claims are consistent with an empirical analysis of binding site content in enhancers characterized in Drosophila melanogaster and their orthologs in other Drosophila species. We also characterized a subtle but significant difference between genotypes likely to be sampled by evolution and equally fit genotypes one would obtain by uniform sampling of the fitness landscape, that is, an "evolutionary signature" in enhancer sequences. Finally, we investigated potential effects of other factors, such as rugged fitness landscapes, short local duplications, and noise characteristics of enhancers, on the emergence of homotypic site clustering. Homotypic site clustering is an important contributor to the complexity and function of cis-regulatory sequences. This work provides a simple null hypothesis for its origin, against which alternative adaptationist explanations may be evaluated, and cautions against "evolutionary mirages" present in common features of genomic sequence. The quantitative framework we develop here can be used more generally to understand how mechanisms of enhancer action influence their composition and evolution.

Project description:Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.

Dataset Information

Dynamics of Transcription Factor Binding Site Evolution.

Publications

Dynamics of Transcription Factor Binding Site Evolution.

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