Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites.
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ABSTRACT:
SUBMITTER: Mustonen V
PROVIDER: S-EPMC2527919 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Phenotypic states and evolutionary trajectories available to cell populations are ultimately dictated by complex interactions among DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by interactions between transcription factors (TFs) and their cognate DNA sites. Our study is informed by a comprehensive collection of genomic binding sites and high-throughput in vitro measurements of TF-DNA binding interactions. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding to show that the shape of the inferred fitness functions is in broad agreement with a simple functional form inspired by a thermodynamic model of two-state TF-DNA binding. However, the effective parameters of the model are not always consistent with physical values, indicating selection pressures beyond the biophysical constraints imposed by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, indicating that epistasis is common in the evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience different selection pressures depending on their position in the genome. These findings support the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.
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Project description:BackgroundDivergence of transcription factor binding sites is considered to be an important source of regulatory evolution. The associations between transcription factor binding sites and phenotypic diversity have been investigated in many model organisms. However, the understanding of other factors that contribute to it is still limited. Recent studies have elucidated the effect of chromatin structure on molecular evolution of genomic DNA. Though the profound impact of nucleosome positions on gene regulation has been reported, their influence on transcriptional evolution is still less explored. With the availability of genome-wide nucleosome map in yeast species, it is thus desirable to investigate their impact on transcription factor binding site evolution. Here, we present a comprehensive analysis of the role of nucleosome positioning in the evolution of transcription factor binding sites.ResultsWe compared the transcription factor binding site frequency in nucleosome occupied regions and nucleosome depleted regions in promoters of old (orthologs among Saccharomycetaceae) and young (Saccharomyces specific) genes; and in duplicate gene pairs. We demonstrated that nucleosome occupied regions accommodate greater binding site variations than nucleosome depleted regions in young genes and in duplicate genes. This finding was confirmed by measuring the difference in evolutionary rates of binding sites in sensu stricto yeasts at nucleosome occupied regions and nucleosome depleted regions. The binding sites at nucleosome occupied regions exhibited a consistently higher evolution rate than those at nucleosome depleted regions, corroborating the difference in the selection constraints at the two regions. Finally, through site-directed mutagenesis experiment, we found that binding site gain or loss events at nucleosome depleted regions may cause more expression differences than those in nucleosome occupied regions.ConclusionsOur study indicates the existence of different selection constraint on binding sites at nucleosome occupied regions than at the nucleosome depleted regions. We found that the binding sites have a different rate of evolution at nucleosome occupied and depleted regions. Finally, using transcription factor binding site-directed mutagenesis experiment, we confirmed the difference in the impact of binding site changes on expression at these regions. Thus, our work demonstrates the importance of composite analysis of chromatin and transcriptional evolution.
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Project description:In comparative genomics one analyzes jointly evolutionarily related species in order to identify conserved and diverged sequences and to infer their function. While such studies enabled the detection of conserved sequences in large genomes, the evolutionary dynamics of regulatory regions as a whole remain poorly understood. Here we present a probabilistic model for the evolution of promoter regions in yeast, combining the effects of regulatory interactions of many different transcription factors. The model expresses explicitly the selection forces acting on transcription factor binding sites in the context of a dynamic evolutionary process. We develop algorithms to compute likelihood and to learn de novo collections of transcription factor binding motifs and their selection parameters from alignments. Using the new techniques, we examine the evolutionary dynamics in Saccharomyces species promoters. Analyses of an evolutionary model constructed using all known transcription factor binding motifs and of a model learned from the data automatically reveal relatively weak selection on most binding sites. Moreover, according to our estimates, strong binding sites are constraining only a fraction of the yeast promoter sequence that is under selection. Our study demonstrates how complex evolutionary dynamics in noncoding regions emerges from formalization of the evolutionary consequences of known regulatory mechanisms.
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Project description:Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available.
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Project description:Recent comparative genomic studies have identified many human accelerated elements (HARs) with elevated substitution rates in the human lineage. However, it remains unknown to what extent transcription factor binding sites (TFBSs) are under accelerated evolution in humans and other primates. Here, we introduce two pooling-based phylogenetic methods with dramatically enhanced sensitivity to examine accelerated evolution in TFBSs. Using these new methods, we show that more than 6000 TFBSs annotated in the human genome have experienced accelerated evolution in Hominini, apes, and Old World monkeys. Although these TFBSs individually show relatively weak signals of accelerated evolution, they collectively are more abundant than HARs. Also, we show that accelerated evolution in Pol III binding sites may be driven by lineage-specific positive selection, whereas accelerated evolution in other TFBSs might be driven by nonadaptive evolutionary forces. Finally, the accelerated TFBSs are enriched around developmental genes, suggesting that accelerated evolution in TFBSs may drive the divergence of developmental processes between primates.
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Project description:In mammals, the cytosine in CG dinucleotides is typically methylated producing 5-methylcytosine (5mC), a chemically less stable form of cytosine that can spontaneously deaminate to thymidine resulting in a T•G mismatched base pair. Unlike other eukaryotes that efficiently repair this mismatched base pair back to C•G, in mammals, 5mCG deamination is mutagenic, sometimes producing TG dinucleotides, explaining the depletion of CG dinucleotides in mammalian genomes. It was suggested that new TG dinucleotides generate genetic diversity that may be critical for evolutionary change. We tested this conjecture by examining the DNA sequence properties of regulatory sequences identified by DNase I hypersensitive sites (DHSs) in human and mouse genomes. We hypothesized that the new TG dinucleotides generate transcription factor binding sites (TFBS) that become tissue-specific DHSs (TS-DHSs). We find that 8-mers containing the CG dinucleotide are enriched in DHSs in both species. However, 8-mers containing a TG and no CG dinucleotide are preferentially enriched in TS-DHSs when compared with 8-mers with neither a TG nor a CG dinucleotide. The most enriched 8-mer with a TG and no CG dinucleotide in tissue-specific regulatory regions in both genomes is the AP-1 motif ( TG: A(C)/GT CA: N), and we find evidence that TG dinucleotides in the AP-1 motif arose from CG dinucleotides. Additional TS-DHS-enriched TFBS containing the TG/CA dinucleotide are the E-Box motif (G CA: GC TG: C), the NF-1 motif (GG CATG: CC), and the GR (glucocorticoid receptor) motif (G-A CATG: T-C). Our results support the suggestion that cytosine methylation is mutagenic in tetrapods producing TG dinucleotides that create TFBS that drive evolution.
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Project description:Flower development is controlled by the action of key regulatory transcription factors of the MADS-domain family. The function of these factors appears to be highly conserved among species based on mutant phenotypes. However, the conservation of their downstream processes is much less well understood, mostly because the evolutionary turnover and variation of their DNA-binding sites (BSs) among plant species have not yet been experimentally determined. Here, we performed comparative ChIP (chromatin immunoprecipitation)-seq experiments of the MADS-domain transcription factor SEPALLATA3 (SEP3) in two closely related Arabidopsis species: Arabidopsis thaliana and A. lyrata which have very similar floral organ morphology. We found that BS conservation is associated with DNA sequence conservation, the presence of the CArG-box BS motif and on the relative position of the BS to its potential target gene. Differences in genome size and structure can explain that SEP3 BSs in A. lyrata can be located more distantly to their potential target genes than their counterparts in A. thaliana. In A. lyrata, we identified transposition as a mechanism to generate novel SEP3 binding locations in the genome. Comparative gene expression analysis shows that the loss/gain of BSs is associated with a change in gene expression. In summary, this study investigates the evolutionary dynamics of DNA BSs of a floral key-regulatory transcription factor and explores factors affecting this phenomenon.
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Project description:Here we describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness.
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Project description:A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the degree of evolutionary conservation of a predicted TFBS. Candidate motifs are constructed by growing compact motif blocks and by concatenating two candidate blocks, allowing 0-15 degenerate positions in between. The resultant patterns are statistically evaluated for their ability to distinguish between target and nontarget genes. Then, a position-based ranking procedure is proposed to enhance the signals of true motifs by collecting position concurrences. Empirical tests on 32 known yeast TFBSs show that the method is highly accurate in identifying gapped motifs, outperforming current methods, and it also works well on ungapped motifs. Predictions on additional 54 TFs successfully discover 11 gapped and 38 ungapped motifs supported by literature. Our method achieves high sensitivity and specificity for predicting experimentally verified TFBSs.
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Project description:To represent the sequence specificity of transcription factors, the position weight matrix (PWM) is widely used. In most cases, each element is defined as a log likelihood ratio of a base appearing at a certain position, which is estimated from a finite number of known binding sites. To avoid bias due to this small sample size, a certain numeric value, called a pseudocount, is usually allocated for each position, and its fraction according to the background base composition is added to each element. So far, there has been no consensus on the optimal pseudocount value. In this study, we simulated the sampling process by artificially generating binding sites based on observed nucleotide frequencies in a public PWM database, and then the generated matrix with an added pseudocount value was compared to the original frequency matrix using various measures. Although the results were somewhat different between measures, in many cases, we could find an optimal pseudocount value for each matrix. These optimal values are independent of the sample size and are clearly correlated with the entropy of the original matrices, meaning that larger pseudocount vales are preferable for less conserved binding sites. As a simple representative, we suggest the value of 0.8 for practical uses.
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