Biophysical Fitness Landscapes for Transcription Factor Binding Sites

Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between 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 the interactions between transcription factors (TFs) and their cognate genomic sites. Our study is informed by high-throughput in vitro measurements of TF-DNA binding interactions and by a comprehensive collection of genomic binding sites. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding energy for a collection of 12 yeast TFs, and show that the shape of the predicted fitness functions is in broad agreement with a simple thermodynamic model of two-state TF-DNA binding. However, the effective temperature of the model is not always equal to the physical temperature, indicating selection pressures in addition to biophysical constraints caused by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, showing that epistasis is common in 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 a spectrum of selection pressures depending on their position in the genome. These findings argue for 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

Genome-Wide Analysis of Natural Selection on Human Cis-Elements

Background: It has been speculated that the polymorphisms in the non-coding portion of the human genome underlie much of the phenotypic variability among humans and between humans and other primates. If so, these genomic regions may be undergoing rapid evolutionary change, due in part to natural selection. However, the non-coding region is a heterogeneous mix of functional and non-functional regions. Furthermore, the functional regions are comprised of a variety of different types of elements, each under potentially different selection regimes. Findings and Conclusions: Using the HapMap and Perlegen polymorphism data that map to a stringent set of putative binding sites in human proximal promoters, we apply the Derived Allele Frequency distribution test of neutrality to provide evidence that many human-specific and primate-specific binding sites are likely evolving under positive selection. We also discuss inherent limitations of publicly available human SNP datasets that complicate the inference of selection pressures. Finally, we show that the genes whose proximal binding sites contain high frequency derived alleles are enriched for positive regulation of protein metabolism and developmental processes. Thus our genome-scale investigation provide

Evolution of Regulatory Sequences in 12 Drosophila Species

Characterization of the evolutionary constraints acting on cis-regulatory sequences is crucial to comparative genomics and provides key insights on the evolution of organismal diversity. We study the relationships among orthologous cis-regulatory modules (CRMs) in 12 Drosophila species, especially with respect to the evolution of transcription factor binding sites, and report statistical evidence in favor of key evolutionary hypotheses. Binding sites are found to have position-specific substitution rates. However, the selective forces at different positions of a site do not act independently, and the evidence suggests that constraints on sites are often based on their exact binding affinities. Binding site loss is seen to conform to a molecular clock hypothesis. The rate of site loss is transcription factor–specific and depends on the strength of binding and, in some cases, the presence of other binding sites in close proximity. Our analysis is based on a novel computational method for aligning orthologous CRMs on a tree, which rigorously accounts for alignment uncertainties and exploits binding site predictions through a unified probabilistic framework. Finally, we report weak purifying selection on short deletions, providing important clues about overall spatial constraints on CRMs. Our results present a complex picture of regulatory sequence evolution, with substantial plasticity that depends on a number of factors. The insights gained in this study will help us to understand the combinatorial control of gene regulation and how it evolves. They will pave the way for theoretical models that are cognizant of the important determinants of regulatory sequence evolution and will be critical in genome-wide identification of non-coding sequences under purifying or positive selection

Probabilistic Clustering of Sequences: Inferring new bacterial regulons by comparative genomics

Genome wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene by gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, 'PROCSE' (probabilistic clustering of sequences), uses Monte-Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data, and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from weight matrices (WMs) when these WMs are unknown. Our analysis suggests that the set of all putative sites for a single genome (e.g. E. coli) is largely inadequate for clustering. When sites from different genomes are combined and all the homologous sites from the various species are used as a block, clustering becomes feasible. We predict 50-100 new regulons as well as many new members of existing regulons, potentially doubling the number of known regulatory sites in E. coli.Comment: 27 pages including 9 figures and 3 table

Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution

The myriad protein-coding genes found in present-day eukaryotes arose from a combination of speciation and gene duplication events, spanning more than one billion years of evolution. Notably, as these proteins evolved, the individual residues at each site in their amino acid sequences were replaced at markedly different rates. The relationship between protein structure, protein function, and site-specific rates of amino acid replacement is a topic of ongoing research. Additionally, there is much interest in the different evolutionary constraints imposed on sequences related by speciation (orthologs) versus sequences related by gene duplication (paralogs). A principal aim of this dissertation is to evaluate and characterize several broad trends in eukaryote protein evolution. To this end, I use sequence-based computational predictors of protein structure (intrinsic disorder and protein secondary structure) and protein function (predicted functional domains), in addition to Bayesian phylogenetic inference methods, to analyze thousands of homologous protein sequence clusters from four eukaryotic lineages: animals, plants, fungi and protists. Using these data, I performed large-scale factorial analyses, testing the correlation between protein structure/function and rates of sequence evolution. The combined results of these analyses somewhat corroborate the findings of previous research in the field, but they also illuminate a subtle interaction among multiple drivers of protein sequence evolution, which is consistently observed across multiple eukaryote groups. Furthermore, using the results of Bayesian phylogenetic analysis on real and simulated protein sequence alignments, I show that orthologous and paralogous proteins exhibit significantly different overall patterns of sequence divergence, indicating that paralogs tend to evolve under relaxed selective pressure. The acquisition of homologous biological sequence clusters is a prominent component of computational biological research. To assist in the identification of protein families within large sequence databases, I implement a simple, graph-based single-linkage clustering procedure, and I demonstrate its capacity to recover homologous subunits of the Rpt regulatory ring in the 26S proteasome complex

Metabolic and Chaperone Gene Loss Marks the Origin of Animals: Evidence for Hsp104 and Hsp78 Sharing Mitochondrial Clients

The evolution of animals involved acquisition of an emergent gene repertoire for gastrulation. Whether loss of genes also co-evolved with this developmental reprogramming has not yet been addressed. Here, we identify twenty-four genetic functions that are retained in fungi and choanoflagellates but undetectable in animals. These lost genes encode: (i) sixteen distinct biosynthetic functions; (ii) the two ancestral eukaryotic ClpB disaggregases, Hsp78 and Hsp104, which function in the mitochondria and cytosol, respectively; and (iii) six other assorted functions. We present computational and experimental data that are consistent with a joint function for the differentially localized ClpB disaggregases, and with the possibility of a shared client/chaperone relationship between the mitochondrial Fe/S homoaconitase encoded by the lost LYS4 gene and the two ClpBs. Our analyses lead to the hypothesis that the evolution of gastrulation-based multicellularity in animals led to efficient extraction of nutrients from dietary sources, loss of natural selection for maintenance of energetically expensive biosynthetic pathways, and subsequent loss of their attendant ClpB chaperones.Comment: This is a reformatted version from the recent official publication in PLoS ONE (2015). This version differs substantially from first three arXiV versions. This version uses a fixed-width font for DNA sequences as was done in the earlier arXiv versions but which is missing in the official PLoS ONE publication. The title has also been shortened slightly from the official publicatio

Inherent limitations of probabilistic models for protein-DNA binding specificity

The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible