Evolution of transcription factor DNA binding sites

Abstract

In bioinformatics, binding of transcription regulatory factors to the cognate binding sites is usually described by sequence-specific binding energy, which is estimated from a training sample of sites. This model implies that all binding sites with binding energy above some threshold are functional and site sequence variations should be considered neutral until they do not reduce this energy below the threshold. To quantify this energy, the binding profile (positional weight matrix, PWM) model or consensus-based model is usually applied. Here we show that in many cases available data are not sufficient to construct a relevant PWM, and modified consensus-based model could be more effective to describe binding properties.Further, using the data about binding sites of several transcription factors, we demonstrate that some non-consensus nucleotides in borthologous sitesQ (that is, binding sites of the same factor upstream of orthologous genes), which have been believed to be irrelevant or even hindering the regulation, are evolutionary very stable and specific for the regulated gene. For each two considered genomes, the number of substitutions between non-consensus nucleotides is far less than the expected number of neutral substitutions. Moreover, in several positions of binding sites regulating different genes, there are non-consensus nucleotides conserved in distant genomes. It means that there exists a selection pressure, which results in the stability of non-consensus nucleotides. D

Evidence That Purifying Selection Acts on Promoter Sequences

“…However, the relationship between binding, function, and fitness is not well understood (Mirny and Gelfand 2002). In some instances, there exists a correlation between binding energy and substitution rate (Brown and Callan 2004), but this may not always be the case (Kotelnikova et al 2005). Furthermore, nonbinding nucleotides may exert some effect on transcription (Mai et al 2000;Mirny and Gelfand 2002;Abnizova et al 2007;Wozniak and Hughes 2008) and potentially on fitness.…”

Weak Negative and Positive Selection and the Drift Load at Splice Sites

“…This fact clearly indicates that selection does not always favor the maximal strength of an SS, either because the strong SSs are deleterious by themselves (e.g., because excess site strength impedes regulation; Zheng et al 2000; Ast 2004; Garg and Green 2007) or because of other competing selective constraints (Kotelnikova et al 2005; Stergachis et al 2013). Within exonic parts of SSs, such constraints may arise from negative selection on codon usage; in both intronic and exonic parts of SSs, they may also arise due to selection on splicing regulators or some other noncoding function (e.g., Stergachis et al 2013).…”

Incorporating evolution of transcription factor binding sites into annotated alignments

“…Moses et al (2003) use yeast species for analysis and propose the use of an evolutionary model (Halpern and Bruno 1998) for modelling position-specifi c evolution of TFBSs. Kotelnikova et al (2005) conclude from bacterial binding site data that TFBSs exhibit selectional constraints on degenerate positions also. As would be expected, positions with clear bias towards a single nucleotide tend to maintain their nucleotide preference throughout evolution, implying strong conservation.…”

“…Much research has gone into the study of the evolution of binding sites, (McCue et al 2002;Gerland and Hwa 2002;Dermitzakis and Clark 2002;Moses et al 2003;Berg et al 2004;Ludwig et al 2005;Kotelnikova et al 2005;Mustonen and Lässig 2005;Wittkopp 2006). It has been shown that (i) binding sites evolve slower than the surrounding sequences and (ii) they exhibit varying rates of evolution at each position.…”