Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks

Abstract

Sequence-specific transcription factors (TFs) bind short DNA sequences in the genome to regulate the expression of target genes. In the last decade, numerous technical advances have enabled the determination of the DNA-binding specificities of many of these factors. Large-scale screens of many TFs enabled the creation of databases of TF DNA-binding specificities, typically represented as position weight matrices (PWMs). Although great progress has been made in determining and predicting binding specificities systematically, there are still many surprises to be found when studying a particular TF's interactions with DNA in detail. Paralogous TFs' binding specificities can differ in subtle ways, in a manner that is not immediately apparent from looking at their PWMs. These differences affect gene regulatory outputs and enable TFs to rewire transcriptional networks over evolutionary time. This review discusses recent observations made in the study of TF-DNA interactions that highlight the importance of continued in-depth analysis of TF-DNA interactions and their inherent complexity. This article is categorized under: Biological Mechanisms > Regulatory Biology.

Keywords: DNA-binding sites; evolution; gene regulatory networks; motifs; specificity; transcription factor-DNA interactions; transcription factors.

Figure 1. Representations of binding specificity

(a) Groups of sequences bound by a TF can be used to create a consensus sequence, represented using IUPAC notation. The group of k-mers themselves can be used to denote sequences bound by the TF. (b) Here, bound sequences are aligned to create a motif, which indicates the probability of each nucleotide at every position within the binding site. Multiple algorithms exist for creating a PWM from high-throughput binding data (reviewed in Stormo, 2013). (c) Machine learning approaches can learn specificity models from binding data, incorporating short k-mer and DNA shape features of the DNA binding sites.

Figure 2. Structural differences between TFs enable divergence in DNA binding

(a) Modular TF families, such as ZFs, contain members with different numbers and arrangements of individual DBDs. (b) Members of a family can contain different amino acids at DNA-contacting positions, as seen in both the fly and mouse HD specificity classes (Berger et al., 2008; Noyes et al., 2008). (c) Differences not at DNA-contacting positions can alter specificity through allosteric mechanisms, as observed in the human ETS factors SAP-1 and Elk-1 (Mo et al., 2000). (d) Protein loops can contact DNA flanking the core recognition motif, adding preferences for DNA shape features, as seen in the yeast S. cerevisiae bHLH proteins Cbf1 and Tye7 (Gordan et al., 2013). (e) DBDs that bind as dimers can recognize sites with different spacer lengths between half sites, as seen in yeast S. cerevisiae bZIP proteins (Gordan et al., 2011). (f) DNA binding along with a co-factor can change the specificity of a TF, as observed in the specificities of the fly Hox protein binding with the cofactors Exd and Hth (Slattery et al., 2011).

Figure 3. Possible effects of cis- and trans- changes to gene regulatory networks

(a) Cis-regulatory mutations to TF binding sites can add or remove genes from a TF’s regulon. (b) Changes to the specificity of trans-acting TFs can rewire the genes regulated by the TF. (c) Gain of co-factor interactions can recruit a TF to newly regulated genes, stabilizing interactions with low affinity binding sites. (d) Cis-regulatory sequences and TFs can co-evolve to maintain the same regulatory logic. (e) TFs can gain new regulatory domains, or interactions with co-factors with regulatory domains, changing the expression of the genes under their control. The arrows denote potentially multiple evolutionary steps.