Perspectives on Gene Regulatory Network Evolution

Abstract

Animal development proceeds through the activity of genes and their cis-regulatory modules (CRMs) working together in sets of gene regulatory networks (GRNs). The emergence of species-specific traits and novel structures results from evolutionary changes in GRNs. Recent work in a wide variety of animal models, and particularly in insects, has started to reveal the modes and mechanisms of GRN evolution. I discuss here various aspects of GRN evolution and argue that developmental system drift (DSD), in which conserved phenotype is nevertheless a result of changed genetic interactions, should regularly be viewed from the perspective of GRN evolution. Advances in methods to discover related CRMs in diverse insect species, a critical requirement for detailed GRN characterization, are also described.

Keywords: DSD; GRN; cis-regulatory module; developmental system drift; enhancer evolution; evodevo.

Figure 1. A gene regulatory network

A hypothetical GRN functioning in two cells is shown. Heavy lines indicate CRMs and lighter-weight lines TF-DNA or protein-protein interactions (→, transcriptional activation; —|, transcriptional repression; —o>—, ligand-receptor binding; —o)— signal transduction process; see [62]). The TF represented by light blue coloring at the upper left is a “master regulator” whose activity impinges on the expression of many other genes in the network, including its own via autoregulation. For an introduction to GRNs, see Levine and Davidson [63].

Figure 2. Modes of GRN evolution

Examples of at least four different modes of GRN evolution have been described. Each panel depicts cells (hexagons) organized into segments or compartments (light versus dark shading). Cells in the ancestral organism (left) that will experience GRN evolution are outlined in bold. (A) GRN evolution occurs “in place” when the ancestral GRN (red) changes to create a GRN with modified activity (blue) and thus a new phenotype in directly homologous cells or tissues. Alternatively, the ancestral state can be maintained while the GRN is co-opted via one or more changes to produce a modified serial homolog of the original structure (B), or a novel structure or phenotype in a non-homologous group of cells (C). Finally, a “repeal, replace, and redeploy” scenario can occur in which the GRN (red) evolves such that the ancestral function is lost from its original location but new activity is gained in a non-homologous group of cells (blue). The original function may be lost altogether or replaced by a different GRN (purple). Adapted from [6].

Figure 3. DSD in the ant wing GRN

A partial schematic of the ant wing development GRN, based on data from Drosophila, is pictured, with genes represented by boxes and genetic interactions by arrows (activation) or capped lines (repression). No CRM-level data are currently available. Genes shaded in green are altered in expression among the wingless castes of at least some of the five species surveyed. Genes shown in blue are expressed identically in all five species; only brk currently meets this criterion. Genes shaded gray (some names have been omitted for simplicity) have not yet been systematically examined. Figure based on [30].

Figure 4. CRM discovery across distantly-related species

(A) The SCRMshaw method [–48] can be used to identify CRMs in diverged insect species. Training data composed of the sequences of known Drosophila melanogaster CRMs with a common function (e.g., expression in the central nervous system (“CNS”), top of figure) are compared to randomly selected non-coding sequences (“BKG” or “background” sequences). A scoring model (“k-mer model and scores”) is then generated that can be used to search the genome of another insect species to predict CRMs. The method has been shown to be effective at least through 345 million years of holometabolous insect evolution. (C) To help reduce the roughly 25% false-positive prediction rate, SCRMshaw predictions can be merged with putative CRM regions predicted from open chromatin profiling methods (B) such as FAIRE-seq [58] or ATAC-seq [64].