Enhancer evolution in chordates: Lessons from functional analyses of cephalochordate cis-regulatory modules

Chordates comprise three major groups, cephalochordates (amphioxus), tunicates (urochordates), and vertebrates. Since cephalochordates were the early branching group, comparisons between amphioxus and other chordates help us to speculate about ancestral chordates. Here, I summarize accumulating data from functional studies analyzing amphioxus cis-regulatory modules (CRMs) in model systems of other chordate groups, such as mice, chickens, clawed frogs, fish, and ascidians. Conservatism and variability of CRM functions illustrate how gene regulatory networks have evolved in chordates. Amphioxus CRMs, which correspond to CRMs deeply conserved among animal phyla, govern reporter gene expression in conserved expression domains of the putative target gene in host animals. In addition, some CRMs located in similar genomic regions (intron, upstream, or downstream) also possess conserved activity, even though their sequences are divergent. These conservative CRM functions imply ancestral genomic structures and gene regulatory networks in chordates. However, interestingly, if expression patterns of amphioxus genes do not correspond to those of orthologs of experimental models, some amphioxus CRMs recapitulate expression patterns of amphioxus genes, but not those of endogenous genes, suggesting that these amphioxus CRMs are close to the ancestral states of chordate CRMs, while vertebrates/tunicates innovated new CRMs to reconstruct gene regulatory networks subsequent to the divergence of the cephalochordates. Alternatively, amphioxus CRMs may have secondarily lost ancestral CRM activity and evolved independently. These data help to solve fundamental questions of chordate evolution, such as neural crest cells, placodes, a forebrain/midbrain, and genome duplication. Experimental validation is crucial to verify CRM functions and evolution.     

Decoding the genome with an integrative analysis tool: combinatorial CRM Decoder

The identification of genome-wide cis-regulatory modules (CRMs) and characterization of their associated epigenetic features are fundamental steps toward the understanding of gene regulatory networks. Although integrative analysis of available genome-wide information can provide new biological insights, the lack of novel methodologies has become a major bottleneck. Here, we present a comprehensive analysis tool called combinatorial CRM decoder (CCD), which utilizes the publicly available information to identify and characterize genome-wide CRMs in a species of interest. CCD first defines a set of the epigenetic features which is significantly associated with a set of known CRMs as a code called ‘trace code’, and subsequently uses the trace code to pinpoint putative CRMs throughout the genome. Using 61 genome-wide data sets obtained from 17 independent mouse studies, CCD successfully catalogued ∼12 600 CRMs (five distinct classes) including polycomb repressive complex 2 target sites as well as imprinting control regions. Interestingly, we discovered that ∼4% of the identified CRMs belong to at least two different classes named ‘multi-functional CRM’, suggesting their functional importance for regulating spatiotemporal gene expression. From these examples, we show that CCD can be applied to any potential genome-wide datasets and therefore will shed light on unveiling genome-wide CRMs in various species.     

MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs

The identification of cis-regulatory modules (CRMs) can greatly advance our understanding of eukaryotic regulatory mechanism. Current methods to predict CRMs from known motifs either depend on multiple alignments or can only deal with a small number of known motifs provided by users. These methods are problematic when binding sites are not well aligned in multiple alignments or when the number of input known motifs is large. We thus developed a new CRM identification method MOPAT (motif pair tree), which identifies CRMs through the identification of motif modules, groups of motifs co-occurring in multiple CRMs. It can identify 'orthologous' CRMs without multiple alignments. It can also find CRMs given a large number of known motifs. We have applied this method to mouse developmental genes, and have evaluated the predicted CRMs and motif modules by microarray expression data and known interacting motif pairs. We show that the expression profiles of the genes containing CRMs of the same motif module correlate significantly better than those of a random set of genes do. We also show that the known interacting motif pairs are significantly included in our predictions. Compared with several current methods, our method shows better performance in identifying meaningful CRMs.     

Systematic identification of mammalian regulatory motifs' target genes and functions

We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.