Towards an Evolutionary Model of Transcription Networks

Abstract

DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs), we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF) and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

“…A large phylogeny is important to be able to systematically observe patterns of conservation and divergence and to study different factors such as gene duplication that can contribute to regulatory network divergence. Existing approaches to infer regulatory networks for multiple species have either not attempted to explicitly model the phylogeny of the species involved (Penfold et al, 2015; Joshi et al, 2014), or their applications have been restricted to two or three species (Xie et al, 2011; Penfold et al, 2015). Extending such approaches to infer genome-scale networks for a large phylogeny with complex orthologies can be computationally expensive.…”

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes

“…Such relationships are naturally represented by a tree, and computational approaches that can incorporate the tree structure while identifying regulatory modules and networks have been useful in understanding evolutionary (Xie et al 2011;Roy et al 2013;Shay et al 2013) and developmental processes . CMINT is based on a previous module inference algorithm that we developed for species lineages (Roy et al 2013) with two major extensions.…”

Functional primate genomics—leveraging the medical potential

“…This approach works in yeasts (see e.g., [91] for a recent review or [92] for a recent analysis in fission yeasts) and starts to be applied in mammals. One example is the modeling of sequence differences, expression, and transcription factor binding in preimplantation development using human, mouse, and cow stem cells that allowed the identification of conserved and species-specific regulatory networks in these species [93]. Extending this approach to primates and to a variety of phenotypes, especially those of medical relevance, should be a worthwhile endeavor:…”