Toward Higher Resolution Co-Expression Networks of Wood Developing Tissues through Data Integration

Trees grow in a diverse array of habitats and must integrate environmental and developmental signals during the formation of woody tissues. Increasing evidence suggests that these signals are routed through complex transcriptional networks where they are precisely aggregated to elicit appropriate gene expression responses. To resolve such signal-aggregating regulatory pathways involved in wood formation, approaches that consider multiple experiments at the same time are needed. In this talk, we present a data integration analysis of diverse genomic datasets from Populus aimed at more precise understanding of transcriptional and regulatory responses during the wood formation process. Using multiple transcriptome profiling (RNA-seq) experiments, we generated higher-resolution gene co-expression networks, and identified both gene modules conserved across multiple datasets and those limited to specific datasets. Modules were identified that have significant correlations with experiment specific phenotypes, and a meta analysis of these datasets reveals that a subset of these same gene modules were highly conserved across multiple datasets. Furthermore, integration of transcription factor binding experiments (ChIP-seq) reveals that conserved gene modules were enriched for binding from three transcription factors (BELLRINGER, ARBORKNOX 1 and ARBORKNOX 2) that play fundamental roles in vascular cambium regulation. Together, this integrated approach increases the specificity of co-expression models and allows for the identification of conserved regulatory interactions, and suggests that a limited number of genes may be broadly involved in integrating environmental and developmental signals during wood formation.