ABSTRACT
BACKGROUND: Gene regulatory networks (GRNs) underlie developmental patterning and morphogenetic processes, and changes in the interactions within the underlying GRNs are a major driver of evolutionary processes. In order to make meaningful comparisons that can provide significant insights into the evolution of regulatory networks, homologous networks from multiple taxa must be deeply characterized. One of the most thoroughly characterized GRNs is the dorsoventral (DV) patterning system of the Drosophila melanogaster embryo. We have developed the wasp Nasonia as a comparative DV patterning model because it has shown the convergent evolution of a mode of early embryonic patterning very similar to that of the fly, and it is of interest to know whether the similarity at the gross level also extends to the molecular level. RESULTS: We used RNAi to dorsalize and ventralize Nasonia embryos, RNAseq to quantify transcriptome-wide expression levels, and differential expression analysis to identify genes whose expression levels change in either RNAi case. This led to the identification of >100 genes differentially expressed and regulated along the DV axis. Only a handful of these genes are shared DV components in both fly and wasp. Many of those unique to Nasonia are cytoskeletal and adhesion molecules, which may be related to the divergent cell and tissue behavior observed at gastrulation. In addition, many transcription factors and signaling components are only DV regulated in Nasonia, likely reflecting the divergent upstream patterning mechanisms involved in producing the conserved pattern of cell fates observed at gastrulation. Finally, several genes that lack Drosophila orthologs show robust and distinct expression patterns. These include genes with vertebrate homologs that have been lost in the fly lineage, genes that are found only among Hymenoptera, and several genes that entered the Nasonia genome through lateral transfer from endosymbiotic bacteria. CONCLUSIONS: Altogether, our results provide insights into how GRNs respond to new functional demands and how they can incorporate novel components.
