Project Description:
Parasitism is the dominant form of existence among flatworms and its evolution within the group involved wholesale changes to their body plan and ontogeny. Such change in form and function is underpinned by the evolution of transcription factors (TF) which act as genomic ‘switches’ that control gene expression during ontogeny. Consistent with this are observations of high rates of TF expansion and loss and of sequence substitution outside of their conserved DNA binding domains. Sequenced genomes typically show an unusually high number of ‘orphan’ (i.e. taxonomically restricted; sometimes referred to as ORFans) genes within otherwise conserved classes of TF (e.g. zinc finger, forkhead, homeobox, basic helix loop helix, etc.). This project will identify the full genomic complements of flatworm TF and determine the level to which they are taxonomically restricted using available, comprehensive suites of gene models from both free-living (planarians, Macrostomum) and parasitic (e.g. bloodflukes, liverflukes, tapeworms, monogeneans) flatworm species. This will be augmented by additional novel genomic data and gene models for key taxa in the group. The expression of tapeworm species- and class-specific TF will be then examined empirically in a model system (Hymenolepis microstoma) using RNAseq and in situ hybridization to determine when and where unique TF are expressed throughout its complex life cycle, giving insight into the role ‘orphan’ TF play in the evolution of the group. The PhD student will be trained in a broad range of contemporary bioinformatic, empirical and imaging techniques that are readily transferable to other biological questions and systems.
Policy Impact of Research:
Elucidating the genetic basis of organismal change is central to our understanding evolution and adaptation and transcription factors are key components of the cis-regulatory mechanisms of the genome. Acquiring the ability to manage large data sets and interrogate them using bioinformatic tools, and to employ empirical tools for genetic manipulations in the laboratory, will help ameliorate identified skill gaps.
