Design of Novel Algorithms for Comparative Analysis of Complex Gene Families and their Application to Nematode Detoxification Pathways
Abstract
Parasitic nematodes present a current and devastating global problem, infecting billions of people, and causing huge production losses in both crops and livestock. There are a limited number of anthelmintic drugs available to treat these infections, and resistance has arisen quickly and spread across the globe. Xenobiotic metabolism is a well-known mechanism of drug resistance in insects, and evidence suggests it may also play a role in the development of drug resistance in parasitic nematodes. Identifying candidate enzymes in the free-living nematodes may help to understand or combat the rising resistance crisis in the parasites. However, identifying many of the protein sequences that may be involved in xenobiotic metabolism can prove challenging due to high sequence divergence and draft quality genome assemblies. This work focuses on novel software to detect hard-to-find genes, as well as methods of performing comparative phylogenetic analyses, both between species and within a population.
In the absence of specific selective pressures, the phylogeny of a multi-species gene family will tend to agree with the underlying species tree. Conversely, adaptive evolution tends to manifest as incongruence in the gene tree as well as lineage-specific expansions and contractions. These properties, collectively termed phylogenetic instability, have been found to be good predictors of proteins that directly interact with the environment. I have developed an algorithm to quantify phylogenetic instability in a gene tree, and show that it correlates exceptionally well with the known substrate specificity of human cytochrome P450 enzymes. I then apply this technique to five detoxification gene families (cyp, fmo, sdr, ugt, gst) from five free-living nematode species (Caenorhabditis elegans, C. briggsae, C. brenneri, C. remanei, and Pristionchus pacificus). These gene families are known to act on both endogenous and xenobiotic molecules, and these new methods allow me to predict which are likely involved in xenobiotic metabolism. This will aid in the study of these enzymes, including their orthologs in the parasitic species.
Description
Keywords
Bioinformatics, Genetics, Parasitology, Veterinary Science
Citation
Curran, D. M. (2017). Design of Novel Algorithms for Comparative Analysis of Complex Gene Families and their Application to Nematode Detoxification Pathways (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25592