Creation of Bioinformatics Approaches to Understand Host-Pathogen Molecular Mimicry

Rich, Kaylee Dawn

Creation of Bioinformatics Approaches to Understand Host-Pathogen Molecular Mimicry

dc.contributor.advisor	Wasmuth, James D.
dc.contributor.author	Rich, Kaylee Dawn
dc.contributor.committeemember	Gilleard, John
dc.contributor.committeemember	Lewis, Ian
dc.contributor.committeemember	Peters, Nathan
dc.date	2024-11
dc.date.accessioned	2024-09-11T19:54:39Z
dc.date.available	2024-09-11T19:54:39Z
dc.date.issued	2024-09-10
dc.description.abstract	Imitation is not the sincerest form of flattery; for some it is the key to survival. Across phyla, pathogens imitating aspects of their host is crucial to their continued survival and proliferation. Also called molecular mimicry, imitation of host biomolecules has been observed to target a wide range of processes, including host signaling pathways and the immune system. Identification of molecular mimicry can provide insights into pathogen virulence and pathogenicity. Additionally, in silico detection methods can predict currently unknown host-pathogen interactions. Here, I describe my development of a bioinformatic method for mimicry detection using protein sequence data, with the ultimate aim to further our collective understanding of molecular mimicry. In Chapter 2, I resurrected and improved upon a bioinformatic k-mer based mimicry detection method, which I assessed on 32 human-infecting pathogens. During my assessment, I exposed potential concerns with the method and recommended avenues for further improvement. Chapter 3 describes my critical evaluation of each step in the pipeline and subsequent development of mimicDetector, a new pipeline for molecular mimicry identification. During my evaluation, I assessed the performance of five protein search tools with two amino acid substitution scoring matrices on varying query lengths. From these results, I determined the combination best suited to mimicry detection and implemented a new filtering method which better assesses similarity between pathogen and host protein sequences. Lastly, in Chapter 4, I applied mimicDetector to a focused investigation of the causative agent of human tuberculosis, Mycobacterium tuberculosis (Mtb). Among my predicted mimics were potentially novel Mtb-human protein interactions as well as a protein frequently implicated in Mtb molecular mimicry. My investigation highlights strategies used by Mtb to subvert host defenses and provides insight into the role mimics play in pathogenicity and virulence.
dc.identifier.citation	Rich, K. D. (2024). Creation of bioinformatics approaches to understand host-pathogen molecular mimicry (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/119690
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	bioinformatics
dc.subject	protein interactions
dc.subject	host-pathogen interactions
dc.subject	parasitology
dc.subject	infectious disease
dc.subject	autoimmunity
dc.subject	pathogenicity
dc.subject	molecular mimicry
dc.subject	mycobacteria
dc.subject	tuberculosis
dc.subject.classification	Bioinformatics
dc.title	Creation of Bioinformatics Approaches to Understand Host-Pathogen Molecular Mimicry
dc.type	doctoral thesis
thesis.degree.discipline	Veterinary Medical Sciences
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.