Thesis
Bioinformatic predictions of the Anaplasma phagocytophilum Type-4 Effectome
Washington State University
Master of Science (MS), Washington State University
05/2024
DOI:
https://doi.org/10.7273/000006929
Abstract
Bacteria have evolved distinct secretion systems capable of transporting substrates across the bacterial outer surface. The Type III and Type IV Secretion Systems (T3SS and T4SS) are employed by pathogenic bacteria to secrete effector proteins into the host cell. Effector proteins function to as virulence factors and can have a variety of different functions. Identification of bacterial effectors remains challenging across different species of bacteria as effector proteins are not conserved across species, and the molecular signatures defining what an effector protein is, are not well understood. Several T4SS effector prediction algorithms have been developed; however, the output of these programs does not overlap well. Our group has developed a machine learning algorithm, OPT4e, that has performed well in de novo predictions of Legionella pneumophila effectors. We have used this program to predict effectors for Anaplasma phagocytophilum, a tick-borne zoonotic pathogen. By examining the predicted T4 effector repertoires for strains that have been sourced from a divergent set of hosts and geographic regions, we can begin to see a core set of effectors predicted in all strains of A. phagocytophiulm, as well as effectors that are unique to some strains. To our knowledge this is the first multistrain analysis of the effectome of A. phagocytophilum.
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Details
- Title
- Bioinformatic predictions of the Anaplasma phagocytophilum Type-4 Effectome
- Creators
- Michael A. Dodd
- Contributors
- Kelly A Brayton (Chair)Dana K Shaw (Committee Member)Susan M Noh (Committee Member)Anders Omsland (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- College of Veterinary Medicine
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 43
- Identifiers
- 99901125241001842
- Language
- English
- Resource Type
- Thesis