Utilizing Bioinformatics to Develop a Tighter Scope for Lyme Disease
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Sam Charlie
CoPIs:
J. Loaisiga-Mora, H. Hidalgo, J.A. Ballesteros
College:
The Dorothy and George Hennings College of Science, Mathematics, and Technology
Major:
Biology
Faculty Research Advisor(s):
Jesus Ballesteros Chavez
Abstract:
Lyme disease, also known as borreliosis, is a vector-borne illness caused by the bacterium Borrelia burgdorferi and its close relatives. Borreliosis alternate ticks (intermediate host), and their definitive host includes diverse mammal species (including humans), although it does not always result in the manifestation of illness. While the most common bacterium species associated with Lyme disease is Borrelia burgdorferi, there are about 50 species under this genus but only a few are consistently documented to carry and transmit the parasite that causes Lyme disease.
Ticks in the genus Ixodes (Arachnida: Parasitiformes) are the only known vector of Lyme. While the genus counts hundreds of known species, only a handful are known to carry and transmit the Borrelia parasite. The most prominent of these species is the black-legged or deer tick: Ixodes scapularis.
Recently, there has been a growing concern about the increase and spread of Lyme disease in the US; due in part to the steady growth of the populations of mammal hosts that are in close contact with human habitation (white-tail deer), potentially triggering a cascade effect that could lead to an increase in the incidence of human infections.
Lyme most usually manifests with multiple nonspecific symptoms, including bull’s-eye-like rashes, flu-like symptoms, joint pain, limb weakness, and chronic fatigue. On top of that, it is notoriously hard to detect and diagnose the presence of the parasite with current serological methods, which rely on detecting antibodies for the parasite and have been suggested to lead to a false positive rate.
The motivation of the current study is to explore, develop, and test a method to detect the presence of Borrelia genomes from genomic sequences of their known hosts. Here, we leverage publicly available genomic sequences from infected human and tick hosts. Our strategy makes use of reference Borrelia, human, and Ixodes scapularis genome sequences. These are used iteratively to identify each species' reads using short-read alignment methods (e.g., Bowtie). Finally, reads potentially belonging to Borrelia are assembled into contigs and validated via whole genome sequence alignment and BLAST. Read aligners to explore qualitatively and quantitatively the genomic signatures of the presence of Borrelia from short-read datasets. Finally, we present and discuss our result in terms of the qualitative accuracy and its potential to provide quantitative data that could offer alternatives for the genomic detection of this parasite; potentially expanding the known range of hosts.