2292: Broad-Range Detection of Canine Tick-Borne Disease and Improved Diagnostics Using Next-Generation Sequencing
Grant Status: Closed
Diagnostic tests based on the detection of DNA of infectious organisms from clinical samples have revolutionized veterinary medicine in the last decades. Currently, diagnostic panels for several vector-borne organisms are available through universities and private laboratories in the USA and abroad. However, the vast majority of results from clinically ill dogs are negative for tick-borne diseases, which frustrates veterinarians and dog owners trying to reach a definitive diagnosis and improve treatment options. These panels are based on the detection of previously known DNA sequences of each pathogen, with little room for detecting new organisms. Consequently, the current assays may suffer from "myopia": a self-fulfilling effect that prevents the detection of new or emerging organisms. Using an innovative approach, the investigators will employ next-generation sequencing (NGS) to overcome the limitations of current diagnostic technology. With NGS, the investigators can generate millions of individual gene sequencing reads from each clinical sample, allowing for the identification and characterization of multiple organisms from a single sample. Testing samples from dogs naturally exposed to tick-borne diseases, NGS will detect not only new organisms but also characterize genetic differences among known organisms. The resulting dataset of a large number of DNA sequences of known tick-borne organisms and previously undetected organisms in naturally-infected dogs will support the development of diagnostic tools to simultaneously advance canine and human health.
Oney, K., Koo, M., Roy, C., Ren, S., Qurollo, B., Juhasz, N. B., Vasconcelos, E. J. R., Oakley, B., & Diniz, P. P. V. P. (2021). Evaluation of a commercial microbial enrichment kit used prior DNA extraction to improve the molecular detection of vector-borne pathogens from naturally infected dogs. Journal of Microbiological Methods, 106163. https://doi.org/10.1016/j.mimet.2021.106163
Vasconcelos, E. J. R., Roy, C., Geiger, J. A., Oney, K. M., Koo, M., Ren, S., Oakley, B. B., & Diniz, P. P. V. P. (2021). Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing. BMC Veterinary Research, 17(1), 262. https://doi.org/10.1186/s12917-021-02969-9
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