Multi-platform Approach for Microbial Biomarker Identification Using Borrelia burgdorferi as a Model.

Autor:	Pflughoeft KJ; DxDiscovery, Inc., Reno, NV, United States.; Department of Microbiology and Immunology, Reno School of Medicine, University of Nevada, Reno, NV, United States., Mash M; DxDiscovery, Inc., Reno, NV, United States.; Department of Microbiology and Immunology, Reno School of Medicine, University of Nevada, Reno, NV, United States., Hasenkampf NR; Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States., Jacobs MB; Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States., Tardo AC; Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States., Magee DM; Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, United States., Song L; Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, United States., LaBaer J; Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, United States., Philipp MT; Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States., Embers ME; Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States., AuCoin DP; DxDiscovery, Inc., Reno, NV, United States.; Department of Microbiology and Immunology, Reno School of Medicine, University of Nevada, Reno, NV, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in cellular and infection microbiology [Front Cell Infect Microbiol] 2019 Jun 11; Vol. 9, pp. 179. Date of Electronic Publication: 2019 Jun 11 (Print Publication: 2019).
DOI:	10.3389/fcimb.2019.00179
Abstrakt:	The identification of microbial biomarkers is critical for the diagnosis of a disease early during infection. However, the identification of reliable biomarkers is often hampered by a low concentration of microbes or biomarkers within host fluids or tissues. We have outlined a multi-platform strategy to assess microbial biomarkers that can be consistently detected in host samples, using Borrelia burgdorferi , the causative agent of Lyme disease, as an example. Key aspects of the strategy include the selection of a macaque model of human disease, in vivo Microbial Antigen Discovery (InMAD), and proteomic methods that include microbial biomarker enrichment within samples to identify secreted proteins circulating during infection. Using the described strategy, we have identified 6 biomarkers from multiple samples. In addition, the temporal antibody response to select bacterial antigens was mapped. By integrating biomarkers identified from early infection with temporal patterns of expression, the described platform allows for the data driven selection of diagnostic targets.
Databáze:	MEDLINE
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