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

by | CIRS, Functional & Integrative Medicine, Lyme Disease & Atypical Infections

https://www.frontiersin.org/articles/10.3389/fcimb.2019.00179/full

Comment; Evolving progress in proteomics may provide a key means of diagnosing the evasive Borrelia genus.

Kathryn J. Pflughoeft1,2, 

Michael Mash1,2, Nicole R. Hasenkampf3, 

Mary B. Jacobs3, 

Amanda C. Tardo3, D. Mitchell Magee4, 

Lusheng Song4, 

Joshua LaBaer4, Mario T. Philipp3, Monica E. Embers3 and 

David P. AuCoin1,2*

  • 1DxDiscovery, Inc., Reno, NV, United States
  • 2Department of Microbiology and Immunology, Reno School of Medicine, University of Nevada, Reno, NV, United States
  • 3Division of Bacteriology and Parasitology, Tulane National Primate Research Center, Tulane University Health Sciences Center, Covington, LA, United States
  • 4Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, United States

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.

Dr. Raymond Oenbrink
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