Cluster analysis of host cytokine responses to biodefense pathogens in a whole blood ex vivo exposure model (WEEM)
1 Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
2 Computation Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
3 Center for Comparative Medicine, University of California at Davis, Sacramento, CA, 95817, USA
4 Department of Pathology and Laboratory Medicine, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
5 Current address: Department of Biostatistics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
BMC Microbiology 2012, 12:79 doi:10.1186/1471-2180-12-79Published: 20 May 2012
Rapid detection and therapeutic intervention for infectious and emerging diseases is a major scientific goal in biodefense and public health. Toward this end, cytokine profiles in human blood were investigated using a human whole blood ex vivo exposure model, called WEEM.
Samples of whole blood from healthy volunteers were incubated with seven pathogens including Yersinia pseudotuberculosis, Yersinia enterocolitica, Bacillus anthracis, and multiple strains of Yersinia pestis, and multiplexed protein expression profiling was conducted on supernatants of these cultures with an antibody array to detect 30 cytokines simultaneously. Levels of 8 cytokines, IL-1α, IL-1β, IL-6, IL-8, IL-10, IP-10, MCP-1 and TNFα, were significantly up-regulated in plasma after bacterial exposures of 4 hours. Statistical clustering was applied to group the pathogens based on the host response protein expression profiles. The nearest phylogenetic neighbors clustered more closely than the more distant pathogens, and all seven pathogens were clearly differentiated from the unexposed control. In addition, the Y. pestis and Yersinia near neighbors were differentiated from the B. anthracis strains.
Cluster analysis, based on host response cytokine profiles, indicates that distinct patterns of immunomodulatory proteins are induced by the different pathogen exposures and these patterns may enable further development into biomarkers for diagnosing pathogen exposure.