Pathogen class-specific transcriptional responses derived from PBMCs accurately discriminate between fungal, bacterial, and viral infections

Immune responses during acute infection often contain canonical elements which are shared across the responses to an array of agents within a given pathogen class (i.e., respiratory viral infection). Identification of these shared, canonical elements across similar infections offers the potential for impacting development of novel diagnostics and therapeutics. In this way, analysis of host gene expression patterns ('signatures') in white blood cells has been shown to be useful for determining the etiology of some acute viral and bacterial infections. In order to study conserved immune elements shared across the host response to related pathogens, we performed in vitro human PBMC challenges with common fungal pathogens (Candida albicans, Cryptococcus neoformans and gattii); four strains of influenza virus (Influenza A/Puerto Rico/08/34 [H1N1, PR8], A/Brisbane/59/2007 [H1N1], A/Solomon Islands/3/2006 [H1N1], and A/Wisconsin/67/2005 [H3N2]); and gram-negative (Escherichia coli) and gram-positive (Streptococcus pneumoniae) bacteria. Exposed human cells were then analyzed for differential gene expression utilizing Affymetrix microarrays. Analysis of pathogen exposure of PBMCs revealed strong, conserved gene expression patterns representing these canonical immune response elements to each broad pathogen class. A 41-gene multinomial signature was developed which correctly classified fungal, viral, or bacterial exposure with 94-98% accuracy. Furthermore, a 21-gene signature consisting of a subset of the discriminatory PBMC-derived genes was capable of accurately differentiating human patients with invasive candidiasis, acute viral infection, or bacterial infection (AUC 0.94, 0.83, and 0.96 respectively). These data reinforce the conserved nature of the genomic responses in human peripheral blood cells upon exposure to infectious agents and highlight the potential for in vitro models to augment our ability to develop novel diagnostic classifiers for acute infectious diseases, particularly devastating fungal infections.