Exploring the heterogeneity of immune response to viral and bacterial infection

Infectious diseases are the result of a complex molecular warfare between the host immune system and the pathogen. Our understanding of these interactions, and consequently diagnosis of infectious diseases is limited by their heterogeneous nature. We hypothesized that leveraging biological and technical heterogeneity present in publicly-available data would improve our understanding of infectious diseases, and identify potential biomarkers for accurate diagnosis. The work presented here emphasizes the importance of incorporating the heterogeneity of host responses to infection; we used thousands of different samples across multiple independent datasets to identify and validate a conserved transcriptional host response to 1) respiratory viral infections and 2) acute bacterial infections. First, we conducted an integrated multi-cohort analysis of publicly available gene expression data and identified a common host gene signature across different viral infections. We downloaded 18 microarray gene expression datasets, comprising 2,939 samples obtained from seven different countries. The meta-virus signature can 1) distinguish viral and bacterial infections, and 2) predict symptom onset in infected individuals. We then conducted a second, independent analysis, and identified an influenza-specific host-response signature, which can distinguish influenza-infected samples from those with bacterial or other respiratory viral infections, can be used as a prognostic marker in influenza-pneumonia patients, and is predictive of response to the flu vaccine. Next, to study the host response to acute bacterial infection, we analyzed over 3,000 human samples from 46 cohorts across 14 countries. We identified a set of 131 genes that identified bacterial infections, with potential applications as a diagnostic and prognostic tool. Then, we conducted in silico cellular deconvolution and determined the contribution of specific immune cell types to this response. Finally, we used a non-linear dimensionality reduction approach to create a model of the transcriptional response to microbial infection, recapitulating specific patient trajectories in disease space. Finally, in order to study the heterogeneity in the response to infection at the single cell level, we characterized the response to bacterial infection of macrophages infected with the intracellular bacteria Salmonella typhimurium. By using live-cell time-lapse microscopy of Salmonella-infected macrophages, we show that single cell response to Salmonella is highly heterogeneous in a clonal population of infected cells. Our data suggest that MAPK and NF-kB signaling pathways have complementary functions, with MAPK activation fine-tuning the immune response in order to distinguish the 'level of threat' in each individual cell. Interestingly, our results reveal a potential set of innate immune pathways being activated by intracellular Salmonella early on during infection, which renders infected cells unresponsive to extracellular stimuli and thus potentially unable to fight the invading pathogen. Exploring macrophage-Salmonella interactions at the single-cell level has provided us with unprecedented insights into the host signaling pathways that play a role during bacterial infection. In conclusion, we took a systems-level approach to identify novel factors involved in the immune response to infection. Defining the metrics that characterize the immune response to different pathogens goes beyond the concept of identifying biomarkers, as our model can also be used as a platform to uncover the biology underlying host-pathogen interactions.

Keywords: Immunology.
Advisor: Khatri Purvesh
Monack Denise M. 
Davis Mark M. 
Schneider David (David Samuel)