Deciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems Approach

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Project Summary Seasonal influenza epidemics, caused by influenza A and B viruses, result in 3–5 million severe cases and 300,000–500,000 deaths globally each year - especially in high-risk groups such as young children, pregnant women, obese individuals, individuals with a compromised immune system, and indigenous populations. The burden of influenza can vary widely between seasons, in part due to characteristics of the circulating viruses, the existing immunity in the population, and the effectiveness of seasonal influenza vaccines against the circulating virus strains. Disease morbidity and mortality increase when a new influenza strain reasserts or jumps the host and becomes capable of infecting humans. In this case, there is no (or minimal) pre-existing antibody-mediated immunity to the new viral strain at the population level, leading to millions of infections and a rapid global spread of the virus. In the absence of antibodies, the severity of the disease can be ameliorated by broadly cross-reactive cellular immunity. However, the precise mechanism of how immune cells mediate recovery in some individuals, but not others is far from clear. NIAID has made significant investments in the generation of data to improve our understanding of infectious diseases, their progression, risk, and severity as well as treatment and prevention. Not only subject of specific programs, such as CEIRS (Centers of Excellence for Influenza Research and Surveillance) and the ongoing efforts in CIVICs (Collaborative Influenza Vaccine Innovation Centers), but in particular, omics-related programs have generated high-throughput genomic, proteomic, and integrated "omic" data sets, and provided other related resources to the scientific community to promote basic and applied research in infectious diseases. We will make use of these open access datasets and resources available via the Bioinformatics Resource Centers (BRCs) in this application. In particular, we will utilize immune epitope, viral sequence and antiviral drug information from the Influenza Research Database (IRD) and combine these data with other public information from studies of human cohorts infected with the influenza virus. Single-cell data will provide sufficient cellular detail and will serve as “scaffold” in the case that only bulk data is available. In our view, a comprehensive and truly predictive model of these complex relationships can only be achieved through the systematic, integrative, and multi-dimensional OMICS approach that we offer. Host response to vaccination and to influenza infection is the result of complex traits that involve a combination of host factors along with entire networks of transcripts, proteins, glycans and metabolites. Together these responses impact cellular, tissue, and whole organism behaviors. Thus, the host responses to vaccination and infection are an emergent property of molecular networks. The goal of this integrated systems biology approach is to understand mechanisms of heterogeneous response to Influenza by determining how the interactions among biological components compare between high-risk and lower risk populations. Such findings will significantly improve therapeutic options in the fight against these threatening infectious diseases. All the models and the software tools developed through this project will be shared with the community.