Quantitative determination of ecological niches for polymicrobial colonization in OM Otitis media (OM) is a common pediatric disease usually caused by polymicrobial infection of the sterile middle ear by commensal bacteria that normally reside in the upper respiratory tract (URT). OM presents a unique and simple paradigm for studying basic mechanisms of host-microbiota interactions, where breakdown of homeostasis between a small community of commensal bacteria, nontypeable H. influenzae (NTHI), S. pneumoniae (Sp), and, M. catarrhalis (Mcat), and the host's immune system by a viral infection (such as RSV or influenza A) can lead to a diseased state. The mechanisms that underlie breakdown of the homeostasis between commensal bacteria and the host are not well understood. Multiple nonlinear interlinked processes spanning a wide range of spatial dimensions (molecular to organ level) and time scales (seconds to days) determine interactions between the main players (bacteria, viruses, immune responses) involved in OM. Therefore, it is difficult to decipher the underlying mechanisms solely through experiments. We propose to use a synergistic combination of computational tools rooted in statistical physics, non-linear population dynamics, and statistics, with wet experiments in the chinchilla URT to determine and quantify ecological niches that underlie polymicrobial infection/colonization of the URT in OM.
In specific aim 1, we will quantitatively determine ecological niches for infection of the chinchilla middle ear by NTHI, Sp, and Mcat.
In aim 2, we will determine mechanisms underlying synergy between one virus and one type of commensal bacteria in the chinchilla URT that could lead to infection of the middle ear by the bacterial species, and in the last aim, we will determine mechanisms that lead to multispecies infection of the middle ear by Mcat and NTHI upon a synergistic viral infection.
OM is one of the most commonly diagnosed pediatric diseases. Vaccines and other therapeutic strategies are inadequate in controlling OM because the mechanisms underlying this polymicrobial infection remain poorly understood. The developed mechanistic in silico model with predictive capabilities will very likely provide strategies for therapeutic interventions and vaccine development against OM.
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