Project Summary This innovative integrated systems biology application seeks to delineate the complex host/pathogen interactions occurring at the alveolar level that lead to unsuccessful response to therapy in serious pneumonia. To achieve this objective, we will leverage our unique access to alveolar fluid collected as part of routine clinical care in mechanically ventilated patients with suspected pneumonia in our medical intensive care unit. Bronchoalveolar lavage fluid will be obtained serially from well characterized mechanically ventilated patients with Pseudomonas or Acinetobacter pneumonia. Both of these CDC-designated serious hazard level pathogens have clinical failure rates as high as 50%. A robust clinical definition will allow comparison of both host and pathogen signatures associated with failure of therapy vs. success. These clinical specimens and extensive patient phenomics will anchor two mutually supportive and iterative research projects. Project One will deploy robust tools for flow sorting myeloid cell populations, isolating RNA from these populations, and performing RNA-Seq and epigenomics (ATAC-Seq) to compare successful and unsuccessful host response. Project Two will focus on both specific pathogen genomic profiles associated with unsuccessful outcome and the differential microbiome response. Changes in microbiome communities will be comprehensively assessed by shotgun deep sequencing to detect bacteriophage, other virus, and fungal DNA, in addition to bacterial. A Technical Core will perform cell sorting of BAL macrophage and lymphocyte subsets, RNA sequencing, and whole genome methylation, as well as perform the mouse pneumonia model studies. A Data Management and Bioinformatics Core will develop tools to reduce the dimensionality of these large comprehensive datasets, including the clinical phenomics, and provide them to the modeling core and the research community in an interactive format. The Modeling Core will then use an ecosystem-based approach to the complex adaptive system combined with unique machine learning tools and neural networks to generate biomarkers in the host, pathogen and/or microbiome predictive of successful pneumonia outcome. Predictive biomarkers developed in the modeling core will be validated in a prospective cohort of patients in whom analogous data will be generated. These data will be used iteratively to refine or improve the model/biomarkers. Biomarkers will also be validated in an independent human cohort and tested for causality in standard murine models and in a humanized alveolar macrophage mouse model. The Administrative Core will perform the outward-facing role of education and outreach to the community and sponsor, as well as regularly exchanging datasets, analytic tools, and specimens with NIH-sponsored/approved repository sites.
|Effective start/end date||1/17/18 → 12/31/22|
- National Institute of Allergy and Infectious Diseases (3U19AI135964-05S1)
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