Systems Biology Modeling of Severe Community-Acquired Pneumonia

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Project Summary/Abstract – Project 1 Pandemic community-acquired pneumonia (CAP) secondary to infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) brought the public health importance of CAP into sharp focus. Investigators in the Successful Clinical Response in Pneumonia Therapy (SCRIPT) systems biology center developed a robust research infrastructure to prospectively collect 1,567 serial distal respiratory samples from 595 patients with severe CAP and hospital acquired pneumonia (HAP) requiring mechanical ventilation and analyze these clinical samples using state-of-the art multi-omics approaches. We leveraged these data to generate a systems model of SARS-CoV-2 pathogenesis and applied it toward a successful clinical trial of Auxora, a calcium release activated channel inhibitor, that resulted in a 53% reduction in 30-day mortality in a phase II trial. In Super-SCRIPT (SCRIPT2), we propose to leverage and expand the longitudinal clinical and molecular data in SCRIPT. By applying machine learning to clinical data, we observe that patients with severe pneumonia undergo transitions between distinct, clinically recognizable states over the course of their hospitalization that are associated with more or less favorable outcomes. These transitions will serve as the foundation for a model incorporating preliminary data generated from BAL and serum analysis that includes single-cell RNA-sequencing of more than 500,000 bronchoalveolar lavage cells, cytokine levels, proteomic, T cell epigenomic, and microbiome analyses. We will use these clinical and molecular data to test the hypothesis that machine learning approaches applied to a latent space model of disease pathogenesis can identify molecular predictors of favorable and unfavorable clinical transitions/outcomes during the clinical course of CAP. A corollary hypothesis is that perturbations of these determinants during controlled clinical trials of pharmacologic interventions will allow iteration of the models’ predictive capabilities. We will address these hypotheses in three Specific Aims: Aim 1. To identify clinical predictors of favorable and unfavorable clinical transitions/outcomes over the course of CAP in patients requiring hospitalization. Aim 2. To determine distinct host or pathogen genomic features that predict favorable or unfavorable clinical transitions/outcomes in patients with severe CAP. Aim 3. To identify pathways that can be targeted for therapy with existing or newly developed therapeutics. SCRIPT2 draws on successful collaborations between clinicians, biologists and data scientists to organize clinical data, process distal lung samples and integrate disparate datasets into latent space models to develop large scale models of pneumonia that can be rapidly translated into care pathways and novel therapies.