HPMI: Host Pathogen Mapping Initiative

宿主病原体图谱2.0计划

THE HOST PATHOGEN MAP INITIATIVE 2.0 OVERALL SUMMARY The Host-Pathogen Map Initiative (HPMI) 2.0 is an interdisciplinary program that aims to improve our understanding of the interactions between host cellular systems and respiratory pathogens and builds upon the success of its previous iteration. The ultimate goals are identifying therapeutic targets, treatment modalities, and predicting disease severity. HPMI 2.0 will focus its efforts on the bacterial pathogen Mycobacterium tuberculosis (Mtb) and viral respiratory pathogens such as SARS-CoV-2 and variants of concern, e.g. omicron and delta, as well as influenza, parainfluenza virus, and respiratory syncytial virus, among other RNA viruses. We will use proteomics, genetics and structural biology approaches to study the host factors relevant to these infectious diseases in disease-relevant cell models and patient samples, and combine our data with existing -omic datasets. Network and structure modeling approaches will be used to integrate these datasets to make testable predictions about proteins, complexes and pathways in the host regulating infection as well as disease prognosis. Our proposal will be centered on the profiling of human samples to decipher networks underlying infectious respiratory diseases. To generate clinically relevant datasets on viral respiratory disease, we will use human lung primary cells and three-dimensional human airway organoids (HAO) for systems biology analyses, determine the global proteome of plasma samples from SARS-CoV-2-infected patients, and integrate our data with clinical datasets. To uncover the determinants of the heterogeneity of susceptibility to TB, we will utilize genome-wide association studies (GWAS) for TB susceptibility genes combined with whole genome sequences of the infecting Mtb strain in a cohort of TB patients in Vietnam at the Oxford University Clinical Research Unit (OUCRU). We will further profile alveolar macrophages and peripheral blood derived macrophages from healthy donors to understand mechanisms of innate immune responses to Mtb infection and cell-type specific features. We hypothesize that different TB clinical outcomes are regulated by specific molecular networks in infected macrophages. Our models for the identification of combinatorial biomarkers to predict disease outcomes will be assessed on the Mtb-infected cohort in Vietnam and COVID-19 patients at UCSF. Lastly, we will explore potential commonalities between Mtb and SARS-CoV-2, as severe forms of TB and COVID-19 are accompanied by exacerbated inflammatory responses and “cytokine storms”. A better molecular understanding of the functions and mechanisms of host-pathogen complexes may reveal new therapeutic strategies for intervention, including strategies of host-directed therapies that circumvent the limitations of current drug regimens using antibiotics or antivirals where mutations in the pathogen proteins can diminish drug efficacy. Our host-pathogen cell maps will help build interpretable deep learning systems using state-of-the-art modeling approaches for prediction of infectious disease risk, disease severity and combinatorial risk factors for M. tuberculosis and SARS-CoV-2 infection in clinical settings.