Profiling the Lung Transcriptome for Precision Diagnosis of Respiratory Infections using Host/Pathogen Metagenomic Sequencing

项目总结/文摘

PROJECT SUMMARY/ABSTRACT As a young clinician-investigator, I am focused and committed to improving human wellbeing through patient-oriented respiratory infection research, applied genomics and clinical medicine. This K23 will provide the support and training needed to build a successful research career focused on improving the diagnosis and treatment of respiratory diseases using advanced genomic technologies. Working with my mentor and co-mentors, I have assembled a K23 Research Committee of outstanding and dedicated faculty who are leaders in genomics, pulmonary and critical care medicine, infectious diseases, immunology and bioinformatics. They have helped me devise a robust training plan to acquire expertise in three key areas: 1) clinical research design and analysis, 2) biostatistics and epidemiology and 3) bioinformatics. This plan includes coursework, direct mentoring, hands-on experiences, and weekly manuscript peer review sessions, grant writing seminars and career advisement via the UCSF K Scholars program. The goals of my proposal are inspired by the many critically ill patients that I have cared for with acute respiratory illnesses who receive empiric, sometimes ineffective, and in many cases protracted treatments for acute respiratory illnesses because available diagnostics are unable to provide an informative microbiologic diagnosis. These experiences have made me realize the outstanding need for better assays that can provide a data driven – and not empiric – approach to treating severe lower respiratory tract infections (LRTIs). My K23 proposal directly addresses this need by engaging metagenomic next generation sequencing (mNGS) to assay both the host transcriptome and microbial pathogens from the airways of critically ill patients. An actively enrolling, prospective cohort of adults with acute respiratory failure requiring mechanical ventilation will be studied via three specific aims. Aim 1 will develop a host gene expression classifier that distinguishes LRTI from non-infectious acute respiratory conditions. Aim 2 will evaluate the performance of mNGS for pathogen detection in patients with clinically adjudicated LRTI. Aim 3 will determine the performance of mNGS genome-based antimicrobial resistance prediction in patients with drug-resistant bacterial LRTI. This proposal incorporates metagenomics and bioinformatics with a focus on patient-centered pulmonary infection research. The results generated from this work will provide the foundation for a subsequent prospective clinical trial evaluating the impact of mNGS diagnostics on patient outcomes. This proposal directly aligns with my career goals of becoming an independent physician-scientist working to advance the field of pulmonary medicine by developing new tools that enhance clinical diagnosis and inform precision treatment strategies. Through patient-focused molecular medicine research inspired by unique and challenging cases, I aim to reduce the burden of respiratory infections.