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

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $160,405

## Abstract

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 respirat...

## Key facts

- **NIH application ID:** 9987702
- **Project number:** 5K23HL138461-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Charles Langelier
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $160,405
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987702

## Citation

> US National Institutes of Health, RePORTER application 9987702, Profiling the Lung Transcriptome for Precision Diagnosis of Respiratory Infections using Host/Pathogen Metagenomic Sequencing (5K23HL138461-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9987702. Licensed CC0.

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