SUMMARY
Lower respiratory tract infections (LRTI) lead to more deaths each year in children than any other infectious
disease category. Despite this, the underlying microbial pathogens are rarely identified due to the limitations of
existing microbiologic tests, resulting in inappropriate antimicrobial use and other adverse outcomes. Viral-
bacterial co-infections and non-infectious inflammatory syndromes resembling LRTI, common in critically ill
patients, further complicate diagnosis. To address the need for improved respiratory diagnostics, we will leverage
an integrated host/microbe metagenomic next-generation sequencing (iHM-mNGS) approach, recently
developed by our group, that simultaneously profiles three central elements of LRTI: the pathogen, microbiome
and host response, from a single sample of respiratory fluid.
We will accomplish our three aims by studying an established prospective, multicenter cohort of 455 critically
ill children with acute respiratory failure requiring mechanical ventilation. Aim 1 will develop and test iHM-mNGS
classifiers designed to: a) accurately diagnose and differentiate LRTI from non-infectious acute respiratory
conditions, and b) rule-out bacterial LRTI with high certainty to permit judicious antimicrobial use. Aim 2 will
develop and test a mNGS model for detecting and differentiating LRTI pathogens from airway commensal
microbes, and then determine the capacity of the model to identify new, previously missed pathogens, in patients
with clinically adjudicated LRTI but negative standard clinical testing. Aim 3 will leverage CRISPR/Cas9 targeted
enrichment methods developed by our group to detect pathogen antimicrobial resistance genes, which could
more quickly inform appropriate antimicrobial therapy. We will develop and test a model to accurately predict
bacterial antimicrobial resistance without a need for culture, and then determine the utility of this approach as a
rapid diagnostic using real-time Nanopore sequencing.
This study will address the need for better LRTI diagnostics by developing and testing advanced, culture-
independent methods that integrate host response and unbiased pathogen detection to achieve accurate LRTI
diagnosis and rule-out in a large multicenter cohort. Our methods aim to change the paradigm of pulmonary
diagnostics by simultaneously profiling host transcripts and microbial sequences from a single sample of
respiratory fluid.
