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.
Efforts to improve outcomes of lower respiratory tract infections (LRTI), the leading cause of infectious disease-related death in children worldwide, have been hampered by a lack of diagnostic tests capable of reliably informing decisions for targeted antimicrobial therapy. We will develop and test a novel metagenomic sequencing-based approach for LRTI diagnosis and rule-out by studying an existing prospective, multicenter cohort of critically ill children with acute respiratory failure. Our methods aim to change the paradigm of pulmonary diagnostics by enabling simultaneous detection of pathogens, antimicrobial resistance, the airway microbiome and the host?s response from a single sample of respiratory fluid.
