Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children
Eran Mick, … , Peter M. Mourani, Charles R. Langelier
Eran Mick, … , Peter M. Mourani, Charles R. Langelier
Published April 3, 2023
Citation Information: J Clin Invest. 2023;133(7):e165904. https://doi.org/10.1172/JCI165904.
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Clinical Research and Public Health Infectious disease Pulmonology

Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children

Abstract

BACKGROUND Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODS We used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTS The host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSION Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDING Support for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.

Authors

Eran Mick, Alexandra Tsitsiklis, Jack Kamm, Katrina L. Kalantar, Saharai Caldera, Amy Lyden, Michelle Tan, Angela M. Detweiler, Norma Neff, Christina M. Osborne, Kayla M. Williamson, Victoria Soesanto, Matthew Leroue, Aline B. Maddux, Eric A.F. Simões, Todd C. Carpenter, Brandie D. Wagner, Joseph L. DeRisi, Lilliam Ambroggio, Peter M. Mourani, Charles R. Langelier

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Figure 1

Study overview.

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Study overview. Pediatric patients with acute respiratory failure requir...
Pediatric patients with acute respiratory failure requiring mechanical ventilation were clinically adjudicated into 4 LRTI status groups. The patients in the Definite and No Evidence groups, whose LRTI status was presumed to be known, were used to develop an integrated host/microbe mNGS classifier for LRTI and to evaluate its performance by cross-validation. The classifier was then applied to the patients in the Suspected and Indeterminate groups, whose LRTI status was considered uncertain. The integrated mNGS classifier takes into account a host probability of LRTI, derived from the host gene counts, and features of any viral or bacterial/fungal pathogens, derived from the nonhost (microbial) taxon counts.