A four-gene signature from blood to exclude bacterial etiology of lower respiratory tract infection in adults

Ann R. Falsey (), Derick R. Peterson, Edward E. Walsh, Soumyaroop Bhattacharya, Andrea M. Baran, Chinyi Chu, Angela R. Branche, Daniel P. Croft, Michael Peasley, Anthony M. Corbett, John Ashton and Thomas J. Mariani
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Ann R. Falsey: University of Rochester, Department of Medicine
Derick R. Peterson: University of Rochester, Department of Biostatistics and Computational Biology
Edward E. Walsh: University of Rochester, Department of Medicine
Soumyaroop Bhattacharya: University of Rochester, Department of Pediatrics and Center for Children’s Health Research
Andrea M. Baran: University of Rochester, Department of Biostatistics and Computational Biology
Chinyi Chu: University of Rochester, Department of Pediatrics and Center for Children’s Health Research
Angela R. Branche: University of Rochester, Department of Medicine
Daniel P. Croft: University of Rochester, Department of Medicine
Michael Peasley: University of Rochester, Department of Medicine
Anthony M. Corbett: University of Rochester, Clinical and Translational Science Institute
John Ashton: University of Rochester, Genomics Research Center
Thomas J. Mariani: University of Rochester, Department of Pediatrics and Center for Children’s Health Research

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Unnecessary antibiotic use is a major driver of antimicrobial resistance, an urgent public health threat. Acute respiratory infection (ARI) is a leading cause of inappropriate antibiotic use, creating an unmet need for improved diagnostics to identify bacterial etiology in ARI. In this work we show a 4-gene signature defining the absence of bacterial ARI which may be useful for managing antibiotics in ARI. Hospitalized adults with ARI underwent comprehensive microbiologic testing and those with definitive viral (n = 280), bacterial (n = 129), or mixed viral-bacterial infection (n = 95) had whole blood RNA sequencing. A hard-thresholded, mostly relaxed, LASSO-constrained logistic regression model is used to select a parsimonious gene set (ITGB4, ITGA7, IFI27, FAM20A) highly capable of discriminating any bacterial from nonbacterial infection (cross-validated AUC = 0.90). The 4-gene signature is validated in five independent adult RNAseq cohorts (AUC = 0.89−0.98), two adult microarray cohorts (AUC = 0.73–0.90), and one pediatric pneumonia RNAseq cohort (AUC 0.74). Thresholding the 4-gene risk score to yield 90% sensitivity to detect bacterial infection results in 71% specificity and 91% negative predictive value.

Date: 2025
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DOI: 10.1038/s41467-025-65361-3

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