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Peer-reviewedCC BYRespiratory research

Integrated host-microbe biomarkers for early diagnostic classification of paediatric LRTI in mechanically ventilated children.

  • Zongtai Wu1,2
  • Gehad Youssef3,4,5,6
  • Iain R L Kean7
  • Zhenguang Zhang7
  • John A Clark7
  • Nazima Pathan7,8
  • Namshik Han9,10,11,12,13,14
14 affiliations
  1. 1Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
  2. 2Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  3. 3Milner Therapeutics Institute, University of Cambridge, Cambridge, UK. [email protected].
  4. 4Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK. [email protected].
  5. 5Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. [email protected].
  6. 6Department of Quantum Information, Institute for Convergence Science Academy, Yonsei University, Seoul, Republic of Korea. [email protected].
  7. 7Department of Paediatrics, University of Cambridge, Cambridge, UK.
  8. 8Paediatric Intensive Care Unit, Addenbrooke's Hospital, Cambridge, UK.
  9. 9Milner Therapeutics Institute, University of Cambridge, Cambridge, UK. [email protected].
  10. 10Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK. [email protected].
  11. 11Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. [email protected].
  12. 12Department of Quantum Information, Institute for Convergence Science Academy, Yonsei University, Seoul, Republic of Korea. [email protected].
  13. 13Department of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea. [email protected].
  14. 14Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. [email protected].
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Abstract

Background

Early diagnosis of paediatric lower respiratory tract infection (LRTI) in intensive care is difficult because clinical features overlap with non-infectious respiratory failure and conventional microbiology has limited sensitivity. We aimed to integrate airway microbial and host-response signals to identify early diagnostic biomarkers in mechanically ventilated children.Methods

We re-analysed airway metagenomic sequencing and tracheal aspirate transcriptomics from 261 ventilated paediatric intensive care unit (PICU) patients using differential expression, protein-protein interaction network analysis, and machine-learning feature selection. Findings were evaluated in an exploratory assessment of biologically related cytokine markers within an independent prospective cohort (RASCALS; n = 100 enrolled, n = 78 analysed) using non-bronchoscopic mini-bronchoalveolar lavage (mini-BAL), plasma cytokines, and clinician-adjudicated diagnoses.Results

Respiratory syncytial virus and Haemophilus influenzae were the only pathogens consistently enriched in LRTI. Host transcriptomics showed activation of interferon, cytokine, and chemokine signalling. Network-guided feature selection identified a seven-gene panel (IRF7, FFAR3, GZMB, FABP4, FN1, CXCL5, BCAR1) with high diagnostic performance in cross-validation (median F1 0.94), comparable to a published 14-gene model. In RASCALS, a logistic model using mini-BAL IL-1β, IL-4, and IL-8 classified bacterial LRTI with 65% accuracy (sensitivity 70%, specificity 63%), while blood IL-6 and TRAIL achieved 82% accuracy (sensitivity 79%, specificity 83%).Conclusions

Integrating airway microbial profiles with host-response biomarkers supports earlier and more specific LRTI diagnostic classification in ventilated children. The seven-gene panel and cytokine combinations are candidates for rapid PCR- or immunoassay-based bedside tests to inform antimicrobial stewardship.

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Cite

BibTeXRIS
BibTeX
@article{wu2026integrated,
  title = {{Integrated host-microbe biomarkers for early diagnostic classification of paediatric LRTI in mechanically ventilated children.}},
  author = {Zongtai Wu and Gehad Youssef and Iain R L Kean and Zhenguang Zhang and John A Clark and Nazima Pathan and Namshik Han},
  journal = {Respiratory research},
  year = {2026},
  month = jul,
  doi = {10.1186/s12931-026-03829-6},
  url = {https://europepmc.org/article/MED/42958270},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Integrated host-microbe biomarkers for early diagnostic classification of paediatric LRTI in mechanically ventilated children.
AU  - Zongtai Wu
AU  - Gehad Youssef
AU  - Iain R L Kean
AU  - Zhenguang Zhang
AU  - John A Clark
AU  - Nazima Pathan
AU  - Namshik Han
PY  - 2026
DA  - 2026/07/27
JO  - Respiratory research
DO  - 10.1186/s12931-026-03829-6
UR  - https://europepmc.org/article/MED/42958270
AB  - Background  Early diagnosis of paediatric lower respiratory tract infection (LRTI) in intensive care is difficult because clinical features overlap with non-infectious respiratory failure and conventional microbiology has limited sensitivity. We aimed to integrate airway microbial and host-response signals to identify early diagnostic biomarkers in mechanically ventilated children.Methods  We re-analysed airway metagenomic sequencing and tracheal aspirate transcriptomics from 261 ventilated paediatric intensive care unit (PICU) patients using differential expression, protein-protein interaction network analysis, and machine-learning feature selection. Findings were evaluated in an exploratory assessment of biologically related cytokine markers within an independent prospective cohort (RASCALS; n = 100 enrolled, n = 78 analysed) using non-bronchoscopic mini-bronchoalveolar lavage (mini-BAL), plasma cytokines, and clinician-adjudicated diagnoses.Results  Respiratory syncytial virus and Haemophilus influenzae were the only pathogens consistently enriched in LRTI. Host transcriptomics showed activation of interferon, cytokine, and chemokine signalling. Network-guided feature selection identified a seven-gene panel (IRF7, FFAR3, GZMB, FABP4, FN1, CXCL5, BCAR1) with high diagnostic performance in cross-validation (median F1 0.94), comparable to a published 14-gene model. In RASCALS, a logistic model using mini-BAL IL-1β, IL-4, and IL-8 classified bacterial LRTI with 65% accuracy (sensitivity 70%, specificity 63%), while blood IL-6 and TRAIL achieved 82% accuracy (sensitivity 79%, specificity 83%).Conclusions  Integrating airway microbial profiles with host-response biomarkers supports earlier and more specific LRTI diagnostic classification in ventilated children. The seven-gene panel and cytokine combinations are candidates for rapid PCR- or immunoassay-based bedside tests to inform antimicrobial stewardship.
SN  - 1465-9921
SN  - 1465-993X
ER  -