Developing an Early Diagnostic Signature and Deciphering the Microbial-Host Dynamics in Lower Respiratory Tract Infection (LRTI) in Paediatric Intensive Care Unit (PICU) Patients

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1. 

   PREreview

   Dec 18, 2025

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17981544.

   This study leverages a multi-omics approach, combining host transcriptomic profiling and airway microbial analysis, to identify early diagnostic biomarkers of pediatric lower respiratory tract infections (LRTI). Analyzing a cohort of 261 critically ill children from publicly available datasets, Wu et al. identify key pathogens such as RSV and Haemophilus influenzae, and develop a streamlined seven-gene host signature that performs comparably to larger gene panels. These findings are validated in an independent prospective cohort, the RASCALS study, demonstrating promising diagnostic accuracies. The integration of machine learning and network analyses offers deep insights into host immune …

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17981544.

   This study leverages a multi-omics approach, combining host transcriptomic profiling and airway microbial analysis, to identify early diagnostic biomarkers of pediatric lower respiratory tract infections (LRTI). Analyzing a cohort of 261 critically ill children from publicly available datasets, Wu et al. identify key pathogens such as RSV and Haemophilus influenzae, and develop a streamlined seven-gene host signature that performs comparably to larger gene panels. These findings are validated in an independent prospective cohort, the RASCALS study, demonstrating promising diagnostic accuracies. The integration of machine learning and network analyses offers deep insights into host immune pathways and host-microbe interactions, with a gene signature that could potentially be used as a diagnostic tool in the future to predict progression of pediatric LRTI. We recommend minor revisions to clarify methods and provide further discussion to contextualize the study.

   Major Comments:

   • In the methods section, the authors state that samples were taken within 24 hours of patient enrollment, but do not specify the timing of sample collection relative to disease onset, which would be helpful in understanding the diagnostic window of these biomarkers.

     • Authors should include more details in the methods section about when samples were collected relative to symptom onset, hospital admission, or initiation of therapy, as these factors influence biomarker performance.

   • Authors do not include an analysis or discussion on potential confounding factors such as prior antibiotic treatment, co-infections, or underlying comorbidities. This could involve subgroup analyses if data are available.

     • Authors should add a subsection in the discussion discussing the influence of these factors on biomarker performance, or outlining strategies for future research to control or adjust for these variables.

   • The authors mention microbial detection and associated analyses in section 3.4, where they discuss the identification of dominant microbial strains such as RSV and Haemophilus influenzae. Specifically, they note that viral counts were adjusted against water controls and that microbial counts were filtered accordingly: "To control for contaminants, microbial counts were adjusted against water controls. After filtering, viruses were detected in 91% of LRTI samples (107/117) versus 16% of controls (8/50)..." However, the detailed methodology for adjusting microbial load is not elaborated on.

     • Authors could clarify by specifying the exact methods used for contamination control, such as the thresholds set or the statistical models employed during microbial count adjustment, and how microbial abundance was quantified and normalized post-filtering. A supplementary figure showing the pre- and post-filtering microbial distributions could be included to improve clarity.

   Minor Comments:

   • The integration of multi-omics data and classification algorithms is a notable strength of this study, and the development of a simplified seven-gene panel is highly relevant for clinical translation. However, future studies should focus on prospective validation in more diverse and larger cohorts, including different age groups and healthcare settings.

     • Authors could explicitly mention plans for such validation or discuss ongoing efforts to test the biomarkers' performance elsewhere.

   • The focus on key pathogens, such as RSV and H. influenzae, enhances clinical relevance; however, expanding pathogen detection to encompass other atypical or emerging pathogens would improve comprehensive diagnostic capacity.

     • The authors could add a paragraph in the discussion to address the potential of expanding their pathogen panel or mention ongoing or planned studies aimed at including wider pathogen detection.

   Competing interests

   The author declares that they have no competing interests.

   Use of Artificial Intelligence (AI)

   The author declares that they did not use generative AI to come up with new ideas for their review.

   Read the original source
2. 

   Version published to 10.1101/2025.10.31.25335414 on medRxiv

   Nov 6, 2025