Abstract
Respiratory microbial dysbiosis is associated with acute respiratory distress syndrome (ARDS) and hospital-acquired pneumonia (HAP) in critically ill patients. However, we lack reproducible respiratory microbiome signatures that can increase our understanding of these conditions and potential treatments. Here, we analyze 16S rRNA sequencing data from 2,177 respiratory samples collected from 1,029 critically ill patients (21.7% with ARDS and 26.3% with HAP) and 327 healthy controls, sourced from 17 published studies. After data harmonization and pooling of individual patient data, we identified microbiota signatures associated with ARDS, HAP and prolonged mechanical ventilation. Microbiota signatures for HAP and prolonged mechanical ventilation were characterized by depletion of a core group of microbes typical of healthy respiratory samples, and the ARDS microbiota signature was distinguished by enrichment of potentially pathogenic respiratory microbes, including Pseudomonas and Staphylococcus. Using machine learning models, we identified clinically informative, three- and four-factor signatures that predicted ARDS, HAP and prolonged mechanical ventilation with relatively high accuracy (area under the curve of 0.751, 0.72 and 0.727, respectively). We validated the signatures in an independent prospective cohort of 136 patients on mechanical ventillation and found that patients with microbiome signatures associated with ARDS, HAP or prolonged mechanical ventilation had longer times to successful extubation than patients lacking these signatures (hazard ratios of 1.56 (95% confidence interval (CI) 1.07–2.27), 1.51 (95% CI 1.02–2.23) and 1.50 (95% CI 1.03–2.18), respectively). Thus, we defined and validated robust respiratory microbiome signatures associated with ARDS and HAP that may help to identify promising targets for microbiome therapeutic modulation in critically ill patients.
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Data availability
Correspondence and requests should be addressed to A.R. The 16S rRNA sequence dataset of the validation cohort has been deposited in a secure file by CHU Nantes (HAP2 data hub) and in the BioProject repository (PRJNA1020542). The data from the studies included in the meta-analysis can be found at figshare at https://figshare.com/s/46c402c6465c8a289c18, https://doi.org/10.6084/m9.figshare.3496412, https://doi.org/10.6084/m9.figshare.3496538, https://doi.org/10.6084/m9.figshare.3485201 and in the BioProject repository: PRJEB26875, PRJEB13056, PRJEB20913, PRJNA267584, SRP062137, PRJEB20665, PRJNA678854, PRJNA595346, SRP112361, PRJNA553560, PRJNA553560, SRP076183, PRJNA339755. The accession for each study is reported in Supplementary Tables 1–3.
Code availability
All analyses were performed using publicly available software and published code as described in the Methods. All custom codes generated for data analyses in this study are reported in the Methods.
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Acknowledgements
We thank the Genomics and Bioinformatics Core Facility of Nantes (GenoBiRD, Biogenouest) for its technical support and the Biological Resource Center for Biobanking of CHU Nantes (Centre de Ressources Biologiques, Nantes, France; BRIF no. BB-0033-00040). We thank D. Flattres Duchaussoy (CHU Nantes) for administrative assistance. The study was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 847782 (HAP2 project). A.R. and R.D. received grants from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 847782. A.R. received funding from the Agence National de la Recherche (ANR) PROGRAM project and the MSD Avenir grant (Phenomenon project). R.D. was funded by the NIH (grants R01HL144599 and K24HL159247). S.V.L. was funded by the NIH (grants RO1 HL143998, P01 AI148104 and P01 AI128482). G.D.K. received funding from the NIH (grants K23 HL139987 and R03 HL162655) and Karius. B.J.K. received funding from the NIH (grant K23 AI121485). R.P.D. received funding from the NIH (grants R01 HL144599 and K24 HL159247) and Horizon H2020 (grant no. 847782). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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E.M., G.D.K., R.P.D. and A.R. selected studies, performed data analyses, interpreted results and drafted the manuscript. J.E.R., Q.L.B., B.J.K., A.P., S.L. and C.S.C. interpreted results and revised the manuscript. All authors have approved the final manuscript for publication.
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A.R. has received grants from MSD and bioMerieux, and consulting fees from MSD. G.D.K. has received funding from Karius. S.V.L. is a co-founder and board member of Siolta Therapeutics and has received consulting fees from Sanofi. All other authors have no competing interests.
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Montassier, E., Kitsios, G.D., Radder, J.E. et al. Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia. Nat Med 29, 2793–2804 (2023). https://doi.org/10.1038/s41591-023-02617-9
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DOI: https://doi.org/10.1038/s41591-023-02617-9
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