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Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia
Published on: June 28, 2018
A multi-omics case-control study identifying oropharyngeal microbiome-metabolite patterns that characterize secondary
Hong Zhang1, Ran He1, Lei Xu1
1Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Abstract:
Secondary bacterial pneumonia is a severe complication of influenza;howeve the biological determinants that distinguish progression from uncomplicated infection remain poorly understood. We investigated the oropharyngeal microbiome and plasma metabolome as potential discriminators of pneumonia development. In this study, we report a cross-sectional case-control study conducted during the 2022-2023 influenza season to identify and internally validate a microbiome-metabolite profile that characterizes pneumonia cases from uncomplicated influenza. We enrolled 236 consecutive influenza patients from Jiangsu Province, China (October 2023-December 2024): 59 with secondary pneumonia and 177 uncomplicated controls. Oropharyngeal swabs were subjected to 16S rRNA V3-V4 sequencing; plasma metabolomics was performed by UPLC-MS/MS in both ion modes. Seven machine-learning algorithms were compared; Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression was selected because it yielded the highest cross-validated discrimination. Microbial composition distinguished groups, not richness. Pneumonia cases showed enrichment of Synergistota and Bifidobacteriaceae with depletion of Bacillaceae (β-diversity p = 0.057). Controls exhibited enriched glycolysis and lipid metabolism pathways; pneumonia cases showed elevated degradation pathways (GLUCARDEG and GALLATE-DEGRADATION). Plasma metabolomics revealed a lipid depletion signature: phospholipids PC(O-16:0/0:0) and PS(14:0/18:3(9Z,12Z,15Z)) were significantly reduced (area under the (receiver operating characteristic) curves (AUCs) = 0.69-0.71). Small Molecule Pathway Database (SMPDB) pathway analysis demonstrated suppressed anabolic (tyrosine, steroid, and purine metabolism) and enhanced catabolic (beta-oxidation of very long-chain fatty acids) pathways. Machine learning identified Peptococcus as the top indicator (LASSO AUC = 0.65); Shapley Additive Explanation (SHAP) analysis revealed a monotonic risk increase with abundance. Oropharyngeal dysbiosis and systemic metabolic reprogramming characterize influenza cases that progress to secondary pneumonia. Peptococcus and four metabolites form an internally validated exploratory profile associated with secondary pneumonia; external validation and performance optimization are warranted.
Insights
The oropharyngeal microbiome and plasma metabolome can predict secondary bacterial pneumonia after influenza. Specific microbial changes and metabolic reprogramming, including elevated Peptococcus, characterize patients who develop pneumonia.
Area of Science:
- Microbiology and Metabolomics
- Infectious Disease Pathogenesis
- Computational Biology and Machine Learning
Background:
- Secondary bacterial pneumonia is a critical complication of influenza, but the underlying biological factors are not fully understood.
- Identifying biomarkers to predict pneumonia development in influenza patients is crucial for timely intervention and improved outcomes.
Purpose of the Study:
- To identify and validate a microbiome-metabolite profile that distinguishes secondary pneumonia cases from uncomplicated influenza.
- To investigate the role of oropharyngeal microbial composition and plasma metabolome in predicting pneumonia development.
Main Methods:
- A cross-sectional case-control study involving 236 influenza patients (59 pneumonia, 177 controls).
- Oropharyngeal swabs analyzed using 16S rRNA sequencing; plasma metabolomics performed via UPLC-MS/MS.
- Machine learning algorithms, including LASSO logistic regression, used for profile identification and validation.
Main Results:
- Microbial composition, not richness, differentiated pneumonia cases, showing enrichment of Synergistota and Bifidobacteriaceae, and depletion of Bacillaceae.
- Pneumonia cases exhibited altered metabolic pathways, including suppressed anabolic processes and enhanced catabolic pathways like beta-oxidation.
- Machine learning identified Peptococcus as a key microbial indicator, and a profile of four metabolites showed association with secondary pneumonia.
Conclusions:
- Oropharyngeal dysbiosis and systemic metabolic reprogramming are characteristic of influenza patients who develop secondary pneumonia.
- An exploratory microbiome-metabolite profile, including Peptococcus and specific metabolites, shows potential for identifying secondary pneumonia risk.
- Further external validation and optimization of the identified profile are necessary for clinical application.
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