Metabolomic Profiling of Infectious Parapneumonic Effusions Reveals Biomarkers for Guiding Management of Children with Streptococcus pneumoniae Pneumonia

Abstract

Metabolic markers in biofluids represent an attractive tool for guiding clinical management. The aim of this study was to identify metabolic mechanisms during the progress of pleural infection in children with Streptococcus pneumoniae pneumonia. Forty children diagnosed with pneumococcal pneumonia were enrolled and analysis of pleural fluid metabolites categorized by complicated parapneumonic effusions (CPE) and non-CPE was assessed by using 1H-NMR spectroscopy. Multivariate statistical analysis including principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were performed. Metabolites identified were studied in relation to subsequent intervention procedures by receiver operating characteristic (ROC) curve analysis. Ten metabolites significantly different between CPE and non-CPE were identified. A significantly lower level of glucose for glycolysis was found in CPE compared to non-CPE. Six metabolites involving bacterial biosynthesis and three metabolites involving bacterial fermentation were significantly higher in CPE compared to non-CPE. Glucose and 3-hydroxybutyric acid were the metabolites found to be useful in discriminating from receiving intervention procedures. Metabolic profiling of pleural fluid using 1H-NMR spectroscopy provides direct observation of bacterial metabolism in the progress of pneumococcal pneumonia. An increase in the metabolism of butyric acid fermentation of glucose could potentially lead to the need of aggressive pleural drainage.

Discovery and Predictive Modeling of Urine Microbiome, Metabolite and Cytokine Biomarkers in Hospitalized Patients with Community Acquired Pneumonia

“…Conversely, we found uridine to be significantly increased in the urine of all patients with pneumonia. This is in keeping with previous reports of uridine transiently increasing in the lung and BAL fluid of mice with viral pneumonia from influenza 42 .…”

“…cranberry juice) or aspirin and is also a biomarker of OH radicals 40,41 . Succinate was significantly decreased in patients with pneumonia in our studies as well as two other metabolite profiling studies of pneumonia from human pleural fluid and mouse urine infected with S. pneumoniae 18,42 . We found citrate and succinate, metabolites related to the citric acid cycle, to be significantly reduced in all three groups with CAP ( Table 3).…”

Gut microbial‐derived butyrate is inversely associated with IgE responses to allergens in childhood asthma

“…Obtained buckets related to ppm ranges were chosen, and metabolites were identified by Chenomx NMR Suite 8.0 professional software (Chenomx Inc). The normalized 1 H‐NMR bucket data were uploaded to MetaboAnalyst 4.0 (http://www.metaboanalyst.ca) for further analysis . The spectral variables were generalized log‐transformed (glog), mean‐centered, and scaled using Pareto scaling for identifying metabolites contributing to the discrimination between groups using partial least‐squares discriminant analysis (PLS‐DA).…”

Metabolomic Analysis Reveals Distinct Profiles in the Plasma and Urine Associated with IgE Reactions in Childhood Asthma

“…Spot plasma and urine samples prior to spectrum acquisition were prepared as described previously [11,12]. In brief, 500 µL of plasma was mixed with 500 µL phosphate buffer in deuterium water containing 0.08% 3-(trimethylsilyl)-propionic-2,2,3,3-d 4 acid sodium salt (TSP), whereas 900 µL of urine was mixed with 100 µL of 1.5 M phosphate buffer in deuterium water containing 0.04% TSP as an internal chemical shift reference standard.…”

“…As previously established NMR data analysis methods [12], the normalized 1 H-NMR spectra data were transformed using generalized log transformation (glog) and uploaded to MetaboAnalyst 4.0 (http://www.metaboanalyst.ca) to identify metabolites used for discrimination between the groups using partial least squares-discriminant analysis (PLS-DA). Spectral variables were mean-centered and scaled using Pareto scaling.…”