Metabolomic Analysis in Severe Childhood Pneumonia in The Gambia, West Africa: Findings from a Pilot Study

被引:84
作者
Laiakis, Evagelia C. [1 ,2 ]
Morris, Gerard A. J. [3 ]
Fornace, Albert J., Jr. [1 ,2 ,4 ]
Howie, Stephen R. C. [3 ]
机构
[1] Georgetown Univ, Lombardi Comprehens Canc Ctr, Dept Biochem & Mol & Cellular Biol, Washington, DC USA
[2] Georgetown Univ, Lombardi Comprehens Canc Ctr, Dept Oncol, Washington, DC USA
[3] Med Res Council UK Labs, Bacterial Dis Programme, Banjul, Gambia
[4] Dankook Univ, WCU Res Ctr Nanobiomed Sci, Cheonan, South Korea
基金
英国医学研究理事会;
关键词
GAMMA-RADIATION EXPOSURE; URIC-ACID; GUT MICROBIOTA; BIOMARKERS; CHILDREN; INFLAMMATION; NUCLEOTIDES; MORTALITY; PROFILES; ETIOLOGY;
D O I
10.1371/journal.pone.0012655
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
Background: Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting. Methodology/Principal Findings: Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). 'Unsupervised' (blinded) data were analyzed by Principal Component Analysis (PCA), while 'supervised' (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5'-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size. Conclusions/Significance: Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.
引用
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页码:1 / 9
页数:9
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