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. 2010 Sep 9;5(9):e12655.
doi: 10.1371/journal.pone.0012655.

Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study

Evagelia C Laiakis  1 , Gerard A J Morris, Albert J Fornace, Stephen R C Howie
Affiliations

Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study

Evagelia C Laiakis et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Map of The Gambia, showing hospitals and major health centres and the coastal region in which the study was conducted.
Figure 2
Figure 2. Analysis of the control and pneumonia samples utilizing SIMCA-P+ and Random Forests revealed differences in ion abundance between the two groups.
Panels A and B show PCA scores plots for urine (ESI+ mode) and for plasma (ESI - mode), respectively. PCA analysis is an unsupervised method of extracting information, where the classes (i.e. experimental groups) are unknown. Panels C and D show the S-plots constructed from the supervised OPLS analysis of urine (ESI+ mode) and plasma (ESI- mode) respectively. Ions with the highest abundance and correlation in the pneumonia group with respect to the controls are present on the upper far right hand quadrant, whereas ions with the lowest abundance and correlation in the pneumonia group with respect to the control group are residing in the lower far left hand quadrant. Ions are marked with either their identity or a number corresponding to Table 1. Panels E and F show heatmaps for urine (ESI+ mode) and plasma (ESI- mode) respectively. The heatmaps were constructed based on the top fifty metabolites of importance, which were extracted with Random Forests analysis. Variable differences are revealed between the control and pneumonia groups, with verified and unknown ions marked on the right corresponding to Table 1. The parallel analysis of the samples with SIMCA-P+ and Random Forests allows for the ability to verify that ions, which are identified through both ways (i.e. hypoxanthine), are highly significant, as depicted through two completely different algorithms. Additionally, it allows for the increase of the numbers of ions that are potential candidates for biomarkers.
Figure 3
Figure 3. Relative changes of urinary ions that were verified with tandem mass spectrometry.
Data is represented at the mean ± SE of the peak areas extracted through the TOFMS data (with * representing p<0.05 and ** representing p<0.01). The real peak areas were normalized to each sample's respective creatinine ([M+H]+ = 114.0667 m/z) peak area.
Figure 4
Figure 4. Relative changes of the plasma ions that were verified with tandem mass spectrometry.
Data is represented as mean ± SE of the peak areas extracted through the TOFMS data (with * representing p<0.05 and ** representing p<0.01). Unlike the urine samples, plasma samples do not require normalization to a particular metabolite since the volumes obtained are tightly controlled. Uric acid (Panel A) is upregulated in pneumonia plasma levels, although not statistically significant (p = 0.119). This is in contrast to the urine findings. Hypoxanthine and glutamic acid levels in Panels B and C are significantly upregulated in pneumonia samples. On the other hand, L-tryptophan and adenosine-5′-diphosphate (ADP) in Panels D and E are significantly downregulated in pneumonia samples.
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