Metabolomic Profiling of Mice Serum during Toxoplasmosis Progression Using Liquid Chromatography-Mass Spectrometry - PubMed (original) (raw)

Metabolomic Profiling of Mice Serum during Toxoplasmosis Progression Using Liquid Chromatography-Mass Spectrometry

Chun-Xue Zhou et al. Sci Rep. 2016.

Abstract

Better understanding of the molecular changes associated with disease is essential for identifying new routes to improved therapeutics and diagnostic tests. The aim of this study was to investigate the dynamic changes in the metabolic profile of mouse sera during T. gondii infection. We carried out untargeted metabolomic analysis of sera collected from female BALB/c mice experimentally infected with the T. gondii Pru strain (Genotype II). Serum samples were collected at 7, 14 and 21 day post infection (DPI) from infected and control mice and were subjected to liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS)-based global metabolomics analysis. Multivariate statistical analysis identified 79 differentially expressed metabolites in ESI + mode and 74 in ESI - mode in sera of T. gondii-infected mice compared to the control mice. Further principal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) identified 19 dysregulated metabolites (5 in ESI + mode and 14 in ESI - mode) related to the metabolism of amino acids and energy metabolism. The potential utility of these metabolites as diagnostic biomarkers was validated through receiver operating characteristic (ROC) curve analysis. These findings provide putative metabolite biomarkers for future study and allow for hypothesis generation about the pathophysiology of toxoplasmosis.

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Figures

Figure 1

Figure 1. Histopathological lesions in mouse tissues infected with Toxoplasma gondii Pru strain (10 cysts per mouse by oral route, H&E stain).

(A) A section of spleen from infected mice at 14 DPI. Pentagram indicates the largely scattered white pulp; (B) A section of spleen from normal mice at 14 DPI showing no histological abnormalities; (C) Brain histology of infected mice at 21 DPI. Arrow indicates the tissue cyst; (D) Brain histology of normal mice at 21 DPI without any pathological changes. Magnifications: 40X (A,B); 100X (C,D).

Figure 2

Figure 2. Representative total ion current (TIC) chromatograms of infected mice serum obtained in

(A) positive ion mode (ESI+) and (B) negative ion mode (ESI-). Y-axis represents the intensity.

Figure 3

Figure 3. OPLS-DA score plot of T. gondii infected and control groups.

(A) The 7D infected group (7I) and the 7D control group (7C) (R2X = 0.427, R2Y = 0.985,Q2 = 0.829 ESI+; R2X = 0.392, R2Y = 0.987, Q2 = 0.801 ESI−); (B) The 14D infected group (14I) and the 14D control group (14C) (R2X = 0.699, R2Y = 0.996, Q2 = 0.934 ESI+; R2X = 0.771, R2Y = 1, Q2 = 0.944 ESI−); (C) The 21D infected group (21I) and the 21D control group (21C) (R2X = 0.506, R2Y = 0.975,Q2 = 0.877 ESI+; R2X = 0.505, R2Y = 0.997, Q2 = 0.852 ESI−). In the OPLS-DA score plot, each data point represents one mouse serum sample, and the distance between points in the plot indicates the similarity between samples. (a) ESI+; (b) ESI-. x- and y-axes represent PC1 and PC2, respectively.

Figure 4

Figure 4. Comparison of the mice serum metabolomes during the course of infection.

Heat maps representing the significantly changed metabolites between infected groups and the corresponding control groups in ESI+ mode (A–C). Individual samples are separated using hierarchical clustering, with the dendrogram scaled to represent the distance between each branch. Heat maps show a clear separation of metabolomic profile between different groups. Normalized signal intensities (log2 transformed and row adjustment) are visualized as a color spectrum and the scale from least abundant to highest ranges is from −3.0 to 3.0. Green indicates low expression, whereas red indicates high expression of the detected metabolites. (A) 7D infected group vs 7D control; (B) 14D infected group vs 14D control; (C) 21D infected group vs 21D control; (D) Venn diagram shows a clear separation among sample groups at the three indicated infection time points in the ESI + mode; (E) Venn diagram shows a clear separation among sample groups at three indicated infection time points in the ESI- mode.

Figure 5

Figure 5. OPLS-DA score plot showing a clear separation between the infected serum samples at different time points after infection.

(A) The 7D infected group vs the 14D infected group (R2X = 0.782, R2Y = 1, Q2 = 0.953 ESI+; R2X = 0.717, R2Y = 1, Q2 = 0.946 ESI−); (B) The 21D infected group vs the 7D infected group (R2X = 0.433, R2Y = 0.997, Q2 = 0.952 ESI+; R2X = 0.517, R2Y = 0.996, Q2 = 0.954 ESI−); (C) The 21D infected group vs the 14D infected group (R2X = 0.604, R2Y = 0.988, Q2 = 0.93 ESI+; R2X = 0.539, R2Y = 0.957, Q2 = 0.808 ESI−). (a) ESI+, (b) ESI−. X axis is PC1, Y axis is PC2.

Figure 6

Figure 6. Heat map showing the significantly dysregulated metabolites identified between different infected groups in ESI+ mode.

Individual samples are separated using hierarchical clustering, with the dendrogram scaled to represent the distance between each branch. Heat maps show a clear separation of the metabolomic profile between different infected groups. Normalized signal intensities (log2 transformed and row adjustment) are visualized as a color spectrum and the scale from least abundant to highest ranges is from −2.0 to 2.0. Green color indicates low expression, and red color indicates high expression of the detected metabolites. (A) 7D infected group vs 21D infected group; (B) 7D infected group vs 14D infected group; (C) 14D infected group vs 21D infected group.

Figure 7

Figure 7. 3D plot showing clear separation between T. gondii infected samples and controls using PCA analysis in ESI+ mode

(A) and ESI- mode (B).

Figure 8

Figure 8. Comparisons of different matabolites panels based on ROC curves.

ROC curves are generated by Monte-Carlo cross validation (MCCV) using balanced subsampling. PLS-DA algorithm was selected as classification and feature ranking method. In each MCCV, two-thirds of the samples are used to evaluate the feature (metabolite) importance. Different panels of important features are then used to build classification models, which is validated on the one-third samples that were left out. The different models with specific feature numbers and their corresponding AUCs are shown on the figure. (A) Marker metabolites detected in ESI+ mode, (B) Maker metabolites detected in ESI- mode. Var. (variables) indicates the number of selected features.

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