Time-resolved metabolomics analysis of individual differences during the early stage of lipopolysaccharide-treated rats

Abstract

Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production and eventually cause fatal sepsis syndrome. Individual toxicity difference of LPS has been widely reported. In our study we observed that two thirds of the rats (24/36) died at a given dose of LPS, while the rest (12/36) survived. Tracking the dynamic metabolic change in survival and non-survival rats in the early stage may reveal new system information to understand the inter-individual variation in response to LPS. As the time-resolved datasets are very complex and no single method can elucidate the problem clearly and comprehensively, the static and dynamic metabolomics methods were employed in combination as cross-validation. Intriguingly, some common results have been observed. Lipids were the main different metabolites between survival and non-survival rats in pre-dose serum and in the early stage of infection with LPS. The LPS treatment led to S-adenosly-methionine and total cysteine individual difference in early stage, and subsequent significant perturbations in energy metabolism and oxidative stress. Furthermore, cytokine profiles were analyzed to identify potential biological associations between cytokines and specific metabolites. Our collective findings may provide some heuristic guidance for elucidating the underlying mechanism of individual difference in LPS-mediated disease.

Figure 1

(a) Scheme of the animal experiment protocol in the present study. D1: a rat died from 2 h to 6 h; D15: 15 rats died from 6 h to 12 h; D6: 6 rats died within 12 h to 24 h; D2: 2 rats died from 24 h to 48 h. (b) The LPS group (n = 36) animals were injected with LPS (10 mg/kg) and mortality was monitored for 72 h.

Figure 2. The time-dependent trajectory of the metabolic profiles of the control and the LPS groups.

Each dot represents the average metabolic status at a certain time; bar lines indicate the SEs of PC1 and PC2 in the PCA model.

Figure 3. A heatmap representing the metabolic changes in survival and non-survival rats within 0 h to 6 h.

Green square indicates a reduction of up to two folds, white square indicates no significant fold change, and red square indicates an increase of up to two folds.

Figure 4. SUS plot of survival and non-survival rats at 2 h and 6 h, respectively.

All metabolites not significant (|p(corr)| <0.5) in either model were removed in this plot to enhance visualization.

Figure 5. Leverage/SPE scatter plots of the ASCA variables submodels for phenotype, time and their interactions.

Metabolites in red have high loadings that follow the expression patterns of the submodels. Metabolites in blue have expression patterns that are different from the major patterns.

Figure 6

(a) Time course of serum concentrations of a panel of cytokines. MIP, macrophage in flammatory protein. The nonparametric Mann-Whitney test was employed to assess the statistical significance between S and NS (n = 8). (*)p < 0.05, (**)p < 0.01. (b) A correlation heatmap of satistical associations of metabolites to cytokines. Blue squares indicate significant negative correlations, black squares indicate nonsignificant correlations (p > 0.05), and yellow squares indicate significant positive correlations.

Figure 7. Schematic overview of the time course analysis of the survival and non-survival rats.