Review on UHPLC-Q-TOF/MS Based plasma Metabolomics Reveals the Metabolic Perturbations by Manganese Exposure in Rat Models

Review on UHPLC-Q-TOF/MS Based plasma Metabolomics Reveals the Metabolic Perturbations by Manganese Exposure in Rat Models

Research Background

Manganese is a trace metal element necessary for human beings and other animals, but the accumulation of high concentrations of manganese in the brain can cause manganese toxicity. At present, there is no biomarker can be used as the gold standard for judging manganese toxicity. Therefore, this paper uses untargeted metabolomics to find biomarkers for determining manganese toxicity from the level of metabolites.

Sample Source

Rat plasma (experimental group) fed for 5 weeks with 200 mg/L manganese and rat plasma (control) fed without manganese for 5 weeks.

Technical Route

untargeted metabolomics (UHPLC-Q-TOF/MS, see fig. below)



Research Result

Manganese toxicity can cause liver damage in rats: Manganese treatment reduces the ratio of rat liver weight to rat body weight (LW/BW) (Fig. A), but it also causes a significant increase in plasma cholesterol in rats (Fig. B). Simultaneously, it was found that the manganese content in the liver of the manganese-treated group was significantly higher than that of the control group (Fig. C ). These results demonstrate that manganese-fed rats can cause the accumulation of manganese in the rat liver.Through histological observation of the liver, it was found that the manganese treatment group can cause gangrene and nucleus dissolution in the liver of rats (Fig. D). The above results prove that the manganese treatment group is effective, and it is also the first time that the rat is susceptible to manganese attack, resulting in its liver damage.


The author first evaluated the stability and reproducibility of untargeted metabolomics by quality control samples (QC): principal component analysis (PCA) in positive and negative ion collection mode found that QC samples were well clustered together (see green spots in the image below), which proves that the stability and reproducibility of this metabolomics is better.



Normalization and multidimensional statistical analysis of metabolomics data: In order to reduce systematic bias and technical bias, metabolomics data needs to be normalized. The experimental data is deleted after normalization and normalized. The metabolic data in the model showed a normal distribution (see fig. below).



In order to detect ion peaks that distinguish between the control and treatment groups, a supervised principal component analysis (PLS-DA) analyzes the two sets of data to maximize the distinction between the two groups and to find differential metabolites. In the PLS-DA analysis, the two groups can be well distinguished in positive and negative ion mode (fig. below). The importance value (VIP) of a variation map can also be derived by establishing the PLS-DA model, which is an important value for finding differential metabolites.



Screening for differential metabolites by screening conditions VIP>1 and P value<0.05, the authors screened 36 differentially expressed metabolites (see table below), and these 36 differentially expressed metabolites may be potential biomarkers of manganese toxicity.

The KEGG pathway analysis of these differential metabolites revealed that these 36 differential metabolites were mainly involved in purine metabolism, tryptophan metabolism, tyrosine metabolism, phenylalanine metabolism, taurine, hypotaurine metabolism and others. Many of the amino acid metabolisms (see table 2), these metabolic pathways are closely related to manganese treatment.



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