Untargeted Metabolomic Analysis of Rat Neuroblastoma Cells as a Model System to Study the Biochemical Effects of the Acute Administration of Methamphetamine

Untargeted metabolomics reveal pathways associated with neuroprotective effect of oxyresveratrol in SH-SY5Y cells

Oxyresveratrol has been documented benefits for neurodegenerative disease. However, the specific molecular mechanisms and pathways involved is currently limited. This study aimed to investigate the potential neuroprotective mechanisms of oxyresveratrol using rotenone-induced human neuroblastoma SH-SY5Y cytotoxicity. Cells were divided into the following groups: control, rotenone, and oxyresveratrol pre-treated before being exposed to rotenone. Cellular assays were performed to investigate neuroprotective effects of oxyresveratrol. The results showed that 20 μM oxyresveratrol was effective in preventing rotenone-induced cell death and decreasing ROS levels in the cells. The alteration of metabolites and pathways involved in the neuroprotective activities of oxyresveratrol were further investigated using LC-QTOF-MS/MS untargeted metabolomics approach. We hypothesized that oxyresveratrol's neuroprotective effects would be associated with neurodegenerative pathways. A total of 294 metabolites were identified. 7,8-dihydrobiopterin exhibited the highest VIP scores (VIP > 3.0; p < 0.05), thus considered a biomarker in this study. Our results demonstrated that pretreatment with oxyresveratrol upregulated the level of 7,8-dihydrobiopterin compared to the positive control. Pathway analysis verified that 7,8-dihydrobiopterin was primarily associated with phenylalanine, tyrosine, and tryptophan metabolism (impact = 1, p < 0.001), serving as essential cofactors for enzymatic function in the dopamine biosynthesis pathway. In conclusion, oxyresveratrol may be benefit for the prevention of neurodegenerative diseases by increasing 7,8-dihydrobiopterin concentration.

Metabolomics changes after rTMS intervention reveal potential peripheral biomarkers in methamphetamine dependence

Methamphetamine is one of the most commonly used drugs around the world, leading to serious public health and psychiatric problems. Due to the lackness of objective laboratory evaluation indicators, the molecular mechanisms of methamphetamine dependence still remain unclear. Previous evidence demonstrated that repetitive transcranial magnetic stimulation (rTMS) may be useful in treating drug addiction. The aim of this study was to identify and validate plasma metabolomics biomarkers in patients with methamphetamine use disorder before and after rTMS intervention. An untargeted gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) based metabolomics approach was applied to characterize the metabolic profile of forty methamphetamine dependent subjects and thirty-eight healthy controls in peripheral blood mononuclear cells (PBMCs). Patients were randomized to receive either rTMS or sham over the DLPFC for four weeks (20 daily sessions, 900 pulses per day). Cognitive function were assessed before and after rTMS intervention. Eight PBMC metabolites responsible for distinguishing real rTMS from sham treatment were identified. These metabolites were mainly involved in energy metabolism and oxidative stress. Compared with baseline, the expression of three metabolites was reversed after rTMS intervention: alpha-tocopherol, glyceric acid and fumaric acid. Changes of the alpha-tocopherol were associated with cognitive function improvement following rTMS. These findings suggest that energy metabolism and oxidative stress system may be associated with the effect of rTMS on cognitive function in methamphetamine dependence, and warrant further investigation.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

The Potential Regulatory Network of Glutamate Metabolic Pathway Disturbance in Chinese Han Withdrawal Methamphetamine Abusers

Objects

To explore the long-term influence of methamphetamine abuse on metabolomics character, with gas chromatography-mass spectrometry (GS-MS) technology, and the potential regulatory network using the bioinformatics method.

Methods

Forty withdrawal methamphetamine abusers (WMA) were recruited from Shanghai Gaojing Forced Isolation Detoxification Institute. Forty healthy controls (HC) were recruited from society. GS-MS technology was used to detect metabolic products in serum. A bioinformatics method was used to build a regulatory network. Q-PCR was used to detect the candidate gene expressions, and ELISA was used to detect the regulatory enzyme expressions.

Results

Four pathways were significantly changed in the MA compared to the HC: (1) the arginine synthesis pathway, (2) alanine, aspartic acid and glutamate metabolic pathway, (3) cysteine and methionine metabolic pathway, and (4) the ascorbate and aldarate pathway (enrichment analysis p < 0.05, Impactor factor > 0.2). When focusing on the ‘Alanine, aspartate, and glutamate metabolism’ pathway, a regulatory network was established, and the expression of candidate regulatory genes and enzymes was verified. It was found that the expression of DLG2 (Discs large MAGUK scaffold protein 2), PLA2G4 (Phospholipase A2 group IVE), PDE4D (Phosphodiesterase 4D), PDE4B (Phosphodiesterase 4B), and EPHB2 (Ephrin type-B receptor 2) were significantly different between the two groups (p < 0.05), However, after adjusting for age and BMI, only DLG2, PLA2G4, and EPHB2 remained significant (p < 0.05). The expression of enzymes was not significantly different (p > 0.05).

Conclusion

Methamphetamine abuse influences the metabolic process in the long term, and DLG2, PLA2G4, and EPHB2 may regulate the glutamate metabolism pathway.

Metabolomics profiling of methamphetamine addicted human serum and three rat brain areas

Methamphetamine (METH) has already been a serious problem all over the world. The identification of related biomarkers and pathways is helpful to evaluate the degree of METH addiction, develop appropriate treatment during abstinence, and explore the mechanism. Here, it is the first time to perform metabolomics profiling of METH addicted human serum and three regions of METH-induced conditioned place preference (CPP) rat brain by using UHPLC-MS/MS and matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI), respectively. Untargeted metabolomics analysis demonstrated clear differences between METH abusers and the healthy control by finding 35 distinct expressed metabolites in serum, including 5 TCA intermediates, 17 amino acids and 13 other biomolecules, 15 of which were newly identified following METH exposure. By using MALDI-MSI, the relative quantification and distribution of 14 metabolites were investigated in the nucleus accumbens (NAc), dorsal hippocampus (dHPC) and ventral hippocampus (vHPC) of CPP rat brain. Taken together, METH addiction could influence energy metabolism, amino acids metabolism, and phospholipids metabolism. A multi-parameter model consisting of these related metabolites can be established as a METH addiction biomarker in the future. The mapping of phospholipids provided new insights into the mechanism of METH addiction. Notably, the trend of metabolite changes in NAc and dHPC was almost the same, while it was opposite between dHPC and vHPC. It seems that NAc and dHPC were the two regions more susceptible to METH administration in the brain. And dHPC and vHPC play different roles in METH addiction proved by metabolites mapping.

To explore the mechanism of METH addiction, the metabolomics profiling of METH addicted human serum and rat brain were performed using UHPLC-MS/MS and MALDI-MSI, respectively. 35 differentially expressed metabolites were identified in the serum.