UPLC/Q-TOF-MS-based metabolomics and molecular docking analysis of Bifidobacterium adolescentis exposure to levofloxacin.

Antibiotic-associated diarrhea is a common adverse reaction caused by the widespread use of antibiotics. The decrease in probiotics is one of the reasons why antibiotics cause drug-induced diarrhea. However, few studies have addressed the intrinsic mechanism of antibiotics inhibiting probiotics. To investigate the underlying mechanism of levofloxacin against Bifidobacterium adolescentis, we used a metabolomics mass spectrometry-based approach and molecular docking analysis for a levofloxacin-induced B. adolescentis injury model. The results showed that levofloxacin reduced the survival rate of B. adolescentis and decreased the number of B. adolescentis. The untargeted metabolomics analysis identified 27 potential biomarkers, and many of these metabolites are involved in energy metabolism, amino acid metabolism and the lipid metabolism pathway. Molecular docking showed that levofloxacin can bind with aminoacyl-tRNA synthetase and lactic acid dehydrogenase. This result provides a novel insight into the mechanism of the adverse reactions of levofloxacin.

Metabolomics and molecular docking analysis of antibiotic exposure in Bifidobacterium adolescentis.

Bifidobacterium adolescentis is a probiotic. This research aimed to investigate the mechanism of antibiotics led to decrease in the number of B. adolescentis. The metabolomics approach was employed to explore the effects of amoxicillin on metabolism of B.adolescentis, while MTT assay and scanning electron microscopy were applied to analyse changes in viability and morphology of bacteria. Molecular docking was used to illuminate the mechanism by which amoxicillin acts on a complex molecular network. The results showed that increasing the concentration of amoxicillin led to a gradual decrease in the number of live bacteria. Untargeted metabolomics analysis identified 11 metabolites that change as a result of amoxicillin exposure. Many of these metabolites are involved in arginine and proline metabolism, glutathione metabolism, arginine biosynthesis, cysteine, and methionine metabolism, and tyrosine and phenylalanine metabolism. Molecular docking revealed that amoxicillin had a good binding effect on the proteins AGR1, ODC1, GPX1, GSH, MAT2A, and CBS. Overall, this research provides potential targets for screening probiotic regulatory factors and lays a theoretical foundation for the elucidation of its mechanisms.

Integrated Gut Microbiota and Urine Metabolite Analyses of T2DM with NAFLD Rat Model.

Globally 80% type 2 diabetes mellitus (T2DM) patients suffer nonalcoholic fatty liver disease (NAFLD). The interplay of gut microbiota and endogenous metabolic networks has not yet been reported in the setting of T2DM with NAFLD. As such, this study utilized 16S rRNA gene sequencing to assess the changes in intestinal flora and nuclear magnetic resonance spectroscopy (1H NMR) to identify potential metabolites in a T2DM with NAFLD rat model. Spearman correlation analysis was performed to explore the relationship between gut microbiota and metabolites. Results revealed that among T2DM with NAFLD rats, diversity indexes of intestinal microbiota were distinctly decreased while levels of 18 bacterial genera within the intestinal tract were significantly altered. In addition, levels of eight metabolites mainly involved in the synthesis and degradation of ketone bodies, the TCA cycle, and butanoate metabolism were altered. Correlation analysis revealed that gut bacteria such as Blautia, Ruminococcus torques group, Allobaculum, and Lachnoclostridium strongly associate with 3-hydroxybutyrate, acetone, acetoacetate, 2-oxoglutarate, citrate, creatinine, hippurate, and allantoin. Our findings can provide a basis for future development of targeted treatments.

Predictive serum biomarkers of patients with cerebral infarction.

OBJECTIVES: Stroke is the third most common cause of death and also causes seizures and disability. Biomarkers are abnormal signal indicators at the biological level that are present before the organism is seriously affected and are more sensitive to early diagnosis than are traditional imaging methods. Early diagnosis of stroke can prevent the progression of the disease. However, there are currently no widely accepted biomarkers for stroke that have been applied clinically. METHODS: A serum metabonomics method based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to identify potential biomarkers and metabolic pathways of cerebral infarction. The receiver-operating characteristic (ROC) curve was used to verify the diagnostic and classification abilities of the biomarkers, and a support vector machine (SVM) model was developed for the prediction of cerebral infarction. RESULTS: Principal component analysis revealed a clear separation between the normal and cerebral infarction groups. A total of 13 potential serum biomarkers were identified, which were mainly involved in linoleic acid metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; arachidonic acid metabolism; and fatty acid biosynthesis. The ROC curve analysis showed that the potential biomarkers had high specificity and sensitivity for the diagnosis of cerebral infarction. The SVM model had good diagnostic ability and could accurately distinguish the control group from the cerebral infarction group. DISCUSSION: The metabonomics approach may be a useful bioanalytical method for understanding the pathophysiology of cerebral infarction and may provide an experimental basis for the development of clinical biomarkers for stroke.

Urinary metabonomics study of anti-depressive mechanisms of Millettia speciosa Champ on rats with chronic unpredictable mild stress-induced depression.

As a traditional Chinese medicine (TCM), Millettia speciosa Champ (MSC), exerts a wide range of pharmacological activities. Our research group previously found that MSC has antidepressant effects, but the specific antidepressant mechanisms remain unclear. Therefore, in this study, urine metabolomics based on ultra-performance liquid chromatography/quadrupole time of flight mass spectrometry (UPLC-Q-TOF/MS) combined with pharmacodynamics was used to explore the pathogenesis of depression and the antidepressant effects of MSC. The results showed that MSC treatment could significantly improve chronic unpredictable mild stress (CUMS)-induced depression. Urine metabolic showed that the profiles of the CUMS model group were significantly separated from the control group, while the drug-treated groups were closer to the control group, especially the MSC group treated with a 14 g/kg dose of MSC. Furthermore, 9 metabolites, including glutaric acid, L-isoleucine, L-Dopa, sebacic acid, 3-methylhistidine, allantoin, caprylic acid, tryptophol, and 2-phenylethanol glucuronide, were identified as potential biomarkers of depression. Metabolic pathway analysis showed that these potential biomarkers were mainly involved in valine, leucine, and isoleucine biosynthesis, aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine degradation, tyrosine metabolism, histidine metabolism, fatty acid biosynthesis, and pentose and glucuronate interconversions. Through Receiver operating characteristic (ROC) analysis and Pearson correlation analysis, the combination of L-isoleucine, sebacic acid, and allantoin, were further screened out as potential pharmacodynamic biomarkers associated with the efficacy of MSC. This study suggests that the integration of metabolomics with pharmacodynamics helps to further understand the pathogenesis of depression and provides novel insight into the efficacy of TCM.