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Mechanistic Insights into the Metabolic Pathways using High-Resolution Mass Spectrometry and Predictive Models in Pancreatic β-Cell Lines (β-TC-6).
* 1 , 2 , 2
1  City University of New York Graduate School of Public Health and Health Policy
2  CUNY GC Advanced Science Research Center
Academic Editor: Francisco José Perez


Objectives: We have previously shown that inhibition of the mTORC1 nutrient-sensing complex by rapamycin and mTORC1/mTORC2 Inhibition by either Torin-2 or RapaLink-1 have differential effects on the global untargeted metabolomics in vivo and in vitro cell culture models.

Methods: In this study, we leveraged the mummichog Python algorithm to analyze the high-dimension untargeted metabolomics data to model the biochemical pathways and metabolic networks and predict their functional activity. We used pancreatic beta-cell culture (Beta TC6) and incubated the cells with either Rapalink-1, rapamycin or the vehicle control for 24 hours. Cells were harvested and snap-frozen in liquid nitrogen. Cells were extracted in ethanol, and the supernatant was collected. The untargeted metabolomics was performed using the high-resolution mass spectrometry LC-MS/MS HILIC peak detection of ESI positive and negative polarity modes. The data were collected using Bruker's maXis-II ESI-Q-q-TOF coupled to Dionex Ultimate-3000 U(H)PLC system using Sequant ZIC-HILIC 150x2.1 mm column (Bruker, Hamburg, Germany). We compared the HRMS-based untargeted precision metabolomics (LC-MS/MS) between groups using positive and negative polarity modes to capture both hydrophilic and hydrophobic metabolites. We employed the XCMS plus bioinformatics platform to link mTOR-regulated metabolites to the predicted biological pathways using the mummichog Python algorithm. Statistical significance (p< 0.001) was assessed by ANOVA and Ranked order data by Whitney-Cox followed by ad-hoc unpaired t-test.

Results: The Cluster heatmap deconvolution and cloud plots analysis show the differential pattern of metabolites between rapamycin and Rapalink-treated pancreatic beta cell lines. Mapping the downstream metabolites data onto predictive metabolic pathways and activity networks revealed that the top pathways affected included the pentose phosphate pathway, dopamine and ubiquinol degradation pathways in the ESI positive polarity mode, and creatine synthesis/glycine degradation and nicotine degradation pathways in the ESI negative polarity mode.

Conclusions: The high-resolution untargeted metabolomics can be leveraged as a proxy of the internal exposome yielding high-dimensional data that provide mechanistic insights into metabolic and signaling pathways and the underlying biology. This approach will have beneficial applications of the internal exposome in determining the optimal precision nutrition pathways for personalized medicine.

Funded by PSC-CUNY Grant 54-101 and the City University of New York, GC Advanced Science Research Center Seed Grant Award # 95649-00. XCMS Plus is a platform for analyzing untargeted metabolomics with an integrated METLIN in silico fragmentation tandem MS database.

Keywords: Precision nutrition; internal exposome; untargeted metabolomics; High-resolution mass spectrometry