Dataset Information

Stable isotope resolved metabolomics of lung cancer in a SCID mouse model.

ABSTRACT: We have determined the time course of [U-(13)C]-glucose utilization and transformations in SCID mice via bolus injection of the tracer in the tail vein. Incorporation of (13)C into metabolites extracted from mouse blood plasma and several tissues (lung, heart, brain, liver, kidney, and skeletal muscle) were profiled by NMR and GC-MS, which helped ascertain optimal sampling times for different target tissues. We found that the time for overall optimal (13)C incorporation into tissue was 15-20 min but with substantial differences in (13)C labeling patterns of various organs that reflected their specific metabolism. Using this stable isotope resolved metabolomics (SIRM) approach, we have compared the (13)C metabolite profile of the lungs in the same mouse with or without an orthotopic lung tumor xenograft established from human PC14PE6 lung adenocarcinoma cells. The (13)C metabolite profile shows considerable differences in [U-(13)C]-glucose transformations between the two lung tissues, demonstrating the feasibility of applying SIRM to investigate metabolic networks of human cancer xenograft in the mouse model.

SUBMITTER: Fan TW

PROVIDER: S-EPMC3109995 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Stable isotope resolved metabolomics of lung cancer in a SCID mouse model.

Fan Teresa W-M TW Lane Andrew N AN Higashi Richard M RM Yan Jun J

Metabolomics : Official journal of the Metabolomic Society 20110601 2

We have determined the time course of [U-(13)C]-glucose utilization and transformations in SCID mice via bolus injection of the tracer in the tail vein. Incorporation of (13)C into metabolites extracted from mouse blood plasma and several tissues (lung, heart, brain, liver, kidney, and skeletal muscle) were profiled by NMR and GC-MS, which helped ascertain optimal sampling times for different target tissues. We found that the time for overall optimal (13)C incorporation into tissue was 15-20 min ...[more]

PMID: 21666826

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Project description:Conventional two-dimensional (2D) cell cultures are grown on rigid plastic substrates with unrealistic concentration gradients of O?, nutrients, and treatment agents. More importantly, 2D cultures lack cell?cell and cell?extracellular matrix (ECM) interactions, which are critical for regulating cell behavior and functions. There are several three-dimensional (3D) cell culture systems such as Matrigel, hydrogels, micropatterned plates, and hanging drop that overcome these drawbacks but they suffer from technical challenges including long spheroid formation times, difficult handling for high throughput assays, and/or matrix contamination for metabolic studies. Magnetic 3D bioprinting (M3DB) can circumvent these issues by utilizing nanoparticles that enable spheroid formation and growth via magnetizing cells. M3DB spheroids have been shown to emulate tissue and tumor microenvironments while exhibiting higher resistance to toxic agents than their 2D counterparts. It is, however, unclear if and how such 3D systems impact cellular metabolic networks, which may determine altered toxic responses in cells. We employed a Stable Isotope-Resolved Metabolomics (SIRM) approach with 13C?-glucose as tracer to map central metabolic networks both in 2D cells and M3DB spheroids formed from lung (A549) and pancreatic (PANC1) adenocarcinoma cells without or with an anti-cancer agent (sodium selenite). We found that the extent of 13C-label incorporation into metabolites of glycolysis, the Krebs cycle, the pentose phosphate pathway, and purine/pyrimidine nucleotide synthesis was largely comparable between 2D and M3DB culture systems for both cell lines. The exceptions were the reduced capacity for de novo synthesis of pyrimidine and sugar nucleotides in M3DB than 2D cultures of A549 and PANC1 cells as well as the presence of gluconeogenic activity in M3DB spheroids of PANC1 cells but not in the 2D counterpart. More strikingly, selenite induced much less perturbation of these pathways in the spheroids relative to the 2D counterparts in both cell lines, which is consistent with the corresponding lesser effects on morphology and growth. Thus, the increased resistance of cancer cell spheroids to selenite may be linked to the reduced capacity of selenite to perturb these metabolic pathways necessary for growth and survival.

Project description:BackgroundStable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism. However, contributions to the observed intensities arising from the stable isotope's natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks. This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment.ResultsWe present both a computationally feasible analytical solution and an algorithm to this newly posed deisotoping problem, which both work with any amount of 13C or 15N stable-isotope enrichment. We demonstrate this algorithm and correct for the effects of 13C natural abundance on a set of raw isotopologue intensities for a specific phosphatidylcholine lipid metabolite derived from a 13C-tracing experiment.ConclusionsCorrection for the effects of 13C natural abundance on a set of raw isotopologue intensities is computationally feasible when the raw isotopologues are isotopically resolved and identified. Such correction makes qualitative interpretation of stable isotope tracing easier and is required before attempting a more rigorous quantitative interpretation of the isotopologue data. The presented implementation is very robust with increasing metabolite size. Error analysis of the algorithm will be straightforward due to low relative error from the implementation itself. Furthermore, the algorithm may serve as an independent quality control measure for a set of observed isotopologue intensities.

Dataset Information

Stable isotope resolved metabolomics of lung cancer in a SCID mouse model.

Publications

Stable isotope resolved metabolomics of lung cancer in a SCID mouse model.

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