Metabolomics

, 13:123 | Cite as

MAIMS: a software tool for sensitive metabolic tracer analysis through the deconvolution of 13C mass isotopologue profiles of large composite metabolites

  • Dries Verdegem
  • Hunter N. B. Moseley
  • Wesley Vermaelen
  • Abel Acosta Sanchez
  • Bart Ghesquière
Original Article
  • 51 Downloads

Abstract

Introduction

Metabolic tracer analysis (MTA) is a collection of principles, rules and tools for the interpretation of stable isotope incorporation patterns. One example is the GAIMS algorithm for the deconvolution of the UDP-GlcNAc 13C mass isotopologue profile. GAIMS has been presented as a powerful, yet currently unavailable, proof-of-concept-only technique.

Objectives

We aimed to build a tool inspired by the original GAIMS algorithm, providing identical functionality and straightforward extensibility towards alternative composite metabolites.

Methods

We implemented MAIMS by applying Multistart metaheuristics combined with an efficient hybrid stopping rule to solve the non-convex optimization underlying the deconvolution problem. By testing our tool on several theoretical datasets, we were able to confirm its robust and reproducible performance.

Results

MAIMS is capable of finding the individual contributions of specifically labeled molecular subunits to large composite metabolites (such as UDP-GlcNAc and ATP) upon U-13C-glucose administration and thereby hinting on the activity of several metabolic pathway activities. Applied to proliferating endothelial cells (ECs), MAIMS led to several interesting metabolic insights and generally proved to be a sensitive way for relatively measuring specific pathway activities and for detecting compartmentalized pools of precursor metabolites.

Conclusion

MAIMS is a powerful and extendible tool for isotopologue profile deconvolution tasks and is freely available on github as an open-source Python (Python 2.7 and 3.5 + compliant) script for command line usage. (http://github.com/savantas/MAIMS).

Keywords

Metabolic tracer analysis (MTA) Stable isotope resolved metabolomics (SIRM) UDP-GlcNAc ATP Isotopologue deconvolution Endothelial cells 

Supplementary material

11306_2017_1250_MOESM1_ESM.pdf (243 kb)
Supplementary material 1 (PDF 242 KB)

References

  1. Boender, C. G. E., & Kan, A. H. G. R. (1987). Bayesian stopping rules for Multistart global optimization methods. Mathematical Programming, 37(1), 59–80. doi:10.1007/Bf02591684.CrossRefGoogle Scholar
  2. Bond, M. R., & Hanover, J. A. (2015). A little sugar goes a long way: the cell biology of O-GlcNAc. The Journal of Cell Biology, 208(7), 869–880. doi:10.1083/jcb.201501101.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Buescher, J. M., Antoniewicz, M. R., Boros, L. G., Burgess, S. C., Brunengraber, H., Clish, C. B., et al. (2015). A roadmap for interpreting (13)C metabolite labeling patterns from cells. Current Opinion in Biotechnology, 34, 189–201. doi:10.1016/j.copbio.2015.02.003.CrossRefPubMedPubMedCentralGoogle Scholar
  4. De Bock, K., Georgiadou, M., Schoors, S., Kuchnio, A., Wong, B. W., Cantelmo, A. R., et al. (2013). Role of PFKFB3-driven glycolysis in vessel sprouting. Cell, 154(3), 651–663. doi:10.1016/j.cell.2013.06.037.CrossRefPubMedGoogle Scholar
  5. Dick, T., Wong, E., &, Dann, C. (2014). How many random restarts are enough?.Google Scholar
  6. Hardiville, S., & Hart, G. W. (2014). Nutrient regulation of signaling, transcription, and cell physiology by O-GlcNAcylation. Cell Metabolism, 20(2), 208–213. doi:10.1016/j.cmet.2014.07.014.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Jaffe, E. A., Nachman, R. L., Becker, C. G., & Minick, C. R. (1973). Culture of human endothelial cells derived from umbilical veins. Identification by morphologic and immunologic criteria. The Journal of Clinical Investigation, 52(11), 2745–2756. doi:10.1172/JCI107470.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Jones E., Oliphant T. & Peterson P. (2001). Scipy: Open source scientific tools for Python.Google Scholar
  9. Joyner, D., Certik, O., Meurer, A., & Granger, B. E. (2012). Open source computer algebra systems: SymPy. ACM Communications in Computer Algebra, 45(3/4), 225–234. doi:10.1145/2110170.2110185.CrossRefGoogle Scholar
  10. Marti, R., Resende, M. G. C., & Ribeiro, C. C. (2013). Multi-start methods for combinatorial optimization. European Journal of Operational Research, 226(1), 1–8. doi:10.1016/j.ejor.2012.10.012.CrossRefGoogle Scholar
  11. Moseley, H. N., Lane, A. N., Belshoff, A. C., Higashi, R. M., & Fan, T. W. (2011). A novel deconvolution method for modeling UDP-N-acetyl-d-glucosamine biosynthetic pathways based on (13)C mass isotopologue profiles under non-steady-state conditions. BMC Biology, 9, 37. doi:10.1186/1741-7007-9-37.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Ruan, H. B., Singh, J. P., Li, M. D., Wu, J., & Yang, X. (2013). Cracking the O-GlcNAc code in metabolism. Trends in Endocrinology and Metabolism: TEM, 24(6), 301–309. doi:10.1016/j.tem.2013.02.002.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Schoors, S., Bruning, U., Missiaen, R., Queiroz, K. C. S., Borgers, G., Elia, I., et al. (2015). Fatty acid carbon is essential for dNTP synthesis in endothelial cells. Nature, 520(7546), 192. doi:10.1038/nature14362.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Tibbetts, A. S., & Appling, D. R. (2010). Compartmentalization of mammalian folate-mediated one-carbon metabolism. Annual Review of Nutrition, 30, 57–81. doi:10.1146/annurev.nutr.012809.104810.CrossRefPubMedGoogle Scholar
  15. Zecchin, A., Stapor, P. C., Goveia, J., & Carmeliet, P. (2015). Metabolic pathway compartmentalization: An underappreciated opportunity? Current Opinion in Biotechnology, 34, 73–81. doi:10.1016/j.copbio.2014.11.022.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Metabolomics Expertise Center, VIB Center for Cancer Biology (CCB)VIBLeuvenBelgium
  2. 2.Department of Oncology, Metabolomics Expertise CenterKU LeuvenLeuvenBelgium
  3. 3.Department of Molecular and Cellular BiochemistryUniversity of KentuckyLexingtonUSA

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