Bayesian multi-model-based 13 C 15 N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb), this is particularly important in informing the development of effective drugs targeting Mtb’s metabolism. Here we performed 13 C 15 N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13 C 15 N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13 C-MFA. We quantified CN fluxes for amino acid and, for the first time, nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria, and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.

Authors 11

1. Khushboo Borah

   University of Surrey

   Affiliation as printed

   Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

2. Martin Beyß Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 52425 Jülich, Germany

   RWTH Aachen University, Computational Systems Biotechnology, 52062 Aachen, Germany

   Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 545 Jülich, Germany

3. Ye Xu

   University of Surrey

   Affiliation as printed

   Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

4. Jim Barber

   University of Surrey

   Affiliation as printed

   Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

5. Catia Costa

   University of Surrey

   Affiliation as printed

   Faculty of Engineering and Physical Sciences, University of Surrey, UK, GU2 7XH

6. Jane Newcombe

   University of Surrey

   Affiliation as printed

   Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

7. Axel Theorell

   Forschungszentrum Jülich · ETH Zurich

   Affiliation as printed

   Computational Systems Biology, ETH Zürich, 4058 Basel, Switzerland

   Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 52425 Jülich, Germany

   Computational Systems Biology, ETH Zürich, 408 Basel, Switzerland

   Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 545 Jülich, Germany

8. Melanie Jane Bailey

   University of Surrey

   Affiliation as printed

   Faculty of Engineering and Physical Sciences, University of Surrey, UK, GU2 7XH

9. Dany J. V. Beste

   University of Surrey

   Affiliation as printed

   Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

10. Johnjoe McFadden corresponding

    University of Surrey

    Affiliation as printed

    Faculty of Health and Medical Sciences, University of Surrey, UK, GU2 7XH

11. Katharina Nöh corresponding

    Forschungszentrum Jülich

    Affiliation as printed

    Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 52425 Jülich, Germany

    Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, 545 Jülich, Germany