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Quantitation of metabolic activity from isotope tracing data using automated methodology

Nature Metabolism volume 6, pages 2207–2209 (2024)Cite this article

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Metabolic flux analysis (MFA) is a computational approach to deciphering labelling patterns based on machine learning principles. Differing from typical machine learning algorithms that train a model from known datasets to make predictions, the commonly used MFA algorithm trains a metabolic network with data from isotope tracing experiments and directly outputs the learned information — that is, all fluxes in the network that best fit data3,5 (Fig. 1b). However, as a machine learning algorithm, current MFA methods often lack systematic evaluation and benchmarking, a standard practice in broader machine learning and artificial intelligence applications6. Issues such as algorithmic convergence, flux estimation accuracy and result robustness in MFA studies have been raised but remain largely unaddressed3, limiting the effectiveness and broader adoption of these automated tools in metabolic research.

To advance the capabilities of MFA for complex metabolic networks and extensive isotope tracing datasets, we developed an automated analysis methodology alongside a large-scale metabolic network model. This model comprises over 100 fluxes across key pathways, including glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway (PPP), one-carbon metabolism, and several amino acid (AA) biosynthetic pathways (Fig. 1c, Supplementary Methods). Compared to contemporary MFA tools7,8,9, a notable feature of our methodology is the incorporation of organelle compartmentalization, facilitating accurate quantification of exchange fluxes between mitochondria and cytosol in eukaryotic cells (Fig. 1c). While other tools typically require tens of minutes to obtain a solution7,9, our methodology can generate an optimized solution, with fluxes that accurately explain the labelling pattern from a 13C tracing experiment on cultured cell lines, within 2 s on a desktop computer10 (Supplementary Fig. 1a–d). Nonetheless, a challenge arose from the observation that these optimized solutions could diverge significantly, showing considerable variability in certain net fluxes even with similar loss values (Fig. 1d, Supplementary Fig. 1e,f).To address this problem, we developed an optimization-averaging algorithm that refines the computation process by selecting a subset of solutions with minimal loss (selected solutions) from the pool of optimized solutions and averaging them to produce a new, more stable solution set (averaged solutions) (Fig. 1e, Supplementary Methods). These solutions, along with those generated using the typical strategy used in contemporary software (Supplementary Methods, Supplementary Fig. 1c), were benchmarked using simulated 13C tracing datasets generated from a known flux vector (Supplementary Fig. 2a). The results demonstrated that, relative to the benchmark, the optimization-averaging algorithm effectively reduced flux variability and improved the accuracy of the results in approximating the known flux, even with varying levels of data availability (Fig. 1f, Supplementary Figs. 2b–e and 3a–e).

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Fig. 1: Development and verification of our automated methodology.

Data availability

The tracing data used for method development from cultured HCT116 cells10, along with in vivo labelling data from patients who received isotope infusions13, were obtained from previous studies. Data for the eight cultured cancer cell lines were generated in this study. All these isotope-tracing data files can be downloaded from the Data and Results section of the GitHub repository at https://github.com/LocasaleLab/Automated-MFA-2023.

Code availability

All source code is available from the GitHub repository at https://github.com/LocasaleLab/Automated-MFA-2023.

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Acknowledgements

We express our sincere appreciation to the members of the Locasale laboratory for valuable discussions. Special thanks are extended to Y. Sun, J. Rutter and A. Kennedy for help with editing the manuscript. Support from National Institutes of Health (R01CA193256) and the American Cancer Society (RSG-16-214-01-TBE) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA

    Shiyu Liu, Xiaojing Liu & Jason W. Locasale

  2. Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC, USA

    Shiyu Liu & Jason W. Locasale

  3. Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA

    Xiaojing Liu & Jason W. Locasale

Authors
  1. Shiyu Liu
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  2. Xiaojing Liu
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  3. Jason W. Locasale
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Contributions

S.L. and J.W.L. designed the study and wrote the manuscript. S.L. and J.W.L. designed the algorithm, S.L. wrote the software package and performed the data analysis. X.L. performed labelling experiments of cultured cells and collected all experimental data. All authors have read, edited and approved the final manuscript.

Corresponding author

Correspondence to Jason W. Locasale.

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The authors declare no competing interests.

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Peer review information

Nature Metabolism thanks Almut Schulze and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Table 1 and Methods

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Liu, S., Liu, X. & Locasale, J.W. Quantitation of metabolic activity from isotope tracing data using automated methodology. Nat Metab 6, 2207–2209 (2024). https://doi.org/10.1038/s42255-024-01144-2

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