Abstract
Individual tissues perform highly specialized metabolic functions to maintain whole-body metabolic homeostasis. Although Drosophila serves as a powerful model for studying human metabolic diseases, modeling tissue-specific metabolism has been limited in this organism. To address this gap, we reconstruct 32 tissue-specific genome-scale metabolic models (GEMs) by integrating a curated Drosophila metabolic network with pseudo-bulk single-nuclei transcriptomics data, revealing distinct metabolic network structures and subsystem coverage across tissues. We validate enriched pathways identified through tissue-specific GEMs, particularly in muscle and fat body, using metabolomics and pathway analysis. Moreover, to demonstrate the utility in disease modeling, we apply muscle-GEM to investigate high sugar diet (HSD)-induced metabolic dysregulation. Constraint-based semi-quantitative flux and sensitivity analyses identify altered NAD(H)-dependent reactions and distributed control of glycolytic flux, including GAPDH. This prediction is further validated through in vivo 13C-glucose isotope tracing study. Notably, decreased glycolytic flux, including GAPDH, is linked to increased redox modifications. Finally, our pathway-level flux analyses identify dysregulation in fructose metabolism. Together, this work establishes a quantitative framework for tissue-specific metabolic modeling in Drosophila, demonstrating its utility for identifying dysregulated reactions and pathways in muscle in response to HSD.
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Data availability
All data generated in this study, including 13C-glucose isotope tracing, high-sugar-diet (HSD) metabolomics, regional metabolomics, and redox proteomics, are provided in the Source Data. Processed and analyzed data are provided in Supplementary Data. The protein mass spectrometry raw data have been deposited to the ProteomeXchange via the MassIVE with the dataset identifier MSV000100288. Source data are provided with this paper.
Code availability
MATLAB and R scripts used for reconstruction of genome-scale metabolic models and flux analyses are available on GitHub (https://github.com/sunjjmoon/FlyTissueGEMs). An achieved version of the code used in this study is available on Zenodo: https://doi.org/10.5281/zenodo.17684286115.
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Acknowledgements
We thank members of the Perrimon laboratory for useful discussions and experimental advice, including Dr. Stephanie Mohr for feedback on the manuscript. We also thank to the Research Computing Group at Harvard Medical School for access to the O2 High Performance Compute Cluster. We additionally thank Dr. Anush Chiappino-Pepe and Dr. Steven Marygold for valuable feedback on genome-scale metabolic modeling work; Dr. Jason Tennessen for valuable feedback on fly metabolism; Dr. Travis Nemkov for valuable feedback on metabolomics analysis; and Dr. Safak Yilmaz and Dr. Marian Walhout for valuable feedback on genome-scale metabolic models. A.K. was supported by Postdoctoral Fellowship Program (Nurturing Next-generation Researchers) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A14039622). The mass spectrometry work was partially funded by NIH grants 5P01CA120964 (J.M.A.) and 5P30CA006516 (J.M.A.). This research was supported by NIH/NIDDK 1R01DK136945, NIH NIAMS R01 AR057352, Cancer 509 Research UK (CGCATF-2021/100022) and the National Cancer Institute (1OT2 CA278685-01). N.P. is an investigator of Howard Hughes Medical Institute. This article is subject to HHMI’s Open Access to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication.
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S.J.M. and N.P. designed the study. Y.H. performed bioinformatics analyses related to the Fly Cell Atlas datasets. S.J.M. performed the computational analyses and follow-up experiments. S.J.M. and J.M.A. performed metabolomics and subsequent analyses. S.J.M., M.D., and A.D. performed redox proteomics and subsequent analyses. A.R.K. performed the AlphaFold structural analysis. N.P. supervised the work. S.J.M. and N.P. wrote the manuscript. S.J.M., Y.H., M.D., A.R.K., J.M.A., A.D., and N.P. reviewed and edited the manuscript.
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Moon, S.J., Hu, Y., Dzieciatkowska, M. et al. Modeling tissue-specific Drosophila metabolism identifies high sugar diet-induced metabolic dysregulation in muscle at reaction and pathway levels. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68395-3
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DOI: https://doi.org/10.1038/s41467-026-68395-3
