A systems-level, semi-quantitative landscape of metabolic flux in C. elegans

Hefei Zhang, Xuhang Li, L. Tenzin Tseyang, Gabrielle E. Giese, Hui Wang, Bo Yao, Jingyan Zhang, Rachel L. Neve, Elizabeth A. Shank, Jessica B. Spinelli, L. Safak Yilmaz and Albertha J. M. Walhout ()
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Hefei Zhang: University of Massachusetts Chan Medical School
Xuhang Li: University of Massachusetts Chan Medical School
L. Tenzin Tseyang: University of Massachusetts Chan Medical School
Gabrielle E. Giese: University of Massachusetts Chan Medical School
Hui Wang: Fudan University
Bo Yao: Fudan University
Jingyan Zhang: Fudan University
Rachel L. Neve: University of Massachusetts Chan Medical School
Elizabeth A. Shank: University of Massachusetts Chan Medical School
Jessica B. Spinelli: University of Massachusetts Chan Medical School
L. Safak Yilmaz: University of Massachusetts Chan Medical School
Albertha J. M. Walhout: University of Massachusetts Chan Medical School

Nature, 2025, vol. 640, issue 8057, 194-202

Abstract: Abstract Metabolic flux, or the rate of metabolic reactions, is one of the most fundamental metrics describing the status of metabolism in living organisms. However, measuring fluxes across the entire metabolic network remains nearly impossible, especially in multicellular organisms. Computational methods based on flux balance analysis have been used with genome-scale metabolic network models to predict network-level flux wiring1–6. However, such approaches have limited power because of the lack of experimental constraints. Here, we introduce a strategy that infers whole-animal metabolic flux wiring from transcriptional phenotypes in the nematode Caenorhabditis elegans. Using a large-scale Worm Perturb-Seq (WPS) dataset for roughly 900 metabolic genes7, we show that the transcriptional response to metabolic gene perturbations can be integrated with the metabolic network model to infer a highly constrained, semi-quantitative flux distribution. We discover several features of adult C. elegans metabolism, including cyclic flux through the pentose phosphate pathway, lack of de novo purine synthesis flux and the primary use of amino acids and bacterial RNA as a tricarboxylic acid cycle carbon source, all of which we validate by stable isotope tracing. Our strategy for inferring metabolic wiring based on transcriptional phenotypes should be applicable to a variety of systems, including human cells.

Date: 2025
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DOI: 10.1038/s41586-025-08635-6

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