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

• 成果类型: Article
• 署名作者: Zhang, Hefei; Li, Xuhang; Tseyang, L. Tenzin; Giese, Gabrielle E.; Wang, Hui; Yao, Bo; Zhang, Jingyan; Neve, Rachel L.; Shank, Elizabeth A.; Spinelli, Jessica B.; Yilmaz, L. Safak; Walhout, Albertha J. M.
• 署名单位: University of Massachusetts System; UMass Chan Medical School; University of Massachusetts Worcester; University of Massachusetts System; University of Massachusetts Worcester; UMass Chan Medical School; Fudan University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2667
• DOI: 10.1038/s41586-025-08635-6
• 发表日期: 2025-04-03
• 关键词: pentose-phosphate pathway

摘要

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, 2, 3, 4, 5-6. 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.