Scholtes, C. & Giguere, V. Transcriptional control of energy metabolism by nuclear receptors. Nat. Rev. Mol. Cell Biol. 23, 750–770 (2022).
Desvergne, B., Michalik, L. & Wahli, W. Transcriptional regulation of metabolism. Physiol. Rev. 86, 465–514 (2006).
Giese, G. E., Nanda, S., Holdorf, A. D. & Walhout, A. J. M. Transcriptional regulation of metabolic flux: a C. elegans perspective. Curr. Opin. Syst. Biol. 15, 12–18 (2019).
Watson, E., Yilmaz, L. S. & Walhout, A. J. M. Understanding metabolic regulation at a systems level: metabolite sensing, mathematical predictions and model organisms. Annu. Rev. Genet. 49, 553–575 (2015).
Zhang, H. et al. Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq. Preprint at bioRxiv https://doi.org/10.1101/2025.02.02.636107 (2025).
Ishii, N. et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316, 593–597 (2007).
Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).
Donati, S. et al. Multi-omics analysis of CRISPRi-knockdowns identifies mechanisms that buffer decreases of enzymes in E. coli metabolism. Cell Syst. 12, 56–67 (2021).
Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. https://doi.org/10.1038/msb4100050 (2006).
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
Kemmeren, P. et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740–752 (2014).
Messner, C. B. et al. The proteomic landscape of genome-wide genetic perturbations. Cell 186, 2018–2034 (2023).
Przybyla, L. & Gilbert, L. A. A new era in functional genomics screens. Nat. Rev. Genet. 23, 89–103 (2022).
Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559–2575 (2022).
Watson, E. & Walhout, A. J. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy. Trends Endocrinol. Metab. 25, 502–508 (2014).
Watts, J. L. & Ristow, M. Lipid and carbohydrate metabolism in Caenorhabditis elegans. Genetics 207, 413–446 (2017).
Yilmaz, L. S. & Walhout, A. J. M. Worms, bacteria and micronutrients: an elegant model of our diet. Trends Genet. 30, 496–503 (2014).
Zhang, J., Holdorf, A. D. & Walhout, A. J. C. elegans and its bacterial diet as a model for systems-level understanding of host-microbiota interactions. Curr. Opin. Biotechnol. 46, 74–80 (2017).
MacNeil, L. T., Watson, E., Arda, H. E., Zhu, L. J. & Walhout, A. J. M. Diet-induced developmental acceleration independent of TOR and insulin in C. elegans. Cell 153, 240–252 (2013).
Watson, E. et al. Interspecies systems biology uncovers metabolites affecting C. elegans gene expression and life history traits. Cell 156, 759–770 (2014).
Watson, E. et al. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans. eLife 5, e17670 (2016).
Bulcha, J. T. et al. A persistence detector for metabolic network rewiring in an animal. Cell Rep. 26, 460–468 (2019).
Giese, G. E. et al. Caenorhabditis elegans methionine/S-adenosylmethionine cycle activity is sensed and adjusted by a nuclear hormone receptor. eLife 9, e60259 (2020).
Yilmaz, L. S. & Walhout, A. J. A Caenorhabditis elegans genome-scale metabolic network model. Cell Syst. 2, 297–311 (2016).
Yilmaz, L. S. et al. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Mol. Syst. Biol. 16, e9649 (2020).
Walker, M. D. et al. WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization. Genetics https://doi.org/10.1093/genetics/iyab089 (2021).
Bhattacharya, S. et al. A metabolic regulatory network for the Caenorhabditis elegans intestine. iScience 25, 104688 (2022).
MacNeil, L. T. et al. Transcription factor activity mapping of a tissue-specific gene regulatory network. Cell Syst. 1, 152–162 (2015).
Amaral, L. A., Scala, A., Barthelemy, M. & Stanley, H. E. Classes of small-world networks. Proc. Natl Acad. Sci. USA 97, 11149–11152 (2000).
Zhang, H. et al. A systems-level, semi-quantitative landscape of metabolic flux in C. elegans. Nature https://doi.org/10.1038/s41586-025-08635-6 (2025).
Burgard, A. P., Nikolaev, E. V., Schilling, C. H. & Maranas, C. D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14, 301–312 (2004).
Roux, A. E. et al. Individual cell types in C. elegans age differently and activate distinct cell-protective responses. Cell Rep. 42, 112902 (2023).
McGhee, J. D. The C. elegans Intestine (Wormbook, 2007).
Walker, A. K. et al. A conserved SREBP-1/phosphatidylcholine feedback circuit regulates lipogenesis in metazoans. Cell 147, 840–852 (2011).
Van Gilst, M. R., Hajivassiliou, H., Jolly, A. & Yamamoto, K. R. Nuclear hormone receptor NHR-49 controls fat consumption and fatty acid composition in C. elegans. PLoS Biol. 3, e53 (2005).
Pathare, P. P., Lin, A., Bornfeldt, K. E., Taubert, S. & Van Gilst, M. R. Coordinate regulation of lipid metabolism by novel nuclear receptor partnerships. PLoS Genet. 8, e1002645 (2012).
Zeng, L. et al. Nuclear receptors NHR-49 and NHR-79 promote peroxisome proliferation to compensate for aldehyde dehydrogenase deficiency in C. elegans. PLoS Genet. 17, e1009635 (2021).
Cherepanova, N., Shrimal, S. & Gilmore, R. N-Linked glycosylation and homeostasis of the endoplasmic reticulum. Curr. Opin. Cell Biol. 41, 57–65 (2016).
Froehlich, J. J., Rajewsky, N. & Ewald, C. Y. Estimation of C. elegans cell- and tissue volumes. MicroPubl. Biol. https://doi.org/10.17912/micropub.biology.000345 (2021).
Kaletsky, R. et al. Transcriptome analysis of adult Caenorhabditis elegans cells reveals tissue-specific gene and isoform expression. PLoS Genet. 14, e1007559 (2018).
Shi, X. et al. A Caenorhabditis elegans model for ether lipid biosynthesis and function. J. Lipid Res. 57, 265–275 (2016).
Meng, L. M. & Nygaard, P. Identification of hypoxanthine and guanine as the co-repressors for the purine regulon genes of Escherichia coli. Mol. Microbiol. 4, 2187–2192 (1990).
Torres, R. J. & Puig, J. G. Hypoxanthine-guanine phosophoribosyltransferase (HPRT) deficiency: Lesch-Nyhan syndrome. Orphanet. J. Rare Dis. 2, 48 (2007).
Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, eaaz1482 (2020).
Zielinski, D. C. et al. Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci. Rep. 7, 41241 (2017).
Jain, M. et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040–1044 (2012).
Narayan, A., Berger, B. & Cho, H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. 39, 765–774 (2021).
King, Z. A. et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515–D522 (2016).
Li, X. et al. Data and code for ‘Systems-level design principles of metabolic rewiring in an animal’. Zenodo https://doi.org/10.5281/zenodo.14198997 (2025).
Holdorf, A. D. et al. WormCat: an online tool for annotation and visualization of Caenorhabditis elegans genome-scale data. Genetics 214, 279–294 (2020).
Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
Nanda, S. et al. Systems-level transcriptional regulation of Caenorhabditis elegans metabolism. Mol. Syst. Biol. 19, e11443 (2023).
