Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal’s physiology.
Feedback loops in metabolic network regulation. Click to enlarge.
Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with “omics” data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms.
Watson E, Yilmas LS, Walhout AJ (2015) Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms. Annu. Rev. Genet. 49, 553-575.
This workshop will focus on how we can quantitatively measure and catalog in a computable fashion, all protein-protein interactions and other key interactions in various human cell types. Talks from experts will be complemented by extensive discussions.
Co-sponsored by the Icahn School of Medicine at Mount Sinai and the University of Massachusetts Medical School
Completing the Walhout lab hat trick of three successfully defended doctoral dissertations in six weeks is Dr. Emma Watson who defended her thesis titled “Diet-responsive gene networks rewire metabolism in the nematode Caenorhabditis elegans to provide robustness against vitamin B12 deficiency.” Dr. Watson is now pursuing post-doctoral training with Dr. Stephen Elledge at Harvard Medical School. Best wishes Emma! Booo-yah!
A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Therefore, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs. This GRN shows only a modest overlap with physical interactions, indicating that many regulatory interactions are indirect. We applied nested effects modeling to uncover the information flow between TFs in the intestine that converges on a small set of physical TF-promoter interactions. We found numerous cell non-autonomous regulatory interactions, illustrating tissue-to-tissue communication. Our study illuminates the complexity of gene regulation in the context of a living animal.
MacNeil LT, Pons C, Arda HE, Giese GE, Myers CL, Walhout AJM (2015) Transcription Factor Activity Mapping of a Tissue-Specific In Vivo Gene Regulatory Network. Cell Systems 1, 152-162.
Congratulations to Dr. Ashlyn Ritter on successfully defending her doctoral dissertation “Complex expression dynamics and robustness in C. elegans insulin networks.” Great job Ash! We are all very proud of you!