Steady state isotope tracing in C. elegans (GC-MS analyses)

The metabolic network, composed of thousands of metabolic reactions, is essential for any organism. Despite its importance, a systems level understanding of how metabolic network is wired by reactions that carry flux in unperturbed condition and rewired by transcriptionally activating or repressing metabolic genes in response to metabolic perturbations are still lacking in any animal. Here, we apply Worm Perturb-Seq (WPS), a high-throughput, whole-animal RNAi and RNA-seq method, to ~900 metabolic genes in the nematode Caenorhabditis elegans. We derive a metabolic gene regulatory network (mGRN) in which 385 perturbations are connected to 9,414 genes by more than 110,000 interactions. The mGRN has a highly modular structure in which 22 perturbation clusters connect to 44 gene expression programs, involving metabolism, stress response and other functions. The mGRN reveals different modes of transcriptional rewiring from simple reaction and pathway compensation to rerouting and more complex network coordination. Using metabolic network modeling, we discover a design principle of transcriptional rewiring we name the ‘compensation/repression’ (CR) model. The CR model explains the majority of the transcriptional responses in metabolic genes and reveals a high-level of compensation and repression in five core metabolic functions related to energy and biomass.

Leveraging the comprehensive data from WPS, we find that the transcriptional response to metabolic gene perturbations can be integrated with the genome-scale metabolic network models to infer a highly constrained, semi-quantitative flux distribution of unperturbed animal. 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.

GC-MS analyses is reported in the current study MTBLS11742.
LC-MS analyses is reported in MTBLS11741.