Model‐driven multi‐omic data analysis elucidates metabolic immunomodulators of macrophage activation

Genome-scale metabolic network reconstruction and analysis of the murine leukemic macrophage cell line RAW 264.7 reveal a complementary relationship between how known metabolic immunomodulators are biochemically processed and their role in macrophage activation.
The RAW 264.7 metabolic model was constructed based on transcriptomic and proteomic data, and validated for its quantitative accuracy in the prediction of growth rate, ATP, and nitric oxide production. Metabolic network-based analyses identified well-established critical metabolite effectors and intracellular pathways that impact activation or suppression of M1- and M2-metabolic activation phenotypes. Three levels of high-throughput data (transcriptomic, proteomic, and metabolomic) were analyzed in the context of the model-based predictions to elucidate underlying metabolic mechanisms of macrophage activation. Results suggest a potential contending link between de-novo nucleotide synthesis and macrophage activation phenotypes at a glutamine junction.
Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors.