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

Abstract

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.

Figure 1

Reaction deletion analysis differentiates metabolic differences observed for M1 and M2 activation. The difference between reaction essentiality for M1 and M2 activation is shown. In the top portion, the reactions are grouped by subsystem and rank ordered in terms of importance for M1 activation. Only a few subsystems were differentially important. Largely differential subsystems are shown in reaction detail. The reaction importance differences seen for oxidative phosphorylation and the shuttling of NADH equivalent reflects known metabolic flux variations seen in M1 and M2 activation.

Figure 2

Network sensitivity analysis recapitulates literature-supported immunomodulatory metabolites. Five objective functions were evaluated for activating and suppressing metabolites based on magnitude and directionality of slope. Support from previously published experimental studies was enriched toward metabolites that were predicted to be most effective. Metabolites with literature support and discussed in our analysis are denoted by (†). Metabolites denoted with (*) were excluded as those results are due to artifacts of the network.

Figure 3

Randomized sampling elucidates intracellular mechanisms for observed macrophage activation and suppression. Tryptophan induces a shift to a ketogenic-like state, increasing metabolic usage of leucine and lysine. To balance the redox potential shift, there is a significantly greater use of the malate-aspartate shuttle, diverting glutamate from activation pathways. In addition, increased nucleotide synthesis shifts metabolic resources toward nucleotide intermediates PRPP and CRP. PRPP and CRP are produced from glutamine and glucose, respectively, diverting metabolic resources from nitric oxide, proline, putrescine, and ATP generation.

Figure 4

High-throughput data support in-silico predictions. (A) Reporter metabolites provide a global analysis of the expression data. Major changes pertained to predicted pathways of activation and suppression. Green nodes are scaled by degree of enrichment. Circled metabolites in red and blue represent significantly changed metabolites detected by GC–MS. (B) Directionality of in-silico predictions was in high accordance with the transcriptional and proteomic response of LPS-stimulated cells. Pycr2, Oat, and Gls expression contradicted model predictions, but the proteomics data confirmed the predictions. Only 24 h transcriptomics data are shown due to sparsity of proteomic data. MP – Model Prediction, metabolite, and reaction abbreviations are provided in Supplementary information.