Results: 4

1.
Figure 2

Figure 2. From: Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

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.

Aarash Bordbar, et al. Mol Syst Biol. 2012;8:558-558.
2.
Figure 1

Figure 1. From: Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

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.

Aarash Bordbar, et al. Mol Syst Biol. 2012;8:558-558.
3.
Figure 3

Figure 3. From: Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

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.

Aarash Bordbar, et al. Mol Syst Biol. 2012;8:558-558.
4.
Figure 4

Figure 4. From: Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

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 .

Aarash Bordbar, et al. Mol Syst Biol. 2012;8:558-558.

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