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

TitleModel-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.
Publication TypeJournal Article
Year of Publication2012
AuthorsBordbar A, Mo ML, Nakayasu ES, Schrimpe-Rutledge AC, Kim Y-M, Metz TO, Jones MB, Frank BC, Smith RD, Peterson SN, Hyduke DR, Adkins JN, Palsson BO
JournalMol Syst Biol
Volume8
Pagination558
PubMed Date2012-6-28
ISSN1744-4292
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.

Alternate JournalMol. Syst. Biol.
PubMed ID22735334

Location

Location

417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

User Login