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

Molecular Systems Biology

Aarash Bordbar²  Monica L Mo²  Ernesto S Nakayasu³  Alexandra C Schrimpe-Rutledge³  Young-Mo Kim³  Thomas O Metz³  Marcus B Jones¹  Bryan C Frank³  Richard D Smith³  Scott N Peterson¹  Daniel R Hyduke²  Joshua N Adkins³ 

[1] J. Craig Venter Institute, Rockville, MD, USA;Department of Bioengineering, University of California San Diego, La Jolla, CA, USA;Pacific Northwest National Laboratory, Richland, WA, USA

关键词: constraint‐based modeling;    immunometabolism;    metabolic network reconstruction;    RAW 264.7;   

DOI  :  10.1038/msb.2012.21

来源: Wiley

【 摘 要 】

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.

Synopsis

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.

【 授权许可】

CC BY-NC-ND   
Copyright © 2012 EMBO and Macmillan Publishers Limited

Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.