Summary of Study ST000084

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000075. The data can be accessed directly via it's Project DOI: 10.21228/M86K5H This work is supported by NIH grant, U2C- DK119886.

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Study IDST000084
Study TitleModel-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation
Study Typegrowth condition, timecourse
Study SummaryMacrophages 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. A genome-scale metabolic network for the RAW 264.7 cell line was constructed 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 were identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. This study demonstrates that the role of metabolism in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors. This submission corresponds to the metabolomics data from this study.
Institute
Pacific Northwest National Laboratory
DepartmentBiological Separation and Mass Spectrometry
Last NameMetz
First NameThomas
Emailthomas.metz@pnnl.gov
Submit Date2014-06-25
Num Groups2
Total Subjects12
Raw Data AvailableYes
Raw Data File Type(s)cdf, d
Uploaded File Size102 M
Analysis Type DetailGC-MS
Release Date2014-08-06
Release Version1
Thomas Metz Thomas Metz
https://dx.doi.org/10.21228/M86K5H
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Combined analysis:

Analysis ID AN000136
Analysis type MS
Chromatography type GC
Chromatography system Agilent 7890A
Column Agilent HP5-MS (30m × 0.25mm, 0.25 um)
MS Type EI
MS instrument type Single quadrupole
MS instrument name Agilent 5975C
Ion Mode POSITIVE
Units Peak area
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