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.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
Study ID | ST000084 |
Study Title | Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation |
Study Type | growth condition, timecourse |
Study Summary | 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. 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 |
Department | Biological Separation and Mass Spectrometry |
Last Name | Metz |
First Name | Thomas |
thomas.metz@pnnl.gov | |
Submit Date | 2014-06-25 |
Num Groups | 2 |
Total Subjects | 12 |
Raw Data Available | Yes |
Raw Data File Type(s) | cdf, d |
Uploaded File Size | 102 M |
Analysis Type Detail | GC-MS |
Release Date | 2014-08-06 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
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 |
MS:
MS ID: | MS000112 |
Analysis ID: | AN000136 |
Instrument Name: | Agilent 5975C |
Instrument Type: | Single quadrupole |
MS Type: | EI |
MS Comments: | An Agilent GC 7890A coupled with a single quadrupole MSD 5975C (Agilent Technologies, Inc.; Santa Clara, CA, USA) was used, and the samples were blocked and analyzed in random order for each experiment. Data were collected over the mass range 50-550 m/z. A mixture of FAMEs (C8-C28) was analyzed once per day together with the samples for retention index alignment purposes during subsequent data analysis. |
Ion Mode: | POSITIVE |
Scan Range Moverz: | 50-550 m/z |