Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA171843 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Model-driven multi-omic data analysis of the RAW 264.7 cell line elucidates metabolic immunomodulators of macrophage activation
2015-11-11 | PXD003055 | Pride
Project description:Macrophages are central players in the immune response and manifest divergent phenotypes to control inflammation and innate immunity. Signaling factors are traditionally recognized as the stimuli governing macrophage functions. In recent years, metabolism’s importance has been reemphasized as critical signaling and regulatory pathways of human diseases and processes, ranging from cancer to aging, often converge on metabolic responses. In this study, we assessed metabolic features that play critical roles in macrophage function. We constructed a genome-scale metabolic network for the RAW 264.7 cell line, an oft-used in vitro model. We determined immunomodulators of activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked critical suppressive effectors were assessed, identifying a suppressive role for nucleotide synthesis. Furthermore, we demonstrate how metabolic mechanisms of macrophage activation can be identified by analyzing multi-omic data of LPS-stimulated RAW cells in the context of our predictions. Our study demonstrates metabolism’s role in regulating macrophage activation may be greater than previously anticipated. The RAW 264.7 (ATTC) cell line was stimulated for 24 hours with LPS. Treated cells were washed twice with Dulbecco’s PBS and harvested for high-throughput analyses. Labeled cDNA was prepared as described (Jones et al. 2010). A mixture Cy3-labeled control cDNA and Cy5-labeled were hybridized to Agilent Mouse GE 4x44K v2 Microarray (Agilent Technologies) and processed. Image analysis and intra-chip normalization were performed with Feature Extraction 9.5.3.1 (Agilent). Data were analyzed with MeV (tm4.org) or with custom python scripts
2012-01-24 | E-GEOD-35237 | biostudies-arrayexpress
Project description:Macrophages are central players in the immune response and manifest divergent phenotypes to control inflammation and innate immunity. Signaling factors are traditionally recognized as the stimuli governing macrophage functions. In recent years, metabolism’s importance has been reemphasized as critical signaling and regulatory pathways of human diseases and processes, ranging from cancer to aging, often converge on metabolic responses. In this study, we assessed metabolic features that play critical roles in macrophage function. We constructed a genome-scale metabolic network for the RAW 264.7 cell line, an oft-used in vitro model. We determined immunomodulators of activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked critical suppressive effectors were assessed, identifying a suppressive role for nucleotide synthesis. Furthermore, we demonstrate how metabolic mechanisms of macrophage activation can be identified by analyzing multi-omic data of LPS-stimulated RAW cells in the context of our predictions. Our study demonstrates metabolism’s role in regulating macrophage activation may be greater than previously anticipated.
2012-01-24 | GSE35237 | GEO
Project description: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. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.
2012-08-02 | E-GEOD-39785 | biostudies-arrayexpress
Project description: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.
2012-08-03 | GSE39785 | GEO
Project description: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.
2012-09-05 | MTBLS23 | MetaboLights
Project description: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.
2014-06-25 | ST000084 | MetabolomicsWorkbench
Project description:We try to investigate the mechanism of kidney fibrosis from data-driven way using multi-omic data, to explore hub gene and key molecules of key interaction network.
2024-03-28 | PXD051046 |
Project description:An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we performed phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrated the time series. Structural characteristics and dynamic nature of the network were analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points due to process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.
2021-08-24 | GSE166336 | GEO
