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:Dysregulated kinase activity drives oncogenic signalling, perturbs cellular homeostasis, and promotes tumour progression. Despite major success in targeting kinases therapeutically, the downstream consequences of kinase inhibition and the mechanisms underlying drug resistance remain incompletely understood. One of the most frequent oncogenic kinase mutations, BRAFV600E, constitutively activates the MAPK pathway and represents a major therapeutic target in melanoma and other cancers. However, the functional relevance of most phosphorylation events downstream of BRAF signalling is unknown, limiting mechanistic interpretation and rational therapeutic design. Here, we established a global, multi-omic model of BRAF inhibition response in BRAFV600E-mutant melanoma cells by integrating time-resolved phosphoproteomics, biophysical PTM-proteomics, transcriptomics, and thermal proteome profiling. Our ultradeep phosphoproteomic analysis revealed widespread phosphorylation changes upon Dabrafenib treatment, while biophysical phosphoproteomics uncovered phosphorylation events associated with altered solubility and subcellular localisation, indicative of biomolecular condensation and nuclear reorganisation. Integration of these modalities into a network-based mechanistic model enabled the prioritisation of functionally relevant phosphorylation sites and kinases. Experimental validation confirmed CDK9, CLK3, and TNIK as key regulators of BRAFV600E signalling and as candidate targets for combinatorial inhibition strategies capable of re-sensitising resistant melanoma cells in a synthetic lethal manner. The transcription factor ETV3 emerged from the network as a previously unrecognised effector of oncogenic BRAF signalling. Using phosphosite-specific biophysical data, imaging, and FRAP experiments, we demonstrated that ETV3 phosphorylation controls its DNA-binding kinetics. Functional assays combining ETV3 knockdown, metabolomics, and drug screening revealed that ETV3 modulates transcriptional and metabolic responses to BRAF inhibition, linking oncogenic signalling to metabolic rewiring. Together, this study provides a comprehensive systems-level framework that connects phosphorylation dynamics to protein function and cellular phenotype, highlights ETV3 as a novel signalling node, and illustrates how multi-omic, site-resolved network models can reveal actionable mechanisms of kinase-driven oncogenesis.
2026-01-01 | E-MTAB-16292 | biostudies-arrayexpress
Project description:Epstein-Barr virus (EBV) contributes to over 200,000 cancers annually, predominantly aggressive lymphomas originating from hypoxic germinal centers (<1% O2). However, conventional models fail to recapitulate the physiologically relevant hypoxic microenvironment which profoundly influences B-cell metabolic remodeling during transformation. Here, we establish an ex vivo model of EBV-driven B-cell transformation under 1% O2, demonstrating robust transformation and super-enhancer activation of oncogenic regulators, including MYC. Multi-omic analyses reveal distinct metabolic adaptations to hypoxia. Unlike normoxic B-cells, which rely on stearyl desaturase 1 and fatty acid oxidation to mitigate lipotoxicity, hypoxically transformed B-cells suppress fatty acid synthesis while upregulating glycerophospholipid metabolism and lipid droplet formation to buffer excess saturated lipids. Consequently, these cells exhibit heightened dependence on extracellular unsaturated fatty acids to support membrane biogenesis for proliferation. Our findings provide the first physiologically relevant model of EBV-driven B-cell transformation under hypoxia, uncovering metabolic vulnerabilities that could inform targeted therapeutic strategies for EBV-associated malignancies.
2025-05-07 | GSE293238 | GEO
Project description:Replicative immortality is a hallmark of cancer, driven by activation of telomere maintenance mechanisms (TMM), that is yet to be therapeutically exploited. To expedite discoveries that will enable development of TMM-targeted therapeutics, we have generated a resource of telomere biology metrics for a pan-cancer panel of 976 cell lines. We have also produced new proteomic data from data-independent mass spectrometry for most of these cell lines. These data link to pre-existing multi-omic data, drug sensitivity and molecular dependency data from CRISPR/Cas9 knock-out screens for most cell lines. The TMM data illustrate a previously unappreciated range and heterogeneity in telomere biology measures, including telomere biology states that diverge from the binary model of TMM activation involving either telomerase or Alternative Lengthening of Telomeres (ALT). The multi-omic data were applied in conjunction with the TMM data to derive proteomic and transcriptomic predictors of ALT and telomerase activity levels. Our investigations also revealed molecular vulnerabilities of ALT cancer cells and identified an investigational drug, Pevonedistat, with activity that strongly correlates with telomerase activity levels. These discoveries illustrate the potential for leveraging this new resource of telomere biology metrics to realise the potential for TMM-directed cancer therapeutics and companion diagnostics.
2025-10-17 | PXD058664 | Pride
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