The effect of the Rocaglate on the macrophage gene expression
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE113085 | GEO | 2018/10/11
REPOSITORIES: GEO
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Project description:The effect of the Rocaglate on the macrophage gene expression
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Project description:Drug-resistant bacteria represent a significant global threat. Given the dearth of new antibiotics, host-directed therapies (HDTs) are especially desirable. As IFN-gamma (IFN?) plays a central role in host resistance to intracellular bacteria, including Mycobacterium tuberculosis, we searched for small molecules to augment the IFN? response in macrophages. Using an interferon-inducible nuclear protein Ipr1 as a biomarker of macrophage activation, we performed a high-throughput screen and identified molecules that synergized with low concentration of IFN?. Several active compounds belonged to the flavagline (rocaglate) family. In primary macrophages a subset of rocaglates 1) synergized with low concentrations of IFN? in stimulating expression of a subset of IFN-inducible genes, including a key regulator of the IFN? network, Irf1; 2) suppressed the expression of inducible nitric oxide synthase and type I IFN and 3) induced autophagy. These compounds may represent a basis for macrophage-directed therapies that fine-tune macrophage effector functions to combat intracellular pathogens and reduce inflammatory tissue damage. These therapies would be especially relevant to fighting drug-resistant pathogens, where improving host immunity may prove to be the ultimate resource.
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Project description:This model is based on:
Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment.
Abstract:
Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.
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Project description:<p><strong>OBJECTIVES:</strong> We investigated the effects of gut microbiota and serum metabolic levels on the patients with Budd-Chiari syndrome (B-CS), and their guidance for clinical management strategies.</p><p><strong>DESIGN:</strong> 214 B-CS subjects including untreated case (n=93) and treated case (n=121), were enrolled in this study. Gut microbiota and serum metabolome were analyzed using shotgun metagenomics and liquid chromatography mass spectrometry (LC-MS). 41 controls were included.</p><p><strong>RESULTS:</strong> The gut microbiota in B-CS patients showed greater abundance of Campylobacter and lower Saccharomyces, Deinococcus and Thiomonas (P < 0.05). 13 differential metabolites such as taurocholate and (R)−3−Hydroxybutyric acid were identified in B-CS patients. The random forest (RF) models showed that serum metabolome could effectively identify B-CS from healthy controls and RF-metabolomics exhibited perfect discrimination (AUC = 100%, 95% CI: 100% – 100%), which was significantly higher than that achieved by RF-metagenomics (AUC =58.48%, 95% CI: 38.46% – 78.5%). The results of association analysis showed that Campylobacter concisus and taurocholate were significantly positively correlated in B-CS patients with clinical manifestations (P < 0.05). Moreover, Actinobacteria was significantly increased in untreated B-CS patients vs. treated B-CS patients (P < 0.05). Campylobacter and Veillonella were significantly increased in treated B-CS patients vs. health controls (P < 0.05). In addition, Fewer discrepant microbes were found between treated and untreated groups for the patients without clinical manifestation.</p><p><strong>CONCLUSIONS:</strong> We identified major alterations in the gut microbiota and serum metabolome of B-CS patients. Alterations in fecal metagenomics and serum metabolomics guided management strategies for the patients with Budd-Chiari syndrome.</p>
2021-10-20 | MTBLS2319 | MetaboLights