Dataset Information

Transcriptome analysis of CD133 overexpressed U87MG glioblastoma cells

ABSTRACT: This study investigated the biological function of CD133 by ectopic expression of CD133 in U87MG cell line. Although CD133 is widely used as a cancer stem cell marker, there are a few studies that examined its own biological functions. While a number of loss-of-function studies about CD133 have shown that CD133 have effects on cancer progression, there are few gain-of-function studies about functions of CD133. Thus, we identified the potential function of CD133 by its overexpression in U87MG glioblastoma cell line. Though there were no significant changes in cell growth and sphere forming ability, elevated IL-1β and its downstream chemokines (CCL3, CXCL3, CXCL5) may function as chemoattractants which affect recruitment of Ly6G+ neutrophils surrounding necrotic regions in vivo and migration of neutrophil-like dHL-60 cells. Taken together, this results imply that CD133 can regulate IL-1β signaling, and promotes the environmental change.

ORGANISM(S): Homo sapiens

PROVIDER: GSE99385 | GEO | 2017/08/03

SECONDARY ACCESSION(S): PRJNA388345

REPOSITORIES: GEO

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Project description:Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Healthy Condition This is the model with healthy state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetano et al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb]. This model is described in the article: Effects of IL-1β-Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms. Palmér R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B. CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118. Abstract: Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. This model is hosted on BioModels Database and identified by: MODEL1604270002. To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Disease Condition This is the model with disease state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetano et al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb]. This model is described in the article: Effects of IL-1β-Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms. Palmér R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B. CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118. Abstract: Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. This model is hosted on BioModels Database and identified by: MODEL1604270002. To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Transcriptome analysis of CD133 overexpressed U87MG glioblastoma cells

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