Fibroblasts and iPS cells from patients with insulin receptor mutations
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE78891 | GEO | 2016/03/04
SECONDARY ACCESSION(S): PRJNA314319
REPOSITORIES: GEO
Similar Datasets
Project description:Inherited genetic variants of insulin receptor induced cellular signaling have long been suspected to contribute to the development of type-2- diabetes mellitus. In this report we discuss a heterozygous mutation in the first coding exon of the proto-oncogene Ha-Ras (Ha-RasA11P) that we have identified in a patient with familial premature aging syndrome. The patient has atopic sklerodermic skin alterations, insulin resistance as well as disturbances in lipid metabolism. In vitro analyzes have shown that this mutation disrupted not only the signal transduction of the insulin receptor but also other receptor tyrosine kinases, such as IGF-1, EGF, PDGF. These results demonstrate that HA-Ras has a significant role in insulin sensitivity in humans. We used microarrays to determine differences in gene expression due to a deactivating Ha-Ras mutation reducing c-fos expression in a patient with lipodystrophy and a Werner-like syndrome. Cultued fibroblasts of non diabetic controls and a patient with deactivating Ha-Ras mutation were analyzed at identical passage number and growth conditions without further additional treatment.
2013-08-07 | E-GEOD-49566 | biostudies-arrayexpress
Project description:We developed a novel network inference approach, Biologically Anchored Knowledge Expansion (BAKE), to analyze large volume gene expression data obtained from a mouse model of insulin resistance progression. Both genetic aspects and dietary factors, specifically high caloric high-fat high-sugar diets, contribute to the progression of insulin resistance. To mimic genetic predisposition, we used a mouse model with double heterozygous deletion of early insulin signaling pathway intermediates, insulin receptor (IR) and insulin receptor substrate 1 (IRS1) genes. These mice were fed with high-fat (Western) or low-fat (Chow) diet for 8 and 16 weeks starting at 8 weeks of age. Gene expression data was collected from adipocytes isolated from these mice. Applying BAKE analysis to the adipocyte gene expression data, we demonstrate that we can accurately discover a novel regulatory gene in the insulin signaling pathway.
2015-12-31 | GSE76428 | GEO
Project description:We developed a novel network inference approach, Biologically Anchored Knowledge Expansion (BAKE), to analyze large volume gene expression data obtained from a mouse model of insulin resistance progression. Both genetic aspects and dietary factors, specifically high caloric high-fat high-sugar diets, contribute to the progression of insulin resistance. To mimic genetic predisposition, we used a mouse model with double heterozygous deletion of early insulin signaling pathway intermediates, insulin receptor (IR) and insulin receptor substrate 1 (IRS1) genes. These mice were fed with high-fat (Western) or low-fat (Chow) diet for 8 and 16 weeks starting at 8 weeks of age. Gene expression data was collected from adipocytes isolated from these mice. Applying BAKE analysis to the adipocyte gene expression data, we demonstrate that we can accurately discover a novel regulatory gene in the insulin signaling pathway. The mouse model of double heterozygous deletion of insulin receptor (IR) and insulin receptor substrate 1 (IRS1) was originally introduced as a polygenic model to study the development of type 2 diabetes. This mouse model, on an atherosclerosis-prone ApoE null background (IR+/- IRS1+/- ApoE-/-), also shows increased atherosclerotic lesions due to impaired insulin signaling. For our study we used female double heterozygous mice (IR+/- IRS1+/-, 'Dhet' mice or 'Dâ mice) on an ApoE null background (ApoE-/-, âEâ) fed with a Western (high-fat) diet for 8 (DW8, n=5) and 16 (DW16, n=9) weeks starting at 8 weeks of age or with a Chow (low-fat) diet (DC8, n=7; DC16, n=5). There were also ApoE null mice (ApoE-/-, 'Eâ) fed either Western diet for 8 (EW8, n=6) and 16 (EW16, n=8) weeks or Chow diet for 16 weeks (EC16, n=5) starting at 8 weeks of age.
2015-12-31 | E-GEOD-76428 | biostudies-arrayexpress
Project description:Inherited genetic variants of insulin receptor induced cellular signaling have long been suspected to contribute to the development of type-2- diabetes mellitus. In this report we discuss a heterozygous mutation in the first coding exon of the proto-oncogene Ha-Ras (Ha-RasA11P) that we have identified in a patient with familial premature aging syndrome. The patient has atopic sklerodermic skin alterations, insulin resistance as well as disturbances in lipid metabolism. In vitro analyzes have shown that this mutation disrupted not only the signal transduction of the insulin receptor but also other receptor tyrosine kinases, such as IGF-1, EGF, PDGF. These results demonstrate that HA-Ras has a significant role in insulin sensitivity in humans. We used microarrays to determine differences in gene expression due to a deactivating Ha-Ras mutation reducing c-fos expression in a patient with lipodystrophy and a Werner-like syndrome.
2013-08-07 | GSE49566 | GEO
Project description:Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been implicated in insulin resistance and type 2 diabetes mellitus (T2DM). To define GK's role in insulin resistance, we examined gene expression in brown adipose tissue in a glycerol kinase (Gyk) knockout (KO) mouse model using microarray analysis. Global gene expression profiles of KO mice were distinct from wild type (WT) with 668 genes that were differentially expressed. These included genes involved in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Real-Time (RT) PCR analysis confirmed the differential expression of selected genes involved in lipid and carbohydrate metabolism. PathwayAssist analysis confirmed direct and indirect connections between GK and genes in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Network Component Analysis (NCA) showed that the transcription factors, peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element binding factor 1 (SREBP-1), SREBP-2, signal transducer and activator of transcription 3 (STAT3), STAT5, trans-acting transcription factor 1 (SP1), CCAAT/enhancer binding protein alpha (CEBP-α), cAMP responsive element binding protein 1 (CREB), glucocorticoid receptor (GR), and PPAR-α have altered activity in the KO mice. NCA also revealed the individual contribution of these transcription factors on the expression of genes altered in the microarray data. This study elucidates the transcription network of Gyk and further confirms a role for Gyk, a simple Mendelian disorder, in insulin resistance and T2DM, a common complex genetic disorder. Experiment Overall Design: 7 samples are analyzed; 3 wildtype and 4 KO. the WT samples are used as control and KO samples are used as the experimental group.
2007-11-22 | E-GEOD-4656 | biostudies-arrayexpress
Project description:Brännmark2013 - Insulin signalling in human adipocytes (diabetic condition)
The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under diabetic condtion
This model is described in the article:
Insulin Signaling in Type 2 Diabetes: EXPERIMENTAL AND MODELING ANALYSES REVEAL MECHANISMS OF INSULIN RESISTANCE IN HUMAN ADIPOCYTES.
Brännmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Strålfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.
Abstract:
Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.
This model is hosted on BioModels Database
and identified
by: MODEL1304160000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
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.
2024-09-02 | BIOMD0000000449 | BioModels
Project description:Brännmark2013 - Insulin signalling in human adipocytes (normal condition)
The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under normal condtion
This model is described in the article:
Insulin Signaling in Type 2 Diabetes: EXPERIMENTAL AND MODELING ANALYSES REVEAL MECHANISMS OF INSULIN RESISTANCE IN HUMAN ADIPOCYTES.
Brännmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Strålfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.
Abstract:
Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.
This model is hosted on BioModels Database
and identified
by: MODEL1304190000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
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.
2024-09-02 | BIOMD0000000448 | BioModels
Project description:We carried out a high throughput analysis of insulin-induced kinase signaling pathways in primary fibroblasts from 35 unrelated individuals. We found that extensive individual variation exists in induction of various signaling pathways. ERK signaling displayed the greatest variation, which led to extensive variation in expression of downstream target genes. Our results suggest that phenotypic variation in kinase signaling mediates variation in downstream processes of insulin response. Future study of such phenotypic variation is important to linking genetic variants to individual susceptibility to complex diseases such as diabetes. Passage-matched primary fibroblasts from 35 unrelated newborns (foreskin) were treated with 100 nM insulin. We measured gene expression levels in each of 35 individuals at baseline, and 1 hour and 6 hours after insulin treatment using expression arrays. In order to examine effects of ERK inhibition on insulin-induced gene expression, we treated fibroblasts from 4 individuals with DMSO or 10uM of U0126 for 1 hour, followed by insulin treatment for one hour and 6 hours. We then harvested cells and measured gene expression in each sample using expression arrays.
2015-08-03 | E-GEOD-21891 | biostudies-arrayexpress
Project description:Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been implicated in insulin resistance and type 2 diabetes mellitus (T2DM). To define GK’s role in insulin resistance, we examined gene expression in brown adipose tissue in a glycerol kinase (Gyk) knockout (KO) mouse model using microarray analysis. Global gene expression profiles of KO mice were distinct from wild type (WT) with 668 genes that were differentially expressed. These included genes involved in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Real-Time (RT) PCR analysis confirmed the differential expression of selected genes involved in lipid and carbohydrate metabolism. PathwayAssist analysis confirmed direct and indirect connections between GK and genes in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Network Component Analysis (NCA) showed that the transcription factors, peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element binding factor 1 (SREBP-1), SREBP-2, signal transducer and activator of transcription 3 (STAT3), STAT5, trans-acting transcription factor 1 (SP1), CCAAT/enhancer binding protein alpha (CEBP-α), cAMP responsive element binding protein 1 (CREB), glucocorticoid receptor (GR), and PPAR-α have altered activity in the KO mice. NCA also revealed the individual contribution of these transcription factors on the expression of genes altered in the microarray data. This study elucidates the transcription network of Gyk and further confirms a role for Gyk, a simple Mendelian disorder, in insulin resistance and T2DM, a common complex genetic disorder. Keywords: genotype state analysis
2007-03-11 | GSE4656 | GEO
Project description:Insulin resistance, a harbinger of the metabolic syndrome, is a state of compromised hormonal response for which transcriptional dysregulation is a major contributor. Genetic or pharmacological inhibition of the nuclear receptor ERRα preserves insulin sensitivity during diet-induced obesity. However, how ERRα integrates insulin signaling at the molecular level with whole body metabolism remains unknown. Herein, we reveal that insulin-mediated gene expression requires the nuclear stabilization of ERRα through a GSK3β/FBXW7 axis. Liver-specific deletion of GSK3β or FBXW7 or mice harboring mutations of ERRα phosphosites (ERRα3SA) co-targeted by GSK3β/FBXW7 result in accumulated ERRα proteins that no longer respond to insulin. ERRα3SA mice display reprogrammed liver and muscle transcriptomes, resulting in insulin resistance and compromised metabolic homeostasis, effects largely reversed by pharmacological inhibition of ERRα. These findings uncovered a previously unrecognised ERRα-dependent insulin regulatory pathway that could be targeted to improve insulin resistance and associated metabolic diseases.
2022-03-16 | GSE182000 | GEO