Dataset Information

The dynamic clustering of insulin receptor underlies its signaling and is disrupted in insulin resistance

ABSTRACT: Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in metabolic diseases such as type 2 diabetes. We report here that IR is incorporated into dynamic clusters at the plasma membrane, in the cytoplasm and in the nucleus of human hepatocytes and adipocytes. Insulin stimulation promotes further incorporation of IR into these dynamic clusters in insulin-sensitive cells but not in insulin-resistant cells, where both IR accumulation and dynamic behavior are reduced. Treatment of insulin-resistant cells with metformin, a first-line drug used to treat type 2 diabetes, can rescue IR accumulation and the dynamic behavior of these clusters. This rescue is associated with metformin’s role in reducing reactive oxygen species that interfere with normal dynamics. These results indicate that changes in the physico-mechanical features of IR clusters contribute to insulin resistance and have implications for improved therapeutic approaches.

INSTRUMENT(S): Liquid Chromatography MS - alternating - hilic

SUBMITTER: Alessandra Dall'Agnese

PROVIDER: MTBLS6505 | MetaboLights | 2022-12-05

REPOSITORIES: MetaboLights

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			Action	DRS
	MTBLS6505	Other
	FILES	Other
	a_MTBLS6505_TD2Met_LC-MS_alternating_hilic_metabolite_profiling.txt	Txt
	a_MTBLS6505_gluc_LC-MS_alternating_hilic_metabolite_profiling.txt	Txt
	a_MTBLS6505_lipo_LC-MS_alternating_hilic_metabolite_profiling.txt	Txt

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The dynamic clustering of insulin receptor underlies its signaling and is disrupted in insulin resistance.

Dall'Agnese Alessandra A Platt Jesse M JM Zheng Ming M MM Friesen Max M Dall'Agnese Giuseppe G Blaise Alyssa M AM Spinelli Jessica B JB Henninger Jonathan E JE Tevonian Erin N EN Hannett Nancy M NM Lazaris Charalampos C Drescher Hannah K HK Bartsch Lea M LM Kilgore Henry R HR Jaenisch Rudolf R Griffith Linda G LG Cisse Ibrahim I II Jeppesen Jacob F JF Lee Tong I TI Young Richard A RA

Nature communications 20221206 1

Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in metabolic diseases such as type 2 diabetes. We report here that IR is incorporated into dynamic clusters at the plasma membrane, in the cytoplasm and in the nucleus of human hepatocytes and adipocytes. Insulin stimulation promotes further incorporation of IR into these dynamic clusters in insulin-sensitive cells but not in insulin-resistant cells, where both IR accumulation and dynamic behavior are redu ...[more]

PMID: 36473871

Similar Datasets

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

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.

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.

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.