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Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

ABSTRACT: Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

SUBMITTER: Tu Z

PROVIDER: S-EPMC3516550 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

Tu Zhidong Z Keller Mark P MP Zhang Chunsheng C Rabaglia Mary E ME Greenawalt Danielle M DM Yang Xia X Wang I-Ming IM Dai Hongyue H Bruss Matthew D MD Lum Pek Y PY Zhou Yun-Ping YP Kemp Daniel M DM Kendziorski Christina C Yandell Brian S BS Attie Alan D AD Schadt Eric E EE Zhu Jun J

PLoS genetics 20121206 12

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression p ...[more]

PMID: 23236292

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Project description:Lorcaserin is a serotonergic agonist specific to the 5-hydroxytryptamine 2c receptor (5-HT2CR) that is FDA approved for the long-term management of obesity with or without at least one weight-related comorbidity. Lorcaserin can restrain patients' appetite and improve insulin sensitivity and hyperinsulinemia mainly through activating 5-HT2CR in the hypothalamus. It is known that the mCPP, a kind of 5-HT2CR agonist, decreases plasma insulin concentration in mice and previous research in our laboratory found that mCPP inhibited glucose-stimulated insulin secretion (GSIS) by activating 5-HT2CR on the β cells. However, the effect of lorcaserin on GSIS of pancreatic β cell has not been studied so far. The present study found that 5-HT2CR was expressed in both mouse pancreatic β cells and β-cell-derived MIN6 cells. Dose-dependent activation of 5-HT2CR by lorcaserin suppressed GSIS and SB242084 or knockdown of 5-HT2CR abolished lorcaserin's effect in vitro. Additionally, lorcaserin also suppressed GSIS in high-fat diet (HFD)-fed mice in dose-dependent manner. Lorcaserin did not change insulin synthesis ATP content, but lorcaserin decrease cytosolic free calcium level [(Ca2+)i] in MIN6 cells stimulated with glucose and also inhibit insulin secretion and (Ca2+)i in MIN6 treated with potassium chloride. Furthermore, stimulation with the L-type channel agonist, Bay K8644 did not restore GSIS in MIN6 exposed to lorcaserin. Lorcaserin inhibits the cAMP generation of MIN6 cells and pretreatment with the Gα i/o inhibitor pertussis toxin (PTX), abolished lorcaserin-induced suppression of GSIS in β cells, while membrane-permeable cAMP analogue db-cAMP had same effect as PTX. These date indicated lorcaserin coupled to PTX-sensitive Gα i/o proteins in β cells reduced intracellular cAMP level and Ca2+ influx, thereby causing GSIS dysfunction of β cell. These results highlight a novel signaling mechanism of lorcaserin and provide valuable insights into the further investigation of 5-HT2CR functions in β-cell biology and it also provides guidance for the clinical application of lorcaserin.

Project description:BackgroundBecause insulin is the main regulator of glucose homeostasis, quantitative models describing the dynamics of glucose-induced insulin secretion are of obvious interest. Here, a computational model is introduced that focuses not on organism-level concentrations, but on the quantitative modeling of local, cellular-level glucose-insulin dynamics by incorporating the detailed spatial distribution of the concentrations of interest within isolated avascular pancreatic islets.MethodsAll nutrient consumption and hormone release rates were assumed to follow Hill-type sigmoid dependences on local concentrations. Insulin secretion rates depend on both the glucose concentration and its time-gradient, resulting in second-and first-phase responses, respectively. Since hypoxia may also be an important limiting factor in avascular islets, oxygen and cell viability considerations were also built in by incorporating and extending our previous islet cell oxygen consumption model. A finite element method (FEM) framework is used to combine reactive rates with mass transport by convection and diffusion as well as fluid-mechanics.ResultsThe model was calibrated using experimental results from dynamic glucose-stimulated insulin release (GSIR) perifusion studies with isolated islets. Further optimization is still needed, but calculated insulin responses to stepwise increments in the incoming glucose concentration are in good agreement with existing experimental insulin release data characterizing glucose and oxygen dependence. The model makes possible the detailed description of the intraislet spatial distributions of insulin, glucose, and oxygen levels. In agreement with recent observations, modeling also suggests that smaller islets perform better when transplanted and/or encapsulated.ConclusionsAn insulin secretion model was implemented by coupling local consumption and release rates to calculations of the spatial distributions of all species of interest. The resulting glucose-insulin control system fits in the general framework of a sigmoid proportional-integral-derivative controller, a generalized PID controller, more suitable for biological systems, which are always nonlinear due to the maximum response being limited. Because of the general framework of the implementation, simulations can be carried out for arbitrary geometries including cultured, perifused, transplanted, and encapsulated islets.

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Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

Publications

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

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