Homo sapiens
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PROVIDER: PRJNA121687 | ENA |
REPOSITORIES: ENA
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Project description:(original Title) Phenothiazine Neuroleptics Signal To The Human Insulin Promoter As Revealed By A Novel Human b-Cell Line Based High-Throughput Screen. To address the current deficiency in human beta-cell models, we have developed a cell line from human islets in which the expression of insulin and other beta-cell restricted genes are modulated by an inducible form of the bHLH transcription factor E47. In an effort to probe the global pattern of gene expression in T6PNE in an unbiased fashion, oligonucleotide microarray analysis was performed on T6PNE in the presence and absence of E47 induction and compared against human islets. Our analysis reveals that T6PNE express a substantial percentage of the b-cell specific geneset, and that this is further enhanced by the induction of E47. This cell line, T6PNE, was then screened against a library of known drugs for those that increase insulin promoter activity. Interestingly, members of the phenothiazine class of neuroleptics increased insulin gene expression upon short term exposure. Chronic treatment, however, resulted in suppression of insulin promoter activity, consistent with the effect of phenothiazines observed clinically to induce diabetes in chronically treated patients. In addition to providing insights into previously unrecognized targets and mechanisms of action of phenothiazines, the novel cell line described here provides a broadly applicable platform for mining new molecular drug targets and central regulators of beta-cell differentiated function. Gene expression studies in: Three human islet samples; Five T6PNE samples; Seven T6PNE induced with E47.
2010-08-06 | E-GEOD-18821 | biostudies-arrayexpress
Project description:(original Title) Phenothiazine Neuroleptics Signal To The Human Insulin Promoter As Revealed By A Novel Human b-Cell Line Based High-Throughput Screen. To address the current deficiency in human beta-cell models, we have developed a cell line from human islets in which the expression of insulin and other beta-cell restricted genes are modulated by an inducible form of the bHLH transcription factor E47. In an effort to probe the global pattern of gene expression in T6PNE in an unbiased fashion, oligonucleotide microarray analysis was performed on T6PNE in the presence and absence of E47 induction and compared against human islets. Our analysis reveals that T6PNE express a substantial percentage of the b-cell specific geneset, and that this is further enhanced by the induction of E47. This cell line, T6PNE, was then screened against a library of known drugs for those that increase insulin promoter activity. Interestingly, members of the phenothiazine class of neuroleptics increased insulin gene expression upon short term exposure. Chronic treatment, however, resulted in suppression of insulin promoter activity, consistent with the effect of phenothiazines observed clinically to induce diabetes in chronically treated patients. In addition to providing insights into previously unrecognized targets and mechanisms of action of phenothiazines, the novel cell line described here provides a broadly applicable platform for mining new molecular drug targets and central regulators of beta-cell differentiated function.
2010-08-07 | GSE18821 | GEO
Project description:This model is from the article:
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework.
Brannmark C, Palmer R, Glad ST, Cedersund G, Stralfors P.
J Biol Chem.2010 Jun 25;285(26):20171-9.
20421297,
Abstract:
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.
2024-09-02 | BIOMD0000000343 | BioModels
Project description:The study was completed to compare expression profiles of primary human beta cells (in the form of adult human islets), to the expression profile of hESC-derived beta-like cells. A HES3 line modified by homologous recombination to express GFP under the insulin promoter allowed us to FACS sort the hESC-derived cells into purified insulin-positive (presumably beta-like cells), and insulin-negative populations.
2012-09-09 | GSE40709 | GEO
Project description:Yugi2014 - Insulin induced signalling (PFKL
phosphorylation) - model 1
Insulin induces phosphorylation and activation of liver-type
phosphofructokinase 1, which thereby controls a key reaction in
glycolysis. This mechanism is revealed using the mathematical
model. In this model, the PFKL phosphorylation time courses are
obtained from experimental data.
Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio
University, Japan) for his kind advice in converting the model
from MATLAB to SBML.
This model is described in the article:
Reconstruction of insulin
signal flow from phosphoproteome and metabolome data.
Yugi K, Kubota H, Toyoshima Y,
Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y,
Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K,
Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4):
1171-1183
Abstract:
Cellular homeostasis is regulated by signals through
multiple molecular networks that include protein
phosphorylation and metabolites. However, where and when the
signal flows through a network and regulates homeostasis has
not been explored. We have developed a reconstruction method
for the signal flow based on time-course phosphoproteome and
metabolome data, using multiple databases, and have applied it
to acute action of insulin, an important hormone for metabolic
homeostasis. An insulin signal flows through a network, through
signaling pathways that involve 13 protein kinases, 26
phosphorylated metabolic enzymes, and 35 allosteric effectors,
resulting in quantitative changes in 44 metabolites. Analysis
of the network reveals that insulin induces phosphorylation and
activation of liver-type phosphofructokinase 1, thereby
controlling a key reaction in glycolysis. We thus provide a
versatile method of reconstruction of signal flow through the
network using phosphoproteome and metabolome data.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000540.
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 | BIOMD0000000540 | BioModels
Project description:The study was completed to compare expression profiles of primary human beta cells (in the form of adult human islets), to the expression profile of hESC-derived beta-like cells. A HES3 line modified by homologous recombination to express GFP under the insulin promoter allowed us to FACS sort the hESC-derived cells into purified insulin-positive (presumably beta-like cells), and insulin-negative populations. Expression profile of adult human islets from cadaveric donors is compared to insulin-positive and insulin-negative populations of hESC-derived beta-like cells
2012-09-09 | E-GEOD-40709 | biostudies-arrayexpress
Project description:Yugi2014 - Insulin induced signalling (PFKL
phosphorylation) - model 2
Insulin induces phosphorylation and activation of liver-type
phosphofructokinase 1, which thereby controls a key reaction in
glycolysis. This mechanism is revealed using the mathematical
model. In this model, the PFKL phosphorylation time courses are
calculation from the signalling pathway model developed by Kubata
et al. (2012) (
MODEL1204060000
- Kubota2012_InsulinAction_AKTpathway).
Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio
University, Japan) for his kind advice in converting the model
from MATLAB to SBML.
This model is described in the article:
Reconstruction of insulin
signal flow from phosphoproteome and metabolome data.
Yugi K, Kubota H, Toyoshima Y,
Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y,
Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K,
Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4):
1171-1183
Abstract:
Cellular homeostasis is regulated by signals through
multiple molecular networks that include protein
phosphorylation and metabolites. However, where and when the
signal flows through a network and regulates homeostasis has
not been explored. We have developed a reconstruction method
for the signal flow based on time-course phosphoproteome and
metabolome data, using multiple databases, and have applied it
to acute action of insulin, an important hormone for metabolic
homeostasis. An insulin signal flows through a network, through
signaling pathways that involve 13 protein kinases, 26
phosphorylated metabolic enzymes, and 35 allosteric effectors,
resulting in quantitative changes in 44 metabolites. Analysis
of the network reveals that insulin induces phosphorylation and
activation of liver-type phosphofructokinase 1, thereby
controlling a key reaction in glycolysis. We thus provide a
versatile method of reconstruction of signal flow through the
network using phosphoproteome and metabolome data.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000541.
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 | BIOMD0000000541 | BioModels
Project description:We report non-canonical PAM sequences for SpyCas9 that were revealed from a novel growth-based PAM screen in E. coli.
2020-06-25 | GSE125959 | GEO
Project description:Human breast cancer cell line MCF-7 is usually sensitive to chemotherapy drug BMS-554417, an insulin receptor (IR) and insulin-like growth factor receptor (IGFR) inhibitor. However, through step-wise increase in BMS-554417 doses in culture media, we were able able to screen and select a single MCF-7 clone that is BMS-554417 resistant. It is cross resistant to BMS-536924. This new line of MCF-7 cells was named as MCF-7R4. The transcriptome profiling of both MCF-7 and MCF-7R4 was performed using Affymetrix HG-U133 plus2.0 GeneChip arrays.
2009-11-06 | GSE18912 | GEO
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. 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)
2015-12-15 | E-GEOD-73034 | biostudies-arrayexpress