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Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
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2012 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 279, no 6, 987-999 p.Article in journal (Refereed) Published
Abstract [en]

Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling.

Place, publisher, year, edition, pages
Wiley-Blackwell , 2012. Vol. 279, no 6, 987-999 p.
Keyword [en]
conclusive mathematical modeling, core prediction, insulin signaling, mechanistic explanation, rat adipocytes
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-76963DOI: 10.1111/j.1742-4658.2012.08488.xISI: 000301336800009OAI: oai:DiVA.org:liu-76963DiVA: diva2:523990
Note
Funding Agencies|European Commission Network of Excellence Biosim||Ostergotland County Council||Novo Nordisk Foundation||Lions||Swedish Diabetes Association||Swedish Research Council||Available from: 2012-04-27 Created: 2012-04-27 Last updated: 2017-12-07
In thesis
1. Insulin signaling dynamics in human adipocytes: Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes
Open this publication in new window or tab >>Insulin signaling dynamics in human adipocytes: Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Type 2 diabetes is characterized by raised blood glucose levels caused by an insufficient insulin control of glucose homeostasis. This lack of control is expressed both through insufficient release of insulin by the pancreatic beta-cells, and through insulin resistance in the insulin-responding tissues. We find insulin resistance of the adipose tissue particularly interesting since it appears to influence other insulin-responding tissues, such as muscle and liver, to also become insulin resistant.

The insulin signaling network is highly complex with cross-interacting intermediaries, positive and negative feedbacks, etc. To facilitate the mechanistic understanding of this network, we obtain dynamic, information-rich data and use model-based analysis as a tool to formally test different hypotheses that arise from the experimental observations. With dynamic mathematical models, we are able to combine knowledge and experimental data into mechanistic hypotheses, and draw conclusions such as rejection of hypotheses and prediction of outcomes of new experiments.

We aim for an increased understanding of adipocyte insulin signaling and the underlying mechanisms of the insulin resistance that we observe in adipocytes from subjects diagnosed with type 2 diabetes. We also aim for a complete picture of the insulin signaling network in primary human adipocytes from normal and diabetic subjects with a link to relevant clinical parameters: plasma glucose and insulin. Such a complete picture of insulin signaling has not been presented before. Not for adipocytes and not for other types of cells.

In this thesis, I present the development of the first comprehensive insulin signaling model that can simulate both normal and diabetic data from adipocytes – and that is linked to a whole-body glucose-insulin model. In the linking process we conclude that at least two glucose uptake parameters differ between the in vivo and in vitro conditions (Paper I). We also perform a model analysis of the early insulin signaling dynamics in rat adipocytes and conclude that internalization is important for an apparent reversed order of phosphorylation seen in these cells (Paper II). In the development of the first version of the comprehensive insulin signaling model, we introduce a key parameter for the diabetic state – an attenuated feedback (Paper III). We finally continue to build on the comprehensive model and include signaling to nuclear transcription via ERK and report substantial crosstalk in the insulin signaling network (Paper IV).

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 67 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1389
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-104725 (URN)10.3384/diss.diva-104725 (DOI)978-91-7519-430-1 (ISBN)
Public defence
2014-03-28, Eken, Campus US, Linköpings universitet, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2014-02-24 Created: 2014-02-24 Last updated: 2014-05-06Bibliographically approved

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Nyman, ElinFagerholm, SiriStrålfors, PeterCedersund, Gunnar

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