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  • 1.
    Bergqvist, Niclas
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AstraZeneca RandD, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Stenkula, Karin G.
    Lund University, Sweden.
    A systems biology analysis connects insulin receptor signaling with glucose transporter translocation in rat adipocytes2017In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, no 27, p. 11206-11217Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is characterized by insulin resistance, which arises from malfunctions in the intracellular insulin signaling network. Knowledge of the insulin signaling network is fragmented, and because of the complexity of this network, little consensus has emerged for the structure and importance of the different branches of the network. To help overcome this complexity, systems biology mathematical models have been generated for predicting both the activation of the insulin receptor (IR) and the redistribution of glucose transporter 4 (GLUT4) to the plasma membrane. Although the insulin signal transduction between IR and GLUT4 has been thoroughly studied with modeling and time-resolved data in human cells, comparable analyses in cells from commonly used model organisms such as rats and mice are lacking. Here, we combined existing data and models for rat adipocytes with new data collected for the signaling network between IR and GLUT4 to create a model also for their interconnections. To describe all data (amp;gt;140 data points), the model needed three distinct pathways from IR to GLUT4: (i) via protein kinase B (PKB) and Akt substrate of 160 kDa (AS160), (ii) via an AS160-independent pathway from PKB, and (iii) via an additional pathway from IR, e.g. affecting the membrane constitution. The developed combined model could describe data not used for training the model and was used to generate predictions of the relative contributions of the pathways from IR to translocation of GLUT4. The combined model provides a systems-level understanding of insulin signaling in rat adipocytes, which, when combined with corresponding models for human adipocytes, may contribute to model-based drug development for diabetes.

  • 2.
    Brannmark, Cecilia
    et al.
    University of Gothenburg, Sweden.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Komai, Ali M.
    University of Gothenburg, Sweden.
    Axelsson, Tom
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    El Hachmane, Mickael F.
    University of Gothenburg, Sweden.
    Musovic, Saliha
    University of Gothenburg, Sweden.
    Paul, Alexandra
    Chalmers University of Technology, Sweden.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AstraZeneca RandD, Sweden.
    Olofsson, Charlotta S.
    University of Gothenburg, Sweden.
    Mathematical modeling of white adipocyte exocytosis predicts adiponectin secretion and quantifies the rates of vesicle exo- and endocytosis2017In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, no 49, p. 20032-20043Article in journal (Refereed)
    Abstract [en]

    Adiponectin is a hormone secreted from white adipocytes and takes part in the regulation of several metabolic processes. Although the pathophysiological importance of adiponectin has been thoroughly investigated, the mechanisms controlling its release are only partly understood. We have recently shown that adiponectin is secreted via regulated exocytosis of adiponectin-containing vesicles, that adiponectin exocytosis is stimulated by cAMP-dependent mechanisms, and that Ca2+ and ATP augment the cAMP-triggered secretion. However, much remains to be discovered regarding the molecular and cellular regulation of adiponectin release. Here, we have used mathematical modeling to extract detailed information contained within our previously obtained high-resolution patch-clamp time-resolved capacitance recordings to produce the first model of adiponectin exocytosis/secretion that combines all mechanistic knowledge deduced from electrophysiological experimental series. This model demonstrates that our previous understanding of the role of intracellular ATP in the control of adiponectin exocytosis needs to be revised to include an additional ATP-dependent step. Validation of the model by introduction of data of secreted adiponectin yielded a very close resemblance between the simulations and experimental results. Moreover, we could show that Ca2+-dependent adiponectin endocytosis contributes to the measured capacitance signal, and we were able to predict the contribution of endocytosis to the measured exocytotic rate under different experimental conditions. In conclusion, using mathematical modeling of published and newly generated data, we have obtained estimates of adiponectin exo- and endocytosis rates, and we have predicted adiponectin secretion. We believe that our model should have multiple applications in the study of metabolic processes and hormonal control thereof.

  • 3.
    Brännmark, Cecilia
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Bergenholm, Linnéa
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Ekstrand, Eva-Maria
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Insulin Signaling in Type 2 Diabetes: Experimental and Modeling Analyses Reveal Mechanisms of Insulin Resistance in Human Adipocytes2013In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 288, no 14, p. 9867-9880Article in journal (Refereed)
    Abstract [en]

    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 in humans. We developed a dynamic mathematical model of insulin signaling – normally and in diabetes – based on quantitative steady-state and dynamic time-course data on signaling intermediaries in human mature adipocytes. At the core of insulin resistance is attenuation of a positive feedback from mammalian target of rapamycin in complex with raptor (mTORC1) to the insulin receptor substrate-1 (IRS1), which explains reduced sensitivity and signal strength throughout the signaling network. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

  • 4.
    Magnusson, Rasmus
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMedical DMPK AstraZeneca RandD, Sweden.
    Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients2017In: Bioscience Reports, ISSN 0144-8463, E-ISSN 1573-4935, Vol. 37, article id BSR20160514Article in journal (Refereed)
    Abstract [en]

    The molecular mechanisms of insulin resistance in Type 2 diabetes have been extensively studied in primary human adipocytes, and mathematical modelling has clarified the central role of attenuation of mammalian target of rapamycin (mTOR) complex 1 (mTORC1) activity in the diabetic state. Attenuation of mTORC1 in diabetes quells insulin-signalling network-wide, except for the mTOR in complex 2 (mTORC2)-catalysed phosphorylation of protein kinase B (PKB) at Ser(473) (PKB-S473P), which is increased. This unique increase could potentially be explained by feedback and interbranch cross-talk signals. To examine if such mechanisms operate in adipocytes, we herein analysed data from an unbiased phosphoproteomic screen in 3T3-L1 adipocytes. Using a mathematical modelling approach, we showed that a negative signal from mTORC1-p70 S6 kinase (S6K) to rictor-mTORC2 in combination with a positive signal from PKB to SIN1-mTORC2 are compatible with the experimental data. This combined cross-branch signalling predicted an increased PKB-S473P in response to attenuation of mTORC1 - a distinguishing feature of the insulin resistant state in human adipocytes. This aspect of insulin signalling was then verified for our comprehensive model of insulin signalling in human adipocytes. Introduction of the cross-branch signals was compatible with all data for insulin signalling in human adipocytes, and the resulting model can explain all data network-wide, including the increased PKB-S473P in the diabetic state. Our approach was to first identify potential mechanisms in data from a phosphoproteomic screen in a cell line, and then verify such mechanisms in primary human cells, which demonstrates how an unbiased approach can support a direct knowledge-based study.

  • 5.
    Magnusson, Rasmus
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mariotti, Guido
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Köpsén, Mattias
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Jornsten, Rebecka
    University of Gothenburg, Sweden.
    Linde, Joerg
    Hans Knoell Institute, Germany; Hans Knoell Institute, Germany.
    Nordling, Torbjorn
    National Cheng Kung University, Taiwan; Science Life Lab, Sweden.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Schulze, Sylvie
    Hans Knoell Institute, Germany.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Zhang, Hanmin
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    LASSIM-A network inference toolbox for genome-wide mechanistic modeling2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 6, article id e1005608Article in journal (Refereed)
    Abstract [en]

    Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

  • 6.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Insulin signaling dynamics in human adipocytes: Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes2014Doctoral 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).

    List of papers
    1. A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis
    Open this publication in new window or tab >>A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis
    Show others...
    2011 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 286, no 29, p. 26028-26041Article in journal (Refereed) Published
    Abstract [en]

    Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

    Place, publisher, year, edition, pages
    American Society for Biochemistry and Molecular Biology, 2011
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-70109 (URN)10.1074/jbc.M110.188987 (DOI)000293073000061 ()
    Note

    This research was originally published in: Elin Nyman, Cecilia Brännmark, Robert Palmér, Jan Brugård, Fredrik Nyström, Peter Strålfors and Gunnar Cedersund, A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis, 2011, Journal of Biological Chemistry, (286), 29, 26028-26041. http://dx.doi.org/10.1074/jbc.M110.188987 © the American Society for Biochemistry and Molecular Biology http://www.asbmb.org/

    Available from: 2013-04-11 Created: 2011-08-19 Last updated: 2017-12-08Bibliographically approved
    2. Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes
    Open this publication in new window or tab >>Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes
    Show others...
    2012 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 279, no 6, p. 987-999Article 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
    Keywords
    conclusive mathematical modeling, core prediction, insulin signaling, mechanistic explanation, rat adipocytes
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-76963 (URN)10.1111/j.1742-4658.2012.08488.x (DOI)000301336800009 ()
    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
    3. Insulin Signaling in Type 2 Diabetes: Experimental and Modeling Analyses Reveal Mechanisms of Insulin Resistance in Human Adipocytes
    Open this publication in new window or tab >>Insulin Signaling in Type 2 Diabetes: Experimental and Modeling Analyses Reveal Mechanisms of Insulin Resistance in Human Adipocytes
    Show others...
    2013 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 288, no 14, p. 9867-9880Article in journal (Refereed) Published
    Abstract [en]

    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 in humans. We developed a dynamic mathematical model of insulin signaling – normally and in diabetes – based on quantitative steady-state and dynamic time-course data on signaling intermediaries in human mature adipocytes. At the core of insulin resistance is attenuation of a positive feedback from mammalian target of rapamycin in complex with raptor (mTORC1) to the insulin receptor substrate-1 (IRS1), which explains reduced sensitivity and signal strength throughout the signaling network. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-84999 (URN)10.1074/jbc.M112.432062 (DOI)000317114000027 ()
    Available from: 2012-10-30 Created: 2012-10-30 Last updated: 2017-12-07Bibliographically approved
    4. The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/2
    Open this publication in new window or tab >>The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/2
    Show others...
    2014 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Insulin acutely controls metabolism in adipocytes, but also nuclear transcription through the “mitogenic” signaling pathway mediated by Map-kinases ERK1/2 (ERK). The cellular metabolic response to insulin is attenuated in insulin resistance and type 2 diabetes, but whether this involves also signaling through ERK is unclear. Based on experimental data from primary mature human adipocytes from diabetic and nondiabetic individuals, we demonstrate a network-wide, model-based analysis of insulin signaling through ERK to phosphorylation of transcription factor Elk1 integrated with signaling for “metabolic” control. We use minimal modeling to analyze the idiosyncratic phosphorylation dynamics of ERK, i.e. a slow phosphorylation response that returns to basal in response to insulin, and conclude that sequestration of ERK is the simplest explanation to data. We also demonstrate a significant cross-talk between ERK and mTORC1 signaling to ribosomal protein S6 for control of protein synthesis. A reduced sensitivity and reduced maximal phosphorylation of ERK in response to insulin in the diabetic state can be explained by the same mechanisms that generate insulin resistance in the control of metabolism.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-104724 (URN)
    Available from: 2014-02-24 Created: 2014-02-24 Last updated: 2014-02-24Bibliographically approved
  • 7.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Cardiovascular and Metabolic Diseases IMED Biotech Unit, AstraZeneca RandD, Gothenburg, Sweden.
    Bartesaghi, Stefano
    Cardiovascular and Metabolic Diseases IMED Biotech Unit, AstraZeneca RandD, Gothenburg, Sweden.
    Melin Rydfalk, Rebecka
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Eng, Sandra
    Cardiovascular and Metabolic Diseases IMED Biotech Unit, AstraZeneca RandD, 431 83 Gothenburg, Sweden.
    Pollard, Charlotte
    Advanced Drug Delivery, Pharmaceutical Science IMED Biotech Unit, AstraZeneca RandD, Gothenburg, Sweden.
    Gennemark, Peter
    Cardiovascular and Metabolic Diseases IMED Biotech Unit, AstraZeneca RandD, Gothenburg, Sweden.
    Peng, Xiao-Rong
    Cardiovascular and Metabolic Diseases IMED Biotech Unit, AstraZeneca RandD, Gothenburg, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Systems biology reveals uncoupling beyond UCP1 in human white fat-derived beige adipocytes2017In: NPJ systems biology and applications, ISSN 2056-7189, Vol. 3, article id 29Article in journal (Refereed)
    Abstract [en]

    Pharmaceutical induction of metabolically active beige adipocytes in the normally energy storing white adipose tissue has potential to reduce obesity. Mitochondrial uncoupling in beige adipocytes, as in brown adipocytes, has been reported to occur via the uncoupling protein 1 (UCP1). However, several previous in vitro characterizations of human beige adipocytes have only measured UCP1 mRNA fold increase, and assumed a direct correlation with metabolic activity. Here, we provide an example of pharmaceutical induction of beige adipocytes, where increased mRNA levels of UCP1 are not translated into increased protein levels, and perform a thorough analysis of this example. We incorporate mRNA and protein levels of UCP1, time-resolved mitochondrial characterizations, and numerous perturbations, and analyze all data with a new fit-for-purpose mathematical model. The systematic analysis challenges the seemingly obvious experimental conclusion, i.e., that UCP1 is not active in the induced cells, and shows that hypothesis testing with iterative modeling and experimental work is needed to sort out the role of UCP1. The analyses demonstrate, for the first time, that the uncoupling capability of human beige adipocytes can be obtained without UCP1 activity. This finding thus opens the door to a new direction in drug discovery that targets obesity and its associated comorbidities. Furthermore, the analysis advances our understanding of how to evaluate UCP1-independent thermogenesis in human beige adipocytes.

  • 8.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Palmér, Robert
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Brugård, Jan
    MathCore Engn.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Endocrinology and Gastroenterology UHL.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis2011In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 286, no 29, p. 26028-26041Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

  • 9.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Insulin signaling - mathematical modeling comes of age2012In: Trends in endocrinology and metabolism, ISSN 1043-2760, E-ISSN 1879-3061, Vol. 23, no 3, p. 107-115Article, review/survey (Refereed)
    Abstract [en]

    Signaling pathways that only a few years ago appeared simple and understandable, albeit far from complete, have evolved into very complex multi-layered networks of cellular control mechanisms, which in turn are integrated in a similarly complex whole-body level of control mechanisms. This complexity sets limits for classical biochemical reasoning, such that a correct and complete analysis of experimental data while taking the full complexity into account is not possible. In this Opinion we propose that mathematical modeling can be used as a tool in insulin signaling research, and we demonstrate how recent developments in modeling - and the integration of modeling in the experimental process - provide new possibilities to approach and decipher complex biological systems more efficiently.

  • 10.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Jullesson, David
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes2012In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 279, no 6, p. 987-999Article in journal (Refereed)
    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.

  • 11.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMED DMPK AstraZeneca R&D, Mölndal, Sweden.
    Lindgren, Isa
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, The Institute of Technology.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundengård, Karin
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    Cervin, Ida
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Arbring, Theresia
    Linköping University, Department of Science and Technology, Physics and Electronics. Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering.
    Altimitas, Jordi
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Mathematical modeling improves EC50 estimations from classical dose–response curves2015In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 5, p. 951-962Article in journal (Refereed)
    Abstract [en]

    The beta-adrenergic response is impaired in failing hearts. When studying beta-adrenergic function in vitro, the half-maximal effective concentration (EC50) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50. Instead, we have applied a mathematical modeling approach to interpret the full dose–response curvein a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose–response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180–525 nM) is higher than the classically interpreted EC50 (46–191 nM). Mathematical modeling thus makes it possible to reinterpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible.

  • 12.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Biomedical Engineering.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes2014In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 289, no 48, p. 33215-33230Article in journal (Refereed)
    Abstract [en]

    The response to insulin is impaired in type 2 diabetes. Much information is available about insulin signaling, but understanding of the cellular mechanisms causing impaired signaling and insulin resistance is hampered by fragmented data, mainly obtained from different cell lines and animals. We have collected quantitative and systems-wide dynamic data on insulin signaling in primary adipocytes and compared cells isolated from healthy and diabetic individuals. Mathematical modeling and experimental verification identified mechanisms of insulin control of the MAPKs ERK1/2. We found that in human adipocytes, insulin stimulates phosphorylation of the ribosomal protein S6 and hence protein synthesis about equally via ERK1/2 and mTORC1. Using mathematical modeling, we examined the signaling network as a whole and show that a single mechanism can explain the insulin resistance of type 2 diabetes throughout the network, involving signaling both through IRS1, PKB, and mTOR and via ERK1/2 to the nuclear transcription factor Elk1. The most important part of the insulin resistance mechanism is an attenuated feedback from the protein kinase mTORC1 to IRS1, which spreads signal attenuation to all parts of the insulin signaling network. Experimental inhibition of mTORC1 using rapamycin in adipocytes from non-diabetic individuals induced and thus confirmed the predicted network-wide insulin resistance.

  • 13.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/22014Manuscript (preprint) (Other academic)
    Abstract [en]

    Insulin acutely controls metabolism in adipocytes, but also nuclear transcription through the “mitogenic” signaling pathway mediated by Map-kinases ERK1/2 (ERK). The cellular metabolic response to insulin is attenuated in insulin resistance and type 2 diabetes, but whether this involves also signaling through ERK is unclear. Based on experimental data from primary mature human adipocytes from diabetic and nondiabetic individuals, we demonstrate a network-wide, model-based analysis of insulin signaling through ERK to phosphorylation of transcription factor Elk1 integrated with signaling for “metabolic” control. We use minimal modeling to analyze the idiosyncratic phosphorylation dynamics of ERK, i.e. a slow phosphorylation response that returns to basal in response to insulin, and conclude that sequestration of ERK is the simplest explanation to data. We also demonstrate a significant cross-talk between ERK and mTORC1 signaling to ribosomal protein S6 for control of protein synthesis. A reduced sensitivity and reduced maximal phosphorylation of ERK in response to insulin in the diabetic state can be explained by the same mechanisms that generate insulin resistance in the control of metabolism.

  • 14.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden.
    Rozendaal, Yvonne J W
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Helmlinger, Gabriel
    AstraZeneca, Pharmaceuticals LP, Waltham, MA, USA.
    Hamrén, Bengt
    AstraZeneca, Gothenburg, Sweden.
    Kjellsson, Maria C
    Uppsala University, Uppsala, Sweden.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    van Riel, Natal A W
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Gennemark, Peter
    AstraZeneca R&D, Gothenburg, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes2016In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, p. 1-14Article, review/survey (Refereed)
    Abstract [en]

    We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

  • 15.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Multilevel-Modeling, Core Predictions, and the Concept of Final Conclusions2012In: Biosimulation in Biomedical Research, Health Care and Drug Development / [ed] Mosekilde, Erik; Sosnovtseva, Olga; Rostami-Hodjegan, Amin, Wien: Springer, 2012, p. 311-328Chapter in book (Other academic)
  • 16.
    Palmér, Robert
    et al.
    Wolfram MathCore AB, Linköping, Sweden;.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Wolfram MathCore AB, Linköping, Sweden;.
    Penney, Mark
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge, UK.
    Marley, Anna
    Bioscience, Astra Zeneca, Alderley Park, UK.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Agoram, Balaji
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge.
    Effects of IL-1β-Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms2014In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 3, no 6, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

  • 17.
    Rajan, Meenu Rohini
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Brannmark, Cecilia
    Univ Gothenburg, Sweden.
    Olofsson, Charlotta S.
    Univ Gothenburg, Sweden.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Inhibition of FOXO1 transcription factor in primary human adipocytes mimics the insulin-resistant state of type 2 diabetes2018In: Biochemical Journal, ISSN 0264-6021, E-ISSN 1470-8728, Vol. 475, p. 1807-1820Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is characterized by insulin resistance in the expanding adipose tissue of obesity. The insulin resistance manifests in human adipocytes as system-wide impairment of insulin signalling. An exception is the regulation of transcription factor FOXO1 (forkhead box protein O1), which is phosphorylated downstream of mTORC2 (mammalian/mechanistic target of rapamycin in complex with raptor) and is therefore not exhibiting impaired response to insulin. However, the abundance, and activity, of FOXO1 is reduced by half in adipocytes from patients with diabetes. To elucidate the effect of reduced FOXO1 activity, we here transduced human adipocytes with a dominant-negative construct of FOXO1 (DN-FOXO1). Inhibition of FOXO1 reduced the abundance of insulin receptor, glucose transporter-4, ribosomal protein S6, mTOR and raptor. Functionally, inhibition of FOXO1 induced an insulin-resistant state network-wide, a state that qualitatively and quantitatively mimicked adipocytes from patients with type 2 diabetes. In contrast, and in accordance with these effects of DN-FOXO1, overexpression of wild-type FOXO1 appeared to augment insulin signalling. We combined experimental data with mathematical modelling to show that the impaired insulin signalling in FOXO1-inhibited cells to a large extent can be explained by reduced mTORC1 activity - a mechanism that defines much of the diabetic state in human adipocytes. Our findings demonstrate that FOXO1 is critical for maintaining normal insulin signalling of human adipocytes.

  • 18.
    Rajan, Meenu Rohini
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Cardiovascular and Metabolic Diseases, Innovative Medicines, and Drug Metabolism and Pharmacokinetics, AstraZeneca Research and Development, Gothenburg, Sweden .
    Kjölhede, Preben
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes2016In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 291, no 30, p. 15806-15819Article in journal (Refereed)
    Abstract [en]

    Insulin resistance is a major aspect of type 2 diabetes (T2D), which results from impaired insulin signaling in target cells. Signaling to regulate forkhead box protein O1 (FOXO1) may be the most important mechanism for insulin to control transcription. Despite this, little is known about how insulin regulates FOXO1 and how FOXO1 may contribute to insulin resistance in adipocytes, which are the most critical cell type in the development of insulin resistance. We report a detailed mechanistic analysis of insulin control of FOXO1 in human adipocytes obtained from non-diabetic subjects and from patients with T2D. We show that FOXO1 is mainly phosphorylated through mTORC2-mediated phosphorylation of protein kinase B at Ser(473) and that this mechanism is unperturbed in T2D. We also demonstrate a cross-talk from the MAPK branch of insulin signaling to stimulate phosphorylation of FOXO1. The cellular abundance and consequently activity of FOXO1 are halved in T2D. Interestingly, inhibition of mTORC1 with rapamycin reduces the abundance of FOXO1 to the levels in T2D. This suggests that the reduction of the concentration of FOXO1 is a consequence of attenuation of mTORC1, which defines much of the diabetic state in human adipocytes. We integrate insulin control of FOXO1 in a network-wide mathematical model of insulin signaling dynamics based on compatible data from human adipocytes. The diabetic state is network-wide explained by attenuation of an mTORC1-to-insulin receptor substrate-1 (IRS1) feedback and reduced abundances of insulin receptor, GLUT4, AS160, ribosomal protein S6, and FOXO1. The model demonstrates that attenuation of the mTORC1-to-IRS1 feedback is a major mechanism of insulin resistance in the diabetic state.

  • 19.
    Sips, Fianne L. P.
    et al.
    Eindhoven University of Technology, Netherlands.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMED DMPK AstraZeneca RandD, Sweden.
    Adiels, Martin
    University of Gothenburg, Sweden.
    Hilbers, Peter A. J.
    Eindhoven University of Technology, Netherlands.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    van Riel, Natal A. W.
    Eindhoven University of Technology, Netherlands.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 9, p. e0135665-Article in journal (Refereed)
    Abstract [en]

    In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.

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