A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes

Nyman, Elin; Rajan, Meenu Rohini; Fagerholm, Siri; Brännmark, Cecilia; Cedersund, Gunnar; Strålfors, Peter

doi:10.1074/jbc.m114.608927

Cited by 58 publications

(100 citation statements)

References 63 publications

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“…The insulin signaling network is vast, and highly interconnected with other networks [122], which makes establishing the individual roles and T mechanisms of the players in the network very hard, and thus suitable for modeling [34,53,54,[123][124][125][126]. In Figure 32, a reduced network featuring some of the key players of the insulin signaling system is shown.…”

Section: Insulin Signaling System In Human Adipocytesmentioning

confidence: 99%

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Johansson¹

2017

Linköping Studies in Science and Technology. Dissertations

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The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model.Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data.In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discri...

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Section: Insulin Signaling System In Human Adipocytesmentioning

confidence: 99%

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Johansson¹

2017

Linköping Studies in Science and Technology. Dissertations

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“…In a series of papers [9,10,22,23], the intracellular insulin resistance mechanisms in human adipocytes have been unravelled, with a combination of systematic experimental and mathematical modelling approaches. The experimental data consist of both dynamic time-series (i.e.…”

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

“…Considering research in diabetes, multilevel models have helped us unravel how insulin resistance, i.e. a reduced cellular response to insulin, arises from specific parts of the intracellular protein interaction network, how this resistance spreads to the rest of the network and even how this resistance may affect other organs [9,10]. This multi-level model is currently being used by several drug development companies to help them identify promising drug targets.…”

Section: Introductionmentioning

confidence: 99%

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

et al. 2016

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One contribution of 12 to a theme issue 'The Human Physiome: a necessary key to the creative destruction of medicine'. 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 decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

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“…The positive or negative effect of this feedback is debated (reviewed in [40]) but in human adipocytes phosphorylation of IRS1 at serine 307 is shown to be required for proper insulin signaling [41,42]. Cell growth is promoted by insulin via PKB and mTORC1 activation of the ribosomal protein S6 kinase (S6K) leading to increased translation of proteins (Figure 3).…”

Section: Insulin Signalingmentioning

confidence: 99%

“…Last and most important is an attenuated positive feedback from mTORC1 to the phosphorylation at serine 307 of IRS1 in T2D adipocytes. This attenuation of the feedback affects insulin signaling through IRS1, PKB, mTOR and ERK branches of the insulin signaling network [42].…”

Section: Insulin Resistance and Type 2 Diabetesmentioning

confidence: 99%

Human Adipocytes : Proteomic Approaches

Jufvas¹

2016

Linköping University Medical Dissertations

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Type 2 diabetes is characterized by increased levels of glucose in the blood originating from insulin resistance in insulin sensitive tissues and from reduced pancreatic insulin production. Around 400 million people in the world are diagnosed with type 2 diabetes and the correlation with obesity is strong. In addition to life style induction of obesity and type 2 diabetes, there are indications of genetic and epigenetic influences. This thesis has focused on the characterization of primary human adipocytes, who play a crucial role in the development of type 2 diabetes. Histones are important proteins in chromatin dynamics and may be one of the factors behind epigenetic inheritance. In paper I, we characterized histone variants and posttranslational modifications in human adipocytes. Several of the specific posttranslational histone modifications we identified have been characterized in other cell types, but the majority was not previously known. Moreover, we identified a variant of histone H4 on protein level for the first time. In paper II, we studied specific histone H3 methylations in the adipocytes. We found that overweight is correlated with a reduction of H3K4me2 while type 2 diabetes is associated with an increase of H3K4me3. This shows a genome-wide difference in important chromatin modifications that could help explain the epidemiologically shown association between epigenetics and metabolic health. Caveolae is a plasma membrane structure involved in the initial and important steps of insulin signaling. In paper III we characterized the IQGAP1 interactome in human adipocytes and suggest that IQGAP1 is a link between caveolae and the cytoskeleton. Moreover, the amount of IQGAP1 is drastically lower in adipocytes from type 2 diabetic subjects compared with controls implying a potential role for IQGAP1 in insulin resistance. In conclusion, this thesis provides new insights into the insulin signaling frameworks and the histone variants and modifications of human adipocytes

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A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes

Cited by 58 publications

References 63 publications

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

Human Adipocytes : Proteomic Approaches

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