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* This work was supported by the Swedish Diabetes Fund, Novo Nordic Foundation, University of Linköping, and the Swedish Research Council. This article contains supplemental Figs. S1–S3, Tables S1 and S2, and Models S1–S4. 1 Both authors contributed equally to this work.
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, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.
Insulin is a prime controller of energy homeostasis in the human body. Dysfunction in the insulin control perturbs energy homeostasis with consequences in the form of disease such as type 2 diabetes (T2D)
and its corollaries cardiovascular disease, nephropathy, and neuropathy. Insulin control of target cells is relayed from the insulin receptor (IR) at the cell surface to different cellular processes, such as glucose uptake and protein synthesis, through an intracellular signaling network. In obesity the expanding adipose tissue, for poorly understood reasons, responds to the hypertrophy and hyperplasia of the adipocytes with a resistance to the hormone. Energy homeostasis is tolerably maintained despite insulin resistance in the adipose, muscle, and liver tissues as the insulin producing β-cells compensate by releasing more insulin. Eventually, after many years, the β-cells often fail to compensate and T2D can be diagnosed. Present understanding of insulin signaling is based on identification and sequencing of individual signaling intermediaries in a wide variety of different cell types and model organisms. There are many observations of differences between signaling in diabetic and normal target tissues of insulin, but there is neither consensus on their relative importance nor on how they relate to each other. We need a systems approach to examine and understand insulin resistance, where systems wide quantitative data are obtained in a consistent fashion and analyzed using mathematical modeling. Earlier models of insulin signaling are based on limited data and data from different cell types, and parameter values are often arbitrarily chosen (
In an integrated experimental/modeling approach we have earlier analyzed the very early phase of insulin signaling in human adipocytes, restricted to signaling between the insulin receptor (IR) and the immediate downstream insulin receptor substrate-1 (IRS1) (
). The model required both internalization of the receptor and a feedback from IRS1 to IR to explain experimental data. However, no analysis is available for the insulin-signaling network in adipocytes, normally and in T2D, as reviewed in Ref.
We now report a quantitative and comprehensive systems analysis of insulin signaling dynamics normally and in T2D. This systems analysis rests on two pillars: (i) collection of dynamic and steady-state data of key signaling intermediaries in primary human mature adipocytes from non-diabetic individuals and, in parallel, from obese patients with T2D; and (ii) mathematical modeling analysis that translates the systems wide data to systems wide mechanistic insights. Our analyses indicate that almost all signaling intermediaries are altered in T2D, and that most alterations may be explained by a single original effect: attenuation of a positive feedback from mammalian target of rapamycin (mTOR) in complex with raptor (mTORC1) to IRS1.
The data presented herein are unique in the ways they have been obtained to construct a mathematical model of insulin signaling in type 2 diabetes of obesity. First, all data are obtained from human mature adipocytes, which is where the obesity related insulin resistance likely starts. This also means that all data are from the same cell type. Second, data are collected throughout the signaling network, in a consistent fashion, allowing for combining the data in a systems analysis. Third, data are obtained in parallel with cells from both non-diabetic subjects and obese patients with T2D, making it possible to identify diabetes-specific signaling in T2D (which is a human disease). Fourth, the data consist of both dose-response data at quasi steady-states and relatively highly resolved time courses. Such time courses provide a crucial function in unraveling complex systems, especially using mathematical modeling. These data (supplemental Material) are thus also a valuable resource for future models incorporating data on additional signaling intermediaries and additional signaling branches of the network, e.g. to control of lipolysis.
To identify a systems wide mechanistic hypothesis is a complex task and we therefore took different and complementing approaches. First, we used traditional visual inspection and direct analysis of data, to identify how key signaling properties, such as time scales, sensitivity to insulin, and maximal effects of insulin, evolve through the network. Second, using conclusive minimal modeling (without relying on details in data and specific parameter values in the model) we rejected several hypotheses and showed that a feedback to IRS1 can explain the data. Third, through a dynamic mathematical model that takes all the details in data into account, we further refined understanding of the feedback to IRS1. This detailed dynamic model shows that although differences between normal and diabetic signaling appear throughout the system, the majority of them can be explained by attenuation of a positive feedback from mTORC1 to IRS1 alone. The model we have developed is important because it constitutes a first quantitative systems wide description of the mechanisms involved in insulin signaling and insulin resistance in human adipocytes, which also includes a link to the whole body level. Moreover, key properties of the model were corroborated by independent experiments and we have demonstrated how the model can be used to simulate the action of drugs.
The model is relatively complex with many estimated parameters; it is therefore necessary to consider the robustness and uniqueness of the obtained conclusions. One aspect of robustness considers the choice of parameter values. In the model more than 40 parameters were fitted to data and the exact parameter values obtained are not unique. In particular, changes in the relative weight allotted to different aspects of data can affect model predictions. However, the qualitative minimal modeling was done in a conclusive fashion, such that those conclusions are not dependent on parameter values or weights in the cost function. Another type of robustness concerns the model structure. Our presented model is not the final description of insulin signaling and insulin resistance, but it is the first such model. Future work will provide improvements of the model structure, both regarding feedbacks and cross-talk with other signaling branches, such as insulin signaling through the MAP kinase pathway to mTORC1 and signaling for transcriptional control, as well as signaling in other cell types. In particular data on the antilipolytic action of insulin can extend our model to include control of lipolysis, which is a major metabolic function of the adipocytes and also a major aberration in T2D. Such an extension is consequently also important for the model to be really useful. This will be particularly interesting as it involves cross-talk with signaling through the β-adrenergic receptor and thus requires modeling the action of two interacting hormones.
The literature is replete with data implicating the phosphorylation of IRS1 at different serine/threonine residues in positive or negative control of insulin signaling and in positive or negative feedback loops of insulin signaling (
). The importance of cell type and experimental conditions implicating negative or positive effects is illustrated by the phosphorylation of IRS1 at serine 312 (human sequence, corresponding to serine 307 in murine sequence), an established negative effect in different experimental setups (
) have previously found that phosphorylation of IRS1 at serine 307 in response to insulin is associated with a positive feedback. The systems approach we used herein demonstrates that a positive feedback to IRS1 can best explain the experimental data for the whole system. Although the phosphorylation of IRS1 at serine 307 fits the bill, parallel phosphorylation at other sites in IRS1 may convey or add to the positive feedback signal. Conclusive demonstration of the sites involved requires identification of all possible phosphorylation sites and their systematic evaluation. Nevertheless, our findings establish that a positive feedback signal to IRS1 is attenuated in T2D, and this was mimicked by different treatments that also inhibit phosphorylation of IRS1 at Ser-307: inhibition of mTORC1 with rapamycin (
We found that activation of mTORC1 is attenuated in adipocytes from patients with T2D whether or not they were on treatment with the insulin-sensitizing drug metformin (Fig. 9). In addition, attenuation of mTORC1 activity in adipocytes from non-diabetic subjects also directly correlates with the extent of insulin resistance of the adipocyte donors (
). This is important as many conditions associated with insulin resistance, such as inflammation, ER-stress, hypoxia, and mitochondrial dysfunction, are all well known to inhibit mTORC1. Unfortunately it is not possible to draw any strong conclusions from the knock-out of mTORC1 regarding its normal function in human adipocytes. In animal studies, adipose specific knock-out of raptor has caused partial transdifferentiation of white adipocytes to brown, with e.g. expression of the mitochondrial uncoupling protein UCP1 (
It should be noted that our data and model describe the two states, non-diabetes and diabetes, and are not describing the transition from the non-diabetic to the diabetic state, although they do suggest possible pathogenic mechanisms of insulin resistance. Attenuation of mTORC1 activity may be a logical response to obesity considering that, although the size of human adipocytes vary, their maximal size appears to be <0.3 mm in diameter (
). Cells can only get so big, there is an upper limit beyond which cellular integrity and function are compromised. Attenuation of mTORC1 signaling is an effective means for the adipocyte to restrict accumulation of triacylglycerol and further cell growth. Indeed it has been reported that large adipocytes are more insulin resistant than smaller from the same individual (
). It can be noted that this mechanism for insulin resistance in obesity is in concord with the current view of how obesity induces insulin resistance in other tissues through ectopic storage of fat as a result of filled adipose stores (reviewed in Ref.
). It remains to understand how mTORC1 can sense and respond to cell size. However, a disappointing implication is that insulin resistance in adipocytes cannot be cured by restoring insulin signaling; not without consequences such as necrosis of oversized cells. Instead, recruiting more adipocytes to relieve the pressure on big cells should improve insulin sensitivity. Interestingly, this is a mechanism of action of the thiazolidinedione class of peroxisome proliferator-activated receptor-γ agonists (
) that have been widely used to successfully improve insulin sensitivity in the adipose tissue in treatment of T2D.
We thank Dr. Preben Kjølhede for unfailingly supplying biopsies from surgery and Annelie Castell, Dr. Anna Danielsson, Niklas Hed, David Janzén, David Jullesson, and Dr. Anita Öst for their involvement in some of the experimental or modeling work.
Adipocytes exhibit abnormal subcellular distribution and translocation of vesicles containing glucose transporter 4 and insulin-regulated aminopeptidase in type 2 diabetes mellitus. Implications regarding defects in vesicle trafficking.