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A comprehensive mechanistic model of adipocyte signaling with layers of confidence
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Mathematics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9058-7049
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
Department of Physiology/Metabolic Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg.ORCID iD: 0000-0001-8824-3151
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4261-0291
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2022 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Adipocyte cellular signaling, normally and in type 2 diabetes, is far from fully studied. We have earlier developed detailed dynamic mathematical models for some well-studied, and partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data is key. There exists such data for the insulin response of adipocytes, as well as prior knowledge on possible protein-protein interactions associated with a confidence level. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. In our new method, we first establish a core model by connecting our partially overlapping models of adipocyte cellular signaling with focus on: 1) lipolysis and fatty acid release, 2) glucose uptake, and 3) the release of adiponectin. We use the phosphoproteome data and prior knowledge to identify phosphosites adjacent to the core model, and then try to add the adjacent phosphosites to the model. The additions of the adjacent phosphosites is tested in a parallel, pairwise approach with low computation time. We then iteratively collect the accepted additions into a layer, and use the newly added layer to find new adjacent phosphosites. We find that the first 15 layers (60 added phosphosites) with the highest confidence can correctly predict independent inhibitor-data (70-90 % correct), and that this ability decrease when we add layers of decreasing confidence. In total, 60 layers (3926 phosphosites) can be added to the model and still keep predictive ability. Finally, we use the comprehensive adipocyte model to simulate systems-wide alterations in adipocytes in type 2 diabetes. This new method provide a tool to create large models that keeps track of varying confidence.Competing Interest StatementThe authors have declared no competing interest.

Place, publisher, year, edition, pages
2022.
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:liu:diva-184907DOI: 10.1101/2022.03.11.483974OAI: oai:DiVA.org:liu-184907DiVA, id: diva2:1657450
Available from: 2022-05-11 Created: 2022-05-11 Last updated: 2023-12-28Bibliographically approved
In thesis
1. A comprehensive dynamic model of the adipocyte
Open this publication in new window or tab >>A comprehensive dynamic model of the adipocyte
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The adipose tissue contributes to energy homeostasis by storing excess energy as triglycerides when the energy status is high, and by releasing fatty acids when the energy status is low. In addition to the involvement in energy homeostasis, the adipose tissue also has a function of hormonal control exerted by the release of adipokines such as adiponectin. Dysregulations in the signaling pathways of these two functions are involved in the development of type 2 diabetes and related complications such as cardiovascular disease. These signaling pathways are too complex to be fully unraveled without a systematic framework, such as mathematical modeling. Previous modeling works have investigated the insulin signaling pathways leading to glucose uptake in primary human adipocytes in response to insulin stimulation. Furthermore, experimental works have investigated how adrenergic stimuli and varying concentrations of intracellular mediators triggers the release of adiponectin from 3T3‐L1 adipocytes, and how insulin and adrenergic stimuli influence lipolysis in primary human adipocytes. Additionally, large‐scale phosphoproteomic data for insulin signaling in 3T3‐L1 adipocytes have become available. However, these experimental data had not been systematically investigated using mathematical modeling. In this thesis, I have used mathematical modeling to study three aspects of the adipocyte: 1) adiponectin release, 2) lipolysis, and 3) intracellular crosstalk between the pathways of glucose uptake, lipolysis, and adiponectin release. Finally, I have developed a new method for automatic model expansion.

In Paper I, we used mathematical modeling to test a hypothesis of the mechanisms controlling adiponectin exocytosis in 3T3‐L1 cells. We found that the hypothesis had to be revised in order to be in agreement with the available experimental data. We used the revised model to quantify the balance between the exocytosis and the endocytosis, and to predict the amount of released adiponectin in response to additional experiments.

In Paper II, we extended the adiponectin exocytosis model from Paper I with mechanisms for how extracellular adrenergic stimulation trigger adiponectin exocytosis. We also used the model to quantify the effect of a decreased amount of β3‐adrenergic receptors on the adrenergically stimulated adiponectin exocytosis.

In Paper III, we tested a hypothesis of the impact of, and crosstalk between, insulin and adrenergic stimulation on the lipolysis. We used the model to test three different actions by insulin on the lipolysis, and to predict fatty acid release in vivo in response to stimulations with epinephrine and insulin.

In Paper IV, we combined the models from Paper I‐III with a previously published model for glucose uptake. We then used the connected model as a core model to which additional signaling data could be added using a new method for automatic model expansion. This new method incorporates prior‐knowledge and large‐scale data to expand a core model with thousands of additional phosphosites into a comprehensive model of the adipocyte. The comprehensive expanded model can propagate the effect of type 2 diabetes from the core model to a substantial part of the phosphoproteome, and could thus facilitate the finding of new drug targets or treatment regimens for type 2 diabetic patients.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 79
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2226
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:liu:diva-184876 (URN)10.3384/9789179293130 (DOI)9789179293123 (ISBN)9789179293130 (ISBN)
Public defence
2022-06-10, Belladonna, Building 511, Campus US, Linköping, 14:00 (English)
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Available from: 2022-05-09 Created: 2022-05-09 Last updated: 2023-12-28Bibliographically approved

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Lövfors, WilliamJönsson, CeciliaNyman, ElinCedersund, Gunnar

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