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Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients

Rasmus Magnusson^1,2, Mika Gustafsson², Gunnar Cedersund^1,3

¹Department of Biomedical Engineering, Linköping University, Sweden.

Bioscience Reports

|December 18, 2016

Abstract:

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 Ser⁴⁷³ (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.

Insights

Mathematical modeling reveals cross-talk between mTORC1 and mTORC2 signaling pathways in adipocytes. This explains increased protein kinase B phosphorylation, a key feature of insulin resistance in type 2 diabetes.

Area of Science:

Molecular biology
Cellular signaling
Diabetes research

Background:

Insulin resistance in type 2 diabetes is linked to impaired mammalian target of rapamycin (mTOR) complex 1 (mTORC1) activity.
Despite general attenuation, mTOR complex 2 (mTORC2)-mediated phosphorylation of protein kinase B (PKB) at Ser473 (PKB-S473P) increases in the diabetic state.
This paradoxical increase suggests complex feedback and cross-talk mechanisms within insulin signaling pathways.

Purpose of the Study:

To investigate the molecular mechanisms underlying the differential regulation of mTORC1 and mTORC2 signaling in insulin resistance.
To determine if inter-branch cross-talk signals explain the increased PKB-S473P in the context of mTORC1 attenuation.
To develop and validate a comprehensive mathematical model of insulin signaling in adipocytes that accounts for these cross-talk mechanisms.

Main Methods:

Analysis of phosphoproteomic screen data from 3T3-L1 adipocytes.
Development and application of mathematical modeling to identify signaling interactions.
Experimental verification of predicted cross-talk mechanisms in primary human adipocytes.

Main Results:

Mathematical modeling identified a negative signal from mTORC1-p70 S6 kinase (S6K) to mTORC2 and a positive signal from PKB to SIN1-mTORC2.
This cross-branch signaling model accurately predicted the increased PKB-S473P upon mTORC1 attenuation, consistent with the diabetic state.
The validated model successfully explained network-wide insulin signaling data in human adipocytes, including the elevated PKB-S473P.

Conclusions:

Cross-talk between mTORC1 and mTORC2 signaling pathways is a critical mechanism driving insulin resistance in type 2 diabetes.
Mathematical modeling, combined with phosphoproteomic data and primary cell validation, provides a powerful approach to elucidate complex cellular signaling networks.
Understanding these intricate signaling dynamics offers potential targets for therapeutic interventions in diabetes.

Read full analysis

Keywords:

SIN1 Type 2 diabetes computational models mTORC1 mTORC2 protein kinase B

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