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Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism

BACKGROUND: Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascu...

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Autores principales: Sadria, Mehrshad, Layton, Anita T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135154/
https://www.ncbi.nlm.nih.gov/pubmed/34016143
http://dx.doi.org/10.1186/s12964-021-00706-1
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author Sadria, Mehrshad
Layton, Anita T.
author_facet Sadria, Mehrshad
Layton, Anita T.
author_sort Sadria, Mehrshad
collection PubMed
description BACKGROUND: Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals. METHODS: To understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+ -consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α. RESULTS: The model simulates the interactions among key regulators such as AKT, mTORC1, AMPK, NAD+ , and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases. CONCLUSIONS: This study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12964-021-00706-1.
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spelling pubmed-81351542021-05-20 Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism Sadria, Mehrshad Layton, Anita T. Cell Commun Signal Research BACKGROUND: Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals. METHODS: To understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+ -consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α. RESULTS: The model simulates the interactions among key regulators such as AKT, mTORC1, AMPK, NAD+ , and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases. CONCLUSIONS: This study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12964-021-00706-1. BioMed Central 2021-05-20 /pmc/articles/PMC8135154/ /pubmed/34016143 http://dx.doi.org/10.1186/s12964-021-00706-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sadria, Mehrshad
Layton, Anita T.
Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title_full Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title_fullStr Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title_full_unstemmed Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title_short Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism
title_sort interactions among mtorc, ampk and sirt: a computational model for cell energy balance and metabolism
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135154/
https://www.ncbi.nlm.nih.gov/pubmed/34016143
http://dx.doi.org/10.1186/s12964-021-00706-1
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