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A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients
Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose ho...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270629/ https://www.ncbi.nlm.nih.gov/pubmed/37319295 http://dx.doi.org/10.1371/journal.pone.0287325 |
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author | Khoshnejat, Maryam Banaei-Moghaddam, Ali Mohammad Moosavi-Movahedi, Ali Akbar Kavousi, Kaveh |
author_facet | Khoshnejat, Maryam Banaei-Moghaddam, Ali Mohammad Moosavi-Movahedi, Ali Akbar Kavousi, Kaveh |
author_sort | Khoshnejat, Maryam |
collection | PubMed |
description | Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated. |
format | Online Article Text |
id | pubmed-10270629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102706292023-06-16 A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients Khoshnejat, Maryam Banaei-Moghaddam, Ali Mohammad Moosavi-Movahedi, Ali Akbar Kavousi, Kaveh PLoS One Research Article Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated. Public Library of Science 2023-06-15 /pmc/articles/PMC10270629/ /pubmed/37319295 http://dx.doi.org/10.1371/journal.pone.0287325 Text en © 2023 Khoshnejat et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Khoshnejat, Maryam Banaei-Moghaddam, Ali Mohammad Moosavi-Movahedi, Ali Akbar Kavousi, Kaveh A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title | A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title_full | A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title_fullStr | A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title_full_unstemmed | A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title_short | A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
title_sort | holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270629/ https://www.ncbi.nlm.nih.gov/pubmed/37319295 http://dx.doi.org/10.1371/journal.pone.0287325 |
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