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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0287325 |
| _version_ | 1797394431495634944 |
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| author | Maryam Khoshnejat Ali Mohammad Banaei-Moghaddam Ali Akbar Moosavi-Movahedi Kaveh Kavousi |
| author_facet | Maryam Khoshnejat Ali Mohammad Banaei-Moghaddam Ali Akbar Moosavi-Movahedi Kaveh Kavousi |
| author_sort | Maryam Khoshnejat |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T00:19:37Z |
| format | Article |
| id | doaj.art-c27c448d54a346b8a6c629c556277097 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-09T00:19:37Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c27c448d54a346b8a6c629c5562770972023-12-12T05:36:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01186e028732510.1371/journal.pone.0287325A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients.Maryam KhoshnejatAli Mohammad Banaei-MoghaddamAli Akbar Moosavi-MovahediKaveh KavousiType 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.https://doi.org/10.1371/journal.pone.0287325 |
| spellingShingle | Maryam Khoshnejat Ali Mohammad Banaei-Moghaddam Ali Akbar Moosavi-Movahedi Kaveh Kavousi A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0287325 |
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