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Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis

Insulin resistance (IR) is a precursor event that occurs in multiple organs and underpins many metabolic disorders. However, due to the lack of effective means to systematically explore and interpret disease-related tissue crosstalk, the tissue communication mechanism in pathogenesis of IR has not b...

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Autores principales: Yang, Linlin, Yang, Linquan, Wang, Xing, Xing, Hanying, Zhao, Hang, Xing, Yuling, Zhou, Fei, Wang, Chao, Song, Guangyao, Ma, Huijuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805208/
https://www.ncbi.nlm.nih.gov/pubmed/35116003
http://dx.doi.org/10.3389/fendo.2021.756785
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author Yang, Linlin
Yang, Linquan
Wang, Xing
Xing, Hanying
Zhao, Hang
Xing, Yuling
Zhou, Fei
Wang, Chao
Song, Guangyao
Ma, Huijuan
author_facet Yang, Linlin
Yang, Linquan
Wang, Xing
Xing, Hanying
Zhao, Hang
Xing, Yuling
Zhou, Fei
Wang, Chao
Song, Guangyao
Ma, Huijuan
author_sort Yang, Linlin
collection PubMed
description Insulin resistance (IR) is a precursor event that occurs in multiple organs and underpins many metabolic disorders. However, due to the lack of effective means to systematically explore and interpret disease-related tissue crosstalk, the tissue communication mechanism in pathogenesis of IR has not been elucidated yet. To solve this issue, we profiled all proteins in white adipose tissue (WAT), liver, and skeletal muscle of a high fat diet induced IR mouse model via proteomics. A network-based approach was proposed to explore IR related tissue communications. The cross-tissue interface was constructed, in which the inter-tissue connections and also their up and downstream processes were particularly inspected. By functional quantification, liver was recognized as the only organ that can output abnormal carbohydrate metabolic signals, clearly highlighting its central role in regulation of glucose homeostasis. Especially, the CD36–PPAR axis in liver and WAT was identified and verified as a potential bridge that links cross-tissue signals with intracellular metabolism, thereby promoting the progression of IR through a PCK1-mediated lipotoxicity mechanism. The cross-tissue mechanism unraveled in this study not only provides novel insights into the pathogenesis of IR, but also is conducive to development of precision therapies against various IR associated diseases. With further improvement, our network-based cross-tissue analytic method would facilitate other disease-related tissue crosstalk study in the near future.
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spelling pubmed-88052082022-02-02 Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis Yang, Linlin Yang, Linquan Wang, Xing Xing, Hanying Zhao, Hang Xing, Yuling Zhou, Fei Wang, Chao Song, Guangyao Ma, Huijuan Front Endocrinol (Lausanne) Endocrinology Insulin resistance (IR) is a precursor event that occurs in multiple organs and underpins many metabolic disorders. However, due to the lack of effective means to systematically explore and interpret disease-related tissue crosstalk, the tissue communication mechanism in pathogenesis of IR has not been elucidated yet. To solve this issue, we profiled all proteins in white adipose tissue (WAT), liver, and skeletal muscle of a high fat diet induced IR mouse model via proteomics. A network-based approach was proposed to explore IR related tissue communications. The cross-tissue interface was constructed, in which the inter-tissue connections and also their up and downstream processes were particularly inspected. By functional quantification, liver was recognized as the only organ that can output abnormal carbohydrate metabolic signals, clearly highlighting its central role in regulation of glucose homeostasis. Especially, the CD36–PPAR axis in liver and WAT was identified and verified as a potential bridge that links cross-tissue signals with intracellular metabolism, thereby promoting the progression of IR through a PCK1-mediated lipotoxicity mechanism. The cross-tissue mechanism unraveled in this study not only provides novel insights into the pathogenesis of IR, but also is conducive to development of precision therapies against various IR associated diseases. With further improvement, our network-based cross-tissue analytic method would facilitate other disease-related tissue crosstalk study in the near future. Frontiers Media S.A. 2022-01-18 /pmc/articles/PMC8805208/ /pubmed/35116003 http://dx.doi.org/10.3389/fendo.2021.756785 Text en Copyright © 2022 Yang, Yang, Wang, Xing, Zhao, Xing, Zhou, Wang, Song and Ma https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Yang, Linlin
Yang, Linquan
Wang, Xing
Xing, Hanying
Zhao, Hang
Xing, Yuling
Zhou, Fei
Wang, Chao
Song, Guangyao
Ma, Huijuan
Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title_full Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title_fullStr Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title_full_unstemmed Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title_short Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis
title_sort exploring the multi-tissue crosstalk relevant to insulin resistance through network-based analysis
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805208/
https://www.ncbi.nlm.nih.gov/pubmed/35116003
http://dx.doi.org/10.3389/fendo.2021.756785
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