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Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes
We sought to identify common key regulators and build a gene-metabolite network in different nonalcoholic fatty liver disease (NAFLD) phenotypes. We used a high-fat diet (HFD), a methionine-choline-deficient diet (MCDD) and streptozocin (STZ) to establish nonalcoholic fatty liver (NAFL), nonalcoholi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5291835/ https://www.ncbi.nlm.nih.gov/pubmed/28082742 http://dx.doi.org/10.1038/emm.2016.123 |
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author | Liu, Xiao-Lin Ming, Ya-Nan Zhang, Jing-Yi Chen, Xiao-Yu Zeng, Min-De Mao, Yi-Min |
author_facet | Liu, Xiao-Lin Ming, Ya-Nan Zhang, Jing-Yi Chen, Xiao-Yu Zeng, Min-De Mao, Yi-Min |
author_sort | Liu, Xiao-Lin |
collection | PubMed |
description | We sought to identify common key regulators and build a gene-metabolite network in different nonalcoholic fatty liver disease (NAFLD) phenotypes. We used a high-fat diet (HFD), a methionine-choline-deficient diet (MCDD) and streptozocin (STZ) to establish nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH) and NAFL+type 2 diabetes mellitus (T2DM) in rat models, respectively. Transcriptomics and metabolomics analyses were performed in rat livers and serum. A functional network-based regulation model was constructed using Cytoscape with information derived from transcriptomics and metabolomics. The results revealed that 96 genes, 17 liver metabolites and 4 serum metabolites consistently changed in different NAFLD phenotypes (>2-fold, P<0.05). Gene-metabolite network analysis identified ccl2 and jun as hubs with the largest connections to other genes, which were mainly involved in tumor necrosis factor, P53, nuclear factor-kappa B, chemokine, peroxisome proliferator activated receptor and Toll-like receptor signaling pathways. The specifically regulated genes and metabolites in different NAFLD phenotypes constructed their own networks, which were mainly involved in the lipid and fatty acid metabolism in HFD models, the inflammatory and immune response in MCDD models, and the AMPK signaling pathway and response to insulin in HFD+STZ models. Our study identified networks showing the general and specific characteristics in different NAFLD phenotypes, complementing the genetic and metabolic features in NAFLD with hepatic and extra-hepatic manifestations. |
format | Online Article Text |
id | pubmed-5291835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52918352017-02-06 Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes Liu, Xiao-Lin Ming, Ya-Nan Zhang, Jing-Yi Chen, Xiao-Yu Zeng, Min-De Mao, Yi-Min Exp Mol Med Original Article We sought to identify common key regulators and build a gene-metabolite network in different nonalcoholic fatty liver disease (NAFLD) phenotypes. We used a high-fat diet (HFD), a methionine-choline-deficient diet (MCDD) and streptozocin (STZ) to establish nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH) and NAFL+type 2 diabetes mellitus (T2DM) in rat models, respectively. Transcriptomics and metabolomics analyses were performed in rat livers and serum. A functional network-based regulation model was constructed using Cytoscape with information derived from transcriptomics and metabolomics. The results revealed that 96 genes, 17 liver metabolites and 4 serum metabolites consistently changed in different NAFLD phenotypes (>2-fold, P<0.05). Gene-metabolite network analysis identified ccl2 and jun as hubs with the largest connections to other genes, which were mainly involved in tumor necrosis factor, P53, nuclear factor-kappa B, chemokine, peroxisome proliferator activated receptor and Toll-like receptor signaling pathways. The specifically regulated genes and metabolites in different NAFLD phenotypes constructed their own networks, which were mainly involved in the lipid and fatty acid metabolism in HFD models, the inflammatory and immune response in MCDD models, and the AMPK signaling pathway and response to insulin in HFD+STZ models. Our study identified networks showing the general and specific characteristics in different NAFLD phenotypes, complementing the genetic and metabolic features in NAFLD with hepatic and extra-hepatic manifestations. Nature Publishing Group 2017-01 2017-01-13 /pmc/articles/PMC5291835/ /pubmed/28082742 http://dx.doi.org/10.1038/emm.2016.123 Text en Copyright © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Original Article Liu, Xiao-Lin Ming, Ya-Nan Zhang, Jing-Yi Chen, Xiao-Yu Zeng, Min-De Mao, Yi-Min Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title | Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title_full | Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title_fullStr | Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title_full_unstemmed | Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title_short | Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
title_sort | gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5291835/ https://www.ncbi.nlm.nih.gov/pubmed/28082742 http://dx.doi.org/10.1038/emm.2016.123 |
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