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Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease
The heterogeneity of biological processes driving the severity of nonalcoholic fatty liver disease (NAFLD) as reflected in the transcriptome and the relationship between the pathways involved are not well established. Well-defined associations between gene expression profiles and disease progression...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715650/ https://www.ncbi.nlm.nih.gov/pubmed/31467298 http://dx.doi.org/10.1038/s41598-019-48746-5 |
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author | Hoang, Stephen A. Oseini, Abdul Feaver, Ryan E. Cole, Banumathi K. Asgharpour, Amon Vincent, Robert Siddiqui, Mohammad Lawson, Mark J. Day, Nathan C. Taylor, Justin M. Wamhoff, Brian R. Mirshahi, Faridoddin Contos, Melissa J. Idowu, Michael Sanyal, Arun J. |
author_facet | Hoang, Stephen A. Oseini, Abdul Feaver, Ryan E. Cole, Banumathi K. Asgharpour, Amon Vincent, Robert Siddiqui, Mohammad Lawson, Mark J. Day, Nathan C. Taylor, Justin M. Wamhoff, Brian R. Mirshahi, Faridoddin Contos, Melissa J. Idowu, Michael Sanyal, Arun J. |
author_sort | Hoang, Stephen A. |
collection | PubMed |
description | The heterogeneity of biological processes driving the severity of nonalcoholic fatty liver disease (NAFLD) as reflected in the transcriptome and the relationship between the pathways involved are not well established. Well-defined associations between gene expression profiles and disease progression would benefit efforts to develop novel therapies and to understand disease heterogeneity. We analyzed hepatic gene expression in controls and a cohort with the full histological spectrum of NAFLD. Protein-protein interaction and gene set variation analysis revealed distinct sets of coordinately regulated genes and pathways whose expression progressively change over the course of the disease. The progressive nature of these changes enabled us to develop a framework for calculating a disease progression score for individual genes. We show that, in aggregate, these scores correlate strongly with histological measures of disease progression and can thus themselves serve as a proxy for severity. Furthermore, we demonstrate that the expression levels of a small number of genes (~20) can be used to infer disease severity. Finally, we show that patient subgroups can be distinguished by the relative distribution of gene-level scores in specific gene sets. While future work is required to identify the specific disease characteristics that correspond to patient clusters identified on this basis, this work provides a general framework for the use of high-content molecular profiling to identify NAFLD patient subgroups. |
format | Online Article Text |
id | pubmed-6715650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67156502019-09-13 Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease Hoang, Stephen A. Oseini, Abdul Feaver, Ryan E. Cole, Banumathi K. Asgharpour, Amon Vincent, Robert Siddiqui, Mohammad Lawson, Mark J. Day, Nathan C. Taylor, Justin M. Wamhoff, Brian R. Mirshahi, Faridoddin Contos, Melissa J. Idowu, Michael Sanyal, Arun J. Sci Rep Article The heterogeneity of biological processes driving the severity of nonalcoholic fatty liver disease (NAFLD) as reflected in the transcriptome and the relationship between the pathways involved are not well established. Well-defined associations between gene expression profiles and disease progression would benefit efforts to develop novel therapies and to understand disease heterogeneity. We analyzed hepatic gene expression in controls and a cohort with the full histological spectrum of NAFLD. Protein-protein interaction and gene set variation analysis revealed distinct sets of coordinately regulated genes and pathways whose expression progressively change over the course of the disease. The progressive nature of these changes enabled us to develop a framework for calculating a disease progression score for individual genes. We show that, in aggregate, these scores correlate strongly with histological measures of disease progression and can thus themselves serve as a proxy for severity. Furthermore, we demonstrate that the expression levels of a small number of genes (~20) can be used to infer disease severity. Finally, we show that patient subgroups can be distinguished by the relative distribution of gene-level scores in specific gene sets. While future work is required to identify the specific disease characteristics that correspond to patient clusters identified on this basis, this work provides a general framework for the use of high-content molecular profiling to identify NAFLD patient subgroups. Nature Publishing Group UK 2019-08-29 /pmc/articles/PMC6715650/ /pubmed/31467298 http://dx.doi.org/10.1038/s41598-019-48746-5 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hoang, Stephen A. Oseini, Abdul Feaver, Ryan E. Cole, Banumathi K. Asgharpour, Amon Vincent, Robert Siddiqui, Mohammad Lawson, Mark J. Day, Nathan C. Taylor, Justin M. Wamhoff, Brian R. Mirshahi, Faridoddin Contos, Melissa J. Idowu, Michael Sanyal, Arun J. Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title | Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title_full | Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title_fullStr | Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title_full_unstemmed | Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title_short | Gene Expression Predicts Histological Severity and Reveals Distinct Molecular Profiles of Nonalcoholic Fatty Liver Disease |
title_sort | gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715650/ https://www.ncbi.nlm.nih.gov/pubmed/31467298 http://dx.doi.org/10.1038/s41598-019-48746-5 |
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