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Sexually-dimorphic targeting of functionally-related genes in COPD

BACKGROUND: There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods...

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Autores principales: Glass, Kimberly, Quackenbush, John, Silverman, Edwin K, Celli, Bartolome, Rennard, Stephen I, Yuan, Guo-Cheng, DeMeo, Dawn L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269917/
https://www.ncbi.nlm.nih.gov/pubmed/25431000
http://dx.doi.org/10.1186/s12918-014-0118-y
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author Glass, Kimberly
Quackenbush, John
Silverman, Edwin K
Celli, Bartolome
Rennard, Stephen I
Yuan, Guo-Cheng
DeMeo, Dawn L
author_facet Glass, Kimberly
Quackenbush, John
Silverman, Edwin K
Celli, Bartolome
Rennard, Stephen I
Yuan, Guo-Cheng
DeMeo, Dawn L
author_sort Glass, Kimberly
collection PubMed
description BACKGROUND: There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism. RESULTS: Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways. CONCLUSIONS: Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-014-0118-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-42699172014-12-18 Sexually-dimorphic targeting of functionally-related genes in COPD Glass, Kimberly Quackenbush, John Silverman, Edwin K Celli, Bartolome Rennard, Stephen I Yuan, Guo-Cheng DeMeo, Dawn L BMC Syst Biol Research Article BACKGROUND: There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism. RESULTS: Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways. CONCLUSIONS: Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-014-0118-y) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-28 /pmc/articles/PMC4269917/ /pubmed/25431000 http://dx.doi.org/10.1186/s12918-014-0118-y Text en © Glass et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Glass, Kimberly
Quackenbush, John
Silverman, Edwin K
Celli, Bartolome
Rennard, Stephen I
Yuan, Guo-Cheng
DeMeo, Dawn L
Sexually-dimorphic targeting of functionally-related genes in COPD
title Sexually-dimorphic targeting of functionally-related genes in COPD
title_full Sexually-dimorphic targeting of functionally-related genes in COPD
title_fullStr Sexually-dimorphic targeting of functionally-related genes in COPD
title_full_unstemmed Sexually-dimorphic targeting of functionally-related genes in COPD
title_short Sexually-dimorphic targeting of functionally-related genes in COPD
title_sort sexually-dimorphic targeting of functionally-related genes in copd
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269917/
https://www.ncbi.nlm.nih.gov/pubmed/25431000
http://dx.doi.org/10.1186/s12918-014-0118-y
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