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Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials
Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential h...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Beilstein-Institut
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734442/ https://www.ncbi.nlm.nih.gov/pubmed/26885455 http://dx.doi.org/10.3762/bjnano.6.252 |
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author | Williams, Andrew Halappanavar, Sabina |
author_facet | Williams, Andrew Halappanavar, Sabina |
author_sort | Williams, Andrew |
collection | PubMed |
description | Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO(2)), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO(2) particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties. |
format | Online Article Text |
id | pubmed-4734442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Beilstein-Institut |
record_format | MEDLINE/PubMed |
spelling | pubmed-47344422016-02-16 Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials Williams, Andrew Halappanavar, Sabina Beilstein J Nanotechnol Full Research Paper Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO(2)), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO(2) particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties. Beilstein-Institut 2015-12-21 /pmc/articles/PMC4734442/ /pubmed/26885455 http://dx.doi.org/10.3762/bjnano.6.252 Text en Copyright © 2015, Williams and Halappanavar https://creativecommons.org/licenses/by/2.0https://www.beilstein-journals.org/bjnano/termsThis is an Open Access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: (https://www.beilstein-journals.org/bjnano/terms) |
spellingShingle | Full Research Paper Williams, Andrew Halappanavar, Sabina Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title | Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title_full | Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title_fullStr | Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title_full_unstemmed | Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title_short | Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
title_sort | application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials |
topic | Full Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734442/ https://www.ncbi.nlm.nih.gov/pubmed/26885455 http://dx.doi.org/10.3762/bjnano.6.252 |
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