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A complex network approach reveals a pivotal substructure of genes linked to schizophrenia
Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused o...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755767/ https://www.ncbi.nlm.nih.gov/pubmed/29304112 http://dx.doi.org/10.1371/journal.pone.0190110 |
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author | Monaco, Alfonso Monda, Anna Amoroso, Nicola Bertolino, Alessandro Blasi, Giuseppe Di Carlo, Pasquale Papalino, Marco Pergola, Giulio Tangaro, Sabina Bellotti, Roberto |
author_facet | Monaco, Alfonso Monda, Anna Amoroso, Nicola Bertolino, Alessandro Blasi, Giuseppe Di Carlo, Pasquale Papalino, Marco Pergola, Giulio Tangaro, Sabina Bellotti, Roberto |
author_sort | Monaco, Alfonso |
collection | PubMed |
description | Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder. |
format | Online Article Text |
id | pubmed-5755767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57557672018-01-26 A complex network approach reveals a pivotal substructure of genes linked to schizophrenia Monaco, Alfonso Monda, Anna Amoroso, Nicola Bertolino, Alessandro Blasi, Giuseppe Di Carlo, Pasquale Papalino, Marco Pergola, Giulio Tangaro, Sabina Bellotti, Roberto PLoS One Research Article Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder. Public Library of Science 2018-01-05 /pmc/articles/PMC5755767/ /pubmed/29304112 http://dx.doi.org/10.1371/journal.pone.0190110 Text en © 2018 Monaco et al http://creativecommons.org/licenses/by/4.0/ 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 author and source are credited. |
spellingShingle | Research Article Monaco, Alfonso Monda, Anna Amoroso, Nicola Bertolino, Alessandro Blasi, Giuseppe Di Carlo, Pasquale Papalino, Marco Pergola, Giulio Tangaro, Sabina Bellotti, Roberto A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title | A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title_full | A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title_fullStr | A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title_full_unstemmed | A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title_short | A complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
title_sort | complex network approach reveals a pivotal substructure of genes linked to schizophrenia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755767/ https://www.ncbi.nlm.nih.gov/pubmed/29304112 http://dx.doi.org/10.1371/journal.pone.0190110 |
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