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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...

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Autores principales: Monaco, Alfonso, Monda, Anna, Amoroso, Nicola, Bertolino, Alessandro, Blasi, Giuseppe, Di Carlo, Pasquale, Papalino, Marco, Pergola, Giulio, Tangaro, Sabina, Bellotti, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
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.
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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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