A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

Abstract

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.

Fig 1. Flowchart of the methodology.

After the identification of a WGCNA community with a target gene, correlation measures have been applied to build a co-expression gene network. The combined study of the network’s topological properties with information entropy, through a threshold analysis, led to the detection of a pivotal community for the target gene.

Fig 2. Dendrogram of the network with defined module colors.

This dendrogram was obtained by average linkage hierarchical clustering. The color spectrum underneath the dendrogram indicates the module assignment determined by the Dynamic Tree Cut. The DRD2 gene is contained in the maroon module. Almost 3000 genes are isolated (grey module).

Fig 3. Adjacency matrix of the maroon module.

This matrix illustrates the similarity between gene expressions using the correlation matrix. Color gradient indicates the strength of the connections between genes.

Fig 4. Trend of the main topological properties of the network.

The vertical lines highlight an optimal range of threshold values between 0.40 and 0.55 in which three of the four network properties are maximized. For visual clarity, threshold values in which the topological properties are not significant have been omitted.

Fig 5. Pivotal Module Index, as a function of threshold.

Pivotal Module Index as a function of threshold in the optimal range of the network topological properties obtained using four different community detection methods. Statistical errors were estimated by means of the bootstrap procedure.

Fig 6. Average PMI distribution.

Average Pivotal Module Index (PMI) distribution for the four community detection methods fitted with a linear model. The blue area represents 95% confidence intervals for the fitted function. Statistical errors were estimated by means of the bootstrap procedure. In the top right corner the reduced χ² and fit parameters are been shown.

Fig 7. Betweenness entropy as a function of threshold.

Information entropy based on betweenness as a function of threshold for the network (panel A) and for the DRD2 community (panel B). Both distributions present a maximum threshold value equal to 0.45.

Fig 8. Dice index for the DRD2 community.

The Dice index as a function of threshold calculated in relation to the DRD2 community obtained with a threshold value equal to 0.45. The statistical errors were estimated by means of the bootstrap procedure.