Temporal analysis of hippocampal CA3 gene coexpression networks in a rat model of febrile seizures

Abstract

Complex febrile seizures during infancy constitute an important risk factor for development of epilepsy. However, little is known about the alterations induced by febrile seizures that make the brain susceptible to epileptic activity. In this context, the use of animal models of hyperthermic seizures (HS) could allow the temporal analysis of brain molecular changes that arise after febrile seizures. Here, we investigated temporal changes in hippocampal gene coexpression networks during the development of rats submitted to HS. Total RNA samples were obtained from the ventral hippocampal CA3 region at four time points after HS at postnatal day (P) 11 and later used for gene expression profiling. Temporal endpoints were selected for investigating the acute (P12), latent (P30 and P60) and chronic (P120) stages of the HS model. A weighted gene coexpression network analysis was used to characterize modules of coexpressed genes, as these modules might contain genes with similar functions. The transcriptome analysis pipeline consisted of building gene coexpression networks, identifying network modules and hubs, performing gene-trait correlations and examining changes in module connectivity. Modules were functionally enriched to identify functions associated with HS. Our data showed that HS induce changes in developmental, cell adhesion and immune pathways, such as Wnt, Hippo, Notch, Jak-Stat and Mapk. Interestingly, modules involved in cell adhesion, neuronal differentiation and synaptic transmission were activated as early as 1 day after HS. These results suggest that HS trigger transcriptional alterations that could lead to persistent neurogenesis, tissue remodeling and inflammation in the CA3 hippocampus, making the brain prone to epileptic activity.

Keywords: Coexpression networks; Epilepsy; Febrile seizures; Gene expression profile; Microarray; Network analysis.

Fig. 1.

Analyses of module preservation and intramodular connectivity changes between HS and CTRL networks. Genes were ranked according to their intramodular connectivity, and changes in ranking positions were determined between networks for identifying nodes and modules associated with gain or loss of connectivity. The summary statistic Zsummary is used to assess preservation of module density and connectivity between the networks. Zsummary<2 denotes no preservation, 2<Zsummary<10 indicates weak to moderate evidence of preservation, and Zsummary>10 suggests strong module preservation.

Fig. 2.

Module-trait relationships from the WGCNA analysis, in which networks were built using all microarray samples. Module names are displayed on the left, and the correlation coefficients to the hyperthermic seizures (HS) group are shown at the top of each row. The corresponding P-values for each module are displayed at the bottom of each row within parentheses. The rows are colored based on the correlation of the module with the HS group: red for positive and green for negative correlation.

Fig. 3.

Significant genes correlated with the HS group in each module, based on the analysis depicted in Fig. 2. Specific genes in each module were significantly associated with the HS group. (A) The number of genes in each module that displayed a P-value for gene significance (GS) <0.1. (B-L) Scatter plots constructed using the K_within (x-axis) and GS (y-axis) values for each gene in their corresponding modules.

Fig. 4.

Pie charts showing the percentage of genes for selected enriched biological functions and pathways at different time points after hyperthermic seizures. (A-D) The functions identified in the modules that gain connectivity in the HS networks. Networks were constructed using the HS and CTRL samples separately, to allow comparison between HS and CTRL networks at each time point.