Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy.

Silvia Yumi Bando,Hung-Tzu Wen,Alexandre V Silva,Carlos Alberto Moreira-Filho,Luiz Hm Castro,Luciana R Pimentel-Silva,Filipi Nascimento Silva,Luciano Da Fontoura Costa,Edson Amaro

doi:10.1371/journal.pone.0079913

Abstract

We previously described – studying transcriptional signatures of hippocampal CA3 explants – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.

Highlights

Epilepsy prevalence ranges from 0.5–1.0% in developed countries to 1.4–5.7% in developing countries [1,2]
We previously described – studying transcriptional signatures of hippocampal CA3 explants obtained at epilepsy surgery – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy (RMTLE) have different pathomechanisms and constitute two distinct genomic phenotypes as shown by CA3 transcriptional co-expression networks [16]
We developed a software to obtain visualizations of large complex networks based on the Fruchterman-Reingold algorithm (FR) [35], which is a force-directed technique based on molecular dynamics employing both attractive and repulsive forces between nodes [36]

Summary

Introduction

Epilepsy prevalence ranges from 0.5–1.0% in developed countries to 1.4–5.7% in developing countries [1,2]. We previously described – studying transcriptional signatures of hippocampal CA3 explants obtained at epilepsy surgery – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy (RMTLE) have different pathomechanisms and constitute two distinct genomic phenotypes as shown by CA3 transcriptional co-expression networks [16]. Comparative analysis of CA3 transcriptional interaction networks revealed molecular mechanisms underlying RMTLE phenotypes and indicated potential therapeutic targets [16]. This network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). We were able to study the whole set of CA3 valid transcripts in order to: i) obtain complete transcriptional networks, here called CO networks, for FS and NFS phenotypic groups; ii) examine how CO and DE networks are related; iii) identify genomic and molecular mechanisms underlying FS and NFS phenotypes, contributing for the discovery of potential targets for therapeutic interventions

Materials and Methods

Results

Discussion