Epileptogenesis-related genes revisited
Introduction
Epilepsies are the second most-common neurologic disorder after stroke (Porter, 1993). It is estimated that approximately 0.8% of the population is affected by some form of epilepsy. In approximately 30% of cases, epilepsy is a result of an insult to the brain, such as traumatic brain injury (TBI), stroke, brain infection, prolonged complex febrile seizures, or status epilepticus (SE) (Hauser, 1997). In such cases, the initial insult is commonly followed by a latency period (epileptogenesis) that can last for months or years before the appearance of spontaneous seizures and epilepsy diagnosis (Pitkanen and Sutula, 2002).
During the latency period, several phenomena can occur in parallel, including neuronal loss, dendritic and axonal plasticity, neurogenesis, gliosis, remodeling of the extracellular matrix, and alterations in gene expression (Parent et al., 1997; Bazan and Serou, 1999; Clark and Wilson, 1999; Coulter and DeLorenzo, 1999; Endo et al., 1999; Covolan et al., 2000; Wu et al., 2000). Such phenomena might reflect a response of the brain to the insult. At least some of the molecular alterations are also involved in the chronic remodeling of neuronal circuits, which eventually leads to the development of epilepsy. Hypothetically, identification of key molecular changes will provide a better understanding of epileptogenesis and point to targets that can be used to modify the epileptogenic process and, in the most optimistic scenario, develop antiepileptogenic treatments.
Studies of gene expression following potentially epileptogenic events like SE, ischemia, or TBI have a long history, and a vast amount of data has been gathered using traditional molecular biology methods (Nedivi et al., 1993; Koistinaho and Hokfelt, 1997; Zagulska-Szymczak et al., 2001). Nevertheless, these studies have focused on a limited number of pre-selected genes at a time. Recent technologic developments allow for the analysis of gene expression at the level of the whole transcriptome (microarrays and serial analysis of gene expression), and thus, provide an unbiased insight into the ensemble of molecular events that occur in the brain following various types of injuries. Brain trauma can trigger alterations in gene expression that partly represent a normal physiologic response to the injury. On the other hand, each of the components of the pathologic circuitry reorganization requires an expression of a particular set of genes.
Just a few years ago, there were less than a handful of large-scale molecular profiling studies of epileptogenic brain (Hendriksen et al., 2001; Elliott et al., 2003; Lukasiuk et al., 2003). A conspicuous feature of these data is that there is a very little overlap between the data sets of genes with altered expression (Lukasiuk and Pitkanen, 2004). This might be related to technical issues, including the brain area or cell population selected for the analysis, the use of different experimental platforms with a limited selection of gene probes, the limited sensitivity of methods for global analysis of gene expression in brain tissue, and the use of different kinds of animal models at different stages of disease development (Lukasiuk and Pitkanen, 2004). There are also specific problems in data interpretation related to analysis of the whole transcriptome. For example, there is only a limited amount of information or no information available on the biologic function of most of the altered genes.
The recent explosion in the application of large-scale molecular profiling in studies investigating the consequences of epileptogenic brain insults as well as advances in bioinformatics has provided a large amount of new data that can be analyzed with novel tools to extract the meaningful information from the noise. For example, databases allowing functional annotations of genes are updated regularly (e.g., http://www.geneontology.org/), and innovative tools that allow for the analysis of gene expression data have been developed.
The present study was fueled by the idea that an unbiased analysis of gene expression will highlight the most-prominent metabolic pathways or other phenomena that underlie reorganization of the epileptogenic circuitry in the brain, and eventually, guide our efforts to identify candidate targets for antiepileptogenic treatments. Here, we reanalysed and compared lists of genes that are regulated by potentially epileptogenic stimuli and are available from publications. We particularly searched for (i) highly represented functional gene classes (GO terms) within the data sets, and (ii) individual genes that appeared in several data sets, and therefore, could be of particular importance for epileptogenesis.
Section snippets
Selection of papers for analysis and creation of gene lists
The main goal of this project was to identify common features in the molecular responses to epileptogenic stimuli across different animal models. Therefore, papers that describe alterations in the transcriptome following SE or TBI, which are known to trigger epileptogenesis in experimental animals, were selected for analysis, as summarized in Table 1. A few papers describing gene expression in epileptic tissue were also included for comparison. The analysis required extensive database searches,
Results and discussion
Our analysis revealed that various epileptogenic insults induce statistically significant changes in gene expression in functionally linked genes that were predefined as GO terms. The number of over-represented GO terms associated with a particular gene list was, however, variable. In some cases, many biologic or molecular processes were indicated, whereas others had no significant changes. The lack of significant findings in some data sets could relate, for example, to the small number of
Concluding remarks
We used novel bioinformatics tools to compare the available literature on global analysis of gene expression following epileptogenic insults. We aimed at identifying (i) highly represented functional gene classes within the data sets and (ii) individual genes that appear in several data sets, and therefore, might be of particular importance for epileptogenesis. Analysis of their function indicated some trends in post-injury gene expression that have been somewhat underappreciated in the
Abbreviations
- AHS
ammon horn sclerosis
- CCI
controlled cortical impact injury
- FPI
lateral fluid percussion injury
- GO
gene ontology
- SE
status epilepticus
- TBI
traumatic brain injury
- TLE
temporal-lobe epilepsy
Acknowledgments
The work was supported by The Polish State Committee for Scientific Research grant No. 2 P04A 052 26 (to K.L.) and the Academy of Finland, the Sigrid Juselius Foundation, the Finnish Cultural Foundation, and the Paulo Foundation (to A.P.). We apologize to all distinguished colleagues whose original articles are not cited in this review. Due to the vast number of issues discussed and space constraints, we were forced to refer only to databases and review articles whenever possible.
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