Epilepsia, 48(Suppl. 8):28–32, 2007 doi: 10.1111/j.1528-1167.2007.01342.x
MOLECULAR NATURE OF STATUS EPILEPTICUS
Gene and protein expression in experimental status epilepticus ∗
Katarzyna Lukasiuk and †‡Asla Pitk¨anen
∗ The Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland; †A.I. Virtanen Institute for Molecular Sciences, University of Kuopio, Kuopio, Finland; and ‡Department of Neurology, Kuopio University Hospital, Kuopio, Finland
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 re-
sult 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). Changes in gene expression leading to network reorganization and neuronal hyperexcitability are crucial to understanding epilepsy and have been extensively studied.
Address correspondence and reprint requests to Prof. Asla Pitk¨anen, A.I. Virtanen Institute for Molecular Sciences, University of Kuopio, PO Box 1627, FIN-70 211 Kuopio, Finland. E-mail:
[email protected] Blackwell Publishing, Inc. C International League Against Epilepsy
Table 1. Cellular and metabolic processes pathways (according to KEGG) detected in various datasets containing lists of genes changing the level of expression 1 day following status epilepticus Elliott et al. (2003) ALS Antigen processing and presentation Apoptosis Axon guidance Basal transcription factors Calcium signaling Cell adhesion molecules Cell communication Cell cycle Complement and coagulation cascade Cytokine-cytokine receptor interaction ECM receptor interaction FC epsilon RI signaling Focal adhesion Gap junction Hematopoietic cell lineage Huntington’s disease Insulin signaling JAK-stat signaling Leukocyte transendothelial migration LTP MAPK signaling Natural killer-mediated cytotoxicity Neuroactive ligand-receptor interaction Neurodegenerative disorders Phospatidiloinositol signaling Regulation of actin cytoskeleton Ribosome TGF-beta signaling Tight junction Type I diabetes mellitus Wnt signaling
Hunsberger et al. (2005)
Lukasiuk et al. (2003)
Tang et al. (2002)
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29 Experimental Status Epilepticus
Figure 1. Selected cellular and metabolic processes pathways interactions detected in datasets containing lists of genes changing the level of expression 1 day following status epilepticus. Each graph presents the network of associations between protein products of genes provided by each gene list, which was found in KEGG pathway database. Nodes of the networks represent proteins and lines connecting nodes represent interactions between proteins. The thickness of lines codes for number of cellular pathways in which given connection has been described. The nodes and lines indicated in dark gray represent the pathway named in column heading. C ILAE Epilepsia Epilepsia, 48(Suppl. 8):28–32, 2007 doi: 10.1111/j.1528-1167.2007.01342.x
30 K. Lukasiuk and A. Pitk¨anen
Figure 1. Continued. C ILAE Epilepsia
Epilepsia, 48(Suppl. 8):28–32, 2007 doi: 10.1111/j.1528-1167.2007.01342.x
31 Experimental Status Epilepticus The application of recent technologic developments that allow the analysis on the level of whole genom or proteom supported by novel bioinformatic tools provides an unbiased insight into molecular events that occur in the brain. Global analysis of gene expression has been successfully employed to decipher molecular response of the brain to seizures (Sandberg et al., 2000; French et al., 2001; Newton et al., 2003; Flood et al., 2004; Li X et al., 2005), status epilepticus (Nedivi et al., 1993; Hevroni et al., 1998; Hendriksen et al., 2001; Liang and Seyfried, 2001; Tang et al., 2002; Becker et al., 2003; Elliott et al., 2003; Lukasiuk et al., 2003; Hunsberger et al., 2005; Wilson et al., 2005; Gorter et al., 2006), kindling (Liang and Seyfried, 2001; Potschka et al., 2002; Arai et al., 2003; Gu et al., 2004), as well as alterations in pattern of gene expression in both experimental (Bo et al., 2002; Arai et al., 2003) and human (Crino et al., 2001; Becker et al., 2002; Kim et al., 2003; Rakhade et al., 2005; Arion et al., 2006; Jamali et al., 2006; Ozbas-Gerceker et al., 2006) epilepsy. Each of above works leads to detection of changes in expression of up to hundreds of genes that supposedly are of importance for epilepsy. Data on global analyses of gene expression lead not only to the identification of new epilepsy-related genes but also to formulation of several hypotheses implying role in epilepsy for particular processes or metabolic pathways. For example, the potential role for electrical activity (Arion et al., 2006), angiogenesis (Newton et al., 2003), neurotrophic factors (Newton et al., 2003), immune response (Gorter et al., 2006; Lukasiuk et al., 2006), or events recapitulating development (Elliott et al., 2003) has been suggested. A number of studies concentrated on immediate and delayed consequences of SE on gene expression. In-depth analysis of already existing data aiming at pinpointing most prominent metabolic pathways affected could lead to identification of most important players in the SEinduced sequel and hopefully will help to find targets for future treatments. We had successfully applied such strategy in our recent metaanalysis of available data on epileptogenesis-related alterations of gene expression. In above analysis, we have detected over- and under-representation of number of functional groups of genes, so called GO-terms (gene ontology terms defined by the Gene Ontology Consortium, http://www.geneontology.org/) across different experimental models of epileptogenesis (Lukasiuk et al., 2006). Over-represented GO terms include, for example, immune response genes. Crucial role of immune response genes has been corroborated recently by new data on transcriptome response following electrically induced SE (Gorter et al., 2006). Here, we employed similar approach, but this time we were looking not for GOterms, but for functional pathways which are represented by genes detected as changing expression level following SE. To do this, we have used published lists of genes that change expression level 1 day after pilocarpine (El-
liott et al., 2003), kainic acid (Tang et al., 2002; Hunsberger et al., 2005), or electrical stimulation-induced SE (Lukasiuk et al., 2003). Gene list were used as published by the authors if GenBank accession numbers were provided with the list. If gene list contained only gene names, the GenBank accession numbers were imputed using Clone/ Gene ID Converter (http://idconverter.bioinfo.cnio.es). Each gene list was analyzed with Pathway Miner (BioRag – Bio Resource for Array Genes at http://www.biorag.org) that classifies and extracts network of associated genes based on pathways. Only pathways defined by KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www. genome.jp/kegg/pathway.html) are presented. As summarized in Table 1 and Fig. 1, genes changing expression level following SE represent several cellular pathways and number of these pathways are represented across different experimental models. Such commonly found interactions belong to pathways involved in MAPK kinase signaling, regulation of actin cytoskeleton, leukocyte transendothelial migration, calcium signaling, or cell cycle. Further research is required to explain the meaning of these data in context of SE-induced alterations in brain function.
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