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Physiol. Genomics 30: 242-252, 2007. First published April 24, 2007; doi:10.1152/physiolgenomics.00288.2006
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Received 28 December 2006; accepted in final form 18 April 2007.
Physiological Genomics 30:242-252 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

Expression changes in mouse brains following nicotine-induced seizures: the modulation of transcription factor networks

Merav Kedmi and Avi Orr-Urtreger

Genetic Institute, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Nicotine, acting through the neuronal nicotinic acetylcholine receptors (nAChRs), can induce seizures in mice. We aimed to study brain transcriptional response to seizure and to identify genes whose expression is altered after nicotine-induced seizures. Whole brains of untreated mice were compared with brains 1 h after seizure activity, using Affymetrix U74Av2 microarrays. Experimental groups included wild-type mice and both nicotine-induced seizure-sensitive and -resistant nAChR mutant mice. Each genotype group received different nicotine doses to generate seizures. This approach allowed the identification of significantly changed genes whose expression was dependent on seizure activity, nicotine administration, or both but not on the type of nAChR subunit mutation or the amount of nicotine injected. Significant expression changes were detected in 62 genes (P < 0.05, false discovery rate correction). Among them, gene ontology functional annotation analysis determined that the most significantly overrepresented categories were of genes encoding MAP kinase phosphatases, regulators of transcription and nucleosome assembly proteins. In silico bioinformatic analysis of the promoter regions of the 62 changed genes detected significant enrichments of 16 transcription regulatory elements (TREs), creating a network of transcriptional regulatory responses to seizures. The TREs for activating transcription factor and serum response factor were most significantly enriched, supporting their association with seizure activity. Our data suggest that nicotine-induced seizure in mice is a useful model to study seizure activity and its global brain transcriptional response. The differentially expressed genes detected here can help us to understand the molecular mechanisms underlying seizures in animal models and may also serve as candidate genes to study epilepsy in humans.

nicotinic acetylcholine receptor; microarray; gene expression


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
EPILEPSY IS A WIDESPREAD neurological condition affecting 0.5–1% of the population (29), with ~5% of the population experiencing at least one seizure episode during their lifetime. About one-half of all epilepsies are idiopathic epilepsies that are considered to have a genetic background (reviewed by Ref. 17). Autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) was the first idiopathic epilepsy for which a genetic basis was identified when a mutation in the human {alpha}4-nicotinic acetylcholine receptor (nAChR) subunit gene was associated with familial cases of ADNFLE (65). Later, mutations in the ß2-nAChR subunit gene were also detected in ADNFLE patients (18, 55). Very recently, a heterozygous missense mutation in the {alpha}2-nAChR subunit gene was detected in familial sleep-related epilepsy in which seizures are associated with nocturnal wandering and ictal fear (3).

In mice, nicotine, which acts through the nAChRs, can induce seizures when administered in high doses (50), and repeated nicotine administration can induce kindling (an experimental model for progressive epilepsy) (4). Mice heterozygous for the L250T "gain of function" mutation in the {alpha}7-nAChR subunit ({alpha}7+/T mice) have previously shown increased sensitivity to nicotine-induced seizures (11, 32), and mice with a knockin mutation in the {alpha}4-nAChR subunit were also hypersensitive to this effect of nicotine (27, 28). Conversely, mice with a deficiency in the {alpha}5- or ß4-nAChR subunit and mice with a null mutation in both subunits were resistant to the convulsant effect of nicotine (40, 60, 61). In addition, heterozygous mice for the {alpha}3-nAChR subunit null mutation also showed resistance to this effect of nicotine (60).

The brain response to seizures is complex and includes increased neuronal activity, neuronal cell death, and ultimately changes in synaptic plasticity and network reorganization (7, 58). These responses are accompanied by stimulation of the expression of numerous genes. Gene expression changes in the brain following seizure activity were previously investigated using different models of seizure induction, including acute and chronic electroconvulsive seizures (2, 16, 43, 52), amygdaloid kindling seizures (12, 48), and kainic acid- (36, 67, 71), pentylenetetrazol- (26, 62), and pilocarpine-induced seizures (5, 23). These analyses revealed altered expression of transcription factors and other immediate early genes (2, 12, 36), followed by expression induction of growth factors (52) and genes involved in stress and immune responses (67), signaling pathways (2), neuronal plasticity, and others (48).

Similarly, chronic and acute administration of nicotine induces transcriptional changes in various genes in the brain. These alterations include genes associated with plasticity (63), immediate early response genes, including transcription factors (6), and genes encoding for growth factors and receptors, as well as genes involved in several distinct signaling pathways (44, 46). However, to date, gene expression changes following nicotine-induced seizures have not been described.

We aimed to detect the genes whose expression was affected by nicotine-induced seizure activity. Therefore, we determined the brain transcriptomes following seizure in three different types of mice: mice that are sensitive to nicotine-induced seizures ({alpha}7+/T mice), mice that are resistant to nicotine-induced seizures (ß4–/– mice), and wild-type control mice.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animals.
All of the mice used in this study were from congenic lines that were backcrossed onto a C57BL/6J background for seven generations after germline transmission. The mice used for expression analyses were deficient for the ß4-nAChR subunit [ß4–/– mice (73)], heterozygous for the L250T mutation in the {alpha}7-nAChR subunit [{alpha}7+/T mice (53)], and wild-type littermate control mice. Each experimental mouse was genotyped twice, once before and once after the experiment. The PCR reaction conditions and primer pairs used for genotyping the {alpha}7+/T and ß4–/– mice were as previously described (32, 40).

All mice were 6–9 wk old. Before the experiments, the mice were housed in groups of two to five per cage under a 12:12-h light-dark cycle, with food and water ad libitum. All procedures were approved by the institutional animal and care committee, in accordance with the National Research Council's "Guide for the Care and Use of Laboratory Animals."

Analysis of brain expression.
Whole brain (including the cerebrum, olfactory bulbs, thalamus, cerebellum, brain stem, and proximal tip of the spinal cord) expression profiles were determined in two experimental groups of mice: 16 mice that were not treated with nicotine and 12 mice 1 h after experiencing nicotine-induced seizure. The brains were collected 1 h following seizure activity to detect early transcriptional responses to nicotine-induced seizures. The untreated group included six wild-type mice, five {alpha}7+/T mice, and five ß4–/– mice. The group of mice that underwent nicotine-induced seizures included three wild types, five {alpha}7+/T mice, and four ß4–/– mice. Different doses of nicotine were injected intraperitoneally (ip) for each genotype group of mice to achieve a similar seizure score in all three genotypes. Table 1 describes the amounts of nicotine injected and the seizure characterizations of the three different mouse genotypes. The dose-response curves and the percentages of {alpha}7+/T and ß4–/– mice that developed score 4–5 seizures following nicotine injections were previously described (11, 32, 40, 60). The two experimental groups included mice with variable sensitivity to nicotine-induced seizure, allowing us to compare untreated brains of mice with brains following seizure.


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  		Table 1. List of mice that underwent nicotine-induced seizures whose brains were used for the microarray analysis

 
The global expression profiles were analyzed using the Murine Genome U74Av2 array (Affymetrix, Santa Clara, CA). Total RNA isolation, target preprocessing, and hybridization to the U74Av2 microarray (Affymetrix) were conducted as previously described (41). Microarray experiments were designed to comply with minimum information about a microarray experiment (MIAME) guidelines (10). Of the data from the 28 microarrays presented here, 6 of untreated wild-type and 5 of untreated ß4–/– brains were previously published by our laboratory [Ref. 41; Gene Expression Omnibus (GEO) accession no. GSE5320].

Data analysis.
The statistical algorithm, implanted in Affymetrix Suite version 5.0 software [Microarray Suite (MAS)5, Affymetrix], generated a signal value (which designates a relative measure of the abundance of the transcript), a detection P value (which indicates the reliability of the transcript's detection call), and detection call (present, absent, or marginal) for each transcript on the microarray. The detection calls were calculated, based on the detection P value, as following: probe sets with P value >0.06 were designated as absent, P value >0.04 and P value <0.06 as marginal, and P value <0.04 as present. For interarray comparisons, data from each array were scaled using MAS5 software, and the mean intensity for each array was adjusted by a scaling factor to a set target intensity of 150. Raw data have been deposited at the National Center for Biotechnology Information (NCBI) GEO (http://www.ncbi.nlm.nih.gov/geo) and are accessible through GEO series accession number GSE6614.

The signal values were normalized both per each gene and per the entire microarray by dividing each signal by the median of the gene and by the median of the microarray, respectively. The normalized data were then subjected to filtering, leaving only 6,931 probe sets that were present or marginal in at least 3 of the 28 tested arrays. Following filtration, statistical analysis was applied to the data from all 28 microarrays to find genes that significantly distinguish between brains of untreated mice and brains of mice that underwent seizures. Genes that were differentially expressed between the two groups (untreated vs. postnicotine-induced seizure mice) were chosen by t-test with false discovery rate (FDR) correction using the significance analysis of microarray (SAM) method (70). The t-test was conducted with FDR correction for multiple comparisons with a P value cutoff of 0.05.

Gene ontology annotation analysis.
The genes that significantly differentiate between brains of untreated mice and postnicotine-induced seizure mice were categorized into gene ontologies (GOs) according to their molecular functions, associated biological processes, and/or cellular components in a species-independent manner, using Expression Analysis Systematic Explorer (EASE) software online [http://david.abcc.ncifcrf.gov/ (19, 37)]. EASE score with a P value threshold of 0.05 was applied to detect the significantly overrepresented GO annotations among the differentially expressed genes compared with the "detected" genes, which were present or marginal in at least 3 of the 28 tested microarrays.

In silico transcriptional regulatory network analysis.
Promoter analysis was done using the promoter integration in microarray analysis (PRIMA) tool (22) implanted in the EXPANDER v3 program suite (64). PRIMA performs statistical tests to detect transcription factors whose binding sites are significantly enriched in the target set compared with the background set. The significantly up- or downregulated genes following the nicotine-induced seizure activity were used as the target sets, and all the genes on the U74Av2 microarray were used as the background set for PRIMA analysis. PRIMA uses position weight matrices (PWMs) as models for transcription regulatory elements (TREs) that bind transcription factors. PWMs that represent human or mouse transcription factor binding sites were obtained from the TRANSFAC v8.2 database (49). The analyzed promoter regions span from 2,000 bp upstream to 200 bp downstream of the putative transcription start site. Significant overrepresentation of TREs was defined by a threshold of P < 0.005.

Pathway mining.
Genes that significantly differentiated between the two experimental groups of mice were mined for known pathways. The pathway mining was performed using the bioresource for array genes (BioRag) website (http://www.biorag.org/pathway.html) by searching the Kegg, GenMapp, and Biocarta databases of pathways.

Identification of molecular pathways and associations between genes.
For the construction of genetic networks between the differentially expressed genes, they were imported as Affymetrix's probe set numbers into PathwayStudio software (Ariadne Genomics, Rockville, MD). PathwayStudio is software for visualization and exploration of biological pathways, gene regulation networks, and protein-protein interactions. PathwayStudio is embedded with ResNet, a molecular interaction and pathway database that contains 500,000 functional links for ~50,000 proteins, extracted from 4.5 million Medline abstracts and full-text articles (as of February 26, 2005). This database contains biological information on proteins, cellular processes, and small molecules including their interaction, modification, and regulation.

Validation by quantitative real-time RT-PCR assay.
Real-time RT-PCR analyses using LightCycler (Roche Applied Science, Mannheim, Germany) were performed to determine the expression levels of Fos, Cyr61, Ptgs2, Dusp6, Dusp1, Klf10, Per1, and Dusp11 genes in whole brain RNA. Quantitative RT-PCRs were a technical replication for the microarrays results. The complementary DNA (cDNA) synthesis was performed using 1 µg of total RNA, 100 units of Superscript III RT (Invitrogen, Carlsbad, CA), 125 µM each dNTP (Pharmacia, Uppsala, Sweden), and 75 ng/µl random primers (Invitrogen) in a total volume of 10 µl; 0.02 µl of the cDNA was used for each real-time RT-PCR analysis of all genes, except for Gapdh, where 0.002 µl was used. Quantitative RT-PCRs were performed using LightCycler FastStart DNA Master SYBR Green I (Roche Applied Science). The PCR reactions were performed in a total volume of 10 µl, with 3 mM MgCl2 and 0.5 µM each primer (except for Gapdh, where primer concentrations of 0.2 µM were used). All primer pair sequences (Sigma-Genosys, Rehovot, Israel) and their product sizes are detailed in Table 2. The quantification procedure was done using known copy numbers of the gene's purified PCR product as a standard curve. The log concentrations of the gene of interest (x) and the normalization gene (Gapdh; y) were calculated from the standard curve using LightCycler 5.1 software (Roche Applied Science). The expression levels were then normalized to Gapdh expression levels (x/y), resulting in a normalized expression value, and then compared among the different groups of mice. Amplified products were verified by electrophoresis on 2% agarose gel stained with ethidium bromide (Sigma-Aldrich) and confirmed by sequencing using BigDye Terminator v1.1 on ABI PRISM 310 Genetic Analyzer (Applied Biosystems, Foster City, CA).


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  		Table 2. Primer pairs for real-time quantitative RT-PCR analyses

 
Additional statistical analyses.
Results are expressed as means ± SD. Statistical significance was determined by Student's t-test or by two-way ANOVA (SPSS, Chicago, IL). Graphs were constructed with Prism software (Prism, GraphPad Software, San Diego, CA).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Differentially expressed genes in brains of mice following nicotine-induced seizures: a transcriptional response that is not dependent on mice genotypes.
We detected 62 genes that significantly differentiated between the untreated mice and mice after nicotine-induced seizures. Table 3 summarizes the fold changes of these gene expressions and their functions. Subjecting the differentially expressed genes to hierarchical clustering (21) demonstrated that the samples were indeed clustered into two distinct groups according to the treatment. Among the differentially expressed genes were 44 upregulated and 18 downregulated genes (Table 3). Of these genes, the highest increase in expression level was 3.34-fold, and the expression of the most downregulated gene was changed by 0.49-fold. Among the 62 significantly changed genes, 36 are novel genes that, to our knowledge, were not previously detected as changed following either seizure activity or after nicotine treatment (Table 3).


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  		Table 3. The 62 genes that significantly differentiated between brains of mice that underwent nicotine-induced seizures and brains of untreated mice

 
In addition, to detect genes that are significantly changed following seizures in each of the three groups of mouse genotypes tested, we compared brain expression in each specific genotype group after nicotine-induced seizure activity and in untreated mice of the same genotype. t-Tests with a cutoff of P < 0.05, without correction for multiple comparisons, were applied separately for each genotype group. These comparisons resulted in the detection of 559 differentially expressed transcripts between untreated and treated wild-type mice and 995 and 167 transcripts between untreated and nicotine-treated {alpha}7+/T and ß4–/– mice, respectively. Interestingly, 10 genes (Fos, Atf3, Egr1, Egr2, Dusp6, Sox18, Cyr61, Errfi1, Iigp2, and Gad1) were common to all 3 comparisons, whereas 9 of these genes are included in the list of the 62 significantly changed genes that resulted from the FDR comparison of all genotype groups together.

Genes that regulate transcription are overrepresented within the groups of up- and downregulated genes.
To detect the possible functions of the 62 differentially expressed genes, they were classified into GO annotation categories. GO annotation groups that were significantly overrepresented among the up/downregulated genes, compared with the 6,931 detected genes, were defined using the EASE online program [http://david.abcc.ncifcrf.gov/ (19, 37)].

Notably, genes that regulate transcription in a DNA-dependent fashion were significantly enriched among the lists of up- and downregulated genes compared with the 6,931 detected genes (P = 0.004 and P = 0.01, respectively), with 12 upregulated genes and 7 downregulated genes belonging to this annotation group. It is worth noting that NetAffx annotation analysis resulted in a similar identification of 13 upregulated genes belonging to the "regulation of transcription, DNA dependent" GO category (Table 3; http://www.affymetrix.com/analysis/index.affx).

Other GO annotations that were significantly overrepresented among the upregulated genes included "cell cycle" (7 genes), "regulation of cell growth" (3 genes), and "MAP kinase phosphatase activity" (2 genes; P = 0.013, P = 0.04, and P = 0.0355, respectively). The latter two genes are members of the dual-specificity protein phosphatase (Dusp) subfamily. Their expression level increased by 1.57- and 1.56-fold, respectively. Interestingly, another member of this family, the Dusp11, was significantly downregulated by 0.74-fold in brains of mice that underwent nicotine-induced seizures.

A significant number of genes that were up- or downregulated following nicotine-induced seizure activity encode for nuclear proteins (29 of 62 genes, 47%). The expression level of 19 genes that belonged to the "nucleus" annotation category was increased, and 10 nuclear genes showed decreased expression. This cellular component GO annotation was significantly overrepresented in both the up- and the downregulated groups of genes compared with the 6,931 detected genes (P = 0.0056 and P = 0.016, respectively).

Differentially expressed genes share common TREs in their promoter sequences.
The 62 genes that discriminated most between untreated mice and mice that underwent seizures were subjected to promoter analysis using PRIMA software. Using this software we identified TREs that were overrepresented in the presumed promoter regions of these 62 genes. The genes that carry these TREs in their promoter area are listed in the Supplemental Table (supplemental data are available at the online version of this article).

The most significantly overrepresented TRE in the promoters of the 44 upregulated genes was for the activating transcription factor (ATF; Table 4, P = 4.03 x 10–6). One of its components, the Atf3, was upregulated by 2.17-fold in brains of mice that experienced nicotine-induced seizure activity. The second most significantly overrepresented TRE in the promoters of the upregulated genes was for the serum response factor (SRF) transcription factor (Table 4, P = 2.5 x 10–5), which was 3.5 times more abundantly represented in these promoters compared with its representation in the promoters of all the genes on the U74Av2 microarray. The Srf mRNA expression level was not evaluated, since it is not represented on the U74Av2 microarray. Expression of the co-factors of Srf, the family of Ets-domain transcription factors, Elk1, Elk3, and Elk4 genes, was not changed after nicotine-induced seizures. Of note, the Elk3 and Elk4 transcripts were "absent" in all samples. Furthermore, our in silico analysis of the 62 significantly changed genes identified six upregulated genes, Junb, Fos, Fosb, Erg1, Erg2, and Cyr61, that are known targets of the SRF transcription factor (Supplemental Table). These genes, in addition to Nr4a1, which was also upregulated following nicotine-induced seizures, were also identified as targets for SRF by Sun et al. (66), using a computational approach. Two additional novel SRF targets, the Txnip and Dusp6 genes, which were identified by Sun et al., were also upregulated following nicotine-induced seizures. Finally, by using the PRIMA software, we were able to identify two more novel putative SRF target genes, Plk2 and 5730557B15Rik (Supplemental Table).


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  		Table 4. TREs that were significantly overrepresented among the up- and downregulated genes following nicotine-induced seizure activity

 
TREs for MZF1 transcription factor were significantly overrepresented among the promoters of the 18 downregulated genes (Table 4, P = 8.78 x 10–4). The TREs for this transcription factor were 4.54 times more frequently represented in the promoters of the downregulated genes compared with their representation in the promoters of all the genes on the microarray.

The 62 differentially expressed genes participate in pathways involved in cellular and regulatory processes.
The 62 genes that distinguish between brains of untreated mice and mice that underwent nicotine-induced seizures were classified to known pathways. The pathway mining was performed using the BioRag website (http://www.biorag.org/pathway.html) by searching the Kegg, GenMapp, and Biocarta databases of pathways. The differentially expressed genes were found to be involved in several pathways associated with regulatory processes. Pathways that included two or more differentially expressed genes are listed in Table 5. Of note, 4 of the 62 differentially expressed genes belong to the MAP kinase signaling pathway. The expression of all of these four genes increased in brains of mice that underwent nicotine-induced seizures.


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  		Table 5. Cellular pathways that are involved in the response to nicotine-induced seizures

 
The interactions among the 62 differentially expressed genes were also analyzed using the PathwayStudio. This program searches for published associations between genes or proteins and builds pathways based on these associations. By searching for direct interactions among the genes, two pathways were constructed (Supplemental Figure). One contains members of the AP-1 transcription factor family (Fos, Fosb, Junb) and immediate early genes, such as Nr4a1, Egr1, and Egr2, and also contains the two members of the Dusp subfamily, Dusp1 and Dusp6, that negatively regulate proteins that belong to the MAP kinase superfamily. The genes included in this pathway are involved in the regulation of cellular proliferation. The second pathway includes the Nfkbia, Ptgs2, and Mig1 (Errfi1) genes. Interestingly, Nfkbia and three of the genes that were included in the former pathway (Fos, Dusp1, and Nr4a1) are also a part of the MAP kinase signaling pathway (see Table 5).

Validation of expression by quantitative RT-PCR.
The expression levels of 8 of the 62 significantly changed genes in brains of postnicotine-induced seizure mutant {alpha}7+/T and ß4–/– mice and wild-type mice were compared with brains of untreated mice from the same genotype groups and confirmed by quantitative RT-PCR analysis. The validation experiment included nine brains from each group of untreated and nicotine-induced seizure mice, three from each genotype (Fig. 1). With the use of real-time quantitative RT-PCR, the expression levels of Fos, Cyr61, Ptgs2, Dusp6, Dusp1, Klf10, Per1, and Dusp11 genes were analyzed, and all were found to be significantly changed following nicotine-induced seizure activity. (P < 0.05, Student's t-test, n = 9; Fig. 1).


Figure 1
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  		Fig. 1. Quantitative RT-PCR analysis of differentially expressed genes in brains of mice following nicotine-induced seizures and in untreated controls. The significant expression changes following nicotine-induced seizure were validated (P < 0.05, Student's t-test). Bars represent means ± SD. The expression levels of each gene, analyzed by real-time quantitative RT-PCR, were determined using known copy nos. as standard curves and normalization to Gapdh expression (y-axis). Light gray represents the expression in brains of untreated mice, and dark gray represents the expression level 1 h after nicotine-induced seizure. Each experimental group included 9 brains, 3 from each of the following genotypes: {alpha}7+/T knockin, ß4–/– knockout, and wild-type mice.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The global gene expression of brains of mice following nicotine-induced seizures was presented in this study. This is, to our knowledge, the first description of a nicotine-induced seizure-dependent expression analysis. Furthermore, our experimental model allowed us to detect a specific group of significantly changed genes whose expression patterns are likely mediated by the nicotine-induced seizure activity and not by the nAChR subunit mutation or the amount of nicotine injected. These expression changes could be due to the seizure activity, nicotine, or both. Indeed, our experimental model detected significantly up- and downregulated genes that were previously reported as differentially expressed following seizures or nicotine, further demonstrating the strength of this experimental model. For example, the upregulation of Dusp1 and Dusp6 genes described here was also shown following electroconvulsive seizures (43) and kainic acid-induced seizures (9).

Another example is the overexpression of Nr4a1 (the nuclear receptor subfamily 4, group A, member 1) and Ptgs2 (prostaglandin-endoperoxide synthase 2) genes following seizures and nicotine application. It was shown that the expression of Nr4a1 was elevated following induction of electroshock-evoked maximal seizure in the mouse brain CA1 area (31). Increased expression of Nr4a1 was also reported after acute intermittent administration of nicotine in rat parietal cortex (6) as well as in prefrontal cortex of acute nicotine-treated rats (63) and following acute nicotine exposure in pheochromocytoma cells (38). Upregulation of Ptgs2 (Cox2) expression, a known molecular response to seizure activity, was reported following electroconvulsive seizures in rats (2, 52), after hippocampal kindling (68), and after pilocarpine-induced (69) and kainic acid-induced (34) seizures. It is interesting to note that postseizure treatment with rofecoxib, a selective inhibitor of Ptgs2, diminished the hippocampal cell loss observed in rats following kainic acid-induced seizures (45). Increased expression levels of Ptgs2 were also detected after nicotine treatment of lipopolysaccharide-stimulated microglial cells (20) and human gingival fibroblasts (15). These data suggest that Ptgs2 modulates some of the adverse effects of either nicotine or seizures, and that these effects might be, at least partially, prevented by treatment with Ptgs2 inhibitors.

Our experiments also identified novel genes that were not previously associated with seizure activity or nicotine. Among them, the most upregulated gene in brains of mice following nicotine-induced seizures was the immediate early gene Cyr61 (cysteine-rich protein 61 gene). This gene encodes a secreted protein involved in cell adhesion, migration, and proliferation (54). Cyr61 expression levels were reportedly increased following toxic stimuli through activation of the c-Jun NH2-terminal kinase (JNK) signaling pathway (42) and after stimulation of the muscarinic acetylcholine receptors (1). Together, these findings suggest that Cyr61 expression is upregulated as a response to some toxins, including nicotine.

Another novel gene that was upregulated after nicotine-induced seizures was Per1 (period homolog 1), a transcription factor that is a part of the period genes family. Per1 is expressed in a circadian pattern in the suprachiasmatic nucleus, the primary circadian pacemaker in the mammalian brain, and is essential for the maintenance of a functioning circadian clock (59). Of note, all tissues used in this study were collected in the afternoon (between 3:00 and 5:00 PM). Although there is so far no data linking Per1 and seizure activity, there is evidence that seizure susceptibility might be affected by circadian modulation, and epileptic seizures may tend to occur at preferential times of the day (56). For example, in ADNFLE, which was associated with mutations in {alpha}4- and ß2-nAChR subunits, seizures occur at night (65). In contrast, in patients with mesial temporal lobe epilepsies, seizures are more frequent during the light phase of the day, specifically between 1:30 and 4:30 PM (57). The mechanisms underlying this association are not clear and might include the involvement of the sleep-wake cycle, core body temperature, melatonin, or other hormones (56). Therefore, our data suggest Per1 as a candidate circadian rhythm gene that might be involved in seizure modulation.

Activation of the MAP kinase (MAPK) family following seizure activity was previously described (25, 39). Our model of nicotine-induced seizures detected four differentially overexpressed genes, Fos, Nr4a1, Dusp1, and Nfkbia, involved in the MAPK signaling pathway. In addition, two genes, Dusp1 and Dusp6, that were upregulated and one gene, Dusp11, that was downregulated are members of the Dusp subfamily. These phosphatases negatively regulate MAPKs by dephosphorylating both the phosphoserine/threonine and phosphotyrosine residues (24). Dusp1 and Dusp6 were shown to specifically inhibit different MAPKs (13, 30). In addition, overexpression of Dusp1 and Dusp6 was previously reported following electroconvulsive seizures (43) and kainic acid-induced seizures (9). Dusp1 mRNA was induced 3 h after kainic acid seizures, whereas Dusp6 showed delayed induction and was increased only after 48 h. It was suggested that Dusp1 might be involved in axonal remodeling and protection of neurons from p38- and JNK/SAPK-dependent apoptosis, and that increased expression of Dusp6 may facilitate neuronal death (9). Our data demonstrated that members of the MAPK inhibitor subfamily, Dusp1, Dusp6, and Dusp11 genes, are involved in brain responses to nicotine-induced seizures as well. Since brains in our study were harvested 1 h after seizure activity, our results also demonstrated, concomitantly, the expression changes of MAPK signaling and specific MAPK inhibitor genes in this time frame as a part of the early response to seizure activity.

Expression changes of immediate early genes (IEGs) are the first transcriptional events in brain responses to seizure activity and nicotine treatment (reviewed by Refs. 7, 8). These stimuli induce neurotransmission changes by increasing intracellular calcium, leading to changes in cell functions, such as synaptic plasticity and density. IEGs are induced rapidly and transiently without the need for prior protein synthesis and serve as targets for second messengers that link membrane stimuli to the nucleus. Protein products of IEGs are often transcription factors, therefore acting as third messengers (7, 8). Of note, a large proportion (19/62) of the differentially expressed genes following nicotine-induced seizures were genes that regulate transcription. Furthermore, the GO annotation category of genes that regulate transcription in a DNA-dependent manner was significantly overrepresented among both the up- and the downregulated genes compared with its representation in the microarray. Similar overrepresentation of this functional category was also demonstrated in a microarray-based meta-analysis of brain expression following seizure activity and during epileptogenesis (47). Taken together, these data indicate a central role for the early response of transcription factors to seizure activity in the brain.

Specific stimulation of transcription regulatory networks by nicotine-induced seizure activity was also demonstrated here by analyzing the significant enrichment of TREs among the promoters of the differentially expressed genes. The TRE for ATF was the most significantly overrepresented TRE among the promoters of the upregulated genes. Atf3, a member of the ATF/CREB family of transcription factors, was 2.17-fold upregulated in brains of mice after nicotine-induced seizure activity. Since Atf3 is a transcriptional repressor that can repress the activity of its own promoter (72), it is not surprising that, in our in silico promoter analysis, one of the genes whose promoter carried TREs for ATF was Atf3 itself (Supplemental Table). Atf3 acts both as a cellular response to stress and as a stimulator of proliferation in multiple tissues. In the nervous system, Atf3 is expressed only in injured neurons and is therefore considered a marker for nerve injury. Its induction has been demonstrated following several types of cell injury, including seizures (33). Therefore, the upregulation of Atf3 expression following nicotine-induced seizures, and the enrichment of TREs that bind Atf3 in the promoters of the upregulated genes, signal the cell damage in response to seizure induced by nicotine.

The second most significantly overrepresented TRE in the promoters of the upregulated genes following nicotine-induced seizures was SRF. Upregulation of the Srf protein was previously detected following two models of rodent seizures, kainic acid-induced seizures (35) and pilocarpine-induced status epilepticus (51). The SRF transcription factor is required for the expression of many genes, including IEGs, cytoskeletal genes, and muscle-specific genes (14). Sun et al. (66) performed a computational analysis to identify known and novel target genes of SRF. A large proportion of those targets encode cytoskeleton/contractile proteins, while all the known SRF targets that were significantly upregulated after nicotine-induced seizures were classified as IEGs. These results suggest that SRF may specifically regulate IEG expression in response to seizure activity.

Together, these results suggest that transcription regulatory networks, which involve the SRF and ATF, modulate gene expression after nicotine-induced seizure and also following seizure activity in other animal models and possibly, in human epilepsy as well. Furthermore, since most of their targets are immediate early transcription factors, they might, in turn, promote a secondary broader wave of gene expression.

In summary, the results presented here, together with previously published data, allow us to hypothesize the following chain of molecular events as a response to nicotine-induced seizure activity. Changes in intracellular calcium activate the MAPK signaling pathway, leading to the induction of immediate early transcription factor genes, such as Fos, Fosb, Junb, and Atf3. These transcription factors further regulate the expression of downstream genes, among them negative regulators of specific MAPKs, such as Dusp1. Finally, the global brain transcriptional response to nicotine-induced seizures in mice is a useful model to study seizure disorders. The differentially expressed genes detected here can help us to understand the molecular mechanisms underlying seizure activity in this model, as well as in other animal models of seizures, and may also serve as candidate genes to study epilepsy in humans.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported by the M. K. Humanitarian Fund and by the Wolfson Foundation.


   ACKNOWLEDGMENTS
 
We thank Sonia Soloviov for technical assistance. This work was performed in partial fulfillment of the requirements for the Ph.D. degree of M. Kedmi, Sackler Faculty of Medicine, Tel Aviv University, Israel.


   FOOTNOTES
 
Address for reprint requests and other correspondence: A. Orr-Urtreger, The Genetic Institute, Tel-Aviv Sourasky Medical Center, 6 Weizmann St., Tel Aviv, 64239 Israel (e-mail: aviorr{at}tasmc.health.gov.il).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
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