correlation of transcriptome profile with electrical ... · correlation of transcriptome profile...

Correlation of transcriptome profile with electrical activity in

temporal lobe epilepsy

Dominique Arion,a Michael Sabatini,a Travis Unger,a Jesus Pastor,c

Lidia Alonso-Nanclares,b Inmaculada Ballesteros-Yanez,b Rafael Garcıa Sola,c

Alberto Munoz,b Karoly Mirnics,a,* and Javier DeFelipe b,*

aDepartment of Psychiatry and Neurobiology, University of Pittsburgh, School of Medicine, W1655 Biomedical Science Tower, Pittsburgh, PA 15261, USAbInstituto Cajal, CSIC, Madrid, SpaincDepartamento de Neurocirugıa, Hospital de la Princesa, Madrid, Spain

Received 18 May 2005; revised 7 November 2005; accepted 4 December 2005

Available online 9 February 2006

The biology underlying epileptic brain activity in humans is not well

understood and likely depends on changes in gene expression. We

performed a microarray transcriptome profiling of 12 anterolateral

temporal cortical samples originating from five individuals who suffered

with temporal lobe epilepsy for at least 10 years. Prior to partial

lobectomy, intraoperative electrocorticography was performed on the

cortical surface of each patient. These recordings showed characteristic

differences in frequency and amplitude that were defined as ‘‘spiking’’

(abnormal) or ‘‘non-spiking’’ (normal). Between the transcriptome of

the two sample groups, transferrin (TF) was the most differentially

expressed gene. Furthermore, gene expression profiling also revealed a

downregulation of multiple GABA system-related genes (GABRA5,

GABRB3, ABAT) in the spiking samples and an upregulation of

oligodendrocyte and lipid metabolism transcripts (MOG, CA2, CNP,

SCD, PLP1, FA2H, ABCA2). In addition, several transcripts related to

the classical MAPK cascade showed expression level alterations between

the spiking and non-spiking samples (G3BP2, MAPK1, PRKAR1A, and

MAP4K4). Out of 12 genes chosen for verification by RT qPCR, 9

showed significant expression changes in the microarray-predicted

direction. Furthermore, the microarray and qPCR data were highly

correlated (r = 0.98; P < 0.001). We conclude that abnormal electrical

brain activity in the spiking samples is strongly correlated with gene

expression changes and we speculate that some of the observed

transcriptome changes may be directly involved in the induction or

prevention of the ictal events seen in epilepsy.

D 2005 Elsevier Inc. All rights reserved.

Keywords: Temporal epilepsy; DNA microarray; Transcriptome; qPCR;

Ictal activity; Transferrin

Introduction

Epilepsies are the second most common neurological disorders

next to stroke, and carry an estimated 2.1% lifetime incidence.

Intractable epilepsy refers to the roughly 30–40% of cases that are

resistant to medical management. A great variety of pathologies

and brain alterations may cause intractable epilepsy, including

various types of tumors, alterations of development, trauma, and

infections (Kuzniecky and Barkovich, 1996; Beaumont and

Whittle, 2000; Degen et al., 2002). However, these alterations

are not intrinsically epileptogenic, since some patients develop

epilepsy whereas others with similar abnormalities do not; some

may even develop epilepsy after a variable delay. The causes of the

underlying epilepsy are often unknown (Cowan, 2002; Devinsky,

2004). Thus, a number of hypothesis to explain seizure activity are

aimed to answer the question of what alterations are necessary in

the neuronal circuitry to induce epilepsy, and why does this happen

in some patients but in others it does not (DeFelipe, 1999; Engel,

2003; Nair et al., 2004).

Temporal lobe epilepsy (TLE) is one of the most common

forms of intractable epilepsy. It has been estimated that about 15%

of patients with intractable temporal lobe epilepsy (TLE) have the

potential for a surgical cure (Engel, 2003). Patients with TLE

experience recurring episodes of intense neural activity originating

from the medial or lateral temporal lobe. It is believed that over

time the ictal episodes result in neuronal damage and subsequently

lead to cognitive decline. These outcomes are thought to be the

result of an array of changes that occur secondary to repeated

epileptic episodes; these changes include cellular-level alterations

such as hyper-excitability, metabolic changes, cytoskeletal

changes, or apoptosis and circuit-level changes including neuronal

loss, sprouting, and gliosis (Pitkanen and Sutula, 2002). Accord-

ingly, it is suspected that alterations in the expression of a large

number of genes must be responsible for these functional changes.

While some of these expression differences may be a consequence

0969-9961/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.nbd.2005.12.012

* Corresponding authors. K. Mirnics is to be contacted at fax: +1 412

624 9910. J. DeFelipe, Instituto Cajal, CSIC, Dr. Arce 37, 28002 Madrid,

Spain.

E-mail addresses: [email protected] (K. Mirnics),

[email protected] (J. DeFelipe).

Available online on ScienceDirect (www.sciencedirect.com).

www.elsevier.com/locate/ynbdi

Neurobiology of Disease 22 (2006) 374 – 387

www.neurorgs.com - Unidad de Neurocirugía RGS

of tissue injury at the epileptic focus, it is likely that others may

play a pivotal role in regulating abnormal neuronal electrical

activity.

Recently, microarrays have been successfully used to examine

gene expression differences in the post-mortem human brain

(Ginsberg et al., 1999; Colantuoni et al., 2001; Hakak et al., 2001;

Colangelo et al., 2002; Hemby et al., 2002; Blalock et al., 2004;

Evans et al., 2004). For example, transcriptome profiling by

microarrays has been successfully used for uncovering critical

pathophysiology mechanisms associated with schizophrenia, Alz-

heimer’s disease, multiple sclerosis, and others (Glanzer et al.,

2004; Mirnics and Pevsner, 2004; Pierce and Small, 2004).

However, due to experimental design and cohort differences,

microarray studies of epilepsy have resulted in the generation of

datasets with a relatively low degree of agreement. Animal models

to date suggest that genes associated with structural proteins,

metabolism, and signal transduction may play a critical role in the

pathogenesis of epilepsy (Hendriksen et al., 2001; Becker et al.,

2003; Elliott and Lowenstein, 2004; Elliott et al., 2003; Lukasiuk

et al., 2003; Majores et al., 2004).

Surgical samples from humans with intractable TLE provide a

unique opportunity to examine human brain with essentially no

post-mortem interval. The only previous report using surgically

harvested samples described gene expression changes in Ammon’s

horn sclerosis with a background of epilepsy (Becker et al., 2002).

In this comparison of Ammon’s horn sclerosis secondary to

epilepsy with normal Ammon’s horn removed either for epilepsy

or oligodendroglioma, Becker and colleagues found 21 differen-

tially expressed genes, including upregulations of Ataxin-3 and

GFAP, and a downregulation of calmodulin. This study, however,

was limited by the low complexity of the DNA array platform of

588 probesets. In addition, the degree of cell loss and gliosis is

remarkably variable between different fields of the sclerotic

hippocampal formation within a given epileptic patient and

between epileptic patients (Mathern et al., 2002; Arellano et al.,

2004). Therefore, the heterogeneity of the damage in the sclerotic

hippocampus of epileptic patients makes it difficult to reach

meaningful conclusions regarding the possible relationships

between neuropathology, epilepsy and changes in gene expression.

In this study, we chose to examine gene expression differences in

normal histopathologically resected tissue that differed on the basis

of electrocorticography signatures. Intraoperative surface electro-

corticography recorded from temporal lobe tissue demonstrated

distinct frequency and amplitude signatures that permitted the

classification of either ‘‘spiking’’ (S) or ‘‘non-spiking’’ (NS) in each

recorded region. S activity has been causally associated with the

onset of an ictal discharge and is commonly used to localize seizure

focus in partial epilepsy patients awaiting surgery (Lieb et al., 1981).

Recently, however, several studies suggested that interictal dis-

charges were instead interfering with the onset of ictal activity

(Avoli, 2001; de Curtis and Avanzini, 2001; Librizzi and de Curtis,

2003). The exact relationship between the interictal spikes and ictal

onset remains poorly understood. Thus, the present study was

designed to compare the transcriptome profile of anterolateral

temporal cortical samples with S and NS activity originating from

patients who have suffered from chronic epilepsy for more than 10

years. Using high-density oligonucleotide microarrays we found 76

probes differentially expressed between the S and NS samples. The

observed expression differences in the S/NS comparison included

(1) an upregulation of several oligodendrocyte and lipid metabolism

transcripts, (2) a downregulation of GABA-related genes and (3) a

differential expression ofmultiple signal transduction genes. Finally,

we identified transferrin as the most differentially expressed

transcript between the two sample types.

Materials and methods

Human brain samples

A total of 5 patients (4 male and 1 female) were included in this

study. Mean age and period of intractable epilepsy were 31.4 T 4.0

and 21.8 T 5.7 years, respectively. All patients were evaluated pre-

surgically with scalp electroencephalography (EEG), interictal

single photon emission computer tomography (SPECT), magnetic

resonance imaging (MRI) 1.5 T and video-electroencephalography

(v-EEG) including 19 scalp electrodes placed using international

10–20 system and foramen-ovale (FO) electrodes in 4 cases. See

Table 1 for more clinical details. Informed consent, approved by

ethical committee of University Hospital de la Princesa (Madrid,

Spain), was obtained individually for all patients.

Electrocorticography and surgical removal of temporal lobe cortex

Briefly, electrocorticography (ECoG) was performed with a

grid of 4 � 5 electrodes (Pt/Ir) embedded in Sylastic, 1.2 mm in

diameter and 1 cm center-to-center inter-electrode distance (Add-

TechR), placed directly over exposed lateral temporal cortex (Fig.

1). In four cases, a row of 4 electrodes was placed in the mesial

temporal lobe. Recordings were performed with a 32-channel Easy

EEG II (CadwellR), sampled at 400 Hz, with a bandwidth of 1–70

Hz, during a minimum period of 20 min.

Spiking (S) areas were identified as electrodes showing spikes

(<80 ms) or sharp waves (80–200 ms) (Ajmone-Marsan and

O’Connor, 1973; Chatrian and Quesney, 1999) with a mean

frequency greater than 1 spike/min (Fig. 2). Non-spiking (NS)

areas were defined as electrodes where no spikes, sharp waves or

slow activity were observed. Photographs of placement of electro-

des were taken before grid removal, and the anatomical location of

the S and NS areas were identified prior to tissue excision. The

same neurosurgeon (R.G.S.) intervened on all patients. Tailored

temporal lobectomy plus amigdalohyppocampectomy was per-

formed under electrocorticography (ECoG) guidance in all but one

case (H211). In the latter case, cortectomy following ECoG

guidance was performed. The outcomes were evaluated following

Engel’s scale (Engel, 1987) for at least one year after surgery.

After surgery, the lateral temporal neocortices of all patients and

the mesial temporal structures from patients H213 and H217 were

available for standard neuropathological assessment. Mesial

structures from patients H212 and H215 were absorbed during

surgical removal and, therefore, could not be examined. The lateral

neocortices in all cases were histologically normal. However,

alterations were found in the hippocampal formations of two

patients: patient H213 showed hippocampal sclerosis and patient

H217 showed hippocampal dysplasia characterized by an abnormal

cortical lamination of CA1 (bi-layer) and the presence of ectopic

clusters of small neurons in CA4.

Microarray sample preparation and hybridization

Surgical samples, obtained at the Neurosurgery Department

at Hospital de la Princesa, were snap-frozen on dry ice and

D. Arion et al. / Neurobiology of Disease 22 (2006) 374–387 375


transported to the University of Pittsburgh where the oligonu-

cleotide microarray experiments took place. Briefly, the brain

material was homogenized and total RNA was isolated using

TrizolR reagent (Invitrogeni). RNA quality was assessed using

the Agilent 2100 Bioanalyzer. Only samples with a 28 s to 18 s

ratio of 1.75 or greater were considered for further analysis.

These samples were primed with a standard T7-oligo(dT) primer

and the cDNA synthesis was performed from 7 Ag of total

RNA according to the AffymetrixR manufacturer’s protocol.

Amplified antisense RNA (aRNA) was produced using an in

vitro transcription directed by T7 polymerase. Fifteen micro-

grams of the cleaned and fragmented aRNA were hybridized to

the Affymetrix HG_U133A microarrays (>22,000 probe sets)

using an Affymetrix Genechip Fluidics Station 400. Microarray

images were scanned using an Affymetrix scanner. Image

segmentation and generation of DAT files were performed

using Microarray Analysis Suite 5.0 (MAS5).

Data analysis

In the present study, we compared the gene expression

signature of S to NS cortical epilepsy samples. All samples

reported exceptional quality on the microarrays with average 5V:3Vratios for actin and GAPDH not exceeding 1:1.2. Segmented

images were normalized and log2 transformed using Robust

Microarray Analysis (RMA) (Irizarry et al., 2003). RMA-generated

expression levels were subjected to a Student’s two-tailed t test

between the S and NS sample groups. To control for false

discovery on a limited sample size, we employed a dual strategy in

determining differentially expressed genes. Genes were considered

differentially expressed between the S and NS samples if they (1)

reported a statistical significance of P < 0.01 and (2) reported a

groupwise average log2 difference whose absolute value exceeded

0.263. This strategy allowed us to eliminate small expression

changes that may be statistically significant, which are a major

source of type I errors. While the statistical criteria of P < 0.01

Fig. 1. Intraoperative photograph illustrating the location of electrodes over

temporal lobe in patient H215. Empty arrows indicate electrodes recording

spiking activity, whereas solid arrows recorded only non-spiking activity in

this patient (see Fig. 2). Dashed line indicates the line of surgical resection.

Table

1

Dem

ographic

andclinical

dataofthestudiedpatients

Patient

Sex

Age

(years)

Ageof

onset

(years)

Tim

eof

epilepsy

(years)

Frequency

Medication

Scalp

EEG

MRI

SPECT

v-EEG

Surgery

Outcome***

(1year)

FO

Focus

H211

F27

14

13

Daily

VPA,CBZ,PHE

L,O-T

SW

Norm

al*

Norm

alNo

L,TN

L,Tcortectomy

II

H212

M24

124

Weekly

CBZ,TPM

R,TSW

L,MS

L,MTL,TN

Yes**

L,MTL

L,Tcortectomy,

AHC

I

H213

M38

28

10

Daily

OxCBZ,TPM,

CLRZ

L,F-T

SW

Norm

alR,MTLTN

Yes

L,MTL

L,T

cortectomy,

AHC

I

H215

M43

10

33

Weekly

CBZ,PHE

L,F-T

SW

R,TN

atrophy

L,MTLTN

Yes

L,MTL

L,T

cortectomy,

AHC

I

H217

M24

10

23

Weekly

CBZ,VPA,TGB

R,F-T

SW

RVenousang.

L,MTL,TN

Yes**

R,MTL

R,Tcortectomy,

AHC

I

AHC:am

igdalohyppocampectomy,CBZ:carbam

azepine,CLRZ:clorazepate,F:female,FO:foramen-ovaleelectrodes,F-T:fronto-tem

poral,L:left,M:male,MS:mesialsclerosis,MTL:mesialtemporallobe,

OxCBZ:oxcarbazepine,

O-T:occipito-tem

poral,PHE:phenytoin,R:right,SW:sharpwaves,T:temporal,TN:lateraltemporalneocortex,TGB:tiagabine,

TPM:topiram

ate,

VPA:valproate.

*Previouslefttemporallobectomy.

**Only

interictal,notseizuresrecorded.

***Engel’sclassification(Engel,1998).

D. Arion et al. / Neurobiology of Disease 22 (2006) 374–387376


alone would identify 269 differentially expressed genes, our

combined approach (P < 0.01 and |ALR| > 0.263) reduced the

dataset to 76 probesets reporting differential expression.

Regardless of the stringency of our combined criteria, we

required an unbiased estimate of the remaining noise (false

discovery rate—FDR) in our dataset. As any experimental error is

dataset specific, we chose a permutation-based FDR assessment on

this dataset which was accomplished in the following manner: (1)

we divided all the microarrays used into two random groups

containing the same number of experimental and control arrays, (2)

we performed the same significance analysis on this array group

that was used in the original experimental design, (3) we repeated

steps 1 and 2 nine additional times, and (4) we averaged the number

of genes identified across all 10 trials to report a false discovery rate.

For a graphical representation of the FDR procedure, see

Supplemental Material 1. True discovery was defined as the

number of differentially expressed probesets � average number

of probesets obtained in the FDR permutation tests.

On the experimental dataset, a two-way clustering of the

differentially expressed genes was performed using Genes@Work

developed by IBM (Lepre et al., 2004). This clustering was

performed on the log2 RMA expression levels using Euclidian

distance. Expression levels and statistical parameters were

imported into a searchable, custom-built MS Access database that

will be publicly available from the author’s Web site. All

microarray data will be deposited into GEO in a MIAME/MGED3

format at the time of publication.

qPCR verification of data

cDNA synthesis was performed using two independent reverse

transcriptions for each sample with the High Capacity cDNAArchive

KitR from Applied Biosystems. For each reaction, we used 50 ng of

the same total RNA that was used for microarray analysis. Priming

was performed with random hexamers. For each sample, amplified

product differences were measured with 4 independent replicates

using SYBR Green chemistry-based detection (Mimmack et al.,

2004). h-actin was used as the endogenous reference gene since (1) ithas been established as a stable reference gene in previous studies

(Chen et al., 2001) and (2) it did not display significant variation in

gene expression between S and NS samples in the microarray studies.

The efficiency for each primer set was assessed prior to qPCR

measurements, and a primer set was considered valid if its efficiency

was between 97 and 100%. The qPCR reactions were carried out in an

ABI Prism 7000 thermal cycler (Applied Biosystems Inc.) and the

fluorescence data obtained were quantified using the ABI Prism 7000

SDS software with the auto baseline and auto threshold detection

options selected. These quantified data were exported to Microsoft

Fig. 2. Intraoperative electrocorticographic recording from the temporal neocortex. Electrocorticographic recording of a patient (H215) showing spiking (empty

arrows) and non-spiking (solid arrows) electrical activity areas from which resected tissue was selected for transcriptome profile analysis.



Excel for significance testing. A Student’s one-tailed t test was used

for statistical analysis to determine whether the qPCR data confirmed

the microarray prediction.

Results

Intraoperative electrocorticography reveals distinct tissue

phenotype

Four patients were diagnosed with mesial temporal lobe epilepsy

(MTLE) and one patient was diagnosed with neocortical epilepsy.

The patient with neocortical epilepsy (H211) had a temporal

lobectomy 2 years before, but seizure control was still poor. All

patients were assessed using ECoG.

In patient H211, ECoG reported slow sharp waves (1120 T 75

AV; 148 T 8ms; n = 115) intermittently mixed with smaller spikes. In

patients H212 and H213, there was no paroxysmal activity observed

in the lateral temporal cortex; while in patients H215 and H217,

well-defined NS and S areas were identified (Fig. 2). These subjects

showed high voltage spikes (898 T 124 AV and 80 T 4 ms; n = 89;

mean T SEM) in the S areas.

In total, we obtained 12 samples, 6 S and 6 NS. S samples

originated from brains H211 (samples 1 and 2), H215 (samples 9

and 10) and H217 (samples 11 and 12). NS samples originated

from brains H212 (samples 3 and 4), H213 (samples 6 and 7),

H215 (sample 8) and H217 (sample 13).

Differentially expressed transcripts in S and NS tissue

The surgically resected human temporal lobe tissue was

processed for oligonucleotide microarray analysis. We obtained

expression data for each of the 14,500 genes represented on the

Affymetrix Human U133A 2.0 arrays. Across the six S arrays, an

average of 46.6% of the genes were called present according to the

MAS 5.0 Present/Absent call (tau = 0.015); across the six NS arrays,

45.8% of the genes were similarly called present. We imposed two

group-wise criteria on these expression data to select genes that were

differentially regulated between the S and NS resected human

temporal lobe samples: (1) S to NS |ALR| > 0.263 and (2) S to NS t

test P value <0.01. These criteria selected 76 transcripts that

represent 70 distinct human genes. A two-way unsupervised

clustering (genes and samples) of these 76 probesets resulted in

separation of samples into two distinct clusters (Fig. 3), accurately

predicting the S/NS phenotype in this sample. Of the 76 transcripts

selected by these criteria, 30 are reduced in S samples and 46 are

enriched in S samples. Using a custom designed false discovery rate

analysis with the same selection criteria, we estimate the FDR at

5.7% (Supplementary Material 1). The individual expression levels

of these genes (log base 2), as well as the gene symbol, accession #,

LocusLink ID, and group-wise summary statistics appear in Table 2.

In this subset upregulated transcripts have a mean ALR = 0.44

Fig. 3. Unsupervised two-way hierarchical clustering of 76 differentially

expressed genes across the S and NS samples. Both genes (rows) and

samples (columns) were clustered based on RMA log2 expression levels

using Genes@Work. Distance was calculated using Euclidian distance. Red

represents increased expression, green represents decreased expression.

Each small square denotes a normalized expression signal in a single

sample and for a single gene. Numbers on the right represent HG_U133A

GeneChip probesets, letters denote corresponding gene symbols. For more

data on the genes, see Table 2. Note that in this unsupervised clustering, the

spiking (red) and non-spiking (black) samples separated into two distinct

clusters (vertical dendrogram).



Table 2

Differentially expressed genes (P < 0.01 and |ALR| > 0.263*)

(continued on next page)



(35.7% increase); downregulated transcripts have a mean ALR =

�0.36 (28.3% decrease). Note that for six of the regulated genes

(GPR37, GABRA5, MOG, NPIP, G3BP2 and TF) two unique

probesets reported similar expression signatures across subjects.

At the functional level, several gene groups are heavily

represented in the group of differentially regulated transcripts.

GABA genes

In the set of transcripts that is deficient in the S tissue, four

probesets are related to GABA-related functions as described by

the Gene Ontology Biological Process or Molecular Function

Descriptions (Table 2). Of these four probesets, two are targets of

the GABA A a5 Receptor (GABRA5) sequence. The remaining

probesets target the GABA A h3 (GABRB3) sequence and the 4-

Aminobutyrate Aminotransferase (ABAT) sequence respectively.

These four GABA-related transcripts have a mean ALR = �0.35

(27.4% decrease). There are no members of the GABA-related

transcripts in the set of genes that is enriched in S tissue.

To obtain global expression data for the GABA-related genes, we

identified every probeset on the arrays directed toward components

of this system that were called present in all samples. This list

includes sequences directed toward receptors, receptor modulators,

synthesis enzymes, transporters, and degradation enzymes. A total

of 38 elements appeared on this list. Twelve of these elements,

directed at 11 unique gene sequences, had a S/NS t testP value of 0.1

or lower, and nine had a P value of 0.05 or lower (Fig. 4). Eleven of

these transcripts were deficient in S tissue while only one was

enriched in S tissue (GAD2). Note that two of the three transcripts

most decreased in S are directed against the same gene (GABRA5).

Oligodendrocyte genes

In the set of transcripts that is enriched in the S tissue, ten

probesets are specifically expressed in oligodendrocyte cells.

These ten probesets are targeted against sequences from eight

unique genes: CNP, CA2, PLP1, ASPA, EDG2, MOG, ABCA2

and TF (Table 2). These oligodendrocyte specific transcripts

have a mean ALR = 0.69 (61.3% increase). Oligodendrocyte-

related transcript decreases were not observed in the S samples.

Two additional transcript enrichments in the S tissue are also

noteworthy in the context of myelination: SCD, FA2H. Both are

linked to lipid metabolism and play a role in the myelination

function of oligodendrocytes. In summary, note that 12 of the

17 most upregulated transcripts are specifically expressed in

oligodendrocytes.

MAPK signaling

Our analysis identified MAPK1 and several other genes that

relate to the classical MAPK signaling cascade: G3BP2, MAPK1,

PRKAR1A, and MAP4K4 (Table 2). Three out of these four

transcripts were deficient in S tissue with an ALR = �0.32 (25%

decrease), while MAP4K4 had an ALR = 0.32 (25% increase).

Transferrin

The most regulated transcript identified by our microarray

analysis was TF (Figs. 5 and 6). Two unique probesets report

1.90-fold and 1.99-fold upregulations in the S tissue. TF was

also the most regulated gene among the group of 12 transcripts

chosen for qPCR analysis (Fig. 6). The S/NS average DDCt for

TF was 0.87, which corresponds to a 1.82-fold change in the

microarray-predicted direction.

qPCR verification of microarray data

In order to validate the microarray findings we selected 12

transcripts for real-time quantitative PCR (qPCR) analysis. These

transcripts were chosen from the gene groups highlighted by the

microarray analysis, and represent potential biologically salient

genes with respect to epilepsy. These selected transcripts have |ALR|

values ranging between 0.99 (98.8% change) and 0.32 (24.9%

change) with a mean |ALR| = 0.54 (47.4% change). For all 12 genes,

the expression difference reported by qPCR agrees in direction with

that reported by microarray analysis (Fig. 6A). The qPCR findings

Differentially expressed genes between 6 S and 6 NS samples. Seventy six probes showed differential expression across the whole microarray data set using the

selection criteria established based on FDR: 1) |ALR| > 0.263 and 2) t test P value < 0.01. Gene probes are sorted according to their ALR. Highlighted probe sets

belong to one of three functional systems: GABA (yellow), signaling (blue) or oligodendrocyte (orange) genes. Genes denoted by ** are represented by multiple

probe sets that give rise to converging data. Individual data for all 12 samples and for all 76 gene probesets described in this table are clustered in Fig. 3. (Avg NS

and Avg S—RMA reported average log 2 ratio across NS and S samples; pVal—two-tailed groupwise t test P value).

* ALR = 1 equals a 2– fold change.

** Multiple probe sets against the same transcript.

Table 2 (continued)



reached significance in nine of the 12 transcripts. The qPCR DDCt

and the microarray ALR are strongly correlated with r = 0.98 (P <

0.001) (Fig. 6B).

Discussion

In the current study, we identified a transcriptome signature for S

tissue versus NS tissue in the background of intractable epilepsy.

While the sample size used in our study was limited, it provided

several robust findings. We reported a differential expression of

>20% (|ARL| > 0.263; P value < 0.01) for 76 probes in the S/NS

comparison. Among the transcriptional changes several functional

gene groups emerged. These included cell signaling genes (MAPK1,

PRKAR1A, PLA2G4C, MAP4K4, G3BP2, GPR37), GABA genes

(GABRA5, GABRB3, ABAT) and oligodendrocytes genes (CNP,

CA2, SCD, PLP1, FA2H, ASPA,MOG, EDG2, ABC2). In addition,

transferrin was the most upregulated individual gene in this study.

Fig. 4. GABA system gene expression in S/NS samples. The x axis represents different gene probes that belong to the GABA system, the y axis represents

mean ALR differences between the S/NS samples. Out of 12 probesets representing 11 differentially expressed GABA-related genes, 10 displayed decreased

levels of expression. Bars are color-coded based on t test significance (Black: P < 0.01, gray: 0.01 < P < 0.05, white: 0.05 < P < 0.1).

Fig. 5. Correlation between qPCR and RMA-normalized gene expression levels for Transferrin (TF). The x axis represents individual samples, non-spiking

(NS—green shades) or spiking (S—red shades); the first y axis represents RMA-normalized log2 signal while the second y axis depicts �DCt obtained in the

qPCR verification experiment. Diamonds represent TF probe 1 and triangles represent TF probe 2 on the microarrays. In NS samples dark green triangles and

light green diamonds depict the expression signal of the two TF microarray probes, while the yellow-green open circles depict the qPCR �DCt measurements.

Similarly in S samples, orange triangles and pink diamonds depict the expression levels of the two TF microarray probes and red open circles denote the qPCR

�DCt measurements. Solitary markers represent the mean measurement for each corresponding sample and experiment group. Note that the two TF probesets

and qPCR measurements reported a similar expression difference between the S and NS samples.



From these data, we can address the molecular differences

between S and NS tissues, however, their relationship to

epileptogenesis will require a better understanding of where

the seizure focus lies in relation to interictal spike focus.

Additionally, the functional interpretation of our gene expression

data set will also greatly depend on the role of the inter-ictal

activity observed in the S samples: a gene expression change

may be beneficial if the inter-ictal spiking is a biological

compensation or it may represent a harmful event if the inter-

ictal spikes represent a pre-ictal event.

S and NS tissue: relationship with epileptogenesis?

S or interictal activity has historically been used to localize the

epileptogenic area and has been considered to lead to seizure onset,

a phenomenon called interictal to ictal transition. In focal chronic

epilepsy, enhancing sodium current in hippocampal slices is able to

induce both interictal spiking and spontaneous ictal events (Otoom

et al., 1998). Inversely, antiepileptic drugs reducing sodium current

are also able to decrease the duration of the interictal spikes. In the

kindling-induced model of epilepsy, there is an increase in spiking

activity preceding the appearance of the ictal event (Wada and Sata,

1974). Recently, however, multiple studies suggest that inter-ictal

spikes may have a role in protection against the onset of an ictal

discharge. Within the background of recurring ictal events,

interictal spikes could then represent a pre-ictal event or an attempt

at controlling neuronal activity by maintaining a low level of

excitation (Engel and Ackermann, 1980; de Curtis and Avanzini,

2001). In this sense, it is interesting to note that in patients

diagnosed with mesial temporal lobe epilepsy, it is usual to find

interictal activity in the lateral temporal cortex. In fact, 41 out of 52

patients (78.8%) surgically intervened in the Hospital de la

Princesa during the last 3 years showed S activity in lateral cortex

(unpublished data), raising the question about the origin and

meaning of this activity. Alarcon et al. (1997) studied the

relationship between patterns of discharge propagation during

acute electrocorticography and surgical treatment of temporal lobe

epilepsy and found a significant association between a poor

outcome and the retaining of regions where discharges showed the

earliest peaks (‘‘leading regions’’). Interestingly, of the 23 cases

with mesial temporal sclerosis studied by these authors, the main

leading regions were localized in the hippocampus in only 43% of

the patients, whereas in the remaining 57%, these leading regions

were localized either in the histopathologically normal temporal

neocortex or in the extrahippocampal basal temporal cortex. These

results are in accordance with previous findings emphasizing the

Fig. 6. qPCR validation of microarray data. 12 genes were selected for verification. (A) Statistical parameters obtained in the microarray and qPCR

experiments. AVG NS: average ALR across NS samples, AVG S: average ALR across S samples; ALR2: spiking/non-spiking average log2 ratio, DCt: delta

threshold cycle (Ct) calculated with respect to beta-actin; DDCt: difference between S/NS DCts; pVal: t test P value. Note that in the qPCR verification

experiment all 12 genes showed an expression change in the predicted direction, with 9 genes reaching statistical significance. (B) x axis denotes GeneChip

RMA ALR between S/NS samples; y axis denotes difference between average NS-S �DDCts. Symbols represent the 12 genes chosen for verification. Green

symbols depict genes that belong to the GABA regulated gene groups, red symbols represent genes that belong to the oligodendrocyte gene group while the

blue symbol corresponds to a gene that belongs to the signaling molecule gene group. Purple symbols depict two other genes chosen for their potential

functional significance vis-a-vis ictal events. Note that the correlation between the microarray and qPCR data was highly significant (r = 0.98; P < 0.001).



contribution of the neocortex to seizure development and

propagation in mesial temporal epilepsy.

In our study, the uncovered gene expression differences may be

one or more of the following: (1) expression changes that cause

spiking, (2) expression changes that are physiological adaptations

to the spiking, and (3) expression changes that are harmful

adaptations to the spiking. While these changes are hard to tease

apart, the known function of the individual genes may provide us

with clues in order to understand the biological role of these

observed gene expression changes. We believe that these data will

be very informative for building future hypotheses about the

molecular causes of epilepsy and its cellular-level compensations.

Transcriptome differences between the S and NS tissue: GABA

system transcripts

We observed a prominent downregulation of GABA system

transcripts in the spiking samples, and this expression deficit

appeared in both the presynaptic GABA synthesis components

in the interneuronal compartment (GAD, ABAT, GAT) and the

postsynaptic receptor binding sites (GABA A receptor subunits).

In the context of epilepsy, this is not an unexpected finding

since the loss of GABA inhibition and hyperexcitability are

common features found during epileptiform activity in the brain.

Multiple lines of evidence suggest a causal relationship between

GABA inhibition and seizure onset, including the antiepileptic

actions of drugs acting on GABA receptors and ABAT (Sperk

et al., 2004). Furthermore, decreased level of the mRNA of

GABA A receptor subunits a 1 and g 1 have been shown to

result in epileptic seizures in mice (Yun et al., 2003). In

addition, GABA A receptor beta 3 subunit (GABRB3) knockout

mice exhibit electroencephalograph abnormalities, seizures and

behavioral characteristics that are similar to Angelman’s

syndrome (DeLorey et al., 1998; DeLorey and Olsen, 1999).

Finally, human patients with temporal lobe epilepsy also show a

reduced expression of GABA A receptors (McDonald et al.,

1991).

Based on these finding and our results, we argue that the overall

down-regulation of GABA system genes may be a core molecular

feature associated with the S phenotype. In this context, the down-

regulation of a number of GABA genes found in our microarray

study would be consistent with the state of hyperexcitability seen

in the S samples and could be instrumental to the onset of ictal and/

or interictal events.

Transcriptome differences between the S and NS tissue: signaling

molecules

One of the major group of genes found to be differentially

expressed in our S/NS comparison contains genes involved in

signal transduction. For this group of genes, transcripts showed

both inductions and repressions in S tissue compared to NS

tissue. The physiological importance of these genes may follow

from their known rapid and robust expression changes following

brain trauma (Skaper et al., 2001; Matzilevich et al., 2002;

Long et al., 2003; Lukasiuk and Pitkanen, 2004), a state that

often predisposes the development of epilepsy. In our dataset a

number of signaling genes showed transcript level alterations

(MAPK1, PRKAR1A, PLA2G4C, MAP4K4, G3BP2, GPR37).

From this group, dysregulation of the mitogen-activated protein

kinase 1 gene (MAPK1) transcript may be particularly relevant

since it regulates a wide variety of cellular processes such as

proliferation, differentiation and development. Furthermore,

recent data strongly suggest that the activation of MAPK1

might contribute to acute brain injury and sustained epileptiform

activity (Chu et al., 2004; Merlo et al., 2004; Zhao et al.,

2004). Hence, we speculate that the downregulation of MAPK1

in the S tissue might represent a negative feedback response to

the oxidative stress sustained by these neuronal cells. Con-

versely, if the interictal spiking represents an attempt to

suppress future ictal events (de Curtis and Avanzini, 2001),

then the downregulation of MAPK1 may be related to this

suppression.

Two additional molecules may be functionally significant in S

tissue. NDRG2 down regulation possibly reflects neuronal damage

in response to abnormal and intense electrical cortical activity

(Mitchelmore et al., 2004). Increased phospholipase A2 levels

(PLA2) have been directly linked to epileptogenesis (Bazan et al.,

2002; Yegin et al., 2002), particularly at early time points

following neuronal injury (Kolko et al., 2003; Long et al., 2003).

These studies would suggest that the direction of the transcript

changes we observed in these two signaling molecules are

detrimental to the tissue function and therefore do not represent

meaningful compensatory events.

Transcriptome differences between the S and NS tissue:

oligodendrocyte transcripts

Glial gene transcripts were robustly increased in the S/NS

comparison (TF, CNP, SCD, PLP1, EDG2, ASPA, ABCA2, FAH2,

MOG and CAPN3). We successfully validated the expression

changes seen on the microarray by qPCR for 6 genes belonging to

this group (TF, CNP, SCD, PLP1, MOG and CAPN3). Many of

these genes have altered transcript levels in neurological disorders.

For example, down-regulation of carbonic anhydrase II (CA2) and

2V,3V-cyclic nucleotide 3Vphosphodiesterase (CNP) have been

associated with neurodegenerative diseases (Gravel et al., 1996;

Vlkolinsky et al., 2001; Casini et al., 2003) while mutations in PLP1

have been associated with Pelizaeus–Merzbacher-like syndrome

(Fahim and Riordan, 1986). Furthermore, at early stages of

epileptogenesis in a rat model (Hendriksen et al., 2001; Rall et al.,

2003), expression of myelin oligodendrocyte glycoprotein (MOG)

may be responsible for both glial proliferation and neuronal survival.

FA2H, encoding a fatty acid 2-hydroxylase is likely involved in the

formation of essential myelin elements like sphingolipids and

galactosylceramides (Alderson et al., 2004). ASPA is also involved

in myelin synthesis and has been shown to suppress tonic

convulsions in spontaneously epileptic rats (Seki et al., 2004).

Another upregulated gene, ABCA2 is critical to the onset of

myelination in the central nervous system and in lipid transport

necessary for the myelination process (Zhou et al., 2001; Schmitz

and Kaminski, 2002; Tanaka et al., 2003). EDG2 is also involved in

myelination through its activation by lysophosphatidic acid LPA

(Handford et al., 2001; Yoshida and Ueda, 2001). In addition,

Stearoyl-CoA-desaturase (SCD), expressed in both neurons and

oligodendrocytes and also increased in our experiment, is a marker

of neuronal regeneration in a rat axotomy model (Schmitt et al.,

2003; Breuer et al., 2004). Through its product, oleic acid, it has a

documented neurotrophic role (Velasco et al., 2003; Rodriguez-

Rodriguez et al., 2004). Finally, calpain 3 (CAPN3) has been

associated with regulation of cell survival and neurogenesis

(Sorimachi et al., 1997; Konig et al., 2003). It is interesting to note



that calpains may also be involved in the activation of the MAPK

signaling pathway and could therefore play a role in cell survival

through this anti-apoptosis pathway (Veeranna et al., 2004).

All of these genes have been previously linked to increased

myelination or are essential for myelin metabolism. The transcrip-

tion of these genes appears to be commonly orchestrated, and may

represent increased myelination. Based on the common role of all

these genes we speculate that this increased myelination could be a

compensatory event aimed at suppression of epileptiform activity.

This compensation may either result from an upregulation of the

number of transcripts for these genes in each oligodendrocyte, or

from an increase in the proportion of oligodendrocytes compared

to other cell types. Interestingly, CA2 inhibitors, which target

another oligodendrocyte specific gene, have a well-documented

anticonvulsant action (Masereel et al., 2002; Ilies et al., 2004). We

observed an up-regulation of CA2 transcripts in our array data. The

array data alone do not discount either that the CA2 increase is

spiking-associated but proconvulsive, or that the CA2 increase is

spiking-associated but anticonvulsive. Given the reported anticon-

vulsant effect of CA2 inhibitors, the former may be more likely

than the latter.

Transferrin expression increase in the S samples: a protective role?

Transferrin (TF) was the most robustly upregulated gene in the

comparison between S and NS samples. Transferrin is an iron

binding protein that is synthesized and stored by oligodendrocytes

in the brain (Bloch et al., 1985; Connor and Menzies, 1995, 1996;

Connor et al., 1990). Based on existing literature findings, we find

support for four, potentially converging mechanisms of transferrin

action that may be relevant for our findings.

First, a causal link between the level of transferrin and

myelination has been suggested (Connor et al., 1993). More

recently, it has also been shown that overexpression of transferrin

in a transgenic mouse model was responsible for a 30% increase in

myelin components (Saleh et al., 2003). This finding strongly

supports a direct role for transferrin in the myelination process and

may suggest that myelination could be critical for maintaining the

proper balance between excitation and inhibition.

Second, a direct correlation between iron accumulation and

epileptogenesis has been previously suggested (Campbell et al.,

1984; Ikeda, 2001). The locally synthesized transferrin in S tissue

could play a role in increased iron metabolism taking place in cells

that are the siege of hyperexcitability. Consequently, an increase in

TF expression would represent an attempt to re-establish the

intracellular iron equilibrium thus possibly suppressing ictal events.

A third possible pathway leading to increased transferrin

expression is linked to the metabolism of manganese. Manganese

is dynamically coupled to the electrophysiological activity of

neurons, and transferrin has been associated with manganese

transport to the brain (Takeda, 2003). Interestingly, in patients

suffering with epilepsy low blood manganese concentration has

been found (Papavasilious et al., 1979). It is also worth noting that

calcium-independent phospholipase A2 antagonists are able to

inhibit endocytic recycling of transferrin and the transferrin receptor

(de Figueiredo et al., 2001). Both PLA2 and TF are overexpressed in

S samples versus NS samples in our study. We speculate that the

product of these genes act in concert to maintain or restore adequate

iron or manganese concentration.

Finally, transferrin has been also reported to behave as a

neurotrophic factor in cell culture (Aizenman and de Vellis, 1987;

Mescher et al., 1997). The up-regulation of both SCD and TF

would then be a direct response to neuronal damage taking place

after the interictal phase.

In summary, by comparing S samples displaying epileptiform

electrical activity to NS samples, we were able to uncover

differential expression of genes that may represent a core feature

of the disease at the molecular level. These included altered levels of

signaling transcripts, a general downregulation of several GABA

system-related genes and an upregulation of multiple oligodendro-

cyte genes. Some of these genes are current targets of anticonvulsant

drugs, while others, pending further verification, may represent

appealing novel drug discovery leads.

Acknowledgments

Support for the studies is provided by Project 2 of NIMH

Center Grant MH45156 (KM), R01 MH067234-01A1 (KM) and

K02 MH070786 (KM).

Appendix A. Supplementary data

Supplementary data associated with this article can be found in

the online version at doi:10.1016/j.nbd.2005.12.012.

References

Aizenman, Y., de Vellis, J., 1987. Brain neurons develop in a serum and

glial free environment: effects of transferrin, insulin, insulin-like growth

factor-I and thyroid hormone on neuronal survival, growth and

differentiation. Brain Res. 406, 32–42.

Ajmone-Marsan, C., O’Connor, M., 1973. Electrocorticography. In:

Ajmone-Marsan, C., O’Connor, M. (Eds.), Handb. Electroencephalogr.

Clin. Neurophysiol., vol. 10. Elsevier, Amsterdam, pp. 3–49.

Alarcon, G., Garcia Seoane, J.J., Binnie, C.D., Martin Miguel,

M.C., Juler, J., Polkey, C.E., Elwes, R.D., Ortiz Blasco, J.M.,

1997. Origin and propagation of interictal discharges in the

acute electrocorticogram. Implications for pathophysiology and

surgical treatment of temporal lobe epilepsy. Brain 120 (Pt. 12),

2259–2282.

Alderson, N.L., Rembiesa, B.M., Walla, M.D., Bielawska, A., Bielaw-

ski, J., Hama, H., 2004. The human FA2H gene encodes a fatty

acid 2-hydroxylase. J. Biol. Chem. 279 (47), 48562–48568.

Arellano, J.I., Munoz, A., Ballesteros-Yanez, I., Sola, R.G., DeFelipe, J.,

2004. Histopathology and reorganization of chandelier cells in the

human epileptic sclerotic hippocampus. Brain 127, 45–64.

Avoli, M., 2001. Do interictal discharges promote or control seizures?

Experimental evidence from an in vitro model of epileptiform

discharge. Epilepsia 42 (Suppl. 3), 2–4.

Bazan, N.G., Tu, B., Rodriguez de Turco, E.B., 2002. What synaptic lipid

signaling tells us about seizure-induced damage and epileptogenesis.

Prog. Brain Res. 135, 175–185.

Beaumont, A., Whittle, I.R., 2000. The pathogenesis of tumour associated

epilepsy. Acta Neurochir. (Wien) 142, 1–15.

Becker, A.J., Chen, J., Paus, S., Normann, S., Beck, H., Elger, C.E.,

Wiestler, O.D., Blumcke, I., 2002. Transcriptional profiling in

human epilepsy: expression array and single cell real-time qRT-

PCR analysis reveal distinct cellular gene regulation. NeuroReport

13, 1327–1333.

Becker, A.J., Chen, J., Zien, A., Sochivko, D., Normann, S., Schramm,

J., Elger, C.E., Wiestler, O.D., Blumcke, I., 2003. Correlated stage-

and subfield-associated hippocampal gene expression patterns in



experimental and human temporal lobe epilepsy. Eur. J. Neurosci. 18,

2792–2802.

Blalock, E.M., Geddes, J.W., Chen, K.C., Porter, N.M., Markesb-

ery, W.R., Landfield, P.W., 2004. Incipient Alzheimer’s disease:

microarray correlation analyses reveal major transcriptional and

tumor suppressor responses. Proc. Natl. Acad. Sci. U. S. A. 101,

2173–2178.

Bloch, B., Popovici, T., Levin, M.J., Tuil, D., Kahn, A., 1985. Transferrin

gene expression visualized in oligodendrocytes of the rat brain by using

in situ hybridization and immunohistochemistry. Proc. Natl. Acad. Sci.

U. S. A. 82, 6706–6710.

Breuer, S., Pech, K., Buss, A., Spitzer, C., Ozols, J., Hol, E.M., Heussen,

N., Noth, J., Schwaiger, F.W., Schmitt, A.B., 2004. Regulation of

stearoyl-CoA desaturase-1 after central and peripheral nerve lesions.

BMC Neurosci. 5, 15.

Campbell, K.A., Bank, B., Milgram, N.W., 1984. Epileptogenic effects of

electrolytic lesions in the hippocampus: role of iron deposition. Exp.

Neurol. 86, 506–514.

Casini, A., Caccia, S., Scozzafava, A., Supuran, C.T., 2003. Carbonic

anhydrase activators. The selective serotonin reuptake inhibitors

fluoxetine, sertraline and citalopram are strong activators of isozymes

I and II. Bioorg. Med. Chem. Lett. 13, 2765–2768.

Chatrian, G.E., Quesney, L.F., 1999. Intraoperative electrocorticography. In:

Engel, J.J., Pedley, T.A. (Eds.), Epilepsy, The Comprehensive Text

Book. Lippincot, Williams and Wilkins, Philadelphia, pp. 1749–1765.

Chen, J., Sochivko, D., Beck, H., Marechal, D., Wiestler, O.D., Becker,

A.J., 2001. Activity-induced expression of common reference genes in

individual cns neurons. Lab. Invest. 81, 913–916.

Chu, C.T., Levinthal, D.J., Kulich, S.M., Chalovich, E.M., DeFranco,

D.B., 2004. Oxidative neuronal injury. The dark side of ERK1/2.

Eur. J. Biochem. 271, 2060–2066.

Colangelo, V., Schurr, J., Ball, M.J., Pelaez, R.P., Bazan, N.G., Lukiw,

W.J., 2002. Gene expression profiling of 12633 genes in Alzheimer

hippocampal CA1: transcription and neurotrophic factor down-

regulation and up-regulation of apoptotic and pro-inflammatory

signaling. J. Neurosci. Res. 70, 462–473.

Colantuoni, C., Jeon, O.H., Hyder, K., Chenchik, A., Khimani, A.H.,

Narayanan, V., Hoffman, E.P., Kaufmann, W.E., Naidu, S., Pevsner, J.,

2001. Gene expression profiling in postmortem Rett Syndrome brain:

differential gene expression and patient classification. Neurobiol. Dis. 8,

847–865.

Connor, J.R., Menzies, S.L., 1995. Cellular management of iron in the

brain. J. Neurol. Sci. 134, 33–44 (Suppl.).

Connor, J.R., Menzies, S.L., 1996. Relationship of iron to oligodendrocytes

and myelination. Glia 17, 83–93.

Connor, J.R., Menzies, S.L., St Martin, S.M., Mufson, E.J., 1990. Cellular

distribution of transferrin, ferritin, and iron in normal and aged human

brains. J. Neurosci. Res. 27, 595–611.

Connor, J.R., Roskams, A.J., Menzies, S.L., Williams, M.E., 1993.

Transferrin in the central nervous system of the shiverer mouse myelin

mutant. J. Neurosci. Res. 36, 501–507.

Cowan, L.D., 2002. The epidemiology of the epilepsies in children. Ment.

Retard. Dev. Disabil. Res. Rev. 8, 171–181.

de Curtis, M., Avanzini, G., 2001. Interictal spikes in focal epileptogenesis.

Prog. Neurobiol. 63, 541–567.

de Figueiredo, P., Doody, A., Polizotto, R.S., Drecktrah, D., Wood, S.,

Banta, M., Strang, M.S., Brown, W.J., 2001. Inhibition of transferrin

recycling and endosome tubulation by phospholipase A2 antagonists.

J. Biol. Chem. 276, 47361–47370.

DeFelipe, J., 1999. Chandelier cells and epilepsy. Brain 122 (Pt. 10),

1807–1822.

Degen, R., Ebner, A., Lahl, R., Leonhardt, S., Pannek, H.W., Tuxhorn, I.,

2002. Various findings in surgically treated epilepsy patients with

dysembryoplastic neuroepithelial tumors in comparison with those of

patients with other low-grade brain tumors and other neuronal migration

disorders. Epilepsia 43, 1379–1384.

DeLorey, T.M., Olsen, R.W., 1999. GABA and epileptogenesis: comparing

gabrb3 gene-deficient mice with Angelman syndrome in man. Epilepsy

Res. 36, 123–132.

DeLorey, T.M., Handforth, A., Anagnostaras, S.G., Homanics, G.E.,

Minassian, B.A., Asatourian, A., Fanselow, M.S., Delgado-Escueta,

A., Ellison, G.D., Olsen, R.W., 1998. Mice lacking the beta3 subunit of

the GABAA receptor have the epilepsy phenotype and many of the

behavioral characteristics of Angelman syndrome. J. Neurosci. 18,

8505–8514.

Devinsky, D., 2004. Diagnosis and treatment of temporal lobe epilepsy.

Rev. Neurol. Dis. 1, 2–9.

Elliott, R.C., Lowenstein, D.H., 2004. Gene expression profiling of seizure

disorders. Neurochem. Res. 29, 1083–1092.

Elliott, R.C., Miles, M.F., Lowenstein, D.H., 2003. Overlapping microarray

profiles of dentate gyrus gene expression during development- and

epilepsy-associated neurogenesis and axon outgrowth. J. Neurosci. 23,

2218–2227.

Engel, J.J., 1987. Outcome with respect to epileptic seizures. In: Engel

Jr., J. (Ed.), In Surgical treatment of Epilepsies. Raven Press, New York,

pp. 553–571.

Engel Jr., J., 1998. Classifications of the international league against

epilepsy: time for reappraisal. Epilepsia 39, 1014–1017.

Engel Jr., J., 2003. A greater role for surgical treatment of epilepsy: why

and when? Epilepsy Curr. 3, 37–40.

Engel Jr., J., Ackermann, R.F., 1980. Interictal EEG spikes correlate with

decreased, rather than increased, epileptogenicity in amygdaloid kindled

rats. Brain Res. 190, 543–548.

Evans, S.J., Choudary, P.V., Neal, C.R., Li, J.Z., Vawter, M.P.,

Tomita, H., Lopez, J.F., Thompson, R.C., Meng, F., Stead, J.D.,

Walsh, D.M., Myers, R.M., Bunney, W.E., Watson, S.J., Jones,

E.G., Akil, H., 2004. Dysregulation of the fibroblast growth factor

system in major depression. Proc. Natl. Acad. Sci. U. S. A. 101,

15506–15511.

Fahim, S., Riordan, J.R., 1986. Lipophilin (PLP) gene in X-linked myelin

disorders. J. Neurosci. Res. 16, 303–310.

Ginsberg, S.D., Crino, P.B., Hemby, S.E., Weingarten, J.A., Lee, V.M.,

Eberwine, J.H., Trojanowski, J.Q., 1999. Predominance of neuronal

mRNAs in individual Alzheimer’s disease senile plaques. Ann. Neurol.

45, 174–181.

Glanzer, J.G., Haydon, P.G., Eberwine, J.H., 2004. Expression profile

analysis of neurodegenerative disease: advances in specificity and

resolution. Neurochem. Res. 29, 1161–1168.

Gravel, M., Peterson, J., Yong, V.W., Kottis, V., Trapp, B., Braun, P.E.,

1996. Overexpression of 2V,3V-cyclic nucleotide 3V-phosphodiesterase intransgenic mice alters oligodendrocyte development and produces

aberrant myelination. Mol. Cell. Neurosci. 7, 453–466.

Hakak, Y., Walker, J.R., Li, C., Wong, W.H., Davis, K.L., Buxbaum, J.D.,

Haroutunian, V., Fienberg, A.A., 2001. Genome-wide expression

analysis reveals dysregulation of myelination-related genes in chronic

schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 98, 4746–4751.

Handford, E.J., Smith, D., Hewson, L., McAllister, G., Beer, M.S., 2001.

Edg2 receptor distribution in adult rat brain. NeuroReport 12, 757–760.

Hemby, S.E., Ginsberg, S.D., Brunk, B., Arnold, S.E., Trojanowski, J.Q.,

Eberwine, J.H., 2002. Gene expression profile for schizophrenia:

discrete neuron transcription patterns in the entorhinal cortex. Arch.

Gen. Psychiatry 59, 631–640.

Hendriksen, H., Datson, N.A., Ghijsen, W.E., van Vliet, E.A., da Silva,

F.H., Gorter, J.A., Vreugdenhil, E., 2001. Altered hippocampal gene

expression prior to the onset of spontaneous seizures in the rat post-

status epilepticus model. Eur. J. Neurosci. 14, 1475–1484.

Ikeda, M., 2001. Iron overload without the C282Y mutation in patients with

epilepsy. J. Neurol., Neurosurg. Psychiatry 70, 551–553.

Ilies, M.A., Masereel, B., Rolin, S., Scozzafava, A., Campeanu, G.,

Cimpeanu, V., Supuran, C.T., 2004. Carbonic anhydrase inhibitors:

aromatic and heterocyclic sulfonamides incorporating adamantyl moi-

eties with strong anticonvulsant activity. Bioorg. Med. Chem. 12,

2717–2726.

Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., Speed,



T.P., 2003. Summaries of affymetrix GeneChip probe level data.

Nucleic Acids Res. 31, e15.

Kolko, M., Rodriguez de Turco, E.B., Diemer, N.H., Bazan, N.G.,

2003. Neuronal damage by secretory phospholipase A2: modula-

tion by cytosolic phospholipase A2, platelet-activating factor, and

cyclooxygenase-2 in neuronal cells in culture. Neurosci. Lett. 338,

164–168.

Konig, N., Raynaud, F., Feane, H., Durand, M., Mestre-Frances, N., Rossel,

M., Ouali, A., Benyamin, Y., 2003. Calpain 3 is expressed in astrocytes

of rat and Microcebus brain. J. Chem. Neuroanat. 25, 129–136.

Kuzniecky, R.I., Barkovich, A.J., 1996. Pathogenesis and pathology of

focal malformations of cortical development and epilepsy. J. Clin.

Neurophysiol. 13, 468–480.

Lepre, J., Rice, J.J., Tu, Y., Stolovitzky, G., 2004. Genes@Work: an

efficient algorithm for pattern discovery and multivariate feature

selection in gene expression data. Bioinformatics 20, 1033–1044.

Librizzi, L., de Curtis, M., 2003. Epileptiform ictal discharges are prevented

by periodic interictal spiking in the olfactory cortex. Ann. Neurol. 53,

382–389.

Lieb, J.P., Engel Jr., J., Gevins, A., Crandal, P.H., 1981. Surface and deep

EEG correlates of surgical outcome in temporal lobe epilepsy. Epilepsia

22, 515–538.

Long, Y., Zou, L., Liu, H., Lu, H., Yuan, X., Robertson, C.S., Yang, K., 2003.

Altered expression of randomly selected genes in mouse hippocampus

after traumatic brain injury. J. Neurosci. Res. 71, 710–720.

Lukasiuk, K., Pitkanen, A., 2004. Large-scale analysis of gene expression

in epilepsy research: is synthesis already possible? Neurochem. Res. 29,

1169–1178.

Lukasiuk, K., Kontula, L., Pitkanen, A., 2003. cDNA profiling of

epileptogenesis in the rat brain. Eur. J. Neurosci. 17, 271–279.

Majores, M., Eils, J., Wiestler, O.D., Becker, A.J., 2004. Molecular

profiling of temporal lobe epilepsy: comparison of data from human

tissue samples and animal models. Epilepsy Res. 60, 173–178.

Masereel, B., Rolin, S., Abbate, F., Scozzafava, A., Supuran, C.T., 2002.

Carbonic anhydrase inhibitors: anticonvulsant sulfonamides incorporat-

ing valproyl and other lipophilic moieties. J. Med. Chem. 45, 312–320.

Mathern, G.W., Adelson, P.D., Cahan, L.D., Leite, J.P., 2002. Hippocampal

neuron damage in human epilepsy: Meyer’s hypothesis revisited. Prog.

Brain Res. 135, 237–251.

Matzilevich, D.A., Rall, J.M., Moore, A.N., Grill, R.J., Dash, P.K., 2002.

High-density microarray analysis of hippocampal gene expression

following experimental brain injury. J. Neurosci. Res. 67, 646–663.

McDonald, J.W., Garofalo, E.A., Hood, T., Sackellares, J.C., Gilman,

S., McKeever, P.E., Troncoso, J.C., Johnston, M.V., 1991. Altered

excitatory and inhibitory amino acid receptor binding in hippo-

campus of patients with temporal lobe epilepsy. Ann. Neurol. 29,

529–541.

Merlo, D., Cifelli, P., Cicconi, S., Tancredi, V., Avoli, M., 2004. 4-Amino-

pyridine-induced epileptogenesis depends on activation of mitogen-activated

protein kinase ERK. J. Neurochem. 89, 654–659.

Mescher, A.L., Connell, E., Hsu, C., Patel, C., Overton, B., 1997. Transferrin

is necessary and sufficient for the neural effect on growth in amphibian

limb regeneration blastemas. Dev. Growth Differ. 39, 677–684.

Mimmack, M.L., Brooking, J., Bahn, S., 2004. Quantitative polymerase

chain reaction: validation of microarray results from postmortem brain

studies. Biol. Psychiatry 55, 337–345.

Mirnics, K., Pevsner, J., 2004. Progress in the use of microarray technology

to study the neurobiology of disease. Nat. Neurosci. 7, 434–439.

Mitchelmore, C., Buchmann-Moller, S., Rask, L., West, M.J., Troncoso,

J.C., Jensen, N.A., 2004. NDRG2: a novel Alzheimer’s disease

associated protein. Neurobiol. Dis. 16, 48–58.

Nair, D.R., Mohamed, A., Burgess, R., Luders, H., 2004. A critical review

of the different conceptual hypotheses framing human focal epilepsy.

Epileptic Disord. 6, 77–83.

Otoom, S., Tian, L.M., Alkadhi, K.A., 1998. Veratridine-treated brain

slices: a cellular model for epileptiform activity. Brain Res. 789,

150–156.

Papavasilious, P.S., Kutt, H., Miller, S.T., Rosal, V., Wang, Y.Y., Aronson,

R.B., 1979. Seizure disorders and trace metals: manganese tissue levels

in treated epileptics. Neurology 29, 1466–1473.

Pierce, A., Small, S.A., 2004. Combining brain imaging with microarray:

isolating molecules underlying the physiologic disorders of the brain.

Neurochem. Res. 29, 1145–1152.

Pitkanen, A., Sutula, T.P., 2002. Is epilepsy a progressive disorder?

Prospects for new therapeutic approaches in temporal-lobe epilepsy.

Lancet Neurol. 1, 173–181.

Rall, J.M., Matzilevich, D.A., Dash, P.K., 2003. Comparative analysis of

mRNA levels in the frontal cortex and the hippocampus in the basal

state and in response to experimental brain injury. Neuropathol. Appl.

Neurobiol. 29, 118–131.

Rodriguez-Rodriguez, R.A., Tabernero, A., Velasco, A., Lavado, E.M., Medina,

J.M., 2004. The neurotrophic effect of oleic acid includes dendritic

differentiation and the expression of the neuronal basic helix–loop–helix

transcription factor NeuroD2. J. Neurochem. 88, 1041–1051.

Saleh, M.C., Espinosa de los Monteros, A., de Arriba Zerpa, G.A.,

Fontaine, I., Piaud, O., Djordjijevic, D., Baroukh, N., Garcia Otin,

A.L., Ortiz, E., Lewis, S., Fiette, L., Santambrogio, P., Belzung, C.,

Connor, J.R., de Vellis, J., Pasquini, J.M., Zakin, M.M., Baron, B.,

Guillou, F., 2003. Myelination and motor coordination are increased in

transferrin transgenic mice. J. Neurosci. Res. 72, 587–594.

Schmitz, G., Kaminski, W.E., 2002. ABCA2: a candidate regulator

of neural transmembrane lipid transport. Cell. Mol. Life Sci. 59,

1285–1295.

Schmitt, A.B., Breuer, S., Liman, J., Buss, A., Schlangen, C., Pech, K., Hol,

E.M., Brook, G.A., Noth, J., Schwaiger, F.W., 2003. Identification of

regeneration-associated genes after central and peripheral nerve injury

in the adult rat. BMC Neurosci. 4, 8.

Seki, T., Matsubayashi, H., Amano, T., Kitada, K., Serikawa, T., Sasa, M.,

Sakai, N., 2004. Adenoviral gene transfer of aspartoacylase ameliorates

tonic convulsions of spontaneously epileptic rats. Neurochem. Int. 45,

171–178.

Skaper, S.D., Facci, L., Strijbos, P.J., 2001. Neuronal protein kinase

signaling cascades and excitotoxic cell death. Ann. N. Y. Acad. Sci.

939, 11–22.

Sorimachi, H., Ishiura, S., Suzuki, K., 1997. Structure and physiological

function of calpains. Biochem. J. 328 (Pt. 3), 721–732.

Sperk, G., Furtinger, S., Schwarzer, C., Pirker, S., 2004. GABA and its

receptors in epilepsy. Adv. Exp. Med. Biol. 548, 92–103.

Takeda, A., 2003. Manganese action in brain function. Brain Res. Brain

Res. Rev. 41, 79–87.

Tanaka, Y., Yamada, K., Zhou, C.J., Ban, N., Shioda, S., Inagaki, N., 2003.

Temporal and spatial profiles of ABCA2-expressing oligodendrocytes

in the developing rat brain. J. Comp. Neurol. 455, 353–367.

Veeranna,, Kaji, T., Boland, B., Odrljin, T., Mohan, P., Basavarajappa, B.S.,

Peterhoff, C., Cataldo, A., Rudnicki, A., Amin, N., Li, B.S., Pant, H.C.,

Hungund, B.L., Arancio, O., Nixon, R.A., 2004. Calpain mediates

calcium-induced activation of the erk1,2 MAPK pathway and cytoskel-

etal phosphorylation in neurons: relevance to Alzheimer’s disease. Am.

J. Pathol. 165, 795–805.

Velasco, A., Tabernero, A., Medina, J.M., 2003. Role of oleic acid as a

neurotrophic factor is supported in vivo by the expression of GAP-43

subsequent to the activation of SREBP-1 and the up-regulation of

stearoyl-CoA desaturase during postnatal development of the brain.

Brain Res. 977, 103–111.

Vlkolinsky, R., Cairns, N., Fountoulakis, M., Lubec, G., 2001.

Decreased brain levels of 2V,3V-cyclic nucleotide-3V-phosphodiesterasein Down syndrome and Alzheimer’s disease. Neurobiol. Aging 22,

547–553.

Wada, J.A., Sata, M., 1974. Generalized convulsive seizures induced by

daily electrical stimulation of the amygdala in cats. Correlative electro-

graphic and behavioral features. Neurology 24, 565–574.

Yegin, A., Akbas, S.H., Ozben, T., Korgun, D.K., 2002. Secretory

phospholipase A2 and phospholipids in neural membranes in an

experimental epilepsy model. Acta Neurol. Scand. 106, 258–262.



Yoshida, A., Ueda, H., 2001. Neurobiology of the Edg2 lysophosphatidic

acid receptor. Jpn. J. Pharmacol. 87, 104–109.

Yun, J., Gaivin, R.J., McCune, D.F., Boongird, A., Papay, R.S., Ying, Z.,

Gonzalez-Cabrera, P.J., Najm, I., Perez, D.M., 2003. Gene expression

profile of neurodegeneration induced by alpha1B-adrenergic receptor

overactivity: NMDA/GABAA dysregulation and apoptosis. Brain 126,

2667–2681.

Zhao, W., Bianchi, R., Wang, M., Wong, R.K., 2004. Extracellular signal-

regulated kinase 1/2 is required for the induction of group I metabotropic

glutamate receptor-mediated epileptiform discharges. J. Neurosci. 24,

76–84.

Zhou, C., Zhao, L., Inagaki, N., Guan, J., Nakajo, S., Hirabayashi, T.,

Kikuyama, S., Shioda, S., 2001. Atp-binding cassette transporter

ABC2/ABCA2 in the rat brain: a novel mammalian lysosome-associated

membrane protein and a specific marker for oligodendrocytes but not for

myelin sheaths. J. Neurosci. 21, 849–857.



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