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  • Research article
  • Open Access

Identification of novel gene and pathway targets for human epilepsy treatment

  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1 and
  • 2Email author
Biological Research201649:3

https://doi.org/10.1186/s40659-015-0060-5

©  Jin et al. 2016

  • Received: 13 October 2015
  • Accepted: 2 December 2015
  • Published:

Abstract

Background

The aim of this study was to explore epilepsy-related mechanism so as to figure out the possible targets for epilepsy treatment.

Methods

The gene expression profile dataset GES32534 was downloaded from Gene Expression Omnibus database. We identified the differentially expressed genes (DEGs) by Affy package. Then the DEGs were used to perform gene ontology (GO) and pathway enrichment analyses. Furthermore, a protein–protein interaction (PPI) network was constructed with the DEGs followed by co-expression modules construction and analysis.

Results

Total 420 DEGs were screened out, including 214 up-regulated and 206 down-regulated genes. Functional enrichment analysis revealed that down-regulated genes were mainly involved in the process of immunity regulation and biological repairing process while up-regulated genes were closely related to transporter activity. PPI network analysis showed the top ten genes with high degrees were all down-regulated, among which FN1 had the highest degree. The up-regulated and down-regulated DEGs in the PPI network generated two obvious sub-co-expression modules, respectively. In up-co-expression module, SCN3B (sodium channel, voltage gated, type III beta subunit) was enriched in GO:0006814 ~ sodium ion transport. In down-co-expression module, C1QB (complement C1s), C1S (complement component 1, S subcomponent) and CFI (complement factor I) were enriched in GO:0006955 ~ immune response.

Conclusion

The immune response and complement system play a major role in the pathogenesis of epilepsy. Additionally, C1QB, C1S, CFI, SCN3B and FN1 may be potential therapeutic targets for epilepsy.

Keywords

  • Epilepsy
  • Differentially expressed genes
  • Functional annotation
  • Protein–protein interaction network
  • Co-expression module

Background

Epilepsy characterized by epileptic seizures, is a kind of neurological disorder [1]. Epileptic seizure is defined as “a state produced by an abnormal excessive neuronal discharge within the central nervous system” in terms of the mechanism. Seizure may originate from any part of the brain and spread to other areas [2]. It has been reported that there are approximately one million people diagnosed with seizure or epilepsy in an emergency department annually in the United States [3]. Patients with epilepsy or seizure often present decreased level of consciousness and disorder to response to environment. Therefore, better understanding of the mechanisms underlying alteration of consciousness in epilepsy is important for the treatment of patients with epilepsy [4].

Currently, the pathogenesis of epilepsy has been widely investigated. Epilepsy may develop from the up-regulated excitatory circuits or down-regulated excitatory due to brain injury [5]. A previous study indicated that a loss of glutamine synthetase was the direct factor for high level of extracellular glutamate that played a key role in the occurrence of epileptic seizures [6]. Turrin et al. [7] suggested a different mechanism related with pro-inflammatory expression in regulating the innate immune reaction in response to seizures, which gave a new perspective for epilepsy related neuropathology. Furthermore, it is reported that specific inflammatory pathways are chronically activated during epileptogenesis and persist in chronic epileptic tissue, suggesting they may contribute to the etiopathogenesis of temporal lobe epilepsy [8]. However, current studies for genetic mechanism underlying epilepsy has not been clearly defined. Besides, there are not enough epilepsy related candidate genes which can be used as treatment targets.

In the present study, we investigated the global gene expression profile GSE32534, which was supplied by Niesen et al. [9]. Additionally, He et al. [10] used this expression profile to perform gene set enrichment analysis and identified several epilepsy related transcription factors and pathways. However, they did not analyze the protein–protein interaction (PPI) network and co-expression modules associated with epilepsy. PPI and co-expression modules analyses can provide new insights into protein function, besides, they may help to uncover the generic organization principles of functional cellular networks [11]. Therefore, based on the dataset of GSE32534, we firstly identified the differentially expressed genes (DEGs) between healthy and epilepsy specimens. Then, gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed on the selected DEGs to explore the biological roles of these DEGs. Finally, we constructed and analyzed PPI network and co-expression modules. We aimed to further explore the molecular mechanism underlying epilepsy and explore novel gene targets for epilepsy therapy.

Methods

Data pre-processing and differentially expressed gene analysis

The epilepsy related dataset (accession number: GSE32534) that was contributed by Niesen et al. [9] was downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) based on Affymetrix Rat Genome U34 Array (Affymetrix Inc., Santa Clara, CA, USA). Data from ten peritumoral cortex tissue specimens were available for further analysis, including five samples from epilepsy and five from non-epilepsy patients with low grade brain tumor.

The probe-level data were converted into expression matrix by RMA (function in Affy package) in R language. Probe numbers were converted into gene names using Bioconductor annotation function in R language combined with annotation information in microarray platform GPL570 (HG-U133_Plus_2). The expression values of multiple probes for a given gene were reduced to a single value by taking the average expression value. After the expression data were normalized [12], the DEGs (epilepsy vs. non-epilepsy) were identified by Limma package [13] in R language. Genes with |log2FC (fold change)| > 1 and p < 0.05 were defined as DEGs.

Functional enrichment analysis

Database for annotation, visualization, and integrated discovery (DAVID, http://david.abcc.ncifcrf.gov/) [14, 15], an online tool, consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene or protein lists [16]. In this study, the GO functional and KEGG pathway enrichment analyses for the screened DEGs was performed by DAVID. The multiple testing correction was performed using Benjamini–Hochberg (HB) method [17], and fold discovery rate (FDR) <0.05 was chosen as the cut-off criterion.

Construction of protein–protein interaction network

Search tool for the retrieval of interacting genes/proteins (STRING, http://string-db.org/) [18] was used to construct protein–protein interaction network among DEGs. Genes with correlation coefficient higher than 0.7 were mapped to PPI network, which was visualized by Cytoscape (http://www.cytoscape.org/) [19]. Igraph package in R language was used to calculate the connectivity degree of each node.

Construction of co-expression module

Expression similarity coefficient was calculated to select co-expression genes among DEGs. The co-expression module was constructed by the DEGs with expression similarity coefficient >0.99 and p < 0.001. GO and KEGG pathway enrichment analyses were performed for modular genes using the DAVID online tool.

Results

DEGs identification

After preprocessing, we obtained the expression values of 19,944 genes. The normalization showed a good result on a wide variety of array (Fig. 1). Total 420 DEGs were screened out, including 214 up-regulated and 206 down-regulated genes. Hierarchical clustering indicated that the DEGs could clearly distinguish epilepsy samples from the normal controls (Fig. 2).
Fig. 1
Fig. 1

Box plot of expression data before and after normalization. The horizontal axis stands for different samples, while the vertical axis represents expression value. The black line in the box represents the expression median for each sample. a Data before normalization. b Data after normalization. After preprocessing, the black lines of box plot are almost on the same straight line, indicating a high level of normalization

Fig. 2
Fig. 2

Cluster analysis of the differentially expressed genes. Each row represents a single gene and each column a single sample. The data are colored red for high abundance/up-regulation and green for low abundance/down-regulation

GO functional and KEGG pathway enrichment analyses

The results of GO and KEGG enrichment analyses were displayed in Table 1. The significantly enriched GO terms of up-regulated DEGs were closely associated with cell–cell signaling, synaptic transmission and transmission of nerve impulse. The down-regulated DEGs were significantly enriched in immunity regulation and repairing process of injured tissues. The significant pathway that enriched by up- and down-regulated DEGs was hsa04720:Long-term potentiation and hsa04510:Focal adhesion, respectively.
Table 1

GO and KEGG pathway enrichment analysis for identified DEGs

Category

Term

Count

p value

Up-regulated genes

GOTERM_BP_FAT

GO:0050877 ~ neurological system process

26

2.15E−04

GOTERM_BP_FAT

GO:0019226 ~ transmission of nerve impulse

22

2.01E−11

GOTERM_BP_FAT

GO:0006811 ~ ion transport

21

4.75E−05

GOTERM_BP_FAT

GO:0007267 ~ cell–cell signaling

20

4.97E−06

GOTERM_BP_FAT

GO:0007268 ~ synaptic transmission

19

5.63E−10

GOTERM_CC_FAT

GO:0031224 ~ intrinsic to membrane

70

0.007687949

GOTERM_CC_FAT

GO:0016021 ~ integral to membrane

65

0.030340215

GOTERM_CC_FAT

GO:0005886 ~ plasma membrane

63

4.81E−06

GOTERM_CC_FAT

GO:0044459 ~ plasma membrane part

48

9.49E−08

GOTERM_CC_FAT

GO:0005887 ~ integral to plasma membrane

23

0.00337979

GOTERM_MF_FAT

GO:0005216 ~ ion channel activity

15

1.16E−05

GOTERM_MF_FAT

GO:0022838 ~ substrate specific channel activity

15

1.63E−05

GOTERM_MF_FAT

GO:0015267 ~ channel activity

15

2.40E−05

GOTERM_MF_FAT

GO:0022803 ~ passive transmembrane transporter activity

15

2.46E−05

GOTERM_MF_FAT

GO:0022836 ~ gated channel activity

13

2.69E−05

KEGG_PATHWAY

hsa04720:Long-term potentiation

7

1.89E−05

KEGG_PATHWAY

hsa05014:Amyotrophic lateral sclerosis (ALS)

5

9.61E−04

KEGG_PATHWAY

hsa04020:Calcium signaling pathway

7

0.003316868

KEGG_PATHWAY

hsa04080:Neuroactive ligand-receptor interaction

8

0.005123351

KEGG_PATHWAY

hsa04310:Wnt signaling pathway

5

0.037620947

Down-regulated genes

GOTERM_BP_FAT

GO:0006955 ~ immune response

40

1.23E−16

GOTERM_BP_FAT

GO:0006952 ~ defense response

30

1.65E−10

GOTERM_BP_FAT

GO:0009611 ~ response to wounding

26

3.53E−09

GOTERM_BP_FAT

GO:0007155 ~ cell adhesion

26

7.74E−07

GOTERM_BP_FAT

GO:0022610 ~ biological adhesion

26

7.94E−07

GOTERM_MF_FAT

GO:0005198 ~ structural molecule activity

20

4.49E−05

GOTERM_MF_FAT

GO:0005509 ~ calcium ion binding

18

0.018905046

GOTERM_MF_FAT

GO:0005201 ~ extracellular matrix structural constituent

13

1.09E−10

GOTERM_MF_FAT

GO:0042802 ~ identical protein binding

13

0.041784269

GOTERM_MF_FAT

GO:0004857 ~ enzyme inhibitor activity

12

1.53E−04

GOTERM_CC_FAT

GO:0005576 ~ extracellular region

70

4.18E−15

GOTERM_CC_FAT

GO:0005886 ~ plasma membrane

69

0.002166744

GOTERM_CC_FAT

GO:0044421 ~ extracellular region part

50

4.52E−17

GOTERM_CC_FAT

GO:0005615 ~ extracellular space

30

2.56E−08

GOTERM_CC_FAT

GO:0005578 ~ proteinaceous extracellular matrix

28

1.34E−14

KEGG_PATHWAY

hsa04510:Focal adhesion

16

4.33E−07

KEGG_PATHWAY

hsa05322:Systemic lupus erythematosus

14

3.27E−09

KEGG_PATHWAY

hsa04610:Complement and coagulation cascades

13

4.92E−10

KEGG_PATHWAY

hsa04512:ECM-receptor interaction

13

5.21E−09

KEGG_PATHWAY

hsa04060:Cytokine–cytokine receptor interaction

10

0.02253563

Count represents the gene number

PPI network analysis

In the PPI network, rare interactions were observed between the up- and the down-regulated genes, while most interactions were observed within the up-regulated genes or the down-regulated genes (Fig. 3). Besides, nodes degree was analyzed for genes in PPI network to obtain the information of hub nodes. Ten DEGs were considered as hub genes, such as FN1 (fibronectin 1), COL1A2 (collagen alpha-2), and C1QB (complement C1s) (Table 2).
Fig. 3
Fig. 3

PPI network. Green nodes represent down-regulated genes while red nodes represent up-regulated genes. The size of the nodes is positively correlated with the degrees of genes. The thickness of line is determined by the correlation coefficient provided by STRING

Table 2

Top genes with high node degrees in PPI network

Node

Degree

log2FC

FN1

39

−1.024861275

COL1A2

30

−2.064034307

DCN

28

−1.628988128

CTSS

28

−1.089500003

COL3A1

28

−2.33411558

COL1A1

28

−2.131930656

CD163

26

−1.470072732

C1QB

25

−1.864805026

LUM

23

−2.114081932

PTPRC

21

−1.083176333

Co-expression modules

The co-expression modules were constructed with up- and down-regulated genes, respectively (Fig. 4). As shown in Table 3, obviously enriched GO terms of genes in up-co-expression module were GO:0006814 ~ sodium ion transport, GO:0030695 ~ GTPase regulator activity and GO:0060589 ~ nucleoside-triphosphatase regulator activity. The down-regulated genes in the co-expression modules were mainly associated with immunity regulation and cell damage repairing processes. For instance, SCN3B (sodium channel, voltage gated, type III beta subunit) was enriched in GO:0006814 ~ sodium ion transport. C1QB, C1S (complement component 1, S subcomponent), and CFI (complement factor I) were enriched in GO:0006955 ~ immune response and GO:0050778 ~ positive regulation of immune response.
Fig. 4
Fig. 4

Co-expression module. Green nodes represent down-regulated genes while red nodes represent up-regulated genes

Table 3

GO functional and KEGG pathway enrichment analysis on genes in co-expression modules

ID

Term

Description

Count

p value

Up-co-expression

GOTERM_BP_FAT

GO:0016310 ~ phosphorylation

5

0.023081277

GOTERM_BP_FAT

GO:0006793 ~ phosphorus metabolic process

5

0.043264378

GOTERM_BP_FAT

GO:0006796 ~ phosphate metabolic process

5

0.043264378

GOTERM_BP_FAT

GO:0006915 ~ apoptosis

4

0.049975204

GOTERM_BP_FAT

GO:0006814 ~ sodium ion transport

3

0.014081453

GOTERM_CC_FAT

GO:0005886 ~ plasma membrane

11

0.02927495

GOTERM_CC_FAT

GO:0044459 ~ plasma membrane part

8

0.033090626

GOTERM_MF_FAT

GO:0030695 ~ GTPase regulator activity

4

0.020009788

GOTERM_MF_FAT

GO:0060589 ~ nucleoside-triphosphatase regulator activity

4

0.021203692

GOTERM_MF_FAT

GO:0008289 ~ lipid binding

4

0.02652355

GOTERM_MF_FAT

GO:0031402 ~ sodium ion binding

3

0.012457524

GOTERM_MF_FAT

GO:0005543 ~ phospholipid binding

3

0.027145214

Down-co-expression

GOTERM_BP_FAT

GO:0006955 ~ immune response

9

1.01E−07

GOTERM_BP_FAT

GO:0050778 ~ positive regulation of immune response

6

2.45E−07

GOTERM_BP_FAT

GO:0048584 ~ positive regulation of response to stimulus

6

2.73E−06

GOTERM_BP_FAT

GO:0002684 ~ positive regulation of immune system process

6

2.84E−06

GOTERM_BP_FAT

GO:0006952 ~ defense response

6

2.72E−04

GOTERM_MF_FAT

GO:0048154 ~ S100 beta binding

2

0.003999687

GOTERM_MF_FAT

GO:0048306 ~ calcium-dependent protein binding

2

0.031586688

GOTERM_MF_FAT

GO:0042803 ~ protein homodimerization activity

3

0.042687029

GOTERM_MF_FAT

GO:0004175 ~ endopeptidase activity

3

0.052602322

GOTERM_MF_FAT

GO:0005509 ~ calcium ion binding

4

0.05926993

GOTERM_CC_FAT

GO:0005576 ~ extracellular region

7

0.020898219

KEGG_PATHWAY

hsa04610:Complement and coagulation cascades

3

0.003647135

KEGG_PATHWAY

hsa04612:Antigen processing and presentation

3

0.005241774

KEGG_PATHWAY

hsa05322:Systemic lupus erythematosus

3

0.007393725

Count represents the gene number

Discussion

Epilepsy is defined as a chronic brain disorder and its genetic mechanism has not been elucidated clearly, which hampered the development of novel preventive and treatment management. In this study, we explored the molecular mechanism of human epilepsy by using bioinformatics methods and attempted to give a new insight to the design of good therapy. Total 214 up-regulated genes and 206 down-regulated DEGs between epilepsy and control group were screened out. According to PPI network analysis, top ten DEGs with high node degrees were all down-regulated. Among them, FN1 showed the highest degree, which was closely associated with cell transport activity.

The data of GO analysis showed that the down-regulated DEGs in epilepsy patients were significantly enriched in immune response and defense response related biological processes. Recent evidence has suggested that the innate and adaptive immune reaction is involved in the epilepsy, which was implicated in the inflammatory processes within the brain [20]. It has been proved that the immune system and the nervous system maintain extensive communication [21]. The inflammation of brain contributed to the local neuronal excitability that resulted in seizures [22]. Importantly, several inflammatory mediators, including temporal lobe epilepsy and cortical dysplasia-related epilepsy, have been detected in surgically resected brain tissue from epilepsy patients [23]. Our results further supported that immune response and inflammatory processes might constitute a crucial mechanism in the pathophysiology of epilepsy.

Specially, in our study, several DEGs were found significantly enriched in immune regulation related terms in the co-expression module, which was consistent with the results of GO functional analysis. These DEGs included C1QB, C1S, CFI, PTPRC, HLA-DRA and B2M. Previous study has found that the complement system represents an essential effector of both humoral immunity and cellular immunity [24]. Among these DEGs in the co-expression module, C1QB and C1S encoded the subunits of human complement component C1 and play roles in the complement activation pathway. C1 deficiency and impaired activation of the complement classical pathway generally leads to severe immune complex disease [25]. C1QB has been found to be a epilepsy related gene and the expression of C1QB mRNA declines in brain peritumoral tissues of patients with tumor-induced epilepsy [26]. Besides, expression of various complement components, such as C1q, C3c, and C3d, has been observed in reactive astrocytes within the sclerotic hippocampus of people with temporal lobe epilepsy [27]. Interestingly, protein encoded by CFI is essential for regulating the complement cascade. CFI can inhibit the activation of complements by the inactivation of C3b and C4b and the normal combination of C3 and C5. The persistence of complement activation could contribute to a sustained inflammatory response and could destabilize neuronal networks [24]. Thus, the down-regulation of CFI may promote the activation of complements to accelerate the progression of epilepsy. Importantly, CFI has significant interconnectivity with C1S in this paper. Besides, CFI, C1S and C1QB jointly enriched in the complement activation pathway according to the results of GO and pathway enrichment analysis. Taken together, we proposed that complement system might play key roles in the development of epilepsy by dysregulating the expression of C1QB, C1S and CFI.

In addition to down-co-expression module, in the up-co-expression module, SCN3B was found enriched in GO terms related to sodium ion transport. The rapid and selective transport of sodium ion through sodium channels is essential for initiating action potentials within excitable cells [28]. Study has found that changes in the function of many ion channels, including sodium channels, contribute to the epileptogenesis [29]. Baek et al. [30] reported that voltage-gated sodium channels could initiate action potentials in brain neurons, and mutation of the channels was associated with several forms of inherited epilepsy. Importantly, SCN3B, an enriched DEGs of this term, is demonstrated to encode voltage-gated sodium channels. In adult brain, there are four accessory β subunits, including SCN1B, SCN2B, SCN3B and SCN4B. The β subunits are thought to affect trafficking and gating of the voltage-gated sodium channels [31]. Therefore, we speculated that SCN3B might play an important role in the progression of epilepsy through sodium channels.

Furthermore, in our study, FN1 was found to have the highest degree in PPI network. FN1 is a multifunctional, extracellular matrix glycoprotein which contains several distinct domains that may bind to cell surfaces. Fibronectin has been reported to play a role in cellular morphology, phagocytosis, and wound repair [32]. Moreover, fibronectin was reported to be involved in astroglial proliferation. Previous study revealed that astrocytes play a major role in the regulation of the immune/inflammatory response in several human central nervous system diseases [33, 34]. In epilepsy-associated pathologies, astrocytes in tissues of patients with epilepsy undergo significant changes in their physiological properties [35]. As a result, we can predict that FN1 may be a candidate molecular marker associated with the occurrence and progression of epilepsy patients by affecting the astrocytes.

In conclusion, our study provided a comprehensive bioinformatics analysis of DEGs and functions which may be involved in the progression of epilepsy. Immune response, complement system and sodium ion transport may play major roles in the pathogenesis of epilepsy. The dysregulation of C1QB, C1S, CFI, SCN3B and FN1 may be used potential gene targets for epilepsy treatment. Although there are some novel findings, a lack of experimental evidences is a limitation in our study. Further experimental studies should be conducted to validate our findings in the future.

Abbreviations

HB: 

Benjamini–Hochberg

COL1A2 : 

collagen alpha-2

DAVID: 

database for annotation, visualization, and integrated discovery

DEGs: 

differentially expressed genes

FDR: 

fold discovery rate

FN1 : 

fibronectin 1

KEGG: 

Kyoto encyclopedia of genes and genomes

GEO: 

gene expression omnibus

GO: 

gene ontology

STRING: 

search tool for the retrieval of interacting genes/proteins

Declarations

Authors’ contributions

YJ carried out the molecular studies, CZ participated in the sequence alignment, LC, XL and SP drafted the manuscript. DJ carried out the study. JM participated in the sequence alignment. JL and BW participated in the design of the study and performed the statistical analysis. YJ conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

None.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Neurology, Jilin Oilfield General Hospital, Songyuan, 131200, China
(2)
The Second Division of Neurosurgery, Departments of Neurosurgery, The China-Japan Union Hospital of Jilin University, 126 Xiantai Street, Changchun, 130033, Jilin Province, China

References

  1. Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, Engel J, Forsgren L, French JA, Glynn M. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014;55:475–82.PubMedView ArticleGoogle Scholar
  2. Lunardi MDS, Souza FPDSD, Xikota JC, Walz R, Lin K. Epilepsy perception amongst education professionals. J Epilepsy Clin Neurophysiol. 2012;18:79–84.View ArticleGoogle Scholar
  3. Patel AD. Variables associated with emergency department and/or unplanned hospital utilization for children with epilepsy. Epilepsy Behav. 2014;31:172–5.PubMedView ArticleGoogle Scholar
  4. Chen WC, Chen EY, Gebre RZ, Johnson MR, Li N, Vitkovskiy P, Blumenfeld H. Epilepsy and driving: potential impact of transient impaired consciousness. Epilepsy Behav. 2014;30:50–7.PubMedPubMed CentralView ArticleGoogle Scholar
  5. McPhee SJ, Lingappa VR, Ganong WF, Lange JD. Pathophysiology of disease: an introduction to clinical medicine. Lange Medical Books/McGraw–Hill; 2000.Google Scholar
  6. Eid T, Thomas M, Spencer D, Runden-Pran E, Lai J, Malthankar G, Kim J, Danbolt N, Ottersen O, De Lanerolle N. Loss of glutamine synthetase in the human epileptogenic hippocampus: possible mechanism for raised extracellular glutamate in mesial temporal lobe epilepsy. Lancet. 2004;363:28–37.PubMedView ArticleGoogle Scholar
  7. Turrin NP, Rivest S. Innate immune reaction in response to seizures: implications for the neuropathology associated with epilepsy. Neurobiol Dis. 2004;16:321–34.PubMedView ArticleGoogle Scholar
  8. Ravizza T, Gagliardi B, Noé F, Boer K, Aronica E, Vezzani A. Innate and adaptive immunity during epileptogenesis and spontaneous seizures: evidence from experimental models and human temporal lobe epilepsy. Neurobiol Dis. 2008;29:142–60.PubMedView ArticleGoogle Scholar
  9. Lenburg ME, Liou LS, Gerry NP, Frampton GM, Cohen HT, Christman MF. Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data. BMC Cancer. 2003;3:31.PubMedPubMed CentralView ArticleGoogle Scholar
  10. He K, Xiao W, Lv W. Comprehensive identification of essential pathways and transcription factors related to epilepsy by gene set enrichment analysis on microarray datasets. Int J Mol Med. 2014;34:715–24.PubMedPubMed CentralGoogle Scholar
  11. Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S. A human protein–protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957–68.PubMedView ArticleGoogle Scholar
  12. Fujita A, Sato JR, Rodrigues Lde O, Ferreira CE, Sogayar MC. Evaluating different methods of microarray data normalization. BMC Bioinform. 2006;7:469.View ArticleGoogle Scholar
  13. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Da Wei Huang BTS, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2008;4:44–57.View ArticleGoogle Scholar
  15. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4:P3.PubMedView ArticleGoogle Scholar
  16. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.View ArticleGoogle Scholar
  17. Yekutieli D, Benjamini Y. Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Inference. 1999;82:171–96.View ArticleGoogle Scholar
  18. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C. STRING v9. 1: protein–protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41:D808–15.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedPubMed CentralView ArticleGoogle Scholar
  20. Vezzani A, French J, Bartfai T, Baram TZ. The role of inflammation in epilepsy. Nat Rev Neurol. 2011;7:31–40.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Steinman L. Elaborate interactions between the immune and nervous systems. Nat Immunol. 2004;5:575–81.PubMedView ArticleGoogle Scholar
  22. Rodgers KM, Hutchinson MR, Northcutt A, Maier SF, Watkins LR, Barth DS. The cortical innate immune response increases local neuronal excitability leading to seizures. Brain. 2009;132:2478–86.PubMedPubMed CentralView ArticleGoogle Scholar
  23. Choi J, Nordli DR, Alden TD, DiPatri A, Laux L, Kelley K, Rosenow J, Schuele SU, Rajaram V, Koh S. Cellular injury and neuroinflammation in children with chronic intractable epilepsy. J Neuroinflammation. 2009;6:38.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Vezzani A. Innate immunity and inflammation in temporal lobe epilepsy: new emphasis on the role of complement activation. Epilepsy Curr. 2008;8:75–7.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Petry F, Hauptmann G, Goetz J, Grosshans E, Loos M. Molecular basis of a new type of C1q-deficiency associated with a non-functional low molecular weight (LMW) C1q: parallels and differences to other known genetic C1q-defects. Immunopharmacology. 1997;38:189–201.PubMedView ArticleGoogle Scholar
  26. Niesen CE, Xu J, Fan X, Li X, Wheeler CJ, Mamelak AN, Wang C. Transcriptomic profiling of human peritumoral neocortex tissues revealed genes possibly involved in tumor-induced epilepsy. PLoS One. 2013;8:e56077.PubMedPubMed CentralView ArticleGoogle Scholar
  27. Aronica E, Ravizza T, Zurolo E, Vezzani A. Astrocyte immune responses in epilepsy. Glia. 2012;60:1258–68.PubMedView ArticleGoogle Scholar
  28. Corry B, Thomas M. Mechanism of ion permeation and selectivity in a voltage gated sodium channel. J Am Chem Soc. 2012;134:1840–6.PubMedView ArticleGoogle Scholar
  29. Jung S, Bullis JB, Lau IH, Jones TD, Warner LN, Poolos NP. Downregulation of dendritic HCN channel gating in epilepsy is mediated by altered phosphorylation signaling. J Neurosci. 2010;30:6678–88.PubMedPubMed CentralView ArticleGoogle Scholar
  30. Baek J-H, Rubinstein M, Scheuer T, Trimmer JS. Reciprocal changes in phosphorylation and methylation of mammalian brain sodium channels in response to seizures. J Biol Chem. 2014;289:15363–73.PubMedPubMed CentralView ArticleGoogle Scholar
  31. Frank HY, Catterall WA. Overview of the voltage-gated sodium channel family. Genome Biol. 2003;4(10):1186.Google Scholar
  32. Engvall E, Ruoslahti E. Binding of soluble form of fibroblast surface protein, fibronectin, to collagen. Int J Cancer. 1977;20:1–5.PubMedView ArticleGoogle Scholar
  33. de Rivero Vaccari JP, Dietrich WD, Keane RW. Activation and regulation of cellular inflammasomes: gaps in our knowledge for central nervous system injury. J Cereb Blood Flow Metab. 2014;34:369–75.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Müller N, Ackenheil M. Psychoneuroimmunology and the cytokine action in the CNS: implications for psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatry. 1998;22:1–33.PubMedView ArticleGoogle Scholar
  35. Coulter DA, Eid T. Astrocytic regulation of glutamate homeostasis in epilepsy. Glia. 2012;60:1215–26.PubMedPubMed CentralView ArticleGoogle Scholar

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