Oncotarget

Research Papers:

New differentially expressed genes and differential DNA methylation underlying refractory epilepsy

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Oncotarget. 2016; 7:87402-87416. https://doi.org/10.18632/oncotarget.13642

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Xi Liu, Shu Ou, Tao Xu, Shiyong Liu, Jinxian Yuan, Hao Huang, Lu Qin, Hui Yang, Lifen Chen, Xinjie Tan _ and Yangmei Chen

Abstract

Xi Liu1,*, Shu Ou1,*, Tao Xu1, Shiyong Liu2, Jinxian Yuan1, Hao Huang3, Lu Qin1, Hui Yang2, Lifen Chen1, Xinjie Tan1, Yangmei Chen1

1Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China

2Epilepsy Center of PLA, Department of Neurosurgery, Xinqiao Hospital, The Third Military Medical University, Shapingba District, Chongqing, 400037, China

3Department of Neurology, Affiliated Hospital of Zunyi Medical College, Zunyi, 563003, China

*These authors have contributed equally to this work

Correspondence to:

Xinjie Tan, email: [email protected]

Yangmei Chen, email: [email protected]

Keywords: refractory epilepsy, human, epigenetics, DNA methylation, gene expression

Received: September 28, 2016     Accepted: November 08, 2016     Published: November 26, 2016

ABSTRACT

Epigenetics underlying refractory epilepsy is poorly understood, especially in patients without distinctive genetic alterations. DNA methylation may affect gene expression in epilepsy without affecting DNA sequences. Herein, we analyzed genome-wide DNA methylation and gene expression in brain tissues of 10 patients with refractory epilepsy using methylated DNA immunoprecipitation linked with sequencing and mRNA Sequencing. Diverse distribution of differentially methylated genes was found in X chromosome, while differentially methylated genes appeared rarely in Y chromosome. 62 differentially expressed genes, such as MMP19, AZGP1, DES, and LGR6 were correlated with refractory epilepsy for the first time. Although general trends of differentially enriched gene ontology terms and Kyoto Encyclopedia of Genes and Genome pathways in this study are consistent with previous researches, differences also exist in many specific gene ontology terms and Kyoto Encyclopedia of Genes and Genome pathways. These findings provide a new genome-wide profiling of DNA methylation and gene expression in brain tissues of patients with refractory epilepsy, which may provide a basis for further study on the etiology and mechanisms of refractory epilepsy.


INTRODUCTION

65 millions of people were affected by epilepsy in the world according to International League Against Epilepsy (ILAE) [1], and approximately 36% of epilepsy patients were drug-resistant [2]. Varieties of genes encoding channels, receptors, transporters, synaptic transmission, etc. have been associated with different types of epilepsy, among which, some were associated with refractory epilepsy [3, 4, 5, 6]. Many environmental factors, such as economic situation, diet, trauma, stroke, etc. were also associated with epilepsy or seizure [7, 8, 9, 10].

Epigenetic modifications, including DNA methylation, histone modification and aberrant microRNA expression, can affect genomic reprogramming, tissue-specific gene expression and global gene silencing without affecting DNA sequence itself [11, 12]. The most common form of DNA methylation occurs at the 5’carbon of cytosine in CpG dinucleotides which often locates in CpG islands within the promoters [13]. More recently, DNA methylation is raised as one of the main epigenetic mechanisms in epilepsy [14]. Previous genome-wide DNA methylation profiling in epileptic animal models presented altered DNA methylation in promoters of genes, and identified many genes that were associated with epilepsy [15].

DNA methylation in promoter may decrease gene expression, for example, increased methylation of reelin promoter resulted in the decrease of reelin expression in epilepsy model [16]. Katja Kobow and colleagues found a genome-wide distinctive DNA methylation pattern in rat models of pilocarpine-induced epilepsy in addition to an inverse relationship between gene expression and DNA methylation in promoter, exon and intron [17]. Meanwhile, ketogenic diet could attenuate seizure progression via ameliorating DNA methylation [17]. Selective changes in genome-wide DNA methylation and increased DNA-methyltransferase were also discovered in patients with temporal lobe epilepsy (TLE) [18, 19]. However, the profiling of genome-wide DNA methylation and gene expression in patients with refractory epilepsy remains unclear.

In this study, methylated DNA immunoprecipitation linked with sequencing (MeDIP-Seq) and mRNA sequencing (mRNA-Seq) were used to analyze the pattern of genome-wide DNA methylation and gene expression, as well as the relationship between DNA methylation and gene expression. Our findings identified a new distribution pattern of DNA methylation and gene expression in refractory epilepsy patients. Most of the differentially methylated genes (DMG) were methylated in gene element of coding sequences (CDS) and introns. More importantly, some new refractory epilepsy-related genes that have not been documented previously were found in this study.

RESULTS

Demographic and clinical characteristics of subjects

The mean age (mean±SD) of the 10 epileptic samples (5 male/5 female) was 17.10±5.84, and the age of epilepsy onset was 6.49±6.16. The mean age of the 10 controls (7 male/3 female) was 39.00±17.40. The detailed data of demographic and clinical characteristics of epileptic samples were presented in Table 1 & Supplementary Table S1.

Table 1: Clinical characteristics of patients and controls

Characteristics

Patients

Controls

Age (year)

17.10±5.84

39.00±17.40

Age of onset (year)

6.49±6.16

N/A

Sex (male/female)

5/5

7/3

Frequency of seizures per month

 

N/A

 <10

5

 

 10~100

1

 

 >100

4

 

Pattern of seizures

 

N/A

 GS

4

 

 GTCS+CPS

4

 

 PS

2

 

AEDs before operation

 

N/A

 VPA

9

 

 CMZ

6

 

 TPM

3

 

 PB

2

 

 OMZ

2

 

 LEV

5

 

 LTG

2

 

 PHT

1

 

 CZP

1

 

GS: generalized seizure; GTCS: generalized tonic clonic seizure; CPS: complex partial seizure; PS: partial seizure; VPA: valproic acid; CMZ: carbamazipine; TPM: topiramate; PB: phenobarbital; OMZ: oxcarbazepine; LEV: levetiracetam; LTG: lamotrigine; PHT: phenytoin; CZP: clonazepam; N/A: non applicable.

No significant difference in distribution of DNA methylation reads

In each sample, 81632654 methylation reads (49 bp) were sequenced. In epileptic samples, an average 71.20% of the reads were uniquely mapped to the reference genome, and in controls, 70.77% of the reads were uniquely mapped. There was no significant difference of uniquely mapped reads between the two groups by T test (Supplementary Table S2).

Moreover, no significant difference was identified between epileptic samples and controls in 1) genome coverage distribution across sequencing depth, 2) distribution of CpG, CHG, and CHH sites across sequencing depth, 3) reads distribution in genome regions with different CpG density, 4) distribution of reads in different gene elements and repetitive elements, 5) distribution of reads around CpG island and gene body.

No significant difference in distribution of DNA methylation peaks

In each epileptic sample, an average of 115128.10±21674.80 peaks were identified, covering an average of 152796189.80±26659961.36 bp and 4.87±0.85% of human genome. In controls, the mean identified peaks were 111020.20±25956.50, covering 148106055.60±25489074.79 bp and 4.72±0.81% of human genome (Supplementary Table S2). No significant difference between the two groups was found. In addition, no significant difference in the number of peaks with different length, the distribution of peaks with different CpG density and distribution of peaks in gene elements (including peak number and peak coverage) was observed between epileptic samples and control.

Analysis of differentially methylated regions (DMR) and DMGs

The median of DMRs identified by pairwise comparison was 7604.50, covering a median of 8580791.00 bp. The distribution of DMGs in paired samples was mapped to human genome using Circos [20] (Figure 1). DMGs appeared on all of chromosomes extensively except for Y chromosome in refractory epilepsy patients.

Distribution of differentially methylated genes (DMGs) across genome.

Figure 1: Distribution of differentially methylated genes (DMGs) across genome. Hyper-methylated (purple) and hypo-methylated (green) regions in epileptic samples vs. controls were targeted to each chromosome. Diverse distribution of DMGs was found in X chromosome, while DMGs appeared rarely in Y chromosome.

The significant enriched gene ontology (GO) terms of DMGs were presented in Table 2. Most of the DMGs were differentially methylated in gene element of CDS and introns. Significant enrichment of DMGs was observed in GO terms of binding of various molecules, such as ATP binding, ion binding, cation binding, and nucleoside binding. In addition, DMG enrichment was also identified in the GO terms involved in receptor activity, transporter activity, kinase activity, transducer activity and channel activity. Those DMGs participate mainly in the biological processes of adhesion and ion transport.

Table 2: GO enrichment analysis of differentially methylated genes

Element

Category

Terms

CDS

C

apical part of cell; apical plasma membrane; axonemal dynein complex; basement membrane; cell surface; cytoplasm; cytoskeleton; cytoskeleton; dynein complex; extracellular matrix; extracellular matrix part;
extracellular region; extracellular region part; extracellular space; insoluble fraction; integral to plasma membrane; intracellular; intracellular organelle; intracellular organelle part;
intracellular part; intrinsic to plasma membrane; membrane; membrane attack complex; membrane fraction; membrane part; membrane-bounded organelle; myosin complex; myosin filament;
nucleolus; organelle; organelle part; plasma membrane; plasma membrane part; platelet alpha granule lumen; proteinaceous extracellular matrix; striated muscle thick filament

 

F

GTPase binding; adenyl nucleotide binding; adenyl ribonucleotide binding; ATP binding; calcium ion binding;
calmodulin binding; collagen binding; extracellular matrix structural constituent; GTPase regulator activity; guanyl-nucleotide exchange factor activity; microtubule motor activity;
motor activity; NAD(P)H oxidase activity; nucleoside binding; phosphotransferase activity, alcohol group as acceptor; purine nucleoside binding; purine nucleotide binding;
Ras guanyl-nucleotide exchange factor activity; Rho guanyl-nucleotide exchange factor activity; small GTPase binding

 

P

cell adhesion; biological adhesion

Intron

C

synapse; actin cytoskeleton; basement membrane; basolateral plasma membrane; cell; cell junction; cell part;
cell projection; cytoskeleton; extracellular matrix; extracellular region part; integral to membrane;
integral to plasma membrane; intrinsic to membrane; intrinsic to plasma membrane;
membrane; membrane part; neuron projection; plasma membrane; plasma membrane part;
postsynaptic membrane; presynaptic membrane; proteinaceous extracellular matrix; synapse part

 

F

actin binding; adenyl nucleotide binding; adenyl ribonucleotide binding; ATP binding; binding; cadherin binding;
calcium channel activity; calcium ion binding; cation binding; cation channel activity; cell adhesion molecule binding; channel activity; cytoskeletal protein binding; diacylglycerol binding;
extracellular-glutamate-gated ion channel activity; gated channel activity; glutamate receptor activity; GTPase regulator activity; guanyl-nucleotide exchange factor activity;
ion binding; ion channel activity; ion transmembrane transporter activity;
ionotropic glutamate receptor activity; kinase activity; ligand-gated channel activity;
ligand-gated ion channel activity; metal ion binding; metal ion transmembrane transporter activity;
molecular transducer activity; nucleoside binding; nucleoside-triphosphatase regulator activity;
passive transmembrane transporter activity; phosphoric diester hydrolase activity;
phosphotransferase activity, alcohol group as acceptor; protein kinase activity;
protein tyrosine kinase activity; protein tyrosine phosphatase activity; purine nucleoside binding;
Rho GTPase activator activity; signal transducer activity; transferase activity, transferring phosphorus-containing groups; transmembrane receptor protein kinase activity;
transmembrane receptor protein tyrosine kinase activity

 

P

cell adhesion; biological adhesion; homophilic cell adhesion; cell-cell adhesion; ion transport; metal ion transport

5’-UTR

C

membrane attack complex

 

F

calcium-dependent protein binding

 

P

none

3’-UTR

C

glycerol-3-phosphate dehydrogenase complex

 

F & P

none

Promoter

C

synaptosome

 

F

transmembrane receptor activity; receptor activity; G-protein coupled receptor activity; signal transducer activity;
molecular transducer activity; olfactory; voltage-gated ion channel activity; voltage-gated channel activity

 

P

G-protein coupled receptor protein signaling pathway; cell surface receptor linked signal transduction;
sensory perception of chemical stimulus; inorganic; anion transport

Gene expression profiling

In each epileptic sample, an average of 78044550.80±8806016.67 reads covering 7024009572.00±792541500.40 bp were sequenced, among which an average 83.71% of reads were uniquely mapped to reference genome, and 66.95% of reads were uniquely mapped to reference genes. In controls, an average of 79185195.80±9170582.72 reads covering 7126667622.00±825352445.10 bp were sequenced, among which an average 83.80% of reads were uniquely mapped to reference genome, and 65.99% of reads were uniquely mapped to reference genes. No significant difference was found between the two groups by T test (p<0.05 was considered statistically significant). (Supplementary Table S3).

A total of 21353 genes were sequenced, among which 17665 genes were expressed by all samples. The sequencing coverage of 65.01% and 65.45% genes was over 90% in epileptic samples and controls, respectively, indicating a good sequencing quality.

Pairwise comparison identified 8850 differentially expressed genes (DEG), among which 885 genes were differentially expressed in ≥5 of the 10 pairs (Figure 2). Out of the 8850 DEGs, 246 were epilepsy-related genes according to NCBI Gene (http://www.ncbi.nlm.nih.gov/gene), and 34 epilepsy-related genes were differentially expressed in ≥5 of the 10 pairs (Table 3). However, only 3 out of the 65 genes differentially expressed in ≥8 pairs of the 10 pairs were epilepsy-related genes according to NCBI Gene (Table 4), which suggested that some new DEGs, including AZGP1, MMP19, DES, LGR6, SERPINA3, CX3CR1, DUSP5, EGR4, GPR37, etc. might be correlated with refractory epilepsy.

The gene expression signature of differentially expressed genes in &ge; 5 pairs of samples.

Figure 2: The gene expression signature of differentially expressed genes in ≥ 5 pairs of samples. (purple: expression up; green: expression down; black: no difference). The hierarchical cluster showed a distinct expression signature with viariation between the paired samples.

Table 3: Differentially expressed epilepsy-relate genes in ≥ 5 pairs of samples

Symbol

description

Epilepsy-Related Disorders

AQP1

aquaporin 1

refractory epilepsy/mesial temporal lobe sclerosis

ATF3

activating transcription factor 3

refractory mTLE

C3

complement component 3

TLE/FS/seizures following acute viral infection

CALB2

calbindin 2

TLE/FCD/LTP

CCR5

chemokine (C-C motif) receptor 5

refractory epilepsy/infantile-onset epilepsy/SUDEP/SE

EGR1

early growth response 1

IAE/focal epilepsy

EMP1

epithelial membrane protein 1

refractory epilepsy

GRIN2B

glutamate receptor, ionotropic, N-methyl D-aspartate 2B

West syndrome/FCD/TLE/anti-NMDAR encephalitis

HLA-DQA1

major histocompatibility complex, class II, DQ alpha 1

IGE/JME/absence epilepsy

HP

haptoglobin

IGE/familial epilepsy

IL1A

interleukin 1, alpha

TLE/FS

IL1B

interleukin 1, beta

epilepsy

IL1RN

interleukin 1 receptor antagonist

TLE

IL6

interleukin 6

TLE/FS/familial epilepsy/refractory epilepsy

CXCL8

chemokine (C-X-C motif) ligand 8

refractory epilepsy

IL18

interleukin 18

seizures in MS

ITGA2

integrin, alpha 2

refractory epilepsy

KCNA1

potassium channel, voltage gated shaker related subfamily A, member 1

SUDEP/TLE/partial epilepsy/Myokymia

NPY

neuropeptide Y

absence epilepsy/TLE/SE

OPRM1

opioid receptor, mu 1

tonic-clonic seizures/TLE/IGE/SE/IAE

PDYN

prodynorphin

TLE/FLTLE

RELN

reelin

TLE

PTGS2

prostaglandin-endoperoxide synthase 2

mTLE/absence seizures/poststroke seizures

SCN1B

sodium channel, voltage gated, type I beta subunit

Genetic GEFS+/Dravet Syndrome/convulsions with gastroenteritis/BPEI/LQTS/brugada syndrome

SCN5A

sodium channel, voltage gated, type V alpha subunit

Dravet syndrome/SUDEP/BFNS/LQTS/brugada syndrome

CCL2

chemokine (C-C motif) ligand 2

refractory epilepsy/SE

CCL4

chemokine (C-C motif) ligand 4

TLE

CDKL5

cyclin-dependent kinase-like 5

West syndrome/early-onset epileptic encephalopathies

CNTN2

contactin 2 (axonal)

PME/Autosomal recessive epilepsy/Autoimmune epilepsy

TNF

tumor necrosis factor

TLE/refractory epilepsy

TRPC4

transient receptor potential cation channel, subfamily C, member 4

generalized epilepsy with photosensitivity

CACNA1H

calcium channel, voltage-dependent, T type, alpha 1H subunit

IGE/CAE/generalized epilepsy syndromes

PLCB1

phospholipase C, beta 1 (phosphoinositide-specific)

early infantile epilepsy syndromes/malignant migrating partial seizures in infancy

ERMN

ermin, ERM-like protein

epileptic seizure/oligodendrocytes and epilepsy

mTLE: mesial temporal lobe epilepsy; TLE: temporal lobe epilepsy; FS: febrile seizures; FCD: focal cortical dysplasia; LTP: long-term potentiation; SUDEP: sudden unexpected death in epilepsy; SE: status epilepticus; IAE: idiopathic absence epilepsy; IGE: idiopathic generalized epilepsy; JME: juvenile myoclonic epilepsy; MS: multiple sclerosis; FLTLE: familial lateral temporal lobe epilepsy; GEFS+: generalized epilepsy with febrile seizure plus; BPEI: benign partial epilepsy in infancy; LQTS: long QT syndrome; BFNS: benign familial neonatal seizure; PME: progressive myoclonic epilepsy; CAE: childhood absence epilepsy.

Table 4: Differentially expressed genes in ≥8 pairs of the samples

Symbol

Description

Symbol

Description

SERPINA3

serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3

SOCS3

suppressor of cytokine signaling 3

AQP1a

aquaporin 1 (Colton blood group)

CH25H

cholesterol 25-hydroxylase

AZGP1

alpha-2-glycoprotein 1, zinc-binding

CD163

CD163 molecule

BMP5

bone morphogenetic protein 5

CARTPT

CART prepropeptide

CHRM5

cholinergic receptor, muscarinic 5

KCNH4

potassium channel, voltage gated eag related subfamily H, member 4

CX3CR1

chemokine (C-X3-C motif) receptor 1

CDH19

cadherin 19, type 2

DES

desmin

BLNK

B-cell linker

DUSP5

dual specificity phosphatase 5

GCNT4

glucosaminyl (N-acetyl) transferase 4, core 2

EGR4

early growth response 4

TNFRSF12A

tumor necrosis factor receptor superfamily, member 12A

FCGR3A

Fc fragment of IgG, low affinity IIIa, receptor (CD16a)

CD244

CD244 molecule, natural killer cell receptor 2B4

FOS

FBJ murine osteosarcoma viral oncogene homolog

LGR6

leucine-rich repeat containing G protein-coupled receptor 6

FOSB

FBJ murine osteosarcoma viral oncogene homolog B

STRA6

stimulated by retinoic acid 6

GPR37

G protein-coupled receptor 37 (endothelin receptor type B-like)

SH3TC2

SH3 domain and tetratricopeptide repeats 2

GRIN2Ba

glutamate receptor, ionotropic, N-methyl D-aspartate 2B

DCSTAMP

dendrocyte expressed seven transmembrane protein

HBA2

hemoglobin, alpha 2

CNDP1

carnosine dipeptidase 1 (metallopeptidase M20 family)

HBB

hemoglobin, beta

PNMA6A

paraneoplastic Ma antigen family member 6A

SERPIND1

serpin peptidase inhibitor, clade D (heparin cofactor), member 1

SLC5A11

solute carrier family 5 (sodium/inositol cotransporter), member 11

HDC

histidine decarboxylase

KIF19

kinesin family member 19

HLA-DRB5

major histocompatibility complex, class II, DR beta 5

FREM3

FRAS1 related extracellular matrix 3

HSD11B1

hydroxysteroid (11-beta) dehydrogenase 1

DLGAP1-AS3

DLGAP1 antisense RNA 3

HTR3A

5-hydroxytryptamine (serotonin) receptor 3A, ionotropic

C20orf166-AS1

C20orf166 antisense RNA 1

TNC

tenascin C

NPAS4

neuronal PAS domain protein 4

IL6a

interleukin 6

HMGA1P7

high mobility group AT-hook 1 pseudogene 7

ITK

IL2-inducible T-cell kinase

C11orf96

chromosome 11 open reading frame 96

LRP2

low density lipoprotein receptor-related protein 2

TMEM233

transmembrane protein 233

MBP

myelin basic protein

TMEM215

transmembrane protein 215

MC4R

melanocortin 4 receptor

CTXN3

cortexin 3

MMP19

matrix metallopeptidase 19

LOC643711

platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 (30kDa) pseudogene

NPPA

natriuretic peptide A

LOC100130331

POTE ankyrin domain family, member F pseudogene

VCAM1

vascular cell adhesion molecule 1

CD24

CD24 molecule

ZFP36

ZFP36 ring finger protein

BRE-AS1

BRE antisense RNA 1

NR4A3

nuclear receptor subfamily 4, group A, member 3

LINC00507

long intergenic non-protein coding RNA 507

BAIAP3

BAI1-associated protein 3

 

 

a: Epilepsy-related genes according to NCBI Gene (http://www.ncbi.nlm.nih.gov/gene).

Pairwise comparison presented a total of 395 GO terms significantly enriched in epileptic samples compared to controls (Component 68, Function 70, Process 257). 77 GO terms were significantly enriched in ≥5 epileptic samples compared to controls, including 12 component terms, 23 function terms and 42 process terms (Table 5). The GO enrichment analysis of gene expression revealed that the biological functions of DEGs are mainly correlated with channel activity, transporter activity, and receptor activity. Furthermore, the biological processes mainly targeted by DEGs were immune system, biological regulation, response to stimulus, signaling, development, and behavior.

Table 5: Enriched GO terms and KEGG pathways in epileptic sample

Term

Genes

Term

Genes

GO Component

 

 

 

GO:0071944-Cell periphery

516

GO:0016020-Membrane

2319

GO:0009986-Cell surface

48

GO:0044425-Membrane part

1912

GO:0005576-Extracellular region

333

GO:0016021-Integral to membrane

509

GO:0044421-Extracellular region part

326

GO:0031224-Intrinsic to membrane

1654

GO:0031012-Extracellular matrix

114

GO:0005886-Plasma membrane

511

GO:0005578-Protenaceous extracellular matrix

44

GO:0044459-Plasma membrane part

488

GO Function

 

 

 

GO:0015267-Channel activity

172

GO:0001653-Peptide receptor activity

39

GO:0005261-Cation channel activity

113

GO:0008528-Peptide receptor activity, G-protein coupled

36

GO:0005216-Ion channel activity

167

GO:0060089-Molecular transducer activity

586

GO:0022838-Substrate-specific channel activity

167

GO:0004871-Signal transducer activity

506

GO:0005215-Transporter activity

402

GO:0005515-Protein binding

1337

GO:0022857-Transmembrane transporter activity

241

GO:0005102-Receptor binding

286

GO:0015075-Ion transmembrane transporter activity

276

GO:0001871-Pattern binding

59

GO:0022803-Passive transmembrane transporter activity

173

GO:0042277-Peptide binding

50

GO:0022892-Substrate-specific transporter activity

367

GO:0030246-Carbohydrate binding

120

GO:0022891-Substrate-specific transmembrane transporter activity

220

GO:0030247-Polysaccharide binding

57

GO:0004872-Receptor activity

347

GO:0005539-Glycosaminoglycan binding

52

GO:0004888-Transmembrane receptor activity

225

 

 

GO Process

 

 

 

GO:0001775-Cell activation

421

GO:0009605-Response to external stimulus

42

GO:0007154-Cell communication

120

GO:0009617-Response to bacterium

201

GO:0030154-Cell differentiation

1390

GO:0009991-Response to extracellular stimulus

287

GO:0002376-Immune system process

449

GO:0031667-Response to nutrient levels

79

GO:0032501-Multicellular organismal process

604

GO:0009611-Response to wounding

168

GO:0032501-Biological regulation

160

GO:0023052-Signaling

75

GO:0050789-Regulation of biological process

1153

GO:0019932-Second-messenger-mediated signaling

308

GO:0048518-Positive regulation of biological process

146

GO:0023033-Signaling pathway

96

GO:0065008-Regulation of biological quality

326

GO:0007166-Cell surface receptor linked signaling pathway

1022

GO:0050793-Regulation of developmental process

283

GO:0007186-G-protein coupled receptor protein signaling pathway

434

GO:0002682-Regulation of immune system process

898

GO:0009653-Anatomical structrue morphogenesis

67

GO:0051239-Regulation of multicellular organismal process

1211

GO:0048856-Anatomical structure development

89

GO:0050896-Response to stimulus

496

GO:0007275-Multicellular organismal development

239

GO:0042221-Response to chemical stimulus

527

GO:0032502-Developmental process

614

GO:0010033-Response to organic substance

1625

GO:0048869-Cellular developmental process

332

GO:0006950-Response to stress

669

GO:0048731-System development

1342

GO:0006952-Defense response

259

GO:0048513-Organ development

906

GO:0009719-Response to endogenous stimulus

201

GO:0009888-Tissue development

824

GO:0009725-Response to hormone stimulus

350

GO:0007610-Behavior

988

GO:0048545-Response to steroid hormone stimulus

59

GO:0006811-Ion transport

222

GO:0031960-Response to corticosteroid stimulus

615

GO:0048878-Chemical homeostasis

182

KEGG Pathway

 

 

 

ko05143-African trypanosomiasis

36

ko04640-Hematopoietic cell lineage

110

ko05330-Allograft rejection

38

ko04672-Intestinal immune network for IgA production

37

ko05146-Amoebiasis

120

ko05140-Leishmaniasis

87

ko05310-Asthma

24

ko04670-Leukocyte transendothelial migration

151

ko05320-Autoimmune thyroid disease

43

ko05144-Malaria

47

ko04662-B cell receptor signaling pathway

92

ko04650-Natural killer cell mediated cytotoxicity

109

ko04020-Calcium signaling pathway

177

ko04080-Neuroactive ligand-receptor interaction

244

ko04514-Cell adhesion molecules

137

ko04380-Osteoclast differentiation

127

ko04062-Chemokine signaling pathway

151

ko04145-Phagosome

197

ko04610-Complement and coagulation cascades

113

ko05020-Prion diseases

54

ko04060-Cytokine-cytokine receptor interaction

171

ko05323-Rheumatoid arthritis

78

ko04512-ECM-receptor interaction

155

ko05150-Staphylococcus aureus infection

85

ko04666-Fc gamma R-mediated phagocytosis

116

ko04940-Type I diabetes mellitus

45

ko05332-Graft-versus-host disease

43

 

 

In Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, 75 pathways were significantly enriched in epileptic samples compared to controls, while 27 pathways were significantly enriched in ≥5 epileptic samples compared to controls (Table 5). DEGs mainly participate in calcium signaling pathway, neuroactive ligand-receptor interaction, and pathways involved in inflammation, immune response, and autoimmune diseases.

DNA methylation and gene expression

The distribution of hyper-, hypo- and unmethylated gene expression levels in different gene elements were presented in Figure 3. The trend of gene expression of the three groups were similar, and in all the 5 elements, the percentage of hyper-methylated genes with log2(RPKM ratio) (RPKM ratio = RPKM of epileptic sample/RPKM of control) approximately ranging from 1 to 3 were higher than that of hypo-methylated and unmethylated genes. Generally, no significant relationship in modulation was found between DNA methylation and gene expression.

The distribution of hyper-, hypo- and unmethylated gene expression levels in different gene elements.

Figure 3: The distribution of hyper-, hypo- and unmethylated gene expression levels in different gene elements. No significant relationship was found between DNA methylation and gene expression in all the 5 gene elements, and the percentage of hyper-methylated genes with log2(RPKM ratio) approximately ranging from 1 to 3 were higher than that of hypo-methylated and unmethylated genes.

DISCUSSION

It is the first genome-wide report on DNA methylation and gene expression in refractory epilepsy patients. 62 differentially expressed genes such as MMP19, AZGP1, DES, and LGR6 were discovered to be correlated with refractory epilepsy, and diverse distribution of differentially methylated genes was found in X chromosome instead of in Y chromosome.

Although many genes were observed differentially methylated or expressed, the general distribution of DNA methylation reads, DNA methylation peaks and mRNA sequencing reads were similar between refractory epileptic samples and controls, which indicate no significant difference in global DNA methylation and global gene expression between the two groups. Inconsistent with previous report which identified decreased DNA methylation in Y chromosome of TLE patients [18], our study presented no difference in DNA methylation in Y chromosome between refractory epileptic samples and controls. It is noteworthy that 4 out of the 5 male epileptic samples in our study were frontal lobe epilepsy, suggesting a possible difference in DNA methylation in Y chromosome between frontal lobe epilepsy and TLE. On the contrary to a previous research which found no change of DNA methylation in X chromosome in rat models of epilepsy [17], we identified diverse DMR distribution on X chromosome in all patients. This difference may be attributed to the much more complicated environmental factors involved by human beings or the difference between species.

In pairwise comparison of gene expression analysis, we identified distinct gene expression signatures. 34 DEGs are correlated with epilepsy or seizure and 14 of these genes are associated with refractory epilepsy, such as AQP1 [21], CCR5 [22], EMP1 [23], CXCL8 [24], ITGA2 [25], and CCL2 [26]. For the first time, 62 DEGs differentially expressed in ≥8 pairs of samples were found related to epilepsy/seizure in our study. These newly-identified refractory epilepsy-related genes may possibly reveal new mechanisms of refractory epilepsy. In all the DEGs, only MMP19 and AZGP1 were differentially expressed in all the 10 pairs. Compared to the controls, 9 of the 10 epileptic samples showed increased expression of MMP19, while 1 epileptic sample showed decreased expression. MMP19 and other matrix metalloproteinases can cleave and remodel the extracellular matrix, including tenascin and laminin, and thus influence synapse formation and remodeling, N-methyl-D-aspartate receptor activity, learning and memory, and hippocampal long-term potentiation [27]. Inhibition of MMP19 and other matrix metalloproteinases may prevent development of epilepsy at the early stage of epileptogenesis [28]. Meanwhile, we found the expression of AZGP1 were decreased in 8 of the 10 epileptic samples, and increased in 2 of the 10 epileptic samples when compared to controls. AZGP1 encodes Zinc-a2-glycoprotein, which is an adipokine participates in lipid mobilization, lipolytic effect, and immune response [29, 30]. Moreover, both DES in 9 pairs and LGR6 in 8 pairs showed consistently increased expression in epileptic samples. To the best of our knowledge, it is the first time that MMP19, AZGP1, DES, and LGR6 were reported to be correlated with refractory epilepsy.

Significant enrichment of DMGs in GO terms of binding, transport, and enzymatic activity were found, which is consistent with previous studies [18]. Interestingly, most of the DMGs were differentially methylated in CDS and intron, while previous research showed differential methylation in all the gene elements in rat models of chronic epilepsy induced by pilocarpine [17]. These findings indicate DNA methylation in CDS and intron may play critical roles in refractory epilepsy besides promoter methylation which has been a very popular target in research on epilepsy [18, 31]. The GO enrichment analysis of gene expression revealed a trend similar to a previous report [17] that the DEGs are mainly correlated with biological functions such as protein binding, receptor binding, channel activity, transporter activity, and receptor activity, as well as being involved in biological processes such as immune system, biological regulation, response to stimulus, signaling, development, and behavior.

The change of DNA methylation in this study is not exactly corresponded with alteration of gene expression. Kobow and his colleagues found that DNA methylation in promoter, exon and intron were inversely correlated with gene expression in rat models of chronic epilepsy induced by pilocarpine [17], but our study found that most of the hyper- and hypo-methylated genes were not differentially expressed in epilepsy patients, and the percentage of hyper-methylated genes with log2(RPKM ratio) ranging from 1 to 3 were higher than that of hypo-methylated and unmethylated genes, which indicate a complicated modulation between DNA methylation and gene expression in refractory epilepsy in human beings.

In KEGG analysis of gene expression, DEGs significantly enriched in calcium signaling pathway, neuroactive ligand-receptor interaction, and pathways participating in inflammation, immune response, autoimmune diseases. Calcium signaling pathway has been increasingly recognized as a vital factor in epileptogenesis and the excess synchronization, and hyperexcitability of neurons for seizures can be linked to various calcium signaling pathways [32]. The aberrantly neuroactive ligand-receptor interaction can enhance the susceptibility to epileptic seizures [33, 34]. It may explain partially that the drugs regulating the function of calcium signaling pathway and neuroactive ligand-receptor interaction are able to alleviate the seizure frequency [35]. The roles of immune response and inflammation in epilepsy have been recognized in previous studies [36]. Autoimmune epilepsy frequently present drug-resistance which can be controlled by immunosuppressive and immunomodulatory therapies [36, 37]. Consistent with Lukic and his colleagues’ study, we also identified that prion disease was significantly targeted by DEGs which indicate both refractory epilepsy and prion diseases may share some common pathway [38].

MATERIALS AND METHODS

Study approval

The research protocol was approved by the Ethics Committees of the Second Affiliated Hospital of Chongqing Medical University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration. Written informed consent was obtained from all individual participants included in the study or their proxies.

Patients and tissues preparation

Resected brain tissues were retrospectively but consecutively collected from 10 patients with refractory epilepsy and 10 patients with post-traumatic intracranial hypertension who underwent surgical treatment since 2008 to 2014. Patients with refractory epilepsy were diagnosed following the definition of ILAE [39]. Briefly, all patients were resistant to maximum doses of at least three anti-epileptic drugs (AED), and evaluated by detailed history, neurological examination, neuropsychological test and neuroimaging data. For presurgical evaluation and epileptogenic zones identification, a combined assessment of ictal simiology, brain magnetic resonance imaging, video-electroencephalography, sphenoidal electrode monitoring and intracerebral electroencephalography and intraoperative electrocorticography were applied. After evaluation, standard en bloc resection was performed. No refractory epilepsy patient received adjustment of AEDs during the 2 months before surgery. Brain tissues as control from the 10 post-trauma intracranial hypertension patients were identified normal by neuropathologist. These patients had no history of epilepsy or exposure to AEDs. All the resected brain tissues were immediately frozen in liquid nitrogen and then stored at -80°C.

The 10 epileptic samples and 10 controls were paired, the difference in genome-wide DNA methylation and gene expression between the paired samples were analyzed using MeDIP-seq and mRNA-seq.

DNA methylation profiling

Genomic DNA was extracted using QIAamp DNA Micro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instruction. Extracted DNA was fragmented to a size of 100-500 bp by sonication (Bioruptor NGS, Digenode, Liege, Belgium), and subjected to DNA-end repair, 3’-dA overhang and ligation of sequencing adaptors according to manufacturer’s instruction (Paired-End DNA Sample Prep kit, Illumina, San Diego, USA) and denatured to single-stranded. Then the methylated DNA was immunoprecipitated by 5mc antibody (Magnetic Methylated DNA Immunoprecipitation kit, Diagenod, Liege, Belgium). After Real-time Quantitative polymerase chain reaction (PCR) (TaqMan Probe, Applied Biosystems, Thermo Fisher Scientific, Waltham, USA) validation and quality control of sample library (Agilent 2100 BioAnalyzer, Agilent, Santa Clara, USA), electrophoretically selected DNA fragments sizing from 200-300 bp were subjected to high-throughput sequencing (Illumina HiSeqTM 2000, Illumina, San Diego, USA). Sequencing strategy was Single-end 50 bp, and reads size was 49 bp.

Filtered MeDIP-Seq data (Adapters, reads containing more than 10% bases undetermined, and low quality reads were removed. Low quality read means the quality values (Q) of more than 50% bases in this read were ≤20, Q=-10lg(rate of sequencing error)) was mapped to reference genome using SOAP software, version 2.21 (Website: http://soap.genomics.org.cn)[40], only unique alignments with no more than 2 mismatches were included for further analysis. The reference genome data and the data used for annotation of all aligned genes were from UCSC Genome Bioinformatics Download (Reference genome: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/chr*.fa.gz. Reference genes: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromOut.tar.gz.).

To describe the distribution of MeDIP-Seq data on genome, the following items were calculated: 1) Genome coverage distribution across sequencing depth; 2) Distribution of CpG, CHG and CHH sites varies with sequencing depth; 3) Reads distribution in genome regions with different CpG density; 4) Distribution of reads in different gene elements, including CpG islands, promoters, 5’-Untranslated regions (UTR), CDS, introns, 3’-UTR, repeat regions and each class of repetitive elements (Repeat dataset is obtained from RepeatMasker (Transposons) and Tandem Repeats Finder (Tandom repeats), and is available at: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromOut.tar.gz); 5) Distribution of MeDIP-Seq reads around CpG island and gene body.

Whole genome scanning of enrichment region of methylation/Peak was based on a defined analysis model, MACS 1.4.0 (Website: http://liulab.dfci.harvard.edu/MACS/) with default parameters [41]. The following items were calculated: 1) Distribution of peaks with different length; 2) Distribution of peaks with different CpG density; 3) Number and coverage of peaks in gene elements (promoter, 5’-UTR, CDS, intron, and 3’-UTR).

DMGs based on peak of all the paired samples were analyzed. Briefly, peaks of the paired two samples were merged as candidate DMRs. For each candidate DMR, the number of reads of each sample was calculated and tested to get true DMRs. The downtrend DMRs indicate that the number of reads of the control sample was larger than the epileptic sample and the uptrend DMRs indicate the opposite. DMGs were defined as the genes overlapping DMRs. Genes with an element merely overlap uptrend DMRs were considered hyper-methylated with such an element, genes with an element merely overlap downtrend DMRs were considered hypo-methylated with such an element.

To clarify the biological functions of DMGs, GO enrichment analysis was performed. Briefly, DMGs were mapped to GO terms in reference database (http://www.geneontology.org) and gene numbers for every term were calculated and tested to identify the significantly enriched GO terms.

Gene expression profiling

RNA was extracted using Trizol method. After DNase I treatment, mRNA was isolated with magnetic beads with Oligo (dT) and fragmented. Then cDNA was synthesized using the mRNA fragments as templates. The synthesized cDNA fragments were purified and subjected to end reparation, single nucleotide adenine addition and adapter connection. cDNA fragments suitable for PCR amplification were selected with electrophoresis. Quality control of sample library was performed using Agilent 2100 Bioanaylzer (Agilent, Santa Clara, USA) and Applied Biosystems StepOnePlus Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific, Waltham, USA). The library was sequenced using Illumina HiSeqTM 2000 (Illumina, San Diego, USA).

After sequencing quality control, the mRNA-Seq data was mapped to reference genome and reference genes using SOAP software, version 2.21 (Website: http://soap.genomics.org.cn) [40]. Then the distribution of reads on reference genome and genes was calculated and coverage analysis was performed. After alignment quality control, the DEGs were selected. And for further analysis, expression pattern analysis of DEGs were also performed.

To clarify the biological functions of DEGs, GO enrichment analysis of DEGs was performed as described above. To further clarify the biological functions of DEGs, KEGG pathway analysis was performed using the same calculating formula as GO enrichment analysis with database available at http://www.kegg.jp/kegg/.

Correlation analysis of DNA methylation and gene expression

The distribution of hyper-, hypo- and unmethylated gene expression levels in different gene elements were calculated to analyze the relationship between DNA methylation and gene expression as previously described [42].

Statistics and analysis

To identify true DMRs, the numbers of reads were calculated and assessed using chi-square statistics and False discovery rate (FDR) statistics (p≤0.01, and the difference of read numbers should be more than twice). To identify significantly enriched GO terms and KEGG pathways, gene numbers for every term or pathway were calculated and then assessed using hypergeometric test, p-value of hypergeometric test was corrected using Bonferroni Correction [43]. GO terms with corrected p-value ≤0.01, and KEGG pathways with corrected p-value ≤0.05 were considered significantly enriched. In selection of DEGs, the gene expression level was calculated using RPKM method [44], and DEGs were selected as previously described [45]. The adjusted p-value was calculated using Benjamini, Yekutieli. 2001 FDR method [46] and DEGs was defined as genes with FDR≤0.001 and the RPKM difference between the paired samples should be more than twice. Hierarchical cluster was performed to analyze the expression pattern of DEGs using Cluster [47] and presented using Java Treeview [48]. The DEGs were clustered by Euclidean distance.

Abbreviations

CDS=coding sequences; DMG=differentially methylated genes; DMR=Differentially Methylated Regions; DEG=differentially expressed genes; GO=gene ontology; ILAE=International League Against Epilepsy; KEGG=Kyoto Encyclopedia of Genes and Genome; MeDIP-Seq=methylated DNA immunoprecipitation linked with sequencing; mRNA-Seq=mRNA sequencing; PCR=polymerase chain reaction; RPKM=Reads Per Kilobases per Millionreads; TLE=temporal lobe epilepsy; UTR=Untranslated regions.

ACKNOWLEDGMENTS

The authors sincerely thank BGI tech (Shenzhen, China) for technique support.

CONFLICTS OF INTEREST

The authors have declared that no competing financial interests exists.

FUNDING

This study is funded by National Science Foundation of China (81571259 & 81571167), and Chongqing Municipal Public Health Bureau (2015ZDXM011).

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