research article gene expression profile analysis in

Research ArticleGene Expression Profile Analysis in Epilepsy byUsing the Partial Least Squares Method

Dong Wang, Xixiao Song, Yan Wang, Xia Li, Shanshan Jia, and Zhijing Wang

Department of Neurology, Xi’an Children’s Hospital, Xi’an, Shaanxi 710003, China

Correspondence should be addressed to Dong Wang; [email protected]

Received 11 December 2013; Accepted 29 January 2014; Published 12 May 2014

Academic Editors: J. T. Efird, M. Godfrey, and W. Hall

Copyright © 2014 Dong Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose. Epilepsy is a common chronic neurological disorder. We aim to investigate the underlying mechanism of epilepsy withpartial least squares- (PLS-) based gene expression analysis, which is more sensitive than routine variance/regression analysis.Methods. Two microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database. PLS analysis wasused to identify differentially expressed genes. Gene ontology and network analysis were also implemented. Results. A total of752 genes were identified to be differentially expressed, including 575 depressed and 177 overexpressed genes in patients. For GOenrichment analysis, except for processes related to the nervous system, we also identified overrepresentation of dysregulated genesin angiogenesis. Network analysis revealed two hub genes, CUL3 and EP300, which may serve as potential targets in furthertherapeutic studies. Conclusion. Our results here may provide new understanding for the underlying mechanisms of epilepsypathogenesis and will offer potential targets for producing new treatments.

1. Introduction

Epilepsy is a common chronic neurological disorder, whichhas devastating effects on patients and their families. Thereare about 50 million epilepsy patients worldwide and theoccurrence in developing countries is more than twice thatin developed countries [1]. Currently there are more than20 antiepileptic drugs available for epilepsy patients [2];however, multidirectional interactions between seizures andthe medications are still challenging for treating patients [3].Exploring the biological alterations of patients may provideinsights into the pathology and new targets for treatments.

Large-scale microarray expression strategy has providedgreater ease for investigating the underlying mechanismsof epilepsy. Several gene expression profiling studies havebeen carried out earlier and most of them used the routinevariance/regression analysis [4–6]. However, this procedurecannot remove unaccounted array specific factors, such ascertain demographic profiles. Compared with the routineanalysis, previous studies [7, 8] proposed that partial leastsquares- (PLS-) based analysis is more robust in proceeding

gene expression profile datawith higher sensitivity.Therefore,using PLS analysis may provide new understanding of thepathogenesis of epilepsy.

In the current study, to identify truly differentiallyexpressed genes between epilepsy patients and normal con-trols, we carried out a PLS analysis with two combined datasets from the Gene Expression Omnibus (GEO) database.Gene ontology (GO) enrichment analysis was also carriedout for the selected genes to capture the biological relevantsignatures. A network constructed by proteins encoded bydysregulated genes was used to identify keymolecules amongthe differentially expressed genes. Our results here mayprovide new understanding on the pathogenesis of epilepsy.

2. Materials and Methods

2.1. Microarray Data. Two data sets (GSE4290 andGSE50161) from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database, which include 23 epilepsy patients and 13healthy controls, were used in this study.

Hindawi Publishing Corporatione Scientific World JournalVolume 2014, Article ID 731091, 5 pageshttp://dx.doi.org/10.1155/2014/731091

2 The Scientific World Journal

Table 1: Characteristics of the expression profile used in this study.

Accession ID DescriptionGSM97800 Brain tissue from epilepsy patientGSM97803 Brain tissue from epilepsy patientGSM97804 Brain tissue from epilepsy patientGSM97805 Brain tissue from epilepsy patientGSM97807 Brain tissue from epilepsy patientGSM97809 Brain tissue from epilepsy patientGSM97811 Brain tissue from epilepsy patientGSM97812 Brain tissue from epilepsy patientGSM97816 Brain tissue from epilepsy patientGSM97817 Brain tissue from epilepsy patientGSM97820 Brain tissue from epilepsy patientGSM97825 Brain tissue from epilepsy patientGSM97827 Brain tissue from epilepsy patientGSM97828 Brain tissue from epilepsy patientGSM97833 Brain tissue from epilepsy patientGSM97834 Brain tissue from epilepsy patientGSM97840 Brain tissue from epilepsy patientGSM97846 Brain tissue from epilepsy patientGSM97848 Brain tissue from epilepsy patientGSM97849 Brain tissue from epilepsy patientGSM97850 Brain tissue from epilepsy patientGSM97853 Brain tissue from epilepsy patientGSM97855 Brain tissue from epilepsy patientGSM1214936 Brain tissue from normal controlGSM1214937 Brain tissue from normal controlGSM1214938 Brain tissue from normal controlGSM1214939 Brain tissue from normal controlGSM1214940 Brain tissue from normal controlGSM1214941 Brain tissue from normal controlGSM1214942 Brain tissue from normal controlGSM1214943 Brain tissue from normal controlGSM1214944 Brain tissue from normal controlGSM1214945 Brain tissue from normal controlGSM1214946 Brain tissue from normal controlGSM1214947 Brain tissue from normal controlGSM1214948 Brain tissue from normal control

The twodata setswere both based on theGPL570platformAffymetrix Human Genome U133 Plus 2.0 Array. Detailedinformation of the samples is listed in Table 1.

2.2. Detection of Differentially Expressed Genes. Normaliza-tion of raw intensity values was carried out with robust mul-tiarray analysis (RMA) [9]. The resulting log2-transformedexpression values of all probes were used for further PLSanalysis [10, 11], which is a dimension reduction method formodeling without imposing strong assumptions, to estimatethe effects for each probe in epilepsy patients. Briefly, NIPALSalgorithm [12] was firstly used to obtain PLS latent variablesderived from the expression profile; variable importance onthe projection (VIP) [13] was then calculated to estimate

the effect of the expressed probes on the disease status ofthe patients. Finally, the empirical distribution of PLS-basedVIP was obtained with a permutation procedure (𝑁 =10000 times) and false discovered rate (FDR) of each probewas calculated based on the empirical distribution. Probeswith FDR value less than 0.05 were selected as differentiallyexpressed genes in this study.

2.3. Enrichment Analysis. Identified differentially expressedprobes were annotated by using the simple omnibus formatin text (SOFT) format files. All genes were then mapped totheGeneOntology database [14], which provides a controlledvocabulary of terms for describing gene product charac-teristics. Hyper geometric distribution test was carried outto identify GO items enriched with differentially expressedgenes.

2.4. Network Analysis. Most proteins function through inter-actions with other proteins. Proteins with more inter-actions with other proteins are supposed to play moreimportant roles in biological processes. To identify keymolecules among the differentially expressed genes, we con-structed an interaction network with the proteins encodedby selected genes by using the software Cytoscape (V2.8.3, http://www.cytoscape.org/) [15]. Interaction informa-tion of the proteins was obtained from the NCBI database(http://ftp.ncbi.nlm.nih.gov/gene/GeneRIF/). The number oflinks (interactions) for each protein was defined as its degree.Proteins with degrees more than 10 were selected as hubmolecules in this study.

3. Results

After quality control, two samples (GSM1214938 andGSM1214939) were excluded from subsequent analysis dueto aberrant RNA degradation. Thus, 23 epilepsy patientsand 11 healthy controls were used in PLS analysis. Sampleclassification according to the three selected latent variablesis illustrated in Figure 1. After FDR control, a total of752 genes were identified to be differentially expressed,including 575 depressed and 177 overexpressed genes inpatients. The top ten GO items enriched with differentiallyexpressed genes are listed in Table 2. Most of them (60%)are related to the nervous system, including nervoussystem development (GO:0007399), central nervous systemmyelin maintenance (GO:0007399), neuroligin clustering(GO:0007399), synapse assembly (GO:0007416), spinal cordmotor neuron differentiation (GO:0021522), and glial celldevelopment (GO:0021782).

Figure 2 represents the interaction network of proteinsencoded by selected genes. Two proteins, CUL3 and EP300,were identified to be hub molecules, with degrees of 56 and21, respectively.

4. Discussion

Pathophysiology of epilepsy is highly complex. Gene expres-sion profiling is useful in investigating the underlying mech-anism of epilepsy. For the data analysis, creating a suitable

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Table 2: The top 10 GO items enriched with differentially expressed genes.

GO id GO description GO class 𝑃 valueGO:0007155 Cell adhesion Process 3.74𝐸 − 05

GO:0072659 Protein localization to plasma membrane Process 4.75𝐸 − 05

GO:0001525 Angiogenesis Process 2.64𝐸 − 04

GO:0007399 Nervous system development Process 9.26𝐸 − 04

GO:0032286 Central nervous system myelin maintenance Process 1.43𝐸 − 03

GO:0097118 Neuroligin clustering Process 1.43𝐸 − 03

GO:0007416 Synapse assembly Process 1.59𝐸 − 03

GO:0021522 Spinal cord motor neuron differentiation Process 2.57𝐸 − 03

GO:0005509 Calcium ion binding Function 4.01𝐸 − 03

GO:0021782 Glial cell development Process 4.18𝐸 − 03

−0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 −0.50

0.51

−0.3−0.2−0.1

00.10.20.30.40.50.6

2nd PLS scores

1st PLS scores

3rd

PLS

scor

es

EpilepsyNormal

Figure 1: Sample classification by using the three selected partialleast squares (PLS) latent variables.

model to handle small sample sizes and large number ofgenes [7] remains challenging. Previous studies [7, 8] havedemonstrated better performance of the PLS-based methodthan common variance/regression analysis, which cannotremove hidden biological effects. Here we used PLS analysisto identify differentially expressed genes between epilepsypatients and healthy controls.

As shown in Figure 1, the selected three latent variablesperformed well in classification of the samples. GO enrich-ment analysis of the selected genes revealed the overrep-resentation of differentially expressed genes in the nervoussystem.This is consistent with previous studies. For example,glial cells have been reported to play prominent roles inseizure precipitation and recurrence [16], and the glial celldevelopment was identified to be enriched with dysregulatedgenes in our study (𝑃 = 4.18×10−3). In addition, angiogenesis(GO:0001525) was also found to be overrepresented withdysregulated genes. Dysregulation of angiogenesis may berelated to the dysfunction of blood-brain barrier, contribut-ing to epileptogenesis [17]. Signs of angiogenesis were also

reported to be corresponding with seizure-induced neuronaldeath in animal models of familial epilepsy [18]. Our resultshere further confirmed the involvement of angiogenesisprocess in the pathogenesis of epilepsy.

According to the network analysis, CUL3 was identifiedto be a hub molecule with the highest degree (Figure 2).CUL3 is a core component and scaffold protein of an E3ubiquitin ligase complex. Previous expression studies havenot reported the differential expression of CUL3 in epilepsypatients. However, E3 ubiquitin ligase may affect the synapticfunctions in the central nervous system and the stability ofkainate receptors, which form a class of glutamate receptorsimplicated in epilepsy [19, 20]. Our results suggested thatCUL3 may serve as a potential target in therapeutic studies.

EP300 is another hub gene with the degree of 21. Noreport of this gene and epilepsy has been proposed before.However, protein encoded by this gene is a transcriptionalcoactivator, which stimulates CREB-dependent gene expres-sion. Seizure disorder was reported to be more frequent inRubinstein-Taybi syndrome patients with CREBBP muta-tions [21]. In addition, the promoted signaling mechanismof EP300 is important in the neuronal survival process [22]and this gene was related to other neuronal disorders, suchas familial Alzheimer’s disease [22]. Thus, the correlation ofthis gene and epilepsy pathogenesis may involve the activityof CREB-dependent proteins and further investigation iswarranted.

In summary, using two data sets from the GEO database,we carried out PLS-based gene expression analysis to inves-tigate the underlying pathology of epilepsy. Except for pro-cesses related to the nervous system, we also identifiedoverrepresentation of dysregulated genes in angiogenesis.Network analysis revealed two hub genes, CUL3 and EP300,which may serve as potential targets in further therapeuticstudies. Our results here may provide new understanding forthe underlyingmechanisms of epilepsy pathogenesis and willoffer potential targets for producing new treatments.

Conflict of Interests

The authors have no financial conflict of interests.

4 The Scientific World Journal

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The Scientific World Journal 5

Acknowledgment

This study is supported by the Science and TechnologyPlanning Project of Xi’an, Shaanxi, China (YF07152).

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