Expression Analysis Systematic Explorer (EASE) analysis reveals differential gene expression in...

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Research Report

Expression Analysis Systematic Explorer (EASE) analysisreveals differential gene expression in permanent andtransient focal stroke rat models

Gregory Ford, Zhenfeng Xu, Alicia Gates, Ju Jiang, Byron D. Ford⁎

Department of Anatomy and Neurobiology, Neuroscience Institute, Morehouse School of Medicine, 720 Westview Drive,SW, MRC 222, Atlanta, GA 30310, USA

A R T I C L E I N F O

⁎ Corresponding author. Fax: +1 404 752 1041.E-mail address: [email protected] (B.D. For

0006-8993/$ – see front matter © 2005 Elsevidoi:10.1016/j.brainres.2005.11.090

A B S T R A C T

Article history:Accepted 22 November 2005Available online 10 January 2006

To gain greater insight on the molecular mechanisms that underlie ischemic stroke, wecompared gene expression profiles in transient (tMCAO) and permanent middle cerebralartery occlusion (pMCAO) stroke models using Expression Analysis Systematic Explorer(EASE) pathway analysis software. Many transcripts were induced in both stroke models,including genes associated with transcriptional pathways, cell death, stress responses andmetabolism. However, EASE analysis of the regulated genes indicated molecular functionsand biological processes unique to each model. Pathways associated with tMCAO includedinflammation, apoptosis and cell cycle, while pMCAO was associated with the induction ofgenes encoding neurotransmitter receptors, ion channels, growth factors and signalingmolecules. An intriguing finding was the involvement of tyrosine kinases and phosphatasesfollowing pMCAO. These results provide evidence that neuronal death following tMCAO andpMCAO involves distinct mechanisms. These findings may give new insight to themolecular mechanisms involved in stroke andmay lead to novel neuroprotective strategies.

© 2005 Elsevier B.V. All rights reserved.

Classification terms:Neuronal deathIschemia

Keywords:erbB receptorInflammationIschemiaMCAOMicroarrayReperfusion

1. Introduction

Ischemic stroke is a major cause of death and invalidity inWestern society. However, mechanisms involved in ische-mia-induced brain injury remain poorly understood. Thedevelopment of strategies to treat acute stroke has beenunsuccessful in clinical trials, therefore, new technologiesfor investigating the mechanisms involved in stroke may beuseful to elucidate novel approaches to design beneficialtherapeutic treatments for stroke. Much of the currentknowledge regarding the development, function and path-ophysiology of the central nervous system (CNS) has

d).

er B.V. All rights reserved

evolved from the traditional “one function, one gene”approach that investigates the manipulation of a singlegene or few genes in order to study neuropathology and todiscover potential therapeutic strategies. However, it isclear that complex CNS cellular functions involve thesimultaneous and concerted induction and repression ofmultiple genes. With the completion of the sequence of thehuman genome and the increasing number of genomicsequences available for other model organisms, it is nowpossible to simultaneously examine the expression ofthousands of transcripts in cells and tissues (Lipshutz et al.,1999; Schulze and Downward, 2001).

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Fig. 1 – The scatter plots show the correlation of thehybridization signal intensity for the expressed genesbetween replicate control and MCAO experiments (n = 3 foreach). Following both pMCAO (a) and tMCAO (b), there was adramatic increase in the number of genes upregulated by2-fold of more. The black lines indicate 2-fold and 10-foldincrease or decrease in signal intensity. The comparisonbetween tMCAO and pMCAO reveals that the majority ofgenes expressed in both models are similar in intensity;however, there are a number of genes that are differentiallyexpressed between the two groups.

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Several previous studies have examined gene expressionprofiles in brain tissues following ischemia in rodent strokemodels using microarray technology or similar means toelucidate themechanisms involved in the neuronal death thatfollows ischemia (Bates et al., 2001; Bowler et al., 2002; Lu et al.,2003, 2004; Raghavendra Rao et al., 2002; Schmidt-Kastner etal., 2002; Schroeter et al., 2003; Soriano et al., 2000; Stenzel-Poore et al., 2003). These studies revealed that cerebralischemia induced the expression of genes associated with anumber of general functions including apoptosis, inflamma-tion and metabolism. The identification of these specificcellular and molecular pathways has led to their implicationin the progression of neuronal injury following stroke (Dirnaglet al., 1999; Iadecola and Alexander, 2001; Lo et al., 2003).

There are two major acute in vivo focal stroke modelsused to study the mechanisms involved in ischemic stroke(reviewed in Longa et al., 1989; McBean and Kelly, 1998;Mhairi Macrae, 1992; Yanamoto et al., 2003). In thepermanent middle cerebral artery occlusion model(pMCAO), the MCA is blocked for a fixed period of timeuntil the animal is sacrificed. Transient (ischemia–reperfu-sion) MCAO (tMCAO) is generated by occluding the MCA fora limited time period. The occlusion is then removed andblood flow is restored (reperfusion) to the ischemic brain.Although it is known that reperfusion improves outcomefollowing ischemia, mechanisms associated with reperfu-sion can produce neuronal injury that contributes to infarctprogression. Reperfusion injury has been shown to involvefree radical formation, inflammation and expression ofadhesion molecules (Barone and Feuerstein, 1999; delZoppo et al., 2000; Iadecola and Alexander, 2001; Stoll etal., 1998; Traystman et al., 1991). There is disagreement inthe field to which model is the most appropriate torepresent human stroke. It has been suggested that thepMCAO model is more appropriate because blood flow isreduced and does not change much during the first fewhours following stroke (STAIR, 1999). Also, reperfusionresulting from endogenous thrombolytic mechanisms occursdays rather than hours after stroke so this model may notbe as relevant to the normal human condition. However,with the new thrombolytic therapies now available, thetMCAO model has become more clinically significant.Thrombolytic therapy is currently used to acutely restoreblood flow to the brain following ischemia and has beenshown to be effective in the treatment of ischemic stroke.Therefore, it is necessary to understand the distinctmechanisms that contribute to neuronal death induced byischemia (occlusion) and reperfusion in order to determineappropriate therapeutic strategies.

In this study, we compare and contrast gene expressionprofiles in rat tMCAO and pMCAO stroke models using a new,powerful pathway data analysis tool, Expression AnalysisSystematic Explorer (EASE). EASE is a bioinformatics programthat provides statistical methods that facilitates the biologicalinterpretation of gene lists derived from results of a micro-array analysis (Hawse et al., 2003, 2004, 2005; Hosack et al.,2003; Warrenfeltz et al., 2004; Zeng et al., 2004). Recently, ourlaboratory used EASE analysis and showed that the neuro-protective effect of neuregulin-1 following tMCAO involvedthe attenuation of pro-inflammatory and oxidative stress

related genes induced following ischemia (Xu et al., 2004). Inthis study, EASE analysis of the regulated genes indicatedmolecular functions and biological processes unique to eachstroke model. These finding may provide novel views regard-ing the molecular mechanisms involved in stroke and lead tonew neuroprotective strategies.

Fig. 3 – Gene cluster analysis of genes upregulated by pMCAOand tMCAO. Hierarchical clustering demonstrates differentiallyexpressed genes in the replicates of the three experimentalconditions (a). High signal (red) to low signal (green) intensitiesin probes are shown that demonstrate a significant change inexpression between sham (S), pMCAO (P) and tMCAO (T). Panelb represents a subset of the clustered genes that are uniquelyexpressed in either the pMCAO or tMCAO model.

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2. Results

2.1. Gene expression profiles of rat brains followingtransient and permanent MCAO

Rats were sacrificed 24 h after permanent (pMCAO) ortransient (tMCAO) stroke. RNA isolated from the braintissues was used to examine gene expression profiles ineach condition. In these experiments, we used the Affyme-trix rat genome U34A chip that has 8784 probe sets (genes).TTC staining of brains in parallel studies showed thatpMCAO resulted in slightly larger infarct volumes (∼15%larger; not shown) than in tMCAO stroke models, aspreviously reported (Longa et al., 1989; McBean and Kelly,1998; Mhairi Macrae, 1992; Yanamoto et al., 2003). Followingboth pMCAO and tMCAO, there was a dramatic increase inthe number of genes increased and decreased 2-fold or moreby ischemia (Figs. 1a, b). Although there were similarities inthe number of genes expressed following pMCAO andtMCAO, the scatter plot indicates that there are differencesin the patterns of genes induced in the two models (Fig. 1c).Genes that were upregulated in only one condition werethen identified to determine whether there is a specificgenomic response for each experimental condition. TheVenn diagram illustrates that there were 3115 genes inducedin both stroke models compared to control in all 3 replicates.However, an additional 288 genes were upregulated 2-fold ormore only in the tMCAO model (Fig. 2). After, pMCAO, 320genes were uniquely upregulated in brain tissues. Eightgenes were found to be upregulated in the sham controlscompared to the stroke models. Hierarchical cluster analysis

Fig. 2 – Venn diagrams show the numbers of genes differentially expressed in tMCAO and pMCAO animals comparedto sham controls.

Table 1 – Representive genes increased 2-fold or morespecifically after tMCAO or pMCAO

Genbank Common Foldchange

Gene title_affymetrix

Increased in tMCAOAI169327 Timp1 21.58 Tissue inhibitor of

metalloproteinase 1Z75029 Hspa1a 14.95 Heat shock 70 kDa

protein 1AAI179610 Hmox1 7.057 Heme oxygenase 1X52498 Tgfb1 6.549 Transforming growth factor,

beta 1M61875 Cd44 4.638 CD44 antigenAA892559 Cntf 4.107 Ciliary neurotropic factorU24441 Mmp9 3.97 Matrix metalloproteinase 9AA891041 Junb 3.851 Jun-B oncogeneAI102562 Mt1a 3.437 MetallothioneinX06769 c-fos 3.392 c-fos oncogeneX13044 Cd74 3.359 CD74 antigenH31839 Bak1 2.964 BCL2-antagonist/killer 1X55286 Hmgcr 2.626 3-Hydroxy-3-methylglutaryl-

Coenzyme A reductaseAF003523 Bad 2.615 bcl-2-associated death agonistAB016160 Gabbr1 2.444 Gamma-aminobutyric

acid (GABA) B receptor, 1X69903 Il4r 2.383 Interleukin-4 receptorU08257 Grik4 2.276 Glutamate receptor, ionotropic,

kainate 4Z24721 Sod3 2.269 Superoxide dismutase 3U59184 Bax 2.235 Bcl2-associated X proteinX75207 Ccnd1 2.192 Cyclin D1U77777 Il18 2.066 Interleukin-18AJ006971 Dapkl 2.05 Death-associated like kinaseM98820 Il1b 2.011 Interleukin-1β

Increased in pMCAOAA849036 Gucy1a3 4.562 Guanylate cyclase 1, soluble,

alpha 3M60654 Adra1d 3.95 Adrenergic receptor, alpha 1dD64050 Ptprr 3.505 Protein tyrosine phosphatase,

receptor type, RY00766 Scn3a 3.361 Sodium channel, voltage-gated,

type III, alpha polypeptideM36419 Gria2 3.155 Glutamate receptor,

ionotropic, 2D38101 Cacna1d 3.013 Calcium channel, voltage-

dependent, L type, alpha 1Dsubunit

J05087 Atp2b3 2.849 ATPase, Ca++ transporting,plasma membrane 3

U04998 Ptprz1 2.618 Protein tyrosine phosphatase,receptor-type, Z polypeptide 1

D25233 Rb1 2.556 Retinoblastoma 1X15468 Gabrb3 2.509 Gamma-aminobutyric acid

receptor, subunit beta 3U08256 Grid2 2.386 Glutamate receptor, ionotropic,

delta 2M35162 Gabrd 2.321 Gamma-aminobutyric acid A

receptor, deltaAA893191 Ppap2c 2.303 Phosphatidic acid phosphatase

type 2cAB006013 Rgs8 2.268 Regulator of G-protein

signaling 8U88324 Gnb1 2.248 Guanine nucleotide binding

protein, beta 1

Table 1 (continued )

Genbank Common Foldchange

Gene title_affymetrix

AF063102 Cirl2 2.168 Calcium-independentalpha-latrotoxin receptorhomolog 2

D14419 Ppp2r2a 2.138 Protein phosphatase 2(formerly 2A), regulatorysubunit B (PR 52)

L19180 Ptprd 2.123 Protein tyrosine phosphatase,receptor type, D

M91466 Adora2b 2.085 Adenosine A2B receptorD38261 Ppp2r2c 2.049 Protein phosphatase 2

(formerly 2A), regulatorysubunit B (PR 52)

M31809 Ppp3cb 2.041 Protein phosphatase 3,catalytic subunit, beta isoform

S55933 Gabra4 2.021 Gamma-aminobutyric acid(GABA-A) receptor, subunitalpha 4

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identified expression profiles of genes that are uniquelyassociated with either tMCAO or pMCAO (Fig. 3). The genesunique to tMCAO included a number of inflammatory andoxidative stress genes such as heme oxygenase-1, interleu-kin-1β and monocyte chemoattractant protein-1 (MCP-1),which have been previously implicated in the progression ofinfarct formation following stroke (Barone and Feuerstein,1999; del Zoppo et al., 2000; Iadecola and Alexander, 2001;Stoll et al., 1998). On the other hand, pMCAO resulted in theinduction of genes that were more related to metabolicactivity and cell signaling, that included a number of ionchannels, signaling molecules and neurotransmitter recep-tors (glutamate, GABA and adenosine receptors). We alsoobserved a distinction in the transcription factors upregu-lated in pMCAO and tMCAO. Following tMCAO, the upregu-lated transcription factors included c-fos, junB, egr-1, C/EBPand USF1. After pMCAO, NF1/B, Fra, p53 and Rb1 wereinduced. This suggests that divergent transcriptional path-ways regulate the mechanisms involved in the two strokemodels.

A representative list of differentially expressed annotat-ed genes between pMCAO and tMCAO models, includingnames and the GenBank accession numbers, is shown inTable 1. Previous microarray studies have already con-firmed by RT-PCR that the genes shown here are upregu-lated following ischemia (Bates et al., 2001; Bowler et al.,2002; Lu et al., 2003, 2004; Raghavendra Rao et al., 2002;Schmidt-Kastner et al., 2002; Schroeter et al., 2003; Sorianoet al., 2000; Stenzel-Poore et al., 2003). Therefore, we chosea small subset of genes just to confirm the pattern ofdifferential gene expression demonstrated by our micro-array studies. RT-PCR results showed agreement with thecorresponding microarray results (Fig. 4).

2.2. EASE analysis of gene ontology annotations

A stringent set of genes specifically upregulated after tMCAOor pMCAO by 2-fold or more was imported into EASE to test forover-representation of annotation classes. Genes identified tobe differentially expressed by 2-fold or greater according to the

Table 2 – Categories of genes increased by 2-fold or moreonly in tMCAO model

System Gene category EASEscore

GO cellularcomponent

Membrane 7.88E−06


Integral to membrane 1.34E−05

GO molecularfunction

Transmembrane receptor activity 0.00288801

GO biologicalprocess

Blood vessel development 0.00478682


Angiogenesis 0.00478682


Death 0.0059315


Lung development 0.00937932


Respiratory tube development 0.00937932


Cell death 0.0093897


Plasma membrane 0.01191674


Neurotransmitter binding 0.01400756


Neurotransmitter receptor activity 0.01400756


Receptor activity 0.01430494


Apoptosis 0.01604764


Programmed cell death 0.01604764


Actin filament 0.01606251


Signal transducer activity 0.02320101


Alpha-type channel activity 0.02327398


Amine receptor activity 0.02771067


Channel/pore class transporteractivity

0.0344453


Chloride transport 0.03584039


Interleukin-1\, Type I\, Activatingreceptor activity

0.03651176


Interleukin-1\, Type I\, Activatingbinding

0.03651176


Inflammatory response 0.0413547


Regulation of cell cycle 0.04325525


Voltage-gated potassium channelcomplex

0.0435763


Translation 0.04580235


Ion channel activity 0.04697467


Subtilase activity 0.04757923


Chemoattractant activity 0.04801377


Innate immune response 0.04998097

Fig. 4 – The RNA expression of selected genes wasassessed by RT-PCR in sham (S), tMCAO (T) and pMCAO (P).

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microarray analysis and determined to be present by GCOS inall three replicates were analyzed with EASE. Categories withan EASE score of less than 0.05 are listed in Tables 2–5. Genesspecifically upregulated after tMCAO were classified into 1833GO categories (it should be noted that each gene can fall intomultiple GO categories, thus resulting in more categories thangenes in the set). However, only 31 of these categories weredetermined to be significantly over-represented based on theEASE score (Table 2). The EASE analysis of gene expressionindicated that several biological processes were over-repre-sented compared to the overall distribution including: celldeath/apoptosis, inflammation and chemoattraction. This isconsistent with observations that indicate that reperfusionresults in the initiation of oxidative stress mechanisms andinflammatory responses, including the induction of cytokinesand cell adhesion molecules. Gene categories associated withangiogenesis were also upregulated after tMCAO. The molec-ular function and cellular component categories over-repre-sented suggested that there were plasma membrane-associated receptors and ion channels that were involvedwith tMCAO.

In contrast, pMCAO was associated with genes encodingneurotransmitter receptors, ion channels, growth factors andsignaling molecules. Of the 1598 categories that the genesupregulated by pMCAOwere assigned to, 36 were significantlyover-represented and 16 of those were found to be unique topMCAO (Table 3). The pattern suggests that these genesencode primarily transmembrane proteins associated withneurotransmission and ion channel activity. An interestingfinding in this group was the over-representation of genesrelated to dephosphorylation and phosphatase activity. Thegenes in this group were three transmembrane proteintyrosine phosphatases (PTPs): protein tyrosine phosphatase,receptor type, R (Ptprr), rat receptor-linked protein tyrosinephosphatase (PTP-P1) and protein tyrosine phosphatase,receptor-type, Z polypeptide 1/phosphacan. Conversely, thegenes downregulated following pMCAO were associated withphosphorylation and kinase activity (Table 4). The genes

Table 3 – Categories of genes increased by 2-fold or moreonly in pMCAO model





Membrane 1.86E−07


Transmembrane receptoractivity

1.67E−05


Receptor activity 8.13E−05




Neurotransmitter receptoractivity

0.001249702









0.003935624








Integral to plasma membrane 0.014943558


Extracellular ligand-gated ionchannel activity

0.016691064






Interleukin-1\, Type I\,Activating binding

0.018908176


Interleukin-1\, Type I\,Activating receptor activity

0.018908176


Chloride transport 0.019550959


G-protein-coupled receptoractivity

0.022309383


Inorganic anion transport 0.022670923


Translation 0.024920333


Dephosphorylation 0.027768898


Ion transport 0.033611179


Perception of chemicalsubstance

0.033800187


Chemosensory perception 0.033800187


Synaptic transmission 0.036881626


Ligand-gated ion channelactivity

0.03725556


Respiratory tube development 0.039422561


Lung development 0.039422561




Interleukin-1 receptor activity 0.041114732


Interleukin-1 binding 0.041114732


Protein–tyrosine–phosphataseactivity

0.042557953


Protein amino aciddephosphorylation

0.043736565


G-protein-coupled receptorprotein signaling pathway

0.0492019

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downregulated by pMCAO were also categorize with immuneand inflammatory functions, indicating that the immuneresponse is suppressed in the pMCAO model in contrast totMCAO where genes related to receptor and ion channelactivity were downregulated (Table 5).

3. Discussion

The results of this study demonstrate that distinct geneexpression profiles relate to the mechanisms associated withischemia/occlusion and reperfusion following stroke. The truepower of bioinformatics tools such as EASE, is the ability to usethemicroarray data generated to examine significant changesin patterns of gene expression to elucidate signaling pathwaysthat may serve as key master regulatory factors or potentialtherapeutic targets. This technology can also facilitate thedevelopment of new ideas and identify genes that had notbeen previously associated with ischemia and other CNSdisorders. Consistent with that notion, our laboratory usedEASE analysis to show that neuroprotective effect of NRG-1following tMCAO involved the attenuation of pro-inflamma-tory and oxidative stress related genes induced followingischemia (Xu et al., 2004).

In agreement with previous gene expression studies, EASEanalysis showed that reperfusion was associated with apo-ptotic cell death, inflammation and mononuclear chemotaxis(Fig. 5) (Bates et al., 2001; Bowler et al., 2002; Lu et al., 2003,2004; Raghavendra Rao et al., 2002; Schmidt-Kastner et al.,2002; Schroeter et al., 2003; Soriano et al., 2000; Stenzel-Pooreet al., 2003). We also observed that genes encoding proteinsthat facilitate mononuclear migration and activation wereinduced by transient ischemia. These included interleukin-1β,CD44 and the chemokine JE/MCP-1 that is known to promotemononuclear infiltration into the brain and subsequentneuronal injury following ischemia (Barone and Feuerstein,1999; del Zoppo et al., 2000; Hughes et al., 2002; Iadecola andAlexander, 2001; Stoll et al., 1998). Previous reports haveshown that interleukin-1β is induced following pMCAO;however, it was shown to peak at 3–12 h, then decline overthe next several hours and days (Buttini et al., 1994; Wang etal., 1997). The 24 h time point chosen in these studies likelyexplains the difference between these results and our studies.

On the contrary, pMCAO was characterized by the down-regulation of genes related to inflammation and apoptosis. Thepathways upregulated the pMCAO models were related to

Table 4 – Categories of genes decreased by 2-fold or moreonly in pMCAO model



Transmembrane receptoractivity

0.000469088


Integral to membrane 0.000952047


Response to external stimulus 0.001075262




Extracellular 0.001246256


Innate immune response 0.001296002




Enzyme inhibitor activity 0.001875889


Response to pest/pathogen/parasite

0.004125618


Chemotaxis 0.004261643


Taxis 0.004261643


Defense response 0.004512163


Receptor activity 0.004585856


Immune response 0.004672974


Response to wounding 0.005696042


Response to biotic stimulus 0.006804746


Cytokine activity 0.006916838




Chemokine receptor binding 0.008849204


Chemokine activity 0.008849204


Protease inhibitor activity 0.011635151


Endopeptidase inhibitoractivity

0.011635151


Chemoattractant activity 0.012055921


Membrane 0.018913836


Phosphorus metabolism 0.022199385


Phosphate metabolism 0.022199385


Protein modification 0.02493462


Response to chemicalsubstance

0.025006337


G-protein-coupled receptorbinding

0.025773325


Protein amino acidphosphorylation

0.028271511



0.02934481





0.02934481


Enzyme regulator activity 0.029610812


Protein kinase activity 0.034525284


Response to abiotic stimulus 0.038191393


Cytokine binding 0.038605539


Excretion 0.042529257








Perception of chemicalsubstance

0.047126013


Phosphorylation 0.049030598

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neurotransmission and ion channel function rather thaninflammation. An intriguing finding in this study was thespecific over-representation of dephosphorylation/phospha-tase categories following pMCAO. Protein tyrosine phosphory-lation is thought to play an important role in the regulation ofneural function. Although it is unclear whether the inducedgenes are related to neuronal death or neuroprotection, severallines of evidence suggest a role for PTPs in the progression ofcellular injury. Protein tyrosine phosphorylation has beenimplicated in delayed neuronal death in themature hippocam-pus through glutamate overload after ischemia–reperfusion(Ohtsuki et al., 1996). Furthermore, administrationof radicicol, aselective inhibitor of tyrosine kinases, attenuated stimulationof tyrosine phosphorylation and hippocampal degenerationafter ischemia or kainic acid injection. In the heart, the tyrosinephosphatase inhibitor bis(maltolato)oxovanadium was shownto attenuate myocardial reperfusion injury by opening ATP-sensitive potassium channels (Liem et al., 2004). Recently, thePTP inhibitor sodium orthovanadate was shown to activate theVEGF receptor KDR/flk-1 and accelerate angiogenesis in a ratmodel of hindlimb ischemia, suggesting that PTP inhibitors canbe used as a drug for therapeutic angiogenesis in peripheralischemic diseases (Sugano et al., 2004). Interestingly, the VEGFreceptor, flt-1, is one of the tyrosine kinases downregulatedfollowing pMCAO. Taken together, it is plausible that inhibitorsof PTPs may be useful as therapeutic agents for protectingneurons from ischemia in the absence of reperfusion.

In conclusion, we have provided evidence of distinctmechanisms involved in ischemic and reperfusion injuryfollowing stroke by analyzing gene expression profiles inanimal models. We acknowledge that these studies representonly one time point during the course of ischemic injury andstudies are underway to characterize additional time-depen-dent mechanisms. Nonetheless, consistent with this observa-tion, a number of studies present pharmacological and

Table 5 – Categories of genes decreased by 2-fold or moreonly in tMCAO model





Membrane 5.81E−08


Transmembrane receptor activity 7.74E−08


Receptor activity 4.12E−07


Signal transducer activity 1.70E−05


G-protein-coupled receptor activity 0.000568406









0.003198427






Neurotransmitter receptor activity 0.005455711



0.007836313



0.007836313


Rhodopsin-like receptor activity 0.012294622










response to external stimulus 0.020441565


G-protein-coupled receptor proteinsignaling pathway

0.021114596


Integral to plasma membrane 0.023877108


Taxis 0.024241124


Chemotaxis 0.024241124


Cytokine activity 0.024771458


Immune response 0.025736925


Extracellular 0.025739421




Perception of chemical substance 0.025891292


Response to chemical substance 0.026970974




Hematopoietin/interferon-class(D200-domain) cytokinereceptor activity

0.028283227


Cyclic-nucleotide-mediatedsignaling

0.028878979


G-protein signaling\, coupled to cyclicnucleotide second messenger

0.032146817


Defense response 0.033106852


Physiological process 0.037562225


Protein modification 0.040338532


Serotonin receptor signaling pathway 0.044346083






Response to wounding 0.04974029

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molecular evidence that discrete mechanisms are involved intransient and permanent ischemia-mediated neuronal death.For example, several treatments, including hyperbaric oxygenpreconditioning (Xiong et al., 2000) and dextromethorphan(Britton et al., 1997) are known to be neuroprotective againsttransient, but not permanent MCAO. In addition, activation ofperoxisome proliferator-activated receptor (PPAR)-γ has beenshown to reduce infarct size in tMCAO but not pMCAO(Shimazu et al., 2005). It was suggested that the distinctionresults from the upregulation of the oxidative stress regulatorsuperoxide dismutase. On the other hand, the PPAR-αactivation was shown to protect neurons from pMCAO(Inoue et al., 2003). Similarly, an anti-intercellular adhesionmolecule-1 antibody reduced ischemic damage after tMCAObut not pMCAO (Zhang et al., 1995). These results takentogether indicate that the mechanisms that lead to neuronaldeath from ischemia and reperfusion injury are unique andsuggest that separate therapeutic strategies should beemployed depending on whether the vessel obstructionremains or if blood flow has been restored. Neuroprotectiontherapy targeting these independent pathways could lead tonovel treatments for ischemic stroke in humans.

4. Experimental procedures

4.1. Middle cerebral artery occlusion and tissue collection

Adult male Sprague–Dawley rats (n = 3 for each experimentalcondition) weighing 250–300 g were subjected to tMCAO orpMCAO as previously described (Parker et al., 2002; Xu and Ford,2005; Xu et al., 2004). For tMCAO, a nylon suture was inserted 18to 20 mm from the bifurcation of the common carotid artery toocclude the MCA. After 1.5 h of ischemia, the nylon suture waswithdrawn and the ischemic brain tissue was reperfused for 22.5h before sacrificing. In the pMCAO, the suture was left in placefor 24 h prior to sacrificing the animals. Animals were killedafter 24 h and the brains were removed and sliced into 2 mm

Fig. 5 – Differential gene expression in permanent and transient focal stroke. EASE analysis showed that reperfusion wasassociatedwith apoptotic cell death, inflammation andmononuclear chemotaxis. In contrast, pMCAOwas characterized by thedownregulation of genes related to inflammation and apoptosis. Permanent MCAO was associated with an increase in genesencoding neurotransmitter receptors, ion channels, growth factors and signaling molecules, specifically pathways related todephosphorylation and phosphatase activity.

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coronal sections (approximately +3.0 to −5.0 from bregma) usinga brain matrix. The ipsilateral tissues from the two middle slices(+1 to −3 from bregma) of sham, pMCAO and tMCAO subjectedanimals were used for subsequent RNA isolation while the outertwo slices were used to confirm infarct formation by stainingwith 2,3,5-triphenyltetrazolium chloride (TTC). Total RNA wasextracted with TRIzol Reagent (Life Technologies, Rockville, MD,USA), cleaned (RNAqueous Kit, Ambion, Austin, TX, USA) andconverted to double-stranded cDNA (Invitrogen, SuperscriptChoice System, Carlsbad CA, USA) using a T7-(dT)24 primer.Cleanup of double-stranded cDNA used Phase Lock Gels(Eppendorf, Westbury, NY, USA)-Phenol/Chloroform/Isoamyl Al-cohol (Sigma, St. Louis, MO, USA). cRNA was synthesized using aRNA transcript labeling kit (Enzo Diagnostics, Farmingdale, NY,USA). Biotin labeled cRNA was cleaned up using a GeneChipSample Cleanup Module (Affymetrix Inc., Santa Clara, CA, USA)and then quantified using a spectrophotometer. Twenty micro-grams of the in vitro transcription product was fragmented byplacing at 94 °C for 35 min in fragmentation buffer. Followingfragmentation, 15 μg of the biotinylated cRNA was hybridized toan Affymetrix Rat Genome U34A GeneChip. The chips werehybridized at 45 °C for 16 h, and then washed, stained withstreptavidin–phycoerythrin and scanned according to manufac-turing guidelines.

4.2. Microarray data analysis

Initial data analysis was performed by Affymetrix GCOS software.Single array analysis was used to build the databases of geneexpression profiles. Three chips were used for each group: shamoperated animals, tMCAO and pMCAO. GCOS normalized andanalyzed data for each chip. Detection P value (set at P b 0.05) wasused to statistically determinewhether a transcript was expressedon a chip. The software generated a present (P), marginal (M) orabsent (A) call for each transcript based on the P value. Data werethen imported into Genespring software (Silicon Genetics, Red-wood City, CA) for further analysis. To obtain differentiallyexpressed genes for each condition, Genespring software wasused to compare replicates of the control arrays (sham operatedanimals) to replicate arrays from the stroke models. We deter-mined the genes that changed 2-fold or more and generated a list

for each condition. Hierarchical clustering algorithms were usedto generate expression profile patterns of genes differentiallyregulated in either tMCAO or pMCAO using the Genespringsoftware. Clusters displaying genes induced or suppressed inonly one model (compared to sham) were selected for furtheranalysis.

Genes identified to be differentially expressed by 2-fold orgreater according to themicroarray analysis and determined to bepresent by GCOS in all three replicates were analyzed forsignificant functional clusters of genes using the EASE (ExpressionAnalysis Systematic Explorer) version 1.21 bioinformatics soft-ware package (Hosack et al., 2003). EASE is a bioinformaticsprogram that provides statistical methods (reported as an EASEscore) that facilitate the biological interpretation of gene listsderived from results of a microarray analysis. We employed EASEanalysis to find gene categories that are over-represented in anannotated class of genes (Hawse et al., 2003, 2004, 2005; Hosack etal., 2003; Warrenfeltz et al., 2004; Zeng et al., 2004). Over-representation describes a class of genes that have similarfunctions regardless of their expression level, and appear moreoften in a list of interest than would normally be predicted bytheir distribution among all genes assayed. An EASE score iscalculated for likelihood of over-representation of biologicalprocesses, molecular functions and cellular component categoriesusing the Gene Ontology (GO) public database. Categories with anEASE score of less than 0.05 were determined to be significantlyover-represented.

4.3. RNA isolation and RT-PCR

Ipsilateral and contralateral slices were prepared as previouslydescribed and RNA was isolated using TRIzol according to themanufacturer's instructions (Life Technologies, Gaithersburg,Maryland). To quantify mRNA expression, gene-specific primerswere employed for RT-PCR using the Qiagen (Valencia, California)One-step kit (30 min at 50 °C, 15 min at 95 °C, then 25 cycles ofdenaturation at 94 °C for 30 s, annealing at 60 °C for 30 s andextension at 72 °C for 30 s with a 10 min final extension period at72 °C) and a BioRad iCycler. Relative expression was indicated as aratio of the control housekeeping gene (glyceraldehyde-3 phos-phate dehydrogenase; GAPDH).

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Acknowledgments

This work was supported by NIH grant NS34194, an NSFCenter for Behavioral Neuroscience Cooperative Agreement(#IBN-9876754) and the W.M. Keck Foundation. The inves-tigation was conducted in a facility constructed withsupport from Research Facilities Improvement Grant #C06RR-07571 from the National Center for Research Resources,NIH.

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