Neurobiology of Disease 62 (2014) 508–520
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Hippocampal subregion-specific microRNA expression during epileptogenesis in experimental temporal lobe epilepsy Jan A. Gorter a,b,1, Anand Iyer c,1, Ian White d, Anna Colzi e, Erwin A. van Vliet a,b, Sanjay Sisodiya f,⁎,1, Eleonora Aronica a,c,b,⁎,1 a
Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, The Netherlands SEIN - Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands Department of (Neuro)Pathology, Academic Center, University of the Netherlands, The Netherlands d UCB Celltech, Experimental Medicine and Diagnostics, 208 Bath Road, Slough, Berkshire, UK e UCB Pharma, Neurosciences Discovery Medicine, Chemin du Foriest, B-1420 Braine-l'Alleud, Belgium f Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK b c
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Article history: Received 19 July 2013 Revised 11 October 2013 Accepted 24 October 2013 Available online 31 October 2013 Keywords: Rat Hippocampus Array miRNA Epileptogenesis Pathways
a b s t r a c t Since aberrant miRNA expression has been implicated in numerous brain diseases, we studied miRNA expression and miRNA regulation of important signaling pathways during temporal lobe epileptogenesis in order to identify possible targets for epilepsy therapy. The temporal profile of miRNA expression was analyzed in three brain regions (CA1; dentate gyrus, DG; parahippocampal cortex, PHC) associated with epileptogenesis in a rat model for temporal lobe epilepsy. Tissue was obtained after electrically-induced status epilepticus (SE) at 1 day (n = 5), 1 week (n = 5) and 3-4 months (n = 5), and compared with control tissue (n = 10) using the Exiqon microRNA arrays which contain capture probes targeting all miRNAs for rat (p b 0.01, and a 1.5 fold up- or downregulation). Expression of three blood plasma miRNAs from the same group of rats was also investigated in rats in order to determine whether plasma miRNAs could serve as potential biomarkers of the epileptogenic process. Molecular pathways potentially altered by the expression of multiple miRNAs were identified using a web-based algorithm, DIANA. In CA1 and DG, more upregulated than downregulated miRNAs were present during each stage after SE. The highest numbers of upregulated miRNAs were encountered during the chronic stage in the DG. In PHC, a high number of downregulated miRNAs were detected. Key pathways involved, based upon quantitatively altered miRNA expression were: axon guidance, MAPK signaling pathway, focal adhesion, TGFβ, ErbB-, Wnt- and mTOR signaling, and regulation of actin skeleton. Expression of plasma miRNAs was differentially regulated after induction of SE. This study identified several signaling pathways possibly involved in temporal lobe epileptogenesis, not previously indicated by RNA microarray studies. These include miRNAs that regulate the ErbB and Wnt pathways and focal adhesion, which may represent interesting new targets for therapeutic interventions. © 2013 Elsevier Inc. All rights reserved.
Introduction Several large-scale genomic studies in both human and experimental temporal lobe epilepsy (TLE) have shown altered expression of Abbreviations: miRNAs, microRNAs; DG, dentate gyrus; PHC, parahippocampal cortex; MAPK, mitogen-activated protein kinase; TGFβ, transforming growth factor beta; mTOR, mammalian target of rapamycin; TLE, temporal lobe epilepsy; SE, status epilepticus; RIN, RNA Integrity Number; MEF2C, myocyte enhancer factor 2C; SV2A, synaptic vesicle protein 2A; Limk1, LIM domain kinase 1; ERK, extracellular signal-regulated kinase; JNK, c-JUN N-terminal kinase; MEK1, mitogen-activated protein kinase kinase. ⁎ Corresponding authors: E. Aronica, Dep. of (Neuro)Pathology, Academic Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands. Fax: +31 20 5669522 or S. Sisodiya, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK. Fax: +44 20 3448 8615. E-mail addresses:
[email protected] (S. Sisodiya),
[email protected] (E. Aronica). Available online on ScienceDirect (www.sciencedirect.com). 1 These authors contributed equally to this work. 0969-9961/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.nbd.2013.10.026
genes associated with key biological processes. Such work has led to the development of new therapeutic strategies, some in preclinical trials (for review see Aronica and Crino, 2011; Pitkanen and Lukasiuk, 2011; Vezzani et al., 2010). More recently, microRNAs (miRNAs) have also been identified as a new class of post-transcriptional regulators of numerous biological processes within the central nervous system (Gantier, 2010; Quinn and O'Neill, 2011; Sonkoly et al., 2008). MiRNAs bind to mRNA targets leading to degradation of the transcript or repression of its translation. MiRNAs introduce a new level of regulatory complexity to pathogenic processes underlying different neurological disorders, including epilepsy (Bian and Sun, 2011). Differential expression of several miRNAs has been shown in animal TLE models (Aronica et al., 2010; Hu et al., 2012; Jimenez-Mateos et al., 2011, 2012; Liu et al., 2010; Omran et al., 2012; Risbud et al., 2011; Song et al., 2011), and human TLE (Kan et al., 2012; Omran et al., 2012). For example, specific miRNAs, such as miR-146a (Aronica et al., 2010; Iyer
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et al., 2012; Omran et al., 2012) and miR-134 (Jimenez-Mateos et al., 2012) are upregulated in both experimental and human epilepsy. The use of exogenous miRNA analogs or related RNAi (interference) approaches represents a promising new strategy for epilepsy therapy (antiepileptogenic therapies) (Boison, 2010). Interestingly, silencing miR-134 after status epilepticus (SE) in mice reduces the subsequent occurrence of spontaneous seizures and exerts neuroprotective actions (Jimenez-Mateos et al., 2012) and reduction of hippocampal miR-132 levels has been shown to reduce seizure-induced neuronal death (Jimenez-Mateos et al., 2011). Conversely, silencing of miR-146a induces a pro-inflammatory response exerting pro-ictogenic effects (Iori et al., 2013). These recent studies suggest that it might be possible to modify epileptogenesis using strategies that target miRNAs. SE leads to altered expression of a large number of genes (Becker et al., 2003; Gorter et al., 2006; Lukasiuk et al., 2006): a concomitant qualitative and quantitative change in the expression of the associated miRNAs might also be expected. However, although several published studies have used miRNA arrays, a large-scale miRNA expression study at different stages of epileptogenesis has not been reported so far. The present study was designed to identify the spatiotemporal dynamics of a large number of miRNAs potentially involved in the epileptogenic process. We analyzed samples both at sequential time points (including the early stages of epileptogenesis) and in distinct brain areas considered to have different sensitivities to, and roles in, epileptogenesis. Moreover, we analyzed blood plasma expression levels of three miRNAs (known to be associated with the immune response (Danger et al., 2013; Davidson-Moncada et al., 2010; Liu et al., 2010; Xu et al., 2013) and found to be strongly upregulated in our array analysis), in the same groups of rats in order to investigate whether plasma miRNAs could serve as potential biomarkers of the epileptogenic process. We used an established rat model for TLE in which SE is induced by electrical stimulation (Gorter et al., 2001). This model is characterized by extensive neuronal death, glial activation and mossy fiber sprouting during the first months after SE and spontaneous seizures that start after a short latent period of approximately 1 week (Gorter et al., 2001, 2002, 2006). The comparison of miRNA expression patterns between different brain regions within the same animal offers the unique possibility to detect the most relevant miRNAs and their associated pathways that are involved in TLE epileptogenesis, and may direct future strategies aimed at preventing or modifying the epileptogenic process.
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form of a succession of trains of pulses every 13 s. Each train had a duration of 10 s and consisted of biphasic pulses (pulse duration, 0.5 ms; maximal intensity, 500 μA). Stimulation was stopped when the rats displayed sustained forelimb clonus and salivation for minutes, which usually occurred within 1 h. Stimulation never lasted longer than 90 min. EEG signals were amplified via a field effect transistor on the headstage and then led to a differential amplifier (CyberAmp; Molecular Devices, Burlingame, CA), amplified (20×), filtered (1–60 Hz), and sampled by a seizure detection program at a frequency of 200 Hz per channel (Harmonie; Stellate Systems, Montreal, Quebec, Canada). EEG recordings were visually monitored and screened for seizure activity. Behavior was observed during electrical stimulation and several hours thereafter. Immediately after termination of the stimulation, periodic epileptiform discharges (PEDs) occurred at a frequency of 1–2 Hz and lasted several hours (SE). During this period rats had frequent seizures as observed by both their behavior and electroencephalography (EEG). The end of SE could be clearly defined by the disappearance of 1–2 Hz PEDs. In previous studies we have noticed that rats that exhibit SE for at least 4 h all develop seizures in later life (Gorter et al., 2001; van Vliet et al., 2004). Seizure groups Rats were killed at three successive time points: (1) at 1 day after SE (group D; acute stage; n = 5); (2) at 1 week after SE (group W; n = 6) (the rats in this group did not exhibit spontaneous seizures during the first week, i.e., they were in the latent period); and (3) at 3–4 months after SE (group M; n = 5); for this latter group, we selected rats that exhibited daily seizures; age-matched controls (3 months, n = 5 and 4–7 months of age, n = 5) that were implanted but not stimulated except for field potential recordings, were also included (group C; n = 10). The scheme is presented in Fig. 1a. The EEG of all chronic epileptic rats was monitored for at least one week, to ensure that daily seizures occurred. When they exhibited an increasing number of seizures during the first 4 weeks after SE, they were disconnected and reconnected during the last week before killing, for quantification of their daily seizures. They were sacrificed around three to four months after SE. Tissue collection After decapitation, the hippocampus and the parahippocampal region [(PHC), which includes mainly the entorhinal cortex and parts
Materials and methods Experimental animals Adult male Sprague Dawley rats (Harlan Laboratories, Horst, The Netherlands) weighing 300–500 g were used in this study, which was approved by the University Animal Welfare committee. The rats were housed individually in a controlled environment (21 ± 1 °C; humidity, 60%; lights on from 8:00 A.M. to 8:00 P.M.; food and water available ad libitum). Electrode implantation and seizure induction At 2–3 months of age, rats were anesthetized with an intramuscular injection of ketamine (57 mg/kg; Alfasan, Woerden, The Netherlands) and xylazine (9 mg/kg; Bayer, Leverkusen, Germany), and placed in a stereotactic apparatus. To record hippocampal EEG, a pair of insulated stainless-steel electrodes (70 μm wire diameter; tips were 80 μm apart) were implanted into the left dentate gyrus under electrophysiological control as described previously (Gorter et al., 2001). A bipolar stimulation electrode (distance between tips 500 µm) was implanted in the angular bundle. Several weeks after electrode implantation, rats underwent tetanic stimulations (50 Hz) of the hippocampus in the
Fig. 1. a) Timeline representing the time points after status epilepticus (SE) when rats were sacrificed in order to determine the miRNA profiles. During the acute stage (1 day after SE; D) rats can still have occasional seizures in the aftermath of the SE. During the latent stage which on average lasts 1 week (W) rats do not exhibit seizures. Rats that were sacrificed during the chronic stage (3 months after SE; M) exhibited daily seizures. b) Schematic representations of the dissected areas from the hippocampus (CA1, CA3, DG) and the parahippocampal cortex (in yellow) that had been removed by dissection at the ventrocaudal part underneath the rhinal fissure.
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of the perirhinal and posterior piriform cortex] were removed under RNase-free conditions by incision at the ventrocaudal part underneath the rhinal fissure until ~5 mm posterior to bregma. Both hippocampi were sliced into smaller parts (200–300 μm) and the CA1 and dentate gyrus (DG) region were cut out of the slices in 4 °C saline solution under a dissection microscope (see Fig. 1b). All material was frozen on dry ice and stored at −80 °C until use. MiRNA array analyses were performed on ipsilateral hippocampal CA1, DG and PHC from each rat in each group (three chips per animal; total n = 25) [control (C), n = 10; 1 day (D), n = 5; 1 week (W), n = 5; chronic (M), n = 5], yielding a data set of 75 miRNA arrays. Plasma was separated from blood collected from the neck of the decapitated rats in K2-EDTA tubes (Becton Dickinson, NJ, USA). The plasma was aliquoted and stored at −80 °C until further use. One aliquot was used every time for isolation of RNA.
RNA isolation and quality control For RNA isolation, brain frozen material was homogenized in QIAzol Lysis Reagent (Qiagen Benelux, Venlo, The Netherlands). Total RNA including the miRNA fraction was isolated using the miRNeasy kit (Qiagen Benelux, Venlo, The Netherlands) according to the manufacturer's instructions. The concentration and purity of RNA were determined at 260/280 nm using a NanoDrop spectrophotometer (Ocean Optics, Dunedin, FL, USA). Sample RNA quality control was performed using an Agilent 2100 bioanalyzer. A RIN (RNA Integrity Number) value of greater than 7 was considered to be of good quality for array profiling (Schroeder et al., 2006). Briefly, 1000 μL of QIAzol Lysis Reagent and 1.5 μL of MS2 RNA (0.8 μg/μL; Roche, USA) were mixed with 200 μL of homogenate. After phase separation with chloroform, RNA was precipitated from the upper aqueous phase using 100% ethanol. The solution was passed through RNeasy MinElute spin columns provided with the kit to bind the RNA which was then extensively washed and eluted in RNase free water. The concentration and purity of RNA were determined at 260/280 nm using a NanoDrop spectrophotometer (Ocean Optics, Dunedin, FL, USA).
Array slide quality control using spike-ins (labeling controls) Spike-in controls were added in various concentrations to both the Hy3™ and the Hy5™ labeling reactions, to evaluate the labeling reaction, hybridization, and the overall performance of the array experiment. The high correlation seen (r2 N 0.95) for signal intensities across all slides for both Hy3™ and Hy5™ channels indicated that both labeling and hybridization were successful. Fifty-two different spike-in controls were added in concentrations covering the full signal range. Each spike-in control has four replicates of capture probes on the array.
MiRNA array data analysis All calculations were done in the software R/bioconductor or Excel. The background threshold was calculated for each individual microarray slide as 1.2 times the 25th percentile of the overall signal intensity of the slide. MiRNAs with intensities above threshold in less than 20% (or two) of the samples were removed from the final dataset used for the expression analysis. For the present data set that contains 692 probe IDs (miRNAs), 322 miRNAs were included, while the other probes were discarded by this filtering procedure. Comparisons were made between control and experimental samples, taken at the same time points, requiring at least p b 0.01 for a significant difference (ANOVA). Because array analysis deals with large numbers of multiple comparisons, it is necessary to take into consideration the false discovery rate (FDR, Benjamini et al., 2001). Because of the number of samples per group is not very high (n = 5) we also included a uniform fold-change threshold of 1.5 fold up- or downregulation which further reduced the false positives. Fold changes in miRNA expression were calculated by dividing the mean intensity signal by the mean intensity signal from the reference samples. Hierarchical (unsupervised) clustering analysis (using Euclidean distance measures) was performed using the Spotfire Decision Site for Functional Genomics program (version 7.3). The log ratio values have been used for the analysis.
Pathway analysis MiRNA array profiling All profiling experiments were conducted at Exiqon Services, Denmark. The reference was created as a pool of all the samples. Total RNA (550 ng) from both sample and reference was labeled with Hy3™ and Hy5™ fluorescent labels, respectively, using the miRCURY LNA™ miRNA Hi-Power Labeling Kit following the procedure described by the manufacturer. The Hy3™-labeled samples and a Hy5™-labeled reference RNA sample were mixed pair-wise and hybridized to the miRCURY LNA™ miRNA Array 6th Gen (Exiqon, Denmark), which contains capture probes targeting all miRNAs for human, mouse or rat registered in the miRBASE 16.0 (http://www. mirbase.org/; Griffiths-Jones et al., 2008). The hybridization was performed according to the miRCURY LNA™ miRNA Array Instruction manual using a Tecan HS4800™ hybridization station (Tecan, Austria). After hybridization, the microarray slides were scanned and stored in an ozone-free environment (ozone level below 2.0 ppb) to prevent bleaching of the fluorescent dyes. The miRCURY LNA™ miRNA array slides were scanned using the Agilent G2565BA Microarray Scanner System (Agilent Technologies, Inc., USA) and the image analysis was carried out using the ImaGene® 9 (miRCURY LNA™ miRNA Array Analysis Software, Exiqon, Denmark). The quantified signals were background corrected (Normexp with offset value 10, see Ritchie et al., 2007) and normalized using the global LOWESS (LOcally WEighted Scatterplot Smoothing) regression algorithm (Cleveland, 1979).
In order to identify molecular pathways that are altered by the expression of multiple miRNAs, each list of significantly regulated miRNAs was submitted to DIANA mirPath, a web-based computational tool that identifies potentially altered molecular pathways by the expression of multiple microRNAs (http://diana.cslab.ece.ntua.gr/ pathways). This program performs an enrichment analysis of miRNA target genes, and compares each set of miRNA targets to all known KEGG pathways. We used the DIANA-microT4 algorithm for target prediction. Only exact miRNA matches were included in the input list. We submitted separate input lists of upregulated and downregulated miRNAs at each stage (D, W, M). Since the number of miRNAs that can be imported and processed was limited by mirPath, the tables represent pathways generated from input lists obtained at each specific stage and region as well as from all three (or two regions) combined. Those pathways that are generated from lists of N3 regulated miRNAs in all 3 (or 2 combined or single) regions are presented in Supplementary Table S7. The graphical output of the program (Figs. S1–6) provides an overview of the parts of the pathway modulated by miRNAs. Furthermore we used miRWalk, a database that provides information on predicted and validated target gene binding sites of miRNAs (http://www.umm.uniheidelberg.de/apps/zmf/mirwalk; Dweep et al., 2011). The Validated Targets module hosts experimentally verified miRNA interaction information associated with genes, pathways, organs, diseases, cell lines, all known Mendelian disorders (OMIM) and literature on miRNAs (Dweep et al., 2011).
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TaqMan miRNA qPCR assays miRNA expression levels were determined using the TaqMan miRNA qPCR assays (Applied Biosystems, Foster City, CA). We analyzed miR-21-5p, mir-34c-5p, miR-137-3p, miR-142-5p, miR-146a-5p, and miR-485-5p and the small RNA Rnu6B in the brain tissues. MiR-21-5p, miR-146a-5p and miR142-5p were determined in the plasma; miR-23a was used as reference for plasma (Blondal et al., 2013). cDNA was generated using the TaqMan MicroRNA reverse transcription kit (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions and the PCRs were run on a Roche LightCycler 480 thermo cycler (Roche Applied Science, Basel, Switzerland). Quantification of data was performed using the computer program LinRegPCR in which linear regression on the Log (fluorescence) per cycle number data is applied to determine the amplification efficiency per sample (Ramakers et al., 2003; Ruijter et al., 2009). The starting concentration of each specific product was divided by the starting concentration of reference genes (Rnu6B for tissues and miR-23a for plasma; Blondal et al., 2013) and this ratio was compared between stimulated/control groups.
In situ hybridization In situ hybridization for miR-146a was performed using a 5′ fluorescein labeled 19-mer antisense oligonucleotide containing Locked Nucleic Acid and 2′OME RNA moieties (FAM — AacCcaTggAauTcaGuuCucA, capitals indicate LNA, lower case indicates 2′OME RNA). The oligos were synthesized by RiboTask ApS, Odense, Denmark. The hybridizations were done on 6 μm sections of paraffin embedded tissue as described previously (Aronica et al., 2010). Signal was detected with chromogens 3-amino-9-ethyl carbazole (St. Louis, MO, USA) or Vector NovaRED (Vector Laboratories, Burlingame, CA, USA) and the nuclei were stained with hematoxylin. In situ hybridization for miR-21 was performed on paraffin section using a 5′–3′double digoxigenin (DIG)-labeled (miR-21; 5′ DIGTcaAcaTcaGucTgaTaaGcuA-DIG; Ribotask A/s, Odense, Denmark). The probe was hybridized at 53 °C for 1 h and the hybridization was detected with alkaline phosphatase (AP) labeled-anti DIG (Roche, USA). NBT (nitro-blue tetrazolium chloride)/BCIP (5-bromo-4-chloro3′-indolyphosphate p-toluidine salt) was used as chromogenic substrate
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for AP. Negative control assays were performed without probes and without primary antibody (sections were blank). For the doublestaining, combining immuno-cytochemistry with in situ hybridization, sections were first processed for ISH and then processed for immunocytochemistry as previously described (Aronica et al., 2003) with GFAP (GFAP; monoclonal mouse, Sigma, San Louis, Mo, USA 1:4000), NeuN (neuronal nuclear protein; mouse clone MAB377; Chemicon, Temecula, CA, USA; 1:2000) or Iba1 (ionized calcium binding adaptor molecule 1; polyclonal rabbit; WAKO, Osaka, Japan 1:200). Signal was detected with chromogen 3-amino-9-ethyl carbazole (St. Louis, MO, USA).
Results Hierarchical clustering First, we investigated in control animals whether each region can be characterized by its own miRNA expression profile using hierarchical clustering analysis that included all 322 miRNAs. The clustering separated the expression patterns of the different regions (CA1, DG, PHC; Fig. 2a). It has been previously reported that each brain region might have its own characteristic miRNA expression pattern (He et al., 2007; Olsen et al., 2009). Here we show region-specific miRNA expression characteristics, within the hippocampus (CA1 vs DG) and the nearby input region, PHC. We then determined that there was a clear separation between epileptogenesis groups (Fig. 2b). The samples cluster in the 1 day (D) and 1 week (W) and chronic groups (M) after SE. Chronic and control groups are next to each other and one chronic sample is in the cluster of the control group, indicating that these two groups are more similar than the others. Heatmaps constructed for DG and PHC revealed similar clustering of groups (data not shown). We calculated the number of miRNAs that were significantly up- or downregulated with respect to control expression. Significance was determined with a minimum fold change of 1.5× compared to control and a p b 0.01. Using these criteria, we found more upregulated than downregulated miRNAs at each time point (D, W, M) in each region except in the PHC. For M, many more miRNAs were upregulated in DG (37) than in CA1 (7) or PHC (22). An overview of the significant up- and downregulated miRNAs in the different subregions during the acute stage (D), the latent stage (W) and the chronic stage (M) is shown in Fig. 3a.
Fig. 2. a) The heat maps show the result of two-way hierarchical clustering of the miRNAs and samples. The clustering is done using complete-linkage method together with the Euclidean distance measure. a) Each row represents a miRNA (total 322) and each column represents a sample from CA1, DG and PHC region of control rats; the miRNA clustering tree is shown on the left. The sample clustering tree is shown on the top; it shows nice separation of brain areas (n = 5 for CA1, DG and PHC of control rats). b) The heat map shows the result of two-way hierarchical clustering of the miRNAs and samples taken at 3 different post-SE stages (D, W, M) and controls (C); the sample clustering (top dendrogram) shows nice separation of each specific epileptic stage (n = 5 for each group). Note that one chronic (M) sample clusters with the control samples. The unsupervised hierarchical clustering analysis was performed on all samples and the 322 miRNAs that were included in the analysis using Spotfire Decision Site for Functional Genomics program. Red represents high expression, white average expression and blue low expression.
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Venn diagrams of significantly regulated miRNAs Venn diagrams in Fig. 3b show the number of miRNAs that are upregulated during the three post-SE stages in each brain region. In CA1, many miRNAs were uniquely regulated in each period. In total, 18 miRNAs were up at 1 day, 16 at 1 week and 7 at 3 months after SE. miR-21 was upregulated in both acute stage and latent stage. miR-143, miR-23a, and miR-27a were upregulated in both latent stage and chronic stage. In DG, 20 miRNAs were up at 1 day, 15 at 1 week and 37 in the chronic phase. 4 miRNAs were upregulated at all three time points: miR-132, miR-132*, and miR-21 and miR-212. During both the acute stage and the latent period (and not chronic), miR-142-5p was upregulated. During both latent period and chronic stage (and not in acute stage), three miRNAs were upregulated: miR-146a, miR-212*, and miR-23a. During both acute and chronic stages (but not in latent period), only miR-223 was upregulated. In PHC, 31 miRNAs were up at 1 day, 37 at 1 week and 22 in the chronic stage. Two miRNAs (miR-21 and miR-223) were upregulated during all three time periods. During the acute and latent stages, twelve miRNAs were upregulated: miR-142-5p, miR-294, miR-32*, miR-351, miR-3596c, miR-466b-1*, miR-466b-2*, miR-466c*, miR-466d, miR-675, miR-758*, and miR-883*. During both latent stage and chronic stages (and not in acute stage), six miRNAs were upregulated: miR-193, miR-199a-3p, miR-199a-5p, miR-23a, miR-27a and miR-34a. Next, we constructed Venn diagrams of miRNAs that were downregulated at each specific stage after SE. The Venn diagrams in Figs. 3c and S1b show that the number of downregulated miRNAs in CA1 and DG
was low during all three stages. In PHC region, 68 miRNAs were downregulated during the acute stage, 102 during the latent stage and 7 during the chronic stage. The largest overlap was shown during the acute and latent phases (44 miRs). Mir-137 was the only one downregulated during all three stages. The other specific miRNAs can be found in the Supplementary tables. Time-dependent miRNA expression changes of upregulated miRNAs In order to better appreciate the dynamics and the magnitude of miRNA changes, we investigated the expression patterns of the miRNAs which had dynamic changes in expression pattern in DG, CA1 and PHC during the acute stage, the latent stage and the chronic stage. Three expression patterns were most abundant: one where miRNA expression peaked at 1 day, one peaking at 1 week and one peaking at 3–4 months after SE. We compared the expression levels with control levels (set at 1). Since the highest number of miRNAs was regulated in PHC, we compared expression in PHC with the expression pattern of the same miRNAs in the two other regions. In the chronic stage, when DG had the most upregulated miRNA expression, we compared expression in DG with expression of the same miRNAs in CA1 and PHC. Figs. 4a–c show the top 5 miRNAs that were significantly upregulated in PHC during the acute stage, the latent stage and the chronic stage (compared to control levels = 1). Note that the pattern in CA1 and DG is similar but that during the acute and latent stages the extent of upregulation is higher in PHC than in CA1 or DG. On the other hand during the chronic stage (M), expression levels are higher in DG compared to expression in the other regions.
Fig. 3. a) The number of altered miRNAs (1.5× greater or lesser than control expression and p b 0.01) per region at 1 day (D), 1 week (W) and 3 months (M) after SE. b) Venn diagrams representing the number of upregulated miRNAs (N1.5 fold and p b 0.01) during the three different epileptic stages (D, W, M) for CA1, DG and PHC are indicated. c) Venn diagrams representing the number of downregulated miRNAs (N1.5 fold and p b 0.01) during the three different epileptic stages (D, W, M) for CA1, DG and PHC are indicated.
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Fig. 4. Fold change patterns at 1 day, (D) 1 week, (W) and at 3–4 months (M) after SE of the top 5 miRNAs that were significantly upregulated (a–c) or downregulated (d–f) at 1 day, (D) 1 week (W) or during the chronic epileptic stage (M) in PHC, CA1 and DG. Note that the pattern in PHC is often similar to the pattern in CA1 and DG and that during the acute and latent stages the extent of upregulation is higher on average in PHC than in CA1 or DG. On the other hand during the chronic stage (M) significantly changed DG miRNAs express a higher-fold change than in PHC. The individual miRNAs are indicated in the boxes to the left of each plot.
Time-dependent miRNA expression changes of downregulated miRNAs We distinguished three main expression patterns based on the downregulated miRNAs in the PHC, where most downregulated miRNAs were detected: one pattern with significantly decreased levels at 1 day, one with significantly decreased levels at 1 week and one with significantly decreased levels at 3-4 months after SE. The latter often displayed lowest levels at 1 week after SE and higher, but still decreased, levels in the chronic stage. Patterns in the three regions were often, but not always, similar. The top 5 downregulated miRNAs are presented in the panels (d–f) in Fig. 4. Simultaneous analysis of multiple miRNAs The combinatorial effect of co-expressed miRNAs in the modulation of a given pathway can be determined by the simultaneous analysis of multiple miRNAs (Artmann et al., 2012). As an example we present here the outcome of analysis of the list that consists of 11 miRNAs that are upregulated in all 3 regions at 1 week post-SE. The program revealed various relevant pathways (with the number of genes affected by the set of miRNAs in each case; see Supplementary Table S-7). Different cancer related pathways – that are composed of a number of more specific signaling pathways – were omitted from these lists. The top 10 (which includes those specific signaling pathways) includes: axon guidance, MAPK signaling pathway, focal adhesion, ErbB signaling pathway, regulation of actin cytoskeleton, Wnt signaling pathway, insulin signaling pathway, tight junction, GnRH signaling pathway and adherens junction (see Supplementary Table S-7). Of the top 10 pathways that emerged from the 11 downregulated miRNAs (in CA1 and PHC combined) at 1 week, six were similar
to the list of pathways that were indicated by upregulated miRNAs. Fig. 5 shows the genes in the MAPK signaling pathway that are regulated by altered miRNA expression in the DG at 3–4 months post-SE. Several of the same genes are also regulated by the downregulated miRNAs (not shown, but see Supplementary Table S7). Various pathways also emerge as significantly changed during the two other stages. Axon guidance, MAPK signaling, TGFβ signaling, Wnt signaling, ErbB signaling, focal adhesion and mTOR signaling pathways are represented at almost every stage in both lists that are produced by the upand downregulated miRNAs respectively. Some of these pathways (mTOR, ErbB signaling and axon guidance) and their affected genes are shown in Supplementary Figs. S2–7. Interestingly, the combinatorial analysis showed that one gene can be affected by several different miRNAs that are downregulated, while at the same time this gene can also be targeted by other upregulated miRNAs (see Supplementary Table S-7). Verification of differentially expressed miRNA We selected miRNAs, differentially regulated in the three different regions and at different time points for validation with PCR. Significantly higher expression levels, as compared to controls, were detected for miR-146a-5p and miR-21-5p at 1 week post-SE in DG, CA1 and PHC (p b 0.001, Figs. 6a and b). The pattern of expression of both these miRNAs was similar to that observed with the array. Similarly, miR-142-5p was upregulated in all regions at 1 week post-SE (Fig. 6c), whereas miR-34c-5p (Fig. 6d) showed a tendency towards increased expression in DG and PHC at 3 months post-SE reflecting the pattern observed in the array. A significantly decreased expression of miR-137-3p was observed at 1 week post-SE in CA1 and PHC (p b 0.05
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Fig. 5. The graphical output of the DIANA mirPath program provides an overview of the genes in the MAPK pathway (KEGG) that are affected by an upregulation of miR expression in the DG during the chronic stage (in yellow).
and p b 0.001 respectively) whereas the expression reverted to control levels at 3-4 months post-SE (Fig. 6e). A similar expression pattern was also observed for miR-485-5p (p b 0.05) only in the PHC (Fig. 6f). Moreover, miR-485 showed a tendency towards decreased expression at 1 week and 3-4 months post-SE in both DG and CA1 although the difference was not statistically significant. In situ hybridization at 1 week post-SE showed increased expression of miR-21 in both neurons and glial cells (inset in Figs. 6a and 7) and miR-146a in reactive astrocytes (inset in Fig. 6b). Double labeling confirmed miR-21 expression in neurons and microglial cells, showing variable expression in reactive astrocytes (Fig. 7).
Detection of miRNA in plasma The expression of three of the significantly upregulated miRNAs, miR-21-5p, miR-146-5p and 142-5p was assessed in the plasma of the different animal groups. Significantly increased expression of miR-21-5p was observed at 1 week post-SE as compared to control plasma (Fig. 8; p b 0.01). The pattern of expression at the different time points was similar to that observed in both array from brainderived RNA and PCR validation of the different hippocampal areas. However, the pattern of expression of miR-146a-5p and 142-5p was different from the pattern observed in brain areas; miR-146a-5p showed a tendency towards increased expression at 3-4 months postSE, but not at 1 week after SE and 142-5p showed increased expression at 24 h but not at 1 week after SE (Fig. 8).
Discussion We report dynamic changes in miRNA expression during the process of epileptogenesis in the post-SE model of TLE in the rat. The uniqueness of our study is a systematic evaluation of the miRNAome in different regions of the hippocampus with differential sensitivities to epileptogenesis at defined time points in a well-characterized rat model of epileptogenesis. This expression profile was then used to select and assess three significantly deregulated miRNAs in blood plasma for identification of possible biomarkers for epileptogenesis. We observed that plasma levels of three inflammation-associated miRNAs changed considerably in the plasma, suggesting that these could function as biomarkers during different post-SE stages. The miRNA expression profiles showed stage-specific changes in miRNA expression. The expression patterns during each stage were similar for many miRNAs among the three regions, but the extent of expression could differ considerably, and significant differences were also observed. In PHC, many more downregulated miRNAs were detected than in the other two regions. Many of the individual miRNAs that we found to be significantly deregulated during epileptogenesis have been previously detected in different types of cancer, neurodegenerative diseases, ischemia, injury and epilepsy. Below we discuss these findings and compare the miRNA expression patterns with what has been reported in other studies. We acknowledge some limitations to the interpretation of our results which are inherent to large-scale genomic studies. First, the detected changes in miRNA expression (also in specific anatomical
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Fig. 6. Quantitative real-time PCR of miR-21-5p (a), miR-146a-5p (b), miR-142-5p (c), miR-34c-5p (d), miR-137-3p (e) and miR-485-5p (f) expression (fold change, compared to control expression) in DG, CA1 and PHC in rats at 24 h, 1 week and 3–4 months after status epilepticus (SE; *p b 0.05, **p b 0.01, ***p b 0.001 compared to controls; #p b 0.05, ##p b 0.01, ### p b 0.001 comparison between different time points). Insets in a: in situ hybridization analysis for miR-21 in control (a; CA1) and 1 week post-SE (b; CA1) showing increased expression in both neurons (arrows) and glial cells (arrow-heads); scale bar: 80 μm. Insets in b: in situ hybridization analysis for miR-146a in control (a; CA1) and at 1 week post-SE (b; CA1) with increased expression, particularly in astroglial cells (high magnification is shown in c); inset in c shows co-localization with the astroglial marker GFAP (purple); scale bar: a–b 160 μm; c, 40 μm.
subfields) may reflect the altered cellular composition within tissue samples, including neuronal cell loss as well as reactive astrogliosis and microglia activation. As an example we show the cellular distribution of miR-146a that was present in neurons in control hippocampus (Aronica et al., 2010) and at 1 week post-SE miR-146a expression was also detected in glial cells with typical astroglia morphology, particularly in the areas of prominent gliosis. The expression pattern of miR-21 at 1 week post-SE shows that this miRNA is upregulated in both neurons and glial cells. Second, the functional role of distinct miRNAs should be considered with precaution. More cellular localization studies in both experimental models and human tissue, as well as functional studies in vitro and in vivo will represent an essential extension of this study and can establish the functional significance of the individual miRNA pathway regulation in TLE and other epilepsy associated pathologies.
Region-specific miRNA expression patterns during epileptogenesis There was a large overlap of upregulated miRNAs between all three regions during the acute and latent stages (D and W). This might reflect the pathological changes – that include cell death and gliosis – that are most extensive during the first week after SE in all three brain regions (Gorter et al., 2003, 2006), while sprouting and neurogenesis start later after SE in the DG. In the DG, most upregulated miRNAs (37) were detected during the chronic stage, which indicates that during this stage the DG has quite different regulations than CA1, where only 7 miRNAs were upregulated, or – to a lesser extent – PHC, where 22 miRNAs were significantly upregulated. Since axon guidance is also indicated as significant process in the other two regions at this chronic stage (Supplementary Tables S7), the exact significance of this late increase of miRNAs within the DG is presently not known and requires
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Fig. 7. In situ hybridization analysis of miR-21 expression in hippocampal tissue of control rats and after induction of SE. Panels a, c, e, g: control hippocampus showing low expression of miR-21 in the different hippocampal subfields (c, CA1; e, dentate gyrus, DG; g, parahippocampal cortex, PHC). Panels b, d, f, h: hippocampus 1 week post-SE showing increased miR-21 expression within the different hippocampal regions, including CA1 (d), DG (f) and PHC (h). Insets (a) in d, f, h: high magnification showing strong neuronal expression (arrows; arrowheads indicate positive glial cells). Inset (b) in d shows miR-21 expression in a pyramidal neuron (NeuN positive, red); insets (c) in d and (b) in f show co-localization with the microglial marker Iba1 (red); insets (c–d) in f show co-localization with the astroglial marker GFAP (red). Scale bar in a: a–b: 500 μm; c–h: 100 μm.
more functional knowledge of the specific miRNAs involved and additional bioinformatics evaluation. While in both CA1 and DG, more upregulated than down-regulated miRNAs were present during all three stages, the PHC region showed mostly downregulated miRNAs, especially in the D and W groups. Considering that decreased miRNA expression leads to less target mRNA degradation and/or less suppression of translation, this suggests that PHC reacts in a different biological fashion after the initial insult than CA1 and/or DG, where only a few miRNAs were downregulated during all three stages. However, it is likely that those changes are not only related to the neuropathological changes that occur after SE. It is tempting to speculate that this distinct miRNA regulation might be related to the hyperexcitability that is
increased in PHC (entorhinal cortex) compared to both DG and CA1 (Kumar and Buckmaster, 2006; Kumar et al., 2007; Tolner et al., 2005, 2007), but this needs to be substantiated with experimental proof and more knowledge about miRNA function in relation to excitability. The miRNAs that were significantly changed in the W group had the most conspicuous pattern since their highest (or lowest) expression was most often at this specific time point, while expression of the same miRNA at the other time points was not necessarily significantly different from control expression. Previous gene expression studies in several TLE rat models indicate that the immune and inflammatory responses are the most prominent processes during this latent stage (Aronica and Gorter, 2007; Gorter et al., 2006; Lukasiuk et al., 2006),
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Fig. 8. Quantitative real-time PCR of miR-21-5p, miR-146a-5p and miR-142-5p expression (fold change, compared to control expression) in plasma of rats at 24 h, 1 week and 3 months after status epilepticus (SE; *p b 0.05, **p b 0.01, compared to controls; ## p b 0.01, comparison between different time points).
supporting the relevance of inflammation in the pathophysiology of TLE (Aronica and Crino, 2011; Vezzani et al., 2011a). In the W group, we identified significant up- and downregulation of miRNAs involved in inflammatory processes such as TGFβ signaling and Toll-like and interleukin-1 receptor signaling, both of which represent major proepileptogenic pathways, activated in experimental and human epilepsy (Aronica and Gorter, 2007; Cacheaux et al., 2009; Gorter et al., 2006; Maroso et al., 2011; Vezzani et al., 2013). These inflammationassociated genes are targeted by the expression of miRNAs such as miR-146a, miR-19b, miR-20b and miR-21, which are upregulated, and miR-33 (amongst others), which are downregulated during this stage. The upregulation of miR-146a at 1 week after SE has been previously demonstrated in the CA3 region in the same rat model (Aronica et al., 2010). This miRNA has also been shown to be upregulated in the hippocampus of lithium-pilocarpine-induced SE rat model in three independent studies (Hu et al., 2012; Omran et al., 2012; Song et al., 2011), as well as in human TLE (Aronica et al., 2010; Omran et al., 2012). In situ hybridization demonstrated increased expression in astrocytes, particularly at 1 week after SE, which corresponds to the time of maximal astroglial activation (Aronica and Gorter, 2007). In a recent study, we confirmed the induction of this miRNA by IL-1β in human astrocytes and provided evidence of its role as a negativefeedback regulator of the astrocyte-mediated inflammatory response (Iyer et al., 2012). At 1 week post-SE, miR-21 is one of the most upregulated miRNAs in all three regions. This miRNA, overexpressed in many pathological conditions including various cancers, cardiovascular disease, after brain injury and ischemia, can be induced by NF-κB and has been suggested to act as negative-feedback regulator of Toll-like receptor signaling via targeting of the pro-inflammatory tumor suppressor PDCD4 (Sheedy et al., 2010; for review see Kumarswamy et al., 2011; O'Connell et al., 2012). In the pilocarpine-induced SE rat model, both miR-21 and its co-transcript miR-21* have been shown to be upregulated in the hippocampus at 4 and 48 h. However, only miR-21 is still upregulated three weeks after SE, suggesting differential posttranscriptional processing of these miRNAs after SE (Risbud et al., 2011). Accordingly, in our study, miR-21* peaks at 1 day after SE in CA1, DG and PHC, whereas miR-21 expression is significantly increased during the latent period (1 week post-SE) in all three regions; a tendency to increased expression was observed in DG and PHC also three months after SE. Besides the suggested role of miR-21 in the regulation of the immune response (Kumarswamy et al., 2011; O'Connell et al., 2012), this miRNA has been indicated as a candidate for regulating neurotrophin-3 signaling in the hippocampus following SE (Risbud et al., 2011). Moreover, a critical neuronal transcription factor, myocyte enhancer factor 2C (MEF2C, involved in MAPK signaling; indicated in
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red lined box in Fig. 5), has also been identified as a target of miR-21 and miR-21*, suggesting a critical role for this miRNA in neuronal dysfunction and neurodegeneration (Yelamanchili et al., 2010). Interestingly, mutations in the MEF2C gene have been described in patients with severe mental retardation and epilepsy (Bienvenu et al., 2013; Nowakowska et al., 2010). Recent studies indicate expression of this miRNA in both neuronal and glial cells (Bhalala et al., 2012; Buller et al., 2010; Zhang et al., 2012), suggesting also a cell-specific temporal expression pattern after ischemic injury (Ziu et al., 2011). We also looked at expression of several interesting plasticity-related candidate miRNAs such as mir-485 which has been implicated in homeostatic plasticity and regulation of SV2A expression (Cohen et al., 2011). Immunohistochemistry has previously indicated a significant decrease of SV2A expression in CA1 and DG during latent and chronic stages (van Vliet et al., 2009). However, no significant change in miR-485 expression was found in any region except for a downregulation in PHC during the acute and latent stages. Silencing of miR-134, which is upregulated in a kainate mouse model with local hippocampal damage, has been shown to have anti-seizure effects (Jimenez-Mateos et al., 2012), possibly via negatively regulating dendritic spine size by inhibiting translation of LIM domain kinase 1 (Limk1) (Schratt et al., 2006). In the present study, miR-134 was eliminated by our filtering procedure. However, notably, we found that miR-138, which also inhibits translation of LIMK1, was downregulated in all three regions, especially during the chronic stage. In contrast, miR-223, which targets glutamate receptor subunits (Harraz et al., 2012) was upregulated during acute and chronic time points in both PHC and DG. Overexpression of this miR has been reported to be neuroprotective against NMDA induced excitotoxicity and carotid artery occlusion induced damage. Pathway analysis Combinatiorial analysis identified a series of genes and pathways that were targeted by the miRNAs altered during epileptogenesis. A notable finding was that the pathways that were regulated by miRNAs at the different stages did not appear to differ much among the three brain regions. Although during the acute and latent stages this could be expected (considering the extensive overlap of deregulated miRNAs between regions), the presence of distinct pathways would be expected during the chronic phase, in particular when comparing the DG with the other two regions. Also when we analyzed the list with exclusively deregulated genes in DG only at 3–4 months post-SE, the top 10 list of pathways is not very different (see Tables S7). Another notable fact is that several affected genes that were targets of upregulated miRNAs also appeared to be targets of downregulated miRNAs. Thus, miRNAs up- and downregulated after SE might target the same gene leading to opposite effects on gene expression. This is reminiscent of results from microarray mRNA studies in rat TLE models, where activation of a specific process was often counterbalanced by activation of inhibitors of this process (e.g. proteolysis was affected by upregulation of protease-encoding genes, as well as their cognate protease inhibitor genes; Gorter et al., 2006; Lukasiuk et al., 2006, 2011). Ultimately, the expression of an mRNA will depend on the balance between the number and extent (fold-change) of up- and downregulated miRNAs that target a specific gene. In addition to the dual pressure on mRNAs by different miRNAs, we note also that there is extensive crosstalk among the different signaling pathways that increase the complexity of the response and of its interpretation. MAPK signaling Mitogen-activated protein kinases (MAPKs) are serine–threonine kinases that mediate intracellular signaling associated with a variety of cellular activities that include survival, death, proliferation and differentiation. MAPK signaling involves different pathways including ERK
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signaling, p38 and JNK signaling which all can be activated by factors such as pro-inflammatory cytokines, epidermal growth factor and stressors. Deregulation of MAPK signaling has been implicated in various cancers and a variety of neurodegenerative diseases such as Alzheimer's, Parkinson's and motor neuron diseases. Inhibitors for ERK, MEK or Jun are being developed as potential therapeutic drugs for Alzheimer's disease. Mice with a constitutively active form of MEK1 have spontaneous epileptic seizures (Nateri et al., 2007), indicating also a causative relation of MAPK signaling with the development of epilepsy. We found many upregulated miRNAs during epileptogenesis that are involved in this pathway in both CA1 and DG, including miRNAs that are also associated with inflammation (see above). In PHC, more genes are affected by the high number of downregulated miRNAs which suggests that brain targeting focused on this pathway might be too limited. Axon guidance The most well-known example of synaptic reorganization and axon guidance in TLE is the sprouting of mossy fibers, which in this rat model starts immediately after SE and continues during the first few months, well into the chronic epileptic stage (Nateri et al., 2007). The mechanisms orchestrating the complex series of steps required for mossy fiber sprouting (or indeed any brain reorganization) are poorly understood. miRNA changes drive alteration in gene expression and could conceivably fulfill the role of coordinating complex changes in patterns of gene expression. In keeping with this possibility, ‘axon guidance’ is in the top three pathways at each time point measured in our study. Semaphorin and ephrin genes are a particular focus of a variety of miRNAs (see figure S2 in Appendix). Decreased expression of Sema3A mRNA in the PHC could potentially contribute to mossy fiber sprouting (Holtmaat et al., 2003), while ephrin interaction can regulate epileptogenesis and mossy fiber sprouting (Xu et al., 2003). A recent study showed that inactivation of ephrinB2 (that can phosphorylate NR2B) greatly reduces seizure frequency in mice with an active form of MEK1, linking MAPK signaling to axon guidance molecules and NMDA receptor signaling (Nateri et al., 2007). Other pathways that also appeared in the top ten of the pathway list included TGFβ signaling, ErbB (epidermal growth factor receptor) signaling, mTOR signaling, Wnt signaling, insulin signaling pathway, ubiquitin-mediated proteolysis and focal adhesion. Several of these pathways (mTOR and ErbB signaling) are illustrated in the Appendix. Microarray studies have previously shown that several of these pathways are involved in epileptogenesis (Cacheaux et al., 2009; Gorter et al., 2006; Lukasiuk et al., 2006). Anti-epileptogenic therapies have previously focused on several of these pathways e.g. by (pharmacological) inhibition of mTOR by rapamycin (Parker et al., 2013; Russo et al., 2013; van Vliet et al., 2012; Zeng et al., 2008) and TGFβ signaling (Cacheaux et al., 2009; Friedman et al., 2009). MiRNAs that can target specific genes controlling these pathways might also be interesting candidates for anti-epileptogenesis. The Wnt pathway has been implicated in epilepsy (Busceti et al., 2007), in particular in relation to the neurodegenerative aspects of TLE. Upregulation of Neuregulin-1 (NRG1)-ErbB4 signaling after seizures has also recently been reported (Tan et al., 2012). Interestingly, seizures can be suppressed by intracerebral infusion of NRG1 and exacerbated by inhibiting ErbB4 activation or depletion of ErbB4 in a subset of interneurons (Tan et al., 2012). Moreover, expression of ErbB4 is downregulated in human epileptogenic tissue (Li et al., 2012). Thus ErbB activators or miRNAs that increase ErbB signaling could have anti-epilept(ogen)ic potential and should be tested further. miRNAs as biomarker Recently, extracellular miRNAs have been identified in various body fluids, including plasma and serum and have been suggested to
represent clinically useful biomarkers for various diseases, including central nervous system (CNS) diseases (for reviews see Jin et al., 2013; Zhang et al., 2013). The source of these circulating miRNAs is still a matter of discussion. They may derive from circulating blood, but recent studies indicate that they may be also released from tissues (including brain tissue) under pathological conditions. Several mechanisms have been suggested, including passive leakage from damaged cells (necrotic or apoptotic cells), as well as mechanisms of active secretion via microvesicles (i.e. exosomes), or via a microvesiclefree RNA-binding protein-dependent pathway (for reviews see Jin et al., 2013; Zhang et al., 2013). Moreover, the secreted miRNAs may represent a new form of intercellular communication, acting as signaling molecules (Jin et al., 2013; Lehmann et al., 2012; Zhang et al., 2013). Whether peripheral blood closely reflects the changes in miRNA expression in the brain is still an open question (Gallego et al., 2012). However, the fact that miR-146a is increased at later time points in plasma than in brain suggests that other mechanisms also play a role. An increasing number of studies have recently addressed the potential role of circulating miRNAs for the diagnosis and therapy in different CNS disorders (for review see Jin et al., 2013). Brain-enriched plasma miRNAs have also been recently proven to represent potential biomarkers for detection of mild cognitive impairment (Sheinerman et al., 2012). Circulating miRNAS (such as miR-21) may also serve as useful biomarkers in patients with glial tumors (Ilhan-Mutlu et al., 2012). Until now, however, limited information has been provided concerning the roles of circulating miRNAs in epilepsy-associated conditions. We hypothesized at the outset that deregulated brain miRNAs might be present and altered also in blood in TLE. Since miRNAs are very stable in plasma samples (Mitchell et al., 2008), they could be an ideal class of biomarkers for the prediction of epilepsy or drug response. We observed significant changes in plasma levels of miR-21 at an early stage (latent period). The change in miR-21 plasma expression corresponded with the change observed in different brain regions. For miR-146a, the increased plasma level appeared later, when chronic seizures were present, whereas miR-142 was increased during the acute stage. Liu et al. (2010) reported changes in plasma miRNA levels after pilocarpineinduced SE. These miRNAs are also associated with regulation of the immune response, suggesting that miRNAs might be suitable biomarkers at early stages of epileptogenesis, possibly to indicate the extent of associated brain injury. In order to assess whether these miRNAs are specific and sensitive enough to be future biomarkers, ROC (receiver operating characteristic) analysis will be needed in a larger number of samples. Moreover, considering that epileptogenesis is a very complex process, evaluation of additional miRNAs, selected on the basis of the present study, will be carried out in future studies.
Conclusion We have presented a large number of miRNAs that are altered after electrically-induced SE. Many of these miRNAs have been previously noted in relation to other neurodegenerative diseases and seem to regulate a survival response. Some of the pathways that emerged, most notably focal adhesion, the ErbB and Wnt signaling pathways, are relatively unexplored in relation to epilepsy. Silencing or activation of miRNAs that target genes involved in these pathways, or specific pharmacological interference, could be considered in vivo in order to establish whether they are promising therapeutic anti-epileptogenic strategies. Moreover, the parallel increased expression in blood of three deregulated brain miRNAs during epileptogenesis shows that they could serve as potential biomarkers. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.nbd.2013.10.026.
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Acknowledgments This work was supported by the National Epilepsy Fund — “Power of the Small”, the Hersenstichting Nederland (NEF 13-01, EA) and an unrestricted grant from UCB Pharma (SS, AE, JAG), and supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The authors are grateful to J. Anink and F. Bruin (Neuropathology, AMC) for their technical help. References Aronica, E., Crino, P.B., 2011. Inflammation in epilepsy: clinical observations. Epilepsia 52 (Suppl. 3), 26–32. Aronica, E., Gorter, J., 2007. Gene expression profile in temporal lobe epilepsy. Neuroscientist 13, 1–9. Aronica, E., et al., 2003. Expression and cell distribution of group I and group II metabotropic glutamate receptor subtypes in Taylor-type focal cortical dysplasia. Epilepsia 44, 785–795. Aronica, E., et al., 2010. Expression pattern of miR-146a, an inflammation-associated microRNA, in experimental and human temporal lobe epilepsy. 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