Brain Advance Access originally published online on September 26, 2008
Brain 2008 131(11):3034-3050; doi:10.1093/brain/awn233
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Array analysis of epilepsy-associated gangliogliomas reveals expression patterns related to aberrant development of neuronal precursors
1Department of Neuropathology, 2Department of Pathology, University of Bonn Medical Center, Bonn, 3Gene Center, Ludwig-Maximilians-University, Munich, 4Department of Epileptology and 5Department of Neurosurgery, University of Bonn Medical Center, Bonn, Germany
Correspondence to: Albert J. Becker, MD, Department of Neuropathology, University of Bonn Medical Center, Sigmund-Freud-Strasse 25, D-53105 Bonn, Germany E-mail: albert_becker{at}uni-bonn.de
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Summary |
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Gangliogliomas, the most frequent neoplasms in patients with pharmacoresistant focal epilepsies, are characterized by histological combinations of glial and dysplastic neuronal elements, a highly differentiated phenotype and rare gene mutations. Their molecular basis and relationship to other low-grade brain tumours are not completely understood. Systematic investigations of altered gene expression in gangliogliomas have been hampered by their cellular complexity, the lack of suitable control tissue and of sensitive expression profiling approaches. Here, we have used discrete microdissected ganglioglioma and adjacent control brain tissue obtained from the neurosurgical access to the tumour of identical patients (n = 6) carefully matched for equivalent glial and neuronal elements in an amount sufficient for oligonucleotide microarray hybridization without repetitive amplification. Multivariate statistical analysis identified a rich profile of genes with altered expression in gangliogliomas. Many differentially expressed transcripts related to intra- and intercellular signalling including protein kinase C and its target NELL2 in identical ganglioglioma cell components as determined by real-time quantitative RT–PCR (qRT–PCR) and in situ hybridization. We observed the LIM-domain-binding 2 (LDB2) transcript, critical for brain development during embryogenesis, as one of the strongest reduced mRNAs in gangliogliomas. Subsequent qRT–PCR in dysembryoplastic neuroepithelial tumours (n = 7) revealed partial expression similarities as well as marked differences from gangliogliomas. The demonstrated gene expression profile differentiates gangliogliomas from other low-grade primary brain tumours. shRNA-mediated silencing of LDB2 resulted in substantially aberrant dendritic arborization in cultured developing primary hippocampal neurons. The present data characterize novel molecular mechanisms operating in gangliogliomas that contribute to the development of dysplastic neurons and an aberrant neuronal network.
Key Words: glioneuronal; development; migration; network; epilepsy; dysplastic
Abbreviations:
ARF3, ADP-ribosylation factor 3; DNETs, dysembryoplastic neuroepithelial tumours; GFP, green fluorescent protein; HSJ2, heat shock 40 kDa protein 4; LDB2, LIM-domain-binding 2; LMO4, LIM domain only 4; MMP2, matrix metallopeptidase 2; NGFR, nerve growth factor receptor; PRKCB1, protein kinase C β-1; ST6GalNAc4, 
-N-acetyl-neuraminyl-2,3-β-galactosyl-1,3-N-acetyl-galactosaminide -2,6-sialyltransferase; TRIB1, tribbles homolog 1
Received February 7, 2008. Revised August 19, 2008. Accepted August 20, 2008.
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Introduction |
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Gangliogliomas, the most frequent tumours encountered in pharmacoresistant patients with focal epilepsies are composed of dysplastic neuronal and proliferative, neoplastic glial cells that comprise a considerable spectrum of cellular and histological architectures (Fig. 1) (Luyken et al., 2003
; Becker et al., 2007). Their focal nature and differentiated glioneuronal phenotype, the expression of the stem cell epitope CD34 and the benign clinical course suggest an origin from developmentally compromised or dysplastic precursor lesions (Blümcke et al., 2002; Fassunke et al., 2004). However, so far the molecular basis of gangliogliomas as well as their neuropathological relationship to other primary low-grade brain tumours such as dysembryoplastic neuroepithelial tumours (DNETs) or diffuse and pilocytic astrocytomas (Rorive et al., 2006; Daumas-Duport et al., 2007; Sharma et al., 2007) are poorly understood. Genetic studies have argued against the contribution of mutational events in genes involved in other brain tumours to play a major role in gangliogliomas (Becker et al., 2006). To control seizures, many patients with pharmacoresistant epilepsy due to gangliogliomas have to undergo neurosurgery. The resulting biopsy specimens provide a unique opportunity to analyse the molecular profiles of these glioneuronal tumours.
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Expression microarrays enable the comprehensive analysis of transcript alterations (Becker et al., 2003
; Lukasiuk et al., 2003; Crino and Becker, 2006; Kang et al., 2007; Aronica et al., 2008). However, central nervous and glioneuronal tumour tissue samples pose a particular challenge to expression array studies in several regards. The complex cellular composition of glioneuronal tumours needs its equivalent in the control tissue used. In a recent microarray study on gangliogliomas, tissue samples from brain autopsies have been used as control (Aronica et al., 2008), which require careful monitoring for potential variability due to mRNA-degradation and expression differences based on heterogeneity of genetic background of individuals. In an intriguing attempt to overcome cellular heterogeneity on the resulting data, individual ganglioglioma cell components were used for expression array analysis (Samadani et al., 2007). However, this approach relies on the substantial amplification of mRNA and might thereby limit the number of transcripts to be analysed.
In the present analysis, we have included only gangliogliomas, for which pre-existing brain tissue from the neurosurgical access to the neoplasm was available from the same patient. Thereby, we aimed to minimize inter-individual gene expression variability, which can profoundly contaminate differential gene expression patterns (Cheung et al., 2005; Spielman et al., 2007). Careful microdissection of ganglioglioma and control tissue resulted in mRNA amounts sufficient for high-density oligonucleotide array hybridization (Affymetrix, Santa Barbara, CA, USA) without repetitive mRNA amplification steps. In concert with real-time quantitative RT–PCR (qRT–PCR) and in situ hybridization for confirmation and further cellular resolution, we found 94 genes to be differentially expressed in gangliogliomas, including several novel genes of particular interest for the development of these tumours, i.e. protein kinase C and its target NELL2 as well as LIM-domain-binding 2 (LDB2), a transcriptional coactivator critical during embryonal brain development (Ostendorff et al., 2006). The present findings identify novel aberrant gene expression patterns in gangliogliomas that contribute to further elucidate the molecular basis of these tumours and differentiate them from other low-grade brain neoplasms.
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Surgical specimens
Biopsy samples were obtained from ganglioglioma patients with chronic pharmacoresistant temporal lobe epilepsy (TLE) in the Epilepsy Surgery Program at Bonn University (Supplementary Table S1, n = 6). Surgical removal of tumours was necessary to achieve seizure control in all patients after standardized pre-surgical evaluation using a combination of non-invasive and invasive procedures (Kral et al., 2002
; Kral et al., 2003). As for ganglioglioma patients and also for patients with DNETs (n = 7), informed and written consent was obtained from all patients for additional studies. Procedures were carried out in accordance with the Declaration of Helsinki and were approved by the local ethics committee. Patients have been carefully matched with respect to clinico-epidemiological criteria to minimize structural variability. Each specimen has been subjected to standardized neuropathological evaluation by two independent neuropathologists and classified according to the World Health Organization classification of tumours (Becker et al., 2007; Daumas-Duport et al., 2007).
Oligonucleotide microarray experiments
Transcriptome-wide expression analyses were performed by cRNA oligonucleotide microarrays (Affymetrix) similarly as described in detail before (Becker et al., 2003). Briefly, sample preparation, hybridization to HG U133A microarrays (Affymetrix), washing, staining and scanning in a GeneArray scanner (Agilent, Palo Alto, CA, USA) were performed according to the manufacturer's recommendations. Fresh frozen tissue samples were cut into 12 µm sections and were screened for control and tumour areas after haematoxylin–eosin staining. In order to establish the statistical possibility of pairwise-testing approaches only snap frozen tissue with valuable discrimination between tumour and control tissue from the neurosurgical access of the same patient was involved in this study. RNA was isolated from thin tissue slices using Qiagen RNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The RNA quality was evaluated according to several recently established quality markers, including the 3' : 5' signal ratios of reference gene transcripts (HUM-GAPDH 3.0, HUM-18S-rRNS 2.6) and RNA integrity number (RIN) (Stan et al., 2006). Due to absence of significant mRNA degradation based on these quality markers including RIN 
7 (RIN: 7.46 ± 0.2), the present samples were suitable for subsequent experiments. Approximately 2.3 µg of each total RNA was subjected to further microarray analysis. After reverse-transcription into cDNA using T7 RNA polymerase promoter sequences, cRNA amplification with biotinylated nucleotides was catalysed by T7 RNA polymerase and cRNA was fragmented to a mean size of 50 bp prior to hybridization to the chips.
Signal intensities for further analysis were determined by the GeneChip Microarray Suite (MAS) 5.0 software (Affymetrix) including RMA analysis. Subsequent data analyses were performed in R (v.1.9.1). Visual inspection of pairwise scatterplots and Q–Q plots confirmed that there were no hybridization failures or outliers, and no substantial rescaling of the data was necessary for array comparability. Cyclic lowess (locally weighted linear regression) was used as a normalization procedure (Yang et al., 2002). After normalization, the expression values showed the typical heavily right-skewed distribution on the absolute scale (Dudoit et al., 2002; Ballman et al., 2004). We excluded low-abundant gene transcripts, i.e. expression values, which had a mean expression value below 100 in both conditions. Apparently, there is no consensus concerning an optimal threshold for expression array analysis. The present conditions are similar to a recent approach (Becker et al., 2003). They represent a reasonable compromise between identification of differentially expressed low-abundant genes and avoiding augmented false positive results (Tang et al., 2002; Miller et al., 2008). This pre-filtering reduces the number of false positives in the low-transcript abundance region, which is prone to a large measurement error. According to an error model of Rocke and Durbin (2001), the error of the log-transformed data was approximately normally distributed for higher transcript abundance. Differential expression was estimated by paired t-tests for each transcript using log-transformed expression data. Detailed information on P-values for individual genes is presented in Table 1. Accounting for multiple testing issues, the P-values were adjusted by the Benjamini–Hochberg algorithm.
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Real-time polymerase chain reaction
Relative quantification (
Ct) was carried out for mRNA analysis in gangliogliomas as well as DNETs. In each case, adjacent control tissue of the same patient was obtained. The reference gene β-actin was used for normalization (Fink et al., 1998; Chen et al., 2001). qRT–PCR (ABI PRISM 7700) was carried out in a 13 µl reaction volume containing 6.25 µl SYBR Green PCR Master Mix (Applied Biosystems, Darmstadt, Germany), 0.375 µl forward and reverse primers (10 pmol/µl; Supplementary Table S2), 3.0 µl DEPC–H2O and 2.5 µl cDNA dissolved in DEPC–H2O. Duplicate reactions were carried out for each transcript. After pre-incubation for 10 min at 95°C, the PCR reaction was performed (20 s at 94°C followed by 30 s at 59°C and 40 s at 72°C, 50x). The SYBR Green fluorescence signal was measured in each cycle.
RNA in situ hybridization
Cellular resolution of expression patterns was obtained by RNA in situ hybridization. Thin tissue slices (12 µm) were cut on a cryostat, thaw-mounted on silane-coated slides, fixed with 4% (w/v) paraformaldehyde in phosphate buffer saline (PBS), dehydrated, and stored under ethanol and finally hybridized for 15–20 h in hybridization buffer [50% deionized formamide/10% (v/v) dextran sulphate (50%)/0.3 M NaCl/30 mM Tris/HCl (pH 7.4)/4 mM EDTA/1x Denhardt's/0.5 mg/ml polyadenylic acid/0.5 mg/ml denatured salmon sperm DNA] containing the radiolabelled probe with a specific radioactivity of 3500 c.p.m./µl. The labelled oligonucleotide probes were generated with terminal transferase by using [35S] dATPS (Amersham, England, UK). Anti-sense oligonucleotides used for LDB2, NELL2 and protein kinase C β-1 (PRKCB1) are presented in Supplementary Table S3. Optimal oligonucleotide sequences (45-mers, 50–60% GC, low hairpin and dimer formation probability) were selected by using Oligo 5.0 (NBI, Plymouth, MN, USA). Hybridizations with a 1000-fold excess of the respective unlabelled oligonucleotide served as negative controls. After hybridization, sections were washed twice with 1x saline-sodium citrate (SSC)/0.1% (v/v) 2-mercaptoethanol at room temperature for 20 min, once with 1x SSC/0.1% (v/v) 2-mercaptoethanol at 57°C for 45 min, once with 1x SSC at room temperature for 5 min and once with 0.1x SSC at room temperature for 2 min. Sections were then dehydrated, air-dried and exposed to Kodak BioMax film (Eastman Kodak, NewHaven, CT, USA) for 2 weeks at room temperature. Slides were dipped in photographic emulsion (Kodak NTB3; Eastman Kodak), incubated for 6–9 weeks and developed in Kodak D-19 developer (Eastman Kodak) for 3.5 min. For analysis with bright-field optics and to confirm cytoarchitecture, sections were counterstained with cresyl violet.
Plasmid construction
The vector pSuper.neo+gfp (VEC PBS 0006, Oligoengine, Seattle, USA) was used for shRNA studies. The procedure for preparing constructs coding for shRNA was described previously (Brummelkamp et al., 2002). The gene-specific targeting sequence (19 nt) separated by a short spacer from the reverse complement of the same sequence and five thymidines (T5) as termination signal was subcloned (for detailed information see Supplementary Material). The specific murine target sequence for LDB2 was designated shRNA-mLDB2 and the human control shRNA for LDB2 was designated shRNA-hLDB2. Sequences were confirmed by NCBI Blast and had no homology with any other vertebrate genes (Supplementary Tables S4 and S5). The full-length mLDB2 (NCBI accession number NM_010698
[GenBank]
) was cloned by PCR, with sense primer 5'-GCG AGA TCT CCA AGA TGT CCA GCA CAC CA-3' (BglII restriction site) and an antisense primer 5'-GCG GTC GAC TCT GGG AAG CCT GGG GTG-3' (SalI restriction site) using RZPD clone (IRAKp961M10176Q2, BC079611
[GenBank]
) as template. The fragment was inserted into the mammalian expression vector pCMV-myc2 (generous gift of A. Maximov, Dallas, USA). The yellow fluorescent protein from the pVenus vector (generously provided by M. Schwarz, Heidelberg, Germany) was also cloned by PCR with the sense primer 5'-GCG GTC GAC AAG TGA GCA AGG GCG AGG AG-3' (SalI restriction site) and the antisense primer 5'-GCG GGA TCC ATT ACT TGT ACA GCT CGT CCA TGC-3' (BamHI restriction site). The vector pCherry (generously provided by T. Sudhof, Dallas, USA) encodes for a red fluorescent protein, which was co-transfected when using the vector pCMV-myc2-mLDB2-Venus. The obtained vectors were subsequently sequenced to verify the integrity of the fusion protein. pGUR-shGFP was kindly provided by B. Sabatini (Boston, USA).
Western blot analysis
Equal amounts of protein extracts (20 µg/sample) from transfected 293T cells were separated by SDS–PAGE and blotted to nitrocellulose over night (45 mA). After blocking with 5% skim milk solution and 5% fetal calf serum, membranes were incubated with mouse anti-β-actin for control and rabbit polyclonal anti-green fluorescent protein (anti-GFP) antibodies followed by decoration with peroxidase labelled anti-mouse or anti-rabbit IgG, respectively. The bound complexes were detected with the ECL-detection system (Amersham Pharmacia biotech, UK).
Neuron culture and transfection
Primary hippocampal neuron cultures were prepared from embryonic day 19 (E19) mouse brains (Banker and Goslin, 1998). Cells were plated on poly-D-lysine coated (0.01%) in 24-well plates at a density of 1 50 000 cells/well. Hippocampal cultures were grown in Basal Medium Eagle (BME; Invitrogen, Carlsbad, CA, USA) supplemented with 2% B27 (Invitrogen), 1% glucose (Invitrogen) and 1% fetal calf serum (Invitrogen). At 5 d in vitro (DIV) hippocampal neurons were transfected using 1 µl lipofectamine 2000 and 1 µg plasmid DNA/well for 3 h. The cells were washed twice with supplemented BME and the original medium was transferred back on the cells. In case of co-transfection, the DNA of interest was labelled with GFP and mixed with pCherry in a 1 : 0.3 ratio.
Image analysis and quantification
Digital images of single transfected neurons (at high magnification, x400) were subjected to morphometric analyses and quantification using ImageJ software (Wayne Rasband, NIH, MD, USA). For measurements of primary, secondary and tertiary dendrites, each branch was traced from branching point to the tip, and the number of pixels was automatically calculated. All quantifications were performed by investigators ‘blind’ to the experimental condition. Data were obtained from three independent neuronal preparations and transfections and in each transfection three wells were transfected with the identical combination of plasmids.
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This comprehensive gene expression analysis of gangliogliomas including the neuropathological spectrum of architectures presented in Fig. 1 was performed from histologically verified tumour as well as adjacent normal brain tissue, both matched for equal amounts of grey and white matter. All microarray hybridizations were subjected to quality assessment and fulfilled technical requirements for reliable data mining strategies. The full array data set will be available on ArrayExpress (‘gangliogliomas’; http://www.ebi.ac.uk/microarray-as/ae/). Statistically more than one-third of the transcripts were assigned ‘present’. The average background signal was 67 compared to an average signal genes designated present (338), marginal (85) or absent (32). Comparative data mining between tumour and control tissue was performed by R (V.1.9.1). Pairwise Q–Q plots were used to address the data integrity. All Q–Q plots aligned well along the main diagonal, confirming the comparability of the expression distributions across different samples (Fig. 2A). Established normalization and pre-filtering procedures diminished false positive low-abundant transcripts.
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Subsequently, we have carried out different clustering approaches to the data. Unsupervised cluster analysis based on large sets of expression data provides information on the global effects that differentiate samples under study (Brehelin et al., 2008
). In this respect, we performed cluster analysis with all present genes based on euclidean distance as a sample-wise distance measure and average linkage as a set-wise distance measure. This analysis demonstrated tumours and controls to be fairly well separated (Fig. 2B). Intriguingly, in the euclidean distance matrix only Patients 3 and 5 had their respective tumour and control samples closest to each other. These data suggest, that even in highly differentiated tumours such as gangliogliomas, the parameter ‘tumour’ appears to be generally more distinctive than ‘identical genetic background’ with respect to gene expression patterns. We excluded contamination effects between tumour and control tissue to be the cause of close sampling between samples of Patients 3 and 5.
Although it needs to be emphasized that there is no gold standard for 2-way hierarchical cluster analyses of large-scale gene expression data, it is useful as an explorative tool displaying the predominant differences across samples, together with the genes that are best suited to differentiate between them (Raychaudhuri et al., 2001). Using a paired t-test followed by Benjamini-Hochberg correction for multiple testing, we observed 94 transcripts to be differentially expressed between gangliogliomas and controls. These transcripts fulfilled several stringent criteria, i.e. expression higher than the normalized median and a two-sided fold-change of at least 1.5 between gangliogliomas and controls. These data mining settings allowed to detect particularly transcripts with both stable and relevant expression alterations. These 94 transcripts are significantly differentially expressed between gangliogliomas and controls (Table 1). Nevertheless, we observed a certain amount of variability in expression between different gangliogliomas (Fig. 2C). This feature may partly reflect the cellular spectrum within and between gangliogliomas (Becker et al., 2007). By clustering genes based on similar expression patterns hierarchically into nearby places we obtained a condition genetree (Fig. 2C). This analysis revealed two large clusters of transcripts that showed either high levels of expression in controls and low abundance in gangliogliomas or vice versa.
In order to validate the differential gene expression data from the array experiments described above, we used real-time RT–PCR and concentrated on representative transcripts from the low-, intermediate- and high copy range in gangliogliomas and controls. In parallel to this aspect and similarly to previous array analyses of brain disorders (Gorter et al., 2006; Kang et al., 2007), in these verification analyses we followed transcripts with a potential relevance to pathogenetic processes as discussed in detail subsequently. Expression levels were determined in duplicates and normalized to the reference gene β-actin by the Ct method (Chen et al., 2001). Tribbles homolog 1 (TRIB1) and ST6GalNAc4 as low copy transcripts, NELL2, PRKCB1 and LDB2 as intermediate abundant mRNAs and AI201594
[GenBank]
, heat shock 40 kDa protein 4 (HSJ2) as well as ADP-ribosylation factor 3 (ARF3) as high copy genes confirmed expression differences between gangliogliomas and controls previously observed by expression arrays (Fig. 3). Furthermore, we used in situ hybridization to resolve differential transcript expression of several of these transcripts between glial and neuronal components of gangliogliomas (Fig. 4). In order to compare gene expression in gangliogliomas with the second major entity of epilepsy-associated glioneuronal neoplasms, we carried out real-time RT–PCR for the transcripts addressed before in gangliogliomas also in DNETs (Fig. 5).
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The set of 94 differentially expressed genes in gangliogliomas (Fig. 2) was subjected to a gene ontology analysis (NetAffx-Portal). Differential expression of more than individual genes related to a circumscribed number of elementary cellular functions and molecular pathways as follows (Table 1).
Regulation of chromatin state and transcription factors
Gangliogliomas are characterized by dysplastic, often binuclear neuronal components and proliferation active glial cells (Blümcke et al., 1999). These neuropathological characteristics are reflected by chromosomal aberrations in the distinct cellular components (Hoischen et al., 2008) and may relate to aberrant transcriptional control. The chromodomain-protein coding gene CDY1 is strongly reduced in expression in gangliogliomas, which can contribute to impaired chromatin assembly since several chromodomain-proteins have been reported as tumour suppressors that are important for chromatin rearrangement. Further, impaired function of chromodomain-proteins underlies defects in developing tissues (Bagchi et al., 2007; Hurd et al., 2007). The B-cell CLL/lymphoma 11A (Bcl11A) gene that encodes for a Kruppel-like zinc-finger protein, has been shown to be relevant for B-cell development and is abundantly expressed in brain (Kuo and Hsueh, 2007). Reduced expression of Bcl11A in gangliogliomas may be associated with the lack of maturation in its cellular components. Modified nuclear gene activation patterns have substantial consequences for several cellular functions, which are mediated by altered intracellular signalling.
Intracellular signal transduction
Expression of multiple genes involved in intracellular signalling was altered in gangliogliomas. The differentially expressed genes were associated with distinct cellular compartments. Expression array and real-time RT–PCR data showed that HSJ2, a co-chaperone of Hsc70 was lower in expression in gangliogliomas as compared to controls. HSJ2 has been suggested to play a role in protein import into mitochondria (Oh et al., 1993; Kanazawa et al., 1997). Impairment of mitochondria function and mitochondrial DNA damage have been reported in the course of epileptic seizures (Baron et al., 2007). Accordingly, this differential expression is not restricted to gangliogliomas but also present in DNETs (Fig. 5). ARF3 is a GTP-binding protein involved in protein trafficking that may modulate vesicle budding and uncoating within the Golgi apparatus (Tsai et al., 1991; Takeya et al., 2000). It is strongly up-regulated in mantle zone during brain development (Takeya et al., 2000). ARF3 mRNA levels are lower in gangliogliomas than in controls (Table 1, Fig. 3). This finding may relate to aberrant protein trafficking and irregular migration not only of ganglioglioma but also DNET cell components (Fig. 5).
A further interesting class of molecules altered in astrocytic tumour cells as well as under epileptic conditions is related to ganglioside biosynthesis (Yu et al., 1987; Abate et al., 2006). -N-acetyl-neuraminyl-2,3-β-galactosyl-1,3-N-acetyl-galactosaminide -2,6-sialyltransferase (ST6GalNAc4) is substantially reduced in expression in gangliogliomas (Table 1, Fig. 3; Harduin-Lepers et al., 2000). It belongs to the glycosyltransferase 29 family and is involved in the biosynthesis of ganglioside GD1A from GM1B (Harduin-Lepers et al., 2000). Recent data from astrocytoma cell lines have suggested that reduced presence of gangliosides including GD1A relates to a less invasive biological behaviour with respect to growth and vascularization stimuli of tumour cells (Abate et al., 2006). Low expression of ST6GalNAc4 in gangliogliomas is well compatible with this benign phenotype. Remarkably, altered expression of ST6GalNAc4 is not present in DNETs, which are characterized by a fundamentally different, multilocular growth pattern (Fig. 5).
Several differentially expressed transcripts such as the inositol 1,4,5-trisphosphate 3-kinase B, the protein kinase C iota, AKT3 and the Rho-type GTPase-activating protein RICH2 (Table 1) point towards further alterations within phosphatidylinositol 3-kinase pathway signalling in gangliogliomas (Schick et al., 2007b). The phosphatidylinositol 3-kinase signalling pathway is critically involved in the development of malformative dysplastic precursor lesions as well as gangliogliomas (Schick et al., 2006; Schick et al., 2007a). Expression of PRKCB1 is substantially reduced in gangliogliomas as demonstrated by expression array and real-time qRT–PCR data (Figs 2 and 3
). Furthermore, in situ hybridization analysis suggested that PRKCB1 is reduced in small glial as well as dysplastic neuronal cell elements in gangliogliomas (Fig. 4). PRKCB1 is a calcium-activated, phospholipid-dependent, serine- and threonine-specific enzyme. PRKCB1 is activated by diacylglycerol, and in turn phosphorylates a range of cellular proteins. Protein Kinase C (PKC) also serves as the receptor for phorbol esters, a class of tumour promoters (Niino et al., 1992). Induction of several PKC isoforms has been reported after epileptic seizures promoting glutamate-mediated cell death (McNamara et al., 1999). Lower expression of PRKCB1 in gangliogliomas may antagonize seizure-induced cell death in these tumours an aspect that is potentially shared by DNETs (Fig. 5).
Transduction of extracellular signals and cell adhesion
mRNA levels of the protein kinase C-binding protein NELL2 are also lower in gangliogliomas as well as DNETs (Table 1, Figs 3 and 5) (Watanabe et al., 1996). In situ hybridization revealed a similar cellular pattern of reduced expression for NELL2 in gangliogliomas compared to controls as observed for PRKCB1 (Fig. 4). Impaired expression of NELL2 in gangliogliomas can contribute to aberrant cellular positioning and differentiation in glioneuronal tumours (Nelson et al., 2004).
Compared to diffusely infiltrating astrocytomas as well as to highly circumscribed, i.e. pilocytic astrocytomas, gangliogliomas can present with an intermediate infiltrating growth pattern. This biological feature may be reflected by increased expression of only individual extracellular matrix proteases, i.e. the matrix metallopeptidase 2 (MMP2) and tissue plasminogen activator (PLAT) (Landau et al., 1994; Fillmore et al., 2001). Abundant expression of these genes reflects the moderate invasive growth character of gangliogliomas, by which these glioneuronal tumours differ from the more frequent diffusely infiltrating glial brain tumours as well as by their low-proliferation activity.
Control of cell cycle and proliferation
Cell cycle activity and proliferation of gangliogliomas are generally low; however, regulation of cell cycle and proliferation of cellular components certainly constitute general key features of tumour growth. Considering a central aspect of ganglioglioma development, i.e. neoplastic transformation of the glial component from pre-existing dysplastic precursor lesions, impaired cell cycle control and increased proliferation are critical for tumourigenesis. A lower overall neuronal density recently observed in cortical dysplasias suggested as precursor lesions for gangliogliomas relates to inadequate local proliferation or secondary degeneration of neural precursors (Thom et al., 2005). Aberrant expression of cell growth and apoptosis-associated genes and proteins has been reported in cortical dysplasias as well as in gangliogliomas (Prayson, 1999; Kim et al., 2003). TP53-inducible protein 3, abundantly expressed in gangliogliomas has been found to impair cell cycle regulation (Nicholls et al., 2004), and can be involved in aberrant growth control in gangliogliomas. In the ganglioglioma series under study, only individual cellular ganglioglioma components accumulate the TP53 protein, not suggesting a mutation of the underlying gene (data not shown). TP53-inducible protein 3 has not been reported to be abundantly expressed in other low-grade brain tumours such as diffuse astrocytomas (WHO grade II) or pilocytic astrocytomas.
TRIB1 transcripts are almost 2-fold induced in gangliogliomas as compared to controls, which was also confirmed by qRT–PCR (Table 1, Fig. 3). Interestingly, this up-regulation is not observed in DNETs (Fig. 5). TRIB1 interacts with and regulates activation of MAP kinases (Kiss-Toth et al., 2004). The MAP kinase pathway is centrally involved in cellular differentiation, growth and programmed cell death, characteristics which are substantially disturbed in gangliogliomas and may contribute to aberrant proliferation as well as differentiation of glial and neuronal components in gangliogliomas.
Development and differentiation
Cytological and histological characteristics suggest compromised differentiation of neuronal precursors during cortex development to play a role in gangliogliomas (Schwartzkroin and Walsh, 2000; Golden, 2001; Bentivoglio et al., 2003). In the present expression array data set, several differentially expressed genes relate to the development of the central nervous system and the differentiation of neural precursor cells. Of this group, the most abundant transcript in gangliogliomas is the nerve growth factor receptor (NGFR, p75) (Tapia-Arancibia et al., 2004). Whereas, neurotrophins such as brain-derived neurotrophic factor (BDNF) were observed increased in expression in the neuronal ganglioglioma components, increased p75 was reported in reactive microglia cell components within gangliogliomas (Aronica et al., 2001; Aronica et al., 2004).
LIM domain-containing proteins constitute important regulators that determine cellular fate and differentiation during embryogenesis (Dawid et al., 1998). Here, we observed two LIM domain-interacting transcripts, i.e. LIM domain only 4 (LMO4) and LDB2, to be substantially lower in expression in gangliogliomas as compared to controls (Table 1). Reduced expression of LMO4 in gangliogliomas reflects the benign biological behaviour of gangliogliomas in contrast to malignant neuroblastic tumours (Aoyama et al., 2005). LDB2 binds the LIM domain of two families of transcription factors, LIM-homeodomain proteins (LIM-HD) and LIM-only proteins (LMO). It has been recently shown that the interaction of LDBs with LIM domains is critical for the development of neurons (Fernandez-Funez et al., 1998). LIM-HD transcription factors are substantially expressed in neurons and essential for neuronal development (Lundgren et al., 1995; Pfaff et al., 1996; Benveniste et al., 1998; Way and Chalfie, 1988). LDB2 is significantly reduced in expression in gangliogliomas (Table 1, Fig. 3). The overall expression level of LDB2 in gangliogliomas is remarkably higher than in DNETs (Figs. 3 and 5). Furthermore, we did not observe a significant down-regulation of LDB2 mRNA in DNETs as compared to controls, although a certain tendency in this direction could be detected (Fig. 5). By in situ hybridization, we demonstrated that reduced expression of LDB2 mRNA is present in dysplastic, clustered neuronal components but not in glial cell elements of gangliogliomas (Fig. 4).
Considering the unique neuropathological aspect of gangliogliomas, i.e. dysplastic and immature neuronal cell components, we focused our further analysis on the functional role of LDB2 for neuronal development. To this end, we applied an shRNA approach to selectively knock-down the expression of murine LDB2 in primary hippocampal cells. We first tested the efficiency and the specificity of the knock-down in HEK293T cells that had been co-transfected with plasmids encoding a LDB2-eGFP fusion protein and an shRNA targeted against murine LDB2 (shRNA-mLDB2). Levels of the LDB2-eGFP fusion protein were reduced by 80% 120 h after transfection (Fig. 6A and B). Using the same assay, an shRNA against the human LDB2 homologue (shRNA-hLDB2) did not result in a significant knock-down of LDB2-eGFP even though there is a sequence homology of >98% between murine and human LDB2 cDNAs (Fig. 6A and B). In the subsequent experiments, shRNA-hLDB2 and an unspecific shRNA against GFP (pGUR-shGFP) were therefore used to control for off-target effects. We then transfected primary hippocampal neurons with the plasmid encoding the shRNA against murine LDB2 and the control plasmids pSuper, shRNA-hLDB2 and pGUR-shGFP (Fig. 6C–F). Transfection of primary hippocampal neurons with the plasmid encoding pCMV-LDB2, i.e. resulting in overexpression of LDB2, did not result in significant morphological differences as compared to controls (data not shown). Whereas, native primary hippocampal neurons showed a densely arborized pattern of processes, neurons with reduced expression of LDB2 revealed substantial structural impairment, i.e. aberrant arborization. These alterations are absent in all control conditions (Fig. 6C–F). Quantitatively, we observed a substantial reduction of the amount of dendrites reflecting the complexity of the dendritic tree in primary hippocampal neurons transfected with shRNA-mLDB2. This alteration affected the distal, tertiary dendritic compartment but also primary and secondary dendrites (Fig. 6G). LDB2 and its interacting partners represent a novel family of molecules potentially involved in aberrant development of ganglioglioma components.
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Gangliogliomas are complex glioneuronal brain tumours frequently associated with pharmacoresistant epilepsies. Gene expression studies using microarrays allow a comprehensive analysis of transcript alterations that reflect pathomechanisms operating in gangliogliomas. By using a stringent clustering approach, we have observed 94 transcripts to be differentially expressed between gangliogliomas and control tissue. Generally, such alterations of gene expression are the consequence of modified epigenetic regulation and/or mutations of promoters that control gene expression. However, not only such factors intrinsic to ganglioglioma cell components but also the interaction of tumour elements with the environment including immune mechanisms, propensity for infiltration and, in functional regards, epileptogenicity influence the expression profile of these tumours. Some of the identified gene profiles are new; other results extend or confirm previous findings on gene expression alterations in gangliogliomas as outlined for individual gene clusters above. Here, we will focus on distinct aspects of gangliogliomas based on our microarray and subsequent analyses related to aberrant development of neuronal precursors and glial components as well as differences in expression profiles of gangliogliomas and other low-grade primary brain tumours.
Focal epilepsies constitute multifactorial disorders (Elger, 2002). The pathogenesis of focal epileptic attacks is not entirely understood and the relative contribution to epileptogenicity of lesional versus perilesional areas is variable between individual patients (Wong, 2008), which limits the significance of differential gene expression patterns in this respect. Lesions can disrupt the anatomic architecture and conduction routes of the central nervous system. Seizures are elicited by deafferenting areas of the cerebral cortex or by immediate physical effects such as stretching of brain tissue, impairment of the extracellular space, altered homoeostasis of neurotransmitter clearance as well as ion equilibrium, which contributes to aberrant synchronous discharges (Heinemann, 2004; Wolf et al., 1995a; Binder and Steinhauser, 2006). In patients with electrically inert lesions such as glial scars and diffuse gliomas, these mechanisms will constitute the most likely scenario. Remarkably, although epileptic seizures are not infrequent in diffusely infiltrating gliomas, the frequency is much less than in glioneuronal tumours (Pace et al., 1998).
In glioneuronal tumours and malformations, the population of dysplastic neuronal cells inherent to these lesions may actively participate in the generation of seizures in addition to the effects described above. This can be either through aberrant release of neurotransmitters or neuromodulators into the adjacent brain tissue or through the electrical activity of their intrinsic neuronal network (Wolf et al., 1996; Cepeda et al., 2007; Aronica et al., 2008). The fact that epilepsy is substantially more frequent in gangliogliomas than in glial tumours argues for an active contribution of the neuronal component to epileptogenesis in these neoplasms. In addition, the interplay between lesional and surrounding tissue contributes to epileptogenesis, which constitutes an important factor for the outcome of epilepsy surgery (Wong, 2008).
Interestingly, it had been previously shown, that neurochemical profiles from ganglioglioma neuronal components are not substantially different from neurons in normal control CNS tissue (Wolf et al., 1995b), and also in the present microarray analysis we do not observe numerous expression changes of neurotransmitter receptors or ion channels in gangliogliomas. Dysplastic neurons substantially contribute to aberrant signal transmission within the neuronal network (Knafo et al., 2001). Many differentially expressed transcripts observed in the present study relate to aberrant differentiation of neuronal ganglioglioma components (Table 1, Figs 3 and 4). These dysplastic neuronal cells may contribute to an epileptogenic network, which in individual patients is not limited to the lesion but extends to the perilesional area. However, e.g. our LDB2 quantitative transcript analyses and in situ hybridization demonstrate a selectively reduced presence in neuronal ganglioglioma components, putatively contributing to their dysplastic differentiation. Our observation that neurons with reduced levels of LDB2 exhibit an altered morphological phenotype, characterized by a decrease in dendrite density, is the first functional evidence linking LDB2 to the molecular mechanisms underlying impaired development of a regular cortical architecture. Since neuronal migration and differentiation processes rely on intercellular signalling (Wu et al., 1999; Williams and Truman, 2004), a reduced dendrite density of developing neurons with silenced LDB2 during development of the brain may contribute to an abnormal neuronal network. Clearly, such individual alterations do not constitute factors sufficient to elicit epileptic attacks. Further alterations are required to manifest epileptic seizures such as growth of a neoplastic glial component and the interaction with perilesional factors and e.g. invading immune cells (Wong, 2008). Remarkably, many patients with glioneuronal tumours do not manifest epilepsy in early childhood but acquire seizures later in life. This may reflect glioneuronal tumours to constitute highly dynamic entities, which at a certain point overcome a virtual threshold to manifest seizures.
As described here, gangliogliomas have rather specific anatomical and clinical features. Does the microarray data argue for molecular neuropathological mechanisms in gangliogliomas distinct from other low-grade brain lesions? A variety of studies using expression microarray approaches have been conducted in cortical malformations as well as low-grade glial and glioneuronal tumours. Certainly, microarray data obtained from different sources may only be compared with great caution since substantial variability in the studies can contaminate the comparisons and differences with respect to factors such as the array platform used, the detection thresholds, control tissue, linear dynamic range of the arrays, etc. have considerable influence on the data.
In cortical dysplasias, distinct transcriptional profiles were found as compared to our present data (Kim et al., 2003). A remarkable finding of this study was a substantially increased expression of anti-apoptotic elements and decreased expression of pro-apoptotic factors. Considering that we did not observe substantial overlap between differential gene expression in gangliogliomas of our collective and the reported expression profile in cortical dysplasias, the data suggest different molecular pathways to operate in both lesions. Whether the cortical dysplasias analysed by Kim and colleagues (2003) can constitute precursor lesions for gangliogliomas remains unresolved, since the group of cortical dysplasias analysed there is heterogeneous.
With respect to the literature as well as our present analysis, there are intriguing similarities as well as differences in expression patterns between gangliogliomas and DNETs. This may be particularly remarkable since DNETs show distinct neuropathological features, i.e. a composition of small oligodendroglia-like cell components as well as ‘floating’ neurons, which generally do not share the dysplastic features of neuronal ganglioglioma components (Daumas-Duport et al., 2007). Increased presence of reactive microglia in gangliogliomas as well as DNETs has been interpreted as potential mechanism to contribute to epileptogenesis in glioneuronal tumours (Aronica et al., 2005). We observed expression signals in the ganglioglioma samples that may reflect microglia activation (Table 1). Furthermore, it was shown that DNETs share distinct features with gangliogliomas with respect to the expression of BDNF and TrkB (Aronica et al., 2001).
Our present array analysis showed NGFR as the most abundant transcript in gangliogliomas. Tyrosine kinase receptors trkA, -B and -C were observed to be strongly expressed in neuronal components of gangliogliogmas, FCD and DNET (Aronica et al., 2004). Similarly, to these partially shared expression differences, our present data suggest gangliogliomas and DNETs to share some differential expression patterns (Figs 3 and 5), i.e. reduced expression of NELL2, PRKCB1, HSJ2 and ARF3. However, we identified several gene expression patterns that were specifically altered only in gangliogliomas such as for TRIB1, ST6GalNac4 and LDB2, related to distinct dysplastic features of these glioneuronal tumours.
Even though several studies have addressed gene expression using microarrays in gangliogliomas before, the experimental approaches applied were fundamentally different from ours and the potential for comparison is thereby limited. Clearly, gene expression data obtained from tissue specimens of gangliogliomas result in a mixed signal derived from glial and neuronal elements as well. We have here used in situ hybridization to resolve differential gene expression between these cell components. A more direct approach was recently taken by obtaining gene expression patterns from single ganglioglioma cell components (Samadani et al., 2007). Nevertheless, substantial overlap is present with respect to gene groups and functional clusters differentially expressed in this analysis and our present data. Differential gene expression in dysplastic neurons strongly relates to the downstream component (mTOR) of the insulin receptor pathway (Samadani et al., 2007). P75 NGF receptor which we found strongly up-regulated in our present set of gangliogliomas was reported to be substantially induced in expression in the astrocytic cell component of gangliogliomas (Samadani et al., 2007). Another highly interesting aspect of ganglioglioma pathology has been recently highlighted by data obtained from microarrays using different detection algorithms as well as autopsy control tissue. This study suggested the presence of a substantial inflammatory and innate immune reaction in gangliogliomas (Aronica et al., 2008). We also found microglia-related transcripts, e.g. CD14, to be induced in expression in gangliogliomas. It has been reported that CD14 mediates dendritic impairment by innate immune cells (Milatovic et al., 2004), which may contribute to aberrant architecture of dysplastic neuronal components of gangliogliomas.
Our present data suggest substantially different molecules and pathways to be involved in ganglioglioma development as compared to low-grade diffusely infiltrating astrocytomas and pilocytic astrocytomas. Recent data showed the most prominent difference between diffusely and pilocytic astrocytomas related to induction of genes encoding adhesion- and infiltration-related molecules in diffuse astrocytomas, well compatible with their specific growth pattern (Rorive et al., 2006; Sharma et al., 2007). Vice versa, a number of genes induced in pilocytic astrocytomas related to anti-migration, which is well compatible with the circumscribed growth pattern of pilocytic astrocytomas. Our analysis of gangliogliomas reveals an intermediate phenotype. Some transcripts related to invasions such as MMP2 and tissue plasminogen activator were induced in expression. This may reflect the generally only minor infiltration behaviour of gangliogliomas.
Taken together, the present study identifies new genes and molecular cascades to be differentially expressed in gangliogliomas as well as extends the knowledge on genes and pathways previously implicated in the pathogenesis of glioneuronal tumours. Particularly, it contributes to differentiate molecular cascades that play a role in distinct low-grade brain tumours and the aberrant differentiation of ganglioglioma cell components. Therefore, the present data provide a basis to develop functional models for glioneuronal tumours.
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Deutsche Forschungsgemeinschaft (SFB TR3, C6, B8 to A.J.B. and S.S., KForG ‘Innate Immunity’ TP2 to A.J.B. and Emmy Noether program to S.S.); Deutsche Krebshilfe (to J.F., M.M. and A.J.B.); Bundesministerium für Bildung und Forschung (NGFNplus to A.J.B. and S.S.); European Union EPICURE (to A.J.B.); the BONFOR program of the University of Bonn Medical Center (to A.J.B., M.M. and S.S.).
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Acknowledgement |
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We thank B. Sabatini, M. Schwarz, T. Südhof and A. Maximov for plasmids.
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