Human Molecular Genetics, 2002, Vol. 11, No. 19 2279-2287
© 2002 Oxford University Press
Modulation of polyglutamine-induced cell death by genes identified by expression profiling
1Taisho Laboratory of Functional Genomics, Nara Institute of Science and Technology, 2Core Research for Evolutional Science and Technology, Japan Science and Technology Corporation, 8916-5 Takayama, Ikoma, Nara, 630-0101, Japan and 3Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2XY, UK
Received May 6, 2002; Accepted July 9, 2002
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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The majority of triplet-repeat diseases are caused by mutated genes with an extended polyglutamine tract, exemplified by Huntington's disease (HD). In order to model HD pathogenesis in a controlled system, we developed stable PC12 cell lines that express exon 1 fragments of the huntingtin gene with 23 or 74 polyglutamines driven by an inducible doxycycline (dox)-sensitive promoter (HD-23Q or HD-74Q). We aimed to identify early perturbations induced by the mutation by studying expression levels of 1824 genes at 0, 5, 10 and 18 hours after induction, using adaptor-tagged competitive PCR (ATAC–PCR). At these time points, the cells show no appreciable death or mitochondrial impairment and very low inclusion levels. A total of 126 genes, including 69 known genes, exhibited statistically significant alterations in the HD-74Q cell lines but no changes in the HD-23Q lines. We tested 11 of these genes for their abilities to modulate polyglutamine-induced cell death in transiently transfected cell models. Five genes [glucose transporter 1 (Glut1), phosphofructokinase muscle isozyme (Pfkm), prostate glutathione-S -transferase 2 (Gstm2), RNA-binding motif protein 3 (Rbm3) and KRAB-A interacting protein 1 (Krip-1)] significantly suppressed cell death in both neuronal precursor and non-neuronal cell lines, suggesting that these transcriptional changes were relevant to polyglutamine pathology. The efficient recovery of functionally relevant genes supports the utility of gene expression profiling for discovering pathways related to pathogenesis, and the importance of analyzing molecular events in the early stages of disease.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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There are at least nine neurodegenerative diseases caused by abnormally expanded CAG trinucleotide repeat stretches, which are translated into abnormally long polyglutamine tracts. These diseases include Huntington's disease (HD), spinocerebellar ataxias (SCA) 1, 2, 3, 6 and 7, dentatorubral–pallidoluyisian atrophy (DRPLA) and spinobulbar muscular atrophy (SBMA). HD is associated with expansions of more than 35 repeats (reviewed in 1–3). These diseases share a number of features, including the formation of inclusions by the mutant protein. However, the role of protein aggregation and inclusions in disease pathogenesis is controversial (1–5).
In vitro studies with cell lines have suggested that overexpression of full-length mutant proteins does not induce cell death (6,7). However, N-terminal fragments of the gene product including the expanded polyglutamine tract are toxic to cells and mice (8–11). Accumulation of N-terminal fragments of huntingtin in inclusions supports the involvement of cleavage products in HD pathogenesis (4,12,13).
Genetic and transgenic evidence suggests that a major consequence of the HD mutation is to confer a toxic ‘gain of function’ on the protein (3,14,15). Perutz et al. (15) proposed that polyglutamine expansions cause a conformational change in the polypeptide that promotes misfolding and aggregation of the disease protein. Recent work has suggested that expanded polyglutamines may induce pathology by interfering with components of the transcriptional machinery, including cAMP-responsive element-binding protein (CREB), CREB-binding protein (CBP) and TATA box-binding protein-associated factor II 130 kDa (TAFII130) (16–24).
In this context, we believe that it is important to study expression levels of a large number of genes in cells expressing mutant or wild-type huntingtin, as a means of identifying genes and pathways that are relevant to pathogenesis. We have focused on early changes occurring before the onset of mitochondrial abnormalities and cell death, in order to exclude the confounding secondary changes that are likely to occur in overtly unhealthy cells. The analysis was done using stable inducible PC12 cell lines that express an exon 1 fragment of the huntingtin gene with various polyglutamine lengths under control of a doxycycline (dox)-sensitive promoter, which we have previously characterized (24). In order to follow temporal changes in gene expression, we measured expression levels of 1824 genes at four different early time points after transgene induction using high-throughput RT–PCR, adaptor-tagged competitive PCR (ATAC–PCR) (25). We identified 126 genes exhibiting changes that were specific to cell lines expressing the mutated gene. We then tested if these genes were functionally relevant to expanded polyglutamine-induced cell death. We introduced 11 genes selected from 69 known genes into in vitro experimental systems described previously (24), and found that eight genes modulated cell death in non-neuronal cells (COS7). Among the six genes showing protective effects in COS7 cells, five (Glut1, Pfkm, Gstm2, Rbm3 and Krip-1) also significantly reduced polyglutamine-induced cell death in neuronal cells (SK-N-SH).
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Quantitative analysis of gene expression by ATAC–PCR
We have previously characterized our PC12-inducible stable cell lines (Tet-On) expressing an exon 1 fragment of the huntingtin gene with 23 or 74 glutamines, fused downstream of enhanced green fluorescent protein (EGFP) (called HD-23Q and HD-74Q). In these cells, induction of cell death is time and polyglutamine length-dependent (24). In our gene expression studies, we have chosen induction times where the cells were not obviously unhealthy (
18 hours) (24). There was no evidence of any deficit in mitochondrial enzyme activity in the HD-74Q lines after 18 hours of induction, compared with the same lines in the uninduced state, or HD-23Q lines in the uninduced state. Cell death was not detectably increased in the HD-74Q lines versus the HD-23Q lines induced for <24 hours (<2% cells with abnormal nuclei in either HD-23Q or HD-74Q). Inclusions were found in 13% [±5.2% (SD)] of HD-74Q cells after 18 hours of induction, and no inclusions were seen in HD-23Q cells (24). In order to control for clonal and experimental variability, we analyzed two independent HD-74Q and two independent HD-23Q clonal lines. Cells were harvested at 0 (uninduced), 5, 10 and 18 hours after induction with 1 µg/ml dox. We selected genes for analysis by selecting transcripts from cDNA libraries constructed from HD-74Q cells induced for 18 hours and uninduced HD-23Q cells (see Materials and Methods). A total of 12 476 EST clones were sequenced from these libraries, and 4006 unique sequences were obtained. We designed 1824 gene-specific primers for ATAC–PCR for genes selected in descending order of abundance estimated from the transcript frequencies in the libraries. Among rare species (those appearing only once), known genes were prioritized. Since one ATAC–PCR reaction is able to assay four samples together, 14 592 independent ATAC–PCR reactions were performed to make a data matrix consisting of 1824 genesx4 cell lines (2 each for HD-23Q and HD-74Q)x4 time pointsx2 calibrations. We obtained clear PCR products from 1415 genes. The expression data may be obtained as a supplement to this paper from our website (http://love2.aist-nara.ac.jp/laboratory/index_frame.html).
Classification of genes by expression patterns
We noticed that there were some differences in gene expression between the two HD-23Q lines and between the two HD-74Q lines. The difference was partly due to experimental error. However, some of the differences were probably due to real variations between the clonal lines, possibly resulting from different transgene integration sites and copy numbers. In order to identify genes showing changes that were likely to be due to the polyglutamine expansion, we selected genes whose expression levels exhibited similar significant changes in both HD-74Q cell lines but no significant changes in HD-23Q cell lines (see Materials and Methods for the statistical approach). 126 genes (8.7%) met these criteria. The expression pattern of the 126 genes was then classified into six clusters by Ward's method (26). The expression patterns of the 126 genes are shown in the color plot in Table 1. To characterize functional aspects of each cluster, we assigned functional and/or structural annotations (Table 1). Cluster 1 is characterized by its rapid downregulation of genes, and includes transcription factors such as Arix1 homeodomain protein (Arix1), p53, ribosomal proteins, lipid-binding proteins such as ATP synthase lipid-binding protein P2 and P3 precursor, and the 11 kDa diazepam-binding inhibitor (Dbi). Cluster 2 also represents genes that were downregulated in the induced HD-74Q lines, but these were downregulated later than the genes in cluster 1. No nuclear proteins were included in this cluster. Genes included in both clusters 3 and 4 were initially expressed at very low levels, then were transiently upregulated after 5 or 10 hours before being downregulated again. The transcriptional repressor Krip-1 belongs to cluster 3. Membrane proteins such as brain Glut1 and the poliovirus receptor homolog Mph were in cluster 4. Genes included in cluster 5 were initially expressed at significantly low levels, then the level was elevated after 10 hours and remained elevated at 18 hours. Several genes related to the function of RNA processing, such as pre-mRNA cleavage factor 1 (HpbrII-4) and ring-finger domain protein 10 (Rnf10), belong to this cluster. Most of the genes related to protein folding, such as transactivating protein Bridge, 20S proteasome subunit RC10-II and HLA-B-associated transcript 3 (Bat3), were induced later and were grouped in cluster 6. A large number of genes related to carbohydrate metabolism and nuclear functions were observed to be perturbed in the induced HD-74Q lines, but they were not restricted to one expression pattern.
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Overexpression of genes with altered expression in HD-74Q lines protects cells against polyglutamine toxicity
We selected 11 genes that were identified above in order to test if they were functionally involved in the cell death caused by expanded polyglutamines. Each one of these genes (or control empty vector DNA) was initially co-transfected into COS7 cells with another vector expressing an exon 1 HD gene fragment containing 74 glutamines (pEGFP–HD74Q). We used transient transfection in COS7 and later in SK-N-SH cells, since these cells are more accessible to such experiments than PC12 cells, which transfect poorly. Cell death was quantified by counting EGFP-positive cells with nuclear fragmentation/condensation 48 hours after transfection. Because constructs were not based on the same vector system and their expression levels were not comparable, we focused on the resulting biological activities. We have previously shown that pEGFP–HD74Q expression results in significantly higher rates of cell death compared with pEGFP–HD23Q (10,24). Nuclear fragmentation was detected by fluorescence microscopy using DAPI staining. Figure 1A summarizes a series of experiments determining the proportions of EGFP-positive cells with nuclear fragmentation expressing pEGFP–HD74Q co-transfected with selected genes from the ATAC–PCR experiments, compared with cells co-transfected with pEGFP–HD74Q and an empty vector control (pBudCE4). Odds ratios (ORs; see Materials and Methods) were considered to be the most appropriate summary statistic for multiple independent experiments of this type, because the percentage of cells with nuclear abnormalities under specified conditions varied between experiments on different days, whereas the relative change in the proportion of cells nuclear abnormalities induced by a perturbation is expected to be more constant (10,24).
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Among the 11 genes tested in COS7 cells, three genes [thiol-specific antioxidant protein 2 (Aop2), lamina-associated polypeptide-binding protein 1 (L2bp1) and clusterin] increased the proportions of EGFP-positive dead cells compared with control cells (‘enhancer’ genes in Fig. 1A). We next confirmed data obtained from COS7 cells in the SK-N-SH neuronal cell line, and focused on the six genes that decreased polyglutamine-induced cell death (‘suppressor’ genes with P-value <0.001 in Fig. 1A) such as Glut1, Pfkm, Gstm2, Rbm3, Krip-1 and BRG1-associated factor 60b (BAF60b). We concentrated on the potentially protective genes, since we believed that these might inform us about pathways that could be possibly perturbed for therapeutic benefit. Among the six genes that were protective in COS7 cells, five (Glut1, Pfkm, Gstm2, Rbm3 and Krip-1) still significantly reduced polyglutamine-induced cell death in SK-N-SH cells (Fig. 1B). In order to test whether Glut1, Pfkm1, Gstm2, Rbm3 and Krip-1 reduced apoptosis in cells expressing wild-type huntingtin constructs, we studied cells co-transfected with each of these genes and pEGFP–HD23Q in a 3 : 1 ratio. EGFP-positive cells were analyzed by quantitative flow cytometry using a FACSort flow cytometer (Becton Dickinson) 48 hours after transfection (25 000–30 000 cells per sample were examined). We used 7-aminoactinomycin D (5 mg/ml) (7-AAD, a G-C base-specific DNA intercalator) to identify non-viable cells, and observed no significant reduction in the percentages of dead cells in populations transfected with Glut1, Pfkm1, Gstm2, Rbm3 and Krip-1, compared with pcDNA3 (data not shown).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Stable transfected cell lines with inducible expression of mutant proteins are powerful tools for analyzing the early steps in a disease process that is believed to be cell-autonomous (3,14,15). Our inducible PC12 cell lines recapitulate many of the features of HD seen in vivo. The polyglutamine expansion is associated with the formation of aggregates with electron-microscopic structures similar to those seen in vivo, decreased neurite outgrowth and caspase-dependent cell death (24). In the cycling PC12 lines, cell death occurs more slowly than in other in vitro model systems (e.g. transient transfection in COS7 and SK-N-SH cells). This is advantageous for gene expression studies, since it allows us to analyze early time points where changes are less likely to be due to induction of secondary pathways. Accordingly, we have studied changes in gene expression at time points before there is any mitochondrial dysfunction or overt cell death.
In order to provide additional insights, we have analyzed multiple time points. This allows cluster analysis, but also enables the detection of transient changes in expression that may be frequently missed when single time points are analyzed. In our method using electroporation of plasmid DNA, it is not possible to control the location of a polyglutamine insertion in genome of each clonal line (24). In order to minimize false-positive data due to clonal variation, we studied two clonal lines with each repeat length, and focused on genes that showed the same response between the clonal lines. Also, we focused on genes that showed a temporal response in the mutant lines but no significant change in expression over time in the wild-type lines. We used ATAC–PCR, which allows relative quantitation of transcript levels. The initial set of genes that we analyzed was selected on the basis of their abundance either in an uninduced wild-type line or a mutant line induced for 18 hours. Although the number of genes that we analyzed is not as large as that found on Affymetrix arrays, most of genes in our set were confirmed to be expressed at quantifiable levels in PC12 cells. Since very rare transcript species generally do not give meaningful signals with microarrays, it is unlikely that we obtained data from fewer genes than what could have been meaningfully assayed with the standard Affymetrix rat arrays. The Smirnov–Valis test is suitable for selecting significant changes over background deviation. We used this test and selected the genes that showed a greater response than the possible changes caused by both clonal lines and the experimental noises. 126 genes showed significant perturbations in expression in mutant lines but no significant changes over time in the wild-type lines. These included 69 known genes. In order to start testing the functional relevance of these changes, we selected 11 candidates for further analysis, with the aim of identifying genes/pathways that repress polyglutamine-induced cytotoxicity. The high positive rate in the functional assays suggested that our analytical approach had the potential to identify primary changes that are relevant to the biological process. Only one of the five suppressors that we identified was represent on the Rat Affymetrix arrays that we had studied previously (Gstm2) (24). The three Affymetrix probe sets that interrogated this gene each showed small non-significant increases or decreases for HD-74Q versus HD-23Q. However, it is important to note that we previously only studied the 18 hour time point, where ATAC–PCR showed only a 1.7-fold increase for HD-74Q versus HD-23Q. The power of the current study is enhanced by analysis of multiple time points.
Altogether, four genes involved in glucose metabolism (Glut1, Pfkm, Aldolase A and Enolase) had altered expression levels in our system. Furthermore, augmented expression of Glut1 and Pfkm (key proteins regulating glycolysis) rescued both COS7 and SK-N-SH cells from polyglutamine-induced death. These observations may be relevant to the bioenergetic defects seen in HD (27,28). Several studies suggested that enhanced glucose uptake protects cells from hypoxic injury (29,30). Overexpression of Glut1 was reported to rescue the cells from toxic insults such as apoptosis induced by glycogen synthase kinase 3ß (Gsk3ß) and hypoxia (29,31). All of these protective effects may be based on a common biological mechanism.
Three genes involved in oxidative stress, Gstm2, Aop2 and peroxiredoxin IV (Prx IV ), changed their expression levels in our model. These data are compatible with in vivo evidence for free-radical damage in an HD mouse (32), oxidative damage in HD parietal cortex (33), increased levels of reactive oxygen species in HD cell models and protection against polyglutamine-induced cell death in tissue culture by antioxidants (34). Glutathione-S-transferases (GSTs) are known to catalyze the conjugation of the antioxidant protein glutathione to reactive oxygen species. Several studies have reported that cell death was induced by suppression of GSTs, such as hGSTA2-2 (35) and GST-P (36). In addition, in a Drosophila SCA1 model, overexpression of the Gst55F gene, which is most similar to human 
-class Gsts, suppressed the disease phenotype (37). GSTs may be generally involved in the pathogenesis of polyglutamine diseases. Against expectation, overexpression of Aop2 enhanced cell death in COS7 cells. However, interpretation of reinforcement of cell death needs caution, because proteins could be cytotoxic at high expression level.
KRIP-1 (also known as TIF1ß) is a PHD zinc finger- and bromodomain-containing co-repressor of Krüppel-associated box (KRAB)-mediated repression (38). KRIP-1 and the structurally related proteins TIF1 and PML are members of the RBCC (ring-finger–B boxes–coiled coil) subfamily of the ring-finger family of zinc-binding proteins. KRIP-1 and TIF1 interact with and function as co-repressors of the KRAB-A repression domain present in the N-terminal regions of approximately one-third of all vertebrate Krüppel-type zinc-finger proteins. TIF1 and TIF1ß (KRIP-1) have also been proposed to play a dual role in the control of transcription, being involved both in co-repression through their interactions with the KRAB-A domain and in hormone-dependent co-activation through their interactions with various nuclear hormone receptors (reviewed in 38). Our finding that overexpression of Krip-1 inhibited polyglutamine-induced cell death suggests that transcriptional pathways other than CREB/CBP/TAFII130 may be involved in polyglutamine disease pathogenesis.
Rbm3 was progressively downregulated over time in our mutant cell lines, and overexpression of this gene rescued polyglutamine-induced cell death. The mechanisms for this rescue are unclear, since the function of the Rbm3 gene product has not been elucidated—as far as we are aware, our data are the first to suggest that it may protect against certain cell death pathways. Rbm3 is a gene that is induced after mild cold stress (39,40). Our results support the proposition that some of the gene products that are induced after mild cold shock may protect cells against both cold shock and other stressors (41). It is interesting to note that all of the suppressors identified are expressed in the brain (42–46).
In conclusion, our data suggest that some of the early cellular responses to polyglutamine expansions include protective responses against cell death, and provide new hypotheses for future studies aiming to elucidate the pathogenic basis of HD, as well as possible therapeutic directions.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Plasmid constructs and establishment of inducible PC12 cell lines
We have previously characterized stable inducible PC12 cell lines expressing pEGFP–HD23Q and pEGFP–HD74Q (N-terminal exon 1 fragments of huntingtin with 23 and 74 glutamine repeats respectively, fused downstream of EGFP) (10,47). The expression of the recombinant huntingtin was confirmed by a modification of ATAC–PCR (25). After dox treatment, the expression levels of pEGFP–HD23Q and pEGFP–HD74Q were increased three to five times and maintained (data not shown). Glut1 (H-K03195M), Pfkm (H-U24183M), Aop2 (H-D14662M) and Gstm2 (H-M63509M) constructs were GeneStorm human clones (Invitrogen, the Netherlands), and L2bp1 (3599828), Rbm3 (3449185), clusterin (4150452) and Baf60b (2647494) constructs were IMAGE clones (HGMP Resource Centre, Cambridge, UK). HLA-B-associated transcript 3 (Bat3) (48), Krip-1 (49) and chromosome region maintenance 1 protein (Crm1) (50) constructs were gifts from Toshinori Ozaki (Chiba Cancer Center Research Institute, Chiba, Japan), Joseph V. Bonventre (Harvard Medical School, Boston, MA, USA) and Minoru Yoshida (University of Tokyo, Tokyo, Japan), respectively.
ATAC–PCR assay
Two 3'-directed cDNA libraries were constructed using total RNAs from one line of uninduced HD-23Q cells (0 h) and one line of HD-74Q cells induced with dox for 18 h, as described previously (24). A total of 4006 unique EST sequences were obtained from single-pass sequencing of 12 476 cDNA clones. We selected 1824 genes for primer design, prioritizing known genes and abundant unknown genes. ATAC–PCR reactions were performed using seven adaptors, instead of six as previously described (25). Three adaptors were assigned for control cDNAs, which were made from RNA of the HD-23Q cell line, and the other four adaptors were assigned for sample cDNAs. The sequences of the seven adaptors were as follows:
(1S) 5'-GTACATATTGTCGTTAGAACGCG-3'
(1L) 5'-GATCCGCGTTCTAACGACAATATGTAC-3'
(2S) 5'-GTACATATTGTCGTTAGAACGCGACT-3'
(2L) 5'-GATCAGTCGCGTTCTAACGACAATATGTAC-3'
(3S) 5'-GTACATATTGTCGTTAGAACGCGCATACT-3'
(3L) 5'-GATCAGTATGCGCGTTCTAACGACAATATGTAC-3'
(4S) 5'-GTACATATTGTCGTTAGAACGCGATCCATACT-3'
(4L) 5'-GATCAGTATGGATCGCGTTCTAACGACAATATGTAC-3'
(5S) 5'-GTACATATTGTCGTTAGAACGCGTCAATCCATACT-3'
(5L) 5'-GATCAGTATGGATTGACGCGTTCTAACGACAATATGTAC-3'
(6S) 5'-GTACATATTGTCGTTAGAACGCGTACTCAATCCATACT-3'
(6L) 5'-GATCAGTATGGATTGAGTACGCGTTCTAACGACAATATGTAC-3'
(7S) 5'-GTACATATTGTCGTTAGAACGCGCTATACTCAATCCATACT-3'
(7L) 5'-GATCAGTATGGATTGAGTATAGCGCGTTCTAACGACAATATGTAC-3'
Three adaptors (adaptors 1, 4 and 7) were used to make calibration curves of control cDNA, and the other four (adaptors 2, 3, 5 and 6) were used for sample cDNAs. Each sample was assayed twice with different calibrations. One calibration used 10x, 3x and 1x equivalents of the control sample with three different adaptors compared with 1x equivalent of each of the four test samples. The other calibration used 1x, 3x and 10x equivalents of the control sample compared with a 3x equivalent of each of the four test samples. The relative expression level of each gene was calculated from two calibration curves based on the control samples, and the data calculated from the calibration curve with the better quality were used for the subsequent analysis.
Selection of genes induced by the extended polyglutamine
Two independent clonal cell lines with HD-23Q and two independent clonal lines with HD-74Q were analyzed. We determined the transcripts that showed significant differences between mutant and wild-type lines as follows. First, the baseline variation of each gene was calculated by determining the absolute differences between all pairwise comparisons of the four different cell lines for the uninduced (0 h) time point, and two cell lines for other time points. Second, absolute differences between all possible different time points (six combinations: 0 and 5 h; 0 and 10 h; 0 and 18 h; 5 and 10 h; 5 and 18 h; 10 and 18 h) were determined for each cell line, and we selected those that were significantly higher than the baseline variations (P-value <0.01 by the Smirnov–Valis test) (51). Third, changes in gene expression common to both HD-74Q or HD-23Q cell lines were identified. Lastly, genes exhibiting changes observed in the HD-74Q cell lines but not in the HD-23Q cell lines were selected.
Cell cultures and co-transfection experiments
African green monkey kidney cells (COS7) and human neuroblastoma cells (SK-N-SH) were used for co-transfection studies. Both cells were grown in Dulbecco's Modified Eagle's Medium (DMEM, SIGMA, Dorset, UK) with 100 IU/ml penicillin/streptomycin, 2 mM L-glutamine, 1 mM sodium pyruvate and 10% FBS (Life Technologies, Paisley, UK) at 37°C and 5% CO2. Rat pheochromocytoma cells (PC12 Tet-On) (CLONTECH) were cultured in DMEM with 100 IU/ml penicillin/streptomycin, 2 mM L-glutamine, 10% heat-inactivated horse serum, 5% FBS, 100 µg/ml G418 and 28 mg/ml Hygromycin B (CALBIOCHEM, CA, USA) at 37°C and 10% CO2. For transfection of COS7, cells were seeded to 60–80% confluency on sterile coverslips in six-well plates for 24 h before transfection, and then exposed to a mixture of 10 µl/well of LipofectAMINE Reagents (Life Technologies) and 2 µg/well of plasmid DNA mixture in serum-free medium. After 4 h, the medium was supplemented with 10% FBS. For transfection of SK-N-SH, cells were seeded to 40–50% confluency for 24 h before the transfection. After 5 h, the medium was supplemented with 10% FBS. In co-transfection experiments, 3 : 1 ratios of the selected genes to pEGFP–HD74Q were used, in order to ensure that practically all cells expressing the pEGFP–HD74Q constructs also expressed the potential modifier gene (data not shown). The same amount of empty vector pBudCE4 (Invitrogen) DNA was used as a control for the potential modifier genes. In all such experiments, we used a total of 2 µg of DNA per 3.5 cm dish. At 48 h after transfection, cells on coverslips were washed with 1x PBS, fixed with 4% paraformaldehyde in 1x PBS for 30 min, and mounted in antifadent supplemented with 4',6-diamidino-3-phenylindole (DAPI) (3 mg/ml) to allow visualization of nuclear morphology.
Statistical analysis
We counted 500–600 EGFP-positive cells per slide (blinded) in multiple random visual fields. The morphology of DAPI-stained nuclei was considered as abnormal when the nucleus showed apoptotic morphology (fragmentation or condensation). Each experiment was performed twice in triplicate. Pooled estimates from multiple experiments for the changes in the proportions of cells with fragmented nuclei induced by potential modifier genes were calculated as odds ratios (OR) with 95% confidence intervals [(% cells expressing the test construct with fragmented nuclei/% cells expressing the test construct without fragmented nuclei)/(% cells expressing control construct with fragmented nuclei/% cells expressing control construct without fragmented nuclei)]. OR and P-values were determined by unconditional logistical regression analysis, using the general loglinear analysis option of SPSS version 9.1 software (SPSS, Chicago, IL, USA).
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ACKNOWLEDGEMENTS |
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We thank Dr C. Maruyama and Ms I. Ikeda for technical assistance. We thank Dr R. Matoba for discussions. We are grateful to the Japan Science and Technology Corporation (K.K.), the Hereditary Disease Foundation (A.W. and D.C.R.), Merck Sharp and Dohme (A.W. and J.S.), Action Research (J.C.) and Glaxo Wellcome (D.C.R.). J.S. and J.C. are grateful for Sackler studentships. D.C.R. is a Wellcome Trust Senior Fellow in Clinical Science.
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FOOTNOTES |
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* To whom correspondence should be addressed. Tel: +81 743725583; Fax: +81 743725589; Email: kkato{at}bs.aist-nara.ac.jp
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