Longitudinal analysis of gene expression and behaviour in the HdhQ150 mouse model of Huntington's disease

Brain Research Bulletin 88 (2012) 199–209

Contents lists available at SciVerse ScienceDirect

Brain Research Bulletin journal homepage: www.elsevier.com/locate/brainresbull

Research report

Longitudinal analysis of gene expression and behaviour in the HdhQ150 mouse model of Huntington’s disease夽 Peter Giles a , Lyn Elliston a , Gemma V. Higgs b , Simon P. Brooks b , Stephen B. Dunnett b , Lesley Jones a,∗ a b

MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK Brain Research Group, School of Bioscience, Cardiff University, Cardiff CF10 4AX, UK

a r t i c l e

i n f o

Article history: Received 15 August 2011 Received in revised form 28 September 2011 Accepted 1 October 2011 Available online 6 October 2011 Keywords: Huntington’s disease Neurodegeneration Gene expression Transgenic mouse models Behaviour

a b s t r a c t Substantial transcriptional changes are seen in Huntington’s disease (HD) brain and parallel early changes in gene expression are observed in mouse models of HD. Analysis of behaviour in such models also shows substantial deficits in motor, learning and memory tasks. We examined the changes in the transcriptional profile in the HdhQ150 mouse model of HD at 6, 12 and 18 months and correlated these changes with the behavioural tasks the animals had undertaken. Changes in gene expression over time showed a significant enrichment of RNAs altered in abundance that related to cognition in both HdhQ150 and wild-type animals. The most significantly down-regulated mRNA between genotypes over the whole time-course was Htt itself. Other changes between genotypes identified at 6 months related to chromatin organization and structure, whilst at 18 months changes related mainly to intracellular signalling. Correlation of the changes in gene product abundance with phenotypic changes revealed that weight and detection of the opposite position of the platform in the water maze seemed to correlate with the chromatin alterations whereas changes in the rotarod performance related mainly to intracellular signalling and homeostasis. These results implicate alterations in specific molecular pathways that may underpin changes in different behavioural tasks. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Huntington’s disease (HD) is a devastating progressive neurodegeneration that leads inexorably to disability and death. It is caused by an expansion of a CAG triplet repeat at the 5 end of the HTT gene, which is translated to give an expanded glutamine tract in the gene product, huntingtin (HTT) [44]. The mutation has been introduced into a number of different animal models to recapitulate the human disease [9,25,47] and to allow a dissection of the disease mechanisms, as well as investigation into possible treatments [19,29]. The mouse models of HD include transgenic models with various truncations of the protein and long CAG repeats, transgenic models with full length huntingtin in artificial chromosomes and knock in models where the mouse CAG repeat has been replaced with a longer CAG repeat [5,9]. Transcriptional changes have been observed in HD brain [24] and in mouse models of HD [5] and these appear early in the

夽 This article is part of a Special Issue entitled ‘HD Transgenic Mouse’. ∗ Corresponding author at: Department of Psychological Medicine, 2nd Floor Henry Wellcome Building, Heath Park, Cardiff, Wales CF14 4XN, UK. Tel.: +44 029206 87054; fax: +44 029206 87068. E-mail address: [email protected] (L. Jones). 0361-9230/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.brainresbull.2011.10.001

development of the phenotype in animals [27]. The transcriptional alterations in mouse model striata are consistent with those seen in human caudate [24,27] indicating that the events in the striata of the models recapitulate the human disease at a molecular level. Notably, treatments that ameliorate the changes in transcription can improve the phenotype in mouse models, whether directed at transcriptional mechanisms or not [16,17,22,35,45]. Many of the mouse models recapitulate other aspects of human HD. Motor phenotypes tend to be most widely assessed in animals though various studies have revealed that cognitive and other behavioural changes can be detected before motor symptoms become apparent. We have measured both the response to behavioural tasks as well as striatal gene expression in the HdhQ150 model of HD [30]. This study uses one of the most genetically accurate knock in models of HD, in which the 7 CAG repeat tract of mouse Htt has been replaced by a long CAG repeat. Mouse Htt was previously designated Hdh and it is from this designation that the name of the HdhQ150 model derives. The behavioural phenotype develops relatively slowly, but is very similar to that of models that develop their phenotype more rapidly [49]. It provides an opportunity to examine the trajectory of any changes at the molecular and behavioural levels in detail over months with a more precise resolution of the timing of events than in a model with a rapidly advancing phenotype that moves from onset to death in weeks [17].

200

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

We examined global gene expression changes in the striata from wild-type and homozygous mice from the HdhQ150 mouse line at 6, 12 and 18 months. We observed alterations in gene expression at all time points which overlapped with changes seen in human HD brain and in other mouse models of the disease. We examined the correlation of gene expression with behavioural data from the animals and identified differentially expressed genes enriched in gene ontology (GO) processes relating to weight, rotarod and water maze phenotypes. 2. Materials and methods 2.1. Samples Heterozygous HdhQ150/+ mice on the original 129/Ola x C57BL6/J background [30] were bred in house and genotypes ascertained using tail tip DNA (Laragen Inc., Los Angeles). CAG repeat lengths in the HdhQ150 animals were 124 ± 6 (range 118–130). Wild-type (WT) and homozygous HdhQ150/150 (HdhQ150) animals of both sexes were used in the experiments (21% male). The animals were housed as sex matched littermate groups and had access to food and water ad libitum. All experiments were carried out in accordance with the United Kingdom Animals (Scientific Procedures) Act of 1986, and subject to local ethical review. The behavioural data relating to the complete cohort of mice are given in Brooks et al. [6]. 2.2. Gene expression From this experimental group 15 homozygous (HdhQ150/Q150 ) (12 females and 3 males) and 14 WT (Hdh+/+ ) mice (11 females and 3 males) were used for gene expression studies. The homozygous HdhQ150/Q150 animals had repeat lengths of 124 ± 6 (range 118–130). Brains from age matched mice from each genotype were harvested at 6, 12 and 18 months and micro-dissected into striatum, motor cortex, cerebellum, prefrontal cortex and hippocampus.1 The dissected brain samples were snap frozen in liquid nitrogen and stored at −80 ◦ C. Total RNA was extracted from micro-dissected striata for gene expression analysis as previously described [35]. RNA quality was determined using an Agilent RNA 6000 Nano Kit and Agilent 2100 Bioanalyser (Agilent Technologies, Santa Clara, USA). Samples with RIN (RNA integrity number) values greater than 7.5 were selected for subsequent analysis. For each RNA sample, cDNA was generated from 100 ng total RNA using an Ambion® WT expression kit (Applied Biosystems, Carlsbad, CA, USA), followed by fragmentation, labelling and hybridisation to a Mouse GeneChip Gene 1.0 ST Array. An Affymetrix WT Terminal Labelling and Hybridisation kit was used according to the manufacturer’s protocol. Gene Chips were processed using a Fluidics station 450 and a GeneChip scanner 3000 7G (Affymetrix UK Ltd., High Wycombe, UK).

its highest correlation to theoretical expression profiles). The gene expression data are available through GEO accession number GSE32417. 2.3.1. Variation of behaviour/expression correlation with genotype The effect of genotype on behaviour and gene expression was tested using the methodology outlined in Hodges et al. [23]. The behavioural data used in the analysis are presented in Brooks et al. [6]. The individual behavioural data for each animal were derived from the most recent test prior to sacrifice for that task. Phenotypes examined in the correlation analysis included weight, rotarod, balance beam, water maze and pre-pulse inhibition and startle [6]. Briefly, significant correlations (FDR, p < 0.05) were identified between behaviours and expression data for probesets identified as significantly different between WT and HdhQ150 (1659 probe sets). Data for the probe sets showing a significant correlation were then subject to linear regression using behaviour as the dependent variable, and differences in the models of the WT and HdhQ150 data were assessed using ANOVA. Probeset–behaviour combinations with significant differences (p < 0.05) were classified into one of eight bins [23] depending on the significance and the direction of correlation in each genotype group. Figure S1 shows a flow diagram of the analysis. If the interaction term was not significant (p > 0.05) the behaviour/probeset combination was assigned to bins 1 and 2 where the correlation between the behavioural measure and probeset expression was either positively (bin 1) or negatively (bin 2) correlated. If the interaction term was significant (p < 0.05) the behaviour/probeset combination was assigned to one of bins 3–8 depending on the significance and the direction of correlation with each genotype. 2.3.2. Determining biological themes The resultant gene lists from the differential gene expression, time course ANOVA and behaviour/expression correlation were analysed for overrepresentation of genes in pathways against GO Biological Process gene sets using the Bioconductor GOstats package with the conditional hypergeometric test (which only uses those terms that were not already significant when testing a higher order (parent) term). Changes in expression of genes in GO gene sets were assessed using Gene Set Analysis [43] against the whole dataset. 2.3.3. Comparison with other data Comparison with differentially expressed genes from human HD brain [24] and other HD models [23,35] was calculated using hypergeometric tests on the top 500 ranked genes in gene lists for differently expressed genes between WT and HdhQ150. To enable comparisons between different array platforms where the species was identical, probesets in gene lists were first converted to unique Entrez Gene IDs and the overlap calculated using these. Where overlaps were made between data for different species, data was first converted to Entrez Gene IDs and then to Homologene IDs which were used to calculate the overlap between lists. In addition, a graphical representation of the overlap, along with information about the relative direction of changes was generated using the method of Kuhn [27].

3. Results 2.2.1. Quantitative real-time PCR Quantitative real-time PCR (QPCR) was performed, using 100 ng DNAsed RNA from the same mRNA preparation as that used for the GeneChip analysis above. QPCR was carried out using Power SYBR® Green RNA-to-CTTM 1-Step Kit (ABI, Carlsbad, CA) and gene-specific oligonucleotide primer pairs spanning intron exon boundaries. Two control genes were used: Ubc (F 5 -GAGTTCCGTCTGCTGTGTGA-3 ; R 5 -CCTCCAGGGTGATGGTCTTA-3 ) and Actn (F 5 -TCTGTGTGGATTGGTGGCTCTA-3 ; R 5 -CTGCTTGCTGATCCACATCTG-3 ). The Htt amplicons used oligonucleotide primers designed with primer3 software (www.genome.wi.mit.edu/cgibin/primer/primer3 www.cgi) and sequence data from the Ensembl database (www.ensembl.org) across exons 2–3 (F 5 CATTGTGGCACAGTCTCTCAG-3 ; R 5 -CATAGCGATGCCCAAGAGTT-3 ): exons 8–9 (F 5 -GGTGACACGGAAAGAAATGG-3 ; R 5 -ACATTGTGGTCTTGGTGCTG-3 ): exons 39–40 (F 5 -GCACACTGCTCATGTGTCTG-3 ; R 5 -GTCTAGTGGCAGCTGCTGTG-3 ): exons 61–62 (F 5 -GCTGTCCAACCTCAAAGGAA-3 ; R 5 -GTGGCACACATTACCAGCAC3 ) (Invitrogen, Carlsbad, CA). Analysis used the 2−CT method [3,31]. 2.3. Gene expression analysis An analysis of GeneChip expression data was undertaken using R/Bioconductor. Expression values were computed using robust multichip average (RMA) (affy package [18]), with testing for differential gene expression by age or genotype performed using moderated t-tests in LIMMA [39]. Changes in gene response over time were identified using TANOVA [50]. Genes with a false discovery rate (FDR) [2] corrected p < 0.05 were extracted and the data for these genes classified in three patterns representing an up, no change or down difference in expression over the time course (this was done separately for the WT and HdhQ150 animals tagging each gene with

1 Only data from the striata are analysed in the present report, for budgetary reasons.

3.1. The effects of age on gene expression Examining the genes altered over the time course within each genotype between 6 and 12 months, 6160 mRNAs (probesets) are altered in abundance in the WT animals and 7596 mRNAs in the HdhQ150 animals (FDR p < 0.05). Of these, 3818 overlapped in both sets (62% and 50% respectively, Fig. 1A). The probesets and corresponding genes are given in Table S1. Between 12 and 18 months, many fewer mRNAs are altered: 729 in WT mice and 1219 in HdhQ150 mice, of which 519 are common to both cohorts (42% and 71% respectively, p = 1.24 × 10−69 , Fig. 1B). A GO term enrichment analysis (Table 1) shows that relatively few pathways were significantly enriched in genes altered in abundance between 6 and 12 months, despite the large number of genes contributing to the signal detected, but seven such pathways are identical between WT and HdhQ150 mice and the 8th pathway in the HdhQ150 striatum analysis is 11th in the WT list (GO:0007606 sensory perception of chemical stimulus; see Table S2). These processes include perception of smell, cognition and intracellular signalling. The smaller number of genes altered in abundance between 12 and 18 months revealed a larger number of significant processes enriched compared with 6–12 months, though these are generally less significant. Although there is a substantial overlap in processes between WT and HdhQ150 striata, it is not nearly so complete as in the 6–12 month data. Ten processes are common to both time periods, just less than half of all processes identified for each genotype.

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

201

Fig. 1. The overlap between striatal genes differentially expressed between different ages of mice.

These include ion transport, neuron-specific and intracellular signalling processes. Many of the other significant processes in each list are related to these processes and often have similar transcripts within them (Table S2). One process that is much more overrepresented in the HdhQ150 than the WT striata is GO:0006468, protein amino acid phosphorylation, (9.8% vs 5.1%, p < 0.0005).

3.2. Effects of genotype on gene expression Analysis between genotypes shows that 1659 probesets are dysregulated between HdhQ150 and WT animals when using data from all time points (nominal p < 0.05), of which 892 are up-regulated and 767 are down-regulated in the HdhQ150 striata. Analysing the

Table 1 Pathways altered with age in mouse caudate in HdhQ150 mice. ID

6m vs 12m GO:0007608 GO:0050890 GO:0007186 GO:0007165 GO:0032501 GO:0050789 GO:0019236 GO:0007606 12m vs 18m GO:0006812 GO:0006813 GO:0030182 GO:0006171 GO:0032990 GO:0007268 GO:0051179 GO:0022008 GO:0030814 GO:0030802 GO:0000904 GO:0009187 GO:0006140 GO:0007242 GO:0010646 GO:0007190 GO:0031281 GO:0051349 GO:0007611 GO:0019935 GO:0045761 GO:0046903 GO:0007188 GO:0031175 GO:0048667 GO:0048858 GO:0032989 GO:0044057 GO:0006814 GO:0050801 GO:0055082 GO:0006468 GO:0006836

Term

6 month

12 month

FDR p

Count

Global

FDR p

Count

Global

Sensory perception of smell Cognition G-protein coupled receptor protein signalling pathway Signal transduction Multicellular organismal process Regulation of biological process Response to pheromone Sensory perception of chemical stimulus

3.18E−164 6.82E−145 6.18E−125 2.72E−61 6.96E−39 1.76E−10 6.65E−04 ns

629 715 758 1072 1132 1448 44

1098 1429 1669 3381 3951 6038 100

1.49E−253 1.38E−217 7.92E−210 1.22E−102 2.18E−61 5.94E−34 2.87E−18 2.92E−06

803 908 996 1380 1433 1900 72 18

1098 1429 1672 3381 3951 6038 100 21

Cation transport Potassium ion transport Neuron differentiation cAMP biosynthetic process Cell part morphogenesis Synaptic transmission Localization Neurogenesis Regulation of cAMP metabolic process Regulation of cyclic nucleotide biosynthetic process Cell morphogenesis involved in differentiation Cyclic nucleotide metabolic process Regulation of nucleotide metabolic process Intracellular signalling cascade Regulation of cell communication Activation of adenylate cyclase activity Positive regulation of cyclase activity Positive regulation of lyase activity Learning or memory Cyclic-nucleotide-mediated signalling Regulation of adenylate cyclase activity Secretion G-protein signalling, coupled to cAMP nucleotide second messenger Neurite development Cell morphogenesis involved in neuron differentiation Cell projection morphogenesis Cellular structure morphogenesis Regulation of system process Sodium ion transport Ion homeostasis Cellular chemical homeostasis Protein amino acid phosphorylation Neurotransmitter transport

1.28E−06 1.00E−05 4.68E−05 1.69E−04 2.15E−04 2.19E−04 4.08E−04 4.96E−04 7.25E−04 1.09E−03 1.45E−03 1.91E−03 2.36E−03 2.71E−03 2.76E−03 6.22E−03 7.96E−03 7.96E−03 2.25E−02 3.18E−02 3.42E−02 3.84E−02 4.38E−02

47 22 38 13 24 21 88 40 12 12 24 14 12 44 42 9 9 9 12 10 8 23 9

493 142 395 58 195 155 1540 468 55 57 216 82 61 597 538 36 37 37 75 54 34 244 45

1.63E−07 8.68E−08 4.29E−02 1.00E−03 ns 2.29E−08 2.79E−04 6.83E−04 2.99E−03 4.76E−03 ns 5.00E−02 1.13E−02 2.08E−02

65 31 37 15

493 142 323 58

33 134 50 14 14

153 1629 421 55 57

15 14 79

78 61 877

10 10 10

36 37 37

29

230

36 32 32 56 24 22 32 30 64 10

221 191 195 488 147 128 239 221 656 35

ns ns ns ns ns ns ns ns ns ns

4.27E−02 5.55E−02 5.55E−02 ns ns ns 7.26E−02 ns 1.38E−05 4.86E−05 8.08E−05 4.86E−04 4.51E−03 4.98E−03 8.66E−03 1.31E−02 1.93E−02 2.84E−02

The probesets and genes that were significant over time are given in Table S1 and the full list of pathways in Table S2. Count is total number of significantly differentially expressed probesets in that GO category and global is the total number of genes in that GO category.

202

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

Fig. 2. The overlap between striatal genes differentially expressed between genotypes at different ages of mice.

time points individually, at 6 months 4130, at 12 months 1060 and at 18 months 1487 probesets are altered in expression (nominal p < 0.05; Table S3, Fig. 2A). Between 6 and 12 months 148 probesets are seen as altered in abundance at both times and between 12 and 18 months there are 173 such probesets (Table S3, Fig. 2B). In these animals the most significantly dysregulated mRNA between genotypes over all times is Htt itself (p = 1.24 × 10−6 , absolute fold change 1.25, Fig. 3A, Figure S2 and Table S3). This difference is observed at all time points and does not appear to be progressive: this was confirmed using QPCR in striatal mRNA from 12 month old mice amplifying multiple exons across Htt (Fig. 3B). The other genes dysregulated between genotypes are given in Table 2. Some, such as Gpx6, the most substantially dysregulated gene, are substantially down-regulated by 6 months and remain down-regulated by a similar amount through the time course. Others, such as Ryr1, Slc4a11, Scn4b and Penk1 decrease in abundance over time. Most up-regulated genes shown in Table 2 show a progressive up-regulation over time. Table 2 indicates that the down-regulations tend to be significant by 6 months whereas the up-regulations tend to show much greater significances and fold changes by 18 months. Examination of the gene sets revealed by the mRNAs altered in expression (Table 3) shows that overall between genotypes there are 4 significantly overrepresented processes, and that these are all connected to chromatin organization. Separating the probesets into up- and down-regulated RNAs reveals that these categories relate to down-regulated probesets and all of the significant categories except that of protein repair are populated by a very similar set of histone genes (Table S4). The three genes in the protein repair category are all methionine sulphoxide reductases. The up-regulated

probesets relate to ion homeostasis and action potential regulation: there is a subset of genes common to all these processes and all contained within GO:0019228 regulation of action potential in neuron, Grik2, Pmp22, Drd1a, Cldn11, Ugt8a, Gje1, Lgi4 and Plp1 (see Table S4 for a full list of genes in all categories). Inspection of the overrepresented processes at the individual time-points reveals that the categories identified by down-regulated genes are likely to be driven by the changes in expression at 6 months which include all the same pathways, whereas the up-regulated genes identify a set of functional categories very similar to those detected by expression changes at 18 months, which relate to intracellular signalling pathways. No processes were significantly overrepresented between genotypes at 12 months. Examination of the changes in expression over time using TANOVA [50] shows that, for all patterns of expression in the striata, more genes are down-regulated in the HdhQ150 (Table 4, Table S5). The single pattern with the largest gene membership is that where the genes are reduced in abundance in the HdhQ150s but do not change in WT mouse brain: 68 genes. This is also the only category to reveal any overrepresented pathways. These are intracellular signalling pathways, similar to the 18 month overrepresented processes revealed by the direct comparison by genotype above, but are more focused on specifically cAMP-related processes. Examination of the processes revealed by all the genes down-regulated in the HdhQ150 model also highlights significant enrichment in intracellular signalling and cAMP pathways. The enrichment of the three most significant processes after FDR correction is due to the same six genes in each case: Adora2a, Drd1a, Adcy5, Chrm4, Drd2 and Htr1b, with the first five also contributing to the enrichment of the other two significant processes.

B) 2.5 6 month

12 month

Age

18 month

1

1.5

2

Q150

0.5

8.9 8.7

All

WT

0

Relative expression level

Q150

9.1

WT

8.5

Log2 expression level

9.3

A)

Exon 2−3

Exon 8−9

Exon 39−40

Exon 61−62

Htt

Fig. 3. Differential expression of Htt between HdhQ150 and WT mice. (A) Down-regulation of Htt expression given by the murine GeneChip ST analysis by age. p-Values are given in Table S2. (B) Down-regulation of Htt expression at different points along the mRNA, shown by QPCR in 12m striatal RNA. p-Values for the difference between WT and HdhQ150 striatal mRNA: exons 2–3, p = 1.7 × 10−4 ; exons 8–9, p = 9.7 × 10−4 ; exons 39–40, p = 1.7 × 10−3 ; exons 61–62, p = 2.9 × 10−3 .

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

203

Table 2 Probesets dysregulated between genotypes. Gene ID

All p-Value

Down in HdhQ150 1.24E−06 Htt Gpx6 3.63E−06 Car2 8.05E−06 Ddit4l 2.45E−05 Ryr1 3.93E−05 5.17E−05 Il33 7.96E−05 Dusp18 1.13E−04 Cntnap3 Qdpr 1.64E−04 Iqub 1.95E−04 Lba1 2.31E−04 Slc39a2 2.43E−04 2.55E−04 Dgat2l6 3.27E−04 Fa2h 3.28E−04 Slc4a11 3.46E−04 Cap1 4.05E−04 Traip Grm3 4.29E−04 4.34E−04 Gba2 Scn4b 4.60E−04 Ppm2c 5.34E−04 5.78E−04 Insig1 5.97E−04 Car11 6.09E−04 Plk5 Cntnap3 6.12E−04 Cldn11 6.67E−04 Sec14l3 6.91E−04 Cntnap3 7.25E−04 7.45E−04 Wdr78 7.60E−04 Penk1 8.33E−04 Phex Arpp19 8.89E−04 Npl 9.57E−04 Up in HdhQ150 5.64E−06 Sfmbt2 7.28E−06 Tmc3 2.87E−05 Polr2a 3.88E−05 Enpp6 Msrb2 5.92E−05 Lsm7 6.34E−05 Lrrn3 1.18E−04 Nfya 1.38E−04 2.05E−04 Ccdc109a 2.53E−04 Cxx1b 2.71E−04 Olig1 2.90E−04 Tns3 3.05E−04 Elf1 3.56E−04 Cbx4 3.95E−04 Fat1 4.07E−04 Cxx1b 4.49E−04 Lsm7 5.01E−04 Il17rb 5.40E−04 S100b 5.69E−04 Creb3l2 6.24E−04 Wdr8 Kcng1 7.13E−04 Cxxc4 7.51E−04 7.64E−04 Thrap3 8.18E−04 Smoc1

6m

12m

18m

log 2FC

AbsFC

p-Value

log 2FC

AbsFC

p-Value

log 2FC

AbsFC

p-Value

log 2FC

AbsFC

−0.325 −1.039 −0.354 −0.472 −0.566 −0.386 −0.334 −0.360 −0.219 −0.312 −0.385 −0.317 −0.259 −0.236 −0.383 −0.161 −0.289 −0.171 −0.167 −0.624 −0.288 −0.357 −0.252 −0.351 −0.381 −0.211 −0.244 −0.460 −0.293 −0.454 −0.479 −0.357 −0.205

1.253 2.054 1.278 1.387 1.481 1.306 1.260 1.283 1.164 1.242 1.306 1.246 1.197 1.178 1.304 1.118 1.222 1.126 1.123 1.542 1.221 1.281 1.191 1.275 1.302 1.157 1.184 1.375 1.225 1.370 1.394 1.281 1.152

1.51E−04 8.08E−05 2.18E−03 1.10E−02 3.68E−02 5.26E−04 8.74E−03 4.10E−03 2.47E−04 1.05E−03 6.95E−02 1.04E−03 8.57E−02 9.29E−03 7.75E−02 4.68E−02 4.08E−03 3.53E−02 6.41E−02 4.68E−03 1.30E−02 6.18E−04 9.81E−02 6.32E−01 2.33E−02 4.86E−03 7.99E−02 1.52E−02 3.90E−02 1.00E−03 2.23E−03 2.33E−02 6.50E−03

−0.369 −1.128 −0.404 −0.358 −0.329 −0.511 −0.276 −0.346 −0.356 −0.393 −0.276 −0.283 −0.160 −0.288 −0.219 −0.114 −0.229 −0.170 −0.124 −0.505 −0.292 −0.526 −0.137 −0.070 −0.368 −0.291 −0.136 −0.471 −0.253 −0.417 −0.592 −0.380 −0.240

1.292 2.185 1.323 1.282 1.257 1.425 1.211 1.271 1.280 1.313 1.211 1.217 1.117 1.221 1.164 1.082 1.172 1.125 1.090 1.419 1.224 1.440 1.100 1.050 1.291 1.224 1.099 1.386 1.192 1.335 1.507 1.301 1.181

5.86E−03 2.44E−03 9.01E−03 7.56E−03 2.14E−04 1.06E−01 5.86E−03 7.33E−04 3.86E−02 2.65E−01 8.30E−03 1.14E−02 1.05E−02 6.30E−03 1.25E−02 1.97E−03 5.34E−03 1.49E−02 6.20E−03 2.60E−03 7.29E−03 4.94E−02 4.37E−03 1.54E−03 4.20E−02 1.84E−02 3.70E−02 6.40E−02 7.10E−02 1.46E−02 7.52E−02 8.93E−02 9.44E−03

−0.278 −0.911 −0.300 −0.500 −0.761 −0.227 −0.332 −0.445 −0.165 −0.145 −0.463 −0.272 −0.318 −0.273 −0.390 −0.216 −0.272 −0.192 −0.231 −0.581 −0.367 −0.292 −0.282 −0.521 −0.360 −0.231 −0.234 −0.403 −0.239 −0.323 −0.397 −0.292 −0.240

1.212 1.881 1.231 1.414 1.694 1.171 1.259 1.362 1.121 1.105 1.379 1.207 1.247 1.208 1.310 1.161 1.207 1.142 1.174 1.496 1.290 1.224 1.216 1.435 1.284 1.173 1.176 1.322 1.180 1.251 1.316 1.224 1.181

3.87E−03 4.42E−04 8.22E−03 2.62E−03 3.36E−03 5.77E−03 4.18E−03 4.12E−02 1.99E−01 1.62E−03 3.04E−02 6.84E−04 2.02E−02 3.33E−01 5.87E−04 3.14E−02 1.25E−04 9.49E−02 1.17E−01 7.41E−05 1.93E−01 2.20E−01 1.05E−03 1.07E−02 2.43E−02 4.81E−01 7.67E−04 1.96E−02 5.08E−03 1.10E−05 2.86E−02 2.34E−02 1.69E−01

−0.316 −1.172 −0.363 −0.593 −0.628 −0.446 −0.377 −0.279 −0.119 −0.446 −0.411 −0.388 −0.304 −0.108 −0.614 −0.157 −0.424 −0.147 −0.136 −0.914 −0.182 −0.195 −0.370 −0.466 −0.440 −0.075 −0.415 −0.551 −0.426 −0.729 −0.503 −0.442 −0.134

1.245 2.253 1.286 1.509 1.546 1.362 1.299 1.213 1.086 1.362 1.330 1.309 1.235 1.077 1.531 1.115 1.342 1.107 1.099 1.884 1.134 1.145 1.293 1.382 1.356 1.054 1.333 1.465 1.344 1.658 1.417 1.358 1.098

0.292 0.417 0.284 0.416 0.192 0.215 0.224 0.200 0.139 0.175 0.261 0.181 0.195 0.256 0.284 0.171 0.172 0.166 0.269 0.166 0.130 0.310 0.140 0.120 0.272

1.224 1.336 1.218 1.334 1.142 1.160 1.168 1.148 1.101 1.129 1.198 1.134 1.145 1.194 1.218 1.126 1.127 1.122 1.205 1.122 1.094 1.240 1.102 1.087 1.207

2.48E−02 1.63E−03 1.18E−02 2.57E−03 8.06E−02 3.13E−03 2.97E−03 3.61E−03 5.52E−03 4.56E−03 3.91E−03 6.24E−02 5.53E−02 3.38E−01 5.27E−03 4.27E−03 1.21E−02 2.23E−02 2.25E−02 2.46E−02 6.80E−02 4.27E−05 4.52E−02 7.16E−02 8.64E−02

0.174 0.305 0.210 0.330 0.119 0.256 0.262 0.208 0.155 0.208 0.243 0.136 0.142 0.069 0.287 0.214 0.194 0.182 0.216 0.160 0.093 0.530 0.122 0.106 0.189

1.128 1.235 1.157 1.257 1.086 1.194 1.199 1.155 1.113 1.155 1.183 1.099 1.103 1.049 1.220 1.160 1.144 1.134 1.162 1.117 1.066 1.444 1.088 1.076 1.140

1.13E−04 2.43E−04 2.20E−03 6.57E−02 5.94E−03 2.96E−02 1.34E−02 5.14E−02 3.54E−02 2.81E−02 2.10E−02 9.09E−02 5.95E−02 1.40E−02 1.73E−02 4.28E−02 2.98E−02 1.13E−01 9.25E−02 9.70E−02 1.65E−03 1.65E−02 8.36E−03 6.43E−02 1.07E−01

0.389 0.410 0.315 0.238 0.202 0.190 0.251 0.151 0.127 0.182 0.245 0.129 0.162 0.282 0.274 0.172 0.179 0.111 0.187 0.132 0.172 0.298 0.189 0.097 0.222

1.309 1.329 1.244 1.180 1.150 1.141 1.190 1.111 1.092 1.134 1.185 1.094 1.119 1.216 1.209 1.126 1.132 1.080 1.138 1.095 1.126 1.230 1.140 1.069 1.166

3.93E−03 2.49E−05 2.17E−03 2.04E−06 2.34E−03 4.47E−02 1.68E−01 4.67E−03 3.10E−02 1.40E−01 3.16E−03 9.22E−04 1.91E−03 7.76E−04 8.66E−03 1.78E−01 1.31E−01 1.15E−02 2.95E−04 9.90E−03 6.70E−02 8.99E−01 2.03E−01 1.07E−02 3.36E−03

0.298 0.544 0.340 0.764 0.259 0.193 0.141 0.243 0.140 0.126 0.329 0.307 0.305 0.438 0.328 0.117 0.133 0.219 0.467 0.227 0.107 0.016 0.095 0.165 0.448

1.230 1.458 1.265 1.698 1.196 1.143 1.103 1.184 1.102 1.091 1.256 1.237 1.236 1.354 1.255 1.084 1.097 1.164 1.383 1.170 1.077 1.011 1.068 1.121 1.364

Probesets annotated to genes with a significant difference (p < 0.001) between the two genotypes are given. FC, fold change; AbsFC, absolute fold change.

3.3. Comparison with human HD and other mouse models The overlap of the top 500 probesets altered in abundance between the HdhQ150 striata and human caudate is substantial (p = 5.53 × 10−10 ) [24]. There is also a significant overlap with human cerebellum (p = 0.007) but no overlap with either BA4 or human BA9 cortex. The HdhQ150 striatal genes altered in abundance overlapped significantly with those seen in both R6/2 (p = 0.004) [35] and R6/1 brain (p = 5.02 × 10−5 ) [23]. The direction

of these changes is also concordant (Fig. 4). Examination of the comparison over the time course reveals significant overlap of the HdhQ150 differential expression data at 6 months only with the R6/1 data (p = 0.004: Figure S3A). By 12 months there is a substantial concordant overlap with the human caudate data (p = 1.51 × 10−9 ) and the R6/1 and R6/2 data (p = 3.88 × 10−10 and p = 0.002 respectively: Figure S3B) and this is continued through to 18 months in comparison with these tissues (human caudate, p = 8.86 × 10−13 ; R6/1, p = 2.63 × 10−14 ; R6/2, p = 2.68 × 10−5 : Figure S3C). Human

204

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

Table 3 Genesets identified by genes differentially expressed between WT and HdhQ150 striata. ID

Term

Processes identified by the probesets differentially expressed between HdhQ150 and WT GO:0006333 Chromatin assembly or disassembly GO:0006334 Nucleosome assembly GO:0006323 DNA packaging Chromosome organization GO:0051276 Processes identified by probesets up-regulated in HdhQ150 striata Ion homeostasis GO:0050801 GO:0055082 Cellular chemical homeostasis GO:0019228 Regulation of action potential in neuron GO:0042592 Homeostatic process GO:0042391 Regulation of membrane potential Processes identified by probesets downregulated in HdhQ150 striata Chromatin assembly or disassembly GO:0006333 GO:0006334 Nucleosome assembly GO:0006323 DNA packaging GO:0051276 Chromosome organization Cellular macromolecular complex assembly GO:0034622 GO:0043933 Macromolecular complex subunit organization GO:0022607 Cellular component assembly GO:0030091 Protein repair Processes identified by differential expression at specific time points 6m Nucleosome assembly GO:0006334 Chromatin assembly or disassembly GO:0006333 GO:0006323 DNA packaging GO:0051276 Chromosome organization GO:0034622 Cellular macromolecular complex assembly GO:0043933 Macromolecular complex subunit organization GO:0022607 Cellular component assembly 12m None 18m Regulation of nucleotide biosynthetic process GO:0030808 GO:0030799 Regulation of cyclic nucleotide metabolic process GO:0009190 Cyclic nucleotide biosynthetic process Regulation of cAMP biosynthetic process GO:0030817 GO:0001963 Synaptic transmission, dopaminergic GO:0051971 Positive regulation of transmission of nerve impulse

FDR p

Count

Global

8.10E−08 9.59E−07 2.28E−05 1.17E−02

26 21 22 39

102 75 96 326

1.37E−05 1.43E−05 5.20E−03 4.84E−02 5.00E−02

24 23 8 30 11

239 221 37 529 97

5.52E−12 6.87E−12 1.42E−09 2.34E−06 4.81E−05 2.82E−04 9.89E−04 4.72E−02

24 21 21 33 28 31 30 3

102 75 96 326 278 358 361 3

4.65E−13 8.05E−12 1.71E−10 2.50E−07 5.69E−04 1.02E−02 1.95E−02

23 25 23 37 28 30 30

75 102 96 326 278 358 370

2.38E−03 3.56E−03 1.29E−02 1.66E−02 2.06E−02 2.06E−02

8 8 8 7 4 4

57 60 71 53 11 11

The full list of differentially expressed genes between genotypes is given in Table S3 and the full pathway analysis in Table S4. Count is total number of significantly differentially expressed probesets in that GO category and global is the total membership of that GO category.

cerebellum differential gene expression does not show significant overlap with differential gene expression with 6 or 12 month HdhQ150 striatum but there is a trend towards concordant overlap with the 18 month HdhQ150 striatal expression differences (Figure S3C, p = 0.088). 3.4. Gene expression and behaviour We examined the relationship of gene expression to the behavioural measures in the individual mice. The longitudinal profile of behavioural changes seen in this cohort of mice is given in Brooks et al. [6]. The earliest noted change was in initial platform location in the water maze (4 months) with opposite location deficit noted at 8 months and reverse location at 6 months. Startle deficits were also noted at 6 months, weight changes from 14 months and rotarod deficits were only seen at 21 months. Table 4 Numbers of probesets falling into each TANOVA pattern. Q150

↑ ↔ ↓ All

WT

All

↓

↔

↑

8 19 49 76

32 40 68 140

33 7 15 55

73 66 132 271

The complete list of probeset identities and significances are given in Table S5.

Of the 1659 probesets that are nominally significantly different between the WT and HdhQ150 striata, 538 show a significant correlation with a behavioural measure. There is considerable overlap in the genes detected as correlated with related behaviours. The two startle tasks with different loudness show very similar gene expression patterns with 228 genes correlated in common of the 258 altered with a 120 dB startle and the 366 altered with a 105 dB startle (p = 0). The genes correlated with the three water maze measures also show a significant overlap, though much less than seen for the startle task (maze initial vs maze new p = 303 × 10−59 ). Table 5 shows that the majority of changes fall into correlation bins 1 and 2: genes that alter proportionally as the behaviour alters or that alter inversely as the behaviour alters. The identities of the genes correlated with phenotypes are given in Table S6 and correlation plots for individual genes and behaviours are given in supplementary Figure S4. An overrepresentation analysis gives only three behavioural/gene expression patterns that indicate any pathways that survive FDR correction: weight (bin 1), rotarod (bin 2) and maze opposite (bin 1) (Table 6). The two behaviours related to bin 1, where the gene expression increases as weight increases or the time taken to locate the platform in the opposite arm of the water maze increases, relate to histone genes, implicating chromatin alterations. All the significant processes are the result of the same set of histone genes, down-regulated in the HdhQ150 striata, and the most specific subgrouping to which these genes belong is nucleosome assembly. A smaller subset of these genes also gives the overrepresentation seen in the maze opposite bin 1 category.

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

HdhQ150 vs BA4 Cortex

1000

3000

5000

7000

9000

0.2 Frequency 0.0 −0.4

−0.2

Frequency 0.0

−0.4

−0.4

−0.2

−0.2

Frequency 0.0

0.2

0.2

0.4

0.4

HdhQ150 vs Cerebellum

0.4

HdhQ150 vs Caudate

205

1000

3000

5000

7000

Rank

HdhQ150 vs R6/1

HdhQ150 vs R6/2

9000

1000

3000

5000

7000

9000

Rank

Frequency 0.0 −0.2 −0.4

−0.4

−0.2

Frequency 0.0

0.2

0.2

0.4

0.4

Rank

1000

5000

9000

1000

Rank

3000

5000

Rank

Fig. 4. Correlation of direction of expression changes in HD model striata and human HD caudate. Frequency represents the fraction of the top 200 HdhQ150 expression changes that map to a particular bin of ranked data (1000 genes per bin) in the other dataset, which is then split to identify same or different direction of expression change in datasets. A higher frequency of concordant (green) rather than discordant (red) in the first bins indicates a similarity between the HdhQ150 and model or human HD gene expression signature.

The bin 2 rotarod enrichments are of receptor and ion homeostasis genes which are up-regulated in the HdhQ150 animals. 4. Discussion The large number of gene expression changes seen between 6 and 12 and 12 and 18 months in these mouse striata were

unexpected, as the youngest animals examined were 6 months, when most developmental changes might already be expected to have occurred. The changes were seen in both WT and HdhQ150 striata, with a large overlap in the genes changed and very similar sets of overlapping pathways identified, indicating the robust nature of these data. It is possible that the genes altered relate to the behavioural tasks that these mice were undertaking at the time

Table 5 Probeset numbers within behaviour–gene expression correlation bins. Behaviour

Weight Rotarod Maze initial Maze new Maze opposite Startle 105 dB Startle 120 dB

# correlated probesets

119 238 91 209 224 366 258

Correlation bin 1

2

61 85 63 107 123 158 102

52 102 27 97 91 201 130

3

4

5

6

7

1 6

1 3

1 17

3 25

4 1 3

3 3 2 10

2 3 2 12

2 1

8

1

Cells highlighted were subjected to over-representation analysis. The total list of genes used in this analysis and the allocation to bins is given in supplementary table* S5.

In Bin 1 Weight the same 13 genes gave the overrepresentation for all these processes and are given once only. In Bin 2 Rotarod the same 8 genes gave the overrepresentation in the first two processes (GO:55082 and GO:0050801). Count is total number of significantly differentially expressed probesets in that GO category and global is the total membership of that GO category.

Hist1h4m, Hist1h2ao, Hist1h4b, Hist4h4, Hist1h4d 75 96 102 5 5 5 1.69E−05 5.60E−05 7.48E−05 Nucleosome assembly DNA packaging Chromatin assembly or disassembly

8.70E−03 2.87E−02 3.83E−02

Drd1a, Htr1b, Adcy5, Adora2a Itpr1, Drd1a, Kcnk2, Adora2a, Slc26a5, Gpr6, Cacna1e, Prkcb, Lmo2, Ppp3ca

Itpr1, Drd1a, Kcnk2, Adora2a, Slc26a5, Gpr6, Prkcb, Ppp3ca

221 239 55 529 8 8 4 10 5.64E−06 1.00E−05 0.000102 0.000102 Cellular chemical homeostasis Ion homeostasis Regulation of cAMP metabolic process Homeostatic process

4.29E−03 7.61E−03 7.73E−02 7.73E−02

75 96 102 278 326 358 370 13 13 13 13 13 13 13 9.47E−20 2.94E−18 6.75E−18 4.18E−12 3.26E−11 1.08E−10 1.65E−10 1.81E−22 5.63E−21 1.30E−20 8.05E−15 6.29E−14 2.09E−13 3.18E−13

Global Count FDR p p Process

Nucleosome assembly DNA packaging Chromatin assembly or disassembly Cellular macromolecular complex assembly Chromosome organization Macromolecular complex subunit organization Cellular component assembly

Bin 1 Weight GO:0006334 GO:0006323 GO:0006333 GO:0034622 GO:0051276 GO:0043933 GO:0022607 Bin 2 Rotarod GO:0055082 GO:0050801 GO:0030814 GO:0042592 Bin 1 Maze opposite GO:0006334 GO:0006323 GO:0006333

ID

Table 6 Overrepresentation analysis in the behaviour–gene expression correlation bins.

Hist1h4m, Hist1h2af, Hist1h2ao, Hist1h3a, Hist1h2an, Hist1h4b, Hist2h3b, Hist1h2ak, Hist2h2aa2, Hist1h3g, Hist1h1b, Hist2h2aa1 Hist2h2ac

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

Gene symbols

206

of analysis [6]. Both the WT and the HdhQ150 mice underwent a battery of tests from 4 months of age, including the water maze, rotarod and PPI/startle tasks. The changes in expression observed from 6 to 12 months identify processes including cognition that might well relate to behavioural training and as these are seen in both WT and HdhQ150 mice this could be consistent with both genotypes being able to alter gene expression as part of task learning. Alterations in gene expression are known to be intimately involved in the processes of learning and memory [5,14,20,28]. The changes in expression observed from 12 to 18 months show some similarities to those from 6 to 12 months, in particular in identifying G-protein signalling as a process highlighted by differentially expressed striatal genes. There are fewer genes which identify other processes at the later time, and these do not show as significant an overrepresentation as at the earlier time point. Again, the most overrepresented processes are the same in both WT and HdhQ150 mice though one notable difference is the overrepresentation of amino-acid phosphorylation in the HdhQ150 animals. Our results indicate that Htt itself is expressed at lower levels in the striata of homozygous HdhQ150 animals than in WT animals. This has been observed previously in this line of mice [12]. These mice have endogenous mouse Htt, with the 7 CAG repeats normally present replaced by a long CAG tract, though in the mice used in this study the repeat had shortened from the original 150 CAGs to around 124 CAGs. There are a number of possible explanations for the apparent reduced Htt expression. The cDNA for the GeneChip experiment was produced by random priming of the mRNA, but that for the QPCR by gene specific priming. It is possible that the CAG repeat itself alters the structure of the RNA locally around the repeat, inducing changes that reduce priming from the RNA. These repeat structures are known to adopt unusual conformations [33] and may well prevent efficient access of the reverse transcriptase to the nucleic acid. The average length of random primed cDNA is shorter than that of oligo dT RNA and Htt mRNA is 11–14 kb in length. Thus if the mRNA were to adopt conformations that prevented the reverse transcriptase accessing the template this would be most likely to occur at the 5 end of the RNA, close to the repeat. This is consistent with GeneChip ST analysis (Fig. 1A and Figure S2) but is not apparent in the QPCR analysis (Fig. 1B) which shows a very similar reduction in Htt mRNA levels for primer pairs spread along the gene. The gene specific priming of these reactions would mean that the RNA would have to be inaccessible for almost its entire length to give such similar results at the 5 and 3 ends of the transcript. It therefore seems most likely that the reduction in Htt mRNA levels occurs in the striata of the HdhQ150 mice. The reduced levels of RNA could result from altered structures in the DNA related to the CAG repeat that lead to reduced transcription or to relative instability of the RNA produced making it more susceptible to degradation. Altered chromatin and DNA structure in HTT DNA consistent with its down-regulation have been observed in human and HD model mouse brain previously [15,37,40] and increased instability of HTT mRNA has been observed in vitro [11]. This observation may also account for the reduced phenotype seen in very highly expanded CAG repeat transgenic mice [4,13,34], if repeat length is inversely correlated with Htt expression. Examination of other expression differences between genotypes shows two main types of processes: ones related to chromatin structure and ones related to neurotransmission. At 6 months the overrepresented processes that are different between the genotypes mainly relate to genes involved in chromatin structure. Altered histone acetylation which leads to altered chromatin structure has previously been implicated in HD [37] and as an early event in HD models [38]. Moreover, interventions altering chromatin structure in HD models have been investigated

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

as therapeutics and have proved to ameliorate the phenotype of the animals [16,17,22,26,36,40,45]. Alterations in chromatin structure are thought to underlie learning and memory [14]. These differences in gene expression could well underlie the altered performance seen in some of the tasks by the knock in animals which may not have the same capacity for chromatin remodelling as the WT animals. By 18 months the differences largely relate to neurotransmission and intracellular signalling, and chromatin-related genes in the striatum do not appear to be different between the WT and HdhQ150 animals. The TANOVA analysis shows a consistent result but indicates that genes down-regulated in the HdhQ150, that do not alter in expression in WT striata, are largely responsible for this observation. It may be that the chromatin-related changes seen in young mice are linked to events close to the initial molecular pathology, and that the later changes, mainly up-regulations of expression, relate to secondary and compensatory events that serve to ameliorate damage. This is consistent with the observation that the early chromatin-related changes are down-regulations, more likely to be part of the initial pathology in HD [27,32,38]. In common with previous studies [23,27,42], we found the changes in gene expression between genotypes to overlap very significantly with those previously detected in both human HD caudate and R6 mouse model striata, and that these concordant overlaps increased over time. The lack of concordant changes with human cerebellum and cortical expression differences is likely to be due to there being many fewer changes observed in these tissues and also to their regionally specific gene expression profiles. Comparison of mouse striatal gene expression and human caudate gene expression show high concordance whilst the concordance with other brain tissues is much less [41]. The increased concordance of human HD caudate expression changes with those seen at later time-points in the HdhQ150 striatal gene expression trajectory might be expected, as the human brains had substantial pathology and consequent cell loss [24]. A reduction in striatal volume has been observed in the striata of the HdhQ150 animals by 6 months [1] though this is accompanied by increased cell numbers, reinforcing previous findings in both human and mouse model studies that individual cells have gene expression changes intrinsic to those cells [27,38,51]. In the analysis of correlated behavioural and gene expression changes it is difficult to separate out the effects of the genotype on progressive behavioural phenotype and progressive gene expression phenotype. The two are intimately connected. By 4–6 months of age these animals are showing deficits in some behavioural tasks [6,7], and it is likely that more sensitive tasks will pick up earlier deficits [46]. Tasks such as the water maze and set shifting, both showing deficits by 4 months of age in the knock in mice, involve learning and memory as well as some motor skills: notably correlation with gene expression in one of the water maze tasks, finding the location of the platform in the opposite quadrant of the maze, picks out enriched nucleosome and chromatin modifying genes. These chromatin-related changes occur early in phenotype development and may well impact on learning and memory [14]. It is likely that the power to detect correlation with some tasks is reduced by their late onset in this mouse line that develops its phenotype only slowly. The up-regulations in genes associated with ion homeostasis, including many ion channel genes, correlate with reduced rotarod performance in the animals. These expression changes only become significant by 18 months and may well be a compensatory effect which have an knock-on effect on rotarod performance: the changes in rotarod performance are not significant until 21 months in these animals though there is a trend to reduced performance earlier than this. This is relevant to the preclinical testing of therapeutics using mouse models, as rotarod is frequently used as a functional readout in such trials [8,48], and it is possible it is not

207

tapping into the primary, or early, pathology of the disease process [21]. Performance on the rotarod requires both learning and motor skills [6]. D’Amours et al. [10] examined the gene expression changes in the striatum of 12 week old mice after training on the rotarod and identified some of the same processes noted in this study as altered both over time and by genotype in the knock in animals. Notably, they detected up-regulation of genes associated with chromatin remodelling and neuronal function. They also detected significant alterations in genes implicating mitochondrial and ribosomal function which the present study did not find to be significantly altered, though our experiment surveyed a larger number of RNAs and thus may well have excluded such findings through relatively stringent multiple testing criteria. The alterations we see in chromatin remodelling may contribute to deficits in the water maze and previously observed deficits in set-shifting tasks [7]. Our results indicate that alterations in chromatin modifying genes are an early event in these animals whilst changes in neuronal activation are relatively late events. The changes in synaptic plasticity may well be attempting to compensate for deficits in other systems that are interfering with neuronal signalling. We detected substantially fewer significant changes in mRNA abundance between the mutant and the wild-type striata in the HdhQ150 (5.7% of total probesets on GeneChip) than in the R6/1 model (15% of total: [23]). This is likely to underlie the many fewer genes that we detected as correlated with behavioural changes in the HdhQ150 mice when we performed a similar analysis of gene expression and behaviour to that presented for the R6/1 striata in Hodges et al. [23]. As in our previous study using R6/1 mice [23] we have here also detected that different subsets of the gene expression changes in the HdhQ150 mouse striata are correlated with different phenotypic outcomes. These animals develop their behavioural phenotype more slowly than some transgenic models and changes in gene expression in their striata are also less marked, though they occur from the earliest measured point at 6 months. However, the gene expression changes that do occur parallel those occurring in other HD model mice and these changes probably underlie the observed changes in behaviour in these animals. Acknowledgements This work was funded by CHDI. We thank Central Biotechnology Services at the School of Medicine, Cardiff University, for technical support. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.brainresbull.2011.10.001. References [1] Z. Bayram-Weston, E.M. Torres, L. Jones, S.B. Dunnett, S.P. Brooks, Light and electron microscopic characterization of the evolution of cellular pathology in the Hdh((CAG)150) Huntington’s disease knock-in mouse, Brain Res. Bull. 88 (2012) 189–198. [2] Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. B 57 (1995) 289–300. [3] C.L. Benn, H. Fox, G.P. Bates, Optimisation of region-specific reference gene selection and relative gene expression analysis methods for pre-clinical trials of Huntington’s disease, Mol. Neurodegener. 3 (2008) 17. [4] C.L. Benn, C. Landles, H. Li, A.D. Strand, B. Woodman, K. Sathasivam, S.H. Li, S. Ghazi-Noori, E. Hockly, S.M. Faruque, J.H. Cha, P.T. Sharpe, J.M. Olson, X.J. Li, G.P. Bates, Contribution of nuclear and extranuclear polyQ to neurological phenotypes in mouse models of Huntington’s disease, Hum. Mol. Genet. 14 (2005) 3065–3078.

208

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209

[5] K.R. Bowles, S.P. Brooks, S.B. Dunnett, L. Jones, Gene expression and behaviour in mouse models of Huntington’s disease, Brain Res. Bull. 88 (2012) 276–284. [6] S. Brooks, G. Higgs, L. Jones, S.B. Dunnett, Longitudinal analysis of the behavioural phenotype in Hdh((CAG)150) Huntington’s disease knock-in mice, Brain Res. Bull. 88 (2012) 182–188. [7] S.P. Brooks, H. Betteridge, R.C. Trueman, L. Jones, S.B. Dunnett, Selective extradimensional set shifting deficit in a knock-in mouse model of Huntington’s disease, Brain Res. Bull. 69 (2006) 452–457. [8] M. Chen, V.O. Ona, M. Li, R.J. Ferrante, K.B. Fink, S. Zhu, J. Bian, L. Guo, L.A. Farrell, S.M. Hersch, W. Hobbs, J.P. Vonsattel, J.H. Cha, R.M. Friedlander, Minocycline inhibits caspase-1 and caspase-3 expression and delays mortality in a transgenic mouse model of Huntington disease, Nat. Med. 6 (2000) 797–801. [9] Z.R. Crook, D. Housman, Huntington’s disease: can mice lead the way to treatment? Neuron 69 (2011) 423–435. [10] G. D’Amours, G. Bureau, M.J. Boily, M. Cyr, Differential gene expression profiling in the mouse brain during motor skill learning: focus on the striatum structure, Behav. Brain Res. 221 (2010) 108–117. [11] M. de Mezer, M. Wojciechowska, M. Napierala, K. Sobczak, W.J. Krzyzosiak, Mutant CAG repeats of Huntingtin transcript fold into hairpins, form nuclear foci and are targets for RNA interference, Nucleic Acids Res. 39 (2011) 3852–3863. [12] K.T. Dixon, J.A. Cearley, J.M. Hunter, P.J. Detloff, Mouse Huntington’s disease homolog mRNA levels: variation and allele effects, Gene Expr. 11 (2004) 221–231. [13] I. Dragatsis, D. Goldowitz, N. Del Mar, Y.P. Deng, C.A. Meade, L. Liu, Z. Sun, P. Dietrich, J. Yue, A. Reiner, CAG repeat lengths > or =335 attenuate the phenotype in the R6/2 Huntington’s disease transgenic mouse, Neurobiol. Dis. 33 (2009) 315–330. [14] C. Dulac, Brain function and chromatin plasticity, Nature 465 (2010) 728–735. [15] D. Duzdevich, J. Li, J. Whang, H. Takahashi, K. Takeyasu, D.T. Dryden, A.J. Morton, J.M. Edwardson, Unusual structures are present in DNA fragments containing super-long Huntingtin CAG repeats, PLoS One 6 (2011) e17119. [16] R.J. Ferrante, J.K. Kubilus, J. Lee, H. Ryu, A. Beesen, B. Zucker, K. Smith, N.W. Kowall, R.R. Ratan, R. Luthi-Carter, S.M. Hersch, Histone deacetylase inhibition by sodium butyrate chemotherapy ameliorates the neurodegenerative phenotype in Huntington’s disease mice, J. Neurosci. 23 (2003) 9418–9427. [17] G. Gardian, S.E. Browne, D.K. Choi, P. Klivenyi, J. Gregorio, J.K. Kubilus, H. Ryu, B. Langley, R.R. Ratan, R.J. Ferrante, M.F. Beal, Neuroprotective effects of phenylbutyrate in the N171-82Q transgenic mouse model of Huntington’s disease, J. Biol. Chem. 280 (2005) 556–563. [18] L. Gautier, L. Cope, B.M. Bolstad, R.A. Irizarry, affy—analysis of Affymetrix GeneChip data at the probe level, Bioinformatics 20 (2004) 307–315. [19] J.M. Gil, A.C. Rego, The R6 lines of transgenic mice: a model for screening new therapies for Huntington’s disease, Brain Res. Rev. 59 (2009) 410–431. [20] Z. Guan, M. Giustetto, S. Lomvardas, J.H. Kim, M.C. Miniaci, J.H. Schwartz, D. Thanos, E.R. Kandel, Integration of long-term-memory-related synaptic plasticity involves bidirectional regulation of gene expression and chromatin structure, Cell 111 (2002) 483–493. [21] M.A. Hickey, K. Gallant, G.G. Gross, M.S. Levine, M.F. Chesselet, Early behavioral deficits in R6/2 mice suitable for use in preclinical drug testing, Neurobiol. Dis. 20 (2005) 1–11. [22] E. Hockly, V.M. Richon, B. Woodman, D.L. Smith, X. Zhou, E. Rosa, K. Sathasivam, S. Ghazi-Noori, A. Mahal, P.A. Lowden, J.S. Steffan, J.L. Marsh, L.M. Thompson, C.M. Lewis, P.A. Marks, G.P. Bates, Suberoylanilide hydroxamic acid, a histone deacetylase inhibitor, ameliorates motor deficits in a mouse model of Huntington’s disease, Proc. Natl. Acad. Sci. U.S.A. 100 (2003) 2041–2046. [23] A. Hodges, G. Hughes, S. Brooks, L. Elliston, P. Holmans, S.B. Dunnett, L. Jones, Brain gene expression correlates with changes in behavior in the R6/1 mouse model of Huntington’s disease, Genes Brain Behav. 7 (2008) 288–299. [24] A. Hodges, A.D. Strand, A.K. Aragaki, A. Kuhn, T. Sengstag, G. Hughes, L.A. Elliston, C. Hartog, D.R. Goldstein, D. Thu, Z.R. Hollingsworth, F. Collin, B. Synek, P.A. Holmans, A.B. Young, N.S. Wexler, M. Delorenzi, C. Kooperberg, S.J. Augood, R.L.M. Faull, J.M. Olson, L. Jones, R. Luthi-Carter, Regional and cellular gene expression changes in human Huntington’s disease brain, Hum. Mol. Genet. 15 (2006) 965–977. [25] J.C. Jacobsen, C.S. Bawden, S.R. Rudiger, C.J. McLaughlan, S.J. Reid, H.J. Waldvogel, M.E. MacDonald, J.F. Gusella, S.K. Walker, J.M. Kelly, G.C. Webb, R.L. Faull, M.I. Rees, R.G. Snell, An ovine transgenic Huntington’s disease model, Hum. Mol. Genet. 19 (2010) 1873–1882. [26] M.O. Kim, P. Chawla, R.P. Overland, E. Xia, G. Sadri-Vakili, J.H. Cha, Altered histone monoubiquitylation mediated by mutant huntingtin induces transcriptional dysregulation, J. Neurosci. 28 (2008) 3947–3957. [27] A. Kuhn, D.R. Goldstein, A. Hodges, A.D. Strand, T. Sengstag, C. Kooperberg, K. Becanovic, M.A. Pouladi, K. Sathasivam, J.H. Cha, A.J. Hannan, M.R. Hayden, B.R. Leavitt, S.B. Dunnett, R.J. Ferrante, R. Albin, P. Shelbourne, M. Delorenzi, S.J. Augood, R.L. Faull, J.M. Olson, G.P. Bates, L. Jones, R. Luthi-Carter, Mutant huntingtin’s effects on striatal gene expression in mice recapitulate changes observed in human Huntington’s disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage, Hum. Mol. Genet. 16 (2007) 1845–1861.

[28] J.M. Levenson, J.D. Sweatt, Epigenetic mechanisms: a common theme in vertebrate and invertebrate memory formation, Cell. Mol. Life Sci. 63 (2006) 1009–1016. [29] J.Y. Li, N. Popovic, P. Brundin, The use of the R6 transgenic mouse models of Huntington’s disease in attempts to develop novel therapeutic strategies, NeuroRx 2 (2005) 447–464. [30] C.H. Lin, S. Tallaksen-Greene, W.M. Chien, J.A. Cearley, W.S. Jackson, A.B. Crouse, S. Ren, X.J. Li, R.L. Albin, P.J. Detloff, Neurological abnormalities in a knock-in mouse model of Huntington’s disease, Hum. Mol. Genet. 10 (2001) 137–144. [31] K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using realtime quantitative PCR and the 2(−Delta Delta C(T)) method, Methods 25 (2001) 402–408. [32] R. Luthi-Carter, S.A. Hanson, A.D. Strand, D.A. Bergstrom, W. Chun, N.L. Peters, A.M. Woods, E.Y. Chan, C. Kooperberg, D. Krainc, A.B. Young, S.J. Tapscott, J.M. Olson, Dysregulation of gene expression in the R6/2 model of polyglutamine disease: parallel changes in muscle and brain, Hum. Mol. Genet. 11 (2002) 1911–1926. [33] C.T. McMurray, Mechanisms of trinucleotide repeat instability during human development, Nat. Rev. Genet. 11 (2010) 786–799. [34] A.J. Morton, D. Glynn, W. Leavens, Z. Zheng, R.L. Faull, J.N. Skepper, J.M. Wight, Paradoxical delay in the onset of disease caused by super-long CAG repeat expansions in R6/2 mice, Neurobiol. Dis. 33 (2009) 331–341. [35] A.J. Morton, M.J. Hunt, A.K. Hodges, P.D. Lewis, A.J. Redfern, S.B. Dunnett, L. Jones, A combination drug therapy improves cognition and reverses gene expression changes in a mouse model of Huntington’s disease, Eur. J. Neurosci. 21 (2005) 855–870. [36] L. Quinti, V. Chopra, D. Rotili, S. Valente, A. Amore, G. Franci, S. Meade, M. Valenza, L. Altucci, M.M. Maxwell, E. Cattaneo, S. Hersch, A. Mai, A. Kazantsev, Evaluation of histone deacetylases as drug targets in Huntington’s disease models. Study of HDACs in brain tissues from R6/2 and CAG140 knock-in HD mouse models and human patients and in a neuronal HD cell model, PLoS Curr. 2 (2010). [37] H. Ryu, J. Lee, S.W. Hagerty, B.Y. Soh, S.E. McAlpin, K.A. Cormier, K.M. Smith, R.J. Ferrante, ESET/SETDB1 gene expression and histone H3 (K9) trimethylation in Huntington’s disease, Proc. Natl. Acad. Sci. U.S.A. 103 (2006) 19176–19181. [38] G. Sadri-Vakili, B. Bouzou, C.L. Benn, M.O. Kim, P. Chawla, R.P. Overland, K.E. Glajch, E. Xia, Z. Qiu, S.M. Hersch, T.W. Clark, G.J. Yohrling, J.H. Cha, Histones associated with downregulated genes are hypo-acetylated in Huntington’s disease models, Hum. Mol. Genet. 16 (2007) 1293–1306. [39] G.K. Smyth, Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments, 2004. [40] E.C. Stack, S.J. Del Signore, R. Luthi-Carter, B.Y. Soh, D.R. Goldstein, S. Matson, S. Goodrich, A.L. Markey, K. Cormier, S.W. Hagerty, K. Smith, H. Ryu, R.J. Ferrante, Modulation of nucleosome dynamics in Huntington’s disease, Hum. Mol. Genet. 16 (2007) 1164–1175. [41] A.D. Strand, A.K. Aragaki, Z.C. Baquet, A. Hodges, P. Cunningham, P. Holmans, K.R. Jones, L. Jones, C. Kooperberg, J.M. Olson, Conservation of regional gene expression in mouse and human brain, PLoS Genet. 3 (2007) e59. [42] A.D. Strand, Z.C. Baquet, A.K. Aragaki, P. Holmans, L. Yang, C. Cleren, M.F. Beal, L. Jones, C. Kooperberg, J.M. Olson, K.R. Jones, Expression profiling of Huntington’s disease models suggests that brain-derived neurotrophic factor depletion plays a major role in striatal degeneration, J. Neurosci. 27 (2007) 11758–11768. [43] A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, J.P. Mesirov, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. U.S.A. 102 (2005) 15545–15550. [44] The Huntington’s Disease Collaborative Research Group, A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes, Cell 72 (1993) 971–983. [45] E.A. Thomas, G. Coppola, P.A. Desplats, B. Tang, E. Soragni, R. Burnett, F. Gao, K.M. Fitzgerald, J.F. Borok, D. Herman, D.H. Geschwind, J.M. Gottesfeld, The HDAC inhibitor 4b ameliorates the disease phenotype and transcriptional abnormalities in Huntington’s disease transgenic mice, Proc. Natl. Acad. Sci. U.S.A. 105 (2008) 15564–15569. [46] R.C. Trueman, S.P. Brooks, L. Jones, S.B. Dunnett, The operant serial implicit learning task reveals early onset motor learning deficits in the Hdh knockin mouse model of Huntington’s disease, Eur. J. Neurosci. 25 (2007) 551–558. [47] S. von Horsten, I. Schmitt, H.P. Nguyen, C. Holzmann, T. Schmidt, T. Walther, M. Bader, R. Pabst, P. Kobbe, J. Krotova, D. Stiller, A. Kask, A. Vaarmann, S. Rathke-Hartlieb, J.B. Schulz, U. Grasshoff, I. Bauer, A.M. Vieira-Saecker, M. Paul, L. Jones, K.S. Lindenberg, B. Landwehrmeyer, A. Bauer, X.J. Li, O. Riess, Transgenic rat model of Huntington’s disease, Hum. Mol. Genet. 12 (2003) 617–624. [48] N.I. Wood, A.J. Morton, Chronic lithium chloride treatment has variable effects on motor behaviour and survival of mice transgenic for the Huntington’s disease mutation, Brain Res. Bull. 61 (2003) 375–383. [49] B. Woodman, R. Butler, C. Landles, M.K. Lupton, J. Tse, E. Hockly, H. Moffitt, K. Sathasivam, G.P. Bates, The HdhQ150/Q150 knock-in mouse model of HD and the R6/2 exon 1 model develop comparable and widespread molecular phenotypes, Brain Res. Bull. 72 (2007) 83–97.

P. Giles et al. / Brain Research Bulletin 88 (2012) 199–209 [50] B. Zhou, W. Xu, D. Herndon, R. Tompkins, R. Davis, W. Xiao, W.H. Wong, M. Toner, H.S. Warren, D.A. Schoenfeld, L. Rahme, G.P. McDonald-Smith, D. Hayden, P. Mason, S. Fagan, Y.M. Yu, J.P. Cobb, D.G. Remick, J.A. Mannick, J.A. Lederer, R.L. Gamelli, G.M. Silver, M.A. West, M.B. Shapiro, R. Smith, D.G. Camp II, W. Qian, J. Storey, M. Mindrinos, R. Tibshirani, S. Lowry, S. Calvano, I. Chaudry, M.A. West, M. Cohen, E.E. Moore, J. Johnson, L.L. Moldawer, H.V. Baker, P.A. Efron, U.G. Balis, T.R. Billiar, J.B. Ochoa, J.L. Sperry, C.L. Miller-Graziano, A.K. De, P.E. Bankey, C.C. Finnerty, M.G. Jeschke, J.P. Minei, B.D. Arnoldo, J.L. Hunt, J. Horton, J.P. Cobb, B. Brownstein, B. Freeman, R.V. Maier, A.B. Nathens, J. Cuschieri,

209

N. Gibran, M. Klein, G. O’Keefe, Analysis of factorial time-course microarrays with application to a clinical study of burn injury, Proc. Natl. Acad. Sci. U.S.A. 107 (2010) 9923–9928. [51] B. Zucker, R. Luthi-Carter, J.A. Kama, A.W. Dunah, E.A. Stern, J.H. Fox, D.G. Standaert, A.B. Young, S.J. Augood, Transcriptional dysregulation in striatal projection- and interneurons in a mouse model of Huntington’s disease: neuronal selectivity and potential neuroprotective role of HAP1, Hum. Mol. Genet. 14 (2005) 179–189.