Human Molecular Genetics, 2013, Vol. 22, No. 18
doi:10.1093/hmg/ddt214
Advance Access published on May 12, 2013
3641–3653
Genome-wide loss of 5-hmC is a novel epigenetic
feature of Huntington’s disease
Fengli Wang1,{, Yeran Yang2,{, Xiwen Lin3,{, Jiu-Qiang Wang1,{, Yong-Sheng Wu2,{, Wenjuan Xie1,
Dandan Wang1, Shu Zhu1, You-Qi Liao2, Qinmiao Sun1, Yun-Gui Yang2, Huai-Rong Luo4,
Caixia Guo2,∗ , Chunsheng Han3,∗ and Tie-Shan Tang1,∗
1
Received February 17, 2013; Revised April 17, 2013; Accepted May 6, 2013
5-Hydroxymethylcytosine (5-hmC) may represent a new epigenetic modification of cytosine. While the dynamics of
5-hmC during neurodevelopment have recently been reported, little is known about its genomic distribution and
function(s) in neurodegenerative diseases such as Huntington’s disease (HD). We here observed a marked reduction of the 5-hmC signal in YAC128 (yeast artificial chromosome transgene with 128 CAG repeats) HD mouse brain
tissues when compared with age-matched wild-type (WT) mice, suggesting a deficiency of 5-hmC reconstruction in
HD brains during postnatal development. Genome-wide distribution analysis of 5-hmC further confirmed the diminishment of the 5-hmC signal in striatum and cortex in YAC128 HD mice. General genomic features of 5-hmC are
highly conserved, not being affected by either disease or brain regions. Intriguingly, we have identified disease-specific (YAC128 versus WT) differentially hydroxymethylated regions (DhMRs), and found that acquisition of DhmRs
in gene body is a positive epigenetic regulator for gene expression. Ingenuity pathway analysis (IPA) of genotypespecific DhMR-annotated genes revealed that alternation of a number of canonical pathways involving neuronal development/differentiation (Wnt/b-catenin/Sox pathway, axonal guidance signaling pathway) and neuronal function/survival (glutamate receptor/calcium/CREB, GABA receptor signaling, dopamine-DARPP32 feedback
pathway, etc.) could be important for the onset of HD. Our results indicate that loss of the 5-hmC marker is a
novel epigenetic feature in HD, and that this aberrant epigenetic regulation may impair the neurogenesis, neuronal
function and survival in HD brain. Our study also opens a new avenue for HD treatment; re-establishing the native
5-hmC landscape may have the potential to slow/halt the progression of HD.
INTRODUCTION
DNA methylation constitutes a critical epigenetic modification
that plays pivotal roles in chromatin structure remolding, gene
silencing, embryonic development, differentiation and maintenance of cellular identity (1 –5). In mammals, methylations at the
fifth position of cytosine (5 mC) are catalyzed by de novo
methyltransferases (DNMT3a, DNMT3b) and subsequently
maintained by DNMT1 (6 – 9). In mammalian central nervous
system (CNS), 5 mC plays a significant role in the temporal
control of neural differentiation and neurodevelopment
(10,11), and alternation in the 5 mC profile of genomic DNA
has been recently linked to some neurological disorders such
as hereditary sensory and autonomic neuropathy type-1 (12),
∗
To whom correspondence should be addressed at: State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Zoology,
Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing, 100101, China. Tel: +86-1064807296; Fax: +86-1064807313;
Email: tangtsh@ioz.ac.cn (T.-S.T.); The State Key Laboratory of Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen
West Road, Chaoyang District, Beijing, 100101, China. Email: hancs@ioz.ac.cn (C.H.) Laboratory of Disease Genomics and Individual Medicine, Beijing
Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China. Email: guocx@big.ac.cn (C.G.)
†
Equal contribution.
# The Author 2013. Published by Oxford University Press. All rights reserved.
For Permissions, please email: journals.permissions@oup.com
Downloaded from http://hmg.oxfordjournals.org/ at Institute of Zoology, CAS on November 3, 2013
From the State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Zoology, 2Laboratory of
Disease Genomics and Individual Medicine, Beijing Institute of Genomics, 3The State Key Laboratory of Reproductive
Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China and 4State Key Laboratory of
Phytochemisty, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China
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Human Molecular Genetics, 2013, Vol. 22, No. 18
autosomal-dominant cerebellar ataxia deafness and narcolepsy
(ADCA-DN) (13), Rett syndrome-like phenotypes (14), HD
(15) and Alzheimer’s disease (16).
Recent studies demonstrate that 5 mC can be converted to 5hydroxymethylcytosine (5-hmC) by the ten-eleven translocation
(TET) family proteins through Fe (II) and alpha-ketoglutaratedependent hydroxylation (17,18). 5-hmC is preferentially
enriched in several cell types, including self-renewing and pluripotent stem cells (17,19) and certain neuronal cells (20). Although 5-hmC in mouse embryonic stem (ES) cells exists at an
extremely high level, it decreases significantly during ES cell
differentiation (17,21), and then reconstructs during the developmental process resulting in an elevated 5-hmC level in terminally differentiated cells, such as Purkinje neurons (20). The fact
that 5-hmC is roughly 10-fold more enriched in neurons than in
some peripheral tissues or ES cells (20,22– 24) suggests that
5-hmC is a stable epigenetic modification which may provide
an epigenetic signature specific to a particular neuronal function
or dysfunction in the brain. Consistent with this notion, recent
studies with genome-wide mapping of 5-hmC in the cerebellum
and hippocampus revealed the enrichment of 5-hmC in developmentally activated genes or cell type-active genes (24– 26).
These studies point to a critical role of TET-mediated 5-hmC
modification in CNS developmental processes and hint at the
possibility that deviation of 5-hmC levels from these signature
patterns may associate with diseases.
Huntington’s disease (HD) is an autosomal-dominant inherited, fatal neurodegenerative disorder characterized by chorea,
gradual but inexorable cognitive decline and psychiatric disturbances (27,28). HD is caused by the expanded polyglutamine
(polyQ) stretch in the amino-terminus of huntingtin protein
(HTT), a 350 kDa ubiquitously expressed protein of unknown
function (27,29,30). Despite the extensive studies regarding
the gain-of-toxic function of polyQ expansion in HTTexp
(31– 43), the exact mechanism(s) underlying the cause of neurodegeneration in HD remain obscure. Given that 5-hmC may
function as a new epigenetic marker in the CNS, we sought to
determine whether HTTexp might derange this new epigenetic
modification.
A yeast artificial chromosome transgenic mouse model of HD
(YAC128, with 128Q-stretch) is a well-characterized HD mouse
model that harbors a full-length mutant huntingtin, which can
faithfully recapitulate many of the behavioral and neuropathological features of the human condition (38,44– 48). Since the
striatum is the most vulnerable region relative to other regions
such as cortex in HD, in this study, both striatum and cortex
regions from WT and HD mice were analyzed, and this pairwise
comparisons between each age, genotype and tissue could
provide novel insights into the HD-specific links between
5-hmC modification and HD pathogenesis.
By employing a selective chemical labeling method for
5-hmC reported recently (49), we have generated the first
genome-wide maps of 5-hmC in striatum and cortex from both
WT and YAC128 HD mice brains. Herein, we provide evidence
showing that loss of the 5-hmC marker is a new epigenetic
feature of HD, and that this aberrant epigenomic regulation of
5-hmC may impair neurogenesis, neuronal survival and functions in HD brain at a very early stage, contributing to the striatal
and cortical neuronal loss in advanced HD. Of potentially therapeutic significance, our study also opens a new possible avenue
for HD treatment by showing it may be possible to target the cellular/biochemical pathways, and thereby potentially reestablishing the 5-hmC levels and landscape in HD brains.
RESULTS
Marked reduction of 5-hmC in whole genome of YAC128 HD
mouse brain
Basing on the fact that 5-hmC is highly enriched in terminally
differentiated neurons (20,22– 24) and the importance of
5-hmC in developmental processes (24,25,26), we sought to determine whether HTTexp might derange this new epigenetic
modification. YAC128 mouse is a HD mouse model that can
faithfully recapitulate the pathological progression of the
disease in humans. The main characteristic of the disease is neuronal loss in the striatum region of the brain. However, neurons
that are absent from the cortex region also occurs in advanced
HD patients. Dot-blots of 5-hmC from striatum and cortex
DNA samples of 6-week-, 3-month- and 8-month-old mice
(n ¼ 5) revealed that the levels of 5-hmC increased substantially
with age in both striatum and cortex, with the highest 5-hmC
levels peaking at 3 months of age (Fig. 1A and F). Surprisingly,
YAC128 HD striatum and cortex samples exhibited markedly
lower content of 5-hmC when compared with age-matched wildtype (WT) brain tissues (Fig. 1A and F). To test whether the
change of 5-hmC levels is specific for striatum and cortex,
DNA samples of hippocampus, cerebellum and peripheral
tissues—tails from WT and YAC128 HD mice were also compared. The reduction of 5-hmC levels in YAC128 mouse
brains could also be detected in hippocampus but not in cerebellum and peripheral tissues such as tails (Supplementary Material, Fig. S1), indicating that the decrease of 5-hmC levels in
YAC128 HD is brain regional specific and neuronal tissue specific. Immunofluorescence (IF) experiments further confirmed
a significant reduction of 5-hmC in both striatum and cortex of
HD brains (Fig. 1B – E), suggesting that a significant reduction
of 5-hmC is a genomic feature in HD brains. Consistent with a
recent study of the 5-hmC distribution in cerebellum and hippocampus (24), we also observed the perfect colocalization of the
5-hmC signal with NeuN, a marker of mature neurons, in both
striatum and cortex, indicating that 5-hmC build up may occur
primarily in neurons in mammalian brains.
To gain a genome-wide view of 5-hmC distribution, we next
employed a 5-hmC-specific chemical labeling and enrichment
technology coupled with high-throughput deep sequencing of
5-hmC-containing DNA fragments (49). Striatal and cortical
DNA samples from the brains of 3-month-old WT/YAC128
mice were prepared and deeply sequenced to determine the
effect of HTTexp on the 5-hmC epigenome at the early stage of
HD. About 41.3, 40.8 42.6 and 34.1 million reads were obtained
from WT striatum, YAC128 striatum, WT cortex and the
YAC128 cortex DNA samples, respectively. When the reads
from these four samples were mapped to the mouse reference
genome (mm9), reads mapped to multiple locations were filtered
out, leaving 20.4, 16.6, 22.9 and 18.4 million uniquely mapped
reads that were used for subsequent analysis (Table 1). Clusters
of read (peaks) were then identified by using MACS software
(50). Overall 7.97 × 104, 3.68 × 104, 6.99 × 104 and 4.23 × 104
Human Molecular Genetics, 2013, Vol. 22, No. 18
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Figure 1. Quantification of the 5-hmC content in WT and YAC128 HD mouse brain tissues. (A) Dot-blot assay of 5-hmC in WT/YAC128 mouse striatum and cortex
genome. 50, 20 and 10 ng striatal and cortical DNA samples from 6-week-, 3-month- and 8-month-old WT and YAC128 mice were dot-blotted using a 5-hmC-specific
antibody. 5-hmC-containing DNA was used as the positive control, and normal C, 5-mC, 5-carC-containing DNA were used as the negative controls to verify the
specificity of 5-hmC antibody. (B–E) IF of 5-hmC (red) in striatum and cortex regions from 6-week-old WT (B) and YAC128 (C), 4-month-old WT (D) and
YAC128 (E) mouse brains. Brain sections were counterstained with NeuN antibody and Hochest33342 to visualize neurons (green) and nuclei (blue). (F) Quantification of the striatal and cortical 5-hmC content in WT and YAC128 DNA samples extracted at 6 weeks, 3 and 8 months of age. Striatal and cortical 5-hmC signals were
normalized to 5-hmC density of 6-week-old YAC128 striatum and cortex, respectively (n ¼ 5, mean + s.e.; ∗ P , 0.05; ∗∗ P , 0.01).
peaks were detected in the four samples, respectively (Table 1,
Supplementary Material, Dataset S1–4).
Genome-scaled distribution and epigenomic characteristics
of 5-hmC in YAC128 brains
The genome-scaled density of 5-hmC reads of each sample was
determined using the binned data and visualized in an IGV
Browser (IGV 1.4.2, http://www.broadinstitute.org/igv/). As
shown in Figure 2A, the global 5-hmC density of WT striatum
and cortex tissues was much higher than those in YAC128 counterparts, which was consistent with the dot-blot results, the
5-hmC IF results and the peak counts. The 5-hmC distribution
pattern in different chromosomes was unremarkable among
four samples and was consistent with previous results from cerebellum and hippocampus (24) (Fig. 2B). Clustering analysis
clearly showed distinctive patterns of cortex- and striatumspecific 5-hmC enrichment (Fig. 2C). The same brain regions
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Table 1. Summary of sequencing data
influences the expression of 436 striatal genes and 199 cortical
genes in HD at a very early stage.
Samples
Total reads
Uni-map
reads
Peak
numbers
Gene
numbers
WT striatum
WT cortex
Yac128
striatum
Yac128 cortex
41321310
42684513
40863760
20449138
22975167
16658192
79774
69976
36826
12215
11683
8862
34137033
18413874
42361
9672
A read is a putative 5-hmC-containing DNA fragment that has been sequenced.
Uni-map reads are the reads that map to a unique locus in the genome. A peak is a
cluster of uni-map reads identified using the MACS software. Genes that contain
peaks can also be identified.
with different genotypes (WT or YAC128) exhibit similar
cluster distribution patterns, though the genotype-specific
cluster distribution pattern could still be noted in both WT and
YAC128 samples (Fig. 2C).
Our genome-wide mapping results showed that 5-hmC was
enriched in gene bodies (including exons, introns, 5′ and 3′
UTRs), and depleted in intergenic regions (Fig. 2D – F). In addition, 5-hmC distributed in repetitive elements similarly among
the four samples, with 80% of the repetitive element-mapped
reads distributing in LINEs, SINEs and LTRs (Fig. 2G). We
also examined the levels of 5-hmC in CpG islands of the
genome in comparison with their flanking sequences. 5-hmC
was enriched in intragenic CpG islands but depleted in promoter
and intergenic CpG islands (Fig. 3A – D). Therefore, the distribution patterns of 5-hmC in different categories of genomic regions
are highly conserved and not affected by genotypes (WT and
YAC128) and neuronal tissues (striatum and cortex).
Identification of disease-related differential 5-hmC regions
(DhMRs) in YAC128 brain tissues
Recent studies reported that DhMRs varies between different
mouse brain development stages, different brain tissues (cerebellum versus hippocampus) (24) and even different neuronal
cell types (26), suggesting that genomic 5-hmC enriched loci
may serve as a novel epigenetic modification involved in gene
regulation. Therefore, we asked whether expression of HTTexp
in brains may perturb the DhMR construction. Pairwise comparisons of 5-hmC peaks between WT and YAC128 samples from
either striatum or cortex allowed us to identify the
disease-related (i.e., genotype-specific) DhMRs at the genomewide level (Table 2). YAC128 mice at 3 months of age do not
show any motor and pathological defects, therefore, identification of HTTexp-gained/lost DhMRs of 3-month-old mice can
provide new insights into the links between 5-hmC modification
and HD onset. We identified 747 DhMRs in striatum of which 49
were up-regulated and 698 were down-regulated in YAC128
striatum comparing with WT striatum (Fig. 3E and F)
(Table 2, Supplementary Material, Dataset S5), localized to 30
and 406 genes, respectively. Meanwhile, 362 DhMRs were identified from cortex samples, 38 were up-regulated and 324 were
down-regulated in YAC128 cortex relative to WT cortex
(Fig. 3G and H) (Table 2, Supplementary Material, Dataset
S5), corresponding to 28 and 171 genes, respectively. Thus,
HTTexp-induced perturbance of 5-hmC epigenome potentially
Correlation of disease-related DhMRs with gene
transcription
Whether 5-hmC modification positively or negatively regulate
gene expression remains elusive. We therefore investigated the
correlation of 5-hmC-enrichment in genes with the gene
mRNA levels. Nine genes were selected based on their 5-hmC
contents in different genotypes of different tissues, Creb5,
Dnm2 and Gnaq manifested significantly higher 5-hmC peaks
in WT striatum than in YAC128. Grm3, Gabbr2, Trpc7 had
higher 5-hmC peaks in WT cortex than in YAC128. Ryr3,
Grid2 and Capn2 possessed significantly higher 5-hmC peaks
in YAC128 cortex than in WT. Our Q-PCR results revealed
that, except in Grm3 gene, all other DhMRs-containing genes
(such as striatal Creb5, Dnm2, Gnaq and cortical Gabbr2 and
Trpc7 in WT brains, and cortical RyR3, Grid2, Capn in
YAC128 brains) exhibited a positive correlation with their
mRNA levels in the tissues of two different genotypes
(Fig. 4A – F; Supplementary Material, Fig. S2). The genomic
view of 5-hmC-enriched regions in Creb5, Trpc7 and Ryr3 indicated that DhMRs are located mainly in gene bodies (Fig. 4B, D
and F, Supplementary Material, Fig. S2).
Implications of aberrant 5-hmC epigenome on HD
pathogenesis
Gene ontology analysis of the 436 genes with disease-related
DhMRs in striatum and 199 genes in cortex allowed us to identify
the canonical pathways and biological functions which could be
important for HD onset. The ingenuity pathway analysis (IPA) of
striatum-DhMRs-containing genes cataloged a number of canonical pathways such as Wnt/b-catenin signaling, axonal guidance signaling, GABA receptor signaling, glutamate receptor
signaling, Dopamine-DAPRR32 feedback in cAMP signaling,
mTOR signaling and synaptic long-term potentiation (Supplementary Material, Fig. S4A and Fig. 5A and B; Supplementary
Material, Dataset S7). A few of these pathways such as glutamate
receptor signaling, CREB signaling in neurons also appeared in
IPA-cataloged pathways of cortex-DhMRs-genes (Supplementary Material, Fig. S4C; Supplementary Material, Dataset S7),
suggesting that striatum and cortex may share some common
pathological mechanisms in HD. Intriguingly, striatum-specific
IPA-clustered pathways such as Wnt/b-catenin/Sox pathway,
axonal guidance signaling, GABA receptor signaling and
Dopamine-DAPRR32 feedback in cAMP signaling clearly represent striatum selective pathological mechanisms. In support of
the effectiveness of our IPA pathway identification results, aberrant glutamate receptor/calcium signaling/dopamine signaling/
neuronal CREB signaling pathways have been previously implicated in HD pathogenesis (35,39,40,42,46,51– 59) (Fig. 5A).
More interestingly, we observed that dysregulation of the Wnt/
b-catenin/Sox pathway and the axonal guidance signaling
pathway, which are very important for neurogenesis and
neuron survival (60,61), could be a new pathological mechanism(s) for HD onset (Fig. 5B). Genes involved in each specific
pathway are listed in Supplementary Material, Dataset S7.
Human Molecular Genetics, 2013, Vol. 22, No. 18
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Figure 2. Genomic mapping and features of 5-hmC across two brain regions in WT/YAC128 mice. (A) Genome-scale of 5-hmC profiles and enrichment in mouse striatum and cortex. The heatmap represents the read densities which have been equally scaled and then normalized based on the total number of mapped reads per sample. (B)
Chromosomal read densities of 5-hmC in cortex and striatum from WT/YAC128 mice brains. The chromosome-wide densities were determined as reads per chromosome
divided by the total number of reads in millions. Expected values were determined by dividing 106 by the total genome length and multiplying by the chromosome length.
(C) Heatmap of 5-hmC levels of RefSeq genes. Five distinct clusters of genes were identified based on the dynamic changes of their 5-hmC levels in WT/YAC128 striatum
and cortex. The heatmap represents the normalized 5-hmC tag density of each gene. (D) Normalized densities of 5-hmC reads (per kilobase per million) in regions with
various genomic features (obtained from the University of California Santa Cruz tables, mm9) including promoters (22 kb to +0.5 kb relative to TSS), exons (5′ UTR,
3′ UTR, coding exons), introns and intergenic regions. (E) Normalized peak densities of DhMR (differential 5-hmC region) in regions with various genomic features,
including promoters (22 kb to +0.5 kb relative to TSS), exons (5′ UTR, 3′ UTR, coding exons), introns and intergenic regions. The dynamic peaks were identified
between WT and YAC128 with each brain region (cortex or striatum). (F) Normalized 5-hmC read densities across the transcript unit of all reference genes in striatum
and cortex from both WT and YAC128 brains. Each protein-coding gene body was normalized to 0–100%. 5-hmC densities are shown along the transcript unit with both
5′ and 3′ flanking regions. 5-hmC densities were normalized to the total number of aligned reads from each sample (in millions). (G) Normalized densities of 5-hmC reads
on repetitive sequences of striatum and cortex samples from WT/YAC128 mice brains. For each type of repeat (annotated by RepeatMasker), relative enrichment of
5-hmC between different samples at different repeat classes was assessed by determining the fraction of total reads aligned to each class of repetitive element.
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Figure 3. Characterization of 5-hmC around CGIs and identification of disease-related DhMRs. (A–D) Normalized 5-hmC read densities across the CGIs belonging to
the promoter (Red), intragenic (Green) and intergenic (Blue) regions of each sample. Each CpG island was normalized to 0 –100%. 5-hmC tag density was plotted from
500% upstream to 500% downstream of the normalized CGIs. (E–H) Disease-related (i.e., genotype-specific) DhMRs. DhMRs identified in bidirectional comparisons of 5-hmC peaks between WT striatum and YAC128 striatum were assigned to genotype-specific (i.e., disease-related) striatum-DhMRs (WT versus YAC128,
698 DhMRs; YAC128 versus WT, 49 DhMRs); Disease-related cortex-DhMRs were identified similarly (WT versus YAC128, 324 DhMRs; YAC128 versus WT, 38
DhMRs).
Table 2. DhMRs identified between samples of pairs
Control
WT striatum
WT cortex
Yac128
striatum
Yac128 cortex
Treatment
WT
striatum
WT
cortex
Yac128
striatum
Yac128
cortex
0
361
698
1182
0
ND
49
ND
0
ND
38
149
ND
324
425
0
DhMRs of a pair of samples were identified using the MACS software with one
sample as the control and the other as the treatment. The DhMRs identified in such
a way represent 5-hmC regions whose 5-hmC contents were higher in the
treatment sample. Another set of DhMRs was acquired if the control and
treatment samples were switched. Eight pairwise comparisons between striatum
and cortex in WT and YAC128 were conducted. The comparisons of certain pairs
of samples were not defined and were labeled as ND.
IPA of DhMR-annotated genes cataloged a variety of functions such as tissue development, embryonic development,
nervous system development and function, organ development,
cell-to-cell signaling and interaction, behavior, cellular assembly and organization, molecular transport, cardiovascular
disease and genetic disorder that may be related to striatum pathology (Supplementary Material, Fig. S4B; Supplementary Material, Dataset S8). Similar to IPA-pathway results, some of
these functions also appeared in IPA cataloged functions of
cortex DhMR genes (Supplementary Material, Fig. S4D;
Supplementary Material, Dataset S8), suggesting that some dysfunctions affected by HTTexp were similar in striatum and cortex
in HD. A number of striatum-specific IPA-clustered functions
such as behavior, molecular transport, cardiovascular disease
and genetic disorder may represent striatum selective pathological mechanisms. Genes involved in each specific function
are listed in Supplementary Material, Dataset S8.
DISCUSSION
In this study, we observed that mutant huntingtin (HTTexp) is
associated with a marked reduction of the 5-hmC landscape, indicating that loss of the 5-hmC marker is a new epigenetic feature
of HD. Moreover, we found that acquisition of DhMRs in gene
bodies is a positive regulator for neuronal gene expression.
Thus, by perturbing the normal regulation of 5-hmC epigenome,
HTTexp could disturb gene expression and cause neuronal dysfunction long before any signs of neuronal death. We identified
a number of pathways involving neuronal development/maturation and neurotransmitter signaling that could be important
for the onset of HD. Our study also points to a new avenue for
HD treatment, reestablishing 5-hmC levels and landscape may
have the potential to slow/halt the progression of HD.
YAC128 mice as models of HD
Selective degeneration of striatal neurons is a pathologic hallmark of HD, and neurons missing in the cortex region have
Human Molecular Genetics, 2013, Vol. 22, No. 18
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Figure 4. Association of disease-related DhMRs with gene transcription. (A) The Creb5 gene showed a higher transcription level in WT striatum than in YAC128
striatum (n ¼ 4, P , 0.05). (B) Genomic view one of the DhMRs lost in YAC128 striatum comparing with WT striatum located in Creb5 gene. (C) The Trpc7 gene
showed a higher transcription level in WT cortex than in YAC128 cortex (n ¼ 4, P , 0.05). (D) Genomic view one of the DhMRs lost in YAC128 cortex comparing
with WT cortex located in Trpc7 gene. (E) The Ryr3 gene showed a higher transcription level in YAC128 cortex than in WT cortex (n ¼ 4, P , 0.05). (F) Genomic
view one of the DhMRs acquired in YAC128 cortex comparing with WT cortex located in Ryr3 gene. Data are represented as mean + s.e. (∗ P , 0.05 comparing with
the gene transcription of WT tissue). WTC, WT cortex; YAC128C, YAC128 cortex; WTS, WT striatum; YAC128S, YAC128 striatum.
also been shown to occur in advanced HD patients. Several HD
mouse models have been created to study the underlying
mechanisms for HD (38). YAC128 transgenic mice express a
mutant huntingtin gene containing 128 CAG repeats under the
human endogenous regulatory elements (44). YAC128 mice
exhibit slow progressive motor deficits, cognitive impairment,
shortened lifespan and progressive selective degeneration of
striatal neurons, thereby recapitulating many key features of
the human disease (38,44– 48); therefore, the results obtained
from YAC128 HD mice are expandable to human disease conditions. YAC128 HD mice do not show apparent motor and neuropathological deficits at 3 months of age (44,46). Since we aimed
to explore the potential role of 5-hmC in HD onset, we generated
detailed 5-hmC epigenomic maps from mouse brains at 3 months
of age which represents the early stage of the disease. Because
we were using a mixture of genomic DNAs extracted from five
mice from either WT or YAC128 brains, the variations of
different individuals in 5-hmC epigenome can be excluded in
our study.
Loss of 5-hmC in HD neurons
It has been recently recognized that 5-hmC distribution undergoes an interesting dynamic change in the entire genome
during development. The level of 5-hmC in mouse ES cells is
quite high. It decreases significantly during ES cell
differentiation (17,21), then rebounds during the developmental
process resulting in elevated 5-hmC in terminally differentiated
cells, particularly in neurons (20,23,24,49,62). Consistent with a
recent report that 5-hmC levels increase during postnatal development in cerebellum and hippocampus (24), we observed a substantial increase in 5-hmC in both striatum and cortex regions,
with the highest level observed at 3 months of age, followed
by reduced levels at 8 months of age, suggesting that continuous
remodeling of the 5-hmC epigenomic architecture takes place in
striatum and cortex. More importantly, by comparing 5-hmC
levels between age-matched DNA samples from WT and HD
mouse brain, we found that 5-hmC markedly decreased in both
striatum and cortex in YAC128 mice as early as 6 weeks of
age, indicating that HTTexp somehow causes deficiencies in
the 5-hmC landscape in HD neurons (Fig. 1A and F). A genomewide mapping of 5-hmC reads density of 3-month-old brain
tissues authentically confirmed the significant diminishment of
5-hmC in YAC128 striatum (55% reduction in peak number)
and cortex (40% reduction in peak number) (Fig. 2A,
Table 1). Thus, genome-wide loss of the 5-hmC marker is a new
epigenetic feature of HD, and the decrease of 5-hmC signal
could be potentially regarded as a biomarker for HD.
The mechanism(s) underlying the deficiency of 5-hmC reconstruction during postnatal neuronal development in HD brain
need to be resolved. Dysfunction of Tet family members presumably influences 5-hmC levels (17,18), and MeCP2 may also
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interfering with 5-hmC homeostasis may play a role in downregulation of 5-hmC in HD. Intriguingly, we observed that the
significant down-regulation of Tet2, Tet3 and up-regulation of
MeCP2 may account for one of the molecular mechanisms
underlying global loss of 5-hmC in YAC128 striatum (Supplementary Material, Fig. S3A). However, expression levels of
Tet2, Tet3 and MeCP2 failed to show detectable changes in
YAC128 cortex (Supplementary Material, Fig. S3B), suggesting
that tissue-specific mechanisms may apply to 5-hmC diminishment in HD cortex. Unlike the striatum-specific reduction of
the expression of Tet2 and Tet3 in YAC128 HD mice, Tet1 expression decreases in both striatum and cortex of YAC128
brains (Supplementary Material, Fig. S3), suggesting that reduction of 5-hmC levels in cortex may be caused by lower expression of Tet1 in YAC128 brains. HTTexp has been shown to bind
directly to DNA (66), raising the possibility that HTTexp may directly affect the accessibility of epigenetic modifiers to genome.
HTTexp also change the neuronal activity (35,40,42,51–54), and
normal neuronal activity has been known to cause localized loss
of DNA methylation (65,67–69). Other factors such as
HTTexp-altered histone modifications (70–75) and DNA-bound
transcription factors (15,43,76,77) affecting DNA methylation
could also influence 5-hmC levels indirectly. The full mechanistic
details of mutant HTT-induced loss of 5-hmC in HD neurons
remain to be determined.
Epigenomic features of 5-hmC in HD brains
Figure 5. Pathway analyses of the disease-related DhMRs-located genes. The
functions data represent the logarithm of P-values calculated by Fisher’s exact
test, with a threshold for statistical significance, P , 0.05. Canonical pathways
with P , 0.05 were defined as significant. Genes involved in each specific
pathway and function are listed in Supplementary Material, Datasets S7 and 8,
respectively. (A) Enrichment of genes acquired/lost DhMRs in YAC128 striatum
in glutamate receptor signaling, dopamine/cAMP/DARPP32 feedback pathway,
CREB signaling in neuron and LTP. Color indicates the strength of fold change of
5-hmC read densities. Red represents 5-hmC peak decrease in YAC128; Green
represents 5-hmC peak increase in YAC128. The fold change value of each
gene encoding the protein in the signaling is listed below. GNAI2 (Gai), 4.38;
CAMK4, 8.33; PRKAG2 (PKA), 4.27; GNAQ (Gaq), 4.11; CACNA1C
(VDCC-a1C, b4), 4.97; PPP2R2C (PP2A, regulatory subunit B), 4.11;
KCNJ6, 6.55; CREB5, 4.71; CACNA1A (VDCC-a1A, P/Q), 5.62; GNB1
(Gb1), 5.05; GRIA1 (AMPAR1), 8.63; GRID2 (IGluRd2, ionotropic glutamate
receptor delta 2), 8.79. (B) Enrichment of genes acquired/lost DhMRs in
YAC128 striatum in the wnt/b-catenin/Sox pathway. Color indicates the strength
of fold change of 5-hmC read densities. Red represents 5-hmC peak decrease in
YAC128; Green represents 5-hmC peak increase in YAC128. The fold change
value of each gene encoding the protein in the signaling is listed below.
WNT4, 5.37; WNT9B, 4.35; WIF1, 6.7; SFRP1, 4.21; LRP6, 6.16; NLK, 6.94;
TLE3 (Groucho), 4.55; GNAQ (Gaq), 4.11; PPP2R2C (PP2A, regulatory
subunit B), 4.11; SOX5, 4.93; TCF4, 4.45.
regulate 5-hmC levels through neuronal activity-dependent
DNA demethylation (63– 65). No Tet and MeCP2 mutations
have been reported in HD, suggesting that other mechanism(s)
Large-scale methylome data indicate that cytosine methylation
is present throughout mammalian genomes (in intergenic
regions, coding regions, mobile elements and certain promoters)
and an inverse correlation between cytosine methylation and
CpG density: CpG-poor DNA, which comprises most of the
genome, shows high levels of cytosine methylation, whereas
CpG islands remain mostly unmethylated (78,79). We observed
that 5-hmC peaks tended to be depleted in CpG islands in promoter and intergenic regions, but were highly enriched in the
intragenic CpG islands, suggesting that promoter and intergenic
CpG may have a specific mechanism to protect from 5-hmC
modification. Consistent with recent reports of 5-hmC distribution in hippocampus and cerebellum (24) and in ES cells, (80 –
83), our data revealed that the distribution patterns of 5-hmC
in different categories of genomic regions are highly conserved
in different brain tissues and not penetrated by HTTexp.
Correlation of DhMR with gene transcription
It is well established that methylated DNA at the gene promoter
region blocks the binding of transcriptional factors and thus
represses gene expression (84). While 5-hmC has been proposed
to serve as either a stable epigenetic modification to DNA that is
distinct from 5-mC or an intermediate of 5-mC demethylation,
the association between 5-hmC enrichment and the gene transcription activity remains an open question (19, 49, 80, 81,
85– 87). Genome-wide expression profiling of Tet1-depleted
mouse ES cells revealed that Tet1 may have both repressive
and activating functions on its direct target genes (81,88,89),
and a reduced 5-hmC level was associated with both up- and
down-regulated genes in melanomas compared with nevi (90),
suggesting that 5-hmC may play dual functions in transcription
Human Molecular Genetics, 2013, Vol. 22, No. 18
regulation in ES or cancer cells. In this study, we examined the
expression level of genes which were genotype-specifically
acquired DhMRs in WT or in YAC128 brain tissues, and
found that eight out of nine genes with DhMRs are positively correlated with gene transcription (Fig. 4A, C and E, Supplementary
Material, Fig. S2A – L). We did not find any examples of gene
transcription that were negatively associated with 5-hmC enrichment. The genomic IGV view of disease-related DhMRs of those
genes indicated that 5hmC-enriched loci occurred mainly in
introns of the gene bodies (Fig. 4B, D and F, Supplementary
Material, Fig. S2A – L), suggesting that intragenic 5-hmCenrichment is likely a positive epigenetic regulator of gene expression. Our findings are in agreement with a recent report
that 5-hmC is associated with developmentally activated genes
and actively transcribed genes in cerebellum and hippocampus
of mouse brains (24). Furthermore, in cerebellum neurons,
many actively transcribed genes display both significant
5-hmC enrichment within the gene bodies and the depletion of
5 mC (26). Therefore, it is possible that the regulation of
5-hmC and associated gene transcription in neuronal cells are
different from that in dividing cells such as ES and cancer cells.
Functional implications of disease-related DhMRs
In this study, by cataloging the genes acquiring genotypespecific DhMRs in striatum and cortex samples, we identified
a number of changed pathways and functions that may be important for HD onset. Some pathways such as glutamate receptor signaling, calcium signaling, dopamine-DAPRR32 feedback
signaling and neuronal CREB signaling have been previously
associated with neurodegeneration in HD (35, 39, 40, 42, 46,
51– 54, 56– 59, 91). It is well established that CREB-mediated
transcription plays a key role in neuronal functions and neuronal
survival. Several lines of evidence (15,39,57– 59) including the
present study (Fig. 5A) observe that CREB functionality is significantly decreased in HD neurons. Moreover, our data here
demonstrated that not only CREB itself but also upstream
members such as AMPAR, VDCC-a1C, IGluR-d2 (ionotropic
glutamate receptor d2) and CaMKIV were down-regulated due
to decreased 5-hmC during the early stage in HD. Therefore, epigenetic alternations in the 5-hmC marker of CREB pathway
members may contribute to the early pathogenic process in HD.
It is somewhat surprising that pathways involving neurogenesis/neuronal differentiation such as Wnt/b-catenin/Sox
pathway and axonal guidance signaling are highly enriched in
IPA analysis of disease-related striatum-DhMR-marked genes
(Supplementary Material, Fig. S4A,B and 5B; Supplementary
Material, Dataset S7). The Wnt/b-catenin/Sox pathway regulates both the proliferation of neural progenitor cells and their
differentiation to neurons in the subventricular and subgranular
zones (SVZs) (92– 94). Moreover, the Wnt signaling pathway is
an obligate component of neural progenitor cell differentiation
into neurons via activation of NeuroD1 (60,61), which is a key
factor for neurogenesis, differentiation and neuron survival.
Genes associated with neurogenesis and neuronal differentiation
(Supplementary Material, Dataset S7) such as Wnt4, Wnt9B,
LRP6, NLK, Sox5, SFRP1, Slit1, Slit3, EPHA7 and Kalrn show
decreased 5-hmC signals and presumably lower expression
levels in HD striatum. Our findings here are consistent with the
recent literature, indicating that disturbed neurogenesis is
3649
involved in pathogenesis of HD (95,96). Our data also suggest
that delayed and/or insufficient endogenous neurogenesis to
counteract the striatum neuropathology may occur at a very
early stage of HD. Several reagents that potentiated SVZ neurogenesis have been shown to be beneficial in HD mouse models
(97– 100), manifesting the important role of impaired neurogenesis in HD onset. Therefore, reconstructing the 5-hmC epigenome in HD brains could form a new therapeutic target against
HD onset/progression.
MATERIALS AND METHODS
Preparation of genomic DNA
YAC128 HD transgenic mice were obtained from The Jackson
Laboratory (Bar Harbor, ME, USA). Generation and breeding of
YAC128 transgenic mice (FVBN_NJ background strain) have
been described previously (44). Striatum and cortex regions
were dissected from 6-week-, 3-month-, and 8-month-old
female mouse brains as described previously (53,101). Genomic
DNA was prepared using a Wizard Genomic DNA Purification
kit (Promega Cat.:A1120) by following the manufacturer’s
instructions. Equal amounts of genomic DNAs extracted from
either striatum or cortex in WT mice (n ¼ 5) were pooled together
and named as WT striatum or WT cortex DNA sample, respectively. YAC128 mouse brain striatum and cortex DNA samples were
prepared similarly. The four groups of genomic DNA samples
were named as WT striatum, YAC128 striatum, WT cortex and
YAC128 cortex, respectively. All animal experiments were
reviewed and approved by the Institute of Zoology Institutional
Animal Care and Use Committee and were conducted according
to the committee’s guidelines.
Immuno-dot-blot assay
Genomic DNA from 6-week-, 3-month- and 8-month-old WT
and YAC128 mice brain striatum and cortex was denatured in
TE buffer for 10 min at 958C and immediately chilled on ice
for 5 min. Dot blot was performed on a Bio-Dot Apparatus
(#170-6545,Bio-Rad). 10, 20 and 50 ng of each DNA sample
was spotted on the positively charged nylon membrane, respectively, then the membrane was baked for 2 h at 808C until completely dry, followed by UV254 crosslink for 10 min to fix
DNA on the membrane. The membrane was then blocked
briefly with 5% non-fat milk for 1.5 h at room temperature.
The primary rabbit anti-5-hydroxymethylytosine antibody
(1:10000, #39769, Active Motif) was applied to the membrane
and incubated at RT for 1 h or overnight at 48C. After incubation
with a peroxidase-conjugated anti-rabbit IgG secondary antibody, the signal was visualized by using ECL (Millipore). The
dot-blot densities were analyzed with Image J software. The
5-hmC signals were normalized to 6-week-old YAC128 striatum 5-hmC density. The 5-hmC-containing DNA was used as
a positive control, and the normal C, 5-mC, 5-carC-containing
DNA were used as the negative controls to verify the specificity
of 5-hmC antibody.
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Human Molecular Genetics, 2013, Vol. 22, No. 18
Immunofluorescent stainings of 5-hmC
Six-week- and 4-month-old WT and YAC128 mice were
anesthetized with pentobarbital sodium (8 mg per kg) and perfused with phosphate-buffered saline, followed by perfusion
with 4% paraformaldehyde (W/V). Brains were then dehydrated
in 30% sucrose (W/V). Brain sections (35 mm) were prepared
with a cryomicrotome (Leica, CM1900). For 5-hmC immunostaining, sections were treated with 1 M HCl at 378C for 30 min
followed by blocking with 5% FBS in PBS. For primary antibodies, we used a rabbit antibody against 5-hmC (1:20 000,
#39769, Active Motif), mouse antibody against neuronal
nuclei (1:500, #MAB377, Millipore). For secondary antibodies,
we used donkey against mouse IgG conjugated with Alexa488
(1:500, #A11008, Invitrogen) and donkey against rabbit IgG
conjugated with Alexa546 (1:500, #A11031, Invitrogen). Sections were counter-stained with the nuclear dye Hochest33324
(#B2261, Sigma). All images of striatum and cortex were
taken using a microscope (Leica DMI6000 B TIRF MC) with
the same capture parameters.
5-hmC specific chemical labeling and affinity purification
Purified genomic DNA was sonicated into short fragments by
Covaris DNA shearing with microTUBEs according to manufacturer’s instructions. Then 5-hmC labeling reactions were performed in 75 ml solution containing 50 mM HEPES buffer
(pH7.9), 250 mM MgCl2, 100 mM UDP-6-N3-glucose and 80U
b-glucosyltransferase. The reaction was incubated for 2 h at
378C. After the reaction, DNA substrates were purified and
buffer-exchanged in H2O via Bio-Rad-Spin columns according
to the manufacturer’s instructions. Click chemistry was performed with the addition of 150 mM biotin into the DNA solution
and incubated for 2 h at 378C. The DNA samples were then purified by Invitrogen Dynabeads MyOneTM Streptavidin C1
according to manufacturer’s instructions.
Sequencing of 5-hmC-enriched genomic DNA
5-hmC enriched genomic DNA libraries were generated following the Illumina protocol for ‘Preparing Samples for CHIP sequencing of DNA’. Then, 20 ng of 5-hmC enriched DNA was
used to initiate the protocol. DNA fragments were gel purified
after the adapter ligation step. PCR-amplified DNA libraries
were quantified on an Agilent 2100 Bio analyzer using a quantitative PCR. We performed 100 bp single end sequencing on Illumina Hiseq2000 to get a 5-hmC-enriched DNA fragment
sequence.
Sequence alignment and peak identification
FASTQ sequence files of WT striatum, YAC128 striatum, WT
cortex and YAC128 cortex were aligned to the mouse genome
(mm9) using Bowtie 0.12.7. In order to improve the quality of
the reads, we removed 5 and 10 bases from the 5′ and 3′ ends
of the reads, respectively. A read containing the remaining 85
bases was mapped to the genome with no more than three mismatches in the first 75 bp. If a read was mapped to a unique
locus of the genome, it was named as a uni-map read. The
analysis of genomic distribution of 5-hmC was conducted
using these uni-map reads.
Peak identification was performed using a Poisson-based peak
identification algorithm (MACS) (50) with uni-map reads. Parameters were as follows: effective genome size ¼ 2.7 × 109; tag
size ¼ 85; band width ¼ 300; q-value cutoff ¼ 0.05; other parameters were set to default values.
DhMRs identification
The MACS software was used again to identify DhMRs (differentially hydroxymethylated regions) between two samples with
one as the control and the other as the treatment. These DhMRs
represented peaks whose 5-hmC content was significantly
higher in the treatment sample than in the control sample.
Then another set of DhMRs were identified by switching the
control and treatment samples. The parameters were set as the
following: effective genome size ¼ 2.7 × 109; tag size ¼ 85;
band width ¼ 300; q-value cutoff ¼ 0.05; other parameters
were set to default values. Genotype-specific (i.e.,
disease-related) DhMRs were identified by bidirectional comparisons between WT and YAC128 samples from striatum (or
from cortex).
Mapping 5-hmC reads to various genomic regions
Mapping of 5-hmC reads, or peaks, or the DhMRs to different
types of genomic regions such as exons and introns was performed by comparing the coordinates of uni-map reads with
the coordinates of these genomic regions, which were obtained
from the University of California Santa Cruz tables for mm9:
RefSeq Whole Gene, 5′ UTR, Exon, Intron, 3′ UTR, coding
regions, promoter and Intragenic region based on RefSeq
Whole Gene. A read/peak/DhMR was mapped to a given
genomic region if they overlapped by ≥1 bp based on their coordinates in the genome. For mapping 5-hmC reads to repetitive
elements, reads were aligned to the RepeatMasker track of
NCBI37v1/mm9 using Bowtie, using the same parameters as
in sequence alignment. Aligned reads were assigned to repeat
classes defined by RepeatMasker.
Evaluation of the 5-hmC content in different genomic
regions
The content of 5-hmC in a particular type of genomic region such
as exons, introns, UTRs or a user-defined window was quantified
using the number of reads (uni-map reads, more exactly) as the
measurement. As the sequencing depth of each sample varied,
the number of reads was first normalized by the total reads of
the sample to give a value with the unit being reads per million
(RPM). A RPM value was further converted to a density value
by dividing the former with the length of a genomic region.
For evaluating the 5-hmC contents in genomic regions such as
chromosomes, gene bodies, exons, introns, UTRs, promoters,
intergenic regions, CpG islands, DhMRs, the numbers of reads
in the peaks that mapped to these regions were used. Any read
that was not in a peak was not counted. For the repetitive elements, however, all reads were counted without considering
whether they were in a peak, and abundance in a repeat class
Human Molecular Genetics, 2013, Vol. 22, No. 18
was represented by the percentage of reads mapped to this class
in all repeat-mapped reads.
Read coverage and visualization
Genomic views of read coverage were generated using Integrated Genomics Viewer tools and browser (IGV 1.4.2, http://
www.broadinstitute.org/igv/, last accessed date on May 14,
2013) with a window size of 300.
3651
FUNDING
This work was supported by grants from the National Basic Research Program of China (2011CB965003, 2012CB944702),
NSFC (30970931, 30970588 and 31170730) and by a grant
from the Knowledge Innovation Program of Chinese Academy
of Sciences (KSCX2-YW-R-148). This work was also supported
by One-Hundred-Talent Program of CAS (to Dr C. Guo and
Dr Q. Sun).
REFERENCES
Ingenuity pathway analysis
Genes with ≥1 DhMRs were listed and used as input dataset for
the IPA software (Ingenuity Systems, www.ingenuity.com;
Redwood City, CA, USA). By applying IPA, the gene sets can
be functionally annotated and the biologically relevant pathways
could be identified.
RNA isolation and mRNA level of DhMR-containing genes
The brain striatum and cortex samples from four WT mice and
four YAC128 mice of 3-month-old were collected. Total RNA
was extracted from 30 mg of each sample using the Tissue
RNA Extraction kit (Tiangen) following the standard manual.
The complementary DNA (cDNA) of 1 mg RNA sample was
reverse-transcribed using the First Strand cDNA Synthesis kit
(Promega). The cDNA was diluted with sterilized ddH2O at
the rate of 1:4 for Q-PCR.
The expression of DhMR-enriched genes in the four WT and
four YAC128 mice striatum and cortex samples was detected
and Q-PCRs were performed in triplicate. The gene expression
levels were quantified relative to the expression of mouse
GAPDH gene, employing an optimized comparative Ct (DDCt)
value method. The primers designed for target genes and internal
control GAPDH are shown in Supplementary Material,
Table S1.
Statistics
Data are expressed as the mean + standard error of the mean
(s. e. m.), and statistical significance of differences between different groups was assessed using the t-test or ANOVA with P ,
0.05.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at HMG online.
ACKNOWLEDGMENTS
We apologize to those investigators whose work we could not
cite due to the reference limit, and gratefully acknowledge
their contributions to HD field. We thank Qiaochu Wang, and
Yun Wang for help with maintaining the YAC mouse colony,
Ilya Bezprozvanny for facilitating transportation of mice
strains and Paula L. Fischhaber for proofreading the manuscript.
Conflict of Interest statement. None declared.
1. Li, E., Bestor, T.H. and Jaenisch, R. (1992) Targeted mutation of the DNA
methyltransferase gene results in embryonic lethality. Cell, 69, 915–926.
2. Li, E., Beard, C. and Jaenisch, R. (1993) Role for DNA methylation in
genomic imprinting. Nature, 366, 362– 365.
3. Jaenisch, R. and Bird, A. (2003) Epigenetic regulation of gene expression,
how the genome integrates intrinsic and environmental signals. Nat.
Genet., 33(Suppl), 245–254.
4. Reik, W. (2007) Stability and flexibility of epigenetic gene regulation in
mammalian development. Nature, 447, 425– 432.
5. Zemach, A., McDaniel, I.E., Silva, P. and Zilberman, D. (2010)
Genome-wide evolutionary analysis of eukaryotic DNA methylation.
Science, 328, 916– 919.
6. Okano, M., Bell, D.W., Haber, D.A. and Li, E. (1999) DNA
methyltransferases Dnmt3a and Dnmt3b are essential for de novo
methylation and mammalian development. Cell, 99, 247 –257.
7. Bestor, T.H. (2000) The DNA methyltransferases of mammals. Hum. Mol.
Genet., 9, 2395–2402.
8. Goll, M.G. and Bestor, T.H. (2005) Eukaryotic cytosine
methyltransferases. Annu. Rev. Biochem., 74, 481– 514.
9. Rottach, A., Leonhardt, H. and Spada, F. (2009) DNA
methylation-mediated epigenetic control. J. Cell. Biochem., 108, 43– 51.
10. Wu, H., Coskun, V., Tao, J., Xie, W., Ge, W., Yoshikawa, K., Li, E., Zhang,
Y. and Sun, Y.E. (2010) Dnmt3a-dependent nonpromoter DNA
methylation facilitates transcription of neurogenic genes. Science, 329,
444–448.
11. Hutnick, L.K., Golshani, P., Namihira, M., Xue, Z., Matynia, A., Yang,
X.W., Silva, A.J., Schweizer, F.E. and Fan, G. (2009) DNA
hypomethylation restricted to the murine forebrain induces cortical
degeneration and impairs postnatal neuronal maturation. Hum. Mol.
Genet., 18, 2875– 2888.
12. Klein, C.J., Botuyan, M.V., Wu, Y., Ward, C.J., Nicholson, G.A.,
Hammans, S., Hojo, K., Yamanishi, H., Karpf, A.R., Wallace, D.C. et al.
(2011) Mutations in DNMT1 cause hereditary sensory neuropathy with
dementia and hearing loss. Nat. Genet., 43, 595– 600.
13. Winkelmann, J., Lin, L., Schormair, B., Kornum, B.R., Faraco, J., Plazzi,
G., Melberg, A., Cornelio, F., Urban, A.E., Pizza, F. et al. (2012) Mutations
in DNMT1 cause autosomal dominant cerebellar ataxia, deafness and
narcolepsy. Hum. Mol. Genet., 21, 2205–2210.
14. Psoni, S., Sofocleous, C., Traeger-Synodinos, J., Kitsiou-Tzeli, S.,
Kanavakis, E. and Fryssira-Kanioura, H. (2012) MECP2 mutations and
clinical correlations in Greek children with Rett syndrome and associated
neurodevelopmental disorders. Brain Dev., 34, 487– 495.
15. Ng, C.W., Yildirim, F., Yap, Y.S., Dalin, S., Matthews, B.J., Velez, P.J.,
Labadorf, A., Housman, D.E. and Fraenkel, E. (2013) Extensive changes in
DNA methylation are associated with expression of mutant huntingtin.
Proc. Natl Acad. Sci. USA, 110, 2354–2359.
16. Irier, H.A. and Jin, P. (2012) Dynamics of DNA methylation in aging and
Alzheimer’s disease. DNA Cell. Biol., 31(Suppl 1), S42– 48.
17. Tahiliani, M., Koh, K.P., Shen, Y., Pastor, W.A., Bandukwala, H., Brudno,
Y., Agarwal, S., Iyer, L.M., Liu, D.R., Aravind, L. et al. (2009) Conversion
of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by
MLL partner TET1. Science, 324, 930–935.
18. Ito, S., D’Alessio, A.C., Taranova, O.V., Hong, K., Sowers, L.C. and
Zhang, Y. (2010) Role of Tet proteins in 5mC to 5hmC conversion, ES-cell
self-renewal and inner cell mass specification. Nature, 466, 1129–1133.
19. Pastor, W.A., Pape, U.J., Huang, Y., Henderson, H.R., Lister, R., Ko, M.,
McLoughlin, E.M., Brudno, Y., Mahapatra, S., Kapranov, P. et al. (2011)
Genome-wide mapping of 5-hydroxymethylcytosine in embryonic stem
cells. Nature, 473, 394– 397.
3652
Human Molecular Genetics, 2013, Vol. 22, No. 18
20. Kriaucionis, S. and Heintz, N. (2009) The nuclear DNA base
5-hydroxymethylcytosine is present in Purkinje neurons and the brain.
Science, 324, 929–930.
21. Szwagierczak, A., Bultmann, S., Schmidt, C.S., Spada, F. and Leonhardt,
H. (2010) Sensitive enzymatic quantification of 5-hydroxymethylcytosine
in genomic DNA. Nucleic Acids Res., 38, e181.
22. Munzel, M., Globisch, D., Bruckl, T., Wagner, M., Welzmiller, V.,
Michalakis, S., Muller, M., Biel, M. and Carell, T. (2010) Quantification of
the sixth DNA base hydroxymethylcytosine in the brain. Angew. Chem. Int.
Ed. Engl., 49, 5375–5377.
23. Li, W. and Liu, M. (2011) Distribution of 5-hydroxymethylcytosine in
different human tissues. J. Nucleic Acids, 2011, 870726.
24. Szulwach, K.E., Li, X., Li, Y., Song, C.X., Wu, H., Dai, Q., Irier, H.,
Upadhyay, A.K., Gearing, M., Levey, A.I. et al. (2011) 5-hmC-mediated
epigenetic dynamics during postnatal neurodevelopment and aging. Nat.
Neurosci., 14, 1607– 1616.
25. Wang, T., Pan, Q., Lin, L., Szulwach, K.E., Song, C.X., He, C., Wu, H.,
Warren, S.T., Jin, P., Duan, R. et al. (2012) Genome-wide DNA
hydroxymethylation changes are associated with neurodevelopmental
genes in the developing human cerebellum. Hum. Mol. Genet., 21, 5500–
5510.
26. Mellen, M., Ayata, P., Dewell, S., Kriaucionis, S. and Heintz, N. (2012)
MeCP2 binds to 5hmC enriched within active genes and accessible
chromatin in the nervous system. Cell, 151, 1417–1430.
27. Vonsattel, J.P. and DiFiglia, M. (1998) Huntington disease. J. Neuropathol.
Exp. Neurol., 57, 369– 384.
28. MacDonald, M.E., Gines, S., Gusella, J.F. and Wheeler, V.C. (2003)
Huntington’s disease. Neuromolecular Med., 4, 7–20.
29. A novel gene containing a trinucleotide repeat that is expanded and unstable
on Huntington’s disease chromosomes. (1993) The Huntington’s Disease
Collaborative Research Group. Cell, 72, 971– 983.
30. Young, A.B. (2003) Huntingtin in health and disease. J. Clin. Invest., 111,
299– 302.
31. Tobin, A.J. and Signer, E.R. (2000) Huntington’s disease: the challenge for
cell biologists. Trends Cell Biol., 10, 531– 536.
32. Ross, C.A. (2002) Polyglutamine pathogenesis: emergence of unifying
mechanisms for Huntington’s disease and related disorders. Neuron, 35,
819– 822.
33. Harjes, P. and Wanker, E.E. (2003) The hunt for huntingtin function:
interaction partners tell many different stories. Trends Biochem. Sci., 28,
425– 433.
34. Sugars, K.L. and Rubinsztein, D.C. (2003) Transcriptional abnormalities in
Huntington disease. Trends Genet., 19, 233–238.
35. Bezprozvanny, I. and Hayden, M.R. (2004) Deranged neuronal calcium
signaling and Huntington disease. Biochem. Biophys. Res. Commun., 322,
1310–1317.
36. Li, S. and Li, X.J. (2006) Multiple pathways contribute to the pathogenesis
of Huntington disease. Mol. Neurodegener., 1, 19.
37. Hodges, A., Strand, A.D., Aragaki, A.K., Kuhn, A., Sengstag, T., Hughes,
G., Elliston, L.A., Hartog, C., Goldstein, D.R., Thu, D. et al. (2006)
Regional and cellular gene expression changes in human Huntington’s
disease brain. Hum. Mol. Genet., 15, 965–977.
38. Kuhn, A., Goldstein, D.R., Hodges, A., Strand, A.D., Sengstag, T.,
Kooperberg, C., Becanovic, K., Pouladi, M.A., Sathasivam, K., Cha, J.H.
et al. (2007) 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, 1845–1861.
39. Cui, L., Jeong, H., Borovecki, F., Parkhurst, C.N., Tanese, N. and Krainc, D.
(2006) Transcriptional repression of PGC-1alpha by mutant huntingtin
leads to mitochondrial dysfunction and neurodegeneration. Cell, 127,
59– 69.
40. Fan, M.M. and Raymond, L.A. (2007) N-Methyl-D-aspartate (NMDA)
receptor function and excitotoxicity in Huntington’s disease. Prog.
Neurobiol., 81, 272–293.
41. Trushina, E. and McMurray, C.T. (2007) Oxidative stress and
mitochondrial dysfunction in neurodegenerative diseases. Neuroscience,
145, 1233– 1248.
42. Bezprozvanny, I. (2009) Calcium signaling and neurodegenerative
diseases. Trends Mol. Med., 15, 89–100.
43. Seredenina, T. and Luthi-Carter, R. (2012) What have we learned from
gene expression profiles in Huntington’s disease? Neurobiol. Dis., 45,
83– 98.
44. Slow, E.J., van Raamsdonk, J., Rogers, D., Coleman, S.H., Graham, R.K.,
Deng, Y., Oh, R., Bissada, N., Hossain, S.M., Yang, Y.Z. et al. (2003)
Selective striatal neuronal loss in a YAC128 mouse model of Huntington
disease. Hum. Mol. Genet., 12, 1555– 1567.
45. Van Raamsdonk, J.M., Murphy, Z., Slow, E.J., Leavitt, B.R. and Hayden,
M.R. (2005) Selective degeneration and nuclear localization of mutant
huntingtin in the YAC128 mouse model of Huntington disease. Hum. Mol.
Genet., 14, 3823–3835.
46. Tang, T.S., Chen, X., Liu, J. and Bezprozvanny, I. (2007) Dopaminergic
signaling and striatal neurodegeneration in Huntington’s disease.
J. Neurosci., 27, 7899–7910.
47. Fernandes, H.B., Baimbridge, K.G., Church, J., Hayden, M.R. and
Raymond, L.A. (2007) Mitochondrial sensitivity and altered calcium
handling underlie enhanced NMDA-induced apoptosis in YAC128 model
of Huntington’s disease. J. Neurosci., 27, 13614–13623.
48. Mazarei, G., Neal, S.J., Becanovic, K., Luthi-Carter, R., Simpson, E.M. and
Leavitt, B.R. (2010) Expression analysis of novel striatal-enriched genes in
Huntington disease. Hum. Mol. Genet., 19, 609– 622.
49. Song, C.X., Szulwach, K.E., Fu, Y., Dai, Q., Yi, C., Li, X., Li, Y., Chen,
C.H., Zhang, W., Jian, X. et al. (2011) Selective chemical labeling reveals
the genome-wide distribution of 5-hydroxymethylcytosine. Nat.
Biotechnol., 29, 68– 72.
50. Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein,
B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W. et al. (2008)
Model-based analysis of ChIP-Seq (MACS). Genome. Biol., 9, R137.
51. Zeron, M.M., Hansson, O., Chen, N., Wellington, C.L., Leavitt, B.R.,
Brundin, P., Hayden, M.R. and Raymond, L.A. (2002) Increased sensitivity
to N-methyl-D-aspartate receptor-mediated excitotoxicity in a mouse
model of Huntington’s disease. Neuron, 33, 849 –860.
52. Tang, T.S., Tu, H., Chan, E.Y., Maximov, A., Wang, Z., Wellington, C.L.,
Hayden, M.R. and Bezprozvanny, I. (2003) Huntingtin and
huntingtin-associated protein 1 influence neuronal calcium signaling
mediated by inositol-(1,4,5) triphosphate receptor type 1. Neuron, 39, 227–
239.
53. Tang, T.S., Slow, E., Lupu, V., Stavrovskaya, I.G., Sugimori, M., Llinas,
R., Kristal, B.S., Hayden, M.R. and Bezprozvanny, I. (2005) Disturbed
Ca2+ signaling and apoptosis of medium spiny neurons in Huntington’s
disease. Proc. Natl Acad. Sci. USA, 102, 2602– 2607.
54. Fan, M.M., Fernandes, H.B., Zhang, L.Y., Hayden, M.R. and Raymond,
L.A. (2007) Altered NMDA receptor trafficking in a yeast artificial
chromosome transgenic mouse model of Huntington’s disease.
J. Neurosci., 27, 3768–3779.
55. Jordt, S.E. and Ehrlich, B.E. (2007) TRP channels in disease. Subcell.
Biochem., 45, 253–271.
56. Mittal, S.K. and Eddy, C. (2013) The role of dopamine and glutamate
modulation in Huntington disease. Behav. Neurol., 26, 255– 263.
57. Sugars, K.L., Brown, R., Cook, L.J., Swartz, J. and Rubinsztein, D.C.
(2004) Decreased cAMP response element-mediated transcription: an
early event in exon 1 and full-length cell models of Huntington’s disease
that contributes to polyglutamine pathogenesis. J. Biol. Chem., 279, 4988–
4999.
58. Okamoto, S., Pouladi, M.A., Talantova, M., Yao, D., Xia, P., Ehrnhoefer,
D.E., Zaidi, R., Clemente, A., Kaul, M., Graham, R.K. et al. (2009) Balance
between synaptic versus extrasynaptic NMDA receptor activity influences
inclusions and neurotoxicity of mutant huntingtin. Nat. Med., 15, 1407–
1413.
59. Choi, Y.S., Lee, B., Cho, H.Y., Reyes, I.B., Pu, X.A., Saido, T.C., Hoyt,
K.R. and Obrietan, K. (2009) CREB is a key regulator of striatal
vulnerability in chemical and genetic models of Huntington’s disease.
Neurobiol. Dis., 36, 259– 268.
60. Kuwabara, T., Hsieh, J., Muotri, A., Yeo, G., Warashina, M., Lie, D.C.,
Moore, L., Nakashima, K., Asashima, M. and Gage, F.H. (2009)
Wnt-mediated activation of NeuroD1 and retro-elements during adult
neurogenesis. Nat. Neurosci., 12, 1097– 1105.
61. Inestrosa, N.C. and Arenas, E. (2010) Emerging roles of Wnts in the adult
nervous system. Nat Rev. Neurosci., 11, 77– 86.
62. Haffner, M.C., Chaux, A., Meeker, A.K., Esopi, D.M., Gerber, J.,
Pellakuru, L.G., Toubaji, A., Argani, P., Iacobuzio-Donahue, C., Nelson,
W.G. et al. (2011) Global 5-hydroxymethylcytosine content is significantly
reduced in tissue stem/progenitor cell compartments and in human cancers.
Oncotarget, 2, 627– 637.
63. Zhou, Z., Hong, E.J., Cohen, S., Zhao, W.N., Ho, H.Y., Schmidt, L., Chen,
W.G., Lin, Y., Savner, E., Griffith, E.C. et al. (2006) Brain-specific
Human Molecular Genetics, 2013, Vol. 22, No. 18
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
phosphorylation of MeCP2 regulates activity-dependent Bdnf
transcription, dendritic growth, and spine maturation. Neuron, 52, 255 –
269.
Nelson, E.D., Kavalali, E.T. and Monteggia, L.M. (2008)
Activity-dependent suppression of miniature neurotransmission through
the regulation of DNA methylation. J. Neurosci., 28, 395 –406.
Ma, D.K., Jang, M.H., Guo, J.U., Kitabatake, Y., Chang, M.L.,
Pow-Anpongkul, N., Flavell, R.A., Lu, B., Ming, G.L. and Song, H. (2009)
Neuronal activity-induced Gadd45b promotes epigenetic DNA
demethylation and adult neurogenesis. Science, 323, 1074–1077.
Benn, C.L., Sun, T., Sadri-Vakili, G., McFarland, K.N., DiRocco, D.P.,
Yohrling, G.J., Clark, T.W., Bouzou, B. and Cha, J.H. (2008) Huntingtin
modulates transcription, occupies gene promoters in vivo, and binds
directly to DNA in a polyglutamine-dependent manner. J. Neurosci., 28,
10720–10733.
Chen, W.G., Chang, Q., Lin, Y., Meissner, A., West, A.E., Griffith, E.C.,
Jaenisch, R. and Greenberg, M.E. (2003) Derepression of BDNF
transcription involves calcium-dependent phosphorylation of MeCP2.
Science, 302, 885–889.
Martinowich, K., Hattori, D., Wu, H., Fouse, S., He, F., Hu, Y., Fan, G. and
Sun, Y.E. (2003) DNA methylation-related chromatin remodeling in
activity-dependent BDNF gene regulation. Science, 302, 890–893.
Guo, J.U., Ma, D.K., Mo, H., Ball, M.P., Jang, M.H., Bonaguidi, M.A.,
Balazer, J.A., Eaves, H.L., Xie, B., Ford, E. et al. (2011) Neuronal activity
modifies the DNA methylation landscape in the adult brain. Nat. Neurosci.,
14, 1345–1351.
Faber, P.W., Barnes, G.T., Srinidhi, J., Chen, J., Gusella, J.F. and
MacDonald, M.E. (1998) Huntingtin interacts with a family of WW domain
proteins. Hum. Mol. Genet., 7, 1463–1474.
Sadri-Vakili, G., Bouzou, B., Benn, C.L., Kim, M.O., Chawla, P.,
Overland, R.P., Glajch, K.E., Xia, E., Qiu, Z., Hersch, S.M. et al. (2007)
Histones associated with downregulated genes are hypo-acetylated in
Huntington’s disease models. Hum. Mol. Genet., 16, 1293–1306.
Kazantsev, A.G. and Thompson, L.M. (2008) Therapeutic application of
histone deacetylase inhibitors for central nervous system disorders. Nat.
Rev. Drug Discov., 7, 854 –868.
McFarland, K.N., Das, S., Sun, T.T., Leyfer, D., Xia, E., Sangrey, G.R.,
Kuhn, A., Luthi-Carter, R., Clark, T.W., Sadri-Vakili, G. et al. (2012)
Genome-wide histone acetylation is altered in a transgenic mouse model of
Huntington’s disease. PLoS One, 7, e41423.
Ryu, H., Lee, J., Hagerty, S.W., Soh, B.Y., McAlpin, S.E., Cormier, K.A.,
Smith, K.M. and Ferrante, R.J. (2006) ESET/SETDB1 gene expression and
histone H3 (K9) trimethylation in Huntington’s disease. Proc. Natl Acad.
Sci. USA, 103, 19176– 19181.
Cedar, H. and Bergman, Y. (2009) Linking DNA methylation and histone
modification, patterns and paradigms. Nat. Rev. Genet., 10, 295– 304.
Hogart, A., Lichtenberg, J., Ajay, S.S., Anderson, S., Margulies, E.H. and
Bodine, D.M. (2012) Genome-wide DNA methylation profiles in
hematopoietic stem and progenitor cells reveal overrepresentation of ETS
transcription factor binding sites. Genome Res., 22, 1407– 1418.
Stadler, M.B., Murr, R., Burger, L., Ivanek, R., Lienert, F., Scholer, A., van
Nimwegen, E., Wirbelauer, C., Oakeley, E.J., Gaidatzis, D. et al. (2011)
DNA-binding factors shape the mouse methylome at distal regulatory
regions. Nature, 480, 490–495.
Lister, R., Pelizzola, M., Dowen, R.H., Hawkins, R.D., Hon, G.,
Tonti-Filippini, J., Nery, J.R., Lee, L., Ye, Z., Ngo, Q.M. et al. (2009)
Human DNA methylomes at base resolution show widespread epigenomic
differences. Nature, 462, 315– 322.
Meissner, A., Mikkelsen, T.S., Gu, H., Wernig, M., Hanna, J., Sivachenko,
A., Zhang, X., Bernstein, B.E., Nusbaum, C., Jaffe, D.B. et al. (2008)
Genome-scale DNA methylation maps of pluripotent and differentiated
cells. Nature, 454, 766 –770.
Ficz, G., Branco, M.R., Seisenberger, S., Santos, F., Krueger, F., Hore,
T.A., Marques, C.J., Andrews, S. and Reik, W. (2011) Dynamic regulation
of 5-hydroxymethylcytosine in mouse ES cells and during differentiation.
Nature, 473, 398 –402.
Williams, K., Christensen, J., Pedersen, M.T., Johansen, J.V., Cloos, P.A.,
Rappsilber, J. and Helin, K. (2011) TET1 and hydroxymethylcytosine in
transcription and DNA methylation fidelity. Nature, 473, 343– 348.
Cimmino, L., Abdel-Wahab, O., Levine, R.L. and Aifantis, I. (2011) TET
family proteins and their role in stem cell differentiation and
transformation. Cell Stem Cell, 9, 193–204.
3653
83. Wu, H., D’Alessio, A.C., Ito, S., Wang, Z., Cui, K., Zhao, K., Sun, Y.E. and
Zhang, Y. (2011) Genome-wide analysis of 5-hydroxymethylcytosine
distribution reveals its dual function in transcriptional regulation in mouse
embryonic stem cells. Genes Dev., 25, 679– 684.
84. Bird, A. (2002) DNA methylation patterns and epigenetic memory. Genes
Dev., 16, 6– 21.
85. Szulwach, K.E., Li, X., Li, Y., Song, C.X., Han, J.W., Kim, S., Namburi, S.,
Hermetz, K., Kim, J.J., Rudd, M.K. et al. (2011) Integrating
5-hydroxymethylcytosine into the epigenomic landscape of human
embryonic stem cells. PLoS Genet., 7, e1002154.
86. Jin, S.G., Wu, X., Li, A.X. and Pfeifer, G.P. (2011) Genomic mapping of
5-hydroxymethylcytosine in the human brain. Nucleic Acids Res., 39,
5015– 5024.
87. Wu, H., D’Alessio, A.C., Ito, S., Xia, K., Wang, Z., Cui, K., Zhao, K., Sun,
Y.E. and Zhang, Y. (2011) Dual functions of Tet1 in transcriptional
regulation in mouse embryonic stem cells. Nature, 473, 389– 393.
88. Wu, H. and Zhang, Y. (2011) Mechanisms and functions of Tet
protein-mediated 5-methylcytosine oxidation. Genes Dev., 25, 2436–
2452.
89. Xu, Y., Wu, F., Tan, L., Kong, L., Xiong, L., Deng, J., Barbera, A.J., Zheng,
L., Zhang, H., Huang, S. et al. (2011) Genome-wide regulation of 5hmC,
5mC, and gene expression by Tet1 hydroxylase in mouse embryonic stem
cells. Mol. Cell, 42, 451– 464.
90. Lian, C.G., Xu, Y., Ceol, C., Wu, F., Larson, A., Dresser, K., Xu, W., Tan,
L., Hu, Y., Zhan, Q. et al. (2012) Loss of 5-hydroxymethylcytosine is an
epigenetic hallmark of melanoma. Cell, 150, 1135–1146.
91. Wang, J.Q., Chen, Q., Wang, X., Wang, Q.C., Wang, Y., Cheng, H.P., Guo,
C., Sun, Q. and Tang, T.S. (2013) Dysregulation of mitochondrial calcium
signaling and superoxide flashes cause mitochondrial genomic DNA
damage in Huntington’s disease. J. Biol. Chem., 288, 3070–3084.
92. Lie, D.C., Colamarino, S.A., Song, H.J., Desire, L., Mira, H., Consiglio, A.,
Lein, E.S., Jessberger, S., Lansford, H., Dearie, A.R. et al. (2005) Wnt
signalling regulates adult hippocampal neurogenesis. Nature, 437,
1370– 1375.
93. Adachi, K., Mirzadeh, Z., Sakaguchi, M., Yamashita, T., Nikolcheva, T.,
Gotoh, Y., Peltz, G., Gong, L., Kawase, T., Alvarez-Buylla, A. et al.
(2007) Beta-catenin signaling promotes proliferation of progenitor
cells in the adult mouse subventricular zone. Stem Cells, 25,
2827– 2836.
94. Qu, Q., Sun, G., Li, W., Yang, S., Ye, P., Zhao, C., Yu, R.T., Gage, F.H.,
Evans, R.M. and Shi, Y. (2010) Orphan nuclear receptor TLX activates
Wnt/beta-catenin signalling to stimulate neural stem cell proliferation and
self-renewal. Nat. Cell. Biol., 12, 31– 40; sup pp 31–39.
95. Simpson, J.M., Gil-Mohapel, J., Pouladi, M.A., Ghilan, M., Xie, Y.,
Hayden, M.R. and Christie, B.R. (2011) Altered adult hippocampal
neurogenesis in the YAC128 transgenic mouse model of Huntington
disease. Neurobiol. Dis., 41, 249–260.
96. Gil-Mohapel, J., Simpson, J.M., Ghilan, M. and Christie, B.R. (2011)
Neurogenesis in Huntington’s disease: can studying adult neurogenesis
lead to the development of new therapeutic strategies? Brain Res., 1406,
84– 105.
97. Grote, H.E., Bull, N.D., Howard, M.L., van Dellen, A., Blakemore, C.,
Bartlett, P.F. and Hannan, A.J. (2005) Cognitive disorders and
neurogenesis deficits in Huntington’s disease mice are rescued by
fluoxetine. Eur. J. Neurosci., 22, 2081–2088.
98. Cho, S.R., Benraiss, A., Chmielnicki, E., Samdani, A., Economides, A. and
Goldman, S.A. (2007) Induction of neostriatal neurogenesis slows disease
progression in a transgenic murine model of Huntington disease. J. Clin.
Invest., 117, 2889–2902.
99. Duan, W., Peng, Q., Masuda, N., Ford, E., Tryggestad, E., Ladenheim, B.,
Zhao, M., Cadet, J.L., Wong, J. and Ross, C.A. (2008) Sertraline slows
disease progression and increases neurogenesis in N171–82Q mouse
model of Huntington’s disease. Neurobiol. Dis., 30, 312– 322.
100. Peng, Q., Masuda, N., Jiang, M., Li, Q., Zhao, M., Ross, C.A. and Duan, W.
(2008) The antidepressant sertraline improves the phenotype, promotes
neurogenesis and increases BDNF levels in the R6/2 Huntington’s disease
mouse model. Exp. Neurol., 210, 154–163.
101. Tang, T.S., Tu, H., Wang, Z. and Bezprozvanny, I. (2003) Modulation of
type 1 inositol (1,4,5)-trisphosphate receptor function by protein kinase a
and protein phosphatase 1a. J. Neurosci., 23, 403– 415.