Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Exploring Genetic Factors Involved in Huntington Disease Age of Onset: E2F2 as a New Potential Modifier Gene

Abstract

Age of onset (AO) of Huntington disease (HD) is mainly determined by the length of the CAG repeat expansion (CAGexp) in exon 1 of the HTT gene. Additional genetic variation has been suggested to contribute to AO, although the mechanism by which it could affect AO is presently unknown. The aim of this study is to explore the contribution of candidate genetic factors to HD AO in order to gain insight into the pathogenic mechanisms underlying this disorder. For that purpose, two AO definitions were used: the earliest age with unequivocal signs of HD (earliest AO or eAO), and the first motor symptoms age (motor AO or mAO). Multiple linear regression analyses were performed between genetic variation within 20 candidate genes and eAO or mAO, using DNA and clinical information of 253 HD patients from REGISTRY project. Gene expression analyses were carried out by RT-qPCR with an independent sample of 35 HD patients from Basque Country Hospitals. We found suggestive association signals between HD eAO and/or mAO and genetic variation within the E2F2, ATF7IP, GRIN2A, GRIN2B, LINC01559, HIP1 and GRIK2 genes. Among them, the most significant was the association between eAO and rs2742976, mapping to the promoter region of E2F2 transcription factor. Furthermore, rs2742976 T allele patient carriers exhibited significantly lower lymphocyte E2F2 gene expression, suggesting a possible implication of E2F2-dependent transcriptional activity in HD pathogenesis. Thus, E2F2 emerges as a new potential HD AO modifier factor.

Citation: Valcárcel-Ocete L, Alkorta-Aranburu G, Iriondo M, Fullaondo A, García-Barcina M, Fernández-García JM, et al. (2015) Exploring Genetic Factors Involved in Huntington Disease Age of Onset: E2F2 as a New Potential Modifier Gene. PLoS ONE 10(7): e0131573. https://doi.org/10.1371/journal.pone.0131573

Editor: David Blum, Inserm U837, FRANCE

Received: January 30, 2015; Accepted: June 3, 2015; Published: July 6, 2015

Copyright: © 2015 Valcárcel-Ocete et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by Basque Government grants (PE08UN78,09+UEGV096/C01 and IT634-13) received by AA and by University of the Basque Country (UPV/EHU) grant (UFI 11/20) received by AMZ. The European Huntington’s Disease Network is funded by CHDI Foundation, Inc. (http://chdifoundation.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Huntington’s disease (HD) (OMIM 143100) is a neurodegenerative disorder characterized by movement abnormalities (chorea, hypokinesia), cognitive decline and psychiatric disturbances, which most often become noticeable between the ages of 35 and 50 [1]. HD is caused by an expanded CAG trinucleotide repeat (>39 CAGs, CAGexp) in exon 1 of the HTT gene. Expanded alleles result in an elongated polyglutamine tract in the Huntingtin protein (HTT), which leads to defects in intracellular trafficking and signalling pathways, as well as in nervous system development during embryogenesis [2]. Assessment of Huntington disease age of onset (AO), that is, the point in time when a carrier of the expanded allele develops unequivocal HD signs [3], remains to be clearly defined at the phenotypic level, as different criteria are being used to estimate AO. Involuntary movements, such as chorea, are the most distinctive HD symptom that can be established with reliability, and their debut commonly defines HD age of onset [4]. However, motor symptoms are often preceded by cognitive and/or psychiatric decline [5]. Consequently, some authors have defined AO as the age at which the first possible symptom is detected [6–8].

Multiple studies have shown an inverse correlation between HD age of onset and CAGexp. However, this correlation only accounts for a fraction of the total AO variability, which ranges between 42% and 73% [6–10]. This range may be attributed, among others, to the characteristics of the studied population, the HD phenotypes considered (i.e. inclusion or not of juvenile HD cases) and/or the criteria used to define the age of onset [11,12].

AO variability not explained by CAGexp shows strong heritability (40% to 56%) [12,13], suggesting the contribution of additional genetic factors in determining AO in HD. Indeed, genetic variation influencing AO has been reported previously. Association signals have been replicated for some of those genes (e.g. GRIK2 [10,14,15], GRIN2A and GRIN2B [16–18], TCERG1 [10,19], HAP1 [20,21], ADORA2A [8,21], PPARGC1A [22,23], ATG7 [24,25]), but not for others (e.g. TP53, UCHL1, DFFB, APOE and MTHFR [26], GSTO1 [27], ASK1 or MAP3K5 and MAP2K6 [28]).

Most polymorphisms exhibiting association signals with HD AO are located in non-coding regions. They may affect mRNA splicing or transcriptional regulation [18], for example, or they may represent markers in linkage disequilibrium (LD) with AO modifiers. However, none of the associated SNPs has been functionally validated to date, and little is known about the mechanisms by which these polymorphisms may affect AO in HD.

The aim of this study is to improve our understanding of the contribution of AO modifiers to HD pathogenesis. To this end, we have studied the effect of genetic variation within 20 candidate genes on HD age of onset. We replicate some of the previously reported association signals. Moreover, we identify genetic variation in the E2F2 promoter region that associates with HD AO and E2F2 gene expression, suggesting a potential molecular explanation for this association.

Material and Methods

Patients and phenotype data

The European Huntington’s Disease Network (EHDN) provided DNA samples and clinical data from 284 individuals forming part of the REGISTRY project. The clinical data supplied included age (from 18 to 82 years old), sex (146 men and 138 women), self-assigned ethnicity (99.3% had European origin), mutated CAG repeat number (CAGexp), symptoms and AO information (S1 Table). Additionally, the EHDN obtained a written informed consent, in compliance with the Declaration of Helsinki, Internal Conference of Harmonisation-Good Clinical Practice (ICH-GCP), and local regulations, from each participant. AO was estimated by the raters, following the Unified Huntington's Disease Rating Scale (UHDRS’99) and the Hamilton Depression Rating Scale (HDRS). Two definitions of AO provided by EHDN were used: the earliest AO (eAO), i.e., the age of the patient at which the first unequivocal signs of HD (motor, cognitive or psychiatric) appeared, and the motor AO (mAO) i.e., the age at which the first motor symptoms appeared. The eAO data was available for all individuals in the sample; the mAO data was available for 196 individuals.

Only European origin individuals with adult-onset HD (> 20 years old) were considered in the study, and single outliers with CAGexp repeat number outside the 40–53 range were excluded [29,30]. Individuals with psychiatric symptoms as their first HD manifestation and with family history of mental disease were excluded to avoid possible effects of hereditary psychiatric disorders. The final number of selected patients in the eAO and mAO groups was 255 and 180, respectively. The analyzed individuals were from Germany (N = 62), Italy (N = 40), United Kingdom (N = 36), Poland (N = 31), The Netherlands (N = 25), Spain (N = 17), Denmark (N = 11), Norway (N = 8), Austria (N = 7), Portugal (N = 6), Finland (N = 5), Belgium (N = 4), Czech Republic (N = 2) and Sweden (N = 1).

For gene expression analyses, blood samples from 35 European origin individuals belonging to 27 HD families from hospitals of the Basque Country were collected after clinical and molecular HD diagnosis. Most of patient’s ancestors come from Spanish regions other than the Basque Country, and two have Basque ancestry (their four grandparents were Basque). This sample was composed of 20 men and 15 women with ages ranging between 28 and 83 years old. Details of each sample are shown in S2 Table. Written informed consent was obtained from all patients and the study was approved by the Ethics Committee for Clinical Research of Euskadi and by the Ethics Committee for Research and Teaching of the University of the Basque Country (UPV/EHU).

Genes, SNPs and Genotypes

Twenty candidate genes were targeted. Seventeen genes were selected based on the following three criteria: (a) reported association with HD AO in previous studies (BDNF [31], DFFB [7], GRIK2 [10,14,15], GRIN2A and GRIN2B [16–18], HAP1 [20,21], PPARGC1 [22,23] and TCERG1 [10,19]); (b) participation of the gene product in a pathway or process altered in HD (CASP6 [32,33], CASP8 [34], E2F1 [35] and E2F2 [36]); (c) direct interaction of the gene product with HTT (CDK5 [37,38], HIP1 [39], SGK1 [40,41], SIRT1 [42] and SP1 [43]). Functionally, these genes are involved in processes such as apoptosis (DFFB [44,45], CASP8 [34,46], CASP6 [32,33] and HIP1 [46,47]), neuronal survival (BDNF [48,49], SGK1 [40,41] and CDK5 [37,38]), glutamatergic synapse/transmission (GRIK2, GRIN2A and GRIN2B [50,51]), transcriptional control/cell proliferation (E2F1 [35], E2F2 [36], SP1 [52], PPARGC1 [53], TCERG1 [19] and SIRT1 [54,55]) or intracellular trafficking (HAP1 [56]). The remaining three genes were selected on the basis of their physical proximity to suggestive signals observed in our preliminary studies: CCL26 (mapping close to HIP1), LINC01559 and ATF7IP (both mapping close to GRIN2B).

Using HapMap CEU population data, a total of 117 SNPs not in LD (D’< 0.7) were selected in the 20 genes, all with intermediate allele frequencies and located within exonic, intronic or regulatory regions to ensure allele detection and to aim a comprehensive coverage of the majority of common variation in each gene. LD information used in the SNP selection process was obtained from the Centre for Genomics and Global Health (http://www.gmap.net/marker/). Some of the selected SNPs had been studied in previous association analyses between genetic polymorphisms and AO in HD. Details about each genotyped SNP are shown in S3 Table.

SNPs were genotyped using SNPlex genotyping system (Life Technologies) and these genotypes are shown in S4 Table. Two individuals were excluded from the analyses due to call-rates < 50%. Twenty-seven SNP genotypes failed or showed < 90% call-rate, and two SNPs were not in Hardy-Weinberg equilibrium, and were therefore excluded (S3 Table). Altogether, a total of 88 SNP genotype data were used for genetic association analyses on 253 individuals with eAO data and on 178 individuals with mAO data.

Association analysis

T–test and Mann-Whitney U test (SPSS Ver.17.0, SPSS Inc) were used to compare the CAGexp and AO mean and median, respectively, of Southern European (Portugal, Spain and Italy) and the rest of the populations (called Northern Europeans).

Population stratification was examined with F-statistics according to the unbiased fixation index (FST) proposed by Weir and Cockerham (1984) [57] using the FSTATv2.9.3 software [58]. The standard deviations of F-statistics and the confidence intervals were calculated with bootstrapping (100,000 permutations) over loci.

The correlation between CAGexp and the logarithm (log) of eAO or mAO, and the contribution of CAGexp to the variability of each AO were estimated by correlation analysis and simple linear regression analysis (SPSS), respectively. The association between log AO and each SNP was estimated by multiple linear regression analysis (SPSS) according to dominant, recessive and additive model based on the minor allele, and corrected using Bonferroni procedure. In all association analyses, log AO was used as dependent variable and CAGexp as independent variable. LD information from the 253 patients was obtained using Haploview v4.0 [59].

Reverse transcription quantitative PCR (RT-qPCR) analysis

Total RNA was obtained from peripheral blood mononuclear cells of patients from 5 hospitals of the Basque Country using TRIzol reagent (Life Technologies), following the manufacturer’s instructions. The RNA was treated with DNase, purified using RNeasy kit (Qiagen), quantified with NanoDrop ND-1000 and examined for RNA integrity (RIN>7) with 2100 Bioanalyzer (Agilent Technologies).

E2F2 gene expression was analyzed with both SYBR Green-based and Taqman-based assays (Life Technologies). The cDNA was synthesized using 2 μg (for SYBR Green-based assays) or 500ng (for Taqman-based assays) of total RNA using High-capacity cDNA Reverse transcription kit (Life Technologies). For SYBR Green-based assays, forward (5´ACG TGC TGG AAG GCA TCC 3´) and reverse (5´GCT CCG TGT TCA TCA GCT CC 3´) primers, located in exons 3 and 4 of E2F2, respectively, were used. For Taqman-based assays, Hs00918089_m1 probe, which hybridizes with the 3–4 exon boundary of the E2F2 gene, was used. The reference genes for normalization were selected according to their reported stability in leukocytes [60]. For SYBR Green assays, GAPDH, HPRT1, UBC and YWHAZ genes were tested (with Hs00266705_g1, Hs99999909_m1, Hs00824723_m1 and Hs01122447_g1 Taqman probes, respectively); for Taqman assays B2M, RPLP0, UBC and YWHAZ genes were tested (with Hs00984230_m1, Hs0299885_s1, Hs 01871556_s1 and Hs03044281_g1 Taqman probes, respectively). PCR reactions were run in triplicate, using 25ng/μl of cDNA and 900 nM of primers (in SYBR Green-based analyses) and 9 ng/μl of cDNA and 250nM of Taqman probes (in Taqman-based analyses), in 20 μl of final volume. Reactions were carried out on an ABI Prism 7900HT Fast Real-Time PCR System Unit (Life Technologies) with standard cycling conditions. Serial cDNA dilutions were performed to calculate standard curves in order to determine the PCR efficiency for each gene. Results were analyzed with the Sequence Detection System (SDS) Software v2.4 (Life Technologies) to obtain the Cq (quantification cycle) values for each sample. Samples with >0.5 standard deviation were excluded.

The geNorm algorithm included in DataAssist v2.0 software (http://www.lifetechnologies.com/us/en/home/technical-resources/software-downloads/dataassist-software.html) was used to estimate the stability of genes for normalization. cDNA quantity was normalized relative to UBC and YWHAZ reference genes in SYBR Green assays and to B2M and YWHAZ reference genes in Taqman assays. The comparisons in gene expression levels between groups were carried out with Relative Expression Software (REST) [61] for SYBR Green data analyses and with DataAssist software for Taqman data analyses.

DNA samples from the Basque Country were genotyped for rs2742976 E2F2 with the same methodology as for the samples collected from REGISTRY. E2F2 mRNA levels were compared among the different rs2742976 E2F2 genotypes.

Results

Absence of geographical stratification in the analyzed patient sample

The eAO of the EHDN sample analyzed in this work ranged from 21 to 73 years. Similarly, the mAO ranged from 21 to 70 years. Mean and median CAGexp values were very similar in both eAO and mAO (mean values of 44.11±2.91 and 44.30±3.11, respectively, and a median value of 44 CAGs in both AOs).

Given the diverse origin of the HD patients tested in this study, several statistical analyses were applied to examine possible genetic divergences between Southern European and Northern European individuals, which could lead to false positive associations [30]. No significant differences were detected when the mean and the median of CAGexp, eAO or mAO were compared (Tables 1 and 2). Similarly, the eAO and mAO distributions across CAGexp alleles did not show different AO patterns between Southern and Northern European populations (P = 0.958 (eAO) and P = 0.945 (mAO) in Kolmogorov-Smirnov test) (S1 and S2 Figs, respectively). Finally, stratification analysis using the SNP genotype information for all analyzed loci reflected low and no significant level of genetic differentiation between the two groups (FST index = 0.001±0.001; P = 0.5). Therefore, subsequent analyses were carried out without correcting for the geographical origin of the patients.

thumbnail
Download:
Table 1. CAGexp and eAO comparisons between samples from Southern and Northern European populations.

https://doi.org/10.1371/journal.pone.0131573.t001

thumbnail
Download:
Table 2. CAGexp and mAO comparisons between samples from Southern and Northern European populations.

https://doi.org/10.1371/journal.pone.0131573.t002

Correlations between HD age of onset and genetic variation in candidate genes

Consistent with previous studies, CAGexp was significantly and negatively correlated with eAO (P<0.0001, R = -0.758) and mAO (P<0.0001, R = -0.824). In our sample, CAGexp accounted for 57.3% of eAO variability and 67.6% of mAO variability (Tables 3 and 4).

thumbnail
Download:
Table 3. Multiple linear regression analysis between SNP genotypes and eAO.

https://doi.org/10.1371/journal.pone.0131573.t003

thumbnail
Download:
Table 4. Multiple linear regression analysis between SNP genotypes and mAO.

https://doi.org/10.1371/journal.pone.0131573.t004

The association analysis carried out using genotype data of 88 single nucleotide polymorphisms, CAGexp and HD age of onset, revealed eight association signals (uncorrected P-value <0.05) with eAO (Table 3) and seven with mAO (Table 4). Four of the association signals overlapped in eAO and mAO: rs2742976 in the E2F2 gene, rs10845757 in the LINC01559 gene, rs10744030 in the GRIN2B gene, and rs8049651 in the GRIN2A gene. In contrast, SNPs located in the ATF71P gene (rs11055896 and rs3213764), in the region between ATF7IP and GRIN2B gene promoter regions (rs7966469) and in LINC01559 (rs12423809), only exhibited suggestive association with eAO, whereas SNPs mapping the GRIK2 (rs2782901), the HIP1 (rs2240133) and the GRIN2B (rs4764011) genes showed significant association only with mAO. With the exception of rs3213764 within ATF7IP, which is an exonic missense SNP, and rs8049651 within GRIN2A, which is a synonymous SNP, all other AO-associated SNPs in our study lie within non-coding regions of the genome.

Of note, six SNPs that correlated with eAO and three that correlated with mAO reside within ATF7IP, GRIN2B and LINC01559 genes, all of which are located in the same genomic region (12p13.1), but not in LD (with the exception of rs12423809 and rs10845757, both in LINC01559 gene, with a D’ value of 0.93 in our sample).

Remarkably, the observed E2F2 association signal was significant after multiple test correction (Bonferroni corrected P-value < 0.05) and explained as much as 5.1% of the eAO variability not explained by CAGexp. More specifically, the T allele of the rs2742976 SNP of the E2F2 gene significantly associated with a 3 year AO delay.

E2F2 gene expression analysis relative to the rs2742976 genotype in HD patients

Interestingly, rs2742976 is located within a putative STATx transcription factor-binding site [62] in the promoter region (-289 G>T) of the E2F2 gene, suggesting that this polymorphism may affect E2F2 gene expression. Therefore, we tested if there was a correlation between the rs2742976 genotype and E2F2 gene expression in an independent HD patient sample (N = 35) collected in the Basque Country. From each individual, DNA and RNA samples were extracted from peripheral blood mononuclear cells. DNA samples were used to establish the E2F2 rs2742976 genotype, and total RNA to quantify the relative E2F2 mRNA expression. To ensure the robustness of the results, two methods were used (Taqman-based and SYBR Green-based assays) to measure the steady-state E2F2 mRNA expression in HD individuals.

Interestingly, a significant correlation between E2F2 rs2742976 genotype and E2F2 gene expression was observed (Fig 1). Specifically, individuals with TT genotype showed significantly lower E2F2 mRNA expression relative to individuals with GG genotype (P = 0.020 and P = 0.046 in Taqman-based assay and SYBR Green-based assays, respectively). In addition, a significantly lower E2F2 expression was detected in samples with GT genotype relative to samples with GG genotype in SYBR Green-based assays (P = 0.044). Altogether, these results suggest that the presence of the T allele in the E2F2 rs2742976 promoter SNP may account for a lower E2F2 gene expression.

thumbnail
Download:
Fig 1. RT-qPCR analysis of E2F2 gene expression in HD patients, according to E2F2 rs2742976 genotype.

Two methods were used. In Taqman assay, the expression of E2F2 gene was analyzed in 31 samples (N TT = 4; N GT = 12, N GG = 15) with Hs00918089_m1 Taqman probe; the expression values were normalized respect to expression of B2M and YWHAZ reference genes. In SYBR Green assay, the E2F2 gene expression was estimated in 31 samples (NTT = 5; NGT = 14, NGG = 12); the expression values were normalized to expression of UBC and YWHAZ reference genes. Results are expressed as fold over respective GG individuals. Asterisk denotes statistically significant differences (P<0.05) between GG and any other group, according to DataAssist software analysis (T-test) or REST software analysis (Pair Wise Reallocation Randomization test).

https://doi.org/10.1371/journal.pone.0131573.g001

Discussion

Age of onset in HD is inversely correlated with the CAG repeat length in the mutated HTT allele. However, other genetic factors are thought to play a role in this complex character. In an attempt to extract as much information as possible from our sample, we have carried out association analyses considering two AO definitions, mAO and eAO. Testing eAO has the advantage of encompassing all the HD phenotypes (motor, cognitive and psychiatric), which involves all the patients available in the sample; analyzing mAO, although it results in a smaller sample size, is considered more reproducible due to its specific nature and reliability of this criterion for determining AO [4,63].

In our study, the detected mAO variability explained by CAGexp (67.6%) lies within the range of previously observed values for motor onset age in Europeans (31–73%) [9,16], and is comparable to that reported by Ramos and collaborators (65.3%), who considered the same CAG repeat range as in our study [30]. Regarding eAO, to our knowledge no other reports have examined eAO variability within the CAG range considered in our work, and no direct comparisons can be made. However, the eAO variability explained by CAGexp obtained by us (57.3%) is well within the range of 49% to 73% detected in other general studies [6,64].

Among the detected AO-associated signals, the association between the E2F2 gene and the age of onset is particularly compelling (Bonferroni corrected P-value <0.05). In our study, individuals with the E2F2 rs2742976 T allele showed on average a 3-year delay in eAO. The relationship between E2F2 gene and HD has not been previously assessed. E2F2 encodes a transcription factor that regulates the cell cycle, and is known to play a critical role in lymphocyte quiescence [65] and in neuronal terminal differentiation [36], through the regulation of target gene expression. Interestingly, SNP rs2742976 is located within a putative STATx transcription factor-binding site in the E2F2 promoter region [62], and allele differences in this SNP could potentially modulate the expression of the E2F2 gene itself. Indeed, HD patients with E2F2 rs2742976 T allele showed significantly lower E2F2 mRNA expression levels in lymphocytes (P-value <0.05). This change in expression level seems to be dependent on T allele dose, given that the heterozygote (GT) shows intermediate expression levels relative to the two homozygous genotypes. Thus, the detected association between the T allele in E2F2 rs2742976 and both a delay in eAO and lower E2F2 gene expression level in lymphocytes hints to a potential involvement of E2F2 in the pathogenesis of HD, a possibility that warrants further study. Moreover, although HD pathology is thought to involve mainly brain-associated defects, our observations support the view that studies on gene expression profiling in blood cells and other peripheral tissues could help identify biomarkers for HD disease progression [66–68] and provide clues to HD pathology.

None of the other suggestive eAO-association signals were significant after multiple test correction, although some of them may be worth examining further. Genetic variants within the GRIN2A and GRIN2B genes encoding the NR2A and NR2B subunits of the N-methyl-D-Aspartic acid (NMDA) receptors [51], have been associated previously with HD age of onset [16–18], although the molecular mechanism involved in these associations remains to be elucidated [18]. This is the case of the polymorphisms rs2650427 [18] and rs1969060 [16,18,69] within the GRIN2A gene, and the polymorphisms rs1806201 [16,18] and rs890 [16] within the GRIN2B gene. Two of those SNPs (GRIN2A rs1969060 and GRIN2B rs1806201) did not associate with AO in our study, but other genetic variants within the GRIN2A and the GRIN2B genes showed uncorrected P-values < 0.05: rs8049651 in GRIN2A, and rs10744030, rs4764011 and rs7966469 in GRIN2B. Of these, only rs4764011 had been previously analyzed, although no association to mAO had been detected [17]. Intriguingly, four additional suggestive signals that were not in LD were detected in the vicinity of the GRIN2B gene, encompassing the genes ATF7IP and LINC01559. These three genes extend over 2 Mb in 12p13.1, a genomic region previously related with intellectual disability [70]. Most associated SNPs mapping GRIN2A and the 12p13.1 region are located in non-coding regions, which raises the possibility that they may affect chromatin organization [71].

The detected mAO-association signals were not significant after Bonferroni correction (probably due to the smaller size of this sample). However, suggestive signals specific for the mAO analysis may be worth following in a bigger sample. In this regard, a non-coding polymorphism mapping the HIP1 gene (rs2240133) was found associated specifically with mAO. HIP1 encodes an HTT-interacting protein known to be involved in apoptosis [72,73], and mutant HIP1 expression produces HTT aggregation and subsequent cell death [74]. The relationship between HIP1 polymorphisms and HD AO has not been reported before. Similarly, genetic variation within the GRIK2 gene (rs2782901) suggestively associated with mAO but no eAO. GRIK2 encodes the GluR6 subunit of the kainate glutamate receptors, which are involved in synaptic plasticity [75]. Mutations in the GRIK2 gene have been repeatedly associated with HD AO [10,14,15].

In summary, our work, which should be considered a hypothesis-generating study, has explored the contribution of genetic variation in several candidate genes as eAO and mAO modifiers. The observed statistically significant and suggestive association signals should be followed-up to improve our knowledge of HD pathogenesis. Importantly, the highly significant E2F2 signal should be further studied given that the presence of the T allele at E2F2 rs2742976 associates with HD age of onset and with E2F2 gene expression levels. We speculate that lower levels of E2F2 gene expression in HD symptomatic patients could be associated with a delay in AO.

Supporting Information

S1 Appendix. Contributor information.

Members of the European Huntington’s Disease Network (EHDN) that have collaborated in collecting biological samples and clinical data.

https://doi.org/10.1371/journal.pone.0131573.s001

(PDF)

S1 Fig. Box plot representing the variance of eAO per CAGexp in samples from Southern and Northern European populations.

No differences were observed in eAO distribution across CAGexp alleles between both groups.

https://doi.org/10.1371/journal.pone.0131573.s002

(TIF)

S2 Fig. Box plot representing the variance of mAO per CAGexp in samples from Southern and Northern European populations.

No differences were observed in mAO distribution across CAGexp alleles between both groups.

https://doi.org/10.1371/journal.pone.0131573.s003

(TIF)

S1 Table. EHDN’s dataset general information.

Age: patient’s age at the time of blood collection; Sex: M (male) and F (female); Ethnicity: 1 = Caucasian; 2 = American-Black; 3 = Asian-West. Country: country of origin. CAGexp: CAG number repeat in expanded allele; eAO: earliest onset, age of the earliest unequivocal symptoms of Huntington's onset; mAO: motor onset, age of the first motor symptoms §: samples excluded from the analysis; * samples with eAO information excluded from the analysis.-: No data.

https://doi.org/10.1371/journal.pone.0131573.s004

(PDF)

S2 Table. General information and expression analysis (RT-qPCR analysis) of the HD patients from Basque Hospitals.

Age: patient’s age at the time of blood collection; Sex: M (male) and F (female); CAGexp: CAG number repeat in expanded allele; Genotypes in E2F2 rs2742976 and Cq values in RT-qPCR analysis for the E2F2 and reference genes analyzed using SYBR Green and Taqman based assays are showed. NA: Not Analyzed.

https://doi.org/10.1371/journal.pone.0131573.s005

(PDF)

S3 Table. SNPs genotyped in this study.

For each SNP, the genomic region (cytoband) and the closest gene is defined as well as the SNP type, chromosome location (according to genome assembly GRCh38), minor and major allele, HapMap CEU minor allele frequency (MAF), genotyping quality control (QC) notes and references if they have been previously studied in HD. * Indicates tag-SNPs. HWE: Hardy-Weinberg Equilibrium.

https://doi.org/10.1371/journal.pone.0131573.s006

(PDF)

S4 Table. Genotypes in the SNPs analyzed in each DNA sample from EHDN’s collection.

https://doi.org/10.1371/journal.pone.0131573.s007

(PDF)

Acknowledgments

Technical and human support provided by General Research Services SGIker [University of the Basque Country (UPV/EHU), Ministry of Economy and Competitiveness (MINECO), Basque Government (GV/EJ), European Regional Development Fund (ERDF), and European Social Fund (ESF)] is gratefully acknowledged. We also wish to acknowledge the time and effort of all EHDN participants in this study. EHDN is supported and funded by the CHDI Foundation Inc. Special thanks to Asunción Martínez and Michael Orth for their support concerning EHDN issues and to Bernhard Landwehrmeyer for his relevant suggestions.

Author Contributions

Conceived and designed the experiments: LVO GAA AA. Performed the experiments: LVO. Analyzed the data: LVO GAA AA. Contributed reagents/materials/analysis tools: GAA MGB JMFG ELG JMLD JRO AAA JMPR RACR JEN CS. Wrote the paper: LVO GAA AMZ AA. Participated in design of the statistical analysis: MI. Participated in design of the statistical and expression analysis: AF.

References

  1. 1. Roos RA. Huntington's disease: a clinical review. Orphanet J Rare Dis. 2010;5: 40. pmid:21171977
  2. 2. Ross CA, Tabrizi SJ. Huntington's disease: from molecular pathogenesis to clinical treatment. Lancet Neurol. 2011;10: 83–98. pmid:21163446
  3. 3. Orth M, Schwenke C. Age-at-onset in Huntington disease. PLOS Currents Huntington Disease. 2011;3: RRN1258.
  4. 4. Tabrizi SJ, Langbehn DR, Leavitt BR, Roos RA, Durr A, Craufurd D, et al. Biological and clinical manifestations of Huntington's disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol. 2009;8: 791–801. pmid:19646924
  5. 5. Paulsen JS, Langbehn DR, Stout JC, Aylward E, Ross CA, Nance M, et al. Detection of Huntington's disease decades before diagnosis: the Predict-HD study. Journal of Neurology Neurosurgery and Psychiatry. 2008;79: 874–880.
  6. 6. Brinkman RR, Mezei MM, Theilmann J, Almqvist E, Hayden MR. The likelihood of being affected with Huntington disease by a particular age, for a specific CAG size. American Journal of Human Genetics. 1997;60: 1202–1210. pmid:9150168
  7. 7. Chattopadhyay B, Baksi K, Mukhopadhyay S, Bhattacharyya NP. Modulation of age at onset of Huntington disease patients by variations in TP53 and human caspase activated DNase (hCAD) genes. Neurosci Lett. 2005;374: 81–86. pmid:15644269
  8. 8. Dhaenens CM, Burnouf S, Simonin C, Van BE, Duhamel A, Defebvre L, et al. A genetic variation in the ADORA2A gene modifies age at onset in Huntington's disease. Neurobiol Dis. 2009;35: 474–476. pmid:19591938
  9. 9. Taherzadeh-Fard E, Saft C, Andrich J, Wieczorek S, Arning L. PGC-1alpha as modifier of onset age in Huntington disease. Mol Neurodegener. 2009;4: 10. pmid:19200361
  10. 10. Chattopadhyay B, Ghosh S, Gangopadhyay PK, Das SK, Roy T, Sinha KK, et al. Modulation of age at onset in Huntington's disease and spinocerebellar ataxia type 2 patients originated from eastern India. Neurosci Lett. 2003;345: 93–96. pmid:12821179
  11. 11. Andresen JM, Gayán J, Djoussé L, Roberts S, Brocklebank D, Cherny SS, et al. The relationship between CAG repeat length and age of onset differs for Huntington's disease patients with juvenile onset or adult onset. Ann Hum Genet. 2007;71: 295–301. pmid:17181545
  12. 12. Wexler NS, Lorimer J, Porter J, Gomez F, Moskowitz C, Shackell E, et al. Venezuelan kindreds reveal that genetic and environmental factors modulate Huntington's disease age of onset. Proc Natl Acad Sci USA. 2004;101: 3498–3503. pmid:14993615
  13. 13. Djoussé L, Knowlton B, Hayden M, Almqvist EW, Brinkman R, Ross C, et al. Interaction of normal and expanded CAG repeat sizes influences age at onset of Huntington disease. Am J Med Genet A. 2003; 119A: 279–282. pmid:12784292
  14. 14. Rubinsztein DC, Leggo J, Chiano M, Dodge A, Norbury G, Rosser E, et al. Genotypes at the GluR6 kainate receptor locus are associated with variation in the age of onset of Huntington disease. Proc Natl Acad Sci USA. 1997;94: 3872–3876. pmid:9108071
  15. 15. Zeng W, Gillis T, Hakky M, Djousse L, Myers RH, MacDonald ME, et al. Genetic analysis of the GRIK2 modifier effect in Huntington's disease. BMC Neurosci. 2006;7: 62. pmid:16959037
  16. 16. Arning L, Kraus PH, Valentin S, Saft C, Andrich J, Epplen JT. NR2A and NR2B receptor gene variations modify age at onset in Huntington disease. Neurogenetics. 2005;6: 25–28. pmid:15742215
  17. 17. Arning L, Saft C, Wieczorek S, Andrich J, Kraus PH, Epplen JT. NR2A and NR2B receptor gene variations modify age at onset in Huntington disease in a sex-specific manner. Hum Genet. 2007;122: 175–182. pmid:17569088
  18. 18. Saft C, Epplen JT, Wieczorek S, Landwehrmeyer GB, Roos RA, de Yebenes JG, et al. NMDA receptor gene variations as modifiers in Huntington disease: a replication study. PLoS Curr. 2011;3: RRN1247.
  19. 19. Holbert S, Denghien I, Kiechle T, Rosenblatt A, Wellington C, Hayden MR, et al. The Gln-Ala repeat transcriptional activator CA150 interacts with huntingtin: Neuropathologic and genetic evidence for a role in Huntington's disease pathogenesis. Proc Natl Acad Sci USA. 2001; 98: 1811–1816. pmid:11172033
  20. 20. Metzger S, Rong J, Nguyen HP, Cape A, Tomiuk J, Soehn AS, et al. Huntingtin-associated protein-1 is a modifier of the age-at-onset of Huntington's disease. Hum Mol Genet. 2008;17: 1137–1146. pmid:18192679
  21. 21. Taherzadeh-Fard E, Saft C, Wieczorek S, Epplen JT, Arning L. Age at onset in Huntington's disease: replication study on the associations of ADORA2A, HAP1 and OGG1. Neurogenetics. 2010;11: 435–439. pmid:20512606
  22. 22. Weydt P, Soyal SM, Gellera C, Didonato S, Weidinger C, Oberkofler H, et al. The gene coding for PGC-1alpha modifies age at onset in Huntington's Disease. Mol Neurodegener. 2009;4: 3. pmid:19133136
  23. 23. Che HV, Metzger S, Portal E, Deyle C, Riess O, Nguyen HP. Localization of sequence variations in PGC-1alpha influence their modifying effect in Huntington disease. Mol Neurodegener. 2011;6: 1. pmid:21211002
  24. 24. Metzger S, Saukko M, Van Che H, Tong L, Puder Y, Riess O, et al. Age at onset in Huntington's disease is modified by the autophagy pathway: implication of the V471A polymorphism in Atg7. Hum Genet. 2010;128: 453–459. pmid:20697744
  25. 25. Metzger S, Walter C, Riess O, Roos RA, Nielsen JE, Craufurd D, et al. The V471A polymorphism in autophagy-related gene ATG7 modifies age at onset specifically in Italian Huntington disease patients. PLoS One. 2013; 8: e68951. pmid:23894380
  26. 26. Gusella JF & MacDonald ME. Huntington's disease: the case for genetic modifiers. Genome Med. 2009;8: 80.
  27. 27. Arning L, Jagiello P, Wieczorek S, Saft C, Andrich J and Epplen JT. Glutathione S-Transferase Omega 1 variation does not influence age at onset of Huntington’s disease. BMC Med Genet. 2004;5: 7. pmid:15040808
  28. 28. Arning L, Monté D, Hansen W, Wieczorek S, Jagiello P, Akkad DA, et al. ASK1 and MAP2K6 as modifiers of age at onset in Huntington's disease. J Mol Med. 2008;86: 485–490. pmid:18327563
  29. 29. Lee JM, Ramos EM, Lee JH, Gillis T, Mysore JS, Hayden MR, et al. CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashion. Neurology. 2012;78: 690–695. pmid:22323755
  30. 30. Ramos EM, Latourelle JC, Lee JH, Gillis T, Mysore JS, Squitieri F, et al. Population stratification may bias analysis of PGC-1α as a modifier of age at Huntington disease motor onset. Hum. Genet. 2012;131: 1833–40. pmid:22825315
  31. 31. Alberch J, Lopez M, Badenas C, Carrasco JL, Mila M, Munoz E, et al. Association between BDNF Val66Met polymorphism and age at onset in Huntington disease. Neurology. 2005; 65: 964–965. pmid:16186551
  32. 32. Graham RK, Deng Y, Slow EJ, Haigh B, Bissada N, Lu G, et al. Cleavage at the caspase-6 site is required for neuronal dysfunction and degeneration due to mutant huntingtin. Cell. 2006;125: 1179–1191. pmid:16777606
  33. 33. Graham RK, Deng Y, Carroll J, Vaid K, Cowan C, Pouladi MA, et al. Cleavage at the 586 amino acid caspase-6 site in mutant huntingtin influences caspase-6 activation in vivo. J Neurosci. 2010;30;15019–15029. pmid:21068307
  34. 34. U M, Miyashita T, Ohtsuka Y, Okamura-Oho Y, Shikama Y, Yamada M. Extended polyglutamine selectively interacts with caspase-8 and -10 in nuclear aggregates. Cell Death Differ.2001;8:377–86. pmid:11550089
  35. 35. Polager S, Ofir M, Ginsberg D. E2F1 regulates autophagy and the transcription of autophagy genes. Oncogene. 2008;27:4860–4864. pmid:18408756
  36. 36. Persengiev SP, Li J, Poulin ML, Kilpatrick DL. E2F2 converts reversibly differentiated PC12 cells to an irreversible, neurotrophin-dependent state. Oncogene. 2001;20: 5124–5131. pmid:11526501
  37. 37. Luo S, Vacher C, Davie, JE, Rubinsztein DC. Cdk5 phosphorylation of huntingtin reduces its cleavage by caspases: implications for mutant huntingtin toxicity. J Cell Biol.2005;169: 647–656. pmid:15911879
  38. 38. Anne SL, Saudou F, Humbert S. Phosphorylation of huntingtin by cyclin-dependent kinase 5 is induced by DNA damage and regulates wild-type and mutant huntingtin toxicity in neurons. J Neurosci.2007;2: 7318–7328.
  39. 39. Kaltenbach LS, Romero E, Becklin RR, et al. Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet.2007; 3: E82. pmid:17500595
  40. 40. Rangone H, Poizat G, Troncoso J, Ross CA, MacDonald ME, Saudou F, et al. The serum- and glucocorticoid-induced kinase SGK inhibits mutant huntingtin-induced toxicity by phosphorylating serine 421 of huntingtin. Eur J Neurosci.2004;19: 273–9. pmid:14725621
  41. 41. Warby SC, Montpetit A, Hayden AR, Carroll JB, Butland SL, Visscher H, et al. CAG expansion in the Huntington disease gene is associated with a specific and targetable predisposing haplogroup. Am J Hum Genet.2009;84:351–66. pmid:19249009
  42. 42. Cohen DE, Cui L, Supinski A, Savas JN, Mazzulli JR, Yates JR, et al. Sirt1 mediates neuroprotection from mutant huntingtin by activation of the TORC1 and CREB transcriptional pathway. Nat Med.2011;18:159–65 pmid:22179316
  43. 43. Dunah AW, Jeong H, Griffin A, Kim YM, Standaert DG, Hersch SM, et al. Sp1 and TAFII130 transcriptional activity disrupted in early Huntington's disease. Science.2002;296: 2238–2243. pmid:11988536
  44. 44. Enari M, Sakahira H, Yokoyama H, Okawa K, Iwamatsu A, Nagata S. A caspase-activated DNase that degrades DNA during apoptosis, and its inhibitor ICAD. Nature.1998;391:43–50. pmid:9422506
  45. 45. Liu X, Li P, Widlak P, Zou H, Lou X, Garrard WT, Wang X. The 40-kDa subunit of DNA fragmentation factor induces DNA fragmentation and chromatin condensation during apoptosis. Proc Natl Acad Sci U S A.1998; 95:8461–6. pmid:9671700
  46. 46. Gervais FG, Singaraja R, Xanthoudakis S, Gutekunst CA, Leavitt BR, Metzler M, et al. Recruitment and activation of caspase-8 by the Huntingtin-interacting protein Hip-1 and a novel partner Hippi. Nat Cell Biol.2002;4:95–105. pmid:11788820
  47. 47. Metzler M, Gan L, Wong TP, Liu L, Helm J, Liu L, et al. NMDA receptor function and NMDA receptor-dependent phosphorylation of huntingtin is altered by the endocytic protein HIP1. J Neurosci.2007;27: 2298–308. pmid:17329427
  48. 48. Zuccato C, Ciammola A, Rigamonti D, Leavitt BR, Goffredo D, Conti L, et al. Loss of huntingtin-mediated BDNF gene transcription in Huntington's disease. Science.2001;293: 493–498. pmid:11408619
  49. 49. Gauthier LR, Charrin BC, Borrell-Pages M, Dompierre JP, Rangone H, Cordelières FP, et al. Huntingtin controls neurotrophic support and survival of neurons by enhancing BDNF vesicular transport along microtubules. Cell.2004;118:127–38. pmid:15242649
  50. 50. Matute C. Therapeutic potential of kainate receptors. CNS Neurosci Ther. 2011;17: 661–9. pmid:21129167
  51. 51. Fan MYM and Raymond LA. N-Methyl-D-aspartate (NMDA) receptor function and excitotoxicity in Huntington’s disease. Progress in neurology. 2007;81: 272–293.
  52. 52. Chen-Plotkin AS, Sadri-Vakili G, Yohrling GJ, Braveman MW, Benn CL, Glajch KE, et al. Decreased association of the transcription factor Sp1 with genes downregulated in Huntington’s disease. Neurobiol Dis. 2006; 22:233–41. pmid:16442295
  53. 53. McGill JK & Beal MF. PGC-1alpha, a new therapeutic target in Huntington’s disease? Cell.2006;127:465–8. pmid:17081970
  54. 54. Jeong H, Then F, Melia TJ Jr, Mazzull JR, Cui L, Savas JN, et al. Acetylation targets mutant huntingtin to autophagosomes for degradation. Cell,2009;137:60–72. pmid:19345187
  55. 55. Jiang M, Wang J, Fu J, Du L, Jeong H, West T, et al. Neuroprotective role of Sirt1 in mammalian models of Huntington's disease through activation of multiple Sirt1 targets. Nat Med.2011;18:153–8. pmid:22179319
  56. 56. Wu LL & Zhou XF. Huntingtin associated protein 1 and its functions. Cell Adh Migr. 2009;3:71–6. pmid:19262167
  57. 57. Weir BS, Cockerman CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38: 1358–1370.
  58. 58. Goudet J. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available from http://www.unil.ch/izea/softwares/fstat.html. Updated from Goudet (1995) (2001).
    • 59. Barrett JC, Fry B, Maller J and Daly MJ. Haploview. analysis and visualization of LD and haplotype maps. Bionformatics. 2005;2: 263–265.
    • 60. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002;7: 1–11.
    • 61. Pffaf MW, Horgan GW and Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Research. 2002;30: e36. pmid:11972351
    • 62. Lu M, Liu Z, Yu H, Wang LE, Li G, Sturgis EM, et al. Combined effects of E2F1 and E2F2 polymorphisms on risk and early onset of squamous cell carcinoma of the head and neck. Mol Carcinog. 2012;51 Suppl 1: E132–E141. pmid:22344756
    • 63. Kobal J, Lovrečič L, Peterlin B. Chapter 4. Biomarkers for Huntington's Disease. In: Huntington's Disease—Core Concepts and Current Advances. InTechOpen; 2012.pp. 101–120.
      • 64. Andrew SE, Goldberg YP, Kremer B, Telenius H, Theilmann J, Adam S, et al. The relationship between trinucleotide (CAG) repeat length and clinical features of Huntingtons disease. Nature Genetics. 1993, 4: 398–403. pmid:8401589
      • 65. Infante A, Laresgoiti U, Fernández-Rueda J, Fullaondo A, Galán J, Díaz-Uriarte R, et al. E2F2 represses cell cycle regulators to maintain quiescence. Cell Cycle. 2008;7: 3915–3927. pmid:19066456
      • 66. Colciago C, Cislaghi G, Silani V, Ciammola A. Huntington’s disease: the current state of research with peripheral tissues. Exp Neurol. 2009;2: 385–397.
      • 67. Chang KH, Cheng YC, Wu YR, Lee WF and Chen CM. Downregulation of genes involved in metabolism and oxidative stress in peripheral leukocytes of Huntington’s disease patients. PLoS One 2012;7: e46492. pmid:23029535
      • 68. Marchina E, Misasi S, Bozaato A, Ferraboli S, Agosti C, Rozzini L, et al. Gene expression profile in fibroblast of Huntington’s disease patients and controls. J Neurol Sci. 2014;337: 42–46. pmid:24296361
      • 69. Andresen JM, Gayán J, Cherny SS, Brocklebank D, Alkorta-Aranburu G, Addis EA; et al. Replication of twelve association studies for Huntington’s disease residual age of onset in large Venezuelan kindreds. J Med Genet. 2007;44: 44–50. pmid:17018562
      • 70. Andrieux J, Labalme A, Lesca G, Cordier MP, Boute O, Sanlaville D,et al. Interstitial 12p13.1 deletion involving GRIN2B in three patients with intellectual disability. Am J Med Genet. 2013;161: 2564–9.
      • 71. Raychaudhuri S. Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases. Curr Opin Genet Dev. 2013;23: 635–41. pmid:24287333
      • 72. Hackam AS, Yassa AS, Singaraja R, Metzler M, Gutekunst CA, Gan L, et al. Huntingtin interacting protein 1 induces apoptosis via a novel caspase-dependent death effector domain. J Biol Chem. 2000;275: 41299–41308. pmid:11007801
      • 73. Majumder P, Chattopadhyay B, Mazumder A, Das P, Bhattacharyya NP: Induction of apoptosis in cells expressing exogenous Hippi, a molecular partner of huntingtin-interacting protein Hip1. Neurobiol Dis. 2006;22: 242–256. pmid:16364650
      • 74. Fontaine SN, Bauer SP, Lin X, Poorfarahani S, Ybe JA. Replacement of charged and polar residues in the coiled-coiled interface of huntingtin-interacting protein 1 (HIP1) causes aggregation and cell death. FEBS Lett. 2012;586: 3030–6. pmid:22835334
      • 75. Huettner JE. Kainate receptors and synaptic transmission. Prog Neurobiol. 2003;70: 387–407. pmid:14511698