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Erschienen in: BMC Medical Genetics 1/2012

Open Access 01.12.2012 | Research article

Polymorphic genes of detoxification and mitochondrial enzymes and risk for progressive supranuclear palsy: a case control study

verfasst von: Lisa F Potts, Alex C Cambon, Owen A Ross, Rosa Rademakers, Dennis W Dickson, Ryan J Uitti, Zbigniew K Wszolek, Shesh N Rai, Matthew J Farrer, David W Hein, Irene Litvan

Erschienen in: BMC Medical Genetics | Ausgabe 1/2012

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Abstract

Background

There are no known causes for progressive supranuclear palsy (PSP). The microtubule associated protein tau (MAPT) H1 haplotype is the major genetic factor associated with risk of PSP, with both oxidative stress and mitochondrial dysfunction also implicated. We investigated whether specific single nucleotide polymorphisms (SNPs) in genes encoding enzymes of xenobiotic detoxification, mitochondrial functioning, or oxidative stress response, including debrisoquine 4-hydroxylase, paraoxonase 1 and 2, N-acetyltransferase 1 and 2 (NAT2), superoxide dismutase 1 and 2, and PTEN-induced putative kinase are associated with PSP.

Methods

DNA from 553 autopsy-confirmed Caucasian PSP cases (266 females, 279 males; age at onset 68 ± 8 years; age at death 75 ± 8) from the Society for PSP Brain Bank and 425 clinical control samples (197 females, 226 males; age at draw 72 ± 11 years) from healthy volunteers were genotyped using Taqman PCR and the SequenomiPLEX Gold assay.

Results

The proportion of NAT2 rapid acetylators compared to intermediate and slow acetylators was larger in cases than in controls (OR = 1.82, p < 0.05). There were no allelic or genotypic associations with PSP for any other SNPs tested with the exception of MAPT (p < 0.001).

Conclusions

Our results show that NAT2 rapid acetylator phenotype is associated with PSP, suggesting that NAT2 may be responsible for activation of a xenobiotic whose metabolite is neurotoxic. Although our results need to be further confirmed in an independent sample, NAT2 acetylation status should be considered in future genetic and epidemiological studies of PSP.
Hinweise

Competing interests

The authors declare that they have no competing interests. This research was conducted in accordance with institutional review board approved procedures.

Authors' contributions

LFP participated in study conception and design, carried out NAT genotyping, assisted in data analysis and was primarily responsible for drafting the manuscript. ACC performed statistical analysis and assisted in data interpretation and manuscript preparation. OAR participated in study design and iPLEX genotyping and manuscript critique. RR helped with study design and DNA preparation. DWD, RJU and ZWK provided samples and were involved in manuscript review and critique. SNR assisted in statistical analysis and data interpretation. MJF participated in study design and manuscript critique. DWH participated in study design, data analysis, and manuscript critique. IL was responsible for study conception, design and manuscript review and critique.

Background

Progressive supranuclear palsy (PSP) is the most common atypical parkinsonian disorder. Classically, patients present with progressive postural instability and falls followed by slow and hypometric vertical saccades and eventually vertical supranuclear gaze palsy.
Neuropathologically, PSP is characterized by deposits of four-repeat microtubule associated protein tau (encoded by the MAPT gene) aggregates in neurons and glia of the basal ganglia and brain-stem [1]. Additionally, there is mitochondrial dysfunction, decreased ATP levels and inflammation in the brains of PSP patients [24]. The MAPT H1 haplotype has been consistently reported to be associated with PSP; however, it is also common in the general population, suggesting that gene-gene or gene-environment interactions are likely required for the development of this disease [5, 6]. Recently, MAPT H1 was also associated with risk of Parkinson's disease (PD) suggesting shared pathways of disease [7]. Early-onset PD and PSP can present with a similar phenotype and be misdiagnosed, supporting common links between the two disorders. The product of PTEN-induced putative kinase (PINK1, PARK6), associated with early-onset PD, is involved in mitochondrial respiration and protection from oxidative damage, which are pathways that have also been linked to risk of PSP [813]. PINK-1 polymorphisms are also associated with PD and it acts in conjunction with parkin to regulate mitochondrial functioning. Although the mechanisms by which PINK1 acts are not fully understood; research suggests that it is crucial for healthy mitochondrial respiration and ATP production [8]. Considering the role of PINK1 in mitochondrial functioning along with its previous links to PD, specific PINK1 SNPs were included in this study to determine if there is also an association with PSP.
Consumption of annonaceous fruit and teas, which contain mitochondrial inhibitors, has been associated with an atypical parkinsonian disorder similar to PSP in the French West Indies [14, 15]. Considering that mitochondrial impairment is observed in PSP brains, mitochondrial complex-1 inhibitors and other chemical neurotoxins, such as organophosphates, are hypothesized as risk factors for PSP [1618]. These and other potentially toxic compounds are metabolized by the products of several genes: debrisoquine 4-hydroxylase (CYP2D6), paraoxonase (PON) 1 and 2, N-acetyltransferase (NAT) 1 and 2, and superoxide dismutase (SOD) 1 and 2 [1013, 1922]. CYP2D6 is found in the brain and is involved in metabolism of MPTP, herbicides (paraquat) and organophosphate pesticides [11, 12]. Reduced in 5-10% of Caucasians, genetic polymorphisms of this enzyme have been widely studied in PD and results suggest that there is an association of the poor metabolizer phenotype with disease development [23, 24]. Moreover, the combination of pesticide exposure and CYP2D6 poor metabolizer phenotype doubles PD risk [11, 20]. PON1 hydrolyzes phosphoric acid esters, organophosphates and aromatic carboxylic acid esters and blocks the formation of free radicals. With low PON1 activity, these pesticides are not metabolized and the cell is subject to increased oxidative stress [19]. The PON 1 M allele, which is correlated with decreased protein levels, has been shown to be associated with PD [25, 26] and the M/M genotype was recently reported to be associated with early onset PD [27]. Additionally, decreased PON1 activity was overrepresented in PD patients from agriculturally exposed areas [19]. NAT1 and NAT2 are involved in the biotransformation of drugs and environmental toxins (xenobiotics) [28]. These enzymes transfer the acetyl group from acetyl-coenzyme A (acetyl CoA) to an amino group on aromatic amines and hydrazine compounds. In addition, following N-hydroxylation, they can further activate xenobiotics via O-acetylation [29]. There are a number of SNPs reported in NAT1 and NAT2, which lead to slow and rapid acetylator phenotypes. The acetylation status of an individual might determine how they respond to xenobiotic exposures, therefore presenting the NAT genes as candidates for gene-environment interaction studies. The slow acetylator phenotype is reported to be associated with PD, but inconsistent results warrant further investigation [3034]. SOD is an important antioxidant enzyme, which converts superoxide anions (O2-) to hydrogen peroxide (H2O2). Considering the antioxidant properties of the enzyme, polymorphisms resulting in decreased SOD activity would be expected to have detrimental effects on the cell; however, recent studies suggest the opposite is true [3537]. The mechanism behind this gain of function toxicity remains unknown, but it is proposed to be a result of either 1) disrupting the balance of O2- and H2O2, or 2) self-aggregation. Numerous SOD polymorphisms have been found to be associated with amyotrophic lateral sclerosis (ALS) [38], and may play a role in PD and AD pathogenesis [39].
To determine if genetic polymorphisms in toxicant metabolism increases risk for developing PSP, we investigated associations between PSP and specific single nucleotide polymorphisms (SNPs) in the aforementioned genes.

Methods

Sample

DNA samples from 545 autopsy-confirmed PSP cases collected between 1993 and 2008 at the PSP Society Brain Bank were included [40]. All cases were from the US and Canada. Control DNA samples (n = 426) were randomly selected from an existing repository of control samples at the Mayo clinic, Jacksonville. All controls were healthy spouses or caregivers of patients at the Mayo Clinic in Jacksonville, FL and free from neurological disorders. All samples were from adults over the age of 33 (see Table 1 for demographic information). Institutional review board (IRB)-approved protocols, including informed consent, were followed to obtain all DNA samples.
Table 1
Characteristics of PSP Cases and Controls
 
Case (N = 545)a
Control (N = 426)
 
n
%
n
%
p
Gender
    
0.490
Female
266
49
197
47
 
Male
279
51
226
53
 
Race
    
--
Caucasian
545
100
426
100
 
Age b
    
< 0.001
Median(Min, Max)
76(44,98)
 
73(33,92)
  
Mean(SD)
75.4(8.0)
 
72(10.7)
  
Clinical Diagnosis
     
PSP
468
87
   
Otherc
68
13
   
Falls
     
Yes
482
99
   
No
3
1
   
Dementia
     
Yes
278
82
   
No
62
18
   
Eye Mov. Abn.
     
Yes
349
96
   
No
13
4
   
Rigidity
     
Yes
374
98
   
No
9
2
   
Instability
     
Yes
454
99
   
No
4
1
   
Dysarthria
     
Yes
370
97
   
No
10
3
   
Dysphagia
     
Yes
352
92
   
No
31
8
   
AD Path.
     
Yes
50
9
   
No
495
91
   
Family History
     
Yes
322
73
   
No
121
27
   
aNs of cases for characteristics including and following "clinical diagnosis" may not total 545 as information for these variables was not available for every case.% represent% of those with data. bAge represents age at death for cases and age at blood draw for controls. cOther clinical diagnoses included Alzheimer disease, Parkinson's disease, corticobasal degeneration, multiple system atrophy, Parkinson's disease with dementia, vascular dementia, progressive non-fluent aphasia and dementia. Abbreviations: p = p-value (chi-squared), Eye Mov. Abn. = eye movement abnormalities, AD Path. = presence of coexisting Alzheimer's pathology

Genotyping

Within 48 hours of collection, DNA was extracted by standard protocols and stored at -80°C until used. NAT1 (rs4987076, rs5030839, rs4986782, rs1057123, and rs15561) and NAT2 (rs1208, rs1801279, rs1801280, rs1799929, rs1799930, rs1799931, and rs1041983) genotyping was performed using Taqman PCR methodology on an ABI Prism 7700 sequence detection system as previously published [41, 42]. All other genotyping was performed on a Sequenom Mass Array iPLEX platform using the Gold Assay (San Diego, CA) as described previously [43] (see Table 2 for rs numbers). Primer sequences are available upon request. The rs numbers tested here also included in the recent GWAS on PSP are rs1043424, rs662, rs7493, rs1801280, rs1799930, rs1799931, rs1799929, and rs1041983 [44].
Table 2
Case-control comparison of SNP genotypes
 
Total
n
Total
n
n-controls
n-cases
  
p
 
q
SNP rs marker (gene name)
Controls (freq)a
Cases (freq)a
0
1
2
0
1
2
OR
CI
trend
recess.
dom.
Holm
rs1135840
395
500
133
183
79
145
240
115
1.14
0.94-1.37
0.174
0.367
0.188
1
(CYP2D6)
0.44
0.56
0.34
0.56
0.2
0.39
0.5
0.23
      
rs3738136
412
521
375
36
1
469
48
4
1.11
0.73-1.7
0.625
0.338
0.78
1
(PINK-1)
0.44
0.56
0.91
0.19
0
0.9
0.19
0.01
      
rs1043424
414
520
217
165
32
281
208
31
0.89
0.72-1.1
0.27
0.19
0.478
1
(PINK-1)
0.44
0.56
0.52
0.4
0.18
0.54
0.4
0.16
      
rs2234694
412
521
372
38
2
478
41
2
0.84
0.54-1.28
0.411
0.813
0.408
1
(SOD1)
0.44
0.56
0.9
0.19
0
0.92
0.18
0
      
rs4880
413
523
107
206
100
116
264
143
1.14
0.95-1.38
0.157
0.22
0.279
1
(SOD2)
0.44
0.56
0.36
0.5
0.24
0.22
0.5
0.37
      
rs662
414
521
211
160
43
256
217
48
1.03
0.85-1.26
0.757
0.65
0.497
1
(PON1)
0.44
0.56
0.51
0.49
0.1
0.59
0.42
0.09
      
rs854560
412
521
181
176
55
213
242
66
1.02
0.84-1.23
0.875
0.661
0.606
1
(PON1)
0.44
0.56
0.44
0.43
0.13
0.41
0.56
0.13
      
rs705381
409
523
231
148
30
293
201
29
0.98
0.79-1.21
0.845
0.355
0.831
1
(PON1)
0.44
0.56
0.6
0.46
0.11
0.66
0.48
0.16
      
rs7493
413
523
237
148
28
315
181
27
0.9
0.73-1.12
0.349
0.265
0.535
1
(PON2)
0.44
0.56
0.67
0.46
0.17
0.6
0.45
0.15
      
rs12026
412
521
237
147
28
314
180
27
0.9
0.73-1.12
0.364
0.267
0.558
1
(PON2)
0.44
0.56
0.68
0.46
0.17
0.6
0.45
0.15
      
rs1052553
406
324
231
148
27
281
40
3
0.23
0.16-0.32
< 0.001
0.001
< 0.001
< 0.001
(MAPT)
0.56
0.44
0.67
0.46
0.17
0.97
0.12
0.01
      
Logistic regression trend test comparing genotype differences between cases and controls, adjusted for age (age at death for cases, age at draw for controls). aFrequency = number of samples in a particular variable category (i.e. "cases" or "0, 1, 2") divided by the total n for that variable (i.e. "cases + controls" or "0+1+2"). "0" represents common allele homozygous genotype for SNP tested, 1 = heterozygote, 2 = minor allele homozygous. OR = odds ratios for increase in the number of alleles (i.e. from 0 to 1 or 1 to 2); CI = 95% confidence intervals for ORs; p = p-values reported for trend test as well as for recessive (recess.) and dominant (dom.) models;q = q-values for multiple testing adjustment using Holm's method.

Data analysis

Statistical analyses were performed using R software (R Development Core Team 2009). Chi-squared, Fisher's exact, student t-test, or Wilcoxon rank sum analyses were used to test for differences in demographic variables between cases and controls. For each iPLEX SNP variable, the Cochran-Armitage and chi-squared tests were used to test additive, dominant, and recessive genetic models. In addition, logistic regression was used to test these same genetic models while adjusting for significant demographic variables (i.e. age). Logistic regression models were also used to determine whether specific NAT1 or NAT2 genotypes or NAT2 phenotypes were associated with PSP. NAT2 phenotypes may be accurately assigned according to genotype [22]; therefore, NAT2 analysis was initially restricted to phenotypic evaluation, which was followed by genotypic analysis. Overall significance of the associations was determined using the omnibus chi-squared test for the model. If the omnibus chi-squared test was not significant, then individual genotypes were not considered significant even if the associated p-value (p) was < 0.05. Odds ratios (OR), 95% confidence intervals (CI) and p-values were determined for each variable. Associations with p < 0.05 were considered significant. Based on the outcome of the primary analysis, t-test or Wilcoxon rank sum test was applied to determine whether means/medians were different between NAT2 phenotypes for age at onset, age at death or disease duration in cases. NAT2 genotype, NAT1 genotype and iPLEX SNP associations were all tested independently each with either a large number of groups or a low number of tests; nevertheless, when p-values were less than 0.05, adjustments were made for multiple testing using the Holm correction [45]. NAT2 phenotype tests were modeled independently from SNP analyses. Furthermore, while multiple SNPs were determined to input the phenotypes, only two phenotypes were compared (i.e. rapid versus slow/intermediate), therefore no multiple testing correction was needed as previously described for testing the NAT2 phenotype association with colorectal cancer [46].

Results

On average, cases were older than controls at sample collection time (Table 1, p < 0.001), with age at collection time for PSP cases being age at death. Trend analysis of the iPLEX SNPs showed no between-group differences in genotypes (Table 2), with the exception of rs1052553 (MAPT H1 OR = 4.35, CI = 3.08-6.25, p < 0.001), which is a known association [47]. Each marker was confirmed to be in Hardy-Weinberg equilibrium in controls. Minor allele frequencies (MAFs) for rs numbers 1043424 and 705381 were higher in both our PSP and control populations compared to that reported for the general (Caucasian) population. For rs numbers 4880 and 1052553 only the PSP sample differed from the general population (Table 3). There were no between-group differences for NAT1 genotypes (Table 4). NAT2 slow and intermediate phenotypes did not differ between groups (p = 0.96), thus these groups were combined and compared against the rapid phenotype for further analyses. Phenotypic analysis showed cases had a significantly higher proportion of NAT2 rapid acetylators (OR = 1.82, CI = 1.05-3.28, p = 0.037) compared to intermediate and slow (Table 5). The omnibus chi-squared test for NAT2 genotypes was not significant (Table 6). Since NAT2 rapid phenotype was associated with PSP, rank sum analyses were used to determine whether NAT2 acetylation status predicted either age at onset or disease duration. NAT2 phenotype was not associated with age at onset or age at death. For disease duration the overall test was also not significant; however, individual pairwise comparisons for disease duration using a t-test (unequal variances, Table 7) corroborated results for association of NAT2 rapid phenotype with disease (Table 5). For example, mean disease duration was shorter for rapid NAT2 phenotype (6.6 yrs.) compared to slow (7.5 yrs. p = 0.025).
Table 3
Allele Frequencies of SNPs vs. General Population
 
Allele
MAF
Controls
q
Cases
  
Marker
Mn/Mj
popa
MAF
CI
p
Holm
MAF
CI
p
q
rs1135840
G/C
0.43
0.432
0.40-0.47
0.954
1
0.47
0.44-0.50
0.012
0.081
rs3738136
A/G
0.06
0.046
0.03-0.06
0.109
0.543
0.054
0.04-0.07
0.432
1.000
rs1043424
C/G
0.34
0.277
0.25-0.31
< 0.001
0.001
0.26
0.23-0.29
< 0.001
< 0.001
rs2234694
C/A
0.042
0.051
0.04-0.07
0.231
0.925
0.043
0.03-0.06
0.91
1.000
rs4880
C/T
0.45
0.492
0.46-0.53
0.018
0.145
0.526
0.50-0.56
< 0.001
< 0.001
rs662
G/A
0.33
0.297
0.27-0.33
0.048
0.337
0.3
0.27-0.33
0.046
0.273
rs854560
T/A
0.38
0.347
0.31-0.38
0.056
0.337
0.359
0.33-0.39
0.171
0.854
rs705381
T/C
0.18
0.254
0.23-0.29
< 0.001
< 0.001
0.248
0.22-0.28
< 0.001
< 0.001
rs7493
G/C
0.24
0.247
0.22-0.28
0.668
1
0.225
0.20-0.25
0.261
1.000
rs12026
G/C
0.24
0.246
0.22-0.28
0.699
1
0.225
0.20-0.25
0.258
1.000
rs1052553
G/Ab
0.21
0.249
0.22-0.28
0.008
0.068
0.071
0.05-0.09
< 0.001
< 0.001
MAFs of studied population (control/case) compared with the general population (MAF pop) using test of proportions. aMinor allele frequencies listed on NCBI from CEU data (CEU = Utah residents with north and western European ancestry). bNote G = H2, A = H1. Mn/Mj -Minor Allele/Major Allele; MAF = Minor Allele Frequency; CI = 95% confidence intervals for determined MAF for noted population (i.e. control or case); p = p-value (Chi-squared); q = q-values for multiple testing adjustment using Holm's method.
Table 4
Case-control comparison of NAT1 genotypes
 
Controls(N = 426)
Cases (N = 545)
   
NAT1 Genotype
n
%
n
%
OR
CI
p
NAT1*4/*4
211
50.8
268
49.7
1
  
NAT1*10/*11A
4
1.0
5
0.9
1.03
0.26-4.35
0.967
NAT1*10/*10
14
3.4
22
4.1
1.22
0.61-2.52
0.583
NAT1*4/*3
13
3.1
23
4.3
1.50
0.74-3.14
0.271
NAT1*4/*14A or *10/*14B
13
3.1
19
3.5
1.25
0.60-2.66
0.552
NAT1*10/*14A
4
1.0
6
1.1
1.12
0.31-4.45
0.863
NAT1*4/*10
138
33.3
165
30.6
0.94
0.70-1.26
0.677
NAT1*4/*11A or *3/*11B
13
3.1
21
3.9
1.21
0.59-2.55
0.607
NAT1*10/*15
0
0.0
1
0.2
--
--
--
NAT1*11A/*14A
0
0.0
1
0.2
--
--
--
NAT1*3/*10
2
0.5
4
0.7
1.65
0.32-12.10
0.567
NAT1*3/*14A
0
0.0
1
0.2
--
--
--
NAT1*4/*11B
0
0.0
1
0.2
--
--
--
NAT1*4/*15
3
0.7
2
0.4
0.37
0.05-2.39
0.298
Missing
11
 
6
    
Logistic regression analysis of individual NAT1 genotypes, adjusted for age. NAT1*4/*4 used as reference. Overall chi-squared p = 0.99 (likelihood ratio test). Genotypes with 10 or less counts in either group (case or control) were not included in overall test of significance. OR = odds ratio, CI = 95% confidence intervals for ORs, p = p-value
Table 5
Comparisons Between NAT2 Phenotypes
 
Controls(N = 426)
Cases(N = 545)
    
NAT2 Phenotype
n
%
n
%
OR
CI
p
q
Slow
241
56.6
299
54.9
1
-
-
-
Intermediate
161
37.8
198
36.4
0.993
0.76-1.31
0.959
0.959
Rapid
19
4.5
42
7.7
1.82
1.04-3.30
0.042a
0.084
Logistic regression analysis of individual NAT2 phenotypes, adjusted for age OR = odds ratio, CI = 95% confidence intervals for ORs, p = p-value, q = q-values for multiple testing adjustment using Holm's method. aWhen intermediate and slow phenotypes are combined (due to p = 0.96) and used as reference, the p-value for rapid is 0.037, and no multiple testing correction is needed since it is a single hypothesis
Table 6
Case-control comparison of NAT2 genotypes
 
Controls(N = 426)
Cases (N = 545)
    
NAT2 Genotype (phenotype)
n
%
n
%
OR
CI
p
q
NAT2*4/*4 (rapid)
19
4.5
42
7.7
1
   
NAT2*4/*5(intermediate)
108
25.4
116
21.3
0.49
0.26-0.88
0.021
0.168
NAT2*4/*6 (intermediate)
49
11.5
69
12.7
0.6
0.30-1.16
0.133
0.532
NAT2*4/*7 (intermediate)
4
0.9
13
2.4
1.43
0.43-5.65
0.576
1
NAT2*5/*6 (slow)
106
24.9
130
23.9
0.55
0.29-0.99
0.053
0.318
NAT2*5/*7 (slow)
5
1.2
10
1.8
0.86
0.26-3.10
0.809
1
NAT2*6/*6 (slow)
42
9.9
47
8.6
0.46
0.23-0.92
0.029
0.203
NAT2*6/*7 (slow)
5
1.2
7
1.3
0.56
0.15-2.21
0.394
1
NAT2*5/*5 (slow)
82
19.2
104
19.1
0.58
0.31-1.07
0.086
0.430
NAT2*5/*14 (slow)
1
0.2
0
0
--
--
--
 
NAT2*6/*14 (slow)
0
0
1
0.2
--
--
--
 
Missing
5
 
6
 
--
--
--
 
Logistic regression analysis of individual NAT2 genotypes, adjusted for age. NAT2*4/*4 used as reference. OR = odds ratio; CI = 95% confidence intervals for ORs; p = p-value; q = q values for multiple testing adjustment using Holm's method. Overall chi-squared p = 0.25 (likelihood ratio test). Genotypes with 10 or less counts in either group (case or control) were not included in overall test of significance
Table 7
Survival of PSP cases by NAT2 phenotype
NAT2 Phenotype
Median(Min, Max)
Mean(SD)
p-values
Age at Onset
   
Rapid
68.5(51,85)
68.5(8.5)
> 0.05
Intermediate
68(41,89)
68.1(8.6)
> 0.05
Slow
68(47,90)
67.9(8.1)
--
Age at Death
   
Rapid
75.5(58,89)
75.3(7.9)
> 0.05
Intermediate
76(44,98)
75.3(8.3)
> 0.05
Slow
76(53,95)
75.4(7.9)
--
Disease Duration (yrs.)
   
Rapida
6(2,12)
6.6(2.2)
0.025, 0.078, 0.028
Intermediateb
7(2,31)
7.4(3.6)
0.675
Slow
7(0,27)
7.5(3.3)
--
Pairwise comparisons of age at onset, age at death, and disease duration by NAT2 phenotype (t-test with unequal variances). ap-values = rapid vs. slow, rapid vs. intermediate, rapid vs. slow + intermediate, respectively. bp-value = slow vs. intermediate. Overall p-values from Wilcoxon rank sum tests were all > 0.1

Discussion

Our primary analysis revealed that none of the iPLEX SNPs was proportionally different between cases and controls except for MAPT rs1052553, which is a known association. On the other hand, significant differences were detected when comparing MAFs of cases with reported MAFs for the general population. There were no differences in NAT1 or NAT2 genotypes between cases and controls. NAT2 rapid acetylator phenotype was more frequent in PSP cases than controls while intermediate and slow acetylator phenotypes were less frequent in cases.
Although trend analysis did not show differences between cases and controls for the iPLEX SNPs (i.e. except for rs1052553), cases did differ from the general population (CEU) in some MAFs. Of particular interest is SOD2 rs4880, which differed from the general population in cases, but not controls. Though not conclusive, this suggests a possible association of rs4880 with PSP. The MAPT H1 allele is known to be associated with PSP; however, it is the major allele. Consistent with previous studies, we found that MAPT genotype and MAFs differed between PSP cases, with the H1 allele conferring risk [6, 44]. Furthermore, MAF comparisons indicate the H2 allele is protective, as it had a lower frequency in our cases compared to the general population (Table 3). Our results also suggest that NAT2 rapid acetylator status might increase risk for developing PSP. This is consistent with NAT2-catalyzed toxicant activation (perhaps via O-acetylation). Therefore, a higher rate of acetylation would result in a higher concentration of toxic metabolite in the system. NAT2 catalyzes the O-acetylation of N-arylhydroxylamines resulting in bioactivation [48].
This is an observational study, therefore more emphasis should be placed on the estimated odds ratio and precision of the confidence intervals rather than on p-values [49]. Nevertheless, these trends must be confirmed by additional studies. Our results did not provide statistical evidence for an effect of NAT2 phenotype on onset age, age at death or disease duration. In accord with our finding that NAT2 rapid phenotype is more frequent in cases than controls, pairwise comparisons did show a trend supporting a potential link between rapid phenotype and shorter disease duration (Table 7). It is important to note that this particular analysis may have been underpowered for detecting differences in the outcome parameters since the lack of disease onset and duration information for many cases substantially decreased the sample size.
Our findings are noteworthy as NAT2*4, which confers the rapid phenotype, was designated originally as the "wild-type" allele http://​louisville.​edu/​medschool/​pharmacology/​nat/​ since it is common among many ethnic groups other than Europeans or Caucasians [29]. Although the frequency of NAT2*4 is not as common among Caucasians (which is the group analyzed in our study), this association may still be similar to the MAPT H1 haplotype association with PSP (i.e. MAPT H1 is associated with increased PSP risk, but is also very common in the general population with a frequency of 0.78) [6]. Therefore, even though our results suggest the rapid acetylator phenotype increases risk for PSP, this is only one of potentially numerous factors that converge to determine individual risk for disease. On the other hand, our finding is contrary to recent findings that NAT2 rapid acetylator genes enhance the protective effect of smoking in PD (De Palma et al. 2010) and reports suggesting that the NAT2 slow acetylator phenotype increases risk for PD [5052]. PSP is a tauopathy and PD is a synucleinopathy, thus, these are two distinct diseases that may have distinct pathogenic mechanisms and risk factors [53]. There are varying reports of NAT2 polymorphisms associating with PD, PSP, and AD. While many suggest that slow alleles or phenotypes increase disease risk [31, 32, 5052], others indicate increased risk with rapid or intermediate conferring genotypes and protection by slow alleles or genotypes [13, 54]. Still others suggest there are no links between these diseases and NAT polymorphisms [18, 30, 33, 55, 56]. In view of these conflicting reports on the role of NAT genetic polymorphisms in neurodegeneration together with our results, additional studies are needed to determine whether NAT alleles or genotypes conferring rapid acetylation increase risk for neurodegenerative diseases or if the slow alleles/genotypes are protective or vice versa.

Conclusions

The control series we used was more geographically confined than our PSP population and the CEU population from which the general population MAFs were derived. Interestingly, for some of the MAFs our control population differed from the general population. This could explain why our genotype comparisons between cases and controls were not significant. Therefore, MAF comparisons between our PSP sample and the general/CEU population augment our case-control analyses. The main strength of this study was the large sample of pathologically well-characterized PSP cases from a single center. On the other hand, the clinical information was not collected in a systematic or standardized manner and controls were clinical, not pathological controls. Considering that PSP is a relatively rare disease, a still larger sample size may be necessary to detect smaller, yet biologically significant differences and investigate interaction effects. Likewise, as 514 of the PSP cases analyzed here were also included in the GWAS, this finding should be confirmed in an independent cohort. Although these findings need to be replicated, this data provides useful information to guide future genetic studies on PSP as it indicates that NAT2 rapid acetylator status should be considered as a potential risk factor for PSP in studies investigating gene-gene and gene-environment interactions. Furthermore, our results are consistent with the recent genome-wide association study (GWAS) on PSP that did not find any associations with SNPs rs1043424, rs662, rs7493 or any individual NAT2 SNPs [44]. The NAT2rs numbers tested here and included in the recent GWAS on PSP are rs1801280, rs1799930, rs1799931, rs1799929, and rs1041983 [44]. Though we did not find an association with any individual NAT2 SNPs, when we used the SNPs to input NAT2 phenotype we observed a significant association between imputed rapid NAT2 acetylator phenotype and PSP. This result is important since this method of testing NAT2 phenotype association with disease has been shown to be more useful than looking at individual SNPs [57, 58]. Thus, our study is quite different from the GWAS, and with respect to NAT2, much more powerful in terms of biological plausibility. Additionally, this study reveals the odds ratios and confidence intervals for a number of biologically relevant SNPs that have not been previously investigated in association studies on PSP. Our results provide support for the multiple-hit hypothesis and demonstrate the multifaceted nature of identifying risk factors for neurodegenerative diseases such as PSP.

Acknowledgements

We thank the patients and their families as well as those individuals who donated samples to the bio-repository. Thanks to research coordinators Jennifer Lash, Jill Searcy, and Audrey Strongosky from Mayo Clinic Jacksonville for assistance with sample collection and to Mark Doll, Alexandra Soto-Ortolaza and Jennifer Adamson for technical assistance. The Society for Progressive Supranuclear Palsy brain bank is supported by a grant to Dr. Dickson from CurePSP, Inc. This research was supported by the University of Louisville Center for Environmental Genomics and Integrative Biology award number P30ES014443. Dr. Litvan is partially supported by NIH R01 PAS-03-092, National Parkinson Foundation, Parkinson Support Center of Kentuckiana. Dr. Litvan is founder and CEO of the Litvan Neurological Research Foundation, whose mission is to increase awareness, determine the cause/s and search for a cure for neurodegenerative disorders presenting with either parkinsonian or dementia symptoms (501c3). Dr. Rai is supported by the Wendell Cherry Chair endowment for clinical trial research and the JG Brown Cancer Center. Dr. Hein was supported by NIH R01-CA034627. Dr. Wszolek is partially supported by the NIH 1RC2-NS070276, P01-NS057567, Mayo Clinic Florida (MCF) Research Committee CR programs (MCF #90052030 and MCF #90052030). Dr. Uitti is partially supported by the NIH P01- NS057567, Mayo Clinic Florida (MCF) Research Committee CR programs (MCF #90052030 and MCF #90052030). Drs. Dickson, Uitti, Wszolek, Ross and Rademakers are supported by NIH P50-NS072187, NIH P50-NS072187-01S2, and NIH P50-AG16574.
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://​creativecommons.​org/​licenses/​by/​2.​0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests

The authors declare that they have no competing interests. This research was conducted in accordance with institutional review board approved procedures.

Authors' contributions

LFP participated in study conception and design, carried out NAT genotyping, assisted in data analysis and was primarily responsible for drafting the manuscript. ACC performed statistical analysis and assisted in data interpretation and manuscript preparation. OAR participated in study design and iPLEX genotyping and manuscript critique. RR helped with study design and DNA preparation. DWD, RJU and ZWK provided samples and were involved in manuscript review and critique. SNR assisted in statistical analysis and data interpretation. MJF participated in study design and manuscript critique. DWH participated in study design, data analysis, and manuscript critique. IL was responsible for study conception, design and manuscript review and critique.
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Metadaten
Titel
Polymorphic genes of detoxification and mitochondrial enzymes and risk for progressive supranuclear palsy: a case control study
verfasst von
Lisa F Potts
Alex C Cambon
Owen A Ross
Rosa Rademakers
Dennis W Dickson
Ryan J Uitti
Zbigniew K Wszolek
Shesh N Rai
Matthew J Farrer
David W Hein
Irene Litvan
Publikationsdatum
01.12.2012
Verlag
BioMed Central
Erschienen in
BMC Medical Genetics / Ausgabe 1/2012
Elektronische ISSN: 1471-2350
DOI
https://doi.org/10.1186/1471-2350-13-16

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