FASEB J. Mp Biomedicals
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published as doi: 10.1096/fj.08-107086.
 (The FASEB Journal. 2008;22:3135-3145.)
© 2008 FASEB
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Supplemental Data
Right arrow All Versions of this Article:
fj.08-107086v1
22/9/3135    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Franks, P. W.
Right arrow Articles by Timmons, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Franks, P. W.
Right arrow Articles by Timmons, J. A.

Genomic variants at the PINK1 locus are associated with transcript abundance and plasma nonesterified fatty acid concentrations in European whites

P. W. Franks*,{dagger},1,2, C. Scheele{ddagger},§,¶,1, R. J. F. Loos{dagger}, A. R. Nielsen, F. M. Finucane{dagger}, C. Wahlestedt||, B. K. Pedersen, N. J. Wareham{dagger} and J. A. Timmons{ddagger},§

* Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University Hospital, Umeå, Sweden;

{dagger} MRC Epidemiology Unit, Institute of Metabolic Sciences, Cambridge, UK;

{ddagger} The Wenner-Gren Institute, Arrhenius Laboratories, Stockholm University, Stockholm, Sweden;

§ School of Life Sciences, Heriot-Watt University, Edinburgh, UK;

Center of Inflammation and Metabolism, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and

|| Department of Biochemistry, Scripps Research Institute, Jupiter, Florida, USA

2Correspondence: Genetic Epidemiology and Clinical Research Group, Dept. of Public Health and Clinical Medicine, Division of Medicine, Umeå University Hospital, Umeå 90 187, Sweden. E-mail: paul.franks{at}medicin.umu.se


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The purpose of this study was to characterize associations between PINK1 genotypes, PINK1 transcript levels, and metabolic phenotypes in healthy adults and those with type 2 diabetes (T2D). We measured PINK1 skeletal muscle transcript levels and 8 independent PINK1 single nucleotide polymorphisms (SNPs) in a cohort of 208 Danish whites and in a cohort of 1701 British whites (SNPs and metabolic phenotypes only). Furthermore, we assessed the effects of PINK1 transcript ablation in primary adipocytes using RNA interference (RNAi). Six PINK1 SNPs were associated with PINK1 transcript levels (P<0.04 to P<0.0001). Obesity modified the association between PINK1 transcript levels and T2D risk (interaction P=0.005); transcript levels were inversely related with T2D in obese (n=105) [odds ratio (OR) per SD increase in expression levels=0.44; 95% confidence interval (CI): 0.23, 0.84; P=0.013] but not in nonobese (n=103) (OR=1.20; 95% CI: 0.82, 1.76; P=0.34) individuals. In the British cohort, several PINK1 SNPs were associated with plasma nonesterified fatty acid concentrations. Nominal genotype associations were also observed for fasting glucose, 2-h glucose, and maximal oxygen consumption, although these were not statistically significant after correcting for multiple testing. In primary adipocytes, Pink1 knockdown affected fatty acid binding protein 4 (Fabp4) expression, indicating that PINK1 may influence substrate metabolism. We demonstrate that PINK1 polymorphisms are associated with PINK1 transcript levels and measures of fatty acid metabolism in a concordant manner, whereas our RNAi data imply that PINK1 may indirectly influence lipid metabolism.—Franks, P. W., Scheele, C., Loos, R. J. F., Nielsen, A. R., Finucane, F. M., Wahlestedt, C., Pedersen, B. K., Wareham, N. J., Timmons, J. A. Genomic variants at the PINK1 locus are associated with transcript abundance and plasma nonesterified fatty acid concentrations in European whites.


Key Words: oxidative metabolism • mitochondria • diabetes • single nucleotide polymorphism • gene-environment interaction


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
IMPAIRMENTS IN THE METABOLIC CAPABILITIES of skeletal muscle, adipose tissue, and liver, which contribute to systemic increases in glucose and fatty acid levels, may reflect alterations in mitochondrial function (1) . Such alterations, which lead to oxidative stress and impaired β-cell function, a primary cause of type 2 diabetes (1) , are likely to be a consequence of physical inactivity and genetic defects.

We recently reported a study in which we aimed to identify novel mitochondria-related genes responsive to physical activity in humans, which may also influence the development of type 2 diabetes (2) . We determined that aerobic training increases phosphatase and tensin homolog-induced putative kinase 1 (PINK1) transcript abundance, and with forced inactivity PINK1 transcript abundance declines. We also showed that PINK1 expression is lower in skeletal muscle tissue from people with type 2 diabetes than in nondiabetic control subjects. In addition, we observed that PINK1 transcript abundance correlates with plasma glucose levels, whereas ablation of PINK1 expression impairs glucose transport (2) .

Although we have demonstrated that environmental influences can regulate PINK1 expression in vivo, this does not rule out the possibility that genetic factors can also contribute to variations in PINK1 transcript abundance. The gene encoding the PINK1 protein (PINK1) is involved in the pathogenesis of rare, autosomal recessive forms of familial Parkinson’s disease (3) . Parkinson’s disease is characterized by bradykinesia, rigidity, and tremor. Both the inherited and the more common idiopathic forms of Parkinson’s disease appear to involve mitochondrial dysfunction and oxidative stress (4 , 5) . PINK1 occupies the PARK6 locus on the short arm of chromosome 1 and seems to be essential for maintaining normal mitochondrial function in Drosophila (6 7 8) . The PINK1 protein localizes to both the mitochondrial inner membrane and matrix (9) and the cytosol (10 , 11) and its main biochemical function is thought to be as a serine threonine kinase (12) . The exact role for PINK1 in the cell is unclear, although it has recently been demonstrated to protect against oxidative stress-related damage (13 14 15) and apoptosis (14 , 16) . Indeed, both reactive oxygen species (ROS) and apoptosis are considered important pathophysiological antecedents of various chronic aging-related diseases including type 2 diabetes.

The overarching objective of this study was to determine whether allelic variation at the PINK1 locus is associated with PINK1 transcript abundance and phenotypes for oxidative energy metabolism. We genotyped two cohorts of middle-aged European white adults for PINK1 gene tagging polymorphisms, assessed the association between these variants and the relevant phenotypes for oxidative energy metabolism, and followed up our observations using reverse genetics approaches in a primary cell model.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Danish cohort
The associations between PINK1 gene variants and skeletal muscle PINK1 transcript levels were assessed in an ethnically homogeneous cohort of 208 participants aged between 22 and 84 yr, from the Copenhagen and Frederiksberg regions of Denmark. Participants with metabolic diseases known to affect body composition and metabolism, other than type 2 diabetes, were excluded. Individuals with insulin-treated diabetes were also excluded, as were those with recent or ongoing infection, a history of malignant disease, or treatment with antiinflammatory drugs. Permission to undertake the study was obtained from the Ethics Committee of Copenhagen and Frederiksberg Communities, Denmark. Measures of body composition and glucose tolerance and muscle biopsies were performed as described previously (2) . In brief, participants in this cohort attended the clinic in the morning after an overnight fast. They abstained from taking any prescribed medication for 24 h before testing to avoid any acute pharmacodynamic responses. Participants with type 2 diabetes abstained from taking oral hypoglycemic medication for 1 week before testing. After written informed consent was obtained, a history was taken and a physical examination was performed. Height and weight were measured using calibrated equipment from which body mass index (BMI) was derived. A muscle biopsy sample was taken from the vastus lateralis in the right thigh, using the percutaneous needle method with suction (17) . Muscle tissue was immediately snap-frozen in liquid nitrogen and subsequently stored at –80°C until expression analyses were performed. Immediately after the muscle biopsy, a standard 75-g oral glucose tolerance test (OGTT) was performed, with glucose levels measured at time 0 and 120 min after glucose loading. Obesity was defined according to the recommendations of the World Health Organization (BMI≥30 kg/m2 indicates obese; BMI<30 kg/m2 indicates nonobese) (18) .

Medical Research Council (MRC) Ely population-based cohort
The associations between PINK1 gene variants and various markers of oxidative energy metabolism were assessed in 1701 participants aged between 35 and 79 yr from the MRC Ely Study, an ethnically homogeneous population-based cohort from the Eastern Anglia region of the UK. The study design, methods, and measurements have been described in detail elsewhere (19 , 20) . Individuals without diagnosed type 2 diabetes were recruited to the study between 1990 and 2003, using a population-based sampling frame of all 35- to 65-yr-olds living in or around the city of Ely in Cambridgeshire. Participants underwent a maximum of 3 examinations at approximately 5-year intervals (phases 1–3). Data reported here pertain to individuals for whom complete clinical measurements and genotype data were available for analysis. If more than one data point was available, the most recent was selected for analysis. Of the outcome measures, glucose and anthropometric data were available for almost the entire cohort (n=1581–1701), whereas nonesterified fatty acid (NEFA) and maximal oxygen consumption (VO2max) data were available for approximately half of the cohort (n=706–928). Age and BMI data were available at all time points, and the relevant data point was used for each analysis. Almost all data pertaining to glucose and anthropometric measurements were derived from phase 3 of the study, whereas NEFA data were derived almost exclusively from phase 2 (conducted ~5 yr earlier). None of the participants had prevalent type 2 diabetes at entry into the study, although 139 individuals had incident, screen-detected diabetes diagnosed at their study examination. Approval for the study was obtained from the Cambridge Local Research Ethics Committee. At each examination, participants in the MRC Ely cohort attended the testing site the morning after an overnight fast. After written informed consent was obtained, anthropometric measures including height and weight (from which BMI was calculated) were taken as described above, and a standard 75-g OGTT was performed with blood samples taken at time 0, 30 min, and 120 min. NEFA levels were measured using an enzymatic assay (Roche Molecular Biochemicals, Lewes, Sussex, UK), as described previously (19) . Total cholesterol and triglyceride levels were measured using methods described previously (21) . After completion of the OGTT, participants underwent a submaximal exercise stress test to estimate VO2max as described previously (21) .

Both studies were performed in accordance with the principles of the Declaration of Helsinki as revised in 2000.

Genetic analyses
Gene expression analysis
Skeletal muscle biopsy samples from the Danish cohort were homogenized in TRIzol (Invitrogen, Carlsbad, CA, USA) using a motor-driven homogenizer (Polytron; Kinematica, Newark, NY, USA) and total RNA was isolated as described previously (22) . Total RNA was dissolved in RNase-free water, quantified using a spectrophotometer (Pharmacia Biotech, Piscataway, NJ, USA), and reverse-transcribed using reverse transcription reagents (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s protocol. Briefly, 2 µg of total RNA was reverse-transcribed in a reaction volume of 40 µl including random hexamers. Real-time quantitative PCR (qPCR) was performed using an ABI Prism 7900 sequence detection system (Applied Biosystems, Foster City, CA, USA). PINK1 primers and an MGB probe were designed using Primer Express software (Applied Biosystems) (PINK1 forward primer: 5'-GGACACGAGACGCTTGCA; PINK1 reverse primer: 5'-TTACCAATGGACTGCCCTATCA; and PINK1 MGB probe: 5'-TTTCGGCTGGAGGAGTA). A preoptimized primer and probe assay for 18S rRNA was used as an endogenous control (Applied Biosystems). To assess amplification efficiency, 2-fold dilution series were performed for PINK1 and 18S, respectively. Based on these, cDNA was diluted 1:50 for PINK1 and 1:500 for 18S to ensure linear amplification. Primers and probes were premixed with TaqMan Universal Master Mix (Applied Biosystems) and distributed into 384-well MicroAmp Optical barcode plates (Applied Biosystems). cDNA aliquots of 4 µl were added in triplicate. The amplification of genomic DNA typically amounted to a maximum of <1% of the target gene when the TRIzol protocol was used. For gene expression analysis of the short interfering (si) RNA experiments, cells were harvested for RNA isolation in TRIzol at day 3 of culture (after 48 h of transfection). RNA (1 µg in a 20-µl reaction volume) was reverse-transcribed using a High-Capacity cDNA kit (Applied Biosystems) according to the manufacturer’s protocol. Gene expression analysis was performed using the ABI Prism 7900 sequence detection system as described above, except that SYBR Green Master Mix (Applied Biosystems) was used for detection instead of a probe. Murine Pink1 and fatty acid binding protein 4 (Fabp4) primers were obtained from the Roche universal probe library (Roche Molecular Biochemicals) [Pink1 (murine) forward primer: 5'-GCGAAGCCATCTTAAGCAAA; Pink1 (murine) reverse primer: 5'-TGGGACCATCTCTGGATCTT; Fabp4 (murine) forward primer: 5'-CGCAGACGACAGGAAGGT; and Fabp4 (murine) reverse primer: 5'-TTCCATCCCACTTCTGCAC]. The same preoptimized primer and probe assay for 18S rRNA (Applied Biosystems), as used for the human study, were also used as an endogenous control for the cell culture samples. RefSeq accession numbers are as follows: PINK1 (human): NM_032409; Pink1 (murine): NM_026880; and Fabp4 (murine): NM_024406.

Genotyping
DNA from the Danish cohort was extracted by a secondary phase separation of the remaining TRIzol homogenate according to the manufacturer’s protocol with modifications. Briefly, after careful removal of the RNA aqueous phase, 0.5 ml of back extraction buffer (4 M guanidine thiocyanate, 50 mM sodium citrate, 1 M Tris, and water) was added. After intense vortex, samples were spun at 12,000 g for 15 min at 4°C cycle threshold (CT). The upper aqueous phase containing DNA was transferred and precipitated with 0.4 ml of isopropanol and washed with 0.5 ml of ethanol. The DNA pellet was resolved in 8 mM NaOH. From the MRC Ely cohort, DNA was extracted from EDTA whole blood as described previously (23) .

Genotyping of the SNP markers was performed using Custom TaqMan SNP Genotyping Assays (Applied Biosystems, Warrington, UK). The genotyping assays were conducted with 10 ng of genomic DNA in a 5-µl 384-well TaqMan assay using a PTC-225 Thermal Cycler (MJ Research, Watertown, MA, USA), with cycling at 95°C for 10 min and then 40 cycles of 15 s at 92°C and 1 min at 60°C. The ABI Prism 7900HT sequence detection system (Applied Biosystems) was used for end point detection and allele calling. The duplicate controls used in both cohorts were 100% concordant, and all of the SNPs genotyped were in Hardy-Weinberg equilibrium (P>0.05). The call rates for all SNPs genotyped in the Danish cohort were ≥99.9% and in the Ely cohort were ≥98.0%.

Cell culture and transfection
All cell culture reagents were purchased from GIBCO Invitrogen (Stockholm, Sweden) unless otherwise stated. For primary brown preadipocyte cell culture and differentiation, male NMRI mice (ages 3–4 wk; B&K, Stockholm, Sweden) were sacrificed by CO2, and brown adipose tissue (from the interscapular, cervical, and axillary depots) was isolated as described previously (24) . In brief, minced tissue was digested in a collagenase (type II; Sigma-Aldrich, St. Louis, MO, USA) -containing buffer, for 30 min at 37°C. The cell suspension was filtered and kept on ice for 20 min. After the top layer (mature adipocytes) was discarded, the suspension was filtered and washed in Dulbecco’s modified Eagle’s medium (DMEM) and resuspended in 0.5 ml of culture medium per animal. Cells were cultured in 6-well plates (Falcon, Cowley, UK) in DMEM supplemented with 10% (v/v) newborn calf serum (Invitrogen), 2.4 nM insulin, 25 µg/ml sodium ascorbate, 10 mM HEPES, 4 mM glutamine, 50 U/ml penicillin, and 50 µg/ml streptomycin, and 2 ml of cell suspension was added into each well.

After 24 h in culture, cells were transfected using 0.32% Lipofectamine 2000 (Invitrogen) and 20 nM siRNA in 2.5 ml of newborn calf serum-containing cell culture media (without penicillin/streptavidin) according to the manufacturer’s protocol and as described previously (2) . At day 3 of culture (after 48 h of transfection), cells were harvested for RNA isolation in TRIzol. The siRNA sequences were one prevalidated siRNA (ID 1199, targeting exons 1 and 2, named P1), one predesigned siRNA (ID 180640, targeting exon 3, named P3), and a control siRNA, predesigned in such a way that it would not target any gene in the murine genome (Silencer Negative Control #1; Ambion Inc., Austin, TX, USA). All siRNAs were designed and chemically synthesized by Ambion Inc. (sequences can be obtained on request).

Statistical analysis
All statistical analyses for the human data were performed using SAS software (version 9.1; SAS Institute, Inc., Cary, NC, USA). Tagging polymorphisms were identified with pairwise tagging in the phase 2 HapMap CEU panel (build 35) (25) using Tagger (Haploview software version 4.0; http://www.broad.mit.edu/mpg/haploview) (Fig. 1 ). By using this approach, all common variants (i.e., minor allele frequency ≥5%) that lie within the PINK1 region should be captured with an r2 cutoff of 0.90. We were unable to type rs3121680, which tags rs512550. However, we included five additional PINK1 genotypes, which incorporated coding variants and genotypes within the neighboring DDOST gene (see Supplemental Table S1). Linkage disequilibrium (LD) between pairs of genotypes was also assessed using Haploview version 4.0. In the British and Danish cohorts, generalized linear regression models were used to assess associations between different PINK1 genotypes and various phenotypic characteristics. When the numbers of individuals with the minor homozygous genotype were less than 5, minor allele carriers were combined before analyses (i.e., to form a dominant model). Interactions between obesity and genotype or transcript level were tested using generalized linear models by fitting an interaction term (BMIxgenotype or BMIxtranscript level) with additional adjustment for covariates including BMI and genotype or transcript level (see ref. 26 for a detailed overview of statistical interaction modeling). To control for the number of statistical tests performed for the genotype analyses, we used Holm’s procedure (27) to adjust P values within each hypothesis set. Hypothesis sets comprised all tests of genotypic association for each specific phenotype (e.g., transcript level, NEFA or glucose concentrations, or level of VO2max). Holm’s procedure (27) is an adaptation of the Bonferroni correction, in which all hypothesis tests undertaken are ranked according to their P value and the denominator for each correction is defined by the number of tests within that rank-ordered list with higher P values. Thus, the least significant hypothesis test is divisible by 1, the second least significant hypothesis test by 2, and so on until all P values within the list have been corrected. For the siRNA experiments, 18S-adjusted CT values for each sample were related to the mean adjusted CT value for the control samples (untreated, Lipofectamine control, and siRNA control) in each experiment, and then the mean adjusted CT value for all experiments was calculated. The results are presented as a percentage of mRNA abundance for control samples (mean±SE). ANOVA was used to analyze both the human gene expression levels and the cell-based responses. When a significant F ratio was achieved, post hoc analysis (Tukey) was used to make individual comparisons and generate P values. A 2-sided value of P < 0.05 was considered statistically significant.


Figure 1
View larger version (64K):
[in this window]
[in a new window]

  		Figure 1. Linkage disequilibrium (pairwise r2) between PINK1 genotypes in the UK cohort (n=1701).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Participant characteristics for the Danish and British populations are shown in Tables 1 and 2 , respectively. Pairwise LD (r2) is shown in Fig. 1 for the 10 PINK1 genotypes in the British cohort. Two pairs of SNPs were in near perfect LD (rs1043424 vs. rs12410193: r2=0.99 and rs622525 vs. rs3131713: r2=0.98); therefore, only the results for 8 independent SNPs are reported below. All genotypes were in Hardy Weinberg equilibrium (P>0.1) (see Supplemental Table S1). Genotype counts are shown in Table 3 . Because most of these genotypes were selected as tagging SNPs, marking distinct genomic regions, the level of pairwise LD is moderate to low for the tagging SNPs.


View this table:
[in this window]
[in a new window]

  		Table 1. Danish cohort


View this table:
[in this window]
[in a new window]

  		Table 2. UK MRC Ely study (population-based cohort)


View this table:
[in this window]
[in a new window]

  		Table 3. Genotype counts (n) for each genotype and phenotype combination

PINK1 transcript levels and type 2 diabetes (Danish cohort)
Within the Danish cohort, we examined the relationship between diabetes status and levels of skeletal muscle PINK1 transcripts. Of the 208 individuals in the cohort, 88 individuals had fasting or 2-h glucose concentrations consistent with a diagnosis of type 2 diabetes (cases) and 89 individuals had normal 2-h glucose concentrations (controls). The remainder of the Danish cohort (n=41) had impaired fasting and/or 2-h glucose concentrations and to maximize the sample size were also included as controls. Using the case-control set described above and after adjustment for 18S, age, sex, and BMI, we observed a significant interaction between PINK1 transcript levels and obesity (P=0.005) on type 2 diabetes risk. Among obese participants (n=105), the association between PINK1 transcript levels and type 2 diabetes was inverse and statistically significant [odds ratio (OR) per SD increase in expression levels=0.44; 95% confidence interval (CI): 0.23, 0.84; P=0.013]. In nonobese participants (n=103), PINK1 transcript levels were not significantly related with type 2 diabetes (OR per SD increase in expression levels=1.20; 95% CI: 0.82, 1.76; P=0.34).

Associations between PINK1 genotypes and PINK1 transcript levels (Danish cohort)
We assessed the association between PINK1 variants and skeletal muscle PINK1 transcript levels. As shown in Table 4 , five of these variants (rs10799655, P=0.005; rs3738136, P=0.039; rs2298299, P=0.005; rs650616, P=0.0006; and rs622525, P<0.0001) were significantly associated with PINK1 transcript levels after adjustment for 18S, age, sex, and BMI. In general, the minor allele was associated with higher PINK1 expression levels, with the exception of the one SNP in the coding region, rs3738136, and one of the intergenic SNPs, rs2298299, for which the minor alleles were associated with a lower expression of PINK1.


View this table:
[in this window]
[in a new window]

  		Table 4. Associations between PINK1 genotypes and levels of PINK1 transcript (Danish cohort only), glucose (fasting and 2 h), NEFA (fasting and 30 min), and VO2max (British cohort only)

Associations between PINK1 genotypes and NEFA concentrations (British cohort)
As described above, the association between PINK1 transcript levels and type 2 diabetes was observed in obese but not lean individuals, indicating that obesity modifies the metabolic effects of the PINK1 gene. Therefore, we tested for interaction between PINK1 genotypes and BMI on NEFA concentrations. In these models, significant interactions were observed for several PINK1 genotypes (rs1043424, rs1079965, rs3738136, rs622525, rs1241019, and rs650616) on fasting NEFA and 30-min NEFA concentrations (Fig. 2 A, B). The interaction terms for several of the remaining SNPs approached statistical significance (P<0.1) (data not shown). Therefore, we stratified the cohort by obese (BMI≥30 kg/m2) and nonobese (BMI<30 kg/m2) individuals and repeated the analyses. In nonobese individuals, the PINK1 genotypes were inversely associated with fasting NEFA (rs1043424, P=0.005; rs3738136, P=0.014; rs3820321, P=0.027; rs1079965, P=0.003; rs1241019, =0.003; and rs650616, P=0.0008) and 30-min NEFA (rs1043424, P=0.004; rs1241019, P=0.003; and rs650616, P=0.0007) concentrations; with the exception of rs3738136, the direction of these associations is consistent with the PINK1 genotype-transcript level results in the Danish cohort. By contrast, no significant inverse associations were observed for any PINK1 genotypes and NEFA concentrations in obese individuals (Fig. 2A, B ).


Figure 2
View larger version (16K):
[in this window]
[in a new window]

  		Figure 2. Interaction between PINK1 genotypes and BMI on fasting (A) and 30 min (B) NEFA levels in the MRC Ely cohort. •, nonobese individuals; {circ}, obese individuals. Data are means ± SE, adjusted for age, sex, and BMI. P values for main effects and interaction models are from the linear regression models reported in the Results section.

Associations between PINK1 genotypes and maximal oxidative capacity (VO2max) (British cohort)
VO2max is expressed relative to body mass; therefore, we did not test for gene-obesity interactions with this phenotype. In the main effect models, which were tested in the entire British cohort, 3 SNPs (rs3738136, rs1043424, and rs650616) were nominally associated with VO2max (P<0.05). As shown in Table 3 , the magnitude of the association at each locus corresponded to ~1 ml/kg/min higher VO2max per copy of the minor allele.

Associations between PINK1 genotypes and glucose concentrations (British cohort)
We tested for gene-obesity interactions on glucose levels, but none was statistically significant. Therefore, we tested for associations between the 8 PINK1 allelic variants and fasting and 2-h glucose levels in the entire UK cohort. Only one SNP was nominally statistically associated with 2-h glucose levels (rs1079965, P=0.029).

Adjustment for multiple hypothesis testing
We undertook adjustments for multiple hypotheses testing to help elucidate which of our findings are robust to type 1 error. In these analyses, the associations with transcript levels for genotypes rs10799655 (Padjusted=0.02), rs2298299 (Padjusted=0.025), rs650616 (Padjusted=0.0036), and rs622525 (Padjusted=0.0007) remained statistically significant. In lean individuals in the obesity-dependent analyses on plasma fasting NEFA levels, genotypes rs1079965 (Padjusted=0.015), rs3738136 (Padjusted=0.042), rs1043424 (Padjusted=0.02), rs650616 (Padjusted=0.0056), and rs1241019 (Padjusted=0.018) remained significant, and for the 30-min NEFA analyses, genotypes rs1043424 (Padjusted=0.02), rs650616 (Padjusted=0.0049), and rs1241019 (Padjusted=0.018) remained significant. No genotype associations with glucose, NEFAs or VO2max when tested in the entire cohort remained statistically significant. Similarly, none of the associations with NEFAs in the obese group was statistically significant after adjustment for multiple testing (data not shown).

Fabp4 is down-regulated in primary adipocytes following Pink1 knockdown (murine cell studies)
To determine the mechanisms that underlie the PINK1 genotype associations with PINK1 transcript levels and NEFA metabolism, we isolated murine brown preadipocytes and established an RNAi model in these cells. Cells were transfected at day 1 and harvested at day 3 of culture. Gene expression was measured by qPCR. By day 3 of culture, Pink1 was nearly completely ablated by using siRNAs targeting two different sites of Pink1 (Fig. 3 A). The ablation of Pink1 led to a reduction in the expression of the lipid chaperone Fabp4 significantly for both siRNAs (Fig. 3B ) which was evaluated in response to the clinical association data. By contrast, other markers of mitochondrial biogenesis or differentiation were unaffected by Pink1 knockdown (Supplemental Figs. S1 and S2).


Figure 3
View larger version (14K):
[in this window]
[in a new window]

  		Figure 3. Gene expression after Pink1 knockdown in preadipocytes. Primary cultures of murine brown preadipocytes were transfected with siRNAs targeting Pink1 exon 1–2 (P1) and exon 3 (P3) at day 1 and harvested for Pink1 (A) and Fabp4 (B) gene expression analysis day 3 of culture (n=3–4). Controls were untreated (UT), Lipofectamine-treated (LF), and siRNA control transfected (Ctrl) cells. When there was no significant difference between the controls UT, LF, and Ctrl, these were pooled to increase the power of the statistical analysis. Gene expression was analyzed by use of real-time qPCR. *P < 0.05, **P < 0.01, ***P < 0.001 vs. UT; Figure 3 Figure 3 Figure 3P < 0.001 vs. LF; ###P < 0.001vs. Ctrl; 1-way ANOVA with Tukey’s post hoc test.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The present study implicates genetic variation at the PINK1 locus in disturbed oxidative energy metabolism. We found evidence of consistent associations between PINK1 expression levels in skeletal muscle and type 2 diabetes, between PINK1 genotypes and muscle expression levels, and between PINK1 genotypes and levels of NEFAs and maximal oxidative capacity. Several of the genotype associations with NEFA levels were strong and inverse in nonobese people and were absent in obese people. Notably, one of the most consistently associated variants in this study (rs622525) was also nominally inversely associated with type 2 diabetes in a recent genome-wide scan of Scandinavian adults (per allele OR=0.83; 95% CI=0.69, 0.99; P<0.05) (28) . In our study, the rs622525 genotype was strongly positively associated with PINK1 transcript levels and weakly inversely associated with glucose and NEFA levels. Elsewhere, in the Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) study, a type 2 diabetes case-control consortium of ~50,000 individuals (29) , several of the SNPs genotyped in our cohorts (or their proxies) were associated with type 2 diabetes. For example, the rs3820321 genotype was nominally associated with increased type 2 diabetes risk (z risk score=2.36; P=0.018); in the present study, this genotype tended to be associated with lower maximal aerobic capacity in the overall cohort and was nominally associated with decreased NEFA levels in lean individuals. The genotype associations for rs3820321 were also in a consistent direction for expression and glucose and NEFA levels, although none was statistically significant. Moreover, the CDA rs2072671 variant, which in European whites is in modest long-range LD with rs1043424 (HapMap D'=0.84; r2=0.19) and rs622525 (HapMap D'=0.00; r2=0.26), was significantly associated with a decreased risk of type 2 diabetes in the DIAGRAM study (P=0.0045) (29) . Consistently, in the present study, rs1043424 was robustly associated with lower fasting and 30-min NEFA levels in lean individuals and nominally associated with decreased fasting and 30-min NEFA concentrations and higher maximal oxygen uptake in the overall cohort.

PINK1 conveys a BMI-dependent phenotype and influences oxidative metabolism
Mutations in the coding region of PINK1 have been linked to Parkinson’s disease (3 , 30 , 31) . In this study, we observed significant associations between 5 of the 8 selected PINK1 SNPs and PINK1 skeletal muscle mRNA expression levels. Five of these SNPs locate to intergenic regions of PINK1, and the minor allele at 4 of these loci corresponds with increased PINK1 mRNA levels. The minor allele for one SNP located in the coding region (rs3738136) corresponded with decreased PINK1 expression. This is consistent with a previous report, in which the minor allele at this locus was associated with the development of Parkinson’s disease (30) . However, in the current study, the same SNP was also associated with lower NEFA levels in nonobese individuals, which is inconsistent with the hypothesized effect of this variant. Therefore, given that in this study the associations between rs3738136 and transcript level or NEFA concentration were nominally statistically significant, we conclude that one or both of these observations may be false.

Although some of the genotypes associated with transcript level have inverse associations (i.e., the minor allele is associated with lower expression levels), others are positively associated. There are several plausible explanations for these differences. One is that because these are mainly tag SNPs, in some instances the minor allele tags the minor unobserved functional allele and in other cases it tags the major unobserved functional allele. It is also possible that minor and major alleles have different effects on transcription, depending on their location within the gene.

In the current study, several PINK1 genotypes are associated with PINK1 expression levels. In a concordant direction, the same SNPs were tentatively associated with maximal aerobic capacity and plasma levels of glucose and NEFA, suggesting a role for PINK1 in oxidative energy metabolism. This conclusion is supported by our recent finding that PINK1 influences glucose uptake (2) . It has been suggested that PINK1 protects the cell against ROS (13 14 15) and consequent apoptosis (14 , 16) . Indeed, the lipid peroxidation product malondialdehyde, an early marker of Parkinson’s disease, was significantly increased in cultured fibroblasts from patients with Parkinson’s disease homozygous for PINK1 gene mutations (15) . Importantly, intracellular accumulation of fatty acid metabolites can contribute to the production of ROS (32) . Thus, as well as directly protecting against ROS in an antioxidant manner, PINK1 may influence the production of ROS via the regulation of intracellular lipid metabolism or acetyl group accumulation in the mitochondria.

It is also possible that PINK1 participates in several pathways promoting fitness and survival of the cell. The assumption of a regulatory role in mitochondrial oxidation is consistent with the severe degenerative mitochondrial phenotypes observed in adult Drosophila when the PINK1 homolog is ablated (6 7 8) and the ability to rescue this phenotype with antioxidants (13) . Whereas Drosophila experience severe apoptosis after PINK1 ablation, mice are more robust to this insult (33 , 34) , probably because in mice and other mammals adequate compensatory mechanisms exist. However, the Pink1–/– mouse is characterized by a reduction in the evoked release of dopamine (34) , a defect likely to occur due to impaired energy homeostasis, which broadly supports our interpretation of the current cell and human data.

To determine whether variation in PINK1 expression might explain the genotype associations with NEFA, we knocked down Pink1 in a mitochondria-rich primary adipocyte model (murine brown adipocytes). We had established previously that reduced PINK1 expression results in impaired cell glucose metabolism (2) . In the present study, we demonstrated that loss of PINK1 expression reduced the expression of the fatty acid transport gene, Fabp4, without any evidence of loss of mitochondria or cellular apoptosis. FAPB4 is a lipid chaperone highly expressed in adipocytes but is also present in human skeletal muscle (22 , 35) . FABP4 acts as a fatty acid transporter between intracellular compartments (36) and is regulated by the level of intracellular fatty acids via a peroxisome proliferator response element (37) . We hypothesize that the ablation of PINK1 impairs mitochondrial oxidation, resulting in reduced lipid oxidation and a subsequent feedback suppression of FABP4 gene expression.

Obesity is associated with impaired skeletal muscle mitochondrial fatty acid oxidation (38) , which in turn can lead to an accumulation of intracellular fatty acid metabolites and disrupted insulin signaling (1) . The adverse effects on metabolic risk are mediated in part by the activity of the FABP4 gene. For example, Fabp4–/– fat-fed mice become obese yet are less susceptible to insulin resistance and hyperinsulinemia (39 , 40) . Furthermore, the absence of Fabp4 results in reduced lipolysis and lipolysis-induced insulin secretion in obese mice (40) . The exact mechanisms underlying these phenomena are unknown, although a functional compensation by keratinocyte fatty acid binding protein has been observed (41) . In the present study, PINK1 genotypes were robustly inversely associated with plasma NEFA levels in nonobese individuals but not in obese individuals. This finding that PINK1 variants have a marked effect on NEFA levels in people with a normal body mass, whereas the effect is lost in obese individuals, possibly due to additional factors dominating the regulation of mitochondrial capacity or fatty acid oxidation. Furthermore, obese individuals with diabetes had lower skeletal muscle PINK1 gene expression than nondiabetic obese individuals in one of our cohorts, which is likely to be a consequence of both genetic and lifestyle factors (2) . Taken together, this suggests that relative PINK1 deficiency could contribute to impaired NEFA metabolism and that obesity (or factors associated with obesity, such as physical inactivity) interacts with PINK1 to modify this relationship.

In summary, our findings demonstrate that polymorphisms at the PINK1 locus are associated with altered expression of this gene in skeletal muscle (a tissue that we can readily study) and thus it is possible that genetic variation influences PINK1 expression in other major organs associated with insulin action. These polymorphisms are also associated with markers of impaired oxidative substrate metabolism and thus our observations suggest that PINK1 sequence variants influence metabolic fitness and susceptibility to type 2 diabetes. However, many of our findings are at best nominally statistically significant, indicating that confirmatory studies, which are larger than those reported here, are needed if statistically robust effects of PINK1 variation on oxidative energy metabolism are to be shown.


   ACKNOWLEDGMENTS
 
We are grateful to the individuals who took part in the studies described herein. We thank Tomas Waldén for his technical support with DNA isolation from the Danish cohort and for the brown adipose dissections. We also thank Matt Sims, Stewart Laing, and Cathy Elks for expert genotyping assistance. The MRC Ely study was supported by the UK Medical Research Council and the Wellcome Trust (N.J.W.). The Danish cohort was supported by the Commission of the European Communities (contract LSHM-CT-2004-005272 EXGENESIS and Danish National Research Foundation grant DG 02-512-555. P.W.F. was supported by grants from the Swedish Diabetes Association, Novo Nordisk, the Swedish Heart-Lung Foundation, and Västerbottens Health Authority. J.A.T. was supported by the Swedish Diabetes Association, the Swedish Parkinson’s Society, and SSF Genome Canada. The funding agencies did not influence study design, data collection, data analysis, the decision to publish, or the preparation of the manuscript.


   FOOTNOTES
 
1 These authors contributed equally to this work.

Received for publication January 26, 2008. Accepted for publication April 24, 2008.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Lowell, B. B., Shulman, G. I. (2005) Mitochondrial dysfunction and type 2 diabetes. Science 307,384-387[Abstract/Free Full Text]
  2. Scheele, C., Nielsen, A. R., Walden, T. B., Sewell, D. A., Fischer, C. P., Brogan, R. J., Petrovic, N., Larsson, O., Tesch, P. A., Wennmalm, K., Hutchinson, D. S., Cannon, B., Wahlestedt, C., Pedersen, B. K., Timmons, J. A. (2007) Altered regulation of the PINK1 locus: a link between type 2 diabetes and neurodegeneration?. FASEB J. 21,3653-3665[Abstract/Free Full Text]
  3. Valente, E. M., Abou-Sleiman, P. M., Caputo, V., Muqit, M. M., Harvey, K., Gispert, S., Ali, Z., Del Turco, D., Bentivoglio, A. R., Healy, D. G., Albanese, A., Nussbaum, R., Gonzalez-Maldonado, R., Deller, T., Salvi, S., Cortelli, P., Gilks, W. P., Latchman, D. S., Harvey, R. J., Dallapiccola, B., Auburger, G., Wood, N. W. (2004) Hereditary early-onset Parkinson’s disease caused by mutations in PINK1. Science 304,1158-1160[Abstract/Free Full Text]
  4. Muqit, M. M., Gandhi, S., Wood, N. W. (2006) Mitochondria in Parkinson disease: back in fashion with a little help from genetics. Arch. Neurol. 63,649-654[Abstract/Free Full Text]
  5. Schapira, A. H. (2007) Mitochondrial dysfunction in Parkinson’s disease. Cell Death Differ. 14,1261-1266[CrossRef][Medline]
  6. Clark, I. E., Dodson, M. W., Jiang, C., Cao, J. H., Huh, J. R., Seol, J. H., Yoo, S. J., Hay, B. A., Guo, M. (2006) Drosophila pink1 is required for mitochondrial function and interacts genetically with parkin. Nature 441,1162-1166[CrossRef][Medline]
  7. Park, J., Lee, S. B., Lee, S., Kim, Y., Song, S., Kim, S., Bae, E., Kim, J., Shong, M., Kim, J. M., Chung, J. (2006) Mitochondrial dysfunction in Drosophila PINK1 mutants is complemented by parkin. Nature 441,1157-1161[CrossRef][Medline]
  8. Yang, Y., Gehrke, S., Imai, Y., Huang, Z., Ouyang, Y., Wang, J. W., Yang, L., Beal, M. F., Vogel, H., Lu, B. (2006) Mitochondrial pathology and muscle and dopaminergic neuron degeneration caused by inactivation of Drosophila Pink1 is rescued by Parkin. Proc. Natl. Acad. Sci. U. S. A. 103,10793-10798[Abstract/Free Full Text]
  9. Gandhi, S., Muqit, M. M., Stanyer, L., Healy, D. G., Abou-Sleiman, P. M., Hargreaves, I., Heales, S., Ganguly, M., Parsons, L., Lees, A. J., Latchman, D. S., Holton, J. L., Wood, N. W., Revesz, T. (2006) PINK1 protein in normal human brain and Parkinson’s disease. Brain 129,1720-1731[Abstract/Free Full Text]
  10. Takatori, S., Ito, G., Iwatsubo, T. (2008) Cytoplasmic localization and proteasomal degradation of N-terminally cleaved form of PINK1. Neurosci. Lett. 430,13-17[CrossRef][Medline]
  11. Haque, M. E., Thomas, K. J., D'Souza, C., Callaghan, S., Kitada, T., Slack, R. S., Fraser, P., Cookson, M. R., Tandon, A., Park, D. S. (2008) Cytoplasmic Pink1 activity protects neurons from dopaminergic neurotoxin MPTP. Proc. Natl. Acad. Sci. U. S. A. 105,1716-1721[Abstract/Free Full Text]
  12. Sim, C. H., Lio, D. S., Mok, S. S., Masters, C. L., Hill, A. F., Culvenor, J. G., Cheng, H. C. (2006) C-terminal truncation and Parkinson’s disease-associated mutations down-regulate the protein serine/threonine kinase activity of PTEN-induced kinase-1. Hum. Mol. Genet. 15,3251-3262[Abstract/Free Full Text]
  13. Wang, D., Qian, L., Xiong, H., Liu, J., Neckameyer, W. S., Oldham, S., Xia, K., Wang, J., Bodmer, R., Zhang, Z. (2006) Antioxidants protect PINK1-dependent dopaminergic neurons in Drosophila. Proc. Natl. Acad. Sci. U. S. A. 103,13520-13525[Abstract/Free Full Text]
  14. Pridgeon, J. W., Olzmann, J. A., Chin, L. S., Li, L. (2007) PINK1 protects against oxidative stress by phosphorylating mitochondrial chaperone TRAP1. PLoS Biol. 5,e172[CrossRef][Medline]
  15. Hoepken, H. H., Gispert, S., Morales, B., Wingerter, O., Del Turco, D., Mulsch, A., Nussbaum, R. L., Muller, K., Drose, S., Brandt, U., Deller, T., Wirth, B., Kudin, A. P., Kunz, W. S., Auburger, G. (2007) Mitochondrial dysfunction, peroxidation damage and changes in glutathione metabolism in PARK6. Neurobiol. Dis. 25,401-411[CrossRef][Medline]
  16. Petit, A., Kawarai, T., Paitel, E., Sanjo, N., Maj, M., Scheid, M., Chen, F., Gu, Y., Hasegawa, H., Salehi-Rad, S., Wang, L., Rogaeva, E., Fraser, P., Robinson, B., St George-Hyslop, P., Tandon, A. (2005) Wild-type PINK1 prevents basal and induced neuronal apoptosis, a protective effect abrogated by Parkinson disease-related mutations. J. Biol. Chem. 280,34025-34032[Abstract/Free Full Text]
  17. Bergstrom, J., Hultman, E. (1966) Muscle glycogen synthesis after exercise: an enhancing factor localized to the muscle cells in man. Nature 210,309-310[CrossRef][Medline]
  18. World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic World Health Organization Geneva. Technical Report Series 894
  19. Franks, P. W., Ekelund, U., Brage, S., Luan, J., Schafer, A. J., O'Rahilly, S., Barroso, I., Wareham, N. J. (2007) PPARGC1A coding variation may initiate impaired NEFA clearance during glucose challenge. Diabetologia 50,569-573[CrossRef][Medline]
  20. Ekelund, U., Franks, P. W., Sharp, S., Brage, S., Wareham, N. J. (2007) Increase in physical activity energy expenditure is associated with reduced metabolic risk independent of change in fatness and fitness. Diabetes Care 30,2101-2106[Abstract/Free Full Text]
  21. Franks, P. W., Ekelund, U., Brage, S., Wong, M. Y., Wareham, N. J. (2004) Does the association of habitual physical activity with the metabolic syndrome differ by level of cardiorespiratory fitness?. Diabetes Care 27,1187-1193[Abstract/Free Full Text]
  22. Timmons, J. A., Larsson, O., Jansson, E., Fischer, H., Gustafsson, T., Greenhaff, P. L., Ridden, J., Rachman, J., Peyrard-Janvid, M., Wahlestedt, C., Sundberg, C. J. (2005) Human muscle gene expression responses to endurance training provide a novel perspective on Duchenne muscular dystrophy. FASEB J. 19,750-760[Abstract/Free Full Text]
  23. Barroso, I., Luan, J., Middelberg, R. P., Harding, A. H., Franks, P. W., Jakes, R. W., Clayton, D., Schafer, A. J., O'Rahilly, S., Wareham, N. J. (2003) Candidate gene association study in type 2 diabetes indicates a role for genes involved in β-cell function as well as insulin action. PLoS Biol. 1,E20[CrossRef][Medline]
  24. Nechad, M., Nedergaard, J., Cannon, B. (1987) Noradrenergic stimulation of mitochondriogenesis in brown adipocytes differentiating in culture. Am. J. Physiol. 253,C889-C894[Medline]
  25. The International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437,1299-1320[CrossRef][Medline]
  26. Brambor, T., Roberts Clarks, W., Golder, M. (2006) Understanding interaction models: improving empirical analyses. Pol. Anal. 14,63-82
  27. Holm, S. (1979) A simple sequentially rejective Bonferroni test procedure. Scand. J. Stat. 6,5-70
  28. Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., Hughes, T. E., Groop, L., Altshuler, D., Almgren, P., Florez, J. C., Meyer, J., Ardlie, K., Bengtsson Bostrom, K., Isomaa, B., Lettre, G., Lindblad, U., Lyon, H. N., Melander, O., Newton-Cheh, C., Nilsson, P., Orho-Melander, M., Rastam, L., Speliotes, E. K., Taskinen, M. R., Tuomi, T., Guiducci, C., Berglund, A., Carlson, J., Gianniny, L., Hackett, R., Hall, L., Holmkvist, J., Laurila, E., Sjogren, M., Sterner, M., Surti, A., Svensson, M., Svensson, M., Tewhey, R., Blumenstiel, B., Parkin, M., Defelice, M., Barry, R., Brodeur, W., Camarata, J., Chia, N., Fava, M., Gibbons, J., Handsaker, B., Healy, C., Nguyen, K., Gates, C., Sougnez, C., Gage, D., Nizzari, M., Gabriel, S. B., Chirn, G. W., Ma, Q., Parikh, H., Richardson, D., Ricke, D., Purcell, S. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316,1331-1336[Abstract/Free Full Text]
  29. Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., Hu, T., de Bakker, P. I., Abecasis, G. R., Almgren, P., Andersen, G., Ardlie, K., Bostrom, K. B., Bergman, R. N., Bonnycastle, L. L., Borch-Johnsen, K., Burtt, N. P., Chen, H., Chines, P. S., Daly, M. J., Deodhar, P., Ding, C. J., Doney, A. S., Duren, W. L., Elliott, K. S., Erdos, M. R., Frayling, T. M., Freathy, R. M., Gianniny, L., Grallert, H., Grarup, N., Groves, C. J., Guiducci, C., Hansen, T., Herder, C., Hitman, G. A., Hughes, T. E., Isomaa, B., Jackson, A. U., Jorgensen, T., Kong, A., Kubalanza, K., Kuruvilla, F. G., Kuusisto, J., Langenberg, C., Lango, H., Lauritzen, T., Li, Y., Lindgren, C. M., Lyssenko, V., Marvelle, A. F., Meisinger, C., Midthjell, K., Mohlke, K. L., Morken, M. A., Morris, A. D., Narisu, N., Nilsson, P., Owen, K. R., Palmer, C. N., Payne, F., Perry, J. R., Pettersen, E., Platou, C., Prokopenko, I., Qi, L., Qin, L., Rayner, N. W., Rees, M., Roix, J. J., Sandbaek, A., Shields, B., Sjogren, M., Steinthorsdottir, V., Stringham, H. M., Swift, A. J., Thorleifsson, G., Thorsteinsdottir, U., Timpson, N. J., Tuomi, T., Tuomilehto, J., Walker, M., Watanabe, R. M., Weedon, M. N., Willer, C. J., Illig, T., Hveem, K., Hu, F. B., Laakso, M., Stefansson, K., Pedersen, O., Wareham, N. J., Barroso, I., Hattersley, A. T., Collins, F. S., Groop, L., McCarthy, M. I., Boehnke, M., Altshuler, D. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40,638-645[CrossRef][Medline]
  30. Wang, F., Feng, X., Ma, J., Zou, H., Chan, P. (2006) A common A340T variant in PINK1 gene associated with late-onset Parkinson’s disease in Chinese. Neurosci. Lett. 410,121-125[CrossRef][Medline]
  31. Grunewald, A., Breedveld, G. J., Lohmann-Hedrich, K., Rohe, C. F., Konig, I. R., Hagenah, J., Vanacore, N., Meco, G., Antonini, A., Goldwurm, S., Lesage, S., Durr, A., Binkofski, F., Siebner, H., Munchau, A., Brice, A., Oostra, B. A., Klein, C., Bonifati, V. (2007) Biological effects of the PINK1 c. 1366C>T mutation: implications in Parkinson disease pathogenesis. Neurogenetics 8,103-109[CrossRef][Medline]
  32. Boden, G. (2002) Interaction between free fatty acids and glucose metabolism. Curr. Opin. Clin. Nutr. Metab. Care 5,545-549[CrossRef][Medline]
  33. Zhou, H., Falkenburger, B. H., Schulz, J. B., Tieu, K., Xu, Z., Xia, X. G. (2007) Silencing of the Pink1 gene expression by conditional RNAi does not induce dopaminergic neuron death in mice. Int. J. Biol. Sci. 3,242-250[Medline]
  34. Kitada, T., Pisani, A., Porter, D. R., Yamaguchi, H., Tscherter, A., Martella, G., Bonsi, P., Zhang, C., Pothos, E. N., Shen, J. (2007) Impaired dopamine release and synaptic plasticity in the striatum of PINK1-deficient mice. Proc. Natl. Acad. Sci. U. S. A. 104,11441-11446[Abstract/Free Full Text]
  35. Fischer, H., Gustafsson, T., Sundberg, C. J., Norrbom, J., Ekman, M., Johansson, O., Jansson, E. (2006) Fatty acid binding protein 4 in human skeletal muscle. Biochem. Biophys. Res. Commun. 346,125-130[CrossRef][Medline]
  36. Haunerland, N. H., Spener, F. (2004) Fatty acid-binding proteins—insights from genetic manipulations. Prog. Lipid Res. 43,328-349[CrossRef][Medline]
  37. Tontonoz, P., Hu, E., Graves, R. A., Budavari, A. I., Spiegelman, B. M. (1994) mPPAR{gamma}2: tissue-specific regulator of an adipocyte enhancer. Genes Dev. 8,1224-1234[Abstract/Free Full Text]
  38. Steinberg, G. R. (2007) Inflammation in obesity is the common link between defects in fatty acid metabolism and insulin resistance. Cell Cycle 6,888-894[Medline]
  39. Hotamisligil, G. S., Johnson, R. S., Distel, R. J., Ellis, R., Papaioannou, V. E., Spiegelman, B. M. (1996) Uncoupling of obesity from insulin resistance through a targeted mutation in aP2, the adipocyte fatty acid binding protein. Science 274,1377-1379[Abstract/Free Full Text]
  40. Uysal, K. T., Scheja, L., Wiesbrock, S. M., Bonner-Weir, S., Hotamisligil, G. S. (2000) Improved glucose and lipid metabolism in genetically obese mice lacking aP2. Endocrinology 141,3388-3396[Abstract/Free Full Text]
  41. Shaughnessy, S., Smith, E. R., Kodukula, S., Storch, J., Fried, S. K. (2000) Adipocyte metabolism in adipocyte fatty acid binding protein knockout mice (aP2–/–) after short-term high-fat feeding: functional compensation by the keratinocyte [correction of keritinocyte] fatty acid binding protein. Diabetes 49,904-911[Abstract]




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Supplemental Data
Right arrow All Versions of this Article:
fj.08-107086v1
22/9/3135    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Franks, P. W.
Right arrow Articles by Timmons, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Franks, P. W.
Right arrow Articles by Timmons, J. A.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS