Uncoupling protein 2 haplotype does not affect human brain structure and function in a sample of community-dwelling older adults

Verena Heise, Enikő Zsoldos, Sana Suri, Claire Sexton, Anya Topiwala, Nicola Filippini, Abda Mahmood, Charlotte L Allan, Archana Singh-Manoux, Mika Kivimäki, Clare E Mackay, Klaus P Ebmeier, Verena Heise, Enikő Zsoldos, Sana Suri, Claire Sexton, Anya Topiwala, Nicola Filippini, Abda Mahmood, Charlotte L Allan, Archana Singh-Manoux, Mika Kivimäki, Clare E Mackay, Klaus P Ebmeier

Abstract

Uncoupling protein 2 (UCP2) is a mitochondrial membrane protein that plays a role in uncoupling electron transport from adenosine triphosphate (ATP) formation. Polymorphisms of the UCP2 gene in humans affect protein expression and function and have been linked to survival into old age. Since UCP2 is expressed in several brain regions, we investigated in this study whether UCP2 polymorphisms might 1) affect occurrence of neurodegenerative or mental health disorders and 2) affect measures of brain structure and function. We used structural magnetic resonance imaging (MRI), diffusion-weighted MRI and resting-state functional MRI in the neuroimaging sub-study of the Whitehall II cohort. Data from 536 individuals aged 60 to 83 years were analyzed. No association of UCP2 polymorphisms with the occurrence of neurodegenerative disorders or grey and white matter structure or resting-state functional connectivity was observed. However, there was a significant effect on occurrence of mood disorders in men with the minor alleles of -866G>A (rs659366) and Ala55Val (rs660339)) being associated with increasing odds of lifetime occurrence of mood disorders in a dose dependent manner. This result was not accompanied by effects of UCP2 polymorphisms on brain structure and function, which might either indicate that the sample investigated here was too small and underpowered to find any significant effects, or that potential effects of UCP2 polymorphisms on the brain are too subtle to be picked up by any of the neuroimaging measures used.

Conflict of interest statement

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

References

    1. Krauss S, Zhang CY, Lowell BB. The mitochondrial uncoupling-protein homologues. Nat Rev Mol Cell Biol. 2005;6(3):248–61. .
    1. Diano S, Urbanski HF, Horvath B, Bechmann I, Kagiya A, Nemeth G, et al. Mitochondrial uncoupling protein 2 (UCP2) in the nonhuman primate brain and pituitary. Endocrinology. 2000;141(11):4226–38. Epub 2000/11/23. doi: .
    1. Nakase T, Yoshida Y, Nagata K. Amplified expression of uncoupling proteins in human brain ischemic lesions. Neuropathology: official journal of the Japanese Society of Neuropathology. 2007;27(5):442–7. .
    1. Donadelli M, Dando I, Fiorini C, Palmieri M. UCP2, a mitochondrial protein regulated at multiple levels. Cell Mol Life Sci. 2014;71(7):1171–90. doi: .
    1. Cardoso S, Correia S, Carvalho C, Candeias E, Placido AI, Duarte AI, et al. Perspectives on mitochondrial uncoupling proteins-mediated neuroprotection. J Bioenerg Biomembr. 2015;47(1–2):119–31. Epub 2014/09/15. doi: .
    1. Andrews ZB, Horvath B, Barnstable CJ, Elsworth J, Yang L, Beal MF, et al. Uncoupling protein-2 is critical for nigral dopamine cell survival in a mouse model of Parkinson's disease. J Neurosci. 2005;25(1):184–91. doi: .
    1. Dietrich MO, Andrews ZB, Horvath TL. Exercise-induced synaptogenesis in the hippocampus is dependent on UCP2-regulated mitochondrial adaptation. J Neurosci. 2008;28(42):10766–71. doi: ;
    1. Dietrich MO, Horvath TL. The role of mitochondrial uncoupling proteins in lifespan. Pflugers Arch. 2010;459(2):269–75. doi: ;
    1. Andersen G, Dalgaard LT, Justesen JM, Anthonsen S, Nielsen T, Thorner LW, et al. The frequent UCP2 -866G>A polymorphism protects against insulin resistance and is associated with obesity: a study of obesity and related metabolic traits among 17 636 Danes. Int J Obes (Lond). 2013;37(2):175–81. Epub 2012/02/22. doi: .
    1. Qian L, Xu K, Xu X, Gu R, Liu X, Shan S, et al. UCP2 -866G/A, Ala55Val and UCP3 -55C/T polymorphisms in association with obesity susceptibility—a meta-analysis study. PLoS One. 2013;8(4):e58939 doi: ;
    1. Rose G, Crocco P, De Rango F, Montesanto A, Passarino G. Further support to the uncoupling-to-survive theory: the genetic variation of human UCP genes is associated with longevity. PLoS One. 2011;6(12):e29650 doi: ;
    1. Marmot M, Brunner E. Cohort Profile: the Whitehall II study. Int J Epidemiol. 2005;34(2):251–6. Epub 2004/12/04. doi: .
    1. Filippini N, Zsoldos E, Haapakoski R, Sexton CE, Mahmood A, Allan CL, et al. Study protocol: The Whitehall II imaging sub-study. BMC Psychiatry. 2014;14:159 Epub 2014/06/03. doi: ;
    1. Goldberg D W P. A user’s guide to the general health questionnaire. London: GL Assessment Limited; 2006.
    1. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. Epub 2005/04/09. doi: .
    1. Radloff L. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement. 1977;1:385–401.
    1. Wechsler D. Test of Premorbid Functioning. UK Version (TOPF UK) Bloomington, MN: Pearson Inc; 2011.
    1. First M, Gibbon M, Spitzer R, Williams J. User's Guide for the Structured Clinical Interview for DSM-IV-TR Axis I Disorders—Research Version—(SCID-I for DSM-IV-TR), November 2002 Revision: New York: Biometric Research Department, New York State Psychiatric Intitute; 2002.
    1. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, et al. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS One. 2008;3(10):e3583 doi: ;
    1. van der Kouwe AJ, Benner T, Salat DH, Fischl B. Brain morphometry with multiecho MPRAGE. Neuroimage. 2008;40(2):559–69. doi: ;
    1. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208–19. doi: .
    1. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57. Epub 2001/04/11. doi: .
    1. Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage. 2013;80:125–43. doi: ;
    1. Behrens T, Woolrich M, Jenkinson M, Johansen-Berg H, Nunes R, Clare S, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med. 2003;50(5):1077–88. doi:
    1. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005;360(1457):1001–13. Epub 2005/08/10. doi: ;
    1. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage. 2014;90:449–68. doi: ;
    1. Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage. 2014;95:232–47. doi: ;
    1. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 2009;48(1):63–72. Epub 2009/07/04. doi: ;
    1. Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, Vickers J, et al. Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain. 2007;130(Pt 9):2375–86. Epub 2007/08/19. doi: .
    1. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56(3):907–22. doi: ;
    1. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–505. Epub 2006/04/21. doi: .
    1. Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, et al. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci USA. 2009;106(17):7209–14. doi: .
    1. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage. 2009;44(1):83–98. Epub 2008/05/27. doi: .
    1. Gaunt TR, Rodriguez S, Day IN. Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX'. BMC bioinformatics. 2007;8:428 doi: ;
    1. Kivimaki M, Shipley MJ, Allan CL, Sexton CE, Jokela M, Virtanen M, et al. Vascular risk status as a predictor of later-life depressive symptoms: a cohort study. Biol Psychiatry. 2012;72(4):324–30. doi: ;
    1. Gorgolewski KJ, Varoquaux G, Rivera G, Schwarz Y, Ghosh SS, Maumet C, et al. : a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in neuroinformatics. 2015;9:8 doi: ;
    1. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45(12):1452–8. doi: ;
    1. Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez DG, Saad M, et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet. 2014;46(9):989–93. doi: ;
    1. Major Depressive Disorder Working Group of the Psychiatric GC, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013;18(4):497–511. doi: ;
    1. Wray NR, Pergadia ML, Blackwood DH, Penninx BW, Gordon SD, Nyholt DR, et al. Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol Psychiatry. 2012;17(1):36–48. doi: ;
    1. Manji H, Kato T, Di Prospero NA, Ness S, Beal MF, Krams M, et al. Impaired mitochondrial function in psychiatric disorders. Nat Rev Neurosci. 2012;13(5):293–307. doi: .

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