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Exploring the Genetic Association of the ABAT Gene with Alzheimer’s Disease

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A Correction to this article was published on 22 January 2021

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Abstract

Accumulating evidence demonstrated that GABAergic dysfunction contributes to the pathogenesis of Alzheimer’s disease (AD). The GABA aminotransferase (ABAT) gene encodes a mitochondrial GABA transaminase and plays key roles in the biogenesis and metabolism of gamma-aminobutyric acid (GABA), which is a major inhibitory neurotransmitter. In this study, we performed an integrative study at the genetic and expression levels to investigate the potential genetic association between the ABAT gene and AD. Through re-analyzing data from the currently largest meta-analysis of AD genome-wide association study (GWAS), we identified genetic variants in the 3’-UTR of ABAT as the top AD-associated SNPs (P < 1 × 10−4) in this gene. Functional annotation of these AD-associated SNPs indicated that these SNPs are located in the regulatory regions of transcription factors or/and microRNAs. Expression quantitative trait loci (eQTL) analysis and luciferase reporter assay showed that the AD risk alleles of these SNPs were associated with a reduced expression level of ABAT. Further analysis of mRNA expression data and single-cell transcriptome data of AD patients showed that ABAT reduction in the neuron is an early event during AD development. Overall, our results indicated that ABAT genetic variants may be associated with AD through affecting its mRNA expression. An abnormal level of ABAT will lead to a disturbance of the GABAergic signal pathway in AD brains.

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References

  1. Alzheimer’s Association (2020) 2020 Alzheimer’s disease facts and figures. Alzheimers Dement 16:391–460

  2. Huang Y, Mucke L (2012) Alzheimer mechanisms and therapeutic strategies. Cell 148(6):1204–1222

    Article  CAS  Google Scholar 

  3. Querfurth HW, LaFerla FM (2010) Alzheimer’s disease. N Engl J Med 362(4):329–344

    Article  CAS  Google Scholar 

  4. De Strooper B, Karran E (2016) The cellular phase of Alzheimer’s disease. Cell 164(4):603–615

    Article  Google Scholar 

  5. Sims R, Hill M, Williams J (2020) The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci 23(3):311–322

    Article  CAS  Google Scholar 

  6. Karch CM, Goate AM (2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77(1):43–51

    Article  CAS  Google Scholar 

  7. Zhang DF, Xu M, Bi R, Yao YG (2019) Genetic analyses of Alzheimer’s disease in China: achievements and perspectives. ACS Chem Neurosci 10(2):890–901

    Article  Google Scholar 

  8. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C et al (2013)Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45(12):1452–1458

    Article  CAS  Google Scholar 

  9. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK et al (2019)Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 51(3):404–413

  10. Bis JC, Jian X, Kunkle BW, Chen Y, Hamilton-Nelson KL, Bush WS et al (2020) Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol Psychiatry 25(8):1859–1875

    Article  CAS  Google Scholar 

  11. Zhang DF, Fan Y, Xu M, Wang G, Wang D, Li J, Kong LL, Zhou H et al (2019) Complement C7 is a novel risk gene for Alzheimer’s disease in Han Chinese. Natl Sci Rev 6(2):257–274

  12. Jonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson PV, Snaedal J, Bjornsson S, Huttenlocher J et al (2013) Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 368(2):107–116

  13. Zhou X, Chen Y, Mok KY, Zhao Q, Chen K, Hardy J et al (2018) Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer’s disease pathogenesis. Proc Natl Acad Sci U S A 115(8):1697–1706

    Article  CAS  Google Scholar 

  14. Ridge PG, Mukherjee S, Crane PK, Kauwe JS (2013) Alzheimer’s disease: analyzing the missing heritability. PLoS One 8(11):e79771

    Article  CAS  Google Scholar 

  15. Palop JJ, Mucke L (2016) Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat Rev Neurosci 17(12):777–792

    Article  CAS  Google Scholar 

  16. Bi D, Wen L, Wu Z, Shen Y (2020) GABAergic dysfunction in excitatory and inhibitory (E/I) imbalance drives the pathogenesis of Alzheimer’s disease. Alzheimers Dement 16(9):1312–1329

    Article  Google Scholar 

  17. Owens DF, Kriegstein AR (2002) Is there more to GABA than synaptic inhibition? Nat Rev Neurosci 3(9):715–727

    Article  CAS  Google Scholar 

  18. Chen WF, Maguire S, Sowcik M, Luo W, Koh K, Sehgal A (2015) A neuron-glia interaction involving GABA transaminase contributes to sleep loss in sleepless mutants. Mol Psychiatry 20(2):240–251

    Article  CAS  Google Scholar 

  19. Prevot T, Sibille E (2020) Altered GABA-mediated information processing and cognitive dysfunctions in depression and other brain disorders. Mol Psychiatry. https://doi.org/10.1038/s41380-020-0727-3

  20. Martin-Belmonte A, Aguado C, Alfaro-Ruiz R, Moreno-Martinez AE, de la Ossa L, Martinez-Hernandez J et al (2020) Density of GABAB receptors is reduced in granule cells of the hippocampus in a mouse model of Alzheimer’s disease. Int J Mol Sci 21(7)

  21. Martin-Belmonte A, Aguado C, Alfaro-Ruiz R, Moreno-Martinez AE, de la Ossa L, Martinez-Hernandez J et al (2020) Reduction in the neuronal surface of post and presynaptic GABAB receptors in the hippocampus in a mouse model of Alzheimer’s disease. Brain Pathol 30(3):554–575

    Article  CAS  Google Scholar 

  22. Limon A, Reyes-Ruiz JM, Miledi R (2012) Loss of functional GABA(A) receptors in the Alzheimer diseased brain. Proc Natl Acad Sci U S A 109(25):10071–10076

    Article  CAS  Google Scholar 

  23. Garcia-Marin V, Blazquez-Llorca L, Rodriguez JR, Boluda S, Muntane G, Ferrer I et al (2009) Diminished perisomatic GABAergic terminals on cortical neurons adjacent to amyloid plaques. Front Neuroanat 3:28

    Article  Google Scholar 

  24. Brawek B, Chesters R, Klement D, Muller J, Lerdkrai C, Hermes M et al (2018) A bell-shaped dependence between amyloidosis and GABA accumulation in astrocytes in a mouse model of Alzheimer’s disease. Neurobiol Aging 61:187–197

    Article  CAS  Google Scholar 

  25. Park JH, Ju YH, Choi JW, Song HJ, Jang BK, Woo J et al (2019) Newly developed reversible MAO-B inhibitor circumvents the shortcomings of irreversible inhibitors in Alzheimer’s disease. Sci Adv 5(3):eaav0316

    Article  CAS  Google Scholar 

  26. Lu MH, Zhao XY, Xu DE, Chen JB, Ji WL, Huang ZP, Pan TT, Xue LL et al (2020) Transplantation of GABAergic interneuron progenitor attenuates cognitive deficits of Alzheimer’s disease model mice. J Alzheimers Dis 75(1):245–260

  27. Govindpani K, Calvo-Flores Guzman B, Vinnakota C, Waldvogel HJ, Faull RL, Kwakowsky A (2017) Towards a better understanding of GABAergic remodeling in Alzheimer’s disease. Int J Mol Sci 18(8)

  28. Najm R, Jones EA, Huang Y (2019) Apolipoprotein E4, inhibitory network dysfunction, and Alzheimer’s disease. Mol Neurodegener 14(1):24

    Article  Google Scholar 

  29. Rice HC, de Malmazet D, Schreurs A, Frere S, Van Molle I, Volkov AN et al (2019) Secreted amyloid-beta precursor protein functions as a GABABR1a ligand to modulate synaptic transmission. Science 363(6423):eaao4827

    Article  CAS  Google Scholar 

  30. Calvo-Flores Guzman B, Kim S, Chawdhary B, Peppercorn K, Tate WP, Waldvogel HJ et al (2020)Amyloid-Beta1-42-induced increase in GABAergic tonic conductance in mouse hippocampal CA1 pyramidal cells. Molecules 25(3):693

  31. Zheng J, Li HL, Tian N, Liu F, Wang L, Yin Y, Yue L, Ma L et al (2020) Interneuron accumulation of phosphorylated tau impairs adult hippocampal neurogenesis by suppressing GABAergic transmission. Cell Stem Cell 26(3):331–345 e336

  32. Sherif FM, Ahmed SS (1995) Basic aspects of GABA-transaminase in neuropsychiatric disorders. Clin Biochem 28(2):145–154

    Article  CAS  Google Scholar 

  33. Ambrad Giovannetti E, Fuhrmann M (2019) Unsupervised excitation: GABAergic dysfunctions in Alzheimer’s disease. Brain Res 1707:216–226

    Article  CAS  Google Scholar 

  34. Koenig MK, Hodgeman R, Riviello JJ, Chung W, Bain J, Chiriboga CA, Ichikawa K, Osaka H et al (2017) Phenotype of GABA-transaminase deficiency. Neurology 88(20):1919–1924

  35. Aoyagi T, Wada T, Nagai M, Kojima F, Harada S, Takeuchi T et al (1990) Increased gamma-aminobutyrate aminotransferase activity in brain of patients with Alzheimer’s disease. Chem Pharm Bull (Tokyo) 38(6):1748–1749

    Article  CAS  Google Scholar 

  36. Aoyagi T, Wada T, Kojima F, Nagai M, Harada S, Takeuchi T et al (1990) Increase in aminobutyrate aminotransferase and cholineacetyltransferase in cerebrum of aged rats. Chem Pharm Bull (Tokyo) 38(6):1750–1752

    Article  CAS  Google Scholar 

  37. Sherif F, Gottfries CG, Alafuzoff I, Oreland L (1992) Brain gamma-aminobutyrate aminotransferase (GABA-T) and monoamine oxidase (MAO) in patients with Alzheimer’s disease. J Neural Transm Park Dis Dement Sect 4(3):227–240

    Article  CAS  Google Scholar 

  38. Hooli BV, Kovacs-Vajna ZM, Mullin K, Blumenthal MA, Mattheisen M, Zhang C, Lange C, Mohapatra G et al (2014) Rare autosomal copy number variations in early-onset familial Alzheimer’s disease. Mol Psychiatry 19(6):676–681

  39. Beecham GW, Bis JC, Martin ER, Choi SH, DeStefano AL, van Duijn CM et al (2017) The Alzheimer’s disease sequencing project: study design and sample selection. Neurol Genet 3(5):e194

    Article  Google Scholar 

  40. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779

  41. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26(18):2336–2337

  42. Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, Hilton JA, Jain K et al (2018) The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res 46(D1):D794–D801

  43. Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48(D1):D127–D131

    Article  CAS  Google Scholar 

  44. Liu W, Wang X (2019) Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol 20(1):18

    Article  CAS  Google Scholar 

  45. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46(3):310–315

    Article  CAS  Google Scholar 

  46. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L et al (2018) Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun 9(1):2282

    Article  Google Scholar 

  47. GTEx Consortium (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45(6):580–585

  48. GTEx Consortium (2015) Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348(6235):648–660

  49. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, Ruderfer DM, Oh EC et al (2016) Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19(11):1442–1453

  50. Ng B, White CC, Klein HU, Sieberts SK, McCabe C, Patrick E, Xu J, Yu L et al (2017) An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci 20(10):1418–1426

  51. Xu M, Zhang DF, Luo R, Wu Y, Zhou H, Kong LL, Bi R, Yao YG (2018) A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized hub genes as important upstream regulators in Alzheimer’s disease. Alzheimers Dement 14(2):215–229

    Article  Google Scholar 

  52. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, Menon M, He L et al (2019)Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570(7761):332–337

  53. Bam M, Yang X, Busbee BP, Aiello AE, Uddin M, Ginsberg JP, Galea S, Nagarkatti PS et al (2020) Increased H3K4me3 methylation and decreased miR-7113-5p expression lead to enhanced Wnt/beta-catenin signaling in immune cells from PTSD patients leading to inflammatory phenotype. Mol Med 26(1):110

  54. Wegerer M, Adena S, Pfennig A, Czamara D, Sailer U, Bettecken T, Müller-Myhsok B, Modell S et al (2013) Variants within the GABA transaminase (ABAT) gene region are associated with somatosensory evoked EEG potentials in families at high risk for affective disorders. Psychol Med 43(6):1207–1217

  55. Besse A, Wu P, Bruni F, Donti T, Graham BH, Craigen WJ, McFarland R, Moretti P et al (2015) The GABA transaminase, ABAT, is essential for mitochondrial nucleoside metabolism. Cell Metab 21(3):417–427

  56. Devine MJ, Kittler JT (2018) Mitochondria at the neuronal presynapse in health and disease. Nat Rev Neurosci 19(2):63–80

    Article  CAS  Google Scholar 

  57. Mattson MP, Gleichmann M, Cheng A (2008) Mitochondria in neuroplasticity and neurological disorders. Neuron 60(5):748–766

    Article  CAS  Google Scholar 

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Availability of Data and Material

All data generated or analyzed in this study are included in the current manuscript and supplementary materials.

Funding

This study was supported by the National Natural Science Foundation of China (31730037, 31970560 and 31970965), Yunnan Province (2019FA027), the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (CAS) (XDB02020003), the Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-SMC005), and the International Partnership Program of CAS (152453KYSB20170031).

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  1. Quanzhen Zheng and Rui Bi contributed equally to this work.

Authors and Affiliations

  1. College of Life Sciences, Anhui Normal University, Wuhu, 241002, Anhui, China

    Quanzhen Zheng & Ya-Ping Lu

  2. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China

    Quanzhen Zheng, Rui Bi, Min Xu, Deng-Feng Zhang & Yong-Gang Yao

  3. Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China

    Rui Bi, Deng-Feng Zhang & Yong-Gang Yao

  4. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China

    Rui Bi & Yong-Gang Yao

  5. Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, 410011, China

    Li-Wen Tan

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  1. Quanzhen Zheng
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Contributions

Y.-G.Y. and R.B. designed the study; M.X. and D.-F.Z. analyzed the data; R.B. and Q.Z. performed the experiments; R.B., Q.Z., and Y.-G.Y. drafted the manuscript; all authors revised and approved the manuscript.

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Correspondence to Yong-Gang Yao.

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The original online version of this article was revised: Assignment of affiliation number to “Yong-Gang Yao” should be 2, 3, 4.

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Zheng, Q., Bi, R., Xu, M. et al. Exploring the Genetic Association of the ABAT Gene with Alzheimer’s Disease. Mol Neurobiol 58, 1894–1903 (2021). https://doi.org/10.1007/s12035-020-02271-z

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