Advertisement

Common Variant in TREM1 Influencing Brain Amyloid Deposition in Mild Cognitive Impairment and Alzheimer’s Disease

  • Yue-Song Liu
  • Wen-Jing Yan
  • Chen-Chen Tan
  • Jie-Qiong Li
  • Wei Xu
  • Xi-Peng Cao
  • Lan TanEmail author
  • Jin-Tai YuEmail author
  • Alzheimer’s Disease Neuroimaging Initiative
Original Article

Abstract

Triggering receptor expressed on myeloid cells-1 (TREM1) has been reported to associate with Alzheimer’s disease (AD) pathology. Recently, TREM1 variant rs2234246A was reported to regulate TREM-1 protein and mRNA levels. We explored the effect of rs2234246 on AD specific biomarker (amyloid-β PET) to look into the role of this TREM1 locus in AD pathogenesis. We calculated the association of the TREM1 locus with amyloid deposition at baseline and follow-up using both multiple linear models and mixed effect models respectively in 522 the Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects. We also analyzed the association of TREM1 with this marker in subregions during the AD process. In the pooled sample, TREM1 rs2234246A was associated with the levels of mean standard uptake volume ratios (SUVRs) at baseline (p = 0.02) and the length of follow-up (p = 0.04) in the cross-sectional analysis and longitudinal study. Subgroup analyses showed no correlation between rs2234246A and amyloid deposition in the cognitively normal (CN) group. In the mild cognitive impairment (MCI) group, TREM1 rs2234246A reached significance at baseline (p = 0.04) and the length of follow-up (p = 0.04). In the AD group, TREM1 rs2234246A was associated with mean SUVR at baseline (p < 0.001) and the length of follow-up (p = 0.001). In subregion analyses, TREM1 rs2234246A was detected to be related to Aβ deposition. This study demonstrated an association between TREM1 variant rs2234246 and brain amyloidosis. Our findings implied that this variant is involved in AD by influencing Aβ neuropathology.

Keywords

Alzheimer’s disease TREM1 PET-Aβ Aβ deposition 

Notes

Funding Information

This study was supported by grants from the National Natural Science Foundation of China (81771148, 91849126), the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. Data acquisition and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). Funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, ADNI also gets substantial donations from the following institutions: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research give finical support to the ADNI clinical sites in Canada. Private sector fund raising is facilitated by the Foundation for the National Institutes of Health (www.fnih.org). For this study, the grantee organization is the Northern California Institute for Research and Education, and the coordinator is the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are spread by the Laboratory for Neuro Imaging at the University of Southern California.

Compliance with Ethical Standards

The current work was approved by the Institutional Review Board of all participating centers. The written informed consent was given by all participants or authorized representative office.

Supplementary material

12640_2019_105_MOESM1_ESM.docx (26 kb)
ESM 1 (DOCX 26 kb)

References

  1. Aldasoro Arguinano AA, Dade S, Stathopoulou M, Derive M, Coumba Ndiaye N, Xie T, Masson C, Gibot S, Visvikis-Siest S (2017) TREM-1 SNP rs2234246 regulates TREM-1 protein and mRNA levels and is associated with plasma levels of L-selectin. PLoS One 12:e0182226CrossRefGoogle Scholar
  2. Alzheimer’s A (2016) 2016 Alzheimer’s disease facts and figures. Alzheimers Dement 12:459–509CrossRefGoogle Scholar
  3. Apostolova LG, Risacher SL, Duran T, Stage EC, Goukasian N, West JD, Do TM, Grotts J, Wilhalme H, Nho K, Phillips M, Elashoff D, Saykin AJ, Alzheimer's Disease Neuroimaging I (2018) Associations of the top 20 Alzheimer disease risk variants with brain amyloidosis. JAMA Neurol 75:328–341CrossRefGoogle Scholar
  4. Ben Bouallegue F, Mariano-Goulart D, Payoux P, Alzheimer's Disease Neuroimaging I (2017) Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database. Alzheimers Res Ther 9:32CrossRefGoogle Scholar
  5. Benjamin DJ, Berger JO, Johannesson M, Nosek BA, Wagenmakers EJ, Berk R, Bollen KA, Brembs B, Brown L, Camerer C, Cesarini D, Chambers CD, Clyde M, Cook TD, De Boeck P, Dienes Z, Dreber A, Easwaran K, Efferson C, Fehr E, Fidler F, Field AP, Forster M, George EI, Gonzalez R, Goodman S, Green E, Green DP, Greenwald AG, Hadfield JD, Hedges LV, Held L, Hua Ho T, Hoijtink H, Hruschka DJ, Imai K, Imbens G, Ioannidis JPA, Jeon M, Jones JH, Kirchler M, Laibson D, List J, Little R, Lupia A, Machery E, Maxwell SE, McCarthy M, Moore DA, Morgan SL, Munafo M, Nakagawa S, Nyhan B, Parker TH, Pericchi L, Perugini M, Rouder J, Rousseau J, Savalei V, Schonbrodt FD, Sellke T, Sinclair B, Tingley D, Van Zandt T, Vazire S, Watts DJ, Winship C, Wolpert RL, Xie Y, Young C, Zinman J, Johnson VE (2018) Redefine statistical significance. Nat Hum Behav 2:6–10CrossRefGoogle Scholar
  6. Bettens K, Sleegers K, Van Broeckhoven C (2013) Genetic insights in Alzheimer’s disease. Lancet Neurol 12:92–104CrossRefGoogle Scholar
  7. Blautzik J, Brendel M, Sauerbeck J, Kotz S, Scheiwein F, Bartenstein P, Seibyl J, Rominger A, Alzheimer's Disease Neuroimaging I (2017) Reference region selection and the association between the rate of amyloid accumulation over time and the baseline amyloid burden. Eur J Nucl Med Mol Imaging 44:1364–1374CrossRefGoogle Scholar
  8. Chetelat G, Villemagne VL, Bourgeat P, Pike KE, Jones G, Ames D, Ellis KA, Szoeke C, Martins RN, O'Keefe GJ, Salvado O, Masters CL, Rowe CC, Australian Imaging B, Lifestyle Research G (2010) Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol 67:317–324PubMedGoogle Scholar
  9. Colonna M (2003) TREMs in the immune system and beyond. Nat Rev Immunol 3:445–453CrossRefGoogle Scholar
  10. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980CrossRefGoogle Scholar
  11. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355CrossRefGoogle Scholar
  12. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198CrossRefGoogle Scholar
  13. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, Fiske A, Pedersen NL (2006) Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 63:168–174CrossRefGoogle Scholar
  14. Golde TE, Schneider LS, Koo EH (2011) Anti-abeta therapeutics in Alzheimer’s disease: the need for a paradigm shift. Neuron 69:203–213CrossRefGoogle Scholar
  15. Hardy J, Bogdanovic N, Winblad B, Portelius E, Andreasen N, Cedazo-Minguez A, Zetterberg H (2014) Pathways to Alzheimer’s disease. J Intern Med 275:296–303CrossRefGoogle Scholar
  16. Harry GJ (2013) Microglia during development and aging. Pharmacol Ther 139:313–326CrossRefGoogle Scholar
  17. Ioannidis JPA (2018) The proposal to lower P value thresholds to .005. JAMA 319:1429–1430CrossRefGoogle Scholar
  18. Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, Foster NL, Petersen RC, Weiner MW, Price JC, Mathis CA, Alzheimer's Disease Neuroimaging I (2009) Relationships between biomarkers in aging and dementia. Neurology 73:1193–1199CrossRefGoogle Scholar
  19. Jiang T, Tan L, Chen Q, Tan MS, Zhou JS, Zhu XC, Lu H, Wang HF, Zhang YD, Yu JT (2016a) A rare coding variant in TREM2 increases risk for Alzheimer’s disease in Han Chinese. Neurobiol Aging 42(217):e211–e213Google Scholar
  20. Jiang T, Zhang YD, Gao Q, Zhou JS, Zhu XC, Lu H, Shi JQ, Tan L, Chen Q, Yu JT (2016b) TREM1 facilitates microglial phagocytosis of amyloid beta. Acta Neuropathol 132:667–683CrossRefGoogle Scholar
  21. Jiang T, Wan Y, Zhou JS, Tan MS, Huang Q, Zhu XC, Lu H, Wang HF, Chen Q, Tan L, Zhang YD, Tan L, Yu JT (2017) A missense variant in TREML2 reduces risk of Alzheimer’s disease in a Han Chinese population. Mol Neurobiol 54:977–982CrossRefGoogle Scholar
  22. Klesney-Tait J, Turnbull IR, Colonna M (2006) The TREM receptor family and signal integration. Nat Immunol 7:1266–1273CrossRefGoogle Scholar
  23. Koivunen J, Scheinin N, Virta JR, Aalto S, Vahlberg T, Nagren K, Helin S, Parkkola R, Viitanen M, Rinne JO (2011) Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology 76:1085–1090CrossRefGoogle Scholar
  24. Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963–974CrossRefGoogle Scholar
  25. Landau SM, Breault C, Joshi AD, Pontecorvo M, Mathis CA, Jagust WJ, Mintun MA, Alzheimer's Disease Neuroimaging I (2013a) Amyloid-beta imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods. J Nucl Med 54:70–77CrossRefGoogle Scholar
  26. Landau SM, Lu M, Joshi AD, Pontecorvo M, Mintun MA, Trojanowski JQ, Shaw LM, Jagust WJ, Alzheimer's Disease Neuroimaging I (2013b) Comparing positron emission tomography imaging and cerebrospinal fluid measurements of beta-amyloid. Ann Neurol 74:826–836CrossRefGoogle Scholar
  27. Landau SM, Fero A, Baker SL, Koeppe R, Mintun M, Chen K, Reiman EM, Jagust WJ (2015) Measurement of longitudinal beta-amyloid change with 18F-florbetapir PET and standardized uptake value ratios. J Nucl Med 56:567–574CrossRefGoogle Scholar
  28. Mawuenyega KG, Sigurdson W, Ovod V, Munsell L, Kasten T, Morris JC, Yarasheski KE, Bateman RJ (2010) Decreased clearance of CNS beta-amyloid in Alzheimer’s disease. Science 330:1774CrossRefGoogle Scholar
  29. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944CrossRefGoogle Scholar
  30. Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, Koeppe RA, Mathis CA, Weiner MW, Jagust WJ, Alzheimer's Disease Neuroimaging I (2009) Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain 132:1310–1323CrossRefGoogle Scholar
  31. Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43:2412–2414CrossRefGoogle Scholar
  32. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74:201–209CrossRefGoogle Scholar
  33. Roostaei T, Nazeri A, Felsky D, De Jager PL, Schneider JA, Pollock BG, Bennett DA, Voineskos AN, Alzheimer's Disease Neuroimaging I (2017) Genome-wide interaction study of brain beta-amyloid burden and cognitive impairment in Alzheimer's disease. Mol Psychiatry 22:287–295CrossRefGoogle Scholar
  34. Satoh K, Abe-Dohmae S, Yokoyama S, St George-Hyslop P, Fraser PE (2015) ATP-binding cassette transporter A7 (ABCA7) loss of function alters Alzheimer amyloid processing. J Biol Chem 290:24152–24165CrossRefGoogle Scholar
  35. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ, Alzheimer's Disease Neuroimaging I (2012) The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 8:S1–S68CrossRefGoogle Scholar
  36. Winblad B, Amouyel P, Andrieu S, Ballard C, Brayne C, Brodaty H, Cedazo-Minguez A, Dubois B, Edvardsson D, Feldman H, Fratiglioni L, Frisoni GB, Gauthier S, Georges J, Graff C, Iqbal K, Jessen F, Johansson G, Jonsson L, Kivipelto M, Knapp M, Mangialasche F, Melis R, Nordberg A, Rikkert MO, Qiu C, Sakmar TP, Scheltens P, Schneider LS, Sperling R, Tjernberg LO, Waldemar G, Wimo A, Zetterberg H (2016) Defeating Alzheimer’s disease and other dementias: a priority for European science and society. Lancet Neurol 15:455–532CrossRefGoogle Scholar
  37. Yan Q, Nho K, Del-Aguila JL, Wang X, Risacher SL, Fan KH, Snitz BE, Aizenstein HJ, Mathis CA, Lopez OL, Demirci FY, Feingold E, Klunk WE, Saykin AJ, Alzheimer's Disease Neuroimaging I, Cruchaga C, Kamboh MI (2018) Genome-wide association study of brain amyloid deposition as measured by Pittsburgh Compound-B (PiB)-PET imaging. Mol PsychiatryGoogle Scholar
  38. Yu JT, Jiang T, Wang YL, Wang HF, Zhang W, Hu N, Tan L, Sun L, Tan MS, Zhu XC, Tan L (2014) Triggering receptor expressed on myeloid cells 2 variant is rare in late-onset Alzheimer’s disease in Han Chinese individuals. Neurobiol Aging 35(937):e931–e933Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yue-Song Liu
    • 1
  • Wen-Jing Yan
    • 2
  • Chen-Chen Tan
    • 3
  • Jie-Qiong Li
    • 3
  • Wei Xu
    • 3
  • Xi-Peng Cao
    • 4
  • Lan Tan
    • 1
    Email author
  • Jin-Tai Yu
    • 5
    Email author
  • Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Department of Neurology, Qingdao Municipal HospitalDalian Medical UniversityQingdaoChina
  2. 2.Department of NeurologyThe Affiliated Hospital of Qingdao UniversityQingdaoChina
  3. 3.Department of Neurology, Qingdao Municipal HospitalQingdao UniversityQingdaoChina
  4. 4.Clinical Research Center, Qingdao Municipal HospitalQingdao UniversityQingdaoChina
  5. 5.Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina

Personalised recommendations