Abstract
Tau and amyloid beta (Aβ) proteins accumulate along neuronal circuits in Alzheimer’s disease. Unraveling the genetic background for the regional vulnerability of these proteinopathies can help in understanding the mechanisms of pathology progression. To that end, we developed a novel graph theory approach and used it to investigate the intersection of longitudinal Aβ and tau positron emission tomography imaging of healthy adult individuals and the genetic transcriptome of the Allen Human Brain Atlas. We identified distinctive pathways for tau and Aβ accumulation, of which the tau pathways correlated with cognitive levels. We found that tau propagation and Aβ propagation patterns were associated with a common genetic profile related to lipid metabolism, in which APOE played a central role, whereas the tau-specific genetic profile was classified as ‘axon related’ and the Aβ profile as ‘dendrite related’. This study reveals distinct genetic profiles that may confer vulnerability to tau and Aβ in vivo propagation in the human brain.
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Data availability
All neuroimaging and clinical data that support the findings of this study are available from https://nmr.mgh.harvard.edu/lab/harvard-aging-brain-study/public-data-releases. HABS data curation is overseen by Aaron P. Schultz (aschultz@nmr.mgh.harvard.edu) at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
References
Braak, H. & Braak, E. Evolution of the neuropathology of Alzheimer’s disease. Acta Neurol. Scand. Suppl. 165, 3–12 (1996).
Thal, D. R., Rüb, U., Orantes, M. & Braak, H. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 58, 1791–1800 (2002).
Braak, H., Del Tredici, K., Schultz, C. & Braak, E. Vulnerability of select neuronal types to Alzheimer’s disease. Ann. N. Y. Acad. Sci. 924, 53–61 (2000).
Wu J. W. et al. Neuronal activity enhances tau propagation and tau pathology in vivo. Nat. Neurosci. 19, 1085–1092 (2016).
Khan, U. A. et al. Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer’s disease. Nat. Neurosci. 17, 304–311 (2014).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Naj, A. C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat. Genet. 43, 436–441 (2011).
Roussotte, F. F. et al. Combined effects of Alzheimer risk variants in the CLU and ApoE genes on ventricular expansion patterns in the elderly. J. Neurosci. 34, 6537–6545 (2014).
Karch, C. M. & Goate, A. M. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol. Psychiatry 77, 43–51 (2015).
Beecham, G. W. et al. Genome-wide association meta-analysis of neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet. 10, e1004606 (2014).
Allen, M. et al. Association of MAPT haplotypes with Alzheimer’s disease risk and MAPT brain gene expression levels. Alzheimers Res. Ther. 6, 39 (2014).
Shen, E. H., Overly, C. C. & Jones, A. R. The Allen Human Brain Atlas. Comprehensive gene expression mapping of the human brain. Trends Neurosci. 35, 711–714 (2012).
Jones, A. R., Overly, C. C. & Sunkin, S. M. The Allen Brain Atlas: 5 years and beyond. Nat. Rev. Neurosci. 10, 821–828 (2009).
Shi, Y. et al. ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature 549, 523–527 (2017).
Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 280–292 (2011).
Dagley, A. et al. Harvard Aging Brain Study: dataset and accessibility. Neuroimage 144, 255–258 (2017).
Johnson, K. A. et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann. Neurol. 79, 110–119 (2016).
Sepulcre, J., Sabuncu, M., Becker, A., Sperling, R. & Johnson, K. In vivo characterization of the early states of the amyloid-beta network. Brain 136, 2239–2252 (2013).
Sepulcre, J. et al. In vivo tau, amyloid, and gray matter profiles in the aging brain. J. Neurosci. 36, 7364–7374 (2016).
Freer, R. et al. A protein homeostasis signature in healthy brains recapitulates tissue vulnerability to Alzheimer’s disease. Sci. Adv. 2, e1600947 (2016).
Chien, D. et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T807. J. Alzheimers Dis. 34, 457–468 (2013).
Maruyama, M. et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79, 1094–1108 (2013).
Okamura, N. et al. Novel 18F-labeled arylquinoline derivatives for noninvasive imaging of tau pathology in Alzheimer disease. J. Nucl. Med. 54, 1420–1427 (2013).
Villemagne, V. L. et al. In vivo evaluation of a novel tau imaging tracer for Alzheimer’s disease. Eur. J. Nucl. Med. Mol. Imaging 41, 816–826 (2014).
Schöll, M. et al. PET imaging of tau deposition in the aging human brain. Neuron 89, 971–982 (2016).
Sepulcre, J. et al. Hierarchical organization of tau and amyloid deposits in the cerebral cortex. JAMA Neurol. 74, 813–820 (2017).
Arnold, S. E., Hyman, B. T., Flory, J., Damasio, A. R. & Van Hoesen, G. W. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cereb. Cortex 1, 103–116 (1991).
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
Hyman, B. T., Van Hoesen, G. W., Damasio, A. R. & Barnes, C. L. Alzheimer’s disease: cell-specific pathology isolates the hippocampal formation. Science 225, 1168–1170 (1984).
Kalus, P., Braak, H., Braak, E. & Bohl, J. The presubicular region in Alzheimer’s disease: topography of amyloid deposits and neurofibrillary changes. Brain Res. 494, 198–203 (1989).
Buckner, R. L. et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29, 1860–1873 (2009).
Greicius, M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 21, 424–430 (2008).
Greicius, M. D., Srivastava, G., Reiss, A. L. & Menon, V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl Acad. Sci. USA 101, 4637–4642 (2004).
Kuchibhotla, K. V. et al. Neurofibrillary tangle-bearing neurons are functionally integrated in cortical circuits in vivo. Proc. Natl Acad. Sci. USA 111, 510–514 (2014).
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009).
Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216–1227 (2012).
Sepulcre, J. et al. Tau and amyloid β proteins distinctively associate to functional network changes in the aging brain. Alzheimers Dement. 13, 1261–1269 (2017).
Hansson, O. et al. Tau pathology distribution in Alzheimer’s disease corresponds differentially to cognition-relevant functional brain networks. Front. Neurosci. 11, 167 (2017).
Serrano-Pozo, A., Frosch, M. P., Masliah, E. & Hyman, B. T. Neuropathological alterations in Alzheimer disease. Cold Spring Harb. Perspect. Med. 1, a006189 (2011).
Bero, A. W. et al. Neuronal activity regulates the regional vulnerability to amyloid-β deposition. Nat. Neurosci. 14, 750–756 (2011).
Marquié, M. et al. Validating novel tau positron emission tomography tracer [F-18]-AV-1451 (T807) on postmortem brain tissue. Ann. Neurol. 78, 787–800 (2015).
Jacobs, H. I. L. et al. Structural tract alterations predict downstream tau accumulation in amyloid-positive older individuals. Nat. Neurosci. 21, 424–431 (2018).
Marioni, R. E. et al. Genetic stratification to identify risk groups for Alzheimer’s disease. J. Alzheimers Dis. 57, 275–283 (2017).
De Strooper, B. & Karran, E. The cellular phase of Alzheimer’s disease. Cell 164, 603–615 (2016).
Nelson, P. T. et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol. 71, 362–381 (2013).
Duyckaerts, C. et al. PART is part of Alzheimer disease. Acta Neuropathol. 129, 749–756 (2015).
Theendakara, V. et al. Direct transcriptional effects of apolipoprotein E. J. Neurosci. 36, 685–700 (2016).
Mormino, E. C. et al. Early and late change on the preclinical Alzheimer’s cognitive composite in clinically normal older individuals with elevated amyloid β. Alzheimers Dement. 13, 1004–1012 (2017).
Ashburner, J. & Friston, K. J. Voxel-based morphometry—the methods. Neuroimage 11, 805–821 (2000).
Mathis, C. A. et al. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J. Med. Chem. 46, 2740–2754 (2003).
Grubbs, F. E. Procedures for detecting outlying observations in samples. Technometrics 11, 1–21 (1969).
Donoho, D. L. & Grimes, C. Image manifolds which are isometric to Euclidean space. J. Math. Imaging Vis. 23, 5–24 (2005).
Meyer, F. G. & Shen, X. Perturbation of the eigenvectors of the graph Laplacian: application to image denoising. Appl. Comput. Harmon. Anal. 36, 326–334 (2014).
Zhang, F. & Hancock, E. R. Graph spectral image smoothing using the heat kernel. Pattern Recognit. 41, 3328–3342 (2008).
Greve, D. N. et al. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. Neuroimage 92, 225–236 (2014).
Hanseeuw, B. J. et al. Fluorodeoxyglucose metabolism associated with tau–amyloid interaction predicts memory decline. Ann. Neurol. 81, 583–596 (2017).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).
Van Essen, D. C. A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28, 635–662 (2005).
Cho, H. et al. In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann. Neurol. 80, 247–258 (2016).
Grothe, M. J. et al. In vivo staging of regional amyloid deposition. Neurology 89, 2031–2038 (2017).
Chhatwal, J. P. et al. Temporal T807 binding correlates with CSF tau and phospho-tau in normal elderly. Neurology 87, 920–926 (2016).
Hedden, T. et al. Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J. Neurosci. 32, 16233–16242 (2012).
French, L. & Paus, T. A FreeSurfer view of the cortical transcriptome generated from the Allen Human Brain Atlas. Front. Neurosci. 9, 1–5 (2015).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4 (2008).
Lopes, C. T. et al. Cytoscape Web: an interactive web-based network browser. Bioinformatics 27, 2347–2348 (2011).
Acknowledgements
We thank the investigators and staff of the Harvard Aging Brain Study, Massachusetts Alzheimer’s Disease Research Center, the individual research participants, and their families and caregivers. We also thank the PET Core of the MGH, the Harvard Center for Brain Science Neuroimaging Core and the Athinoula A. Martinos Center for biomedical imaging support. This research was supported by grants from the National Institutes of Health (NIH) (K23-EB019023 to J.S.; T32EB013180 to L.O.-T.; R01HL137230 and P41-EB022544 to G.E.-F.; R01-AG027435-S1 to R.A.S. and K.A.J.; P50-AG00513421 and R01AG046396 to K.A.J. and R.A.S.; P01-AG036694 to R.A.S. and K.A.J.; and RF1AG052653 to Q.L.); Massachusetts ADRC; Alzheimer’s Association (NIRG-11-205690 to J.S.; IIRG-06-32444 to R.A.S. and K.A.J.; and ZEN-10-174210 to K.A.J.); and the Alzheimer Forschung Initiative e.V. (to M.J.G.). The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
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J.S. contributed to the design, analysis and interpretation of the data and preparation of the manuscript. M.J.G. contributed to the design, analysis and interpretation of the data and preparation of the manuscript. F.d.U. contributed to the analysis of the data and preparation of the manuscript. L.O.-T. contributed to the analysis of the data and preparation of the manuscript. I.D. contributed to the analysis and interpretation of the data and preparation of the manuscript. H.-S.Y. contributed to the analysis of the data and preparation of the manuscript. H.I.L.J. contributed to the interpretation of the data and preparation of the manuscript. B.H. contributed to the interpretation of the data and preparation of the manuscript. Q.L. contributed to the interpretation of the data and preparation of the manuscript. G.E.-F. contributed to the interpretation of the data and preparation of the manuscript. R.A.S. contributed to the design and interpretation of the data and preparation of the manuscript. K.A.J. contributed to the design and interpretation of the data and preparation of the manuscript.
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Sepulcre, J., Grothe, M.J., d’Oleire Uquillas, F. et al. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med 24, 1910–1918 (2018). https://doi.org/10.1038/s41591-018-0206-4
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DOI: https://doi.org/10.1038/s41591-018-0206-4
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