AD-linked R47H- TREM2 mutation induces disease-enhancing proinflammatory microglial states in mice and humans
Open accessAbstract
The hemizygous R47H variant of TREM2 , a microglia-specific gene in the brain, increases risk for late-onset Alzheimer’s disease (AD). In this study, we identified a subpopulation of microglia with disease-enhancing proinflammatory signatures associated with the R47H mutation in human AD brains and tauopathy mouse brains. Using transcriptomic analysis at the single-nuclei level from AD patients with the R47H or the common variant (CV )-TREM2 with matched sex, pathology and APOE status, we found that the R47H mutation was associated with cell type- and sex-specific transcriptional changes in human AD brains, with microglia exhibiting the most robust alterations. Further characterization revealed that R47H-associated microglial subpopulations had enhanced inflammatory signatures including hyperactivation of Akt, one of the signaling pathways downstream of TREM2. In a newly-generated tauopathy knock-in mouse model expressing one allele of human TREM2 ( hTREM2) with either the R47H mutation or CV, R47H induced and exacerbated tau-mediated spatial memory deficits in female mice. Single-cell transcriptomic analysis of microglia from these mice also revealed transcriptomic changes induced by R47H that had significant overlaps with R47H microglia in human AD brains, including robust increases in proinflammatory cytokines, activation of Syk-Akt-signaling, and elevation of a subset of disease-associated microglial signatures. Strikingly, pharmacological Akt inhibition largely reversed the enhanced inflammatory signatures induced by R47H in primary microglia treated with tau fibrils. By unraveling the disease-enhancing properties of the R47H mutation in mouse and human, our findings shed light on an immune-linked AD subtype and provide new directions for modulating microglial immune responses to treat AD.
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The most characterised variant of TREM2 (p.R47H) shows defects in ligand binding, which impacts the ability of TREM2 to recognise damage, and induce microglial changes, resulting in a dampened response to pathological insults such as Aβ deposition.
“The most characterised variant of TREM2 (p.R47H) shows defects in ligand binding [6, 37, 38, 97], which impacts the ability of TREM2 to recognise damage, and induce microglial changes, resulting in a dampened response to pathological insults such as Aβ deposition.”
From TREM2/PLCγ2 signalling in immune cells: function, structural insight, and potential therapeutic modulation (2021) · cites this paper as [38]
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Experimental — accuracy not yet measuredTREM2 p.R47H is associated with increased expression of pro-inflammatory cytokines.
“increased expression of pro-inflammatory cytokines [38]”
From TREM2/PLCγ2 signalling in immune cells: function, structural insight, and potential therapeutic modulation (2021) · cites this paper as [38]
It has been suggested that p.R47H leads to hyperactivation of Akt downstream of Trem2.
“it has been suggested that p.R47H leads to hyperactivation of Akt downstream of Trem2 [38]”
From TREM2/PLCγ2 signalling in immune cells: function, structural insight, and potential therapeutic modulation (2021) · cites this paper as [38]
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · see citing works
Identification of a unique TGF-β–dependent molecular and functional signature in microglia
Butovsky, O., Jedrychowski, M. P., Moore, C. S., Cialic, R., Lanser, A. J., Gabriely, G., Koeglsperger, T., Dake, B., Wu, P. M., Doykan, C. E., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3599 · Cited by 2,543 · see citing works
TREM2 Maintains Microglial Metabolic Fitness in Alzheimer’s Disease
Ulland, T. K., Song, W. M., Huang, S. C.-C., Ulrich, J. D., Sergushichev, A., Beatty, W. L., Loboda, A. A., Zhou, Y., Cairns, N. J., Kambal, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.023 · Cited by 1,263 · see citing works
Self-citationApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy
Initiative, A. D. N., Shi, Y., Yamada, K., Liddelow, S. A., Smith, S. T., Zhao, L., Luo, W., Tsai, R. M., Spina, S., Grinberg, L. T., et al. (2017) · Nature · doi:10.1038/nature24016 · Cited by 1,220 · see citing works
Self-citationLi, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N. F., Okamoto, J., Gulati, G., Bennett, M. L., Sun, L. O., Clarke, L. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.006 · Cited by 1,129 · see citing works
Self-citationARUK Consortium, Sims, R., GERAD/PERADES, C., van der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3916 · Cited by 1,126 · see citing works
Self-citationTakahashi, K., Rochford, C. D. P., & Neumann, H. (2005) · The Journal of Experimental Medicine · doi:10.1084/jem.20041611 · Cited by 1,124 · see citing works
TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques
Wang, Y., Ulland, T. K., Ulrich, J. D., Song, W., Tzaferis, J. A., Hole, J. T., Yuan, P., Mahan, T. E., Shi, Y., Gilfillan, S., et al. (2016) · Journal of Experimental Medicine · doi:10.1084/jem.20151948 · Cited by 840 · see citing works
TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis
Kleinberger, G., Yamanishi, Y., Suárez-Calvet, M., Czirr, E., Lohmann, E., Cuyvers, E., Struyfs, H., Pettkus, N., Wenninger-Weinzierl, A., Mazaheri, F., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3009093 · Cited by 798 · see citing works
Self-citationTREM2 sustains microglial expansion during aging and response to demyelination
Poliani, P. L., Wang, Y., Fontana, E., Robinette, M. L., Yamanishi, Y., Gilfillan, S., & Colonna, M. (2015) · Journal of Clinical Investigation · doi:10.1172/jci77983 · Cited by 525 · see citing works
Leyns, C. E. G., Ulrich, J. D., Finn, M. B., Stewart, F. R., Koscal, L. J., Remolina Serrano, J., Robinson, G. O., Anderson, E., Colonna, M., & Holtzman, D. M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710311114 · Cited by 517 · see citing works
Transcriptional and Translational Differences of Microglia from Male and Female Brains
Guneykaya, D., Ivanov, A., Hernandez, D. P., Haage, V., Wojtas, B., Meyer, N., Maricos, M., Jordan, P., Buonfiglioli, A., Gielniewski, B., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.001 · Cited by 477 · see citing works
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
Raber, J., Wong, D., Buttini, M., Orth, M., Bellosta, S., Pitas, R. E., Mahley, R. W., & Mucke, L. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.18.10914 · Cited by 364 · see citing works
TREM2 deficiency impairs chemotaxis and microglial responses to neuronal injury
Mazaheri, F., Snaidero, N., Kleinberger, G., Madore, C., Daria, A., Werner, G., Krasemann, S., Capell, A., Trümbach, D., Wurst, W., et al. (2017) · The EMBO Reports · doi:10.15252/embr.201743922 · Cited by 351 · see citing works
The Triggering Receptor Expressed on Myeloid Cells 2 Binds Apolipoprotein E
Bailey, C. C., DeVaux, L. B., & Farzan, M. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.677286 · Cited by 303 · see citing works
Altered microglial response to Aβ plaques in APPPS1-21 mice heterozygous for TREM2
Ulrich, J. D., Finn, M. B., Wang, Y., Shen, A., Mahan, T. E., Jiang, H., Stewart, F. R., Piccio, L., Colonna, M., & Holtzman, D. M. (2014) · Molecular Neurodegeneration · doi:10.1186/1750-1326-9-20 · Cited by 302 · see citing works
Wunderlich, P., Glebov, K., Kemmerling, N., Tien, N. T., Neumann, H., & Walter, J. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.517540 · Cited by 296 · see citing works
Pattern Recognition by TREM-2: Binding of Anionic Ligands
Daws, M. R., Sullam, P. M., Niemi, E. C., Chen, T. T., Tchao, N. K., & Seaman, W. E. (2003) · The Journal of Immunology · doi:10.4049/jimmunol.171.2.594 · Cited by 291 · see citing works
The <i>MS4A</i> gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk
Deming, Y., Filipello, F., Cignarella, F., Cantoni, C., Hsu, S., Mikesell, R., Li, Z., Del-Aguila, J. L., Dube, U., Farias, F. G., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aau2291 · Cited by 271 · see citing works
Impact of TREM2R47H variant on tau pathology–induced gliosis and neurodegeneration
Gratuze, M., Leyns, C. E. G., Sauerbeck, A. D., St-Pierre, M.-K., Xiong, M., Kim, N., Serrano, J. R., Tremblay, M.-È., Kummer, T. T., Colonna, M., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci138179 · Cited by 252 · see citing works
Sayed, F. A., Telpoukhovskaia, M., Kodama, L., Li, Y., Zhou, Y., Le, D., Hauduc, A., Ludwig, C., Gao, F., Clelland, C., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811411115 · Cited by 229 · see citing works
Environment matters: microglia function and dysfunction in a changing world
Hanamsagar, R., & Bilbo, S. D. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.007 · Cited by 122 · see citing works
Distinct Signaling Pathways Regulate TREM2 Phagocytic and NFκB Antagonistic Activities
Yao, H., Coppola, K., Schweig, J. E., Crawford, F., Mullan, M., & Paris, D. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00457 · Cited by 110 · see citing works
Roussos, P., Katsel, P., Fam, P., Tan, W., Purohit, D. P., & Haroutunian, V. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.10.013 · Cited by 103 · see citing works
Flagged first, then by citations
Research works printed but not linked: 17
- Guerreiro, R., et al. TREM2 variants in Alzheimer's disease. The New England journal of medicine 368, 117-127 (2013).
- Zheng, H., et al. TREM2 Promotes Microglial Survival by Activating Wnt/beta-Catenin Pathway. The Journal of neuroscience : the official journal of the Society for Neuroscience 37, 1772-1784 (2017).
- Hsieh, C.L., et al. A role for TREM2 ligands in the phagocytosis of apoptotic neuronal cells by microglia. Journal of neurochemistry 109, 1144-1156 (2009).
- Zhong, L., et al. Amyloid-beta modulates microglial responses by binding to the triggering receptor expressed on myeloid cells 2 (TREM2). Mol Neurodegener 13, 15 (2018).
- Zhong, L., et al. Soluble TREM2 induces inflammatory responses and enhances microglial survival. The Journal of experimental medicine 214, 597-607 (2017).
- Jay, T.R., et al. Disease Progression-Dependent Effects of TREM2 Deficiency in a Mouse Model of Alzheimer's Disease. The Journal of neuroscience : the official journal of the Society for Neuroscience 37, 637-647 (2017).
- Gamberger, D., Lavrač, N., Srivatsa, S., Tanzi, R.E. & Doraiswamy, P.M. Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease. Sci Rep 7, 6763 (2017).
- Shi, Y., et al. Microglia drive APOE-dependent neurodegeneration in a tauopathy mouse model. The Journal of experimental medicine 216, 2546-2561 (2019).
- Finak, G., et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16, 278 (2015).
- Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate -a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Statistical Methodology 57, 289-300 (1995).
- Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).
- Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
- Dobin, A., et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England) 29, 15-21 (2013).
- Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X., Google Brain. TensorFlow: A system for large-scale machine learning. USENIX Association 12th USENIX Symposium on Operating Systems Design and Implementation(2016).
- He, K., Gkioxari, G., Dollár, P., Girshick, R. Mask R-CNN. arXiv:1703.06870 (2017). 100. Abdulla, W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub repository (2017). 101. Lombardot, B. Manual drift correction ImageJ plugin. Fiji (2016). 102. Team, R.C. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2017). 103. · arXiv
- Wickham, H. ggplot2 -Elegant Graphics for Data Analysis, (Springer, 2009). 104.
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis, (Springer-Verlag New York, 2016).
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Magno, L., Bunney, T. D., Mead, E., Svensson, F., & Bictash, M. N. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00436-5 · Cited by 64 · see citing works
- Authors
- Sayed, F. A., Kodama, L., Udeochu, J. C., Fan, L., Carling, G. K., Le, D., Li, Q., Zhou, L., Mathys, H., Wang, M., Niu, X., Mazutis, L., Jiang, X., Wang, X., Wong, M. Y., Gao, F., Telpoukhovskaia, M., Tracy, T. E., Frost, G., Zhou, Y., Li, Y., Brendel, M., Qiu, Y., Cheng, Z., Yu, G., Hardy, J., Coppola, G., Gong, S., Wang, F., DeTure, M. A., Zhang, B., Xie, L., Dickson, D. W., Luo, W., & Gan, L.
- Affiliations
- Gladstone Institutes (US)
- Weill Cornell Medicine (US)
- Stanford Medicine (US)
- Massachusetts Institute of Technology (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of California, Los Angeles (US)
- Cornell University (US)
- The Graduate Center, CUNY (US)
- Virginia Tech (US)
- UCL Queen Square Institute of Neurology (GB)
- Mayo Clinic (US)
- Corresponding author
- Gan, Li
- ORCID
- Sayed, Faten A.
- Kodama, Lay
- Udeochu, Joe C.
- Fan, Li
- Carling, Gillian K.
- Le, David
- Li, Qingyun
- Zhou, Lu
- Mathys, Hansruedi
- Wang, Minghui
- Niu, Xiang
- Jiang, Xueqiao
- Wang, Xueting
- Wong, Man Ying
- Gao, Fuying
- Telpoukhovskaia, Maria
- Tracy, Tara E.
- Frost, Georgia
- Li, Yaqiao
- Brendel, Matthew
- Qiu, Yue
- Cheng, Zuolin
- Hardy, John
- Coppola, Giovanni
- Gong, Shiaoching
- Wang, Fei
- DeTure, Michael A.
- Zhang, Bin
- Xie, Lei
- Dickson, Dennis W.
- Luo, Wenjie
- Gan, Li
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2020-07-25
- OpenAlex
- W3044915822
- Cited by
- 3
- References
- 26
- Citations by year
- 2021: 2 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Science Foundation (T32GM007618)
- Memorial Sloan-Kettering Cancer Center
- Gladstone Institutes
- Wellcome Trust (202903/Z/16/Z)
- Alzheimer's Society
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (P2BSP3, 202903, 151885)
- UK Dementia Research Institute
- Weill Cornell Medical College
- University of California, San Francisco (T32GM007618)
- Medical Research Council (MR/N026004/, MR/N026004/1, 202903/Z/16/Z)
- National Institute of Mental Health (MH110504, R01MH110504)
- National Institute of General Medical Sciences (T32GM007618)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Keywords
- Alzheimer's disease, microglia, tauopathy, Akt signaling, single-nucleus transcriptomics, sex differences, tau fibrils, spatial memory impairment, AKT inhibition
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