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Multi-omic comparison of Alzheimer’s variants in human ESC–derived microglia reveals convergence at APOE

Liu, Tongfei, Zhu, Bing, Liu, Yan, Zhang, Xiaoming, Yin, Jun, Li, Xiaoguang, Jiang, LuLin, Hodges, Andrew P., Rosenthal, Sara Brin, Zhou, Lisa, Yancey, Joel, McQuade, Amanda, Blurton-Jones, Mathew, Tanzi, Rudolph E., Huang, Timothy Y., Xu, Huaxi·2020·Journal of Experimental Medicine
DOI 10.1084/jem.20200474PMC PMC7953740
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Abstract

Variations in many genes linked to sporadic Alzheimer’s disease (AD) show abundant expression in microglia, but relationships among these genes remain largely elusive. Here, we establish isogenic human ESC–derived microglia-like cell lines (hMGLs) harboring AD variants in CD33, INPP5D, SORL1, and TREM2 loci and curate a comprehensive atlas comprising ATAC-seq, ChIP-seq, RNA-seq, and proteomics datasets. AD-like expression signatures are observed in AD mutant SORL1 and TREM2 hMGLs, while integrative multi-omic analysis of combined epigenetic and expression datasets indicates up-regulation of APOE as a convergent pathogenic node. We also observe cross-regulatory relationships between SORL1 and TREM2, in which SORL1R744X hMGLs induce TREM2 expression to enhance APOE expression. AD-associated SORL1 and TREM2 mutations also impaired hMGL Aβ uptake in an APOE-dependent manner in vitro and attenuated Aβ uptake/clearance in mouse AD brain xenotransplants. Using this modeling and analysis platform for human microglia, we provide new insight into epistatic interactions in AD genes and demonstrate convergence of microglial AD genes at the APOE locus.

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  • SORLA reduces APP and BACE1 interactions while directly binding and facilitating Aβ internalization and lysosomal degradation.

    “In addition, SORLA reduces APP and BACE1 (Spoelgen, von Arnim et al. 2006) interactions while directly binding and facilitating Aβ internalization and lysosomal degradation (Caglayan, Takagi-Niidome et al. 2014, Liu, Zhu et al. 2020).”

    From SORLA up-regulation suppresses pathological effects in aged tauopathy mouse brain (2026) · cites this paper as Liu, Zhu et al. 2020

    The Results section on the SORL1–TREM2–APOE regulatory axis reports that SORL1 loss or suppression reduces Aβ binding and uptake in human ESC-derived microglia, supporting a role in internalization. It does not show that SORLA directly binds Aβ or facilitates its lysosomal degradation. The APP–BACE1 interaction clause is attached to a different citation. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • In addition to trafficking and internalizing Aβ, SORLA may also potentially affect tau accumulation.

    “This suggests that in addition to trafficking and internalizing Aβ (Caglayan, Takagi-Niidome et al. 2014, Liu, Zhu et al. 2020), SORLA may also potentially affect tau accumulation.”

    From SORLA up-regulation suppresses pathological effects in aged tauopathy mouse brain (2026) · cites this paper as Liu, Zhu et al. 2020

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Research works printed but not linked: 56

  1. Abud, 2017, iPSC-Derived Human Microglia-like Cells to Study Neurological Diseases, Neuron, 94, 278, 10.1016/j.neuron.2017.03.042 · DOI · Paper page
  2. Andersen, 2005, Neuronal sorting protein-related receptor sorLA/LR11 regulates processing of the amyloid precursor protein, Proc. Natl. Acad. Sci. USA, 102, 13461, 10.1073/pnas.0503689102 · DOI · Paper page
  3. Bakker, 1999, Myeloid DAP12-associating lectin (MDL)-1 is a cell surface receptor involved in the activation of myeloid cells, Proc. Natl. Acad. Sci. USA, 96, 9792, 10.1073/pnas.96.17.9792 · DOI
  4. Bouchon, 2001, A DAP12-mediated pathway regulates expression of CC chemokine receptor 7 and maturation of human dendritic cells, J. Exp. Med, 194, 1111, 10.1084/jem.194.8.1111 · DOI · Paper page
  5. Brawek, 2014, Impairment of in vivo calcium signaling in amyloid plaque-associated microglia, Acta Neuropathol, 127, 495, 10.1007/s00401-013-1242-2 · DOI
  6. Chatterjee, 2018, Epigenetic Drug Repositioning for Alzheimer’s Disease Based on Epigenetic Targets in Human Interactome, J. Alzheimers Dis, 61, 53, 10.3233/JAD-161104 · DOI
  7. Choi, 2014, MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments, Bioinformatics, 30, 2524, 10.1093/bioinformatics/btu305 · DOI
  8. Corces, 2017, An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues, Nat. Methods, 14, 959, 10.1038/nmeth.4396 · DOI · Paper page
  9. Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol, 26, 1367, 10.1038/nbt.1511 · DOI · Paper page
  10. Cox, 2011, Andromeda: a peptide search engine integrated into the MaxQuant environment, J. Proteome Res, 10, 1794, 10.1021/pr101065j · DOI
  11. Del-Aguila, 2019, TREM2 brain transcript-specific studies in AD and TREM2 mutation carriers, Mol. Neurodegener, 14, 18, 10.1186/s13024-019-0319-3 · DOI
  12. Dobin, 2013, STAR: ultrafast universal RNA-seq aligner, Bioinformatics, 29, 15, 10.1093/bioinformatics/bts635 · DOI · Paper page
  13. Dubey, 2018, Recent studies on cellular and molecular mechanisms in Alzheimer’s disease: focus on epigenetic factors and histone deacetylase, Rev. Neurosci, 29, 241, 10.1515/revneuro-2017-0049 · DOI
  14. Fjorback, 2012, Retromer binds the FANSHY sorting motif in SorLA to regulate amyloid precursor protein sorting and processing, J. Neurosci, 32, 1467, 10.1523/JNEUROSCI.2272-11.2012 · DOI · Paper page
  15. Gerakis, 2019, Brain organoids: a next step for humanized Alzheimer’s disease models?, Mol. Psychiatry, 24, 474, 10.1038/s41380-018-0343-7 · DOI · Paper page
  16. Hasselmann, 2019, Development of a Chimeric Model to Study and Manipulate Human Microglia In Vivo, Neuron, 103, 1016, 10.1016/j.neuron.2019.07.002 · DOI · Paper page
  17. Heinz, 2010, Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities, Mol. Cell, 38, 576, 10.1016/j.molcel.2010.05.004 · DOI · Paper page
  18. Hickman, 2013, The microglial sensome revealed by direct RNA sequencing, Nat. Neurosci, 16, 1896, 10.1038/nn.3554 · DOI · Paper page
  19. Hollingworth, 2011, Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease, Nat. Genet, 43, 429, 10.1038/ng.803 · DOI · Paper page
  20. Jonsson, 2013, Variant of TREM2 associated with the risk of Alzheimer’s disease, N. Engl. J. Med, 368, 107, 10.1056/NEJMoa1211103 · DOI · Paper page
  21. Jung, 2000, Analysis of fractalkine receptor CX(3)CR1 function by targeted deletion and green fluorescent protein reporter gene insertion, Mol. Cell. Biol, 20, 4106, 10.1128/MCB.20.11.4106-4114.2000 · DOI · Paper page
  22. Kang, 2009, Amyloid-beta dynamics are regulated by orexin and the sleep-wake cycle, Science, 326, 1005, 10.1126/science.1180962 · DOI · Paper page
  23. Kedaigle, 2018, Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data, Methods Mol. Biol, 1711, 13, 10.1007/978-1-4939-7493-1_2 · DOI
  24. Keren-Shaul, 2017, A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease, Cell, 169, 1276, 10.1016/j.cell.2017.05.018 · DOI · Paper page
  25. Kim, 2010, Epigenetic memory in induced pluripotent stem cells, Nature, 467, 285, 10.1038/nature09342 · DOI
  26. Konttinen, 2019, PSEN1DeltaE9, APPswe, and APOE4 Confer Disparate Phenotypes in Human iPSC-Derived Microglia, Stem Cell Reports, 13, 669, 10.1016/j.stemcr.2019.08.004 · DOI · Paper page
  27. Kunkle, 2019, Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing, Nat. Genet, 51, 414, 10.1038/s41588-019-0358-2 · DOI · Paper page
  28. Lambert, 2009, Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease, Nat. Genet, 41, 1094, 10.1038/ng.439 · DOI · Paper page
  29. Li, 2011, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome, BMC Bioinformatics, 12, 323, 10.1186/1471-2105-12-323 · DOI · Paper page
  30. Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol, 15, 550, 10.1186/s13059-014-0550-8 · DOI · Paper page
  31. Mancuso, 2019, Stem-cell-derived human microglia transplanted in mouse brain to study human disease, Nat. Neurosci, 22, 2111, 10.1038/s41593-019-0525-x · DOI · Paper page
  32. Martin, 2011, Cutadapt removes adapter sequenes from high-throughput sequencing reads, EMBnet. J, 17, 10, 10.14806/ej.17.1.200 · DOI · Paper page
  33. Mathys, 2019, Author Correction: Single-cell transcriptomic analysis of Alzheimer’s disease, Nature, 571, 10.1038/s41586-019-1329-6 · DOI
  34. Murray, 2019, APOE epsilon4 is also required in TREM2 R47H variant carriers for Alzheimer’s disease to develop, Neuropathol. Appl. Neurobiol, 45, 183, 10.1111/nan.12474 · DOI · Paper page
  35. Najm, 2019, Apolipoprotein E4, inhibitory network dysfunction, and Alzheimer’s disease, Mol. Neurodegener, 14, 24, 10.1186/s13024-019-0324-6 · DOI · Paper page
  36. Nakamagoe, 2011, A Japanese case with Nasu-Hakola disease of DAP12 gene mutation exhibiting precuneus hypoperfusion, Intern. Med, 50, 2839, 10.2169/internalmedicine.50.5891 · DOI
  37. Parkhurst, 2013, Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor, Cell, 155, 1596, 10.1016/j.cell.2013.11.030 · DOI · Paper page
  38. Polo, 2010, Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells, Nat. Biotechnol, 28, 848, 10.1038/nbt.1667 · DOI
  39. Ran, 2013, Genome engineering using the CRISPR-Cas9 system, Nat. Protoc, 8, 2281, 10.1038/nprot.2013.143 · DOI · Paper page
  40. Rougerie, 2013, Generation of membrane structures during phagocytosis and chemotaxis of macrophages: role and regulation of the actin cytoskeleton, Immunol. Rev, 256, 222, 10.1111/imr.12118 · DOI · Paper page
  41. Scherzer, 2004, Loss of apolipoprotein E receptor LR11 in Alzheimer disease, Arch. Neurol, 61, 1200, 10.1001/archneur.61.8.1200 · DOI · Paper page
  42. Shannon, 2003, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res, 13, 2498, 10.1101/gr.1239303 · DOI · Paper page
  43. Shi, 2019, Microglia drive APOE-dependent neurodegeneration in a tauopathy mouse model, J. Exp. Med, 216, 2546, 10.1084/jem.20190980 · DOI · Paper page
  44. So, 2019, Application of CRISPR genetic screens to investigate neurological diseases, Mol. Neurodegener, 14, 41, 10.1186/s13024-019-0343-3 · DOI
  45. Speicher, 2019, Generating microglia from human pluripotent stem cells: novel in vitro models for the study of neurodegeneration, Mol. Neurodegener, 14, 46, 10.1186/s13024-019-0347-z · DOI
  46. Tuncbag, 2016, Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package, PLOS Comput. Biol, 12, 10.1371/journal.pcbi.1004879 · DOI
  47. Uchihara, 1995, ApoE immunoreactivity and microglial cells in Alzheimer’s disease brain, Neurosci. Lett, 195, 5, 10.1016/0304-3940(95)11763-M · DOI · Paper page
  48. Vardarajan, 2015, Coding mutations in SORL1 and Alzheimer disease, Ann. Neurol, 77, 215, 10.1002/ana.24305 · DOI · Paper page
  49. Wang, 2017, WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit, Nucleic Acids Res, 45, W130, 10.1093/nar/gkx356 · DOI
  50. Wu, 2014, Analysis of microglial migration by a micropipette assay, Nat. Protoc, 9, 491, 10.1038/nprot.2014.015 · DOI
  51. Yajima, 2015, ApoE-isoform-dependent cellular uptake of amyloid-beta is mediated by lipoprotein receptor LR11/SorLA, Biochem. Biophys. Res. Commun, 456, 482, 10.1016/j.bbrc.2014.11.111 · DOI · Paper page
  52. Yan, 2009, Characterizing the appearance and growth of amyloid plaques in APP/PS1 mice, J. Neurosci, 29, 10706, 10.1523/JNEUROSCI.2637-09.2009 · DOI · Paper page
  53. Yin, 2017, Immune hyperreactivity of Abeta plaque-associated microglia in Alzheimer’s disease, Neurobiol. Aging, 55, 115, 10.1016/j.neurobiolaging.2017.03.021 · DOI · Paper page
  54. Zhang, 2016, Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse, Neuron, 89, 37, 10.1016/j.neuron.2015.11.013 · DOI · Paper page
  55. Zhao, 2018, TREM2 Is a Receptor for beta-Amyloid that Mediates Microglial Function, Neuron, 97, 1023, 10.1016/j.neuron.2018.01.031 · DOI · Paper page
  56. Zhou, 2020, Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease, Nat. Med, 26, 131, 10.1038/s41591-019-0695-9 · DOI · Paper page

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Metadata

Authors
Liu, T., Zhu, B., Liu, Y., Zhang, X., Yin, J., Li, X., Jiang, L., Hodges, A. P., Rosenthal, S. B., Zhou, L., Yancey, J., McQuade, A., Blurton-Jones, M., Tanzi, R. E., Huang, T. Y., & Xu, H.
Affiliations
  • Neuroscience Initiative, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 1
  • Center for Computational Biology and Bioinformatics, University of California, San Diego School of Medicine, La Jolla, CA 6
  • Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA 2
  • Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 5
ORCID
Venue
Journal of Experimental Medicine
Publisher
Rockefeller University Press
Published
2020-09-17
Volume
217
Issue
12
ISSN
  • 0022-1007
  • 1540-9538
DOI
10.1084/jem.20200474
PMC
PMC7953740
Cited by
64
References
34
Language
en
Licence
Funders
  • National Institutes of Health (R01 AG061875, R01 AG056130, R01 AG044420, R21 AG059217, R01 AG038710, R01 NS046673, R01 AG056114)
  • Tanz Family Fund
  • Cure Alzheimer’s Fund
  • National Cancer Institute (P30CA030199)

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