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A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease

Nature Neuroscience volume 20, pages 1052–1061 (2017)Cite this article

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Abstract

A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

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Figure 1: AD-survival and myeloid eQTL associations.
Figure 2: SPI1 (PU.1) expression and ChIP-seq analysis.
Figure 3: PU.1 modulates the phagocytic activity of BV2 microglial cells.
Figure 4: Genes regulated with differential expression of Spi1 in BV2 microglial cells.

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Acknowledgements

We thank the patients, control subjects and their family members for participating in and supporting the research projects included in this study. We thank M. Diamond (UT Southwestern Medical Center) for the BV2 cell line and the Flow Cytometry CORE at the Icahn School of Medicine at Mount Sinai Hospital.

IGAP: This study incorporated imputed summary results from the GERAD1 genome-wide association study. GERAD was supported by the Wellcome Trust, the MRC, Alzheimer's Research UK (ARUK) and the Welsh government. ADGC and CHARGE were supported by the US National Institutes of Health, National Institute on Aging (NIH-NIA), including grants U01 AG032984 and R01 AG033193. CHARGE was also supported by Erasmus Medical Center and Erasmus University.

Cardiff University was supported by the Wellcome Trust, Medical Research Council (MRC), Alzheimer's Research UK (ARUK) and the Welsh Assembly Government. Cambridge University and Kings College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer′s Society, Ulster Garden Villages, N. Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer's Institute for Research on Aging supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer's and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and by the Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research (BMBF), Competence Network Dementia and Competence Network Degenerative Dementia and by the Alfried Krupp von Bohlen und Halbach-Stiftung. The GERAD Consortium also used samples ascertained by the NIMH AD Genetics Initiative.

The KORA F4 studies were financed by Helmholtz Zentrum München; German Research Center for Environmental Health; BMBF; German National Genome Research Network and the Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the Heinz Nixdorf Foundation (Dr. jur. G.Schmidt, Chairman) and BMBF. Coriell Cell Repositories is supported by NINDS and the Intramural Research Program of the National Institute on Aging. We acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the MRC and the Wellcome Trust, which was genotyped by the Wellcome Trust Case Control Consortium and the Type-1 Diabetes Genetics Consortium, sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International.

ADNI: Data collection and sharing for this project was funded by the Alzheimer′s Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; 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 is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

We thank the Cardiogenics (European Project reference LSHM-CT-2006-037593) project for providing summary statistics for the cis-eQTL-based analyses. We also thank the ENCODE Consortium and R. Myers' lab (HAIB) for providing ChIP-seq datasets.

This work was supported by grants from the National Institutes of Health (U01 AG049508, R01-AG035083 and RF-AG054011 (to A.M.G.) and R01-AG044546 and RF1AG053303 (to C.C.)), the JPB Foundation (to A.M.G.) and F Prime (to A.M.G.). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991 and P01 AG026276. Kuan-lin Huang received fellowship funding in part from the Ministry of Education in Taiwan and the Lucille P. Markey Special Emphasis Pathway in Human Pathobiology. Ke Hao is partially supported by the National Natural Science Foundation of China (Grant Nos. 21477087 and 91643201) and by the Ministry of Science and Technology of China (Grant No. 2016YFC0206507). This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

Author information

Author notes

  1. Kuan-lin Huang, Edoardo Marcora and Gerard D Schellenberg: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, Saint Louis, Missouri, USA

    Kuan-lin Huang

  2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York,, New York, USA

    Edoardo Marcora, Antonio F Di Narzo, Manav Kapoor, Andrew McKenzie, Towfique Raj, Bin Zhang, Ke Hao & Alison M Goate

  3. Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York,, New York, USA

    Edoardo Marcora, Anna A Pimenova, Manav Kapoor, Sarah Bertelsen, Towfique Raj, Alan E Renton & Alison M Goate

  4. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA

    Sheng Chih Jin

  5. Department of Psychiatry, Washington University in St. Louis, Saint Louis, Missouri, USA

    Oscar Harari, Yuetiva Deming, John Budde, Jorge L Del-Aguila, Maria Victoria Fernandez, Laura Ibañez & Carlos Cruchaga

  6. Nuffield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Benjamin P Fairfax

  7. Department of Genetics, Washington University in St. Louis, Saint Louis, Missouri, USA

    Jake Czajkowski & Ingrid Borecki

  8. Department of Neurology, Boston University School of Medicine, Boston, Massachussets, USA

    Vincent Chouraki & Sudha Seshadri

  9. Inserm, U1167, RID-AGE –Risk factors and molecular determinants of aging-related diseases, Lille, France

    Benjamin Grenier-Boley, Céline Bellenguez, Jean Charles Lambert & Philippe Amouyel

  10. Univ. Lille - Excellence laboratory Labex DISTALZ, Lille, France

    Benjamin Grenier-Boley, Céline Bellenguez, Jean Charles Lambert & Philippe Amouyel

  11. Institut Pasteur de Lille, Lille, France

    Benjamin Grenier-Boley, Céline Bellenguez, Jean Charles Lambert & Philippe Amouyel

  12. Icelandic Heart Association, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    Albert Smith

  13. Department of Epidemiology, University of Washington, Seattle, Washington, USA

    Annette Fitzpatrick

  14. Department of Medicine, University of Washington, Seattle, Washington, USA

    Joshua C Bis

  15. Department of Biostatistics, Boston University School of Public Health, Boston, Massachussetts, USA

    Anita DeStefano & Lindsay A Farrer

  16. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Hieab H H Adams, M Arfan Ikram, Sven van der Lee & Cornelia van Duijn

  17. Psychological Medicine and Clinical Neurosciences, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

    Rebecca Sims, Valentina Escott-Price & Julie Williams

  18. Taub Institute on Alzheimer's Disease and the Aging Brain, Gertrude H. Sergievsky Center, and Department of Neurology, Columbia University, New York,, New York, USA

    Richard Mayeux

  19. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA

    Jonathan L Haines

  20. Department of Ophthalmology, Boston University School of Medicine, Boston, Massachussets, USA

    Lindsay A Farrer

  21. Department of Epidemiology, Boston University School of Public Health, Boston, Massachussetts, USA

    Lindsay A Farrer

  22. The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA

    Lindsay A Farrer & Margaret A Pericak-Vance

  23. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA

    Margaret A Pericak-Vance

  24. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA

    Lenore Launer

  25. Centre Hospitalier Universitaire de Lille, U1167, Lille, France

    Philippe Amouyel

  26. Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA

    Gerard D Schellenberg

  27. Department of Biology, Brigham Young University, Provo, Utah, USA

    John S K Kauwe

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Consortia

The International Genomics of Alzheimer's Project

The Alzheimer's Disease Neuroimaging Initiative

Contributions

A.M.G., E.M. and K.-L.H. conceived and designed the experiments. K.-L.H., S.C.J., O.H., A.D., M.K., J.C., J.C.L., V.C., C.B., B.G.-B., Y.D., A.M., T.R., A.E.R., J.L.D.-A., M.V.F, L.I., B.Z., I.B., C.C. and E.M. performed data analysis. A.A.P. performed phagocytosis assays, western blotting and qPCR validation. S.B., B.P.F., J.B., R.S., V.E.-P., R.M., J.L.H., L.A.F., M.A.P.-V., S.S., J.W., P.A., G.D.S., J.S.K.K., K.H. and C.C. provided and processed the data. A.M.G. supervised data analysis and functional experiments. K.-L.H., A.A.P., E.M. and A.M.G. wrote and edited the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Alison M Goate.

Ethics declarations

Competing interests

A.M.G. is on the scientific advisory board for Denali Therapeutics and has served as a consultant for AbbVie and Cognition Therapeutics.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Result and quality control analysis of the IGAP AD-survival meta-analysis.

(a) Manhattan plot and QQ-plot of the GWAS. The final meta-analysis showed little evidence of genomic inflation (λ = 1.026). (b) The average standard error versus the number of cohorts with consistent directionalities of effect sizes.

Supplementary Figure 2 Kaplan-Meier plots of AAOS associations.

Kaplan-Meier plots of survival analysis associations in the ADGC cohort of (a) rs1057233, (b) rs10919252, (c) rs567075, (d) rs7867518, (e) rs7930318, (f) rs4803758.

Supplementary Figure 3 Forest plots of AAOS associations.

Forest plots of survival analysis associations across IGAP cohorts of (a) rs1057233, (b) rs10919252, (c) rs567075, (d) rs7867518, (e) rs7930318, (f) rs4803758.

Supplementary Figure 4 Cell-type-specific expression of eQTL-associated genes in brain.

Cell-type specific expression of MS4A4A (no mouse homolog available), SPI1, MYBPC3, MS4A6A and SELL in human and mouse brains based on the brain RNA-Seq database.

Supplementary Figure 5 Linkage disequilibrium (LD) plot of SNPs of interest in the SPI1 (CELF1) locus.

Supplementary Figure 6 SMR plots showing the associations at the SPI1 locus.

SMR plots showing the associations at the SPI1/CELF1 locus from AAOS GWAS and eQTLs in (a) monocytes and (b) macrophages.

Supplementary Figure 7 SPI1 (PU.1) ChIP-seq binding sites and other epigenetic signatures at AD-associated loci in human CD14+ monocytes.

PU.1 binding sites, DNase I hypersensitive sites, histone modifications, and chromatin states at the locus of (a) ABCA7, (b) APOE, (c) BIN1, (d) SPI1, (e) PICALM, and (f) TYROBP.

Supplementary Figure 8 Analysis of phagocytosis in BV2 microglial cells.

(a) Flow cytometry histograms of BV2 cells transfected with pcDNA3 (pcDNA) or pcDNA3-FLAG-PU.1 (FLAG-PU.1) with pCMV-GFP for overexpression and scrambled shRNA (shSCR) or shRNA targeting PU.1 (shA, shB and shD) in pGFP-V-RS vector for knock-down of PU.1 after 3 hours of incubation with pHrodo-labeled zymosan. Cells were gated on GFP+ population. (b) Flow cytometry analysis of the number of gated cells in a presented as mean ± s.d., pcDNA 67.03 ± 6.883, pcDNA + 1 μM Cyt 15.64 ± 16.24, FLAG-PU.1 82.71 ± 4.74, shSCR 77.17 ± 3.115, shA 48.63 ± 2.285, shB 28.92 ± 2.495, shD 22.76 ± 1.595. pcDNA vs pcDNA + 1 μM Cyt P < 0.0001, pcDNA vs FLAG-PU.1 P = 0.0306, shSCR vs shA P = 0.0002, shSCR vs shB P < 0.0001, shSCR vs shD P < 0.0001. F(6,13) = 58.68, n = 3. (c) Flow cytometry analysis of the geometric mean fluorescent pHrodo intensity in a presented as mean ± s.d., pcDNA 10952 ± 2206, pcDNA + 1 μM Cyt 1533 ± 47, FLAG-PU.1 15226 ± 2701, shSCR 13129 ± 4617, shA 9937 ± 2168, shB 8872 ± 2019, shD 8754 ± 1856. pcDNA vs pcDNA + 1 μM Cyt P = 0.0092. F(6,13) = 6.228, n = 3. (d) Flow cytometry histograms of BV2 cells transfected as in (a) and gated on GFP- populations. (e) Flow cytometry analysis of number of gated cells in d presented as mean ± s.d., pcDNA 63.92 ± 6.575, pcDNA + 1 μM Cyt 14.21 ± 13.66, FLAG-PU.1 67.54 ± 4.826, shSCR 68.31 ± 5.784, shA 67.27 ± 4.144, shB 65.19 ± 4.268, shD 60.3 ± 2.181. pcDNA vs pcDNA + 1 μM Cyt P < 0.0001. F(6,13) = 22.53, n = 3. (f) Flow cytometry analysis of geometric mean fluorescent pHrodo intensity in d presented as mean ± s.d., pcDNA 9186 ± 2863, pcDNA + 1 μM Cyt 1545 ± 147, FLAG-PU.1 9931 ± 2458, shSCR 9849 ± 3012, shA 10903 ± 2949, shB 10912 ± 2494, shD 10934 ± 2685. pcDNA vs pcDNA + 1 μM Cyt P = 0.0367. F(6,13) = 3.473, n = 3. (g) Phagocytic index of BV2 GFP- cells analyzed in (e) and (f) presented as mean ± s.d., pcDNA 0.5954 ± 0.2223, pcDNA + 1 μM Cyt 0.0209 ± 0.0189, FLAG-PU.1 0.6745 ± 0.188, shSCR 0.6765 ± 0.2274, shA 0.7382 ± 0.2255, shB 0.7131 ± 0.1742, shD 0.6612 ± 0.1748. pcDNA vs pcDNA + 1 μM Cyt P = 0.0331. F(6,13) = 3.53, n = 3. Cytochalasin D (Cyt) treatment was used as a negative control. * P < 0.05, ** P < 0.01, *** P < 0.001, one-way ANOVA with Sidak’s post hoc multiple comparisons test between selected groups.

Supplementary Figure 9 Expression levels of genes related to phagocytosis that were not affected by altered Spi1 expression.

BV2 cells were transiently transfected with pcDNA3-FLAG-PU.1 and pCMV-GFP or pGFP-V-RS-shB against PU.1. pcDNA3 and pGFP-V-RS-shSCR were used as controls. RNA was extracted from sorted GFP+ cells and used for qPCR validation of expression levels for genes of interest. Values are presented as mean ± s.d., n = 4 samples collected independently.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9. (PDF 1328 kb)

Supplementary Methods Checklist (PDF 196 kb)

Supplementary Note (PDF 68 kb)

Supplementary Table 1

Quality control of the age-at-onset survival GWAS. The number of filtered SNPs and samples are shown for each of the 7 cohort included for the IGAP meta-analysis. Additionally, the Schoenfeld P values were recorded for each of the 22 top AAOS GWAS SNPs across the 7 IGAP cohorts, and the covariates in Cox models for the ADGC and GERAD cohorts. (XLSX 16 kb)

Supplementary Table 2

Association results from the previous IGAP logistic regression and the matching cohort logistic regression of the 22 top AD-associated SNPs in AAOS GWAS. (XLSX 12 kb)

Supplementary Table 3

The 22 top SNPs significantly associated with age-at-onset defined survival in AD and their tag SNPs (R2 ≥ 0.8). (XLSX 476 kb)

Supplementary Table 4

Significant cis-eQTL associations between the 22 survival-associated SNPs across 10 brain regions in the BRAINEAC dataset. Bonferroni-corrected threshold: P=0.05/292,000 probes = 1.7x10-7. (XLSX 8 kb)

Supplementary Table 5

Significant cis-eQTL associations between the 22 survival-associated SNPs across tissues in the GTEx dataset. (XLSX 72 kb)

Supplementary Table 6

LD score regression analysis results for IGAP AD and PGC SCZ GWAS SNP heritability partitioned using 220 human cell types (CT, and 10 cell type groups, CTG)-specific functional annotations as described by Finucane et al. (XLSX 116 kb)

Supplementary Table 7

Replication of monocyte/macrophage cis-eQTL associations in CD14+ human monocytes from 432 individuals of European ancestry. Bonferroni-corrected threshold: P=0.05/15421 probes = 3.24x10-6. (XLSX 8 kb)

Supplementary Table 8

Coloc analysis results for the SPI1/CELF1, MS4A and SELL loci, using AAOS GWAS SNPs and Cardiogenics monocyte (MC) or macrophage (MP) cis-eQTL datasets. Estimated posterior probabilities for the following mutually exclusive hypotheses are shown in bold when surpassing the threshold PP ≥ 0.8: H0, neither trait has a genetic association in the region; H1: only trait 1 (cis-eQTL) has a genetic association in the region; H2: only trait 2 (GWAS) has a genetic association in the region; H3: both traits are associated, but with different causal variants; H4: both traits are associated and share a single causal variant. (XLSX 88 kb)

Supplementary Table 9

Conditional analysis of monocyte/macrophage SPI1 eQTL associations of 6 SNPs of interest: rs1057233, rs10838698, rs10838699, rs7928163, rs1377416, and rs10838725. Bonferroni-corrected threshold: P=0.05/6 SNPs = 0.00833. (XLSX 11 kb)

Supplementary Table 10

SMR/HEIDI analysis results for 6 SNPs of interest in the SPI1/CELF1 locus: rs1057233, rs10838698, rs10838699, rs7928163, rs1377416 and rs10838725, using AAOS GWAS SNPs and Cardiogenics monocyte (MC) or macrophage (MP) cis-eQTL datasets. Bonferroni-corrected threshold: SMR P=0.05/(6 SNPs x 17 probes) = 0.00049 [green cells]. A nominal significance threshold P ≥ 0.05 [red cells] is used for the HEIDI test as motivated by Zhu et al. Only probes with nominally significant SMR P are shown. (XLSX 84 kb)

Supplementary Table 11

AD-associated genes expressed in human brain myeloid cells with nearby SPI1 (PU.1) binding sites in human blood myeloid cells. (XLSX 45 kb)

Supplementary Table 12

LD score regression analysis results for IGAP AD and PGC SCZ GWAS SNP heritability partitioned using SPI1 (PU.1) ChIP-Seq binding sites in human monocytes and macrophages, as well as SPI1 (PU.1) and POLR2AphosphoS5 ChIP-Seq binding sites in human HL60 cells. (XLSX 13 kb)

Supplementary Table 13

Genes regulated in BV2 microglial cells with differential expression of Spi1. (XLSX 12 kb)

Supplementary Table 14

Primers used for qPCR validation of gene expression in BV2 microglial cells. (XLSX 14 kb)

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Huang, Kl., Marcora, E., Pimenova, A. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease. Nat Neurosci 20, 1052–1061 (2017). https://doi.org/10.1038/nn.4587

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