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PreprintCited by 11

A cortical immune network map identifies a subset of human microglia involved in Tau pathology

Patrick, Ellis, Olah, Marta, Taga, Mariko, Klein, Hans-Ulrich, Xu, Jishu, White, Charles C, Felsky, Daniel, Gaiteri, Chris +6 more·2017
DOI 10.1101/234351
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Abstract

Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimer's disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five gene modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 541 subjects from two cohort studies of brain aging. Two of these transcriptional programs – modules 113 and 114 – relate to the accumulation of β-amyloid, while module 5 relates to tau pathology. These modules are also detectable in the human brain's epigenome, where we replicate these associations. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that VASP , a representative module 5 gene, encodes a protein that is upregulated in activated microglia in AD.

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  • One microglial transcriptional program in the aging dorsolateral prefrontal cortex contributes to the accumulation of tau pathology, while two others may relate to β-amyloid pathology.

    “we observed that one microglial transcriptional program in the aging dorsolateral prefrontal cortex (DLPFC) contributes to the accumulation of tau pathology while two others may relate to β-amyloid pathology6”

    From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [6]

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  1. Jack, C. R., Jr. & Holtzman, D. M. Biomarker modeling of Alzheimer's disease. Neuron 80, 1347- 1358, doi:10.1016/j.neuron.2013.12.003 (2013). · DOI · Paper page
  2. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519-523, doi:10.1126/science.1249547 (2014). · DOI · Paper page
  3. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707-720, doi:10.1016/j.cell.2013.03.030 (2013). · DOI · Paper page
  4. Jones, L. et al. Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease. PLoS One 5, e13950, doi:10.1371/journal.pone.0013950 (2010). · DOI · Paper page
  5. Sara Mostafavi, C. G., Sarah Sullivan, Charles White, Shinya Takasi, Jishu Xu, Mariko Taga, Hans Klein, Ellis Patrick, Vitalina Komashko, Cristin McCable, Robert Smith, Elizabeth Bradshaw, David Root, Lei Yu, Aviv Regev, Lori Chibnik, Julie Schneider, Tracy Young-Pearse, Davi Bennett, Philip De Jager. A molecular network of the aging brain implicates INPPL1 and PLXNB1 in Alzheimer's disease. BioRxiv, 205807, doi:https://doi.org/10.1101/205807 (2017). · DOI
  6. Galatro, T. F. et al. Transcriptomic analysis of purified human cortical microglia reveals age- associated changes. Nat Neurosci 20, 1162-1171, doi:10.1038/nn.4597 (2017). · DOI · Paper page
  7. Rothhammer, V. & Quintana, F. J. Control of autoimmune CNS inflammation by astrocytes. Semin Immunopathol 37, 625-638, doi:10.1007/s00281-015-0515-3 (2015). · DOI · Paper page
  8. Huang, K. L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease. Nat Neurosci 20, 1052-1061, doi:10.1038/nn.4587 (2017). · DOI · Paper page
  9. Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nature genetics 45, 1452-1458, doi:10.1038/ng.2802 (2013). · DOI · Paper page
  10. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic acids research 45, doi:10.1093/nar/gkw1133 (2017). · DOI · Paper page
  11. Maphis, N. et al. Reactive microglia drive tau pathology and contribute to the spreading of pathological tau in the brain. Brain : a journal of neurology 138, 1738-1755, doi:10.1093/brain/awv081 (2015). · DOI · Paper page
  12. Satoh, J. et al. TMEM119 marks a subset of microglia in the human brain. Neuropathology 36, 39-49, doi:10.1111/neup.12235 (2016). · DOI · Paper page
  13. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome biology 7, doi:10.1186/gb-2006-7-10-r100 (2006). · DOI
  14. Chan, G. et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat Neurosci 18, 1556- 1558, doi:10.1038/nn.4126 (2015). · DOI · Paper page
  15. Group, T. N.-R. W. Consensus recommendations for the postmortem diagnosis of Alzheimer's disease. The National Institute on Aging, and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer's Disease. Neurobiology of aging 18, 2 (1997).
  16. Levin, J. Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature methods 7, 709-715, doi:10.1038/nmeth.1491 (2010). · DOI · Paper page
  17. Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nature methods 10, 623-629, doi:10.1038/nmeth.2483 (2013). · DOI
  18. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome biology 10, 1 (2009).
  19. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics 12, 1 (2011).
  20. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127, doi:10.1093/biostatistics/kxj037 (2007). · DOI · Paper page
  21. Schneider, J. A. et al. Cognitive impairment, decline and fluctuations in older community- dwelling subjects with Lewy bodies. Brain 135, 3005-3014, doi:10.1093/brain/aws234 (2012). · DOI · Paper page
  22. Wilson, R., Barnes, L. & Bennett, D. Assessment of lifetime participation in cognitively stimulating activities. Journal of clinical and experimental neuropsychology 25, 634-642, doi:10.1076/jcen.25.5.634.14572 (2003). · DOI
  23. Wilson, R. S. et al. Conscientiousness, dementia related pathology, and trajectories of cognitive aging. Psychology and aging 30, 74-82, doi:10.1037/pag0000013 (2015). · DOI
  24. De Jager, P. L. et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol Aging 33, 1017 e1011-1015, doi:10.1016/j.neurobiolaging.2011.09.033 (2012). · DOI
  25. Gaiteri, C. et al. Identifying robust communities and multi-community nodes by combining top- down and bottom-up approaches to clustering. Sci Rep 5, 16361, doi:10.1038/srep16361 (2015). · DOI · Paper page
  26. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics 25, 25-29, doi:10.1038/75556 (2000). · DOI · Paper page
  27. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707-720, doi:10.1016/j.cell.2013.03.030 (2013). · DOI · Paper page
  28. Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432-441, doi:10.1093/biostatistics/kxm045 (2008). · DOI · Paper page

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Authors
Patrick, E., Olah, M., Taga, M., Klein, H.-U., Xu, J., White, C. C., Felsky, D., Gaiteri, C., Chibnik, L. B., Mostafavi, S., Schneider, J. A., Bennett, D. A., Bradshaw, E. M., & De Jager, P. L.
Publisher
openRxiv
Published
2017-12-14
DOI
10.1101/234351
Cited by
11
References
6
Type
Preprint

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