Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Glial AP1 is activated with aging and accelerated by traumatic brain injury

Abstract

The emergence of degenerative disease after traumatic brain injury (TBI) is often described as an acceleration of normal age-related processes. Whether similar molecular processes occur after injury and in age is unclear. Here, we identify a functionally dynamic and lasting transcriptional response in glia, mediated by the conserved transcription factor AP1. In the early post-TBI period, glial AP1 is essential for recovery, ensuring brain integrity and animal survival. In sharp contrast, chronic AP1 activation promotes human tau pathology, tissue loss and mortality. We show a similar process activates in healthy fly brains with age. In humans, AP1 activity is detected after moderate TBI and correlates with microglial activation and tau pathology. Our data provide key molecular insight into glia, highlighting that the same molecular process drives dynamic and contradictory glia behavior in TBI and possibly age, first acting to protect but chronically promoting disease.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A lasting AP1 transcriptional response to TBI.
Fig. 2: AP1 activation is sustained and is severity-dependent.
Fig. 3: AP1 activates and persists in glia.
Fig. 4: Glial AP1 is activated by ERK and promotes early TBI recovery.
Fig. 5: Age and injury evoke a glial AP1 response.
Fig. 6: Chronic AP1 activity promotes human tau pathology.
Fig. 7: Glial AP1 promotes early TBI recovery but chronically drives tauopathy.
Fig. 8: AP1 activity decades after moderate TBI in humans.

Data availability

The differential gene expression lists generated in this study are included in this article (Supplementary Tables 57). dTBI sequencing data (fastq files and HTSeq counts) that support the findings of this study have been deposited in the Gene Expression Omnibus (accession code GSE171185). Additional data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. 1.

    Corkin, S., Rosen, T. J., Sullivan, E. V. & Clegg, R. A. Penetrating head injury in young adulthood exacerbates cognitive decline in later years. J. Neurosci. 9, 3876–3883 (1989).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Gardner, R. C. et al. Dementia risk after traumatic brain injury vs nonbrain trauma: the role of age and severity. JAMA Neurol. 71, 1490–1497 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Smith, D. H., Johnson, V. E. & Stewart, W. Chronic neuropathologies of single and repetitive TBI: substrates of dementia? Nat. Rev. Neurol. 9, 211–221 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    McKee, A. C. et al. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J. Neuropathol. Exp. Neurol. 68, 709–735 (2009).

    PubMed  Article  Google Scholar 

  5. 5.

    Mackay, D. F. et al. Neurodegenerative disease mortality among former professional soccer players. N. Engl. J. Med. 381, 1801–1808 (2019).

    PubMed  Article  Google Scholar 

  6. 6.

    Johnson, V. E., Stewart, W. & Smith, D. H. Widespread tau and amyloid-β pathology many years after a single traumatic brain injury in humans. Brain Pathol. 22, 142–149 (2012).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Tagge, C. A. et al. Concussion, microvascular injury, and early tauopathy in young athletes after impact head injury and an impact concussion mouse model. Brain 141, 422–458 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Rubenstein, R. et al. Comparing plasma phospho tau, total tau, and phospho tau-total tau ratio as acute and chronic traumatic brain injury biomarkers. JAMA Neurol. 74, 1063–1072 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Zanier, E. R. et al. Induction of a transmissible tau pathology by traumatic brain injury. Brain 141, 2685–2699 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Hickman, S., Izzy, S., Sen, P., Morsett, L. & El Khoury, J. Microglia in neurodegeneration. Nat. Neurosci. 21, 1359–1369 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Liu, L. et al. Glial lipid droplets and ROS induced by mitochondrial defects promote neurodegeneration. Cell 160, 177–190 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Habib, N. et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat. Neurosci. 23, 701–706 (2020).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Liu, C. C. et al. ApoE4 accelerates early seeding of amyloid pathology. Neuron 96, 1024–1032 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Zhao, Y. et al. TREM2 is a receptor for β-amyloid that mediates microglial function. Neuron 97, 1023–1031 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Paolicelli, R. C. et al. TDP-43 depletion in microglia promotes amyloid clearance but also induces synapse loss. Neuron 95, 297–308 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Heckmann, B. L. et al. LC3-Associated endocytosis facilitates β-amyloid clearance and mitigates neurodegeneration in murine Alzheimer’s disease. Cell 178, 536–551 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Ising, C. et al. NLRP3 inflammasome activation drives tau pathology. Nature 575, 669–673 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Venegas, C. et al. Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease. Nature 552, 355–361 (2017).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Narasimhan, S. et al. Pathological tau strains from human brains recapitulate the diversity of tauopathies in nontransgenic mouse brain. J. Neurosci. 37, 11406–11423 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Asai, H. et al. Depletion of microglia and inhibition of exosome synthesis halt tau propagation. Nat. Neurosci. 18, 1584–1593 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Jassam, Y. N., Izzy, S., Whalen, M., McGavern, D. B. & El Khoury, J. Neuroimmunology of traumatic brain injury: time for a paradigm shift. Neuron 95, 1246–1265 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Johnson, V. E. et al. Inflammation and white matter degeneration persist for years after a single traumatic brain injury. Brain 136, 28–42 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Cherry, J. D. et al. Microglial neuroinflammation contributes to tau accumulation in chronic traumatic encephalopathy. Acta Neuropathol. Commun. 4, 112 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24.

    McKee, A. C., Abdolmohammadi, B. & Stein, T. D. The neuropathology of chronic traumatic encephalopathy. Handb. Clin. Neurol. 158, 297–307 (2018).

    PubMed  Article  Google Scholar 

  25. 25.

    McKee, A. C., Stein, T. D., Kiernan, P. T. & Alvarez, V. E. The neuropathology of chronic traumatic encephalopathy. Brain Pathol. 25, 350–364 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Arena, J. D. et al. Tau immunophenotypes in chronic traumatic encephalopathy recapitulate those of ageing and Alzheimer’s disease. Brain 143, 1572–1587 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Collins-Praino, L. E. & Corrigan, F. Does neuroinflammation drive the relationship between tau hyperphosphorylation and dementia development following traumatic brain injury? Brain Behav. Immun. 60, 369–382 (2017).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Freibaum, B. D. et al. GGGGCC repeat expansion in C9orf72 compromises nucleocytoplasmic transport. Nature 525, 129–133 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Zhang, K. et al. The C9orf72 repeat expansion disrupts nucleocytoplasmic transport. Nature 525, 56–61 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Marsh, J. L. & Thompson, L. M. Drosophila in the study of neurodegenerative disease. Neuron 52, 169–178 (2006).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Bier, E. Drosophila, the golden bug, emerges as a tool for human genetics. Nat. Rev. Genet. 6, 9–23 (2005).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Bellen, H. J., Tong, C. & Tsuda, H. 100 years of Drosophila research and its impact on vertebrate neuroscience: a history lesson for the future. Nat. Rev. Neurosci. 11, 514–522 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Warrick, J. M. et al. Suppression of polyglutamine-mediated neurodegeneration in Drosophila by the molecular chaperone HSP70. Nat. Genet. 23, 425–428 (1999).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Auluck, P. K., Chan, H. Y., Trojanowski, J. Q., Lee, V. M. & Bonini, N. M. Chaperone suppression of α-synuclein toxicity in a drosophila model for Parkinson’s disease. Science 295, 865–868 (2002).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Wittmann, C. W. et al. Tauopathy in Drosophila: neurodegeneration without neurofibrillary tangles. Science 293, 711–714 (2001).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Frost, B., Hemberg, M., Lewis, J. & Feany, M. B. Tau promotes neurodegeneration through global chromatin relaxation. Nat. Neurosci. 17, 357–366 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Steffan, J. S. et al. Histone deacetylase inhibitors arrest polyglutamine-dependent neurodegeneration in Drosophila. Nature 413, 739–743 (2001).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Fernandez-Funez, P. et al. Identification of genes that modify ataxin-1-induced neurodegeneration. Nature 408, 101–106 (2000).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Saikumar, J., Byrns, C. N., Hemphill, M., Meaney, D. F. & Bonini, N. M. Dynamic neural and glial responses of a head-specific model for traumatic brain injury in Drosophila. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2003909117 (2020).

  40. 40.

    Shah, E. J., Gurdziel, K. & Ruden, D. M. Mammalian models of traumatic brain injury and a place for Drosophila in TBI research. Front. Neurosci. 13, 409 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Freeman, M. R. Drosophila central nervous system glia. Cold Spring Harb. Perspect. Biol. https://doi.org/10.1101/cshperspect.a020552 (2015).

  42. 42.

    Kremer, M. C., Jung, C., Batelli, S., Rubin, G. M. & Glia, G. U. The glia of the adult Drosophila nervous system. GLIA https://doi.org/10.1002/glia.23115 (2017).

  43. 43.

    Lu, T.-Y. et al. Axon degeneration induces glial responses through Draper-TRAF4-JNK signalling. Nat. Commun. 8, 14355 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Purice, M. D. et al. A novel Drosophila injury model reveals severed axons are cleared through a Draper/MMP-1 signaling cascade. eLife https://doi.org/10.7554/eLife.23611 (2017)

  45. 45.

    Hess, J., Angel, P. & Schorpp-Kistner, M. AP-1 subunits: quarrel and harmony among siblings. J. Cell Sci. 117, 5965–5973 (2004).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Perkins, K. K., Dailey, G. M. & Tjian, R. Novel Jun- and Fos-related proteins in Drosophila are functionally homologous to enhancer factor AP-1. EMBO J. 7, 4265–4273 (1988).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Perkins, K. K., Admon, A., Patel, N. & Tjian, R. The Drosophila Fos-related AP-1 protein is a developmentally regulated transcription factor. Genes Dev. 4, 822–834 (1990).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Kockel, L., Homsy, J. G. & Bohmann, D. Drosophila AP-1: lessons from an invertebrate. Oncogene 20, 2347–2364 (2001).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Külshammer, E. et al. Interplay among Drosophila transcription factors Ets21c, Fos and Ftz-F1 drives JNK-mediated tumor malignancy. Dis. Models Mech. 8, 1279–1293 (2015).

    Google Scholar 

  50. 50.

    Toggweiler, J., Willecke, M. & Basler, K. The transcription factor Ets21C drives tumor growth by cooperating with AP-1. Sci. Rep. 6, 34725 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Chatterjee, N. & Bohmann, D. A. Versatile ΦC31 based reporter system for measuring AP-1 and Nrf2 signaling in Drosophila and in tissue culture. PLoS ONE https://doi.org/10.1371/journal.pone.0034063 (2012)

  52. 52.

    Streit, W. J., Khoshbouei, H. & Bechmann, I. Dystrophic microglia in late-onset Alzheimer’s disease. GLIA 68, 845–854 (2020).

    PubMed  Article  Google Scholar 

  53. 53.

    Streit, W. J., Sammons, N. W., Kuhns, A. J. & Sparks, D. L. Dystrophic microglia in the aging human brain. GLIA 45, 208–212 (2004).

    PubMed  Article  Google Scholar 

  54. 54.

    MacDonald, J. M. et al. The Drosophila cell corpse engulfment receptor Draper mediates glial clearance of severed axons. Neuron 50, 869–881 (2006).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Macdonald, J. M., Doherty, J., Hackett, R. & Freeman, M. R. The c-Jun kinase signaling cascade promotes glial engulfment activity through activation of draper and phagocytic function. Cell Death Differ. 20, 1140–1148 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Doherty, J. et al. PI3K signaling and Stat92E converge to modulate glial responsiveness to axonal injury. PLoS Biol. 12, e1001985 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    Angel, P. & Karin, M. The role of Jun, Fos and the AP-1 complex in cell-proliferation and transformation. Biochim. Biophys. Acta 1072, 129–157 (1991).

    CAS  PubMed  Google Scholar 

  58. 58.

    Karin, M. The regulation of AP-1 activity by mitogen-activated protein kinases. J. Biol. Chem. 270, 16483–16486 (1995).

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Ciapponi, L., Jackson, D. B., Mlodzik, M. & Bohmann, D. Drosophila Fos mediates ERK and JNK signals via distinct phosphorylation sites. Genes Dev. 15, 1540–1553 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Kockel, L., Zeitlinger, J., Staszewski, L. M., Mlodzik, M. & Bohmann, D. Jun in Drosophila development: redundant and nonredundant functions and regulation by two MAPK signal transduction pathways. Genes Dev. 11, 1748–1758 (1997).

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Davie, K. et al. A single-cell transcriptome atlas of the aging drosophila brain. Cell 174, 982–998.e20 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Dobens, L. L., Martin-Blanco, E., Martinez-Arias, A., Kafatos, F. C. & Raftery, L. A. Drosophila puckered regulates Fos/Jun levels during follicle cell morphogenesis. Development 128, 1845–1856 (2001).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Crane, P. K. et al. Association of traumatic brain injury with late-life neurodegenerative conditions and neuropathologic findings. JAMA Neurol. 73, 1062–1069 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Cole, J. H., Leech, R., Sharp, D. J. & Alzheimer’s Disease Neuroimaging, I. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann. Neurol. 77, 571–581 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Moretti, L. et al. Cognitive decline in older adults with a history of traumatic brain injury. Lancet Neurol. 11, 1103–1112 (2012).

    PubMed  Article  Google Scholar 

  66. 66.

    Nordstrom, A. & Nordstrom, P. Traumatic brain injury and the risk of dementia diagnosis: a nationwide cohort study. PLoS Med. 15, e1002496 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Pacifico, R., MacMullen, C. M., Walkinshaw, E., Zhang, X. & Davis, R. L. Brain transcriptome changes in the aging Drosophila melanogaster accompany olfactory memory performance deficits. PLoS ONE 13, e0209405 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Colodner, K. J. & Feany, M. B. Glial fibrillary tangles and JAK/STAT-mediated glial and neuronal cell death in a Drosophila model of glial tauopathy. J. Neurosci. 30, 16102–16113 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Schmidt, M. L., Zhukareva, V., Newell, K. L., Lee, V. M. & Trojanowski, J. Q. Tau isoform profile and phosphorylation state in dementia pugilistica recapitulate Alzheimer’s disease. Acta Neuropathol. 101, 518–524 (2001).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Blennow, K., Hardy, J. & Zetterberg, H. The neuropathology and neurobiology of traumatic brain injury. Neuron 76, 886–899 (2012).

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Velazquez, A., Ortega, M., Rojas, S., Gonzalez-Olivan, F. J. & Rodriguez-Baeza, A. Widespread microglial activation in patients deceased from traumatic brain injury. Brain Inj. 29, 1126–1133 (2015).

    PubMed  Article  Google Scholar 

  72. 72.

    Logan, M. A. et al. Negative regulation of glial engulfment activity by Draper terminates glial responses to axon injury. Nat. Neurosci. 15, 722–730 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Szuts, D. & Bienz, M. An autoregulatory function of δfos during Drosophila endoderm induction. Mech. Dev. 98, 71–76 (2000).

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Nestler, E. J. FosB: a transcriptional regulator of stress and antidepressant responses. Eur. J. Pharmacol. 753, 66–72 (2015).

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Chen, J., Kelz, M. B., Hope, B. T., Nakabeppu, Y. & Nestler, E. J. Chronic Fos-related antigens: stable variants of δFosB induced in brain by chronic treatments. J. Neurosci. 17, 4933–4941 (1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Nestler, E. J., Kelz, M. B. & Chen, J. δFosB: a molecular mediator of long-term neural and behavioral plasticity. Brain Res. 835, 10–17 (1999).

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Martinez-Zamudio, R. I. et al. AP-1 imprints a reversible transcriptional programme of senescent cells. Nat. Cell Biol. 22, 842–855 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Bussian, T. J. et al. Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline. Nature 562, 578–582 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Sharpless, N. E. & Sherr, C. J. Forging a signature of in vivo senescence. Nat. Rev. Cancer 15, 397–408 (2015).

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Gorgoulis, V. et al. Cellular senescence: defining a path forward. Cell 179, 813–827 (2019).

    CAS  PubMed  Article  Google Scholar 

  81. 81.

    Chien, Y. et al. Control of the senescence-associated secretory phenotype by NF-κB promotes senescence and enhances chemosensitivity. Genes Dev. 25, 2125–2136 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Ndoja, A. et al. Ubiquitin ligase COP1 suppresses neuroinflammation by degrading c/EBPβ in microglia. Cell https://doi.org/10.1016/j.cell.2020.07.011 (2020).

  83. 83.

    Wood, R. L. Accelerated cognitive aging following severe traumatic brain injury: a review. Brain Inj. 31, 1270–1278 (2017).

    PubMed  Article  Google Scholar 

  84. 84.

    Shahidehpour, R. K. et al. Dystrophic microglia are associated with neurodegenerative disease and not healthy aging in the human brain. Neurobiol. Aging 99, 19–27 (2021).

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    Ramlackhansingh, A. F. et al. Inflammation after trauma: microglial activation and traumatic brain injury. Ann. Neurol. 70, 374–383 (2011).

    PubMed  Article  Google Scholar 

  86. 86.

    Marschallinger, J. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat. Neurosci. 23, 194–208 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Sheng, L. et al. Social reprogramming in ants induces longevity-associated glia remodeling. Sci. Adv. 6, eaba9869 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271 (2019).

    CAS  PubMed  Article  Google Scholar 

  89. 89.

    Osterwalder, T., Yoon, K. S., White, B. H. & Keshishian, H. A conditional tissue-specific transgene expression system using inducible GAL4. Proc. Natl Acad. Sci. USA 98, 12596–12601 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Saikumar, J. et al. Inducing different severities of traumatic brain injury in drosophila using a piezoelectric actuator. Nat. Protoc. 16, 263–282 (2021).

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    ‘Picard Toolkit’. GitHub Repository (Broad Institute, 2019).

  93. 93.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  95. 95.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Lyne, R. et al. FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biol. 8, R129 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  97. 97.

    Hu, Y. et al. FlyPrimerBank: an online database for Drosophila melanogaster gene expression analysis and knockdown evaluation of RNAi reagents. G3 3, 1607–1616 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. 98.

    Tito, A. J., Cheema, S., Jiang, M. & Zhang, S. A simple one-step dissection protocol for whole-mount preparation of adult drosophila brains. J. Vis. Exp. https://doi.org/10.3791/55128 (2016).

  99. 99.

    Halfmann, R. & Lindquist, S. Screening for amyloid aggregation by semi-denaturing detergent-agarose gel electrophoresis. J. Vis. Exp. https://doi.org/10.3791/838 (2008).

  100. 100.

    Allen Institute for Brain Science. Aging, dementia and traumatic brain injury study. https://aging.brain-map.org/overview/home (2016).

  101. 101.

    Miller, J. A. et al. Neuropathological and transcriptomic characteristics of the aged brain. eLife https://doi.org/10.7554/eLife.31126 (2017).

Download references

Acknowledgements

We thank G. Donahue for early guidance on analyzing RNA-seq data; V. Lee and S. Narasimhan for helpful discussions on tau; R. Bonasio, L. Goodman, E. Lee, A. Perlegos and K. Simeonov for manuscript feedback; F. Carranza and Z. Jin for technical assistance; M. Kayser, G. Ming and D. Meany for helpful thesis feedback; and S. Lindquist for continued inspiration. This work was supported by T32-AG000255, F31-NS111868 (to C.N.B.), a predoctoral Howard Hughes Medical Institute fellowship (to J.S.) and R35-NS097275 (to N.M.B.).

Author information

Affiliations

Authors

Contributions

C.N.B., under the supervision of N.M.B., executed the conceptualization, investigation, formal analysis and visualization; J.S. contributed to RNA-seq study conceptualization. C.N.B. wrote and edited the manuscript, with input from N.M.B. Funding acquisition was obtained by C.N.B., J.S. and N.M.B.

Corresponding author

Correspondence to Nancy M. Bonini.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks David Sharp and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 A lasting AP1 transcriptional response to TBI.

a, Kaplan-Meier survival curve (with Log-rank test) for 10 and 15 dpi RNAseq replicate animals. b, Venn diagram showing the number of common and distinct differentially expressed genes (FDR < 0.05) between post-injury times. Value in parenthesis shows total (up and down) number of DE genes for a given timepoint. c, Results for HOMER de novo motif enrichment among downregulated genes (FDR < 0.05). d, Tile plot showing Reactome pathways enriched among upregulated genes with a predicted AP1 motif (FDR < 0.05). Presence of a colored tile indicates enrichment at a given post-injury time. Tile opacity encodes significance. Color corresponds to the parent process, as defined by the Reactome annotation database (annotated on left). e, Average sham (black) and severe dTBI (red) expression of genes related to the heat shock response at ≤1 dpi (top) and ≥1 dpi (bottom; blue star indicates FDR < 0.05 at a given time). f, Average sham (black) and severe dTBI (red) expression of canonical stress response genes at ≤1 dpi (top) and ≥1 dpi (bottom; blue star indicates FDR < 0.05 at a given time). For full statistical reporting, including exact p-values, see Source Data Extended Data Fig. 1. See Supplementary Table 1 for genotypes.

Source data

Extended Data Fig. 2 Sustained and severity-dependent activation of AP1.

a, Representative z-stacked wholemount brains at 1 dpi in flies with dsRed expressed under a mutated TRE-promoter (MREdsRed; representative of n = 9–11 brains per condition from two independent experiments). b, Mean relative expression of select RNAseq predicted AP1 genes by RT-qPCR at 1, 7 and 15 dpi across injury conditions (each point = 1 biological replicate (9 dissected brains); n = 6 biological replicates per condition; Kruskal–Wallis test with Dunn’s multiple comparison test and Holm adjustment for each gene). Black symbols, sham. Pink symbols, mild dTBI. Red symbols, severe dTBI. Statistical annotations are ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05, .<p < 0.10. For full statistical reporting, including exact p-values, see Source Data Extended Data Fig. 2. See Supplementary Table 1 for genotypes. All scale bars 100 μm.

Source data

Extended Data Fig. 3 Glial AP1 activation is draper and JNK-independent.

a, Z-stack of the first 6 slices (2 μm each) of the antennal lobe with (RU; top) or without (vehicle; bottom) glial draper-RNAi. White arrows highlight hypertrophic glial processes. 1 day after 3rd antennal segment ablation (AL injury; n = 7–9 brains per condition). b, Left, z-stacked whole mount brains at 1 dpi with (RU; top) and without (vehicle; bottom) glial draper-RNAi. Right, quantification relative to left condition (each point = 1 brain; n = 11–14 brains per condition from two independent experiments; p = 2.17e-06, Kruskal–Wallis test with Wilcoxon rank sum test). c, Mean relative expression by RT-qPCR at 1 dpi, with or without glial draper-RNAi (each point = 1 biological replicate (9 dissected brains pooled); n = 6 biological replicates per condition; Kruskal–Wallis test with Dunn’s multiple comparison test). d, Average sham (black) and severe dTBI (red) expression of draper from RNAseq experiment (blue star FDR < 0.05). e, Representative whole mount for lysotracker at 1 dpi (severe) in WT (top; w1118) and draper-/- flies (middle: dTBI; bottom: sham). Right, quantification relative to left condition (each point = 1 brain; n = 7–11 brains per condition from two independent experiments; p = 0.00072, Kruskal–Wallis test with Dunn’s test). f, Western immunoblots for all replicates in Fig. 4a. See Source Data for uncropped gels with molecular weights. g, Quantification of dsRed immunofluorescence for Fig. 4b, relative to left condition (each point = 1 brain; n = 9-10 brains from two independent experiments; p = 0.000238, Kruskal–Wallis test with Wilcoxon test). h, Mean relative expression of basal dsRed and basket by RT-qPCR in 3 d whole flies, with (blue) or without JNK-RNAi (green) under a ubiquitous driver (each point = 1 biological replicate, n = 20 flies per biological replicate; Welch’s t-test). a-g, Black symbols, sham. Red symbols, severe dTBI. Statistical annotations are ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. All statistical tests were two-sided. For full statistical reporting, see Source Data Extended Data Fig. 3. All scale bars 100 μm. See Supplementary Table 1 for genotypes.

Source data

Extended Data Fig. 4 AP1 activation requires ERK.

a, Images from Scope Single Cell Atlas of the Drosophila adult brain61 showing relative expression levels of repo, elav, rolled (ERK), and basket (JNK). b, Western immunoblots for all replicates of Fig. 4c. Samples are biological replicates. c, Quantification of dsRed immunofluorescence fold change for representative whole mount brains shown in Fig. 4d, relative to leftmost condition (each point = 1 brain; n = 9-15 brains per condition pooled from two independent experiments; p = 6.08e-7, one-way ANOVA with Tukey’s test). d, RNAseq data showing average sham (black) and severe dTBI (red) dFos and dJun expression (blue star FDR < 0.05). e, Quantification of dJun-GFP (left; p = 2.07e-5, Student’s t-test) and dFos-GFP (right; p = 2.56e-7, Student’s t-test) immunofluorescence change for Figs. 4e and 4f, relative to sham (each point = 1 brain; n = 7-10 brains per condition from two independent experiments). f, Quantification of dsRed immunofluorescence change for Fig. 4g, relative to left condition (each point = 1 brain; n = 9-15 brains per condition from two independent experiments; p = 0.0040, one-way ANOVA with Tukey’s test). g, pERK/ERK levels at 1 hpi (each point = 1 biological replicate, 8 dissected brains; n = 3 biological replicates per condition/genotype; p = 0.0159, two-way ANOVA). h, Mean relative expression of AP1 target genes by RT-qPCR at 15 dpi, with or without glial ERK RNAi from 12 dpi (each point = 1 biological replicate, 9 dissected brains; n = 6 biological replicates per condition; Kruskal–Wallis test with Dunn’s multiple comparison test and Holm adjustment). i, Western immunoblot for ERK in whole flies with (blue) or without (green) ERK RNAi expressed under an inducible ubiquitous promoter. Right, quantification (each point = 1 biological replicate, 8 whole flies; p = 0.0173, Welch t-test). b-h, Black symbols, sham. Red symbols, severe dTBI. Statistical annotations are ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. All statistical tests were two-sided where applicable. For full statistical reporting, including exact p-values, see Source Data Extended Data Fig. 4. See Source Data for uncropped gels with molecular weights. See Supplementary Table 1 for genotypes.

Source data

Extended Data Fig. 5 Glial AP1 is required for TBI recovery.

a, Representative z-stacked whole mount brains focused on the central brain at 1 dpi in flies expressing a GFP-tagged variant of the AP1 target gene, Ets21c. Ets21c-GFP is detected in a handful of neurons at baseline but is dramatically upregulated in glia by 1 dpi (representative of n = 11-15 brains per condition, two independent experiments). b, Post-injury survival with (RU; dashed line) or without (vehicle; solid line) Ets21c-RNAi expressed in glia (n = 100 per condition, 5 vials of 20 flies; p < 0.0001, Kaplan-Meier analysis with log-rank comparison). c, Representative brain vacuolization at 1 dpi under sham and dTBI conditions, with (RU) or without (vehicle) JNK RNAi expression in glia. Quantification on right, expressed as % of total brain area that is vacuolized (each point = 1 brain; n = 9-10 brains per condition; two-way ANOVA revealed a non-significant interaction but a significant effect of dTBI, p = 6.1e-05). d, Representative brain vacuolization at 1 dpi under sham and dTBI conditions, with (RU) or without (vehicle) puckered expression in neurons. Quantification on right, expressed as % of total brain area that is vacuolized (each point = 1 brain; n = 18-19 brains per condition pooled from two independent experiments; two-way ANOVA revealed a non-significant interaction but a significant effect of dTBI, p = 3.55e-06).e, Post-injury survival with (RU; dashed line) or without (vehicle; solid line) puckered expression in neurons (n = 100 per condition, 5 vials of 20 flies; p < 0.0001, Kaplan-Meier analysis with log-rank comparison). Black symbols, sham. Red symbols, severe dTBI. Statistical annotations are ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. All statistical tests were two-sided where applicable. For full statistical reporting, including exact p-values, see Source Data Extended Data Fig. 5. All scale bars 100 μm. See Supplementary Table 1 for genotypes.

Source data

Extended Data Fig. 6 Sustained glial AP1 activity drives human tau pathology.

a, Representative section of paraffin-embedded head at 10 dpi stained for AT8 when human tau is expressed in neurons (representative of n = 7 animals). b, Membrane immunostained for human tau in phosphatase treated (+) and untreated (-) protein samples isolated from the heads of 5 dpi (repoGS > UAS-hTau) or WT (w1118) flies (n = 30 per sample). c, Pearson correlation (two-sided) between percentage of total brain area vacuolized and number of AT8 + puncta (top) or AT100 + puncta (bottom) at 5 and 10 dpi (combined) in sham, mild or severe dTBI. Data shown is an alternative representation of Fig. 6a. d, Representative sections of paraffin embedded heads immunostained for AT100 (top row) or AT8 (bottom row), highlighting the concentration of phosphorylated tau puncta in neuropil and around vacuoles. Vacuoles are visualized by autofluorescence. e, Representative z-stacked hemisections of paraffin-embedded in WT (left; w1118) or AP1 blocked (right) mild dTBI at 10 dpi, immunostained for AT100. AP1 was blocked by expressing a dominant negative variant of dFos (dFos-DN). f, Quantification of AT00 + puncta (top) and % brain vacuolization (bottom) at 10 dpi with (UAS-dFos-DN) or without AP1 blockade (WT) in glia from 3 dpi (each point = 1 brain, n = 9-10 per condition/genotype pooled from two independent experiments; AT100: p = 0.051, brain vacuolization: p = 0.072, two-way ANOVA with Tukey’s test). g, Post-injury survival with (solid) or without (dashed) UAS-dFos-DN expression in glia in the setting of tau expression (n = 100 per condition, 5 vials of 20 flies; p < 0.0001, Kaplan-Meier analysis with two-sided log-rank comparison). Black symbols, sham. Pink symbols, mild dTBI. Red symbols, severe dTBI. Statistical annotations are ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. All statistical tests were two-sided where applicable. For full statistical reporting, including exact p-values, see Source Data Extended Data Fig. 6. All scale bars 100 μm. See Supplementary Table 1 for genotypes.

Source data

Extended Data Fig. 7 Evidence of AP1 activity after moderate human TBI.

a, TBI exposure history for donors; the same data is visualized two ways. Top shows age at first TBI, with most injuries occurring before age 30. Donors are further separated by duration of loss-of-consciousness. Total lifetime TBI events for each donor are shown by point size. Bottom shows the duration of time between first TBI and death. b, PCA of all samples (left) compared to PCA of samples with outliers removed (right). Each point represents 1 tissue sample. Shape encodes brain region. Black symbols, non-TBI samples. Red symbols, TBI samples. c, Top 5 Molecular Signature Data Base (MSigDb) Hallmark gene sets and top 10 gene ontology (GO) terms enriched among upregulated genes (FDR < 0.10) in TBI donors. d, Violin plots showing expression of all predicted FRA1/JUND target genes in non-TBI (black; n samples = 81) and TBI (red; n samples = 80) exposed donors (FDR < 0.10).

Extended Data Fig. 8 Evidence of a dTBI-specific dFos isoform.

Aligned bigWig files (IGV; aligned to Fly Base d. mel r6.17) for severe dTBI (top row) and sham injury at 24 hpi (bottom row), along with controls (0h; middle row), at the dFos locus (Fly gene name kay or kayak). With dTBI, reads map to dFos-RB, which encodes isoform B.

Supplementary information

Supplementary Information

Supplementary Tables 1–4; Supplementary statistical outputs (detailed statistical results)

Reporting Summary

Supplementary Table 5

Supplementary Table 6

Supplementary Table 7

Source data

Source Data Fig. 2

Full statistical results, including exact P values.

Source Data Fig. 4

Full statistical results, including exact P values.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 5

Full statistical results, including exact P values.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 6

Full statistical results, including exact P values.

Source Data Extended Data Fig. 1

Full statistical results, including exact P values.

Source Data Extended Data Fig. 2

Full statistical results, including exact P values.

Source Data Extended Data Fig. 3

Full statistical results, including exact P values.

Source Data Extended Data Fig. 4

Full statistical results, including exact P values.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Full statistical results, including exact P values.

Source Data Extended Data Fig. 6

Full statistical results, including exact P values.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Byrns, C.N., Saikumar, J. & Bonini, N.M. Glial AP1 is activated with aging and accelerated by traumatic brain injury. Nat Aging 1, 585–597 (2021). https://doi.org/10.1038/s43587-021-00072-0

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing