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.

  • Article
  • Published:

Tau is required for glial lipid droplet formation and resistance to neuronal oxidative stress

Nature Neuroscience volume 27, pages 1918–1933 (2024)Cite this article

Subjects

Abstract

The accumulation of reactive oxygen species (ROS) is a common feature of tauopathies, defined by Tau accumulations in neurons and glia. High ROS in neurons causes lipid production and the export of toxic peroxidated lipids (LPOs). Glia uptake these LPOs and incorporate them into lipid droplets (LDs) for storage and catabolism. We found that overexpressing Tau in glia disrupts LDs in flies and rat neuron–astrocyte co-cultures, sensitizing the glia to toxic, neuronal LPOs. Using a new fly tau loss-of-function allele and RNA-mediated interference, we found that endogenous Tau is required for glial LD formation and protection against neuronal LPOs. Similarly, endogenous Tau is required in rat astrocytes and human oligodendrocyte-like cells for LD formation and the breakdown of LPOs. Behaviorally, flies lacking glial Tau have decreased lifespans and motor defects that are rescuable by administering the antioxidant N-acetylcysteine amide. Overall, this work provides insights into the important role that Tau has in glia to mitigate ROS in the brain.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overexpression of hTau in glia disrupts LDs and glial cell morphology in response to neuronal ROS.
Fig. 2: tau loss disrupts neuronal ROS-induced glial LDs and causes degenerative phenotypes.
Fig. 3: tau is expressed in neurons and glia.
Fig. 4: tau loss in glia contributes to climbing and lifespan defects.
Fig. 5: Glial dTau loss disrupts LD formation and glial cell morphology in response to neuronal ROS.
Fig. 6: Tau-associated disruptions in glial LD formation is conserved in mammalian astrocytes.
Fig. 7: Tau’s function as a microtubule-binding protein impacts LD formation.
Fig. 8: Tau loss in oligodendrocyte-like cells disrupts LD budding from the ER.

Similar content being viewed by others

Data availability

All reagents and data are available upon reasonable request.

References

  1. Haque, M. M., Murale, D. P., Kim, Y. K. & Lee, J.-S. Crosstalk between oxidative stress and tauopathy. Int. J. Mol. Sci. 20, 1959 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alavi Naini, S. M. & Soussi-Yanicostas, N. Tau hyperphosphorylation and oxidative stress, a critical vicious circle in neurodegenerative tauopathies? Oxid. Med. Cell. Longev. 2015, 15197 (2015).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Olzmann, J. A. & Carvalho, P. Dynamics and functions of lipid droplets. Nat. Rev. Mol. Cell Biol. 20, 137–155 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Moulton, M. J. et al. Neuronal ROS-induced glial lipid droplet formation is altered by loss of Alzheimer’s disease-associated genes. Proc. Natl Acad. Sci. USA 118, e2112095118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu, L., MacKenzie, K. R., Putluri, N., Maletić-Savatić, M. & Bellen, H. J. The glia–neuron lactate shuttle and elevated ROS promote lipid synthesis in neurons and lipid droplet accumulation in glia via APOE/D. Cell Metab. 26, 719–737.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ioannou, M. S. et al. Neuron–astrocyte metabolic coupling protects against activity-induced fatty acid toxicity. Cell 177, 1522–1535.e14 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Qi, G. et al. ApoE4 impairs neuron–astrocyte coupling of fatty acid metabolism. Cell Rep. 34, 108572 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schulz, J. G. et al. Glial β-oxidation regulates Drosophila energy metabolism. Sci. Rep. 5, 7805 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mi, Y. et al. Loss of fatty acid degradation by astrocytic mitochondria triggers neuroinflammation and neurodegeneration. Nat. Metab. 5, 445–465 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Haynes, P. R. et al. A neuron–glia lipid metabolic cycle couples daily sleep to mitochondrial homeostasis. Nat. Neurosci. 27, 666–678 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Haney, M. S. et al. APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia. Nature 628, 154–161 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Byrns, C. N. et al. Senescent glia link mitochondrial dysfunction and lipid accumulation. Nature 630, 475–483 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Alzheimer, A. Über eine eigenartige Erkrankung der Hirnrinde. Zentralbl. Nervenh. Psych. 18, 177–179 (1907).

    Google Scholar 

  16. Alzheimer, A. Über eigenartige Krankheitsfälle des späteren Alters. Z. Gesamte Neurol. Psychiatr. 4, 356–385 (1911).

    Article  Google Scholar 

  17. Claes, C. et al. Plaque-associated human microglia accumulate lipid droplets in a chimeric model of Alzheimer’s disease. Mol. Neurodegener. 16, 50 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, Y. et al. Microglial lipid droplet accumulation in tauopathy brain is regulated by neuronal AMPK. Cell Metab. 36, 1351–1370.e8 (2024).

    Article  CAS  PubMed  Google Scholar 

  19. Ralhan, I. et al. Autolysosomal exocytosis of lipids protect neurons from ferroptosis. J. Cell Biol. 222, e202207130 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Windham, I. A. et al. APOE traffics to astrocyte lipid droplets and modulates triglyceride saturation and droplet size. J. Cell Biol. 223, e202305003 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bonda, D. J. et al. A novel perspective on tau in Alzheimer’s disease. Curr. Alzheimer Res. 8, 639–642 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Reddy, P. H. Abnormal tau, mitochondrial dysfunction, impaired axonal transport of mitochondria, and synaptic deprivation in Alzheimer’s disease. Brain Res. 1415, 136–148 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. García-Escudero, V., Martín-Maestro, P., Perry, G. & Avila, J. Deconstructing mitochondrial dysfunction in Alzheimer disease. Oxid. Med. Cell. Longev. 2013, 162152 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Moussavi Nik, S. H. et al. Hypoxia alters expression of zebrafish microtubule-associated protein tau (mapta, maptb) gene transcripts. BMC Res. Notes 7, 767 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Violet, M. et al. A major role for tau in neuronal DNA and RNA protection in vivo under physiological and hyperthermic conditions. Front. Cell. Neurosci. 8, 84 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lei, P. et al. Tau deficiency induces parkinsonism with dementia by impairing APP-mediated iron export. Nat. Med. 18, 291–295 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Lopes, S. et al. Absence of Tau triggers age-dependent sciatic nerve morphofunctional deficits and motor impairment. Aging Cell 15, 208–216 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lei, P. et al. Motor and cognitive deficits in aged tau knockout mice in two background strains. Mol. Neurodegener. 9, 29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Jensen, N. & Oliveira, J. R. M. Basal ganglia vulnerability to oxidative stress. Front. Neurosci. 8, 80 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sultan, A. et al. Nuclear tau, a key player in neuronal DNA protection. J. Biol. Chem. 286, 4566–4575 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Dias-Santagata, D., Fulga, T. A., Duttaroy, A. & Feany, M. B. Oxidative stress mediates tau-induced neurodegeneration in Drosophila. J. Clin. Invest. 117, 236–245 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Saito, T. et al. Disulfide bond formation in microtubule-associated tau protein promotes tau accumulation and toxicity in vivo. Hum. Mol. Genet. 30, 1955–1967 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  34. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kahlson, M. A. & Colodner, K. J. Glial tau pathology in tauopathies: functional consequences. J. Exp. Neurosci. 9, 43–50 (2015).

    CAS  PubMed  Google Scholar 

  36. Nishimura, M. et al. Immunocytochemical characterization of glial fibrillary tangles in Alzheimer’s disease brain. Am. J. Pathol. 146, 1052–1058 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Richetin, K. et al. Tau accumulation in astrocytes of the dentate gyrus induces neuronal dysfunction and memory deficits in Alzheimer’s disease. Nat. Neurosci. 23, 1567–1579 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Leyns, C. E. G. & Holtzman, D. M. Glial contributions to neurodegeneration in tauopathies. Mol. Neurodegener. 12, 50 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Amro, Z., Yool, A. J. & Collins-Praino, L. E. The potential role of glial cells in driving the prion-like transcellular propagation of tau in tauopathies. Brain Behav. Immun. Health 14, 100242 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Narasimhan, S. et al. Human tau pathology transmits glial tau aggregates in the absence of neuronal tau. J. Exp. Med. 217, e20190783 (2020).

    Article  PubMed  Google Scholar 

  41. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hsieh, Y.-C. et al. Tau-mediated disruption of the spliceosome triggers cryptic RNA splicing and neurodegeneration in Alzheimer’s disease. Cell Rep. 29, 301–316.e10 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Higuchi, M. et al. Transgenic mouse model of tauopathies with glial pathology and nervous system degeneration. Neuron 35, 433–446 (2002).

    Article  CAS  PubMed  Google Scholar 

  44. Smolič, T. et al. Astrocytes in stress accumulate lipid droplets. Glia 69, 1540–1562 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Li, F., Artiushin, G. & Sehgal, A. Modulation of sleep by trafficking of lipids through the Drosophila blood–brain barrier. eLife 12, e86336 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sheng, L. et al. Ensheathing glia promote increased lifespan and healthy brain aging. Aging Cell 22, e13803 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Talmat-Amar, Y., Arribat, Y. & Parmentier, M.-L. Vesicular axonal transport is modified in vivo by tau deletion or overexpression in Drosophila. Int. J. Mol. Sci. 19, 744 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bunker, J. M., Wilson, L., Jordan, M. A. & Feinstein, S. C. Modulation of microtubule dynamics by tau in living cells: implications for development and neurodegeneration. Mol. Biol. Cell 15, 2720–2728 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kanai, Y. et al. Expression of multiple tau isoforms and microtubule bundle formation in fibroblasts transfected with a single tau cDNA. J. Cell Biol. 109, 1173–1184 (1989).

    Article  CAS  PubMed  Google Scholar 

  50. Niewiadomska, G., Niewiadomski, W., Steczkowska, M. & Gasiorowska, A. Tau oligomers neurotoxicity. Life 11, 28 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Barbier, P. et al. Role of tau as a microtubule-associated protein: structural and functional aspects. Front. Aging Neurosci. 11, 204 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cowan, C. M., Bossing, T., Page, A., Shepherd, D. & Mudher, A. Soluble hyper-phosphorylated tau causes microtubule breakdown and functionally compromises normal tau in vivo. Acta Neuropathol. 120, 593–604 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Burnouf, S. et al. Deletion of endogenous Tau proteins is not detrimental in Drosophila. Sci. Rep. 6, 23102 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Doerflinger, H., Benton, R., Shulman, J. M. & Johnston, D. S. The role of PAR-1 in regulating the polarised microtubule cytoskeleton in the Drosophila follicular epithelium. Development 130, 3965–3975 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Kanca, O. et al. An efficient CRISPR-based strategy to insert small and large fragments of DNA using short homology arms. eLife 8, e51539 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Venken, K. J. T., He, Y., Hoskins, R. A. & Bellen, H. J. P[acman]: a BAC transgenic platform for targeted insertion of large DNA fragments in D. melanogaster. Science 314, 1747–1751 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Velazquez, R. et al. Acute tau knockdown in the hippocampus of adult mice causes learning and memory deficits. Aging Cell 17, e12775 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. da Cruz, A. B. et al. Disruption of the MAP1B-related protein FUTSCH leads to changes in the neuronal cytoskeleton, axonal transport defects, and progressive neurodegeneration in Drosophila. Mol. Biol. Cell 16, 2433–2442 (2005).

    Article  PubMed Central  Google Scholar 

  60. Vershinin, M., Carter, B. C., Razafsky, D. S., King, S. J. & Gross, S. P. Multiple-motor based transport and its regulation by Tau. Proc. Natl Acad. Sci. USA 104, 87–92 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Ezerskiy, L. A. et al. Astrocytic 4R tau expression drives astrocyte reactivity and dysfunction. JCI Insight 7, e152012 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Silies, M. et al. Glial cell migration in the eye disc. J. Neurosci. 27, 13130–13139 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Atlas, D. Emerging therapeutic opportunities of novel thiol-amides, NAC-amide (AD4/NACA) and thioredoxin mimetics (TXM-peptides) for neurodegenerative-related disorders. Free Radic. Biol. Med. 176, 120–141 (2021).

    Article  CAS  PubMed  Google Scholar 

  64. Iijima-Ando, K., Zhao, L., Gatt, A., Shenton, C. & Iijima, K. A DNA damage-activated checkpoint kinase phosphorylates tau and enhances tau-induced neurodegeneration. Hum. Mol. Genet. 19, 1930–1938 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Franco, B. et al. Shaggy, the homolog of glycogen synthase kinase 3, controls neuromuscular junction growth in Drosophila. J. Neurosci. 24, 6573–6577 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Sung, H.-H. et al. Drosophila ensconsin promotes productive recruitment of kinesin-1 to microtubules. Dev. Cell 15, 866–876 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Coppola, G. et al. Evidence for a role of the rare p.A152T variant in MAPT in increasing the risk for FTD-spectrum and Alzheimer’s diseases. Hum. Mol. Genet. 21, 3500–3512 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Fong, H. et al. Genetic correction of tauopathy phenotypes in neurons derived from human induced pluripotent stem cells. Stem Cell Rep. 1, 226–234 (2013).

    Article  CAS  Google Scholar 

  69. Bugiani, O. et al. Frontotemporal dementia and corticobasal degeneration in a family with a P301S mutation in tau. J. Neuropathol. Exp. Neurol. 58, 667–677 (1999).

    Article  CAS  PubMed  Google Scholar 

  70. Lindquist, S. G. et al. Alzheimer disease-like clinical phenotype in a family with FTDP-17 caused by a MAPT R406W mutation. Eur. J. Neurol. 15, 377–385 (2008).

    Article  CAS  PubMed  Google Scholar 

  71. Hong, M. et al. Mutation-specific functional impairments in distinct tau isoforms of hereditary FTDP-17. Science 282, 1914–1917 (1998).

    Article  CAS  PubMed  Google Scholar 

  72. Rademakers, R. et al. Tau (MAPT) mutation Arg406Trp presenting clinically with Alzheimer disease does not share a common founder in Western Europe. Hum. Mutat. 22, 409–411 (2003).

    Article  CAS  PubMed  Google Scholar 

  73. Singh, J., Sanghavi, P. & Mallik, R. Microtubule motor driven interactions of lipid droplets: specificities and opportunities. Front. Cell Dev. Biol. 10, 893375 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jin, Y., Ren, Z., Tan, Y., Zhao, P. & Wu, J. Motility plays an important role in the lifetime of mammalian lipid droplets. Int. J. Mol. Sci. 22, 3802 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Li, X. et al. Novel role of dynamin-related-protein 1 in dynamics of ER-lipid droplets in adipose tissue. FASEB J. 34, 8265–8282 (2020).

    Article  CAS  PubMed  Google Scholar 

  76. Smolič, T., Zorec, R. & Vardjan, N. Pathophysiology of lipid droplets in neuroglia. Antioxidants 11, 22 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Brasaemle, D. L. Thematic review series: adipocyte biology. The perilipin family of structural lipid droplet proteins: stabilization of lipid droplets and control of lipolysis. J. Lipid Res. 48, 2547–2559 (2007).

    Article  CAS  PubMed  Google Scholar 

  78. Allen, M. et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci. Data 3, 160089 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  81. LoPresti, P. Tau in oligodendrocytes takes neurons in sickness and in health. Int. J. Mol. Sci. 19, 2408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Blanchard, J. W. et al. APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes. Nature 611, 769–779 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hallmann, A.-L. et al. Astrocyte pathology in a human neural stem cell model of frontotemporal dementia caused by mutant TAU protein. Sci. Rep. 7, 42991 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Zhang, Y., Wu, K.-M., Yang, L., Dong, Q. & Yu, J.-T. Tauopathies: new perspectives and challenges. Mol. Neurodegener. 17, 28 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Goodman, L. D. et al. Toxic expanded GGGGCC repeat transcription is mediated by the PAF1 complex in C9orf72-associated FTD. Nat. Neurosci. 22, 863–874 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Dutta, D. et al. A defect in mitochondrial fatty acid synthesis impairs iron metabolism and causes elevated ceramide levels. Nat. Metab. 5, 1595–1614 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Ioannou, M. S., Liu, Z. & Lippincott‐Schwartz, J. A neuron–glia co-culture system for studying intercellular lipid transport. Curr. Protoc. Cell Biol. 84, e95 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Lee, P.-T. et al. A gene-specific T2A-GAL4 library for Drosophila. eLife 7, e35574 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Kanca, O. et al. An expanded toolkit for Drosophila gene tagging using synthesized homology donor constructs for CRISPR-mediated homologous recombination. eLife 11, e76077 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Bischof, J., Maeda, R. K., Hediger, M., Karch, F. & Basler, K. An optimized transgenesis system for Drosophila using germ-line-specific φC31 integrases. Proc. Natl Acad. Sci. USA 104, 3312–3317 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Yang, X. et al. A public genome-scale lentiviral expression library of human ORFs. Nat. Methods 8, 659–661 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Fernius, J., Starkenberg, A., Pokrzywa, M. & Thor, S. Human TTBK1, TTBK2 and MARK1 kinase toxicity in Drosophila melanogaster is exacerbated by co-expression of human Tau. Biol. Open 6, 1013–1023 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Sancak, Y. et al. The Rag GTPases bind raptor and mediate amino acid signaling to mTORC1. Science 320, 1496–1501 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Zhou, Y., Yang, S., Mao, T. & Zhang, Z. MAPanalyzer: a novel online tool for analyzing microtubule-associated proteins. Database 2015, bav108 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51, D418–D427 (2023).

    Article  CAS  PubMed  Google Scholar 

  96. Joiner, W. J., Crocker, A., White, B. H. & Sehgal, A. Sleep in Drosophila is regulated by adult mushroom bodies. Nature 441, 757–760 (2006).

    Article  CAS  PubMed  Google Scholar 

  97. Pfeiffenberger, C., Lear, B. C., Keegan, K. P. & Allada, R. Locomotor activity level monitoring using the Drosophila activity monitoring (DAM) system. Cold Spring Harb. Protoc. 2010, pdb.prot5518 (2010).

    Article  PubMed  Google Scholar 

  98. Cichewicz, K. & Hirsh, J. ShinyR-DAM: a program analyzing Drosophila activity, sleep and circadian rhythms. Commun. Biol. 1, 25 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Goodman, L. D. et al. TNPO2 variants associate with human developmental delays, neurologic deficits, and dysmorphic features and alter TNPO2 activity in Drosophila. Am. J. Hum. Genet. 108, 1669–1691 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Beaudoin, G. M. J. et al. Culturing pyramidal neurons from the early postnatal mouse hippocampus and cortex. Nat. Protoc. 7, 1741–1754 (2012).

    Article  CAS  PubMed  Google Scholar 

  101. Ikeda, H. et al. The estrogen receptor influences microtubule-associated protein tau (MAPT) expression and the selective estrogen receptor inhibitor fulvestrant downregulates MAPT and increases the sensitivity to taxane in breast cancer cells. Breast Cancer Res. 12, R43 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Lu, S.-Z. et al. Suppression of astrocytic autophagy by αB-crystallin contributes to α-synuclein inclusion formation. Transl. Neurodegener. 8, 3 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Mostafavi, S. et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat. Neurosci. 21, 811–819 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Gockley et al. Genome Medicine. https://doi.org/10.1186/s13073-021-00890-2 (2021).

Download references

Acknowledgements

We apologize to investigators whose relevant research was not cited herein due to limits set by the journal. We thank former and present Bellen lab members for their input during investigations, particularly Z. Zou, J. Deger, J. Chang and J. Najm. We thank A. Sehgal for helpful comments during manuscript preparation. We thank H. Tricoire, A. Sehgal and N. Bonini for providing GAL4GeneSwitch fly lines. We thank L. Partridge for providing the dTau antibody. We thank S. Yamamoto and M. Wangler for the resources used during fly line development. Fly stocks were obtained from the Bloomington Drosophila Stock Center (National Institutes of Health (NIH) P40-OD018537) and Vienna Drosophila Resource Center. This work was supported by infrastructure made available by Texas Children’s Neurological Research Institute (National Institute of Child Health and Human Development (NICHD) P50-HD103555), the University of Alberta Faculty of Medicine & Dentistry Cell Imaging Core (RRID SCR_019200) and the Neurovisualization Core at Baylor College of Medicine (BCM) (NICHD U54-HD083092). This research and L.D.G. were supported by the Postdoctoral Fellowship Program in Alzheimer’s Disease Research from the BrightFocus Foundation. This research was supported by the National Institutes of Health (NIH) Common Fund to H.J.B. (National Institute on Aging (NIA R01-AG073260)). Further support to H.J.B. came from NIH awards: Office of the Director (OD R24-OD02205 and OD R24-OD031447) and National Institute of General Medical Sciences (NIGMS R01-GM067858). H.J.B. and J.M.S. received support from NIA U01-AG072439. J.M.S. received additional support from NIA RF1-AG078660. L.D.G. and M.J.M. received support from the Brain Disorders & Development Training Grant at BCM, National Institute of Neurological Disorders and Stroke (NINDS T32-NS043124-18). I.R. was supported by the Canadian Institutes of Health Research (CIHR) Doctoral Award (181551). M.S.I. was supported by a Sloan Research Fellowship from the Alfred P. Sloan Foundation (FG-2021-16349), Canada Research Chairs program (2021-00027) and a CIHR project grant (173321). K.A. received support from a Grant-in-Aid for Scientific Research on Challenging Research (Exploratory) (JSPS KAKENHI 19K21593). K.S. received support from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK R01-DK129815). The transcriptomic data were obtained from the AD Knowledge Portal (https://adknowledgeportal.org). We acknowledge the patients and their families who provided invaluable tissue samples. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, provided all ROSMAP data collected, which was funded by the NIA (P30-AG10161, R01-AG15819, R01-AG17917, R01-AG30146, R01-AG36836, U01-AG32984, U01-AG46152), the Illinois Department of Public Health and the Translational Genomics Research Institute. Mayo Study data were provided by the following sources: The Mayo Clinic Alzheimer’s Disease Genetic Studies, led by N. Ertekin-Taner (not certified by peer review), is made available under a CC-BY-NC 4.0 International license; Gockley et al.104 and Steven G. Younkin, Mayo Clinic, Jacksonville, FL, using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer’s Disease Research Center and the Mayo Clinic Brain Bank. Mayo Data collection was funded through the NIA (P50-AG016574, R01-AG032990, U01-58AG046139, R01-AG018023, U01-AG006576, U01-AG006786, R01-AG025711, R01-AG017216, R01-AG003949), NINDS R01-NS080820, CurePSP Foundation and the Mayo Foundation.

Author information

Authors and Affiliations

  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Lindsey D. Goodman, Shenzhao Lu, Matthew J. Moulton, Ye-Jin Park, Oguz Kanca, Ziyaneh S. Ghaderpour Taleghani, Joshua M. Shulman & Hugo J. Bellen

  2. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA

    Lindsey D. Goodman, Shenzhao Lu, Matthew J. Moulton, Ye-Jin Park, Pinghan Zhao, Oguz Kanca, Ziyaneh S. Ghaderpour Taleghani, Joshua M. Shulman & Hugo J. Bellen

  3. Department of Physiology, University of Alberta, Edmonton, Alberta, Canada

    Isha Ralhan, Julie Jacquemyn & Maria S. Ioannou

  4. Group on Molecular and Cell Biology of Lipids, University of Alberta, Edmonton, Alberta, Canada

    Isha Ralhan, Julie Jacquemyn & Maria S. Ioannou

  5. Center for Metabolic and Degenerative Diseases, The Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases, University of Texas Health Science Center at Houston, Houston, TX, USA

    Xin Li & Kai Sun

  6. Program in Development, Disease Models and Therapeutics, Baylor College of Medicine, Houston, TX, USA

    Ye-Jin Park & Hugo J. Bellen

  7. Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA

    Pinghan Zhao, Joshua M. Shulman & Hugo J. Bellen

  8. Department of Neurology, Baylor College of Medicine, Houston, TX, USA

    Joshua M. Shulman

  9. Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan

    Kanae Ando

  10. Department of Integrative Biology and Pharmacology, Graduate Program in Cell and Regulatory Biology, Graduate School of Biomedical Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA

    Kai Sun

  11. Department of Cell Biology, University of Alberta, Edmonton, Alberta, Canada

    Maria S. Ioannou

  12. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

    Maria S. Ioannou

Authors
  1. Lindsey D. Goodman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isha Ralhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shenzhao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew J. Moulton
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ye-Jin Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pinghan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Oguz Kanca
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ziyaneh S. Ghaderpour Taleghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Julie Jacquemyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Joshua M. Shulman
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kanae Ando
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kai Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Maria S. Ioannou
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hugo J. Bellen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.D.G. designed and implemented this project and wrote the manuscript under the mentorship of H.J.B. I.R. performed rat co-culture studies under the mentorship of M.S.I. The MO3.13 studies were performed by X.L. and S.L. with feedback from L.D.G. and under the mentorship of K.S. and H.J.B., respectively. M.J.M. performed SREBP studies with L.D.G. and developed fly lines used in this study under the mentorship of H.J.B. Y.J.P. performed the Drosophila activity monitoring assays with L.D.G. P.Z. analyzed patient transcriptomic data under the mentorship of J.M.S. O.K. made the tau-CRIMIC allele. Z.G.T. provided technical assistance during tau-CRIMIC characterization under the guidance of L.D.G. J.J. provided technical assistance for rat co-culture studies under the mentorship of M.S.I. K.A. developed and provided hTau[S262] fly lines used in this study.

Corresponding author

Correspondence to Hugo J. Bellen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Glial lipid droplet pathway and characterization of lipid droplets in the fly retina.

(a) Elevating ROS levels in neurons causes an SREBP-dependent production of peroxidated lipids (LPOs). These lipids are exported by ABC-lipid transporters, transferred to glia via apolipoproteins (for example APOE), and endocytosed by the glia. Within the glia, the LPO are integrated into lipid droplets (LDs) via the endoplasmic reticulum (ER). LDs promote lipid β-oxidation by the mitochondria, producing energy. Thus, glial LDs remove the toxic, LPO and protect both cells from ROS-induced damage. A growing list of established and predicted AD risk genes (red text) fit into this pathway, with most genes being required for proper glial LD formation and protection against neuronal ROS5,7,8,9. Created with Biorender.com. (b) The fly retina is composed of ~800 highly structured ommatidia. Diagram: a single ommatidium with seven photoreceptor neurons (blue) surrounded by pigment glia (green). Each photoreceptor contains a lipid-rich rhabdomere (hashed circle) which can be visualized with lipid stains. (c) Elevating ROS in photoreceptors causes LDs to accumulate in surrounding glia. LDs are visualized using neutral lipid dyes, Nile red and BODIPY 493/503. (d) TEM image of the fly retina, including 4 ommatidia. Each ommatidium contains seven visible photoreceptors with the rhabdomeres (R) clustered at the center and the cell body (CB) extending distally. Photoreceptors are surrounded by retinal glia (green). (e) The genetic manipulation used to investigate regulators of glial LD formation in the fly retina. UAS-transgenes are expressed either in mature photoreceptors, using Rh1-GAL4, or in retinal glia, using 54C-GAL4. Rh:ND42 RNAi is used to induce ROS only in the photoreceptors, causing glial LDs. (f) LDs can be induced in photoreceptors either by causing neuronal ROS (Rh:ND42 RNAi) or by overexpressing the activated SREBP transcription factor (nSyb-GAL4 > UAS-SREBP). LDs produced by Rh:ND42 RNAi can be prevented by feeding flies the antioxidant NACA, consistent with5. LDs formed by neuronal SREBP upregulation in non-ROS conditions are not impacted by NACA. Lipid stain = Nile red. Data presented as mean ± SD. A datapoint represents the average LD number per ommatidium per animal (n). n = 7-10 (detailed in Supporting Data 1). Statistics: 1-way ANOVA with Tukey’s multiple comparisons test. P-values = not significant (n.s.) >0.05, **=0.004, ***=0.0002, ****≤0.0001. (g) LDs within retinas co-stain for both Nile red and BODIPY 493/503, validating that these puncta are LDs. (f-g) Retinas from 2d flies were fixed, dissected, and stained with neutral lipid dyes. Arrow = glial LD.

Extended Data Fig. 2 Additional data for hTau overexpression in flies.

(a-b) UAS transgenes were expressed in photoreceptors using Rh1-GAL4. Fixed retinas were stained with Nile red or BODIPY 493/503 to visualize LDs (white puncta, arrow) within retinal glia. (a) Few LDs were seen with UAS-Control (LacZ), UAS-Tau or UAS-TauR406W expression up to 6d. (b) Rh:ND42 RNAi was used to induce neuronal ROS and robust LD formation within glia. No change in LD number was observed when UAS-Tau or UAS-TauR406W were also expressed using Rh1-GAL4. Arrow = glial LD. A datapoint represents the average LD number per ommatidium per animal (n). n = 3-6 (detailed in Supporting Data 1). Data presented as mean ± SD. Statistics: 1-way ANOVA with Tukey’s multiple comparisons test. P-values = not significant, >0.05. (c) UAS-Control or UAS-hTau were expressed in retinal glia using 54C-GAL4 and TEM was performed. Glial cells = green. Shown are representative images for the quantification provided in Fig. 1c. (d) UAS-Ctrl (LacZ) or UAS-hTau were expressed for 10 days in adult brain glia using repoGS, starting at 2-days old (d). Both normal (total Tau) and hyperphosphorylated hTau (AT8, AT100) protein is produced in fly heads, assayed using western immunoblots (WBs). Shown are 3 replicate samples for UAS-hTau collected in parallel.

Extended Data Fig. 3 Assessment of existing tau loss-of-function alleles.

(a) Schematic of the genomic region deleted in tauMR22 (green line) flies and the impacted genes. (b) Schematic of fly tau gene and isoform G. Green line = the deleted genomic region in tau∆ex6-14 (more commonly known as “dTau KO”) flies. Red shows exons known to encode microtubule binding domains (MTBDs) that are deleted. A large exon (orange) that is exclusive to isoform tau-G is intact. (c) PCR was used to confirm the deleted genomic region in tau∆ex6-14 homozygous flies. (d) qPCR was performed on control flies, tau∆ex6-14 homozygous flies, tau∆ex6-14 heterozygous flies, or transheterozygous tau∆ex6-14/Df flies to measure tau-G transcript levels. Df = deficiency allele. tau-G is significantly upregulated in response to the tau∆ex6-14 allele. Data were consistent using two independent deficiency alleles, Df-1 and Df-2. All flies are in a w[1118] background. Total RNA was extracted from 2d fly heads. A datapoint represents the relative expression per technical replicate (n). n = 4-8. Data presented as mean ± SD. Statistics: 1-way ANOVA with Tukey’s multiple comparisons test. P-values = not significant (n.s.) >0.05, *=0.0117, **≤0.01, ****≤0.0001. See Supporting Data 1 for details on n and P-values. (e) In silico analyses were performed on the protein sequence encoded by tau-G to define MTBDs. Unhighlighted text is sequence unique to tau-G and not deleted in tau∆ex6-14 flies. Pink highlighted text is sequence deleted in tau∆ex6-14 flies that is also found in tau-G. Red and green text denotes specific MTBD identified by MAPanalyzer. Italicized text denotes sequence identified as common to MT associated proteins using InterPro. Note the overlap in predictions at two MTBD within the sequence unique to tau-G (red, italicized). These predictions correctly identify established MTBD (green, italicized) within the sequence deleted in tau∆ex6-14. Overall, tau-G is likely upregulated to compensate for the loss of other tau transcripts in tau∆ex6-14 flies.

Extended Data Fig. 4 RNA analysis of tau-CRIMIC flies.

(a) Schematic of the CRIMIC cassette inserted between exons 10 and 11 of tau. This disrupts all transcripts carrying established (red) and predicted (orange) microtubule binding domains. Primer sets (blue) were designed to measure the levels of the different tau transcripts by qPCR. (b-d) qPCR data for flies including yw control, heterozygous tau-CRIMIC/+, and trans-heterozygous tau-CR/Df mutant flies carrying one copy of tau-CRIMIC and one copy of a deficiency (Df) allele. tau transcript levels were consistently decreased using two independent deficiency alleles, Df-1 and Df-2. A datapoint represents the relative expression per technical replicate (n). n = 4-10. Data presented as mean ± SD. Statistics: 1-way ANOVA with Tukey’s multiple comparisons test. P-values = not significant (n.s.) >0.05, *≤0.05, **≤0.01, ***≤0.001, ****≤0.0001. See Supporting Data 1 for details on n and P-values.

Extended Data Fig. 5 Additional data for Drosophila tau RNAi studies.

(a) UAS-Control (Ctrl) RNAi or UAS-tau RNAi was expressed ubiquitously using da-GAL4. Protein extracted from 2d heads was analyzed by WB for dTau levels. A datapoint represents the relative band density per technical replicate (n). n = 6. (b) UAS-Control (LacZ), UAS-Ctrl RNAi or UAS-tau RNAi were expressed in adult flies starting at 2d using the pan-neuronal, drug inducible driver, elavGS. n = 234-325 per genotype. (c) UAS-Ctrl RNAi or UAS-tau RNAi were expressed through development and adulthood using the pan-neuronal driver, elav-GAL4, at 29 °C. Control RNAi n = 161, tau RNAi n = 125. As the GAL4/UAS system is temperature-dependent, this produces higher RNAi expression than standard 25 °C rearing temperatures (see Fig. 3e). (b-c) Survival was measured using Kaplan-Meier plots. P-value ≤ 0.0001 seen with UAS-tau RNAi versus UAS- Ctrl RNAi in b and c, with UAS-tau RNAi versus UAS-Control in c, and UAS- Ctrl RNAi versus UAS-Control in c. UAS-Ctrl RNAi and UAS-tau RNAi are in a w+ background. UAS-Control is in a w* background. (d) UAS-Control (GFP) RNAi-2 or UAS-tau RNAi-2 were expressed in retinal glia using 54C-GAL4. Rh:ND42 RNAi was used to induce neuronal ROS and glial LDs. LDs were visualized in fly retinas from 2d animals using BODIPY 493/503 (white puncta, arrows). A datapoint represents the average LD number per ommatidium per animal (n). n = 6. (e) UAS-Control RNAi or UAS-tau RNAi was expressed in retinal glia using 54C-GAL4 and TEM was performed to visualize morphological changes in the fly retina. Glial cells = green. Shown are representative images for the quantification shown in Fig. 5d. (f) UAS-Control RNAi or UAS-tau RNAi were expressed in glia of 1-2d adult flies for 10d. Fixed brains were stained for LDs using BODIPY 493/503 (white puncta, arrows). Shown are representative images for the quantification shown in Fig. 5f. (g) The total levels of triglycerides (TG) were measured in whole heads from either 10d or 26d flies expressing UAS-RNAi in adult glia using repoGS. A datapoint represents the relative TG levels per technical replicate (n). n = 8. Statistics: unpaired 2-sided student t-test (a, d), Log-rank test (b, c), 1-way ANOVA with Tukey’s multiple comparisons test (g). P-values = not significant (n.s.) >0.05, *≤0.05, **≤0.01, ***≤0.001, ****≤0.0001. Data presented as mean ± SEM. See Supporting Data 1 for details on technical replicates, n, and P-values.

Extended Data Fig. 6 Downregulating dTau in adult glia causes increased daytime sleep.

UAS-Control RNAi or UAS-tau RNAi were expressed in glia of adult animals starting at 2d using the drug-inducible glial driver, repoGS. Starting at 5d, DAM assays were used to measure fly activity over the next 3 days. (a) Average activity profiles in Control (black, n = 32) or tau RNAi (red, n = 31) expressing flies. (b-d) tau RNAi expressing flies have more recorded total and daytime sleep. A datapoint represents the mean measurement per n animal over the 3 days of analysis. Statistics: unpaired 2-sided student t-test. Data presented as mean ± SEM. P-values = not significant (n.s.) >0.05, ****≤0.0001. Experiments were performed in a w+ background.

Extended Data Fig. 7 Astrocyte monocultures require Tau for proper LD formation during stress.

(a) HBSS was used to induce stress in rat astrocytes grown in the absence (monoculture, n = 6) or presence (co-culture, n = 5) of neurons. LDs were then stained in these astrocytes using BODIPY 493/503. A datapoint represents the average LD number or size from 10 glia per experimental replicate (n). (b) WB showing GFP (control) or GFP-hTau expression in astrocyte monocultures prior to co-culturing with neurons. (c-e) LipidTox was used to assess LD number in astrocyte monocultures. CM = complete glia media (control). Glia were treated with HBSS to induce oxidative stress19. A datapoint represents the average LD number from 5-10 glia per experimental replicate (n). n = 3-4. (c) Astrocytes were transduced to overexpress GFP (control) or GFP-hTau. (d-e) Astrocytes were transduced to express RNAi targeting Mapt to a control RNAi. GFP marks cells that were successfully transduced. Statistics: unpaired 2-sided student t-test (a), 1-way Anova with Tukey’s multiple comparisons test (b and c). P-values = not significant (n.s.) >0.05, *≤0.05, **≤0.01, ***≤0.001, ****≤0.0001. Data presented as mean ± SEM. See Supporting Data 1 for details on n and P-values. All images are maximum intensity projections.

Extended Data Fig. 8 Overexpression of futsch in glia disrupts LD formation similar to hTau overexpression.

Rh:ND42 RNAi was used to induce neuronal ROS and glial LD. LDs were visualized in fly retinas of 2d animals using BODIPY 493/503 (white puncta, arrows). UAS-Control (LacZ), UAS-futsch, or UAS-ensconsin (ens) were overexpressed in glia using 54C-GAL4. A datapoint represents the average LD number per ommatidium per animal (n). n = 7-8. Statistics: 1-way ANOVA with Tukey’s multiple comparisons test. P-values = not significant (n.s.) >0.05, *≤0.05, **≤0.01, ***≤0.001, ****≤0.0001. Data presented as mean ± SEM. See Supporting Data 1 for details on n and P-values.

Extended Data Fig. 9 Characterization of 1xUAS-hTau flies.

(a) Schematic of a standard UAS construct for hTau that carries five upstream activation sequences (5xUAS). (b) Schematic of our new 1xUAS construct for hTau that carries one upstream activation sequence (1xUAS). (c) WBs comparing expression of total Tau protein in flies carrying a standard (5x)UAS-GFP-hTau transgene versus flies carrying a 1xUAS-GFP-hTau transgene. For accurate comparisons, transgenes were expressed for 4d using the drug-inducible, ubiquitous driver da-GAL4Geneswitch (daGS) starting in 2d animals. Protein was extracted from whole fly heads. (d) Viability analysis for flies expressing UAS-hTau versus flies expressing 1xUAS-hTau using the tau-CRIMIC allele. (e) WBs showing the successful production of hTau protein when 1xUAS-hTau was expressed using the tau-CRIMIC allele. Protein was extracted from 2d fly heads. Hyperphosphorylated Tau is not detected in these animals using the AT8 antibody (no data to show). Note that expression of hTau using 1xUAS-hTau and the tau-CRIMIC allele can result in secondary bands with this well-established anti-Tau antibody due to the low expression of the protein. In contrast, this same antibody does not produce secondary bands when hTau is expressed at higher levels using 5xUAS-hTau transgenes or a stronger GAL4 driver, as per c and Extended Data Fig. 3b. A datapoint represents the relative band density per technical replicate (n). n = 4. Data presented as mean ± SD. Statistics: 1-way ANOVA w/ Tukey’s multiple comparison’s test. P-values = not significant (n.s.) >0.05. (f) Expression of 1xCtrl or 1xhTau using the tau-CRIMIC allele does not induce LDs. Quantification seen in Fig. 7c.

Extended Data Fig. 10 Additional data for BODIPY-C12/ER tracker studies and expression data on PLIN2 versus MAPT in aged human brains.

(a) Human oligodendrocyte-like MO3.13 cells were transiently transfected with siRNA targeting MAPT or scrambled control. Cells were fixed and stained for total hTau levels (red) or DAPI (blue). Shown are representative images for the quantification of hTau levels provided in Fig. 8b. Note that the level of hTau downregulation varies between cells. This will make quantifications presented in Fig. 8b,c and Extended Data Fig. 10c,d underestimates as cells with intact Tau will be included. (b) Zoomed out images for those seen in Fig. 8c. Dashed square = the representative image used. (c) Total levels of BODIPY-C12 were measured in transfected cells. MAPT siRNA results in an overall increase in BODIPY-C12 levels, indicating defects in the breakdown of externally sourced lipids. (d) Total levels of ER tracker signal were measured in transfected cells, showing ER enlargement with MAPT siRNA-2. (c-d) A datapoint represents the relative fluorescence per cell in an image (n). n = 5. Statistics: 1-way ANOVA with Dunnett’s multiple comparisons test. Data presented as mean ± SD. P-values = not significant (n.s.) >0.05, *≤0.05, **≤0.01, ***≤0.001, ****≤0.0001. (e-g) Publicly available RNA-seq data from human postmortem brains was analyzed for expression levels of MAPT (encodes Tau) and the LD marker, PLIN2. Pearson r correlations were performed to define a correlation in expression between these two genes. Two analyses were performed: (1) includes all individuals independent of clinical status; (2) includes only individuals with a clinical diagnosis of AD (shown in red). (e) RNA-seq data in the MAYO cohort was obtained from temporal cortex (TCX) post-mortem tissue. This cohort includes individuals with AD, progressive supranuclear palsy, and cognitively unimpaired individuals. (f) RNA-seq data in the ROSMAP cohort was obtained from dorsolateral prefrontal cortex (DLPFC) post-mortem tissue. ROSMAP is a prospective study recruiting individuals from the community setting without known dementia who are followed longitudinally and consent for brain donation and autopsy at the time of death. See Supporting Data 1 for details on n and statistics.

Supplementary information

Supplementary Information

Supplementary note and figure.

Reporting Summary

Supplementary Table 1

Supporting Data 1–3.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goodman, L.D., Ralhan, I., Li, X. et al. Tau is required for glial lipid droplet formation and resistance to neuronal oxidative stress. Nat Neurosci 27, 1918–1933 (2024). https://doi.org/10.1038/s41593-024-01740-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-024-01740-1

Search

Advanced 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