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:

Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation

Nature Immunology volume 21, pages 513–524 (2020)Cite this article

Subjects

An Author Correction to this article was published on 13 July 2020

This article has been updated

Abstract

Oxidative stress is a central part of innate immune-induced neurodegeneration. However, the transcriptomic landscape of central nervous system (CNS) innate immune cells contributing to oxidative stress is unknown, and therapies to target their neurotoxic functions are not widely available. Here, we provide the oxidative stress innate immune cell atlas in neuroinflammatory disease and report the discovery of new druggable pathways. Transcriptional profiling of oxidative stress–producing CNS innate immune cells identified a core oxidative stress gene signature coupled to coagulation and glutathione-pathway genes shared between a microglia cluster and infiltrating macrophages. Tox-seq followed by a microglia high-throughput screen and oxidative stress gene network analysis identified the glutathione-regulating compound acivicin, with potent therapeutic effects that decrease oxidative stress and axonal damage in chronic and relapsing multiple sclerosis models. Thus, oxidative stress transcriptomics identified neurotoxic CNS innate immune populations and may enable discovery of selective neuroprotective strategies.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Single cell oxidative stress transcriptome of CNS innate immunity.
Fig. 2: Overlay of CNS innate immune cell clusters with oxidative stress core signature genes.
Fig. 3: Oxidative stress core signature in EAE.
Fig. 4: Bulk Tox-seq and coexpression gene network analysis of ROS+ microglia in EAE.
Fig. 5: Microglia HTS and oxidative stress gene network with Tox-seq data overlay.
Fig. 6: GGT inhibition in innate immune cells.
Fig. 7: Effects of acivicin in EAE.
Fig. 8: Acivicin suppresses demyelination, axonal damage and oxidative stress in chronic EAE.

Similar content being viewed by others

Data availability

The scRNA-seq and bulk RNA-seq data files are available in the Gene Expression Omnibus database under SuperSeries accession code GSE146295. A searchable web resource for the scRNA-seq data is available at https://toxseq.shinyapps.io/scrnaseq_viewer/ and associated source code at https://github.com/gladstone-institutes/BC-AM-1148. The oxidative stress pathway model is available at WikiPathways: https://www.wikipathways.org/instance/WP4466. All data generated and analyzed in this study are available within the paper. Any additional data can be made available from the corresponding author upon reasonable request.

Change history

References

  1. Nikic, I. et al. A reversible form of axon damage in experimental autoimmune encephalomyelitis and multiple sclerosis. Nat. Med. 17, 495–499 (2011).

    CAS  PubMed  Google Scholar 

  2. Locatelli, G. et al. Mononuclear phagocytes locally specify and adapt their phenotype in a multiple sclerosis model. Nat. Neurosci. 21, 1196–1208 (2018).

    CAS  PubMed  Google Scholar 

  3. Fischer, M. T. et al. NADPH oxidase expression in active multiple sclerosis lesions in relation to oxidative tissue damage and mitochondrial injury. Brain 135, 886–899 (2012).

    PubMed  PubMed Central  Google Scholar 

  4. Heppner, F. L., Ransohoff, R. M. & Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci. 16, 358–372 (2015).

    CAS  PubMed  Google Scholar 

  5. Nortley, R. et al. Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signaling to pericytes. Science 365, eaav9518 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Park, L. et al. NADPH-oxidase-derived reactive oxygen species mediate the cerebrovascular dysfunction induced by the amyloid β peptide. J. Neurosci. 25, 1769–1777 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Junker, A. et al. Extensive subpial cortical demyelination is specific to multiple sclerosis. Brain Pathol. https://doi.org/10.1111/bpa.12813 (2020).

  8. Back, S. A., Gan, X., Li, Y., Rosenberg, P. A. & Volpe, J. J. Maturation-dependent vulnerability of oligodendrocytes to oxidative stress-induced death caused by glutathione depletion. J. Neurosci. 18, 6241–6253 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ryu, J. K. et al. Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration. Nat. Immunol. 19, 1212–1223 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Merlini, M. et al. Fibrinogen induces microglia-mediated spine elimination and cognitive impairment in Alzheimer’s disease. Neuron 101, 1099–1108 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Weiner, H. L. A shift from adaptive to innate immunity: a potential mechanism of disease progression in multiple sclerosis. J. Neurol. 255 (Suppl. 1), 3–11 (2008).

    CAS  PubMed  Google Scholar 

  12. Lassmann, H., van Horssen, J. & Mahad, D. Progressive multiple sclerosis: pathology and pathogenesis. Nat. Rev. Neurol. 8, 647–656 (2012).

    CAS  PubMed  Google Scholar 

  13. Mahad, D. H., Trapp, B. D. & Lassmann, H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol. 14, 183–193 (2015).

    CAS  PubMed  Google Scholar 

  14. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    CAS  PubMed  Google Scholar 

  15. 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  Google Scholar 

  16. Schirmer, L. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. JordĂŁo, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    PubMed  Google Scholar 

  21. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    PubMed  Google Scholar 

  22. Diehn, M. et al. Association of reactive oxygen species levels and radioresistance in cancer stem cells. Nature 458, 780–783 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223 (2019).

    CAS  PubMed  Google Scholar 

  25. Ajami, B. et al. Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models. Nat. Neurosci. 21, 541–551 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging and disease. Immunity 48, 380–395 (2018).

    CAS  PubMed  Google Scholar 

  27. Geirsdottir, L. et al. Cross-species single-cell analysis reveals divergence of the primate microglia program. Cell 179, 1609–1622 (2019).

    CAS  PubMed  Google Scholar 

  28. Choi, B. Y. et al. Inhibition of NADPH oxidase activation reduces EAE-induced white matter damage in mice. J. Neuroinflammation 12, 104 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. Davalos, D. et al. Early detection of thrombin activity in neuroinflammatory disease. Ann. Neurol. 75, 303–308 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Davalos, D. et al. Fibrinogen-induced perivascular microglial clustering is required for the development of axonal damage in neuroinflammation. Nat. Commun. 3, 1227 (2012).

    Article  PubMed  Google Scholar 

  31. Petersen, M. A., Ryu, J. K. & Akassoglou, K. Fibrinogen in neurological diseases: mechanisms, imaging and therapeutics. Nat. Rev. Neurosci. 19, 283–301 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Drozdz, R. et al. γ-glutamyl transferase dependent generation of reactive oxygen species from a glutathione/transferrin system. Free Radic. Biol. Med. 25, 786–792 (1998).

    CAS  PubMed  Google Scholar 

  33. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Kutmon, M. et al. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput. Biol. 11, e1004085 (2015).

    PubMed  PubMed Central  Google Scholar 

  35. Corti, A., Franzini, M., Paolicchi, A. & Pompella, A. γ-glutamyl transferase of cancer cells at the crossroads of tumor progression, drug resistance and drug targeting. Anticancer Res. 30, 1169–1181 (2010).

    CAS  PubMed  Google Scholar 

  36. Koga, M. et al. Glutathione is a physiologic reservoir of neuronal glutamate. Biochem. Biophys. Res. Commun. 409, 596–602 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Jiang, X. et al. Quantitative real-time imaging of glutathione. Nat. Commun. 8, 16087 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Tsuji, T., Yamada, K. & Kunieda, T. Characterization of the dwg mutations: dwg and dwgBayer are new mutant alleles of the Ggt1 gene. Mamm. Genome 20, 711–719 (2009).

    CAS  PubMed  Google Scholar 

  39. Shu, Y. et al. Association of serum γ-glutamyltransferase and C-reactive proteins with neuromyelitis optica and multiple sclerosis. Mult. Scler. Relat. Disord. 18, 65–70 (2017).

    PubMed  Google Scholar 

  40. Fischer, M. T. et al. Disease-specific molecular events in cortical multiple sclerosis lesions. Brain 136, 1799–1815 (2013).

    PubMed  PubMed Central  Google Scholar 

  41. Iturria-Medina, Y. et al. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat. Commun. 7, 11934 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kunutsor, S. K. & Laukkanen, J. A. Gamma-glutamyltransferase and risk of future dementia in middle-aged to older Finnish men: a new prospective cohort study. Alzheimers Dement. 12, 931–941 (2016).

    PubMed  Google Scholar 

  43. Mayo, L. et al. Regulation of astrocyte activation by glycolipids drives chronic CNS inflammation. Nat. Med. 20, 1147–1156 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Herrera, A. J., Castano, A., Venero, J. L., Cano, J. & Machado, A. The single intranigral injection of LPS as a new model for studying the selective effects of inflammatory reactions on dopaminergic system. Neurobiol. Dis. 7, 429–447 (2000).

    CAS  PubMed  Google Scholar 

  45. International Multiple Sclerosis Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).

    Google Scholar 

  46. Magliozzi, R. et al. Iron homeostasis, complement, and coagulation cascade as CSF signature of cortical lesions in early multiple sclerosis. Ann. Clin. Transl. Neurol. 6, 2150–2163 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Yates, R. L. et al. Fibrin(ogen) and neurodegeneration in the progressive multiple sclerosis cortex. Ann. Neurol. 82, 259–270 (2017).

    CAS  PubMed  Google Scholar 

  48. Han, M. H. et al. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature 451, 1076–1081 (2008).

    CAS  PubMed  Google Scholar 

  49. Ravuri, C., Svineng, G., Pankiv, S. & Huseby, N. E. Endogenous production of reactive oxygen species by the NADPH oxidase complexes is a determinant of γ-glutamyltransferase expression. Free Radic. Res. 45, 600–610 (2011).

    CAS  PubMed  Google Scholar 

  50. Iadecola, C. The pathobiology of vascular dementia. Neuron 80, 844–866 (2013).

    CAS  PubMed  Google Scholar 

  51. Rodriguez-Rodriguez, A., Egea-Guerrero, J. J., Murillo-Cabezas, F. & Carrillo-Vico, A. Oxidative stress in traumatic brain injury. Curr. Med. Chem. 21, 1201–1211 (2014).

    CAS  PubMed  Google Scholar 

  52. Davalos, D. & Akassoglou, K. Fibrinogen as a key regulator of inflammation in disease. Semin. Immunopathol. 34, 43–62 (2012).

    CAS  PubMed  Google Scholar 

  53. Yamamoto, S. et al. Preventive effect of GGsTop, a novel and selective γ-glutamyl transpeptidase inhibitor, on ischemia/reperfusion-induced renal injury in rats. J. Pharmacol. Exp. Ther. 339, 945–951 (2011).

    CAS  PubMed  Google Scholar 

  54. Birkner, K. et al. β1-integrin- and KV1.3 channel-dependent signaling stimulates glutamate release from Th17 cells. J. Clin. Invest. 103, 715–732 (2020).

    Google Scholar 

  55. Sedlak, T. W. et al. The glutathione cycle shapes synaptic glutamate activity. Proc. Natl Acad. Sci. USA 116, 2701–2706 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Schieber, M. & Chandel, N. S. ROS function in redox signaling and oxidative stress. Curr. Biol. 24, R453–R462 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Saederup, N. et al. Selective chemokine receptor usage by central nervous system myeloid cells in CCR2-red fluorescent protein knock-in mice. PLoS ONE 5, e13693 (2010).

    PubMed  PubMed Central  Google Scholar 

  58. Jung, S. et al. Analysis of fractalkine receptor CX3CR1 function by targeted deletion and green fluorescent protein reporter gene insertion. Mol. Cell. Biol. 20, 4106–4114 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Aronesty, E. ea-utils: command-line tools for processing biological sequencing data. (2011); https://github.com/ExpressionAnalysis/ea-utils

  60. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    PubMed  Google Scholar 

  61. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  62. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. Zambon, A. C. et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Van der Laan, M. J. & Pollard, K. S. A new algorithm for hybrid clustering of gene expression data with visualization and the bootstrap. J. Stat. Plan. Interference 117, 275–303 (2003).

    Google Scholar 

  65. Zuberi, K. et al. GeneMANIA prediction server 2013 update. Nucleic Acids Res. 41, W115–W122 (2013).

    PubMed  PubMed Central  Google Scholar 

  66. Kutmon, M. et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 44, D488–D494 (2016).

    CAS  PubMed  Google Scholar 

  67. Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    CAS  PubMed  Google Scholar 

  68. Ryu, J. K. et al. Blood coagulation protein fibrinogen promotes autoimmunity and demyelination via chemokine release and antigen presentation. Nat. Commun. 6, 8164 (2015).

    PubMed  Google Scholar 

  69. Adams, R. A. et al. The fibrin-derived γ377-395 peptide inhibits microglia activation and suppresses relapsing paralysis in central nervous system autoimmune disease. J. Exp. Med. 204, 571–582 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Akassoglou, K. et al. Oligodendrocyte apoptosis and primary demyelination induced by local TNF/p55TNF receptor signaling in the central nervous system of transgenic mice: models for multiple sclerosis with primary oligodendrogliopathy. Am. J. Pathol. 153, 801–813 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Akassoglou, K. et al. Fibrin depletion decreases inflammation and delays the onset of demyelination in a tumor necrosis factor transgenic mouse model for multiple sclerosis. Proc. Natl Acad. Sci. USA 101, 6698–6703 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank F. Quintana for sharing the NOD EAE protocol, I. Kathiriya for advice on scRNA-seq and the Gladstone Flow Cytometry (M. Cavrois, M. Maiti and N. Raman) and Genomics Cores (R. Chadwick, J. McGuire, Y. Hao and N. Carli) for expert technical assistance with flow cytometry, library preparations and sequencing. We thank S. Zeitlin and S. Chen for helpful advice and testing of cell-imaging algorithms, S.J. Won and R. Swanson for advice, B. Cabriga for technical assistance, G. Maki for graphics and K. Claiborn for editorial assistance. B.G.B. acknowledges support from Younger Family Fund, Jr. S.S.Z. is supported by research grants from the NIH (R01 AI131624, R01 NS092835, R21 NS108159), the National Multiple Sclerosis Society (NMSS) (RG1701-26628; RG-1801-29861), Race to Erase MS, the Weill Institute and the Maisin Foundation. J.W. is supported by NIH/NIGMS R01 GM115622. A.R.P. and K.H. are supported by NRNB NIH/NIGMS P41 GM103504. This research was supported by NMSS Postdoctoral Fellowships FG 1944-A-1, FG-1507-05496 and FG-1708-28925 to S.B., J.K.R. and A.S.M., respectively; the German Research Foundation (DFG) postdoc fellowship to S.B.; UCSF Immunology NIH/NIAID T32 AI007334 to A.S.M.; NIH/NINDS F32 NS096920 to V.A.R.; Race to Erase MS Young Investigator Award and American Heart Association Scientist Development Grant 16SDG30170014 to J.K.R.; the FastForward/NMSS grant to K.A. and M.R.A., the Ray and the Dagmar Dolby Family Fund, the Simon Family Trust, Conrad N. Hilton Foundation (17348) and NIH/NIA RF1 AG064926 and NIH/NINDS R35 NS097976 to K.A.

Author information

Author notes

  1. These authors contributed equally: Andrew S. Mendiola, Jae Kyu Ryu.

Authors and Affiliations

  1. Gladstone Institutes, San Francisco, CA, USA

    Andrew S. Mendiola, Jae Kyu Ryu, Sophia Bardehle, Anke Meyer-Franke, Kim M. Baeten, Kristina Hanspers, Mario Merlini, Sean Thomas, Mark A. Petersen, Alexander Williams, Reuben Thomas, Victoria A. Rafalski, Rosa Meza-Acevedo, Reshmi Tognatta, Zhaoqi Yan, Michael R. Machado, Catherine Bedard, Pamela E. Rios Coronado, Michael A. Pleiss, Alexander R. Pico, Benoit G. Bruneau & Katerina Akassoglou

  2. Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

    Jae Kyu Ryu, Ari J. Green, Scott S. Zamvil & Katerina Akassoglou

  3. Small Molecule Discovery Center, University of California San Francisco, San Francisco, CA, USA

    Kenny Kean-Hooi Ang, Chris Wilson, Samuel J. Pfaff & Michelle R. Arkin

  4. Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, CA, USA

    Kenny Kean-Hooi Ang, Chris Wilson, Samuel J. Pfaff & Michelle R. Arkin

  5. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

    Mark A. Petersen & Benoit G. Bruneau

  6. Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA

    Xiqian Jiang & Jin Wang

  7. Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA

    Ari J. Green

  8. Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA

    Benoit G. Bruneau

Authors
  1. Andrew S. Mendiola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae Kyu Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sophia Bardehle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anke Meyer-Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kenny Kean-Hooi Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chris Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kim M. Baeten
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kristina Hanspers
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mario Merlini
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sean Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mark A. Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alexander Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Reuben Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Victoria A. Rafalski
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Rosa Meza-Acevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Reshmi Tognatta
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Zhaoqi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Samuel J. Pfaff
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Michael R. Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Catherine Bedard
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Pamela E. Rios Coronado
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Xiqian Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Jin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Michael A. Pleiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Ari J. Green
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Scott S. Zamvil
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Alexander R. Pico
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Benoit G. Bruneau
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Michelle R. Arkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Katerina Akassoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.S.M., J.K.R., S.B. and A.M.-F. performed and designed experiments and analyzed data; A.S.M. performed sc-Tox-seq and analyzed scRNA-seq data; J.K.R. performed target identification and validation in vitro and in vivo; S.B. developed the ROS-labeling protocol and performed bulk Tox-seq. A.S.M., R. Thomas, S.T. and A.W. performed bioinformatics analysis. A.M.-F., K.K.A., S.J.P., J.K.R., K.M.B., M.R.M., C.B. and C.W. performed microglia assay development and HTS; M.A.Pe., R. Tognatta, J.K.R. and A.S.M. performed immunohistology; A.S.M. and J.K.R. performed FACS; M.M. performed GSH live-cell imaging assays; X.J. and J.W. provided reagents and analyzed data; P.E.R.C., R.M.-A. and Z.Y. conducted image analysis; A.R.P. and K.H. performed pathway modeling and network analysis; M.A.Pl. and A.J.G. analyzed data; V.A.R., A.R.P., S.S.Z., B.G.B. and M.R.A. designed experiments and analyzed data; K.A. conceived the project, designed the study, designed experiments and analyzed data; A.S.M., J.K.R., S.B. and K.A. wrote the manuscript with input from all authors. All authors contributed to critical review of the manuscript.

Corresponding author

Correspondence to Katerina Akassoglou.

Ethics declarations

Competing interests

K.A. is a co-founder and scientific advisor of Therini Bio. K.A., A.M.-F. and J.K.R. are co-inventors on the J. David Gladstone Institutes’ patent US10,451,611 and pending patent applications US 16/572,365 and EP 15735284.05, covering in vitro fibrin assays. K.A. and J.K.R. are co-inventors on the J. David Gladstone Institutes’ application PCT/2018/052694, covering methods to inhibit neurodegeneration, K.A., M.R.A., K.K.A., A.M.-F. and C.W. are co-inventors on the J. David Gladstone Institutes’ and the Regents of the University of California’s pending patent applications US 15/943,474 and EP 15735033.1, covering assays for inhibition of microglia activation. Their interests are managed in accordance with their respective institutions’ conflict of interest policy. J.W. and X.J. are the coinventors of a patent (WO2016025382A8 and US201462035400P) related to the glutathione probes used in this study. J.W. is the cofounder of CoActigon Inc. and Chemical Biology Probes LLC.

Additional information

Peer review information L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Tox-seq and HTS schematic.

Tox-seq workflow and microglia HTS followed by in vivo validation and drug gene target network analysis.

Extended Data Fig. 2 Validation of single cell oxidative stress transcriptome of CNS innate immunity.

a, Flow cytometry plots of live CD11b+ROS– and live CD11b+ROS+ cells from spinal cord of individual healthy and EAE mice. Cell population (%) is shown inside gate. Data are representative of two independent experiments with similar results. b, Representative histogram plots (top) and quantification of mean fluorescence intensity (MFI, bottom) of ROS production (assessed via DCFDA) in CD11b+ cells from healthy and EAE mice. Data are from n = 4 mice per group (a, b) (mean ± s.d.). *P < 0.05 (two-tailed Mann-Whitney test). c, Violin plots of number (n) Gene (nGene) and unique molecular identifiers (nUMI) post-normalization and shown for each biological replicate for scRNA-seq. Data are from n = 2 mice per group. d, Correlation gene expression scatter plot between healthy (left) or EAE (right) biological replicates from scRNA-seq. Data are from n = 2 healthy and 2 EAE mice depicted as the average gene expression for cluster 1 (n = 14,927 and 12,861 genes for healthy replicate 1 and 2 respectively) and cluster 6 (n = 11,856 and 14,319 genes for EAE replicate 1 and 2 respectively) with Pearson correlation coefficients r = 0.98 and 0.99, respectively. e, UMAP plots (left) of each cells sample type and summarized in table (right). f, Violin plots of log expression levels of potential astrocytic (Gfap), neuronal (Tubb3 and Rbfox3), oligodendrocyte (Mog and Plp1) contaminates in each cluster. Violin plots depict minima and maxima expression levels (14 clusters; n = 8,701 cells from spinal cord of 2 healthy and 2 EAE mice). Genes shown were not differentially expressed. Actb is shown as a reference gene. g, Heat map of top 5 DEGs per single cell cluster. The clusters are ordered by unsupervised hierarchical clustering. Key indicates scaled z-score expression.

Source data

Extended Data Fig. 3 CNS innate immune populations and comparison with canonical myeloid gene signatures.

a, Heat map of oxidative stress subclusted populations from Tox-seq analyzed for select microglia (green text)19,20, monocyte/macrophage (orange text)20,25,26, and CNS-associated macrophage (black text)20 gene markers. Key indicates average gene expression (log scale). Asterisk indicates genes previously validated by cell-fate mapping20. b, Violin plots from Tox-seq clusters MgV and MpI showing single-cell gene expression levels of highly-conserved microglia core genes27. Violin plots depict minima and maxima with points showing single cell expression levels. Data are from n = 2 mice per group. c, Heat maps of top 5 DEGs across microglia (top) or monocytes/macrophages (bottom) single cell subclusters. Key indicates scaled z-score gene expression. d, UMAP plots of monocytes/macrophages subclusted and colored based on FACS-purified EAE CD11b+ROS+ cells (red dots), high expressing NAPDH oxidase (Cybb+ x Cyba+) cells (green dots), and overlay between the two populations (yellow dots). The percent colocalization between the number of FACS-purified EAE CD11b+ROS+ and high expressing NAPDH oxidase cells is shown in key. e, Violin plots of log expression levels of Sparc across all microglia (Mg) and monocyte/macrophage (Mp) clusters. Violin plots depict minima and maxima with points showing single cell expression levels. Data are from n = 2 healthy and 2 EAE mice. f, Confocal microscopy quantification related to Fig. 3g, and presented as percent CX3CR1+ and CCR2+ cells colocalized with gp91-phox and MHC II in spinal cord sections from Cx3cr1GFP/+:Ccr2RFP/+ mice with MOG35-55-induced EAE. n = 4 mice per group (means ± s.e.m.). Each symbol (f) represents an individual mouse.

Extended Data Fig. 4 Bulk RNA-seq of ROS+ and MHC+ CNS innate immune cells.

a, Representative flow cytometry plots of CD45loCD11b+ (microglia) and CD45hiCD11b+ (monocytes) cells sorted based on MHC II and ROS production (assessed via DCFDA) from EAE spinal cord. Data are representative of n = 3 independent experiments with similar results. b, Cell percentages of sorted microglia and monocytes populations. Data are from n = 3 independent experiments with 5 spinal cord EAE tissues pooled per experiment. c, d, Quantification of MFI of ROS production (assessed via DCFDA) (c) and MHC II (d) expression in CD45loCD11b+ and CD45hiCD11b+ cells. Data are from n = 3 experiments (mean ± s.e.m.) ** P = 0.0011, n.s., not significant P = 0.080 (two-tailed unpaired t-test). Each symbol represents an individual mouse. e, Gene expression (FPKM) profiles of select microglia and monocyte/macrophage markers across each sorted population. Base, MHCII–ROS– cells. f, Heat map of bulk RNA-seq cluster analysis from CD45loCD11b+ cells. Six gene clusters (C1-6) were annotated with functions for selected genes based on expression levels. g, Heat map of bulk RNA-seq cluster analysis from CD45hiCD11b+ cells. Four gene clusters (C1-4) were annotated with functions for selected genes based on expression levels. Key indicates normalized z-score expression.

Source data

Extended Data Fig. 5 Effects of acivicin on microglia, macrophage and whole blood cell numbers.

a, Representative images showing morphology and density of primary microglia treated with vehicle or acivicin for 48 h stained with nuclei marker Hoechst or CellMask Red Whole Cell Stain. Quantification of the total number of microglia. Data are representative of one experiment with technical replicates (open symbols). Scale bar, 100 μm. b, Representative confocal images showing morphology and density of vehicle- and acivicin-treated BMDMs treated for 24 h. Scale bar, 100 μm. Insets depict digitally zoomed in regions of interest. Quantification of the total number of BMDMs in the field of view. Data are from n = 7 independent experiment. c, Representative flow cytometric plots of gating strategy for BMDMs treated with acivicin for 24 h or left untreated. Cell viability of single CD45+ cells was assessed by Sytox blue live/dead nuclei stain. Quantification of percent cell viability of BMDMs treated with acivicin or left untreated. Data are from n = 2 independent experiments with n = 4 mice per group. d, Quantification of complete blood count analysis of white blood cells (WBCs), red blood cells (RBC), platelets (PLT) (left) and composition of WBCs (right) of blood samples from healthy mice treated with saline or acivicin for 10 days. Data are from n = 8 mice (saline) or n = 7 mice (acivicin). e, Representative flow cytometric plots of cells expressing CD45 and CD11b from the spinal cord of healthy mice treated with acivicin or saline every day for 14 days. Quantification of total CD45loCD11b+ microglia cell numbers in healthy mice treated with acivicin or saline. Data are from n = 3 mice per group. Each symbol represents an individual experiment (b) or mouse (c-e). Data are shown as means ± s.e.m. (b, d, e) or means ± s.d. (c). P = 0.68 or P > 0.99; n.s., not significant, as determined by two-tailed unpaired Student’s t-test (b) or two-tailed Mann-Whitney test (e).

Source data

Extended Data Fig. 6 Inhibition of GGT in macrophages.

a, b, Dose response of acivicin (a) or GGsTop (b) on GGT activity measured by fluorescent probe gGlu in fibrin-stimulated BMDMs. Data are from n = 4 independent experiments performed in technical duplicates. c, Quantitative PCR analysis of Ccl5, Cxcl3, Cxcl10 and Il1b expression in BMDMs isolated from Ggt1+/+ and Ggt1dwg/dwg mice left unstimulated or stimulated with LPS for 6 hr (key). Data are from n = 4 mice per group. Each symbol represents an individual experiment (a, b) or mouse (c). Data are shown as means ± s.e.m. (a-c). * P < 0.05, **P < 0.01, *** P < 0.001, **** P < 0.0001, as determined by one-way ANOVA with Dunnett’s multiple comparison test (a, b) or two-way ANOVA with Tukey’s multiple comparisons test (c).

Source data

Extended Data Fig. 7 Effects of acivicin in EAE, antigen-presenting cell function and GGT1 expression.

a, Clinical scores of MOG35-55 EAE mice (upward arrow) after daily prophylactic administration of acivicin (5 mg/kg, i.p.) or saline (key) starting day 0. Data are from n = 14 mice (acivicin), n = 15 mice (saline). b, Clinical scores for MOG35-55-induced EAE (upward arrow), followed by prophylactic injection of GGsTop (5 mg/kg, i.p.) or saline (key) every day beginning day 0. Data are from n = 13 mice (MOG35-55 + saline) or n = 12 mice (MOG35-55 + GGsTop). c-f, Flow cytometry analysis of splenocytes from MOG35-55-EAE mice treated with saline or acivicin for 10 days and assessed for IL-17+ or IFN-γ+ CD4+ T cells (c, d), effector memory (TEM), central memory (TCM), and naive CD4+ T cells (e), or CD25+ FoxP3+ Treg cells (f). Data are from n = 5 mice per group. g, h, Flow cytometry analysis of BrdU+ 2D2 T cells activated with LPS primed BMDMs (pulsed with MOG35-55 peptide) or anti-CD3 and anti-CD28 in the presence or absence of acivicin. Data are from n = 3 independent experiments performed in technical duplicates. i. Quantitative PCR analysis of gene expression from spinal cord extracts of unimmunized healthy controls and MOG35-55-EAE mice at peak disease, treated with saline or acivicin. Data are from n = 5 mice (unimmunized healthy control), n = 7 mice (EAE + saline), or n = 7 mice (EAE + acivicin). j, Confocal microscopy of spinal cord sections from NOD mice with chronic MOG35-55 EAE, treated with acivicin or saline every day starting at day 80 until day 113, showing GGT1 and CD11b immunoreactivity. Scale bar, 25 μm. Quantification of GGT1 immunoreactivity in white matter of spinal cord. Data are from n = 7 (EAE + acivicin) and n = 7 (EAE + saline) mice. Each symbol represents an individual mouse (c-f, i, j) or an individual experiment (g,h). Data are shown as means ± s.e.m. (a-j). * P < 0.05, **P < 0.01, *** P < 0.001, **** P < 0.0001; n.s., not significant, as determined by two-tailed permutation test (b), two-way ANOVA with Tukey’s multiple comparisons test (c,d), two-tailed Mann-Whitney test (e,f,j), or one-way ANOVA with Tukey’s multiple comparisons test (h,i).

Source data

Extended Data Fig. 8 Effects of acivicin in LPS-induced neurodegeneration.

a, GGT activity in substantia nigra (SN) extracts 12 h following PBS or LPS injection in mice pretreated with acivicin or saline for 5 d. Data are from n = 6 mice (control, PBS, or LPS + saline) or n = 8 mice (LPS + acivicin). b, c, Microscopy images of brain sections spanning SN 7 d following PBS or LPS injection in mice pretreated with acivicin or saline, tissues were stained for tyrosine-hydroxylase (TH) (b) or Iba-1 (c). Scale bars, 300 µm (b, c). Quantification of TH (b) and Iba-1 (c) immunoreactivity in SN is shown (right). Data are from n = 6 mice (saline) and n = 5 mice (acivicin) (b); n = 6 mice (saline + PBS or saline + LPS) and n = 5 mice (acivicin + PBS or acivicin + LPS) (c).Each symbol represents an individual mouse (a-c). Data are shown as means ± s.e.m. (a-c). * P < 0.05, **P < 0.01, *** P < 0.001, as determined by two-way ANOVA with Tukey’s multiple comparisons test (a), two-tailed Mann-Whitney test (b), or two-way ANOVA with Sidak’s multiple comparisons test (c).

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9.

Source data

Source Data Fig. 5

Statistical Source Data

Source Data Fig. 6

Statistical Source Data

Source Data Fig. 7

Statistical Source Data

Source Data Fig. 8

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 6

Statistical Source Data

Source Data Extended Data Fig. 7

Statistical Source Data

Source Data Extended Data Fig. 8

Statistical Source Data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mendiola, A.S., Ryu, J.K., Bardehle, S. et al. Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation. Nat Immunol 21, 513–524 (2020). https://doi.org/10.1038/s41590-020-0654-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-020-0654-0

This article is cited by

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