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Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation

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

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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.

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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.

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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.

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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.

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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

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  1. Andrew S. Mendiola
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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.

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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.

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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.

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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).

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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).

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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).

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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).

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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

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