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Identification of a protective microglial state mediated by miR-155 and interferon-γ signaling in a mouse model of Alzheimer’s disease

Nature Neuroscience volume 26, pages 1196–1207 (2023)Cite this article

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Abstract

Microglia play a critical role in brain homeostasis and disease progression. In neurodegenerative conditions, microglia acquire the neurodegenerative phenotype (MGnD), whose function is poorly understood. MicroRNA-155 (miR-155), enriched in immune cells, critically regulates MGnD. However, its role in Alzheimer’s disease (AD) pathogenesis remains unclear. Here, we report that microglial deletion of miR-155 induces a pre-MGnD activation state via interferon-γ (IFN-γ) signaling, and blocking IFN-γ signaling attenuates MGnD induction and microglial phagocytosis. Single-cell RNA-sequencing analysis of microglia from an AD mouse model identifies Stat1 and Clec2d as pre-MGnD markers. This phenotypic transition enhances amyloid plaque compaction, reduces dystrophic neurites, attenuates plaque-associated synaptic degradation and improves cognition. Our study demonstrates a miR-155-mediated regulatory mechanism of MGnD and the beneficial role of IFN-γ-responsive pre-MGnD in restricting neurodegenerative pathology and preserving cognitive function in an AD mouse model, highlighting miR-155 and IFN-γ as potential therapeutic targets for AD.

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Fig. 1: Targeting microglial miR-155 enhances MGnD signature in APP/PS1 mice.
Fig. 2: miR-155 ablation primes microglial transition to the pre-MGnD cluster through IFN-γ signaling.
Fig. 3: Targeting interferon-γ signaling abolishes the beneficial effect of miR-155 deletion in response to phagocytic stress.
Fig. 4: miR-155-deficient microglia restrict amyloid-β pathology.
Fig. 5: Deletion of microglial miR-155 enhanced synaptic functions in the brain milieu of APP/PS1 mice at 4 and 8 months of age.
Fig. 6: Microglia-specific miR-155 ablation rescues cognitive function in APP/PS1 mice.

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Data availability

The data that support the findings of this study are available in this paper and Supplementary Information. The Smart-seq2 RNA-seq and scRNA-seq data that support the findings of this study have been deposited into Gene Expression Omnibus under SuperSeries GSE205569.

Code availability

Single-cell and bulk RNA-seq data were analyzed using customized code available at https://github.com/The-Butovsky-Lab/Yin-Herron-et-al.-miR-155-deletion-study/.

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Acknowledgements

We thank R. Krishnan (Ann Romney Center for Neurologic Diseases Flow Cytometry Core facility) and B. Tilton (Boston University Flow Cytometry Core facility) for FACS sorting; Y. Alekseyev, Boston University Single Cell Sequencing Core facility and Boston University Microarray and Sequencing Resource Core Facility for single-cell sequencing; the Single Cell Core at Harvard Medical School for performing the single-cell RNA-seq sample preparation; L. Ding and NeuroTechnology Studio at Brigham and Women’s Hospital for consultation on data acquisition and data analysis; G. Garden for sharing miR-155-flox mice; S. Garamszegi and Genomic Platform of Broad Institute of MIT and Harvard for Smart-seq2 RNA-seq; D. Holtzman for sharing the anti-Apoe and anti-Aβ antibodies. This work was supported by grants from the following organizations to the indicated authors. O.B. and T.I.: NIH-NIA (R01AG054672); O.B.: NIH-NIA (R01AG051812, R01AG075509, R01AG080992, R21AG076982 and R41AG073059), NIH-NINDS (R01NS088137, R21NS104609, R21NS101673), NIH-NEI (R01EY027921), NIH-NIGMS (R01GM132668), Cure Alzheimer’s Fund (ApoE Consortium) and BrightFocus Foundation (2020A016806); T.I. and S.I.: Cure Alzheimer’s Fund, T.I.: NIH-NIA (RF1AG054199, R01AG066429, R01AG072719 and R01AG067763); S.I.: NIH-NIA (R01AG079859), S.H.: NIH-NIA (F31AG071106) and NIH-NIGMS (T32GM008541). M.A.M: NIH-NEI (K12 EY016335, K08 EY030160) and Research to Prevent Blindness Career Development Award.

Author information

Author notes

  1. These authors contributed equally: Zhuoran Yin, Shawn Herron.

Authors and Affiliations

  1. Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Zhuoran Yin, Sebastian Silveira, Kilian Kleemann, Christian Gauthier, Dania Mallah, Yiran Cheng, Milica A. Margeta, Kristen M. Pitts, Jen-Li Barry, Hannah Shorey, Wesley Brandao, Ana Durao, Charlotte Madore, Gopal Murugaiyan & Oleg Butovsky

  2. Evergrande Center for Immunologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Zhuoran Yin & Oleg Butovsky

  3. Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA

    Shawn Herron, Jean-Christophe Delpech, Samuel W. Hersh, Seiko Ikezu & Tsuneya Ikezu

  4. School of Computing, University of Portsmouth, Portsmouth, UK

    Kilian Kleemann

  5. Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA

    Milica A. Margeta & Kristen M. Pitts

  6. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ayshwarya Subramanian

  7. ARCND, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ayshwarya Subramanian

  8. Laboratoire NutriNeuro, UMR 1286, Bordeaux INP, INRAE, University of Bordeaux, Bordeaux, France

    Jean-Christophe Delpech & Charlotte Madore

  9. Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Mark Jedrychowski

  10. Department of Medicine, Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Amrendra K. Ajay

  11. Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA

    Seiko Ikezu & Tsuneya Ikezu

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  1. Zhuoran Yin
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Contributions

O.B., T.I., S.I., Z.Y. and S.H. conceived the study. Z.Y. and S.H. did experimental design, performed experiments, analyzed data and wrote the manuscript. O.B., T.I. and S.I. provided funding, supervision of the study, data interpretation and manuscript editing. J.-C.D. assisted and supervised microglia isolation and behavioral experiments. S.H. performed behavioral tests. S.S., Y.C. and S.W.H. performed experiments. D.M., C.G. and K.K. performed analysis for Smart-seq2 RNA-seq, scRNA-seq and proteomic analysis. A.S. performed statistical analysis of scRNA-seq for comparison of proportions. C.M., W.B., M.A.M., K.M.P., J.-L.B., H.S. and A.D. developed and procured mouse models. M.J. performed mass spectrometry for proteomics. G.M. and A.K.A. performed the luciferase reporter assay.

Corresponding authors

Correspondence to Tsuneya Ikezu or Oleg Butovsky.

Ethics declarations

Competing interests

O.B. is an inventor of a patent for the use of miR-155 inhibitors for the treatment of neurodegenerative diseases. O.B. collaborated with GSK and Regulus Therapeutics, received research funding from Sanofi and GSK, has received honoraria for lectures from and has provided consultancy services to Camp4 and Ono Pharma USA. T.I. consults Takeda. The remaining authors declare no competing interests.

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Nature Neuroscience thanks Evgenia Salta and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 miR-155 is specifically detected in Iba1+ cells in the brain of APP/PS1 mice.

a, Gating strategy for sorting Ly6C–CD11b+Fcrls+ microglia. b, Confocal microscopy images of Iba1 or CD206 immunoreactivity and detection of miR-155 gene expression using miRNAscope in 8-month-old APP/PS1 mice, scale bar: 50 µm. c, Quantification of fluorescence intensity of miR-155 in Iba1+ cells, Iba1– cells, and CD206+ cells per ROI using one-way ANOVA with Tukey’s post hoc analysis, P < 0.0001 (n = 8 ROIs for Iba1+ group, n = 12 ROIs for Iba1– group, n = 5 ROIs for CD206+ group). Data were presented as mean values ± s.e.m.

Extended Data Fig. 2 Targeting microglial miR-155 causes subtle changes in APP/PS1 mice at 8 months old.

a, qPCR of miR-155 expression in microglia from 8-month-old mice showing the efficiency of tamoxifen treatment, P = 0.0003 using one-way ANOVA with Tukey’s post hoc test (n = 5 mice for WT and APP/PS1:miR-155 cKO group, n = 3 mice for miR-155 cKO group, n = 4 mice for APP/PS1 group). b, Heatmap of DEGs identified in microglia of APP/PS1:miR-155 cKO group at 8 months of age, compared to APP/PS1 group using DESeq2 analysis (Two-sided LRT, FDR-corrected P < 0.05, n = 5-6 per sex/group). c, PCA analysis showing no separation among all the groups at 8 months old. d, Venn diagram showing the difference of DEGs between 4- and 8-month-old groups. Data were presented as mean values ± s.e.m. See also Supplementary Table 1.

Extended Data Fig. 3 Targeting microglial miR-155 suppresses homeostatic signature in WT mice.

a, Experimental design. b, Number of DEGs comparing miR-155 cKO vs. WT at 4- and 8 months old (n = 5-6 mice per sex/group). Heat map of DEGs identified in miR-155 cKO microglia, compared to WT microglia for male (c) and female (d) groups using DESeq2 analysis, two-sided Wald test, FDR-corrected P < 0.05 (n = 5-6 mice per sex/group). e, Top-affected pathways based on the comparison of female miR-155 cKO microglia vs. WT microglia at 8 months of age using IPA (DEGs were identified with two-sided Wald test, FDR-corrected P < 0.05). See also Supplementary Table 1.

Extended Data Fig. 4 Male APP/PS1 mice show more robust changes after targeting microglial miR-155.

Venn diagram (a) and violin plot (b) showing the difference of up-regulated DEGs or down-regulated DEGs by comparing APP/PS1:miR-155 cKO vs. APP/PS1 (P < 0.05) between male and female samples at 4 months of age. c, Sex-specific top-15 upregulated and downregulated DEGs with the Log2 fold changes (FC) between APP/PS1 vs. WT group and APP/PS1:miR-155 cKO vs. WT group. DEGs were identified using DESeq2 analysis with FDR-corrected P < 0.05. d, Volcano plots using common DEGs of sex-specific APP/PS1:miR-155 cKO microglia vs. APP/PS1 microglia and non-plaque-associated microglia or plaque-associated microglia, compared to WT microglia (Two-sided LRT, P < 0.05). See also Supplementary Table 1.

Extended Data Fig. 5 miR-155 ablation induces interferon signaling in microglia.

a, GSEA analysis of interferon alpha response and interferon gamma response made using bulk RNA sequencing data from isolated microglia. b, DEG Heatmaps of genes involved in gene ontology terms for phagocytosis, antigen presentation and cellular response to IFN-γ derived from bulk RNAseq data (Two-sided LRT, FDR-corrected P < 0.05, n = 4-6 mice per sex/group). c, Transcription factor functional enrichment analysis in microglia comparing APP/PS1:miR-155 cKO vs. APP/PS1 mice at 4 months old (R_Dorothea, FDR-corrected P < 0.05). d, Workflow of generating miR-155 targetome. e, Volcano plots showing common genes (highlighted) between miR-155 targetome and DEGs from four different comparisons: male and female miR-155 cKO vs. WT at the age of 8 months, APP/PS1:miR-155 cKO vs. APP/PS1 at the age of 4 and 8 months (Two-sided Wald test, P < 0.05, n = 5-13 mice per group). f, Table of common miR-155 targeted genes among four different comparisons in e. See also Supplementary Table 1.

Extended Data Fig. 6 miR-155 ablation sensitizes microglial transition to the pre-MGnD cluster through interferon-γ signaling.

a, Gating strategy for sorting CD11b+CD45+ myeloid cells. b, Unbiased UMAP plots analysis of female APP/PS1 and APP/PS1:miR-155 cKO microglia from single cell sequencing. Initial clustering generated 17 sub-clusters (n = 2 mice per group, 15,429 cells). c, Cell cycle phase analysis for each cluster (clusters 15 and 16 removed). d, Percentage of cell cycle phase within each sub-cluster. e, Volcano plots of DEGs in the proliferating microglia clusters 8 and 9 comparing APP/PS1:miR-155 cKO microglia and APP/PS1 microglia (Two-sided Wald test, P < 0.05). f, Effect-size of differences in the proportion of microglia clusters between genotypes in female APP/PS1:miR-155 cKO mice determined by Poisson regression. The color bar indicates the P value, while the size of the points indicates the effect size. g, UMAP plots analysis of three major microglia clusters identified in male APP/PS1 and APP/PS1:miR-155 cKO microglia from scRNA sequencing (n = 3 mice per group, 43,667 cells). h, Bar charts showing relative percentage of each major microglia cluster in APP/PS1 and APP/PS1:miR-155 cKO mice. i, Violin plots showing the mean z-score of pre-MGnD markers in APP/PS1 and APP/PS1:miR-155 cKO male mice. P = 3.54507e-91 by Kruska-Wallis test, FDR corrected using Benjamini Hochberg. j, Top-affected pathways from IPA analysis of all three clusters based on the comparison of APP/PS1:miR-155 cKO microglia vs. APP/PS1 microglia at 4 months of age (DEGs were identified with two-sided Wilcox FDR-corrected P < 0.05). k, UMAP plots analysis of microglia clusters identified in male APP/PS1 and APP/PS1:miR-155 cKO microglia including subclusters of pre-MGnD. l, Dot plot showing top markers for four microglia clusters (M0, early IFN-responsive pre-MGnD, late IFN-responsive pre-MGnD and MGnD). m, Trajectory analysis showing the transition from M0 to MGnD via IFN-responsive pre-MGnD clusters. n, Dynamic plots showing the expression of selected genes in all four major microglia clusters. o, Effect-size of differences in the proportion of microglia clusters between genotypes in male APP/PS1:miR-155 cKO mice determined by Poisson regression. The color bar indicates the P value, while the size of the points indicates the effect size. Standard error bars were shown. See also Supplementary Table 2.

Extended Data Fig. 7 Validation of Pre-MGnD markers as Stat1 and Clec2d.

a, Confocal microscopy images of Stat1+ and Iba1+ cells in the brain of APP/PS1 and APP/PS1:miR-155 cKO mice at 4 months of age. Scale bar: 50 µm. b, Quantification of Stat1positive are in Iba1+ cells per ROI, P = 0.0001 by two-tailed Student’s unpaired t-test (n = 12 ROIs from 3 mice for APP/PS1 group, n = 18 ROIs from 6 mice for APP/PS1:miR-155 cKO group). c, Comparison of transcript per million (TPM) level of Clec2d between WT and APP/PS1 mice, P = 0.0458 by two-tailed Student’s unpaired t-test (n = 11 APP/PS1, n = 9 APP/PS1:miR-155 cKO). d, FACS plots showing four clusters in WT, 2- and 8-month-old APP/PS1 mice, which are Clec7a–Clec2d– (M0), Clec7a–Clec2d+ (early pre-MGnD), Clec7a+Clec2d+ (late pre-MGnD), and Clec7a+Clec2d– (MGnD). e, Quantification of percentage of four clusters mentioned in d from WT, 2- and 8-month-old APP/PS1 mice, P < 0.0001 for M0 and late pre-MGnD, P = 0.0011 (WT vs. 8-month-old APP/PS1) and P = 0.0084 (2-month-old APP/PS1 vs. 8-month-old APP/PS1) for early pre-MGnD, P = 0.0017 (WT vs. 8-month-old APP/PS1) and P = 0.0014 (2-month-old APP/PS1 vs. 8-month-old APP/PS1) for MGnD as determined by one-way ANOVA (n = 3 WT, n = 4 APP/PS1). f, qPCR Quantification of gene expression of P2ry12, Clec2d, and Clec7a in M0, early pre-MGnD, late pre-MGnD, and MGnD clusters, P < 0.0001 for P2ry12, P = 0.0046 (early pre-MGnD vs. MGnD) and P = 0.0103 (late pre-MGnD vs. MGnD) for Clec2d, P = 0.0136 (M0 vs. early pre-MGnD), P = 0.0060 (M0 vs. late pre-MGnD), and P = 0.0023 (M0 vs. MGnD) for Clec7a as determined by one-way ANOVA (n = 5 mice/group). Data were presented as mean values ± s.e.m.

Extended Data Fig. 8 miR-155 targets Stat1 and regulates Interferon signaling pathway.

a, Illustration of 3’UTR of Stat1 Gaussia luciferase assay co-transfected with miR-155 mimic or miR-155 antagomir into N9 microglial cells; Created with Biorender.com. b, Control plasmid activity in miR-155 mimic and miR-155 antagomir transfected cells. Fold changes were calculated as compared to control mimic or antagomir using two-tailed Student’s unpaired t-test (n = 3). c, Fold changes of luciferase activity after co-transfection of mutant STAT1 3’UTR and control mimic or miR-155 mimic to check the specific binding of miR-155 to Stat1, P = 0.0234 by two-way ANOVA with Holm-Sidak post hoc analysis (n = 3). d, Fold changes of luciferase activity after non-mutated Stat1 3’UTR co-transfected with the non-relevant miRNA mimic (miR-18), P = 0.0072 by one-way ANOVA with post hoc Tukey’s test (n = 3). Data are representative of 2 independent experiments and show biologically independent replicates (b-d). e, Venn diagram depicting overlap of Ifngr1 cKO downregulated genes and APP/PS1:miR-155 cKO upregulated genes. f, Heat map showing expression of genes which were downregulated by Ifngr1 cKO phagocytic microglia and upregulated in APP/PS1:miR-155 cKO microglia. Data were presented as mean values ± s.e.m. See also Supplementary Tables 1, 3.

Extended Data Fig. 9 Decreased 6E10+ amyloid plaque burden and enhanced phagocytosis function in APP/PS1:miR-155 cKO mice.

a, Representative images of 6E10 staining in APP/PS1 and APP/PS1:miR-155 cKO brains at the age of 4 months. Scale bar: 500 µm. b, 6E10 plaque counts normalized to area and percent area 6E10 positivity in 4-month-old mice. P values were determined using two-tailed Student’s unpaired t-test, P = 0.0084 and P = 0.0104, respectively (n = 10 APP/PS1, n = 9 APP/PS1:miR-155 cKO). c, 6E10 plaque counts normalized to area and percent area 6E10 positivity in 8-month-old mice (n = 14 APP/PS1, n = 16 APP/PS1:miR-155 cKO). P values were determined using two-tailed Student’s unpaired t-test. d, Representative images of Thioflavin-S in the cortex and hippocampus of APP/PS1 and APP/PS1:miR-155 cKO mice at 4 months old. Scale bar: 500 µm. P values were determined using two-tailed Student’s unpaired t-test. e, Thioflavin-S plaque counts normalized to area and percent area Thioflavin-S positivity in 4-month-old mice. P values were determined using two-tailed Student’s unpaired t-test (n = 10 APP/PS1, n = 9 APP/PS1:miR-155 cKO, P = 0.09). f, Thioflavin-S plaque counts normalized to area and percent area Thioflavin-S positivity in 8-month-old mice (n = 14 APP/PS1, n = 16 APP/PS1:miR-155 cKO). P values were determined using two-tailed Student’s unpaired t-test. g, Representative images of Thioflavin-S in the cortex and hippocampus of APP/PS1 and APP/PS1:miR-155 cKO mice at 2.5 months of age. Scale bar: 500 µm. h, Quantification of Thioflavin-S+ plaque area in the cortical region at 2.5 months of age using two-tailed Student’s unpaired t-test (n = 6 APP/PS1, n = 5 APP/PS1:miR-155 cKO). i, Representative images of HJ3.4B staining in APP/PS1 and APP/PS1:miR-155 cKO brains. Scale bar: 500 µm. j, Quantification of HJ3.4B+ plaque area normalized to cortical area at 2.5 months of age using two-tailed Student’s unpaired t-test (n = 6 APP/PS1, n = 5 APP/PS1:miR-155 cKO). k, Apoptotic neuron injection scheme. l, Mean fluorescence intensity (MFI) of phagocytic microglia sorted from WT and miR-155 cKO mice following apoptotic neuron injection, P = 0.0042 using two-tailed Student’s unpaired t-test (n = 7 WT, n = 14 miR-155 cKO). Data were combined from two independent experiments and normalized to control group (l). Data were presented as mean values ± s.e.m.

Extended Data Fig. 10 Proteomics analysis of whole-brain tissue showed enhanced synaptic functions in APP/PS1:miR-155 cKO mice at 4 and 8 months of age.

a, Heat maps of significantly changed proteins between male APP/PS1:miR-155 cKO and APP/PS1 whole brain tissue at 4 and 8 months of age (one-way ANOVA with Student’s t-test between APP/PS1 and APP/PS1:miR-155 cKO mice, P < 0.05, n = 3-4 male mice per group). See also Supplementary Table 4.

Supplementary information

Reporting Summary

Supplementary Table 1

RNA-seq data for Fig. 1 and related Extended Data figures.

Supplementary Table 2

scRNA-seq data for Fig. 2 and related Extended Data figures.

Supplementary Table 3

RNA-seq data for Fig. 3.

Supplementary Table 4

Proteomics data for Fig. 5.

Supplementary Table 5

Reagents and resources.

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Yin, Z., Herron, S., Silveira, S. et al. Identification of a protective microglial state mediated by miR-155 and interferon-γ signaling in a mouse model of Alzheimer’s disease. Nat Neurosci 26, 1196–1207 (2023). https://doi.org/10.1038/s41593-023-01355-y

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