Abstract
Sex is a key modifier of neurological disease outcomes. Microglia are implicated in neurological diseases and modulated by microRNAs, but it is unknown whether microglial microRNAs have sex-specific influences on disease. We show in mice that microglial microRNA expression differs in males and females and that loss of microRNAs leads to sex-specific changes in the microglial transcriptome and tau pathology. These findings suggest that microglial microRNAs influence tau pathogenesis in a sex-specific manner.
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Data availability
The bulk RNA-seq and miRNA-seq data that support the findings of this study have been deposited with the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo/). Bulk RNA-seq data have been deposited under accession number GSE122663 and scRNA-seq data have been deposited under accession number GSE135330.
References
Ferretti, M. T. et al. Sex differences in Alzheimer disease: the gateway to precision medicine. Nat. Rev. Neurol. 14, 457–469 (2018).
Mazure, C. M. & Swendsen, J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 15, 451–452 (2016).
Salter, M. W. & Stevens, B. Microglia emerge as central players in brain disease. Nat. Med. 23, 1018–1027 (2017).
Guerreiro, R. et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 368, 117–127 (2013).
Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).
Villa, A. et al. Sex-specific features of microglia from adult mice. Cell Rep. 23, 3501–3511 (2018).
Guneykaya, D. et al. Transcriptional and translational differences of microglia from male and female brains. Cell Rep. 24, 2773–2783.e6 (2018).
Mangold, C. A. et al. Sexually divergent induction of microglial-associated neuroinflammation with hippocampal aging. J. Neuroinflammation 14, 141 (2017).
Kang, S. S. et al. Microglial translational profiling reveals a convergent APOE pathway from aging, amyloid, and tau. J. Exp. Med. 215, 2235–2245 (2018).
Ponomarev, E. D., Veremeyko, T., Barteneva, N., Krichevsky, A. M. & Weiner, H. L. MicroRNA-124 promotes microglia quiescence and suppresses EAE by deactivating macrophages via the C/EBP-α–PU.1 pathway. Nat. Med. 17, 64–70 (2011).
Varol, D. et al. Dicer deficiency differentially impacts microglia of the developing and adult brain. Immunity 46, 1030–1044.e8 (2017).
Sharma, S. & Eghbali, M. Influence of sex differences on microRNA gene regulation in disease. Biol. Sex Differ. 5, 3 (2014).
Asai, H. et al. Depletion of microglia and inhibition of exosome synthesis halt tau propagation. Nat. Neurosci. 18, 1584–1593 (2015).
Yoshiyama, Y. et al. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron 53, 337–351 (2007).
Jicha, G. A., Bowser, R., Kazam, I. G. & Davies, P. Alz-50 and MC-1, a new monoclonal antibody raised to paired helical filaments, recognize conformational epitopes on recombinant tau. J. Neurosci. Res. 48, 128–132 (1997).
Hébert, S. S. et al. Genetic ablation of Dicer in adult forebrain neurons results in abnormal tau hyperphosphorylation and neurodegeneration. Hum. Mol. Genet. 19, 3959–3969 (2010).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).
Bennett, F. C. et al. A combination of ontogeny and CNS environment establishes microglial identity. Neuron 98, 1170–1183.e8 (2018).
Jordão, M. J C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Phansalkar, N., More, S., Sabale, A. & Joshi, M. Adaptive local thresholding for detection of nuclei in diversity stained cytology images. In Proc. International Conference on Communications and Signal Processing 218–220 (IEEE, 2011).
Minami, S. S. et al. Progranulin protects against amyloid β deposition and toxicity in Alzheimer’s disease mouse models. Nat. Med. 20, 1157–1164 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896 (2014).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Krämer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).
Subramanian, A. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).
Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223.e10 (2019).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).
Anders, S., Pyl, P. T. & Huber, W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
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).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Acknowledgements
We thank the Gladstone Histology and Light Microscopy Core and the Weill Cornell Medicine CLC Microscopy and Image Analysis Core Facility for help with imaging, and the Gladstone Bioinformatics Core for help with the analysis of sequencing. Bulk RNA-seq was carried out at the Center for Advanced Technology, University of California, San Francisco. scRNA-seq was carried out at the Weill Cornell Medicine Genomics and Epigenomics Core Facility. FACS was carried out at the Stanford FACS Facility. We thank S. Ordway and K. Claiborn for editing the manuscript, K. M. Ansel and his laboratory members for comments on the manuscript and discussions on miRNA biology, and Y. Fu for the DicerloxP/loxP mice. This work was supported by National Institutes of Health (NIH) grant nos. 1R01AG054214-01A1, U54NS100717, R01AG051390 and a Tau Consortium grant to L.G.; NIH grant nos. 1F30AG062043-02 and T32GM007618 to L.K.; NIH grant no. F31AG058505 to F.A.S.; NIH grant no. U54NS100717, Dr. Miriam and Sheldon G. Adelson Medical Research Foundation grant no. SB180058, Larry L. Hillblom Foundation grant no. 2018-A-0004-NET, and the Edward N. and Della L. Thome Memorial Foundation grant no. SB180126 to K.S.K.; Alzheimer’s Association AACSF grant no. 17-531484, NIH grant no. R25 R25NS070680 and UCSF Clinical and Translational Science Institute grant no. 5TL1TR0018 to C.D.C.; IRACDA Postdoctoral fellowship grant no. K12GM081266-11 to J.C.U.; and grant no. R01MH110504 to G.Y. Gladstone Institutes received support from the National Center for Research Resources grant no. RR18928.
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L.K. and L.G. conceived and designed the research. L.K., E.G., J.I.E., Y.L., Y.Z., F.A.S., Q.L., L.Zhan and D.L. performed the research. L.Zhan, F.A.S., Q.L., L.Zhou, Z.C., G.Y., J.C.U. and K.S.K. contributed new reagents and analytical tools. L.K., E.G., Q.L., Z.C., G.Y. and L.G. analyzed the data. L.K., C.D.C. and L.G. wrote the paper.
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Extended data
Extended Data Fig. 1 Male and female PS19 mice have similar tau pathology.
(a–c,e,f) Representative images of MC1 immunostaining of 9-month-old PS19 female (a) and male (b) hemibrains. Scale bar, 600 μm. Yellow dashed boxes magnified in (c). Scale bar, 300 μm. (e) Representative image of hippocampus. Scale bar, 600 μm. Yellow dashed boxes magnified in (f). Scale bar, 150 μm. 2 independent experimental cohorts were used. (d,g) MC1 density of entire hemibrain (d) and hippocampus (g) of nontransgenic (–) and transgenic (+) male and female mice. n = 11 nontransgenic females, 5 nontransgenic males, 10 PS19 females, and 9 PS19 males. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range.
Extended Data Fig. 2 Male and female microglia have differential transcriptional responses to tau pathology.
(a,c) Venn diagram of differentially expressed (DE) miRNAs (a) and mRNAs (c) comparing microglia from PS19 vs nontransgenic control (Ctrl) mice. DE genes defined as those with log2FC ≥ 1 or ≤ -1 and FDR < 0.05. Purple numbers, up-regulated DE genes; green numbers, down-regulated DE genes. n = 4 Ctrl samples/sex, 5 PS19 samples/sex, 2 mice/sample (a). n = 5 male and 3 female Ctrl samples, 3 male and 4 female PS19 samples, 2 mice/sample (c). * P = 0.05 (a), * P = 0.0146 (c), two-sided Fisher’s exact test. Full list of DEGs in Supplementary Tables 5–7. (b) Volcano plot of male miRNA-seq data from (a). Purple and green dots represent miRNAs upregulated in PS19 samples (11 miRNAs; P ≤ 0.05 by Benjamini-Hochberg correction and log2FC ≥ 1) and downregulated in PS19 samples (43 miRNAs; P ≤ 0.05 and log2FC ≤ –1), respectively. Grey dots are miRNAs not significantly different. Dots with black circles represent those that were analyzed in (d). Vertical dashed lines indicate log2FC ± 1. Horizontal dashed line indicates -log10(0.05). Wald test was used. (d) Bar graph showing 9 Ingenuity Pathway Analysis predicted target coverage of DE mRNAs from male PS19 vs Ctrl microglia (c) by DE miRNAs from male PS19 vs Ctrl microglia (a). Results were filtered for those with opposing miRNA and mRNA log fold changes (i.e. focusing on miRNA and mRNA targets that have anti-correlated expression patterns) and were either experimentally observed or highly predicted to be miRNA-mRNA target interactions.
Extended Data Fig. 3 RNA sequencing of Dicer KO microglia from PS19 mice.
(a) Volcano plot of RNA-seq data from Dicer KO microglia from 3-month-old male and female PS19 mice. Pink, female-enriched; turquoise, male-enriched; grey, not significantly different. Vertical dashed lines indicate log2FC ± 1. Horizontal dashed line indicates -log10(0.05). n = 4 male samples, 2 female samples, 2 mice/sample. Wald test was used. (b) Schematic of the single-cell isolation method. Brains without the cerebellum were harvested from 9-month-old Dicer KO PS19 female and male mice and homogenized. After myelin depletion, cells were sorted using flow cytometry and gated by CD45+;CD11b+ expression. (c) Representative FACS plots showing gating strategy and the cells sequenced. Similar gating strategy was used for all samples sequenced (n = 2 biologically independent animals/sex). (d) Number of cells, proportion and statistics for FACS plots from (c). (e-g) Quality control criteria for the single-cell sequencing data. Fitted curves for histograms of mapped reads (e), numbers of detected genes (f) and ERCC correlation coefficient (g) are labeled in red. Dashed lines are statistical cutoffs. Cells that passed all three criteria were retained for analysis. (h) Scatter plot highlighting cells that passed QC (red) among all cells sequenced. Each dot is a cell. (i) Summary of the numbers of cells sequenced and cells that passed QC (red). (j) t-SNE plot of microglia clusters from 9-month-old Dicer KO PS19 female and male mice. n = 2 biologically independent animals/sex. (k) Heatmap of top genes defining each microglial cluster. (l) Ridge plots of microglial marker expression levels by each microglial cluster.
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Supplementary Table 2.
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Supplementary Tables 1, 3–11.
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Kodama, L., Guzman, E., Etchegaray, J.I. et al. Microglial microRNAs mediate sex-specific responses to tau pathology. Nat Neurosci 23, 167–171 (2020). https://doi.org/10.1038/s41593-019-0560-7
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DOI: https://doi.org/10.1038/s41593-019-0560-7
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