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[Preprint]. 2023 Jun 1:2023.03.07.531562.
doi: 10.1101/2023.03.07.531562.

Microglial senescence contributes to female-biased neuroinflammation in the aging mouse hippocampus: implications for Alzheimer's disease

Affiliations

Microglial senescence contributes to female-biased neuroinflammation in the aging mouse hippocampus: implications for Alzheimer's disease

Sarah R Ocañas et al. bioRxiv. .

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Abstract

Background: Microglia, the brain's principal immune cells, have been implicated in the pathogenesis of Alzheimer's disease (AD), a condition shown to affect more females than males. Although sex differences in microglial function and transcriptomic programming have been described across development and in disease models of AD, no studies have comprehensively identified the sex divergences that emerge in the aging mouse hippocampus. Further, existing models of AD generally develop pathology (amyloid plaques and tau tangles) early in life and fail to recapitulate the aged brain environment associated with late-onset AD. Here, we examined and compared transcriptomic and translatomic sex effects in young and old murine hippocampal microglia.

Methods: Hippocampal tissue from C57BL6/N and microglial NuTRAP mice of both sexes were collected at young (5-6 month-old [mo]) and old (22-25 mo) ages. Cell sorting and affinity purification techniques were used to isolate the microglial transcriptome and translatome for RNA-sequencing and differential expression analyses. Flow cytometry, qPCR, and imaging approaches were used to confirm the transcriptomic and translatomic findings.

Results: There were marginal sex differences identified in the young hippocampal microglia, with most differentially expressed genes (DEGs) restricted to the sex chromosomes. Both sex chromosomally-and autosomally-encoded sex differences emerged with aging. These sex DEGs identified at old age were primarily female-biased and enriched in senescent and disease-associated microglial signatures. Normalized gene expression values can be accessed through a searchable web interface ( https://neuroepigenomics.omrf.org/ ). Pathway analyses identified upstream regulators induced to a greater extent in females than in males, including inflammatory mediators IFNG, TNF, and IL1B, as well as AD-risk genes TREM2 and APP.

Conclusions: These data suggest that female microglia adopt disease-associated and senescent phenotypes in the aging mouse hippocampus, even in the absence of disease pathology, to a greater extent than males. This sexually divergent microglial phenotype may explain the difference in susceptibility and disease progression in the case of AD pathology. Future studies will need to explore sex differences in microglial heterogeneity in response to AD pathology and determine how sex-specific regulators (i.e., sex chromosomal or hormonal) elicit these sex effects.

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Conflict of interest statement

Competing interests - The authors declare that they have no competing interests.

Figures

Figure 1.
Figure 1.. Isolation of hippocampal microglial transcripts by CD11b-MACS and Cx3cr1-TRAP.
A) Schematic of experimental grouping. Male and female C57BL/6N or Cx3cr1-NuTRAP mice were collected at young (5–6 months) and old (22–25 months) timepoints to form four groups: 1) young female (YF), 2) young male (YM), 3) old female (OF), and 4) old male (OM). B) Isolation of hippocampal microglia by CD11b-MACS. The hippocampus from YF, YM, OF, and OM C57BL6/N mice was dissociated by enzymatic and mechanical dissociation with transcription and translation inhibitors. The single-cell suspension was labeled with CD11b microbeads prior to magnetic separation. The CD11b+ fraction was then analyzed by flow cytometry. C) Representative flow cytometry plots of the CD11b and CD45 immunoreactivity from the CD11b-MACS input and positive (pos.) fractions. D) Quantitation of the percentage of singlets that were CD11b+CD45+ from the CD11b-MACS input and pos. fraction (Two-way ANOVA, main effect MACS fraction [Input v. Pos. Fraction], ***p<0.001). E) Isolation of hippocampal microglial translatome by Cx3cr1-TRAP. Mouse hippocampus was homogenized in TRAP lysis buffer containing translation inhibitors. eGFP-labeled polysomes and associated translating RNA (from Cx3cr1+ cells) were magnetically separated using an eGFP antibody and IgG beads. RNA from the pos. fraction was used to generate stranded RNA-Seq libraries for assessment of the microglial translatome. F-I) Cx3cr1-NuTRAP cortex samples were used to assess the cell specificity of cre-mediated induction of the NuTRAP allele by flow cytometry. F) Representative flow cytometry plot of eGFP+ singlets from Cx3cr1-NuTRAP cortex. G) Quantitation of the percent eGFP+ singlets from young and old Cx3cr1-NuTRAP cortex from both sexes. H) Representative flow cytometry plots of the percent CD11b+CD45+ singlets (left) and eGFP+ singlets (right). I) Quantitation of the percent CD11b+CD45+ singlets (left) and eGFP+ singlets (right).
Figure 2.
Figure 2.. Analysis of sex-specific microglial transcriptomic (CD11b-MACS) and translatomic (Cx3cr1-TRAP) age effects from mouse hippocampus.
Stranded RNA-Seq libraries were constructed from microglial RNA isolated by CD11b-MACS (n=3–5/sex/age) or Cx3cr1-TRAP (n=5–7/sex/age) for groups described in Fig. 1A and sequenced on an Illumina NovaSeq6000 platform. Demultiplexed fastq files were aligned and deduplicated in StrandNGS software prior to quantification (featureCounts) and differential expression calling (edgeR) in R. A) Multidimensional scaling (MDS) plot of CD11b-MACS gene expression shows separation in the first dimension by age. Of note, the young samples do not show separation by sex, whereas the old samples can be separated into male and female groups. B) Sets of differentially expressed genes (GLM QLF-test, FDR<0.1) showing an age effect in females (OF v YF) or males (OM v YM) within the CD11b-MACS dataset were compared by Venn diagram. C) MDS plot of Cx3cr1-TRAP gene expression shows separation in the first dimension by age and sex. D) Sets of differentially expressed genes (GLM QLF-test, FDR<0.1) showing an age effect in females (OF v YF) or males (OM v YM) within the Cx3cr1-TRAP dataset were compared by Venn diagram. Volcano plots of differentially expressed genes with age (GLM QLF-test, FDR<0.1, |FC|>1.25) within CD11b-MACS for (E) females and (F) males, in addition to Cx3cr1-TRAP for (G) females and (H) males.
Figure 3.
Figure 3.. Comparison of hippocampal microglial transcriptomic age effects between CD11b-MACS and Cx3cr1-TRAP methods.
A) Upset plot of differentially expressed genes by age with both sexes and across both methods (GLM QLF-test, FDR<0.1, |FC|>1.25). B) Differentially expressed genes with age that were identified in both males and females across both methods (GLM QLF-test, FDR<0.1, |FC|>1.25). C) Ingenuity pathway analysis identified conserved ‘Upstream Regulators’ across both sexes and methods (|z|>2, p<0.05). D) Ingenuity pathway analysis identified conserved ‘Canonical Pathways’ and ‘Diseases and Bio Functions’ across both sexes and methods (|z|>2, p<0.05). E) Top 5 GO Biological Processes for age effects altered in the same direction across both methods and sexes (Hypergeometric test, BH MTC, FDR<0.05). F) Gene expression (CPM) of antigen processing and presentation genes (B2m, H2-K1, H2-D1) (GLM QLF-test, |FC|>1.25, *FDR<0.1 for OF v YF and OM v YM comparisons). G) Gene expression (log10CPM) of lipid localization genes (Apoe, Lplr) (GLM QLF-test, |FC|>1.25, *FDR<0.1 for OF v YF and OM v YM comparisons).
Figure 4.
Figure 4.. Sex-specific age effects in the microglial transcriptome and translatome.
A) Heatmap of female-specific age effects (OF v. YF) identified as significantly upregulated or downregulated in the CD11b-MACS and Cx3cr1-TRAP analyses (GLM QLF-test, FDR<0.1, |FC|>1.25). B) Heatmap of male-specific age effects (OM v. YM) identified as significantly upregulated or downregulated in the CD11b-MACS and Cx3cr1-TRAP analyses (GLM QLF-test, FDR<0.1, |FC|>1.25). C) Select IPA upstream regulators that were identified as differentially regulated by age in females only across the CD11b-MACS and Cx3cr1-TRAP analyses (FDR<0.1). D) Select IPA upstream regulators that were identified as differentially regulated by age in males only across the CD11b-MACS and Cx3cr1-TRAP analyses (FDR<0.1). E) Expression analysis of genes downstream of IPA upstream regulator DDR1 that is altered with age only in females. F-G) Gene expression (CPM) of (F) Glb1 and (G) Bcl2 which change with age in only females (GLM QLF-test, |FC|>1.25, *FDR<0.1 age effect (O v. Y), ^FDR<0.1, sex effect (F v. M)). Boxplots represent the median +/− IQR. H) Expression analysis of genes downstream of IPA upstream regulator USP18 that is altered with age in males only. I-J) Gene expression (CPM) of (I) Irf7 and (J) Ifit1 which change with age in only males (GLM QLF-test, |FC|>1.25, *FDR<0.1 age effect (O v. Y), ^FDR<0.1, sex effect (F v. M)). Boxplots represent the median +/− IQR.
Figure 5.
Figure 5.. Analysis microglial transcriptomic (CD11b-MACS) and translatomic (Cx3cr1-TRAP) sex effects from young and old mouse hippocampus.
A) Sets of differentially expressed genes (GLM QLF-test, FDR<0.1) showing sex effects in young (YF v YM) or old (OF v OM) within the CD11b-MACS dataset were compared by Venn diagram. B) Sets of differentially expressed genes (GLM QLF-test, FDR<0.1) showing sex effects in young (YF v YM) or old (OF v OM) within the Cx3cr1-TRAP dataset were compared by Venn diagram. C) Upset plot of differentially expressed genes by sex from both age groups and across methods (GLM QLF-test, FDR<0.1, |FC|>1.25). D-E) Heatmap of the gene expression of seven DEGs by sex that were identified in young and old mice with both D) CD11b-MACS and E) Cx3cr1-TRAP methodologies. F) Gene Expression (CPM) of Kdm5c and Ddx3x from CD11b-MACS and Cx3cr1-TRAP analyses (GLM QLF-test, |FC|>1.25, *FDR<0.1). G-I) Volcano plots of differentially expressed genes with sex (GLM QLF-test, FDR<0.1, |FC|>1.25) within CD11b-MACS for (G) young and (H) old mice. I) Inset of Figure 5H volcano plot showing differentially expressed genes between old females and old males within the CD11b-MACS analysis. J-L) Volcano plots of differentially expressed genes with sex (GLM QLF-test, FDR<0.1, |FC|>1.25) within Cx3cr1-TRAP for (J) young and (K) old mice. L) Inset of Figure 5K volcano plot showing differentially expressed genes between old females and old males within the CD11b-MACS analysis.
Figure 6.
Figure 6.. Sex differences in the transcriptome (CD11b-MACS) and translatome (Cx3cr1-TRAP) in old microglia.
A) Heatmap of the logFC(OF/OM) from the 23 sex DEGs identified in the old age groups across both the transcriptome (CD11b-MACS) and translatome (Cx3cr1-TRAP). B) Flow cytometry confirmations of the percentage of CD11c (Itgax) (Two-way ANOVA, Sidak’s MTC, *p<0.05, **p<0.01, ***p<0.001). Boxplots represent the median +/− IQR. C-D) Representative confocal fluorescent microscopy images of sagittal brain sections captured in the hippocampus of old (22 mo) C) female and D) male mice show CD11c (pseudo-color green signal) in cells that co-expressed CD11b (red signal). E-F) Flow cytometry confirmations of the percentage of E) CD22 (Cd22), and F) CD282 (Tlr2) positive cells among the eGFP+ cells from Cx3cr1-NuTRAP hippocampus (Two-way ANOVA, Sidak’s MTC, *p<0.05, **p<0.01, ***p<0.001). Boxplots represent the median +/− IQR. G) List of genes with female-biased expression in old microglia that overlap with DAMa (21), ARMb (59), MGnDc (60), and LDAMd (42) markers. H) GSEA normalized enrichment scores comparing sex-biased enrichment of microglial phenotypic markers of DAMa,b,c (21, 59, 60), IRMe (59), LDAMd (42), or homeostatic states in young (YF v YM) or old (OF v OM) groups. I-J) GSEA enrichment plots for I) DAMa,b,c (21, 42, 59) and J) homeostatic marker genes.
Figure 7.
Figure 7.. Pathway analysis of sex effects in the hippocampal microglial transcriptome and translatome.
A) Heatmap of z-scores for IPA canonical pathways, diseases and bio functions, and upstream regulators analyses. B) IPA upstream regulator comparison to assess sex effects in old hippocampal microglia. C) Network topology-based analysis (PPI BIOGRID) of the 19 genes that were more highly expressed in old female microglia compared to old males (Figure 6A) in both the transcriptome and translatome. D-E) Cdkn2a transcript analysis for p19ARF and p16INK4A in the D) transcriptome and E) translatome of hippocampal microglia. F) Heatmap of SASP gene expression (CPM). G) IPA upstream regulator analysis to identify drug regulators of sex differences.

References

    1. Klein SL, Flanagan KL. Sex differences in immune responses. Nature Reviews Immunology. 2016;16(10):626–38. - PubMed
    1. Franceschi C, Campisi J. Chronic Inflammation (Inflammaging) and Its Potential Contribution to Age-Associated Diseases. The Journals of Gerontology: Series A. 2014;69(Suppl_1):S4–S9. - PubMed
    1. Hammond TR, Dufort C, Dissing-Olesen L, Giera S, Young A, Wysoker A, et al. Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity. 2019;50(1):253–71.e6. - PMC - PubMed
    1. Mangold CA, Masser DR, Stanford DR, Bixler GV, Pisupati A, Giles CB, et al. CNS-wide Sexually Dimorphic Induction of the Major Histocompatibility Complex 1 Pathway With Aging. J Gerontol A Biol Sci Med Sci. 2017;72(1):16–29. - PMC - PubMed
    1. Mangold CA, Wronowski B, Du M, Masser DR, Hadad N, Bixler GV, et al. Sexually divergent induction of microglial-associated neuroinflammation with hippocampal aging. J Neuroinflammation. 2017;14(1):141. - PMC - PubMed

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