A glial circadian gene expression atlas reveals cell type and disease-specific reprogramming in response to amyloid pathology or aging

Abstract

While circadian rhythm disruption may promote neurodegenerative disease, how aging and neurodegenerative pathology impact circadian gene expression patterns in different brain cell types is unknown. Here, we used translating ribosome affinity purification methods to define the circadian translatomes of astrocytes, microglia, and bulk cerebral cortex, in healthy mouse brain and in the settings of amyloid-beta plaque pathology or aging. Our data reveal that glial circadian translatomes are highly cell type-specific and exhibit profound, context-dependent reprogramming of rhythmic transcripts in response to amyloid pathology or aging. Transcripts involved in glial activation, immunometabolism, and proteostasis, as well as nearly half of all Alzheimer Disease (AD)-associated risk genes, displayed circadian oscillations, many of which were altered by pathology. Amyloid-related differential gene expression was also dependent on time of day. Thus, circadian rhythms in gene expression are cell- and context dependent and provide important insights into glial gene regulation in health, AD, and aging.

Keywords: Alzheimer’s Disease; aging; astrocyte; circadian; glia; microglia; transcriptome.

Fig. 1:. Analysis of circadian rhythms in astrocytes and microglia using Translating Ribosome Affinity Purification (TRAP)/RiboTag.

A. Schematic showing the steps in TRAP/RiboTag. B. Listing of the mouse lines used and schematic of the lighting paradigm and mouse harvest schedule. C,D. Fold enrichment of cell type specific gene expression (TRAP/Pre-IP) from AstroTRAP mice (C) and mgRiboTag mice (D). E. Representative image of amyloid plaque burden in a 6mo APP/PS1–21 mouse, as assessed by anti-Aβ antibody HJ3.4b. F,G Fold enrichment of cell type specific gene expression (TRAP/Pre-IP) from AstroTRAP-APP mice (F) and mgRiboTag-APP mice (G).

Fig. 2:. Circadian transcriptional rhythms and reprogramming of bulk cortex transcripts in WT and APP/PS1 mice.

A. Heatmaps showing transcripts that were rhythmic in bulk cortex tissue from WT mice (upper) and rhythmic in APP/PS1 mice (lower). In both rows of heatmaps, the genes plotted are in the same order to compare differences in rhythmic expression between mice. B-D. KEGG pathway analysis of cortex transcripts identified as rhythmic (by RAIN analysis) in (B) both WT and APP/PS1 mice, (C) WT mice, or (D) APP/PS1 mice. E. Heatmap showing temporally-coordinated expression of KEGG lysosome pathway genes in bulk cortex from WT mice. F. Radar plot showing acrophase distributions of rhythmic bulk cortex transcripts from WT (grey) or APP/PS1 (orange) mice. G. Pie chart depicting the number of transcripts that gained or lost rhythms in different datasets, based on compareRhythms analysis of rhythmic transcripts in bulk tissue. A total of 2139 were identified as rhythmic by RAIN analysis across all datasets. H-K. Graphs showing circadian expression patterns of transcripts from bulk cortex from WT (grey) or APP/PS1 (orange) mice. H. Core clock genes Per2 and Arntl (Bmal1) remained rhythmic. I. Senescence marker Cdkn1a and glutathione transferase Gstt2 lost rhythmicity in APP/PS1. J. Inflammatory transcripts Nfkbia and Ccl4 gained rhythmicity in APP/PS1. K. Endosomal gene Rab13 changed phase in APP/PS1. Adjusted P values from RAIN are shown. Each datapoint is the average of two mice, each from separate experiments.

Fig. 3:. Astrocyte circadian translatomes and reprogramming in WT and APP/PS1 mice.

A. Heatmaps showing transcripts that were rhythmic in WT astrocytes (upper) and rhythmic in astrocytes from APP/PS1 mice (lower). In both rows of heatmaps, the genes plotted are in the same order to compare differences in rhythmic expression between mice. B-D. KEGG pathway analysis of astrocyte transcripts identified as rhythmic (by RAIN analysis) in (B) both WT and APP/PS1 mice, (C) WT mice, or (D) APP/PS1 mice. E. Heatmap showing temporally-coordinated expression of KEGG insulin signaling pathway genes in astrocytes from WT mice. F. Radar plot showing acrophase distributions of rhythmic astrocyte transcripts from WT (green) or APP/PS1 (purple) mice. G. Pie chart depicting the number of transcripts that gained or lost rhythms in different datasets, based on compareRhythms analysis of rhythmic transcripts in microglia. Total of 2323 were identified as rhythmic by RAIN analysis across all datasets. H-K. Graphs showing circadian expression patterns of transcripts from microglia from WT (green) or APP/PS1 (purple) mice. H. Core clock genes Per2 and Arntl (Bmal1) remained rhythmic. I. Cholesterol response gene Srebf1 lost rhythmicity in APP/PS1. J & K. AD-related transcripts Clu, Picalm, and Chi3l1, as well as chemokine Ccl3 (K), gained rhythmicity in APP/PS1. Adjusted P values from RAIN are shown. Each datapoint is the average of two mice, each from separate experiments.

Fig. 4:. Microglial circadian translatomes and reprogramming in WT and APP/PS1 mice.

A. Heatmaps showing transcripts that were rhythmic in WT microglia (upper) and rhythmic in microglia from APP/PS1 mice (lower). In both rows of heatmaps, the genes plotted are in the same order to compare differences in rhythmic expression between mice. B-D. KEGG pathway analysis of microglial transcripts identified as rhythmic (by RAIN analysis) in (B) both WT and APP/PS1 mice, (C) WT mice, or (D) APP/PS1 mice. E. Heatmap showing temporally-coordinated expression of KEGG proteasome pathways genes in microglia from WT mice. F. Radar plot showing acrophase distributions of rhythmic microglial transcripts from WT (pink) or APP/PS1 (blue) mice. G. Pie chart depicting the number of transcripts that gained or lost rhythms in different datasets, based on compareRhythms analysis of rhythmic transcripts in microglia. Total of 6399 were identified as rhythmic by RAIN analysis across all datasets. H-K. Graphs showing circadian expression patterns of transcripts from microglia from WT (blue) or APP/PS1 (red) mice. H. Core clock genes Per2 and Arntl (Bmal1) remained rhythmic. I. Iba1-encoding gene Aif1 lost rhythmicity in APP/PS1. J. Microglial homeostasis markers Tmem119, P2ry12, and Csfr1 gained rhythmicity in APP/PS1. K. Phagocytosis receptor Mertk remained rhythmic. Adjusted P values from RAIN are shown. Each datapoint is the average of two mice, each from separate experiments.

Fig. 5.. Aging induces rhythmic expression of autophagy and mTOR pathways in astrocytes.

A. Schematic of mouse lines and lighting schedule for aging experiment. B. Heatmaps showing transcripts that were rhythmic in astrocytes from young WT mice (upper) and rhythmic in aged mice (lower). In both rows of heatmaps, the genes plotted are in the same order to compare differences in rhythmic expression between mice. C. KEGG pathway analysis of astrocyte transcripts identified as rhythmic (by RAIN analysis) in aged mice. D. Heatmaps showing temporally-coordinated expression of KEGG endocytic pathway genes only in astrocytes from aged mice. E-G. Graphs showing circadian expression patterns of astrocyte transcripts from young WT (green), APP/PS1 (purple), or aged (brown) mice. E. Core clock genes Per2 and Arntl (Bmal1) remain rhythmic but are induced in aged astrocytes. F,G. Autophagy genes Atg10, Pdpk1, and Ulk1 (F), as well as Mtor (G) are induced and gain rhythmicity in aged astrocytes. Adjusted P values from RAIN are shown. Each datapoint is the average of two mice, each from separate experiments.

Fig. 6.. Aging suppresses microglial core clock oscillation and circadian gene expression.

A. Heatmaps showing transcripts that were rhythmic in microglia from young WT mice (upper) and rhythmic in aged mice (lower). In both rows of heatmaps, the genes plotted are in the same order to compare differences in rhythmic expression between mice. B. KEGG pathway analysis of microglial transcripts identified as rhythmic (by RAIN analysis) in aged mice. C. Heatmaps showing temporally-coordinated expression of KEGG metabolic pathway genes in microglia from young WT, APP/PS1, and aged mice. D-F. Graphs showing circadian expression patterns of transcripts from microglia from WT (blue) or APP/PS1 (red) mice. D. Core clock genes Per2, Arntl (Bmal1), Nr1d1, and Ciart remained rhythmic but were blunted and suppressed in aged microglia. E. Endosomal trafficing gene Rab5c and proteasome subunit Psmd11 are induced and gain rhythmicity in aged microglia. F. The lipoprotein receptor Ldlr and transcription factor Mafg lose rhythmicity in aged microglia. Adjusted P values from RAIN are shown. Each datapoint is the average of two mice, each from separate experiments.

Fig. 7:. Time-of-day of tissue harvest influences differential gene expression in APP/PS1 mice.

A & B. Volcano plots showing differential gene expression in microglia between WT and APP/PS1 mice harvested during the morning (AM) hours (A) or evening (PM) hours (B). 506 DEGs were identified in AM, 627 in PM. C. Comparison of fold change of induction of DEGs in microglia in WT vs. APP/PS1 mice harvested in AM or PM. Brown circles indicate more expression in PM; pink circles indicate more expression in AM. D & E. Genes plotted from microglia in the AM time bin (D) or PM time bin (E) including myeloid transcription factor Spi1, interferon-inducible protein Aim2, and Cd209a, known to regulate phagocytic activity. Volcano plots showing differential gene expression in astrocytes between WT and APP/PS1 mice harvested during the morning (AM) hours (F) or evening (PM) hours (G). 438 DEGs were identified in both AM and PM, though not the same 438. H. Comparison of fold change of induction of DEGs in astrocytes in WT vs. APP/PS1 mice harvested in AM or PM. Dark purple circles indicate more expression in PM; light purple circles indicate more expression in AM. I & J. Genes plotted from astrocytes in the AM time bin (I) or PM time bin (J) including, WNT pathway gene Wnt7a, ciliary neurotrophic factor Cntf, and AD GWAS gene, Clu (Clusterin). In C and H, only genes with fold change <3 were examined-all genes with large fold changes (>3 fold) were identified as DEGs in both AM and PM datasets. Graphs in (D,E,I and J) show mean ± SEM. **p < 0.01, ***p < 0.001 by unpaired two-tailed t test