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TREM1 disrupts myeloid bioenergetics and cognitive function in aging and Alzheimer disease mouse models

Nature Neuroscience (2024)Cite this article

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Abstract

Human genetics implicate defective myeloid responses in the development of late-onset Alzheimer disease. A decline in peripheral and brain myeloid metabolism, triggering maladaptive immune responses, is a feature of aging. The role of TREM1, a pro-inflammatory factor, in neurodegenerative diseases is unclear. Here we show that Trem1 deficiency prevents age-dependent changes in myeloid metabolism, inflammation and hippocampal memory function in mice. Trem1 deficiency rescues age-associated declines in ribose 5-phosphate. In vitro, Trem1-deficient microglia are resistant to amyloid-β42 oligomer-induced bioenergetic changes, suggesting that amyloid-β42 oligomer stimulation disrupts homeostatic microglial metabolism and immune function via TREM1. In the 5XFAD mouse model, Trem1 haploinsufficiency prevents spatial memory loss, preserves homeostatic microglial morphology, and reduces neuritic dystrophy and changes in the disease-associated microglial transcriptomic signature. In aging APPSwe mice, Trem1 deficiency prevents hippocampal memory decline while restoring synaptic mitochondrial function and cerebral glucose uptake. In postmortem Alzheimer disease brain, TREM1 colocalizes with Iba1+ cells around amyloid plaques and its expression is associated with Alzheimer disease clinical and neuropathological severity. Our results suggest that TREM1 promotes cognitive decline in aging and in the context of amyloid pathology.

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Fig. 1: TREM1 deficiency prevents age-associated inflammation and memory decline.
Fig. 2: TREM1 is activated in peripheral MΦ in aging.
Fig. 3: TREM1 suppresses PPP generation of ribose-5P and purine/pyrimidine synthesis in aged MΦ.
Fig. 4: TREM1 deficiency preserves hippocampal function in 5XFAD mice.
Fig. 5: TREM1 deficiency preserves hippocampal spatial memory and brain glucose uptake in APPSwe mice.
Fig. 6: Myeloid TREM1 expression positively associates with increasing AD pathology.

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Data availability

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE229327 for mouse macrophage transcript data and GSE229620 for mouse microglia transcript data. Source data are provided with this paper.

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Acknowledgements

This work was supported by grant no. RF1AG053001K (K.I.A.), grant no. RO1NS100180 (K.I.A.), grant no. RF1AG070131 (K.I.A.), grant no. RF1AG070839 (K.I.A.), grant no. P30 AG0066515 (K.I.A.), American Heart Foundation grant no. 19PABH1345800 (K.I.A.), the Phil & Penny Knight Initiative for Brain Resilience at the Wu Tsai Neurosciences Institute, Stanford University (K.I.A.), The Archer Foundation (K.I.A.), Stanford School of Medicine Dean’s Postdoctoral Fellowship (E.N.W. and E.B.), HHMI Hanna H. Gray Fellows Program (M.R.M.), Burroughs Wellcome Fund PDEP (M.R.M.), an Alzheimer’s Association Research Fellowship (K.A.Z.), the Paul and Daisy Soros Fellowship for New Americans (P.S.M.), the Gerald J. Lieberman Fellowship (P.S.M.) and Marie Skłodowska-Curie Grant no. 888494 (E.B.). K.I.A. is a Chan Zuckerberg–San Francisco Biohub Investigator. Data and tissue were obtained from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) (https://www.bannerhealth.com/services/research/about-banner-research/research-programs/brain-and-body-donation-program) supported by grant no. U24NS072026, grant no. P30AG19610, the Arizona Department of Health Services, the Arizona Biomedical Research Commission and the Michael J. Fox Foundation for Parkinson’s Research. We thank the Stanford Cyclotron & Radiochemistry Facility, the Stanford SCi3 Small-Animal Imaging Facility, the Stanford Shared FACS Facility, Stanford Human Immune Monitoring Core and the Stanford Neuroscience Microscopy Service.

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Authors and Affiliations

  1. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Edward N. Wilson, Congcong Wang, Michelle S. Swarovski, Kristy A. Zera, Hannah E. Ennerfelt, Qian Wang, Aisling Chaney, Esha Gauba, Javier A. Ramos Benitez, Yann Le Guen, Paras S. Minhas, Maharshi Panchal, Yuting J. Tan, Eran Blacher, Chinyere A. Iweka, Haley Cropper, Poorva Jain, Qingkun Liu, Swapnil S. Mehta, Abigail J. Zuckerman, Matthew Xin, Jacob Umans, Jolie Huang, Aarooran S. Durairaj, Michael D. Greicius, Michelle L. James, Marion S. Buckwalter & Katrin I. Andreasson

  2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Edward N. Wilson, Michael D. Greicius, Michelle L. James, Marion S. Buckwalter & Katrin I. Andreasson

  3. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Aisling Chaney, Haley Cropper, Poorva Jain & Michelle L. James

  4. Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA

    Geidy E. Serrano & Thomas G. Beach

  5. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA

    Marion S. Buckwalter

  6. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Melanie R. McReynolds & Joshua D. Rabinowitz

  7. Department of Chemistry, Princeton University, Princeton, NJ, USA

    Melanie R. McReynolds & Joshua D. Rabinowitz

  8. Department of Biochemistry and Molecular Biology, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA

    Melanie R. McReynolds

  9. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Katrin I. Andreasson

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  1. Edward N. Wilson
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Contributions

E.N.W. and K.I.A. conceived and planned this study. E.N.W., T.G.B., M.D.G., M.L.J., M.S.B., J.D.R. and K.I.A. contributed to supervising experimental design. E.N.W., C.W., H.E.E., M.S.S., M.R.M., Q.W., K.A.Z., A.C., J.A.R.B., E.G., P.S.M., E.B., Y.J.T., C.A.I., Y.L.G., M.P., H.C., P.J., Q.L., S.S.M., A.J.Z., M.X., J.U., J.H., A.S.D. and G.E.S. conducted the experiments. E.N.W., C.W., H.E.E., M.S.S., A.C., J.A.R.B., E.G., P.S.M., Y.L.G., A.J.Z., M.X., J.U. and J.H. performed collection of the data and statistical analysis. E.N.W. and K.I.A. wrote the manuscript. All authors reviewed and approved of the manuscript.

Corresponding author

Correspondence to Katrin I. Andreasson.

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Competing interests

M.L.J. and K.I.A. are co-founders and Scientific Advisory Board members of Willow Neuroscience, Inc. The other authors declare no competing interests.

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Nature Neuroscience thanks Michael Heneka, Inhee Mook-Jung 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 Flow cytometry gating for quantification of TREM1 expression in blood, spleen and brain myeloid cells.

a Gating strategy for live CD45 + Cd11b + TREM1+ blood myeloid cells. b Gating strategy for live CD45 + Cd11b + TREM1+ spleen myeloid cells. c Gating strategy for live CD45loCd11b + TREM1+ microglia; also includes gating for P2RY12, CD206, CX3CR1 and Tmem119 microglial markers.

Extended Data Fig. 2 TREM1 amplifies levels of inflammatory factors but does not alter phagocytosis.

a Quantification of immune factors in lysates of peritoneal MΦ isolated from 8.5 mo WT and Trem1-/- mice treated with vehicle or 100 ng/ml LPS for 20 hr. Two-way ANOVA with Tukey’s multiple comparisons shown for WT + LPS vs Trem1-/-+ LPS (n = 5–6 male mice per genotype as shown). b Trem1 deficiency does not alter E.coli phagocytosis in peritoneal MΦ from 2–3 mo male mice. Cells were stimulated with vehicle or increasing concentrations of LPS for 20 h. 2-way ANOVA, effect of LPS ***P < 0.001, no effect of Trem1 genotype basally or with LPS stimulation on 6–11 technical replicate wells as shown.

Source data

Extended Data Fig. 3 TREM1 promotes a pro-inflammatory polarization state in aged peritoneal MΦ.

a Gating strategy for live Cd11b+Ly6G−CD45+Ly6CHi and Cd11b+Ly6G−CD45+Ly6CLo peritoneal MΦ from young WT and Trem1-/- (2 mo) and aged WT and Trem1-/- (22–23 mo) male mice. b Representative gating for MHC II, CD86, and CD71 positive Ly6CHi peritoneal MΦ. c Representative gating for MHC II, CD86, and CD71 positive Ly6CLo peritoneal MΦ. d Multianalyte Luminex quantification of immune factors in young WT (2 mo) and aged WT (23–24 mo) and aged Trem1-/- (23–24 mo) cerebral cortex. One-way ANOVA with Tukey’s multiple comparisons shown (n = 5–6 male mice per group as shown). e Total time exploring (in seconds) during the training phase of the NOR task; ANOVA with Tukey’s multiple comparisons show no significant differences (n = 6–8 female mice as shown). f Speed (pixels/second) of mice during the final trial of the Barnes Maze; ANOVA with Tukey’s multiple comparisons (n = 5–6 male and female mice as shown).

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Extended Data Fig. 4 Trem1 deficiency alters gene expression in aged peripheral MΦ with minimal changes in aged brain microglia.

a Venn diagram of DEGs (all genes q < 0.05) in pairwise comparisons of microglia isolated from aged (18 - 20 mo) vs young (3 mo) mice and pairwise comparisons of microglia isolated from aged Trem1-/- vs aged WT mice (18–20 mo). Blue indicates number of downregulated genes while red indicates the number of upregulated genes (n = 3 microglial samples (2 pooled male mice per sample) per group). b KEGG Pathway enrichment analysis of upregulated genes in aged compared to young microglia; there were no enriched pathways in the comparison of aged WT vs aged Trem1-/- microglia. c Venn diagrams showing the number of DEGs (all genes q < 0.05) in pairwise comparisons of primary mouse MΦ from young WT (2 mo), aged WT (25 mo) and aged Trem1-/- (25 mo) mice. Blue indicates number of downregulated genes while red indicates the number of upregulated genes (n = 3–4 male mice per group). d Pathway enrichment analysis of upregulated genes in aged MΦ vs young and in aged Trem1-/- vs aged WT MΦ. e Heatmap of DEGs (q < 0.05) comprising the Coordinated Lysosomal Expression and Regulation (CLEAR) network. Lysosomal functional categories are indicated for each gene. Aged Trem1-/- mice cluster with the young WT mice. Scale represents z-score values from FPKM. n = 3–4 male mice per group.

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Extended Data Fig. 5 TREM1 deficiency restores youthful metabolism in aged macrophages.

a PCA of metabolites from young (2 mo) WT and Trem1-/- peritoneal MΦ (n = 3 male mice per group). b PCA of significantly regulated metabolites from primary peritoneal MΦ isolated from young WT (2 mo), aged WT (25 mo) and aged Trem1-/- (25 mo) male mice. n = 3–5 male mice per group. c Volcano plot comparing aged WT vs young WT (left) and aged Trem1-/- vs aged WT (right) peritoneal MΦ with Log2 fold change (FC) and -Log10(P) values of metabolites. Significantly regulated metabolites with -log10(P) > 2 and log2 fold change > 1 are shown in blue. d KEGG metabolic pathway enrichment analysis of differentially regulated metabolites between aged WT and young WT macrophages and between aged Trem1-/- vs aged WT MΦ. X-axis shows -Log10(q) value. No pathways were significant following FDR correction for the comparison of aged Trem1-/- vs young WT. Pathway analysis was performed using MetaboAnalyst 5.0; n = 3–5 male mice per group. e Total ion count of Ribose-5P, the precursor of purines and pyrimidines and total ion counts for nucleobases and derivatives, ribo-nucleosides, deoxynucleosides, and nucleoside monophosphates. Data are analyzed by one-way ANOVA with Tukey’s multiple comparisons (n = 3–5 male mice per group). Abbreviations: AMP: adenosine monophosphate, GMP: guanosine monophosphate, UMP: uridine monophosphate, CMP cytidine monophosphate, IMP: inosine monophosphate. n = 3–5 male mice per group as shown. f Transcription factor (TF) enrichment analysis revealing TFs enriched for differentially expressed metabolite enzymes. g FKPM of NRF2. ANOVA followed by Tukey’s multiple comparison (n = 3–4 male mice per group as shown). h FKPM of the 3 pyruvate dehydrogenase complex (PDH) subunits: pyruvate dehydrogenase (PDHA1), dihydrolipoyl transacetylase (DLAT) and dihydrolipoyl dehydrogenase (DLD); ANOVA followed by Tukey’s multiple comparison (n = 3–4 male mice per group as shown).

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Extended Data Fig. 6 TREM1 expression does not significantly change in context of accumulating amyloid.

a (Left) Immunofluorescent staining of TREM1 in 9 mo WT and APPSwe-PS1∆E9 hippocampal stratum lacunosum in IBA1+microglia. 5–7 hippocampal sections per mouse were imaged, n = 5 male mice/genotype. Scale bar = 10 µm. TREM1 colocalizes with Iba1+ cells (white arrows). (Right) Quantification of MFI. b Thioflavin-S (ThioS) fluorescent staining of hippocampus in 9–10 mo 5XFAD and 5XFAD;Trem1+/- female mice. Scale bar = 500 µm. c Soluble levels of cerebral cortical Aß40 and 42 and the ratio of Aß42/40 in 5X FAD and 5XFAD;Trem1 + /- mice. Student’s two tailed t-test (n = 4–6 males/group as shown, 10–13 mo). d Insoluble levels of cerebral cortical Aß40 and 42 and the ratio of Aß42/40 in 5X FAD and 5XFAD;Trem1 + /- mice; Student’s two tailed t-test (n = 4–6 males/group as shown, 10–13 mo). e Quantification of hippocampal immune factors in WT, 5XFAD, and 5XFAD;Trem1+/- 10 mo mice. One-way ANOVA with Tukey post-hoc comparisons (n = 5–6 female mice per group as shown). f Microglial number from Fig. 4i. One-way ANOVA with Tukey’s multiple comparisons (n = 5 male mice per condition).

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Extended Data Fig. 7 Disease-associated microglial (DAM) signature in 5XFAD;Trem1+/- mice.

a PCA of significantly regulated DAM signature genes from primary microglia isolated from WT, 5XFAD and 5XFAD/Trem1+/- mice. Individual microglial samples were pooled from 2 male mice (n = 3 samples per condition). b Hierarchical clustering of DAM signature gene set showing homeostatic, DAM Stage 1 and DAM Stage 2 genes. Scale represents z-score values from FPKM. c Hierarchical clustering of top 170 DEGs (FDR-corrected) belonging to the full DAM signature gene set. Scale represents z-score values from FPKM. Scale represents z-score values from FPKM. d (Left) Percent of CX3CR1+ microglia in young (3 mo) and 13–17 mo WT, 5xFAD and 5xFAD;Trem1+/- mice. (Right) MFI of CX3CR1+ microglia. One-way ANOVA with Tukey’s multiple comparison, not significant (n = 5–8 male and female mice per group as shown). e (Left) Percent of Tmem119+ microglia in young (3 mo) and 13–17 mo WT, 5xFAD and 5xFAD;Trem1+/- mice. (Right) MFI of Tmem119+ microglia. One-way ANOVA with Tukey’s multiple comparisons (n = 4-5 mice per group). f (Left) Total time exploring (in seconds) during the training phase of the NOR task in APPSwe mice; ANOVA with Tukey’s post-hoc test (n = 14-17 male and female mice per condition as shown). g Motor speed (pixels/sec) during the final training session of the Barnes maze; ANOVA with Tukey’s post-hoc test (n = 6 male and female mice per group). h Distance traveled during the final training session of the Barnes maze; ANOVA with Tukey’s post-hoc test (n = 6 male and female mice per group). i Coupling assay tracings of synaptic mitochondria oxygen consumption rates (OCR). Shown over time are the rates of basal Complex II respiration, State III (ADP stimulated respiration), State IV (oligomycin) and State IIIu (State III uncoupled, FCCP) that were consecutively measured over the course of the assay (n = 5-9 male mice per condition). j Diagram of model of action of TREM1 in aging and transgenic mice with amyloid accumulation. In aging, peripheral TREM1 activity contributes to declines in myeloid metabolism and immune functions that lead to age-dependent cognitive decline. In models of amyloid accumulation, both peripheral and microglial TREM1 contribute to cognitive deficits associated with local accumulation of amyloid.

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Extended Data Fig. 8 TREM1 expression in development of AD.

a Immunofluorescent staining of human frontal cortex without addition of TREM1 antibody and secondary only control (red), Iba1 (green), and X-34 (blue) signal. This antibody validation experiment was performed three times. Scale bar = 20 μm. b Immunoblot of TREM1 protein in postmortem human mid frontal gyrus. Clinicopathological diagnoses: non-demented Braak I-II, demented non-AD Braak stages I-II, AD Braak III-IV and AD Braak V-VI. Primary antibody detected human TREM1 band at the molecular weight of positive control (human liver lysates, Cat# HT-314, Zyagen, San Diego, CA). The TREM1 band used for analysis are indicated by blue box. Also shown is the band for ß-actin at 42 kDa. This antibody validation experiment was performed once. c Immunoblot of TREM2 protein in postmortem human mid frontal gyrus. Primary antibody detects human TREM2 band at the molecular weight of positive control (human liver lysates, Cat# HT-314, Zyagen, San Diego, CA). TREM2 bands used for analysis are indicated by blue box. Also shown is the band for ß-actin at 42 kDa. This antibody validation experiment was performed once. d Mendelian Randomization (MR) analyses with Alzheimer’s disease (AD) risk level as exposure and sTREM1 and sTREM2 plasma protein level as outcomes. Blue lines are estimated MR-median weight effects, and the dashed lines indicate the 95% confidence interval for the MR effects.

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Extended Data Table 1 List of Metabolites Detected by LC-MS
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Extended Data Table 2 Demographic Characteristics of Study Participants
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Wilson, E.N., Wang, C., Swarovski, M.S. et al. TREM1 disrupts myeloid bioenergetics and cognitive function in aging and Alzheimer disease mouse models. Nat Neurosci (2024). https://doi.org/10.1038/s41593-024-01610-w

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