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Aging disrupts circadian gene regulation and function in macrophages

Nature Immunology volume 23, pages 229–236 (2022)Cite this article

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Abstract

Aging is characterized by an increased vulnerability to infection and the development of inflammatory diseases, such as atherosclerosis, frailty, cancer and neurodegeneration. Here, we find that aging is associated with the loss of diurnally rhythmic innate immune responses, including monocyte trafficking from bone marrow to blood, response to lipopolysaccharide and phagocytosis. This decline in homeostatic immune responses was associated with a striking disappearance of circadian gene transcription in aged compared to young tissue macrophages. Chromatin accessibility was significantly greater in young macrophages than in aged macrophages; however, this difference did not explain the loss of rhythmic gene transcription in aged macrophages. Rather, diurnal expression of Kruppel-like factor 4 (Klf4), a transcription factor (TF) well established in regulating cell differentiation and reprogramming, was selectively diminished in aged macrophages. Ablation of Klf4 expression abolished diurnal rhythms in phagocytic activity, recapitulating the effect of aging on macrophage phagocytosis. Examination of individuals harboring genetic variants of KLF4 revealed an association with age-dependent susceptibility to death caused by bacterial infection. Our results indicate that loss of rhythmic Klf4 expression in aged macrophages is associated with disruption of circadian innate immune homeostasis, a mechanism that may underlie age-associated loss of protective immune responses.

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Fig. 1: Aging disrupts the diurnal rhythmicity of innate immune functions.
Fig. 2: Aging abolishes rhythmic gene expression in macrophages.
Fig. 3: Chromatin accessibility is globally decreased in aged macrophages but does not account for loss of diurnal transcription.
Fig. 4: Age-dependent loss of KLF4 reduces macrophage circadian function.
Fig. 5: The KLF4 variant may be linked to age-associated differences in antimicrobial immunity in humans.

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Data availability

Transcriptomics data are available under accession number GSE128830 and at Token (ybqjocoontgtluf); ATAC-seq data are available at https://purl.stanford.edu/rc797bt9574. The dataset used for the analyses in the UK BioBank have not been deposited in a public repository but are available after approval of a reasonable application at https://www.ukbiobank.ac.ukl. Source data are provided with this paper.

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Acknowledgements

This work was supported by RO1AG048232 (K.I.A.), RF1AG058047 (K.I.A.), the American Heart Association 19PABH134580007 (K.I.A.), RO1NS100180 (K.I.A.), 1P30 AG066515 (K.I.A.), The Zhang-Jiang Research Fund, T32 Neuroscience Institute (C.T.), NSF GRFP (C.T.), DP2AG067492 (C.A.T.), the Edward Mallinckrodt, Jr. Foundation (C.A.T.), UPenn Institute for Immunology (C.A.T.), UPenn Diabetes Research Center P30-DK-019525 (C.A.T.), Pew Biomedical Scholarship (C.A.T.), Fritz Thyssen Foundation (C.A.T.) and the UPenn Institute on Aging (C.A.T.), Marie Skłodowska-Curie Grant 888494 (E.B.), Stanford School of Medicine Dean’s Postdoctoral Fellowship (E.B.), Medical Scientist Training Program T32 GM07170 (L.L.) and Training Grant in Computational Biology 5-T32-HG-000046-21 (L.L.). We thank L. de Lecea for support in housing mice, the Stanford Shared FACS facility for flow cytometry analysis on LSR instruments (S10RR027431-01) and SCGPM for sequencing on a HiSeq 2000. The ATAC-seq sequencing data were generated on an Illumina HiSeq 4000 that was purchased with funds from NIH S10OD018220 for the Stanford Functional Genomics Facility. Schematic illustrations were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Eran Blacher, Connie Tsai, Lev Litichevskiy.

  2. These authors jointly supervised this work: Katrin I. Andreasson, Christoph A. Thaiss.

Authors and Affiliations

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

    Eran Blacher, Connie Tsai, Chinyere Agbaegbu Iweka & Katrin I. Andreasson

  2. Neurosciences Graduate Program, Stanford University, Stanford, CA, USA

    Connie Tsai

  3. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Lev Litichevskiy, Kai Markus Schneider & Christoph A. Thaiss

  4. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Lev Litichevskiy, Kai Markus Schneider & Christoph A. Thaiss

  5. Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Lev Litichevskiy, Kai Markus Schneider & Christoph A. Thaiss

  6. Department of Genetics, Stanford University, Stanford, CA, USA

    Zohar Shipony

  7. Department of Biology, Stanford University, Stanford, CA, USA

    Bayarsaikhan Chuluun & H. Craig Heller

  8. Center for Translational and Computational Neuro-immunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA

    Vilas Menon

  9. Stanford Immunology Program, Stanford University, Stanford, CA, USA

    Katrin I. Andreasson

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

    Katrin I. Andreasson

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Contributions

E.B. designed, performed and interpreted the experiments and wrote the manuscript. C.T. conceived the study, performed and analyzed RNA-seq experiments and wrote the manuscript. L.L., Z.S., K.M.S. and V.M. performed computational analyses. C.A.I. assisted with FACS and qRT–PCR experiments. B.C. and H.C.H. assisted with circadian experiments. K.I.A. and C.A.T. conceived the study, supervised the participants, interpreted the experiments and wrote the manuscript.

Corresponding authors

Correspondence to Christoph A. Thaiss or Katrin I. Andreasson.

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The authors declare no competing interests.

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Peer review information Nature Immunology thanks Nicolas Cermakian, Anne Curtis, Lora Hooper, Luz Navarro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. L.A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Gating strategy for monocytes and macrophages in bone marrow, blood and spleen.

Mononuclear cells from bone marrow (a) blood (b) and spleen (c) were gated for forward and side-scatter (FSC/SSC), doublets, and live/dead prior to identification of bone marrow (CD45 + CD11b + Ly6G-Ly6C + ), blood (CD45 + CD115 + Ly6G-) and splenic (CD45 + CD11b + Ly6G-) monocytes and macrophages.

Extended Data Fig. 2 Macrophage enrichment validation and RNA-seq quality control and analysis.

a. Gating strategy for measurement of macrophage enrichment of samples at ZT0 and ZT12. b. Percent of live Cd11b+ macrophages in young vs aged at ZT0 and ZT12 (n = 3, 2-way ANOVA age factor p = 0.0065). c. Expression levels of macrophage transcripts, Itgam and Emr1 versus markers of dendritic cells (Itgax), eosinophils (Siglec F), T cells (Cd3d, Cd3e, Cd3g), and B cells (Cd19). Data are mean ± s.e.m, two-sided Mann-Whitney U test. n = 21 mice in each age group and n = 3 in each time group. d-k. Pooled values (d, f, h, j) and time-dependent presentations (e, g, i, k) of sample total cell counts (d-e), input RNA quantity (f-g), RIN scores (h-i) and number of mapped reads (j-k) of circadian RNA-seq analysis of young and aged peritoneal macrophages. (d, f, h, j) Data are mean ± s.e.m, two-sided Mann-Whitney U test. n = 21 mice in each age group and n = 3 in each time group. (e, g, i, k) Lines of best fit were determined using loess, and 95% confidence intervals are shown.

Source data

Extended Data Fig. 3 Algorithm comparison and amplitude assessment for rhythmic transcripts.

a. Venn diagrams of unique and shared rhythmically expressed transcripts in young vs. aged macrophages, compared across different algorithms (JTK_CYCLE, RAIN, BooteJTK, and MetaCycle) and q-values. b. JTK_CYCLE amplitudes of genes in peritoneal macrophages from young and aged mice. Genes are binned by their rhythmicity in neither, both, or individual groups. n = 21 mice in each age group and n = 3 in each time group. Boxes extend from the 25th-75th percentiles, whiskers extend to 1.5 times the IQR, and the center line is the median.

Source data

Extended Data Fig. 4 Loss of circadian rhythmicity of phagocytosis gene expression in aged macrophages.

a. Heatmap of normalized CLEAR network gene expression values of all time points for each age group shows a decrease in overall gene expression in aged as compared to young peritoneal macrophages. b. Circadian RNA-seq expression patterns of phagocytosis-related genes reveal loss of rhythmicity in aged peritoneal as compared to young macrophages. Data are mean ± s.e.m, n = 21 mice in each age group and n = 3 in each time group. Indicated p-values were calculated by JTK_CYCLE on normalized expression values.

Source data

Extended Data Fig. 5 The core circadian clock genes remain rhythmic in aged peritoneal macrophages.

a-j, Individual representations of normalized circadian gene expression values measured by RNA-seq of the positive arm (a-b), negative arm (c-g) and supporting genes (h-j) of the core clock machinery. (k-l) qPCR validation of Bmal1 and Per2 expression in young and aged peritoneal macrophages. Data are mean ± s.e.m, n = 21 mice in each age group and n = 3 in each time group.

Source data

Extended Data Fig. 6 Chromatin accessibility of young and aged macrophages is not rhythmic.

a. Schematic of investigated possible explanations for differentially rhythmic gene expression by chromatin accessibility. b. Chromatin accessibility (as normalized and log2-transformed values) of promoter regions of differentially rhythmic genes between young and aged peritoneal macrophages. Note that rhythmically expressed genes have higher chromatin accessibility compared to an equal number of randomly selected control genes. n = 15-16 mice in each age group and n = 2-3 mice per 4 h time interval. Boxes extend from the 25th-75th percentiles, whiskers extend to 1.5 times the IQR, and the center line is the median. c. Venn diagrams of the numbers of differentially rhythmic genes between the two age groups (RNA-seq) and the numbers of differentially accessible promoter regions assessed by ATAC-seq. d, Scatterplots of amplitude and q-value for open chromatin peaks as assessed by JTK_CYCLE. No element shows q < 0.2 (indicated by dotted line). e, f, Distribution of unadjusted p-values for oscillations of open chromatin peaks (e) and transcripts (f) in young macrophages, zoomed-in to p < 0.1, JTK_CYCLE.

Source data

Extended Data Fig. 7 Two distinct DNA binding motifs of KLF4.

a, b. Circadian expression levels measured by RNA-seq of KLF family members Klf9 and Klf13. Data are mean ± s.e.m, n = 21 mice in each age group and n = 3 in each time group. p-values determined by JTK_CYCLE on normalized expression values. c, d. Depiction of the two known KLF4 binding motifs, MA0039.1 and MA0039.2. q-values from de novo motif discovery on a KLF4 ChIP-seq experiment from ENCODE (https://factorbook.org/experiment/ENCSR265WJC/motif). e. chromVAR30 deviations within all 500 bp peaks indicating KLF4 binding by estimating accessibility within peaks sharing the MA0039.2 motif or annotation. p = 0.935, two-sided Mann-Whitney U test. f. chromVAR30 deviations within peaks associated with differentially rhythmic genes indicating KLF4 binding by estimating accessibility within peaks sharing the MA0039.2 motif or annotation. p = 0.654, two-sided Mann-Whitney U test. (e, f) n = 15-16 mice in each age group and n = 2-3 mice per 4 h time interval. Boxes extend from the 25th-75th percentiles, whiskers extend to 1.5 times the IQR, and the center line is the median.

Source data

Extended Data Fig. 8 Klf4 is controlled by Bmal1 and drives circadian rhythmicity of phagocytosis.

a-d. Circadian RT-PCR expression patterns of Klf4 (a-b), and the phagocytosis-related genes Gba (c) and Rab3d (d) reveal loss of rhythmicity of phagocytosis genes in Klf4-shRNA lentiviral injected young mice as compared to scrambled vector controls. Data are mean ± s.e.m, n = 18 mice in each treatment group and n = 3 in each time group. Indicated p-values were calculated by JTK_CYCLE. e-f. RT-PCR expression of Bmal1 (e) and Circadian RT-PCR expression patterns of Klf4 in Bmal1-shRNA lentiviral injected young mice as compared to scrambled vector controls. Data are mean ± s.e.m, n = 18 mice in each treatment group and n = 3 in each time group. Indicated p-values were calculated by JTK_CYCLE. g. Phagocytosis of fluorescent E. coli particles by Bmal1-shRNA lentiviral and scrambled vector injected young mice. Data are mean ± s.e.m, n = 18 mice in each treatment group and n = 3 in each time group. Indicated p-values were calculated by JTK_CYCLE. h. Crystal structure of the zinc-finger domain of KLF4 in complex with DNA and rs2236599 synonymous mutation site predicted by SWISS-MODEL57.

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Blacher, E., Tsai, C., Litichevskiy, L. et al. Aging disrupts circadian gene regulation and function in macrophages. Nat Immunol 23, 229–236 (2022). https://doi.org/10.1038/s41590-021-01083-0

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