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Lysosomal control of senescence and inflammation through cholesterol partitioning

Nature Metabolism volume 5, pages 398–413 (2023)Cite this article

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Abstract

Whereas cholesterol is vital for cell growth, proliferation, and remodeling, dysregulation of cholesterol metabolism is associated with multiple age-related pathologies. Here we show that senescent cells accumulate cholesterol in lysosomes to maintain the senescence-associated secretory phenotype (SASP). We find that induction of cellular senescence by diverse triggers enhances cellular cholesterol metabolism. Senescence is associated with the upregulation of the cholesterol exporter ABCA1, which is rerouted to the lysosome, where it moonlights as a cholesterol importer. Lysosomal cholesterol accumulation results in the formation of cholesterol-rich microdomains on the lysosomal limiting membrane enriched with the mammalian target of rapamycin complex 1 (mTORC1) scaffolding complex, thereby sustaining mTORC1 activity to support the SASP. We further show that pharmacological modulation of lysosomal cholesterol partitioning alters senescence-associated inflammation and in vivo senescence during osteoarthritis progression in male mice. Our study reveals a potential unifying theme for the role of cholesterol in the aging process through the regulation of senescence-associated inflammation.

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Fig. 1: Cholesterol metabolism is altered to enhance the senescence-associated secretory phenotype.
Fig. 2: The GATA4–NF-κB–ABCA1 axis regulates the SASP through the mTORC1 pathway.
Fig. 3: ABCA1 functions as a lysosomal cholesterol importer to enhance mTORC1-dependent SASP regulation.
Fig. 4: Lysosomal cholesterol accumulated by ABCA1 contributes to microdomains enriched for the mTORC1 scaffolding complex.
Fig. 5: Control of lysosomal cholesterol modulates in vivo senescence during OA progression.

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Data availability

Data supporting the findings of this study are available within the paper and its Supplementary Tables. The RNA-seq data generated by this study are deposited in the Gene Expression Omnibus database under project accession number GSE222676. Publicly available datasets used in this study are as follows: GSE64553 (Extended Data Fig. 2b), GSE57218 (Extended Data Fig. 9a), GTEx v8 (Extended Data Fig. 10a) and Cancer Cell Line Encyclopedia (DepMap portal (Public 21Q1); Extended Data Fig. 10b). Source data are provided with this paper.

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Acknowledgements

We thank J.B. Kim and N. Kim for sharing reagents. We are also grateful to all of the members of the Kang lab and the Kim lab for their support. This work was supported by the Suh Kyungbae Foundation (SUHF-17020068), the National Research Foundation of Korea (NRF-2020R1A5A1018081), and the Samsung Science & Technology Foundation (SSTF-BA2201-09) to C.K. J.-H.K. was supported by the Suh Kyungbae Foundation (SUHF-18010068) and the National Research Foundation of Korea (NRF-2020R1A2C2012300).

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Author notes

  1. These authors contributed equally: Kyeonghwan Roh, Jeonghwan Noh.

Authors and Affiliations

  1. School of Biological Sciences, Seoul National University, Seoul, South Korea

    Kyeonghwan Roh, Jeonghwan Noh, Yeonju Kim, Yeji Jang, Jaejin Kim, Haebeen Choi, Yeonghyeon Lee, Donghyun Kang, Mi-Sung Kim, Jongkyeong Chung, Jin-Hong Kim & Chanhee Kang

  2. Center for Systems Geroscience, Seoul National University, Seoul, South Korea

    Kyeonghwan Roh, Yeonju Kim, Yeji Jang, Jaejin Kim, Haebeen Choi, Yeonghyeon Lee, Mi-Sung Kim, Jongkyeong Chung & Chanhee Kang

  3. Center for RNA Research, Institute of Basic Science, Seoul, South Korea

    Jeonghwan Noh, Donghyun Kang & Jin-Hong Kim

  4. College of Pharmacy, Sunchon National University, Suncheon, South Korea

    Moongi Ji & Man-Jeong Paik

  5. Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea

    Jongkyeong Chung

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Contributions

K.R. and C.K. conceived and designed the project. J.N. and J.-H.K. conceived and designed male mouse OA experiments. K.R., Y.K., Y.J. and C.K. performed most of the experiments and analyzed the data. J.N., D.K. and J.-H.K. performed and analyzed male mouse OA experiments. M.J. and M.-J.P. performed GC–MS/MS and amino acid profiling analysis. J.-H.K. and C.K. supervised the project. H.C. and M.-S.K. provided multiple types of senescent samples for analysis. M.-S.K. performed ChIP experiments. Y.L. performed global protein synthesis analyses. J.K. performed GSVA. J.C. provided intellectual contribution on the project. K.R., J.N., Y.K., Y.J., J.-H.K. and C.K. wrote the paper. Y.K. and Y.J. contributed equally to this work. All authors discussed the results and commented on the paper.

Corresponding authors

Correspondence to Jin-Hong Kim or Chanhee Kang.

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Nature Metabolism thanks Christopher Wiley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Cholesterol metabolism is altered in DNA damage-induced, replicative, and oncogene-induced senescence.

a, Schematic representation of the gene expression profiling for DNA damage-induced senescence followed by GSEA with a specific focus on metabolic pathway. VMH denotes the virtual metabolic human database. PRO and SEN denote sham-irradiated proliferating and DNA damage-induced senescent cells, respectively. DEGs denotes differentially expressed genes. b, GSEA of differentially expressed genes in replicative senescent MRC5 cells for the cholesterol homeostasis pathway [left, false discovery rate (q-value) and normalized enrichment score (NES) calculated by GSEA]. Abundance of the indicated mRNAs analyzed by qRT-PCR upon replicative senescence (middle, n = 3 biologically independent samples, mean ± SEM, two-sided unpaired t test). PRO and SEN (REP) denote proliferating and replicative senescent IMR90 cells, respectively. Enzymatic measurement of cholesterol upon replicative senescence in IMR90 cells (right, n = 3 biologically independent samples, mean ± SEM, two-sided unpaired t test). c, Abundance of the indicated mRNAs analyzed by qRT-PCR upon oncogene-induced senescence (left, n = 3 biologically independent samples, mean ± SEM, two-sided unpaired t test). PRO and SEN (OIS) denote sham-treated and doxycycline-treated IMR90 cells carrying a Dox-inducible (Tet-On) vector expressing H-RasV12. Enzymatic measurement of cholesterol upon oncogene-induced senescence (right, n = 4 biologically independent samples, mean ± SEM, two-sided unpaired t test). d, Enzymatic measurement of cholesterol upon knockdown of the indicated genes during senescence (mean ± SEM, n = 3 biologically independent samples, one-way ANOVA test with Tukey’s multiple comparisons test).

Source data

Extended Data Fig. 2 Cholesterol metabolism is associated with the SASP during DNA damage-induced and oncogene-induced senescence.

a, Enzymatic measurement of cholesterol upon MβCD treatment for 2 days in DMEM supplemented with 15% FBS during DNA damage-induced senescence (n = 3 biologically independent samples, mean ± SEM, two-sided unpaired t test). b, Abundance of the indicated mRNAs analyzed by qRT-PCR upon cholesterol depletion (left, n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). ChDS denotes cholesterol depletion by incubating the cells with DMEM supplemented with 15% charcoal-stripped FBS. Abundance of the indicated proteins analyzed by immunoblotting upon senescence induction in BJ cells (right, n = 2 biologically independent experiments). The cells were starved for amino acids and serum, and then restimulated for the indicated time. c, Abundance of the indicated mRNAs analyzed by qRT-PCR (left, n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). Abundance of the indicated proteins analyzed by immunoblotting (right, n = 3 biologically independent experiments). Lov denotes lovastatin. d-e, Abundance of the indicated mRNAs analyzed by qRT-PCR (mean ± SEM, n = 3 biologically independent samples, one-way ANOVA test with Tukey’s multiple comparisons test). Chol denotes cholesterol stimulation.

Source data

Extended Data Fig. 3 ABCA1 plays a key role in SASP regulation.

a, Abundance of the indicated mRNAs analyzed by qRT-PCR upon senescence induction in IMR90 cells expressing the indicated shRNAs (mean ± SEM, n = 6 and 4 biologically independent samples for IL1A/IL1B/IL8 and HMGCR/LRP1/SCARB1, respectively, RM one-way ANOVA test with Tukey’s multiple comparisons test). b, Abundance of the indicated proteins analyzed by immunoblotting upon senescence induction in BJ cells (top, n = 2 biologically independent experiments). Abundance of the indicated mRNAs analyzed by qRT-PCR upon senescence induction in BJ cells (bottom, n = 2 biologically independent samples, mean ± SEM). PRO and SEN denote sham-treated proliferating and DNA damage-induced senescent cells, respectively. C denotes control ABCA1 WT cells. c, Abundance of the indicated proteins analyzed by immunoblotting (left, n = 3 biologically independent experiments). Abundance of the indicated mRNAs analyzed by qRT-PCR upon oncogene-induced senescence (right, n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). d, Abundance of the indicated mRNAs analyzed by qRT-PCR in IMR90 cells stimulated with or without cholesterol during senescence (mean ± SEM, n = 3 biologically independent samples, one-way ANOVA test with Tukey’s multiple comparisons test).

Source data

Extended Data Fig. 4 The GATA4-NF-kB axis induces ABCA1 during senescence.

a, Abundance of the indicated proteins analyzed by immunoblotting upon senescence induction in IMR90 cells (left, n = 3 biologically independent experiments). Abundance of the indicated mRNAs analyzed by qRT-PCR upon senescence induction in IMR90 cells (right, from 1 biologically independent experiment, two times each experiment was repeated independently with similar results). b, Abundance of the indicated proteins analyzed by immunoblotting upon senescence induction in BJ cells expressing the PmiR-146a-GFP SASP reporter (n = 1 biologically independent experiment). c, Abundance of the indicated proteins analyzed by immunoblotting upon replicative senescence in IMR90 cells (left, n = 2 and 3 biologically independent samples for PRO and SEN (REP), respectively). Abundance of ABCA1 mRNA analyzed by qRT-PCR upon replicative senescence in IMR90 cells (right, n = 2 and 3 biologically independent samples for PRO and SEN (REP), respectively, mean ± SEM, two-sided unpaired t test). PRO and SEN (REP) denote proliferating and replicative senescent IMR90 cells. d, Schematic representation of the promoter region of ABCA1 with the indicated qPCR primers (left). Potential RELA and GATA4 motifs are shown. IMR90 cells carrying a Dox-inducible (Tet-On) vector expressing HA-GATA4 were cross linked with formaldehyde and protein extracts were immunoprecipitated with either HA or RELA antibodies. DNA was eluted, the cross-links were reversed, and the DNA was analyzed by qPCR using the indicated primers. Signals obtained from ChIP were normalized to signals obtained from an input sample (right, n = 3 biologically independent samples, % Input, mean ± SEM, two-sided unpaired t test). e, Abundance of the indicated proteins analyzed by immunoblotting upon senescence induction in IMR90 cells carrying a Dox-inducible (Tet-On) vector expressing dominant negative p53 (Tet-p53DN) (left, n = 3 biologically independent samples). Abundance of the indicated mRNAs analyzed by qRT-PCR (right, n = 3 biologically independent samples, mean ± SEM, two-way ANOVA test with Tukey’s multiple comparisons test).

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Extended Data Fig. 5 ABCA1 is localized in the lysosome.

a, Immunocytochemistry of the indicated proteins (left, n = 2 biologically independent experiments). Scale bar, 50 μm. Abundance of the indicated proteins analyzed by immunoblotting upon LysoIP (right, n = 2 biologically independent experiments). PRO and SEN denote sham-treated proliferating and senescent cells. b, Representative images of cellular cholesterol and lysosome staining with filipin and lysotracker red (Lyso), respectively, upon GATA4 induction (left). Scale bar, 50 μm. Pearson’s correlation coefficient for colocalization of cellular cholesterol and lysosome (right, n = 2 biologically independent experiments, 5-7 randomly chosen fields from each experiment, box plots show the 1st and 3rd quartiles; whiskers show the minimum and maximum values, two-sided unpaired t test).

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Extended Data Fig. 6 Pharmacological modulation of lysosomal cholesterol affects the SASP.

a, Abundance of the indicated mRNAs analyzed by qRT-PCR (n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). SEN (OIS) and Prob denote oncogene-induced senescence and probucol, respectively. b, Abundance of the indicated mRNAs analyzed by qRT-PCR (n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). Glyb denotes glyburide. c, Pearson’s correlation coefficient for colocalization of cellular cholesterol and lysosome (left, n = 2 biologically independent experiments, 10–20 randomly chosen fields from each experiment, box plots show the 1st and 3rd quartiles; whiskers show the minimum and maximum values, one-way ANOVA test with Tukey’s multiple comparisons test). Abundance of the indicated mRNAs analyzed by qRT-PCR (right, n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). SEN denotes DNA damage-induced senescence. d, Abundance of the indicated mRNAs analyzed by qRT-PCR (mean ± SEM, n = 3 biologically independent samples, one-way ANOVA test with Tukey’s multiple comparisons test).

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Extended Data Fig. 7 ABCA1 sustains a lysosomal mTORC1 recruitment during senescence.

Immunocytochemistry of the indicated proteins (left). Scale bar, 50 μm. Pearson’s correlation coefficient for colocalization of mTOR and LAMP2 in the indicated cell lines during senescence upon amino acid starvation (2 h) and refeeding (30 m) (right, n = 3 biologically independent experiments, 10–23 randomly chosen individual cells from each experiment, box plots show the 1st and 3rd quartiles; whiskers show the minimum and maximum values, one-way ANOVA test with Tukey’s multiple comparisons test). PRO and SEN denote sham-treated proliferating and senescent cells.

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Extended Data Fig. 8 Lysosomal amino acid levels do not change during senescence.

Absolute quantification of amino acids upon LysoIP by Gas chromatography-tandem mass spectrometry (GC-MS/MS, n = 3 biologically independent samples, mean ± SEM, two-way ANOVA test with Tukey’s multiple comparisons test). WT and KO denote ABCA1 wild-type and knock-out cells. PRO and SEN denote sham-treated proliferating and senescent cells.

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Extended Data Fig. 9 Cholesterol metabolism and ABCA1 modulate chondrocyte senescence and OA progression.

a, GSEA of differentially expressed genes in human OA for the cholesterol homeostasis pathway (left, top) and the mTOR signaling pathway (left, bottom). False discovery rate (q-value) and normalized enrichment score (NES) calculated by GSEA. Pearson’s correlation analysis of expression of ABCA1 and the indicated genes in human OA (right, n = 73 biologically independent individuals). b, Abundance of the indicated mRNAs analyzed by qRT-PCR (n = 6 biologically independent samples, mean ± SEM, two-sided unpaired t test). PRO and SEN denote proliferating and senescent chondrocytes, respectively. c, Abundance of the indicated mRNAs analyzed by qRT-PCR (n = 3 biologically independent samples, mean ± SEM, one-way ANOVA test with Tukey’s multiple comparisons test). Lov denotes lovastatin. d, Abundance of the indicated proteins analyzed by immunoblotting (n = 2 biologically independent experiments). ChDS denotes cholesterol depletion by incubating the cells with DMEM supplemented with charcoal-stripped FBS. The cells were starved for amino acids and serum, and then restimulated for the indicated time. e, Representative images of immunohistochemistry of the indicated proteins in cartilage sections of male mice. Scale bar, 50 μm. Tidemarks were represented by dotted lines. f, The percentage of weight placed on the DMM-operated limb versus the contralateral limb of sham- and DMM-operated mice treated with or without probucol or glyburide determined using a static weight bearing test (left). Paw withdrawal threshold in response to von Frey filaments in sham-operated and DMM-operated mice treated with or without probucol or glyburide (right). n = 5, 6, 9, 9, 5, 5, 7, and 8 biologically independent animals for Sham w/o Prob, Sham w/ Prob, DMM w/o Prob, DMM w/ Prob, Sham w/o Glyb, Sham w/ Glyb, DMM w/o Glyb, and DMM w/ Glyb, box plots show the 1st and 3rd quartiles; whisker show the minimum and maximum values, two-way ANOVA test with Holm-Sidak’s multiple comparisons test.

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Extended Data Fig. 10 Expression of ABCA1 is associated with the SASP signature and inflammation in multiple tissues during human aging as well as in cancer.

a, Correlation analysis of the SASP GSVA scores and ABCA1 expression in human aging. Samples from the indicated tissues in the Genotype-Tissue Expression (GTEx) database were divided into two age groups [‘Young’ (20~39 years old) and ‘Old’ (60~79 years old)], and the Pearson’s correlation analysis between the SASP GSVA scores and ABCA1 expression was performed for each age group (two-tailed, n = 212, 73, 155, 17, 180, 33, 120, 9, 155, 16, 52, and 30 biologically independent individuals for lung old, lung young, pituitary old, pituitary young, heart old, heart young, brain frontal cortex old, brain frontal cortex young, brain cerebellum old, brain cerebellum young, salivary gland old, and salivary gland young, respectively). b, Box plot analysis comparing the abundance of the indicated genes between ABCA1low and ABCA1high cells (n = 347 and 345 biologically independent cell lines for ABCA1low and ABCA1high, respectively, box plots show the 1st and 3rd quartiles; whiskers show the minimum and maximum values, two-sided unpaired t-test). Cells were categorized into 2 groups [ABCA1low (bottom 25%) and ABCA1high (top 25%)] according to ABCA1 expression in the cancer cell line encyclopedia.

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Roh, K., Noh, J., Kim, Y. et al. Lysosomal control of senescence and inflammation through cholesterol partitioning. Nat Metab 5, 398–413 (2023). https://doi.org/10.1038/s42255-023-00747-5

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