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Aging promotes metabolic dysfunction-associated steatotic liver disease by inducing ferroptotic stress

Nature Aging (2024)Cite this article

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Abstract

Susceptibility to the biological consequences of aging varies among organs and individuals. We analyzed hepatocyte transcriptomes of healthy young and aged male mice to generate an aging hepatocyte gene signature, used it to deconvolute transcriptomic data from humans and mice with metabolic dysfunction-associated liver disease, validated findings with functional studies in mice and applied the signature to transcriptomic data from other organs to determine whether aging-sensitive degenerative mechanisms are conserved. We discovered that the signature enriches in diseased livers in parallel with degeneration. It is also enriched in failing human hearts, diseased kidneys and pancreatic islets from individuals with diabetes. The signature includes genes that control ferroptosis. Aged mice develop more hepatocyte ferroptosis and liver degeneration than young mice when fed diets that induce metabolic stress. Inhibiting ferroptosis shifts the liver transcriptome of old mice toward that of young mice and reverses aging-exacerbated liver damage, identifying ferroptosis as a tractable, conserved mechanism for aging-related tissue degeneration.

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Fig. 1: Liver accumulation of biologically old HCs parallels MASLD severity in people.
Fig. 2: Aging HCs in MASLD lose metabolic functions and exhibit ferroptotic stress.
Fig. 3: Livers of patients with MASLD exhibit ferroptotic stress.
Fig. 4: Ferroptotic stress and lipotoxicity are increased in livers of old mice.
Fig. 5: Aging increases ferroptotic stress and promotes HC susceptibility to lipotoxicity.
Fig. 6: Fer1 protects HCs from aging-associated ferroptotic stress and senescence during diet-induced MASH.
Fig. 7: Fer1 prevents aging-related exacerbation of MASH.
Fig. 8: Liver aging correlates with dysfunction of multiple-organ systems.

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Data availability

All data generated by the present study are included in this article and the Supplementary Information. The bulk RNA-seq data from the 6-week CDA-HFD-fed young versus old mice have been deposited in the GEO with accession no. GSE262457. The snRNA-seq data from the 22-week-old, chow- versus CDA-HFD-fed mice have also been submitted to the GEO and can be accessed with accession no. GSE262939. Additional public transcriptomic datasets for the samples discussed in the present study are available in the GEO database under the following accession nos.: GSE181761 (comparison of old versus young mouse HCs), GSE132042 (old versus young mouse liver), GSE213623 (Duke MASLD cohort), GSE33814 (German MASLD cohort), GSE167523 (Japanese MASLD cohort), GSE135251 (European MASLD cohort), GSE174748 (snRNA-seq on liver of controls and patients with MASLD) and GSE50244 (pancreatic islets). Moreover, the RNA-seq dataset for patients with heart failure was obtained from Zenodo at https://doi.org/10.5281/zenodo.4114616 (ref. 106). The RNA-seq dataset for patients with kidney fibrosis was sourced from E-MTAB-2502 on the EMBL-EBI website at https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-2502. In addition, the RNA-seq dataset for 226 healthy human livers was acquired from the GTEx Portal v.8 at https://gtexportal.org/home. Source data are provided with this paper.

Code availability

The present study did not generate any unique code or algorithm. The algorithms used for the analysis in the present study are all publicly available.

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Acknowledgements

This work was supported by the 2021 AASLD Pinnacle Award to K.D. and NIH grants (nos. R01 AA010154, R01 DK077794 and R56 DK134334) and Sponsored Research Study Agreement (no. 337521) supported by Boehringer Ingelheim Pharmaceuticals, awarded to A.M.D. We thank the patients who donated liver tissue for analysis, M. Abdelmalek and C. Guy and the clinical staff and coordinators who created and maintain the Duke NAFLD Biorepository, S. Gregory and his team at Duke Molecular Physiology Institute for snRNA-seq of control and MASLD liver tissues, and S. Pullen and his team at Boehringer Ingelheim Pharmaceuticals and Z. Man (Duke Department of Neurology) for their assistance in the bioinformatics analysis.

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Author notes

  1. These authors contributed equally: Kuo Du, Liuyang Wang, Ji Hye Jun.

Authors and Affiliations

  1. Department of Medicine, Duke University, Durham, NC, USA

    Kuo Du, Ji Hye Jun, Rajesh K. Dutta, Raquel Maeso-Díaz, Seh Hoon Oh & Anna Mae Diehl

  2. Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA

    Liuyang Wang & Dennis C. Ko

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Contributions

K.D., L.W. and A.M.D. conceived the experiments. K.D., L.W. and J.H.J. performed the experiments. K.D., L.W., J.H.J., R.K.D., R.M.D., S.H.O., D.C.K. and A.M.D. analyzed the data. K.D., L.W., J.H.J. and A.M.D. wrote the paper. K.D. and A.M.D. secured the funding for the study. All authors reviewed and approved the paper.

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Correspondence to Anna Mae Diehl.

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Nature Aging thanks Philippe Lefebvre 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 Top 10 upregulated/downregulated KEGG pathways in old versus young mouse hepatocytes.

Red arrows point to the upregulated pathways of interest, and blue arrows point to the downregulated pathways of interest. p values in GSEA plot was calculated using permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method.

Extended Data Fig. 2 Identification and characterization of the aging hepatocyte gene signature (AHGS).

(A) Number of DEGs in each gene set after applying different adjusted p-value cutoffs (p = 0.05 or 0.01) and log2 fold changes (1 - 5) for DEGs. (B) Jaccard index, examining similarity of overlapping DEGs of primary hepatocytes from old vs young mice fed with chow diet, and liver tissues from old vs young mice fed with CDA-HFD diet, revealed that AHGS with p-value < 0.01 and Log2 FC > 3 resulted in the best gene set by showing a reduced gene number and higher Jaccard similarity. (C) GO:MF (Molecular Function) clustering of the AHGS. Gene Set Enrichment Analysis (GSEA) of AHGS further identified (D) upregulated GO:BP (GO Biological Process) pathways and (E) upregulated HPO (Human Phenotype Ontology) pathways. p values were calculated using: unpaired two-tailed t.test in A, B; permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method in C, D, E.

Extended Data Fig. 3 Age is a major risk factor of MASLD development.

(A) Re-analysis of the Duke MASLD cohort (GSE213623) revealed that the chronological age positively correlates with MASLD histological markers including hepatocyte ballooning, portal inflammation and fibrosis in MASLD patients (control n = 69; MASLD patients n = 299; ballooning score 0, n = 32, score 1, n = 118, score 2, n = 149; portal_inf score 0, n = 146, score 1, n = 142; fibrosis F0F1, n = 97; F2, n = 107; F3F4, n = 95). **p < 0.01; ***p < 0.001; ****p < 0.0001. (B, C) GSEA using KEGG revealed that genes related to longevity and its associated mechanisms (for example nicotinamide; FoxO signaling) are depleted (B), while genes related to programmed cell death (C) are highly enriched in the transcriptomics of MASLD patients. Red arrows point to the pathways of interest. (D) AHGS enrichment increases with chronological age in MASLD patients but not control subjects. (E) AHGS enrichment score with chronological age in RNA-seq data of 226 normal liver samples from GTEx v8 database (https://gtexportal.org/home/). p-values were calculated using Wilcoxon Rank Sum test. Boxplot shows the upper quantile (75%), median (50%) and lower quantile (25%) of overall data distribution (A, D, E). p values in A, D, E were calculated using Wilcoxon Rank Sum test; p values in GSEA plots in B and C were calculated using permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method.

Extended Data Fig. 4 Aging-associated mechanisms are altered during MASLD development.

GSEA of bulk liver RNA seq data from Duke MASLD patients (GSE213623) revealed that (A) genes related to longevity and its associated mechanisms (for example NAD metabolism, sirtuins) are depleted, (B) while genes related to senescence are highly enriched in the transcriptomics of MASLD patients. (C) AHGS was applied to deconvolute bulk liver RNA seq data (GSE135251, heathy control n = 13; MASH n = 12). AHGS is enriched in transcriptomes of MASH patients and distinguishes MASH patients from healthy controls. Boxplot shows the upper quantile (75%), median (50%) and lower quantile (25%) of overall data distribution. p-values were calculated using: permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method in A, B; Wilcoxon Rank Sum test in C.

Extended Data Fig. 5 snRNA-seq analysis and bulk RNA-seq GSEA of MASLD liver.

(A, C) Uniform manifold approximation and projection (UMAP) visualization of liver cells in single-nucleus RNA-seq dataset generated from two MASLD patients or two healthy controls (GSE174748) (A), or mice fed with CDA-HFD diet or chow diet for 22 weeks (C). (B, D) We focused our analysis on the clusters that are most enriched for hepatocyte markers and depleted for non-hepatocyte markers in both human (B) and mouse dataset (D). GSEA of bulk liver RNA-seq data from Duke MASLD patients (GSE213623) revealed that genes related to (E) apoptosis, (F) pyroptosis and (G) necroptosis are all enriched in the liver transcriptomics of MASLD patients. p values in GSEA plots were calculated using permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method.

Extended Data Fig. 6 Livers of MASLD patients exhibit ferroptotic stress.

(A) Duke MASLD cohort patients (GSE213623) with the same fibrosis stage were stratified into young (age ≤ 35), middle age (35 <age < 55) and old groups (age ≥ 55). Expression of ferroptotic-associated genes was compared among the different age groups (age 20-35, fibrosis F0F1, n = 2; F2, n = 9; F3F4, n = 4; age 36-55, fibrosis F0F1, n = 55; F2, n = 61; F3F4, n = 39; age 56-80, fibrosis F0F1, n = 19; F2, n = 37; F3F4, n = 52). Box plot showed the upper quantile (75%), median (50%) and lower quantile (25%) (A) of overall data distribution in MASLD patients with different fibrosis stages. (B) Expression of genes related to iron homeostasis and (C) ferroptotic stress was compared between MASLD patients versus controls (healthy n = 2; MASLD n = 2). The average expression of in each single cell was calculated by the AddModuleScore function of Seurat. Feature plot showed single cells on dimensional reduction plot, with cell color indicating their relative expression levels. Violin plot showed the maxima, upper quantile (75%), median (50%), lower quantile (25%) and minima of overall data distribution in the healthy and MASLD group. p-values were calculated using: Wilcoxon Rank Sum test in A; unpaired two-tailed t.test in B, C.

Extended Data Fig. 7 Ferroptotic stress, lipotoxicity, inflammation and collagen metabolism are increased in livers of old mice.

(A) MDA levels were measured by western blots in livers and primary hepatocytes from chow-fed young (3 months old, n = 4) and old (2 years old, n = 5 for liver lysates, n = 4 for hepatocyte lysates). Data are graphed as mean ± SEM. (B) A published dataset (GSE132042) was re-analyzed to compare the transcriptomes of very old mouse liver ( ≥ 24 months old, n = 7) to young mouse liver ( ≤ 3 months old, n = 12). AHGS was enriched in the transcriptome of liver of aged mice. Boxplot shows the upper quantile (75%), median (50%) and lower quantile (25%) of overall data distribution. GSEA analysis further revealed that the transcriptomes of aged mouse liver are enriched with genes associated with (C) hepatocyte aging, (D) lipid metabolism, (E) inflammation and (F) collagen activity. p-values were calculated using: unpaired two-tailed t.test in A; Wilcoxon Rank Sum test in B; permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method in C, D, E, F.

Source data

Extended Data Fig. 8 Ferrostatin-1 protects hepatocytes from aging-related ferroptotic stress and senescence during diet-induced MASH.

Young and old mice were fed with a chow diet or CDA-HFD diet for 6 weeks. Old mice were intraperitoneally injected with Fer1 or its vehicle every other day during the last 8 days of feeding. (A) Body weights and liver weights were measured on the day of sacrifice (young+veh, n = 9; old+veh, n = 6; old+Fer-1, n = 6). (B) Expression of ferroptosis-related proteins detected by western blotting. (C) Representative Tfrc staining and quantification of the positively-stained areas (young+veh, n = 9; old+veh, n = 6; old+Fer-1, n = 6). Scale bars = 100μm (D) Expression of senescent marker p21 detected by western blotting. (E) GSEA demonstrated that transcriptome of old mice treated with Fer1 is depleted with genes involved in response to oxidative stress and related mortality. Protein expression was quantified by densitomeric analyses of western blots. Data are graphed as mean ± SEM. #p < 0.05 versus young mice + veh; $p < 0.05 versus old mice + veh. p-values were calcualted using: one-way ANOVA in A, B, C, D; permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method in E.

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Extended Data Fig. 9 GSEA of the DEGs that reversed by ferrostatin1 in aged mice.

(A) Venn diagram identified 520 DEGs that were upregulated in old mice (vs young mice + veh) but reversed by Fer1 (vs old mice + veh). GSEA further identified 14 significantly enriched hallmark pathways in these DEGs. (B) Venn diagram identified 658 DEGs that were downregulated in old mice (vs young mice + veh) but reversed by Fer1 (vs old mice + veh). GSEA further identified 7 significantly enriched hallmark pathways in these DEGs. p values were calculated using permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method.

Extended Data Fig. 10 FXR and ferroptotic stress interact to modulate age-dependent susceptibility to MASLD.

Young and old mice were fed with CDA-HFD diet for 6 weeks. These mice were intraperitoneally injected with Fer1 or its vehicle every other day during the last 8 days of feeding. Transcriptomes of liver tissues were analysed by RNA-seq (n = 4 mice/group). (A) GSEA demonstrated that liver transcriptome of old mice is depleted of genes involved in bile acid metabolism and this is reversed by Fer1 treatment in the old mice. (B) Expression of FXR/Nr1h4. (C) GSEA demonstrated that liver transcriptome of old mice is depleted of genes involved in FXR pathway activity, and this is reversed by Fer1 treatment in the old mcie. (D) Expression of FXR/Nr1h4 in liver of Duke NAFLD cohorts (GSE213623). (E) Expression of FXR/Nr1h4 in hepatocytes subpopulations from single-nucleus RNA-seq dataset of two MASLD patients or two healthy controls (GSE174748). The average expression of in each single cell was calculated by the AddModuleScore function of Seurat. Feature plot showed single cells on dimensional reduction plot, with cell color indicating their relative expression levels. Box plot showed the upper quantile (75%), median (50%) and lower quantile (25%) (B, D), while violin plot showed the maxima, upper quantile (75%), median (50%), lower quantile (25%) and minima (E) of overall data distribution. p-values were calcualted using: permutation test, then adjusted for multiple comparison testing using the Benjamini-Hochberg method in A, C; Wilcoxon Rank Sum test in B, D; unpaired two-tailed t.test in E.

Supplementary information

Supplementary Information

Supplementary Tables 2–5.

Reporting Summary

Supplementary Table 1

AHGS Aging_geneSig_l2fc.

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Du, K., Wang, L., Jun, J.H. et al. Aging promotes metabolic dysfunction-associated steatotic liver disease by inducing ferroptotic stress. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00652-w

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