Skip to main content
SpringerLink
Log in

Proteomic changes induced by longevity-promoting interventions in mice

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All sample analysis run order files, mass spectrometry dia-PASEF raw folders corresponding to mouse tissue experiments (.d), FASTA database used to generate the assay libraries (.fasta), mouse hybrid spectral assay library (.txt) and its DIALib-QC report (.tsv), and three unnormalized peptide quantitation data files (.csv) for each tissue have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository [50, 51] with the data set identifier PXD040497 (http://www.ebi.ac.uk/pride). Code describing our statistical data analysis that can be used to reproduce our main results can be found at https://github.com/longevity-consortium/M005MouseLongevityPaper2023.

References

  1. Bartke A, Wright JC, Mattison JA, et al. Extending the lifespan of long-lived mice. Nature. 2001;414:412. https://doi.org/10.1038/35106646

    Article  CAS  PubMed  Google Scholar 

  2. Drake JC, Bruns DR, Peelor FF, et al. Long-lived Snell dwarf mice display increased proteostatic mechanisms that are not dependent on decreased mTORC1 activity. Aging Cell. 2015;14:474–82. https://doi.org/10.1111/acel.12329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gesing A, Masternak MM, Lewinski A, et al. Decreased levels of proapoptotic factors and increased key regulators of mitochondrial biogenesis constitute new potential beneficial features of long-lived growth hormone receptor gene–disrupted mice. J Gerontol: Series A. 2013;68:639–51. https://doi.org/10.1093/gerona/gls231

    Article  CAS  Google Scholar 

  4. Harrison DE, Strong R, Sharp ZD, et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature. 2009;460:392–5. https://doi.org/10.1038/nature08221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Harrison DE, Strong R, Allison DB, et al. Acarbose, 17-α-estradiol, and nordihydroguaiaretic acid extend mouse lifespan preferentially in males. Aging Cell. 2014;13:273–82. https://doi.org/10.1111/acel.12170

    Article  CAS  PubMed  Google Scholar 

  6. Harrison DE, Strong R, Alavez S, et al. Acarbose improves health and lifespan in aging HET3 mice. Aging Cell. 2019;18:e12898. https://doi.org/10.1111/acel.12898

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Miller RA, Harrison DE, Allison DB, et al. Canagliflozin extends life span in genetically heterogeneous male but not female mice. JCI Insight. 2020;5 https://doi.org/10.1172/jci.insight.140019

  8. Tyshkovskiy A, Bozaykut P, Borodinova AA, et al. Identification and application of gene expression signatures associated with lifespan extension. Cell Metab. 2019;30:573–593.e8. https://doi.org/10.1016/j.cmet.2019.06.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li X, Shi X, McPherson M, et al. Cap-independent translation of GPLD1 enhances markers of brain health in long-lived mutant and drug-treated mice. Aging Cell. 2022;21:e13685. https://doi.org/10.1111/acel.13685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Shen Z, Hinson A, Miller RA, Garcia GG. Cap-independent translation: a shared mechanism for lifespan extension by rapamycin, acarbose, and 17α-estradiol. Aging Cell. 2021;20:e13345. https://doi.org/10.1111/acel.13345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ozkurede U, Kala R, Johnson C, et al. Cap-independent mRNA translation is upregulated in long-lived endocrine mutant mice. J Mol Endocrinol. 2019;63:123–38. https://doi.org/10.1530/JME-19-0021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wink L, Miller RA, Garcia GG. Rapamycin, Acarbose and 17α-estradiol share common mechanisms regulating the MAPK pathways involved in intracellular signaling and inflammation. Immun Ageing. 2022;19:8. https://doi.org/10.1186/s12979-022-00264-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li X, Frazier JA, Spahiu E, et al. Muscle-dependent regulation of adipose tissue function in long-lived growth hormone-mutant mice. Aging. 2020;12:8766–89. https://doi.org/10.18632/aging.103380.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li X, McPherson M, Hager M, et al. Four anti-aging drugs and calorie-restricted diet produce parallel effects in fat, brain, muscle, macrophages, and plasma of young mice. Geroscience. 2023; https://doi.org/10.1007/s11357-023-00770-0

  15. Bonkowski MS, Rocha JS, Masternak MM, et al. Targeted disruption of growth hormone receptor interferes with the beneficial actions of calorie restriction. Proc Natl Acad Sci. 2006;103:7901–5. https://doi.org/10.1073/pnas.0600161103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Flurkey K, Papaconstantinou J, Miller RA, Harrison DE. Lifespan extension and delayed immune and collagen aging in mutant mice with defects in growth hormone production. Proc Natl Acad Sci. 2001;98:6736–41. https://doi.org/10.1073/pnas.111158898

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Flurkey K, Astle CM, Harrison DE. Life extension by diet restriction and N-acetyl-L-cysteine in genetically heterogeneous mice. J Gerontol A Biol Sci Med Sci. 2010;65:1275–84. https://doi.org/10.1093/gerona/glq155

    Article  CAS  PubMed  Google Scholar 

  18. Miller RA, Harrison DE, Astle CM, et al. Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell. 2014;13:468–77. https://doi.org/10.1111/acel.12194

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dominick G, Berryman DE, List EO, et al. Regulation of mTOR activity in Snell dwarf and GH receptor gene-disrupted mice. Endocrinology. 2015;156:565–75. https://doi.org/10.1210/en.2014-1690

    Article  CAS  PubMed  Google Scholar 

  20. Macchiarini F, Miller RA, Strong R, et al. Chapter 10 - NIA interventions testing program: a collaborative approach for investigating interventions to promote healthy aging. In: Musi N, Hornsby PJ, editors. Handbook of the Biology of Aging (Ninth Edition). Academic Press; 2021. p. 219–35.

    Chapter  Google Scholar 

  21. Auer H, Mobley J, Ayers L, et al. The effects of frozen tissue storage conditions on the integrity of RNA and protein. Biotech Histochem. 2014;89:518–28. https://doi.org/10.3109/10520295.2014.904927

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. LaBaer J. Improving international research with clinical specimens: 5 achievable objectives. J Proteome Res. 2012;11:5592–601. https://doi.org/10.1021/pr300796m

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Escher C, Reiter L, MacLean B, et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics. 2012;12:1111–21. https://doi.org/10.1002/pmic.201100463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bruderer R, Bernhardt OM, Gandhi T, Reiter L. High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics. 2016;16:2246–56. https://doi.org/10.1002/pmic.201500488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Krasny L, Bland P, Burns J, et al. A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts. Dis Model Mech. 2020;13:dmm044586. https://doi.org/10.1242/dmm.044586

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Midha MK, Campbell DS, Kapil C, et al. DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics. Nat Commun. 2020;11:5251. https://doi.org/10.1038/s41467-020-18901-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bland JM, Altman DG. Statistics notes: calculating correlation coefficients with repeated observations: Part 1—correlation within subjects. BMJ. 1995;310:446. https://doi.org/10.1136/bmj.310.6977.446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bakdash JZ, Marusich LR (2022) rmcorr: repeated measures correlation

  29. Lazar C, Gatto L, Ferro M, et al. Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J Proteome Res. 2016;15:1116–25. https://doi.org/10.1021/acs.jproteome.5b00981

    Article  CAS  PubMed  Google Scholar 

  30. McGurk KA, Dagliati A, Chiasserini D, et al. The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination. Bioinformatics. 2020;36:2217–23. https://doi.org/10.1093/bioinformatics/btz898

    Article  CAS  PubMed  Google Scholar 

  31. Cragg JG. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica. 1971;39:829–44. https://doi.org/10.2307/1909582

    Article  Google Scholar 

  32. StataCorp (2021) Stata statistical software

  33. Sellke T, Bayarri MJ, Berger JO. Calibration of ρ values for testing precise null hypotheses. Am Stat. 2001;55:62–71. https://doi.org/10.1198/000313001300339950

    Article  Google Scholar 

  34. Draghici S, Khatri P, Tarca AL, et al. A systems biology approach for pathway level analysis. Genome Res. 2007;17:1537–45. https://doi.org/10.1101/gr.6202607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Carlson, M (2019) org.Mm.eg.db: Genome wide annotation for Mouse

  36. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Voichita C, Donato M, Draghici S. Incorporating gene significance in the impact analysis of signaling pathways. In: 2012 11th International Conference on Machine Learning and Applications; 2012. p. 126–31.

    Chapter  Google Scholar 

  38. Voichita C, Ansari S, Draghici S (2022) ROntoTools: R onto-tools suite.

  39. Kanehisa M, Furumichi M, Sato Y, et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587–92. https://doi.org/10.1093/nar/gkac963

    Article  CAS  PubMed  Google Scholar 

  40. Steinbaugh MJ, Sun LY, Bartke A, Miller RA. Activation of genes involved in xenobiotic metabolism is a shared signature of mouse models with extended lifespan. Am J Physiol Endocrinol Metab. 2012;303:E488–95. https://doi.org/10.1152/ajpendo.00110.2012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Li X, Bartke A, Berryman DE, et al. Direct and indirect effects of growth hormone receptor ablation on liver expression of xenobiotic metabolizing genes. Am J Physiol Endocrinol Metab. 2013;305:E942–50. https://doi.org/10.1152/ajpendo.00304.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Herrera JJ, Louzon S, Pifer K, et al. Acarbose has sex-dependent and -independent effects on age-related physical function, cardiac health, and lipid biology. JCI Insight. 5:e137474. https://doi.org/10.1172/jci.insight.137474

  43. Mihaylova MM, Shaw RJ. The AMPK signalling pathway coordinates cell growth, autophagy and metabolism. Nat Cell Biol. 2011;13:1016–23. https://doi.org/10.1038/ncb2329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sadagurski M, Cady G, Miller RA. Anti-aging drugs reduce hypothalamic inflammation in a sex-specific manner. Aging Cell. 2017;16:652–60. https://doi.org/10.1111/acel.12590

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mitchell SJ, Madrigal-Matute J, Scheibye-Knudsen M, et al. Effects of sex, strain, and energy intake on hallmarks of aging in mice. Cell Metab. 2016;23:1093–112. https://doi.org/10.1016/j.cmet.2016.05.027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Takemon Y, Chick JM, Gerdes Gyuricza I, et al. Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney. Elife. 2021;10:e62585. https://doi.org/10.7554/eLife.62585

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gerdes Gyuricza I, Chick JM, Keele GR, et al. Genome-wide transcript and protein analysis highlights the role of protein homeostasis in the aging mouse heart. Genome Res. 2022;32:838–52. https://doi.org/10.1101/gr.275672.121

    Article  PubMed  PubMed Central  Google Scholar 

  48. Orwoll ES, Wiedrick J, Nielson CM, et al. Proteomic assessment of serum biomarkers of longevity in older men. Aging Cell. 2020;19:e13253. https://doi.org/10.1111/acel.13253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sebastiani P, Federico A, Morris M, et al. Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans. Aging Cell. 2021;20:e13290. https://doi.org/10.1111/acel.13290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vizcaíno JA, Côté R, Reisinger F, et al. A guide to the proteomics identifications database proteomics data repository. Proteomics. 2009;9:4276–83. https://doi.org/10.1002/pmic.200900402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Perez-Riverol Y, Bai J, Bandla C, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50:D543–52. https://doi.org/10.1093/nar/gkab1038

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Kelly Crebs for excellent technical assistance.

Funding

This work was funded in part by the National Institutes of Health grants, from the National Institute of General Medical Sciences R01 GM087221, the Office of the Director S10OD026936, the National Institute on Aging U19AG023122, and the National Science Foundation award 1920268.

Author information

Authors and Affiliations

  1. Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA

    Adam R. Burns, Jack Wiedrick & Alicia Feryn

  2. Institute for Systems Biology, Seattle, WA, USA

    Michal Maes, Mukul K. Midha, David H. Baxter, Seamus R. Morrone, Timothy J. Prokop, Charu Kapil, Michael R. Hoopmann, Ulrike Kusebauch, Eric W. Deutsch, Noa Rappaport, Kengo Watanabe & Robert L. Moritz

  3. Department of Pathology and Geriatrics Center, University of Michigan School of Medicine, Ann Arbor, MI, USA

    Richard A. Miller

  4. School of Public Health, Oregon Health & Science University-Portland State University, Portland, OR, USA

    Jodi A. Lapidus

  5. Department of Endocrinology, Oregon Health & Science University, Portland, OR, USA

    Eric S. Orwoll

Authors
  1. Adam R. Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jack Wiedrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alicia Feryn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michal Maes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mukul K. Midha
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David H. Baxter
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seamus R. Morrone
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Timothy J. Prokop
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charu Kapil
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael R. Hoopmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ulrike Kusebauch
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eric W. Deutsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Noa Rappaport
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kengo Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Robert L. Moritz
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Richard A. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jodi A. Lapidus
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Eric S. Orwoll
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adam R. Burns.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

Supplemental Figure 1: (A) Venn diagram of the number of proteins detected in each tissue. (B) Dendrogram of similarity in protein abundances across all tissues. (PNG 108 kb)

High resolution image (TIFF 9913 kb)

ESM 2

Supplemental Figure 2: (A-C) Scatterplots comparing mean protein abundances between liver and kidney (A), liver and muscle (B), and kidney and muscle (C) samples across mice of both sexes. (D-F) Scatterplot comparing mean protein abundances between female and male samples in liver (D), kidney (E), and muscle (F) tissues. (PNG 342 kb)

High resolution image (TIFF 1657 kb)

ESM 3

Supplemental Figure 3: Variation in protein abundances explained by mouse background, sex, and longevity intervention for each quantified protein in liver (A), kidney (B), and muscle (C) tissues. Results from fitting a random effects model with intervention nested within background for each protein individually. (D) The abundances by intervention and sex of proteins for which the percent variation explained by intervention was greater than 60% in liver samples. (PNG 746 kb)

High resolution image (TIFF 2405 kb)

ESM 4

Supplemental Figure 4: The log2 fold-change and log10 Bayes factor for proteins within each treatment compared to controls for male (A) and female (B) kidney samples. Dashed vertical lines correspond to a log2 fold-change of +/- 0.5 while the dashed vertical line corresponds to a Bayes factor of 10. (PNG 569 kb)

High resolution image (TIFF 2370 kb)

ESM 5

Supplemental Figure 5: The log2 fold-change and log10 Bayes factor for proteins within each treatment compared to controls for male (A) and female (B) muscle samples. Dashed vertical lines correspond to a log2 fold-change of +/- 0.5 while the dashed vertical line corresponds to a Bayes factor of 10. (PNG 464 kb)

High resolution image (TIFF 2249 kb)

ESM 6

Supplemental Figure 6: Fold-changes in response to each longevity intervention in females (x-axis) compared to males (y-axis) in liver (A), kidney (B), and muscle (C) tissues. Dashed lines indicate log2 fold-changes of +/- 0.5 in males (horizontal) and females (vertical), while the diagonal dotted line marks a 1:1 relationship. Points are colored blue if the protein exhibited an absolute log2 fold-change greater than 0.5 with a Bayes factor greater than 10 in both sexes, red if in males only, yellow if in females only, and grey if in neither. (PNG 1040 kb)

High resolution image (TIFF 3696 kb)

ESM 7

Supplemental Figure 7: (A) Changes in protein abundances across multiple longevity treatments in kidney. Proteins shown are limited to those exhibiting an absolute log2 fold-change ≥ 0.5 and a Bayes factor ≥ 10 in response to at least three interventions of either sex. The color of the points indicates the associated Bayes factor and the size is proportional to the magnitude of the fold-change with closed points indicating a positive change and open points indicating a negative change. (B) GO-Terms that are enriched (with an associated adjusted p-value ≤ 0.01) among proteins with shared responses across longevity promoting interventions in kidney. (PNG 1022 kb)

High resolution image (TIFF 2873 kb)

ESM 8

Supplemental Figure 8: Changes in protein abundances across multiple longevity treatments in muscle. Proteins shown are limited to those exhibiting an absolute log2 fold-change greater than ≥ 0.5 and a Bayes factor ≥ 10 in response to at least three interventions of either sex. The color of the points indicates the associated Bayes factor and the size is correlated with the magnitude of the fold-change with closed points indicating a positive change and open points indicating a negative change. No GO-Terms where are enriched with an associated adjusted p-value ≤ 0.01 among proteins with shared responses across longevity promoting interventions in muscle. (PNG 218 kb)

High resolution image (TIFF 727 kb)

ESM 9

Supplemental Figure 9: Comparison of protein responses to longevity interventions in mouse livers to protein differences between long-lived humans and not in the MrOS study for proteins detected in both datasets for liver, kidney, and muscle tissues. The color of the points indicates the associated Bayes factor and the size is proportional to the magnitude of the fold-change with closed points indicating a positive change and open points indicating a negative change. (PNG 1008 kb)

High resolution image (TIFF 2505 kb)

ESM 10

Supplemental Figure 10: Protein responses to longevity-promoting intervention-by-sex groups in mouse liver, kidney, and muscle for proteins also differentially abundant in centenarians in NECS. Proteins shown are limited to those that exhibit both differential abundance in centenarian humans compared to controls with an adjusted p-value less than 0.001 in NECS and exhibited an absolute log2 fold-change ≥ 0.5 and a Bayes factor ≥ 10 in response to at least one interventions of either sex in mice. The color of the points indicates the associated Bayes factor and the size is proportional to the magnitude of the fold-change with closed points indicating a positive change and open points indicating a negative change. (PNG 1382 kb)

High resolution image (TIFF 3354 kb)

ESM 11

(CSV 4602 kb)

ESM 12

(CSV 5249 kb)

ESM 13

(CSV 2807 kb)

ESM 14

(CSV 119 kb)

ESM 15

(CSV 87 kb)

ESM 16

(CSV 36 kb)

ESM 17

(CSV 6 kb)

ESM 18

(PDF 644 kb)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burns, A.R., Wiedrick, J., Feryn, A. et al. Proteomic changes induced by longevity-promoting interventions in mice. GeroScience 46, 1543–1560 (2024). https://doi.org/10.1007/s11357-023-00917-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-023-00917-z

Keywords

Search

Navigation