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In vivo cyclic induction of the FOXM1 transcription factor delays natural and progeroid aging phenotypes and extends healthspan

Nature Aging volume 2, pages 397–411 (2022)Cite this article

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Abstract

The FOXM1 transcription factor exhibits pleiotropic C-terminal transcriptional and N-terminal non-transcriptional functions in various biological processes critical for cellular homeostasis. We previously found that FOXM1 repression during cellular aging underlies the senescence phenotypes, which were vastly restored by overexpressing transcriptionally active FOXM1. Yet, it remains unknown whether increased expression of FOXM1 can delay organismal aging. Here, we show that in vivo cyclic induction of an N-terminal truncated FOXM1 transgene on progeroid and naturally aged mice offsets aging-associated repression of full-length endogenous Foxm1, reinstating both transcriptional and non-transcriptional functions. This translated into mitigation of several cellular aging hallmarks, as well as molecular and histopathological progeroid features of the short-lived Hutchison–Gilford progeria mouse model, significantly extending its lifespan. FOXM1 transgene induction also reinstated endogenous Foxm1 levels in naturally aged mice, delaying aging phenotypes while extending their lifespan. Thus, we disclose that FOXM1 genetic rewiring can delay senescence-associated progeroid and natural aging pathologies.

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Fig. 1: FOXM1 transgene short-term induction reverts major cellular aging phenotypes of progeroid mouse fibroblasts.
Fig. 2: Cyclic induction of FOXM1 transgene expression resets the accumulation of in vitro aging phenotypes.
Fig. 3: In vivo cyclic induction of transgenic FOXM1 extends the healthspan of progeroid mice.
Fig. 4: Transgenic FOXM1 cyclic induction delays progeroid histopathological features and senescence.
Fig. 5: Enhancement of tumor-suppressive non-transcriptional function of endogenous Foxm1 by FOXM1-dNdK transgene induction.
Fig. 6: Increased expression of FOXM1 extends the healthspan of naturally aged mice.

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Data availability

Targeted-transcriptome sequencing data have been deposited in NCBI’s Gene Expression Omnibus and are available under accession number GSE193147. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the personnel at i3S Scientific Platforms for technical support: animal facility (S. Lamas), CCGEN (P. Magalhães), bioimaging (M. Lázaro), genomics (A. M. Rocha), advanced light microscopy (P. Sampaio) and biosciences screening (A. Maia). The Genomics platform is part of the GenomePT project (POCI-01-0145-FEDER-022184). Bioimaging, ALM and BS platforms are members of the Portuguese Platform of Bioimaging (PPBI-POCI-01-0145-FEDER-022122). We thank B. Wen and E. Li (Cincinnati Children’s Hospital Medical Center) and A. Águas (ICBAS, Porto University) for valuable reagents and technical support in histology. We are grateful to all laboratory members for discussions during this work. The laboratory of E.L. was supported by: Portuguese funds through Fundação para a Ciência e a Tecnologia (FCT) in the framework of the project PTDC/MED-OUT/2747/2020; a grant from the Progeria Research Foundation (PRF 2020-78); FEDER (Fundo Europeu de Desenvolvimento Regional) funds through the COMPETE 2020–Operational Programme for Competitiveness and Internationalization (POCI), Portugal 2020 and by Portuguese funds through FCT, I. P., in the framework of the project POCI-01-0145-FEDER-031120 (PTDC/BIA-CEL/31120/2017); and by POCI-01-0145-FEDER-007274 i3S framework project co-funded by COMPETE 2020/PORTUGAL 2020 through FEDER. V.N.G. was supported by grants from the National Institutes of Health. R.R. was supported by an FCT fellowship (PD/BD/128000/2016). E.L. was supported by grants IF/00916/2014 and CEECIND/00654/2020. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Aging and Aneuploidy Laboratory, i3S – Instituto de Investigação e Inovação em Saúde, IBMC - Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal

    Rui Ribeiro, Joana C. Macedo, José Pedro Castro & Elsa Logarinho

  2. Graduate Program in Areas of Basic and Applied Biology (GABBA), ICBAS - Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal

    Rui Ribeiro

  3. Anatomy Department, Unit for Multidisciplinary Biomedical Research, ICBAS - Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Porto, Portugal

    Madalena Costa

  4. Center for Lung Regenerative Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Vladimir Ustiyan & Vladimir V. Kalinichenko

  5. Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Vladimir Ustiyan, Tanya V. Kalin & Vladimir V. Kalinichenko

  6. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Anastasia V. Shindyapina, Alexander Tyshkovskiy & Vadim N. Gladyshev

  7. Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia

    Alexander Tyshkovskiy & Sergey E. Dmitriev

  8. INEB – Instituto Nacional de Engenharia Biomédica, i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal

    Rita N. Gomes, Francisco Vasques-Nóvoa & Diana S. Nascimento

  9. ICBAS – Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal

    Rita N. Gomes & Diana S. Nascimento

  10. Cardiovascular Research and Development Center, Faculty of Medicine of the University of Porto, Porto, Portugal

    Francisco Vasques-Nóvoa

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Conceptualization: E.L.; methodology: all authors; formal analysis: R.R., R.N.G., V.U., A.V.S., A.T., J.P.C., F.V.-N., V.V.K., and E.L.; investigation: all authors; resources: A.T., D.S.N., V.N.G., T.V.K., V.V.K., and E.L.; data curation: R.R.; writing–original draft: R.R. and E.L.; writing–review and editing: all authors; supervision: E.L.; project administration: E.L.; funding acquisition: E.L.

Corresponding author

Correspondence to Elsa Logarinho.

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Competing interests

The authors declare no competing interests.

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Nature Aging thanks Jan Deursen, David Sinclair 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 Improvement of proliferation fitness of progeroid fibroblasts following FoxM1-dNdK short-term induction (related to Fig. 1).

(a) RT-qPCR analysis of endogenous FoxM1 transcript levels (2-ΔΔCt) following 2- and 4-days induction with doxycycline (dox; 2d, 4d). (b) RT-qPCR analysis of FOXM1-dNdK mRNA levels (2-ΔCt) in LAKI-FoxM1 fibroblasts. (c-f) RT-qPCR analysis of relative CcnB1 (c), Plk1 (d), p21/Cdkn1a (e) and p16/Cdkn2a (f) mRNA levels (2-ΔΔCt). (g) Western blot analysis of progerin/LMNA/C protein levels. Vinculin was used as loading control. (h) Quantification of progerin levels from western blot analysis. (i) Percentage of cells staining positive for the Annexin V-FITC apoptosis marker. (j) Western blot analysis of total FOXM1 protein levels in LAKI MAFs treated with dox for 2 and 4 consecutive days. (k) Quantification of total FOXM1 protein levels. Tubulin was used as loading control and levels were normalized to untreated cells (0d). (l-q) Immunofluorescence analysis of cellular aging phenotypes in LAKI MAFs following dox-treatment. (l) Percentage of proliferative cells (Ki67 + ). (m) Percentage of cells with DNA damage (γH2AX + ). (n) Percentage of cells staining positive for SA-β-gal activity assay. (o) Quantification of the fluorescence intensity levels of H3K9me3 and H4K20me3 epigenetic marks. (p) Percentage of cells with nuclear blebbing. (q) Quantification of the nuclear area. Error bars represent s.d. “n” in all graphs refer to the number of independent experiments, except for (o, q) where n = number of cells. Tbp and Gapdh were used as reference genes and expression levels were normalized to untreated LAKI-FoxM1 fibroblasts (0d). Statistics were performed by ordinary one-way ANOVA with Tukey’s multiple comparison correction (a-f, h, i, k), two-sided Fisher’s exact (l-n, p) and two-sided Kruskal-Wallis with Dunn’s multiple comparison correction (o, q) statistical tests for the indicated comparisons.

Source data

Extended Data Fig. 2 Improvement of aging molecular signature in progeroid fibroblasts under FOXM1-dNdK cyclic induction (related to Fig. 2).

(a) RT-qPCR analysis of endogenous FoxM1 transcript levels (2-ΔΔCt) in LAKI-FoxM1 MAFs under cyclic dox treatment as indicated. (b) RT-qPCR analysis of absolute FOXM1-dNdK transcript levels (2-ΔCt). (c-e) RT-qPCR analysis of Ccnb1 (c), Plk1 (d) and p16/Cdkn2a (e) transcript levels (2-ΔΔCt). (f) Western blot analysis of total FOXM1 protein levels following short-term induction of FOXM1-dNdK expression under serum starvation. (g) Quantification of total FOXM1 protein levels from western blot analysis. Tubulin was used as loading control. (h,i) Quantification of Ki67+ proliferating cells (h) and cells staining positive for SA-β-gal activity assay (i). Scale bar: 10 µm. Error bars represent s.d. “n” in all graphs refer to the number of independent experiments. Tbp and Gapdh were used as reference genes and expression levels were normalized to untreated LAKI-FoxM1 fibroblasts (0d). Statistics were performed by ordinary one-way ANOVA with Tukey’s multiple comparison correction (a-e, g) and two-sided Fisher’s exact (h, i) statistical tests for the indicated comparisons.

Source data

Extended Data Fig. 3 Lifespan extension and improvement of cardiovascular dysfunction in HGPS mice upon FOXM1 transgene induction (related to Fig. 3).

(a) Survival curve of LAKI C57Bl6 parental strain (ref20.) and LAKI C57Bl6xFVB strain (this study). (b,c) Survival curves of LAKI and LAKI-FoxM1 males (b) and females (c) under no-dox diet. (d,e) Survival curves of LAKI and LAKI-FoxM1 males (d) and females (e) under cyclic dox-diet. (f) Mean lifespan (weeks) of the littermates used for survival curve analysis. (g-i) Electrophysiological analysis of ejection fraction (EF) (g), stroke volume (SV) (h) and ratio between left ventricular early (E) to late (A) filling velocities (E/A) (i). (j) Representative images of Masson’s trichrome staining and (k) Sirius Red/Fast Green staining of heart sections of WT, LAKI, and LAKI-FoxM1 littermates. (l) Quantification of interstitial fibrosis. Scale bars: 1 mm (j) and 10 μm (k). Error bars represent s.d. “n” represents individual mice throughout. Statistics were performed by log-rank (Mantel-Cox) (a-e), unpaired two-sided t-test (f), two-sided Kruskal-Wallis with Dunn’s multiple comparison correction (g, i, l) and ordinary one-way ANOVA with Tukey’s multiple comparison correction (h) statistical tests for the indicated comparisons.

Source data

Extended Data Fig. 4 Improvement of HGPS histopathological features and associated molecular signatures upon FOXM1-dNdK cyclic induction (related to Fig. 4).

(a) Quantification of the epidermis layer thickness of telogenic skin. (b) Histological analysis of anagenic skin of 16 weeks-old WT, LAKI, and LAKI-FoxM1 littermates as indicated. Dashed lines delimitate the hypodermal layer. (c,d) Quantification of hypodermis (c) and epidermis (d) layers’ thickness in anagenic skin. (e) RT-qPCR analysis of FOXM1-dNdK expression in the skin (2-ΔCt). (f) RT-qPCR analysis of FoxM1 expression in the skin (2-ΔΔCt). (g-i) Western blot analysis of total FOXM1 (g) and p21 (h,i) protein levels in skin extracts. GAPDH and Vinculin were used as loading controls. (j,k) Immunostaining and quantification of cytokeratin-5 (K5)-expressing keratinocytes in skin. (l) RT-qPCR analysis of FOXM1-dNdK expression in gonadal fat (2-ΔCt). (m) RT-qPCR analysis of FoxM1 expression in gonadal fat (2-ΔΔCt). (n,o) Masson’s Trichrome of aorta sections of the indicated mice and respective quantification. (p) RT-qPCR analysis of FOXM1-dNdK expression in aorta (2-ΔCt). (q) RT-qPCR analysis of FoxM1 expression in the aorta (2-ΔΔCt). (r) 3D modeling of the cortical tibia (diaphysis). (s) Quantification of micro-CT analysis of cortical bone thickness. (t) Cytokine levels in serum samples. (u) RT-qPCR analysis of FOXM1-dNdK expression in gonadal fat (2-ΔCt). (v) RT-qPCR analysis of FoxM1 expression in gonadal fat (2-ΔΔCt). (x) RT-qPCR analysis of senescence markers in the kidney. Scale bars: 50 μm (b, n), 25 μm (j) and 5 mm (r). Tbp and Gapdh were used as reference genes and expression levels were normalized to WT mice (2-ΔΔCt). Error bars represent s.d. “n” represents independent experiments, except for (a, c, d, k, o, s) where n = individual mice. Statistics were performed by two-sided Kruskal-Wallis test with Dunn’s multiple comparison correction (a, d, o) and ordinary one-way ANOVA with Tukey’s multiple comparison correction (c, e, f, i, k-m, p, q, s-x) statistical tests for the indicated comparisons.

Source data

Extended Data Fig. 5 FoxM1 downregulation in the gut, heart, and brain tissues of naturally aged mice (related to Fig. 6).

(a-c) Gene expression analysis of endogenous FoxM1 transcript levels (2-ΔΔCt) in 4-week-old vs. 2-year-old WT mice. Tbp and Gapdh were used as reference genes and expression levels were normalized to 4w-old WT mice. Error bars represent s.d. “n” represents individual mice. Statistics were performed by unpaired two-sided t-test.

Source data

Extended Data Fig. 6 Lifespan extension upon widespread expression of FOXM1-dNdK transgene (related to Fig. 6).

(a,b) Survival curves of Rosa26 and FoxM1-dNdK males (a) and females (b) under no-dox diet. (c,d) Survival curves of Rosa26 and FoxM1-dNdK males (c) and females (d) under cyclic dox-diet. (e) Mean lifespan (weeks) of the littermates used for survival curve analysis. (f) RT-qPCR analysis of endogenous FoxM1 expression in tissues of 88-week-old (±1.7 y) FoxM1-dNdK (2-ΔΔCt). Tbp and Gapdh were used as reference genes and expression levels were normalized to those in Rosa26 mice tissues. (g) Western blot analysis of FOXM1-dNdK protein levels in extracts of skin, kidney, and liver of 88-week-old mice under dox-diet cyclic induction. Vinculin was used as loading control. (h) Immunostaining of FOXM1-dNdK on skin, kidney and liver sections of the mentioned mice. Scale bars: 100 μm (kidney insets, 50 μm). Error bars represent s.d. “n” represents individual mice throughout, except for (f) where n = independent experiments. Statistics were performed by log-rank (Mantel-Cox) (a-d) and unpaired two-sided t-test (e, f) statistical tests.

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Extended Data Fig. 7 Tumor incidence analysis on cohorts under long-term FOXM1 transgene induction (related to Fig. 6).

(a) Representative photographs of 88-week-old Rosa26 and FoxM1-dNdK littermates’ necropsy. (b) Post-mortem solid tumor incidence on the indicated cohorts. “n” represents individual mice. (c) Summarization of spontaneous solid tumor formation observed in cohorts indicated in (b). (d) Representative images of liver and kidney histological sections for exclusion of lymphocyte infiltration in n = 3 animals per genotype. Scale bars: 1 cm (a) and 200 μm (d). Error bars represent s.d. Statistics were performed by two-sided Fisher’s exact statistical test for the indicated comparisons.

Source data

Extended Data Fig. 8 Improvement of senescence-associated histopathological aging features upon long-term FOXM1-dNdK induction (related to Fig. 6).

(a,b) Immunostaining and quantification of PAX7-positive muscle stem cells (arrowheads) in sections of the gastrocnemius muscle of Rosa26 and FoxM1-dNdK mice. (c) Grip-strength test in 14-16w-old Rosa26 and in 88w-old Rosa26 and FoxM1-dNdK littermates (n = 3 animals per genotype). Dashed lines interconnect values of 3 measurements in the same animal obtained with 5 min interval. (d,e) Masson’s Trichrome staining of aorta sections of Rosa26 and FoxM1-dNdK littermates and respective quantification. (f-h) Exclusion of sex bias effect in improved phenotypes upon FOXM1 transgene induction. (i-l) RT-qPCR analysis of senescence markers in the skin (i), kidney (j), gonadal fat (k), and gastrocnemius muscle (l) of 88-week-old Rosa26 and FoxM1-dNdK littermates. Tbp and Gapdh were used as reference genes and expression levels were normalized to Rosa26 mice (2-ΔΔCt). Scale bars: 50 μm (a, d) and 2.5 μm (inset in a). Error bars represent s.d. “n” is the number of mice except in (i-l) where n = independent experiments. Statistics were performed by two-sided Fisher’s exact (b, e), unpaired two-sided t-test (c, i-l), and ordinary one-way ANOVA with Tukey’s multiple comparison correction (f-h) statistical tests for the indicated comparisons.

Source data

Extended Data Fig. 9 Impact of FOXM1 transgene induction in tissues from young animals (related to Fig. 6).

(a) Histological analysis of the aorta in littermate mice as indicated and respective quantification (b) of vascular smooth muscle cells (VSMC) nuclei density and (c) of aortic wall thickness. (d) Histological analysis of telogenic skin. Dashed lines delimitate the hypodermal layer. (e,f) Quantification of hypodermal and epidermal layers’ thickness in telogenic skin. (g) Histological analysis of gonadal fat tissue and (h) quantification of adipocyte cross-sectional area and (i) crown-like structures. (j) Histological analysis of gastrocnemius and (k) quantification of muscle fiber cross-sectional area. Scale bar: 10 μm (a) and 50 μm (d, g, j). Error bars represent s.d. “n” represents the number of individual mice. Statistics were performed by unpaired two-sided t-test.

Source data

Extended Data Fig. 10 Bioinformatic analysis of transcriptomic changes in the aorta and gastrocnemius produced by FOXM1-dNdK induction (related to Fig. 6).

(a) Principal component analysis (PCA) of aorta (circles) and muscle (triangles) samples from 88-week-old Rosa26 (black) and FoxM1-dNdK (green) mice under cyclic dox-diet. (b,c) MSigDB pathways found similarly (b) and oppositely (c) altered in the aorta and muscle. (d) KEGG pathway analysis for the aorta signature. (e) GSEA-analysis in the gastrocnemius using pathways from MSigDB databases. (f) KEGG pathway analysis for the gastrocnemius signature. (g) GSEA enrichment plot for ‘SASP’ custom pathway in the FoxM1-dNdK gastrocnemius. (h) Association of transcriptomic changes induced by FOXM1-dNdK in the muscle with signatures of lifespan-extending interventions (LEI) (GH - growth hormone; Common LEI - common gene expression changes across different interventions) and aging (muscle and murine multi-tissue). (i) Correlation matrix of aggregated gene expression profiles across interventions in comparison with signatures from the aorta and gastrocnemius muscle under long-term induction of FOXM1-dNdK. (j) Pathways altered in lifespan-extending interventions, aging signatures and FOXM1-dNdK overexpressing tissues. Significance scores of functional enrichment analyses (d-h) are described in Methods.

Supplementary information

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Ribeiro, R., Macedo, J.C., Costa, M. et al. In vivo cyclic induction of the FOXM1 transcription factor delays natural and progeroid aging phenotypes and extends healthspan. Nat Aging 2, 397–411 (2022). https://doi.org/10.1038/s43587-022-00209-9

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