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

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.

Main

Aging is characterized by a gradual loss of function occurring at the molecular, cellular, tissue and organismal levels, which is the main risk factor for the development of chronic diseases and declining overall health1. Growing evidence supports the idea that loss of proliferative capacity is a conspicuous feature of cellular aging interrelated with multiple canonical aging hallmarks such as genomic instability, epigenetic dysregulation and cellular senescence2,3. Indeed, dysfunction of the mitotic machinery was shown to trigger deleterious epigenetic alterations and cellular senescence4. Mutant mice carrying hypomorphic alleles of the spindle assembly checkpoint protein BubR1 were reported to develop cellular aging phenotypes and organismal progeroid features5,6. The putative role of mitotic decline in aging was further reinforced by the demonstration that gene expression modulation of the critical proliferation-associated transcription factor FOXM1, which primarily drives the expression of G2/M genes (including BUBR1; ref. 7) rescues several aging hallmarks including the accrual of pro-inflammatory senescent cells8,9. However, the pleiotropic role of FOXM1 in a wide spectrum of biological functions most likely explains its senomorphic effect and our previous observation that ≈60% of the aging-associated transcriptional changes in human dermal fibroblasts are FOXM1 dependent8. Mounting evidence has suggested that besides C-terminal-dependent transcriptional activity in cell proliferation and response to DNA damage and oxidative stress10,11, an N-terminal-dependent non-transcriptional activity accounts for cytoplasmic regulatory functions in mitochondria homeostasis12 and cortical actin stiffness13. In particular, the latter indicated that a non-transcriptional function, via interaction with the Ect2–RhoA–mDia1 axis controlling cortical actin nucleation, is critical for proper centrosome positioning during chromosome segregation and mitotic fidelity13.

In our previous study, we found that transient induction of an N-terminal truncated form of FOXM1 (FOXM1-dNdK) in fibroblasts from patients with Hutchinson–Gilford progeria syndrome (HGPS) enhanced the proliferative capacity and mitotic fidelity while delaying the premature onset of cellular senescence8. HGPS is caused by a heterozygous mutation c.1824C > T;(p.Gly608Gly) in the LMNA gene (encoding nuclear lamins A/C) that activates a cryptic splicing site generating a 50-amino acid truncated isoform of lamin A, referred to as progerin14,15. Progerin cannot be fully processed posttranslationally, remaining farnesylated and accumulating at the nuclear envelope, leading to massive functional and structural nuclear defects, such as heterochromatin disorganization, increased DNA damage, nuclear shape abnormalities, mitotic defects and cellular senescence (all canonical hallmarks of aging)16,17,18. HGPS clinical manifestation includes growth impairment, alopecia, extensive lipodystrophy, dermal and bone abnormalities and cardiovascular alterations, all leading to shortened lifespan (12–14 years) and death by cardiovascular diseases15,19. The gold-standard HGPS mouse model (LAKI mice), designed by ref. 20, carries the causative homozygous mutation LmnaG609G/G609G that phenocopies the main molecular and clinical features of human LMNAG608G/+ HGPS, namely progerin accumulation, loss of subcutaneous adipose tissue, lipodystrophy, skeletal abnormalities, retarded growth, weight loss and shortened lifespan (14–15 weeks)20. So far, lonafarnib, a farnesyltransferase inhibitor, is the only US Food and Drug Administration-approved treatment for HGPS (Zokinvy21), yet with demonstrated mild effects in lifespan extension (±10%), and reported off-target action and in vivo toxicity22,23,24,25. Genome editing of the mutation or transient reprogramming to pluripotency have emerged as exciting therapeutic strategies, albeit not clinically applicable yet20,26,27,28. Other genetic modulations targeting downstream actions of progerin have also been reported29.

To gain further insight into the impact of FOXM1 modulation in pathological and physiological aging, we devised in vivo platforms of FOXM1-dNdK induction using both progeria and natural aging mouse models. Induced expression of the truncated FOXM1 transgene was found to amend the aging-associated decline of endogenous Foxm1 across multiple tissues, thereby acting to reinstate both transcriptional and non-transcriptional functions that translated into attenuated senescence in proliferative and non-proliferative tissues, as well as extended lifespan. FOXM1 is thus disclosed as a promising genetic target for therapeutic strategies against progeria and age-related diseases.

Results

C-terminal FOXM1 transgene induction rescues HGPS cellular progeroid phenotypes

Fibroblasts of patients with HGPS exhibit canonical aging hallmarks, namely persistent DNA damage, premature senescence, global epigenetic dysregulation and abnormal nuclear architecture3,30,31. We have previously shown genomic instability and senescence to be rescued by increased expression of truncated FOXM1-dNdK in fibroblasts from healthy octogenarian and HGPS donors8. Thus, we asked if FOXM1 transgene expression could also amend organismal aging, in both pathological and natural contexts.

We began by crossing a mouse model of ubiquitous and inducible expression of truncated FOXM1 transgene (GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+) with the HGPS mouse model (LAKI, LmnaG6096G/G609G)20,32, to generate GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+LmnaG609G/G609G mice (LAKI-Foxm1). We collected mouse adult fibroblasts (MAFs) from the ear dermis to establish low-passage cultures and test the effect of in vitro short-term transgene induction. MAFs were treated with doxycycline (dox) for 2 and 4 d and FOXM1-dNdK transgene expression was confirmed by immunofluorescence and western blot analyses (Fig. 1a–c and Extended Data Fig. 1a,b). Interestingly, in line with FOXM1 activating its promoter33, transgene induction increased the endogenous Foxm1 levels (Extended Data Fig. 1a), which suggests that non-transcriptional functions (for example, in mitotic fidelity13) are agreeably increased besides transcriptional ones. Short-term transgene induction increased the percentage of cells staining positive for Ki67 proliferation marker by 1.5-fold in comparison to untreated cells (Fig. 1d,e). Also, transcript levels of the Ccnb1 and Plk1 mitotic targets of FOXM1 were upregulated (Extended Data Fig. 1c,d), with a concomitant 1.5-fold increase of the mitotic index in dox-treated LAKI-Foxm1 MAFs (Fig. 1f).

Fig. 1: FOXM1 transgene short-term induction reverts major cellular aging phenotypes of progeroid mouse fibroblasts.
figure 1

a, Representative images of GFP-FOXM1-dNdK immunostaining in dermal fibroblasts from LAKI-Foxm1 mice upon dox treatment for 2 and 4 consecutive days. b, Western blot analysis of total FOXM1 protein levels following short-term continuous induction of FOXM1-dNdK expression for 2 and 4 d. c, Quantification of total FOXM1 protein relative levels. Tubulin was used as loading control and protein levels were normalized to untreated cells (0 d). d, Immunofluorescence of Ki67+ proliferating cells. e, Quantification of Ki67+ cells from immunofluorescence analysis. f, Mitotic index (percentage of phospho-H3-positive cells) of LAKI-Foxm1 fibroblasts’ cultures. g, Immunofluorescence of cells with DNA damage (γH2AX+). h, Quantification of γH2AX+ cells from immunofluorescence analysis. i, Representative images of SA-β-gal activity assay. j, Percentage of cells staining positive for SA-β-gal activity assay. k, Immunofluorescence of heterochromatin-associated epigenetic marks H3K9me3 (gray) and H4K20me3 (red). l, Quantification of the fluorescence intensity levels ratio H3K9me3/H4K20me3. m, DAPI staining for nuclear morphology analysis of LAKI-Foxm1 fibroblasts. n,o, Quantification of nuclear blebbing incidence (n) and of the nuclear area (o) of LAKI-Foxm1 fibroblasts. Scale bars, 10 µm (a, d, g, k and m) and 20 µm (i). Error bars represent the s.d. ‘n’ in all graphs refer to the number of independent experiments, except for l and o where n > 200 cells. Statistics were performed in comparison with untreated control (0 d) using ordinary one-way analysis of variance (ANOVA) with Tukey’s multiple-comparison correction (c), two-tailed Fisher’s exact (e, f, h, j and n) and Kruskal–Wallis test with Dunn’s multiple-comparison correction (l and o) tests.

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HGPS cells typically exhibit exacerbated DNA damage markers34,35. Short-term transgene induction significantly reduced the number of cells staining positive for the γH2AX double-strand breaks marker (Fig. 1g,h). We next asked whether the increase in cell proliferation and decreased DNA damage translated into diminished cellular senescence4. We indeed found a significantly lower percentage of LAKI-Foxm1 MAFs staining positive for senescence-associated β-galactosidase (SA-β-gal) activity, with concurrent downregulation of p21/Cdkn1a and p16/Cdkn2a cell cycle inhibitors (Fig. 1i,j and Extended Data Fig. 1e,f). Because progerin expression widely impairs epigenetic regulation36,37, we also investigated the heterochromatin-associated epigenetic marks H3K9me3 and H4K20me3, which are typically downregulated and upregulated in HGPS cells, respectively36,38,39. Noticeably, FOXM1-dNdK induction rescued the levels of these marks toward a rejuvenated epigenetic state (Fig. 1k,l). Finally, we evaluated the effect of transgene induction in the progerin-driven nuclear blebbing phenotype40. We found a significant reduction in nuclear abnormalities and the nuclear area upon dox treatment (Fig. 1m–o), pointing to restored nuclear envelope architecture. Changes in progerin levels did not account for this improvement (Extended Data Fig. 1g,h). Also, and discarding the possibility of a senolytic effect, we did not observe increased apoptosis following FOXM1-dNdK induction (Extended Data Fig. 1i). Moreover, none of the phenotypes were ameliorated in control LAKI MAFs retrieved from Rosa26-rtTAtg/+LmnaG609G/G609G mice, demonstrating that FOXM1-dNdK induction specifically accounted for the phenotypic rescue (Extended Data Fig. 1j–q).

FOXM1 transgene cyclic induction amends progeria cellular phenotypes

Next, we monitored the perdurance of improved cellular aging hallmarks after truncated FOXM1 induction. To this end, we tested a cyclic induction scheme comprising 4 d of dox treatment followed by evaluation of the cellular phenotypes at 2 and 5 d after dox withdrawal (Fig. 2a–c and Extended Data Fig. 2a,b). We found a sustained reduction of aging phenotypes in LAKI-Foxm1 MAFs under cyclic treatment (Fig. 2d–h). Relevantly, 4 d of re-induction after 5 d of withdrawal was able to prevent the re-accumulation of aging phenotypes, with MAFs exhibiting sustained proliferative capacity (Fig. 2d and Extended Data Fig. 2c,d), lower levels of DNA damage (Fig. 2e), reduced cellular senescence (Fig. 2f,g and Extended Data Fig. 2e) and remodeled heterochromatin-associated epigenetic marks (Fig. 2h).

Fig. 2: Cyclic induction of FOXM1 transgene expression resets the accumulation of in vitro aging phenotypes.
figure 2

a, Representative images of GFP-FOXM1-dNdK immunostaining in dermal fibroblasts from LAKI-Foxm1 mice under cyclic dox treatment as indicated. b, Western blot analysis of total FOXM1 protein levels following cyclic induction of FOXM1-dNdK expression. c, Quantification of total FOXM1 protein levels from western blot analysis. Tubulin was used as loading control and protein levels were normalized to those of untreated cells (0 d). d, Quantification of Ki67+ proliferating cells. e, Quantification of cells with DNA damage (γH2AX+). f, RT–qPCR analysis of p21/Cdkn1a transcript levels (2−ΔΔCt). Tbp and Gapdh were used as reference genes and expression levels were normalized to those of untreated cells. g, Quantification of cells staining positive for SA-β-gal activity assay. h, Quantification of the fluorescence intensity levels ratio between H3K9me3 and H4K20me3 epigenetic marks. Scale bar, 10 µm. Error bars represent the s.d. ‘n’ in all graphs refer to the number of independent experiments, except for h where n > 200 cells. Statistics were performed in comparison to untreated controls (0 d) using ordinary one-way ANOVA with Tukey’s multiple-comparison correction (c and f), two-tailed Fisher’s exact test (d, e and g) and Kruskal–Wallis test with Dunn’s multiple-comparison correction (h).

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We previously demonstrated that FOXM1-dNdK induction increases proliferation and mitotic efficiency in fibroblast cultures from older human adults, which in turn, diluted the senescent cell population while preventing senescence accrual8. To expand this observation, and determine if senescence rescue depends on proliferative capacity, we measured senescence in LAKI-Foxm1 MAFs under non-proliferative culture conditions (Extended Data Fig. 2f–i). We found that serum starvation (deprived of FBS), which induces G0/G1 arrest, mitigated the positive effect of FOXM1-dNdK induction on proliferative rate (Extended Data Fig. 2h) and senescence reduction (Extended Data Fig. 2i). When restoring serum to cells starved for 4 d, proliferation increased (Extended Data Fig. 2h) and the percentage of SA-β-gal-positive cells was again reduced (Extended Data Fig. 2i). FOXM1-dNdK induction specifically accounted for senescence rescue, as senescence levels remained unchanged in controls without dox treatment.

Overall, the results suggest that the beneficial effect of FOXM1-dNdK induction persists over time, under a cyclic ‘on/off’ setup, delaying the emergence and accrual of senescent cells in proliferating fibroblast cultures.

FOXM1 induction improves organismal fitness and extends the lifespan of progeroid mice

Once the global improvement of cellular aging by transgene cyclic induction in fibroblasts from progeroid mice was established, we next evaluated if it has equivalent benefits at the organismal level. We established an in vivo platform of FOXM1-dNdK induction in LAKI-Foxm1 (GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+LmnaG6096G/G609G), LAKI (Rosa26-rtTAtg/+LmnaG6096G/G609G) and wild-type (WT; Lmna+/+) littermates, starting in 4-week-old animals and during 12 weeks, to test the prevention, not the reversion, of the onset of HGPS phenotypes. We followed a cyclic scheme of 3 d under the dox diet followed by 4 d of a non-supplemented diet based on the in vitro validation for its comparable efficacy to continuous induction which, therefore, was not tested in vivo. Notably, FOXM1-dNdK cyclic induction improved the typical weight loss and growth retardation observed in LAKI mice20 (Fig. 3a). In addition, median and maximum lifespans of LAKI-Foxm1 mice under the dox diet were extended by ~25% in comparison to LAKI controls (median, 18.1 versus 14.4 weeks; maximum, 21.9 versus 17.7 weeks; Fig. 3b,c). The mixed genetic background did not account for lifespan differences in LAKI (FVB × C57BL/6) versus the original LAKI strain (C57BL/6)20 (Extended Data Fig. 3a). Sex was also excluded as a potential variable in lifespan extension (Extended Data Fig. 3b–e). Also, LAKI-Foxm1 mice consistently exhibited increased mean lifespan in comparison to their LAKI littermates (Extended Data Fig. 3f). Noteworthy, necropsy analysis of dox-treated LAKI-Foxm1 mice did not reveal any gross signs of hyperplasic tissue growth (Fig. 3d).

Fig. 3: In vivo cyclic induction of transgenic FOXM1 extends the healthspan of progeroid mice.
figure 3

a,b, Body weight (a) and Kaplan–Meier survival curves (b) of LAKI and LAKI-Foxm1 mice under Foxm1-dNdK cyclic induction scheme (3 d dox diet; 4 d dox withdrawal). c,d, Representative photographs of 16-week-old WT, LAKI and LAKI-FoxM1 littermates upon Foxm1-dNdK cyclic induction (c) and respective necropsy analysis (d). e, Representative micro-CT 3D reconstructions of WT, LAKI and LAKI-Foxm1 cranial skeletons. Insets highlight the cranial sutures and the arrowhead points to the loss of the zigzag appearance of the cranial sutures in LAKI mice. f, Whole-skeleton 3D reconstructions. g, Quantification of kyphosis index based on 3D reconstructions. h, Representative micro-CT images segmented for whole-body fat (green). Lungs (marked by an asterisk), which share similar tissue density with adipose, were excluded from the analysis. i, Quantification of the abdominal fat volume based on 3D reconstructions. j, Representative images of the ECG parasternal long-axis view. kn, Cardiac functional analysis of LV end-diastolic volume (EDV) (k), LV posterior wall thickness in diastole (LV PWd) (l), LV mass (m) and cardiac index (CI) (n). o, Representative ECG signal traces. p,q, ECG analysis of heart rate (beats per minute) (p) and QTc interval (q). Scale bar, 1 cm (cf and h). Error bars represent the s.d. ‘n’ refers to the number of individual mice. Statistics were performed using two-way ANOVA (a), log-rank (Mantel–Cox) test (b), ordinary one-way ANOVA with Tukey’s multiple-comparison correction (g, i, kn and p) and Kruskal–Wallis test with Dunn’s multiple-comparison correction (q).

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Next, we investigated for skeletal defects, namely craniofacial abnormalities, such as reduction in the zigzag appearance of the cranial sutures, and kyphosis (marked curvature of the vertebral column)20,41. Three-dimensional (3D) microcomputed tomography (micro-CT) analysis revealed mild improvement of the cranial sutures (Fig. 3e) and a clear decrease of the kyphosis index in dox-treated LAKI-Foxm1 mice versus LAKI controls (Fig. 3f,g and Supplementary Videos 13). Additionally, we evaluated another overt progeroid phenotype, the severe reduction of fat deposits (lipodystrophy). Micro-CT scanning of the whole-body fat 3D distribution revealed attenuation of lipodystrophy upon FOXM1-dNdK induction, with an expansion of the subcutaneous fat deposit and significant increase of the abdominal fat volume (Fig. 3h,i). Finally, we assessed cardiac function and structure, as early death in HGPS is believed to stem primarily from heart failure, which drives the occurrence of eventually fatal cardiac and cerebrovascular events22. Cardiac evaluation comprising functional, electrophysiological and histological analysis was performed in the hearts of 16-week-old mice. Echocardiography (ECG; Fig. 3j) revealed that, alike progeroid mice, LAKI-FoxM1 displayed smaller hearts compared to WT controls. Accordingly, both progeroid groups showed reduced left ventricle (LV) end-diastolic volumes, wall thickness and lower LV mass, compared to the WT group, although partially rescued on LAKI-FoxM1 mice (Fig. 3k–m). Regarding cardiac performance, the LAKI group presented mildly reduced ejection fraction and stroke volume (Extended Data Fig. 3g,h) resulting in a tendency toward a smaller cardiac index despite higher heart rate (Fig. 3n–p). The apparent contractile dysfunction observed in the LAKI group was associated with electrocardiographic alterations, namely prolonged QTc interval (Fig. 3q). Of note, these differences were not observed in LAKI-Foxm1 mice supporting attenuated cardiac dysfunction and electrical remodeling. Additionally, Doppler evaluation demonstrated a lower transmitral valve early to late filling velocities ratio (Extended Data Fig. 3i), disclosing a tendency for diastolic dysfunction in LAKI mice, which was not observed in LAKI-Foxm1 mice. Histological analysis of hearts stained with Masson’s trichrome and Sirius Red/Fast Green excluded fibrosis as contributing to cardiac dysfunction in LAKI mice (Extended Data Fig. 3j–l).

Collectively, these results demonstrate that FOXM1 transgene cyclic induction in vivo leads to an overall improvement of progeroid features, including the death-causing cardiac dysfunction, extending the lifespan of progeria mice.

FOXM1 induction delays progeroid histopathological hallmarks while counteracting senescence

Progerin toxicity leads to severe histological alterations in numerous tissues of patients with HGPS, namely sclerotic skin, lipodystrophy, osteoporosis, aorta medial layer depletion of vascular smooth muscle cells (VSMCs) and adventitial fibrotic thickening19,20,42,43,44,45,46,47. Thus, we investigated these progeroid phenotypes in 16-week-old LAKI-Foxm1 mice under cyclic FOXM1 induction, further complemented by gene expression analysis of senescence markers.

Histological analysis of dorsal skin at different stages of the hair follicle cycle (anagen and telogen) revealed LAKI mice to exhibit severe thinning and atrophy of the subcutaneous fat layer as expected. FOXM1-dNdK induction reestablished skin homeostasis, with increased subcutaneous adipose layer and epidermal layer thickness compared to control mice (Fig. 4a,b and Extended Data Fig. 4a–d). Quantitative PCR with reverse transcription (RT–qPCR) confirmed transgene expression in the skin and, in addition, revealed restored expression of endogenous Foxm1 to equivalent levels as in WT (Extended Data Fig. 4e,f). Importantly, several senescence markers48 upregulated in the skin of LAKI mice were returned to WT levels in LAKI-Foxm1 mice (Fig. 4c). Western blot analysis further validated amended FOXM1 and p21 protein levels in LAKI-Foxm1 mice (Extended Data Fig. 4g–i). Moreover, the expression of cytokeratin-5, a positive modulator and marker of cell proliferation in the basal layer of the epidermis49 was restored (Extended Data Fig. 4j,k).

Fig. 4: Transgenic FOXM1 cyclic induction delays progeroid histopathological features and senescence.
figure 4

a, Histological analysis of telogenic skin of 16-week-old WT, LAKI and LAKI-FoxM1 littermates. Dashed lines delimitate the hypodermal adipose layer. b, Quantification of hypodermis layer thickness. c, RT–qPCR analysis of senescence markers expression in the skin (2−ΔΔCt). d,e, Histological analysis (d) of gonadal fat and quantification (e) of adipocyte cross-sectional area (>150 adipocytes per animal). f, RT–qPCR analysis of senescence marker expression in gonadal fat (2−ΔΔCt). g, Histological analysis of aorta. Arrowheads point to vacuolated smooth muscle cells. h,i, Quantification of the density of VSMC nuclei in the medial wall (h) and the aortic wall thickness (i). j, Masson’s trichrome staining of aorta sections. k, RT–qPCR analysis of senescence markers expression in the aorta (2−ΔΔCt). l, 3D modeling of the trabecular tibia (metaphysis) from littermate mice under cyclic induction. mo, Quantification of trabecular bone mineral density (BMD) (m), trabecular bone volume (BV) (n) and the number of trabeculae (o). Scale bars, 50 μm (a and d), 10 μm (g), 100 μm (j) and 1 mm (l). Error bars represent the s.d. ‘n’ refers throughout to the number of individual mice. In all RT–qPCR analyses (c, f and k), error bars represent the s.d. from n = 3 independent experiments. Tbp and Gapdh were used as reference genes and expression levels were normalized to those of WT mice. Statistics were performed in comparison to WT using ordinary one-way ANOVA with Tukey’s multiple-comparison correction test. BV/TV, BV fraction; HF, hair follicle; TV, tissue volume.

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Histological analysis of gonadal fat revealed marked enlargement of the average adipocyte cross-sectional area in LAKI-Foxm1 versus LAKI mice, resembling the WT adipocyte size (Fig. 4d,e). RT–qPCR analysis confirmed transgene expression in gonadal fat and restored expression of endogenous Foxm1 to equivalent levels as in WT mice (Extended Data Fig. 4l,m). Senescence markers upregulated in gonadal fat from LAKI mice were returned to WT levels in LAKI-Foxm1 mice (Fig. 4f).

Patients with HGPS and LAKI mice exhibit aortic wall thickening due to elastin degradation, collagen deposition and atherosclerosis20,50. The thickening and loss of VSMCs on the aortic medial layer of LAKI mice, as well as the presence of vacuolated VSMCs51, were rescued in LAKI-Foxm1 mice (Fig. 4g–i). Adventitial fibrosis and collagen deposition in the aortic wall were also decreased in LAKI-Foxm1 mice, as shown by Masson’s trichrome staining (Fig. 4j and Extended Data Fig. 4n,o). RT–qPCR analysis confirmed transgene expression in the aorta and restored expression of endogenous Foxm1 in LAKI-Foxm1 (Extended Data Fig. 4p,q). Also, senescence markers were downregulated in LAKI-Foxm1 to WT equivalent levels (Fig. 4k).

Three-dimensional micro-CT of tibias from 16-week-old LAKI mice indicated profound alterations in bone microarchitecture, trabecular bone density and volume, and cortical thickness compared to those of WT mice, which were significantly improved in LAKI-Foxm1 mice (Fig. 4l-o, Extended Data Fig. 4r,s and Supplementary Videos 46). FOXM1 induction also attenuated systemic pro-inflammatory responses, as indicated by decreased levels of interleukin (IL)-6, IL-2, IL-10 and interferon (IFN)-γ cytokines52 in the blood serum of LAKI-Foxm1 versus LAKI mice (Extended Data Fig. 4t).

Altogether, our data show that FOXM1 transgene induction in progeria mice restores endogenous Foxm1 levels, attenuates senescence in several tissues (see also Extended Data Fig. 4u–x for kidney), and prevents major histological progeroid features, ultimately leading to healthspan and lifespan extension.

Transgene induction in cells from naturally aged mice restores endogenous Foxm1 non-transcriptional anti-tumorigenic function

The segmental nature of progeroid syndromes incompletely phenocopies physiological aging. Besides, premature aging syndromes typically emerge from a single loss-of-function mutation rather than the global macromolecular alterations driving the gradual process of natural aging. We thus asked whether FOXM1 transgene induction could also improve the organismal fitness of naturally aged mice, in which Foxm1 was confirmed to be repressed in tissues with distinct proliferation rates (Extended Data Fig. 5). However, considering the long-term induction scheme required and the enduring association between high FOXM1 levels and poor clinical outcome in cancer53, we questioned the tumorigenic risk of this approach.

Opportunely, recent evidence disclosed a FOXM1 non-transcriptional (N-terminal) function that is tumor suppressive13. We noticed that the N terminus truncated FOXM1 transgene upregulated endogenous Foxm1 (Extended Data Figs. 1a and 4f,m,q,v), whose N-terminal function could sustain the security of a long-term induction experiment. Indeed, we found that FOXM1-dNdK induction upregulated endogenous Foxm1 in MAFs from 88-week-old animals (Fig. 5a–c). In line with previous findings showing that Foxm1 insufficiency hyperactivates Ect2–RhoA–mDia1 signaling to drive cancer13, we observed excessive cortical actin polymerization in older versus young MAFs (Fig. 5d,e). FOXM1-dNdK induction reverted this pro-tumorigenic phenotype, but specifically through endogenous Foxm1 upregulation as confirmed by short interfering RNA (siRNA) depletion against endogenous Foxm1 (Fig. 5d,e). Interestingly, senescence was still rescued by FOXM1 transgene transcriptional function, that is, independently of endogenous Foxm1 (Fig. 5f–i).

Fig. 5: Enhancement of tumor-suppressive non-transcriptional function of endogenous Foxm1 by FOXM1-dNdK transgene induction.
figure 5

a,b, Western blot analysis of endogenous FOXM1 protein levels in MAFs from 88-week-old (88w) Foxm1-dNdK mice following specific RNA interference depletion of endogenous Foxm1 (siFoxm1) and 4 d of dox induction. b, Quantification of endogenous FOXM1 protein levels from western blot analysis. Tubulin was used as the loading control and protein levels were normalized to those of untreated cells (0 d). c, RT–qPCR analysis of endogenous Foxm1 transcript levels (2−ΔΔCt) upon siFoxm1 and 4 d of induction of FOXM1-dNdK. Primers were designed to specifically detect the endogenous Foxm1 transcripts and not FOXM1-dNdK mRNA. Expression levels were normalized to those of untreated 88w MAFs (0 d). d, F-actin staining in 4w WT and 88w Foxm1-dNdK MAFs upon siFoxM1 and 4d of dox induction. e, Quantification of mitotic cortical actin intensity in MAFs as shown in d. fh, Immunofluorescence analysis of cellular aging phenotypes in siFoxm1-depleted cells following FOXM1-dNdK induction. f, Percentage of proliferative cells (Ki67+). g, Percentage of cells with DNA damage (γH2AX+). h, Percentage of cells staining positive for SA-β-gal activity. i, RT–qPCR analysis of relative p21/Cdkn1a mRNA levels (2ΔΔCt). Expression levels were normalized to those of untreated fibroblasts (0 d). Scale bar, 10 μm. Error bars represent the s.d. ‘n’ refers to independent experiments. In RT–qPCR analyses, Tbp and Gapdh were used as reference genes. Statistics were performed using ordinary one-way ANOVA with Tukey’s multiple-comparison correction (b, c and i), Kruskal–Wallis test with Dunn’s multiple-comparison correction (e), and two-tailed Fisher’s exact test (fh). a.u., arbitrary units.

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Thus, the results show that FOXM1 transgene expression can correct a pro-tumorigenic cortical actin phenotype through upregulation of endogenous Foxm1 and its N-terminal function, which reassured us to pursue with the evaluation of the lifespan and aging phenotypes in animals under long-term induction.

Transgenic FOXM1 cyclic induction extends the lifespan of naturally aged mice

Eight-week-old transgenic mice (GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+) were kept in the cyclic scheme of 3 d on a dox diet and 4 d of dox withdrawal for 80 weeks. Noticeably, transgene expression significantly extended the median and maximum survival of aged Foxm1-dNdK mice compared to Rosa26 control mice (GFP-FOXM1-dNdK/Rosa26-rtTAtg/+) by ~28% and ~29%, respectively (median, 105.8 versus 82.4 weeks; maximum, 124.1 versus 96.1 weeks), as well as delayed the aging-associated weight loss (Fig. 6a,b). Sex was excluded as a potential variable in lifespan extension (Extended Data Fig. 6a–d). Also, Foxm1-dNdK mice under the dox diet consistently exhibited increased lifespan in comparison to Rosa26 littermates (Extended Data Fig. 6e). RT–qPCR analysis of a broad spectrum of tissues indicated ubiquitous transgene expression in 88-week-old mice under the dox diet, with higher levels in proliferative tissues (for example, gut, skin and spleen) and lowest levels in differentiated tissues (for example, muscle and brain; Fig. 6c). Transgene expression translated in the upregulation of endogenous Foxm1 in Foxm1-dNdK mice in comparison to Rosa26 controls (Extended Data Fig. 6f), even though not necessarily following a direct correlation (for example, kidney and skin), likely due to tissue-specific regulation of Foxm1 transcription. Western blot analysis further confirmed the transgene expression in skin, kidney and liver (Extended Data Fig. 6g), and immunohistochemical analysis validated widespread transgene overexpression in skin, kidney and liver, and thus the efficacy of dox induction (Extended Data Fig. 6h). In line with the possibility of an enhanced non-transcriptional tumor-suppressive function through endogenous Foxm1 upregulation, necropsy analysis of 88-week-old or postmortem analysis of Foxm1-dNdK dox-treated mice did not reveal an increased incidence of solid tumors/dysplasia compared to control mice (Extended Data Fig. 7a–c), nor did it reveal lymphocyte infiltration in the liver and kidney (Extended Data Fig. 7d).

Fig. 6: Increased expression of FOXM1 extends the healthspan of naturally aged mice.
figure 6

a,b, Kaplan–Meier survival and body weight curve of naturally aged Foxm1-dNdK and Rosa26 control littermates under cyclic dox diet (3 d +dox; 4 d −dox). c, RT–qPCR analysis of FOXM1-dNdK expression in tissues of 88-week-old Foxm1-dNdK mice versus Rosa26 (2−ΔCt). Tbp and Gapdh were used as reference genes. d, Histological analysis of telogenic skin. Dashed lines delimitate the hypodermal adipose layer. e, Quantification of hypodermis thickness in telogenic skin. f, Histological analysis of gonadal fat (arrowheads). g, Quantification of adipocyte cross-sectional area (>150 adipocytes per animal). h, Quantification of crown-like structures in gonadal fat. i, Histological analysis of gastrocnemius. j, Quantification of muscle fiber cross-sectional area (>100 fibers per animal). k, Histological analysis of the aorta. l,m, Quantification of aortic wall thickness and VSMC nuclei density. np, Gene-set enrichment analysis in the aorta of 88-week-old Foxm1-dNdK versus Rosa26 mice using pathways from MSigDB databases (n) and custom pathways for cellular senescence87 and SASP88 (o,p). q, Association of transcriptomic changes induced by FOXM1-dNdK in the aorta with signatures of LEIs (GH, growth hormone; common LEI, common gene expression changes across different interventions) and aging (muscle and mouse multi-tissue). Scale bars, 50 μm (d, f and i) and 10 μm (k). Error bars represent the s.d. ‘n’ represents individual mice throughout, except for c where n = 3 independent experiments. Statistics were performed in comparison to Rosa26 mice using log-rank (Mantel–Cox) test (a), two-way ANOVA (b), unpaired two-tailed t-test (c), ordinary one-way ANOVA with Tukey’s multiple-comparison correction (e, j, l and m) and Kruskal–Wallis test with Dunn’s multiple-comparison correction (g and h). Significance scores of functional enrichment analyses (np) are described in Methods. GO, Gene Ontology.

Source data

Transgenic FOXM1 cyclic induction delays natural aging phenotypes

We pursued histopathology evaluation and gene expression analysis of senescence markers in tissues of ±1.7-year-old (±88-week-old) Foxm1-dNdK mice. We found an improvement of the dorsal skin tissue homeostasis, with significantly increased subcutaneous fat (Fig. 6d,e). Extensive changes in adipocyte function occur throughout aging, including smaller adipocyte size and increased formation of crown-like structure, where apoptotic adipocytes and phagocytic macrophages interact and generate inflammatory signaling54. We found mild rescue of the adipocyte size in the gonadal fat deposits of ±1.7-year-old Foxm1-dNdK mice (Fig. 6f,g) and decreased crown-like structure density (Fig. 6h). Another feature of organismal aging is the loss of skeletal muscle fibers (sarcopenia) and satellite stem cell pool exhaustion55. Histological analysis of the gastrocnemius muscle of Foxm1-dNdK versus Rosa26 control mice revealed mild reversion of the age-associated muscle fiber atrophy (Fig. 6i,j), and improved detection of muscle stem (PAX7-positive) cells (Extended Data Fig. 8a,b). Moreover, the maximal muscle strength of combined forelimbs and hind limbs was increased in Foxm1-dNdK versus Rosa26 animals (Extended Data Fig. 8c), thus attesting for improved muscle function. With advancing age, the cardiovascular system undergoes gross morphology and histological alterations50. FOXM1 transgene induction significantly restored aortic aging phenotypes, namely adventitial fibrosis (Extended Data Fig. 8d,e), aortic wall thickening and loss of VSMCs (Fig. 6k–m). Sex was excluded as a potential variable in the adipocyte, muscle fiber and VSMC phenotypes (Extended Data Fig. 8f–h). Noteworthy, to demonstrate that aging phenotypes are really blunted and not simply due to FOXM1 gain of function, we ascertained the impact of FOXM1 transgene overexpression on tissues from young (14- to 16-week-old) animals, that is, at a stage where aging-related phenotypes are absent. We found no significant changes in histological parameters of the aorta, skin, gonadal fat and muscle (Extended Data Fig. 9).

RT–qPCR analysis of skin, kidney, fat and muscle tissues revealed that FOXM1-dNdK induction led to downregulation of several senescence markers compared to Rosa26 control littermates (Extended Data Fig. 8i–l). We also conducted an unbiased molecular signature analysis in the aortic and gastrocnemius tissues using an AmpliSeq mouse transcriptome analysis. To this end, RNA was collected from these tissues from 88-week-old Rosa26 and Foxm1-dNdK mice under a dox diet. Gene-set enrichment analysis (GSEA) using pathways from the Molecular Signature Database (MSigDB) disclosed inflammation and metabolism as primarily altered in both tissues (Extended Data Fig. 10a–c). In the aorta, we found downregulation of pro-inflammatory and apoptosis pathways (for example, IFN-γ, IL-6, complement and tumor necrosis factor), previously linked to cardiovascular disease and vascular cell death in the tunica media56,57,58 (Fig. 6n), as well as downregulation of interrogated ‘cellular senescence’ and ‘senescence-associated secretory phenotype’ (SASP) custom pathways (Fig. 6o,p). Also, it appears that restored aortic aging is independent of cell proliferation (because cell cycle pathways are downregulated) but associated with upregulated oxidative phosphorylation and fatty-acid metabolism (Extended Data Fig. 10b–d). Intriguingly, in the muscle, the pro-inflammatory and adipogenesis pathways were upregulated (Extended Data Fig. 10c,e), although we suspect this could be due to the common adipocyte infiltration in aged gastrocnemius59. Since myofiber size was restored in Foxm1-dNdK mice (Fig. 6j), and myogenesis and ubiquitin-mediated proteolysis pathways were suppressed (Extended Data Fig. 10e,f), this suggests sarcopenia avoidance and an adaptative muscle cell response, as supported by improved xenobiotic metabolism60,61,62,63 (Extended Data Fig. 10e,f). Likely due to underrepresented satellite cell (SC) transcriptome, the SASP pathway (reported as activated in geriatric SCs64) was not downregulated as suggested by RT–qPCR (Extended Data Figs. 8l and 10g). In addition, we applied correlation and GSEA-based association analyses to compare the gene expression signatures with those of aging and lifespan-extending interventions (LEIs), identified previously65. We found that the transgene induction signature in the aorta was positively associated with the signatures of rapamycin and interventions extending maximum and median lifespan, while it was negatively associated with mouse multi-tissue aging signatures (Fig. 6q and Extended Data Fig. 10h–j). FOXM1 induction signatures in the muscle correlated positively with the signatures of rapamycin and common gene expression changes across different interventions, albeit also positively associated with mouse aging (Extended Data Fig. 10h–j). This positive association between our skeletal muscle signature and the aging signatures is mostly due to activated fatty-acid metabolism and inflammation (Extended Data Fig. 10f), possibly caused by infiltrated adipocytes.

Overall, these data support that FOXM1 transgene induction restores aging-associated loss of Foxm1 transcriptional and non-transcriptional functions, attenuates senescence-associated functional decline, and counteracts the accumulation of physiological aging phenotypes by modulating tissue-specific molecular signatures of pro-inflammatory and metabolic pathways, positively correlating with some established LEIs.

Discussion

We have previously shown that FOXM1 (ref. 52) is repressed in fibroblasts from healthy donors along advancing age and in fibroblasts from patients with HGPS. Notably, FOXM1 transgene induction improved the proliferation fitness of octogenarian and HGPS fibroblasts, ultimately delaying senescence onset8. Here, we showed that Foxm1 repression in HGPS and natural aging mouse models contributes to aging phenotypes, and that induction of an N-terminal truncated FOXM1 transgene attenuates those phenotypes, extending the lifespan. Importantly, we demonstrated that transgene expression offsets an aging/progeria-associated decline in full-length endogenous Foxm1, in line with the ability of FOXM1 to bind its promoter33, and which means that transgene expression refurbishes not only transcriptional but also non-transcriptional functions. The transcriptional functions appear to primarily account for senescence attenuation in vitro, as transgene expression in MAFs depleted for endogenous Foxm1 was still able to amend senescence phenotypes. The non-transcriptional functions brought about by restored levels of endogenous Foxm1 critically correct an aging-associated excessive cortical actin nucleation, recently reported as pro-tumorigenic due to induction of chromosomal instability13, but also observed to be coupled with nuclear blebbing in progeria cells66. Thus, it is an overall enhancement of both transcriptional and non-transcriptional functions that expectedly contributes to the healthspan and lifespan extension of the mouse models in this study.

Firstly, we demonstrated that FOXM1 transgene expression in LAKI-Foxm1 MAFs accounts for rapid and sustained improvement in proliferative fitness, DNA damage, and senescence markers. This is in agreement with previously established roles of FOXM1 in cell cycle progression53, DNA damage response10, oxidative stress55 and senescence67. Notably, FOXM1 transgene induction also bypassed other progerin-driven phenotypes, namely nuclear architecture defects and heterochromatin-associated epigenetic shifts, suggesting that it acts through additional mechanisms.

Secondly, we demonstrated that FOXM1 transgene cyclic induction markedly rescued Foxm1 decline and the main progeroid histopathological phenotypes in tissues of mesenchymal origin19,68, namely skin sclerosis, lipodystrophy, kyphosis, osteoporosis and arterial fibrosis. Gene expression analysis of senescence markers in the skin, fat and aortic tissues of LAKI-Foxm1 mice supported that transgene induction is geroprotective, and analysis of serum cytokine levels was suggestive of attenuated systemic inflammation. Because heart failure is the primary cause of early death in HGPS44, we undertook a detailed electrophysiological analysis of cardiac function. We found improved cardiac function parameters upon transgene induction, concurring with attenuated arterial stiffening and fibrosis22. Interestingly, the Sirt7 member of the histone deacetylase family, whose vascular endothelium-targeted gene therapy was shown to extend the lifespan of progeroid mice69, is upregulated by Foxm1 (refs. 8,70), further supporting the ability of Foxm1 to restore vascular aging. Thus, global improvement of progeroid features and 28% median lifespan extension, grades FOXM1 as a promising genetic target for HGPS therapeutics.

Thirdly, we validated the impact of FOXM1 transgene induction in natural aging. Analysis of ±1.7-year-old mice revealed global restoration of the organismal homeostasis and significant lifespan extension. Several age-associated features were improved, such as sclerotic skin, arterial VSMC loss, aortic fibrosis, adipose tissue inflammation, depletion of skeletal muscle PAX7+ SCs and decreased muscle function. Reduced expression of senescence gene markers in the skin, gonadal fat and gastrocnemius muscles of Foxm1-dNdK mice supported senescence attenuation. Thus, FOXM1 genetic intervention could be investigated in disease models with an etiology causally linked to SASP71. Transcriptome analysis of the aorta disclosed repression of major inflammatory/immune response pathways associated with vascular cell aging72 as the molecular signature of FOXM1 intervention. This signature correlated positively with that of maximum LEIs and negatively with mouse multi-tissue aging signature. Surprisingly, RNA-sequencing analysis of the gastrocnemius showed upregulation of pro-inflammatory signaling pathways, albeit the expression of senescence gene markers was downregulated in RT–qPCR analysis. As the gastrocnemius is prone to adipocyte infiltration during aging, adipocytes might account for the inflammatory signature. Notwithstanding, myofiber area and detection of Pax7+ SCs were improved in Foxm1-dNdK mice. We suspect that distinct FOXM1 functions in myoblasts and SCs account for cell-specific and tissue-specific autocrine and paracrine effects73,74,75.

Finally, and noteworthy, increased incidence of spontaneous solid tumors upon prolonged transgene induction was not observed. We reason that any potential pro-tumorigenic properties arising from increased C-terminal transcriptional activity might be offset by the tumor-protective properties of the N-terminal function provided by restored Foxm1 endogenous levels. Moreover, considering that C-terminal transgenic animals live longer, the attenuation of the aging-related functional decline through transcriptional and/or non-transcriptional functions might extend tumor latency, with spontaneous tumors proliferating slower and appearing later in life, as previously demonstrated for other geroprotectors76, and is important to further explore in the future.

Methods

Mouse strains

Procedures involving animals and their care were conducted in compliance with institutional ethical guidelines (i3S Animal Welfare and Ethics Review Body, ORBEA) and with the National and European Union rules (2010/63/EU), under DGAV license (DGAV 0421/000/000/2017). Mice were fed ad libitum and kept on pathogen-free barrier areas, under a 12 h:12 h light–dark cycle, at the i3S animal facility. FVB/N background mice transgenic for dox-inducible expression of constitutively active N-terminal truncated FOXM1 (GFP-FOXM1-dNdKtg/+)32, were crossed with the previously generated Rosa26-rtTAtg/+ mice, kindly provided by R. Sotillo77 (FVB × C57BL/6 mixed background; mentioned as Rosa26 mice), for ubiquitous expression of FOXM1-dNdK transgene (GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+, abbreviated as Foxm1-dNdK mice). The mouse model of HGPS carrying the Lmna causative mutation p.Gly609Gly (LAKI; LmnaG6096G/G609G; C57BL/6 background) was kindly provided by C. López-Otín20. Foxm1-dNdK mice were bred with LAKI mice to obtain progeroid mice with ubiquitous dox-inducible FOXM1-dNdK transgene expression (GFP-FOXM1-dNdKtg/+Rosa26-rtTAtg/+LmnaG6096G/G609G; hereafter, LAKI-Foxm1 mice). All mice were on a mixed FVB × C57BL/6 mixed genetic background. Experiments were performed using littermates of both sexes, randomly assigned to control and experimental groups. Extended information about mouse housing and husbandry can be found in the Supplementary Notes.

In vivo induction of FOXM1-dNdK

Mice started being fed with dox-impregnated food pellets (A155D70623; 625 ppm dox and green food dye; ssniff-Spezialdiäten) at 4 weeks of age (HGPS mouse model) and 8 weeks of age (natural aging model). A cyclic ‘on–off’ scheme of induction was performed by feeding mice for 3 d with a dox-supplemented diet followed by 4 d with a non-supplemented diet until death (lifespan analysis) or the experimental endpoint. Survival curve analysis was terminated when animals reached predefined humane endpoints according to the i3S animal facility. Further information about experimental procedures on animals can be found in Supplementary Notes.

Cell culture and in vitro induction of FOXM1-dNdK

Mouse adult dermal fibroblasts were isolated from ear biopsies of 4-week-old (and 88-week-old in Fig. 5) animals by enzymatic digestion with 25 mg ml−1 collagenase-D (Gibco), and cultured at 37 °C in DMEM/F12 supplemented with GlutaMAX (Gibco), 10% FBS (Gibco) and 1× antibiotic–antimycotic (Gibco). Mouse fibroblasts were then cultured under a low passage number (passage number ≤ 4) and in culture medium supplemented with 250 ng ml−1 dox (D9891, Sigma-Aldrich) for FOXM1-dNdK expression during specific day periods. For serum-starvation experiments, the percentage of FBS in the culture medium was reduced to 1%.

Foxm1 siRNA knockdown

Cells were plated in serum-depleted culture medium and, after 1 h, transfected with siRNA specifically targeting mouse endogenous Foxm1 (SASI_Mm01_00100165, Merck), at a final concentration of 30 or 60 nM as indicated. Transfections were performed using Lipofectamine RNAiMAX in Opti-MEM medium (both from Gibco) according to the manufacturer’s instructions. Transfection medium was replaced by complete medium after 5 h. Protein depletion was confirmed by western blot and qPCR analyses.

Immunofluorescence

Cells were grown on µ-Plate 24-well ibiTreat black plates (82406, ibidi) or on sterilized glass coverslips coated with 50 µg ml−1 fibronectin (F1141, Sigma-Aldrich) and fixed with either 4% paraformaldehyde (PFA; 20 min, room temperature (RT); for anti-GFP (for detection of FOXM1-dNdK), anti-Ki67, anti-H3S10ph and anti-γH2AX antibodies) or methanol (5 min, −20 °C; for anti-H3K9me3 and anti-H4K20me3 antibodies). Afterwards, cells were permeabilized with 0.3% Triton X-100 in PBS for 7 min at RT. After blocking with 10% FBS in PBS-T (PBS + 0.05% Tween-20) for 1 h, cells were incubated overnight at 4 °C with primary antibodies diluted in PBS-T + 5% FBS as follows: rabbit anti-Ki67 (ab15580, Abcam; 1:1,200 dilution), mouse anti-GFP (clone 3E6, A‐11120, Thermo Fisher Scientific; 1:300 dilution), mouse anti-γH2AX (clone JBW301, 05-636, Sigma-Aldrich; 1:1,000 dilution), rabbit anti-H3S10ph (06-570, Sigma-Aldrich; 1:1,500), rabbit anti-H3K9me3 (ab8898, Abcam, 1:2,000 dilution) and mouse anti-H4K20me3 (clone 6F8-D9, sc-134216, Santa Cruz Biotechnology; 1:250 dilution). After washing, cells were incubated at RT for 45 min with goat anti-rabbit Alexa Fluor 568 (A-11011, Thermo Fisher Scientific) and goat anti-mouse Alexa Fluor 647 (A-21235, Thermo Fisher Scientific) secondary antibodies (1:1,500 dilution) and nuclei were counterstained with DAPI (Sigma-Aldrich).

Senescence-associated-β-galactosidase assay

Cells were incubated for 90 min in medium supplemented with 100 nM bafilomycin A1 (B1793, Sigma-Aldrich) to induce lysosomal alkalinization. DDAOG fluorogenic substrate (10 μM, Setareh Biotech) was then added and incubation was carried out for 90 min. Cells were fixed in 4% PFA for 15 min, washed with PBS, and permeabilized with 0.1% Triton X-100 in PBS for 15 min. Finally, nuclei were counterstained with DAPI (Sigma-Aldrich).

F-actin staining

For cortical F-actin staining, cells were fixed with PHEM fixative buffer (4% PFA, 4% sucrose, 0.25% glutaraldehyde, 0.1% Triton X-100, 300 mM PIPES, 125 mM HEPES, 60 mM EGTA and 10 mM magnesium chloride) for 15 min. Cells were then permeabilized with 0.2% Triton X-100 in PBS for 5 min, quenched with 200 mM ammonium chloride, and blocked with 10% FBS in PBS-T for 1 h. F-actin was labeled with rhodamine-conjugated phalloidin (R415, Invitrogen; 1:200 dilution) for 45 min and nuclei were counterstained with DAPI (Sigma-Aldrich).

Image acquisition

Fluorescence microscopy was performed using a Zeiss AxioImager Z1 (Axiovision 4.8 software, Carl Zeiss) equipped with an Axiocam MR and using an EC-Plan-Neofluor ×40/1.3-NA and PlanApo ×63/1.40-NA objectives. Automated fluorescence microscopy was performed using IN Cell Analyzer 2000 (Software v4.5, GE Healthcare), equipped with a Photometrics CoolSNAP K4 camera and using a Nikon ×20/0.45-NA (immunostainings) and a Nikon ×40/0.95-NA (SA-β-gal assay) Plan Fluor objectives. User-defined fluorescence intensity thresholds were set and used consistently for samples within each experiment. Histological and immunohistochemistry slides were imaged using an Axioskop 2 Zeiss microscope (Carl Zeiss) coupled with a Nikon DS-L1 v3.22 camera (Nikon) or with AxioCam MRm (fluorescent)/MRc5 (bright-field) digital cameras (Carl Zeiss).

Apoptosis assay

Cell death (apoptosis) was evaluated by flow cytometry using fluorescein isothiocyanate (FITC)-conjugated Annexin V/Apoptosis detection kit (BioLegend). Briefly, cells were washed twice in cold cell staining buffer and resuspended in 100 μl of Annexin V-binding buffer. Subsequently, 5 μl of fluorescein isothiocyanate-conjugated Annexin V was added to the cell suspension. Cells were incubated for 15 min in the dark, washed with Annexin V-binding buffer, and analyzed immediately using a BD Accuri C6 Flow Cytometer (BD Biosciences).

RNA expression analysis

Total RNA from mouse dermal fibroblasts was extracted using TRIzol reagent (Life Technologies), according to the manufacturer’s protocol. Total RNA from muscle, skin, kidney, fat and aortic tissue was isolated from snap-frozen samples, after mechanical homogenization in a FastPrep-24 instrument (MP Biomedicals), using RNeasy Fibrous Tissue Mini Kit (muscle, aorta, skin and kidney; QIAGEN) or RNeasy Lipid Tissue Mini Kit (fat; QIAGEN), according to the manufacturer’s protocol. After evaluating the quantity and integrity of total RNA using the Experion system (Bio-Rad Laboratories), 1 μg of RNA was reverse transcribed using the iScript cDNA Synthesis Kit (Bio-Rad Laboratories). RT–qPCR was performed using iTaq Universal SYBR Green Supermix in a CFX96/384 Touch Real-Time PCR Detection System and analyzed using the CFX Maestro Software (all from Bio-Rad Laboratories). Absolute and relative expression levels were determined using the ΔCt and ΔΔCt analysis methods, respectively. Primers used are listed in Supplementary Table 1.

Western blotting

Protein samples were collected from cell pellets resuspended in lysis buffer (150 nM sodium chloride, 10 nM Tris-HCl (pH 7.4), 1 nM EDTA, 1 nM EGTA and 0.5% IGEPAL) with protease inhibitors. Extraction of tissue protein was performed by mechanical homogenization with RIPA buffer (89900, Thermo Fisher) supplemented with protease inhibitors. Protein content was determined using the Lowry Method (DC Protein Assay, Bio-Rad Laboratories) according to the manufacturer’s instructions. Equal amounts of protein extracts were then loaded for SDS–PAGE and transferred onto nitrocellulose membranes for western blot analysis. Blocking was performed with 5% non-fat dry milk in TBS-T (50 mM Tris-HCl (pH 7.4), 150 mM sodium chloride, 0.05% Tween-20) during 1 h. Subsequently, membranes were incubated overnight at 4 °C with the indicated antibody. Both primary and secondary antibodies were diluted in TBS-T supplemented with 2% non-fat milk as follows. Primary antibodies: rabbit anti-FOXM1 (13147, ProteinTech; 1:1,000 dilution), rabbit anti-GFP (for detection of Foxm1-dNdK on tissue extracts; A-11122, Invitrogen; 1:1,000 dilution), mouse anti-p21 (clone DF10, sc-56336, Santa Cruz; 1:500 dilution), mouse anti-α-tubulin (clone B-5-1-2, T5168, Sigma-Aldrich; 1:100,000 dilution), rabbit anti-vinculin (clone 42H89L44, 700062, Thermo Fisher Scientific; 1:3,000 dilution), mouse anti-GAPDH (clone 1E6D9, 60004, ProteinTech; 1:30,000 dilution) and mouse anti-lamin A/C (clone 4C11, 4777, Cell Signaling; 1:500 dilution). Secondary antibodies used were horseradish peroxidase (HRP)-conjugated mouse anti-rabbit (SC-2357, Santa Cruz Biotechnology) and goat anti-mouse (SC-2005, Santa Cruz Biotechnology), both at a dilution of 1:8,000, and incubated for 45 min. HRP conjugates were detected using Clarity Western ECL Substrate reagent (Bio-Rad Laboratories) according to the manufacturer’s instructions. A GS-800 calibrated densitometer operated by the Quantity One I-D Analysis Software v4.6 (Bio-Rad Laboratories) was used for quantitative analysis of protein levels.

Histological analysis

For histological analysis, tissue biopsies were collected, fixed overnight in 10% formalin, processed and embedded in paraffin. Paraffin sections were processed for hematoxylin and eosin staining using standard procedures. For heart histological analysis, harvesting was preceded by myocardial injection of 4 M potassium chloride (for diastole arrest).

Immunohistochemistry

Masson’s trichrome (Poly Scientific R&D; HT15-1KT, Sigma-Aldrich) and Sirius Red/Fast Green (9046, Chondrex) staining were performed according to the manufacturer’s protocol. Immunohistochemistry was performed as described previously78,79. Primary antibodies included: goat anti-GFP (ab6673, Abcam; 1:200 dilution), rabbit anti-cytokeratin-5 (clone Poly19055, 905501, BioLegend; 1:200 dilution) and mouse anti-PAX7 (PAX7-c, Developmental Studies Hybridoma Bank; 1:50 dilution). Antibody–antigen complexes were detected using either (1) biotinylated secondary antibody followed by avidin–HRP complex and DAB substrate (both from Vector Lab), or (2) secondary antibody donkey anti-mouse conjugated with Alexa Fluor 594 (R37115, Life Technologies; 1:750 dilution) followed by nuclei counterstaining with DAPI (Vector Lab).

Image analysis

Fixed-cell experiments, histological and immunohistochemistry slides were blindly quantified using ImageJ/Fiji v1.53c software. The quantification of the cortical intensity of F-actin was carried out by subtracting the mean cytoplasmic intensity per unit area to the mean intensity per unit area of the entire cell13.

Three-dimensional microcomputed tomography

X-ray computed micro-CT was performed using a SkyScan 1276 (Bruker) equipment. In vivo (skeleton, body fat) scanning parameters were: image pixel size of 40 μm, 70 kV, 200 μA, 0.5-mm aluminum filter, image averaged on two frames, 180° rotation and a rotation step of 0.8°. Extended information about procedures on live animals can be found in the Supplementary Notes. For ex vivo bone microarchitecture analysis, tibias were scanned using the following parameters: image pixel size of 4 μm, 55 kV, 72 μA, 0.25-mm aluminum filter, image averaged on four frames, 180° rotation, and a rotation step of 0.2°. Three-dimensional reconstructions of tomographic images from individual micro-CT slices were processed and analyzed using Bruker Software (NRecon v1.7.5.0, DataViewer v1.5.6.3, CTAn v1.20.3.0, CTVox v3.3.0-1412 and CTVol v2.3.2.1). Quantification of kyphosis index was adapted from work by Laws & Hoey80. In vivo abdominal fat ratio was quantified from a standardized and referenced range of cross-sectional slices defined relative to the L1 lumbar vertebra. For ex vivo bone microarchitecture analysis, cross-sectional slices of both trabecular (metaphyseal) and cortical (diaphyseal) tibial bone were selected with reference to the growth plate, following the American Society for Bone and Mineral Research parameters.

Targeted-transcriptome sequencing and bioinformatics

Ion Torrent sequencing libraries were prepared according to the AmpliSeq Library prep kit protocol81. Briefly, 10 ng of total RNA was reverse transcribed, the resulting cDNA was amplified for 12 cycles by adding PCR Master Mix, and the AmpliSeq mouse transcriptome gene expression primer pool (targeting 20,767 well-annotated RefSeq genes + 3,163 XM and XR genes; based on GRCm38/mm10). Amplicons were digested with the proprietary FuPa enzyme, then barcoded adaptors were ligated onto the target amplicons. The library amplicons were bound to magnetic beads, and residual reaction components were washed off. Libraries were amplified, re-purified and individually quantified using Agilent TapeStation High Sensitivity tape. Individual libraries were diluted to a 50-pM concentration and pooled equally, with twelve individual samples per pool for further processing. Emulsion PCR, templating and 550-chip loading were performed with an Ion Chef Instrument (Thermo Fisher). Sequencing was performed on an Ion S5XL sequencer (Thermo Fisher). Raw reads were mapped to the mm10 (GRCm38.p6) mouse genome and gene counts were obtained with STAR (v2.7.2b)82. Gene counts were normalized using the Relative Log Expression method83 and log transformed using edgeR84 package (v3.36.0) in R v4.1. Only genes covered by ten or more reads in the analyzed samples were used for downstream analysis. Differential expression levels were calculated with limma85 package (v3.26.9). The rank for each gene was calculated as −log10 of the P value multiplied by 1 if the fold change was positive, and by −1 if the fold change was negative. Ranked lists of genes were used for GSEA with clusterprofiler package (v4.0)86 using pathways from MSigDB v7.4 (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and custom pathways for cellular senescence87 and SASP88. Principal-component analysis was performed with the factoextra package (v1.0.7). Plots were visualized using R v4.1 in Rstudio v2021.09.1 + 372.pro1.

Association of transcriptomic changes induced by FOXM1 intervention with signatures of lifespan extension and aging

Gene expression signatures of LEIs, including signatures of caloric restriction, rapamycin, growth hormone deficiency, common gene expression changes across different interventions, and signatures associated with the effect of interventions on median and maximum lifespan, were obtained from Tyshkovskiy et al.65. Aging signatures, obtained through a meta-analysis of age-related transcriptomic changes, included a muscle signature as well as the mouse multi-tissue signature89. Dependence between gene expression log fold changes associated with FOXM1, lifespan extension and aging were assessed using correlation and GSEA-based analyses89. For the correlation analysis, the Spearman metric was calculated for the top 300 statistically significant genes for each pair of signatures. Clustering was performed with complete hierarchical approach. For the GSEA-based association analysis, we utilized an algorithm developed in work by Tyshkovskiy et al.65. First, for every signature, statistically significant genes were selected, using a false discovery rate threshold of 0.05, and restricted their number to 1,000 genes with the highest absolute log fold change if needed. Then, these genes were divided into upregulated and downregulated groups. These lists were considered as gene sets. Then, genes were ranked according to the differential expression in response to FOXM1 intervention based on their P values, calculated as described in ‘Functional enrichment analysis’. Afterward, GSEA scores were calculated separately for upregulated and downregulated lists of gene sets as described in work by Lamb et al.90 and we defined the final GSEA score as a mean of the two. To calculate statistical significance of the obtained GSEA score, a permutation test was performed, where genes were randomly assigned to the lists of gene sets maintaining their size. To get the P value of association between the response to Foxm1 intervention and a certain signature, the frequencies of real final GSEA scores were calculated as being bigger by absolute value than random final GSEA scores obtained as a result of 5,000 permutations. To adjust for multiple comparisons, Benjamini–Hochberg correction91 was performed. Resulting adjusted P values were converted into significance scores according to:

$${\mathrm{Significance}}\;{\mathrm{score}} = - {{\log _{10}\left( {{\mathrm{adj}}.\;{\mathrm{pv}}} \right) \times {\mathrm{sgn}}({\mathrm{GSEA}}\;{\mathrm{score}})}}$$

where \({\mathrm{adj}}.\;{\mathrm{pv}}\) and \({\mathrm{GSEA}}\;{\mathrm{score}}\) are the Benjamini–Hochberg-adjusted P value and final GSEA score, respectively.

Functional enrichment analysis

For the identification of functions enriched by genes perturbed by FOXM1 intervention, GSEA89 was performed separately for muscle and aorta data on a pre-ranked list of genes sorted based on log10(P value) corrected by the sign of regulation, calculated as:

$$- \log _{10}\left( {\mathrm{{pv}}} \right) \times {\mathrm{sgn}}({\mathrm{lfc}}),$$

where ‘pv’ and ‘lfc’ are the P value and log fold change of a certain gene, respectively, obtained from edgeR output, and ‘sgn’ is a signum function (is equal to 1, −1 and 0 if value is positive, negative and equal to 0, respectively). Kyoto Encyclopedia of Genes and Genomes pathways and molecular function from MSigDB have been used as gene sets for GSEA. The GSEA algorithm was performed via fgsea package (v1.20.0) in R with 5,000 permutations. A q-value cutoff of 0.1 was used to select statistically significant functions. Significance scores of enriched functions were calculated as:

$${\mathrm{Significance}}\;{\mathrm{score}} = - \log _{10}({\mathrm{qv}}) \times {\mathrm{sgn}}({\mathrm{NES}}),$$

where NES and ‘qv’ are normalized enrichment score and q value, respectively.

Similar GSEA analysis was performed for gene expression signatures of aging and LEIs. Overall comparison of functional response associated with FOXM1 intervention, aging and lifespan extension was assessed with Spearman correlation calculated for NES of functions, which demonstrated statistically significant enrichment (q value < 0.1) for at least one signature. Heat maps were built for manually chosen statistically significant functions and colored based on NES. Clustering of functions was performed with a hierarchical complete approach and the Spearman correlation distance.

Cardiac functional characterization

Cardiac function was assessed using high-resolution ECG (40 MHz probe; Vevo 2100 system, Visualsonics) in lightly sedated mice (2% sevoflurane and 100% oxygen)92. In the parasternal long-axis view, motion-mode (M-Mode) was used to determine LV wall thickness, whereas volumes and ejection fraction were estimated by LV tracing and a simplified Simpson’s method. Diastolic function was evaluated by the ratio between early to late filling velocities obtained by using pulsed-wave Doppler evaluation of transmitral inflow obtained from the LV four-chamber view.

Electrophysiological parameters were assessed in lightly sedated mice, using intradermal electrodes placed in a lead II-like configuration. ECG signals were obtained for 5 min using a data acquisition hardware (PowerLab 8/35, ADInstruments) coupled to a signal amplifier (Animal Bio Amp, ADInstruments), with support of LabChart 8 software (ADInstruments).

Grip-strength test

Grip strength was measured using a BIO-GS3 (Bioseb). Mice were placed onto the grid (BIO-GRIPGS) with all four paws attached and gently pulled back to measure grip until the grid was released. The values obtained represent the muscle force (g) obtained in n = 3 trials per animal with an interval of 5 min, and normalized to the respective body weight (g).

Cytokine array

Cytokine levels were measured in mouse serum samples replicates using a Mouse High Sensitivity T Cell 18-Plex Discovery Assay Array (MDHSTC18; Eve Technologies).

Statistical analysis

The data presented represent a minimum of three independent experiments and are shown as the mean ± s.d. Survival curves and statistical analyses were carried out using Prism 8 software (GraphPad). No statistical methods were used to predetermine sample sizes, but sample sizes were similar to those reported in previous publications13,20,40,48. Randomization was normally not applicable to the experimental groups as animals/cells/samples were assigned based on their genotype. Randomization was applied in the distribution of animal litters to different diet regimens (±dox). When possible, data collection and analysis were performed blindly to the experimental conditions (fixed-cell experiments, targeted-transcriptome sequencing, histology, micro-CT, ECG and grip-strength test). No animals or data points were excluded. Normal distribution of the data was tested using the Shapiro–Wilk test, and the appropriate statistical test was used according to data distribution. For lifespan assays, the log-rank (Mantel–Cox) test was performed. All relevant P values are shown in the figures, with P < 0.05 being considered as significant.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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.

References

  1. Kirkwood, T. B. L. Understanding the odd science of aging. Cell 120, 437–447 (2005).

    CAS  PubMed  Article  Google Scholar 

  2. Macedo, J. C., Vaz, S. & Logarinho, E. Mitotic dysfunction associated with aging hallmarks. Adv. Exp. Med. Biol. 1002, 153–188 (2017).

  3. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    PubMed Central  Article  CAS  Google Scholar 

  4. Melo-Pereira, S., Ribeiro, R. & Logarinho, E. Approaches towards longevity: reprogramming, senolysis, and improved mitotic competence as anti-aging therapies. Int. J. Mol. Sci. 20, 938 (2019).

  5. Baker, D. J. et al. BubR1 insufficiency causes early onset of aging-associated phenotypes and infertility in mice. Nat. Genet. 36, 744–749 (2004).

    CAS  PubMed  Article  Google Scholar 

  6. Sieben, C. J. et al. BubR1 allelic effects drive phenotypic heterogeneity in mosaic-variegated aneuploidy progeria syndrome. J. Clin. Invest. 130, 171–188 (2020).

    CAS  PubMed  Article  Google Scholar 

  7. Wan, X. et al. Identification of FoxM1–Bub1b signaling pathway as a required component for growth and survival of rhabdomyosarcoma. Cancer Res. 72, 5889–5899 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Macedo, J. C. et al. FoxM1 repression during human aging leads to mitotic decline and aneuploidy-driven full senescence. Nat. Commun. 9, 2834 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. Laoukili, J. et al. Activation of FoxM1 during G2 requires cyclin A/Cdk-dependent relief of autorepression by the FoxM1 N-terminal domain. Mol. Cell. Biol. 28, 3076–3087 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Zona, S., Bella, L., Burton, M. J., Nestal de Moraes, G. & Lam, E. W. F. FOXM1: an emerging master regulator of DNA damage response and genotoxic agent resistance. Biochim. Biophys. Acta 1839, 1316–1322 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Alvarez-Fernández, M. & Medema, R. H. Novel functions of FoxM1: from molecular mechanisms to cancer therapy. Front. Oncol. 3, 30 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  12. Black, M. et al. FOXM1 nuclear transcription factor translocates into mitochondria and inhibits oxidative phosphorylation. Mol. Biol. Cell https://doi.org/10.1091/mbc.E19-07-0413 (2020).

  13. Limzerwala, J. F. et al. FoxM1 insufficiency hyperactivates Ect2–RhoA–mDia1 signaling to drive cancer. Nat. Cancer 1, 1010–1024 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. De Sandre-Giovannoli, A. et al. Lamin A truncation in Hutchinson–Gilford progeria. Science 300, 2055 (2003).

    PubMed  Article  Google Scholar 

  15. Eriksson, M. et al. Recurrent de novo point mutations in lamin A cause Hutchinson–Gilford progeria syndrome. Nature 423, 293–298 (2003).

    CAS  PubMed  Article  Google Scholar 

  16. Worman, H. J. & Foisner, R. The nuclear envelope from basic biology to therapy. Biochem. Soc. Trans. 38, 253–256 (2010).

    CAS  PubMed  Article  Google Scholar 

  17. Ashapkin, V. V., Kutueva, L. I., Kurchashova, S. Y. & Kireev, I. I. Are there common mechanisms between the Hutchinson–Gilford progeria syndrome and natural aging? Front. Genet. 10, 455 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Carrero, D., Soria-Valles, C. & López-Otín, C. Hallmarks of progeroid syndromes: lessons from mice and reprogrammed cells. Dis. Model. Mech. 9, 719–735 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Hennekam, R. C. M. Hutchinson–Gilford progeria syndrome: review of the phenotype. Am. J. Med. Genet. A 140, 2603–2624 (2006).

    PubMed  Article  CAS  Google Scholar 

  20. Osorio, F. G. et al. Hutchinson–Gilford progeria: splicing-directed therapy in a new mouse model of human accelerated aging. Sci. Transl. Med. 3, 106ra107–106ra107 (2011).

    PubMed  Article  CAS  Google Scholar 

  21. U.S. Food and Drug Administration. FDA approves first treatment for Hutchinson–Gilford Progeria Syndrome and some progeroid laminopathies. https://www.fda.gov/news-events/press-announcements/fda-approves-first-treatment-hutchinson-gilford-progeria-syndrome-and-some-progeroid-laminopathies (2021).

  22. Gordon, L. B. et al. Association of lonafarnib treatment vs no treatment with mortality rate in patients with Hutchinson–Gilford progeria syndrome. JAMA 319, 1687–1695 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Gordon, L. B. et al. Impact of farnesylation inhibitors on survival in Hutchinson–Gilford progeria syndrome. Circulation 130, 27–34 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Misteli, T. Farnesyltransferase inhibition in HGPS. Cell 184, 293 (2021).

    CAS  PubMed  Article  Google Scholar 

  25. Davies, B. S. J. et al. An accumulation of non-farnesylated prelamin A causes cardiomyopathy but not progeria. Hum. Mol. Genet. 19, 2682–2694 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Beyret, E. et al. Single-dose CRISPR–Cas9 therapy extends lifespan of mice with Hutchinson–Gilford progeria syndrome. Nat. Med. 25, 419–422 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Santiago-Fernández, O. et al. Development of a CRISPR–Cas9-based therapy for Hutchinson–Gilford progeria syndrome. Nat. Med. 25, 423–426 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. Scaffidi, P. & Misteli, T. Reversal of the cellular phenotype in the premature aging disease Hutchinson–Gilford progeria syndrome. Nat. Med. 11, 440–445 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Saxena, S. & Kumar, S. Pharmacotherapy to gene editing: potential therapeutic approaches for Hutchinson–Gilford progeria syndrome. GeroScience https://doi.org/10.1007/s11357-020-00167-3 (2020).

  30. Kudlow, B. A., Stanfel, M. N., Burtner, C. R., Johnston, E. D. & Kennedy, B. K. Suppression of proliferative defects associated with processing-defective lamin A mutants by hTERT or inactivation of p53. Mol. Biol. Cell 19, 5238–5248 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Liu, B. et al. Genomic instability in laminopathy-based premature aging. Nat. Med. 11, 780–785 (2005).

    CAS  PubMed  Article  Google Scholar 

  32. Wang, I. C. et al. Increased expression of FoxM1 transcription factor in respiratory epithelium inhibits lung sacculation and causes Clara cell hyperplasia. Dev. Biol. 347, 301–314 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Halasi, M. & Gartel, A. L. A novel mode of FoxM1 regulation: positive auto-regulatory loop. Cell Cycle 8, 1966–1967 (2009).

    CAS  PubMed  Article  Google Scholar 

  34. Musich, P. R. & Zou, Y. DNA-damage accumulation and replicative arrest in Hutchinson–Gilford progeria syndrome. Biochem. Soc. Trans. 39, 1764–1769 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Musich, P. R. & Zou, Y. Genomic instability and DNA damage responses in progeria arising from defective maturation of prelamin A. Aging 1, 28–37 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Zhang, W., Qu, J., Liu, G. H. & Belmonte, J. C. I. The ageing epigenome and its rejuvenation. Nat. Rev. Mol. Cell Biol. 21, 137–150 (2020).

    CAS  PubMed  Article  Google Scholar 

  37. Arancio, W., Pizzolanti, G., Genovese, S. I., Pitrone, M. & Giordano, C. Epigenetic involvement in Hutchinson–Gilford progeria syndrome: a mini-review. Gerontology 60, 197–203 (2014).

    CAS  PubMed  Article  Google Scholar 

  38. Shumaker, D. K. et al. Mutant nuclear lamin A leads to progressive alterations of epigenetic control in premature aging. Proc. Natl Acad. Sci. USA 103, 8703–8708 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Benayoun, B. A., Pollina, E. A. & Brunet, A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat. Rev. Mol. Cell Biol. 16, 593–610 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. Cell 167, 1719–1733 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Fong, L. G. et al. Heterozygosity for Lmna deficiency eliminates the progeria-like phenotypes in Zmpste24-deficient mice. Proc. Natl Acad. Sci. USA 101, 18111–18116 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Varga, R. et al. Progressive vascular smooth muscle cell defects in a mouse model of Hutchinson–Gilford progeria syndrome. Proc. Natl Acad. Sci. USA 103, 3250–3255 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Stehbens, W. E., Delahunt, B., Shozawa, T. & Gilbert-Barness, E. Smooth muscle cell depletion and collagen types in progeric arteries. Cardiovasc. Pathol. 10, 133–136 (2001).

    CAS  PubMed  Article  Google Scholar 

  44. Olive, M. et al. Cardiovascular pathology in Hutchinson–Gilford progeria: correlation with the vascular pathology of aging. Arterioscler. Thromb. Vasc. Biol. 30, 2301–2309 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Wang, Y. et al. Epidermal expression of the truncated prelamin A causing Hutchinson–Gilford progeria syndrome: effects on keratinocytes, hair and skin. Hum. Mol. Genet. 17, 2357–2369 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Kurban, R. S. & Bhawan, J. Histologic changes in skin associated with aging. J. Dermatol. Surg. Oncol. 16, 908–914 (1990).

    CAS  PubMed  Article  Google Scholar 

  47. Merideth, M. A. et al. Phenotype and course of Hutchinson–Gilford progeria syndrome. N. Engl. J. Med. 358, 592–604 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Baker, D. J. et al. Naturally occurring p16 Ink4a-positive cells shorten healthy lifespan. Nature 530, 184–189 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Alam, H., Sehgal, L., Kundu, S. T., Dalal, S. N. & Vaidya, M. M. Novel function of keratins 5 and 14 in proliferation and differentiation of stratified epithelial cells. Mol. Biol. Cell 22, 4068–4078 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Komutrattananont, P., Mahakkanukrauh, P. & Das, S. Morphology of the human aorta and age-related changes: anatomical facts. Anat. Cell Biol. 52, 109–114 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  51. Kim, P. H. et al. Disrupting the LINC complex in smooth muscle cells reduces aortic disease in a mouse model of Hutchinson–Gilford progeria syndrome. Sci. Transl. Med. 10, eaat7163 (2018).

  52. Turner, M. D., Nedjai, B., Hurst, T. & Pennington, D. J. Cytokines and chemokines: at the crossroads of cell signalling and inflammatory disease. Biochim. Biophys. Acta 1843, 2563–2582 (2014).

    CAS  PubMed  Article  Google Scholar 

  53. Barger, C. J., Branick, C., Chee, L. & Karpf, A. R. Pan-cancer analyses reveal genomic features of FOXM1 overexpression in cancer. Cancers 11, 251 (2019).

  54. Mau, T. & Yung, R. Adipose tissue inflammation in aging. Exp. Gerontol. 105, 27–31 (2018).

    CAS  PubMed  Article  Google Scholar 

  55. Conboy, I. M. & Rando, T. A. Aging, stem cells and tissue regeneration: lessons from muscle. Cell Cycle 4, 407–410 (2005).

    CAS  PubMed  Article  Google Scholar 

  56. Tyrrell, D. J. & Goldstein, D. R. Ageing and atherosclerosis: vascular intrinsic and extrinsic factors and potential role of IL-6. Nat. Rev. Cardiol. 18, 58–68 (2021).

    CAS  PubMed  Article  Google Scholar 

  57. Pisano, C., Balistreri, C. R., Ricasoli, A. & Ruvolo, G. Cardiovascular disease in ageing: an overview on thoracic aortic aneurysm as an emerging inflammatory disease. Mediators Inflamm. 2017, 1274034 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. Helske, S. et al. Complement system is activated in stenotic aortic valves. Atherosclerosis 196, 190–200 (2008).

    CAS  PubMed  Article  Google Scholar 

  59. Tuttle, L. J., Sinacore, D. R. & Mueller, M. J. Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J. Aging Res. 2012, 172957 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  60. Wiedmer, P. et al. Sarcopenia–molecular mechanisms and open questions. Ageing Res. Rev. 65, 101200 (2021).

    CAS  PubMed  Article  Google Scholar 

  61. Grevendonk, L. et al. Impact of aging and exercise on skeletal muscle mitochondrial capacity, energy metabolism, and physical function. Nat. Commun. 12, 4773 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Koves, T. R. et al. Mitochondrial overload and incomplete fatty-acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 7, 45–56 (2008).

    CAS  PubMed  Article  Google Scholar 

  63. Li, X. et al. Direct and indirect effects of growth hormone receptor ablation on liver expression of xenobiotic metabolizing genes. Am. J. Physiol. 305, E942–E950 (2013).

    CAS  Google Scholar 

  64. Sousa-Victor, P. et al. Geriatric muscle stem cells switch reversible quiescence into senescence. Nature 506, 316–321 (2014).

    CAS  PubMed  Article  Google Scholar 

  65. Tyshkovskiy, A. et al. Identification and application of gene expression signatures associated with lifespan extension. Cell Metab. 30, 573–593 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Mu, X. et al. Cytoskeleton stiffness regulates cellular senescence and innate immune response in Hutchinson–Gilford Progeria Syndrome. Aging Cell 19, e13152 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Rovillain, E. et al. Activation of nuclear factor-kappa B signalling promotes cellular senescence. Oncogene 30, 2356–2366 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Prokocimer, M., Barkan, R. & Gruenbaum, Y. Hutchinson–Gilford progeria syndrome through the lens of transcription. Aging Cell 12, 533–543 (2013).

    CAS  PubMed  Article  Google Scholar 

  69. Sun, S. et al. Vascular endothelium–targeted Sirt7 gene therapy rejuvenates blood vessels and extends lifespan in a Hutchinson–Gilford progeria model. Sci. Adv. 6, eaay5556 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Yu, W. et al. Silencing forkhead box M1 promotes apoptosis and autophagy through SIRT7/mTOR/IGF2 pathway in gastric cancer cells. J. Cell. Biochem. 119, 9090–9098 (2018).

    CAS  PubMed  Article  Google Scholar 

  71. Paez‐Ribes, M., González‐Gualda, E., Doherty, G. J. & Muñoz‐Espín, D. Targeting senescent cells in translational medicine. EMBO Mol. Med. 11, e10234 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. Maloberti, A. et al. Vascular aging and disease of the large vessels: role of inflammation. High Blood Press. Cardiovasc. Prev. 26, 175–182 (2019).

    PubMed  Article  Google Scholar 

  73. Ferreira, F. J., Carvalho, L., Logarinho, E. & Bessa, J. foxm1 modulates cell non-autonomous response in Zebrafish skeletal muscle homeostasis. Cells 10, 1241 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Hou, Y. et al. The transcription factor Foxm1 is essential for the quiescence and maintenance of hematopoietic stem cells. Nat. Immunol. 16, 810–818 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Chen, Z. et al. A Cdh1–Foxm1–Apc axis controls muscle development and regeneration. Cell Death Dis. 11, 180 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. Anisimov, V. N. Lifespan extension and cancer risk: myths and reality. Exp. Gerontol. 36, 1101–1136 (2001).

    CAS  PubMed  Article  Google Scholar 

  77. Sotillo, R. et al. Mad2 overexpression promotes aneuploidy and tumorigenesis in mice. Cancer Cell 11, 9–23 (2007).

    CAS  PubMed  Article  Google Scholar 

  78. Ustiyan, V. et al. FOXF1 transcription factor promotes lung morphogenesis by inducing cellular proliferation in fetal lung mesenchyme. Dev. Biol. 443, 50–63 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Kalinichenko, V. V. et al. Ubiquitous expression of the forkhead box M1B transgene accelerates proliferation of distinct pulmonary cell types following lung injury. J. Biol. Chem. 278, 37888–37894 (2003).

    CAS  PubMed  Article  Google Scholar 

  80. Laws, N. & Hoey, A. Progression of kyphosis in mdx mice. J. Appl. Physiol. 97, 1970–1977 (2004).

    PubMed  Article  Google Scholar 

  81. Papp, A. C. et al. AmpliSeq transcriptome analysis of human alveolar and monocyte-derived macrophages over time in response to Mycobacterium tuberculosis infection. PLoS ONE13, e0198221 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  82. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Article  Google Scholar 

  83. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  86. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. Coppé, J. P. et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous functions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol. 6, 2853–2868 (2008).

    Article  CAS  Google Scholar 

  88. Hernandez-Segura, A. et al. Unmasking transcriptional heterogeneity in senescent cells. Curr. Biol. 27, 2652–2660 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  PubMed  Article  Google Scholar 

  91. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  92. Sampaio-Pinto, V. et al. Neonatal apex resection triggers cardiomyocyte proliferation, neovascularization and functional recovery despite local fibrosis. Stem Cell Reports 10, 860–874 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

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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.

Author information

Authors and Affiliations

Authors

Contributions

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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