Partial reprogramming by expression of reprogramming factors (Oct4, Sox2, Klf4 and c-Myc) for short periods of time restores a youthful epigenetic signature to aging cells and extends the life span of a premature aging mouse model. However, the effects of longer-term partial reprogramming in physiologically aging wild-type mice are unknown. Here, we performed various long-term partial reprogramming regimens, including different onset timings, during physiological aging. Long-term partial reprogramming lead to rejuvenating effects in different tissues, such as the kidney and skin, and at the organismal level; duration of the treatment determined the extent of the beneficial effects. The rejuvenating effects were associated with a reversion of the epigenetic clock and metabolic and transcriptomic changes, including reduced expression of genes involved in the inflammation, senescence and stress response pathways. Overall, our observations indicate that partial reprogramming protocols can be designed to be safe and effective in preventing age-related physiological changes. We further conclude that longer-term partial reprogramming regimens are more effective in delaying aging phenotypes than short-term reprogramming.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
DNA methylation and RNA-seq data are deposited in the GEO under the accession no. GSE190665 and the SuperSeries under accession no. GSE190986. The metabolomics and lipidomics raw data can be found in Supplementary Tables 8 and 9 (4F mice serum) and 10 and 11 (B6 mice serum). Other data supporting the findings of the study can be requested from the corresponding author.
Ermolaeva, M., Neri, F., Ori, A. & Rudolph, K. L. Cellular and epigenetic drivers of stem cell ageing. Nat. Rev. Mol. Cell Biol. 19, 594–610 (2018).
Keenan, C. R. & Allan, R. S. Epigenomic drivers of immune dysfunction in aging. Aging Cell 18, e12878 (2019).
Melzer, D., Pilling, L. C. & Ferrucci, L. The genetics of human ageing. Nat. Rev. Genet. 21, 88–101 (2020).
Campisi, J. et al. From discoveries in ageing research to therapeutics for healthy ageing. Nature 571, 183–192 (2019).
Smith, H. J., Sharma, A. & Mair, W. B. Metabolic communication and healthy aging: where should we focus our energy? Dev. Cell 54, 196–211 (2020).
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).
Mahmoudi, S., Xu, L. & Brunet, A. Turning back time with emerging rejuvenation strategies. Nat. Cell Biol. 21, 32–43 (2019).
Mahmoudi, S. & Brunet, A. Aging and reprogramming: a two-way street. Curr. Opin. Cell Biol. 24, 744–756 (2012).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Liu, G.-H. et al. Recapitulation of premature ageing with iPSCs from Hutchinson–Gilford progeria syndrome. Nature 472, 221–225 (2011).
Zhang, J. et al. A human iPSC model of Hutchinson Gilford progeria reveals vascular smooth muscle and mesenchymal stem cell defects. Cell Stem Cell 8, 31–45 (2011).
Lapasset, L. et al. Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state. Genes Dev. 25, 2248–2253 (2011).
Abad, M. et al. Reprogramming in vivo produces teratomas and iPS cells with totipotency features. Nature 502, 340–345 (2013).
Ohnishi, K. et al. Premature termination of reprogramming in vivo leads to cancer development through altered epigenetic regulation. Cell 156, 663–677 (2014).
Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. Cell 167, 1719–1733.e12 (2016).
Sarkar, T. J. et al. Transient non-integrative expression of nuclear reprogramming factors promotes multifaceted amelioration of aging in human cells. Nat. Commun. 11, 1545 (2020).
Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature 588, 124–129 (2020).
Doeser, M. C., Schöler, H. R. & Wu, G. Reduction of fibrosis and scar formation by partial reprogramming in vivo. Stem Cells 36, 1216–1225 (2018).
Rodríguez-Matellán, A., Alcazar, N., Hernández, F., Serrano, M. & Ávila, J. In vivo reprogramming ameliorates aging features in dentate gyrus cells and improves memory in mice. Stem Cell Rep. 15, 1056–1066 (2020).
Wang, C. et al. In vivo partial reprogramming of myofibers promotes muscle regeneration by remodeling the stem cell niche. Nat. Commun. 12, 3094 (2021).
Mosteiro, L. et al. Tissue damage and senescence provide critical signals for cellular reprogramming in vivo. Science 354, aaf4445 (2016).
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).
Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).
Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 18, 57 (2017).
Maegawa, S. et al. Caloric restriction delays age-related methylation drift. Nat. Commun. 8, 539 (2017).
Thompson, M. J. et al. A multi-tissue full lifespan epigenetic clock for mice. Aging (Albany NY) 10, 2832–2854 (2018).
Haghani, A. et al. Divergent age-related methylation patterns in long and short-lived mammals. Preprint at bioRxiv https://doi.org/10.1101/2022.01.16.476530 (2022).
Jones, D. L. & Rando, T. A. Emerging models and paradigms for stem cell ageing. Nat. Cell Biol. 13, 506–512 (2011).
Jasper, H. Intestinal stem cell aging: origins and interventions. Annu. Rev. Physiol. 82, 203–226 (2020).
de Haan, G. & Lazare, S. S. Aging of hematopoietic stem cells. Blood 131, 479–487 (2018).
Reddy, P., Memczak, S. & Belmonte, J. C. I. Unlocking tissue regenerative potential by epigenetic reprogramming. Cell Stem Cell 28, 5–7 (2021).
Nevedomskaya, E. et al. CE-MS for metabolic profiling of volume-limited urine samples: application to accelerated aging TTD mice. J. Proteome Res. 9, 4869–4874 (2010).
Wang, X., Ge, J., Tredget, E. E. & Wu, Y. The mouse excisional wound splinting model, including applications for stem cell transplantation. Nat. Protoc. 8, 302–309 (2013).
Pau, G. & Reeder, J. HTSeqGenie: A NGS analysis pipeline. R package (v.4.2.1 and v.4.4.1) version https://bioconductor.org/packages/release/bioc/html/HTSeqGenie.html (2021).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Matys, V. et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006).
Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford) 2016, baw100 (2016).
Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat. Commun. 13, 783 (2022).
Zhou, W., Triche, T. J.Jr., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).
Jaochico, A., Sangaraju, D. & Shahidi-Latham, S. K. A rapid derivatization based LC–MS/MS method for quantitation of short chain fatty acids in human plasma and urine. Bioanalysis 11, 741–753 (2019).
Dunn, W. B. et al. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9, 44–66 (2013).
Kind, T. et al. Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev. 37, 513–532 (2018).
We thank M. Schwarz for administrative support. We also thank R. Russell for help with bioluminescence imaging in p16-luc mice. This study was supported by the Universidad Católica San Antonio de Murcia and Fundación Dr. Pedro Guillén. The cartoon images of mice and tissues were created with BioRender.com.
The authors declare no competing interests.
Peer review information
Nature Aging thanks Jesus Avila, George Daley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
The expression levels of reprogramming factors (Oct4, Sox2, Klf4 and cMyc) were measured in 9 months old 4 F mice tissues after 48 hr of doxycycline treatment. n = 2 (-Dox) and n = 3 (+Dox) independent biological mice. The data are represented as mean ± SD.
Representative images of the liver, kidney, skin, spleen and lung tissues from 7 months treated long-term partial reprogramming mice were collected at the end of the treatment. The tissues from the doxycycline treatment were compared to similar age untreated and young 4 F mice. Similar phenotypes were observed in 5 pairs of untreated and treated mice. Scale bar, 50 μm (liver, kidney, skin and lung) and 100 μm (spleen). Representative images are from n = 1 mouse. Tissues were evaluated from n = 5 (4 F -Dox) and n = 5 (4 F + Dox) independent biological mice.
Scatter plots of DNA methylation age (PredictedAge) (y-axis) versus chronological age (x-axis) of different tissues from 4F mice. DNAm age analysis is based on LUC clocks trained on either individual or pan-tissues. Kidney, Liver, Lung and Spleen: n = 19 (Control) and n = 17 (Treated). Muscle: n = 18 (Control) and n = 17 (Treated). Skin: n = 20 (Control) and n = 16 (Treated). Controls include 4F -Dox and B6 +Dox. Cor: Pearson correlation (cor); p: P-value from two-sided Student’s t-test; Err: Mean absolute error.
Extended Data Fig. 4 Epigenetic clock analysis of tissues from short-term partial reprogrammed mice.
Measurement of DNA methylation age of kidney and skin of 4F mice after short-term partial reprogramming using elastic net (EN) clocks trained on indicated tissues. n = 7 (B6 + doxycycline) and n = 3 (4F + doxycycline) independent biological mice. Age accelerations are the residuals of the predicted age regressed on the chronological age of each sample. The mean differences were examined by a two-sided Student t-test of age acceleration differences between the groups. The P values are reported in each panel. Boxes show the interquartile range of the age accelerations. The notches indicate the 95% confidence interval of the median. The whiskers represent 1.5*IQR length of the age accelerations.
a, Manhattan plots showing the distribution of hyper and hypomethylated CpGs in different tissues from 26 months old mice (4 F -Dox n = 3 and 4 F + Dox n = 3). The EWAS is done by linear regression model of the treatment vs the controls. The coordinates are estimated based on the alignment of Mammalian array probes to mus musculus mm10 genome. Top 15 CpGs was labeled by the neighboring genes. Red dashed line indicates P < 10-4. b, Horizontal bars showing the CpG locations in the genome of tissues. The orange color denotes hypermethylated and blue color denotes hypomethylated CpGs in treated vs control animals. Fisher’s Exact Test is used to examine the proportional change than the background; # two-sided P-value <0.05; odd ratios are reported on the bars with significant proportional change.
a,b, Measurement of DNA methylation age of kidney and skin of 4F mice after long-term partial reprogramming using elastic net (EN) clocks trained on indicated tissues. a, DNA methylation age acceleration in 4F mice after seven months of treatment starting at 15 months of age. n = 7 (B6 + doxycycline) and n = 5 (4F + doxycycline) independent biological mice. b, DNA methylation age acceleration in 4F mice after ten months of treatment starting at 12 months of age. n = 7 (B6 + doxycycline) and n = 3 (4F + doxycycline) independent biological mice. c, DNA methylation age acceleration of kidney and skin of 4F mice after ten months of treatment starting at 12 months of age using Lifespan Uber Correlation (LUC) clocks trained on indicated tissues. n = 7 (B6 + doxycycline) and n = 3 (4F + doxycycline) independent biological mice. Age accelerations are the residuals of the predicted age regressed on the chronological age of each sample. The mean differences were examined by a two-sided Student t-test of age acceleration differences between the groups. The P values are reported in each panel. Boxes show the interquartile range of the age accelerations. The notches indicate the 95% confidence interval of the median. The whiskers represent 1.5*IQR length of the age accelerations.
a, Manhattan plots showing the distribution of hyper and hypomethylated CpGs in different tissues from 22 months old mice. n = 5 (4 F + Dox) and n = 5 (4 F -Dox) independent biological mice. The EWAS is done by linear regression model of the treatment vs the controls. The coordinates are estimated based on the alignment of Mammalian array probes to mus musculus mm10 genome. Top 15 CpGs was labeled by the neighboring genes. Red dashed line indicates P < 10-4. b, Horizontal bars showing the CpG locations in the genome of tissues. The orange color denotes hypermethylated and blue color denotes hypomethylated CpGs in treated vs control animals. # indicates positive association and *indicates negative association. Fisher’s Exact Test is used to examine the proportional change than the background; odd ratios are reported on the bars with significant proportional change. The P values (#,*<0.05, ##,**<0.01, ###,***<0.001, ****<0.0001); the odd ratio values are reported on the bars. c, The list of top hyper and hypomethylated CpGs and the closet gene. The asterisk denotes the genes differentially expressed in skin.
a, Transcriptional profiling of 25 months old 4 F mice after 1 month of treatment by comparing to untreated and young 4 F mice (3 mo). n = 4 (old 4 F -Dox), n = 3 (old 4 F + Dox) and n = 4 (young 4 F -Dox) independent biological mice. b, Plot of principal components (PC1 and PC2) obtained from the principal component analysis of tissues from untreated and treated male and female mice at 26 months of age and young 4 F mice. c, The list of top 20 upregulated and downregulated genes. d, A dot plot showing the downregulation of Gbp7 gene expression in treated mice and comparable to young control. e, Transcription factor motif enrichment analysis in differentially expressed genes.
Extended Data Fig. 9 Transcriptional profiling of long-term treated 4 F mice and doxycycline treated B6 mice.
a, Transcriptional profiling of 22 months old 4 F mice after 7 months of treatment by comparing to untreated B6 mice. n = 5 (B6 -Dox) and n = 5 (4 F + Dox) independent biological mice. b, Gene ontology (GO) analysis of RNA-seq data of skin showing the top upregulated and downregulated genes. DEG list cutoffs defined as P < 0.05 and LFC of 2. c, List of top downregulated and upregulated genes in the skin. d, Plot of principal components (PC1 and PC2) of tissues from 10 months treated 4 F mice analyzed at 22 months of age. n = 3 (4 F -Dox) and n = 3 (4 F + Dox) independent biological mice. e, Plot of principal components (PC1 and PC2) of the skin comparing control and treated mice. f, Volcano plots of RNA-seq data of skin generated by comparison of 5 months doxycycline treated B6 mice (control and treated) and 10 months treated 4 F mice. For B6 + /-Dox controls, n = 2 (B6 -Dox) and n = 2 (B6 + Dox) independent biological mice. For 4 F 10 month treated, n = 3 (4 F -Dox) and n = 3 (4 F + Dox) independent biological mice.
Extended Data Fig. 10 Analysis of skin and skeletal muscle regeneration after long-term partial reprogramming.
a, Representative images of immunostaining of Ki67 positive cells in the skin of 4 F mice at 22 months after long-term partial reprogramming. Scale bar, 50 μm. b, Quantification of Ki67 positive cells in the skin of 4 F mice after short-term and long-term partial reprogramming in 26 (-Dox n = 4 and +Dox n = 3) and 22 months (-Dox n = 4 and +Dox n = 4) 4 F mice, respectively. 3 sections per mice were used for quantification. c, Epidermal thickness of skin in 4 F mice at 22 months after long-term partial reprogramming. Females (-Dox n = 2 and +Dox n = 2) and Males (-Dox n = 2 and +Dox n = 2), 2 sections per mice were used for quantification. d, Representative histological images of TA muscle and quantification of muscle fiber size of Young (3-months) and 7-months treated (starting at 15 months of age until 22 months) 4 F mice 10 days after cardiotoxin injection. Scale bar, 100 μm. e, Immunostaining of Pax7 and dystrophin and quantification of Pax7+ cells in TA muscle 10 days after cardiotoxin injection n = 5 (Young), n = 6 (-Dox) and n = 5 (+Dox) independent biological mice. Scale bar, 50 μm. For all relevant figures, data are represented as mean ± SD. ****P < 0.0001 values according to two-tailed Student’s t-test.
About this article
Cite this article
Browder, K.C., Reddy, P., Yamamoto, M. et al. In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice. Nat Aging 2, 243–253 (2022). https://doi.org/10.1038/s43587-022-00183-2