Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Reprogramming the GRHL2−CDK19 axis by gene therapy alleviates prostate aging

Nature Aging volume 6, pages 252–269 (2026)Cite this article

Subjects

Abstract

The prostate is a multifunctional organ of the male reproductive system whose aging process impairs sexual and urinary function and fertility and increases disease susceptibility, thereby compromising quality of life. However, the mechanisms underlying human prostate aging remain poorly understood. Here we integrated single-nucleus transcriptomics and histological analyses to elucidate the aging mechanisms of the primate prostate. We identified epithelial cell senescence, chronic inflammation and fibrosis as key hallmarks of prostate aging. In young epithelial cells, GRHL2 promotes CDK19 transcription, which sequesters p53, leading to the suppression of p21Waf1/Cip1. Aging-related downregulation of GRHL2 releases p53 from the CDK19−p53 complex, activating p21Waf1/Cip1 transcription and inducing cell senescence. Accordingly, a single injection of a GRHL2-based gene therapy strategy delayed prostate aging and alleviated age-related urinary dysfunction in vivo. Our findings elucidate key mechanisms of primate prostate aging and provide a foundation for developing therapies targeting prostate aging and associated pathologies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Age-related phenotypical changes in monkey prostate.
Fig. 2: Establishment of the single-nucleus transcriptome landscape of monkey prostate aging.
Fig. 3: Transcriptomic profiling of monkey prostate epithelial cells.
Fig. 4: Downregulation of GRHL2 induces human prostate basal epithelial cell senescence.
Fig. 5: CDK19 inhibits p53-mediated transcription of p21Waf1/Cip1 through direct interaction with p53.
Fig. 6: GRHL2 delays senescence of prostate epithelial cells and alleviates prostate dysfunction in aged male mice.

Similar content being viewed by others

Data availability

The raw sequencing data of human epithelial cells reported in this paper have been deposited in the Genome Sequence Archive-Human in the National Genomics Data Center, China National Center for Bioinformation, with accession number HRA009832. The raw sequencing data of animals have been deposited in the Genome Sequence Archive in the National Genomics Data Center, with accession number CRA021601.

Code availability

The code used to perform bioinformatics analysis in this study is available at GitHub (https://github.com/hezan0726/Monkey-prostate-aging).

References

  1. Verze, P., Cai, T. & Lorenzetti, S. The role of the prostate in male fertility, health and disease. Nat. Rev. Urol. 13, 379–386 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Motrich, R. D. et al. Implications of prostate inflammation on male fertility. Andrologia 50, e13093 (2018).

    Article  PubMed  Google Scholar 

  3. Rosen, R. C., Giuliano, F. & Carson, C. C. Sexual dysfunction and lower urinary tract symptoms (LUTS) associated with benign prostatic hyperplasia (BPH). Eur. Urol. 47, 824–837 (2005).

    Article  PubMed  Google Scholar 

  4. Cellek, S., Cameron, N. E., Cotter, M. A., Fry, C. H. & Ilo, D. Microvascular dysfunction and efficacy of PDE5 inhibitors in BPH−LUTS. Nat. Rev. Urol. 11, 231–241 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Infante Hernandez, S., Gomez Rivas, J. & Moreno Sierra, J. Benign prostatic hyperplasia. Med. Clin. (Barc.) 163, 407–414 (2024).

    Article  PubMed  Google Scholar 

  6. Foo, K. T. Pathophysiology of clinical benign prostatic hyperplasia. Asian J. Urol. 4, 152–157 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Crowell, P. D. et al. Expansion of luminal progenitor cells in the aging mouse and human prostate. Cell Rep. 28, 1499–1510 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kwon, O. J. et al. Functional heterogeneity of mouse prostate stromal cells revealed by single-cell RNA-seq. iScience 13, 328–338 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Crowley, L. et al. A single-cell atlas of the mouse and human prostate reveals heterogeneity and conservation of epithelial progenitors. eLife 9, e59465 (2020).

  11. Guo, W. et al. Single-cell transcriptomics identifies a distinct luminal progenitor cell type in distal prostate invagination tips. Nat. Genet. 52, 908–918 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Karthaus, W. R. et al. Regenerative potential of prostate luminal cells revealed by single-cell analysis. Science 368, 497–505 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chen, S. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, Y. et al. Single-cell transcriptomics reveals cell type diversity of human prostate. J. Genet. Genomics 49, 1002–1015 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Yan, Q. et al. Single-cell RNA-sequencing technology demonstrates the heterogeneity between aged prostate peripheral and transitional zone. Clin. Transl. Med. 12, e1084 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cao, Y. et al. The symptoms of benign prostatic hyperplasia patients with stromal-dominated hyperplasia nodules may be associated with prostate fibrosis. Int. J. Gen. Med. 16, 1181–1191 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yang, Y. et al. Metformin decelerates aging clock in male monkeys. Cell 187, 6358–6378 (2024).

    Article  CAS  PubMed  Google Scholar 

  18. Hu, J. et al. Spatially resolved transcriptomic analysis of the adult human prostate. Nat. Genet. 57, 922–933 (2025).

    Article  CAS  PubMed  Google Scholar 

  19. Hirz, T. et al. Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses. Nat. Commun. 14, 663 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bao, H. et al. Biomarkers of aging. Sci. China Life Sci. 66, 893–1066 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhang, W. et al. SIRT6 deficiency results in developmental retardation in cynomolgus monkeys. Nature 560, 661–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. Wang, S. et al. Single-cell transcriptomic atlas of primate ovarian aging. Cell 180, 585–600 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Sun, S. et al. CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord. Nature 624, 611–620 (2023).

    Article  CAS  PubMed  Google Scholar 

  24. Mubiru, J. N. et al. Nonhuman primates as models for studies of prostate specific antigen and prostatic diseases. Prostate 68, 1546–1554 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lewis, R. W., Kim, J. C., Irani, D. & Roberts, J. A. The prostate of the nonhuman primate: normal anatomy and pathology. Prostate 2, 51–70 (1981).

    Article  CAS  PubMed  Google Scholar 

  26. Sun, G. et al. Single-cell profiling identifies hair cell SLC35F1 deficiency as a signature of primate cochlear aging. Nat. Aging 5, 1862–1879 (2025).

    Article  CAS  PubMed  Google Scholar 

  27. Jing, Y. et al. Vitamin C conveys geroprotection on primate ovaries. Cell Stem Cell https://doi.org/10.1016/j.stem.2025.09.008 (2025).

  28. Lu, H. et al. Aging hallmarks of the primate ovary revealed by spatiotemporal transcriptomics. Protein Cell 15, 364–384 (2024).

    Article  CAS  PubMed  Google Scholar 

  29. Gangavarapu, K. J., Jowdy, P. F., Foster, B. A. & Huss, W. J. Role of prostate stem cells and treatment strategies in benign prostate hyperplasia. Am. J. Clin. Exp. Urol. 10, 154–169 (2022).

    PubMed  PubMed Central  Google Scholar 

  30. Hata, J. et al. Mechanism of androgen-independent stromal proliferation in benign prostatic hyperplasia. Int. J. Mol. Sci. 24, 11634 (2023).

  31. Kyprianou, N., Tu, H. & Jacobs, S. C. Apoptotic versus proliferative activities in human benign prostatic hyperplasia. Hum. Pathol. 27, 668–675 (1996).

    Article  CAS  PubMed  Google Scholar 

  32. Oomens, E. H., van Steenbrugge, G. J., van der Kwast, T. H. & Schroder, F. H. Application of the monoclonal antibody Ki-67 on prostate biopsies to assess the fraction of human prostatic carcinoma. J. Urol. 145, 81–85 (1991).

    Article  CAS  PubMed  Google Scholar 

  33. Wolny-Rokicka, E. I. & Grabowska, M. Ki67 and TNFRII as potential clinical markers for effective clinical staging of advanced prostate cancer. Cancers (Basel) 17, 2638 (2025).

  34. Zhong, W. et al. Ki-67 and PCNA expression in prostate cancer and benign prostatic hyperplasia. Clin. Invest. Med. 31, E8–E15 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Zhang, B. et al. SenoIndex: S100A8/S100A9 as a novel aging biomarker. Life Med. 2, lnad022 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Rodriguez-Nieves, J. A. & Macoska, J. A. Prostatic fibrosis, lower urinary tract symptoms, and BPH. Nat. Rev. Urol. 10, 546–550 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Adrian, A. E. et al. Aging-related mitochondrial dysfunction is associated with fibrosis in benign prostatic hyperplasia. J. Gerontol. A Biol. Sci. Med. Sci. 79, glad222 (2024).

  38. Aging Atlas Consortium. Aging Atlas: a multi-omics database for aging biology. Nucleic Acids Res. 49, D825–D830 (2021).

    Article  Google Scholar 

  39. Willcox, B. J. et al. FOXO3A genotype is strongly associated with human longevity. Proc. Natl Acad. Sci. USA 105, 13987–13992 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen, C., Liu, J. & Yu, W. Unlocking heart anti-aging potential: the SIRT2-STAT3-CDKN2B pathway as a bridge between fiction and reality. Life Med. 3, lnae020 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chen, Y. et al. Circadian factors CLOCK and BMAL1 promote nonhomologous end joining and antagonize cellular senescence. Life Med. 3, lnae006 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Jing, Y. et al. Single-nucleus profiling unveils a geroprotective role of the FOXO3 in primate skeletal muscle aging. Protein Cell 14, 497–512 (2023).

    PubMed  Google Scholar 

  43. Sun, G. et al. A single-cell transcriptomic atlas reveals senescence and inflammation in the post-tuberculosis human lung. Nat. Microbiol. 10, 2073–2091 (2025).

    Article  CAS  PubMed  Google Scholar 

  44. Zhang, W. et al. A single-cell transcriptomic landscape of primate arterial aging. Nat. Commun. 11, 2202 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Billis, A. Prostatic atrophy. Clinicopathological significance. Int. Braz. J. Urol. 36, 401–409 (2010).

    Article  PubMed  Google Scholar 

  46. Giroux, M. & Descoteaux, A. Cyclooxygenase-2 expression in macrophages: modulation by protein kinase C-α. J. Immunol. 165, 3985–3991 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Zhang, B. et al. Klf5 acetylation regulates luminal differentiation of basal progenitors in prostate development and regeneration. Nat. Commun. 11, 997 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Scarano, W. R. et al. Cell junctions in the prostate: an overview about the effects of Endocrine Disrupting Chemicals (EDCS) in different experimental models. Reprod. Toxicol. 81, 147–154 (2018).

    Article  CAS  PubMed  Google Scholar 

  49. Chen, A. F. et al. GRHL2-dependent enhancer switching maintains a pluripotent stem cell transcriptional subnetwork after exit from naive pluripotency. Cell Stem Cell 23, 226–238 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Aue, A. et al. A Grainyhead-like 2/Ovo-like 2 pathway regulates renal epithelial barrier function and lumen expansion. J. Am. Soc. Nephrol. 26, 2704–2715 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Liang, C. et al. Stabilization of heterochromatin by CLOCK promotes stem cell rejuvenation and cartilage regeneration. Cell Res. 31, 187–205 (2021).

    Article  CAS  PubMed  Google Scholar 

  52. Wang, B., Han, J., Elisseeff, J. H. & Demaria, M. The senescence-associated secretory phenotype and its physiological and pathological implications. Nat. Rev. Mol. Cell Biol. 25, 958–978 (2024).

    Article  CAS  PubMed  Google Scholar 

  53. Ding, Y. et al. Comprehensive human proteome profiles across a 50-year lifespan reveal aging trajectories and signatures. Cell 188, 5763–5784 (2025).

    Article  CAS  PubMed  Google Scholar 

  54. Dannappel, M. V., Sooraj, D., Loh, J. J. & Firestein, R. Molecular and in vivo functions of the CDK8 and CDK19 kinase modules. Front. Cell Dev. Biol. 6, 171 (2018).

    Article  PubMed  Google Scholar 

  55. Engeland, K. Cell cycle regulation: p53-p21-RB signaling. Cell Death Differ. 29, 946–960 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Galanos, P. et al. Chronic p53-independent p21 expression causes genomic instability by deregulating replication licensing. Nat. Cell Biol. 18, 777–789 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhang, Z. et al. CDK19 regulates the proliferation of hematopoietic stem cells and acute myeloid leukemia cells by suppressing p53-mediated transcription of p21. Leukemia 36, 956–969 (2022).

    Article  CAS  PubMed  Google Scholar 

  58. De Nunzio, C., Presicce, F. & Tubaro, A. Inflammatory mediators in the development and progression of benign prostatic hyperplasia. Nat. Rev. Urol. 13, 613–626 (2016).

    Article  PubMed  Google Scholar 

  59. Gorbunova, V. et al. The role of retrotransposable elements in ageing and age-associated diseases. Nature 596, 43–53 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Zhao, Y., Simon, M., Seluanov, A. & Gorbunova, V. DNA damage and repair in age-related inflammation. Nat. Rev. Immunol. 23, 75–89 (2023).

    Article  CAS  PubMed  Google Scholar 

  61. Jiang, S., Song, C. S. & Chatterjee, B. Stimulation of prostate cells by the senescence phenotype of epithelial and stromal cells: implication for benign prostate hyperplasia. FASEB Bioadv. 1, 353–363 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Fiard, G. et al. Cellular senescence as a possible link between prostate diseases of the ageing male. Nat. Rev. Urol. 18, 597–610 (2021).

    Article  PubMed  Google Scholar 

  63. Wong, L., Hutson, P. R. & Bushman, W. Prostatic inflammation induces fibrosis in a mouse model of chronic bacterial infection. PLoS ONE 9, e100770 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Li, Y. et al. The role of IL-6 in fibrotic diseases: molecular and cellular mechanisms. Int. J. Biol. Sci. 18, 5405–5414 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sheng, J. et al. M2 macrophage-mediated interleukin-4 signalling induces myofibroblast phenotype during the progression of benign prostatic hyperplasia. Cell Death Dis. 9, 755 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Di Micco, R., Krizhanovsky, V., Baker, D. & d’Adda di Fagagna, F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat. Rev. Mol. Cell Biol. 22, 75–95 (2021).

    Article  PubMed  Google Scholar 

  67. Hoeijmakers, J. H. DNA damage, aging, and cancer. N. Engl. J. Med. 361, 1475–1485 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Trastus, L. A. & d’Adda di Fagagna, F. The complex interplay between aging and cancer. Nat. Aging 5, 350–365 (2025).

    Article  PubMed  Google Scholar 

  69. Haston, S. et al. Clearance of senescent macrophages ameliorates tumorigenesis in KRAS-driven lung cancer. Cancer Cell 41, 1242–1260 (2023).

    Article  CAS  PubMed  Google Scholar 

  70. Prieto, L. I. et al. Senescent alveolar macrophages promote early-stage lung tumorigenesis. Cancer Cell 41, 1261–1275 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Mittelbrunn, M. & Kroemer, G. Hallmarks of T cell aging. Nat. Immunol. 22, 687–698 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. McHugh, D., Duran, I. & Gil, J. Senescence as a therapeutic target in cancer and age-related diseases. Nat. Rev. Drug Discov. 24, 57–71 (2025).

    Article  CAS  PubMed  Google Scholar 

  73. Leng, S. X. & Pawelec, G. Single-cell immune atlas for human aging and frailty. Life Med. 1, 67–70 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kong, M. et al. Aging-associated accumulation of mitochondrial DNA mutations in tumor origin. Life Med. 1, 149–167 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wang, Y. X. et al. Function of hematopoiesis and bone marrow niche in inflammation and non-hematopoietic diseases. Life Med. 4, lnaf015 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Li, H. et al. Immunosenescence inventory—a multi-omics database for immune aging research. Nucleic Acids Res. 53, D1047–D1054 (2025).

    Article  CAS  PubMed  Google Scholar 

  77. Dong, Z. et al. Cellular senescence and SASP in tumor progression and therapeutic opportunities. Mol. Cancer 23, 181 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Song, H. et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nat. Commun. 13, 141 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Guo, W. et al. JAK/STAT signaling maintains an intermediate cell population during prostate basal cell fate determination. Nat. Genet. 56, 2776–2789 (2024).

    Article  CAS  PubMed  Google Scholar 

  80. Zhuang, X. et al. Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis. Nature 637, 184–194 (2025).

    Article  CAS  PubMed  Google Scholar 

  81. Petrof, G. et al. Mutations in GRHL2 result in an autosomal-recessive ectodermal dysplasia syndrome. Am. J. Hum. Genet. 95, 308–314 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ming, Q. et al. Structural basis of gene regulation by the Grainyhead/CP2 transcription factor family. Nucleic Acids Res. 46, 2082–2095 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Michaels, Y. S. et al. Precise tuning of gene expression levels in mammalian cells. Nat. Commun. 10, 818 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Sun, Y., Li, Q. & Kirkland, J. L. Targeting senescent cells for a healthier longevity: the roadmap for an era of global aging. Life Med. 1, 103–119 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Zhang, J. et al. Spatiotemporal omics—refining the landscape of precision medicine. Life Med. 1, 84–102 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Sun, G. et al. Single-cell transcriptomic atlas of mouse cochlear aging. Protein Cell 14, 180–201 (2023).

    CAS  PubMed  Google Scholar 

  87. Zheng, F. et al. FOXO3-engineered human mesenchymal stem cells efficiently enhance post-ischemic stroke functional rehabilitation. Protein Cell 16, 365–373 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lei, J. et al. Senescence-resistant human mesenchymal progenitor cells counter aging in primates. Cell 188, 5039−5061 (2025).

  89. Wang, M. et al. Single-nucleus transcriptomics decodes the link between aging and lumbar disc herniation. Protein Cell 16, 667−684 (2025).

  90. Liu, F. et al. Identification of FOXO1 as a geroprotector in human synovium through single-nucleus transcriptomic profiling. Protein Cell 15, 441–459 (2024).

    Article  CAS  PubMed  Google Scholar 

  91. Hill, W. G., Zeidel, M. L., Bjorling, D. E. & Vezina, C. M. Void spot assay: recommendations on the use of a simple micturition assay for mice. Am. J. Physiol. Ren. Physiol. 315, F1422–F1429 (2018).

    Article  CAS  Google Scholar 

  92. Wang, C. et al. RIG-I-driven CDKN1A stabilization reinforces cellular senescence. Sci. China Life Sci. 68, 1646–1661 (2025).

    Article  CAS  PubMed  Google Scholar 

  93. Ma, S. et al. Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging. Cell 187, 7025–7044 (2024).

    Article  CAS  PubMed  Google Scholar 

  94. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023).

    Article  CAS  PubMed  Google Scholar 

  96. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb.) 2, 100141 (2021).

    CAS  PubMed  Google Scholar 

  100. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Harms, K. L. & Chen, X. The functional domains in p53 family proteins exhibit both common and distinct properties. Cell Death Differ. 13, 890–897 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Babamohamadi, M. et al. Recent findings on the role of wild-type and mutant p53 in cancer development and therapy. Front. Mol. Biosci. 9, 903075 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wang, Q. et al. Aging induces region-specific dysregulation of hormone synthesis in the primate adrenal gland. Nat. Aging 4, 396–413 (2024).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank H. Lu, D. Huang and J. Lei for their assistance for tissue disassociation and X. Zhu (Institute of Zoology, Chinese Academy of Sciences) for help in image scanning. We are grateful to L. Bai, L. Tian, S. Qiao, Y. Cai, J. Lu, X. Li, Y. Yang, X. Chen, R. Bai, Q. Chu and J. Chen for their administrative assistance. This work was supported by the National Natural Science Foundation of China (82488301 to G.-H.L. and J.Q., 82125011 to J.Q. and 82471586 to S.W.); the Program of the Beijing Natural Science Foundation (JQ24044 to W.Z.); the National Key Research and Development Program of China (2020YFA0804000 to G.-H.L. and S.W., 2022YFA1103700 to W.Z., S.M. and J.Q., 2022YFA1103800 to S.S. and STI2030-Major Projects-2021ZD0202400 to S.W.); the National Natural Science Foundation of China (92168201 to G.-H.L., 82330044 to G.-H.L., 32341001 to G.-H.L. and G.S., 82361148130 to G.-H.L. and J.Q., 8231101626 to J.Q., 92468303 to S.W., S.S. and X.F., 82361148131 to W.Z., 32121001 to W.Z., 82192863 to W.Z., 82422031 to S.S., 82322025 to S.M. and 82271600 to S.M.); the Non-Communicable Chronic Diseases-National Science and Technology Major Project (2024ZD0530400 to J.Q.); the Program of the Beijing Natural Science Foundation (Z240018 to G.-H.L. and S.W., F251011 to J.Q. and Z230011 to J.Q.); the Shenzhen Medical Research Fund (C2406001 to G.-H.L.); the Chinese Academy of Sciences Project for Young Scientists in Basic Research (YSBR-076 to G.-H.L. and J.Q. and YSBR-012 to W.Z.); Beijing Anzhen Hospital High Level Research Funding (2024AZB3002 to J.Q.); the Informatization Plan of the Chinese Academy of Sciences (CAS-WX2022SDC-XK14 to G.-H.L. and S.M.); the New Cornerstone Science Foundation through the XPLORER PRIZE (2021-1045 to G.-H.L.); the Key Laboratory of Alzheimer’s Disease of Zhejiang Province (ZJAD-2024001 to J.Q.); the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA0460403-05 to W.Z. and XDC0200000 to S.M.); the Initiative Scientific Research Program, Institute of Zoology, Chinese Academy of Sciences (2023IOZ0202 to J.Q., 2024IOZ0103 to J.Q. and 2023IOZ0102 to S.M.); the Excellent Young Talents Program of Capital Medical University (12300927 to S.W. and 12500825 to S.S.); the Excellent Young Talents Training Program for the Construction of Beijing Municipal University Teacher Team (BPHR202203105 to S.W.); the Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (JYY2023-13 to W.Z.); the Chinese Academy of Sciences Youth Interdisciplinary Team to W.Z.; the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2022083 to S.M.); the Chinese Academy of Sciences President’s International Fellowship Initiative (2024PG0022 to G.Q.); the International Partnership Program of the Chinese Academy of Sciences (073GJHZ2023019FN to S.M.); and the Space Medical Experiment Project of the China Manned Space Program (HYZHXMH01012 to G.-H.L.).

Author information

Author notes

  1. These authors contributed equally: Guoqiang Sun, Zan He, Dongliang Lv, Qiaoran Wang.

Authors and Affiliations

  1. State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Guoqiang Sun, Dongliang Lv, Yandong Zheng, Shuai Ma, Jing Qu & Guang-Hui Liu

  2. Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Aging Translational Medicine Center, Beijing Municipal Geriatric Medical Research Center, Beijing Key Laboratory of Environment and Aging, Xuanwu Hospital, Capital Medical University, Beijing, China

    Zan He, Bilan Luo, Yandong Zheng, Jinghao Hu, Si Wang & Guang-Hui Liu

  3. University of Chinese Academy of Sciences, Beijing, China

    Dongliang Lv, Qiaoran Wang, Yandong Zheng, Weiqi Zhang, Jing Qu & Guang-Hui Liu

  4. China National Center for Bioinformation and Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Qiaoran Wang & Weiqi Zhang

  5. Liver Transplant Center, Organ Transplant Center, West China Hospital of Sichuan University, Chengdu, China

    Gang Xu & Jiayin Yang

  6. Laboratory of Liver Transplantation, Key Laboratory of Transplant Engineering and Immunology, NHC, West China Hospital of Sichuan University, Chengdu, China

    Gang Xu & Jiayin Yang

  7. Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

    Feifei Liu, Shuhui Sun & Jing Qu

  8. Department of Urology, The First Affiliated Hospital, Anhui Medical University, Anhui, China

    Peiyu Wang

  9. Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China

    Shuai Ma, Jing Qu & Guang-Hui Liu

  10. Aging Biomarker Consortium (ABC), Beijing, China

    Shuai Ma, Weiqi Zhang, Jing Qu, Si Wang & Guang-Hui Liu

  11. Altos Labs San Diego Institute of Science, San Diego, CA, USA

    Concepcion Rodriguez Esteban & Juan Carlos Izpisua Belmonte

  12. Salk Institute for Biological Studies, La Jolla, CA, USA

    Concepcion Rodriguez Esteban & Juan Carlos Izpisua Belmonte

  13. Key Laboratory of Tissue Repair and Regeneration of PLA, Beijing Key Research Laboratory of Skin Injury, Repair and Regeneration, Medical Innovation Department, PLA General Hospital and PLA Medical College, Beijing, China

    Xiaobing Fu

  14. Sino-Danish College, University of Chinese Academy of Sciences, Sino-Danish Center for Education and Research, Beijing, China

    Weiqi Zhang

Authors
  1. Guoqiang Sun
    View author publications

    Search author on:PubMed Google Scholar

  2. Zan He
    View author publications

    Search author on:PubMed Google Scholar

  3. Dongliang Lv
    View author publications

    Search author on:PubMed Google Scholar

  4. Qiaoran Wang
    View author publications

    Search author on:PubMed Google Scholar

  5. Gang Xu
    View author publications

    Search author on:PubMed Google Scholar

  6. Feifei Liu
    View author publications

    Search author on:PubMed Google Scholar

  7. Peiyu Wang
    View author publications

    Search author on:PubMed Google Scholar

  8. Bilan Luo
    View author publications

    Search author on:PubMed Google Scholar

  9. Yandong Zheng
    View author publications

    Search author on:PubMed Google Scholar

  10. Jinghao Hu
    View author publications

    Search author on:PubMed Google Scholar

  11. Shuhui Sun
    View author publications

    Search author on:PubMed Google Scholar

  12. Shuai Ma
    View author publications

    Search author on:PubMed Google Scholar

  13. Concepcion Rodriguez Esteban
    View author publications

    Search author on:PubMed Google Scholar

  14. Jiayin Yang
    View author publications

    Search author on:PubMed Google Scholar

  15. Xiaobing Fu
    View author publications

    Search author on:PubMed Google Scholar

  16. Juan Carlos Izpisua Belmonte
    View author publications

    Search author on:PubMed Google Scholar

  17. Weiqi Zhang
    View author publications

    Search author on:PubMed Google Scholar

  18. Jing Qu
    View author publications

    Search author on:PubMed Google Scholar

  19. Si Wang
    View author publications

    Search author on:PubMed Google Scholar

  20. Guang-Hui Liu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

G.-H.L., S.W., J.Q. and W.Z. conceived and supervised the project. G.-H.L., S.W., J.Q., W.Z., G.S., Z.H., D.L. and Q.W. wrote the manuscript. G.S., Z.H., D.L. and Q.W. generated the data. G.-H.L., S.W., J.Q., W.Z., G.S., Z.H., D.L., Q.W., G.X., F.L., P.W., B.L., Y.Z., J.H., S.S., S.M., C.R.E., J.Y., X.F. and J.C.I.B. interpreted the data. All authors contributed to manuscript writing and approved the final version.

Corresponding authors

Correspondence to Weiqi Zhang, Jing Qu, Si Wang or Guang-Hui Liu.

Ethics declarations

Competing interests

C.R.E. and J.C.I.B. are employees of Altos Labs. The other authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Vincent Goffin, Stefano Fumagalli, Douglas Strand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Aging phenotypes of monkey prostate.

a, Representative H&E staining images of the prostate TZ in each monkey. b, H&E staining of the prostate CZ (left) and PZ (right) in young and aged monkeys. c, Masson’s Trichrome staining of the prostate CZ (left) and PZ (right) in young and aged monkeys. d, Immunostaining of αSMA in the prostate CZ (left) and PZ (right) from young and aged monkeys. e, Immunostaining of Ki67 in the prostate TZ in each monkey. b-e, Quantitative data are shown as the mean ± s.e.m. Young, n = 5 monkeys; Aged, n = 5 monkeys. Two-tailed Student’s t-test (b, c, d (fold change in αSMA-positive area of PZ), e), two-tailed Welch’s t-test (d (fold change in αSMA-positive area of CZ)) P values are indicated. Scale bars, 200 μm and 100 μm (b), 500 μm and 200 μm (Zoomed in images) (c), 20 μm (d, e). Y, Young; A, Aged.

Source Data

Extended Data Fig. 2 Aging-related inflammatory phenotypes of monkey prostate.

a, SA-β-Gal staining of the prostate CZ (left) and PZ (right) in young and aged monkeys. Nuclear Fast Red was used to counterstain the nuclei. b, Immunostaining of p21Waf1/Cip1 in the prostate CZ (left) and PZ (right) from young and aged monkeys. Arrowheads indicate p21Waf1/Cip1-positive cells. c, Immunostaining of HP1α in the prostate CZ (left) and PZ (right) from young and aged monkeys. d, Immunostaining of CD45 in the prostate TZ from young and aged monkeys. Arrowheads indicate CD45-positive cells. e, Immunostaining of TNF in the prostate CZ (left) and PZ (right) from young and aged monkeys. Arrowheads indicate TNF-positive cells. f, Immunostaining of IL-1β in the prostate CZ (left) and PZ (right) from young and aged monkeys. Arrowheads indicate IL-1β-positive cells. g, Immunostaining of S100A8 in the prostate CZ (left) and PZ (right) from young and aged monkeys. Arrowheads indicate S100A8-positive cells. a-g, Quantitative data are shown as the mean ± s.e.m. Young, n = 5 monkeys (b-g) or 3 monkeys (a); Aged, n = 5 monkeys or 4 monkeys (a). Two-tailed Wilcoxon rank-sum test (b, d), two-tailed Welch’s t-test (e, g (fold change in S100A8-positive cells of CZ)) or two-tailed Student’s t-test (a, c, f, g (fold change in S100A8-positive cells of PZ)) P values are indicated. Scale bars, 20 μm (a, d), 200 μm and 100 μm (Zoomed in images) (b, c, e), 100 μm and 50 μm (Zoomed in images) (f, g). Y, Young; A, Aged.

Source Data

Extended Data Fig. 3 Construction of a single-nucleus transcriptome atlas for monkey prostate aging.

a, Bar plot showing the numbers of cells detected in different samples (top). Boxplots showing the gene numbers (middle), and the percentages of mitochondrial genes (bottom) detected in different samples (Y1: 8,625 cells, Y2: 6,509 cells, Y3: 11,056 cells, Y4: 9,328 cells, Y5: 8,237 cells, A1: 6,598 cells, A2: 8,183 cells, A3: 9,806 cells, A4: 16,399 cells, A5: 4,866 cells). Boxes show the medians and the IQR (25–75%), while the lengths of the whiskers represent 1.5 × IQR. b, UMAP plots showing the distribution of prostate cell types for each monkey. c, Dot plot showing the expression level of representative marker genes across monkey prostate cell types. d, Box plots showing the cell identity scores or each monkey prostate cell type. Y, Young; A, Aged (Y: 6,661 BEs, 23,420 LEs, 4,564 SMCs, 3,736 Fibs, 1,912 ECs, 1,655 TCs, 870 Macs, 937 Neus; A: 5,553 BEs, 29,368 LEs, 3,076 SMCs, 2,098 Fibs, 1,085 ECs, 2,910 TCs, 1,057 Macs, 705 Neus). Boxes show the medians and the IQR (25–75%), while the lengths of the whiskers represent 1.5 × IQR. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests. Y, Young; A, Aged.

Extended Data Fig. 4 The expression of GRHL2 and CDK19 diminishes with age.

a, Immunostaining of E-cadherin, KRT5, and MSMB in CZ and PZ of both young and aged monkey prostates. The scale bar, 20 μm. b-c, Immunostaining of GRHL2 and KRT5 (b) or MSMB (c) in CZ and PZ of both young and aged monkey prostates. The scale bar, 50 μm and 25 μm (Zoomed in images). Arrowheads indicate KRT5- (b) or MSMB-(c) positive cells. a-c, Quantitative data are shown as the mean ± s.e.m. Young, n = 5 monkeys; Aged, n = 5 monkeys. Two-tailed Student’s t-test (a (fold change in intensity of E-cadherin in KRT5-positive cells of CZ), a (fold change in intensity of E-cadherin in MSMB-positive cells of PZ), b, c) or two-tailed Welch’s t-test (a (fold change in intensity of E-cadherin in KRT5-positive cells of PZ), a (fold change in intensity of E-cadherin in MSMB-positive cells of CZ)) P values are indicated.

Source Data

Extended Data Fig. 5 Age-dependent disruption of basal epithelial cell homeostasis.

a, Pseudotime trajectory showing the distribution of monkey prostate epithelial cells with aging. b, Density plots showing the density alteration of young (left) and aged (right) monkey prostate epithelial cells. c, The left heatmap showing the pseudotime-related genes in two patterns. Bar plots on the right represent the functional enrichment analysis of genes in pattern 1 and 2. d, Top, box plots showing the scores of gene sets in pattern 1 and 2 in BE (young BEs, 6,661 cells; old BEs, 5,553 cells). Bottom, a schematic diagram illustrating the status of BEs with prostate aging. Boxes show the medians and the IQR (25–75%), while the lengths of the whiskers represent 1.5 × IQR. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests. Image in d was created with BioRender.com. e, Box plots showing the scores of various cell identity marker genes in young and aged monkey prostate BEs (young BEs, 6,661 cells; old BEs, 5,553 cells). Boxes show the medians and the IQR (25–75%), while the lengths of the whiskers represent 1.5 × IQR. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests. Y, Young; A, Aged.

Extended Data Fig. 6 GRHL2-CDK19-p53-p21Waf1/Cip1 axis protects the basal epithelial cells from aging.

a, Immunostaining of KRT5 in hBEs. The scale bar, 20 μm. b, Schematic diagram depicting the co-culture of hBE and human fibroblast (hFib). Image was created with BioRender.com. c-f, Immunostaining of αSMA(d), Collagen type I (e), Collagen type III (f), and Collagen type VI (g) in hFibs co-cultured with non-senescent (Non-sen) or senescent (Sen) hBEs. g, Volcano plot showing the DEGs in hBEs transfected with si-GRHL2 compared to those transfected with si-NC. h, Bar pot showing the representative enriched terms of DEGs in hBEs transfected with si-GRHL2 compared to those transfected with si-NC. Upper-tailed cumulative hypergeometric test for significance. i, Immunostaining of CDK19 and KRT5 in CZ and PZ of both young and aged monkey prostates. Arrowheads indicate KRT5-positive cells. j, Western blot analysis of CDK19 protein levels in hBEs transduced with lentiviral vectors expressing GAL4 or CDK19. k, Immunostaining of Ki67 in hBEs transduced with lentiviral vectors expressing GAL4 or CDK19. l, SA-β-Gal staining in hBEs transduced with lentiviral vectors expressing GAL4 or CDK19. m, RT-qPCR analysis of IL1A and CCL20 transcript levels in hBEs transduced with lentiviruses expressing GAL4 or CDK19. n, ELISA analysis of IL-6 secretion levels in hBEs transduced with lentiviral vectors expressing GAL4 or CDK19. o and p, Computed model of structure of p53 (k) and CDK19 (p) proteins respectively. q, Computed model of docking residues between p53 and CDK19 proteins. r, Heatmap showing the expected position error of AlphaFold3 prediction. The vertical axis represents the aligned residue positions and the horizontal axis represents the scored residue positions. The color gradient, ranging from light to dark green, corresponds to the expected position error in angstroms, indicating the level of uncertainty in the predicted atomic positions. s, PCA plot showing the within-group consistency and batch effects of the sequenced samples. c-f and i-n, Data were presented as mean ± s.e.m. n = 5 biological repeats. Two-tailed Student’s t-test (c, i, k, l, m (fold change in transcript level of IL1A), n), two-tailed Wilcoxon rank-sum test (d, e) or two-tailed Welch’s t-test (f, j, m (fold change in transcript level of CCL20)) P values are indicated. Scale bars, 20 μm (c-f, k), 50 μm (i, l).

Source Data

Supplementary information

Reporting Summary

43587_2025_1020_MOESM2_ESM.xlsx

Supplementary Table 1. Marker genes of each cell type. Supplementary Table 2. Age-associated DEGs of each cell type. Supplementary Table 3. Gene sets used in this study. Supplementary Table 4. DEGs identified in hBEs after GRHL2 knockdown. Supplementary Table 5. DEGs rescued in aged mouse prostates via GRHL2 overexpression. Supplementary Table 6. Primers and siRNAs used in this study. Supplementary Table 7. Antibodies used in this study.

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 1

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Fig. 4

Uncropped scans of blots and gels.

Source Data Fig. 5

Uncropped scans of blots and gels.

Source Data Fig. 6

Uncropped scans of blots and gels.

Source Data Extended Data Fig. 6

Uncropped scans of blots and gels.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, G., He, Z., Lv, D. et al. Reprogramming the GRHL2−CDK19 axis by gene therapy alleviates prostate aging. Nat Aging 6, 252–269 (2026). https://doi.org/10.1038/s43587-025-01020-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43587-025-01020-y

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing