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.

  • Article
  • Published:

Cxxc finger protein 1 maintains homeostasis and function of intestinal group 3 innate lymphoid cells with aging

Nature Aging volume 3, pages 965–981 (2023)Cite this article

Subjects

Abstract

Aging is accompanied by homeostatic and functional dysregulation of multiple immune cell subsets. Group 3 innate lymphoid cells (ILC3s) constitute a heterogeneous cell population that plays pivotal roles in intestinal immunity. In this study, we found that ILC3s in aged mice exhibited dysregulated homeostasis and function, leading to bacterial and fungal infection susceptibility. Moreover, our data revealed that the enrichment of the H3K4me3 modification in effector genes of aged gut CCR6+ ILC3s was specifically decreased compared to young mice counterparts. Disruption of Cxxc finger protein 1 (Cxxc1) activity, a key subunit of H3K4 methyltransferase, in ILC3s led to similar aging-related phenotypes. An integrated analysis revealed Kruppel-like factor 4 (Klf4) as a potential Cxxc1 target. Klf4 overexpression partially restored the differentiation and functional defects seen in both aged and Cxxc1-deficient intestinal CCR6+ ILC3s. Therefore, these data suggest that targeting intestinal ILC3s may provide strategies to protect against age-related infections.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Intestinal CCR6+ ILC3s are numerically compromised with aging.
Fig. 2: Defective ILC3 function in aged mice compromises host defense against C. rodentium infection.
Fig. 3: Transcriptome profile of ILC3s from young and aged mice at the single-cell level.
Fig. 4: Cxxc1 is required to maintain ILC3 homeostasis.
Fig. 5: Defective ILC3 function in Cxxc1-deficient mice compromises host defense against C. rodentium infection.
Fig. 6: Single-cell profiling reveals similarities in the transcriptional landscape between Cxxc1-deficient and aged gut ILC3s.
Fig. 7: Genome-wide chromatin sequencing and rescue experiments revealed that Klf4 is a potential target of Cxxc1 in CCR6+ ILC3.

Similar content being viewed by others

Data availability

This original data presented in the study can be found on the Gene Expression Omnibus—scRNA-seq (GSE208733, GSE210195, GSE210193 and GSE209592) and CUT&Tag (GSE211017 and GSE210194)—and are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The scRNA-seq code used in this study is available in the GitHub repository at https://github.com/anjin8023/wanglab.git. The CUT&Tag code used in this study is available in the GitHub repository at https://github.com/anjin8023/wanglabCUTTag.git.

References

  1. Nikolich-Zugich, J. The twilight of immunity: emerging concepts in aging of the immune system. Nat. Immunol. 19, 10–19 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Ray, D. & Yung, R. Immune senescence, epigenetics and autoimmunity. Clin. Immunol. 196, 59–63 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sadighi Akha, A. A. Aging and the immune system: an overview. J. Immunol. Methods 463, 21–26 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Hu, B. et al. Transcription factor networks in aged naive CD4 T cells bias lineage differentiation. Aging Cell 18, e12957 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. Dowery, R. et al. Peripheral B cells repress B-cell regeneration in aging through a TNF-α/IGFBP-1/IGF-1 immune-endocrine axis. Blood 138, 1817–1829 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Goldberg, E. L., Shaw, A. C. & Montgomery, R. R. How inflammation blunts innate immunity in aging. Interdiscip. Top. Gerontol. Geriatr. 43, 1–17 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Stotesbury, C. et al. Defective early innate immune response to ectromelia virus in the draining lymph nodes of aged mice due to impaired dendritic cell accumulation. Aging Cell 19, e13170 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fung, I. T. H. et al. Activation of group 2 innate lymphoid cells alleviates aging-associated cognitive decline. J. Exp. Med. 217, e20190915 (2020).

  10. Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Klose, C. S. & Artis, D. Innate lymphoid cells as regulators of immunity, inflammation and tissue homeostasis. Nat. Immunol. 17, 765–774 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Klose, C. S. et al. A T-bet gradient controls the fate and function of CCR6−RORγ+ innate lymphoid cells. Nature 494, 261–265 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Melo-Gonzalez, F. & Hepworth, M. R. Functional and phenotypic heterogeneity of group 3 innate lymphoid cells. Immunology 150, 265–275 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mebius, R. E. Organogenesis of lymphoid tissues. Nat. Rev. Immunol. 3, 292–303 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Meier, D. et al. Ectopic lymphoid-organ development occurs through interleukin 7-mediated enhanced survival of lymphoid-tissue-inducer cells. Immunity 26, 643–654 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Robinette, M. L. et al. Transcriptional programs define molecular characteristics of innate lymphoid cell classes and subsets. Nat. Immunol. 16, 306–317 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhong, C., Zheng, M. & Zhu, J. Lymphoid tissue inducer—a divergent member of the ILC family. Cytokine Growth Factor Rev. 42, 5–12 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rankin, L. C. et al. The transcription factor T-bet is essential for the development of NKp46+ innate lymphocytes via the Notch pathway. Nat. Immunol. 14, 389–395 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Artis, D. & Spits, H. The biology of innate lymphoid cells. Nature 517, 293–301 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Keir, M., Yi, Y., Lu, T. & Ghilardi, N. The role of IL-22 in intestinal health and disease. J. Exp. Med. 217, e20192195 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Pagiatakis, C., Musolino, E., Gornati, R., Bernardini, G. & Papait, R. Epigenetics of aging and disease: a brief overview. Aging Clin. Exp. Res. 33, 737–745 (2021).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Shchukina, I. et al. Enhanced epigenetic profiling of classical human monocytes reveals a specific signature of healthy aging in the DNA methylome. Nat. Aging 1, 124–141 (2021).

    Article  PubMed  Google Scholar 

  24. Dozmorov, M. G., Coit, P., Maksimowicz-McKinnon, K. & Sawalha, A. H. Age-associated DNA methylation changes in naive CD4+ T cells suggest an evolving autoimmune epigenotype in aging T cells. Epigenomics 9, 429–445 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. McCauley, B. S. & Dang, W. Histone methylation and aging: lessons learned from model systems. Biochim. Biophys. Acta 1839, 1454–1462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hsu, C. L., Lo, Y. C. & Kao, C. F. H3K4 methylation in aging and metabolism. Epigenomes 5, 14 (2021).

  27. Sen, P., Shah, P. P., Nativio, R. & Berger, S. L. Epigenetic mechanisms of longevity and aging. Cell 166, 822–839 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sun, D. et al. Epigenomic profiling of young and aged HSCs reveals concerted changes during aging that reinforce self-renewal. Cell Stem Cell 14, 673–688 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Thomson, J. P. et al. CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature 464, 1082–1086 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yang, Y., Yang, Y., Chan, K. & Couture, J. F. Analyzing the impact of CFP1 mutational landscape on epigenetic signaling. FASEB J. 35, e21790 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Sha, Q. Q. et al. CFP1-dependent histone H3K4 trimethylation in murine oocytes facilitates ovarian follicle recruitment and ovulation in a cell-nonautonomous manner. Cell. Mol. Life Sci. 77, 2997–3012 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Sha, Q. Q. et al. Role of CxxC-finger protein 1 in establishing mouse oocyte epigenetic landscapes. Nucleic Acids Res. 49, 2569–2582 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cao, W. et al. CXXC finger protein 1 is critical for T-cell intrathymic development through regulating H3K4 trimethylation. Nat. Commun. 7, 11687 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chun, K. T. et al. The epigenetic regulator CXXC finger protein 1 is essential for murine hematopoiesis. PLoS ONE 9, e113745 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lin, F. et al. Epigenetic initiation of the TH17 differentiation program is promoted by Cxxc finger protein 1. Sci. Adv. 5, eaax1608 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hui, Z. et al. Cxxc finger protein 1 positively regulates GM-CSF-derived macrophage phagocytosis through Csf2rα-mediated signaling. Front. Immunol. 9, 1885 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bilmez, Y., Talibova, G. & Ozturk, S. Expression of the histone lysine methyltransferases SETD1B, SETDB1, SETD2, and CFP1 exhibits significant changes in the oocytes and granulosa cells of aged mouse ovaries. Histochem. Cell Biol. 158, 79–95 (2022).

  38. Verrier, T. et al. Phenotypic and functional plasticity of murine intestinal NKp46+ group 3 innate lymphoid cells. J. Immunol. 196, 4731–4738 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Viant, C. et al. Transforming growth factor-β and Notch ligands act as opposing environmental cues in regulating the plasticity of type 3 innate lymphoid cells. Sci. Signal. 9, ra46 (2016).

    Article  PubMed  Google Scholar 

  40. Shih, H. Y. et al. Developmental acquisition of regulomes underlies innate lymphoid cell functionality. Cell 165, 1120–1133 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rutz, S., Eidenschenk, C. & Ouyang, W. IL-22, not simply a Th17 cytokine. Immunol. Rev. 252, 116–132 (2013).

    Article  PubMed  Google Scholar 

  42. Sanos, S. L., Vonarbourg, C., Mortha, A. & Diefenbach, A. Control of epithelial cell function by interleukin-22-producing RORγt+ innate lymphoid cells. Immunology 132, 453–465 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sonnenberg, G. F., Monticelli, L. A., Elloso, M. M., Fouser, L. A. & Artis, D. CD4+ lymphoid tissue-inducer cells promote innate immunity in the gut. Immunity 34, 122–134 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Collins, J. W. et al. Citrobacter rodentium: infection, inflammation and the microbiota. Nat. Rev. Microbiol. 12, 612–623 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Silberger, D. J., Zindl, C. L. & Weaver, C. T. Citrobacter rodentium: a model enteropathogen for understanding the interplay of innate and adaptive components of type 3 immunity. Mucosal Immunol. 10, 1108–1117 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gladiator, A., Wangler, N., Trautwein-Weidner, K. & LeibundGut-Landmann, S. Cutting edge: IL-17-secreting innate lymphoid cells are essential for host defense against fungal infection. J. Immunol. 190, 521–525 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Busuttil, R., Bahar, R. & Vijg, J. Genome dynamics and transcriptional deregulation in aging. Neuroscience 145, 1341–1347 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Sun, L., Yu, R. & Dang, W. Chromatin architectural changes during cellular senescence and aging. Genes (Basel) 9, 211 (2018).

  49. Shilatifard, A. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 81, 65–95 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Eberl, G. & Littman, D. R. Thymic origin of intestinal αβ T cells revealed by fate mapping of RORγ+ cells. Science 305, 248–251 (2004).

    Article  CAS  PubMed  Google Scholar 

  51. Kondo, M., Weissman, I. L. & Akashi, K. Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661–672 (1997).

    Article  CAS  PubMed  Google Scholar 

  52. Seillet, C. et al. Deciphering the innate lymphoid cell transcriptional program. Cell Rep. 17, 436–447 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Harly, C., Cam, M., Kaye, J. & Bhandoola, A. Development and differentiation of early innate lymphoid progenitors. J. Exp. Med. 215, 249–262 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Qiu, J. et al. The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells. Immunity 36, 92–104 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Seillet, C. & Belz, G. T. Assessment of gene function of mouse innate lymphoid cells for in vivo analysis using retroviral transduction. Methods Mol. Biol. 1953, 231–240 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. Buettner, M. & Lochner, M. Development and function of secondary and tertiary lymphoid organs in the small intestine and the colon. Front. Immunol. 7, 342 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Stehle, C. et al. T-bet and RORα control lymph node formation by regulating embryonic innate lymphoid cell differentiation. Nat. Immunol. 22, 1231–1244 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sawa, S. et al. Lineage relationship analysis of RORγt+ innate lymphoid cells. Science 330, 665–669 (2010).

    Article  CAS  PubMed  Google Scholar 

  59. Sanos, S. L. et al. RORgammat and commensal microflora are required for the differentiation of mucosal interleukin 22-producing NKp46+ cells. Nat. Immunol. 10, 83–91 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Yang, C. E. et al. Aryl hydrocarbon receptor: from pathogenesis to therapeutic targets in aging-related tissue fibrosis. Ageing Res. Rev. 79, 101662 (2022).

    Article  CAS  PubMed  Google Scholar 

  61. He, J., Kallin, E. M., Tsukada, Y. & Zhang, Y. The H3K36 demethylase Jhdm1b/Kdm2b regulates cell proliferation and senescence through p15Ink4b. Nat. Struct. Mol. Biol. 15, 1169–1175 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Tanaka, H. et al. The SETD8/PR-Set7 methyltransferase functions as a barrier to prevent senescence-associated metabolic remodeling. Cell Rep. 18, 2148–2161 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Greer, E. L. et al. Members of the H3K4 trimethylation complex regulate lifespan in a germline-dependent manner in C. elegans. Nature 466, 383–387 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Djeghloul, D. et al. Age-associated decrease of the histone methyltransferase SUV39H1 in HSC perturbs heterochromatin and B lymphoid differentiation. Stem Cell Rep. 6, 970–984 (2016).

    Article  CAS  Google Scholar 

  65. Fraga, M. F. & Esteller, M. Epigenetics and aging: the targets and the marks. Trends Genet. 23, 413–418 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Sarg, B., Koutzamani, E., Helliger, W., Rundquist, I. & Lindner, H. H. Postsynthetic trimethylation of histone H4 at lysine 20 in mammalian tissues is associated with aging. J. Biol. Chem. 277, 39195–39201 (2002).

    Article  CAS  PubMed  Google Scholar 

  67. Chang, J. et al. Setd2 determines distinct properties of intestinal ILC3 subsets to regulate intestinal immunity. Cell Rep. 38, 110530 (2022).

    Article  CAS  PubMed  Google Scholar 

  68. Kapoor, N. et al. Transcription factors STAT6 and KLF4 implement macrophage polarization via the dual catalytic powers of MCPIP. J. Immunol. 194, 6011–6023 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Liao, X. et al. Kruppel-like factor 4 regulates macrophage polarization. J. Clin. Invest. 121, 2736–2749 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dykstra, B. & de Haan, G. Hematopoietic stem cell aging and self-renewal. Cell Tissue Res. 331, 91–101 (2008).

    Article  PubMed  Google Scholar 

  71. Li, Y. et al. Murine embryonic stem cell differentiation is promoted by SOCS-3 and inhibited by the zinc finger transcription factor Klf4. Blood 105, 635–637 (2005).

    Article  CAS  PubMed  Google Scholar 

  72. Blacher, E. et al. Aging disrupts circadian gene regulation and function in macrophages. Nat. Immunol. 23, 229–236 (2022).

    Article  CAS  PubMed  Google Scholar 

  73. Yin, S. et al. Runx3 mediates resistance to intracellular bacterial infection by promoting IL12 signaling in group 1 ILC and NCR+ILC3. Front. Immunol. 9, 2101 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Gao, X. et al. The transcription factor ThPOK regulates ILC3 lineage homeostasis and function during intestinal infection. Front. Immunol. 13, 939033 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tizian, C. et al. c-Maf restrains T-bet-driven programming of CCR6-negative group 3 innate lymphoid cells. eLife 9, e52549 (2020).

  76. Herweijer, H. & Wolff, J. A. Progress and prospects: naked DNA gene transfer and therapy. Gene Ther. 10, 453–458 (2003).

    Article  CAS  PubMed  Google Scholar 

  77. Knapp, J. E. & Liu, D. Hydrodynamic delivery of DNA. Methods Mol. Biol. 245, 245–250 (2004).

    CAS  PubMed  Google Scholar 

  78. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Dan, L. et al. The phosphatase PAC1 acts as a T cell suppressor and attenuates host antitumor immunity. Nat. Immunol. 21, 287–297 (2020).

    Article  Google Scholar 

  80. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).

  84. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).

    Article  CAS  PubMed  Google Scholar 

  86. Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 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 

Download references

Acknowledgements

We thank J. Qiu (Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences) for her generous gifts of C. rodentium. We thank H. Guanghua (Fudan University) for his generous gifts of C. albicans. We thank Y. Q. Zhu (Zhejiang University) for providing S. typhimurium (SL1344, SB300). We thank X. Guo (Institute of Immunology, Tsinghua University School of Medicine) for providing pRK-mIL-22 and control vector (pRK). We thank Y. Li, Y. Huang and W. Yin from the Core Facilities, Zhejiang University School of Medicine, for their technical support. We thank Y. Ding, H. Jin and X. Zhang from Animal Facilities, Zhejiang University, for mice maintenance. This work was supported by grants from the National Natural Science Foundation of China (nos. 32030035, 91442101 and 32100693), the Zhejiang Provincial Natural Science Foundation of China (no. LZ21C080001), Science and Technology Innovation 2030-Major Project (2021ZD0200405), Key Project of Experimental Technology Program of Zhejiang University (no. SZD202203) and Pre-research Projects of Innovation Center of Yangtze River Delta, Zhejiang University (no. 2022ZY008). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Xin Shen, Xianzhi Gao, Yikai Luo.

Authors and Affiliations

  1. Institute of Immunology and Bone Marrow Transplantation Center, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Xin Shen, Xianzhi Gao, Xiaoqian Liu & Lie Wang

  2. Co-Facility Center, Zhejiang University School of Medicine, Hangzhou, China

    Xin Shen

  3. Zhejiang University School of Medicine, Hangzhou, China

    Xin Shen, Xianzhi Gao, Qianying Xu, Zhengwei Huang, Xiaoqian Liu, Di Wang, Linrong Lu & Lie Wang

  4. Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

    Xin Shen, Xianzhi Gao & Lie Wang

  5. Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou, China

    Xianzhi Gao & Lie Wang

  6. Edinburgh Medical School: Biomedical Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK

    Xianzhi Gao

  7. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yikai Luo & Han Liang

  8. Program of Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA

    Yikai Luo & Han Liang

  9. Laboratory Animal Center, Zhejiang University, Hangzhou, China

    Ying Fan, Shenghui Hong, Qianqian Wang & Lie Wang

  10. Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Zuojia Chen & Chuan Wu

  11. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Han Liang

  12. Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China

    Lie Wang

Authors
  1. Xin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianzhi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yikai Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qianying Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shenghui Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhengwei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaoqian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qianqian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zuojia Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Di Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Linrong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Chuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Han Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Lie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.W., H.L. and C.W.: supervision, conceptualization, project administration and writing—review and editing. L.L. and D.W.: writing—review and editing. L.W.: funding acquisition. X.S. and X.G.: investigation, methodology, project administration and writing—original draft. X.G. and Y.L.: data curation and formal analysis. Q.X., Y.F., S.H., Z.H., X.L., Q.W. and Z.C.: investigation and methodology.

Corresponding authors

Correspondence to Chuan Wu, Han Liang or Lie Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks David Withers, Jörg Fritz and the other, anonymous, reviewers 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 Phenotype of ILC3s in the intestine of aged mice.

(a) Flow cytometry of Lin−RORγt+ ILC3s isolated from the siLP of young and aged mice. (b) The percentages (n = 8) and total numbers of ILC3s were compared (n = 10 young, n = 8 aged). (c) Summary of small intestine lengths in young and aged mice (n = 10 young, n = 11 aged). (d, e) The numbers and size of Peyer’s patches in the small intestine of young and aged mice were compared (n = 5). (f) Flow cytometric analysis of CD45.1 and CD45.2 expression in ILC3s. (g) The percentages of donor-derived cells shown in (f) (CD45.2/CD45.1) were compared (n = 11). Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of at least three independent experiments (a-g).

Source data

Extended Data Fig. 2 Phenotype of aged gut CCR6+ ILC3s is not affected by gender.

(a) Flow cytometry of ILC3s from the siLP of young (6- to 8-week-old, female) and aged mice (18-month-old, female). All data showed above gated out Lin+ cells in advance. (b) The percentages and total numbers of ILC3s were compared (n = 8). (c) The Lin−RORγt+ ILC3s shown in (a) were further characterized based on their CCR6 and NKp46 expression (upper). The CD4+ ILC3 subset among the CCR6+ ILC3s was analyzed (below). (d) The percentages and total numbers of all subsets were (n = 8). Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of four independent experiments (a-d).

Source data

Extended Data Fig. 3 Ectopic expression of IL-22 protects aged mice from C. rodentium infection.

(a-e) young (6- to 8-week-old, male) and aged mice (18-month-old, male) were infected with C.rodentium. Six hours after infection, IL-22-expressing plasmid (pRK-mIL-22) or control vector (pRK) was administered into the mice via hydrodynamic injection. (a, b) Colon lengths of young and aged mice (n = 9 young, n = 4 aged). (c) Bacterial counts in faeces (n = 9 young, n = 4 aged). (d) Body weight changes were monitored at the indicated time points (n = 3 aged+vector and aged+IL-22, n = 4 young+vector, n = 5 young+IL-22). (e) H&E staining of colon tissue sections. Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. Data are representative of two independent experiments (a-e).

Source data

Extended Data Fig. 4 Defective ILC3 function in aged mice compromises host defense against C. albicans infection.

(a-g) C. albicans infection model. (a) Representative image of colons in young and aged mice after C. albicans infection on day 7. (b) Colon lengths were counted and plotted in infected mice (n = 5 young, n = 6 aged). (c) Colonies of C. albicans in the faeces were counted by serial dilution (n = 4 young, n = 5 aged). (d) Body weight changes (n = 6). (e) H&E-stained sections of representative colons. (f) Flow cytometric analysis of IL-17A expression in the indicated subsets 7 days after infection. (g) The percentages of IL-17A+ cells were compared (n = 6 young, n = 5 aged). (h) Flow cytometric analysis of IFN-γ expression in NKp46+ ILC3s after stimulation with PMA and ionomycin (left); right, quantification (n = 8 young, n = 9 aged). (i-m) ILC3s (Lin−CD127+CD27−KLRG1−) from young and aged mice were adoptively transferred into NCG mice with C. albicans infection. (i-j) Measurements and statistical analysis of the colon lengths from NCG recipients (n = 5 control and aged transferred, n = 6 young transferred). (k) CFUs in the faeces of NCG recipients 9 days after infection (n = 5 control and aged transferred, n = 6 young transferred). (l) Changes in body weight were recorded at the indicated time points (n = 5 control, n = 7 young transferred, n = 8 aged transferred). (m) Histological analysis of colonic tissues by H&E staining. The data are representative of two independent experiments. Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of three independent experiments (a-h) and two independent experiments (i-m).

Source data

Extended Data Fig. 5 Protein level of Cxxc1 and differentially H3K4me3 modified genes of intestinal ILC3s in aged mice.

(a) Immunoblot images showing Cxxc1 protein in ILC3s sorted from young and aged mice (left). The relative protein content was normalized to β-Actin (right) (n = 3). (b-d) Anti H3K4me3 CUT&Tag in young mice versus aged mice. (b) Table showing top 100 genes with down−regulated H3K4me3 modification regions in aged mice. (c) KEGG pathway enrichment analysis performed by using DAVID. The top 10 highly enriched KEGG pathways are presented. (d) IGV visualizing keys genes related to pathways shown in (c). Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of three independent experiments (a).

Source data

Extended Data Fig. 6 Development of ILC progenitor in the bone marrow and formation of Peyer’s patches are not affected in Cxxc1f/f RorcCre mice.

(a) Flow cytometry of ILC3s from the siLP in Cxxc1f/f RorcCre and Cxxc1f/f mice. All data showed above gated out Lin+ cells in advance. (b) The percentages (n = 9) and total numbers (n = 8) of ILC3s were compared. (c) Flow cytometric analysis of common lymphoid progenitors (CLPs, Lin−CD127+c-KitintSca1intFlt3+); α4β7+ lymphoid progenitors (α-LPs, Lin−CD127+c-Kit+α4β7+); common helper-like innate lymphoid progenitors (ChILPs, Lin-CD127+α4β7+CD25−Flt3−) and common ILC precursors (ILCPs, Lin−CD127+α4β7+PLZF+) in bone marrow in Cxxc1f/f RorcCre mice and their wild-type Cxxc1f/f littermates. The lineage cocktail included TCRγδ, CD3ε, CD19, B220, NK1.1, CD11b, CD11c, Gr-1 and Ter119. (d) The percentages of CLPs, α-LPs, ChILPs, and ILCPs were compared.CLP (n = 6 WT, n = 8 KO), α-LP (n = 8 WT, n = 7 KO), ChILP (n = 8 WT, n = 8 KO), and ILCP (n = 7 WT, n = 6 KO). The cell numbers were compared. CLP (n = 7 WT, n = 9 KO), α-LP (n = 8 WT, n = 8 KO), ChILP (n = 6 WT, n = 6 KO), and ILCP (n = 8 WT, n = 8 KO). (e) Representative images of Peyer’s patches (red arrows) in the small intestine from Cxxc1f/f RorcCre mice and their wild-type Cxxc1f/f littermates. The numbers (f) and size (g) of Peyer’s patches in the small intestine from Cxxc1f/f RorcCre and Cxxc1f/f mice were compared (n = 10). Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of at least two independent experiments (a-g).

Source data

Extended Data Fig. 7 Defective ILC3 function in Cxxc1-deficient mice compromises host defense against C. albicans infection.

(a-g) C. albicans infection model. (a, b) Colon lengths of Cxxc1f/f and Cxxc1f/f RorcCre mice (n = 4). (c) Fungal burden in faeces (n = 5). (d) Body weight changes (n = 5 WT, n = 8 KO). (e) H&E histological analysis of representative colons in infected mice. (f, g) Representative flow plots and quantification of IL-17A production by CCR6+ ILC3s (left), NKp46+ ILC3s (middle), and DN ILC3s (right) isolated from the siLP (n = 7 WT, n = 9 KO). (h-i) Cytokine production in siLP ILC3s from Cxxc1f/f and Cxxc1f/f RorcCre mice. (h) Flow cytometric analysis of IFN-γ expression in NKp46+ ILC3s after stimulation with PMA and ionomycin (left); right, quantification (n = 4 WT, n = 5 KO). (i) S. typhimurium infection model. Representative flow cytometric profiles (left); right, quantification (n = 4). (j-n) Cxxc1f/f or Cxxc1f/f RorcCre ILC3s (Lin−CD127+CD27−KLRG1−) were adoptively transferred into NCG mice with C. albicans infection. Measurements (j) and statistical analysis (k) of the colon lengths from NCG recipients (n = 5 control, n = 7 WT transferred, n = 6 KO transferred). (l) CFUs in the faeces of NCG recipients 9 days after infection (n = 5 control, n = 7 WT transferred, n = 6 KO transferred). (m) Changes in body weight were recorded at the indicated time points (n = 5 control, n = 7 WT transferred, n = 8 KO transferred). (n) Histological analysis of colonic tissues by H&E staining. Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of two independent experiments (a-g, i-n) and three independent experiments (h).

Source data

Extended Data Fig. 8 The epigenetic program of DN ILC3s and NKp46+ ILC3s.

(a, b) Heatmap for genome-wide distribution of Cxxc1-binding signals at peak centers in DN ILC3s (a) and NKp46+ ILC3s (b) sorted from control and Cxxc1-deficient mice by CUT&Tag. (c) Donut chart showing the percentages of Cxxc1-binding at promoter regions, gene body regions, or intergenic regions. (d, e) Heatmaps illustrating enrichment of H3K4me3 CUT&Tag signals at all gene promoters ( ± 3 kb of TSS) in DN ILC3s (d) and NKp46+ ILC3s (e). (f) Donut chart showing the percentages of H3K4me3 peaks at promoter regions, gene body regions, or intergenic regions. Peak annotation was performed by HOMER. UTR, untranslated region. (g, h) Venn diagram showing genes with reduced enrichment of Cxxc1 and H3K4me3 modification in the indicated cells in the graphs (Chi-squared test was used to calculate the P values. Adjusted absolute log2fc value > 0.25 and adjusted P value < 0.05). (i) Overlaid histograms show expression of indicated protein in aged mice (red), control mice (blue) and Isotype (grey). (j) Overlaid histograms show expression of indicated protein in Cxxc1f/f RorcCre mice (red), control mice (blue) and Isotype (grey). The data are representative of two independent experiments (i) and four independent experiments (j).

Extended Data Fig. 9 Homeostasis and function of ILC3s in aged mice can be rescued by Klf4.

(a) Immunoblot images showing Klf4 protein in ILC3 subsets sorted from the young and aged mice. (b) The relative protein content was normalized to β-Actin (n = 3). (c-f) Rescue experiments with ILC3s in aged mice. (c) Flow cytometry of CD45.2+GFP+ ILC3 subsets isolated from the siLP in mice that received retrovirus-transfected CLPs from the young and aged mice. ILC3 subsets are gated as CD45.2+GFP+Lin−RORγt+ and then CCR6+NKp46−, CCR6−NKp46+, or CCR6−NKp46− (upper). The CD4+ ILC3 subset among the CCR6+ ILC3s was analyzed (below). (d) The percentages of the indicated subsets were compared (n = 6 or 7 young +PMX, n = 6,7 or 8 aged +PMX, n = 6,7 or 8 aged +Klf4). (e) Cytokine production in siLP CD45.2+GFP+ ILC3s from the indicated recipient mice. (f) The percentages of IL-22+ ILC3s were compared (n = 8 young+PMX, n = 8 aged+PMX, n = 9 aged+Klf4). The percentages of IL-17A+ ILC3s were compared (n = 8 young+PMX, n = 7 aged+PMX, n = 9 aged+ Klf4). Bar graphs are presented as mean ± SEM. A two-tailed Student’s t-test was performed for comparisons. The data are representative of at least three independent experiments (a-f).

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data Fig. 1.

Source Data Fig. 2

Statistical source data Fig. 2.

Source Data Fig. 3

Statistical source data Fig. 3.

Source Data Fig. 4

Statistical source data Fig. 4.

Source Data Fig. 5

Statistical source data Fig. 5.

Source Data Fig. 6

Statistical source data Fig. 6.

Source Data Fig. 7

Statistical source data Fig. 7.

Source Data Fig. 7

Image source data Fig. 7.

Source Data Extended Data Fig. 1

Statistical source data Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Statistical source data Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Statistical source data Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical source data Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Statistical source data Extended Data Fig. 5.

Source Data Extended Data Fig. 5

Image source data Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Statistical source data Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Statistical source data Extended Data Fig. 7.

Source Data Extended Data Fig. 9

Statistical source data Extended Data Fig. 9.

Source Data Extended Data Fig. 9

Image source data Extended Data Fig. 9.

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

Shen, X., Gao, X., Luo, Y. et al. Cxxc finger protein 1 maintains homeostasis and function of intestinal group 3 innate lymphoid cells with aging. Nat Aging 3, 965–981 (2023). https://doi.org/10.1038/s43587-023-00453-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-023-00453-7

This article is cited by

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