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:

Transcriptional memory of dFOXO activation in youth curtails later-life mortality through chromatin remodeling and Xbp1

Nature Aging volume 2, pages 1176–1190 (2022)Cite this article

Subjects

Abstract

A transient, homeostatic transcriptional response can result in transcriptional memory, programming subsequent transcriptional outputs. Transcriptional memory has great but unappreciated potential to alter animal aging as animals encounter a multitude of diverse stimuli throughout their lifespan. Here we show that activating an evolutionarily conserved, longevity-promoting transcription factor, dFOXO, solely in early adulthood of female fruit flies is sufficient to improve their subsequent health and survival in midlife and late life. This youth-restricted dFOXO activation causes persistent changes to chromatin landscape in the fat body and requires chromatin remodelers such as the SWI/SNF and ISWI complexes to program health and longevity. Chromatin remodeling is accompanied by a long-lasting transcriptional program that is distinct from that observed during acute dFOXO activation and includes induction of Xbp1. We show that this later-life induction of Xbp1 is sufficient to curtail later-life mortality. Our study demonstrates that transcriptional memory can profoundly alter how animals age.

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: Transient expression of dfoxo in early adulthood extends subsequent lifespan.
Fig. 2: dfoxo-switch induces persistent changes in chromatin structure and requires chromatin remodelers for longevity.
Fig. 3: A unique transcriptional program is triggered in the fat body by dfoxo-switch.
Fig. 4: dfoxo-switch flies exhibit a distinct metabolic profile.
Fig. 5: Xbp1 activation accounts for longevity resulting from dfoxo-switch.
Fig. 6: dfoxo-switch counteracts age-related transcriptional dysregulation.

Similar content being viewed by others

Data availability

Raw RNA-seq and ATAC-seq data are available from the GEO under accession number GSE183542. Metabolomics data are available from the MetaboLights repository (study identifier MTBLS3251). All other data are available as Supplementary Data or can be made available by the corresponding author on reasonable request.

Publicly available data obtained and used in the study were: (1) Drosophila female fat body gene expression in different ages (GSE130158) and gene expression of Xbp1 mutant flies at larval stage 2 (GSE99676) from the GEO (https://www.ncbi.nlm.nih.gov/geo/); (2) raw gene count data from mouse gene expression datasets of different ages and tissues from the Tabula Muris Senis project (https://twc-stanford.shinyapps.io/maca/); (3) 5,176 processed peak files of the publicly available Drosophila ChIP–seq datasets following the uniform processing protocol as well as the annotations of each peak file from ChIP-Atlas (https://chip-atlas.org/); (4) fly genome release and annotation from FlyBase (https://flybase.org/).

References

  1. Spitz, F. & Furlong, E. E. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    Article  CAS  Google Scholar 

  2. Lasko, P. Patterning the Drosophila embryo: a paradigm for RNA‐based developmental genetic regulation. Wiley Interdiscip. Rev. RNA 11, e1610 (2020).

    Article  CAS  Google Scholar 

  3. Webb, A. E. & Brunet, A. FOXO transcription factors: key regulators of cellular quality control. Trends Biochem. Sci. 39, 159–169 (2014).

    Article  CAS  Google Scholar 

  4. Latchman, D. S. Transcription factors: an overview. Int. J. Biochem. Cell Biol. 29, 1305–1312 (1997).

    Article  CAS  Google Scholar 

  5. D’Urso, A. & Brickner, J. H. Epigenetic transcriptional memory. Curr. Genet. 63, 435–439 (2017).

    Article  Google Scholar 

  6. Siwek, W., Tehrani, S. S., Mata, J. F. & Jansen, L. E. Activation of clustered IFNγ target genes drives cohesin-controlled transcriptional memory. Mol. Cell 80, 396–409 (2020).

    Article  CAS  Google Scholar 

  7. Bheda, P., Kirmizis, A. & Schneider, R. The past determines the future: sugar source history and transcriptional memory. Curr. Genet. 66, 1029–1035 (2020).

    Article  CAS  Google Scholar 

  8. Kamada, R. et al. Interferon stimulation creates chromatin marks and establishes transcriptional memory. Proc. Natl Acad. Sci. USA 115, E9162–E9171 (2018).

    Article  CAS  Google Scholar 

  9. Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  11. Partridge, L., Deelen, J. & Slagboom, P. E. Facing up to the global challenges of ageing. Nature 561, 45–56 (2018).

    Article  CAS  Google Scholar 

  12. Kuh, D., Karunananthan, S., Bergman, H. & Cooper, R. A life-course approach to healthy ageing: maintaining physical capability. Proc. Nutr. Soc. 73, 237–248 (2014).

    Article  Google Scholar 

  13. Dearden, L., Bouret, S. G. & Ozanne, S. E. Nutritional and developmental programming effects of insulin. J. Neuroendocrinol. 33, e12933 (2021).

    Article  CAS  Google Scholar 

  14. Obata, F., Fons, C. O. & Gould, A. P. Early-life exposure to low-dose oxidants can increase longevity via microbiome remodelling in Drosophila. Nat. Commun. 9, 975 (2018).

  15. Bitto, A. et al. Transient rapamycin treatment can increase lifespan and healthspan in middle-aged mice. elife 5, e16351 (2016).

    Article  Google Scholar 

  16. Dobson, A. J. et al. Nutritional programming of lifespan by FOXO inhibition on sugar-rich diets. Cell Rep. 18, 299–306 (2017).

    Article  CAS  Google Scholar 

  17. Catterson, J. H. et al. Short-term, intermittent fasting induces long-lasting gut health and TOR-independent lifespan extension. Curr. Biol. 28, 1714–1724 (2018).

    Article  CAS  Google Scholar 

  18. Hahn, O. et al. A nutritional memory effect counteracts the benefits of dietary restriction in old mice. Nature Metab. 1, 1059–1073 (2019).

    Article  CAS  Google Scholar 

  19. Murphy, C. T. The search for DAF-16/FOXO transcriptional targets: approaches and discoveries. Exp. Gerontol. 41, 910–921 (2006).

    Article  CAS  Google Scholar 

  20. Corrales, G. M. & Alic, N. Evolutionary conservation of transcription factors affecting longevity. Trends Genet. 36, 373–382 (2020).

    Article  Google Scholar 

  21. Martins, R., Lithgow, G. J. & Link, W. Long live FOXO: unraveling the role of FOXO proteins in aging and longevity. Aging Cell 15, 196–207 (2016).

    Article  CAS  Google Scholar 

  22. Hwangbo, D. S., Gersham, B., Tu, M.-P., Palmer, M. & Tatar, M. Drosophila dFOXO controls lifespan and regulates insulin signalling in brain and fat body. Nature 429, 562–566 (2004).

    Article  CAS  Google Scholar 

  23. Giannakou, M. E. et al. Long-lived Drosophila with overexpressed dFOXO in adult fat body. Science 305, 361–361 (2004).

    Article  CAS  Google Scholar 

  24. Koch, R. E., Phillips, J. M., Camus, M. F. & Dowling, D. K. Maternal age effects on fecundity and offspring egg‐to‐adult viability are not affected by mitochondrial haplotype. Ecol. Evol. 8, 10722–10732 (2018).

    Article  Google Scholar 

  25. Augustin, H. & Partridge, L. Invertebrate models of age-related muscle degeneration. Biochim. Biophys. Acta 1790, 1084–1094 (2009).

    Article  CAS  Google Scholar 

  26. Rera, M., Azizi, M. J. & Walker, D. W. Organ-specific mediation of lifespan extension: more than a gut feeling? Ageing Res. Rev. 12, 436–444 (2013).

    Article  CAS  Google Scholar 

  27. Piper, M. D. & Partridge, L. Drosophila as a model for ageing. Biochim. Biophys. Acta Mol. Basis Dis. 1864, 2707–2717 (2018).

    Article  CAS  Google Scholar 

  28. Giannakou, M. E. et al. Dynamics of the action of dFOXO on adult mortality in Drosophila. Aging Cell 6, 429–438 (2007).

    Article  CAS  Google Scholar 

  29. Poirier, L., Shane, A., Zheng, J. & Seroude, L. Characterization of the Drosophila gene‐switch system in aging studies: a cautionary tale. Aging Cell 7, 758–770 (2008).

    Article  CAS  Google Scholar 

  30. Scialo, F., Sriram, A., Stefanatos, R. & Sanz, A. Practical recommendations for the use of the GeneSwitch Gal4 system to knock-down genes in Drosophila melanogaster. PLoS ONE 11, e0161817 (2016).

    Article  Google Scholar 

  31. Parkhitko, A. A. et al. A genetic model of methionine restriction extends Drosophila health- and life-span. Proc. Natl Acad. Sci. USA 118, e2110387118 (2021).

  32. Alic, N. et al. Interplay of dFOXO and two ETS-family transcription factors determines lifespan in Drosophila melanogaster. PLoS Genet. 10, e1004619 (2014).

    Article  Google Scholar 

  33. Dobson, A. J. et al. Longevity is determined by ETS transcription factors in multiple tissues and diverse species. PLoS Genet. 15, e1008212 (2019).

    Article  Google Scholar 

  34. Alic, N. et al. Cell-nonautonomous effects of dFOXO/DAF-16 in aging. Cell Rep. 6, 608–616 (2014).

    Article  CAS  Google Scholar 

  35. Gargano, J. W., Martin, I., Bhandari, P. & Grotewiel, M. S. Rapid iterative negative geotaxis (RING): a new method for assessing age-related locomotor decline in Drosophila. Exp. Gerontol. 40, 386–395 (2005).

    Article  Google Scholar 

  36. Kundu, S., Horn, P. J. & Peterson, C. L. SWI/SNF is required for transcriptional memory at the yeast GAL gene cluster. Genes Dev 21, 997–1004 (2007).

    Article  CAS  Google Scholar 

  37. Booth, L. N. & Brunet, A. The aging epigenome. Mol. Cell 62, 728–744 (2016).

    Article  CAS  Google Scholar 

  38. Oki, S. et al. ChIP‐Atlas: a data‐mining suite powered by full integration of public ChIP–seq data. EMBO Rep. 19, e46255 (2018).

    Article  Google Scholar 

  39. Matilainen, O., Sleiman, M. S. B., Quiros, P. M., Garcia, S. M. & Auwerx, J. The chromatin remodeling factor ISW-1 integrates organismal responses against nuclear and mitochondrial stress. Nat. Commun. 8, 1818 (2017).

    Article  Google Scholar 

  40. Riedel, C. G. et al. DAF-16 employs the chromatin remodeller SWI/SNF to promote stress resistance and longevity. Nat. Cell Biol. 15, 491–501 (2013).

    Article  CAS  Google Scholar 

  41. Zhou, L., He, B., Deng, J., Pang, S. & Tang, H. Histone acetylation promotes long-lasting defense responses and longevity following early life heat stress. PLoS Genet. 15, e1008122 (2019).

    Article  CAS  Google Scholar 

  42. Zeng, X. et al. Genome-wide RNAi screen identifies networks involved in intestinal stem cell regulation in Drosophila. Cell Rep. 10, 1226–1238 (2015).

    Article  CAS  Google Scholar 

  43. Doiguchi, M. et al. SMARCAD1 is an ATP-dependent stimulator of nucleosomal H2A acetylation via CBP, resulting in transcriptional regulation. Sci. Rep. 6, 20179 (2016).

  44. van der Heide, L. P. & Smidt, M. P. Regulation of FoxO activity by CBP/p300-mediated acetylation. Trends Biochem. Sci. 30, 81–86 (2005).

    Article  Google Scholar 

  45. Martin, E., Heidari, R., Monnier, V. & Tricoire, H. Genetic screen in adult Drosophila reveals that dCBP depletion in glial cells mitigates Huntington disease pathology through a Foxo-dependent pathway. Int. J. Mol. Sci. 22, 3884 (2021).

    Article  CAS  Google Scholar 

  46. Liu, L.-P., Ni, J.-Q., Shi, Y.-D., Oakeley, E. J. & Sun, F.-L. Sex-specific role of Drosophila melanogaster HP1 in regulating chromatin structure and gene transcription. Nat. Genet. 37, 1361–1366 (2005).

    Article  CAS  Google Scholar 

  47. Tyagi, M., Imam, N., Verma, K. & Patel, A. K. Chromatin remodelers: we are the drivers!! Nucleus 7, 388–404 (2016).

    Article  CAS  Google Scholar 

  48. Wang, S. et al. Target analysis by integration of transcriptome and ChIP–seq data with BETA. Nat. Protoc. 8, 2502–2515 (2013).

    Article  CAS  Google Scholar 

  49. Ma, Z. et al. Epigenetic drift of H3K27me3 in aging links glycolysis to healthy longevity in Drosophila. Elife 7, e35368 (2018).

    Article  Google Scholar 

  50. Acosta-Alvear, D. et al. XBP1 controls diverse cell type-and condition-specific transcriptional regulatory networks. Mol. Cell 27, 53–66 (2007).

    Article  CAS  Google Scholar 

  51. Yoshida, H., Matsui, T., Yamamoto, A., Okada, T. & Mori, K. XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell 107, 881–891 (2001).

    Article  CAS  Google Scholar 

  52. Shen, X. et al. Complementary signaling pathways regulate the unfolded protein response and are required for C. elegans development. Cell 107, 893–903 (2001).

    Article  CAS  Google Scholar 

  53. Calfon, M. et al. IRE1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. Nature 415, 92–96 (2002).

    Article  CAS  Google Scholar 

  54. Ryoo, H. D., Domingos, P. M., Kang, M. J. & Steller, H. Unfolded protein response in a Drosophila model for retinal degeneration. EMBO J. 26, 242–252 (2007).

    Article  CAS  Google Scholar 

  55. Huang, S., Xing, Y. & Liu, Y. Emerging roles for the ER stress sensor IRE1α in metabolic regulation and disease. J. Biol. Chem. 294, 18726–18741 (2019).

    Article  CAS  Google Scholar 

  56. Xu, T. et al. The IRE1α–XBP1 pathway regulates metabolic stress-induced compensatory proliferation of pancreatic β-cells. Cell Res. 24, 1137–1140 (2014).

    Article  CAS  Google Scholar 

  57. Huang, H.-W., Zeng, X., Rhim, T., Ron, D. & Ryoo, H. D. The requirement of IRE1 and XBP1 in resolving physiological stress during Drosophila development. J. Cell Sci. 130, 3040–3049 (2017).

    CAS  Google Scholar 

  58. Surani, M. Glycoprotein synthesis and inhibition of protein glycosylation by tunicamycin in preimplantation embryos: influence on compaction and trophoblast adhesion. Cell 18, 217 (1979).

    Article  CAS  Google Scholar 

  59. Taylor, R. C. & Dillin, A. XBP-1 is a cell-nonautonomous regulator of stress resistance and longevity. Cell 153, 1435–1447 (2013).

    Article  CAS  Google Scholar 

  60. Wang, L., Ryoo, H. D., Qi, Y. & Jasper, H. PERK limits Drosophila lifespan by promoting intestinal stem cell proliferation in response to ER stress. PLoS Genet. 11, e1005220 (2015).

    Article  Google Scholar 

  61. Imanikia, S., Sheng, M., Castro, C., Griffin, J. L. & Taylor, R. C. XBP-1 remodels lipid metabolism to extend longevity. Cell Rep. 28, 581–589 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  63. Weigelt, C. M. et al. An insulin-sensitive circular RNA that regulates lifespan in Drosophila. Molecular cell 79, 268–279 (2020).

    Article  CAS  Google Scholar 

  64. Harris, S. E. et al. Age-related gene expression changes, and transcriptome wide association study of physical and cognitive aging traits, in the Lothian Birth Cohort 1936. Aging 9, 2489 (2017).

    Article  CAS  Google Scholar 

  65. Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).

    Article  CAS  Google Scholar 

  66. Hou, Z. & Fuiman, L. A. Nutritional programming in fishes: insights from mammalian studies. Reviews in Fish Biology and Fisheries 30, 67–92 (2020).

    Article  Google Scholar 

  67. Shimazu, T. et al. Suppression of oxidative stress by β-hydroxybutyrate, an endogenous histone deacetylase inhibitor. Science 339, 211–214 (2013).

    Article  CAS  Google Scholar 

  68. 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).

    Article  CAS  Google Scholar 

  69. Yan, Y. et al. HDAC6 suppresses age-dependent ectopic fat accumulation by maintaining the proteostasis of PLIN2 in Drosophila. Dev. Cell 43, 99–111 (2017).

    Article  CAS  Google Scholar 

  70. Peleg, S. et al. Lifespan extension by targeting a link between metabolism and histone acetylation in Drosophila. EMBO Rep. 17, 455–469 (2016).

    Article  CAS  Google Scholar 

  71. Lu, Y.-X. et al. A TORC1–histone axis regulates chromatin organisation and non-canonical induction of autophagy to ameliorate ageing. Elife 10, e62233 (2021).

    Article  CAS  Google Scholar 

  72. Brown, A. K., Maybury-Lewis, S. Y. & Webb, A. E. Integrative multi-omics analysis reveals conserved hierarchical mechanisms of FOXO3 pioneer-factor activity. Preprint at bioRxiv https://doi.org/10.1101/2021.04.06.438676 (2021).

  73. Hatta, M. & Cirillo, L. A. Chromatin opening and stable perturbation of core histone: DNA contacts by FoxO1. J. Biol. Chem. 282, 35583–35593 (2007).

    Article  CAS  Google Scholar 

  74. Iwafuchi-Doi, M. et al. The pioneer transcription factor FoxA maintains an accessible nucleosome configuration at enhancers for tissue-specific gene activation. Mol. Cell 62, 79–91 (2016).

    Article  CAS  Google Scholar 

  75. Lee, S. & Dong, H. H. FoxO integration of insulin signaling with glucose and lipid metabolism. J. Endocrinol. 233, R67–R79 (2017).

    Article  Google Scholar 

  76. Zhao, P. et al. Fat body Ire1 regulates lipid homeostasis through the Xbp1s–FoxO axis in Drosophila. Iscience 24, 102819 (2021).

    Article  CAS  Google Scholar 

  77. Imanikia, S., Özbey, N. P., Krueger, C., Casanueva, M. O. & Taylor, R. C. Neuronal XBP-1 activates intestinal lysosomes to improve proteostasis in C. elegans. Curr. Biol. 29, 2322–2338 (2019).

    Article  CAS  Google Scholar 

  78. Wang, Z. V. et al. Spliced X-box binding protein 1 couples the unfolded protein response to hexosamine biosynthetic pathway. Cell 156, 1179–1192 (2014).

    Article  CAS  Google Scholar 

  79. van der Harg, J. M. et al. The UPR reduces glucose metabolism via IRE1 signaling. Biochim. Biophys. Acta Mol. Cell Res. 1864, 655–665 (2017).

    Article  Google Scholar 

  80. Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature 588, 124–129 (2020).

    Article  CAS  Google Scholar 

  81. Bass, T. M. et al. Optimization of dietary restriction protocols in Drosophila. J. Gerontol. A Biol. Sci. Med. Sci. 62, 1071–1081 (2007).

    Article  Google Scholar 

  82. Larkin, A. et al. FlyBase: updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res. 49, D899–D907 (2021).

    Article  CAS  Google Scholar 

  83. Piper, M. D. & Partridge, L. Protocols to study aging in Drosophila. Methods Mol. Biol. 1478, 291–302 (2016).

  84. Clancy, D. & Kennington, W. A simple method to achieve consistent larval density in bottle cultures. Drosoph. Inf. Serv. 84, 168–169 (2001).

    Google Scholar 

  85. Corrales, G. M. et al. Partial inhibition of RNA polymerase I promotes animal health and longevity. Cell Rep. 30, 1661–1669 (2020).

    Article  Google Scholar 

  86. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  Google Scholar 

  87. Bateman, J. R., Lee, A. M. & Wu, C.-T. Site-specific transformation of Drosophila via ϕC31 integrase-mediated cassette exchange. Genetics 173, 769–777 (2006).

    Article  CAS  Google Scholar 

  88. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC‐seq: a method for assaying chromatin accessibility genome‐wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

    Article  Google Scholar 

  89. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    Article  CAS  Google Scholar 

  90. Alexa, A. & Rahnenführer, J. Gene-set enrichment analysis with topGO. Bioconductor Improv. 27, 1–26 (2009).

    Google Scholar 

  91. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  92. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  Google Scholar 

  93. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  Google Scholar 

  94. Gel, B. et al. regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests. Bioinformatics 32, 289–291 (2016).

    CAS  Google Scholar 

  95. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  97. Villanueva, M. et al. Sensory deprivation in Staphylococcus aureus. Nat. Commun. 9, 523 (2018).

  98. Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).

  99. Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).

    CAS  Google Scholar 

  100. Juricic, P. et al. Long-lasting geroprotection from brief rapamycin treatment in early adulthood by persistently increased intestinal autophagy. Nat. Aging 2, 824–836 (2022).

Download references

Acknowledgements

We thank H. D. Ryoo, L. Partridge and T. Niccoli for providing fly stocks; C. Regnault from Glasgow Polyomics for assistance with Metabolomics analysis; The University of Cambridge Department of Genetics Fly Facility for generating fly stocks; Y. Zhao for help with Tn5 synthesis; A. Vieira and J. Uriach for technical assistance; and the IHA members for support, comments and discussion throughout this project. Fly stocks were obtained from the Bloomington Drosophila Stock Center. This work was supported by a Biotechnology and Biological Sciences Research Council grant (BB/R014507/1) to T.D.S. and N.A. and a Medical Research Council grant (MR/S033939/1) to A.J.D. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. N.A. dedicates this work to the memory of his mother, E. Hodžić.

Author information

Author notes

  1. These authors contributed equally: Guillermo Martínez Corrales, Mengjia Li.

Authors and Affiliations

  1. Institute of Healthy Ageing and the Research Department of Genetics, Evolution, and Environment, University College London, London, UK

    Guillermo Martínez Corrales, Mengjia Li, Tatiana Svermova, Alex Goncalves, Diana Voicu & Nazif Alic

  2. School of Molecular Biosciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Adam J. Dobson

  3. Department of Life Sciences, Imperial College London, London, UK

    Tony D. Southall

Authors
  1. Guillermo Martínez Corrales
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengjia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tatiana Svermova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alex Goncalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diana Voicu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adam J. Dobson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tony D. Southall
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nazif Alic
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.A. and T.D.S. contributed to study and experimental design. G.M.C., M.L., T.S., A.G., D.V. and A.J.D. performed experimental work, and G.M.C., M.L., A.J.D., D.V. and N.A. analyzed the data. N.A., G.M.C. and M.J. wrote the manuscript with inputs from A.J.D. and T.D.S.

Corresponding author

Correspondence to Nazif Alic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Andrey Parkhitko 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 Experimental setup – expression.

a Expression pattern of UAS-n8-GFP, driven with the S106 driver in 7 day-old female guts and fat bodies. All images were taken at exactly the same laser conditions under the confocal microscope, to allow the comparison of GFP levels between the two tissues. Scale bars are 50 µm. b representative example of a S106 > n8-GFP fat body, induced or not with RU486, under different imaging conditions to (a), allowing a better view of GFP induction in fat body cells. Scale bars are 50 µm. c Quantification of dfoxo mRNA levels in S106 alone and S106 UAS-dfoxo both uninduced. Boxplots – quantiles; whiskers – extremes; overlay – individual data points. N = 4, 3 (left to right), p = 1, unpaired two-sided t-test. d Uncropped images of the western-blot membranes shown in Fig. 1c.

Source data

Extended Data Fig. 2 dfoxo-switch – additional lifespans, and climbing ability measurements.

a Driver-alone (S106) RU486-switch control lifespan. Control n = 150 dead/1 censored fly, switch n = 148 dead/0 censored flies, p = 0.69, log-rank test. b UAS-dfoxo alone RU486-switch control lifespan. Control n = 135 dead/2 censored flies, switch n = 136 dead/12 censored flies, p = 0.86, log-rank test. c Male driver-alone (S106) RU486-switch control lifespan. Control n = 126 dead/12 censored flies, RU486-switch n = 143 dead/6 censored flies, p = 0.49, log-rank test. d Male dfoxo-switch lifespan. Control n = 154 dead/6 censored flies, dfoxo-switch n = 142 dead/8 censored flies, p = 0.72, log-rank test. e TiGS > dfoxo-switch lifespan. Control n = 128 dead/3 censored flies, dfoxo-switch n = 141 dead/4 censored flies, p = 0.85, log-rank test. f S106 > aopAct-switch lifespan. Control n = 145 dead/7 censored flies, aopAct-switch n = 147 dead/2 censored flies, p = 0.00274, log-rank test. g Experimental trials of the negative geotaxis assays of dfoxo-switch and control that were combined for the analysis presented in Fig. 1e. N = individual flies, boxplots – quantiles; whiskers – extremes; overlay – individual data points. Experiment 1: effect of dfoxo-switch p = 0.008, age p < 10−4, age-by- dfoxo-switch interaction p = 0.0008, mixed-effects linear model (LM). Experiment 2: effect of dfoxo-switch p > 0.05, age p < 10−4, age-by-dfoxo-switch interaction p = 0.033, mixed-effects LM. Experiment 3: effect of dfoxo-switch p > 0.05, age p < 10−4, age-by-dfoxo-switch interaction p = 0.07, mixed-effects LM. h Experimental trials of S106 > dfoxo-switch lifespans used for the analyses presented in Fig. 1f. Experimental trial 1: control n = 61 dead/0 censored flies, dfoxo-switch n = 76 dead/0 censored flies, p = 0.009, log-rank test. Experimental trial 2: shown in Fig. 1d. Experimental trial 3: control n = 145 dead/1 censored fly, dfoxo-switch n = 145 dead/1 censored fly, p = 1.72189 ×10-5, log-rank test. Experimental trial 4: control n = 125 dead/0 censored flies, dfoxo-switch n = 145 dead/2 censored flies, p = 6.05 ×10-9, log-rank test.

Source data

Extended Data Fig. 3 ATAC-Seq – additional information.

a Schematic distribution of the ATAC peaks on the four main Drosophila chromosomes. Additional peaks were detected on contigs and are not shown. b Violin plots showing the size of all ATAC peaks detected in the gut and fat body, and in the significantly differentially accessible peaks in the fat body. c Distribution of the distance of ATAC peaks from a transcriptional start site (TSS). Proportion of peaks in each distance category are presented from 5’ to 3’ relative to the TSS. d ATAC-qPCR of the levels of sequences near the MED1 locus where an ATAC-Seq peak opened by dfoxo-switch was detected, near Prosap where a peak unaltered by dfoxo-switch was detected, and Sox21b region which contained no peak in ATAC-Seq. Boxplots – quantiles; whiskers – extremes; overlay – individual data points. N = 3 biologically independent samples; effect of dfoxo-switch: MED1 p = 0.00024, Prosap p = 0.59, Sox21b p = 0.76, pairwise comparisons with two-sided unpaired t-tests with pooled SD. e ATAC-qPCR signal intensity for regions near the Xbp1 locus within a peak detected by ATAC-Seq (3’end of the gene) or two regions within the promoter of Xbp1. The levels were normalized to Prosap. Boxplots – quantiles; whiskers – extremes; overlay – individual data points. N = 3 biologically independent samples, effect of region p = 0.0011, effect of dfoxo-switch p > 0.05, mixed effects LM.

Source data

Extended Data Fig. 4 dfoxo-switch dependance on chromatin remodelers – lifespan.

Lifespan curves of the switch in S106 > dfoxo & RNAi (a) or S106 > RNAi (b) with indicated RNAi lines. These were used to generate the analysis shown in Fig. 2e. P values are obtained comparing control vs RU486-switch conditions (after day 23, log-rank test). Detailed statistical analyses including number of flies per experiment are shown in Supplementary Data 2 and 3.

Source data

Extended Data Fig. 5 dfoxo-switch dependance on chromatin remodelers – climbing ability.

a Negative geotaxis assay of dfoxo-switch + mor RNAi at all ages, combining two independent trials. Effect of the switch p = 0.6, age p < 10−4, age-by-switch interaction p = 0.36, mixed-effects LM. b Negative geotaxis assay of dfoxo-switch + iswi RNAi at all ages. Effect of the switch p = 0.12, age p < 10−4, age-by-switch interaction p = 0.14, mixed-effects LM. c Negative geotaxis assay of mor RNAi switch at all ages. Effect of the switch p = 0.02, age p < 10−4, age-by-switch interaction p = 0.006, mixed-effects LM. d Negative geotaxis assays of iswi RNAi switch at all ages. Effect of the switch p = 0.62, age p < 10−4, age-by-switch interaction p = 0.49, mixed-effects LM. N - individual flies, boxplots – quantiles; whiskers – extremes; overlay – individual data points. The negative geotaxis assays of dfoxo-switch that were performed at the same time are shown in Extended Data Fig. 2g, experiments 2 and 3.

Source data

Extended Data Fig. 6 RNA-Seq – additional information.

a qPCR quantification of transcripts detected as differentially expressed in our RNA-Seq data (HDAC6, Pfk, Pepck1) in fat bodies after dfoxo-switch. Effect of dfoxo p = 0.0055, transcript p = 0.0386, and dfoxo-switch-by-transcript interactions p = 0.032, mixed effects LM. b qPCR quantifications of dfoxo-switch targets (MED1, HDAC6, Xbp1s, Pfrx) at week 7 in dfoxo-switch females. Effect of dfoxo-switch p = 0.0133, effect of transcript or dfoxo-switch-by-transcript interaction p > 0.05, mixed effects LM. N – biologically independent samples; boxplots – quantiles; whiskers – extremes; overlay – individual data points. c and d Overlaps of sets of differentially expressed genes between dfoxo-switch (red circles) and dfoxo-acute (grey circles) in the fat body and gut employing previously published gene lists. Overlap p-values from one-sided hypergeometric test. e Bar plot comparing the log2 fold change of the transcripts in common between the sets of genes differentially regulated by dfoxo-acute (meta analysis) and by dfoxo-switch. f qPCR quantifications of transcripts during acute induction of dfoxo in the fat body. HDAC6, Pfk, Xbp1s, Xbp1u were examined as they are all differentially expressed after dfoxo-switch in the fat body (RNA-Seq analysis and qPCR confirmation shown elsewhere). N – biologically independent samples; boxplots – quantiles; whiskers – extremes; overlay – individual data points. Effects of RU486, transcript or RU486-by-transcript interaction p > 0.05, mixed effects LM. g Activating/Repressing Function prediction in BETA applying one-tailed Kolmogorov-Smirnov test. Differentially accessible ATAC peaks explained transcriptional activation (p = 2.97x10-46) and repression (p = 0.028) after dfoxo-switch.

Source data

Extended Data Fig. 7 Additional GO terms enrichment analysis.

Top 5 GO terms and KEGG pathways for genes differentially expressed (DE) a exclusively after dfoxo-switch and b exclusively during dfoxo-acute induction in the fat body. Note that no significant GO enrichment was observed in the set of genes that are differentially expressed in both dfoxo-switch and dfoxo-acute. BP - biological process, CC - cellular component, MF - molecular function.

Source data

Extended Data Fig. 8 Involvement of Xbp1 in the effects of dfoxo-switch – additional information.

a qPCR quantification of transcripts whose levels are increased after dfoxo-switch in fat bodies (hsc70-4, Eip75b, kay) in female S106 > Xbp1s fat bodies, with or without RU486 induction. Effect of RU486 p = 0.0088, effect of transcript or RU486-by-transcript interaction p > 0.05, mixed effects LM. b qPCR quantifications of the same transcripts in fat bodies after dfoxo-switch. Effect of RU486 p = 0.0001, effect of transcript or RU486-by-transcript interaction p > 0.05, mixed effects LM. N – biologically independent samples; boxplots – quantiles; whiskers – extremes; overlay – individual data points. c Survival of dfoxo-switch flies challenged with tunicamycin after 1 week of recovery (day 30; control n = 142 dead/0 censored, dfoxo-switch n = 129 dead/0 censored, p = 0.001, log-rank test). d Same for driver-alone (control n = 114 dead/0 censored, switch n = 131 dead/0 censored, p = 0.21, log-rank test). e Starvation assay of 30-day-old dfoxo-switch flies (control n = 142 dead/0 censored, dfoxo-switch n = 151 dead/0 censored, p = 0.67, log-rank test). f Same for driver-alone (control n = 149 dead/0 censored, switch n = 149 dead/0 censored, p = 0.07, log-rank test). g-j. Survival in the presence of tunicamycin a week after the switch in: S106 > morRNAi (control n = 141 dead/1 censored, switch n = 154 dead/0 censored, p < 6x10-7, log-rank test), S106 > iswiRNAi (control n = 146 dead/0 censored, switch n = 145 dead/0 censored, p = 0.60, log-rank test), S106 > dfoxo morRNAi (control n = 140 dead/0 censored, switch n = 139 dead/0 censored, p < 8x10-7, log-rank test), S106 > dfoxo iswiRNAi (control n = 153 dead/0 censored, switch n = 148 dead/0 censored, p = 0.079 log-rank test).

Source data

Extended Data Fig. 9 Age-related expression changes in the mouse – additional information.

Relationship between the expression changes triggered by dfoxo-switch in the fly fat body and the expression changes caused by ageing of their mouse orthologues (FDR 10%) in the functionally equivalent organs in the mouse. Points – genes; lines with shading – line of best fit and 95% CI; grey – those that are not significantly changed with age, red – those that are significantly changed with age. None of the organs show significant correlation between age-related change and dfoxo-switch change (p > 0.05, LM).

Source data

Supplementary information

Source data

Source Data Fig. 1

Source data for Fig. 1 (additional data are available in Supplementary Data and Extended data Fig. 1).

Source Data Fig. 2

Source data for Fig. 2 (additional data are available in Supplementary Data).

Source Data Fig. 3

Source data for Fig. 3 (additional data are available in Supplementary Data).

Source Data Fig. 4

Source data for Fig. 4 (additional data are available in Supplementary Data).

Source Data Fig. 5

Source data for Fig. 5 (additional data are available in Supplementary Data).

Source Data Fig. 6

Source data for Fig. 6 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 1

Source data for Extended data Fig. 1 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 2

Source data for Extended data Fig. 2 (additional data are available in Supplementary Data).

Source Data Extend ed Data Fig. 3

Source data for Extended data Fig. 3 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 4

Source data for Extended data Fig. 4 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 5

Source data for Extended data Fig. 5 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 6

Source data for Extended data Fig. 6 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 7

Source data for Extended data Fig. 7 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 8

Source data for Extended data Fig. 8 (additional data are available in Supplementary Data).

Source Data Extended Data Fig. 9

Source data for Extended data Fig. 9 (additional data are available in Supplementary Data).

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

Martínez Corrales, G., Li, M., Svermova, T. et al. Transcriptional memory of dFOXO activation in youth curtails later-life mortality through chromatin remodeling and Xbp1. Nat Aging 2, 1176–1190 (2022). https://doi.org/10.1038/s43587-022-00312-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-022-00312-x

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