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:

Heritable shifts in redox metabolites during mitochondrial quiescence reprogramme progeny metabolism

Nature Metabolism volume 3, pages 1259–1274 (2021)Cite this article

Subjects

Abstract

Changes in maternal diet and metabolic defects in mothers can profoundly affect health and disease in their progeny. However, the biochemical mechanisms that induce the initial reprogramming events at the cellular level have remained largely unknown owing to limitations in obtaining pure populations of quiescent oocytes. Here, we show that the precocious onset of mitochondrial respiratory quiescence causes a reprogramming of progeny metabolic state. The premature onset of mitochondrial respiratory quiescence drives the lowering of Drosophila oocyte NAD+ levels. NAD+ depletion in the oocyte leads to reduced methionine cycle production of the methyl donor S-adenosylmethionine in embryos and lower levels of histone H3 lysine 27 trimethylation, resulting in enhanced intestinal lipid metabolism in progeny. In addition, we show that triggering cellular quiescence in mammalian cells and chemotherapy-resistant human cancer cell models induces cellular reprogramming events identical to those seen in Drosophila, suggesting a conserved metabolic mechanism in systems reliant on quiescent cells.

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: Inducing MRQ prematurely in oogenesis causes reprogramming of progeny metabolism.
Fig. 2: Oocytes establish a unique redox state during the onset of quiescence.
Fig. 3: Changes in NAD metabolism are heritable factors that drive the reprogramming of progeny metabolism.
Fig. 4: Inducing MRQ prematurely causes downregulation of redox metabolism genes and defective maternal RNA loading.
Fig. 5: Reprogrammed progeny display lower levels of H3K27me3.
Fig. 6: Defective methionine cycle activity underlies the loss of H3K27me3 and contributes to the reprogramming of progeny metabolism.
Fig. 7: Quiescence has a conserved role in the reprogramming of progeny metabolism.
Fig. 8: A mammalian model of progeny metabolic reprogramming displays reduced levels of H3K27me3.

Similar content being viewed by others

Data availability

RNA-seq and ChIP–seq data for Figs. 4 and 8 are deposited in the GEO database under accession numbers GSE145353, GSE175387,GSE175754, GSE175755, and GSE175756. Source data are provided with this paper. Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding author.

References

  1. Kaur, J. A comprehensive review on metabolic syndrome. Cardiol. Res Pract. 2014, 943162 (2014).

    PubMed  PubMed Central  Google Scholar 

  2. Lopez-Candales, A. Metabolic syndrome X: a comprehensive review of the pathophysiology and recommended therapy. J. Med. 32, 283–300 (2001).

    CAS  PubMed  Google Scholar 

  3. Agnoli, C. et al. Metabolic syndrome and breast cancer risk: a case–cohort study nested in a multicentre italian cohort. PLoS ONE10, e0128891 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Buescher, J. L. et al. Evidence for transgenerational metabolic programming in Drosophila. Dis. Model Mech. 6, 1123–1132 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Rechavi, O. et al. Starvation-induced transgenerational inheritance of small RNAs in C. elegans. Cell 158, 277–287 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Palu, R. A. S., Praggastis, S. A. & Thummel, C. S. Parental obesity leads to metabolic changes in the F2 generation in Drosophila. Mol. Metab. 6, 631–639 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Meng, R. et al. Early famine exposure and adult disease risk based on a 10-year prospective study of Chinese adults. Heart 106, 213–220 (2020).

    PubMed  Google Scholar 

  8. Weiser, N. E. & Kim, J. K. Multigenerational regulation of the Caenorhabditis elegans chromatin landscape by germline small RNAs. Annu. Rev. Genet. 53, 289–311 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ost, A. et al. Paternal diet defines offspring chromatin state and intergenerational obesity. Cell 159, 1352–1364 (2014).

    Article  PubMed  CAS  Google Scholar 

  10. Gkountela, S. et al. DNA demethylation dynamics in the human prenatal germline. Cell 161, 1425–1436 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tang, W. W. et al. A unique gene regulatory network resets the human germline epigenome for development. Cell 161, 1453–1467 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Houtkooper, R. H., Pirinen, E. & Auwerx, J. Sirtuins as regulators of metabolism and healthspan. Nat. Rev. Mol. Cell Biol. 13, 225–238 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shi, L. & Tu, B. P. Acetyl-CoA and the regulation of metabolism: mechanisms and consequences. Curr. Opin. Cell Biol. 33, 125–131 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sieber, M. H., Thomsen, M. B. & Spradling, A. C. Electron transport chain remodeling by GSK3 during oogenesis connects nutrient state to reproduction. Cell 164, 420–432 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hsu, H. J. & Drummond-Barbosa, D. Insulin signals control the competence of the Drosophila female germline stem cell niche to respond to Notch ligands. Dev. Biol. 350, 290–300 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Drummond-Barbosa, D. & Spradling, A. C. Alpha-endosulfine, a potential regulator of insulin secretion, is required for adult tissue growth control in Drosophila. Dev. Biol. 266, 310–321 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. LaFever, L. & Drummond-Barbosa, D. Direct control of germline stem cell division and cyst growth by neural insulin in Drosophila. Science 309, 1071–1073 (2005).

    Article  CAS  PubMed  Google Scholar 

  18. Kockel, L. et al. Dynamic switch of negative feedback regulation in Drosophila Akt–TOR signaling. PLoS Genet. 6, e1000990 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Anderson, L. K. et al. Juxtaposition of C(2)M and the transverse filament protein C(3)G within the central region of Drosophila synaptonemal complex. Proc. Natl Acad. Sci. USA 102, 4482–4487 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hennig, K. M. & Neufeld, T. P. Inhibition of cellular growth and proliferation by dTOR overexpression in Drosophila. Genesis 34, 107–110 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Petrova, B. et al. Dynamic redox balance directs the oocyte-to-embryo transition via developmentally controlled reactive cysteine changes. Proc. Natl Acad. Sci. USA 115, E7978–E7986 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tennessen, J. M. et al. Coordinated metabolic transitions during Drosophila embryogenesis and the onset of aerobic glycolysis. G3 4, 839–850 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ni, J. Q. et al. Vector and parameters for targeted transgenic RNA interference in Drosophila melanogaster. Nat. Methods 5, 49–51 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Tennessen, J. M., Barry, W. E., Cox, J. & Thummel, C. S. Methods for studying metabolism in Drosophila. Methods 68, 105–115 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Contrino, S. et al. modMine: flexible access to modENCODE data. Nucleic Acids Res. 40, D1082–D1088 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Horner, M. A. et al. The Drosophila DHR96 nuclear receptor binds cholesterol and regulates cholesterol homeostasis. Genes Dev. 23, 2711–2716 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sieber, M. H. & Thummel, C. S. The DHR96 nuclear receptor controls triacylglycerol homeostasis in Drosophila. Cell Metab. 10, 481–490 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Obniski, R., Sieber, M. & Spradling, A. C. Dietary lipids modulate Notch signaling and influence adult intestinal development and metabolism in Drosophila. Dev. Cell 47, 98–111 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Reuter, G. et al. Dependence of position-effect variegation in Drosophila on dose of a gene encoding an unusual zinc-finger protein. Nature 344, 219–223 (1990).

    Article  CAS  PubMed  Google Scholar 

  30. Serefidou, M., Venkatasubramani, A. V. & Imhof, A. The impact of one carbon metabolism on histone methylation. Front. Genet. 10, 764 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mitra, M., Ho, L. D. & Coller, H. A. An in vitro model of cellular quiescence in primary human dermal fibroblasts. Methods Mol. Biol. 1686, 27–47 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. De Angelis, M. L., Francescangeli, F., La Torre, F. & Zeuner, A. Stem cell plasticity and dormancy in the development of cancer therapy resistance. Front. Oncol. 9, 626 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Moore, N. & Lyle, S. Quiescent, slow-cycling stem cell populations in cancer: a review of the evidence and discussion of significance. J. Oncol. https://doi.org/10.1155/2011/396076 (2011).

  34. Carone, B. R. et al. Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell 143, 1084–1096 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sharma, U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science 351, 391–396 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Bundgaard, J. & Barker, J. S. Genetic variation for resistance to high-temperature stress of mature sperm—a study in Drosophila. PLoS ONE 12, e0173990 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Gao, J. et al. Heat shock protein expression enhances heat tolerance of reptile embryos. Proc. Biol. Sci. 281, 20141135 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. Lockwood, B. L., Julick, C. R. & Montooth, K. L. Maternal loading of a small heat shock protein increases embryo thermal tolerance in Drosophila melanogaster. J. Exp. Biol. 220, 4492–4501 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. Thayer, Z. M., Wilson, M. A., Kim, A. W. & Jaeggi, A. V. Impact of prenatal stress on offspring glucocorticoid levels: a phylogenetic meta-analysis across 14 vertebrate species. Sci. Rep. 8, 4942 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Leatherman, J. L. & Jongens, T. A. Transcriptional silencing and translational control: key features of early germline development. Bioessays 25, 326–335 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Lovett, J. A. & Goldstein, E. S. The cytoplasmic distribution and characterization of poly(A)+RNA in oocytes and embryos of Drosophilia. Dev. Biol. 61, 70–78 (1977).

    Article  CAS  PubMed  Google Scholar 

  42. Mermod, J. J., Jacobs-Lorena, M. & Crippa, M. Changes in rate of RNA synthesis and ribosomal gene number during oogenesis of Drosophila melanogaster. Dev. Biol. 57, 393–402 (1977).

    Article  CAS  PubMed  Google Scholar 

  43. Rossari, F., Zucchinetti, C., Buda, G. & Orciuolo, E. Tumor dormancy as an alternative step in the development of chemoresistance and metastasis—clinical implications. Cell Oncol. https://doi.org/10.1007/s13402-019-00467-7 (2019).

  44. Cao, D. et al. Inhibition of PGC-1α after chemotherapy-mediated insult confines multiple myeloma cell survival by affecting ROS accumulation. Oncol. Rep. 33, 899–904 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Mastropasqua, F., Girolimetti, G. & Shoshan, M. PGC1α: friend or foe in cancer? Genes 9, 48 (2018).

    Article  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank B. H. Graham, C. Thummel and the Bloomington Drosophila Stock Center for reagents. We thank C. Thummel, M. Buszczak, R. Deberardinas, D. Hattori, J. Repa and G. Demartino for providing insightful comments on the manuscript. C. Xing and UTSW bioinformatics laboratory assisted with our sequencing data. We thank the UT Southwestern Animal Resource Core Facility for their assistance with our xenograft studies. R. Deberardinas and the Children’s Research Institute metabolomics core assisted with our LC–MS experiments. We also thank B. Tu and W. C. Hsieh for helpful discussions and ongoing technical support. Models in Figs. 1a, 6g, 7e and 8a were created using BioRender.com (licence no. MJ22RADMPS). This work is supported by the Welch Foundation (I-2015-20190330 to M.S.), NIH (R01AG067604 to M.S.), the W.W. Caruth Jr Foundation and the UT Southwestern Endowed Scholars programme.

Author information

Author notes

  1. These authors contributed equally: Helin Hocaoglu, Lei Wang, Mengye Yang.

Authors and Affiliations

  1. Department of Physiology, UT Southwestern Medical Center, Dallas, TX, USA

    Helin Hocaoglu, Lei Wang, Mengye Yang, Sibiao Yue & Matthew Sieber

Authors
  1. Helin Hocaoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengye Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sibiao Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew Sieber
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.W., H.H., M.Y., S.Y. and M.S. designed and conducted the experiments. L.W., H.H. and M.S. analysed the data and wrote the manuscript.

Corresponding author

Correspondence to Matthew Sieber.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: George Caputa. Nature Metabolism thanks Filippo Giancotti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Metabolic profiling of reprogrammed progeny.

a) Glycogen levels in progeny from MTD-GFP, MTD-InR-DN, and MTD-TOR-DN mothers fed control or an amino acid-deficient diet(n = 10 biological replicates, p = 1.9E-6, p = 9.27E-5 calculated by one way anova). b) Normalized trehalose levels in progeny from control, InR-DN, and TOR-DN expressing oocytes (n = 10 biological replicates, p = 1.4E-6, p = 4.3E-8 calculated by one way anova). c) Adult glycogen levels from the progeny of parents fed either an amino acid-deficient media or a control media (n = 10 biological replicates). d) A graph showing diet can induce reprogramming of glucose metabolism in yw and Canton S genetic backgrounds (n = 10 biological replicates, p = 1.4E-11, p = 9.2E-10 calculated by students t-test). e) A graph showing diet can induce reprogramming of triglyceride metabolism in yw and Canton s genetic backgrounds(n = 10 biological replicates, p = 4.1E-11, p = 1.9E-7 calculated by students t-test). f) sterol levels in progeny from mothers fed control or an amino acid-deficient diet(n = 10 biological replicates). (g) Glucose levels from the progeny of mothers fed either a control diet or an amino acid-deficient diet(n = 10 biological replicates, p = 0.002 calculated by students t-test). (h, i) Graphs showing that foxo does not induce the reprogramming of progeny glucose and triglyceride metabolism(n = 10 biological replicates). Error bars represent 1X standard deviation.

Extended Data Fig. 2 Changes in TCA cycle profile during quiescence.

a) Glycogen (PA/S staining) staining for wild-type ovarioles. b) Oil Red O staining of wild-type ovarioles. c) A summary of GC/MS data characterizing the TCA cycle changes that occur as oocytes mature and enter quiescence. (n = 10 biological replicates) (Box and whisker plots = The box represents the upper/lower quartile of the data presented the line within the box is the mean of the data presented. The whisker represent the maximum and minimum of the data.). Error bars represent 1X standard deviation.

Extended Data Fig. 3 Metabolism changes in InR-DN expressing oocytes.

A heat map of LC/MS metabolomic data displaying the VIP score of metabolites assayed in our metabolic data. b) A model displaying the mechanisms that produce NAD + . c) LC/MS data showing changes in fatty-acyl carnitines in control (MTD > GFP) and InR-DN expressing oocytes. d) Purine levels in control and InR-DN expressing oocytes. e) Pyrimidine levels in control and InR-DN expressing oocytes. (Box and whisker plots = The box represents the upper/lower quartile of the data presented the line within the box is the mean of the data presented. The whisker represent the maximum and minimum of the data.) f) OCR measurements of follicles from MTD-GFP, MTD-InR-DN, and MTD-GFP follicles cultured with 10 ug/ml Insulin).

Extended Data Fig. 4 Metabolite measurements from mutants lacking a functional pentose phosphate pathway.

Glucose levels (a) and triglyceride levels (b) from adult progeny produced from crossing Pgd,zw/+ or Pgd,zw/Pgd,zw virgins to Oregon R males(n = 10 biological replicates). c) Normalized glucose levels from control and InR-DN expressing progeny that were supplemented with a combination of NA (2 mg/ml) and NR (2 mg/ml) (n = 12 biological replicates) Error bars represent 1X standard deviation.

Extended Data Fig. 5 Gene expression changes in InR-DN expressing oocytes.

a) A table showing the known transcriptional regulators that are down-regulated in quiescent oocytes. b) Gene ontology analysis of the genes downregulated in InR-DN oocytes. (c, g, f) Redox reporter expression in early embryos (4–6 hrs) derived from control and InR-DN expressing oocytes. d, h, i) Redox reporter expression in late embryos (16–20 hrs) derived from control and InR-DN expressing oocytes(n = 30 biological replicates) (Box and whisker plots = The box represents the upper/lower quartile of the data presented the line within the box is the mean of the data presented. The whisker represent the maximum and minimum of the data.). e) Developmental expression of MDH2 showing that this gene is maternally loaded. j) A graph showing that glycolysis and gluconeogenesis genes are down-regulated in quiescent oocytes. Error bars represent 1X standard deviation.

Extended Data Fig. 6 De-repression of intestinal metabolism genes in reprogrammed progeny.

a) A graph highlighting 7 adult-specific intestinal genes that are de-repressed in Progeny of InR-DN expressing oocytes. b) Q-PCR validation of 2 intestinal gene and 2 calcium signaling genes de-repressed in InR-DN progeny (n = 3 biological replicates). c) The developmental expression profile of 7 adult specific intestinal genes de-repressed in InR-DN progeny. C’) RNA-seq mapped reads showing the expression levels of the intestinal gene magro. A graph showing examples of adult/larval-specific intestinal genes that are up-regulated in the embryonic progeny of InR-DN expressing oocytes. d) Tissue expression profile of seven adult and larval expressed intestinal genes that are up-regulated in the embryonic progeny of InR-DN expressing oocytes. e) Diagram of how Hr96 function was examined in reprogrammed progeny. f) The developmental expression profile of 7 adult and larval intestinal genes de-repressed in InR-DN progeny. g) Normalized triglyceride levels from flies overexpressing Hr96 in the enterocytes (n = 10 biological replicates, p = 0.001, calculated by students t-test). Error bars represent 1X standard deviation.

Extended Data Fig. 7 Chromatin changes in reprogrammed progeny.

a) Quantification of relative levels of variegated and normal eyes (suppressed) in wm4 animals fed a control diet or an amino acid-deficient diet(n = 275). b) A diagram of the methionine cycle. The intermediates that are reduced in reprogrammed progeny are highlighted with a red box. c) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains antp. d) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains magro.(red boxes represent genomic regions with altered histone methylation). E) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains ca-beta.

Extended Data Fig. 8 Mitochondrial analysis metabolic reprogramming in mammalian models.

a) An ECAR trace read comparing active and quiescent NIH 3T3 cells(n = 9 biological replicates). A’) ECAR data comparing active and quiescent cells compiled from multiple experiments(n = 9 biological replicates). b) ECAR trace reads for quiescent and reactivated NIH 3T3 cells(n = 9 biological replicates). B’) ECAR data for quiescent and reactivated cells compiled from multiple experiments(n = 9 biological replicates). c) ECAR trace read comparing active and reactivated 3T3 cells(n = 9 biological replicates). (Box and whisker plots = The box represents the upper/lower quartile of the data presented the line within the box is the mean of the data presented. The whisker represent the maximum and minimum of the data.) C’) ECAR data for active and reactivated cells compiled from multiple experiments(n = 9 biological replicates). d) NAD/NADH ratios for active and reactivated 3T3 cells (n = 9 biological replicates). e) Protein measurements showing no difference in protein content in samples used in Seahorse mitochondrial assays(n = 9 biological replicates). f) OCR measurements comparing quiescent and reactivated NIH 3T3 cells compiled from multiple experiments(n = 9). g) Energy map for quiescent and reactivated NIH 3T3 cells(n = 9 biological replicates). h) OCR measurements for active NIH 3T3 cells or quiescent 3T3 cells(n = 9 biological replicates). Error bars represent 1X standard deviation *p < .05 **p < .005, calculated by students t-test.

Extended Data Fig. 9 Chromatin changes in recurrent MCF7 cells.

a) Total number of broad methylation peaks in 2 independent ChIP-seq runs from control and recurrent MCF7 cells. b) LC/MS measurements of the levels of short-chain acyl-carnitines in control and recurrent MCF7 tumors (n = 10 biological replicates) (Box and whisker plots = The box represents the upper/lower quartile of the data presented the line within the box is the mean of the data presented. The whisker represent the maximum and minimum of the data.). c)H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains the 5′-end of Esr1 and its promoter. d) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains KRT18. e) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains BRCA1. f) H3K27-me3, H3K27-ac, and H3K9-me3 ChIP seq profile of the genomic region that contains BRCA1.

Supplementary information

Reporting Summary

Supplementary Table 1

An table of the 93 redox metabolism-related genes that were downregulated in InR-DN-expressing oocytes. This table includes log(FC) values, maternal loading status of the gene, metabolic processes associated with that gene, and human homologues of the metabolic gene.

Source data

Source Data Fig. 3

Metabolite profile for InR-DN oocytes.

Source Data Fig. 6

Unprocessed western blots for Fig. 6b,b′,c,c′.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hocaoglu, H., Wang, L., Yang, M. et al. Heritable shifts in redox metabolites during mitochondrial quiescence reprogramme progeny metabolism. Nat Metab 3, 1259–1274 (2021). https://doi.org/10.1038/s42255-021-00450-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-021-00450-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer