The epigenetic landscape of transgenerational acclimation to ocean warming

Abstract

Epigenetic inheritance is a potential mechanism by which the environment in one generation can influence the performance of future generations1. Rapid climate change threatens the survival of many organisms; however, recent studies show that some species can adjust to climate-related stress when both parents and their offspring experience the same environmental change2,3. Whether such transgenerational acclimation could have an epigenetic basis is unknown. Here, by sequencing the liver genome, methylomes and transcriptomes of the coral reef fish, Acanthochromis polyacanthus, exposed to current day (+0 °C) or future ocean temperatures (+3 °C) for one generation, two generations and incrementally across generations, we identified 2,467 differentially methylated regions (DMRs) and 1,870 associated genes that respond to higher temperatures within and between generations. Of these genes, 193 were significantly correlated to the transgenerationally acclimating phenotypic trait, aerobic scope, with functions in insulin response, energy homeostasis, mitochondrial activity, oxygen consumption and angiogenesis. These genes may therefore play a key role in restoring performance across generations in fish exposed to increased temperatures associated with climate change. Our study is the first to demonstrate a possible association between DNA methylation and transgenerational acclimation to climate change in a vertebrate.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Experimental design and summary of the DMRs.
Fig. 2: Differential methylation patterns.
Fig. 3: Heatmaps of differentially methylated and net aerobic scope-correlated genes.
Fig. 4: DNA methylation patterns for thermal acclimation.

References

  1. 1.

    Jablonka, E. & Raz, G. Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 84, 131–176 (2009).

    Google Scholar 

  2. 2.

    Donelson, J., Munday, P., McCormick, M. & Pitcher, C. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).

    Google Scholar 

  3. 3.

    Salinas, S. & Munch, S. B. Thermal legacies: transgenerational effects of temperature on growth in a vertebrate. Ecol. Lett. 15, 159–163 (2012).

    Google Scholar 

  4. 4.

    Munday, P. L., Warner, R. R., Monro, K., Pandolfi, J. M. & Marshall, D. J. Predicting evolutionary responses to climate change in the sea. Ecol. Lett. 16, 1488–1500 (2013).

    Google Scholar 

  5. 5.

    Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).

    Google Scholar 

  6. 6.

    Bonduriansky, R., Crean, A. J. & Day, T. The implications of nongenetic inheritance for evolution in changing environments. Evol. Appl. 5, 192–201 (2012).

    Google Scholar 

  7. 7.

    Daxinger, L. & Whitelaw, E. Understanding transgenerational epigenetic inheritance via the gametes in mammals. Nat. Rev. Genet. 13, 153–162 (2012).

    CAS  Google Scholar 

  8. 8.

    Ng, S. F. et al. Chronic high-fat diet in fathers programs beta-cell dysfunction in female rat offspring. Nature 467, 963–966 (2010).

    CAS  Google Scholar 

  9. 9.

    Veilleux, H. D. et al. Molecular processes of transgenerational acclimation to a warming ocean. Nat. Clim. Change 5, 1074–1078 (2015).

    CAS  Google Scholar 

  10. 10.

    Donelson, J. M., Wong, M., Booth, D. J. & Munday, P. L. Transgenerational plasticity of reproduction depends on rate of warming across generations. Evol. Appl. 9, 1072–1081 (2016).

    Google Scholar 

  11. 11.

    Rui, L. Energy metabolism in the liver. Compr. Physiol. 4, 177–197 (2014).

    Google Scholar 

  12. 12.

    Das, J. The role of mitochondrial respiration in physiological and evolutionary adaptation. Bioessays 28, 890–901 (2006).

    CAS  Google Scholar 

  13. 13.

    Kashio, M. & Tominaga, M. The TRPM2 channel: A thermo-sensitive metabolic sensor. Channels 11, 426–433 (2017).

    Google Scholar 

  14. 14.

    Kang, H. W., Wei, J. & Cohen, D. E. PC-TP/StARD2: of membranes and metabolism. Trends Endocrinol. Metab. 21, 449–456 (2010).

    CAS  Google Scholar 

  15. 15.

    Puri, V. et al. Cidea is associated with lipid droplets and insulin sensitivity in humans. Proc. Natl Acad. Sci. USA 105, 7833–7838 (2008).

    CAS  Google Scholar 

  16. 16.

    Zhou, Z. et al. Cidea-deficient mice have lean phenotype and are resistant to obesity. Nat. Genet. 35, 49–56 (2003).

    Google Scholar 

  17. 17.

    Portner, H. O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).

    Google Scholar 

  18. 18.

    Fraisl, P., Mazzone, M., Schmidt, T. & Carmeliet, P. Regulation of angiogenesis by oxygen and metabolism. Dev. Cell 16, 167–179 (2009).

    CAS  Google Scholar 

  19. 19.

    Laramee, M. et al. The scaffolding adapter Gab1 mediates vascular endothelial growth factor signaling and is required for endothelial cell migration and capillary formation. J. Biol. Chem. 282, 7758–7769 (2007).

    CAS  Google Scholar 

  20. 20.

    Chao, W. & D’Amore, P. A. IGF2: epigenetic regulation and role in development and disease. Cytokine Growth F. Rev. 19, 111–120 (2008).

    CAS  Google Scholar 

  21. 21.

    de Vries, S. et al. Identification of DEAD-box RNA helicase 6 (DDX6) as a cellular modulator of vascular endothelial growth factor expression under hypoxia. J. Biol. Chem. 288, 5815–5827 (2013).

    Google Scholar 

  22. 22.

    Bard-Chapeau, E. A. et al. Deletion of Gab1 in the liver leads to enhanced glucose tolerance and improved hepatic insulin action. Nat. Med. 11, 567–571 (2005).

    CAS  Google Scholar 

  23. 23.

    Ding, G. L. et al. Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61, 1133–1142 (2012).

    CAS  Google Scholar 

  24. 24.

    Vangeel, E. B. et al. DNA methylation in imprinted genes IGF2 and GNASXL is associated with prenatal maternal stress. Genes Brain Behav. 14, 573–582 (2015).

    CAS  Google Scholar 

  25. 25.

    Gabillard, J. C., Rescan, P. Y., Fauconneau, B., Weil, C. & Le Bail, P. Y. Effect of temperature on gene expression of the Gh/Igf system during embryonic development in rainbow trout (Oncorhynchus mykiss). J. Exp. Zool. A 298, 134–142 (2003).

    Google Scholar 

  26. 26.

    Xie, B., Zhang, L., Zheng, K. & Luo, C. The evolutionary foundation of genomic imprinting in lower vertebrates. Chin. Sci. Bull. 54, 1354 (2009).

    CAS  Google Scholar 

  27. 27.

    Lou, S. et al. Whole-genome bisulfite sequencing of multiple individuals reveals complementary roles of promoter and gene body methylation in transcriptional regulation. Genome Biol. 15, 408 (2014).

    Google Scholar 

  28. 28.

    Hu, W. et al. Glutaminase 2, a novel p53 target gene regulating energy metabolism and antioxidant function. Proc. Natl Acad. Sci. USA 107, 7455–7460 (2010).

    CAS  Google Scholar 

  29. 29.

    Vukotic, M. et al. Rcf1 mediates cytochrome oxidase assembly and respirasome formation, revealing heterogeneity of the enzyme complex. Cell Metab. 15, 336–347 (2012).

    CAS  Google Scholar 

  30. 30.

    Chung, D. J. & Schulte, P. M. Mechanisms and costs of mitochondrial thermal acclimation in a eurythermal killifish (Fundulus heteroclitus). J. Exp. Biol. 218, 1621–1631 (2015).

    Google Scholar 

  31. 31.

    Rangwala, S. M. et al. Estrogen-related receptor gamma is a key regulator of muscle mitochondrial activity and oxidative capacity. J. Biol. Chem. 285, 22619–22629 (2010).

    CAS  Google Scholar 

  32. 32.

    van den Hoogenhof, M. M., Pinto, Y. M. & Creemers, E. E. RNA splicing: regulation and dysregulation in the heart. Circ. Res 118, 454–468 (2016).

    Google Scholar 

  33. 33.

    Sambrook, J. & Russell, D. W. Molecular Cloning: A Laboratory Manual 3rd edn (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 2001).

    Google Scholar 

  34. 34.

    Simpson, J. T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    CAS  Google Scholar 

  35. 35.

    Boetzer, M., Henkel, C. V., Jansen, H. J., Butler, D. & Pirovano, W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 27, 578–579 (2011).

    CAS  Google Scholar 

  36. 36.

    Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).

    CAS  Google Scholar 

  37. 37.

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

    CAS  Google Scholar 

  38. 38.

    Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

  39. 39.

    Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11–34 (2014). 11 12.

    Google Scholar 

  40. 40.

    Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nat. Methods 7, 909–912 (2010).

    CAS  Google Scholar 

  41. 41.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Google Scholar 

  42. 42.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  Google Scholar 

  43. 43.

    Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform. 12, 491 (2011).

    Google Scholar 

  44. 44.

    Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    CAS  Google Scholar 

  45. 45.

    Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M. & Bairoch, A. UniProtKB/Swiss-Prot. Methods Mol. Biol. 406, 89–112 (2007).

    CAS  Google Scholar 

  46. 46.

    Korf, I. Gene finding in novel genomes. BMC Bioinform. 5, 59 (2004).

    Google Scholar 

  47. 47.

    Stanke, M., Steinkamp, R., Waack, S. & Morgenstern, B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 32, W309–W312 (2004).

    CAS  Google Scholar 

  48. 48.

    Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinform. 25, 4.10.1–4.10.14 (2009).

    Google Scholar 

  49. 49.

    Li, W. et al. The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res. 43, W580–W584 (2015).

    CAS  Google Scholar 

  50. 50.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Google Scholar 

  51. 51.

    Krueger F. Trim Galore! v. 0.4 (Babraham Bioinformatics, 2015); http://www.bioinformatics.babraham.ac.uk/projects/trim_galore

  52. 52.

    Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  Google Scholar 

  53. 53.

    Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).

    Google Scholar 

  54. 54.

    Warnes, G. R. et al. gplots: various R programming tools for plotting data R package v. 3.0.1 (R Foundation, 2016); http://CRAN.R-project.org/package=gplots

  55. 55.

    De Leeuw, J. & Mair, P. Multidimensional scaling using majorization: SMACOF in R. J. Stat. Softw. 31, i03 (2009).

  56. 56.

    Nordhausen, K., Sirkia, S., Oja, H. & Tyler, D. ICSNP: Tools for Multivariate Nonparametrics R package v. 1.1-0. (R Foundation, 2015).

  57. 57.

    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).

    Google Scholar 

Download references

Acknowledgements

This study was supported by the Competitive Research Funds OCRF-2014-CRG3-62140408 from the King Abdullah University of Science and Technology. This project was completed under JCU Ethics A1233 and A1415. T.Ryu acknowledges the support from the APEC Climate Center. P.L.M. was supported by the Australian Research Council (ARC) and P.L.M., H.D.V. and J.M.D. were supported by the ARC Centre of Excellence for Coral Reef Studies. We thank C. Ortiz Alvarez and E. J. Steinig (James Cook University) for assisting genomic DNA extraction for methylome sequencing. Figures were enhanced by I. Gromicho, scientific illustrator at King Abdullah University of Science and Technology (KAUST).

Author information

Affiliations

Authors

Contributions

J.M.D. managed the fish rearing experiments and performed metabolism experiments. H.D.V. prepared samples for sequencing. H.D.V. extracted nucleic acids for genome and transcriptiome. T.Ryu extracted nucleic acids for methylome sequencing. T.Ryu, H.D.V. and J.M.D. selected samples for sequencing. T.Ryu designed and performed the computational analysis. T.Ryu and H.D.V. interpreted the results. T.Ryu, T.Ravasi, P.L.M., H.D.V. and J.M.D. wrote the manuscript. T.Ravasi and P.L.M. supervised the overall project.

Corresponding authors

Correspondence to Philip L. Munday or Timothy Ravasi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figures 1 & 2, Supplementary Tables 1–3, 5, 6, 8, 9

Supplementary Table 4

Genomic coordinates of identified differentially methylated regions (DMRs)

Supplementary Table 7

Differentially methylated and net aerobic scope (NAS)-correlated gene information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ryu, T., Veilleux, H.D., Donelson, J.M. et al. The epigenetic landscape of transgenerational acclimation to ocean warming. Nature Clim Change 8, 504–509 (2018). https://doi.org/10.1038/s41558-018-0159-0

Download citation

Further reading