Letter | Published:

Insulin resistance in cavefish as an adaptation to a nutrient-limited environment

Nature volume 555, pages 647651 (29 March 2018) | Download Citation

Abstract

Periodic food shortages are a major challenge faced by organisms in natural habitats. Cave-dwelling animals must withstand long periods of nutrient deprivation, as—in the absence of photosynthesis—caves depend on external energy sources such as seasonal floods1. Here we show that cave-adapted populations of the Mexican tetra, Astyanax mexicanus, have dysregulated blood glucose homeostasis and are insulin-resistant compared to river-adapted populations. We found that multiple cave populations carry a mutation in the insulin receptor that leads to decreased insulin binding in vitro and contributes to hyperglycaemia. Hybrid fish from surface–cave crosses carrying this mutation weigh more than non-carriers, and zebrafish genetically engineered to carry the mutation have increased body weight and insulin resistance. Higher body weight may be advantageous in caves as a strategy to cope with an infrequent food supply. In humans, the identical mutation in the insulin receptor leads to a severe form of insulin resistance and reduced lifespan. However, cavefish have a similar lifespan to surface fish and do not accumulate the advanced glycation end-products in the blood that are typically associated with the progression of diabetes-associated pathologies. Our findings suggest that diminished insulin signalling is beneficial in a nutrient-limited environment and that cavefish may have acquired compensatory mechanisms that enable them to circumvent the typical negative effects associated with failure to regulate blood glucose levels.

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References

  1. 1.

    & The Biology of Caves and Other Subterranean Habitats (Oxford Univ. Press, 2009)

  2. 2.

    Evolution in the Dark, Darwin’s Loss Without Selection (Springer, 2017)

  3. 3.

    , , , & Melanocortin 4 receptor mutations contribute to the adaptation of cavefish to nutrient-poor conditions. Proc. Natl Acad. Sci. USA 112, 9668–9673 (2015)

  4. 4.

    , & Eyeless Mexican cavefish save energy by eliminating the circadian rhythm in metabolism. PLoS ONE 9, e107877 (2014)

  5. 5.

    Oxygen consumption of Astyanax fasciatus (Characidae, Pisces): a comparison of epigean and hypogean populations. Environ. Biol. Fishes 17, 299–308 (1986)

  6. 6.

    The complex origin of Astyanax cavefish. BMC Evol. Biol. 12, 105 (2012)

  7. 7.

    , & The population genomics of repeated evolution in the blind cavefish Astyanax mexicanus. Mol. Biol. Evol. 30, 2383–2400 (2013)

  8. 8.

    & Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806 (2001)

  9. 9.

    , , & Targeting hepatic glucose metabolism in the treatment of type 2 diabetes. Nat. Rev. Drug Discov. 15, 786–804 (2016)

  10. 10.

    et al. Insights into insulin and glucagon responses in fish. Fish Physiol. Biochem. 27, 205–216 (2002)

  11. 11.

    & The insulin signalling pathway. Curr. Biol. 12, R236–R238 (2002)

  12. 12.

    et al. The cavefish genome reveals candidate genes for eye loss. Nat. Commun. 5, 5307 (2014)

  13. 13.

    et al. Rabson Mendenhall Syndrome; a case report. J. Diabetol. 2, 2 (2013)

  14. 14.

    et al. Substitution of Leu for Pro-193 in the insulin receptor in a patient with a genetic form of severe insulin resistance. Hum. Mol. Genet. 2, 1437–1441 (1993)

  15. 15.

    et al. Mutations in insulin-receptor gene in insulin-resistant patients. Diabetes Care 13, 257–279 (1990)

  16. 16.

    , , , & Gene flow and population structure in the Mexican blind cavefish complex (Astyanax mexicanus). BMC Evol. Biol. 12, 9 (2012)

  17. 17.

    , & Genome editing using CRISPR/Cas9-based knock-in approaches in zebrafish. Methods 121-122, 77–85 (2017)

  18. 18.

    & Recent insights into fatty liver, metabolic dyslipidaemia and their links to insulin resistance. Curr. Opin. Lipidol. 21, 329–336 (2010)

  19. 19.

    , & Fish scale is a suitable model for analyzing determinants of skeletal fragility in type 2 diabetes. Endocrine 54, 575–577 (2016)

  20. 20.

    et al. Comparing growth in surface and cave morphs of the species Astyanax mexicanus: insights from scales. Evodevo 8, 23 (2017)

  21. 21.

    et al. Spinal deformity in aged zebrafish is accompanied by degenerative changes to their vertebrae that resemble osteoarthritis. PLoS ONE 8, e75787 (2013)

  22. 22.

    , & Mechanisms of disease: advanced glycation end-products and their receptor in inflammation and diabetes complications. Nat. Clin. Pract. Endocrinol. Metab. 4, 285–293 (2008)

  23. 23.

    , & Advanced glycation end products and diabetic cardiovascular disease. Cardiol. Rev. 20, 177–183 (2012)

  24. 24.

    & Structural biology of insulin and IGF1 receptors: implications for drug design. Nat. Rev. Drug Discov. 1, 769–783 (2002)

  25. 25.

    Restoring sight in blind cavefish. Curr. Biol. 18, R23–R24 (2008)

  26. 26.

    , , , & Astyanax transgenesis and husbandry: how cavefish enters the laboratory. Zebrafish 11, 291–299 (2014)

  27. 27.

    , & Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

  28. 28.

    Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, (2011)

  29. 29.

    & Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010)

  30. 30.

    et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11.10.1–11.10.33 (2013)

  31. 31.

    et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011)

  32. 32.

    , , , & Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193, 929–941 (2013)

  33. 33.

    et al. A mammalian mediator subunit that shares properties with Saccharomyces cerevisiae mediator subunit Cse2. J. Biol. Chem. 279, 5846–5851 (2004)

  34. 34.

    , , , & Flow cytofluorometric analysis of insulin binding and internalization by Swiss 3T3 cells. Cytometry 2, 402–406 (1982)

  35. 35.

    R Development Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016)

  36. 36.

    ggplot2: Elegant Graphics for Data Analysis (Springer, 2009)

Download references

Acknowledgements

We thank Y. Chinchore and C. Sengel for technical advice; X. Gao for bioinformatics support; Z. Zakibe for photographs of the fish; the Aquatics facility at Stowers for fish maintenance and support; the cell culture core at Stowers for cell line maintenance and advice; the molecular biology core at Stowers for design, execution and validation of the CRISPR constructs; the proteomics core; M. Levy for advice and computational modelling of the insulin receptor; A. Herman for help with the genome scan; the Microscopy Resources on the North Quad (MicRoN) core at Harvard Medical School; M. Miller for illustration; and S. Williams, F. Damen, S. Xiong, E. Kingsley and K. Fox for feedback on the manuscript text. This work was supported by a grant from the NIH to C.J.T. (HD089934) and institutional funding to N.R. M.R.R. was supported by a National Research Service Award (DK108495) and R.P. was supported by a grant from the Deutsche Forschungsgemeinschaft (PE 2807/1-1).

Author information

Author notes

    • Misty R. Riddle
    •  & Ariel C. Aspiras

    These authors contributed equally to this work.

Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Misty R. Riddle
    • , Ariel C. Aspiras
    • , Brian Martineau
    • , Megan Peavey
    • , Julius A. Tabin
    •  & Clifford J. Tabin
  2. Stowers Institute for Medical Research, Kansas City, Missouri 64110, USA

    • Karin Gaudenz
    • , Robert Peuß
    • , Jenny Y. Sung
    • , Andrew C. Box
    •  & Nicolas Rohner
  3. College of Biomedical Sciences, University of Minnesota, St. Paul, Minnesota 55108, USA

    • Suzanne McGaugh
  4. Department of Biology, New York University, New York, New York 10003, USA

    • Richard Borowsky
  5. Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas 66160, USA

    • Nicolas Rohner

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Contributions

M.R.R., A.C.A., C.J.T. and N.R. conceived the project and designed research with additional contributions from K.G., R.P. and A.C.B. M.R.R., A.C.A., K.G., R.P., J.Y.S., B.M., M.P., A.C.B., J.A.T., S.M., R.B. and N.R. performed the research. M.R.R., A.C.A., C.J.T. and N.R. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Clifford J. Tabin or Nicolas Rohner.

Reviewer Information Nature thanks K. Kavanagh, S. O’Rahilly and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

  2. 2.

    Supplementary Figure

    This file contains raw uncropped film for the western blot shown in Figure 1F. Both films were from the same set of membranes which were stripped and reprobed. Red outline denotes gels used for the figure and yellow outline shows cropping used.

  3. 3.

    Supplementary Information

    This file contains names of the rivers in which surface Astyanax mexicanus were sampled and the numbers of individual samples collected.

  4. 4.

    Supplementary Information

    This file contains extended text describing the population genomic approach to test the insra locus for signatures of selection.

HTML files

  1. 1.

    Supplementary Data

    This file contains Sequence information of the genes from the insulin signaling pathway (http://www.genome.jp/kegg-bin/show_pathway?hsa04910) from Astyanax mexicanus in FASTA format.

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DOI

https://doi.org/10.1038/nature26136

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