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Genome-wide analysis of cis-regulatory changes underlying metabolic adaptation of cavefish

Nature Genetics volume 54, pages 684–693 (2022)Cite this article

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Abstract

Cis-regulatory changes are key drivers of adaptative evolution. However, their contribution to the metabolic adaptation of organisms is not well understood. Here, we used a unique vertebrate model, Astyanax mexicanus—different morphotypes of which survive in nutrient-rich surface and nutrient-deprived cave waters—to uncover gene regulatory networks underlying metabolic adaptation. We performed genome-wide epigenetic profiling in the liver tissues of Astyanax and found that many of the identified cis-regulatory elements (CREs) have genetically diverged and have differential chromatin features between surface and cave morphotypes, while retaining remarkably similar regulatory signatures between independently derived cave populations. One such CRE in the hpdb gene harbors a genomic deletion in cavefish that abolishes IRF2 repressor binding and derepresses enhancer activity in reporter assays. Selection of this mutation in multiple independent cave populations supports its importance in cave adaptation, and provides novel molecular insights into the evolutionary trade-off between loss of pigmentation and adaptation to food-deprived caves.

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Fig. 1: Analysis of morphotype-biased accessible chromatin regions.
Fig. 2: Morphotype-biased accessible chromatin regions associate with key metabolic pathway genes.
Fig. 3: Analysis of genetic changes underlying accessible chromatin regions.
Fig. 4: Functional validation of differentially accessible CREs.
Fig. 5: Detailed characterization of CRE-15.

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Data availability

Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1538. The ATAC-seq, ChIP-seq and RNA-seq data can be found at GEO accession number GSE153052. Source data are provided with this paper.

Code availability

All of the code used for the analysis can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1538.

References

  1. Wittkopp, P. J. & Kalay, G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. 13, 59–69 (2012).

    Article  CAS  Google Scholar 

  2. Long, H. K., Prescott, S. L. & Wysocka, J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell 167, 1170–1187 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–83 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Thompson, A. C. et al. A novel enhancer near the Pitx1 gene influences development and evolution of pelvic appendages in vertebrates. eLife 7, e38555 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Partha, R. et al. Subterranean mammals show convergent regression in ocular genes and enhancers, along with adaptation to tunneling. eLife 6, e25884 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gore, A. V. et al. An epigenetic mechanism for cavefish eye degeneration. Nat. Ecol. Evol. 2, 1155–1160 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jeffery, W. R. Astyanax surface and cave fish morphs. EvoDevo 11, 14 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Krishnan, J. & Rohner, N. Sweet fish: fish models for the study of hyperglycemia and diabetes. J. Diabetes 11, 193–203 (2019).

    Article  PubMed  Google Scholar 

  9. Bradic, M., Beerli, P., García-de León, F. J., Esquivel-Bobadilla, S. & Borowsky, R. L. Gene flow and population structure in the Mexican blind cavefish complex (Astyanax mexicanus). BMC Evol. Biol. 12, 9 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Herman, A. et al. The role of gene flow in rapid and repeated evolution of cave-related traits in Mexican tetra, Astyanax mexicanus. Mol. Ecol. 27, 4397–4416 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Coghill, L. M., Hulsey, C. D., Chaves-Campos, J., García de Leon, F. J. & Johnson, S. G. Next generation phylogeography of cave and surface Astyanax mexicanus. Mol. Phylogenet. Evol. 79, 368–374 (2014).

    Article  PubMed  Google Scholar 

  12. Riddle, M. R. et al. Insulin resistance in cavefish as an adaptation to a nutrient-limited environment. Nature 555, 647–651 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Aspiras, A. C., Rohner, N., Martineau, B., Borowsky, R. L. & Tabin, C. J. Melanocortin 4 receptor mutations contribute to the adaptation of cavefish to nutrient-poor conditions. Proc. Natl Acad. Sci. USA 112, 9668–9673 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dowling, T. E., Martasian, D. P. & Jeffery, W. R. Evidence for multiple genetic forms with similar eyeless phenotypes in the blind cavefish, Astyanax mexicanus. Mol. Biol. Evol. 19, 446–455 (2002).

    Article  CAS  PubMed  Google Scholar 

  16. Gross, D. S. & Garrard, W. T. Nuclease hypersensitive sites in chromatin. Annu. Rev. Biochem. 57, 159–197 (1988).

    Article  CAS  PubMed  Google Scholar 

  17. Daugherty, A. C. et al. Chromatin accessibility dynamics reveal novel functional enhancers in C. elegans. Genome Res. 27, 2096–2107 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Warren, W. C. et al. A chromosome-level genome of Astyanax mexicanus surface fish for comparing population-specific genetic differences contributing to trait evolution. Nat. Commun. 12, 1447 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhou, V. W., Goren, A. & Bernstein, B. E. Charting histone modifications and the functional organization of mammalian genomes. Nat. Rev. Genet. 12, 7–18 (2011).

    Article  PubMed  CAS  Google Scholar 

  22. Cooper, G. M. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Schmidt, D. et al. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328, 1036–1040 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hong, J.-W., Hendrix, D. A. & Levine, M. S. Shadow enhancers as a source of evolutionary novelty. Science 321, 1314 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wong, E. S. et al. Decoupling of evolutionary changes in transcription factor binding and gene expression in mammals. Genome Res. 25, 167–178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hariprakash, J. M. & Ferrari, F. Computational biology solutions to identify enhancers-target gene pairs. Comput. Struct. Biotechnol. J. 17, 821–831 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database 2017, bax028 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  30. Xiong, S., Krishnan, J., Peuß, R. & Rohner, N. Early adipogenesis contributes to excess fat accumulation in cave populations of Astyanax mexicanus. Dev. Biol. 441, 297–304 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Eissing, L. et al. De novo lipogenesis in human fat and liver is linked to ChREBP-β and metabolic health. Nat. Commun. 4, 1528 (2013).

    Article  PubMed  CAS  Google Scholar 

  33. Ham, M. et al. Glucose-6-phosphate dehydrogenase deficiency improves insulin resistance with reduced adipose tissue inflammation in obesity. Diabetes 65, 2624–2638 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. He, Y. et al. The role of retinoic acid in hepatic lipid homeostasis defined by genomic binding and transcriptome profiling. BMC Genomics 14, 575 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Laurencikiene, J. & Rydén, M. Liver X receptors and fat cell metabolism. Int. J. Obes. 36, 1494–1502 (2012).

    Article  CAS  Google Scholar 

  36. Weissglas-Volkov, D. et al. Common hepatic nuclear factor-4α variants are associated with high serum lipid levels and the metabolic syndrome. Diabetes 55, 1970–1977 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Lu, Y.-H., Dallner, O. S., Birsoy, K., Fayzikhodjaeva, G. & Friedman, J. M. Nuclear Factor-Y is an adipogenic factor that regulates leptin gene expression. Mol. Metab. 4, 392–405 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Truty, M. J., Lomberk, G., Fernandez-Zapico, M. E. & Urrutia, R. Silencing of the transforming growth factor-β (TGFβ) receptor II by Krüppel-like factor 14 underscores the importance of a negative feedback mechanism in TGFβ signaling. J. Biol. Chem. 284, 6291–6300 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Phillips-Cremins, J. E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kentepozidou, E. et al. Clustered CTCF binding is an evolutionary mechanism to maintain topologically associating domains. Genome Biol. 21, 5 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chan, Y. F. et al. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science 327, 302–305 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Bradic, M., Teotónio, H. & Borowsky, R. L. The population genomics of repeated evolution in the blind cavefish Astyanax mexicanus. Mol. Biol. Evol. 30, 2383–2400 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Coetzee, S. G., Coetzee, G. A. & Hazelett, D. J. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31, 3847–3849 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Raile, K. et al. HNF1B abnormality (mature-onset diabetes of the young 5) in children and adolescents: high prevalence in autoantibody-negative type 1 diabetes with kidney defects. Diabetes Care 31, e83 (2008).

    Article  PubMed  Google Scholar 

  45. Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Fisher, S. et al. Evaluating the biological relevance of putative enhancers using Tol2 transposon-mediated transgenesis in zebrafish. Nat. Protoc. 1, 1297–1305 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Parker, H. J., Bronner, M. E. & Krumlauf, R. A Hox regulatory network of hindbrain segmentation is conserved to the base of vertebrates. Nature 514, 490–493 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Evolutionary changes in cis and trans gene regulation. Nature 430, 85–88 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Bilandžija, H., Ma, L., Parkhurst, A. & Jeffery, W. R. A potential benefit of albinism in Astyanax cavefish: downregulation of the oca2 gene increases tyrosine and catecholamine levels as an alternative to melanin synthesis. PLoS ONE 8, e80823 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Krishnan, J. et al. Comparative transcriptome analysis of wild and lab populations of Astyanax mexicanus uncovers differential effects of environment and morphotype on gene expression. J. Exp. Zool. B Mol. Dev. Evol. 334, 530–539 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Jeffery, W. R. Regressive evolution in Astyanax cavefish. Annu. Rev. Genet. 43, 25–47 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 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 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  56. Ou, J. et al. ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data. BMC Genomics 19, 169 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Gu, Z., Eils, R., Schlesner, M. & Ishaque, N. EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. BMC Genomics 19, 234 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinf. 43, 11.10.1–11.10.33 (2013).

    Article  Google Scholar 

  63. Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res. 46, D252–D259 (2018).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful to the cavefish and aquatics core facilities at the Stowers Institute for Medical Research for the support and husbandry of the cavefish and zebrafish. DNA samples for wild-caught Tinaja, Yerbaniz, Piedras and Japonés cavefish were generously provided by R. Borowsky, and B. Jeffery provided Astyanax liver samples for preliminary ChIP experiments. We thank M. Cook for assistance with the motif analysis software, K. Weaver for assistance with high-throughput genotyping, and M. Miller for illustrations. We thank R. Krumlauf and J. Zeitlinger for useful input throughout the study and critical reading of the manuscript. N.R. is supported by institutional funding, National Institutes of Health (NIH) grants 1DP2AG071466-01 and R01 GM127872, and the National Science Foundation (NSF) EDGE award 1923372. R.P. was supported by a grant (no. PE 2807/1-1) from Deutsche Forschungsgemeinschaft.

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  1. Jake VanCampen

    Present address: Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA

  2. Robert Peuß

    Present address: Institute for Evolution and Biodiversity, University of Münster, Münster, Germany

Authors and Affiliations

  1. Stowers Institute for Medical Research, Kansas City, MO, USA

    Jaya Krishnan, Chris W. Seidel, Ning Zhang, Narendra Pratap Singh, Jake VanCampen, Robert Peuß, Shaolei Xiong, Alexander Kenzior, Hua Li, Joan W. Conaway & Nicolas Rohner

  2. Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA

    Nicolas Rohner

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Contributions

J.K. and N.R. designed the study. J.K. performed experiments with critical input from N.P.S. and J.W.C., and support from S.X. R.P. and A.K. collected wild Astyanax samples. J.K., C.W.S., N.Z. and J.V. performed the analyses with support from H.L. J.K. and N.R. wrote the manuscript. All authors read and approved of the manuscript.

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Correspondence to Nicolas Rohner.

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Supplementary Figures 1–6, Supplementary Tables 1–4 and Supplementary Data 1–3.

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Supplementary Data 1

Genomic coordinates of all CREs identified in the study.

Supplementary Data 2

Raw read counts for RNA-seq data.

Source data

Source Data Fig. 5

Unprocessed EMSA gel.

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Krishnan, J., Seidel, C.W., Zhang, N. et al. Genome-wide analysis of cis-regulatory changes underlying metabolic adaptation of cavefish. Nat Genet 54, 684–693 (2022). https://doi.org/10.1038/s41588-022-01049-4

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