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Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris sea urchins

Nature Ecology & Evolution volume 6, pages 1907–1920 (2022)Cite this article

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Abstract

Changes in developmental gene regulatory networks (dGRNs) underlie much of the diversity of life, but the evolutionary mechanisms that operate on regulatory interactions remain poorly understood. Closely related species with extreme phenotypic divergence provide a valuable window into the genetic and molecular basis for changes in dGRNs and their relationship to adaptive changes in organismal traits. Here we analyse genomes, epigenomes and transcriptomes during early development in two Heliocidaris sea urchin species that exhibit highly divergent life histories and in an outgroup species. Positive selection and chromatin accessibility modifications within putative regulatory elements are enriched on the branch leading to the derived life history, particularly near dGRN genes. Single-cell transcriptomes reveal a dramatic delay in cell fate specification in the derived state, which also has far fewer open chromatin regions, especially near conserved cell fate specification genes. Experimentally perturbing key transcription factors reveals profound evolutionary changes to early embryonic patterning events, disrupting regulatory interactions previously conserved for ~225 million years. These results demonstrate that natural selection can rapidly reshape developmental gene expression on a broad scale when selective regimes abruptly change. More broadly, even highly conserved dGRNs and patterning mechanisms in the early embryo remain evolvable under appropriate ecological circumstances.

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Fig. 1: Evolution of life history and genomes.
Fig. 2: Evolution of open chromatin landscape.
Fig. 3: Evolution of transcriptomes.
Fig. 4: Evolutionary change at the ‘top’ of a conserved developmental dGRN.

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Data Availability

Genomes. Sequencing reads used to assemble the Heliocidaris genomes and the genome assemblies themselves are available on the Chinese National GeneBank (CNP0002233) and NCBI (PRJNA869508). Genome assemblies of Heliocidaris erythrogramma and Heliocidaris tuberculata are also available on NCBI (PRJNA827916 and PRJNA827769, respectively). Genome assembly of Lytechinus variegatus is previously published21 and available on NCBI (PRJNA657258). Genome annotations and files associated with whole genome alignments between species are available on Dryad (https://doi.org/10.5061/dryad.sj3tx966v). ATAC-seq. Raw sequencing reads for the ATAC-seq dataset are available on NCBI (PRJNA828607). Alignment files are available on Dryad (https://doi.org/10.5061/dryad.sj3tx966v). Result files associated with the ATAC-seq analyses are available as Supplementary Data 2–4. Bulk RNA-seq. Bulk RNA-seq data were retrieved from ref. 19. scRNA-seq. Raw sequencing reads for the Heliocidaris erythrogramma single-cell ATAC-seq dataset are available on NCBI (PRJNA833141). Sequencing reads from the Lytechinus variegatus scRNA-seq dataset were retrieved from ref. 34 and are available on NCBI (PRJNA765003). Results files associated with the scRNA-seq analyses are available as Supplementary Data 7 and 9.

Code Availability

Code and analyses associated with these result figures are available on GitHub at https://github.com/phillipdavidson/heliocidaris_analyses.

References

  1. Erwin, D. H. & Davidson, E. H. The evolution of hierarchical gene regulatory networks. Nat. Rev. Genet. 10, 141–148 (2009).

    Article  CAS  PubMed  Google Scholar 

  2. Oliveri, P. & Davidson, E. H. Gene regulatory network controlling embryonic specification in the sea urchin. Curr. Opin. Genet Dev. 14, 351–360 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. McClay, D. R. Evolutionary crossroads in developmental biology: sea urchins. Development 138, 2639–2648 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gildor, T. & Ben-Tabou de-Leon, S. Comparative study of regulatory circuits in two sea urchin species reveals tight control of timing and high conservation of expression dynamics. PLoS Genet. 11, e1005435 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yamazaki, A. & Minokawa, T. Expression patterns of mesenchyme specification genes in two distantly related echinoids, Glyptocidaris crenularis and Echinocardium cordatum. Gene Expr. Patterns 17, 87–97 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Erkenbrack, E. M., Davidson, E. H. & Peter, I. S. Conserved regulatory state expression controlled by divergent developmental gene regulatory networks in echinoids. Development https://doi.org/10.1242/dev.167288 (2018).

  7. Hinman, V. F., Nguyen, A. T., Cameron, R. A. & Davidson, E. H. Developmental gene regulatory network architecture across 500 million years of echinoderm evolution. Proc. Natl Acad. Sci. USA 100, 13356–13361 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Koch, N. M. et al. Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record. eLife 11, e72460 (2022).

    Article  CAS  Google Scholar 

  9. Maynard Smith, J. et al. Developmental constraints and evolution: a perspective from the Mountain Lake conference on development and evolution. Q. Rev. Biol. 60.3, 265–287 (1985).

    Article  Google Scholar 

  10. Arthur, W. A Theory of the Evolution of Development (John Wiley & Sons Inc., 1988).

  11. Raff, R. A. Constraint, flexibility, and phylogenetic history in the evolution of direct development in sea urchins. Dev. Biol. 119, 6–19 (1987).

    Article  CAS  PubMed  Google Scholar 

  12. Wray, G. A. Parallel evolution of nonfeeding larvae in echinoids. Syst. Biol. 45, 308–322 (1996).

    Article  Google Scholar 

  13. McEdward, L. R. & Miner, B. G. Larval and life-cycle patterns in echinoderms. Can. J. Zool. 79, 1125–1170 (2001).

    Article  Google Scholar 

  14. Marshall, D. J., Krug, P. J., Kupriyanova, E. K., Byrne, M. & Emlet, R. B. The biogeography of marine invertebrate life histories. Annu Rev. Ecol. Evol. S 43, 97 (2012).

    Article  Google Scholar 

  15. Laegdsgaard, P., Byrne, M. & Anderson, D. T. Reproduction of sympatric populations of Heliocidaris erythrogramma and H. tuberculata (Echinoidea) in New South Wales. Mar. Biol. 110, 359–374 (1991).

    Article  Google Scholar 

  16. Morgan, S. G. Life And Death in the Plankton: Larval Mortality and Adaptation. in Ecology of Marine Invertebrate Larvae (ed. McEdward, L.R.) 279-321 (CRC Press, 2020).

  17. Raff, R. A. Direct-developing sea urchins and the evolutionary reorganization of early development. Bioessays 14, 211–218 (1992).

    Article  CAS  PubMed  Google Scholar 

  18. Raff, R. A. & Byrne, M. The active evolutionary lives of echinoderm larvae. Heredity 97, 244–252 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Israel, J. W. et al. Comparative developmental transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris. PLoS Biol. 14, e1002391 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wray, G. A. Extreme phenotypic divergence and the evolution of development. Curr. Top. Developmental Biol. 146, 79–112 (2021).

    Article  Google Scholar 

  21. Davidson, P. L. et al. Chromosomal-level genome assembly of the sea urchin Lytechinus variegatus substantially improves functional genomic analyses. Genome Biol. Evol. 12, 1080–1086 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Murrell, B. et al. Gene-wide identification of episodic selection. Mol. Biol. Evol. 32, 1365–1371 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang, L. et al. Genetic basis for divergence in developmental gene expression in two closely related sea urchins. Nat. Ecol. Evol. 4, 831–840 (2020).

    Article  PubMed  Google Scholar 

  24. Davidson, P. L., Byrne, M. & Wray, G. A. Evolutionary changes in the chromatin landscape contribute to reorganization of a developmental gene network during rapid life history evolution in sea urchins. Mol. Biol. Evol. 39, msac172 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wray, G. A. & Raff, R. A. Evolutionary modification of cell lineage in the direct-developing sea urchin Heliocidaris erythrogramma. Dev. Biol. 132, 458–470 (1989).

    Article  CAS  PubMed  Google Scholar 

  26. Wray, G. A. & Raff, R. A. Novel origins of lineage founder cells in the direct-developing sea urchin Heliocidaris erythrogramma. Dev. Biol. 141, 41–54 (1990).

    Article  CAS  PubMed  Google Scholar 

  27. Smith, M. S., Turner, F. R. & Raff, R. A. Nodal expression and heterochrony in the evolution of dorsal-ventral and left-right axes formation in the direct developing sea urchin Heliocidaris erythrogramma. J. Exp. Zool. Part B 310b, 609–622 (2008).

    Article  CAS  Google Scholar 

  28. Wilson, K. A., Andrews, M. E. & Raff, R. A. Dissociation of expression patterns of homeodomain transcription factors in the evolution of developmental mode in the sea urchins Heliocidaris tuberculata and H. erythrogramma. Evol. Dev. 7, 401–415 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Love, A. C. & Raff, R. A. Larval ectoderm, organizational homology, and the origins of evolutionary novelty. J. Exp. Zool. Part B 306, 18–34 (2006).

    Article  Google Scholar 

  30. O’Hara, T. & Byrne, M. Australian Echinoderms: Biology, Ecology and Evolution (CSIRO Publishing, 2017).

  31. Cameron, R. A., Hough-Evans, B. R., Britten, R. J. & Davidson, E. H. Lineage and fate of each blastomere of the eight-cell sea urchin embryo. Genes Dev. 1, 75–85 (1987).

    Article  CAS  PubMed  Google Scholar 

  32. Davidson, E. H. Lineage-specific gene expression and the regulative capacities of the sea urchin embryo: a proposed mechanism. Development 105, 421–445 (1989).

    Article  CAS  PubMed  Google Scholar 

  33. Oliveri, P., Tu, Q. & Davidson, E. H. Global regulatory logic for specification of an embryonic cell lineage. Proc. Natl Acad. Sci. USA 105, 5955–5962 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Massri, A. J. et al. Developmental single-cell transcriptomics in the Lytechinus variegatus sea urchin embryo. Development https://doi.org/10.1242/dev.198614 (2021).

  35. Davidson, E. H., Cameron, R. A. & Ransick, A. Specification of cell fate in the sea urchin embryo: summary and some proposed mechanisms. Development 125, 3269–3290 (1998).

    Article  CAS  PubMed  Google Scholar 

  36. Ettensohn, C. A., Illies, M. R., Oliveri, P. & De Jong, D. L. Alx1, a member of the Cart1/Alx3/Alx4 subfamily of paired-class homeodomain proteins, is an essential component of the gene network controlling skeletogenic fate specification in the sea urchin embryo. Development 130, 2917–2928 (2003).

    Article  CAS  PubMed  Google Scholar 

  37. Croce, J. C. & McClay, D. R. Dynamics of Delta/Notch signaling on endomesoderm segregation in the sea urchin embryo. Development 137, 83–91 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Emlet, R. B. Larval spicules, cilia, and symmetry as remnants of indirect development in the direct developing sea urchin Heliocidaris erythrogramma. Dev. Biol. 167, 405–415 (1995).

    Article  CAS  PubMed  Google Scholar 

  39. Revilla-i-Domingo, R., Oliveri, P. & Davidson, E. H. A missing link in the sea urchin embryo gene regulatory network: hesC and the double-negative specification of micromeres. Proc. Natl Acad. Sci. USA 104, 12383–12388 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Oliveri, P., Davidson, E. H. & McClay, D. R. Activation of pmar1 controls specification of micromeres in the sea urchin embryo. Dev. Biol. 258, 32–43 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Yamazaki, A. et al. pmar1/phb homeobox genes and the evolution of the double-negative gate for endomesoderm specification in echinoderms. Development https://doi.org/10.1242/dev.182139 (2020).

  42. Wu, S. Y. & McClay, D. R. The Snail repressor is required for PMC ingression in the sea urchin embryo. Development 134, 1061–1070 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Smith, M. S., Turner, F. R. & Raff, R. A. Nodal expression and heterochrony in the evolution of dorsal–ventral and left–right axes formation in the direct‐developing sea urchin Heliocidaris erythrogramma. J. Exp. Zool. Part B 310.8, 609–622 (2008).

    Article  Google Scholar 

  44. Koop, D. et al. Nodal and BMP expression during the transition to pentamery in the sea urchin Heliocidaris erythrogramma: insights into patterning the enigmatic echinoderm body plan. BMC Dev. Biol. 17, 4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Huang, S., Kang, M. & Xu, A. HaploMerger2: rebuilding both haploid sub-assemblies from high-heterozygosity diploid genome assembly. Bioinformatics 33, 2577–2579 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Harris, R. S. Improved Pairwise Alignment of Genomic DNA (Pennsylvania State Univ., 2007).

  50. Pryszcz, L. P. & Gabaldon, T. Redundans: an assembly pipeline for highly heterozygous genomes. Nucleic Acids Res. 44, e113 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Flynn, J. M., Hubley, R., Goubert, C. & Smit, A. F. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA 117, 9451–9457 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wong, W. Y. & Simakov, O. RepeatCraft: a meta-pipeline for repetitive element de-fragmentation and annotation. Bioinformatics 35, 1051–1052 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Abrusan, G., Grundmann, N., DeMester, L. & Makalowski, W. TEclass—a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics 25, 1329–1330 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Arshinoff, B. I. et al. Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms. Nucleic Acids Res. 50.D1, D970–D979 (2022).

    Article  Google Scholar 

  61. Bruna, T., Hoff, K. J., Lomsadze, A., Stanke, M. & Borodovsky, M. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genom. Bioinform. 3, lqaa108 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Stanke, M., Schoffmann, O., Morgenstern, B. & Waack, S. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics 7, 62 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Bruna, T., Lomsadze, A. & Borodovsky, M. GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins. NAR Genom. Bioinform. 2, lqaa026 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lomsadze, A., Burns, P. D. & Borodovsky, M. Integration of mapped RNA-seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Res. 42, e119 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  67. Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER-P. Curr. Protoc. Bioinforma. 48, 4 11 11–39 (2014).

    Article  Google Scholar 

  68. Storer, J., Hubley, R., Rosen, J., Wheeler, T. J. & Smit, A. F. The Dfam community resource of transposable element families, sequence models, and genome annotations. Mob. DNA 12, 2 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Bateman, A. Uniprot: a universal hub of protein knowledge. Protein Sci. 28, 32–32 (2019).

    Google Scholar 

  71. O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article  PubMed  Google Scholar 

  72. Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  75. Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Hinrichs, A. S. et al. The UCSC genome browser database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  78. 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 

  79. Berrio, A., Haygood, R. & Wray, G. A. Identifying branch-specific positive selection throughout the regulatory genome using an appropriate proxy neutral. BMC Genomics 21, 359 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Siepel, A. & Haussler, D. Phylogenetic estimation of context-dependent substitution rates by maximum likelihood. Mol. Biol. Evol. 21, 468–488 (2004).

    Article  CAS  PubMed  Google Scholar 

  81. Pond, S. L., Frost, S. D. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).

    Article  CAS  PubMed  Google Scholar 

  82. Konopka, A. K. Sequence complexity and composition. eLS http://www.els.net/WileyCDA/ElsArticle/refId-a0005260.html (2005).

  83. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  86. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018).

    Article  PubMed  Google Scholar 

  89. Butler, A. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36.5, 411–420 (2018).

    Article  Google Scholar 

  90. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Edgar, A., Byrne, M. & Wray, G. A. Embryo microinjection of the lecithotrophic sea urchin Heliocidaris erythrogramma. J. Biol. Methods 6, e119 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Rho, H. K. & McClay, D. R. The control of foxN2/3 expression in sea urchin embryos and its function in the skeletogenic gene regulatory network. Development 138, 937–945 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Sydney Institute of Marine Science (SIMS) for facilities, as well as the SIMS staff for their assistance. This work was supported by the National Science Foundation Division of Integrative Organismal Systems (award no. 1929934 to G.A.W.), National Science Foundation Graduate Research Fellowships to H.R.D. and A.J.M., and an Australian Research Council Discovery Grant (award no. DP120102849 to M.B.).

Author information

Authors and Affiliations

  1. Department of Biology, Duke University, Durham, NC, USA

    Phillip L. Davidson, Jane S. Swart, Abdull J. Massri, Allison Edgar, Lingyu Wang, Alejandro Berrio, Hannah R. Devens & Gregory A. Wray

  2. BGI-Qingdao, BGI-Shenzhen, Qingdao, China

    Haobing Guo, Yaolei Zhang & Guangyi Fan

  3. State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China

    Haobing Guo, He Zhang & Guangyi Fan

  4. School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Demian Koop, Paula Cisternas & Maria Byrne

  5. Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China

    Yaolei Zhang

  6. Center for Genomic and Computational Biology, Duke University, Durham, NC, USA

    Gregory A. Wray

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  1. Phillip L. Davidson
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Contributions

L.W., M.B., G.F. and G.A.W. conceived and designed the study. L.W., D.K. and P.C. collected tissues for genomic sequencing. Y.Z. performed genomic DNA extraction and sequencing library preparations. P.L.D., H.G. and H.Z. assembled the genomes. P.L.D. performed genome annotation, alignments and data analysis. P.L.D. and A.B. performed selection analyses. P.L.D. and H.R.D. collected, prepared and analysed ATAC-seq libraries. A.J.M. collected, prepared and analysed scRNA-seq libraries. J.S.S. and A.E. performed embryonic expression and injection assays. P.L.D. and G.A.W. wrote the manuscript, and all authors contributed to manuscript revisions.

Corresponding author

Correspondence to Gregory A. Wray.

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Nature Ecology & Evolution thanks Ina Arnone, Veronica Hinman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Chromatin landscape and expression domain of foxN2/3 is conserved.

Chromatin accessibility nearby foxN2/3 in H. erythrogramma (top, orange) and H. tuberculata (bottom, green), including seven open chromatin regions (OCRs). In-situ hybridization of foxN2/3 in blastula-stage b, H. erythrogramma and c, H. tuberculata embryos. Micrographs derive from a single round of in-situ experiments for each species.

Extended Data Fig. 2 Scaling down number of L. variegatus cells does not substantially affect clustering.

UMAP of non-integrated single cell RNA-seq data from L. variegatus, in which the data has been randomly subsampled to 2065 cells so that the cell number is equivalent to the H. erythrogramma dataset. This subsampling does not change the number and general spatial relationship among clusters in L. variegatus (that is distinct cluster of skeletogenic cells separate from remainder of cells in the embryo).

Extended Data Fig. 3 Three independent methods of integrating single cell RNA-seq data recover similar clustering relationships among L. variegatus and H. erythrogramma cells.

Canonical correlation analysis (CCA), reciprocal principal component analysis (RPCA), and Harmony each recover more numerous, distinct transcriptional states in L. variegatus relative to H. erythrogramma following integration of single cell expression data. All three methods reveal a consistent number of clusters in both species. In particular, H. erythrogramma contains fewer clusters there is more extensive overlap of cells among clusters. These results suggesting that differentiation of discrete cell populations is delayed in the H. erythrogramma embryo.

Extended Data Fig. 4 hesC appears to have lost its ancestral role of repressing larval skeletal cell specification in H. erythrogramma.

Derived expression patterns of hesC in H. erythrogramma at a, cleavage; b, blastula; and c, d, larva stage embryos. Micrographs derive from a single round of in-situ experiments. e, f, Control and MASO knock-down of HesC in H. erythrogramma (early larva; scale bar 100 µm). Polarized light illuminates skeletal elements. HesC knockdown appears to show no phenotype, a dramatic change from the ancestral dGRN. Injection experiments were replicated twice.

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Figs. 1–9.

Reporting Summary

Supplementary Data 1

List of sea urchin dGRN genes.

Supplementary Data 2

List of putatively neutral sites for selection analyses, referenced to H. erythrogramma genome.

Supplementary Data 3

OCR tests for selection results.

Supplementary Data 4

OCR accessibility counts and statistics.

Supplementary Data 5

P values from coding selection analyses.

Supplementary Data 6

Results from bulk RNA-seq analyses.

Supplementary Data 7

scRNA-seq count data.

Supplementary Data 8

Gene models from each species’ genome annotations.

Supplementary Data 9

Marker genes for each cluster from single-cell analyses.

Supplementary Data 10

Putative orthology relationships between genes of H. erythrogramma and L. variegatus from OrthoFinder2.

Supplementary Data 11

Marker genes used to putatively assign cell type identity to scRNA-seq clusters.

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Davidson, P.L., Guo, H., Swart, J.S. et al. Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris sea urchins. Nat Ecol Evol 6, 1907–1920 (2022). https://doi.org/10.1038/s41559-022-01906-9

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