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.

Evolutionary transcriptomics of metazoan biphasic life cycle supports a single intercalation origin of metazoan larvae

A Publisher Correction to this article was published on 20 April 2020

This article has been updated

Abstract

The transient larva-bearing biphasic life cycle is the hallmark of many metazoan phyla, but how metazoan larvae originated remains a major enigma in animal evolution. There are two hypotheses for larval origin. The ‘larva-first’ hypothesis suggests that the first metazoans were similar to extant larvae, with later evolution of the adult-added biphasic life cycle; the ‘adult-first’ hypothesis suggests that the first metazoans were adult forms, with the biphasic life cycle arising later via larval intercalation. Here, we investigate the evolutionary origin of primary larvae by conducting ontogenetic transcriptome profiling for Mollusca—the largest marine phylum characterized by a trochophore larval stage and highly variable adult forms. We reveal that trochophore larvae exhibit rapid transcriptome evolution with extraordinary incorporation of novel genes (potentially contributing to adult shell evolution), and that cell signalling/communication genes (for example, caveolin and innexin) are probably crucial for larval evolution. Transcriptome age analysis of eight metazoan species reveals the wide presence of young larval transcriptomes in both trochozoans and other major metazoan lineages, therefore arguing against the prevailing larva-first hypothesis. Our findings support an adult-first evolutionary scenario with a single metazoan larval intercalation, and suggest that the first appearance of proto-larva probably occurred after the divergence of direct-developing Ctenophora from a metazoan ancestor.

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: Comprehensive transcriptome profiling of the scallop biphasic life cycle.
Fig. 2: Rapid transcriptome evolution and the intercalation origin of trochophore larvae.
Fig. 3: Macro-evolutionary analyses of larval transcriptomes across major metazoan lineages.
Fig. 4: Potential timing of evolutionary origination of metazoan proto-larva.
Fig. 5: Different potential scenarios for evolution of the metazoan biphasic life cycle.

Data availability

All sequencing data have been deposited at the NCBI’s SRA database under bioproject no. PRJNA562987.

Code availability

The software and codes used in this study are publicly available, with corresponding versions indicated in Methods.

Change history

References

  1. 1.

    Morris, S. C. The fossil record and the early evolution of the Metazoa. Nature 361, 219–225 (1993).

    Google Scholar 

  2. 2.

    Sebe-Pedros, A., Degnan, B. M. & Ruiz-Trillo, I. The origin of Metazoa: a unicellular perspective. Nat. Rev. Genet. 18, 498–512 (2017).

    CAS  PubMed  Google Scholar 

  3. 3.

    Rieger, R. M. The biphasic life cycle – a central theme of metazoan evolution. Am. Zool. 34, 484–491 (1994).

    Google Scholar 

  4. 4.

    Strathmann, R. R. Hypotheses on the origins of marine larvae. Ann. Rev. Ecol. Syst. 24, 89–117 (1993).

    Google Scholar 

  5. 5.

    Arenas-Mena, C. Indirect development, transdifferentiation and the macroregulatory evolution of metazoans. Philos. Trans. R. Soc. B 365, 653–669 (2010).

    Google Scholar 

  6. 6.

    Haeckel, E. Die Gastrea-Theorie, der phylogenetische clasification des thierreichs und die homologie der keimblätter. Jena. Z. Med. Naturwiss. 8, 1–55 (1874).

    Google Scholar 

  7. 7.

    Nielsen, C. Life cycle evolution: was the eumetazoan ancestor a holopelagic, planktotrophic gastraea? BMC Evol. Biol. 13, 171 (2013).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Nielsen, C. Animal Evolution: Interrelationships of the Living Phyla (Oxford Univ. Press, 2012).

  9. 9.

    Marlow, H. et al. Larval body patterning and apical organs are conserved in animal evolution. BMC Biol. 12, 7 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Raff, R. A. Origins of the other metazoan body plans: the evolution of larval forms. Philos. Trans. R. Soc. B 363, 1473–1479 (2008).

    Google Scholar 

  11. 11.

    Arendt, D., Technau, U. & Wittbrodt, J. Evolution of the bilaterian larval foregut. Nature 409, 81–85 (2001).

    Google Scholar 

  12. 12.

    Dunn, E. F. et al. Molecular paleoecology: using gene regulatory analysis to address the origins of complex life cycles in the late Precambrian. Evol. Dev. 9, 10–24 (2007).

    CAS  PubMed  Google Scholar 

  13. 13.

    Love, A. C., Lee, A. E., Andrews, M. E. & Raff, R. A. Co‐option and dissociation in larval origins and evolution: the sea urchin larval gut. Evol. Dev. 10, 74–88 (2008).

    CAS  PubMed  Google Scholar 

  14. 14.

    Xu, F. et al. High expression of new genes in trochophore enlightening the ontogeny and evolution of trochozoans. Sci. Rep. 6, 34664 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Haszprunar, G. & Wanninger, A. Molluscs. Curr. Biol. 22, 510–514 (2012).

    Google Scholar 

  16. 16.

    Sigwart, J. D. Zoology: molluscs all beneath the sun, one shell, two shells, more, or none. Curr. Biol. 27, 708–710 (2017).

    Google Scholar 

  17. 17.

    Wanninger, A. & Wollesen, T. The evolution of molluscs. Biol. Rev. 94, 102–115 (2018).

    Google Scholar 

  18. 18.

    Simakov, O. et al. Insights into bilaterian evolution from three spiralian genomes. Nature 493, 526–531 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Page, L. R. Molluscan larvae: pelagic juveniles or slowly metamorphosing larvae? Biol. Bull. 216, 216–225 (2009).

    PubMed  Google Scholar 

  20. 20.

    Nielsen, C. Origin of the trochophora larva. R. Soc. Open Sci. 5, 180042 (2018).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Rouse, G. W. Trochophore concepts: ciliary bands and the evolution of larvae in spiralian Metazoa. Biol. J. Linn. Soc. 66, 411–464 (1999).

    Google Scholar 

  22. 22.

    Takeuchi, T. Molluscan genomics: implications for biology and aquaculture. Curr. Mol. Biol. Rep. 3, 297–305 (2017).

    Google Scholar 

  23. 23.

    De Oliveira, A. L. et al. Comparative transcriptomics enlarges the toolkit of known developmental genes in mollusks. BMC Genomics 17, 905 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Liu, T., Yu, L., Liu, L., Li, H. & Li, Y. Comparative transcriptomes and EVO-DEVO studies depending on next generation sequencing. Comput. Math. Methods Med. 2015, e896176 (2015).

    Google Scholar 

  25. 25.

    Roux, J., Rosikiewicz, M. & Robinson-Rechavi, M. What to compare and how: comparative transcriptomics for Evo-Devo. J. Exp. Zool. 324, 372–382 (2015).

    CAS  Google Scholar 

  26. 26.

    Wang, S. et al. Scallop genome provides insights into evolution of bilaterian karyotype and development. Nat. Ecol. Evol. 1, 0120 (2017).

    Google Scholar 

  27. 27.

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).

    Google Scholar 

  28. 28.

    Levin, M. et al. The mid-developmental transition and the evolution of animal body plans. Nature 531, 637–641 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Paps, J., Xu, F., Zhang, G. & Holland, P. W. H. Reinforcing the egg-timer: recruitment of novel Lophotrochozoa homeobox genes to early and late development in the Pacific oyster. Genome Biol. Evol. 7, 677–688 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Babonis, L. S., Martindale, M. Q. & Ryan, J. F. Do novel genes drive morphological novelty? An investigation of the nematosomes in the sea anemone Nematostella vectensis. BMC Evol. Biol. 16, 114 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Chen, S., Krinsky, B. H. & Long, M. New genes as drivers of phenotypic evolution. Nat. Rev. Genet. 14, 645 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    McDougall, C. & Degnan, B. M. The evolution of mollusc shells. Wiley Interdiscip. Rev. Dev. Biol. 7, e313 (2018).

    PubMed  Google Scholar 

  33. 33.

    Domazet-Lošo, T. & Tautz, D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature 468, 815–818 (2010).

    PubMed  Google Scholar 

  34. 34.

    Quint, M. et al. A transcriptomic hourglass in plant embryogenesis. Nature 490, 98–101 (2012).

    PubMed  Google Scholar 

  35. 35.

    Cheng, X., Hui, J. H. L., Lee, Y. Y., Wan Law, P. T. & Kwan, H. S. A “developmental hourglass” in fungi. Mol. Biol. Evol. 32, 1556–1566 (2015).

    CAS  PubMed  Google Scholar 

  36. 36.

    Drost, H. G., Gabel, A., Grosse, I. & Quint, M. Evidence for active maintenance of phylotranscriptomic hourglass patterns in animal and plant embryogenesis. Mol. Biol. Evol. 32, 1221–1231 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Mchugh, D. Molecular evidence that echiurans and pogonophorans are derived annelids. Proc. Natl Acad. Sci. USA 94, 8006–8009 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Struck, T. H. et al. Annelid phylogeny and the status of Sipuncula and Echiura. BMC Evol. Biol. 7, 57 (2007).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Goto, R. A comprehensive molecular phylogeny of spoon worms (Echiura, Annelida): implications for morphological evolution, the origin of dwarf males, and habitat shifts. Mol. Phylogenet. Evol. 99, 247–260 (2016).

    PubMed  Google Scholar 

  40. 40.

    Phelan, P. Innexins: members of an evolutionarily conserved family of gap-junction proteins. Biochim. Biophys. Acta 1711, 225–245 (2005).

    CAS  PubMed  Google Scholar 

  41. 41.

    Shoshani, L. et al. The polarized expression of Na+, K+-ATPase in epithelia depends on the association between β-subunits located in neighboring cells. Mol. Biol. Cell 16, 1071–1081 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Nielsen, C. Six major steps in animal evolution: are we derived sponge larvae? Evol. Dev. 10, 241–257 (2008).

    PubMed  Google Scholar 

  43. 43.

    Krupinski, T. & Beitel, G. J. Unexpected roles of the Na-K-ATPase and other ion transporters in cell junctions and tubulogenesis. Physiology 24, 192–201 (2009).

    CAS  PubMed  Google Scholar 

  44. 44.

    Arenas-Mena, C., Wong, K. S. & Arandi-Forosani, N. Ciliary band gene expression patterns in the embryo and trochophore larva of an indirectly developing polychaete. Gene Expr. Patterns 7, 544–549 (2007).

    CAS  PubMed  Google Scholar 

  45. 45.

    Jacobs, D. K. et al. Molluscan engrailed expression, serial organization, and shell evolution. Evol. Dev. 2, 340–347 (2000).

    CAS  PubMed  Google Scholar 

  46. 46.

    Shimizu, K., Luo, Y.-J., Satoh, N. & Endo, K. Possible co-option of engrailed during brachiopod and mollusc shell development. Biol. Lett. 13, 20170254 (2017).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Nielsen, C. Trochophora larvae: cell‐lineages, ciliary bands, and body regions. 1. Annelida and Mollusca. J. Exp. Zool. B 302, 35–68 (2004).

    Google Scholar 

  48. 48.

    Phelan, P., Bacon, J. P., Davies, J. A., Stebbings, L. A. & Todman, M. G. Innexins: a family of invertebrate gap-junction proteins. Trends Genet. 14, 348–349 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Simion, P. et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr. Biol. 27, 958–967 (2017).

    CAS  PubMed  Google Scholar 

  50. 50.

    Dunn, C. W. et al. Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 452, 745–749 (2008).

    Google Scholar 

  51. 51.

    Ryan, J. F. et al. The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science 342, 1242592 (2013).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Moroz, L. L. et al. The ctenophore genome and the evolutionary origins of neural systems. Nature 510, 109–114 (2014).

    PubMed Central  Google Scholar 

  53. 53.

    Whelan, N. V., Kocot, K. M., Moroz, L. L. & Halanych, K. M. Error, signal, and the placement of Ctenophora sister to all other animals. Proc. Natl Acad. Sci. USA 112, 5773–5778 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Laumer, C. E. et al. Revisiting metazoan phylogeny with genomic sampling of all phyla. Proc. R. Soc. B 286, 20190831 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Pang, K. & Martindale, M. Q. Ctenophores. Curr. Biol. 18, R1119–R1120 (2008).

    CAS  PubMed  Google Scholar 

  56. 56.

    Nielsen, C. in Evolutionary Ecology of Marine Invertebrate Larvae (eds Carrier, T. J., Reitzel, A. M. & Heyland, A.) Ch. 1, 3–15 (Oxford Univ. Press, 2018).

  57. 57.

    Jägersten, G. Evolution of the Metazoan Life Cycle (Academic Press, 1972).

  58. 58.

    Henry, J. J., Hejnol, A., Perry, K. J. & Martindale, M. Q. Homology of ciliary bands in spiralian trochophores. Integr. Comp. Biol. 47, 865–871 (2007).

    PubMed  Google Scholar 

  59. 59.

    Kean-Howie, J. C., O’Dor, R. K. & Scarratt, D. J. Evolution of feeding strategies throughout the life histories of bivalve molluscs, with emphasis on ontogeny and phylogeny. ICES Mar. Sci. Symp. 199, 5–12 (1995).

    Google Scholar 

  60. 60.

    Christodoulou, F. et al. Ancient animal microRNAs and the evolution of tissue identity. Nature 463, 1084–1088 (2010).

    PubMed Central  Google Scholar 

  61. 61.

    Strathmann, R. R. The evolution and loss of feeding larval stages of marine invertebrates. Evolution 32, 894–906 (1978).

    PubMed  Google Scholar 

  62. 62.

    Bhattachan, P. et al. Ascidian caveolin induces membrane curvature and protects tissue integrity and morphology during embryogenesis. FASEB J. 34, 1345–1361 (2020).

    CAS  PubMed  Google Scholar 

  63. 63.

    Karaiskou, A., Swalla, B. J., Sasakura, Y. & Chambon, J. P. Metamorphosis in solitary ascidians. Genesis 53, 34–47 (2015).

    PubMed  Google Scholar 

  64. 64.

    Domazet-Lošo, T., Brajković, J. & Tautz, D. A phylostratigraphy approach to uncover the genomic history of major adaptations in metazoan lineages. Trends Genet. 23, 533–539 (2007).

    PubMed  Google Scholar 

  65. 65.

    Tautz, D. & Domazet-Lošo, T. The evolutionary origin of orphan genes. Nat. Rev. Genet. 12, 692–702 (2011).

    PubMed  Google Scholar 

  66. 66.

    Wissler, L., Gadau, J., Simola, D. F., Helmkampf, M. & Bornberg-Bauer, E. Mechanisms and dynamics of orphan gene emergence in insect genomes. Genome Biol. Evol. 5, 439–455 (2013).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Palmieri, N., Kosiol, C. & Schlötterer, C. The life cycle of Drosophila orphan genes. eLife 3, e01311 (2014).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Wang, R. & Wang, Z. Science of Marine Shellfsh Culture (China Ocean Univ. Press, 2008).

  69. 69.

    Hu, X. et al. Cloning and characterization of tryptophan 2, 3‐dioxygenase gene of Zhikong scallop Chlamys farreri (Jones and Preston 1904). Aquac. Res. 37, 1187–1194 (2006).

    CAS  Google Scholar 

  70. 70.

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

    CAS  PubMed  Google Scholar 

  71. 71.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq – a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Zambelli, F. et al. RNentropy: an entropy-based tool for the detection of significant variation of gene expression across multiple RNA-Seq experiments. Nucleic Acids Res. 46, e46 (2018).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Chen, S. et al. De novo analysis of transcriptome dynamics in the migratory locust during the development of phase traits. PLoS ONE 5, e15633 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Dutilh, B. E., Huynen, M. A. & Snel, B. A global definition of expression context is conserved between orthologs, but does not correlate with sequence conservation. BMC Genomics 7, 10 (2006).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

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

    Google Scholar 

  79. 79.

    Wang, J. et al. Genome-wide identification and characterization of TRAF genes in the Yesso scallop (Patinopecten yessoensis) and their distinct expression patterns in response to bacterial challenge. Fish Shellfish Immunol. 47, 545–555 (2015).

    CAS  PubMed  Google Scholar 

  80. 80.

    Li, Y. et al. Systematic identification and validation of the reference genes from 60 RNA-Seq libraries in the scallop Mizuhopecten yessoensis. BMC Genomics 20, 288 (2019).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Lalitha, S. Primer premier 5. Biotech. Softw. Internet Rep. 1, 270–272 (2000).

    Google Scholar 

  82. 82.

    Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    PubMed Central  Google Scholar 

  83. 83.

    Aguilera, F., McDougall, C. & Degnan, B. M. Co-option and de novo gene evolution underlie molluscan shell diversity. Mol. Biol. Evol. 34, 779–792 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Domazet-Lošo, T. et al. No evidence for phylostratigraphic bias impacting inferences on patterns of gene emergence and evolution. Mol. Biol. Evol. 34, 843–856 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Domazet-Lošo, T. & Tautz, D. An ancient evolutionary origin of genes associated with human genetic diseases. Mol. Biol. Evol. 25, 2699–2707 (2008).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Drost, H.-G., Gabel, A., Liu, J., Quint, M. & Grosse, I. J. B. myTAI: evolutionary transcriptomics with R. Bioinformatics 34, 1589–1590 (2017).

    PubMed Central  Google Scholar 

  87. 87.

    Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).

    Google Scholar 

  88. 88.

    Zhang, G. et al. The oyster genome reveals stress adaptation and complexity of shell formation. Nature 490, 49–54 (2012).

    PubMed  Google Scholar 

  89. 89.

    Park, C. et al. The developmental transcriptome atlas of the spoon worm Urechis unicinctus (Echiurida: Annelida). Gigascience 7, giy007 (2018).

    PubMed Central  Google Scholar 

  90. 90.

    Hou, X. et al. Transcriptome analysis of larval segment formation and secondary loss in the echiuran worm Urechis unicinctus. Int. J. Mol. Sci. 20, 1806 (2019).

    CAS  PubMed Central  Google Scholar 

  91. 91.

    Li, Y. et al. Sea cucumber genome provides insights into saponin biosynthesis and aestivation regulation. Cell Discov. 4, 29 (2018).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Leclère, L. et al. The genome of the jellyfish Clytia hemisphaerica and the evolution of the cnidarian life-cycle. Nat. Ecol. Evol. 3, 801–810 (2019).

    PubMed  Google Scholar 

  93. 93.

    Gaiti, F. et al. Dynamic and widespread lncRNA expression in a sponge and the origin of animal complexity. Mol. Biol. Evol. 32, 2367–2382 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sebé-Pedrós, A. et al. Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nat. Ecol. Evol. 2, 1176–1188 (2018).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Marlow, H. in Evolutionary Ecology of Marine Invertebrate Larvae (eds Carrier, T. J., Reitzel, A. M. & Heyland, A.) Ch. 2, 16–33 (Oxford Univ. Press, 2018).

Download references

Acknowledgements

We thank W. Liu and Y. Sun for help with SEM analysis of trochophore larvae and C. Cui for help with transcriptomic data analyses. We thank C. Zhao for providing zebrafish embryos and D. Chourrout for helpful discussions. We acknowledge support from the National Natural Science Foundation of China (grant no. U1706203), National Key Research and Development Project (grant no. 2018YFD0900206), National Natural Science Foundation of China (grant no. 31871499), Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (grant no. 2018SDKJ0302-1), major basic research projects of the Shandong Natural Science Foundation (grant no. ZR2018ZA0748), Fundamental Research Funds for the Central Universities (grant nos. 201762001 and 201841001), China Postdoctoral Science Foundation Funded Project (grant no. 2018M642702) and the Taishan Scholar Project Fund of Shandong Province of China.

Author information

Affiliations

Authors

Contributions

S.W. and Z.B. conceived and designed the study. J. Wang, P.L., Y.X. and Y.L. prepared the libraries for transcriptome sequencing. J. Wang, S.W., L. Zhang, S. Lian, X.D., N.H., Q.Z. and S. Liu participated in the landscape profiling of molluscan transcriptomes and transcriptomic analyses of larval evolution. Z.Q., X.Z. and D.K. conducted in situ hybridization experiments. C.K. and Z.H. provided abalone transcriptome data. J. Wei and B.D. provided sea squirt transcriptome data. Z.Q. and Z. Zhang provided spoon worm transcriptome data. Z. Zhou and Y.D. facilitated sample collection and transcriptome analysis of sea cucumber. L. Zhao, Q.X., J. Wang and Y.X. participated in scallop culture and sample collection. B.D., Z.B., X. Huang and X. Hu participated in discussions and provided suggestions for manuscript improvement. S.W., J. Wang, L. Zhang and S. Lian did most of the writing with input from other authors.

Corresponding author

Correspondence to Shi Wang.

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.

Extended data

Extended Data Fig. 1 Summary of expressed genes in various ontogenetic stages of P. yessoensis.

(a) Totally 19,189 genes are expressed (TPM>1) in at least one of 16 ontogenetic transcriptomes and 17.5% of them are universally expressed in all the samples. (b) Comparison of expressed genes among various stages of the whole life cycle.

Extended Data Fig. 2 Expression profiling of transcription factors across ontogenetic stages.

(a) Distribution of gene expression levels for transcription factors (TFs) (orange) and all genes (blue) across different ontogenetic stages. In all samples, TF genes have higher average expression than all genes. The black lines inside the box indicate the median values, and the whiskers extend from the first or third quartiles to the minimum or maximum values. (b) Numbers of TFs expressed in each sample (blue bars) and the proportion of expressed TFs versus all expressed genes, given as a percentage (red points). The numbers of expressed TFs vary in different samples, ranging from 331 in the 2-8cell stage to 577 in the juvenile scallop. In all samples, TFs constitute ~4% of all expressed genes. (c) The expression of ubiquitous TFs spans almost six orders of magnitude while the expression of stage-restricted expressed TFs was usually high but restricted in certain ontogenetic stages. For each ontogenetic stage, gene names are shown for the top five TFs with the largest fold change relative to the average expression level of all other stages. The ubiquitous and restricted TFs are indicated in blue and red, respectively.

Extended Data Fig. 3 Distribution of young/novel genes among 11 molluscan mantle transcriptomes.

Histograms show the distribution of old genes (blue) and young (Bivalvia-specific or Gastropoda-specific) or novel (species-specific) genes (red) among top500 highly expressed genes for each molluscan species. There is a general tendency of higher representation of young/novel genes over old genes among 11 molluscan mantle transcriptomes. The statistical significance was evaluated using the Chi-Square test. **, p <0.05; *, p <0.01.

Extended Data Fig. 4 Distribution of young/novel genes in the group of mantle-exclusive expressed genes and the group of trochophore-mantle shared expressed genes.

Histograms show the distribution of young/novel genes in the group of mantle-exclusive expressed genes (blue) and the group of trochophore-mantle shared genes (red). There is significantly higher representation of novel or young genes in the group of trochophore-mantle shared genes over the group of mantle-exclusive expressed genes (one-sided paired t-test, p-value = 0.008).

Extended Data Fig. 5 Expression patterns of four highly expressed novel genes at scallop trochophore stage.

(a) Whole mount in situ hybridization of four novel genes (T21316, T16918, T04332, T22991). The spatial expression patterns of T21316 and T16918 showed full-range of the shell field while T04332 and T22991 showed marginal expression around the shell field, which suggested their involvement in the early formation of shell field at the trochophore stage. (b) Expression levels of these novel genes showed their high (TPM: 5,475-40,410) and restricted expression in trochophore but drastic decreased expression at fully shell-formed larval stages and barely no expression in adult mantle (TPM: 0.1-1.2).

Extended Data Fig. 6 Transcriptome age index (TAI) across the ontogeny of eight primary larva-bearing animals.

The TAI values of developmental stages are shown for all individual phylostratum levels. The “young-larval-transcriptome” feature is generally observed across Metazoa, supporting the evolutionarily latter appearance of primary larval stages. For H. discus hannai, although such feature is not evident at the last PS level (ps11), it is evident at older PS levels (e.g., ps4-ps10). E, L and AL represent embryonic, larva and adult-like stages.

Extended Data Fig. 7 Spatial expression of larva-related genes in the trochophore larvae of mollusc P. yessoensis and annelid U. unicinctus, in comparison to the direct-developing vertebrate D. rerio.

Whole mount in situ hybridization of caveolin, innexin and ATP1B genes (with the highest expression at the trochophore stage) in the mollusc P. yessoensis and the annelid U. unicinctus. In comparison, spatial expression patterns of two ATP1B genes (ATP1B1a, ATP1B2a) are shown for three ontogenetic stages (early/mid/late somitogenesis after gastrulation) of the direct-developing vertebrate D. rerio. The major expression sites of caveolin and innexin genes were at the apical organ and ciliary bands – the major larval features of trochophore. The expression of ATP1B largely corresponds to the forthcoming adult territory in both indirect-developing and direct-developing animals. No signal in the control group using sense probes confirmed the reliability of the positive signals. at, apical tuft; pt, prototroch; sfi, shell field invagination; tt, telotroch; cc, circumpharyngeal connective; vn, ventral nerve cord; 12 S, 12-somite stage; 18 S, 18-somite stage; 24 hpf, 24 hours post fertilization.

Extended Data Fig. 8 Ontogenetic expression profiling of trochozoan larva-related genes and their spatial expression in early developmental stages of scallop.

(ad) Expression levels of caveolin, innexin and ATP1B genes in four trochozoans P. yessoensis, U. unicinctus, H. discus hannai and C. gigas. In comparison. ATP1B shows stronger early-expression over caveolin/innexin during early developmental stages. (e) Left panel: whole mount in situ hybridization of caveolin, innexin and ATP1B genes in early developmental stages of scallop. The major expression sites of caveolin and innexin genes were largely correspond to the regions with forthcoming larval characteristic features whereas the expression of ATP1B largely corresponds to the forthcoming adult territory (sfi /sf). No signal in the control group using sense probes confirmed the reliability of observed positive signals. Right panel: the scanning electron microscopy (SEM) micrograph of the trochophora larva of scallop P. yessoensis. sfi, shell field invagination; at, apical tuft; pt, prototroch; sf, shell field; cf, ciliated field.

Extended Data Fig. 9 Comparison of relative larval TAI contribution between ps4 and other phylostratum levels.

Across eight metazoan species, the larval TAI contribution by the genes of ps4 is significantly larger than those by other phylotratum levels (except ps11), implicating ps4 (Metazoa) as the potential timing of single proto-larva origin. For each phylotratum, the larval TAI contribution was normalized by fold difference relative to the average value across eight metazoan species. The lines inside the box indicate the median values, the whiskers extend from the first or third quartiles to the minimum or maximum values and the dots show individual data points. For comparisons of ps4 with other phylostratum levels, statistical significance based on one-sided t-test is shown on the top of each box.

Extended Data Fig. 10 Across-Metazoa TAI comparison of three developmental groups (embryonic, primary larva and adult-like/adult).

Based on the TAI data from eight metazoan species, box plots show significantly high TAIs contributed by either all genes (a) or the genes of ps4 (b) in the larval group over embryonic and adult-like/adult groups. For each species, TAI value was normalized by fold difference relative to the average value of TAIs(N) across the ontogeny. The lines inside the box indicate the median values, the whiskers extend from the first or third quartiles to the minimum or maximum values and the dots show individual data points. Statistical significance (p-value based on one-sided t-test) is shown above each box pair in comparison.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Tables 1, 2, 9, 10, 13, 18–21 and 25–27.

Reporting Summary

Supplementary Tables

Supplementary Tables 3–8, 11, 12, 14–17, 22–24 and 28–31.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Zhang, L., Lian, S. et al. Evolutionary transcriptomics of metazoan biphasic life cycle supports a single intercalation origin of metazoan larvae. Nat Ecol Evol 4, 725–736 (2020). https://doi.org/10.1038/s41559-020-1138-1

Download citation

Further reading

Search

Quick links