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Exaptation of an evolutionary constraint enables behavioural control over the composition of secreted venom in a giant centipede

Nature Ecology & Evolution volume 9, pages 73–86 (2025)Cite this article

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Abstract

Venoms are biochemical arsenals that have emerged in numerous animal lineages, where they have co-evolved with morphological and behavioural traits for venom production and delivery. In centipedes, venom evolution is thought to be constrained by the morphological complexity of their venom glands due to physiological limitations on the number of toxins produced by their secretory cells. Here we show that the uneven toxin expression that results from these limitations have enabled Scolopendra morsitans to regulate the composition of their secreted venom despite the lack of gross morphologically complex venom glands. We show that this control is probably achieved by a combination of this heterogenous toxin distribution with a dual mechanism of venom secretion that involves neuromuscular innervation as well as stimulation via neurotransmitters. Our results suggest that behavioural control over venom composition may be an overlooked aspect of venom biology and provide an example of how exaptation can facilitate evolutionary innovation and novelty.

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Fig. 1: Different venom collection techniques lead to compositionally and functionally different venoms.
Fig. 2: S. morsitans has functionally distinct toxins but not functionally differentiated venom glands.
Fig. 3: Serotonin and a forcipular nerve may be involved in venom secretion.
Fig. 4: Volumetric reconstruction of venom gland subunits and proposed secretion mechanism.

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Data availability

Data are available from the corresponding author upon request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE49 partner repository with the dataset identifier PXD048264. Transcriptome data used can be found under NCBI BioProject accessions PRJNA200640 and PRJNA540703. MSI and VolumeScope data are available through UQ eSpace at https://doi.org/10.48610/0156525 (ref. 50).

Code availability

All custom code used are included in Supplementary Data 2 and 3.

References

  1. Schendel, V., Rash, L. D., Jenner, R. A. & Undheim, E. A. B. The diversity of venom: the importance of behavior and venom system morphology in understanding its ecology and evolution. Toxins 11, 666 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zancolli, G. & Casewell, N. R. Venom systems as models for studying the origin and regulation of evolutionary novelties. Mol. Biol. Evol. 37, 2777–2790 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Inceoglu, B. et al. One scorpion, two venoms: prevenom of Parabuthus transvaalicus acts as an alternative type of venom with distinct mechanism of action. Proc. Natl Acad. Sci. USA 100, 922–927 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Walker, A. A. et al. The assassin bug Pristhesancus plagipennis produces distinct venoms in separate gland lumens. Nat. Commun. 9, 755 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Dutertre, S. et al. Evolution of separate predation- and defence-evoked venoms in carnivorous cone snails. Nat. Commun. 5, 3521 (2014).

  6. Hamilton, B. R. et al. Mapping enzyme activity on tissue by functional mass spectrometry imaging. Angew. Chem. 132, 3883–3886 (2020).

    Article  Google Scholar 

  7. Rosenberg, J. & Hilken, G. Fine structural organization of the poison gland of Lithobius forficatus (Chilopoda, Lithobiomorpha). Nor. J. Entomol. 53, 119–127 (2006).

    Google Scholar 

  8. Dugon, M. M. & Arthur, W. Comparative studies on the structure and development of the venom-delivery system of centipedes and a hypothesis on the origin of this evolutionary novelty. Evol. Dev. 14, 128–137 (2012).

    Article  PubMed  Google Scholar 

  9. Undheim, E. A. B. et al. Production and packaging of a biological arsenal: evolution of centipede venoms under morphological constraint. Proc. Natl Acad. Sci. USA 112, 4026–4031 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jami, S., Erickson, A., Brierley, S. M. & Vetter, I. Pain-causing venom peptides: insights into sensory neuron pharmacology. Toxins 10, 15 (2018).

  11. Yin, K., Baillie, G. J. & Vetter, I. Neuronal cell lines as model dorsal root ganglion neurons: a transcriptomic comparison. Mol. Pain 12, 1744806916646111 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Undheim, E. A. B. & King, G. F. On the venom system of centipedes (Chilopoda), a neglected group of venomous animals. Toxicon 57, 512–524 (2011).

    Article  CAS  PubMed  Google Scholar 

  13. Hanselmann, M. et al. Concise representation of mass spectrometry images by probabilistic latent semantic analysis. Anal. Chem. 80, 9649–9658 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Müller, C. H. G., Rosenberg, J. & Hilken, G. Ultrastructure, functional morphology and evolution of recto-canal epidermal glands in Myriapoda. Arthropod Struct. Dev. 43, 43–61 (2014).

    Article  PubMed  Google Scholar 

  15. Baumann, O., Kühnel, D., Dames, P. & Walz, B. Dopaminergic and serotonergic innervation of cockroach salivary glands: distribution and morphology of synapses and release sites. J. Exp. Biol. 207, 2565–2575 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Ali, D. W. The aminergic and peptidergic innervation of insect salivary glands. J. Exp. Biol. 200, 1941–1949 (1997).

    Article  CAS  PubMed  Google Scholar 

  17. Duboscq, O. in Archives De Zoologie Expérimentale Et Générale Vol. 3 (ed. de Lacaze-Duthiers, H.) 481–655 (Centra National de la Recherche Scientifique, 1898).

  18. Rilling, G. Lithobius forficatus (Gustav Fischer Verlag, 1968).

  19. Proctor, G. B. & Carpenter, G. H. Salivary secretion: mechanism and neural regulation. Monogr. Oral Sci. 24, 14–29 (2014).

    Article  PubMed  Google Scholar 

  20. Hu, Y., Converse, C., Lyons, M. C. & Hsu, W. H. Neural control of sweat secretion: a review. Br. J. Dermatol. 178, 1246–1256 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Brown, J. W. & McKnight, C. J. Molecular model of the microvillar cytoskeleton and organization of the brush border. PLoS ONE 5, e9406 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mooseker, M. S. Brush border motility: microvillar contraction in Triton-treated brush borders isolated from intestinal epithelium. J. Cell Biol. 71, 417–433 (1976).

    Article  CAS  PubMed  Google Scholar 

  23. Turcato, A. & Minelli, A. Fine structure of the ventral glands of Pleurogeophilus mediterraneus (Meinert) (Chilopoda, Geophilomorpha). In Proc. 7th International Congress of Myriapodology (ed. Minelli, A.) 165–173 (E. J. Brill, 1990).

  24. Dass, C. M. S. & Jangi, B. S. Ultrastructural organization of the poison gland of the centipede Scolopendra morsitans Linn. Indian J. Exp. Biol. 16, 748–757 (1978).

    Google Scholar 

  25. Antoniazzi, M. M. et al. Comparative morphological study of the venom glands of the centipede Cryptops iheringi, Otostigmus pradoi and Scolopendra viridicornis. Toxicon 53, 367–374 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Just, F. & Walz, B. The effects of serotonin and dopamine on salivary secretion by isolated cockroach salivary glands. J. Exp. Biol. 199, 407–413 (1996).

    Article  CAS  PubMed  Google Scholar 

  27. Hilken, G. & Rosenberg, J. Ultrastructural investigation of a salivary gland in a centipede: structure and origin of the maxilla I-gland of Scutigera coleoptrata (Chilopoda, Notostigmophora). J. Morphol. 267, 375–381 (2006).

    Article  PubMed  Google Scholar 

  28. Kazandjian, T. D. et al. Physiological constraints dictate toxin spatial heterogeneity in snake venom glands. BMC Biol. 20, 148 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Safavi-Hemami, H. et al. Combined proteomic and transcriptomic interrogation of the venom gland of Conus geographus uncovers novel components and functional compartmentalization. Mol. Cell Proteom. 13, 938–953 (2014).

    Article  CAS  Google Scholar 

  30. Marshall, J. et al. Anatomical correlates of venom production in Conus californicus. Biol. Bull. 203, 27–41 (2002).

    Article  PubMed  Google Scholar 

  31. Basak, O. et al. Induced quiescence of Lgr5+ stem cells in intestinal organoids enables differentiation of hormone-producing enteroendocrine cells. Cell Stem Cell 20, 177–190 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2023).

  33. Tang, Y., Horikoshi, M. & Li, W. ggfortify: unified interface to visualize statistical results of popular R packages. R J. 8, 478–489 (2016).

    Article  Google Scholar 

  34. Candiano, G. et al. Blue silver: a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis 25, 1327–1333 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Hale, J. E., Butler, J. P., Gelfanova, V., You, J. S. & Knierman, M. D. A simplified procedure for the reduction and alkylation of cysteine residues in proteins prior to proteolytic digestion and mass spectral analysis. Anal. Biochem. 333, 174–181 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Undheim, E. A. B. et al. Clawing through evolution: toxin diversification and convergence in the ancient lineage Chilopoda (centipedes). Mol. Biol. Evol. 31, 2124–2148 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jenner, R. A., von Reumont, B. M., Campbell, L. I. & Undheim, E. A. B. Parallel evolution of complex centipede venoms revealed by comparative proteotranscriptomic analyses. Mol. Biol. Evol. 36, 2748–2763 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. C. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell Proteom. 5, 144–156 (2006).

    Article  CAS  Google Scholar 

  39. Vetter, I. & Lewis, R. J. Characterization of endogenous calcium responses in neuronal cell lines. Biochem. Pharmacol. 79, 908–920 (2010).

    Article  CAS  PubMed  Google Scholar 

  40. Undheim, E. A. B. et al. Multifunctional warheads: diversification of the toxin arsenal of centipedes via novel multidomain transcripts. J. Proteom. 102, 1–10 (2014).

    Article  CAS  Google Scholar 

  41. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Minh, B. Q., Nguyen, M. A. T. & Von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Han, M. V. & Zmasek, C. M. phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinf. 10, 356 (2009).

  46. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Cardona, A. et al. TrakEM2 software for neural circuit reconstruction. PLoS ONE 7, e38011 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Saalfeld, S., Cardona, A., Hartenstein, V. & Tomančák, P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25, 1984 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).

    Article  CAS  PubMed  Google Scholar 

  50. Schendel, V. Dataset—Exaptation of an evolutionary constraint enables behavioural control over the composition of secreted venom in a giant centipede. UQ eSpace https://doi.org/10.48610/0156525 (2024).

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Acknowledgements

This work was supported by the Australian Research Council (DECRA Fellowship DE160101142 and Discovery Grant DP160104025 to E.A.B.U.), the Norwegian Research Council (FRIPRO-YRT Fellowship no. 287462 to E.A.B.U.), the European Research Council (ERC Starting Grant 101039862 to E.A.B.U.) and the University of Queensland (International UQ Research Training PhD Scholarship to V.S.). We acknowledge the facilities and the scientific and technical assistance of the Australian Microscopy & Microanalysis Research Facility at the Centre for Microscopy and Microanalysis, The University of Queensland. We thank R. Sullivan (Queensland Brain Institute, The University of Queensland) for paraffin embedding samples for MSI. Computations were in part performed on resources provided by Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway.

Author information

Authors and Affiliations

  1. Centre for Advanced Imaging, University of Queensland, St. Lucia, Queensland, Australia

    Vanessa Schendel, Brett R. Hamilton & Samuel D. Robinson

  2. Institute for Molecular Bioscience, University of Queensland, St. Lucia, Queensland, Australia

    Vanessa Schendel, Samuel D. Robinson, Darren Brown, Jennifer L. Stow & Irina Vetter

  3. Centre for Microscopy and Microanalysis, University of Queensland, St. Lucia, Queensland, Australia

    Brett R. Hamilton, Kathryn Green & Marcel E. Sayre

  4. School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia

    Marcel E. Sayre

  5. Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway

    Jan Philip Øyen & Eivind A. B. Undheim

  6. Natural History Museum, University of Oslo, Oslo, Norway

    Kjetil L. Voje

  7. School of Biomedical Sciences, University of Queensland, St. Lucia, Queensland, Australia

    S. Sean Millard & Lachlan D. Rash

  8. School of Pharmacy, University of Queensland, Woolloongabba, Queensland, Australia

    Irina Vetter

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Contributions

E.A.B.U. conceived the study. E.A.B.U. and V.S. designed the study. V.S., B.R.H., S.D.R., K.G., M.E.S., D.B., J.L.S., J.P.O., K.L.V., S.S.M., I.V., L.D.R. and E.A.B.U. contributed to the acquisition and analysis of data. V.S. authored the main draft. All authors contributed to writing the manuscript and approve its final form.

Corresponding author

Correspondence to Eivind A. B. Undheim.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10, Tables 1 and 2, legends for Data 1–3 and Video 1.

Reporting Summary

Supplementary Data 1

Summaries of MALDI peak intensities from EV and DV, as well as proteomic protein abundance estimates for venom components identified in EV, DV and venom gland depletion experiments. (A) Compositional comparison of all EV and all DV, as shown in Fig. 1a. (B) Means of three most abundant (area) peptides for each protein. Areas are shown as parts per million of each respective sample’s summed areas of all high-confidence peptides assigned non-contaminant and non-decoy proteins in the peptide summary. Values were summarized from trimmed ProteinPilot peptide summaries provided in PRIDE under the accession PXD048264 using Supplementary Data 3. This table was used to generate Venn diagrams in Fig. 1a and PCA shown in Fig. 1b. (C) The log2-transformed values from (B), used to label phylogenetic trees in Fig. 1f and Supplementary Figs. 3–5. (D) Peak intensities from aligned MALDI MS spectra, used for PCA in Fig. 1c. (E) Areas of each of the three most abundant peptides selected for each protein in the depletion experiment, as selected using Supplementary Data 2, which have been normalized as parts per million of the total area of each sample in (F). The mean normalized parts per million is shown in (G), which was the table used for PCA shown in Fig. 2, Supplementary Fig. 6 and Supplementary Tables 1 and 2.

Supplementary Data 2

Findtop3.txt. Perl script used to extract top three unique peptides from ProteinPilot peptide summary.

Supplementary Data 3

Summarise_peptides.sh. Shell script used to summarize ProteinPilot peptide summaries from shotgun proteomic analyses of EV and DV.

Supplementary Video 1

The 3D reconstruction of S. morsitans venom gland imaged by MSI. Video of a ‘full’ venom gland. The distributions of four different venom gland components: m/z 6,005, 3,337, 3,640 and 5,815 are visualized as heatmaps: minimum (blue)–maximum (red).

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Schendel, V., Hamilton, B.R., Robinson, S.D. et al. Exaptation of an evolutionary constraint enables behavioural control over the composition of secreted venom in a giant centipede. Nat Ecol Evol 9, 73–86 (2025). https://doi.org/10.1038/s41559-024-02556-9

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