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.

  • Article
  • Published:

Seasonal variation in UVA light drives hormonal and behavioural changes in a marine annelid via a ciliary opsin

Nature Ecology & Evolution volume 5, pages 204–218 (2021)Cite this article

Subjects

Abstract

The right timing of animal physiology and behaviour ensures the stability of populations and ecosystems. To predict anthropogenic impacts on these timings, more insight is needed into the interplay between environment and molecular timing mechanisms. This is particularly true in marine environments. Using high-resolution, long-term daylight measurements from a habitat of the marine annelid Platynereis dumerilii, we found that temporal changes in ultraviolet A (UVA)/deep violet intensities, more than longer wavelengths, can provide annual time information, which differs from annual changes in the photoperiod. We developed experimental set-ups that resemble natural daylight illumination conditions, and automated, quantifiable behavioural tracking. Experimental reduction of UVA/deep violet light (approximately 370–430 nm) under a long photoperiod (16 h light and 8 h dark) significantly decreased locomotor activities, comparable to the decrease caused by a short photoperiod (8 h light and 16 h dark). In contrast, altering UVA/deep violet light intensities did not cause differences in locomotor levels under a short photoperiod. This modulation of locomotion by UVA/deep violet light under a long photoperiod requires c-opsin1, a UVA/deep violet sensor employing Gi signalling. C-opsin1 also regulates the levels of rate-limiting enzymes for monogenic amine synthesis and of several neurohormones, including pigment-dispersing factor, vasotocin (vasopressin/oxytocin) and neuropeptide Y. Our analyses indicate a complex inteplay between UVA/deep violet light intensities and photoperiod as indicators of annual time.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Light intensity ratios provide seasonal information that is different from that provided by the photoperiod.
Fig. 2: UVA/deep violet light affects locomotor behaviour in P. dumerilii.
Fig. 3: P. dumerilii c-opsin1 mediates UVA/deep violet light input via Gi signalling and regulates locomotor activity.
Fig. 4: Loss of c-opsin1 affects brain hormone synthesis.

Similar content being viewed by others

Data availability

All of the data generated or analysed during this study are included within the published article and its Supplementary Information files. The targeted mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via Panorama Public99 with the identifier PXD014682. The light and temperature measurements and untargeted proteomics raw data have been deposited at Dryad (https://doi.org/10.5061/dryad.73n5tb2vv).

References

  1. Korringa, P. Relations between the moon and periodicity in the breeding of marine animals. Ecol. Monogr. 17, 347–381 (1947).

    Article  Google Scholar 

  2. Numata, H. & Helm, B. Annual, Lunar, and Tidal Clocks: Patterns and Mechanisms of Nature’s Enigmatic Rhythms (Springer, 2014).

  3. Tessmar-Raible, K., Raible, F. & Arboleda, E. Another place, another timer: marine species and the rhythms of life. Bioessays 33, 165–172 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shlesinger, T. & Loya, Y. Breakdown in spawning synchrony: a silent threat to coral persistence. Science 365, 1002–1007 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Humphries, M. M., Studd, E. K., Menzies, A. K. & Boutin, S. To everything there is a season: summer-to-winter food webs and the functional traits of keystone species. Integr. Comp. Biol. 57, 961–976 (2017).

    Article  PubMed  Google Scholar 

  6. Burthe, S. et al. Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web. Mar. Ecol. Prog. Ser. 454, 119–133 (2012).

    Article  Google Scholar 

  7. Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313 (2010).

    Article  Google Scholar 

  8. Monecke, S. et al. Circannual phase response curves to short and long photoperiod in the European hamster. J. Biol. Rhythms 24, 413–426 (2009).

    Article  PubMed  Google Scholar 

  9. Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Beaugrand, G., Brander, K. M., Lindley, J. A., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Soreide, J. E., Leu, E., Berge, J., Graeve, M. & Falk-Petersen, S. Timing of blooms, algal food quality and Calanus glacialis reproduction and growth in a changing Arctic. Glob. Change Biol. 16, 3154–3163 (2010).

    Google Scholar 

  13. Häfker, N. S. & Tessmar-Raible, K. Rhythms of behavior: are the times changin’? Curr. Opin. Neurobiol. 60, 55–66 (2020).

    Article  PubMed  CAS  Google Scholar 

  14. Schiesari, L., Kyriacou, C. P. & Costa, R. The hormonal and circadian basis for insect photoperiodic timing. FEBS Lett. 585, 1450–1460 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Collins, B. H., Rosato, E. & Kyriacou, C. P. Seasonal behavior in Drosophila melanogaster requires the photoreceptors, the circadian clock, and phospholipase C. Proc. Natl Acad. Sci. USA 101, 1945–1950 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tataroglu, O. & Emery, P. Studying circadian rhythms in Drosophila melanogaster. Methods 68, 140–150 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hegazi, S. et al. A symphony of signals: intercellular and intracellular signaling mechanisms underlying circadian timekeeping in mice and flies. Int. J. Mol. Sci. 20, 2363 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  18. Hastings, M. H., Maywood, E. S. & Brancaccio, M. The mammalian circadian timing system and the suprachiasmatic nucleus as its pacemaker. Biology (Basel) 8, 13 (2019).

    CAS  Google Scholar 

  19. Dardente, H., Wood, S., Ebling, F. & de Miera, C. S. An integrative view of mammalian seasonal neuroendocrinology. J. Neuroendocrinol. 31, e12729 (2019).

    Article  PubMed  CAS  Google Scholar 

  20. Ranzi, S. Ricerche sulla biologia sessuale degli Anellidi. Pubbl. Staz. Zool. Napoli 11, 271–292 (1931).

    Google Scholar 

  21. Ranzi, S. Maturita sessuale degli Anellidi e fasi lunari. Boll. Soc. Ital. Biol. Sper. 6, 18 (1931).

    Google Scholar 

  22. Zantke, J. et al. Circadian and circalunar clock interactions in a marine annelid. Cell Rep. 5, 99–113 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zantke, J., Oberlerchner, H. & Tessmar-Raible, K. in Annual, Lunar and Tidal Clocks: Patterns and Mechanisms of Nature’s Enigmatic Rhythms (eds Numata, H. & Helm, B.) (Springer Japan, 2015).

  24. Fischer, A. & Dorresteijn, A. W. C. The polychaete Platynereis dumerilii (Annelida): a laboratory animal with spiralian cleavage, lifelong segment proliferation and a mixed benthic/pelagic life cycle. Bioessays 3, 314–325 (2004).

    Article  Google Scholar 

  25. Zantke, J., Bannister, S., Veedin Rajan, V. B., Raible, F. & Tessmar-Raible, K. Genetic and genomic tools for the marine annelid Platynereis dumerilii. Genetics 197, 9–31 (2014).

    Article  CAS  Google Scholar 

  26. Gambi, M. C., Lorenti, M., Russo, G. F., Scipione, M. B. & Zupo, V. Depth and seasonal distribution of some groups of the vagile fauna of the Posidonia oceanica leaf stratum: structural and trophic analyses. Mar. Ecol. 13, 17–39 (1992).

    Article  Google Scholar 

  27. Somaschini, A. et al. Characterization and cartography of some Mediterranean soft-bottom benthic communities (Ligurian Sea, Italy). Sci. Mar. 62, 27–36 (1998).

    Article  Google Scholar 

  28. Galparsoro, I. et al. in Seafloor Geomorphology as Benthic Habitat (eds Harris, P. T. & Baker, E. K.) 493–507 (Elsevier, 2012).

  29. Ribera d’Alcalà, M. et al. Seasonal patterns in plankton communities in a pluriannual time series at a coastal Mediterranean site (Gulf of Naples): an attempt to discern recurrences and trends. Sci. Mar. 68, 65–83 (2004).

    Article  Google Scholar 

  30. Hut, R. A., Paolucci, S., Dor, R., Kyriacou, C. P. & Daan, S. Latitudinal clines: an evolutionary view on biological rhythms. Proc. R. Soc. B Biol. Sci. 280, 20130433 (2013).

    Article  Google Scholar 

  31. Dekens, M. P., Foulkes, N. S. & Tessmar-Raible, K. Instrument design and protocol for the study of light controlled processes in aquatic organisms, and its application to examine the effect of infrared light on zebrafish. PLoS ONE 12, e0172038 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Kojima, D. et al. UV-sensitive photoreceptor protein OPN5 in humans and mice. PLoS ONE 6, e26388 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sato, K. et al. Two UV-sensitive photoreceptor proteins, Opn5m and Opn5m2 in ray-finned fish with distinct molecular properties and broad distribution in the retina and brain. PLoS ONE 11, e0155339 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Yamashita, T. et al. Opn5 is a UV-sensitive bistable pigment that couples with Gi subtype of G protein. Proc. Natl Acad. Sci. USA 107, 22084–22089 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ni, J. D., Baik, L. S., Holmes, T. C. & Montell, C. A rhodopsin in the brain functions in circadian photoentrainment in Drosophila. Nature 545, 340–344 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zwinkels, J. in Encyclopedia of Color Science and Technology (ed. Luo, R.) 1–8 (Springer, 2015).

  37. Maverakis, E. et al. Light, including ultraviolet. J. Autoimmun. 34, J247–J257 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Ricevuto, E., Kroeker, K. J., Ferrigno, F., Micheli, F. & Gambi, M. C. Spatio-temporal variability of polychaete colonization at volcanic CO2 vents indicates high tolerance to ocean acidification. Mar. Biol. 161, 2909–2919 (2014).

    Article  CAS  Google Scholar 

  39. Chapman, J. W., Reynolds, D. R. & Wilson, K. Long-range seasonal migration in insects: mechanisms, evolutionary drivers and ecological consequences. Ecol. Lett. 18, 287–302 (2015).

    Article  PubMed  Google Scholar 

  40. Staples, J. F. Metabolic flexibility: hibernation, torpor, and estivation. Compr. Physiol. 6, 737–771 (2016).

    Article  PubMed  Google Scholar 

  41. Putman, N. Marine migrations. Curr. Biol. 28, R972–R976 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Alerstam, T. & Backman, J. Ecology of animal migration. Curr. Biol. 28, R968–R972 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Yokota, T. & Oishi, T. Seasonal change in the locomotor activity rhythm of the medaka, Oryzias latipes. Int. J. Biometeorol. 36, 39–44 (1992).

    Article  CAS  PubMed  Google Scholar 

  44. Foa, A. et al. Seasonal changes of locomotor activity patterns in ruin lizards Podarcis sicula. I. Endogenous control by the circadian system. Behav. Ecol. Sociobiol. 34, 267–274 (1994).

    Article  Google Scholar 

  45. Sztarker, J. & Tomsic, D. Neuronal correlates of the visually elicited escape response of the crab Chasmagnathus upon seasonal variations, stimuli changes and perceptual alterations. J. Comp. Physiol. A 194, 587–596 (2008).

    Article  Google Scholar 

  46. Varpe, O. Life history adaptations to seasonality. Integr. Comp. Biol. 57, 943–960 (2017).

    Article  PubMed  Google Scholar 

  47. Last, K. S., Olive, P. J. W. & Edwards, A. J. An actographic study of diel activity in the semelparous polychaete Nereis (Neanthes) virens Sars in relation to the annual cycle of growth and reproduction. Invertebr. Reprod. Dev. 35, 141–145 (1999).

    Article  Google Scholar 

  48. Last, K. S. & Olive, P. J. Interaction between photoperiod and an endogenous seasonal factor in influencing the diel locomotor activity of the benthic polychaete Nereis virens. Biol. Bull. 206, 103–112 (2004).

    Article  PubMed  Google Scholar 

  49. Arendt, D., Tessmar-Raible, K., Snyman, H., Dorresteijn, A. W. & Wittbrodt, J. Ciliary photoreceptors with a vertebrate-type opsin in an invertebrate brain. Science 306, 869–871 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Tsukamoto, H., Chen, I. S., Kubo, Y. & Furutani, Y. A ciliary opsin in the brain of a marine annelid zooplankton is ultraviolet-sensitive, and the sensitivity is tuned by a single amino acid residue. J. Biol. Chem. 292, 12971–12980 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Veraszto, C. et al. Ciliary and rhabdomeric photoreceptor-cell circuits form a spectral depth gauge in marine zooplankton. eLife 7, e36440 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bailes, H. J. & Lucas, R. J. Human melanopsin forms a pigment maximally sensitive to blue light (λmax ≈ 479 nm) supporting activation of Gq/11 and Gi/o signalling cascades. Proc. R. Soc. B Biol. Sci. 280, 20122987 (2013).

    Article  CAS  Google Scholar 

  53. Bailes, H. J., Zhuang, L. Y. & Lucas, R. J. Reproducible and sustained regulation of Gαs signalling using a metazoan opsin as an optogenetic tool. PLoS ONE 7, e30774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sugihara, T., Nagata, T., Mason, B., Koyanagi, M. & Terakita, A. Absorption characteristics of vertebrate non-visual opsin, Opn3. PLoS ONE 11, e0161215 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Ballister, E. R., Rodgers, J., Martial, F. & Lucas, R. J. A live cell assay of GPCR coupling allows identification of optogenetic tools for controlling Go and Gi signaling. BMC Biol. 16, 10 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Tichy, A. M., Gerrard, E. J., Sexton, P. M. & Janovjak, H. Light-activated chimeric GPCRs: limitations and opportunities. Curr. Opin. Struct. Biol. 57, 196–203 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. Almenar-Queralt, A. et al. Presenilins regulate neurotrypsin gene expression and neurotrypsin-dependent agrin cleavage via cyclic AMP response element-binding protein (CREB) modulation. J. Biol. Chem. 288, 35222–35236 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hidaka, C., Kashio, T., Uchigaki, D. & Mitsui, S. Vulnerability or resilience of motopsin knockout mice to maternal separation stress depending on adulthood behaviors. Neuropsychiatr. Dis. Treat. 14, 2255–2268 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Nakajima, T. Roles of sulfur metabolism and rhodanese in detoxification and anti-oxidative stress functions in the liver: responses to radiation exposure. Med. Sci. Monit. 21, 1721–1725 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lu, J. & Holmgren, A. The thioredoxin antioxidant system. Free Radic. Biol. Med. 66, 75–87 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Lincoln, G. A., Clarke, I. J., Hut, R. A. & Hazlerigg, D. G. Characterizing a mammalian circannual pacemaker. Science 314, 1941–1944 (2006).

    Article  CAS  PubMed  Google Scholar 

  62. Hankins, M. W., Davies, W. I. L. & Foster, R. G. in Evolution of Visual and Non-visual Pigments (eds Hunt, D. M. et al.) 65–103 (Springer US, 2014).

  63. Nakane, Y., Shimmura, T., Abe, H. & Yoshimura, T. Intrinsic photosensitivity of a deep brain photoreceptor. Curr. Biol. 24, R596–R597 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Halford, S. et al. VA opsin-based photoreceptors in the hypothalamus of birds. Curr. Biol. 19, 1396–1402 (2009).

    Article  CAS  PubMed  Google Scholar 

  65. Hunt, D. M., Hankins, M. W., Collin, S. P. & Marshall, N. J. Evolution of Visual and Non-Visual Pigments (Springer, 2014).

  66. Kiang, N. Y., Siefert, J., Govindjee & Blankenship, R. E. Spectral signatures of photosynthesis. I. Review of Earth organisms. Astrobiology 7, 222–251 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Bracher, A. U. & Tilzer, M. M. Underwater light field and phytoplankton absorbance in different surface water masses of the Atlantic sector of the Southern Ocean. Polar Biol. 24, 687–696 (2001).

    Article  Google Scholar 

  68. Reierth, E., Van’t Hof, T. J. & Stokkan, K. A. Seasonal and daily variations in plasma melatonin in the high-arctic Svalbard ptarmigan (Lagopus mutus hyperboreus). J. Biol. Rhythms 14, 314–319 (1999).

    Article  CAS  PubMed  Google Scholar 

  69. Arnold, W. et al. Circadian rhythmicity persists through the Polar night and midnight sun in Svalbard reindeer. Sci. Rep. 8, 14466 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Häfker, N., Teschke, M., Hüppe, L. & Meyer, B. Calanus finmarchicus diel and seasonal rhythmicity in relation to endogenous timing under extreme polar photoperiods. Mar. Ecol. Prog. Ser. 603, 79–92 (2018).

    Article  CAS  Google Scholar 

  71. Hüppe, L. et al. Evidence for oscillating circadian clock genes in the copepod Calanus finmarchicus during the summer solstice in the high Arctic. Biol. Lett. 16, 20200257 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Ashley, N. T. et al. Revealing a circadian clock in captive arctic-breeding songbirds, Lapland longspurs (Calcarius lapponicus), under constant illumination. J. Biol. Rhythms 29, 456–469 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Reierth, E. & Stokkan, K.-A. Activity rhythm in High Arctic Svalbard ptarmigan (Lagopus mutus hyperboreus). Can. J. Zool. 76, 2031–2039 (1998).

    Article  Google Scholar 

  74. Lu, W., Meng, Q. J., Tyler, N. J., Stokkan, K. A. & Loudon, A. S. A circadian clock is not required in an arctic mammal. Curr. Biol. 20, 533–537 (2010).

    Article  CAS  PubMed  Google Scholar 

  75. Wallace, M. et al. Comparison of zooplankton vertical migration in an ice-free and a seasonally ice-covered Arctic fjord: an insight into the influence of sea ice cover on zooplankton behavior. Limnol. Oceanogr. 55, 831–845 (2010).

    Article  Google Scholar 

  76. Kobelkova, A. et al. Continuous activity and no cycling of clock genes in the Antarctic midge during the polar summer. J. Insect Physiol. 81, 90–96 (2015).

    Article  CAS  PubMed  Google Scholar 

  77. Stelzer, R. J. & Chittka, L. Bumblebee foraging rhythms under the midnight Sun measured with radiofrequency identification. BMC Biol. 8, 93 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Nordtug, T. & Thor, B. M. Diurnal variations in natural light conditions at summer time in arctic and subarctic areas in relation to light detection in insects. Ecography 11, 2020–2209 (1988).

    Article  Google Scholar 

  79. Chittka, L., Stelzer, R. J. & Stanewsky, R. Daily changes in ultraviolet light levels can synchronize the circadian clock of bumblebees (Bombus terrestris). Chronobiol. Int. 30, 434–442 (2013).

    Article  PubMed  Google Scholar 

  80. Tosches, M. A., Bucher, D., Vopalensky, P. & Arendt, D. Melatonin signaling controls circadian swimming behavior in marine zooplankton. Cell 159, 46–57 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sugden, D., Cena, V. & Klein, D. C. Hydroxyindole O-methyltransferase. Methods Enzymol. 142, 590–596 (1987).

    Article  CAS  PubMed  Google Scholar 

  82. Schenk, S. et al. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase. eLife 8, e41556 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Govardovskii, V. I., Fyhrquist, N., Reuter, T., Kuzmin, D. G. & Donner, K. In search of the visual pigment template. Vis. Neurosci. 17, 509–528 (2000).

    Article  CAS  PubMed  Google Scholar 

  84. Stavenga, D. G., Oberwinkler, J. & Postma, M. in Handbook of Biological Physics Vol. 3 (eds Stavenga, D. G. et al.) 527–574 (Elsevier, 2000).

  85. Duffett-Smith, P. & Zwart, J. Practical Astronomy with Your Calculator or Spreadsheet 4th edn (Cambridge Univ. Press, 2011).

  86. Bannister, S. et al. TALE nucleases mediate efficient, heritable genome modifications in the marine annelid Platynereis dumerilii. Genetics 197, 19–31 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Cermak, T. et al. Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting. Nucleic Acids Res. 39, e82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Dahlem, T. J. et al. Simple methods for generating and detecting locus-specific mutations induced with TALENs in the zebrafish genome. PLoS Genet. 8, e1002861 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Meeker, N. D., Hutchinson, S. A., Ho, L. & Trede, N. S. Method for isolation of PCR-ready genomic DNA from zebrafish tissues. Biotechniques 43, 610, 612, 614 (2007).

    Article  PubMed  CAS  Google Scholar 

  90. Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    Article  PubMed  CAS  Google Scholar 

  91. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  92. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  PubMed  Google Scholar 

  93. Caers, J. et al. Peptidomics of neuropeptidergic tissues of the tsetse fly Glossina morsitans morsitans. J. Am. Soc. Mass. Spectrom. 26, 2024–2038 (2015).

    Article  CAS  PubMed  Google Scholar 

  94. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

    Article  CAS  PubMed  Google Scholar 

  95. Dorfer, V. et al. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J. Proteome Res. 13, 3679–3684 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).

    Article  PubMed  CAS  Google Scholar 

  97. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Article  CAS  PubMed  Google Scholar 

  99. Sharma, V. et al. Panorama public: a public repository for quantitative data sets processed in Skyline. Mol. Cell. Proteom. 17, 1239–1244 (2018).

    Article  CAS  Google Scholar 

  100. Qayum, H. A., Klimley, A. P., Newton, R. & Richert, J. E. Broad-band versus narrow-band irradiance for estimating latitude by archival tags. Mar. Biol. 151, 467–481 (2007).

    Article  Google Scholar 

  101. Ritchie, R. J., Larkum, A. W. D. & Ribas, I. Could photosynthesis function on Proxima Centauri b? Int. J. Astrobiol. 17, 147–176 (2018).

    Article  CAS  Google Scholar 

  102. Guehmann, M. et al. Spectral tuning of phototaxis by a Go-opsin in the rhabdomeric eyes of Platynereis. Curr. Biol. 25, 2265–2271 (2015).

    Article  CAS  Google Scholar 

  103. Kiang, N. Y., Siefert, J., Govindjee & Blankenship, R. E. Spectral signatures of photosynthesis. I. Review of Earth organisms. Astrobiology 7, 222–251 (2007).

    Article  CAS  PubMed  Google Scholar 

  104. Depauw, F. A., Rogato, A., Ribera d’Alcalá, M. & Falciatore, A. Exploring the molecular basis of responses to light in marine diatoms. J. Exp. Bot. 63, 1575–1591 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the members of the Tessmar-Raible and Raible groups for discussions; A. Belokurov and M. Borysova for excellent worm care at the MFPL aquatic facility; M. Waldherr, L. Orel and N. Getachew for excellent technical assistance; and N. Hartl for technical assistance with the PRM assays. Targeted proteomics experiments were performed using the Vienna BioCenter Core Facilities (VBCF) instrument pool. K.T.-R. received funding for this research from: the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013; ERC grant agreement 337011) and the Horizon 2020 Programme (ERC grant agreement 819952); the research platform ‘Rhythms of Life’ of the University of Vienna; the Austrian Science Fund (FWF; http://www.fwf.ac.at/en/), including a START award (AY0041321), research project grant (P28970) and SFB grant (SFB F78); and the HFSP (http://www.hfsp.org/; research grant RGY0082/2010). None of the funding bodies were involved in the design of the study, the collection, analysis and interpretation of data, or in writing the manuscript.

Author information

Author notes

  1. Enrique Arboleda

    Present address: Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, Lyon, France

  2. Maximillian Hofbauer

    Present address: loopbio, Vienna, Austria

Authors and Affiliations

  1. Max Perutz Labs, Vienna BioCenter, University of Vienna, Vienna, Austria

    Vinoth Babu Veedin Rajan, N. Sören Häfker, Enrique Arboleda, Birgit Poehn, Thomas Gossenreiter, Maximillian Hofbauer, Markus Hartl & Kristin Tessmar-Raible

  2. Research Platform ‘Rhythms of Life’, Vienna BioCenter, University of Vienna, Vienna, Austria

    Vinoth Babu Veedin Rajan, N. Sören Häfker, Enrique Arboleda, Birgit Poehn, Maximillian Hofbauer & Kristin Tessmar-Raible

  3. Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK

    Elliot Gerrard & Robert J. Lucas

  4. Marine Breeding Systems, St. Gallen, Switzerland

    Christian Mühlestein

  5. Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria

    Andrea Bileck & Christopher Gerner

  6. Stazione Zoologica Anton Dohrn, Napoli, Italy

    Maurizio Ribera d’Alcala & Maria C. Buia

Authors
  1. Vinoth Babu Veedin Rajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sören Häfker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrique Arboleda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Birgit Poehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Gossenreiter
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elliot Gerrard
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maximillian Hofbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christian Mühlestein
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrea Bileck
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Christopher Gerner
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Maurizio Ribera d’Alcala
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Maria C. Buia
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Markus Hartl
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Robert J. Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kristin Tessmar-Raible
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.B.V.R. conducted the experimental work, planned parts of the project, analysed and interpreted the data and designed the figures and data representation for the entire manuscript with additional contributions by others outlined as follows. N.S.H. performed data analyses and interpretation, figure design of the untargeted proteomics experiments, and provided detailed input to writing the ecological parts of the manuscript. E.A. performed the natural light and temperature measurements and data analyses. B.P. conducted the indoor light measurements and helped to design the automated worm locomotor analysis set-up and NELIS. T.G. and M.H. acquired and analysed the targeted proteomics data. E.G. and R.J.L. helped to perform the experimental work and data analyses, designed the experimental plans for the c-Opsin action spectrum and signalling and helped to calculate the Opsin R/M state ratios. M.H. designed the automated worm locomotor analysis set-up. C.M. designed the NELIS illumination set-ups. A.B. and C.G. performed the untargeted proteomics data acquisition and analyses. M.R.d’A. helped to perform the natural light and temperature measurements and provided detailed feedback on ecological perspectives. M.C.B helped to perform the natural light and temperature measurements. K.T.-R. planned the project, conceived of the experimental concepts, discussed, analysed and interpreted the data and wrote the manuscript. All authors provided comments and feedback to the manuscript.

Corresponding author

Correspondence to Kristin Tessmar-Raible.

Ethics declarations

Competing interests

M.H. is the chief executive officer of loopbio, a company developing commercial animal behavioural tracking solutions. C.M. is the chief executive officer of Marine Breeding Systems, a company developing commercial illumination systems for aquaculture. All other authors declare no competing interests.

Additional information

Peer review information Nature Ecology and Evolution thanks Oren Levy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Benchmarking of daylight intensity measurements at 10 m with published measurements and calculations.

The units from our data are in each case converted and plotted corresponding to the units and type of plot used in the respective compared publication. (a) Nightlight data measured for individual wavelengths between August 24–25, 1999100. (b,c) Analysis from (a) performed on natural light data measurement for August 24–25 2010. (c): saturation and noise-equivalent irradiance thresholds are indicated 400 nm (pink line), 500 nm (green line) and 700 nm (red line). (d) Calculations of light irradiance at different ocean depths101. Black arrow points at UVA spectral range clearly present at 10 m water depth and below. (e,f) Our measurements from July 4, 2011 and August 24, 2010 at 12:00 noon for comparison. (g) Irradiance data calculated for different water depths based on in situ measurements of the attenuation coefficients in coastal waters in Corsica using a PhotoreSearch PR-670 spectrophotometer in a custom UW housing on July 4th, 2010, under bright sun at noon102. (h,i) Our measurements from July 4, 2011 at 12:00 noon timepoint. (j) Irradiance in atmosphere and in water at different depths103. (k,l) Representative daylight measurements from July 4, 2011 and August 24, 2010 at 12:00 noon timepoint from our 10 m measurement set for comparsion. (m,n) Average spectral irradiance and individual wavelength penetration under different ocean depths104. (f,i,l) Examplary saturation and noise equivalent irradiance (NEI) levels of the RAMSES hyperspectral radiometer indicated as dots. Panel a reproduced with permission from ref. 100, Springer Nature Ltd; d reproduced with permission from ref. 101, Cambridge University Press; g reproduced with permission from ref. 102, Elsevier; j reproduced with permission from ref. 66, Mary Ann Liebert, Inc.; m and n reproduced with permission from ref. 104, Oxford University Press.

Extended Data Fig. 2 Daytime spectral irradiance and ratios with and without twilight (10 m depth).

(a) Daylight average per day across the year between sunrise to sunset without twilight times for each wavelength. For 3D-rotational graph: Supplementary Data 2. (b) 2-D pcolor plot of (a). (c) Daylight spectrum across the year for each wavelength between astronomical dawn to astronomical dusk (raw data). For 3D-rotational graph: Supplementary Data 3. (d) 2-D pcolor plot of (c). (e) Daytime monthly irradiance averages comparing periods of equal photoperiods. Long day photoperiod example: 14 April – 15 May 2011 (yellow) and 27 July – 27 August 2010 (blue), short day photoperiod example: 9 January – 9 February 2011 (black) and 1 November – 2 December 2010 (red). (f, g) Equinox day spectra without twilight (f) and between astronomical twilight (g). For 3D-plot: Supplementary Data 6 and 9. (h,i) Screenshot of weather data for spring and autumnal equinox days: https://www.timeanddate.com/weather. (j, k) 3D-surface plot of equinox day ratio - September 23, 2010/March 21, 2011 (j) and September 23, 2010/March 24, 2011 (k). For 3D-rotational graph: Supplementary Data 7 and 8. (l) Ratios of wavelengths averaged across the day for fall and spring equinox days including twilight times. Black dotted lines: range of strong c-opsin1 activation. Data were corrected for daylight saving time.

Extended Data Fig. 3 Irradiance differences between 4 m and 10 m water depth.

(a,b) Daylight spectra without twilight of 4 m (a) and 10 m (b) water depth during June 2011. For 3D-rotational graph: Supplementary data 4 and 5. (c,d) Monthly average 2D plot of (a,b) in logarithmic (c) and in linear scale (d). Blue: 4 m, Red: 10 m. (e) Wavelength ratios between monthly average of June 2011-4 m/June 2011-10 m. (f,g) Zoom-in for specific wavelengths ranges from plot (d) for better visualization. Black dotted lines: range of strong c-opsin1 activation. Data were corrected for daylight saving time shifts.

Extended Data Fig. 4 Light spectra and intensity data for experimental light sources.

(a-e) Light intensity and spectra measured using an ILT950 spectrometer (International Light Technologies Inc, Peabody, USA) for (a) standard worm culture illumination, (b,b’) worm culture using ‘NELIS’ in linear plot (b) and logarithmic plot (b’), (c,c’) Behavioural chamber with ‘Nelis’ white light with UVA as linear plot (c) and logarithmic plot (c’). (d,d’) Behavioural chamber with ‘Nelis’ white light and a filter reducing light below 430 nm (UVAR filter, Pixelteq, Salvo Technologies, USA) as linear plot (d) and logarithmic plot (d’), (e,e’) Behavioural chamber with ‘Nelis’ white light matching the intensities of the spectrum >430 nm from (d,d’) as linear (e) and logarithmic plot (e’). Purple dotted line in b-e indicates the c-opsin1 high activation range. (f,g) Picture details of ‘Nelis’ light source.

Extended Data Fig. 5 G-protein signaling analyses, irradiance dose response curves and spectral sensitivity analyses of Platynereis c-opsin1.

(a) Broad spectrum white light from Arc lamp used for G-protein selectivity assay. (b) Wavelength spectra from CoolLED light source used for spectral characterization of Platynereis c-opsin1. (c) Cells transfected with Platynereis c-opsin1 showed no increase in calcium concentration after 2 s white light pulse in calcium bioluminescence assay testing for G𝛼q binding (purple diamonds: Pdu-c-opsin1, red open circles: human melanopsin, black inverted triangles: no opsin, black arrow: 2 s white light pulse). (d) No luminescence increase after 30 s white light pulse indicates that Platynereis c-opsin1 does not signal via G𝛼s (Pdu c-opsin1, Purple diamond; Jellyopsin, red open triangle; no opsin, black inverted triangle; 30 s white light pulse, black arrow). (e) Schematic diagram of Platynereis c-opsin1 chimera with second and third intracellular loop regions replaced by corresponding human melanopsin loop region and downstream signaling. (f) Platynereis c-opsin1-human melanopsin chimera depicted in (e) shows a clear response in the calcium luminescence assay, indicative of G𝛼q-signaling. (g-m) Irradiance dose response curve of Platynereis c-opsin1-melanopsin chimera at 7 different wavelengths.

Extended Data Fig. 6 Platynereis c-opsin1Δ8/Δ8 allele exhibit lowered locomotor activity under longday, including UVA during LD and DD.

(a-c) Double plotted average actogram plot of c-opsin1+/+ (a: n = 17), c-opsin1Δ8/+ (b: n = 12) and c-opsin1Δ8/Δ8 worms (c: n = 20) under longday, including strong UVA under Light-Dark (LD, days 1-5) and Dark-Dark condition (DD, days 6-10, grey shaded). (d) Significant difference in rhythmicity power (PN) exist for c-opsin1Δ8/+ vs c-opsin1Δ8/Δ8 and c-opsin1+/+ vs c-opsin1Δ8/Δ8, but not for c-opsin1+/+ vs c-opsin1Δ8/+ (Mann-Whitney-Wilcoxon test). (e) Percentage of rhythmicity calculated for all genotypes under LD condition (c-opsin1+/+: 100%R, c-opsin1Δ8/+: 100%R and c-opsin1Δ8/Δ8: 90%R+10%WR). (f) c-opsin1Δ8/Δ8 worms showed significant decrease in nocturnal locomotor activity compared to c-opsin1+/+ and c-opsin1Δ8/+ (One-way ANOVA with Sidak’s multiple comparison test). (g) Under DD free-running conditions, significant differences in rhythmicity power (PN) exist for c-opsin1+/+ vs c-opsin1Δ8/Δ8 and c-opsin1+/+ vs c-opsin1Δ8/+, but no difference for c-opsin1Δ8/+ vs c-opsin1Δ8/Δ8 (Mann-Whitney-Wilcoxon test). (h) Percentage of rhythmicity calculated for all genotypes under DD condition (c-opsin1+/+: 76.48%R+17.64%WR+5.88%AR, c-opsin1Δ8/+: 33.33%R+41.67%WR+25%AR and c-opsin1Δ8/Δ8: 30%R+30%WR+40%AR). (i) c-opsin1Δ8/Δ8 worms recorded under DD condition showed significant decrease in nocturnal locomotor activity compared to c-opsin1+/+ and c-opsin1Δ8/+ (One-way ANOVA with Sidak’s multiple comparison test). *p<0.05, ** p<0.01, *** p<0.001. For individual actograms see Supplementary Fig. 2.

Extended Data Fig. 7 Platynereis c-opsin1Δ8/Δ7 transheterozygous worms exhibit lowered locomotor activity under longday, including UVA conditions.

(a,b) Average, double-plotted actogram of c-opsin1+/+ (a: n = 15), c-opsin1Δ8/Δ7 (b: n = 21). 3 days of LD. (c) No difference in power (PN) was observed between c-opsin1+/+ and c-opsin1Δ8/Δ7 (Mann-Whitney-Wilcoxon test). (d) Percentage of rhythmicity calculated for all genotypes under LD condition (c-opsin1+/+: 80%R+13.33WR+6.67AR; c-opsin1Δ8/Δ7: 57.14%R+14.29%WR+28.57AR). (e) c-opsin1Δ8/Δ7 worms showed a significant decrease in nocturnal locomotor activity compared to c-opsin1+/+ (One-way ANOVA with sidak’s multiple comparison test). *p<0.05, ** p<0.01, *** p<0.001. For individual actograms see Supplementary Fig. 3.

Extended Data Fig. 8 Locomotion under long day (LD 16:8) and intermediate photoperiod (LD 12:12) with full and filter-reduced UVA.

Locomotor behaviour of Platynereis c-opsin1Δ8/Δ8 mutant and its corresponding wt siblings. (a,b) Double plotted average actograms of c-opsin1+/+ worms under long day Nelis white light with intense UVA (+UVA) (a: n = 12) and with filter-reduced UVA (-UVA) (b: n = 10). (c) c-opsin1+/+ worms under –UVA conditions showed a significantly decrease locomotor activity compared to worms under +UVA conditions and (d) significant decrease in power (PN) and rhythmicity. (e,f) Double plotted average actograms of c-opsin1Δ8/Δ8 worms under LD16:8 +UVA (e, n = 11) and -UVA (f, n = 10). (g,h) c-opsin1Δ8/Δ8 worms recorded in (e,f) showed no difference in locomotor activity level (g) and rhythmicity (h). (i,j) Double plotted average actograms of c-opsin1+/+ worms under LD 12:12 +UVA (i: n = 9) and -UVA (j: n = 7). (k,l) c-opsin1+/+ worms recorded in (i,j) with a difference in locomotor activity close to statistical significance (k), and no difference in rhythmicity (l). (m,n) Double plotted average actograms of c-opsin1Δ8/Δ8 worms under LD 12:12 +UVA (m: n = 10) and -UVA (n: n = 9). (o,p) c-opsin1Δ8/Δ8 worms recorded in (m,n) showed no difference trend in locomotor activity level (o) and rhythmicity (p). For all statistical comparisons and p values: Supplementary Fig. 7. Statistics: locomotor activity: One-way ANOVA, Sidak’s multiple comparison test, period, power and rhythmicity: Individual worm rhythmicity and power were determined via Lomb-Scargle periodograms using ActogramJ. The averages of multiple worms were tested by Mann-Whitney-Wilcoxon test. *p<0.05, ** p<0.01, *** p<0.001. Locomotor data of individual worms: Supplementary Figs. 8, 9.

Extended Data Fig. 9 Head transcript level analyses for additional candidate genes in c-opsin1Δ/8Δ8 and corresponding wildtypes.

Genes as indicated in each panel. The p-value for differences across time was determined by one-way ANOVA. One-way ANOVA with Sidak’s multiple comparison test was used for differences at specific timepoints. Differences between overall transcript levels (measured as AUC) were tested for by Unpaired student’s t-test with Welch’s correction. n.s.- non significant Data displayed as mean± S.E.M., n = 3BR (5 heads/BR).

Extended Data Fig. 10 Overview of untargeted proteomics experiment under UVA condition.

(a) UVA light used for untargeted proteomics experiment on c-opsin1+/+ and c-opsin1Δ8/Δ8 worms. (b) Sampling scheme. (c) Cellular and pathway model representing differentially regulated protein candidates. For primary data see: Supplementary Tables 13–15. N = 3BRs (20heads/BR), The significance was calculated by two-sided students t-test with adjusted p-value 0.05 (Permutation based FDR correction). Only proteins with least 2 peptides (at least one of it unique) were included in analyses.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10.

Reporting Summary

Peer Review Information

Supplementary Data 1

Rotatable 3D plot corresponding to the stationary image in Fig. 1c.

Supplementary Data 2

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2a.

Supplementary Data 3

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2c.

Supplementary Data 4

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 3a.

Supplementary Data 5

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 3b.

Supplementary Data 6

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2f.

Supplementary Data 7

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2j.

Supplementary Data 8

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2k.

Supplementary Data 9

Rotatable 3D plot corresponding to the stationary image in Extended Data Fig. 2g.

Supplementary Tables

Supplementary Tables 1–17. Primary data from measurements, as well as calculations and statistics (for details, see the individual tab labels).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Veedin Rajan, V.B., Häfker, N.S., Arboleda, E. et al. Seasonal variation in UVA light drives hormonal and behavioural changes in a marine annelid via a ciliary opsin. Nat Ecol Evol 5, 204–218 (2021). https://doi.org/10.1038/s41559-020-01356-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-01356-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing