Limits to the cellular control of sequestered cryptophyte prey in the marine ciliate Mesodinium rubrum

Abstract

The marine ciliate Mesodinium rubrum is famous for its ability to acquire and exploit chloroplasts and other cell organelles from some cryptophyte algal species. We sequenced genomes and transcriptomes of free-swimming Teleaulax amphioxeia, as well as well-fed and starved M. rubrum in order to understand cellular processes upon sequestration under different prey and light conditions. From its prey, the ciliate acquires the ability to photosynthesize as well as the potential to metabolize several essential compounds including lysine, glycan, and vitamins that elucidate its specific prey dependency. M. rubrum does not express photosynthesis-related genes itself, but elicits considerable transcriptional control of the acquired cryptophyte organelles. This control is limited as light-dependent transcriptional changes found in free-swimming T. amphioxeia got lost after sequestration. We found strong transcriptional rewiring of the cryptophyte nucleus upon sequestration, where 35% of the T. amphioxeia genes were significantly differentially expressed within well-fed M. rubrum. Qualitatively, 68% of all genes expressed within well-fed M. rubrum originated from T. amphioxeia. Quantitatively, these genes contributed up to 48% to the global transcriptome in well-fed M. rubrum and down to 11% in starved M. rubrum. This tertiary endosymbiosis system functions for several weeks, when deprived of prey. After this point in time, the ciliate dies if not supplied with fresh prey cells. M. rubrum represents one evolutionary way of acquiring photosystems from its algal prey, and might represent a step on the evolutionary way towards a permanent tertiary endosymbiosis.

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: Light micrographs of Teleaulax amphioxeia and Mesodinium rubrum with corresponding cartoons.
Fig. 2: Workflow and transcriptome features of Teleaulax amphioxeia and Mesodinium rubrum.
Fig. 3: Global transcriptome features of M. rubrum.
Fig. 4: Changes in gene expression of Teleaulax amphioxeia genes in response to sequestration.
Fig. 5: Changes in light- and time-controlled gene expression of free-swimming T. amphioxeia and after sequestration by M. rubrum.
Fig. 6: Transcriptional changes in M. rubrum upon sequestration in different light conditions.

Data availability

The raw sequencing reads produced in this study are deposited in the CNGB Nucleotide Sequence Archive (CNSA) [62, 63] with accession number CNP0000925. The nucleotide sequences and functional annotations of the reference gene sets for Teleaulax amphioxeia and Mesodinium rubrum are deposited in the figshare repository under the link 10.6084/m9.figshare.12360836.

References

  1. 1.

    Altenburger A, Blossom HE, Garcia-Cuetos L, Jakobsen HH, Carstensen J, Lundholm N, et al. Dimorphism in cryptophytes—the case of Teleaulax amphioxeia/Plagioselmis prolonga and its ecological implications. Sci Adv. 2020;6:eabb1611.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Burki F, Kaplan M, Tikhonenkov DV, Zlatogursky V, Minh BQ, Radaykina LV, et al. Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista. Proc R Soc B. 2016;283:20152802.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  3. 3.

    Cavalier-Smith T. Membrane heredity and early chloroplast evolution. Trends Plant Sci. 2000;5:174–82.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Douglas SE, Murphy CA, Spencer DF, Gray MW. Cryptomonad algae are evolutionary chimaeras of two phylogenetically distinct unicellular eukaryotes. Nature. 1991;350:148–51.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Cavalier-Smith T. Principles of protein and lipid targeting in secondary symbiogenesis: euglenoid, dinoflagellate, and sporozoan plastid origins and the eukaryote family tree. J Eukaryot Microbiol. 1999;46:347–66.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Curtis BA, Tanifuji G, Burki F, Gruber A, Irimia M, Maruyama S, et al. Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs. Nature. 2012;492:59–65.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Douglas S, Zauner S, Fraunholz M, Beaton M, Penny S, Deng LT, et al. The highly reduced genome of an enslaved algal nucleus. Nature. 2001;410:1091–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Hoef-Emden K, Archibald JM. Cryptophyta (Cryptomonads). In: Archibald JM, Simpson AGB, Slamovits CH, editors. Handbook of the protists. Cham: Springer International Publishing; 2017. p. 851–91.

  9. 9.

    Ward BA, Follows MJ. Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux. Proc Natl Acad Sci U S A. 2016;113:2958–63.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Herfort L, Peterson TD, Campbell V, Futrell S, Zuber P. Myrionecta rubra (Mesodinium rubrum) bloom initiation in the Columbia River estuary. Estuar Coast Shelf Sci. 2011;95:440–6.

    Article  Google Scholar 

  11. 11.

    Johnson MD, Beaudoin DJ, Laza-Martinez A, Dyhrman ST, Fensin E, Lin S, et al. The genetic diversity of Mesodinium and associated cryptophytes. Front Microbiol. 2016;7:2017.

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Lindholm T. Mesodinium rubrum—a unique photosynthetic ciliate. Adv Aquat Microbiol. 1985;3:1–48.

    Google Scholar 

  13. 13.

    Nowack EC, Melkonian M. Endosymbiotic associations within protists. Philos Trans R Soc B. 2010;365:699–712.

    CAS  Article  Google Scholar 

  14. 14.

    Johnson MD, Oldach D, Delwiche CF, Stoecker DK. Retention of transcriptionally active cryptophyte nuclei by the ciliate Myrionecta rubra. Nature. 2007;445:426–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Hansen PJ, Moldrup M, Tarangkoon W, Garcia-Cuetos L, Moestrup O. Direct evidence for symbiont sequestration in the marine red tide ciliate Mesodinium rubrum. Aquat Micro Ecol. 2012;66:63–75.

    Article  Google Scholar 

  16. 16.

    Kim M, Drumm K, Daugbjerg N, Hansen PJ. Dynamics of sequestered cryptophyte nuclei in Mesodinium rubrum during starvation and refeeding. Front Microbiol. 2017;8:1–14.

    Google Scholar 

  17. 17.

    Nam SW, Park JW, Yih W, Park MG, Shin W. The fate of cryptophyte cell organelles in the ciliate Mesodinium cf. rubrum subjected to starvation. Harmful Algae. 2016;59:19–30.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Juel Hansen P, Fenchel T. The bloom-forming ciliate Mesodinium rubrum harbours a single permanent endosymbiont. Mar Biol Res. 2006;2:169–77.

    Article  Google Scholar 

  19. 19.

    Smith M, Hansen PJ. Interaction between Mesodinium rubrum and its prey: importance of prey concentration, irradiance and pH. Mar Ecol Prog Ser. 2007;338:61–70.

    Article  Google Scholar 

  20. 20.

    Matthew DJ, Diane KS. Role of feeding in growth and photophysiology of Myrionecta rubra. Aquat Micro Ecol. 2005;39:303–12.

    Article  Google Scholar 

  21. 21.

    Fenchel T, Hansen PJ. Motile behaviour of the bloom-forming ciliate Mesodinium rubrum. Mar Biol Res. 2006;2:33–40.

    Article  Google Scholar 

  22. 22.

    Gustafson DE, Stoecker DK, Johnson MD, Van Heukelem WF, Sneider K. Cryptophyte algae are robbed of their organelles by the marine ciliate Mesodinium rubrum. Nature. 2000;405:1049–52.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Peltomaa E, Johnson M. Mesodinium rubrum exhibits genus-level but not species-level cryptophyte prey selection. Aquat Micro Ecol. 2017;78:147–59.

    Article  Google Scholar 

  24. 24.

    Kim GH, Han JH, Kim B, Han JW, Nam SW, Shin W, et al. Cryptophyte gene regulation in the kleptoplastidic, karyokleptic ciliate Mesodinium rubrum. Harmful Algae. 2016;52:23–33.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Lasek-Nesselquist E, Wisecaver JH, Hackett JD, Johnson MD. Insights into transcriptional changes that accompany organelle sequestration from the stolen nucleus of Mesodinium rubrum. BMC Genom. 2015;16:805.

    Article  CAS  Google Scholar 

  26. 26.

    Chen Y, Chen Y, Shi C, Huang Z, Zhang Y, Li S, et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience. 2018;7:1–6.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I. The impact of amplification on differential expression analyses by RNA-seq. Sci Rep. 2016;6:25533.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Ondov BD, Starrett GJ, Sappington A, Kostic A, Koren S, Buck CB, et al. Mash screen: high-throughput sequence containment estimation for genome discovery. Genome Biol. 2019;20:232.

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–100.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30:1575–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27:2987–93.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43.

    Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.

    Article  CAS  Google Scholar 

  44. 44.

    Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, et al. GO::TermFinder-open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004;20:3710–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

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

    Google Scholar 

  47. 47.

    Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE. 2010;5:e13984.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29:1830–1.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Allen JF, de Paula WBM, Puthiyaveetil S, Nield J. A structural phylogenetic map for chloroplast photosynthesis. Trends Plant Sci. 2011;16:645–55.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Qiu H, Lee Jun M, Yoon Hwan S, Bhattacharya D. Hypothesis: gene-rich plastid genomes in red algae may be an outcome of nuclear genome reduction. J Phycol. 2017;53:715–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Grzebyk D, Schofield O, Vetriani C, Falkowski PG. The mesozoic radiation of eukaryotic algae: the portable plastid hypothesis. J Phycol. 2003;39:259–67.

    CAS  Article  Google Scholar 

  53. 53.

    Hehenberger E, Gast RJ, Keeling PJ. A kleptoplastidic dinoflagellate and the tipping point between transient and fully integrated plastid endosymbiosis. Proc Natl Acad Sci USA. 2019;116:17934–42.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    Onuma R, Hirooka S, Kanesaki Y, Fujiwara T, Yoshikawa H, Miyagishima S-Y. Changes in the transcriptome, ploidy, and optimal light intensity of a cryptomonad upon integration into a kleptoplastic dinoflagellate. ISME J. 2020;14:2407–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    McFadden GI. Origin and evolution of plastids and photosynthesis in eukaryotes. Cold Spring Harb Perspect Biol. 2014;6:a016105.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Park MG, Kim M, Kim S. The acquisition of plastids/phototrophy in heterotrophic Dinoflagellates. Acta Protozool. 2014;53:39–50.

    Google Scholar 

  57. 57.

    Johnson MD, Beaudoin DJ. The genetic diversity of plastids associated with mixotrophic oligotrich ciliates. Limnol Oceanogr. 2019;64:2187–201.

    CAS  Article  Google Scholar 

  58. 58.

    Kim M, Kim S, Yih W, Park MG. The marine dinoflagellate genus Dinophysis can retain plastids of multiple algal origins at the same time. Harmful Algae. 2012;13:105–11.

    CAS  Article  Google Scholar 

  59. 59.

    Tourancheau AB, Tsao N, Klobutcher LA, Pearlman RE, Adoutte A. Genetic code deviations in the ciliates: evidence for multiple and independent events. EMBO J. 1995;14:3262–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Heaphy SM, Mariotti M, Gladyshev VN, Atkins JF, Baranov PV. Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in Condylostoma magnum. Mol Biol Evol. 2016;33:2885–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Johnson MD, Beaudoin DJ, Frada MJ, Brownlee EF, Stoecker DK. High grazing rates on cryptophyte algae in Chesapeake Bay. Front Mar Sci. 2018;5:1–13.

    PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Guo X, Chen F, Gao F, Li L, Liu K, You L, et al. CNSA: a data repository for archiving omics data. Database. 2020;2020:baaa055.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Chen Fengzhen YL, Fan Yang, Lina Wang, Xueqin Guo, Fei Gao, Cong Hua, et al. CNGBdb: China National GeneBank DataBase. Hereditas. 2020;42:799–809.

    CAS  Google Scholar 

Download references

Acknowledgements

This project was supported by the Danish Council for Independent Research (Grant number 4181–00484 to PJH), the Major scientific and technological projects of Hainan Province (ZDKJ2019011), the National Key Research and Development Program of China (2018YFC0308401), and the Carlsberg Foundation (2012_01_0515 to LG-C). We acknowledge the China National GeneBank (CNGB) for support with computing resources.

Author information

Affiliations

Authors

Contributions

PJH, NL, UJ, and AA conceived the work. AA, KD, MK, and LG-C did the culturing; AA and LG-C extracted RNA; QL, YZ, and XZ did the sequencing; HC, AA, and QL analyzed the data under the supervision of SL and NL; AA, HC, and QL drafted the manuscript. All authors contributed to writing of the final version of the manuscript.

Corresponding authors

Correspondence to Shuaicheng Li or Nina Lundholm.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Altenburger, A., Cai, H., Li, Q. et al. Limits to the cellular control of sequestered cryptophyte prey in the marine ciliate Mesodinium rubrum. ISME J (2020). https://doi.org/10.1038/s41396-020-00830-9

Download citation

Search

Quick links