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:

Position-dependent termination and widespread obligatory frameshifting in Euplotes translation

Nature Structural & Molecular Biology volume 24, pages 61–68 (2017)Cite this article

Subjects

Abstract

The ribosome can change its reading frame during translation in a process known as programmed ribosomal frameshifting. These rare events are supported by complex mRNA signals. However, we found that the ciliates Euplotes crassus and Euplotes focardii exhibit widespread frameshifting at stop codons. 47 different codons preceding stop signals resulted in either +1 or +2 frameshifts, and +1 frameshifting at AAA was the most frequent. The frameshifts showed unusual plasticity and rapid evolution, and had little influence on translation rates. The proximity of a stop codon to the 3′ mRNA end, rather than its occurrence or sequence context, appeared to designate termination. Thus, a 'stop codon' is not a sufficient signal for translation termination, and the default function of stop codons in Euplotes is frameshifting, whereas termination is specific to certain mRNA positions and probably requires additional factors.

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

Figure 1: Frequent frameshifting in Euplotes.
Figure 2: Identification of amino acids inserted at frameshift sites.
Figure 3: Distribution of codons upstream of stop codons at the frameshift sites and at the sites of translation termination.
Figure 4: Metagene analysis of ribosome profiling and distribution of frameshifting according to transcript levels.
Figure 5: Comparison of ribosomal frameshifting at AAA versus non-AAA frameshifting sites and TAA versus TAG frameshifting sites.
Figure 6: Cross-species comparison and frequency of nucleotide deletions in different hexamers.

Similar content being viewed by others

Accession codes

Primary accessions

BioProject

DDBJ/GenBank/EMBL

Proteomics Identifications Database

Sequence Read Archive

References

  1. Atkins, J.F., Loughran, G., Bhatt, P.R., Firth, A.E. & Baranov, P.V. Ribosomal frameshifting and transcriptional slippage: from genetic steganography and cryptography to adventitious use. Nucleic Acids Res. 44, 7007–7078 (2016).

    PubMed  PubMed Central  Google Scholar 

  2. Baranov, P.V., Atkins, J.F. & Yordanova, M.M. Augmented genetic decoding: global, local and temporal alterations of decoding processes and codon meaning. Nat. Rev. Genet. 16, 517–529 (2015).

    CAS  PubMed  Google Scholar 

  3. Belcourt, M.F. & Farabaugh, P.J. Ribosomal frameshifting in the yeast retrotransposon Ty: tRNAs induce slippage on a 7 nucleotide minimal site. Cell 62, 339–352 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Giedroc, D.P. & Cornish, P.V. Frameshifting RNA pseudoknots: structure and mechanism. Virus Res. 139, 193–208 (2009).

    CAS  PubMed  Google Scholar 

  5. Klobutcher, L.A. & Farabaugh, P.J. Shifty ciliates: frequent programmed translational frameshifting in euplotids. Cell 111, 763–766 (2002).

    CAS  PubMed  Google Scholar 

  6. Vallabhaneni, H., Fan-Minogue, H., Bedwell, D.M. & Farabaugh, P.J. Connection between stop codon reassignment and frequent use of shifty stop frameshifting. RNA 15, 889–897 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Valbonesi, A. & Luporini, P. Biology of Euplotes focardii, an Antarctic ciliate. Polar Biol. 13, 489–493 (1993).

    Google Scholar 

  8. Pucciarelli, S. et al. Molecular cold-adaptation of protein function and gene regulation: the case for comparative genomic analyses in marine ciliated protozoa. Mar. Genomics 2, 57–66 (2009).

    PubMed  Google Scholar 

  9. Baird, S.E. & Klobutcher, L.A. Differential DNA amplification and copy number control in the hypotrichous ciliate Euplotes crassus. J. Protozool. 38, 136–140 (1991).

    CAS  PubMed  Google Scholar 

  10. Prescott, D.M. The DNA of ciliated protozoa. Microbiol. Rev. 58, 233–267 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Wong, L.C. & Landweber, L.F. Evolution of programmed DNA rearrangements in a scrambled gene. Mol. Biol. Evol. 23, 756–763 (2006).

    CAS  PubMed  Google Scholar 

  12. Swart, E.C. et al. The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes. PLoS Biol. 11, e1001473 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Ricard, G. et al. Macronuclear genome structure of the ciliate Nyctotherus ovalis: single-gene chromosomes and tiny introns. BMC Genomics 9, 587 (2008).

    PubMed  PubMed Central  Google Scholar 

  14. Vinogradov, D.V. et al. Draft macronuclear genome of a ciliate Euplotes crassus. Molekuliarnaia biologiia 46, 361–366 (2012).

    CAS  PubMed  Google Scholar 

  15. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R. & Weissman, J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Ingolia, N.T. Ribosome profiling: new views of translation, from single codons to genome scale. Nat. Rev. Genet. 15, 205–213 (2014).

    CAS  PubMed  Google Scholar 

  17. Michel, A.M. & Baranov, P.V. Ribosome profiling: a Hi-Def monitor for protein synthesis at the genome-wide scale. RNA 4, 473–490 (2013).

    CAS  PubMed  Google Scholar 

  18. Baranov, P.V., Gesteland, R.F. & Atkins, J.F. Release factor 2 frameshifting sites in different bacteria. EMBO Rep. 3, 373–377 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Ivanov, I.P. & Atkins, J.F. Ribosomal frameshifting in decoding antizyme mRNAs from yeast and protists to humans: close to 300 cases reveal remarkable diversity despite underlying conservation. Nucleic Acids Res. 35, 1842–1858 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Sharma, V. et al. A pilot study of bacterial genes with disrupted ORFs reveals a surprising profusion of protein sequence recoding mediated by ribosomal frameshifting and transcriptional realignment. Mol. Biol. Evol. 28, 3195–3211 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Pearson, W. Finding protein and nucleotide similarities with FASTA. Curr Protoc. Bioinformatics 4, 3.9 (2004).

    Google Scholar 

  22. Wang, R., Xiong, J., Wang, W., Miao, W. & Liang, A. High frequency of +1 programmed ribosomal frameshifting in Euplotes octocarinatus. Sci. Rep. 6, 21139 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Swart, E.C., Serra, V., Petroni, G. & Nowacki, M. Genetic codes with no dedicated stop codon: context-dependent translation termination. Cell 166, 691–702 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Heaphy, S.M., Mariotti, M., Gladyshev, V.N., Atkins, J.F. & Baranov, P.V. Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in C. magnum. Mol. Biol. Evol. 33, 2885–2889 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Schmieder, R. & Edwards, R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One 6, e17288 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Dziallas, C., Allgaier, M., Monaghan, M.T. & Grossart, H.P. Act together-implications of symbioses in aquatic ciliates. Front. Microbiol. 3, 288 (2012).

    PubMed  PubMed Central  Google Scholar 

  27. Simpson, J.T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).

    PubMed  PubMed Central  Google Scholar 

  29. Warren, R.L., Sutton, G.G., Jones, S.J. & Holt, R.A. Assembling millions of short DNA sequences using SSAKE. Bioinformatics 23, 500–501 (2007).

    CAS  PubMed  Google Scholar 

  30. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Myers, E.W. et al. A whole-genome assembly of Drosophila. Science 287, 2196–2204 (2000).

    CAS  PubMed  Google Scholar 

  32. Huang, X., Wang, J., Aluru, S., Yang, S.P. & Hillier, L. PCAP: a whole-genome assembly program. Genome Res. 13, 2164–2170 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Huang, X. & Yang, S.P. Generating a genome assembly with PCAP. Curr. Protoc. Bioinformatics 11, 11.3 (2005).

    Google Scholar 

  34. Turanov, A.A. et al. Genetic code supports targeted insertion of two amino acids by one codon. Science 323, 259–261 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Lowe, T.M. & Eddy, S.R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32, 11–16 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Gerashchenko, M.V., Lobanov, A.V. & Gladyshev, V.N. Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress. Proc. Natl. Acad. Sci. USA 109, 17394–17399 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Haas, B.J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

    CAS  PubMed  Google Scholar 

  39. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  40. Thorvaldsdóttir, H., Robinson, J.T. & Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    PubMed  Google Scholar 

  41. Depuydt, G. et al. Reduced insulin/insulin-like growth factor-1 signaling and dietary restriction inhibit translation but preserve muscle mass in Caenorhabditis elegans. Mol Cell Prot 12, 3624–3639 (2013).

    CAS  Google Scholar 

  42. Depuydt, G. et al. LC-MS proteomics analysis of the insulin/IGF-1-deficient Caenorhabditis elegans daf-2(e1370) mutant reveals extensive restructuring of intermediary metabolism. J. Proteome Res. 13, 1938–1956 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Petyuk, V.A. et al. Characterization of the mouse pancreatic islet proteome and comparative analysis with other mouse tissues. J. Proteome Res. 7, 3114–3126 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Yang, F., Shen, Y., Camp, D.G. II & Smith, R.D. High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Exp Rev Prot 9, 129–134 (2012).

    CAS  Google Scholar 

  45. Mayampurath, A.M. et al. DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra. Bioinformatics 24, 1021–1023 (2008).

    CAS  PubMed  Google Scholar 

  46. Petyuk, V.A. et al. DtaRefinery, a software tool for elimination of systematic errors from parent ion mass measurements in tandem mass spectra data sets. Mol Cell Prot 9, 486–496 (2010).

    CAS  Google Scholar 

  47. Kim, S. & Pevzner, P.A.M.S.-G.F. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Supported by NIH GM061603 and GM065402 to V.N.G. S.M.H. and P.V.B. are supported by the grants from Wellcome Trust (094423) and Science Foundation Ireland (12/IA/1335). Portions of this research were also supported by NIH GM103493 and the W.R. Wiley Environmental Molecular Science Laboratory (sponsored by DOE and located at Pacific Northwest National Laboratory). Pacific Northwest National Laboratory is operated by the Battelle Memorial Institute under the DOE contract DE-AC05-76RLO-1830. C.M. acknowledges the Italian PNRA and the COST action BM1102 for supporting a part of this work.

Author information

Author notes

  1. Alexei V Lobanov and Stephen M Heaphy: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Alexei V Lobanov, Anton A Turanov, Maxim V Gerashchenko & Vadim N Gladyshev

  2. School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland

    Stephen M Heaphy, John F Atkins & Pavel V Baranov

  3. School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy

    Sandra Pucciarelli, Raghul R Devaraj & Cristina Miceli

  4. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA

    Fang Xie, Vladislav A Petyuk & Richard D Smith

  5. Department of Molecular Biology and Biophysics, University of Connecticut Health Center, Farmington, Connecticut, USA

    Lawrence A Klobutcher

  6. Molecular Biology of Selenium Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Institutes of Health, Bethesda, Maryland, USA

    Dolph L Hatfield

Authors
  1. Alexei V Lobanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen M Heaphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anton A Turanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maxim V Gerashchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandra Pucciarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Raghul R Devaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vladislav A Petyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Richard D Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lawrence A Klobutcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. John F Atkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Cristina Miceli
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dolph L Hatfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Pavel V Baranov
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Vadim N Gladyshev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.V.L., S.M.H., P.V.B. and V.N.G. analyzed the data and wrote the paper with advice from D.L.H. and J.F.A.; A.A.T. and M.V.G. prepared samples for sequencing; S.P., R.R.D., C.M. and L.A.K. performed cell culture maintenance and growth, F.X., V.A.P. and R.D.S. conducted MS analysis. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding authors

Correspondence to Pavel V Baranov or Vadim N Gladyshev.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Features of Euplotes genomes.

(a) Comparison Euplotes genomes in comparison with the genomes of other representative eukaryotes. The tree was constructed based on the sequences of 18S rRNA genes, and archaeal 16S rRNA gene (from Pyrococcus furiosis) was used as an outgroup. *number of contigs with telomeric repeats at both ends. (b) Distribution of telomeric repeat lengths in E. crassus (red) and E. focardii (black) macronuclear genomes. The X axis indicates the observed telomeric repeat number and the Y axis their frequencies. As expected, Euplotes genomes consist of gene-sized chromosomes capped by telomeres. The length of terminal repeats slightly varies; however, most chromosomes in both organisms have a double-stranded telomere length of 3.5 repeats (c) Sequence logo of subtelomeric regions at the 3’ end of E. crassus nanochromosomes. 1000 randomly selected chromosome sequences with telomeric repeat GGGGTTTTGGGGTTTTGGGGTTTTGGGG were chosen for constructing the logo. The logo detects a conserved position-specific sequence motif associated with telomeric repeats. Abundance of high-quality telomeric sequences allowed an unbiased screen for motifs and patterns associated with telomere function. A previously described TCAA motif (Baird S. E. & Klobutcher L. A., Genes Dev 3, 585-597, 1989; Klobutcher, L. A. et al., Proc Natl Acad Sci USA 78, 3015-3019,1981) was readily detected with Weblogo (Crooks, G. E. et al, Genome Res 14, 1188-1190, 2004) in the subtelomeric region due to its conserved position relative to the telomere repeats. An analysis of sequences in the vicinity of telomeres with a pattern discovery suite MEME (Bailey, T. L. et al., Nucl Acids Res 34, W369-373, 2006) did not reveal additional common motifs.

Supplementary Figure 2 Features of the Euplotes transcriptome.

(a) Euplotes Sec and Cys tRNAs that decode TGA codons. Cys tRNA with the GCA anticodon and mitochondrial Trp tRNA with TCA anticodon are shown for comparison. In total we identified 183 tRNA genes in E. crassus and 337 genes in E. focardii based on their genomes analysis. (b) Frequency of introns of different lengths. The X axis indicates the length of introns in nucleotides, and the Y axis shows how many times they are found in the transcriptomes (log scale). Short introns (~25 nucleotides) is a characteristic feature of Euplotes transcriptomes. (c) Frequency of chromosomes with different numbers of RNA molecules transcribed from them. The X axis shows a number of transcripts per chromosome, and the Y axis how many such chromosomes are found in the genome. (d) E. crassus splice sites. Nucleotide conservation around exon-intron junction and intron-exon junctions. E. crassus. Transcriptomes were assembled de novo using Trinity (Haas, B. J. et al., Nature Protoc, 8, 1494-1512, 2013); no genomic template was used for the assembly of the transcriptome to ensure independence of the analysis. The assembly procedure produced 33,701 unique transcripts with an average length of 573 nucleotides in E. crassus. We obtained the E. focardii RNA-seq reads from (Keeling, P. J. et al., PLoS Biol, 12, e1001889, 2014).; this assembly produced 28,869 unique transcripts with an average length of 667 nucleotides. To identify introns we carried out pairwise alignments between the genome and the transcriptome for each species using FASTA (Pearson, W. Curr Protoc Bioinf, Chapter 3, Unit3 9, 2004) In total, we identified 21,798 introns in E. crassus and 18,747 in E. focardii. The most frequent intron length was 25 nucleotides in both E. crassus and E. focardii with 2,895 and 2,631 occurrences, respectively. Using 10,000 intron sequences from E. crassus, we characterized sequence features of the exon-intron donor and intron-exon acceptor sites. We further aligned 32,350 E. crassus transcripts or their fragments (96%) to 18,032 genomic contigs, and similarly aligned 21,233 E. focardii transcripts (74%) to 16,950 genomic contigs. The majority of chromosomes had a single transcript aligning to them, 10,495 in E. crassus and 14,082 in E. focardii. Some chromosomes contained two or more predicted transcripts, which could be, at least in part, due to insufficient sequence coverage. Low coverage can result in missassembly of a single transcript as two or more, when reads matching internal positions are missing.

Supplementary Figure 3 Termination at AAATAA and two mRNAs with long 3′ UTRs.

In each panel ribosome footprints (top) and mRNA-seq reads (middle) are shown for a transcript whose ORF organization is shown at the bottom (red lines correspond to stop codons, and green lines to ATG codons). Identity of stop codons and adjacent 5’ codons is indicated for the site of termination. Translated segments of ORFs are highlighted in blue. (a) An example of mRNA with termination at AAATAA. (b) mRNA of selenoprotein P22. The position of UGA Sec codon is shown in dark blue. (c) A single detected example of an mRNA with a long 3’UTR not containing SECIS structure.

Supplementary Figure 4 Metagene analysis of RNA-seq density surrounding frameshifting sites.

First nucleotide of a stop codon is shown as a zero coordinate. Only minor alteration of density associated with sequencing biases at specific nucleotides of frameshift sites can be seen.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Table 1 and Supplementary Notes 1–4 (PDF 6592 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lobanov, A., Heaphy, S., Turanov, A. et al. Position-dependent termination and widespread obligatory frameshifting in Euplotes translation. Nat Struct Mol Biol 24, 61–68 (2017). https://doi.org/10.1038/nsmb.3330

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nsmb.3330

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research