Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-Pairing Codons in Yeast

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9649)

Abstract

Ribosome profiling quantitatively captures ribosome locations during translation. The resulting profiles of ribosome locations are widely used to study translational speed. However, an accurate estimation of the ribosome location depends on identifying the A-site from ribosome profiling reads, a problem that was previously unsolved. Here, we propose a novel method to estimate the ribosome A-site positions from high-coverage ribosome profiling reads. Our model allows more reads to be used, accurately explains the 3-nt periodicity of ribosome profiling reads from various lengths, and recovers consistent ribosome positions across different lengths. Our recovered ribosome positions are correctly highly skewed towards a single frame within a codon. They retain sub-codon resolution and enable detection of off-frame translational events, such as frameshifts. Our method improves the correlation with other estimates of codon decoding time. Further, the refined profiles show that yeast wobble-pairing codons are translated slower than their synonymous Watson-Crick-pairing codons. These results provide evidence that protein synthetic rate can be tuned by codon usage bias.

Keywords

Ribosome profiling A-site recovery Translation rate 

References

  1. 1.
    Albert, F.W., Muzzey, D., Weissman, J.S., Kruglyak, L.: Genetic influences on translation in yeast. PLoS Genet. 10(10), e1004692 (2014)CrossRefGoogle Scholar
  2. 2.
    Artieri, C.G., Fraser, H.B.: Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res. 24(12), 2011–2021 (2014)CrossRefGoogle Scholar
  3. 3.
    Artieri, C.G., Fraser, H.B.: Evolution at two levels of gene expression in yeast. Genome Res. 24(3), 411–421 (2014)CrossRefGoogle Scholar
  4. 4.
    Brar, G.A., Yassour, M., Friedman, N., Regev, A., Ingolia, N.T., Weissman, J.S.: High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335(6068), 552–557 (2012)CrossRefGoogle Scholar
  5. 5.
    Crick, F.H.: Codon-anticodon pairing: the wobble hypothesis. J. Mol. Biol. 19(2), 548–555 (1966)CrossRefGoogle Scholar
  6. 6.
    Dana, A., Tuller, T.: Determinants of translation elongation speed and ribosomal profiling biases in mouse embryonic stem cells. PLoS Comput. Biol. 8(11), e1002755 (2012)CrossRefGoogle Scholar
  7. 7.
    Dana, A., Tuller, T.: Properties and determinants of codon decoding time distributions. BMC Genomics 15(6), S13 (2014)CrossRefGoogle Scholar
  8. 8.
    Dana, A., Tuller, T.: The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 42(14), 9171–9181 (2014)CrossRefGoogle Scholar
  9. 9.
    Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., Gingeras, T.R.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15–21 (2013)CrossRefGoogle Scholar
  10. 10.
    Dunn, J.G., Foo, C.K., Belletier, N.G., Gavis, E.R., Weissman, J.S.: Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster. Elife 2, e01179 (2013)CrossRefGoogle Scholar
  11. 11.
    Engel, S.R., Cherry, J.M.: The new modern era of yeast genomics: community sequencing and the resulting annotation of multiple Saccharomyces cerevisiae strains at the Saccharomyces Genome Database. Database (Oxford) 2013, bat012 (2013)CrossRefGoogle Scholar
  12. 12.
    Fong, D.C., Saunders, M.: LSMR: an iterative algorithm for sparse least-squares problems. SIAM J. Sci. Comput. 33(5), 2950–2971 (2011)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Gao, X., Wan, J., Liu, B., Ma, M., Shen, B., Qian, S.B.: Quantitative profiling of initiating ribosomes in vivo. Nat. Methods 12(2), 147–153 (2015)CrossRefGoogle Scholar
  14. 14.
    Gardin, J., Yeasmin, R., Yurovsky, A., Cai, Y., Skiena, S., Futcher, B.: Measurement of average decoding rates of the 61 sense codons in vivo. Elife 3, e03735 (2014)CrossRefGoogle Scholar
  15. 15.
    Gerashchenko, M.V., Gladyshev, V.N.: Translation inhibitors cause abnormalities in ribosome profiling experiments. Nucleic Acids Res. 42(17), e134 (2014)CrossRefGoogle Scholar
  16. 16.
    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. U.S.A. 109(43), 17394–17399 (2012)CrossRefGoogle Scholar
  17. 17.
    Guo, H., Ingolia, N.T., Weissman, J.S., Bartel, D.P.: Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466(7308), 835–840 (2010)CrossRefGoogle Scholar
  18. 18.
    Guydosh, N.R., Green, R.: Dom34 rescues ribosomes in 3’ untranslated regions. Cell 156(5), 950–962 (2014)CrossRefGoogle Scholar
  19. 19.
    Ingolia, N.T.: Ribosome profiling: new views of translation, from single codons to genome scale. Nat. Rev. Genet. 15(3), 205–213 (2014)CrossRefGoogle Scholar
  20. 20.
    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(5924), 218–223 (2009)CrossRefGoogle Scholar
  21. 21.
    Ingolia, N.T., Lareau, L.F., Weissman, J.S.: Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147(4), 789–802 (2011)CrossRefGoogle Scholar
  22. 22.
    Lareau, L.F., Hite, D.H., Hogan, G.J., Brown, P.O.: Distinct stages of the translation elongation cycle revealed by sequencing ribosome-protected mRNA fragments. Elife 3, e01257 (2014)CrossRefGoogle Scholar
  23. 23.
    Lee, S., Liu, B., Lee, S., Huang, S.X., Shen, B., Qian, S.B.: Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution. Proc. Natl. Acad. Sci. U.S.A. 109(37), E2424–2432 (2012)CrossRefGoogle Scholar
  24. 24.
    Li, G.W., Burkhardt, D., Gross, C., Weissman, J.S.: Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157(3), 624–635 (2014)CrossRefGoogle Scholar
  25. 25.
    Martens, A.T., Taylor, J., Hilser, V.J.: Ribosome A and P sites revealed by length analysis of ribosome profiling data. Nucleic Acids Res. 43(7), 3680–3687 (2015)CrossRefGoogle Scholar
  26. 26.
    McManus, C.J., May, G.E., Spealman, P., Shteyman, A.: Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res. 24(3), 422–430 (2014)CrossRefGoogle Scholar
  27. 27.
    Michel, A.M., Choudhury, K.R., Firth, A.E., Ingolia, N.T., Atkins, J.F., Baranov, P.V.: Observation of dually decoded regions of the human genome using ribosome profiling data. Genome Res. 22(11), 2219–2229 (2012)CrossRefGoogle Scholar
  28. 28.
    O’Connor, P., Andreev, D., Baranov, P.: Surveying the relative impact of mRNA features on local ribosome profiling read density in 28 datasets. bioRxiv, 018762 (2015)Google Scholar
  29. 29.
    Patro, R., Duggal, G., Kingsford, C.: Salmon: accurate, versatile and ultrafast quantification from RNA-seq data using lightweight-alignment. bioRxiv, 021592 (2015)Google Scholar
  30. 30.
    Pop, C., Rouskin, S., Ingolia, N.T., Han, L., Phizicky, E.M., Weissman, J.S., Koller, D.: Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation. Mol. Syst. Biol. 10, 770 (2014)CrossRefGoogle Scholar
  31. 31.
    dos Reis, M., Savva, R., Wernisch, L.: Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res. 32(17), 5036–5044 (2004)CrossRefGoogle Scholar
  32. 32.
    Sabi, R., Tuller, T.: A comparative genomics study on the effect of individual amino acids on ribosome stalling. BMC Genomics 16(10), S5 (2015)CrossRefGoogle Scholar
  33. 33.
    Shah, P., Ding, Y., Niemczyk, M., Kudla, G., Plotkin, J.B.: Rate-limiting steps in yeast protein translation. Cell 153(7), 1589–1601 (2013)CrossRefGoogle Scholar
  34. 34.
    Stadler, M., Artiles, K., Pak, J., Fire, A.: Contributions of mRNA abundance, ribosome loading, and post- or peri-translational effects to temporal repression of C. elegans heterochronic miRNA targets. Genome Res. 22(12), 2418–2426 (2012)CrossRefGoogle Scholar
  35. 35.
    Stadler, M., Fire, A.: Wobble base-pairing slows in vivo translation elongation in metazoans. RNA 17(12), 2063–2073 (2011)CrossRefGoogle Scholar
  36. 36.
    Tarrant, D., von der Haar, T.: Synonymous codons, ribosome speed, and eukaryotic gene expression regulation. Cell. Mol. Life Sci. 71(21), 4195–4206 (2014)CrossRefGoogle Scholar
  37. 37.
    Vaidyanathan, P.P., Zinshteyn, B., Thompson, M.K., Gilbert, W.V.: Protein kinase A regulates gene-specific translational adaptation in differentiating yeast. RNA 20(6), 912–922 (2014)CrossRefGoogle Scholar
  38. 38.
    Woolstenhulme, C.J., Guydosh, N.R., Green, R., Buskirk, A.R.: High-precision analysis of translational pausing by ribosome profiling in bacteria lacking EFP. Cell Rep. 11(1), 13–21 (2015)CrossRefGoogle Scholar
  39. 39.
    Zupanic, A., Meplan, C., Grellscheid, S.N., Mathers, J.C., Kirkwood, T.B., Hesketh, J.E., Shanley, D.P.: Detecting translational regulation by change point analysis of ribosome profiling data sets. RNA 20(10), 1507–1518 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computational Biology Department, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Biological SciencesCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations