Advertisement

Novel Insights of the Gene Translational Dynamic and Complex Revealed by Ribosome Profiling

  • Zhe WangEmail author
  • Zhenglong Gu
Chapter
Part of the RNA Technologies book series (RNATECHN)

Abstract

Biology research has entered into the big data era. Systems biology approaches, therefore, have become essential tools to elucidate the whole landscape of how cells separate, grow, and resist different stresses. In 2009, a novel RNA technology, termed ribosome profiling, was invented by Dr. Jonathan Weissman Lab from UCSF. Ribosome profiling (Ribo-Seq) is a powerful tool which can provide the most direct readout of the intracellular translation state of a protein including information on the location of translation start/stop sites, ribosome distribution pattern, and even the moving rate of the translating ribosome, at the whole-genome scale and single-nucleotide resolution.

To date, many researchers including our lab have successfully applied ribosome profiling method for diverse purposes. We thus review in this chapter the underlying mechanism and recent advances as regards this fantastic tool. Firstly, we introduce the working mechanism, advantages, and study history of ribosome profiling. Secondly, we discuss the data analysis pipeline, also compare different statistical algorithms and data visualization software. Finally, we review the extensive applications of Ribo-seq, for example, identification of uORF, computation of global translation efficiency (TE), the study of the posttranscriptional regulatory role of RNA binding protein and others. We hope this chapter would be useful for interested systems biology researchers as well as RNA biologists.

Keywords

Ribosome profiling Ribo-Seq RNA-Seq Translation Deep sequencing mRNA Ribosome footprint 

Notes

Acknowledgements

We apologize for not being able to cite many works owing to lack of space.

References

  1. Aeschimann F, Kumari P, Bartake H et al (2017) LIN41 post-transcriptionally silences mRNAs by two distinct and position-dependent mechanisms. Mol Cell 65:476–489CrossRefPubMedGoogle Scholar
  2. Artieri CG, Fraser HB (2014) Evolution at two levels of gene expression in yeast. Genome Res 24:411–421CrossRefPubMedPubMedCentralGoogle Scholar
  3. Baek J, Lee J, Yoon K et al (2017) Identification of unannotated small genes in Salmonella. G3 (Bethesda) 7:983–989CrossRefGoogle Scholar
  4. Barry KC, Ingolia NT, Vance RE (2017) Global analysis of gene expression reveals mRNA superinduction is required for the inducible immune response to a bacterial pathogen. eLife 6:e22707CrossRefPubMedPubMedCentralGoogle Scholar
  5. Benhalevy D, Gupta SK, Danan CH et al (2017) The human CCHC-type zinc finger nucleic acid-binding protein binds G-rich elements in target mRNA coding sequences and promotes translation. Cell Rep 18:2979–2990CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bercovich-Kinori A, Tai J, Gelbart IA et al (2016) A systematic view on influenza induced host shutoff. eLife 5:e18311CrossRefPubMedPubMedCentralGoogle Scholar
  7. Brar GA, Weissman JS (2015) Ribosome profiling reveals the what, when, where and how of protein synthesis. Nat Rev Mol Cell Biol 16:651–664CrossRefPubMedPubMedCentralGoogle Scholar
  8. Brar GA, Yassour M, Friedman N et al (2012) High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335:552–557CrossRefPubMedGoogle Scholar
  9. Calviello L, Mukherjee N, Wyler E et al (2016) Detecting actively translated open reading frames in ribosome profiling data. Nat Methods 13:165–170CrossRefPubMedGoogle Scholar
  10. Chotewutmontri P, Barkan A (2016) Dynamics of chloroplast translation during chloroplast differentiation in maize. PLoS Genet 12:e1006106CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chun SY, Rodriguez CM, Todd PK et al (2016) SPECtre: a spectral coherence-based classifier of actively translated transcripts from ribosome profiling sequence data. BMC Bioinformatics 17:482CrossRefPubMedPubMedCentralGoogle Scholar
  12. Chung BY, Hardcastle TJ, Jones JD et al (2015) The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for Ribo-seq data analysis. RNA 21:1731–1745CrossRefPubMedPubMedCentralGoogle Scholar
  13. Fields AP, Rodriguez EH, Jovanovic M et al (2015) A regression-based analysis of ribosome-profiling data reveals a conserved complexity to mammalian translation. Mol Cell 60:816–827CrossRefPubMedPubMedCentralGoogle Scholar
  14. Gerashchenko MV, Gladyshev VN (2017) Ribonuclease selection for ribosome profiling. Nucleic Acids Res 45:e6CrossRefPubMedGoogle Scholar
  15. Guo H, Ingolia NT, Weissman JS et al (2010) Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835–840CrossRefPubMedPubMedCentralGoogle Scholar
  16. Haft RJ, Keating DH, Schwaegler T (2014) Correcting direct effects of ethanol on translation and transcription machinery confers ethanol tolerance in bacteria. Proc Natl Acad Sci USA 111:E2576–E2585CrossRefPubMedGoogle Scholar
  17. Hsieh AC, Liu Y, Edlind MP et al (2012) The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485:55–61CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hsu PY, Calviello L, Wu HL et al (2016) Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis. Proc Natl Acad Sci USA 113:E7126–E7135CrossRefPubMedGoogle Scholar
  19. Hwang JY, Buskirk AR (2017) A ribosome profiling study of mRNA cleavage by the endonuclease RelE. Nucleic Acids Res 45:327–336CrossRefPubMedGoogle Scholar
  20. Ingolia NT, Ghaemmaghami S, Newman JR et al (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324:218–223CrossRefPubMedPubMedCentralGoogle Scholar
  21. Ingolia NT, Lareau LF, Weissman JS (2011) Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147:789–802CrossRefPubMedPubMedCentralGoogle Scholar
  22. Jan CH, Williams CC, Weissman JS (2014) Principles of ER cotranslational translocation revealed by proximity-specific ribosome profiling. Science 346:1257521CrossRefPubMedPubMedCentralGoogle Scholar
  23. Juntawong P, Girke T, Bazin J et al (2014) Translational dynamics revealed by genome-wide profiling of ribosome footprints in Arabidopsis. Proc Natl Acad Sci USA 111:E203–E212CrossRefPubMedGoogle Scholar
  24. Latif H, Szubin R, Tan J et al (2015) A streamlined ribosome profiling protocol for the characterization of microorganisms. Biotechniques 58:329–332CrossRefPubMedGoogle Scholar
  25. Legendre R, Baudin-Baillieu A, Hatin I et al (2015) RiboTools: a Galaxy toolbox for qualitative ribosome profiling analysis. Bioinformatics 31:2586–2588CrossRefPubMedGoogle Scholar
  26. Liu X, Jiang H, Gu Z et al (2013a) High-resolution view of bacteriophage lambda gene expression by ribosome profiling. Proc Natl Acad Sci USA 110:11928–11933CrossRefPubMedGoogle Scholar
  27. Liu MJ, Wu SH, Wu JF et al (2013b) Translational landscape of photomorphogenic Arabidopsis. Plant Cell 25:3699–3710CrossRefPubMedPubMedCentralGoogle Scholar
  28. Loayza-Puch F, Rooijers K, Buil LC et al (2016) Tumour-specific proline vulnerability uncovered by differential ribosome codon reading. Nature 530:490–494CrossRefPubMedGoogle Scholar
  29. Loayza-Puch F, Rooijers K, Zijlstra J et al (2017) TGFβ1-induced leucine limitation uncovered by differential ribosome codon reading. EMBO Rep 18:549–557CrossRefPubMedPubMedCentralGoogle Scholar
  30. McKinney C, Zavadil J, Bianco C et al (2014) Global reprogramming of the cellular translational landscape facilitates cytomegalovirus replication. Cell Rep 6:9–17CrossRefPubMedGoogle Scholar
  31. McManus CJ, May GE, Spealman P et al (2014) Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res 24:422–430CrossRefPubMedPubMedCentralGoogle Scholar
  32. Merchante C, Brumos J, Yun J et al (2015) Gene-specific translation regulation mediated by the hormone-signaling molecule EIN2. Cell 163:684–697CrossRefPubMedGoogle Scholar
  33. Michel AM, Fox G, M Kiran A et al (2014) GWIPS-viz: development of a ribo-seq genome browser. Nucleic Acids Res 42:D859–D864CrossRefPubMedGoogle Scholar
  34. Michel AM, Mullan JP, Velayudhan V et al (2016) RiboGalaxy: a browser based platform for the alignment, analysis and visualization of ribosome profiling data. RNA Biol 13:316–319CrossRefPubMedPubMedCentralGoogle Scholar
  35. Miranda-CasoLuengo AA, Staunton PM, Dinan AM et al (2016) Functional characterization of the Mycobacterium abscessus genome coupled with condition specific transcriptomics reveals conserved molecular strategies for host adaptation and persistence. BMC Genomics 17:553CrossRefPubMedPubMedCentralGoogle Scholar
  36. Oh E, Becker AH, Sandikci A et al (2011) Selective ribosome profiling reveals the cotranslational chaperone action of trigger factor in vivo. Cell 147:1295–1308CrossRefPubMedPubMedCentralGoogle Scholar
  37. Olexiouk V, Crappé J, Verbruggen S et al (2016) sORFs.org: a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res 44:D324–D329CrossRefPubMedGoogle Scholar
  38. Popa A, Lebrigand K, Paquet A et al (2016) RiboProfiling: a bioconductor package for standard Ribo-seq pipeline processing. F1000Res 5:1309CrossRefPubMedPubMedCentralGoogle Scholar
  39. Reid DW, Shenolikar S, Nicchitta CV (2015) Simple and inexpensive ribosome profiling analysis of mRNA translation. Methods 91:69–74CrossRefPubMedPubMedCentralGoogle Scholar
  40. Schrader JM, Li GW, Childers WS et al (2016) Dynamic translation regulation in Caulobacter cell cycle control. Proc Natl Acad Sci USA 113:E6859–E6867CrossRefPubMedGoogle Scholar
  41. Sendoel A, Dunn JG, Rodriguez EH et al (2017) Translation from unconventional 5′ start sites drives tumour initiation. Nature 541:494–499CrossRefPubMedPubMedCentralGoogle Scholar
  42. Shell SS, Wang J, Lapierre P et al (2015) Leaderless transcripts and small proteins are common features of the mycobacterial translational landscape. PLoS Genet 11:e1005641CrossRefPubMedPubMedCentralGoogle Scholar
  43. Stern-Ginossar N, Weisburd B, Michalski A et al (2012) Decoding human cytomegalovirus. Science 338:1088–1093CrossRefPubMedGoogle Scholar
  44. Sun X, Wang Z, Guo X et al (2016) Coordinated evolution of transcriptional and post-transcriptional regulation for mitochondrial functions in yeast strains. PLoS One 11:e0153523CrossRefPubMedPubMedCentralGoogle Scholar
  45. Wang Z, Sun X, Zhao Y et al (2015) Evolution of gene regulation during transcription and translation. Genome Biol Evol 7:1155–1167CrossRefPubMedPubMedCentralGoogle Scholar
  46. Williams CC, Jan CH, Weissman JS (2014) Targeting and plasticity of mitochondrial proteins revealed by proximity-specific ribosome profiling. Science 346:748–751CrossRefPubMedPubMedCentralGoogle Scholar
  47. Xie SQ, Nie P, Wang Y et al (2016) RPFdb: a database for genome wide information of translated mRNA generated from ribosome profiling. Nucleic Acids Res 44:D254–D258CrossRefPubMedGoogle Scholar
  48. Zhong Y, Karaletsos T, Drewe P et al (2017) RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinformatics 33:139–141CrossRefPubMedGoogle Scholar
  49. Zoschke R, Watkins KP, Barkan A (2013) A rapid ribosome profiling method elucidates chloroplast ribosome behavior in vivo. Plant Cell 25:2265–2275CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Infectious DiseasesWeill Medical College of Cornell UniversityNew YorkUSA
  2. 2.Division of Nutritional SciencesCornell UniversityIthacaUSA

Personalised recommendations