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.

  • Protocol
  • Published:

Simultaneous ribosome profiling of hundreds of microbes from the human microbiome

Nature Protocols volume 16, pages 4676–4691 (2021)Cite this article

Subjects

Abstract

Ribosome profiling enables sequencing of ribosome-bound fragments of RNA, revealing which transcripts are being translated as well as the position of ribosomes along mRNAs. Although ribosome profiling has been applied to cultured bacterial isolates, its application to uncultured, mixed communities has been challenging. We present MetaRibo-Seq, a protocol that enables the application of ribosome profiling directly to the human fecal microbiome. MetaRibo-Seq is a benchmarked method that includes several modifications to existing ribosome profiling protocols, specifically addressing challenges involving fecal sample storage, purity and input requirements. We also provide a computational workflow to quality control and trim reads, de novo assemble a reference metagenome with metagenomic reads, align MetaRibo-Seq reads to the reference, and assess MetaRibo-Seq library quality (https://github.com/bhattlab/bhattlab_workflows/tree/master/metariboseq). This MetaRibo-Seq protocol enables researchers in standard molecular biology laboratories to study translation in the fecal microbiome in ~5 d.

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: MetaRibo-Seq protocol workflow.
Fig. 2: MetaRibo-Seq bioinformatic workflow.
Fig. 3: MetaRibo-Seq quality.

Similar content being viewed by others

Data availability

The data presented in Fig. 3 were generated as part of ref. 27 and can be accessed under BioProject accession PRJNA510123.

Code availability

The pipeline implemented in this paper is available at https://github.com/bhattlab/bhattlab_workflows/tree/master/metariboseq (https://zenodo.org/record/4638134#.YGDevkhKgcg)46. This pipeline creates a de novo assembly from metagenomic reads, maps MetaRibo-Seq reads to the assembly, calls open reading frames across the assembly and creates visualizations such as metagene plots, triplet periodicity histograms and fragment length distribution for aligned MetaRibo-Seq reads. This serves as a baseline assessment of library quality and a starting point for future analysis. Though specifically designed for MetaRibo-Seq, this pipeline would also be generally useful for any situation in which both DNA-sequencing and Ribo-Seq data for an organism with no existing reference genome are available.

References

  1. Zengel, J. M. & Lindahl, L. Diverse mechanisms for regulating ribosomal protein synthesis in Escherichia coli. Prog. Nucleic Acid Res. Mol. Biol. 47, 331–370 (1994).

    Article  CAS  PubMed  Google Scholar 

  2. Meyer, M. M. The role of mRNA structure in bacterial translational regulation. Wiley Interdiscip. Rev. RNA https://doi.org/10.1002/wrna.1370 (2017).

  3. Lindahl, L., Jaskunas, S. R., Dennis, P. P. & Nomura, M. Cluster of genes in Escherichia coli for ribosomal proteins, ribosomal RNA, and RNA polymerase subunits. Proc. Natl Acad. Sci. USA 72, 2743–2747 (1975).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fallon, A. M., Jinks, C. S., Strycharz, G. D. & Nomura, M. Regulation of ribosomal protein synthesis in Escherichia coli by selective mRNA inactivation. Proc. Natl Acad. Sci. USA 76, 3411–3415 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dean, D. & Nomura, M. Feedback regulation of ribosomal protein gene expression in Escherichia coli. Proc. Natl Acad. Sci. USA 77, 3590–3594 (1980).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li, G.-W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lalanne, J.-B. et al. evolutionary convergence of pathway-specific enzyme expression stoichiometry. Cell 173, 749–761.e38 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bucca, G. et al. Translational control plays an important role in the adaptive heat-shock response of Streptomyces coelicolor. Nucleic Acids Res. 46, 5692–5703 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sawyer, E. B., Grabowska, A. D. & Cortes, T. Translational regulation in mycobacteria and its implications for pathogenicity. Nucleic Acids Res. 46, 6950–6961 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Taylor, R. C. et al. Changes in translational efficiency is a dominant regulatory mechanism in the environmental response of bacteria. Integr. Biol. 5, 1393–1406 (2013).

    Article  CAS  Google Scholar 

  11. Morita, Y., Gilmour, C., Metcalf, D. & Poole, K. Translational control of the antibiotic inducibility of the PA5471 gene required for mexXY multidrug efflux gene expression in Pseudomonas aeruginosa. J. Bacteriol. 191, 4966–4975 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Starosta, A. L., Lassak, J., Jung, K. & Wilson, D. N. The bacterial translation stress response. FEMS Microbiol. Rev. 38, 1172–1201 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Jeong, Y. et al. The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2). Nat. Commun. 7, 11605 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mathieu, A. et al. Discovery and function of a general core hormetic stress response in E. coli induced by sublethal concentrations of antibiotics. Cell Rep. 17, 46–57 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  16. Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Becker, A. H., Oh, E., Weissman, J. S., Kramer, G. & Bukau, B. Selective ribosome profiling as a tool for studying the interaction of chaperones and targeting factors with nascent polypeptide chains and ribosomes. Nat. Protoc. 8, 2212–2239 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Oh, E. et al. Selective ribosome profiling reveals the cotranslational chaperone action of trigger factor in vivo. Cell 147, 1295–1308 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li, G.-W., Oh, E. & Weissman, J. S. The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature 484, 538–541 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Freeberg, L., Kuersten, S. & Syed, F. Isolate and sequence ribosome-protected mRNA fragments using size-exclusion chromatography. Nat. Methods 10, i–ii (2013).

    Article  CAS  Google Scholar 

  21. Latif, H. et al. A streamlined ribosome profiling protocol for the characterization of microorganisms. BioTechniques 58, 329–332 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Nakahigashi, K. et al. Comprehensive identification of translation start sites by tetracycline-inhibited ribosome profiling. DNA Res 23, 193–201 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Meydan, S. et al. Retapamulin-assisted ribosome profiling reveals the alternative bacterial proteome. Mol. Cell 74, 481–493.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mangano, K. et al. Genome-wide effects of the antimicrobial peptide apidaecin on translation termination in bacteria. eLife 9, e62655 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hwang, J.-Y. & Buskirk, A. R. A ribosome profiling study of mRNA cleavage by the endonuclease RelE. Nucleic Acids Res. 45, 327–336 (2017).

    Article  CAS  PubMed  Google Scholar 

  26. Mohammad, F., Green, R. & Buskirk, A. R. A systematically-revised ribosome profiling method for bacteria reveals pauses at single-codon resolution. eLife 8, e42591 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fremin, B. J., Sberro, H. & Bhatt, A. S. MetaRibo-Seq measures translation in microbiomes. Nat. Commun. 11, 3268 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mohammad, F. & Buskirk, A. R. Protocol for ribosome profiling in bacteria. Bio Protoc. 9, e3468 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tanca, A. et al. A straightforward and efficient analytical pipeline for metaproteome characterization. Microbiome 2, 49 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ndah, E. et al. REPARATION: ribosome profiling assisted (re-)annotation of bacterial genomes. Nucleic Acids Res. 45, e168 (2017).

    Article  PubMed  CAS  Google Scholar 

  31. Clauwaerts, J., Menschaert, G. & Waegeman, W. DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. Nucleic Acids Res. 47, e36 (2019).

    Article  CAS  Google Scholar 

  32. Sberro, H. et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell 178, 1245–1259.e14 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Durrant, M. G. & Bhatt, A. S. Automated prediction and annotation of small open reading frames in microbial genomes. Cell Host Microbe 29, 121–131.e4 (2021).

    Article  CAS  PubMed  Google Scholar 

  34. Dar, D. et al. Term-seq reveals abundant ribo-regulation of antibiotics resistance in bacteria. Science 352, aad9822 (2016).

    Article  PubMed  CAS  Google Scholar 

  35. Fremin, B. J. & Bhatt, A. S. Structured RNA contaminants in bacterial Ribo-Seq. mSphere 5, e00855-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Mohammad, F., Woolstenhulme, C. J., Green, R. & Buskirk, A. R. Clarifying the translational pausing landscape in bacteria by ribosome profiling. Cell Rep. 14, 686–694 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Marks, J. et al. Context-specific inhibition of translation by ribosomal antibiotics targeting the peptidyl transferase center. Proc. Natl Acad. Sci. USA 113, 12150–12155 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Zinshteyn, B., Wangen, J. R., Hua, B. & Green, R. Nuclease-mediated depletion biases in ribosome footprint profiling libraries. RNA 26, 1481–1488 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. O’Loughlin, S. et al. Polysomes bypass a 50-nucleotide coding gap less efficiently than monosomes due to attenuation of a 5′ mRNA stem-loop and enhanced drop-off. J. Mol. Biol. 432, 4369–4387 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Article  Google Scholar 

  42. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Chung, B. Y. et al. The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for Ribo-seq data analysis. RNA 21, 1731–1745 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Di Tommasso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).

    Article  CAS  Google Scholar 

  45. Kurtzer, G. M., Sochat, V. & Bauer, M. W. Scientific containers for mobility of compute. PLoS One 12, e0177459 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Siranosian, B. bhattlab/bhattlab_workflows: metariboseq_v1.0.0. https://doi.org/10.5281/zenodo.4638134 (2021).

Download references

Acknowledgements

We thank D. Maghini, E. Brooks and S. Vance for helpful comments on the manuscript. This work was funded by the Damon Runyon Clinical Investigator Award, grant nos. NIH R01AI148623 and NIH R01AI143757 as well as grant no. NIH P30 AG047366, which supports the Stanford ADRC. Computational work was supported by NIH S10 Shared Instrumentation grant no. 1S10OD02014101 and by NIH grant no. P30 CA124435.

Author information

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Brayon J. Fremin & Ami S. Bhatt

  2. Divisions of Hematology and Blood & Marrow Transplantation, Department of Medicine, Stanford University, Stanford, CA, USA

    Cosmos Nicolaou & Ami S. Bhatt

Authors
  1. Brayon J. Fremin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cosmos Nicolaou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ami S. Bhatt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.J.F. and A.S.B. conceived the study. B.J.F performed all experiments and data analysis. C.N. wrote the Nextflow computational pipeline. B.J.F. and A.S.B. wrote the paper with input from all authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ami S. Bhatt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Related links

Key references using this protocol

Fremin, B. J. et al. Nat. Commun. 11, 3268 (2020): https://doi.org/10.1038/s41467-020-17081-z

Durrant, M. G. & Bhatt, A.S. Cell Host Microbe 29, 121–131.e4 (2021): https://doi.org/10.1016/j.chom.2020.11.002

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fremin, B.J., Nicolaou, C. & Bhatt, A.S. Simultaneous ribosome profiling of hundreds of microbes from the human microbiome. Nat Protoc 16, 4676–4691 (2021). https://doi.org/10.1038/s41596-021-00592-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00592-4

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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