Letter

Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias

  • Nature Biotechnology volume 36, pages 190195 (2018)
  • doi:10.1038/nbt.4045
  • Download Citation
Received:
Accepted:
Published:

Abstract

Small subunit ribosomal RNA (SSU rRNA) genes, 16S in bacteria and 18S in eukaryotes, have been the standard phylogenetic markers used to characterize microbial diversity and evolution for decades. However, the reference databases of full-length SSU rRNA gene sequences are skewed to well-studied ecosystems and subject to primer bias and chimerism, which results in an incomplete view of the diversity present in a sample. We combine poly(A)-tailing and reverse transcription of SSU rRNA molecules with synthetic long-read sequencing to generate high-quality, full-length SSU rRNA sequences, without primer bias, at high throughput. We apply our approach to samples from seven different ecosystems and obtain more than a million SSU rRNA sequences from all domains of life, with an estimated raw error rate of 0.17%. We observe a large proportion of novel diversity, including several deeply branching phylum-level lineages putatively related to the Asgard Archaea. Our approach will enable expansion of the SSU rRNA reference databases by orders of magnitude, and contribute to a comprehensive census of the tree of life.

  • Subscribe to Nature Biotechnology for full access:

    $250

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Accessions

Primary accessions

European Nucleotide Archive

References

  1. 1.

    , , & Genetic diversity in Sargasso Sea bacterioplankton. Nature 345, 60–63 (1990).

  2. 2.

    , & 16S rRNA sequences reveal numerous uncultured microorganisms in a natural community. Nature 345, 63–65 (1990).

  3. 3.

    et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

  4. 4.

    & Scaling laws predict global microbial diversity. Proc. Natl. Acad. Sci. USA 113, 5970–5975 (2016).

  5. 5.

    , & Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

  6. 6.

    et al. High-resolution phylogenetic microbial community profiling. ISME J. 10, 2020–2032 (2016).

  7. 7.

    , , , & A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic Acids Res. 38, e159 (2010).

  8. 8.

    , , & Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. PeerJ Prepr. 3, e778v1 (2015).

  9. 9.

    et al. INC-Seq: accurate single molecule reads using nanopore sequencing. Gigascience 5, 34 (2016).

  10. 10.

    & A method for high precision sequencing of near full-length 16S rRNA genes on an Illumina MiSeq. PeerJ 4, e2492 (2016).

  11. 11.

    , , & Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).

  12. 12.

    et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).

  13. 13.

    et al. Poly(A) polymerase modification and reverse transcriptase PCR amplification of environmental RNA. Appl. Environ. Microbiol. 71, 1267–1275 (2005).

  14. 14.

    & A comparative study of microbial diversity and community structure in marine sediments using poly(A) tailing and reverse transcription-PCR. Front. Microbiol. 4, 160 (2013).

  15. 15.

    , , , & Parallel, tag-directed assembly of locally derived short sequence reads. Nat. Methods 7, 119–122 (2010).

  16. 16.

    et al. BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads. Genome Biol. 15, 517 (2014).

  17. 17.

    et al. Haplotype-phased synthetic long reads from short-read sequencing. PLoS One 11, e0147229 (2016).

  18. 18.

    & Fidelity of DNA polymerases in DNA amplification. Proc. Natl. Acad. Sci. USA 86, 9253–9257 (1989).

  19. 19.

    et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).

  20. 20.

    , , & Dynamic expression of the translational machinery during Bacillus subtilis life cycle at a single cell level. PLoS One 7, e41921 (2012).

  21. 21.

    Degradation of stable RNA in bacteria. J. Biol. Chem. 278, 45041–45044 (2003).

  22. 22.

    & The ribosomal ribonucleic acid of Agrobacterium tumefaciens. Biochem. J. 149, 17–22 (1975).

  23. 23.

    et al. Occurrence of fragmented 16S rRNA in an obligate bacterial endosymbiont of Paramecium caudatum. Proc. Natl. Acad. Sci. USA 90, 9892–9895 (1993).

  24. 24.

    , & Improved protocols for the illumina genome analyzer sequencing system. Curr. Protoc. Hum. Genet. 62, 18.2.1–18.2.27 (2009).

  25. 25.

    et al. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27, 1159–1161 (2011).

  26. 26.

    et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).

  27. 27.

    et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).

  28. 28.

    et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208–211 (2015).

  29. 29.

    et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).

  30. 30.

    et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31, 533–538 (2013).

  31. 31.

    et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014).

  32. 32.

    , , , & Not all are free-living: high-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa. Mol. Ecol. 24, 4556–4569 (2015).

  33. 33.

    , , & Metacommunity analysis of amoeboid protists in grassland soils. Sci. Rep. 6, 19068 (2016).

  34. 34.

    et al. Metatranscriptomic census of active protists in soils. ISME J. 9, 2178–2190 (2015).

  35. 35.

    et al. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 3, 675–684 (2009).

  36. 36.

    , , , & Status of the archaeal and bacterial census: an update. MBio 7, e00201–e00216 (2016).

  37. 37.

    et al. The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Database 2010, baq013 (2010).

  38. 38.

    et al. MiDAS: the field guide to the microbes of activated sludge. Database 2015, bav062 (2015).

  39. 39.

    , , & Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genomics 16, 1056 (2015).

  40. 40.

    et al. Functionally relevant diversity of closely related Nitrospira in activated sludge. ISME J. 9, 643–655 (2015).

  41. 41.

    et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

  42. 42.

    , & FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).

  43. 43.

    RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

  44. 44.

    et al. Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. USA 100, 14339–14344 (2003).

  45. 45.

    , , , & Back to basics—the influence of DNA extraction and primer choice on phylogenetic analysis of activated sludge communities. PLoS One 10, e0132783 (2015).

  46. 46.

    , & Ligation of single-stranded oligodeoxyribonucleotides by T4 RNA ligase. Anal. Biochem. 158, 171–178 (1986).

  47. 47.

    et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

  48. 48.

    Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  49. 49.

    , & SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

  50. 50.

    R Core Team. R. A language and environment for statistical computing (2016).

  51. 51.

    RStudio Team. RStudio: Integrated Development Environment for R. (2015).

  52. 52.

    tidyverse: Easily Install and Load 'Tidyverse' Packages. (2016).

  53. 53.

    et al. vegan: Community Ecology Package. R package version 2.3–0 (2015).

  54. 54.

    Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).

  55. 55.

    et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).

  56. 56.

    , & Creating the CIPRES Science Gateway for inference of large phylogenetic trees. Proceedings of the Gateway Computing Environments Workshop, 14 November 2010, New Orleans, 1–8 (2010).

Download references

Acknowledgements

The study was funded by the Danish Research Council for Independent Research (FTP, grant 6111-00617B), the Innovation Fund Denmark (1305-00018B, NomiGas), the Villum Foundation (grant VKR 022796 and 13351), and the Poul Due Jensen (Grundfos) Foundation. S.J.M. was supported by a Danish Council for Independent Research grant (no. 4093-00127A). M.A. was supported by a research grant (15510) from VILLUM FONDEN. We thank H. Daims and M. Wagner for insightful discussions of the manuscript.

Author information

Author notes

    • Søren M Karst
    •  & Morten S Dueholm

    These authors contributed equally to this work.a

Affiliations

  1. Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Denmark.

    • Søren M Karst
    • , Morten S Dueholm
    • , Simon J McIlroy
    • , Rasmus H Kirkegaard
    • , Per H Nielsen
    •  & Mads Albertsen

Authors

  1. Search for Søren M Karst in:

  2. Search for Morten S Dueholm in:

  3. Search for Simon J McIlroy in:

  4. Search for Rasmus H Kirkegaard in:

  5. Search for Per H Nielsen in:

  6. Search for Mads Albertsen in:

Contributions

S.M.K., M.S.D. and M.A. conceived the method. S.M.K. and M.S.D. performed wet lab method development and experiments. R.H.K. performed Nanopore sequencing and data analysis. S.M.K. and M.A. developed the bioinformatics pipeline and performed data analysis. S.J.M. performed the phylogenetic analysis. S.M.K., M.S.D., S.J.M., R.H.K., P.H.N. and M.A. wrote the manuscript.

Competing interests

M.A., S.M.K., R.H.K., and P.H.N. are co-owners of DNASense ApS. The other authors declare no competing financial interests.

Corresponding author

Correspondence to Mads Albertsen.

Integrated supplementary information

Supplementary information