Validation of two ribosomal RNA removal methods for microbial metatranscriptomics

Journal name:
Nature Methods
Volume:
7,
Pages:
807–812
Year published:
DOI:
doi:10.1038/nmeth.1507
Received
Accepted
Published online

Abstract

The predominance of rRNAs in the transcriptome is a major technical challenge in sequence-based analysis of cDNAs from microbial isolates and communities. Several approaches have been applied to deplete rRNAs from (meta)transcriptomes, but no systematic investigation of potential biases introduced by any of these approaches has been reported. Here we validated the effectiveness and fidelity of the two most commonly used approaches, subtractive hybridization and exonuclease digestion, as well as combinations of these treatments, on two synthetic five-microorganism metatranscriptomes using massively parallel sequencing. We found that the effectiveness of rRNA removal was a function of community composition and RNA integrity for these treatments. Subtractive hybridization alone introduced the least bias in relative transcript abundance, whereas exonuclease and in particular combined treatments greatly compromised mRNA abundance fidelity. Illumina sequencing itself also can compromise quantitative data analysis by introducing a G+C bias between runs.

At a glance

Figures

  1. Technical reproducibility.
    Figure 1: Technical reproducibility.

    (ad) In the correlation plots, each point indicates the abundance of an individual mRNA transcript in two technical replicates. Analysis of technical replicates (i and ii) of Hyb in Illumina run 1 (a), Exo between runs 1 and 2 (b), Hyb between runs 3 and 4 (c; color-coded by source organisms) and Hyb between runs 3 and 4 after normalizing for G+C content by organism (d). Pearson's product moment correlation coefficient, r, for all data points regardless of source organism is shown. Slopes from linear regression of data points for each organism are indicated in c and d.

  2. Effectiveness of bulk rRNA removal.
    Figure 2: Effectiveness of bulk rRNA removal.

    (a,b) Distribution of reads between rRNAs (divided by community member) and total non-rRNAs for each treatment in experiments 1 (a) and 2 (b). (c) Observed and actual rRNA percentage removals for the three and five treatments in experiments 1 and 2. Dashed lines are simulations of observed and actual rRNA percentage removals, when starting rRNA (in controls, rRNA0) accounted for 94.9% (community 1) and 96.9% (community 2) of total RNA. (d) Actual rRNA percentage removal for each organism. Error bars, s.d. There was no net rRNA removal for Spirochaeta by Exo in experiment 1, indicated by an arbitrary negative value.

  3. Enrichment of mRNA in the synthetic communities.
    Figure 3: Enrichment of mRNA in the synthetic communities.

    (a) Fold enrichment of total mRNA abundance. (b) Percentage improvement in mRNA detection sensitivity.

  4. Fidelity of mRNA relative abundance.
    Figure 4: Fidelity of mRNA relative abundance.

    (a,b) Analysis of all seven samples in experiment 1 (a) and the seven samples from run 3 in experiment 2 (b). Bray Curtis similarities between samples are indicated by a dendrogram showing increasing loss of mRNA fidelity with distance from controls. Increasing loss of fidelity between treatments (y axes) and corresponding controls (x axes) is also visually shown using scatter plots. The average percentage and s.d. of mRNAs in treatments exhibiting greater than twofold difference from respective controls (indicated by diagonal dashed lines) is shown in each scatter plot.

References

  1. Sorek, R. & Cossart, P. Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat. Rev. Genet. 11, 916 (2010).
  2. Neidhardt, F.C. & Umbarger, H.E. Chemical composition of Escherichia coli . in Escherichia coli and Salmonella: Cellular and Molecular Biology 2nd edn., vol. 1 (eds. Neidhardt, F.C. et al.) 1317 (ASM Press, Washington, D.C., 1996).
  3. Karpinets, T.V., Greenwood, D.J., Sams, C.E. & Ammons, J.T. RNA: protein ratio of the unicellular organism as a characteristic of phosphorous and nitrogen stoichiometry and of the cellular requirement of ribosomes for protein synthesis. BMC Biol. 4, 30 (2006).
  4. Zhao, J., Hyman, L. & Moore, C. Formation of mRNA 3′ ends in eukaryotes: mechanism, regulation, and interrelationships with other steps in mRNA synthesis. Microbiol. Mol. Biol. Rev. 63, 405445 (1999).
  5. Urich, T. et al. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE 3, e2527 (2008).
  6. Mao, C., Evans, C., Jensen, R. & Sobral, B. Identification of new genes in Sinorhizobium meliloti using the Genome Sequencer FLX system. BMC Microbiol. 8, 72 (2008).
  7. Su, C. & Sordillo, L.M. A simple method to enrich mRNA from total prokaryotic RNA. Mol. Biotechnol. 10, 8385 (1998).
  8. Pang, X. et al. Bacterial mRNA purification by magnetic capture-hybridization method. Microbiol. Immunol. 48, 9196 (2004).
  9. Frias-Lopez, J. et al. Microbial community gene expression in ocean surface waters. Proc. Natl. Acad. Sci. USA 105, 38053810 (2008).
  10. Shi, Y., Tyson, G.W. & DeLong, E.F. Metatranscriptomics reveals unique microbial small RNAs in the ocean′s water column. Nature 459, 266269 (2009).
  11. Dunman, P.M. et al. Transcription profiling-based identification of Staphylococcus aureus genes regulated by the agr and/or sarA loci. J. Bacteriol. 183, 73417353 (2001).
  12. McGrath, K.C. et al. Isolation and analysis of mRNA from environmental microbial communities. J. Microbiol. Methods 75, 172176 (2008).
  13. Gilbert, J.A. et al. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. PLoS ONE 3, e3042 (2008).
  14. Gilbert, J.A. et al. Potential for phosphonoacetate utilization by marine bacteria in temperate coastal waters. Environ. Microbiol. 11, 111125 (2009).
  15. He, S. et al. Metatranscriptomic array analysis of ′Candidatus Accumulibacter phosphatis′-enriched enhanced biological phosphorus removal sludge. Environ. Microbiol. 12, 12051217 (2010).
  16. Hu, Z., Zhang, A.X., Storz, G., Gottesman, S. & Leppla, S.H. An antibody-based microarray assay for small RNA detection. Nucleic Acids Res. 34, e52 (2006).
  17. Shrestha, P.M., Kube, M., Reinhardt, R. & Liesack, W. Transcriptional activity of paddy soil bacterial communities. Environ. Microbiol. 11, 960970 (2009).
  18. Yoder-Himes, D.R. et al. Mapping the Burkholderia cenocepacia niche response via high-throughput sequencing. Proc. Natl. Acad. Sci. USA 106, 39763981 (2009).
  19. Burgmann, H. et al. Transcriptional response of Silicibacter pomeroyi DSS-3 to dimethylsulfoniopropionate (DMSP). Environ. Microbiol. 9, 27422755 (2007).
  20. Garbeva, P. & de Boer, W. Inter-specific interactions between carbon-limited soil bacteria affect behavior and gene expression. Microb. Ecol. 58, 3646 (2009).
  21. Hewson, I. et al. Microbial community gene expression within colonies of the diazotroph, Trichodesmium, from the Southwest Pacific Ocean. ISME J. 3, 12861300 (2009).
  22. Poretsky, R.S. et al. Comparative day/night metatranscriptomic analysis of microbial communities in the North Pacific subtropical gyre. Environ. Microbiol. 11, 13581375 (2009).
  23. Hewson, I. et al. In situ transcriptomic analysis of the globally important keystone N2-fixing taxon Crocosphaera watsonii . ISME J. 3, 618631 (2009).
  24. Wurtzel, O. et al. A single-base resolution map of an archaeal transcriptome. Genome Res. 20, 133141 (2010).
  25. Schroeder, A. et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 7, 3 (2006).
  26. Stewart, F.J., Ottesen, E.A. & DeLong, E.F. Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J. 4, 896907 (2010).
  27. Rosenkranz, R., Borodina, T., Lehrach, H. & Himmelbauer, H. Characterizing the mouse ES cell transcriptome with Illumina sequencing. Genomics 92, 187194 (2008).
  28. Quail, M.A. et al. A large genome center's improvements to the Illumina sequencing system. Nat. Methods 5, 10051010 (2008).

Download references

Author information

  1. These authors contributed equally to this work.

    • Shaomei He &
    • Omri Wurtzel

Affiliations

  1. Department of Energy Joint Genome Institute, Walnut Creek, California, USA.

    • Shaomei He,
    • Kanwar Singh,
    • Jeff L Froula,
    • Suzan Yilmaz,
    • Susannah G Tringe,
    • Zhong Wang,
    • Feng Chen,
    • Erika A Lindquist &
    • Philip Hugenholtz
  2. Energy Biosciences Institute, University of California-Berkeley, Berkeley, California, USA.

    • Shaomei He &
    • Philip Hugenholtz
  3. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.

    • Omri Wurtzel &
    • Rotem Sorek
  4. Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.

    • Philip Hugenholtz

Contributions

S.H., K.S., S.G.T., F.C., E.A.L. and P.H. planned the experiments, S.H., K.S. and S.Y. executed the experiments, S.H., O.W., J.L.F., Z.W., R.S. and P.H. performed the data analysis and S.H. and P.H. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (688K)

    Supplementary Figures 1–3, Supplementary Tables 1–6, Supplementary Notes 1–4

Additional data